JOURNAL OF BUSINESS STUDIES - University of Rajshahi

Vol. 9July-December 2016

JBS-ISSN 2303-9884

JOURNAL OFBUSINESS STUDIES

FACULTY OF BUSINESS STUDIESUniversity of Rajshahi

Vol. 9, July-December 2016

JBS-ISSN 2303-9884

Journal of Business Studies

Faculty of Business StudiesUniversity of Rajshahi

www.ru.ac.bd/business

Published by : Faculty of Business Studies Dean’s Complex (2nd Floor) University of Rajshahi Rajshahi - 6205, Banglasesh Tel # -0721-711129 Email: [email protected] Web: www.ru.ac.bd/business

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University of Rajshahi(Vol. 9, July-December 2016)

Journal of Business Studies

Editorial Board

Professor Dr. Md. Shibley Sadique

Dean, Faculty of Business Studies

University of Rajshahi

Chief Editor

Professor Dr. Md. Zafor Sadique

Dept. of Management Studies


Member

Professor Dr. Md. Ohidul Islam

Dept. of Management Studies


Member

Professor Dr. Md. Humayun Kabir

Dept. of Accounting & Information Systems


Member

Dr. Md. Anwarul Haque

Dept. of Accounting & Information Systems


Member

Professor Dr. Mohammad Zahid Hossain

Dept. of Finance


Member

Dr. Abu Sadeque Md. Kamruzzaman

Dept. of Finance


Member

Professor A.K.M. Mostafizur Rahman Al-Arif

Dept. of Marketing


Member

Professor Dr. Md. Salim Reza

Dept. of Marketing


Member

Professor Abdul Quddus

Dept. Banking and Insurance


Member

N.B. Views expressed in the articles published in this Journal are of the author(s).

Therefore, neither the Chief Editor nor any Member of the Editorial Board bears

any responsibility of the views expressed in the papers.

Editorial Foreword

Welcome to Vol.9, July-December 2016 issue, of Journal of Business

Studies, an issue which consolidates the January-July 2015, July-

December 2015 and January-July 2016 volumes. This consolidation is

deemed necessary to bridge the accumulated time lag from 2015 to the

present. Indeed it is a much anticipated and long overdue issue. The

editorial board is excited to present this issue which carries a varied range

of submissions; one we trust that will tantalize the minds of the readers of

this journal.

With its broad scope area of coverage inter alia extending from

economics, finance, accounting, management and tourism, this journal is

dedicated to a challenge rather than to a topic or an intersection of topics

per se. This challenge is to address current issues that relates to the field of

business in Bangladesh in particular and the world in general and to

incrementally add to the body of knowledge which is already in existence.

The Journal of Business Studies aims first, to contribute in its role as a

University journal by allowing all the academics, researchers and post-

graduate students of Rajshahi University the opportunity to get their work

peer-refereed and published on an open-access platform. It serves as the

starting point in the journey of getting these articles to be published in

indexed journals. In the foreseeable future, it is the aspiration of this

editorial board to get this journal indexed and accepted worldwide.

Second, the journal aims to encourage and facilitate inter-disciplinary

research on issues that relate to business across the departments within the

Faculty. It is universally acknowledged that “Business Studies” is a

subject which relates to various issues and our aim is to draw on these

academic debates and solicit contributions from a wide variety of

disciplines. Inter-disciplinary research have gained great momentum in the

world of academia, that theories and models are transcending from

disciplines and creating a niche in a ground breaking manner in areas

where it was never considered plausible. The plethora of possibilities is

vast with inter-disciplinary research andin tandem with the current

research practices. Therefore, it is an opportune time for researchers in

Rajshahi University to work collaboratively and address those knowledge

gaps that seeks to be filled.This issue embraces this diversity as you will

notice from the range of papers that it contains.

Moving to the current issue, it contains ten (10) peer-refereed articles

which seek to shed some light on contemporary research questions in the

field of business in Bangladesh. There are a series of empirically proven

research articles which will give you an insight of what is happening in the

related areas. Some of these articles are exploratory in nature and sets the

first step to the development of a more rigorous investigation and thought

provoking journey. I hope to see more extended works in the areas that

have been highlighted in this issue and publication of the same in refereed

journals.

We all know that a journal needs commitment, not only from editors but

also from editorial boards, reviewers and the contributors. Without the

support of my editorial team, Icannot imagine this feat being possible.

Special thanks, also, goes to the reviewers for supporting the editorial

board with their commitment in turning around the articles within a short

span of time and providing their invaluable input to improvise the work by

the contributors. I also thank the contributors for their trust, patience and

timely revisions.

Professor Dr. Md. Shibley Sadique

Chief Editor

Journal of Business Studies &

Dean, Faculty of Business Studies


Contents

1. Agricultural Commercialization in Bangladesh: Are Smallholder

Farmers Market Oriented?

Md. Ataul Gani Osmani

Md. Elias Hossain

01

2. Factors Affecting the Choices for Off-farm Activities in

Bangladesh: A study on Rajshahi District

Dr. A S M Kamruzzaman

26

3. The Economics of Price Volatility in Commodity Futures

Markets: A Survey

Mahmud Hossain Riazi

45

4. Impact of Market Size and Foreign Trade on FDI Inflow in

Bangladesh: A VEC Approach

Rakibul Islam

75

5. Visitors’ Perception towards Tour Destinations: A Study on

Padma Garden

Rudrendu Ray

Md. Abdul Alim Dr. Md Enayet Hossain

95

6. Determinants of Share Prices in Bangladesh: Evidence from

Pharmaceuticals Industry

Ajit Kumar Ghose Md. Solaiman Chowdhury

117

7. Influence of Cognitive and Affective Image on a Recreational

Park: An Empirical Study

Md. Ikbal Hossain

Rebeka Sultana Rekha

Dr. Md. Enayet Hossain

133

8. Performance Evaluation of Selected NCBs and PCBs in

Bangladesh: An Empirical Study

Dr. Mohammad Zahid Hossain Md. Fazle Fattah Hossain

161

9. Succession Plan in Second or Subsequent Generation Family

Owned Firms in Bangladesh- a Study on Rajshahi Division

Md. Shariful Islam

Professor Dr. Md. Amzad Hossain

199

10. Impact of Remittances to the Economic Development of

Bangladesh

Md. Omar Faruque Udayshankar Sarkar

213

Journal of Business Studies, Vol. 9, 2016 1

JBS-ISSN 2303-9884

Agricultural Commercialization in Bangladesh: Are

Smallholder Farmers Market Oriented? Md. Ataul Gani Osmani 1

Md. Elias Hossain 2

Abstract

Agricultural commercialization is a viable mechanism to strengthen the thrust of

improving agriculture. This paper investigates the status of smallholder farmers

of Bangladesh in promoting agricultural commercialization. Using field survey

data from 100 smallholder farmers of Rajshahi district, households‟ market

orientation index is calculated to measure their market orientation status. A One-

way ANOVA analysis is performed to check whether the smallholders are using

more traded inputs in production as they move from low to high level of market

orientation. Moreover, a multiple regression analysis is applied to identify the

factors determining smallholders‟ market orientation. Results show that

smallholder farmers in the study area are not subsistence oriented as, on the

average, 65% of their produced commodities are sold in the market, and that the

sample farmers are moderately market oriented with average market orientation

index 0.59, indicating that they allocate 59% of their cultivable land to

marketable crops. The results of the study indicate that market orientated farmers

are progressively using traded inputs to increase total production and are

significantly influenced by exogenous determinants like farm size, use of

improved seeds, access to extension services and total value of produced cash

crops. These findings suggest that enhancing direct motivation, enforcing farmer-

market contacts and promoting market orientated crop technologies may

facilitate the move of smallholder farmers from subsistence to commercialized

agriculture. Keywords: Commercialization, market orientation, smallholder farmers, traded

inputs, Bangladesh

(I) Introduction

gricultural commercialization is an effective way to transform

agriculture from subsistence to market oriented agricultural

1 Lecturer, Department of Economics, Varendra University, Rajshahi,

Email: [email protected] 2 Professor, Department of Economics, University of Rajshahi,

Email: [email protected]

A

2 Journal of Business Studies, Vol. 9, 2016

JBS-ISSN 2303-9884

production. Commercialization of agriculture increases the ability of the

agriculture dependent developing countries to bolster economic growth

and development (Pingali and Rosegrant, 1995; Timmer, 1997). It is

generally driven by forces like globalization, urbanization, migration, state

of rising per capita income, etc. It also involves a gradual but definite

movement out of subsistence production system to increasingly market

oriented production system with progressive use of purchased (traded)

inputs (Pingali, 2001). Specifically, agricultural commercialization is a

complex and dynamic process involving various linkages between the

farm and industry, where the key agents are the farmers, traders and

processors (Thapliya, 2006). However, the core problem of promoting

agricultural commercialization in Bangladesh is the lack of effective value

chain linkages among the key agents such as input providers, farmers,

traders, processors and service providers (Azad, 2015). In Bangladesh,

market orientation of high valued crops, which generally refers to fish,

livestock products, fruits, spices and vegetables, is one of the potential

avenues of agricultural commercialization (Azad, 2015). Although the

opportunities of commercialization for these high value crops are seized

upon due to growing domestic and global demand, it requires more

advanced post harvest technologies, as high valued agricultural products

are generally more perishable than the traditional staples (Azad, 2015). It

is estimated that post harvest losses are more than 40% for highly

perishable fruits and vegetables in Bangladesh, while in food grains these

losses are estimated as 20-25%.

Agricultural commercialization requires access to agricultural markets,

and access to emerging high-income agricultural markets is seen to be

skewed in favor of large-scale farmers (Balint, 2003). In Bangladesh, most

of the farmers are smallholders and market orientation of them is hindered

by a number of difficulties such as poor quality and high cost of inputs,

high transportation costs, high market charges and unreliable market

information (Sharma et al., 2012). Thus, it is necessary to link smallholder

farmers strongly with market in order to expand demand for agricultural

products and set opportunities for income generation in rural economy

(Pingali, 1997). Market orientation of the smallholder farmers enhances

their purchasing power for food, while enabling re-allocation of their

incomes to high valued non-food agribusiness sectors and off-farm


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enterprises (Davis, 2006). In this context, the government and non-

government organizations (NGOs) in Bangladesh are recently trying to

transform and diversify smallholder agriculture in Bangladesh as it is

prescribed in the policy forums that the development of agriculture sector

is only possible through transformation of subsistence agriculture to

agribusiness or commercialization (Azad, 2015). National Agricultural

Technology Project (NATP), Integrating Smallholders into Expanding

Markets (ISEM) project (2011-2012) and Strengthening Low-cost

Technology Market Systems (SLCTMS) (2011) are the few examples of the efforts to transform Bangladesh agriculture towards commercialization.

Agriculture has continued to play important role in the economy of

Bangladesh as it contributes 16.77% to the GDP and provides employment

for about 47% of the labor force of the country (BBS, 2013). Moreover,

about 67% of total population lives in rural areas (World Bank, 2013) and

within the rural economy, smallholder farmers are the main performers of

agriculture sector in Bangladesh (SFB, 2015). Although these smallholder

farmers have not yet fully utilized agriculture for its multiple functions,

they are now practicing market oriented agriculture that slightly includes

them with the formal market system and the related income mediated

benefits (Razzaque and Hossain, 2007).

Considering the issue of market orientation of smallholder farmers, several

questions have arisen, which remained unanswered in the context of

Bangladesh: (a) to what extent are smallholder farmers market oriented?

(b) are market oriented farmers progressively using purchased inputs in

their production? (c) and what are the factors that mostly determine the

level of market orientation of smallholder farmers in Bangladesh? This

paper is designed to respond to these questions by assessing the state of

market orientation of the smallholder farmers, the pattern of using inputs

by them, and identifying the factors that influence smallholders to be

market oriented.

The paper has the following structure. Section Two provides a brief

review of literature. Section Three deals with the methodology and data

required for the study. Section Four presents the results and discussions

based on the results, while Section Five concludes with some suggestions.


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(II) Literature Review

There are both theoretical and empirical studies that have examined the

process of agricultural commercialization. However, the actual meaning of

the concept of agricultural commercialization is seldom clearly defined

(Pingali and Rosegrant, 1995; Von Braun and Kennedy, 1994). Hinderink

and sterkenburg (1987) observed from an analysis of the relevant literature

that agricultural commercialization is interpreted in different ways and is

measured in various criteria with the consequence that different aspects of

the phenomenon are taken into account. Some researchers including

Pingali and Rosegrant (1995) and Pingali et al. (2005) agreed that

agricultural commercialization leads to more specialization both at a

regional and household level, and at the same time to more diversification

at national level. As the process of structural transformation takes root, it

can be occurred through increasing participation in the rural market

economy to earn higher income, accumulate asset, and thereby the

smallholder households may be lifted out of poverty and food insecurity

through it (Gabre-Madhin and Haggblade, 2004; Haggblade & Hazell,

2010). Thus, the key feature of agricultural transformation is the transition

of smallholder farming from subsistence to commercialized farming in the

process of economic development (Johnston and Mellor, 1961; Johnston,

1970).

There is an on-going debate on the role of smallholder farmers in

economic development. Although smallholder farmers cannot cope with

current trends in market demands (IFPRI, 2005), they are important

players in agricultural growth with their significant shares in agricultural

resources, activities and outputs, as they can efficiently use their land and

cheaper family or local labor in production and directly benefit from

income and food supply growth (Hazell et al., 2007; Pingali, 2010).

Narayanan and Gulati (2002) characterized smallholder farmers as

practicing a mix of commercial and subsistence farming. Another study

defined smallholder farmers as farmers with limited resource endowments,

relative to other farmers in the sector (Dixon et al., 2003). The most

common approach to define small farms is based on the size of

landholding or livestock numbers (Nagayets, 2005; Chamberlin, 2008).

The concept of smallholder farmers in Bangladesh is defined as farmers


JBS-ISSN 2303-9884

with 0.05 to 2.49 acres of cultivable land (GoB, 2008; Sharma et al.,

2012). Thus, the smallholder farmers in Bangladesh are resource poor in

terms of land holding. However, they may improve their livelihood status

through significant market orientation or commercialization, as market

orientation of smallholder farmers leads to gradual decline in real food

prices due to increased competition and lower costs in food marketing and

processing (Jayne et al., 1995). For example, smallholder farmers in

Bangladesh are enjoying better welfare outcomes in terms of more food

and goods as they move through lower to upper level of

commercialization (Osmani, et al., 2015).

Agricultural commercialization mainly entails increased integration of

farmers into the exchange economy and participation in input and output

markets (von Braun and Kennedy, 1994; Pingali and Rosegrant, 1995;

Jaleta et al., 2009). There exists little distinction between market

orientation and market participation as the former means production

decision based on market signals while the latter means the percentage

sales of output (Gebremedhin and Jaleta, 2010; 2012). However,

examining the trend of market orientation is a method of accessing the

farmers‟ participation in the output market so that the objective of

agricultural commercialization can be justified (Adenegan et al., 2013).

Thus, in order to draw policy implications to enhance agricultural

commercialization, it is important to analyze the trend of market

orientation and its determinants (Gebremedhin and Jaleta, 2010). Several

studies have also verified that the degree of market orientation is a major

determinant of competitive advantage (Fritz, 1996; Selnes et al., 1996).

Moreover, commercialized or market oriented farms depend more on

markets to collect their required inputs (improved seed, inorganic

fertilizer, crop protection chemicals etc.) instead of their own produced

inputs (Leavy and Poulton, 2007).

Although market orientation has taken its place in marketing thinking and

business operations of manufacturing firms, it is also important for the

development of agricultural firms (Helfert et al., 2001). It is shown in

some empirical research findings that market orientation is positively

related to aspects such as profitability (Narver and Slater, 1990), new

diversified product (Atuahene-Gima, 1995) and sales growth with


JBS-ISSN 2303-9884

increased sales revenue (Greenley, 1995, Jaworski and Kohli, 1993).

There is evidence of research examining the importance of market

orientation within food industry and related sectors (Harris and Piercy,

1999). More recently market orientation of smallholder farmers has been

examined for different context in different countries (Gebremedhin and

Jaleta, 2012; Goshu et al., 2012; Adenegan et al., 2013).

Agricultural commercialization of smallholder farmers is not researched

intensively in the context of Bangladesh. It is found that

commercialization of smallholder farming in Bangladesh is still not high

enough and the farmers are still producing under the state of subsistence

agriculture (Mahelet, 2007). These farmers receive low welfare outcomes

of commercialization because of market imperfections and high

transaction costs (De Janvry et al.1991). Thus, the smallholder farmers are

not able to take part in the market for reaping the possible benefits of

commercialization unless the mentioned difficulties are removed and

better environment is created (Wegner and Zwart, 2011).

(III) Methodology

Study Area Selection and Data Collection

The present study is related to commercialization of agriculture, and is

based on primary data collected from Durgapur Upazila under Rajshahi

district of Bangladesh. The rationale behind selecting this area is that

Rajshahi is an agriculture based area. Rice is the dominant crop in the area

produced simultaneously with other minor crops such as wheat, potato,

vegetables, jute, maize, oilseeds, pulse, onion, garlic etc. Farming is the

principle occupation of most of the population and their livelihood mostly

depend on agricultural activities. In this area, farming is characterized by

low level of production technology and small size of farm holding. About

79.85% people of the Upazila are farmers and rest 20.15% people are

involved with non-agricultural activities. The present study has been

carried out in three unions, chosen randomly, from Durgapur Upazila of

Rajshahi district namely, Noapara, Deluabari, and Jhaluka. The total

population in Noapara, Deluabari, and Jhaluka are 25041, 25860 and

23028, respectively. Most of the people of these unions earn their

livelihoods from agriculture and most of the farmers are smallholders. The


JBS-ISSN 2303-9884

randomly selected villages, two from each union, are Nondigram,

Kashipur, Vobanipur, Bera, Coupukoria, and Shaheber.

This study is focused on the selected smallholder farmers who are mainly

engaged in agriculture for their livelihood and the data is collected from

randomly selected farmers from the above six villages through a structured

questionnaire. The study focuses on the 2013 production year and

therefore, relied on recalled information. Multistage random sampling

technique is adopted to choose sample farmers from the study area. For

analyzing agricultural commercialization in Bangladesh, the sample has

been selected in such a way that it covers all necessary data required for

the analysis. During the sampling, firstly, the researchers selected three

unions randomly and in the next stage, two villages from each union are

selected randomly. Next, a list of all smallholder farmers is collected from

the agriculture extension office of Durgapur Upazila, and 100 respondents

were selected from the six villages of three sample unions using the simple

random sampling method.

Empirical Methodology

The methodology of the study includes quantitative techniques to obtain

the study objectives of measuring the level of market orientation,

examination of the use of purchased inputs, and estimating the factors

responsible for market orientation in the study area. That is, the methods

include a description of the techniques which are used for analysis and the

empirical design of the study. The study techniques involve descriptive

and econometric analyses. The descriptive analysis involved the use of

statistical tools like frequency tables, percentages and ratios to describe

different socio-economic characteristics, particularly related to market

orientation of the smallholder farmers. Moreover, One-way ANOVA

technique is applied to inspect the progressive use of purchased inputs in

production. To see how the factors affect the level of market orientation, a

multiple regression analysis is used as well.

There are many studies on farm market orientation and progressive

substitution of non-traded inputs for purchased inputs. These studies

defined market orientation in agriculture as a production decision issue

and the degree of allocation of resources (land, labor and capital) to


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production of agricultural products that are meant for exchange or sale

(Hinderink and Sterkenburg, 1987; Immink and Aarcon, 1993). Hence, in

studying the commercialization of agriculture in Bangladesh, the present

study tries to assess the level or extent of market orientation of

smallholder farmers by calculating market orientation index following

Gebremedhin and Jaleta (2010) and Goshu et al. (2012). According to

Gebremedhin and Jaleta (2010) and Goshu et al. (2012), a smallholder

farmer is said to be market oriented if his production plan follows market

signals and he produces commodities that are more marketable. As there

exists a semi or moderate commercial system in Bangladesh (Osmani and

Hossain, 2015), production decision is significantly influenced by both

market signal and home consumption level (Gebremedhin and Jaleta,

2010), noted that all crops produced by a moderately commercialized

farmers may not be marketable in the same proportion. Thus, households

could differ in their market orientation depending on their resource

allocation (land, labor and capital) to the more marketable commodities.

Based on the proportion of total amount sold to total production at farming

system level, firstly, a crop specific marketability index is computed for

each crop produced at farming system level as follows:

N

i

ki

N

i

ki

k

Y

X

CMI

1

1 ; XY kiki and 10 CMIk (1)

Where CMI k is the crop specific marketability index defined as the

proportion of crop k sold ( X ki) to the total amount produced (Y ki )

aggregated over the total households in a farming system. CMI k takes a

value between 0 and 1, indicating that crops mainly produced for markets

have CMI k values closer to 1. After computing CMI k , household‟s market

orientation index in land allocation, MOIi , is computed from the land

allocation pattern of the household weighted by the marketability index of

each crop ( CMI k ) as follows:


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L

LCMI

MOI Ti

k

k

ikk

i1 ; L

Ti > 0 and 0 < 1MOIi (2)

Where, MOIi is market orientation index for household i, Lik is amount

of land allocated to crop k and LTi is the total crop land operated by

household i. This also indicates that with a value of MOIi closer to 1, the

ith household allocates higher proportion of land to more marketable crops

and thus, the household is more market oriented.

Although earlier studies on smallholder market orientation have

considered output market only, a sustainable market orientation requires

integration into input markets as well (Pingali and Rosegrant, 1995). In the

crop mix of the households, market orientation may be justified by the

relative importance of more marketable crops and profit motive of the

households (Pingali and Rosegrant, 1995; Pingali, 2001). According to

Gebremedhin and Jaleta (2010) the realization of profit through market

revenues also requires increased production efficiency using modern

inputs and technologies. Having the background of market orientation, we

adopted a statistical model of One-way ANOVA to inspect whether there

is a rising trend in using purchased inputs in agricultural production by

smallholder farmers working at different levels of market orientation. The

reason is that in the recent years, per capita land holding has rapidly been

reduced and the production system has been converted from organic

system to chemical based system. In order to maximize production, most

of the ignorant farmers of remote rural areas are using purchased inputs

haphazardly such as, improved seeds, chemical fertilizer, insecticides, etc.

Finally, following Gebremedhin and Jaleta (2010), market orientation

index (market orientation) is modeled as a function of different socio-

economic factors to see how the factors affect the level of market

orientation. The functional form is as follows:

)3( )(XfMOI ii


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Where, MOIi = Market Orientation Index, and Xi = Socio-economic

factors that affect the level of market orientation. In the study, market

orientation index is taken as dependent variable following the earlier study

of Gebremedhin and Jaleta (2010). Thus, for the determinants of

household market orientation a multiple regression model is employed,

since the dependent variable is a continuous one. It is found that age,

education, experience of farmer, farm size, fertilizer cost, seed quality,

ownership of oxen, non-farm income, value of produced food crops, value

of produced cash crops etc. affect the degree of market orientation or

commercialization of smallholder farmers (Goshu et al., 2012;

Gebremedhin and Jaleta, 2010). Therefore, a specified regression model is

formulated as follows:

)4( 110109988776655443322110uXXXXXXXXXXMOI i

Where, MOIi is the market orientation index or the level of market

orientation; β0, β1,..,β10 are parameters to be estimated; X1, X2,....., X 9 , 10X

are the explanatory variables that affect the level of market orientation,

and ui is the stochastic error term. The regression Equation (4) shows a

linear relationship between dependent variable and explanatory variables

and the equation is estimated using Ordinary Least Squares (OLS)

method. The explanatory variables that are used in the regression are

shown in Table 1.


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(IV) Results and Discussion

This section provides the results of the estimations towards attaining the

objectives set for this study. To this end, descriptive statistics of collected

data from the questionnaire survey are presented at first. The results from

estimation of households‟ market orientation index are presented in the

next section. After that results from the One-way ANOVA analysis are

presented. Finally, the estimation results of the multiple regression model,

showing the influence of the key socio-economic factors on the level of

market orientation, are discussed.

Table 1: Specification of the Explanatory Variables for Multiple Regression

Models

Variable Name Type Measurement Expected

Sign

Farm size ( X 1 ) Continuous Amount of household‟s land under

cultivation (Bigha) +

Farming Experience

( 2X ) Continuous

Number of years engaged in crop

production (years) +

Education level

( 3X ) Continuous

Formal education of the household

head (years of schooling) +

Cost of Chemical

fertilizer ( 4X ) Continuous

Total value of fertilizer used in the

last production year (Tk.) +

Use of improved

seeds ( 5X ) Continuous % land used improved seeds +

Access to extension

Services ( 6X ) Dummy If access then 1, otherwise o +

Income from

livestock ( 7X ) Continuous

Total value of livestock sold in the

production year (Tk) -

Non-farm income

( 8X ) Continuous

Total income earned from non-farm

activities in the production year -

Value of cash crops

( X 9 ) Continuous Total market value of produced

cash crop (Tk.) +

Value of food crops

( 10X ) Continuous

Total market value of produced food

crops in the production year (Tk.) +


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Descriptive Statistics

Analyses of the demographic and socio-economic characteristics revealed that substantial difference exists among the sample smallholder farmers of the study area. Although farm size, farming experience, education level, cost of chemical fertilizer, use of improved seeds, access to extension services, income from livestock, non-farm income, value of cash crops and value of food crops were hypothesized to be the common factors affecting commercialization, significant variations across farmers with respect to information of these variables were found. Moreover, to check whether all variables used in this study really tap into one construct from the questionnaire, we used Cronbach‟s Alpha Test of Reliability. It is an important concept in the evaluation of assessments and questionnaires which measures the internal consistency or reliability of the variables (Tavakol and Dennick, 2011). The coefficient Alpha (α) in this test ranges from 0 to 1, that is, if all variables are perfectly reliable and measure the same thing (true score), then α is equal to 1 and if there is no true score but only error in the items, then α is equal to 0. In this study, the coefficient Alpha (α) is found as 0.781 which is considered “good level of reliability” as far as social science research is concerned (Cronbach, 1951; Nunnally & Bernstien, 1994). The descriptive statistics of the variables used in the present study are shown in Table 2.

Table 2: Socio-economic Characteristics of Smallholder Farmers

Variables Mean Std. Dev. Min. Max.

Farm size (bigha) 4.01 1.824892 0.65 7

Farming Experience (years) 25.68 11.62623 4 45

Education level (years of schooling) 5.4 5.270463 0 20

Cost of Chemical fertilizer (Tk.) 6467.41 3256.146 1578 17180

Use of improved seeds (% of

cultivated land) 84.48 31.12005 0 100

Income from livestock (Tk.) 20204 25788.87 0 110000

Non-farm income (Tk.) 37252 61529.35 0 400000

Value of cash crops (Tk.) 46126.5 56299.27 0 284000

Value of food crops (Tk.) 57983.3 44550.57 5600 252000

Note: Tk. indicates Bangladeshi currency, taka

Source: Authors‟ calculations according to data from Osmani and Hossain (2013)


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From the table it is found that the average farm size of a sample farmer is

4.01 bigha indicating that most of the farmers in the study area are

smallholders. It is also found that all farmers in the study area do not have

same experience. Table 2 shows that the average experience of the sample

farmers is 25.68 years, where minimum experience is 4 years and

maximum experience is 45 years. The average level of education of

farmers in the study area is 5.4 years of schooling with minimum of no

education and maximum of 20 years of schooling. Chemical fertilizer is an

important input for agricultural production in the study area. The average

cost of chemical fertilizer of the sample farmers is Tk.6467.41 in a crop

year. From the above table, it is observed that in 2013-14 production

season, about 84.48% of cultivated land of the sample farmers was under

the use of improved seeds. It is also seen that average annual income from

livestock asset is Tk.20244, whereas average annual non-farm income is

Tk. 37252. Farmers in the study area produce mainly food and cash crops.

It is found that the average value of produced food crops of the sample

farmers is Tk.57983.3 and that of cash crop is Tk.46126.5.

Level of Market Orientation of Smallholder Farmers

In explaining the level of market orientation of smallholder farmers in

Durgapur Upazila, we adopted a household market orientation index. As

indicated earlier, households‟ commercialization behavior can be reflected

by their land allocation pattern and the crop marketability index is used as

an indicator of the households‟ market orientation. The market orientation

index is computed for specific crops produced in 2013 production season.

The findings of market orientation index reflect that the land allocation

decision of households is designed for profit maximization. Specifically,

on average, smallholder farmers in the study area allocate 59% of their

cultivable land to the production of marketable crops and as the average

market orientation index is about 0.59, indicating a moderate market

orientation of smallholder farmers in the study area (Table 3). The

computed results from crop marketability index and household market

orientation index are presented in Table 3.


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Table 3: Level of Market Orientation of Smallholder Farmers

with Crop Specific Marketability Index (CMI)

Indicators Rice Jute Potato Wheat Maize Mustard Pulse Onion Total

Total

Production

(„000‟Tk.)

4541.25 200.5 3239.8 274.99 760.51 186.98 194.48 790.9 10189.4

Total sales

(„000‟ Tk.) 1983.58 198.4 2528.04 123.69 750.51 146.48 156.5 743.9 6631.1

CMI 0.44 0.99 0.78 0.45 0.99 0.78 0.80 0.94 0.65

Household Market Orientation Index (MOI)

Indicators Obs. Mean Std. Dev. Min Max

MOI 100 0.59 0.24 0.06 0.96

Source: Authors‟ calculations according to data from Osmani and Hossain

(2013)

Analysis of crop specific marketability index indicates that 65% of total

production is sold by the households in the study area. Thus, the

households are considered moderately commercialized as their percentage

of crop sales is well above the midpoint but less than the threshold level

75%. According to Goletti (2005) and Ohen et al. (2013), farmers (small

or large) are said to be commercial if they sell more than 75% of their total

production. However, the crop specific marketability index also revealed

that jute and maize are jointly the most marketable crops in the study area.

Moreover, rice and wheat are the dominant forms of crops produced by

almost every smallholder farmer in the study area. The crop specific

marketability index calculates that only 44% of produced rice and 56% of

produced wheat are sold by the smallholder farmers in the output market

as shown in the above table. This indicates that rice and wheat farmers are

less commercialized as these crops are mainly produced in Bangladesh to

meet the farmers‟ consumption needs. Potato is another food crop

produced by the smallholder farmers where marketability index is

computed as 0.78, which indicates that potato producers are

commercialized. Table 3 also shows that crop marketability indices are


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0.78, 0.80 and 0.94 for mustard, pulse and onion, respectively, although

farmers are less interested in production of these types.

Intensity of Market Orientation by use of Purchased Inputs

According to Gebremedhin and Jaleta (2010), market orientation is

strongly translated into crop output and input market participation.

Moreover, market orientation is geared through the progressive need to

purchased external inputs into production process. Results in Table 4

indicate that purchased input use pattern is an important determinant of

agricultural commercialization in the study area. This is evident from the

analysis of One-way ANOVA examining the relationship between the

levels of market orientation and purchased (traded) input use pattern. In

doing this statistical test, the farm households are categorized into three

groups depending on the value of MOI, such as ≤0.50, ≥0.50 to <0.75, and

≥0.75, and improved seeds, chemical fertilizer and pesticides are taken as

the intensity representatives of market orientation by the use of purchased

inputs. Moreover, as most of the respondents (about 85%) are Boro rice

producers, we only considered input cost of Boro rice production showing

the rising trend of average cost.

Table 4: Intensity of Market Orientation by use of Purchased Inputs

Representatives of Purchased

Inputs

Level of Market Orientation Prob.> F

≤0.50 ≥0.50 to

<0.75

≥0.75.

Average cost of improved

seeds (Tk.) 270.35 476.60 710.77 0.0001***

Average cost of chemical

fertilizer (Tk.) 1978.20 3487.34 5235 0.0001***

Average cost of pesticides

(Tk.) 625.43 1104.32 1657.75 0.0001***

Total Number of Observation 25 47 28 100

Note: *** 1% significance level

Source: Authors‟ calculations according to data from Osmani and

Hossain (2013)


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Table 4 shows the intensity of market orientation of smallholder farmers

by the use of purchased inputs in the production of agricultural

commodities. The results presented in the table showed that the use of

purchased inputs has consistent increasing pattern along the level of

market orientation, from low to high. The One-way ANOVA test results

confirm that the variation in average costs of improved seeds, chemical

fertilizer and pesticides by farm households at different levels of market

orientation is statistically significant at 1% significance level.

Determinants of Market Orientation

A multiple regression model is estimated to examine the factors affecting

farmers‟ market orientation in the study area and the regression model is

estimated by Ordinary Least Squares (OLS) method. In this regression

analysis, farmers‟ market orientation index is used as the dependent

variable to determine farmers‟ preparedness for participation in the market

through efficient allocation of their small landholdings. Table 5 presents

the results of the OLS estimation of factors affecting smallholder farmers‟

market orientation in Durgapur Upazila of Rajshahi district, Bangladesh.

The R-squared value indicates that 49% of the variation in the market

orientation index is explained by the explanatory variables. As the study is

based on the primary data, there is a probability of occurring

heteroscedasticity and multicolinearity problems in the estimation process

of OLS. However, the robust action was taken to remedy the problem of

heteroscedasticity. Moreover, the VIF test is performed to see if the model

suffers from the problem of multicollinearity and incorrect specification.

This test reveals that the model is free from such problems as the average

VIF value for the explanatory variables included in OLS estimation is

1.45.


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Table 5: OLS Estimation Results for Determinants of Market Orientation

Variable Coefficient Robust

Std. Err. T P>|t|

Farm size ( X 1 ) 0.028** 0.013 2.09 0.040

Farming Experience ( 2X ) 0.002 0.002 1.23 0.221

Education level ( 3X ) 0.002 0.004 0.52 0.605

Cost of Chemical fertilizer

( 4X ) 3.45e-06 6.46e-06 0.53 0.595

Use of improved seeds

( 5X ) 0.002*** 0.001 2.86 0.005

Access to extension Services

( 6X ) 0.099** 0.047 2.11 0.038

Income from livestock ( 7X ) -6.38e-08 7.43e-07 -0.09 0.932

Non-farm income ( 8X ) -3.50e-07 3.44e-07 -1.02 0.312

Value of cash crops ( X 9 ) 1.30e-

04*** 3.37e-07 3.86 0.000

Value of food crops ( 10X ) 1.93e-07 4.76e-07 0.41 0.686

Constant 0.146 0.079 1.84 0.069

F( 10, 89) = 11.92; Prob. > F = 0.0000; R-squared =0.4867; Root MSE

=0.17835

Note: *** and ** indicate 1% and 5% significance levels, respectively

Source: Authors‟ calculations according to data from Osmani and

Hossain (2013)

Table 5 shows the results from the OLS estimation of the determinants of

market orientation of smallholder farmers. The result indicates that that

the extent of market orientation by smallholder farmers is significantly

determined by farm size, use of improved seeds, access to extension

services and value of produced cash crops. That is, these variables have

stronger numerical effects on market orientation. Other explanatory

variables have no significant impact on market orientation of the small

holder farmers. It is found that there is a strong significant and positive

relationship between farm size and market orientation in the study area i.e.

(β = 0.028; P = 0.040). This indicates that if farmers‟ farm size is


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increased by one bigha, market orientation index will be increased by

0.028 at 5% significance level. The fact might be that farm households

with large farm size could allocate their land for cash crop production

giving them better position to participate in the output market. The

regression result also revealed that use of improved seeds has a significant

and positive impact (β = 0.002; P = 0.005) on the market orientation

index. It explains that at 1% significance level, farm households‟ market

orientation increases by 0.20% if they use 1% more land for cultivation by

using improved seeds. This is so because use of improved seeds renders

higher production and improved seeds are supposed to be effective to

produce high quality crops resulting from high demand and possible

higher selling price for the crop.

Agricultural extension services appeared effective in inducing market

orientation for Bangladeshi smallholder farmers. The result of table 5

shows that farmers‟ access to extension services are (β = 0.0993; P =

0.038) related significantly and positively with the market orientation in

the study area. This explains that if agricultural extension services are

locally available to the smallholder farmers then their market orientation is

expected to rise by 0.099. The result may be attributed to the effective

monitoring and teaching approach of the extension agents and expert

persons in the study area. Finally, the amount of total cash crop production

(β = 1.30e-06; P = 0.000) is also strongly and positively related with

market orientation of smallholder farmers in the study area. This explains

that as value of cash crop production increases by 1 Tk., the extent of

market orientation increases by 0.00013.

(V) Conclusion

Commercialization is a new paradigm in Bangladesh agriculture.

Generally, Bangladeshi smallholder farmers have integrated into the

market system with their surplus production. This also leads to progressive

substitution of non-traded inputs in favor of purchased inputs in crop

production. Thus, this study puts emphasis on the analysis of the

potentiality of Bangladeshi smallholder farmers in enhancing farmers‟

involvement in commercial agriculture. The calculation of household

market orientation index reveals that on the average, farm households

allocate 59% of their cultivable land to the production of marketed crops.


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This is because of the gradual substitution of complex farming system in

the study area by specialized farmers for specific high value crops in

which every farm decision depends on the market signals. It is also

important to note that as farmers in the study area are moderately market

orientated, they progressively use traded inputs like improved seeds,

chemical fertilizer and pesticides in production. One-way ANOVA test

finds that farm households are overwhelmingly using purchased inputs in

production as they move from lower to higher level of market orientation.

However, one of the key limiting factors in production is that although

farmers are somewhat market oriented, the production system is not yet

fully mechanized. Moreover, ownership or availability of factors is not

likely to be complementary to external inputs for the smallholder farmers

in the study area. Thus, for proper interventions to promote input market

orientation in terms of using more land for cultivation by using traded

inputs may need to address the problem of availability of complementary

inputs.

Moreover, the result of OLS estimation shows that market orientation of

smallholder farmers increases as the farmers with relatively larger farm

size are using more improved seeds and have well accessed to extension

services for production of cash crops. Specifically, these findings suggest

that a holistic approaches should be taken that would enforce farmer-

market contracts and fair input prices, adequate extension services for all

marginal and smallholder farmers and encourage farmers to produce and

trade market oriented crops, such as onion, pulse, maize, jute and potato.

Along these lines, there is a need to promote market oriented crop

technologies and further research on endogenous determinants of market

orientation also deserves better attention.


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Factors Affecting the Choices for Off-farm Activities in

Bangladesh: A Study of Rajshahi District

Dr. A S M Kamruzzaman

Abstract

People living in rural and semi-urban areas in Bangladesh are taking

heterogeneous income generating off-farm activities to reduce poverty. But the

participation in any employment activity or sector depends on both motivational

and ability factors. The capacity of individuals or households to participate in

such activities is not uniform. Poverty, inequality and human skills affect the

capacity of individuals or households to engage in their preferred high

remunerative off-farm activities. This paper has identified some demographic and

socio-economic ability factors of rural individuals and households to engage in

some selected off-farm activities in the study area. These factors were found to

have affected significantly the decisions of households to choose or participate in

some sample off-farm activities. Age of entrepreneur, family size, whether the

head of the family or not, education, training, past experience, social network,

loan diversion for other purposes, household land ownership, percentage of off-

farm income in total household income, percentage of equity and debt fund

invested in business, distance between the local bank branch and the residence of

an entrepreneur, and distance between the local market and the residence of an

entrepreneur were found as significant factors to affect the choices for a

particular off-farm activity in the study. The multinomial logistic regression

analysis was used for modeling the choices of off-farm activities. A randomly

selected, cross-sectional, sample survey data of 300 borrowers from the SECP

program of RAKUB, purposively selected under five categories of sample off-

farm activities, had been used in this study.

Keywords: Rural livelihood diversification, ability factors of rural entrepreneurs,

natural, human, social and financial capital

(I) Introduction

ural and semi-urban people are taking heterogeneous income

generating activities besides their main occupations to reduce the

Associate Professor, Department of Finance, University of Rajshahi

Email : [email protected]

R


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overall risk of livelihood, or to take opportunities for higher remunerative

jobs than less remunerative traditional agriculture (Davis and Bezemer

2004). In developing countries, the resource-poor farmers are usually risk-

averse and, therefore, they will allocate less time to more risky jobs or,

alternatively, they will be willing to accept lower wages in the less-risky

environment. Rural farmers may participate in off-farm activities only to

reduce the overall risk of their incomes or to increase their total returns

(NRI, 2000)1.

Participation in any employment sector depends on both motivational and

ability factors. The first is the incentive or motivation – perhaps higher

return or less risk than alternatives. The second is the capacity of an

individual or household– perhaps certain skills or making necessary

financial commitment. It is often the poorest households who have the

highest motivation to diversify their livelihoods and also have the highest

constraints to diversify. Poverty, inequality in income and wealth, and

human skills affect the ability of an individual or household to engage in

the preferred activity or sector.

Although the choice and the participation in any rural off-farm activity for

self-employment depend on the motivation and the ability factors, the

capacity of households or individuals is not uniform. The analysis of 100

farm-households (Reardon et al 2000) shows a rough pattern of the

capacity of rural households : a positive relationship between non-farm

income share (and level) and total household income or Land-holding in

much of Africa; a negative relationship in much of Latin America, and a

very mixed set of results in Asia. They argue that the positive relationship

and the U-curve relationship (mixed results) reflect high entry barriers for

poor households to engage in nonfarm self-employment activities in

Africa and Asia.

Factors determining access to high remunerative nonfarm jobs can be of

individual, household, region or place, and project specific. Ellis and

Hussein (1998) in their study identified health and nutrition, household

1 Policy and Research on the Rural Non-Farm Economy: A Review of Conceptual, Methodological

and Practical Issues, Draft paper, NRI RNFE Project Team, November 2000.


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composition, access to finance, education, social capital and infrastructure

as determining factors to have access in nonfarm jobs. These factors are

considered as assets, or factors of production, or capital representing the

capacity of household to diversify income or livelihood. In some

livelihood literature (i.e. Barrett and Reardon 2000; Ellis 2000; and

Carney 1998) factors affecting the choice, or the access of households

were classified as: natural capital (access to land or common property

resources); social capital (networks and organizations); human capital

(health and educational status); financial capital; and physical capital (hard

infrastructure, shelter and production equipments).

As population pressure increases and rapid unplanned urbanization takes

place, it has become more difficult for the rural poor to rely only on

agriculture or natural-resource-based activities. Even for many,

livelihoods have become less secure and sources of incomes have more

varied. The alternative to reduce overall risk of livelihoods or to diversity

incomes, and to take opportunities to relatively high remunerative jobs

will require some ability factors of individuals or households suitable to

have better access in it. Therefore, it is important to understand who have

access to alternative or supplementary activities that can bring sustained

and significant improvements in incomes or welfare for the individuals or

households concerned. A clear understanding of the entry-barriers faced

by different groups within the society, or even individuals within a

household is, therefore, very useful and important to academics,

institutions providing micro or SME finance, and particularly for policy

makers. Specially, financial institutions which are providing micro or

SME finance in different activity-based projects to alleviate poverty or to

develop entrepreneurial base for SME, may be benefited from this study

being better informed about the groups of entrepreneurs suitable for

heterogeneous off-farm activities.

(II) Objective of the study

This study attempts to identify the ability factors of rural households that

affect their choices for heterogeneous off-farm activities to diversify

incomes. The study has explored the capacity of individuals or households

to engage in off-farm activities crossing varying levels of entry barriers

with various forms capital (human, financial, social etc). Therefore, the


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study has segregated the socio-economic, demographic and other factors

of rural households that determine the access to the preferred high

remunerative off-farm activities.

(III) Methods of Analysis

Logistic regression method has been used for modeling the choices of

households for some selected sample off-farm activities. Since the forms

of choices or the categories of the dependent variable are more than two or

multi-categorical, the multinomial logistic regression analysis is used to

predict the probability of being in the specific sample category of the

dependent variable for a set of independent variables or factors. A

sequential description on the estimation model, data and variables are

presented below in this section:

Estimation model

The logistic regression models the logit-transformed probability as a linear

relationship with the predictor variables. Let X1, X2, X3, . . . . . . Xn be a set

of predictors or independent variables and Z be the logit for the dependent

variable, then the logistic regression model can be written as follows:

Z = logit (P) = Log (P/1-P) = b 0 + b 1 X 1 + b 2 X 2 + . . . . . . . + b n X n

or

Z = ln [odds (event)] = ln [prob (event) / prob (nonevent)] = ln [prob (event) /

1-prob (event)]

= b 0 + b 1 X 1 + b 2 X 2 + . . . . . . . + b n X n

In terms of probabilities, the above regression equation can be translated

as follows:

P = Exp (b0 + b1 x1 + b2 x2 +. . + bk xk) / 1+ Exp (b0 + b1 x1 + b2 x2 + . . + bk xk)

Where b 0 is the constant and there are n independent (X) variables. Beta

coefficients are used to predict the log odds (logit) of the dependent

variable. To convert the log odds (which is Z, which is the logit) back into

an odds ratio, the natural logarithmic base e is raised to the Zth power:


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odds (event) = exp (Z). Exp (Z) is the log odds of the dependent, or the

estimate of the odds of event.

Logistic regression estimates parameter values for b0, b1, b2 …..bn through

the Maximum Likelihood Estimation (MLE).

Data, Sample and Statistical tool

A cross-sectional sample survey data of 300 borrowers from the SECP

program of RAKUB, randomly collected from eleven than areas of

Rajshahi district, under purposively selected five categories of sample off-

farm activities popular in the sample area has been used in the analysis.

The SPSS package (11.5 Version) was used to analyze the data. The

sample design of the study is presented below:

Table 1: Sample design

Off-farm Activities

Sample Area Animal

raising Poultry Fishery Nursery Others Total

Mohonpur 5 10 15 16 10 56

Tanore 13 6 14 6 12 51

Godadari 4 4 5 0 6 19

Bagmara 4 8 5 0 4 21

Durgapur 7 8 4 2 8 29

Rajshahi 9 12 11 2 5 39

Charghat 5 9 10 7 4 35

Putia 14 9 6 6 15 50

Total 61 66 70 39 64 300

Variables

The list of the variables containing their descriptions and units of

measurements are presented below:


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Dependent variable

Name Definition and measurement

Y choice Choice of off-farm activity

(1 = Animal raising, 2 = Poultry, 3 = Fishery, 4 = Nursery,

5 = Others)

Explanatory variables

Name Definition and measurement

AGE Age (years)

GEN Gender (0 = Female, 1 = Male)

EDU Educational level ( 1 = Primary, 2 = Secondary, 3 = Higher

Secondary,

4 = Graduation & above, 5 = No Schooling )

PEX Past experience (0 = No, 1 = Yes )

TRNG Training (0 = No, 1 = Yes)

HOF Household head (0 = Others, 1 = Himself)

NOFM Size of the family (number of family members)

LAND Household land ownership (1 = Landless and marginal farmer (0

--1.49) acres, 2 = Small and Medium farmer (1.5 -- 4.99) acres, 3 =

Large farmer (5.00+) acres.)

EQINV Equity investment in the project (TK)

REQTD Ratio of equity to debt investment (Equity/Loan)

THIN Household income (TK)

ROFFIN Ratio of household off-farm income (Off-farm income/Total

income)

ASSET Household assets of the borrower (TK)

LSNET Level of social network (0 = No participation, 1 = Participate in

one organization, 2 = Participate in two organizations, 3 =

Participate in three or more organizations)

WLDIV Whether the borrower had loan diversion motive (0 = Yes, 1 = No)

DTBR Distance from a borrower’s residence to local branch (Km)

DTBZ Distance from a borrower’s residence to local market (Km)


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(IV) Data Analysis and Interpretation

Descriptive statistics

Table 1 in the appendix shows the descriptive statistics of the variables

included in the model. The summary data show that among the five

categories of the dependent variable, 20.33 percent sample borrowers have

the choices for animal raising, 22.00 percent for poultry, 23.33 percent for

fishery, 13.00 percent for nursery and 21.33 percent for others activities.

In the set of categorical independent variables, 29 percent are female and

71 percent are male. Thirty one (31) percent sample borrowers have

education up to the primary level, 29 percent have the secondary level,

15.6 percent the higher secondary level, 13 percent the graduation and

above level of education, and 11 percent have no formal education. The

case processing summary also show that 64.33 percent sample borrowers

have the practical work experiences and 35.67 percent have no

experiences. Although training is compulsory in the SECP, 17 percent

sample borrowers are found to have no training and 83 percent have the

formal training. In total, 65 percent sample borrowers have reported

themselves as the heads of their respective families and 35 percent as the

subordinates. The summary data show that 35 percent sample borrowers

participate in one organization, 21 percent in two and 10 percent in three

or more organizations. Thirty four (34) percent sample borrowers have no

participation in any credit organization except the SECP. Sixteen (16)

percent sample borrowers have acknowledged that they have diverted

loans for other purposes and 84 percent have used loans for the right

purposes.

In the total samples, 40 percent households are found as the landless and

marginal (0 – 1.49 acres), 45 percent the small and medium (1.5 – 4.99

acres) and 15 percent the large (5+ acres).

For continuous independent variables, the average age of sample

borrowers is 34 years, average family size is 4.49, average family income

is Tk.86,600 per year, average equity investment in the projects is

Tk.44,100, average asset holding is Tk.78,500, average share of household

off-farm income is 29 percent, average ratio of equity to debt is 1.37,

average distance of local bank branch is 4.75 km and average distance of


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local market from home is 1.38 km. Total 300 cases were processed in the

analysis and there were no missing cases.

Model Fitting Information

Table 2 shows the model fitting information of the regression analysis

which indicates whether this model gives adequate predictions compared

to the null model or the intercept only. The null model gives the initial test

for the model in which the coefficients for all the explanatory variables are

zero.

Table 2: Model fitting information

Model -2 Log

Likelihood Chi-Square df Sig.

Intercept Only 954.824

Final 427.252 527.572 92 0.000

Model fitting information shows that the final model which includes all

the explanatory variables with the intercept is outperforming the null

model at zero percent level of significance. Since the logistic regression

follows the maximum likelihood estimation method, it calculates the

values of -2 log likelihood for both the null and final model and calculates

the chi-square value from the difference of -2 log likelihood values. The

value of the -2 log likelihood statistic ranges from zero to infinity and has

a chi-square distribution with q (the difference in the numbers of

parameters in the two models) degrees of freedom. The statistic -2log

likelihood is used to test the hypothesis that the parameters corresponding

to the deleted variables are zero which implies that the null model and the

final model fit the data equally well. The significance test for the final

model chi-square (after the independent variables have been added) is the

statistical evidence of the presence of a relationship between the

dependent variable and the combination of the independent variables.

Since the chi-square value is large (difference of log likelihood values)

and significant at zero percent level of significance, the null model is to be

rejected. It implies that the coefficients of explanatory variables are not

equal to zero and the final model with all the explanatory variables fits the

data better than the intercept only.


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Overall Classifications

Table 3 shows the results of regression analysis on what correct

percentages the fitted model can predict or correctly classify the groups

or categories of the dependent variable. Ultimately the predictive accuracy

of the regression model is judged by the overall percentage correct

predicted by the model.

Table 3: Results of the regression analysis

Observed Predicted

Animal

raising Poultry Fishery Nursery Others

Percent

Correct

Animal raising 39 3 5 3 11 63.93

Poultry 6 46 10 2 2 69.70

Fishery 4 6 54 0 6 77.14

Nursery 1 4 1 31 2 79.49

Others 5 4 5 0 50 78.13

Overall Percentage 18.33 21 25 12 23.67 73.33

The classification results show that the fitted model gives 73.33 percent

overall correct predictions for the categories of the dependent variable. In

other words, the fitted model with the set of the selected variables has

predicted correctly the overall 73.33 percent choices for off-farm

activities. The fitted model has given much better predictions for all the

categories of the dependent variable compared to the null model.

The benchmark that is usually used to characterize a multinomial logistic

regression model as useful is a 25% improvement over the chance

accuracy. The proportional by chance accuracy rate is used to evaluate the

usefulness of a logistic regression model. The proportional by chance

accuracy rate is computed by squaring and summing the marginal

percentages of the dependent variable (exhibited in the case processing


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summary). Therefore, the proportional by chance accuracy rate of the

model is 25.82 percent (1.25 x 20.65 % = 25.82 %). The classification

accuracy rate of the fitted model is 73.33 percent which is much greater

than the proportional by chance accuracy criteria of 25.82 percent.

Therefore the classification accuracy as well as the adequacy of the fitted

model is satisfactory in this analysis.

Measures of Effect Size or Pseudo R-Square

Table 4 shows that the measurement of effect size or the proportion of

variation explained by the fitted model. There is no widely-accepted direct

analogy to the R2 of OLS regression. The R-squared measures for logistic

regression cannot be compared directly with the R2 of OLS. Nonetheless, a

number of logistic R-squared measures may give an approximation to OLS

R2, not as actual percent of variance explained.

Table 4: Results of Pseudo R-Square

Pseudo R-Square

Cox and Snell 0.828

Nagelkerke 0.864

McFadden 0.553

Likelihood Ratio Tests

Table 5 in the appendix shows the results of the likelihood ratio test of the

logistic regression. The level of significance of each variable indicates

whether the variable has significant overall relationship with the

dependent variable. Results show that all the selected independent

variables are statistically significant at less than ten percent level of

significance. Therefore, the results of the likelihood ratio test indicate that

the independent variables included in the model have significant overall

relationship with the dependent variable.


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Parameter Estimates

Table 6 in the appendix shows the results of parameter estimates of

logistic regression. Parameter estimates show the beta value, standard

error of beta, Wald statistic and its corresponding level of significance,

and odds ratio for each explanatory variable. For better understanding of

the regression results, the probability value of corresponding odds ratio is

also added to the results of parameter estimates. (table7 in the appendix)

(V) Interpretations of Parameter Estimates

The results of parameter estimates reveal the following about the

explanatory variables.

Age

The age of a borrower is found as a significant factor for deciding the

choice of fishery. One additional year of age has decreased the probability

of choosing fishery by 2 percent (.48-.50) compared to the reference

category-other activity. Though the age has increased the probability of

choosing animal-raising by 1 percent (.51-.50) and decreased the

probability of choosing nursery by 2 percent (.48-.50), these results were

not statistically significant (as wald statistics of these were not

significant). In case of poultry, the age of a borrower had no effect.

Family Size

The family size of sample borrowers was found to have significant effect

on deciding the choices for fishery and nursery. An extra member in the

family has increased the probability of choosing fishery by 12 percent

(.62-.50) and decreased the probability of choosing nursery by 2 percent

(.48-.50). It has also increased the probability for choosing animal-raising

by 7 percent (0.57-0.50) and decreased the probability for poultry by 4

percent (0.46-0.50). But these results were not significant.

Household Income

Parameter estimates show that beta coefficients of household income in

animal-raising, poultry, fishery, and nursery are zero. The odds ratios of these

activities were 1 and corresponding probability values were 0.50. These


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results indicate that household income had no effect on the choices for these

activities.

Ratio of Off-farm Income

The ratio of off-farm household income was found as a significant factor for

all the selected categories off-farm activities. It had significant positive effects

on the choices for poultry and nursery but significant negative effects for

animal-raising and fishery. One point increase in the ratio increased the

probability of choosing both poultry and nursery by 47 percent (.97-.50)

compared to the reference category. On the other hand, the probabilities

reduced by 49 percent (0.01-.50) for animal-raising and 50 percent (.00-.50)

for fishery compared to the reference category. Sample borrowers who were

heavily dependent on off-farm incomes had preferred poultry and nursery, on

the other hand, who were marginally or less dependent on it had preferred

animal-raising and fishery.

Equity Investment

Parameter estimates show that beta coefficients of equity investment in all

the selected activities are zero. These results indicate that equity

investment has no effect on the choices for any specific activity.

Ratio of Equity to Debt

The ratio of equity to debt was found as a significant positive factor on the

choices for poultry, fishery and nursery. One point increase in this ratio

had increased the probability of choosing poultry by 37 percent (.87-.50),

the probability of fishery by 38 percent (.88-.50) and the probability of

nursery by 35 percent (.85-.50) compared to the reference category. These

results indicate that sample borrowers who had higher percentages of

equity investment in off-farm projects, had commonly preferred poultry,

fishery and nursery to others off-farm activities.

Household Assets

Parameter estimates show that the beta coefficients of household assets in

all the selected off-farm activities are zero. These results indicate that

household assets has no effect on the choices for any particular activity.


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Distance from Residence to Local Bank Branch

The distance between the local bank branch (source of finance) and the

borrower’s home was found as a significant factor on the choices for

animal-raising and fishery. One kilometer increase in the distance has

decreased the probability of choosing animal-raising by 8 percent (.42-.50)

and fishery by 9 percent (.41-.50) compared to the reference category.

Though this factor has increased the probability of choosing poultry by 3

percent and nursery by 5 percent, these results were not statistically

significant. These results may help draw such conclusions that

entrepreneurs who resided near the local bank branches had shown

preferences for animal-raising and fishery compared to others activities.

Distance from Residence to Local Market

The distance between the local market (local growth and information

center) and the borrower’s home was found as a significant factor on the

choice for nursery only. One kilometer increase in the distance has

increased the probability of choosing nursery by 39 percent (.89-.50)

compared to the reference category. It may imply that entrepreneurs who

usually resided at remote rural areas from the local trade center had clear

preferences for nursery.

Gender

Parameter estimates show that female borrowers were more likely to

choose animal-raising compared to male counterparts as the beta value

was found positive in the category. The female borrowers were found 27

percent more likely to choose animal-raising compared to the male

borrowers (reference category). The beta values of gender in poultry,

fishery and nursery were negative which indicate that female borrowers

were less interested to choose these activities compared to male

borrowers. But these results were not statistically significant in the

analysis.

Education

The educational level of a borrower was found as a significant factor on

the choice for poultry. This particular activity was found as the common

choice of all educated borrowers compared to illiterates (reference

category).


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Past Experience of Work

Past experience of a borrower was found as a significant factor on the choices

for animal-raising and fishery. These results indicate that inexperienced

borrowers were less likely to choose both these activities than experienced

borrowers. The inexperienced borrowers were 37 percent (.13-.50) less

interested to choose animal-raising and 34 percent (.16-.50) less interested to

choose fishery compared to the experienced borrowers (reference category).

Training

Professional training of a borrower was found as a significant factor on

deciding the choice for poultry. Parameter estimates show that the non-

trained sample borrowers were more likely to choose poultry compared to

the trained borrowers (reference category). They were found 47 percent

(.97-.50) more likely to choose poultry.

Head of the Family

Whether a borrower is the head or not of his family was found as a

significant factor on the choice for poultry. Sample borrowers who reported

themselves as not the heads of their families were found more interested to

choose poultry. They were 45 percent (.95-.50) more likely to choose the

activity. This result may help make such observation that young

entrepreneurs were creating self-employment mainly in poultry farming.

Level of Social Network

The level of social network (measured by participation in different

organizations such as co-operatives, NGOs, societies, various others social

organizations except the SECP) was also found as a significant factor.

Borrowers who participated in one social organization (level-1) were

found less interested (39 percent) to choose animal-raising than those who

did not. On the other hand, borrowers who participated in two social

organizations (level-2) were found more interested (41 percent) to choose

nursery. Borrowers who participated in three or more social organizations

(level-3) were found less interested (46 percent) to choose fishery.


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Diversion of Loan

The prior motive of loan diversion of a sample borrower was found as a

significant factor on the choices for poultry and fishery. Borrowers who

diverted loans for other purposes were 47 percent (.03-.50) less interested

to choose both these activities.

Household Land Ownership

The household land ownership of borrowers was found as a significant

factor on deciding the choices for fishery. Beta coefficients of the landless

& marginal farm households (from 0-1.49 acres of land) and the small &

medium farm households (from 1.5-4.9 acres of land) were negative in

fishery. These results indicate that both the landless & marginal and the

small & medium farm households were less likely to choose fishery

compared to large farm households (reference category). The landless &

marginal were 39 percent (.11-.50) and the small & medium were 42

percent (.08-.50) less interested to choose fishery. It clearly indicates that

only the large farm households have exclusive access to fishery.

(VI) Conclusions

Although the choice of a particular off-farm activity for self-employment

depends on both the motivation and the ability factors, the capacity of

households or individuals to participate in off-farm activities is not

uniform. Poverty, inequality in income and wealth, and human skills affect

the ability of an individual or household to engage in the preferred sector.

This study has identified some socio-economic ability factors found to

affect the decisions to engage in the selected off-farm activities. As many

as thirteen socio-economic ability factors of the SECP borrowers were

identified in five most preferred categories of off-farm activities by

borrowers in the study area. Table 8 below shows summary results of

those factors identified by parameter estimates of the fitted model of

logistic regression.


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Table 5: Factors affecting the choices for off-farm activities

Factors Animal-

raising Poultry Fishery Nursery

Age Sig.(-)

Family Size Sig.(+) Sig.(-)

Ratio of Off-farm income Sig.(-) Sig.(+) Sig.(-) Sig.(+)

Ratio of equity to debt Sig.(+) Sig.(+) Sig.(+)

Distance of local bank branch Sig.(-) Sig.(-)

Distance of local market Sig.(+)

Education of borrower Sig.(+)

Past experience Sig.(-) Sig.(-)

Training Sig.(+)

Head of the family Sig.(+)

Social network Sig.(-) Sig.(-)

Motive for loan diversion Sig.(-) Sig.(-)

Household land ownership Sig.(-)

In a nut shell, ratio of off-farm household income, past experience, social

network and distance of local bank branch were found to have significant

negative effects on the choice for animal-raising. Ratio of off-farm

household income, ratio of equity to debt, education, training, and

household head had significant positive effects on the choice for poultry.

Loan diversion was found to have significant negative impact on the

choice for animal-raising. Family size and ratio of equity to debt had

significant positive effects on the choice for fishery. Age, ratio of off-farm

income, distance of local bank branch, past experience, social network,


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loan diversion, and household land ownership had significant negative

effects for fishery. Ratio of off-farm income, ratio of equity to debt, and

distance of local market were found to have significant positive effects on

the choice for nursery but family size had significant negative effect on the

choice for nursery.

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The Economics of Price Volatility in Commodity Futures

Markets: A Survey

Mahmud Hossain Riazi

Abstract

This paper reviews the major contributions concerning commodity futures

markets with special attention paid to the dynamics of futures price volatility.

With the turn of the century, there has been a considerable shift in the subject

matter of volatility literature, the preponderance of the issues of seasonality being

the rather significant phenomenon than the previous research works. Keeping this

in mind, attempt has been made to compare and contrast the existing literatures

of volatility with its current trends and to identify what differences they entail in

their implications to deal with the more practical decision-making issues

regarding storage and hedging behaviour. In that pursuit, this paper addresses

both the theoretical and empirical literatures on futures price volatility and

critically examines them in terms of some more detailed topics like what

commodities they analyze, what models they employ, what techniques they use

for data construction and so on. The discussion will likely to trigger new research

insight in the field of futures price volatility.

Keywords: Commodity futures, volatility, seasonality, time-to-maturity, storage

theory, term-structure models, hedging

(I) Introduction

here is an extensive body of literature on the behaviour of commodity

futures prices. The main aspects of this literature are articulated and

discussed in the review articles of Carter (1999), Gray and Routledge

(1971), Kamara (1982), Blank (1989), Milliaris (1997), Garcia and

Leuthold (2004) and Lautier (2005). The critical areas of research that

dominate this vast literature can be classified into several broad categories:

(i) the issues in price discovery and efficiency of futures market (ii) the

analysis of term-structure of commodity futures price that aims to evaluate

the commodity related derivatives (iii) the identification of nature and

Associate Professor, Department of Economics, University of Rajshahi,


T


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causes of volatility of futures prices and (iv) the theories and empirical

issues in hedging that devise the effective ways of risk management.

With the growth of futures markets, the scope of research on commodity

futures prices has changed significantly during the recent years. The centre

of attention has been to capture, as precisely as possible, the stochastic

behaviour of commodity prices which play a central role for pricing

commodity contingent claims and quantifying their inherent volatility

structure (Schwartz, 1997). To this end, different latent factor models are

employed to determine the term-structure of commodity futures (and

option) prices and their volatility. In addition to this, different time-

varying volatility models for analyzing the volatility dynamics of

commodity prices have been used. These models require complex

mathematical algorithms, and sometimes, numerical techniques for their

solution because the storability of most commodities and their inherent

seasonality in the production or (and) consumption process cause their

stochastic dynamic system to be non-linear. The solutions of the dynamic

systems for futures prices and their volatility enable different economic

agents, especially the hedgers, to manage risk in an effective way.

This paper critically reviews the streams of empirical literature on the

nature and causes of commodity price volatility, especially, the volatility

of agricultural futures prices. However, before going to the thorough

analysis on this topic, the traditional term-structure models deserve a brief

discussion because these models have important implications for hedging

and analyzing seasonality. The effectiveness of an empirical model of

commodity price behaviour depends, to a great extent, on two vital issues:

first, how well this model fits the real world commodity price data and

secondly, and more importantly, how consistent the model is with the

underlying theories that guide the commodity price behaviour.

Accordingly, prior to analyzing the comparative analysis of the

commodity price models a brief analysis of the basic theories of

commodity prices is required.

The organization of this paper is as follows. Section (ii) describes the main

theories of commodity price and their main empirical tests. Section (iii)

analyzes the standard term-structure models. The previous research on the


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volatility of futures prices and their determinants are addressed in section

(iv). Section (v) presents some modelling issues in volatility literature.

Section (vi) reviews the literature on hedging and Section (vii) concludes.

(II) Basic Theories of Commodity Prices

The theory of storage (Kaldor 1940; Working 1949) and the theory of

normal backwardation (Keynes 1923; Hicks 1946) have been embraced as

the two most important theories of commodity price behaviour (Fama and

French, 1987). However, recently the focus of research has been shifted to

a great extent to the theory of storage in a rational expectation setting that

suits best for the explanation of the term-structure of volatility.

Theory of normal backwardation and its empirical test

The theory of normal backwardation explains the relationship between

spot and futures prices in terms of the function of transferring risk. The

theory of normal backwardation states that, in a normal situation, the

commodity markets are characterized by a forward price lying below the

spot price. Central to the analysis of normal backwardation is the existence

of a positive risk premium in futures market contracts. As speculators sell

insurance to the hedgers, the former should receive a positive risk

premium (often called the Keynes-Hicks risk premium) from the later and

this risk premium equals the difference between spot and futures price at

the contract delivery date.

There has been much empirical testing of the theory of normal

backwardation that led to much controversy and debate. It has never been

truly validated nor rejected. Telser (1958) tests the theory of normal

backwardation and rejects it. Cootner (1960), on the other hand, finds

evidence for this theory. Later, Dusak (1973) and Bessembinder (1993)

use the capital asset pricing model to test the presence of risk premia in

futures contracts. Dusak uses futures on wheat, corn and soybeans whereas

Bessembinder uses agricultural futures on live cattle, soybeans, sugar,

wheat, cotton and corn. They conclude that risk premium is not positive

for all commodities. It might be zero or sometimes negative leading to

normal contango. Kolb (1992) analyzes 29 futures contracts (16

agricultural commodities, 4 foreign exchanges, 2 energy futures, 2 bonds,

5 precious metals) and finds evidence against the existence of risk premia.


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In contrast, Fama and French (1987) analyze 21 commodities1 and find

evidence in favour of risk premia. Sorensen (2002) analyzes the term-

structure of corn, soybean and wheat prices and shows that normal

backwardation is valid for soybean and wheat, whereas the case is mixed

for corn with normal backwardation valid for long contract maturities and

contango valid for short contract maturities.

Theory of storage and its empirical tests

The theory of storage explains the relationship between the spot and the

futures price of a commodity in terms of convenience yield, a stream of

implicit revenue associated with the stock of physical holding of the

commodity. Brennan and Schwartz (1985) define convenience yield as the

“flow of services that accrues to an owner of the physical commodity but

not to the owner of a contract”. Kaldor (1940) proposes this theory which

has subsequently been elaborated by Working (1948, 1949), Telser (1958),

Williams (1989), and Brennan (1991). This theory posits that the marginal

value of convenience yield declines as inventory increases and becomes

zero for high inventory level. This inverse relationship is sometimes called

the Kaldor-Working hypothesis. The cost of carrying stocks from one

date to another determines inter-temporal price relation.

A positive convenience yield, through arbitrage, depresses the futures

price relative to spot price (Ng and Pirrong, 1994). This is evident from

the no arbitrage relation between the spot and the futures prices is:

where, is the futures price at t to be delivered at time T, is the spot

price at t, is the physical storage cost during the time span between t

to T, is the interest rate and is the convenience yield. This

1 Ten agricultural commodities (cocoa, coffee, corn, cotton, oats, orange juice, soybeans,

soy meal, soy oil and wheat), two wood products (lumber and plywood), five animal

products (broilers, eggs, cattle, hogs and pork bellies) and four metals (copper, gold,

platinum and silver).


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relationship can be expressed in terms of interest and storage adjusted

spread as:

The spread is inversely related to convenience yield and directly related to

inventory. This equation explains that the spread is below full carrying

charges and storage must be taken place at an opportunity cost, the

convenience yield. Using quarterly data from the United States

Department of Agriculture (USDA) on stocks of inventory, Sorensen

(2002) empirically validates the Kaldor-Working hypothesis for corn,

soybeans and wheat.

Modern version of the theory of storage

The modern version of the storage theory explains the relationship

between the spot and futures prices in terms of the interaction between

stock-out (depletion) and spread in a setting of a competitive storage

model based on rational expectations. Unlike the two theories mentioned

above, this modern theory of storage does not depend on the elusive

concepts like convenience yield or risk premium to explain the prevalence

of spread below full carrying charge in grain futures markets. Rather, it

uses the simple supply-demand fundamentals. This theory originates with

the pioneering work of Gustafson (1958), who explicitly assumes the

impossibility of carrying forward negative inventory, and Muth (1961),

who introduces the assumption of rational expectation2 in the competitive

storage model. Subsequently, Wright and Williams (1982) and Williams

and Wright (1991) formally elaborate the model.

The point of departure for the analysis of storage theory is the dual role

played by storage itself on the time series behaviour of the storable

2 By rational expectation is meant that the producers and the storers in the competitive

storage model are able to make objective calculations about the probability distribution of

yields and price response to the inevitable production shocks (Williams and Wright,

1991).


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commodity. The level of storage determined3 in the competitive storage

model has important price-smoothing properties; First, it can spread the

shocks of a good or bad harvest across a number of periods. Second, the

variance of price movement decreases as the amount of storage increases.

However, storage has an asymmetric effect on price as the price-

smoothing role of storage is limited only to supporting low prices rather

than lowering high prices. This asymmetry is attributed to the non-

negativity constraint on storage as private agents store only when there is

surplus, but, collectively, the market cannot borrow from the future.

The non-negativity constraint has significant implications for explaining

commodity price behaviour. The most important characteristics of

commodity (esp. the grains that are subject to seasonal harvesting) prices

is that they are mean-reverting and converge to a stochastic steady state in

the long-run. This phenomenon is the direct result of backwardation in

commodity prices and a spread below full carrying charges across a crop

year. The current shortage (very low or zero stockpile) leads to stock-outs,

increase spot price and thereby gives rise to backwardation in price. The

stock-out in turn increases the probability of having a small availability in

the next period. However, with a likelihood of successive replenishment of

stocks, the probability of stock-out in the distant periods will gradually

decrease and the spread will be below full carrying charges. This has been

empirically tested in the work of Deaton and Laroque (1992) for thirteen

commodities (bananas, cocoa, coffee, copper, cotton, jute, maize, palm oil,

rice, sugar, tea, tin, wheat) and Ng and Pirrong (1994) for four

commodities (copper, lead, silver and zinc).

The nature of volatility in commodity prices can also be seen in terms of

adjustment of stock. During the time of stock-out and shortage, the non-

negativity constraint does not allow the system to borrow from the future

which breaks the inter-temporal price linkage and price-smoothing role of

storage. Any minor shock can then create disproportionately large price

3 The level of storage and the price in each period are jointly determined in the

competitive storage model –the arbitrage activity of the storers based on rational

expectation determines the level of storage whereas the availability and storage rule

determine price in each period.


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volatility in the system. This phenomenon has been empirically verified by

Suenaga, Smith and Williams (2008) for NYMEX (New York Mercantile

Exchange) natural gas futures and by Suenaga and Smith (2011) for

NYMEX crude oil, heating oil and unleaded gasoline futures.

(III) Price Volatility and the Term-Structure Models

The stochastic term-structure models of commodity prices play a central

role in evaluating commodity-related derivatives and real assets. They try

to reproduce the prices of the derivatives (futures or options on futures)

observed in the market. They also provide a means for the discovery of

futures prices for horizons exceeding exchange traded maturities (Lautier,

2005). These models usually specify the dynamics of the state variables

that are assumed to follow some specific stochastic processes. The

arbitrage reasoning and the construction of a hedging portfolio lead the

model to provide a valuation formula for futures prices. The difference

between the model-implied futures prices and the observed futures prices

are interpreted as representing the risk premium.

The basic ideas that give rise to the formation of term-structure models for

commodity prices are: Black and Scholes‟ (1973) option pricing model;

Cox, Ingersoll and Ross‟ (1981) term-structure models for interest rates;

and Vasicek‟s (1977) application of Ornstein-Uhlenbeck process for

interest rate dynamics. Gibson and Schwartz (1990), Brennan (1991),

Schwartz (1997) and Schwartz and Smith (2000) are the exponents who

successfully introduce and popularize the mean-reverting models for the

valuation of commodity-contingent claims. At the centre of the analysis of

term-structure models for commodity prices is the theory of storage

(Kaldor, 1940; Working, 1948, 1949; Telser, 1958; Brennan, 1958;

Williams, 1989; and Williams and Wright, 1991).

Depending on the number and nature of factors (state variables) and the

specific stochastic process that they are assumed to follow there have been

several types of term-structure models of commodity futures prices. The

spot price of a commodity is thought to be the principal determinant of its

futures price. This leads most one-factor models to suppose the spot price


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to be the only factor, for example, Brennan and Schwartz (1985)4,

Schwartz (1997) and Cortazar and Schwartz (1997). Subsequently, in

order to capture a more realistic stochastic behaviour of commodity prices

a second factor, the convenience yield, is included.

Gibson and Schwartz (1990) and Schwartz and Smith (2000) are two basic

two-factor models that follow the approach of Cox, Ingersoll and Ross‟

(1981) for pricing commodity contingent claims. Gibson and Schwartz

(1990)5 are the first to assume that the convenience yield is

stochastic6 rather than constant and that it follows a mean-reverting

process. Specifically, the dynamics of spot price in this model follows a

geometric Brownian motion. One problem of this model is it does not give

a closed-form solution for the futures prices. Rather, its parameters are

estimated by Seemingly Unrelated Regression (SUR) analysis.

4 Brennan and Schwartz (1985) assume that commodity spot price follows a geometric Brownian

motion (GBM). However, subsequently, many scholars (Dixit and Pindyck, 1994; Cortazar and

Schwartz, 1994; Bessembinder, 1995; Schwartz, 1997) prefer mean-reverting price models over the

GBM process. 5 The spot price of oil, S and the net convenience yield, follow a joint diffusion process

as:

where, and are the increments to standard Brownian motion, and are the

volatilities of spot price and convenience yield respectively and ρ is the correlation co-

efficient between the two Brownian motions. The spot price of oil follows a geometric

Brownian motion whereas the instantaneous convenient yield follows a mean-reverting

process5. As the data on the state variables cannot be observed, proxy variables are used

for them. Using the no-arbitrage argument the value of futures contract, can be

shown by solving a particular differential equation:

subject to the initial condition:

However, there is no closed-form solution for the futures price and so a numerical

technique is resorted to for computation of futures price. The parameters of the model are

estimated using the seemingly unrelated regression analysis using the NYMEX data on

crude oil.

6 Brennan and Schwartz (1985) assume a constant convenience yield.


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Gibson and Schwartz (1990) inspire the formulation of a host of more

sophisticated models, for example, Schwartz (1997), Cortazar and

Schwartz (2003) and Hilliard and Reis (1998). Schwartz (1997) is the

most popular term-structure model of commodity price dynamic. Unlike

Gibson and Schwartz (1990), this model gives a closed-form solution for the commodity contingent claims and is mathematically tractable through

the use of Kalman filter technique. Cortazar and Schwartz (2003) devise a

three factor model which is an extension of a reformulated two-factor

model of Schwartz (1997). This model is more parsimonious than

Schwartz (1997) in terms of number of parameters needed to be estimated.

Hilliard and Reis (1998) extend Schwartz (1997) model in order to

introduce jumps in the spot price process so that it can capture sudden

supply and demand shocks. This model suits better for energy

commodities.

Schwartz and Smith (2000) criticize the Gibson and Schwartz (1990)

tradition for assuming equilibrium price, to which the short-run prices

revert, to be fixed. In contrast to Gibson and Schwartz (1990), they

decompose the spot price into two stochastic factors: the short-term

deviation in price and a long-run equilibrium price level which is assumed

to be uncertain. The short-run deviation in price follows a mean-reverting

process of the Ornstein-Uhlenbeck type7 whereas the long-run equilibrium

price level is assumed to follow a GBM process. This short-term/long-

term model gives a closed-form solution for the futures prices and is

exactly equivalent to the Gibson and Schwartz (1990) model8. This model

is the simplest form of the general affine9 term-structure models. This

model is very realistic and amenable to empirical analysis. First, it avoids

the estimation of convenience yield which is an „elusive‟ concept to many.

7 Uncertainty about the long-run price stems from changes in expectations about existing

supply, technological improvement regarding production and exploration of a

commodity, inflation or any regulation that can affect supply. Conversely, short-run

changes in price refer to any shock that limits the ability of the market to adjust inventory

levels to changing market conditions. 8 “The state variable in each model can be represented as linear combinations of the state

variables in the other” (Schwartz and Smith, 2000). 9 In this formulation, the logarithm of asset price is a linear function (affine function) of

latent (unobservable) state variables.


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Second, the volatility of the futures price is easy to calculate. Third, this

model is parsimonious compared to Gibson and Schwartz (1990) and,

fourth, it can easily incorporate seasonal factor. Sorensen (2002) extends

the affine term-structure model to analyze seasonality in agricultural

commodities (corn, wheat and soybeans futures), whereas Todorova

(2004) applies this model for analyzing seasonality in crude oil and the

natural gas futures market. Subsequently, more complex affine models

have been set by Cassassus and Collin-Dufresne (2005), or in the

stochastic volatility model10

of Richter and Sorensen (2002).

Two-factor models have been extended later by different authors to get a

more precise model for explaining commodity price dynamics. Schwartz

(1997) includes a third factor, the stochastic interest rate. Manoliu and

Tompaidis (2002) introduce a multi-factor model, while Cortazar and

Schwartz (2003) introduce a third factor, long-term spot price return to the

reformulated Schwartz (1997) model. Hilliard and Reis (1998) introduces

a jump in the spot price process of Schwartz (1997) model. However,

there is always a trade-off between model performance and complexity of

the models. Schwartz (1997) compares performance between one-factor,

two-factor and three-factor models. Whereas two-factor model

outperforms one-factor model significantly, the three-factor model only

marginally improves the performance of two-factor models.

Although, the traditional term-structure models are very useful for pricing

commodity contingent claims and devising hedging strategies they have

limited practical use in explaining the pattern of commodity price

volatility. These models provide a crude measure of volatility dynamics of

futures prices that depends on time-to-delivery and the volatility

parameters of the state variables. They can only indicate the volatility of

futures prices to be a decreasing function of time-to-delivery. But, due to

the time-invariant nature of the volatility of state variables, the model

implied volatility measure is unable to explain the time-varying volatility

that stems from seasonality and other shocks in the economy. However,

10 The traditional term-structure models assume the volatility of the underlying state

variables to be constant, whereas in the stochastic volatility models the volatility of the

underlying state variables follows some stochastic process.


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the affine term-structure model can incorporate seasonality and time-

varying volatility in the system (Sorensen, 2002; Richter and Sorensen,

2002).

(IV) Theories and Empirics on the Volatility of Futures Prices

Hypotheses on Price Volatility

Commodity prices, especially the prices of agricultural commodities, are

subject to high degrees of volatility. Production decisions and risk-

management require the producers, commodity traders and policy makers

to have good knowledge about the pattern and causes of price volatility of

agricultural commodities. The price variability of agricultural

commodities has been attributed to a number of factors: (a) Reactions to

information flows (Kyle, 1985; Anderson and Bollerslev, 1997); (b) Time-

to-delivery (Samuelson, 1965; Milonas, 1986; Castelino, 1982); (c)

Seasonality (Anderson, 1985; William and Wright, 1991); (d) Persistence

in volatility (Kenyon et al., 1987) and (e) Trade volume (Cornell, 1981;

Streeter and Tomek, 1992).

The most important hypothesis concerning the dynamic behaviour of

commodity prices is the time-to-maturity effect (Samuelson, 1965). This

hypothesis states that the movements of prices are large for short-term

contracts and small for long-term contracts, indicating that the volatility

exhibits a decreasing pattern along the price curve. As futures contracts

approach their expiration date and incorporate new information, they react

much more strongly to information shocks, due to the ultimate

convergence of futures prices to spot prices upon maturity.

Seasonality is the other source, and to many the biggest source, of

volatility for most agricultural (and energy) commodities. According to

this view, seasonality lies behind the interaction of real economic

variables: very low inventory at the end of the production cycle together

with the impossibility of borrowing from the future break the inter-

temporal arbitrage link of storage. For some commodities, this response to

low inventory is attributed to the inelastic nature of the demand curve, for

example, natural gas in winter (Suenaga, Smith and Williams, 2008). For

other commodities this is the result of both inelastic demand and supply

curve, for example, the commodities that are complementary in production


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but subject to different timing of consumption, such as heating oil and

unleaded gasoline (Suenaga and Smith, 2011).

Empirical Studies on time-to-maturity and seasonality

The time-to-maturity and seasonality effects have been subject to many

empirical tests. Rutledge (1976) examines daily price movements of

silver, cocoa, wheat and soybean contract, respectively, from Commodity

Exchange Incorporated of New York (COMEX), New York Cocoa

Exchange, Kansas City Board of Trade (KC) and Chicago Board of Trade

(CBOT). He rejects the Samuelson hypothesis for wheat and soybean oil,

but accepts it for cocoa. Miller (1979) uses logarithms of the daily closing

price and analyses June and December live beef contracts of the Chicago

Mercantile Exchange (CME) for 1964-1972. She finds no support for the

time-to-maturity effect. Castelino (1982) uses daily data of wheat, corn,

soybeans, soybean meal and soybean oil contracts at CBOT and copper at

COMEX over 1960-1971. The result supports the Samuelson hypothesis.

Anderson (1985) tests both time-to-maturity and the state variable

hypothesis11

for seven agricultural commodities (wheat, corn, oats,

soybeans, soybean oil, cocoa and live cattle) and a metal, silver. The tests

shows that time-to-maturity effect and the state variable hypothesis can

hold at the same time. Besides, for most of the commodities seasonality

effect exceeds maturity effect. The secondary factor is the time-to-

maturity. The other important finding of his study is that the time-to-

maturity holds only for daily price data.

Milonas (1986) provides empirical support for the time-to-maturity effect

for a large number of commodities. On the other hand, Deaton and

Laroque (1992) and Chambers and Bailey (1996) show that Samuelson

effect is a function of storage cost. A high storage cost leads to less

transmission of shocks via inventory across periods and so volatility of

futures price declines rapidly with maturity.

11 State variable hypothesis relates volatility with the supply-demand state variables.

When uncertainty regarding the state variables is resolved, prices incorporate new

information which leads to price volatility. According to the state variable hypothesis,

this process of new information generation at the maturity of a contract is itself seasonal

(Stein, 1979; Anderson and Danthine, 1983).


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Khoury and Yourougou (1993) use daily data on canola, rye, feed barley,

feed wheat, flaxseed and oats from the Winnipeg Commodity Exchange

and test the determinants of volatility. The results show that the price

volatility is affected by the year, calendar month, contract month, time-to-

maturity and trading session effects. Kenyon, et al (1987) analyze five

agricultural commodities: soybean, corn, wheat, live cattle and live hogs,

and find that grain price volatility is affected by seasons and level of

futures price relative to loan rate.

Yang and Brorsen (1993) use a GARCH (1, 1) model to test the effect of

day-of-the-week, time-to-maturity and seasonality on the basis values of

15 different futures prices (7 grains, 5 metals and 3 financial futures).

There is a significant day-of-the-week effect: Mondays have higher

variance and Wednesdays have lower variances, so the results favour the

calendar time hypothesis over the trading time hypothesis. Also,

agricultural futures prices show seasonality in their variance. Streeter and

Tomek (1992) find that the time-to-maturity has a non-linear effect on

price volatility, the volatility decreases in the month before maturity.

However, there is a strong relationship between seasonality and volatility

of soybean futures prices. Hennessy and Wahl (1996) show that

seasonality affects the price volatility of corn, soybean and wheat.

However, there is no influence of time-to-maturity on volatility.

There are many recent empirical studies on the effect of seasonality and

time-to-maturity on the behaviour of commodity futures prices. Manoliu

and Tompaidis (2002); Suenaga, Smith and Williams (2008); Suenaga and

Smith (2011) are significant studies on energy related commodities. On

the other hand, Goodwin and Schnepf (2000); Sorensen (2002); Richter

and Sorensen (2002); Chatrath, Adrangi and Dhanda (2002); Schaefer,

Myers and Koontz (2004); Smith (2005); Kalev and Duong (2008); Karali,

Dorfman, Thurman (2010); Karali and Thurman (2010); Ovararin and

Meade (2010) conduct research on seasonality in agriculture.

Sorensen (2002) analyzes the stochastic behaviour of agricultural

commodity prices under seasonality using CBOT weekly futures price

data of corn, soybean and wheat. This adds a seasonal component to the

short-term/long-term model of Schwartz and Smith (2000). The estimated


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seasonal parameters show that this model fits the CBOT agricultural

futures data better than the Schwartz and Smith (2000). For all three

commodities the estimated seasonal components peak two or three months

before the beginning of US harvest. However, this effect is less

pronounced for soybeans due to the bulk supply of soybeans in the US

market from the Southern hemisphere. On the other hand, the falling

standard deviation of the futures prices with increasing time-to-maturity, a

positive mean-reverting parameter for the stationary state variable and a

smaller value of the estimated volatility parameter for non-stationary

component than the stationary component suggest that the data supports

Samuelson hypothesis.

Manoliu and Tompaidis (2002) add a deterministic seasonal component to

a multi-factor model within the Heath, Jarrow and Morton (1992)

framework and test it with NYMEX daily natural gas futures price. The

estimated model exhibits a strong seasonal variation in price, with a higher

monthly seasonality index for winter months and a lower seasonality index

for summer months. Richter and Sorensen (2002) use the weekly soybean

futures price from CBOT and analyze the effect of seasonality in soybean

futures and options. They extend the Gibson and Schwartz (1990) model

with an additional factor, a stochastic volatility term, and add two

deterministic trigonometric seasonal functions to it, one for the

convenience yield and the other for the stochastic volatility term12

. They

12 As in Gibson and Schwartz (1990), the convenience yield follows a mean-reverting process. If Pt,

δt and υt denote respectively the three state variables – spot commodity price, convenience yield

and seasonally adjusted spot price volatility – the dynamics of the three dimensional process (P, δ,

υ) can be described by a system of stochastic differential equations:

where, β, κ, θ, λP, λδ, λυ, σδ, συ are constant parameters to be estimated. W = (W1, W2, W3) is the

three-dimensional Wiener process where the Ws are assumed correlated one another with

correlation coefficients ρ12, ρ13 and ρ23. The parameters β and κ are the degree of reversion to the

deterministic seasonal pattern in convenience yield and long-run volatility level, θ, respectively.

The parameters λP, λδ, and λυ are respectively the risk premia on uncertainty of commodity price,

convenience yield and volatility. The parameters σδ, συ denote volatility of convenience yield and

stochastic volatility of commodity price, respectively.

The functions α(t) and ν(t) show seasonal patterns in convenience yield and volatilities

and are specified by trigonometric functional forms. In the first two equations, both the changes in


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find high price volatility (a global maximum in the seasonal function) in

soybean prices before US soybean harvest (July). On the other hand,

soybean volatility is low in March before US planting (in May and June).

Besides, the volatility process shows considerable mean-reversion and is

consistent with the theory of storage.

Chatrath, Adrangi and Dhanda (2002) find that daily returns on soybean,

corn, wheat and cotton are highly dependent on seasonality. Besides,

soybean and corn support the time-to-maturity effect. Kalev and Duong

(2008) analyze eight commodities (corn, soybean, soybean oil, soybean

meal, feeder cattle, lean hogs, live cattle and pork bellies) and find

evidence of a time-to-maturity effect.

Smith (2005) uses partially overlapping time-series (POTS)13

model and

performs an empirical test of the theory of storage and the Samuelson

effect with CBOT corn futures price data. The resulting high value in the

proportion of model variance explained by the common factors,

, gives an indication that suggests the data support the

theory of storage. On the other hand, a high proportion of old crop

variance to new crop variance strongly supports a low correlation between

factors and thereby a break in the relation between nearby and distant

futures prices, a backwardated price and low inventory. This model also

supports the time-to-maturity effect.

Suenaga, Smith and Williams (2008) show that highly non-linear volatility

dynamics of natural gas futures prices are attributed to strong seasonality

in demand and storage as well as the time-to-maturity. Their analysis is

built on the principles of storage theory. Using the POTS model of Smith

(2005) in a single-factor setting they analyse the NYMEX data on natural

gas futures price. They argue that both inelastic winter demand and high

spot prices and convenience yield are affected by the same seasonality pattern because seasonality

influences both variables through storage.

Futures price can be found by using the Feynman-Kac formula:

There is no closed-form solution for the futures price.

13 The POTS model is presented in sub-section 5.1


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storage cost peculiar to natural gas reduce inventory level and thereby

limit the price-smoothing property of inventory during winter. Any

information shock about winter gas availability, which normally comes

during the preceding off-peak season (May-September), causes the prices

of winter futures contract to be highly volatile. On the other hand, the

price of early winter futures contract is less volatile as inventory keeps

piling up in this period. That the above pattern of seasonality in price

volatility is consistent with the theory of storage is further supported by

the seasonal pattern of US nationwide gas storage data.

Suenaga and Smith (2011) use the POTS model of Smith (2005) and

analyze the volatility dynamics in the price of three petroleum

commodities – crude oil, unleaded gasoline and heating oil. Using

NYMEX futures price data, they find strong seasonality and time-to-

maturity effects in the highly non-linear volatility pattern of futures prices.

Ovararin and Meade (2010) use daily closest futures prices of rubber, rice

and sugar and test for mean-reversion and seasonality in volatility. They

consider two types of seasonalities: a day-of-the-week seasonality, which

represents investor‟s behaviour, and a yearly seasonality, which

demonstrates the effect of the harvest. A GARCH (1, 1) model is extended

thrice with trigonometric deterministic seasonal functions that capture

mean-reversion, day-of-the-week effect and yearly seasonality. The

estimated results show that the daily return process for the three

commodities are not mean reverting but show day-of-the-week effect and

annual seasonality. Karali and Thurman (2010) study the effect of time-to-

maturity, seasonality, calendar trend and volatility persistence using

CBOT multiple contracts on corn, soybeans, wheat and oats traded each

day. They apply the generalized least square method of Karali and

Thurman (2009) and estimate the model parameters by seemingly

unrelated regression analysis. The study strongly supports the time-to-

maturity effect and seasonality in the pattern of price volatility. The

volatility peaks in the summer, just two months before the harvest.

(V) Issues in Volatility Modelling

This section presents some technical issues that are crucial for volatility

modelling. Basically three issues are stressed here: (i) a comparison


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between models in terms of their relative advantages and disadvantages

(ii) issues in data arrangement (iii) different methods for modelling

seasonality.

Types of Volatility models

The models that are popular for volatility analysis are briefly discussed in

this sub-section. Six models are widely seen in this case.

(a) Constant volatility models for the analysis of commodity contingent

claims (Sorensen, 2002; Manoliu and Tompaidis, 2002).

(b) Historical volatility models in which the volatility is estimated based

on previous historical standard deviation.

(c) Autoregressive conditional heteroskedastic models(ARCH) and

Generalized ARCH (GARCH) models (Yang and Brorsen, 1993;

Ovararin and Meade, 2010)

(d) Stochastic volatility (SV) models (Richter and Sorensen, 2002)

It differs from GARCH model in that the conditional variance in a

stochastic volatility model itself depends on a stochastic process.

(e) Implied standard deviation or (ISD) models

This is the volatility implied by the Black-Scholes option pricing

model.

(f) Partially overlapping time-series analysis (POTS) (Smith, 2005;

Suenaga, Smith and Williams 2008; Suenaga and Smith 2011)

The last model is comparatively new and needs some explanation.

Concerning econometric issues, a serious drawback of the other models

that employ standard no-arbitrage contingent claim valuation models is

that they pay too much attention to the time series properties of the term

structure of futures price and unduly ignore its cross-sectional dimensions.

In practice, multiple contracts of a commodity with different delivery

dates trade simultaneously in a futures market and thereby generate

partially overlapping time series at a point of time. However, in practice,

most studies work with truncated data series in that they „reduce the data

in a single time series‟ by „splicing together the nearby contract that is the

closest to maturity‟. This type of rolling over of futures prices excludes

much of the information about the commodity. In contrast, POTS model

uses all contracts traded on a given day.


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In futures markets, contracts at a point of time are priced by two factors,

some are priced by old-crop factors while the rest are priced by new-crop

factors. The interaction between the two prices provides the same

information as is embedded in the dynamics of inventory in its role of

price smoothing between old and new crops. This line of reasoning allows

POTS to document different features of the theory of storage by only two

latent factors: the old-crop factor and the new-crop factor. In addition to

specifying two factors, POTS model incorporates time-varying conditional

heteroskedasticity and time-to-delivery and cross-sectional variation in

the factor loadings or innovation variances.

Unlike most term-structure models where prices are expressed in levels,

POTS model considers price in first differences. This price change is a

linear combination of the common factors and an idiosyncratic term as:

= +

where, = is the change in futures price, is the 2 x

1 vector of common factors that exhibits time varying conditional

heteroscedasticity, d is the time to maturity, t is the date of price

observation, and represent the factor loading and innovation

standard deviation, respectively14

.

Poon and Granger (2005) compare 93 volatility studies over the last two

decades and find that the implied volatility model outperforms other

models, followed by GARCH and the historical volatility model15

. On the

other hand, GARCH models are always better than ARCH models as the

former nests the latter.

Data Construction16

Data arrangement is a complex issue in any research work on commodity

future markets. The construction of futures price data depends on the 14 The POTS model is estimated in two steps by using iteration method: the first step is

Expectation Maximisation algorithm (Kalman Filter and Newton-Raphson methods)

and the second step is Berndt-Hall-Hall-Hausman algorithm. 15 This review excludes the POTS model. 16 This articulation is done in the manner of Karali, Dorfman and Thurman (2010).


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nature of research questions. Data constructions fall in the four broad

genre as follows:

(a) Data splicing and the formation of a single time series by nearby

contract (Yang and Brorsen, 1993; Khoury and Yourougou, 1993;

Chatrath, Adrangi and Dhanda, 2002).

(b) Data arrangement according to single delivery month contract, for

example, September wheat contract or December corn future

(Kenyon, Kling, Jordan, Seale and McCabe, 1987; Streeter and

Tomek, 1992).

(c) The construction of separate time-series by the delivery horizon:

first closest to maturity, second closest to maturity etc. (Schwartz,

1997; Sorensen, 2002; Richter and Sorensen, 2002).

(d) No splicing; Using all futures contract traded (Smith, 2005;

Suenaga, Smith and Williams 2008; Suenaga and Smith 2011).

Smith (2005) has a significant implication concerning the

formation of one time series data traditionally by splicing nearby

contracts. He refutes the homogeneity of the so-called spliced data set as is

done traditionally. Instead, he suggests that data splicing could be optimal

if rolling over is done two to three months before delivery of corn so that

it can avoid the delivery month inefficiency.

Modelling Seasonality

The seasonal components in commodity prices are incorporated in the

empirical models mainly in three different ways: by introducing dummy

variables or by adding some trigonometric functions or cubic spline

functions to the model. The traditional approach is to use a standard

dummy variable technique (Todorova, 2004; Yang and Brorsen, 1993;

Kenyon et al., 1987).

Following the tradition of Hannan, Terrel and Tuckwell (1970), the recent

practice has been to incorporate in the model some specific deterministic

seasonal components as trigonometric function of time. This approach has

been used by (Gabillon, 1992; Yang and Brorsen, 1993; Sorensen, 2002;

Richter and Sorensen, 2002; Suenaga, Smith and Williams 2008; Karali

and Thurman, 2010; Suenaga and Smith, 2011). This method of modelling


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seasonality is more flexible in dealing with time. Sorensen (2002) for

example, simply adds a seasonal component to the existing state

variables in a Schwartz and Smith (2000) term-structure setting

to get the logarithm of spot price:

where, K determines the number of terms in the sum and and k are

parameters. Similarly, the factor loading and the idiosyncratic error term

in Suenaga, Smith and Williams (2008) are characterized by:

Again, K determines the number of terms and are parameters.

The optimum number of trigonometric terms depends on the trade-off

relation between model flexibility and coefficients‟ sensitivity to outliers.

In Sorensen (2002), the optimum number of terms is 2 whereas in

Suenaga, Smith and Williams (2008) it is 5.

An alternative way to capture the deterministic effect of season is

through the incorporation of cubic spline functions (Smith, 2005) as in the

tradition of Engle and Russell (1998). Splines are sequences of cubic

polynomial functions that are connected at different nodes. In the factor

model of Smith (2005), factor loading and innovation standard deviation

are spline functions of time.


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= + (

= + (

where, is indicator function and and are

parameters. The nodes , .... are chosen a priori.

Another approach to model seasonality components is to use periodic step

function (Manoliu and Tompaidis, 2002). This is also modelled as an

additive deterministic factor with other different state variables in an n-

factor setting. However, this approach is in essence the same as the

dummy variable modelling.

(VI) Hedging

Hedging is a process of portfolio diversification by simultaneously

choosing futures positions and underlying cash positions in order to reduce

price risk. The hedging literature so far pivots around two issues: to find

both the optimal hedge ratio and the index of percentage reduction in price

risk. There are two formulas to find the optimal hedge ratio: (i) the

minimum risk hedge ratio (McKinnon, 1967; Ederington, 1979) and (ii)

the utility maximising optimal ratio (Johnson, 1960; Heifner, 1972).

Anderson and Danthine (1983), Ho (1984), Hey (1987) develop a dynamic

hedging model. The bottom line of dynamic hedging is that the producers

can revise their hedge position over time. Although, conceptually dynamic

hedging models are very appealing, gains from dynamic hedging strategy

are small (Martinez and Zering, 1992). The other reason for which it has

not got much popularity is that long-term risks are, most of the time,

managed by sequential short-term hedges – rollover hedging. Rather, the

time-varying optimal hedging strategy has attracted more attention.

The first formula, the minimum risk hedge ratio, is widely used. By using

this formula to the portfolio theory of hedging the optimal hedge ratio is

obtained. This optimal hedge ratio is defined as the ratio of the covariance

between the return on spot and futures to the variance of the return on

futures price and depends on the specifications of the dynamics of

variances and co-variances. Ederington (1979), Anderson and Danthine


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(1981) assume that the covariance matrix between spot and futures return

is always constant which gives a constant optimal hedge ratio. However,

Baillie and Myers (1991) argue that the standard assumption of a time-

invariant optimal hedge ratio is inappropriate. They argue that the

covariance and the variance of spot and futures return depends on the

distribution of prices which changes over time. They define the optimal

hedge ratio by the ratio of conditional covariance between spot and futures

prices to the conditional variance of the futures price. Time variation in

the conditional covariance matrix is modelled using the multivariate

GARCH model. The daily futures price data of six commodities (Beef,

coffee, corn, cotton, gold, soybeans) over two futures contract periods are

used to calculate the optimal hedge ratio via GARCH model. The result

shows that time-invariant optimal hedge ratio is inappropriate.

Moschini and Myers (2002) use multivariate generalized GARCH model

and reject the null hypothesis that optimal hedge ratio is constant. They

also reject that optimal hedge ratio is solely explained by the seasonality

and time to maturity.

The analysis of Suenaga, Smith and Williams (2008) has a profound

implication in determining the optimal hedging strategy under seasonal

and cross-sectional variation in the volatility of futures price. The central

point of their analysis is that the POTS model causes the factor loadings to

have seasonal and cross sectional variations whereas the traditional term-

structure models determine the factor loadings by the time-to-maturity.

Accordingly, the optimal hedge ratio that varies by contract delivery date

can effectively be explained under the POTS framework rather than under

the traditional term-structure model where optimal portfolio depends on

the nearby contract. The hedger‟s decision variable thus reduces to finding

a specific futures contract that can be included in the optimal portfolio.

The contracts whose highest share of price volatility is explained by the

common factor are included in the optimal portfolio. To avoid

idiosyncratic volatility, at least three-month-ahead contracts should be

used. These criteria suggest that for hedging strategy of NYMEX natural

gas the optimal portfolio should include four contracts: „the December

contract‟ for the period of mid May to mid August (September to

November contracts are avoided due to high idiosyncratic variance


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stemming from maturity effects) and either „the June, July or August

contract‟ for the period of mid September and mid April of the following

year (April to June contracts are avoided because of high price volatility

due to seasonality in storage: low level of inventory limits the inter-

temporal movement of prices through storage). The optimal hedge ratio in

this model is a bit higher (1.2 to slightly more than 2.0) compared to that

implied by the conventional factor models. However, this high optimal

hedge ratio implied by POTS model is due to the high share of price

variance explained by common factor.

(VII) Conclusion

The above review summarizes the mainstream research conducted on

commodity futures prices. The issue emphasised is the volatility of

commodity futures price. The whole array of volatility research has been

considered from the viewpoint of their commodity composition, subject

matter, the data they use, their methodology of estimation and, above all,

the framework within which the analysis is being conducted. From the

analysis, it can be seen that there is a large amount of research on the

agricultural commodities, especially the volatility of corn, wheat and

soybeans. The centre of the analysis is the time to maturity and seasonality

as two commonly observed deterministic patterns in the commodity

futures price volatility. Indeed, this is the area where there is still scope for

further research. Agricultural commodities have prices with time-varying

volatility, which is well-captured by GARCH model. However, there are

few studies that deal with seasonality of agricultural commodities by

contract delivery under the GARCH framework.


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Impact of Market Size and Foreign Trade on FDI Inflow in

Bangladesh: A VEC Approach

Rakibul Islam

Abstract

This study investigated the effect of market size and foreign trading on FDI

inflow in Bangladesh over the period of 1986 to 2012 by using a vector error

correction model. The long term co-integration result identified the positive

impact of GDP and Export on FDI inflow and negative impact of Import on FDI.

Besides significant F value of VECM estimates suggest the overall short run

relationship among the variable under study. In short run, two year lagged export

and one year lagged import has positive impact on FDI inflow in Bangladesh.

The VEC granger causality result revealed strong unidirectional relation of

Export and Import to FDI inflow, FDI inflow to GDP, Import to Export and a

mild unidirectional relation of FDI to Import.

Keywords: Foreign direct investment, co-integration, gross domestic product,

export, import

(I) Introduction

he immense contribution of inflow of Foreign Direct Investment

(FDI) proved to be significant in many theoretical and empirical

researches conducted in many countries at different times by identifying

the improvement of host countries‘ infrastructural, technological,

entrepreneurial, social and financial resources (Adams 2009, Bergten, et

al. 1978, Seid 2002, Romer 1986, 1994, Lucas 1988, Mankiw et al. 1992,

Pugel 2007, Grossman and Helpman 1991, Nair–Reichert and Weinhold

2001). In addition, the study of United Nation Conference on Trade and

Development (UNCTAD) in 1992 also examine the truth of FDI led

growth hypothesis in developing economies and advocate initiatives to

encourage FDI to flourish economic growth in developing nations (Agosin

and Mayer 2000).

Lecturer, Department of Banking and Insurance, University of Rajshahi,

E-mail: [email protected]

T

mailto:[email protected]


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Bangladesh thriving for rapid growth with huge prospect should look

forward to expand FDI inflow to accelerate the momentum. The influence

of market size of a country and its level of foreign trading on FDI inflow

pronounced in different literatures (Charkrabarti 2001, Jordaan 2004)

induces the study conducted for Bangladesh with a more advanced

methodology for exploration and explanation of nexus as well as

anomalies of FDI inflow. Market size hypothesis of FDI predicts the flow

of FDI to the nations with larger market having greater ability to exploit

resources to derive economic of scale. Whereas, level of foreign trading

effect to FDI decision according to the nature and purpose of FDI. Unlike

export base FDI attracted by Open Export Policy, market seeking FDI

moves to that location where import barrier exists (Jordaan 2004). As a

result, the study used export and import separately to identify impact of

foreign trade on FDI inflow in Bangladesh.

The study set to identify the long-run equilibrium relationship between

GDP, Export, Import and FDI inflow and short run causal direction to

evaluate the impact of market size and foreign trading on FDI inflow in

Bangladesh. The remainder of the paper proceeds with Section II

exploring the brief review of empirical literature, Section III explains

theoretical framework of the study, Section IV shows the methodology;

Section V presents empirical results and Section VI draws conclusion.

(II) Brief Review of Empirical Literature

There are different perplexing results found after observing the empirical

literature supporting different theories for FDI. One major thing observed

that FDI and GDP have positive bidirectional relationship (Hsiao and Shen

2003) contrasting the negative on average relationship found by

Mencinger (2003) while Arshad and Muhammad (2012) signifies no

relation between them. It has been found that there are bidirectional

causality between FDI and trade (export and import) in case of developing

countries (Aizenmana and Noy, 2006; Fontagne and Pajot, 2000)

contrasting the unidirectional relationships among the variable found by

others (Pantulu and poon 2002). Again one way relationship observed

between FDI and exports has been found by studies (Pantulu and poon,

2002; Srivastava and sen, 2004) while the bidirectional causation between


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FDI and exports found in developed world (Iqbal et al., 2010). Again, Liu

et al.(2001) reported the existence of unidirectional relationship between

FDI and Exports; Imports and FDI in China. The Following specific

literatures have been presented in brief;

Arshad and Muhammad (2012) analyzed FDI, Investment, Trade (import

and export), Economic growth of Pakistan from 1965 to 2005 by using co-

integrating VAR technique and found long term relationship explaining

import and export effect on GDP but FDI insignificantly affecting GDP

while another long term relationship identifying import and export effect

on FDI but GDP fail to have significant effect on FDI. Therefore they

conclude that FDI and GDP have no significant relation in long run.

Nguyen (2011) used export, import, economic growth data of Malaysia

and Korea from 1970 to 2004 and vector auto regression model had been

employed to explain that all four variables have two way causalities

between each pair except GDP to export in Malaysia, whereas substantial

one way causality from export, import, GDP to FDI, from export and

import to GDP and from export to import observed in Korea.

Sharma and Kaur (2013) examined FDI, trade (export and import) of India

and China from 1976 to 2011 and their Granger- causality result found

that unidirectional causality from FDI to import, FDI to Export and two-

way causality between import and export in China while in case of India

all three variables have bidirectional causality between one another.

Martinez-Martin (2010) test VECM by using annual data from 1993 to

2008 for Spain. They used FDI, export, domestic income, world income

and competitiveness as variables to identify causal relationship among

them. The VECM result found a positive long run relationship exists from

FDI to export.

Mortaza and Narayan (2007) examined FDI inflows, import and export

over GDP, literacy rate and domestic investment and inflation to identify

causal relationship of growth trade liberation and FDI in Bangladesh,

India, Pakistan, Srilanka and Nepal. By employing VAR, panel fixed and

random effect model, they identified unidirectional causality between FDI,

Trade liberation and economic growth for Bangladesh and Pakistan.


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(III) Theoretical Framework

The paper attempts to trace the long-run and short run equilibrium

relationship between FDI, market size, foreign trading of Bangladesh over

the period of 1986–2012 using the time series framework. In doing so, the

study measures FDI as FDI inflow, Market size as GDP at current USD,

Foreign trading in terms of export as export of goods and services at

current USD and foreign trading in terms of import as import of goods and

services at current USD. All data have been collected from data base of

the World Development Indicators (WDI) and central bank of Bangladesh

(Bangladesh Bank).The sample covers twenty seven annual observations

and all the data converted into natural logarithm to minimize the effect of

heteroskedasticity and multicollinearity among the variable.

The empirical estimation proceeds with the checking of the normality of

the distribution by using Jarque-Bera test. Next, it goes to identify the

existence of unit root under a univariate analysis by employing both

Augmented Dickey-Fuller (ADF) and the Phillips-Perron (PP) tests. The

unit root test has to be conducted at the intercept as well as intercept plus

trend regression form. If the study shows unit root that is data distribution

are non-stationary then they shouldn‘t be used in levels rather their first

differences to identify the level of stationary since in time series data two

or more non-stationary data can be stationary if they are integrated at same

order ie. Order I(1).

If it confirms the stationary of the variables at their differences, the study

then proceeds to draw co-integration relationship between variables by

applying the Johansen-Juselius procedure to identify the long run

relationship among the variables. The notable thing is that to run Johansen

co-integration test, all series must have same order of integration, either in

level or in differenced form. That means the difference between two or

more non-stationary series becomes stationary when they move together in

long-run, while they might float separately in short run.

Though the existence of co-integrating relation identifies long run

equilibrium relationships between variables and existence of at least one

causal relationship among variables, it cannot identify the direction of

causal relationship rather it may produce spurious correlation between


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variables. Therefore Vector error correction model (VECM) has

employed, over Vector autoregressive model unable to have error

correction term, to identify direction and sources of causal relationship

and distinguish short run and long run relationship for the variables.

(IV) Methodology

Descriptive statistics

The mean, median, mode of the variables are to be defined in descriptive

statistics with the maximum and minimum level and standard deviation to

have an overall condition of the each of the variable within the time frame

analyzed. Whether the data under each variable are normally distributed or

not is the pre- requisite for further analysis.

Unit Root Test

In order to test for short run dynamics and long run relationship among

time series variables, the time series variables are estimated to identify

stationarity of the series by the unit autoregressive tests. In this paper two

methods are used for detecting a unit autoregressive root: (i) The

Augmented Dickey-Fuller (ADF) Test (Dickey and Fuller 1981) and the

Phillips–Perron (PP) Test (Phillips and Perron 1988).

Augmented Dickey-Fuller Test

The ADF test for a unit autoregressive root tests the null hypothesis H0: δ

= 0 against the alternative H1: δ < 0 in the following regression:

(1)

Where Δ is the first difference operator and ut is a white noise error term

and ρ is the number of lags in the dependent variable. In the hypothesis

testing H0statistic is obtained from the OLS t-statistics testing δ = 0 in

equation (1).

If Yt is stationary around a deterministic linear time trend line, then the

trend‗t‘ i.e., the no. of observation should be added as an explanatory

variable. Alternatively (1) can be written as


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(2)

In the equation (6) Ytis a random walk with drift around a stochastic trend.

Here α2

is an unknown coefficient and the ADF statistic is the OLS t-

statistic testing δ = 0 in (2).

The Phillips–Perron (PP) Test

The results are also verified by Phillips and Perron (1988) test. The test

regression for the PP tests is:

(3)

Where, may be 0, μ, or μ+ βt

and εt

is I(0) and may not be

homoskedastic. Any serial correlation and heteroskedasticity in the error

term εtrectified by the PP tests by a straight modification in the test

statistics tπ= 0 and . The hypothesis testing procedure is the same

asymptotic distributions as the ADF test.

The null hypothesis of a unit root implies that the coefficient of X t−1is zero

i.e., = 0. Series is stationary if null hypothesis is rejected and no

differencing in the series is necessary to induce stationarity. The number

of lags in the dependent variable is chosen by the Akaike Information

Criterion (AIC). Unit root test identifies whether the variables are

stationary or non-stationary. The test is applied on both the original series

(in logarithmic form) and to the first differences. In addition, both models

with and without trend are tried.

Co-integration Test

To identify whether a long run equilibrium relationship exists among time

series variables, Johansen (1988) maximum likelihood approach is readily

used.

The time series variables of FDI function of Bangladesh are considered to

pursue the first order Vector Auto Regressive (VAR) representation

defined as:


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E t= Π11 E t-1 + Π12R t-1 + Π13P t-1 + Π14Q t-1 + ε E (4)

R t= Π21 E t-1 + Π22R t-1 + Π23P t-1 + Π24Q t-1 + ε Rt (5)

P t= Π31 E t-1 + Π32R t-1 + Π33P t-1 + Π34Q t-1 + ε Pt. (6)

Q t= Π41 E t-1 + Π42R t-1 + Π43P t-1 + Π44Q t-1 + ε Q (7)

Subtracting lagged dependent variables from the respective equations, it

can be written in matrix form as follows:

t

t

t

t

Q

P

R

E

=

4441341241

34333231

24232221

14131211

+

1

1

1

1

t

t

t

t

Q

P

R

E

t

t

t

t

Q

P

R

E

where Γ11= Π11-1, Γ22= Π 22-1, Γ33= Π 33-1, Γ44= Π 44-1and Γ12= Π

12 and Γ21= Π 21, Γ31= Π31 and Γ41= Π 41 and Et, Rt, Pt, Qt, are first

difference stationary i.e., I(1). The existence of a co-integrating

relationship depends on the rank of the matrix Γ which must be equal to

one as there can be up to one linearly independent co-integrating vectors.

Johansen‘s procedure gives two likelihood ratio tests for the number of co-

integrating vectors (r) which are found by the trace and the maximum

eigen value tests as follows:

(8)

(9)

λi's are the characteristic roots of the matrix Γ and N is the sample size.

The null hypothesis of at most r cointegrating vectors is tested in both the

trace test as well in the maximum eigen value test. In the trace test, the

alternative hypothesis is that the number of cointegrating vectors is equal

to or less than r+1, whereas it is equal to r+1 in the maximum eigen value

test. The Johansen‘s maximum likelihood procedure is carried out by

replacing Et with lnfdi and Rt with lngdp and Pt with lnex and Qt with lnim

equations (4), (5), (6) and (7) respectively.


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Vector Error Correction Model

The co-integration among variables exclusively illustrates a long run

equilibrium association. However, there may be short run disequilibrium

among them. Vector Error Correction Model (VECM) can be developed to

explore the short run dynamics among the concerned time series variables.

Therefore, an unrestricted VECM (Granger 1988) taking into account up

to ρ lags for FDI.

(10)

Particularly, in this model, the parameter (λ) of the lagged error correction

term (et−1) exhibits the long-run association in time series variables under

study, and also the speed of correction from the short-run to the long-run

equilibrium situation. The lag-length of the variables has been

appropriated through final prediction error (FPE) criterion (H.Akaike

1969) to surmount the under or over parameterization problem which may

provoke in efficiency and bias in the estimates. Remarkably, the parameter

of the error correction term should be negative and statistically significant

in terms of its associated t value to confirm the long-run equilibrium

relationship in the variables. The variation in GDP, Export, and Import

cause the variation in FDI when bi‘s, ci‘s, di‘s are significant in terms of

the F test (Bahmani and Payesteh 1993). The stability of the VEC model

has examined by the inverse roots of the AR characteristic polynomial test

as well as cusum and cusumq.

(V) Empirical Results and Discussion

Descriptive Statistics

The descriptive statistics of the variables under study have been presented

in Table I. The Jarque-Bera test statistics fails to reject the null hypothesis

of normal distribution of each variable, which substantiates the normality

of the series. Besides, the numeric of kurtosis for each variable is found

below 2.5, which indicates the normality of distribution. The figure for

skewness of each variable is found to be mild and negative skewed, except

for the GDP and Import, those have slight positive skewness. The standard

deviation of the series is low compared to mean, which point out a small

coefficient of variation except for FDI inflow. In addition, the range of


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deviation between the maximum and minimum of each individual series is

found to be consistent to the mean. Finally, the mean over median ratio for

each series is seen to be approximately one which represents normality of

distribution.

Table 1 : Descriptive Statistics

lnfdi lngdp lnex lnim

Mean 18.30777 24.56408 22.31386 22.88342

Median 19.75352 24.54523 22.49429 22.86638

Maximum 20.90131 25.47991 23.96669 24.34427

Minimum 12.42081 23.77205 20.84418 21.66994

Std. Dev. 2.638825 0.472010 0.926986 0.776865

Skewness -0.804210 0.357352 -0.055525 0.250871

Kurtosis 2.091514 2.307790 1.910124 2.066305

Jarque-Bera 3.838907 1.113702 1.350182 1.263971

Probability 0.146687 0.573011 0.509110 0.531535

Sum 494.3099 663.2301 602.4741 617.8522

Sum Sq. Dev. 181.0483 5.792619 22.34189 15.69150

Observations 27 27 27 27

Unit Root Test

Augmented Dickey Fuller (ADF) test is used for testing the unit root in

time series data. Here, Lag length of each variable is selected based on

minimum values of Akaike Info Criterion (AIC) statistics and max lag is

2. The test equations include constant and constant plus trend in their

levels as well as their first difference. The results for augmented Dickey

Fuller (ADF) Test presented in Table II and the results for Phillips–Perron

(PP) Test presented in Table III. The results shown in Table II and III

suggest that the null hypothesis of a unit test in the time series cannot be

rejected on levels in a logarithm form. However, all the variables are


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found stationary in their first differences. Therefore; all the variables are

integrated of order one, I(1).

Table 2 : Augmented Dickey Fuller Test (Akaike Info Criterion)

Variable

Level First difference

Intercept Intercept

and trend Intercept

Intercept and

trend

lnfdi -1.138935 -1.741106 -6.545061** -6.466955**

lngdp 0.968198 -0.392707 -3.077729* -3.263629*

lnex 0.761195 -2.072439 -5.093771** -5.005212**

lnim 0.564359 -1.771991 -5.184648** -5.263293**

Note: * and ** denote 5% and 1% level of significance respectively.

Table 3 : Phillips –Perron Test

Variable

Level First difference

Intercept Intercept

and trend Intercept

Intercept and

trend

lnfdi -1.138935 -1.700567 -6.447923** -6.459151**

lngdp 0.642565 -0.975664 -3.436697* -3.602912*

lnex 0.814066 -2.142177 -5.093771** -5.005212**

lnim 0.916780 -1.771991 -5.197546** -5.316699**

Note: * and ** denote 5% and 1% level of significance respectively.

Co-integration test

As presented in the last part, the important point of Vector Autoregressive

model is the number of lag‘s order of variables. An appropriate lag length

of the variables could create the best model with uncorrelated and

homoskedastic residuals. Analysis suggested the lag order of 2 that yields

the minimum Final Prediction error (FPE).


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Table 4 : Results of Johansen co-integration test

Hypothesized No. of

Co-integrated

equation(s) (CEs)

Trace

Statistic Prob.**

Max-Eigen

Statistic Prob.**

55.07076 0.009062 30.24185 0.0223

24.82891 0.167619 13.40352 0.4158

11.42539 0.186649 9.325447 0.2601

r ≤ 3 2.099942 0.147303 2.099942 0.1473

Notes: Trace test and Max-Eigen indicates1 co-integrating eqn(s) at the

0.05 level

* denotes rejection of the hypothesis at the 0.05 level

**MacKinnon-Haug-Michelis (1999) p-values

As all variables are determined I(1), the Co-integration test is performed

for the long run relationship among series by using Johansen co-

integration test. Table IV presents the results of Johansen co-integration

test with a co-integration rank of one in both the trace test and the

maximum Eigen value test; thereby the existence of long run relationship

among the variables has been detected.

Result of Co-integration

The result of long run co-integrated equation where FDI is the function of

GDP, export and import suggest highly significant influence of each

independent variable to the dependent variable. The table identifies long-

run positive relation of GDP and export to FDI and negative association of

import to FDI.

Table 5 : Result of co-integration

lnfdi Co.ef. P value

lngdp 403.1926 0.000

lnex 232.761 0.000

lnim -534.193 0.000


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Vector Error Corrected results

The following Table VI postulates the outcome of the vector error

correction model. To initiate VEC model, the appropriate lag length (lag

02) has been selected by FPE criterion (H. Akaike 1969). The table reveals

the long run equilibrium relationship justified among variables as

estimated parameter (λ) of the error correction term (et−1) is negative and

statistically significant at 1 percent level of significance., implies a long

run causality as well as long run convergence with (-0.0264). Followed by

that result, table VI presents the short run components of estimated vector

error corrected model (VECM). The existence of overall short run

variation found significant as F statistics is 2.558. The result of R square

showed that the short run variation of GDP, Export, and Import explains

37.88 percent variation of FDI on average.


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Table 6 : Vector Error Corrected Results

Coef. Std. Err. z P>z [95% Conf. Interval]

-0.00992 0.533689 -0.02 0.985 -1.05593 1.03609

-0.02644 0.008497 -3.11 0.002 -0.04309 -0.00979

∆lnfdit-1 -0.75438 0.208758 -3.61 0 -1.16354 -0.34522

∆lnfdit-2 -0.41494 0.23115 -1.8 0.073 -0.86799 0.038104

∆lngdpt-1 -8.50997 6.572755 -1.29 0.195 -21.3923 4.372397

∆lngdpt-2 3.177733 6.071985 0.52 0.601 -8.72314 15.0786

∆lnext-1 3.292857 3.766977 0.87 0.382 -4.09028 10.676

∆lnext-2 6.156685 2.273448 2.71 0.007 1.70081 10.61256

∆lnimt-1 12.29244 3.751499 3.28 0.001 4.939633 19.64524

∆lnimt-2 4.882006 3.575868 1.37 0.172 -2.12657 11.89058

R-squared 0.621937 Mean dependent 0.269875

Adj. R-squared 0.378897 S.D. dependent 0.933469

Sum sq. resids 7.576901 Akaike AIC 2.518261

S.E. equation 0.735668 Schwarz SC 3.009117

Log likelihood -20.2191 F-statistic 2.558989

The immediate one year lagged variation of Import and two year lagged

variation of Export has significant positive impact on short run variation

of FDI inflow, while variation of GDP doesn‘t have any significant short

run effect on FDI.VEC short run granger causality (Appendix A4) result

suggests there is a unidirectional relationship exists between FDI to GDP,

Export to FDI while only bidirectional causality found between FDI and

Import. The stability of the VEC model has been ensured through the test

of inverse roots of the AR characteristic polynomial (appendix A3 and

CUSUM and CUSUMQ (appendix A1, A2).


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(VI) Conclusion

This study investigated the effect of market size and foreign trading on

FDI inflow in Bangladesh over the period of 1986 to 2012 by using a

vector error correction model. The results of ADF and PP unit root test

identified that all variables in the study were integrated in order one I(1).

The test statistics (trace and max-eigen) of the Johansen co-integration test

have been conducted on intercept without trend regression identified the

presence of a co-integrated relationship among the variables. Again, the

negative parameter of error correction term validates the existence of long

run equilibrium relationship among the variables with a highly significant

t value. The long term co-integration result identified the positive impact

of GDP and Export on FDI inflow and negative impact Import on FDI.

Besides significant F-value of VECM estimates suggest the overall short

run relationship among the variable under study. In short run, two year

lagged export and one year lagged import have positive impact on FDI

inflow in Bangladesh. The VEC granger causality result showed strong

unidirectional relation of Export and Import to FDI inflow, FDI inflow to

GDP, Import to Export and a mild unidirectional relation of FDI to Import.

Therefore, the VEC model identified a long run equilibrium association in

the variables and short run causal flow between them.

The policy implication of this study can be abridged under following

points. Firstly, the Govt. of Bangladesh should exploit these macro-

economic variables carefully on a long run basis to reap the benefit from

their nexus. Secondly, the positive relation of GDP, Export and FDI

provides the Govt. with information that the growth in GDP and Export

can substantially raise FDI inflow and FDI will in turn accelerate Export

and GDP. Therefore, FDI driven growth and growth led FDI policy should

be advocated simultaneously. In addition, the long run negative

association between FDI and Import should be viewed as import

substituting policy for the country, so that current account deficit can be

lessened in the long run. Thirdly, in short run perspective, Export and

Import have great influence on FDI. The contrasting feature found in

short run case Import has positive relation to FDI against long run can be

rationalized by substantial capital goods import requirement to initiate FDI

that is to establish plants and operations in Bangladesh. Govt. should


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provide special facilities and provision for capital goods import to

accelerate FDI inflow in short run, causing decrease of aggregate import

by producing goods and services to meet the domestic needs and rise in

export to meet the international demand. Furthermore, the long run

positive relation between FDI inflow and GDP hasn‘t been pronounced in

the short run, because FDI follows long run performance of GDP of a

country while factors initiating FDI inflow remains the foreign trading in

Bangladesh. In nutshell, a comprehensive but target oriented sector basis

short run export and import policies focusing long term benefit out of it

should be managed and practiced effectively.

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Country Panels: A New Look at FDI and Economic Growth in

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through Strategic Foreign Direct Investment‖. Nigerian Economic

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May.

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and International Trade: Evidence from the US and Japan.‖

Journal of Economic Geography,3(3):241-259.

Paugel, A. T. 2007. International Economics. McGraw-Hill Irwin: New

York, USA.

Phillips P. C. B. andPerron P., ―Testing for a Unit Root in Time Series

Regression,‖ Biometrika, vol. 75, no. 2, pp. 335–346,1988.

Razin, A., Sadka, E., & Yuen, C. 1999. ―Excessive FDI under Asymmetric

Information.‖ NBER Working Paper 7400, Cambridge.

Romer, P. M. 1986. ―Increasing Returns and Long-run Growth.‖ Journal

of Political Economy, 95(5):1002-1037.

Romer, P. M. 1994. ―The Origin of Endogenous Growth.‖ Journal of

Economic Perspectives, 8(1):3- 22.

S. Johansen, 1988. ―Statistical Analysis of Cointegration Vectors,‖

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te Publishing Company, USA.

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http://joeg.oxfordjournals.org/search?author1=Jyothi+Pantulu&sortspec=date&submit=Submit

http://joeg.oxfordjournals.org/search?author1=Jessie+P.H.+Poon&sortspec=date&submit=Submit

http://joeg.oxfordjournals.org/

http://joeg.oxfordjournals.org/content/3/3.toc


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Appendices:

A1. CUSUM Analysis

-15

-10

-5

0

5

10

15

90 92 94 96 98 00 02 04 06 08 10 12

CUSUM 5% Significance

A2. CUSUMQ Analysis

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

90 92 94 96 98 00 02 04 06 08 10 12

CUSUM of Squares 5% Significance

A3. Inverse roots of AR Characteristics Polynomial

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

Inverse Roots of AR Characteristic Polynomial


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A4. VECM Granger Causality Result

Null Hypothesis: F-Stat Prob.

lngdp does not Granger Cause lnfdi 1.74 0.42

lnfdi does not Granger Cause lngdp 6.13 0.05

lnex does not Granger Cause lnfdi 8.24 0.01

lnfdi does not Granger Cause lnex 2.09 0.35

lnim does not Granger Cause lnfdi 10.84 0.00

lnfdi does not Granger Cause lnim 4.66 0.09

lnex does not Granger Cause lngdp 0.76 0.68

lngdp does not Granger Cause lnex 2.67 0.26

lnim does not Granger Cause lngdp 1.58 0.45

lngdp does not Granger Cause lnim 2.98 0.23

lnim does not Granger Cause lnex 12.37 0.00

lnex does not Granger Cause lnim 0.98 0.61


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Visitors’ Perception towards Tour Destinations: A Study on

Padma Garden

Md. Abdul Alim 1

Rudrendu Ray 2

Dr. Md Enayet Hossain 3

Abstract

This empirical study is conducted to find out the visitor’s perception towards the

tour destination, Padma Garden, Rajshahi in Bangladesh. A convenient sampling

technique was used to collect data. Total thirty one quality attributes were taken

into consideration to find out the choice similarities or dissimilarities towards the

selected destination. A total 199 usable data were collected from Padma Garden

using 5 point Likert Scale. Data were analyzed using SPSS to find out influential

factors which are the most responsible for drawing the attention of the visitors.

Findings reveal six factors; food and beverage, price, accommodation,

environment, safety and security and transportation. However, food and beverage

is appeared as the most influential factor consisting six attributes whereas

transportation appears as less important to the visitors for visiting the destination.

The main contribution of the study is twofold. Theoretically it provides insightful

relationship between visitors’ choice factor and visiting to the destination.

Practically, the destination operators can use the mentioned factors in their

promotional activities.

Keywords: Visitors’ perception, tour destination, Padma garden

(I) Introduction

ourism is a rapid growing industry (Saayman et al., 2001) and has

been greatly contributing in many economies of the world. The

tourism industry generates enormous economic and noneconomic

benefits to both host country and tourists’ home countries. According to

the World Travel and Tourism Council (WTTC), the total contribution of

1 Assistant Professor, Department of Marketing, University of Rajshahi,

Email:[email protected] 2 Assistant Professor, Department of Marketing, University of Rajshahi,

Email:[email protected] 3 Professor, Department of Marketing, University of Rajshahi,


T


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travel and tourism to GDP was USD 7580.9 billion (9.8% of GDP) in

2014, and forecast to rise by 3.7% in 2015 and 3.8% in 2025. In

Bangladesh, although, the total contribution of Travel & Tourism to GDP

was BDT 627.9bn which was 4.1% of GDP in 2014, and is forecast to rise

by 6.0% in 2015 and 6.5% in 2025. The total contribution was 3.6% of

total employment which were 1984000 jobs in number in 2014 (WTTC,

2015).

There are different types of communities, specifically in developing

countries; indeed, tourism has represented a stronger connection to the

rich economic markets (Johnston, 2000; Rodriguez, 1999). In those

countries, tourism has a great contribution to change in household

economies, create new opportunities for employment, new sources of

liquid income, and new information about technologies (Barkin, 1996;

Eadington, & Smith, 1992; Levy and Lerch, 1991; Liu, 2003; Ahmed et

al., 2010). However, in tourism industry, tourism destination is one of the

most frequently used concepts but different stakeholders and tourism

researchers use it differently. In the tourism literature, destinations are

described as places, as regions and as images (Framke, 2002). A

destination abundant of natural resources and/or other attractions can give

competitive advantage (Crouch & Ritchie, 1999).

The advantages of tourism destinations are based on different products,

qualifying determinants of visitation, as well as the fundamental reasons

for potential visitors to choose one destination over another. In addition, a

tourist destination is a place which is very often visited by many locals,

national as well as international visitors. The tourism destination can be a

city, town, historical place, sea-beach, mountain, an amusement park,

museum or religiously important places. However, a park is regarded as a

large garden or area of land used for recreation. It has been recognized as

an important tourism and recreational resources to the local people and the

visitors come from the out of town (Buckley, 2000; Cho, 1988; Uysal,

Mcdonald, 1994). It could also be natural tourist attractions like forests,

rivers, big waterfalls, hills or lakes. It is rigorously studied that why these

destinations are important to the visitors particularly river based

destinations. On the contrary, people can make an ordinary place into an

important tourist destination by their own effort like making amusement


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parks, statues, big hotels or by making a new city or town. Tourists have

different choices and that is why different tourists have different

perceptions towards a particular tour destination (Yeoman, 2008).

It is focused that different authors use the term perception in different

ways. It has emerged surrounding the concept and understanding of what

perception means. In this regard, one of the most widely accepted

definitions was defined by Berkman (2010): “Perception is the way in

which individual gathers, processes, and interprets information from the

environment”, and Gale (1994) stated that perceptions are the beliefs

about what a consumer received from the goods and services. Moreover,

perception has the substantial impact to the visitors for developing the

tourism industry. Mainly it is a process by which a person selects, sort out

and interpret the thing quickly into a meaningful picture of the

environment and accept the product in various ways; it may be favorably,

less favorably or not at all (Dey et al, 2012; Shamsuddin, & Hasan, 2013).

Again, the improvement of the cleanliness, safety and facilities of the

beach could be varying by the opinion and perceptions of the beach users

(Semeoshenkova & Williams, 2011). Thus, it is said that, recreational

services influence the visitors’ choice for selecting a particular tour

destination.

(II) Study Area

Bangladesh is one of the countries with a unique scenic beauty and rich

cultural heritage that she offers to the visitors from home and abroad.

Rajshahi is one of the divisional towns in Bangladesh and it is a growing

tourist destination in the country. The town is located on the bank of the

Padma River. Similarly, with this flourishing entertainment spot, we

foresee Padma Garden as the scope of employment opportunity for several

hundreds of people. It has also been ensured that the ways of earning of

livelihood of the employed by selling various items that the visitors feel a

need including fast-food, toys and flowers. Although the Padma river is

with a meager or without any water now due to diversion of water through

the Farakka Barrage across the border but the efforts of Rajshahi City

Corporation and some private entrepreneurs to turn its embankments and

surrounding land into a green zone with various trees and plants has made

Padma-Garden one of the most attractive places of the city.


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During the last decade, the roads along the Padma-Garden has been

repaired, trees have been planted, various colorful tents with chairs and

benches for sitting and gossiping have been built and all the areas has been

linked with colorful electric bulbs and devices. Those who visited the

riverside a couple of years ago and visiting now will be surprised at the

first sight to watch its beauty and presence of a good number of visitors.

Although there is no statistical evidence of the number of visitors at

Padma Garden, but based on the observation of the researchers by visiting

the said destination, it could be around 2000 visitors visited at the said

destination per day particularly in the evening. However, in the first half

of the day visitors are quite less in number. It is probably due to the

working hours of the local visitors.

It is clear to us that, the contribution of Padma Garden in the local and

national economy of Bangladesh bears an important role. The flow of

economic contribution and the growth and sustainability depends on the

number of visitors’ arrival and facilities they consume. It can be explored

that how sufficient is the provided facilities of the responsible authorities

to the visitors. What is the visitors’ perception towards the various

qualities are to be understood. The purpose of the present study is to

investigate the visitors’ perception towards the quality services at Padma

Garden as a tour destination.

(III) Objective of the study

The main objective of the study is to find out the factors influencing

visitors’ perception towards the Padma Garden, Rajshahi as a tour

destination in Bangladesh.

In relation to the aforementioned objective, the specific objectives are as

follows:

i. To identify the different quality attributes that influence visitors to

select the Padma Garden as their tour destination.

ii. To understand how visitors’ perception varies based on various

factors.

(IV) Literature Review

Baloglu et al., (2003) conducted research on the relationship between

destination performance, overall satisfaction, and behavioral intention for


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distinct segment. The purpose of this study was to gain a better

understanding of short-term visitors of mountain destinations in order to

improve marketing strategies. However, Chheang (2011) examines tourist

perceptions and experiences and argues that tourist perception is positive

and based on cultural enrichment, friendliness and the sense of hospitality

facilities of the local people experiences of the visitors are over than the

expectation. Kamal and Chowdhury (1993) and Hasan and Chowdhury

(1995) conducted studies on the basis of tourism related services. In fact,

these were the studies based on the performance of tourism related

services as well as the contribution to the development of the country’s

tourism industry. Therefore, Henderson (2011) highlighted other factors

that almost influence on inbound and outbound tourist to travel such as

political instability, safety and security and in terms of social

psychological concepts Higginbotham (2011) give emphasis on the

interrelated fields of recreation, leisure and tourism. Likewise, others

studies have been conducted by Hossain and Firozzaman (2003); Alam &

Shamsuddoha (2003); Hossain (2006); Lincoln (2008). These studies

focused that the significance of tourism is viewed from many angles e.g.

economic, social, cultural, political etc. Another study conducted by

Sofique and Parveen (2009) and Ahammed (2010) are directly related to

Cox’s Bazaar tourism regarding economic and socio-cultural effect of

tourism. This study is based on the factors that affect the visitors’

perception for selecting a tour destination. In this regard Ahmed et al.

(2010) conducted a study on factors affecting chooses Bangladesh as a

tourist destination. The study shows that service quality, natural beauty,

security and shopping facility are statistically significant in explaining the

intention for selecting a tour destination in Bangladesh (Cai & Zhang,

2003; Lee et al., 2004; Neal, 2003) and (Yourtseven, 2000) have

conducted research to meet the demand of tourism development. Here,

authors stated mainly of customer satisfaction and finding effective ways

to ascertain customer desire depends on status full occupation and

technically focused.

Food is very sensitive and one of the most priority issues in tourism as

well as services related industry (Donald, 1997). It is not only core value

of food but also packages of benefits -such as presentation of food, overall

environment of seating arrangement, way of approaching of service


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providers etc (Cook et. al., 2007). However, at present food and beverage

are the common motivator of all kinds of existing and potential visitors’

and it is a ceiling trend in individuals mind for selecting a tour destination

(Ahmed et al., 2010).

Zeithaml (1988) defined price as what consumer sacrifice in order to gain

something from a product or services. Again, Berry and Parasuraman

(1991) emphasized on price as what customers actually pay in exchange

for products or services’ they received or a visible sign of services’ level

and quality. But in the destination perspective, Bagwell & Bernheim

(1996); Ngoc & Trinh (2015) describe price as what consumers are willing

to pay more for services at a destination if they think that prestigious

images are associated with it. In these cases, consumers feel interest in

paying higher price for effective goods which are associated with the

destination’s sophistication with greater perceived value (Papatheodorou,

2002).

Middleton and Clarke (1999) stated that accommodation has a functional

role for providing the facilities that makes travel convenient and

comfortable. Hall (1995) regarded accommodation as one of the most

critical components on the demand side as accommodation has a major

influence on visitors the type of who come to a destination. Cooper,

Fletcher, Gilbert and Wanhill (1996) suggested accommodation provides a

necessary support services to satisfy the broader motivation that brought

the visitor to the destination. Some authors (Chi & Qu 2008; Pike, 2009;

Ahmed et al., 2010) have mentioned that accommodation facilities are

most priority aspect for the visitors.

Hasan (1992) and Hall and Page (2000) conducted the four elaborate

studies covering the tourism and tourism environment in Bangladesh. The

study focuses tourism potentiality, major problems and prospects of

tourism, marketing strategies of tourism industry, foreign tourist arrival

trend in Bangladesh. For attracting the both domestic and international

visitors to the tourist destinations environment plays a significant role

(Dunn, 2009). An attitude towards the environment is a measure of how

people would like to experience the landscape based on their personal


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preferences for environmental, social and cultural aspects. These

preferences reflect more basic values or environmental value orientations

Homer and Kahle (1988) and they are often related to the attitudes toward

specific environmental conditions and impacts as well as management and

development options.

In tourism industry safety and security is the synonym for providing

quality services, by ensuring that it helps visitors while thinking to choose

a destination. Tourism is different from any other economic activity;

visitors always seek safe and secure places to enjoy their pastime without

any tension. In this regard, Albrechtsen (2002) noted that “safety is the

protection from unintended incidents while security is the protection from

intended incidents”. Safety is concerned with human life and health’s

protection while security refers to the protection against criminal

activities. Therefore, success or failure of a tourism destination depends on

providing a safe and secure destination (Besculides et al., 2002). We

cannot be complacent, since there is an emerging consensus that crime -

which raises safety issues, is a growing concern among tourism

stakeholders who fear the potential damage that it may inflict on the

perception of safety and, by extension, the industry (Volker & Soree,

2002; Ahmed et al., 2010). However, in recent years researchers reported

that in country like Bangladesh visitors’ safety and security issues are

alarming to the travelers (Embassy Web-pages of America, Norway and

Denmark have been consulted in February 2006). In tourism, different

destinations required different levels of security e.g. safety and security

issue, visitors’ perception at Taj-Mahal is significant. But the Padma

Garden, on the other hand, is not that much of concern to both visitors and

the destination operators as well. Thus, minimum level of peaceful

surrounding environment and political stability of the country would be

the best concern to the local as well as domestic visitors. In this regard,

Lee et al., (2007) stated that safety and security system is not the same for

each destination.

It is difficult to run tourism industry without an effective and efficient

transportation system (Cook et al., 2007). Transport is the major cause for

tourism development and it has both positive and negative effects on


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tourism. To start with, the improved facilities have stimulated tourism and

the expansion of tourism has stimulated transport. In this regards

accessibility is the key functions in the shadow of tourism transport. In

order to access the areas that are mainly aimed, tourists will use any

transportation mode. However, air transport is the main mode for

international tourism (Kroshus, 2003). However, in the context of

Bangladesh transportation facilities are not same for each destination

(Gallarza & Saurab, 2006).

A number of studies are reviewed including above and it is clear to us that

it seems a significant research gap existing in the domestic tourism market

in Bangladesh to ascertain visitors’ perception towards selection of a tour

destination. In previous academic research it was hardly given attention on

the country’s tourism industry. Thus, the present empirical study has an

opportunity to know new knowledge about this area. The present study

aims to gather primary data from the visitors at Padma Garden. This

research paper presents the relevant variables that are affecting to the

visitors’ perception in choosing a particular tour destination. Therefore,

the findings of this study will give new idea and the directions to the

concerned authorities to pace their competitiveness.

(V) Methodology

The study is carried out on Padma Garden, Rajshahi area in Bangladesh.

The reason for choosing this area is that it is the local amusing river based

visiting spot and there is no significant research carried out earlier about

the factors that greatly affect the visitors’ mind for selecting the

destination. Thus the authors interested to identify the factor that affects

the visitors’ perception to Padma Garden. This study mainly follows

positivist paradigm, the field study technique was used for data collection.

The main selection criterion is that the visitors must be on the spot during

interview. The sample population for this study is composed of visitors

who visited Padma Garden, Rajshahi through convenience sampling. It

mainly focuses on the areas of information needed to satisfy the objectives

of this research. Out of 225 sample questionnaire 199 usable questionnaire

were collected with a response rate of over 84% using 5 point Likert scale

ranging from 1 to 5 response categories. Mainly close ended questions

were used in the questionnaire. However, there were some open ended


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questions used to collect demographic information from the respondents.

Largely factor analysis was used for the data analysis. Data were analyzed

using statistical software SPSS to find out the influential factors, which

would be most responsible for drawing attention of the visitors.

(VI) Discussion of Results

Respondents’ Profile

In this study, seven demographic characters have been observed by the

authors over those who visited Padma garden, Rajshahi. Among them

gender, age, profession, home district (place of residence of the visitors)

and the marital status have the significant differences in their counterpart.

Table 1 depicts the data that majority of the visitors are male (81.9%) and

rest of them are female (18.1). Two-third of the visitors’ age group is 21-

30 years (63.8%) whereas visitors over 50 years of age are only 1%;

probably it is due to their weak physical condition and mental

unwillingness. However, in terms of professional status, student carried

the lions’ share (62.8%) to visit the mentioned destination. These young

people have the curiosity to know and attention for utilizing their pastime

in an enjoyable and productive way. It is also observed that geographically

visitors from Rajshahi region have the significant intension to visit Padma

garden that is 62.3%. On the other hand, those form Barisal and Sylhet has

the least tendency to visit this local based visiting spot. Moreover,

precisely two-third of the visitors is unmarried. In addition to that, among

the visitors, educated people have the highest trend to visit Padma garden

where the percentage of graduate and post graduate is 43.2 and 33.7

respectively. Therefore, three-fourth of the visitors is well educated

coming from this city of education. Eventually, visitors or their parents’

monthly income is Tk. 10001-20000 and Tk. 20001-30000, which is

34.1% and 32.2% respectively. On the contrary, over tk. 30000 of income

group has less participation towards this amusing destination.

Measurement

In sampling adequacy author uses KMO and Bartlett’s test to examine the

sample accuracy. The result of Kaiser-Meyer-Olkin Measure illustrates

that the presented sample is quite suitable for factor analysis. (Table 2)

figures that current data is 89% accurate at with 99% significant level.


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From the analysis of data, we find 31 quality variables out of the initially

approached 42 variables. These are important for examining the factors

that affect the visitors’ perception of Padma garden, Rajshahi in

Bangladesh. Based on Eigen value, all 31 variables are selected-value 1

considered into the list of variables. Statistically, 31 variables construct six

factors, which explain almost 60% of the field. So author concentrates on

these factors for the study. Factor 1 explains 31.889% where total Eigen

value is 9.886 (Table 3). With this rational cause, this factor is the top

most priority concern to the current study, which is related to the services

of food & beverage at Padma garden, Rajshahi.

Variables with loading higher than 0.5 are grouped under all factors.

However, factor loading is the correlation between the original variable

with the concerned factor and the key to understanding the nature of that

specific factor (Debasish, 2004). Table 4 has been supplied the Varimax

rotated factor loadings against the earlier mentioned 31 variables

consisting of 6 factors. Moreover, six factors components and the

correlations can be seen in rotating component matrix. Rotation has been

carried out through Varimax rotation method. SPSS (version 18) is used

for analysis. Factor 1 (food and beverage), Factor 2 (price), Factor 3

(accommodation facility), Factor 4 (environment), Factor 5 (safety and

security) and Factor 6 (transportation). However, the total variance

accounts for all the six factors which is 59.041 %.

Factor 1 (Food and Beverage): The study discovered that food and

beverage has great influence on visitors. This factor determines six items,

which is highly correlated with the first factor. However, the factor

loading score for each item is within the acceptable level (from 0.615 to

0.745; See Table 4). In this factor, the Cronbach’s alpha value 0.872

which is quite standard in acceptable level 0.70 or above (O’ Leary-Kelly

& Vokurka, 1998) and mean value of this individual factor is 3.492.

Considering the above mentioned score, the factor and the items are

relevant. As food and beverage is the prime attraction to the visitors to this

short trip destination, therefore destination operators can extend the list of

food items with different taste for getting more attention from the potential

visitors. However, keeping existing quality of the foods could be the first

priority to the authority of Padma Garden.


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Factor 2 (Price): There are also six attributes that are the strong correlated

with this factor. In this regards, service charge at accommodation is

economy (0.696) and price of drinks (0.695). Moreover, the visitors’

perceived benefit in value of food charge and transportation cost is

satisfactory by 0.685 and 0.665 respectively. Furthermore, the cost of

natural sightseeing facilities and goods purchasing facilities are in within

the standard level at 0.658 and 0.647. In the factor analysis, this factor

brings alpha value (0.810) and mean value 3.313. Eventually, as the entire

price concerning item are ranging in minimum level so that it is rightly

constructed. Evidence from the demographic profile shows that, most of

the visitors of Padma Garden are student (62.8%) and approximately two-

third of the visitors or their parents’ monthly income is between Tk. 10001

to Tk. 30000. So that continuation of existing price of the destination

would be suitable to keep the continuous flow of the visitors.

Nevertheless, price of some of the items of this factor is not under control

by the destination operator such as price of drinks, transportation cost etc.

It depends on the country’s overall economic condition. So that it can be

an effective initiative for the planners to control their internal cost that

would led the sustainable growth of this destination.

Factor 3 (Accommodation Facility): Accommodation is one of the most

important basics to the visitors to visit any destination. The current study

identifies five variables where accommodation facility plays a functional

role to influence the visitors to visit this destination. The factor loading

score for each of the five items is within the acceptable range that is from

0.596 to 0.744 Such as physical condition of accommodation (0.744),

room service facilities at hotel (0.716), comfortability at room (0.689),

facilities for shopping in the destination area (0.608) and accommodation

at restaurants (0.596). However, the Cronbach’s alpha score (0.819) is

above the standard level and mean value 3.077. These scores clearly

indicate that the factor is justified. Thus, the destination operator and those

are involved in providing accommodation facilities can work together to

ensure the guests’ comfort at hotel and restaurant. Moreover, usually

shopping facilities at the destination area is not a major concern in this

local visiting spot rather their main intention is entertainment and


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recreation. But unintentional purchase is the part of any kinds of visitors’

cultures particularly, some documentary materials such as- souvenirs and

statue with historical background and images of the destination. In this

case, the authority of the destination can pay their attention for expanding

the range of the products and services to meet the needs of the visitors and

can get extra money.

Factor 4 (Environment): Visitors always tend to seek a comfortable,

enjoyable and pleasant environment for their physical and mental

recreation. The existing study consists of five variables that are highly

correlated with the fourth (Environment) factor. Here visitors have

significant intension to natural environment and weather condition of the

garden that is scored by 0.744 and 0.687 respectively. On the other hand,

chaos free environment and image of the destination is 0.674 and 0.670

accordingly. However, political stability is 0.603. Where Cronbach’s alpha

value 0.776 and mean value of individual factor 3.570. Finally, author

combines this factor as influential because the items logically included

with the context of this green city Rajshahi. In fact, Rajshahi is blessed

and have privileges for its geographical location due to not threatened by

substantial natural disaster. So it is an opportunity to hold its existing

natural and artistic view of Padma Garden. Therefore, the planners and the

decision makers are suggested to enhance its greenage environment for

building a positive impression into the visitors’ mind, which will be

helpful in case of achieving competitive advantage in future.

Factor 5 (Safety & Security): it is often said that nothing can be enjoyable

if their remains insecurity in mind. Rationally, safety & security emerged

as the major concern to the visitors. The security issue is important indeed

for meeting the objectives i.e. recreation, entertainment etc of the visitors.

In this factor, four items considered by analysis ranging from 0.620 to

0.703 (See Table 4) where safety in cultural program and medical issue is

also included as significant variables. This factor is determined by

Cronbach’s Alpha score .803 and mean value 3.190. Therefore, due to

logical reason, the factor safety & security is valid. Safety and security

issues are the primary requirement to the visitors because people can go to

a place when he/she feel like being entertained. Thus, a visitor can get


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pleasure and full charm of recreation when he feels secured. According to

the study result, as rate of gathering is higher in the evening even or even

at night, authority of Padma garden can take a measure through their own

security system to receive more attention from the visitors.

Factor 6 (Transportation): There are five quality attributes that are strongly

correlated except the one (safety in internal transportation). The acceptable

level of items-road transport facility, internal transportation and water

transportation which is 0.674, 0.537 and 0.530 accordingly. However,

efficiency in public transportation is 0.524. The Cronbach’s Alpha is

0.793 and mean value of this single factor is 3.329. But, safety in internal

transportation is scored by 0.434 which seems under the minimum

acceptable level. Moreover, author emphasis on this factor since data were

collected from field sources. In this destination twofold transportation

facilities required to the visitors that are internal and external. In case of

external- destination authority doesn’t have control over them. However,

internally it has high demand to pay attention to make the right balance of

happiness of the visitors. In particular, as this destination has built based

on the Padma River and moving on the river by boat is the key attraction

to the visitors for their adventure. But in the rainy season, water overflows

on the river and it creates big weaves. Therefore, boats, the only way of

water transportation, fall in danger and post incident recovery measure is

inadequate. In this case, the planners, policy makers and the authority of

Padma Garden are strongly encouraged to take immediate steps to launch

live support boat for the visitors’ safety & security.

(VII) Conclusion

The main purpose of this study is to find out different aspects of Padma

garden at Rajshahi that attract visitors. In this study, it is clearly seen that

visitors are attracted towards Padma Garden because of several factors

among which food and beverage is the highest in preference. Thirty one

quality variables have been found and each variable is different from

another. As a result, the authority of this destination can be suggested to

make item wise customized promotional activities. In addition to that, it

would be much better to reconstruct all the factors for the visitors of

moderate economic status especially for students. Because, a certain group

of visitors have least tendency to visit this destination those are


JBS-ISSN 2303-9884

economically strong. On the other hand, in order to get nationwide

attention from expectant visitors, concerned authority is also advised to

make smooth harmony with the other destinations at Rajshahi. As bunch

of attractions are available at Rajshahi such as Varendra Research

Museum, different renowned educational institutions including Rajshahi

University. Visitors would be able to get the pleasure of different places in

a single city. As a result, not only domestic visitors but also international

visitors would be interested to visit this excellent destination with full of

natural beauty. In this case, shopping could be one of the most leading

attractions to the visitors as the country’s best silk factories are situated

here in this city whether domestic or international visitors can collect silk

items directly whether from the showroom or the factory.

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Appendices

Table 1: Profile of Visitors Involved in the Study

Demographic Variables Items Frequency Percent (%)

Gender of the Respondents

Male 163 81.9

Female 36 18.1

Total 199 100

Age of the Respondents

Less than 20 years 27 13.6

21-30 years 127 63.8

31-40 years 34 17.1

41-50 years 9 4.5

Above 50 years 2 1

Total 199 100

Profession of the Respondents

Student 125 62.8

Govt. Employee 25 16.6

Non-govt. Employee 23 11.6

Business 18 9

Unemployed or housekeeper 4 2

Others 4 2

Total 199 100

Marital Status of Respondents

Single 133 66.8

Married 66 33.2

Total 199 100

Education of the Respondents

SSC 11 5.5

HSC 26 13.1

Graduation 86 43.2

Post-Graduation 67 33.7

More 9 4.5

Total 199 100

Monthly Income of the

Respondents/Parents

Tk. Less than 10000 29 14.6

10001-20000 68 34.1

20001-30000 64 32.2

30001-40000 24 12.1

40001-50000 7 3.5

More than 50000 7 3.5

Total 199 100

Home District

Rajshahi 124 62.3

Dhaka 16 8

Chittagong 7 3.5

Khulna 22 11.1

Barisal 5 2.5

Sylhet 2 1

Rangpur 23 11.6

Total 199 100


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Table 2: Data Adequacy Test for Factor Analysis

Kaiser-Meyer Olkin Measure Sampling Adequacy .889

Bartlett’s Test of Sphericity

Approx. Chi-Square 2738.520

Df 465

Sig. .000

Table-3: Variance Explained

Table 4: Rotated Component Matrix and Total Variance Explained

Rotated Component Matrix

Attributes

Component

Food &

Beverage Price

Accommodation

Facility Environment

Safety

&

Security

Transportation

Hygienic food .745

Testiness of

food .726

Presentation of

food .726

Preferable food .719

Available

restaurant .615

Pure drinking

water .615

Component

Initial Eigen Values Extraction Sums of

Squared Loading

Rotation Sums of Squared

Loading

Total % of

Variance

Cumulative

% Total

% of

Variance

Cumulativ

e % Total

% of

Variance

Cumulativ

e %

1 9.886 31.889 31.889 9.886 31.889 31.889 3.768 12.156 12.156

2 2.463 7.945 39.835 2.463 7.945 39.835 3.214 10.369 22.525

3 2.145 6.918 46.753 2.145 6.918 46.753 3.178 10.252 32.777

4 1.345 4.338 51.092 1.345 4.338 51.092 3.061 9.873 42.651

5 1.324 4.271 55.363 1.324 4.271 55.363 2.741 8.843 51.493

6 1.140 3.678 59.041 1.140 3.678 59.041 2.340 7.547 59.041

Note: Extraction Method: Principal Component Analysis


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Attributes

Component

Food &

Beverage Price

Accommodation


Safety

&

Security

Transportation

Service charge

at

accommodation

.696

Price of drinks .695

Value of food

charges .685

Price charge for

transportation .665

Price charge of

natural sight .658

Price charge for

buying goods .647

Physical

condition of

accommodation

.744

Room service

facility .716

Room's

comfort ability .689

Shopping

facilities .608

Restaurant at

accommodation .596

Natural

environment .744

Weather

condition of the

garden

.687

Chaos free

environment .674

Image of

destination .670

Political

stability .603

Safety in

cultural

program

.703

Safety at hotel .688


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Attributes

Component

Food &

Beverage Price

Accommodation


Safety

&

Security

Transportation

Safety at

Padma garden .687

Medical

facilities .620

Road

transportation

facilities

.674

Internal

transportation .537

Water

transportations .530

Efficiency of

public

transportation

.524

Safety in

internal

transportation

.434

Eigen Values 9.886 2.463 2.145 1.345 1.324 1.140

% of Variance 31.889 7.945 6.918 4.338 4.271 3.678

Cumulative % 31.889 39.835 46.753 51.092 55.363 59.041

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 7 iterations.


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Determinants of Share Prices in Bangladesh: Evidence from

Pharmaceuticals Industry

Ajit Kumar Ghose 1

Md Solaiman Chowdhury 2

Abstract

The main focus of this paper is to examine the micro factors as the determinants

of share prices in Bangladesh. The study employs annual panel data over the

period of 2010-2014 pharmaceuticals sectors in Bangladesh. The results revealed

that the dividend per share, size and price earnings ratio have a positive and

significant impact on the share prices of pharmaceuticals sectors. The evidence

also shows that earning per share and return on equity are the crucial

determinants and positively associated with share prices. Moreover, the net asset

value per share positively influences the share prices of pharmaceuticals sector.

Keywords: Panel data, determinants of stock prices, pharmaceuticals sector

(I) Introduction

hare prices of a firm changes every now and then. Because of this

volatility, it is often hard to choose the right investment decision. The

changes in share price is influenced by a number of factors, if these factors

could be identified, it would be much easier for investors to choose the

right share to invest. Different school of thoughts have argued about the

determinants of share price. Some economist (e.g., Ohlson,1995) believe

that share prices are determined by the fundamental factors of a firm,

which are often called as the “micro factor”. Dividend Discount Model

(DDM), Binomial Pricing Model, Residual Income Valuation (RIV) and

Discounted Cash Flows (DCF) are some of the models which recognize

micro factors as the share determinants. While some other economists

(e.g., Sharpe,1964) believe that the share prices are determined by macro-

economic variables. Capital Asset Pricing Model (CAPM), Arbitrage

Pricing Theory (APT) are based on this ideology. On the contrary Keynes

1 MBA Graduate, Department of Finance, University of Rajshahi

Email: [email protected] 2 Assistant Professor, Department of Management Studies, University of

Rajshahi Email: [email protected]

S

https://en.wikipedia.org/wiki/John_Maynard_Keynes


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(1936) holds that stock valuation is not a prediction but a convention,

which serves to facilitate investment and ensure that stocks are liquid,

despite being underpinned by an illiquid business and its illiquid

investments. Keynes (1936) explains price fluctuations in equity markets

by providing fictional beauty contest theory.

"It is not a case of choosing those [faces] that, to the best of one's

judgment, are really the prettiest, nor even those that average opinion

genuinely thinks the prettiest. We have reached the third degree where we

devote our intelligences to anticipating what average opinion expects the

average opinion to be. And there are some, I believe, who practice the

fourth, fifth and higher degrees." (Keynes, 1936, page-149).

Keynes believed that similar behavior was at work within the market. This

means micro and macro factors are not the determinants of share price.

Instead, what most people think their value is, will set the share price.

From the above arguments, it is quite clear that determination of share

price is an inconclusive issue in corporate finance. In Bangladesh, very

few research has been done on this topic (discussed in literature review).

In 2010-11, Dhaka stock exchange(DSE) collapse small investors were

greatly hit. Because of this incident and lack of sufficient research on

share price determinants, the authors are motivated to examine the issue.

The present study attempts to analyze the determinants of share price of

Pharmaceuticals sectors in Bangladesh. The objective is to identify the

relationships among firm fundamental factors (e.g., net asset value per

share, dividend per share, earning per share, firm size, return on equity,

price earnings ratio) of firm on DSE stock price. In the light of the above-

mentioned objective, the remaining part of this study is structured as

follows. In the next Section relevant literature is reviewed, later Section is

followed by study hypothesis and framework and methodology. The

Second last sections provide empirical results and discussions of major

findings and the final Section offers conclusion of this study.

(II) Literature Review

A good number of academic studies have tried to find key determinants of

share price. Determinants of share price vary among the academic

https://en.wikipedia.org/wiki/Stock_valuation#Keynes.27s_view

https://en.wikipedia.org/wiki/Liquidity

https://en.wikipedia.org/wiki/Market_trend

https://en.wikipedia.org/wiki/Stock


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research due to difference in countries studied, market, methodology, and

study period. To investigate determinants of share price, a number of

related research papers have been reviewed in this section.

Zahir et al. (1982)studied the determinants of stock prices in India in 101

industrial giants in the private sector for the year 1976-77 and 1977-78

using multiple linear regression model. They found that dividend per share

emerged as a significant determinant of share price, dividend yield also

emerged highly significant determinant with its negative association with

market price of share. Balkrishan (1984)in his work analyzed the

relationship in the internal factor, i.e. dividend per share, earning per

share, book value, yield with market price of share. A linear regression

model was used to study the relationship of these variables in general

engineering and cotton textile industries. Book value per share and

dividend per share turned out to be the most significant determinants of

market price in both the industries.

Sharma (2011) investigated impacts of fundamental also known as micro

factors including-book value per share, dividend per share, earning per

share, price earnings ratio, yield, dividend payout, and size in terms of sale

and net worth on share price in Indian stock market for the period 1993-94

to 2008-09. He found that book value per share, dividend per share,

earning per share has positive impact on share price.

Srinivasan (2012) identified the fundamental determinants of share prices

in India. The study focuses on six major sectors of Indian economy

namely manufacturing, pharmaceutical, energy, infrastructure, commercial

banking sectors, information technology(IT) and information technology

enabled services (ITES) over the period 2006-2011. Random effects

model has been employed and found that earnings per share and price-

earnings ratio are the crucial determinants of share prices of

manufacturing, pharmaceutical, energy, infrastructure and commercial

banking sectors. The findings indicate that size is a significant factor in

determining the share prices of all sectors under consideration except

manufacturing. Moreover, the book value per share positively influences

the share prices of pharmaceutical, energy, IT and ITES and

Infrastructure. Uddin et al. (2013) examined the impact of internal factors


JBS-ISSN 2303-9884

on stock prices of the companies in financial sector in Bangladesh over the

period 2005- 2011. By using multiple regression analysis, this study

revealed that earnings per share, net asset value, net profit after tax and

price earnings ratio have strong positive relationship with stock prices.

Bhattarai (2014) investigated the determinants of market stock price of

Nepalese Commercial Banks over the period 2006 to 2014. This study

revealed that earnings per share and price-earnings ratio have significant

positive association with share price while dividend yield showed

significant inverse association with share price of banks.

Malik et al. (2014) attempted to explain determinants of share price using

Ohlson (1995) model. According to Ohlson (1995) model book value per

share, earning per share and multiplication of book value per share and

earning per share are the key determinants of share price. Statistical

investigation using fixed effect model shows strong evidence for

applicability of Ohlson model for Karachi Stock Exachange listed

companies.

Jatoi et al. (2014) attempted to explain the share price changes due to only

one micro factor (earning per share) in cement industry of Pakistan over

the period 2009-2013.They used two variable linear regression model and

found that significant positive relationship exists between share price and

earning per share. Almumani (2014) determined the relationship between

Amman stock markets’ stock prices and different quantitative factors.

Results show that six internal factor (e.g., dividend per share, earnings per

share, book value, dividend payout ratio, price earnings ratio and size)

influence Amman stock markets’ stock prices. Applying ratio analysis,

correlation and a liner multiple regression models, this study found that

book value, earnings per share and price earnings ratio have positive

impact on share price of the listed banks in Amman stock exchange over

the study period 2005-2011. The study also found that size has inverse

relationship with share price and other variables such as dividend per

share and dividend payout have insignificant impact on share price.

The study of Arshad et al. (2015) examined the impact of different internal

and external factors that influence the share prices. They conducted their

study on commercial banks in Pakistan over the period 2007-2013. By


JBS-ISSN 2303-9884

using linear multiple regression analysis, they found that earnings per

share has more influence on share prices and has positive and significant

relationship with share prices. Book to market value ratio and Interest rate

have also significant but negative relation with share prices while other

variables (gross domestic product, price earnings ratio, dividend per share,

and leverage) have no relationship with share prices.

The study of Iqbal et al. (2015) identified the fundamental factors that

affect stock price in Oil and Gas and Cement Sector of Karachi Stock

Exchange over the period 2008-2011. By using panel data approach, they

found that earnings per share and book value per share have positive and

significant impact on share price in both sectors. On the other hand,

dividend yield is negatively significant in only cement sector.

(III) Study Hypothesis and Framework

The literature reviewed earlier suggest strong evidence of relationship

between firm specific factors also known as micro factors and share price.

In view of theory and major empirical evidence market share price may be

influenced by net asset value per share, dividend per share, earnings per

share, firm size, return on equity, price earnings ratio. The following

hypothesis and conceptual framework (Figure 1) are developed to test the

effect of these variable on share price in DSE.

H0 = There is no significant influence of Net Asset Value Per

Share(NAVPS), Dividend Per Share(DPS), Earnings Per Share(EPS),

Firm Size(SIZE), Return on Equity(ROE) and Price Earnings Ratio(P/E)

on Share Price.


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Figure 1: Conceptual Framework

Independent variable Dependent variable

Figure 1 shows that NAVPS, DPS, EPS, SIZE, ROE, P/E are the

independent variables and share price is a dependent variable. That means,

the changes in any one of these independent variables will result in change

in share price.

(IV) Research Methodology

This research investigates the relationship between share price and firm

fundamental factors of the listed pharmaceuticals industry on the Dhaka

stock Exchange (DSE). The data were collected from annual reports of the

companies and from the website of DSE. In carrying out this study, a

panel data design was adopted. This is because the research involves

multi-dimensional data as it contains observations on multiple phenomena

of 11 companies observed over a period of five years (2010-2014). After

the capital market collapse in 2010-11, the share prices in the

pharmaceuticals sector and the textile sector showed less volatile. But

after the generalized system of preference(GSP) was withdrawn, the share

Share price

NAVPS

EPS

DPS

SIZE

ROE

P/E


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prices of the textile sector became volatile. So, the pharmaceuticals sector

was the only sector to show stability in share price. Because of this

stability pharmaceuticals sector is purposively chosen.

Table 1: Pharmaceuticals companies used in this study

ACI ORIONINFU

ACIFORMULA PHARMAID

AMBEEPHA RECKITTBEN

GLAXOSMITH RENATA

IBNSINA SQURPHARMA

LIBRAINFU

Table 1 shows the list of the companies used in this study and it is

arranged in alphabetic order. In achieving the objective of this study, we

use ordinary least square (OLS) method. The Hausman test was applied to

choose the most efficient and most suitable method between fixed and

random effect. Pearson product moment Correlation coefficient was also

used, first to conduct the multicollinearity test for all the independent

variables and then to determine the degree and strength of association

between the variables.

(V) Empirical Results

Table 2 provides a summary of the descriptive statistics of the dependent

and independent variables for 11 pharmaceuticals companies for a period

of five years from year 2010 to2014 with a total of 55 observations. The

table 2 includes the mean, median, standard deviation, number of

observations, minimum and maximum for the independent and dependent

variables of the model.

The mean of share price(SP) was 455.72 in Bangladeshi taka(BDT) and

the standard deviation was 461.51. This means, the average share price of

the pharmaceuticals companies in Bangladesh, under the period of study,

was 455.72BDT. Regarding the standard deviation, it means the value of


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share price can deviate from its mean by 461.51BDT.The average value of

NAPVS was 212.54BDT. This implies that on average, the

pharmaceuticals company book value per share was 212.54BDT over the

study period. The maximum value of NAVPS for the study period was

1571.50BDT and a minimum value of 7BDT. The standard deviation was

436.36. Regarding the dividend per share, mean of dividend per share the

sampled firms was 7.84. It reveals that average yearly cash dividend was

nearly 78.4 percent of the face value of share price of sampled

pharmaceuticals companies. The highest dividend per share a company in

a particular year was paid 55 and in the same way the minimum ratio for a

company in a year was 1.

Moreover, EPS has a mean of 14.52 with maximum 68.63 and minimum

6.98. Minimum EPS (negative) is an indication that some firms incurred

loss during the study period, while the maximum is a clear indication that

some firms were able to generate profit.

Further, to check the size of the pharmaceuticals company and its

relationship with share price, natural logarithm of total market

capitalization was used as proxy. The mean of the natural logarithm of

market capitalization over the period 2010 to 2014 was 22.19 and standard

deviation of 2.04. The maximum value was 26.11 while the minimum

value was 17.69. To check profitability and its relationship with the share

price, ROE was used as a proxy. The average profitability was 18 percent.

This means, on the average, for each 1 BDT investment in equity of

pharmaceuticals companies there was 0.18 BDT return. The maximum

value of ROE for the year was 0.84 whereas the minimum value was -

0.04. Also, the standard deviation was 0.15 which indicates there was low

a variation from the mean.

Finally, the average value of the price earnings ratio was 37.27. That is,

average share price was 37.5 multiple of average EPS. The maximum

value and the minimum value was 135.85 and .05 respectively for the

study period.


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Table 2: Descriptive Statistics of the Variables

SP NAVPS DPS EPS LN(SIZE) ROE P/E

Mean 455.7 212.54 7.8 14.5 22.19 0.18 37.27

Median 256.7 55.48 3 5.92 22.22 0.18 28.34

Maximum 1824 1571.5 55 68.6 26.11 0.84 135.9

Minimum 42.5 7 1 -6.98 17.69 -0 -0.05

Std. Dev. 461.5 436.76 11 16.2 2.34 0.15 26.89

Observations 55 55 55 55 55 55 55

The correlation matrix is used to determine the degree of linear

relationship between independent variable and dependent variable. Table 3

shows the Pearson’s correlation matrix for the variables used in the

analysis. As can be seen from the table, the result of correlation between

net asset value per share and share showed a negative coefficient -0.04. It

indicates that if the NAVPS increases it will have a negative impact on

share price. The correlation between dividend per share and share price

showed a positive sign with a coefficient of 0.79. This indicates, if the

pharmaceuticals companies’ DPS increases, the share price also increases.

Besides, earning per share (EPS) had a positive correlation with share

price with a coefficient of 0.93. This implies an increase in profitability

results in increasing share price. There was positive correlation between

size and share price and the coefficient was 0.55. This shows that as the

size of pharmaceuticals companies increase, so does the share price.

Besides, ROE had a positive correlation with share price with a coefficient

of 0.63. This implies an increase in profitability results in increase in share

price. Meanwhile, the correlation result showed negative relationship

between price earnings ratio and share price with a coefficient of 0.15.

This indicates increase in price earnings ratio inversely affect share price.

Generally, the correlation results showed EPS, DPS, SIZE, and

profitability have a positive relation with share price. On the other hand,

share price had negative relation with NAVPS and price earnings ratio.

Malhotra (2007) stated that if the correlation coefficient among variables

should be greater than 0.75 it can cause multicollinearity problems. All


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correlation results are below 0.75, which indicates that multicollinearity is

not a potential problem for this study

Table 3: Correlation matrix

SP NAVPS DPS EPS SIZE ROE P/E

SP 1

NAVPS -0.04 1

DPS 0.79 -0.12 1

EPS 0.93 -0.09 0.66 1

SIZE 0.55 -0.29 0.38 0.63 1

ROE 0.63 -0.38 0.68 0.57 0.22 1

P/E -0.15 0.2 -0.19 -0.3 -0.36 -0.21 1

Fixed effects model (FEM) and Random effects model (REM) are two

classes of panel estimator approaches that can be used in financial

research. This study uses Housman test to find out the model that provides

comparatively more consistent estimate for the study. Tables 4 show the p-

value for the test is 0.52, which indicate that the null hypothesis was failed

to be rejected. Hence, the random effect method was preferable.

Accordingly, REM was employed to estimate the relationship between the

dependent variable and the independent variable.

Table 4: Housman test

Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.

Cross-section random 5.13214 6 0.527

H0 = The random effects method is the preferred regression method.

H1 = The random effects method is not the preferred regression method

The R-Square in Table 5 which is often referred to as the coefficient of

determination of the variables is .9233. The R-Square which is also a

measure of the overall fitness of the model indicates that the model is

capable of explaining about 92.33% of the variability in the share prices of

pharmaceuticals companies. This means that the model explains about

92.33% of the systematic variation in the dependent variable. That is,


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about 7.66% of the variations in market price of the sampled

pharmaceuticals companies are accounted for by other factors not captured

by the model. This result is complimented by the adjusted R- square of

about 91.37%, which in essence is the proportion of total variance that is

explained by the model.

The DW test statistic value in the multivariate regression result is 1.70.

According to DW stat. table, the relevant critical values for the test are

lower critical value(dL) = 1.172 and upper critical value(dU) = 1.638, so 4

− dU = 2.362 and 4 − dL = 2.828. The DW test statistic value is clearly

between 1.638 to 2.362. So, the null hypothesis is not rejected and no

significant residual autocorrelation was presumed. Similarly, findings

from the Fishers ratio (i.e., the F-Statistics) which is a proof of the validity

of the estimated model as reflected in Table 5, indicates that, the F is about

96.349 and a p-value or F(sig) that is equal to 0.000, this invariably

suggests clearly that simultaneously the explanatory variables are

significantly associated with the dependent variable. That is, they strongly

determine the behavior of the market values of share prices

Table 5: Regression result-REM

Variable Coefficient Std. Error t-Statistic Prob.

NAVPS 0.088677 0.039731 2.23193 0.0303

DPS 11.49781 1.777874 6.46717 0.0000

EPS 19.27831 1.421481 13.5621 0.0000

LN(SIZE) 17.22947 8.650343 1.99177 0.0521

ROE 283.7409 136.8093 2.07399 0.0435

P/E 2.287929 0.504109 4.53856 0.0000

C -452.7322 196.0555 -2.3092 0.0253

R-squared 0.923334

Adjusted R-squared 0.913751

F-statistic 96.34927

Prob(F-statistic) 0.00000

Durbin-Watson stat(DW) 1.705467


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The model we estimated is given below:

SPit = β0 + β1NAVPSit + β2DPSit + β3EPSit + β4SIZEit P/Eit + β5ROEit +

β6 P/Eit + εit

Where,

Dependent variable, SP: Stock price

Independent variables, : NAVPS = Net asset value per share

DPS = Dividend per share

EPS = Earnings per share

SIZE = Firm size

ROE = Return on equity

P/E = Price earnings ratio

Table 5 shows that net asset value per share has a positive relationship

with share price. This result basically means that with the influence of

other variable held constant, firm’s net asset value per share will have

positive impact on market price. Empirical findings provided in the Table

5 show that there is a significant positive relationship between dividend

per share and share price of the listed pharmaceuticals companies in DSE.

This is evident in the t-statistics=6.46 with p value 0.000. However,

further empirical finding from the regression analysis shows a positive

relationship between EPS and share price. This is evident in the t-statistics

value of (t-statistics = 13.56and the p-value =.000). The results can be

explained as that an increase in earnings per share will invariably bring

about a significant increase in the market prices of equity shares. Another

empirical finding from the regression analysis shows that there is positive

relationship between P/E ratio and SP. The coefficient of P/E ratio is

2.287which mean that when there is 1-unit increase in price to earnings

ratio, the share prices will increase by BDT.2.287. Finally, variables

LN(SIZE) have significant impact on share price. This indicates that SIZE

have an explanatory power toward stock price movement.


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(VI) Discussions of Major Findings

Net Asset Value Per Share(NAVPS)

This study showed that significant positive relationship between share

price and net asset value per share. The previous literature supports this

finding like the studies of Sharma (2011) Arshad et al. (2015), Uddin et al.

(2013) and Almumani (2014). The reason behind positive relationship

between net asset value per share and share price is that book value per

share is the owner’s funds, a higher book value per share is perhaps

perceived by an investor to be an indicator of the sound financial position

of a company for investing.

Dividend Per Share (DPS)

This model showed that there is positive association between dividend per

share and share price. This result is consistent with results of Zahir and

Khanna (1982), Balkrishan (1984), Malhotra (1987) that dividend per

share has positive and significant impact on market price of share.

Earnings Per Share (EPS)

Significant positive relationship between earning per share and stock price

has been found in the statistical test. It can be explained that when earning

per share increase, it will boost up company’s share price. The present

study tends to support the viewpoints of Sharma (2011), Arshad et al.

(2015), Uddin et al. (2013, and Almumani (2014), Zahir and Khanna

(1982), Balkrishan (1984), Malhotra (1987).

Firm Size (SIZE)

Firm size was used in this study in terms of market capitalization of a

firm. This study showed that relationship between firm size and share

price are statistically being significant. These finding is consistent with

previous researcher’s findings that the firm size is having significant

positive relationship with stock price. Srinivasan (2012) found that

positive relationship exists between stock price and firm size. Chandra

(1981) also found that size has significant positive impact on market price

of share.


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Return on Equity(ROE)

Profitability of a firm greatly influences the market share price. In this

study, return on equity is used as proxy of profitability of firm. Results

show statistically significant positive relationship between return on equity

and share price. This finding is consistent with the findings of Chandra

(1981) that returns have a positive influence on share price.

Price Earnings Ratio(P/E)

Empirical findings from the regression analysis shows positive linear

relationship between price earnings ratio and share price. This outcome is

supported by Uddin et al. (2013), Almumani (2014), and Bhattarai (2014)

that has a significant positive relation exists between price earnings ratio

and share price.

(VII) Conclusion

This study examines the relationship between the selected variables (net asset value per share, dividend per share, earnings per share, firm size, return on equity and price earnings ratio) and the stock price in DSE, by means of panel data techniques (REM)for the period from 2010 to 2014. The conclusion drawn from this study is that a significant relationship exists between the DSE stock prices and all the selected microeconomic variables. Specifically, the study found that net asset value per share, dividend per share, earnings per share, firm size, return on equity, price earnings ratio are positively affecting the stock price. The result of this study has similarities with the result of the previous studies. Like, the study result of Arshad et al. (2015), Uddin et al. (2013, and Almumani (2014), Zahir and Khanna (1982), Balkrishan (1984) and Malhotra (1987), this study also revealed that share price has positive relations with net asset value per share, earnings per share, firm size, return on equity and price earnings ratio. This study has a lot of implications to the managers and the investors. The findings of this study will help managers knowing the factors that affect share price and the factors that needed to be emphasized to maximize share price. Besides, the investors can take their investment decision on the basis of this result. Despite having important implications, this paper also has limitations. This paper is done using the pharmaceuticals sector data only. So, it might not be applicable to the other sector. Future studies need to be done on the determinants of share price including all the industries enlisted in DSE.


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References

Almumani, M. A. (2014), “Determinants of equity share prices of the

listed banks in Amman stock exchange: Quantitative approach”,

International Journal of Business and Social Science, Vol. 5, pp.

91-104.

Arshad, Z., Arshaad, A. R., Yousaf, S., & Jamil, S. (2015), “Determinants

of Share Prices of listed Commercial Banks in Pakistan”,Journal of

Economics and Finance, Vol. 6, pp.56-64.

Balakrishnan (1984), “Determinants of Equity Prices in India”,

Management Accountant, Vol.19, pp. 728-730.

Bhattarai, Y. R. (2014), “Determinants of Share Price of Nepalese

Commercial Banks”,Economic Journal of Development Issues,

Vol. 17, pp. 187-197.

Chandra, P. (1981), “Valuation of equity shares in India”,New Delhi:

Sultan Chand and Sons.

Jatoi, M.Z., Shabir, G.,Hamad, N.,Iqbal N. & Muhammad, K. (2014), “A

Regressional impact of earning per share on market value of share:

A case study cement industry of Pakistan”,International Journal

of Academic Research in Accounting, Finance and Management

Sciences,Vol.4, pp. 221-227.

Hausman J.A (1978), “Specification Tests in Econometrics”,

Econometrica, Vol. 46, pp. 1251- 1271.

Iqbal , A., Ahmed , F., & Zaidi, S. S. (2015),“Determinants of Share

Prices, Evidence from Oil & Gas and Cement Sector of Karachi

Stock Exchange (A Panel Data Approach)”,Journal of Poverty,

Investment and Development, Vol.8, pp. 14-19.

Keynes, John Maynard (1936), The General Theory of Employment,

Interest and Money, New York: Harcourt Brace and Co.

Malhotra N. K.(2007),Basic Marketing Reserach :A Decesion-Making

Apporach, New jersey: Person Education, Inc.


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Malhotra, N., &Tandon, K. (2013), “Determinants of stock prices:

Empirical evidence from NSE 100 companies”, International

Journal of Research in Management &Technology, Vol.3, pp. 86-

95.

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Share Price: Evidence from Karachi Stock Exchange”, The

Romanian Economic Journal, Vol. 4, pp. 97-114.

Ohlson, J. (1995), “Earnings, Book values and Dividends in Equity

valuation”, Contemporary Accounting Research, Vol. 11, pp. 661

– 687.

Sharma, S. (2011), “Determinants of equity share prices in India”, Journal

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equilibrium under conditions of risk”, Journal of Finance, Vol.19,

pp. 425–442.

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pp. 205-228.

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The Chartered Accountant, Vol. 30, pp. 521-523.


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Influence of Cognitive and Affective Image on a Recreational

Park: An Empirical Study

Md. Ikbal Hossain 1

Rebeka Sultana Rekha 2

Dr. Md. Enayet Hossain 3

Abstract

This empirical study is conducted to test the influence of cognitive and affective

image on a recreational Park at Rajshahi in Bangladesh. In total 257 samples are

collected from the visitors at the destination using seven Point Likert-Scale.

Initially an exploratory factor analysis is employed and regression analysis is

performed to test the individual relationship of factor with the cognitive and

affective images using SPSS 15.00. The main outcome of the analysis present

total six cognitive (Entertainment & Recreation Facilities, Food & Beverage

Facilities, Transportation & Safety Facilities, Infrastructure, Price Charges and

Natural & Artificial Environment) and two affective (Pleasant & Relaxing)

influential factors which have significant relationship with cognitive and

affective images. Thus, the eight hypotheses are accepted which will enrich the

existing literatures. The park operator will get insight knowledge for developing

image of the destination. Theoretical implications are discussed including

limitation and future research direction.

Keywords: Cognitive image, affective image, factor analysis, regression analysis

(I) Introduction

oday’s tourism market is very much competitive and should revise to

attract visitors where an image of a destination is the most important

issue. Destination image is described as simply impressions of a place or

perceptions of an area (Echtner & Ritchie 2003). It is also the concept of

expression of all objective knowledge, prejudices, imagination and

emotional thoughts of an individual or group about a particular location

1 Assistant Professor, Department of Marketing, University of Rajshahi,

Email: [email protected] 2 Lecturer, Department of Business Administration, Pabna University of Science and

Technology, Email: [email protected] 3 Professor, Department of Marketing, University of Rajshahi,


T





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(Lawson & Baud (1977). It is a multidimensional constructs comprising of

several primary dimensions (Ahmed 1996; Baloglu & McCleary 1999;

Echtner & Ritchie 1993; Lawson & Baud 1977; Leisen 2001) which is the

growing interest for researchers of the day.

Cognitive and affective are the two major perspectives of analyzing the

image of a destination (Lawson & Baud, 1977). The cognitive image’s is

known as perceptual component which can be interpreted as the whole

beliefs and knowledge about the physical attributes of a destination. The

affective image’s refers to the appraisal of the affective quality of feelings

towards the attributes and the surrounding environments (Baloglu &

McCleary, 1999). The authors also suggest that destination image as the

sum of the perception of cognitive evaluation based on information

sources and age, and affective ones influenced by socio-psychological and

demographical factors. Leisen (2001) states that affective associations

greatly influence the image, an individual has of a destination and

therefore, destination choice. So, destination image has significant effect

on the development of the destination through satisfying visitors, to retain

them and for developing the tourism sector of a country.

Though, tourism sector of Bangladesh is one of the fastest growing sectors

which needs to revise its image for attracting and satisfying visitors, yet

foreign visitors arrival expanded from 207,199 in 2001 to 303,386 visitors

in 2010 (Bangladesh Parjatan Corporation). The growth indicates light of

further scope by maximizing use of existing resources (Islam &

Nuruzzaman 2009). There are seven divisions and 64 districts in

Bangladesh which refer unique tourism flavor individually. The study is

conducted at Rajshahi district which contains more than 20 tourism spots

(such as Zia Park, Central Zoo, Veranda Museum(1st Museum in

Bangladesh), Padma Garden, T-Damp, Sericulture, University of Rajshahi,

Vadra Park, Putia Rajbari and River Bank etc.). Most of the visitors often

visit Zia Park, Central Zoo, Putia Rajbari, Veranda Museum and Padma

Garden (on the bank of River Padma). This empirical study is mainly

focused on Zia Recreational Park to understand the factors and its

relationship with cognitive and affective images.


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(II) An overview of the Recreational Park

Bell, Tyrvainen, Seivanen, Probstl, and Simpson (2007) find that recreation is the activities of people in the nature as the part of their leisure

time. Recreation Park is developed by state to maximize social welfare of

people and for their recreation (Pauta & Siavanen, 2001). This recreational

park is situated in city of education (Rajshahi) in Bangladesh which was

also the special project of the People’s Republic of Bangladesh. Domestic

Engineering & Technology Services (DETS) constructs the park within

the contractual time (2 years) and also get the management authority for

ten years from Rajshahi City Corporation. They get the authority by

making contact of sharing income under the supervision of this city

corporation. This park is situated on 12.21 acres land which has 18

different paid & few non-paid rides for children and adult people (Hossain

& Hossain, 2014). It has been opened to general people on 25th

, February

2006. It is the most attractive and well furnished park in the northern zone

in Bangladesh and opened to visitors from 10.00 am to 8.00 pm in the

whole year. The peak session for the park is from August to February and

most of the visitors come from different educational institutions (schools

and colleges) for education tour, picnic parties from different places, local

picnic parties and the residential people. Hossain and Hossain (2014) also

depict more than 1.2 lakh visitor visit this destination over the year which

also plays an important role for economic contribution and for

employment opportunity in the northern region. This park provides Tk.

35.70 lakh (1$ = 78.00 Taka) respectively to the Rajshahi City

Corporation in 2011 and 2012. There are 82 people employed to conduct

the park activities smoothly. Moreover, many local people are directly and

indirectly dependent on this park for their earnings and livings by

conducting different business activities (Shops, Restaurants, Tea stall and

Transportation services etc.).

(III) Brief Literature Review

Image and Destination Image

Beerli and Martin (2004) depict that an image is seen as a mental picture

formed by a set of attributes that defines the destination in its various

dimension, influences destination selection process. They further suggest a

destination image can be created from an individual’s general knowledge


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or feelings, external influence from friends and relatives, advertisements

and their own past experience. On the other hand, Milman and Pizam

(1995) suggest that destination image consists of three main components;

firstly, the product, for example the quality of attraction, the attitude of the

destination hosts and the environment; secondly the weather or climate

and lastly the facilities available in the destination. Destination managers

or organizations need to consider all three components with equal

importance to retain tourist’s confidence on their products and to influence

their behavior. Beside these, destination image plays two important roles

in behaviors: (1) to influence the destination choice decision-making

process and (2) to condition the after-decision-making behaviors including

participation (on-site experience), evaluation (satisfaction) and future

behavioral intentions (intention to revisit and willingness to recommend)

(Ashworth & Goodall, 1988; Bigne, Sanchez & Sanchez, 2001; Cooper,

Fletcher, Gilbert & Wanhill, 1993; Lee, Lee, & Lee, 2005; Mansfeld,

1992). Moreover, cognitive image have a direct influence a destination

and represent its overall image (Beeril & Martin, 2004).

Therefore, destination image is a view of picture that attracts the visitors

to the destination and makes them spend much more money there. At the

same time, image views different things for different people (White, 2005)

and destination image is the beliefs, thought and impression of a tourist

about a place and the picture in their minds relating to that place (Watkins,

Hassanien & Dale 2006). Beside these, the image of a place is an

important asset (Ryan & Gu 2008). They further emphasize that image

itself is the beginning point of tourists expectation to visit the destination.

Unfortunately, most the image studies only considers cognitive image to

analyze the image of a destination. This study considers both cognitive

and affective components to analyze their influence on the destination

which is also considered in many recent image studies to measure the

overall image in different cultures’ (Baloglu & McCleary, 1999; Beerli &

Martin, 2004; Hosany, Ekinci, & Uysal, 2007; Lee et al., 2005; Martin &

Bosque, 2008; Phillips & Jang, 2008).

Cognitive Image

Cognitive image refers to beliefs and knowledge about an object or place

(Baloglu & Brinberg 1997; Gartner 1993; Walmsley & Jenkins, 1993). It


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is also described as the beliefs and information that visitors have about a

place (Coban, 2012). Cognitive image is directly observable, descriptive

and measurable which may provide more concrete and interpretive

information regarding the uniqueness of a destination (Walmsley &

Young, 1998). It comprises objective reality of destination attributes

(Tasci & Gartne, 2007). Numerous studies are conducted by researchers to

measure the destination image considering only the cognitive image

(Chen, 2001; Chen & Kerstetter, 1999; Leisen, 2001) and different studies

consider different dimensions (Liu, Lin & Wang, 2012). This study

considers six cognitive factors (Entertainment & recreation, Food &

beverage, Infrastructure, Transportation & security, Price charges and

Natural Environment) which are common to different image studies and

contain variables (table 1).

First, entertainment and recreation facilities are one the most attractive

factor which visitors consider to choice and select a destination for

visiting. Though, this study is conducted on a park, it appears very

important to represent the cognitive image of the destination. Then, the

hypothesis is drowned as follow-

Hypothesis 1: The factor entertainment and recreation facilities positively

influence the cognitive image of the destination.

Second, food and beverage is also important to visitors for visiting a

destination. Usually, visitors spend much time there and they need launch

and snacks etc. If a destination can ensure the availability of quality

restaurants, food corners etc; it can attract more visitors. So, it appears

significant to weight the cognitive image of the destination and the

hypothesis is

Hypothesis 2: The factor food and beverage facilities positively influence

the cognitive image of the destination.

Third, infrastructure plays also key role to attract visitors towards a

destination. This study also considers this important factor and proposes

hypothesis to test its’ relationship with cognitive image of the destination.

The hypothesis is in below-


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Hypothesis 3: The factor infrastructure positively influences the cognitive

image of the destination.

Fourth, transportation facilities are also an important indicator to easily

access a destination and safety is important for physical and mental relieve

of visitors. This study considers this factor and tries to test its influence on

cognitive image of the destination. The drawing hypothesis is-

Hypothesis 4: The factor transportation and safety facilities positively

influence the cognitive image of the destination.

Fifth, Price plays an important role to visit or not to visit a destination

and the most successful destination always offers competitive price for its

different offerings to attract more visitors. This study considers it as factor

and the hypothesis is drawing as-

Hypothesis 5: The factor price charges positively influence the cognitive

image of the destination.

Sixth, natural environment is very much important for the mental pleasure

of visitors towards a destination for visiting. Because, they normally visit

a destination with family, friends and relatives etc and this factor plays an

important role to taking final visiting decision to visitors. The study

considers this important factor and is drowned the hypothesis to test its

influence on cognitive image as follow-

Hypothesis 6: The factor natural environment positively influence the

cognitive image of the destination.

Affective Image

Baloglu and McCleary (1999) depict that affective image is the emotional

feelings about the destination attributes and surroundings environment. It

is the evaluation of visitors towards a destination and the evaluation may

be positive or negative (Woodside & Lysonki, 1989). Many studies

consider affective image component with cognitive image to measure the

destination image. This study considers two factors (pleasant place to visit

and relaxing place to visit) which are used in different study also to

measure the affective image (table 1).


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First, the factor pleasant place to visit the park is considered as an

important affective issue. Normally, visitors visit those destinations which

seem pleasant to them. Later on, they take their final decision for visiting

and evaluate the destination on this regard. So, this study considers it and

tries to test its relationship with affective image and the proposed

hypothesis is-

Hypothesis 7: The factor pleasant place to visit positively influences the

affective image of the destination.

Second, the factor relaxing place to visit is also important to visitors. If

they think the destination is free of risk (mentally and physically). If they

can visit with relaxation, it increases more visitors for visiting the

destination. This study considers this important factor for study and the

proposed hypothesis is-

Hypothesis 8: The factor relaxing place to visit positively influences the

affective image of the destination.

This study treats both the cognitive and affective image components

independently by considering its attributes in understanding the image of

the destination which is not seen in previous image studies. Then, it

considers testing the influence of eight factors individually to measure

their relationship with cognitive and affective images. This study has also

been carried out in tourism sector and Zia Park has been chosen as the

study context. The reason of choosing the destination is that there are not

available literatures on this park. But it is the most attractive, amusement

and recreational places not only in Rajshahi city but also in the northern

zone in Bangladesh. Many domestic visitors visit this destination each

year. So, the study is conducted to understand the park image and to look

for main reasons behinds for visiting this destination. To fill up the gaps,

the present study conducts to explore the variables and factors of cognitive

and affective image of the park. In addition to that it also explores the

attributes of cognitive and affective images. Finally, it tests the

relationship of factors with independent cognitive and affective image of

the destination.


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(IV) Research Methodology

Sampling and Data Collection

The study considers quantitative method to collect data which is very

much popular in marketing and social science research. This method is

used for three purposes, First, it reflect the post- positivist philosophical

assumption; Second, it allows simple statistical tools, easy to administrate,

code the data for further processing and resulting in easy writing; Third,

statistically organized image analysis is helpful to compare with other

destinations which might be of interest of the destination operators or

managers. While the study considered the spot visitors as target

populations and the sample size are 257 by considering the convenient

sampling method due to the convenience and availability of the

respondents (Babble, 1990). A total of 350 questionnaires were distributed

and 302 questionnaires were returned, representing a response rate of

86.28%. After screening of completeness of the questionnaires, 257

samples were deemed to final analysis.

Survey Questionnaire Design

The questionnaire was developed based on existing literature which was

tested empirically in previous study (Hossain & Hossain, 2014). It

contained three sections. The first section contained the 34 variables and

10 attributes of cognitive image. The second section contained 10

variables and 2 attributes of affective image of the park. Likert-Scale was

used to indicate the level of agreement of the respondents ranging from 7

= very strongly agree and 1= very strongly disagree for both sections. This

scale is very much popular and widely used to understand and measure

perception, evaluation, beliefs and attitude of customers or visitors toward

an object, brand, place and product (Malhotra, Hall, Shaw, & Crisp, 1996).

This scale also provides more accurate data than 5- point Liker-Scale for

statistical analysis. Secondary sources were used to generate variables and

attributes for both of these sections also. It included brochures, paper

cutting and promotional materials about the destination and review of park

and tourism destination related literatures that focus on destination image,

perceived image, tourists/ visitors attraction, attitude, satisfaction and

cognition (Aksoy 2011; Beerli & Martín 2004; Hossain & Hossain, 2014;

Liu, Liu, Huang & Wen 2010). The last section of the questionnaire was


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related to the socio-demographic information of the respondents to

identify their characteristics. Moreover, there are two questions included

to determine the respondents’ intention to revisit the Park using

dichotomous-scale and for the number of visited time using ordinal-scale.

These scales are also widely used in social science research.

Pilot Test

This empirical study also conducted a pilot test to ensure its clarity,

reliability and comprehensiveness of the questionnaire and thirty (30)

questionnaires were distributed to 25 MBA students and 5 faculty

members (Department of Marketing, University of Rajshahi, Bangladesh)

who were visited the Park at least once. Some modifications to the

wording are made on the basis of recommendation of pilot test.

Scale reliability

A reliability analysis (Cronbach’s Alpha) is used to gauge the reliability of

the instrument’s items which determines the internal consistency or

average correlation of the items. The reliability analysis reveals that the

alpha coefficient is 0.913 for cognitive and 0.852 for affective images,

which exceeds the minimum coefficient (0.5) suggests by Hair, Anderson,

Tatham and Black (1998).

Data analysis

The data are analyzed using the Statistical Package for the Social Sciences

(SPSS 15). Descriptive statistics are used to analyze the distribution of the

data. An exploratory factor analysis is performed to reduce the number of

items to a few correlated dimensions. It has been used to explore the

possible underlying factor structure of a set of observed variables without

imposing a preconceived structure on the outcome (Child, 1990). The

Principal Components and Orthogonal (VARIMAX) rotation methodology

is used and only factors with Eigen value equal or greater than one (1) are

retained. A variable with a factor loading of 0.5 or more is kept in a factor.

Finally, regression analysis is performed where dependent variables are

cognitive and affective images and independent variables are the attained

exploratory factors of the destination. Then, two regression models are

developed for these two images (Cognitive Affective) to test the


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relationship of 8 factors with these images statistically which are given in

below-

1) The regression models for the cognitive image of the Zia Park is-

)1( ............................2101 eXXY nn

Where,

= Dependent Variable: Cognitive Image

= Regression of coefficient intercept

=Regression coefficient of independent variables

= Independent variables (Entertainment & recreation, Food &

beverage, Transportation & Safety, Infrastructure, Price charges

and Natural environment)

e = Random error

2) The regression models for the affective image of the Zia Park is-

(2) ..................221102 eXXXY nn

Where,

Y2 = Dependent Variable: Affective Image

β0 = Regression of coefficient intercept

βn Xn = Regression coefficient of independent variables

X1X2= Independent variables (Pleasant place and Relaxing

place)

e = Random error

(V) Results and Discussions

Profile of Respondents

The useable questionnaires are distributed to 257 respondents,

representing 65.4% male and 34.6% female respectively. Near half of the

respondents are in the age group of 21-30 years, representing 49.4% and

younger than 20 years of age is the second largest portion representing

21.8% of the respondents. Most of the respondents’ professional

background includes students 49%, government employees 16.7% and

private organization employees 17.5%. In addition, the survey reveals that


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the education level of visitors to Zia Park is relatively high, with 45.9%

completed graduation degrees. While 19.8% of the respondents complete

higher secondary certificate education and only 14.8% of the respondents

complete their secondary school certificate education. With regards to

personal monthly income measures in taka, the study reveals that 28.8% of

the visitors report their or their parents’ monthly income in the range

between 10,000Tk to 20,000Tk and 22.6% of the respondents earn less

than 10,000Tk. It denotes that most of the visitors fall in lower level

income group. While 54.5% of the respondents are single and 45.5% are

married respectively. A majority of respondents 81.3% are from the

Rajshahi division which indicates that most of the visitors are local people.

At the same time as, 7.4% of the respondents are from the Dhaka division

and 4.3% are from Khulna division. Whereas over half, 50.2% of

respondents visit this place more than 5 times and 32.7% of the

respondents are 2 to 5 times. Only 17.1 % of the respondents are the first

time visitors at this Park. While 87.2% respondents want to visit this park

again and 10.5 % respondents are in under-consideration (Table 2)

Cognitive image dimension

The results of Bartlett test of Sphericity is significant (x² =3154.055, p =

0.000). The overall value of the Kaiser-Meyer-Olkin overall measure of

sampling adequacy (MSA) is 0.894, which is well above the

recommended threshold of sampling adequacy at the minimum of 0.5

(Hair et al., 1998). These two tests suggest that the data is suitable for

exploratory factor analysis. Base on the Eigen value greater than one,

scree-plot criteria and the percentage of variance criterion, six factors (6)

are retained which capture 64.39% of the total variance. Among the 34

cognitive image attributes, eight has factor loading less than .50. These are

“Availability of mobile network,” “Dustbin facilities,” “Train

transportation facilities,” “Rides ticket price,” “Various trees available at

the park”, “Prayer facilities”, “Free entering facilities” and

“Toilet/washroom facilities”. These are evaluated for possible deletion

following by the criterion of Hair et al. (1998). The dropping of these

variables with low factor loading the total variance explanation increases

more than 3% (from 61.02% to 64.39%). The results of the principle

component analysis with orthogonal (VARIMAX) rotations are shown in


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Table 3. The scale reliability for each factor is tested also for internal

consistency by assessing the item –to-total correlation for each separate

item and Cronbach’s alpha is considered for the consistency of the entire

scale. The rules of thumb suggest that item-to-total correlations exceed .50

and lower limit for Cronbach’s alpha value is .67. The result shows that

the alpha coefficient ranges from .67 to .87 for the six factors (Table 3).

Factors are labeled based on highly loaded items and the common

characteristics of the items they include. They are labeled as

“Entertainment & recreation facilities”, (Factor 1), “Food & beverage

facilities,” (Factor 2). “Transportation and safety facilities”, (Factor 3),

“Infrastructure”, (Factor 4), “Price charges”, (Factor 5), and “Natural &

artificial environment”, (Factor 6). While cognitive image is the result of

the mean value of 10 cognitive attributes. These include the overall belief

of the respondents about rides facility, environmental scenery, safety &

security, food, entertainment & recreation, structure & location, facility

services, management services, price charges and transportation facility).

Where the environmental scenery shows the highest mean value (5.53)

and price charges shows the lowest one (4.36). While, others bear the near

or same value which indicate the respondents’ cognitive image towards

the recreational park is very positive.

In addition, regression analysis is performed to examine the relationship of

six (6) factors with cognitive image of the park taking a 5% significance

level. The regression equation characteristics of cognitive image indicates

a good adjusted R² =0.708 (Table 5). T his indicates that more than 70%

of the variation is explained by the equation where the F-ratio of 100.990

is significant. The regression analysis shows that the cognitive image has

statistically significant beta coefficients (p.000). So, there are a positive

relationship between the independent variables and the dependent

variable.

The factor (entertainment & recreation facilities) shows the result

(Standardized, β=0.126, t = 2.631) which indicates the positive

relationship with the cognitive image. Thus, the first hypothesis is

accepted. While the average value of this factor’s variables are not so high

which indicate the facilities unavailability or poor quality to visitors. The

park operator should give more attention to improve the quality and make


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more noticeable these facilities to develop image and to attract more

visitors. While, factor (food & beverage facilities) shows the regression

results (Standardized, β=0.221, t = 4.595) which denote its positive

relationship with cognitive image. Thus, the second hypothesis is also

accepted. The destination authority or manager should ensure more quality

restaurants and food corners to make available the fast foods and

preferable foods to visitors which indicate the lowest opinion of studies

from the descriptive study.

In addition to, the factor transportation & safety facilities shows the

regression result (Standardized, β=0.433, t = 9.954) which mean the

positive relationship with cognitive image and the hypothesis is accepted.

The facilities within this factor are available to visitors and the destination

should continue it to improve its image. On the other hand, the factor

(infrastructure) shows the results (Standardized, β=0.090, t = 2.005) which

indicate its positive relationship with cognitive image and the fourth

hypothesis is accepted also. It can be recommended from the descriptive

studies that the authority should revise the facilities within this factor

specialty to enlarge the area of the park.

Furthermore, price charges shows the results (Standardized, β=0.143, t =

3.325) which also indicate its positive relationship with the cognitive

image and the hypothesis is accepted. But the descriptive study of this

factor does not show satisfactory result especially to the variable price

charges for buying different goods at the park. The authority or manager

of the park should revise their offerings price and set up competitive price

to develop the park image and attract more visitors. Because most of the

visitors are in lower level income group and residence people and they are

very much price sensitive. Moreover, the factor natural and artificial

environment show the regression results (Standardized, β=0.221, t =

4.595) which show the positive relationship with the cognitive image and

the hypothesis is accepted. The descriptive results of the variables within

this factors show the highest opinion of visitors which indicate the

availability of the facilities in the destination. The operator should

continue these facilities and make more visible to develop the park

cognitive image. The regression model for the cognitive image of the park

is described in below-


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)1( 66554433221101 eXXXXXXY

= 0.605+(0.126x0.074)+(0.221x0.151)+(0.433x0.411)+(0.090x0.073)+

(0.143x0.104)+(0.133X0.107)+0.408

= 0.605 +0.009324+0.033371+0.1777963+0.00657+0.014872+ 0.014231+

0.408

=1.27

Where, Y1 = the dependent variable cognitive image; β0 = the value of

regression coefficient; β1 X1 = Regression coefficient of entertainment &

recreation X beta value of it; β2 X2 = Regression coefficient of food &

beverage X beta value of it; β3 X3 = Regression coefficient of

transportation &safety X beta value of it; β4 X4= Regression coefficient

of infrastructure X beta value of it; β5 X6= Regression coefficient of

price charges X beta value of it; β6 X6 = Regression coefficient of

natural & artificial environment X beta value of it and e = Random

error respectively.

Affective image dimension

The results of Bartlett test of Sphericity is significant (x² =812.057, p =

0.000).The overall value of the Kaiser-Meyer-Olkin overall measure of

sampling adequacy (MSA) is 0.879 which is well above the recommended

threshold of sampling adequacy at the minimum of 0.5 (Hair et al., 1998).

These two tests suggest that the data is suitable for exploratory factor

analysis. Base on the Eigen value greater than one, scree-plot criteria and

the percentage of variance criterion, two (2) factors are chosen which

captures 59.43% of the total variance. Among the 10 affective-images

attributes, one has factor loading less than .50. This is the respondent

evaluation about structure & location of the park. This is evaluated for

possible deletion following by the criterion of Hair et al. (1998). The

dropping of this variable with low communalities (>0.40) and factor

loading less than (0.50), increases the total variance explains

approximately 3% (from 56.49% to 59.43%). The results of the principle

component analysis with orthogonal (VARIMAX) rotations are shown in

Table 4. The scale reliability for each factor is tested also for internal

consistency by assessing the item –to-total correlation for each separate


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item and Cronbach’s alpha for the consistency of the entire scale. The

rules of thumb suggest that item-to-total correlations exceed .50 and lower

limit for Cronbach’s alpha value is 0.773. The result shows that the alpha

coefficient ranges from 0.773 to 0.799 for the two factors. Factors are

labeled based on highly loaded items and the common characteristics of

the items they include. They are labeled as “Pleasant place to visit”,

(Factor 1) and “Relaxing place to visit,” (Factor 2). The first factor

explains of the variance 47.19% and it bears Cronbach’s alpha coefficient

0.799. The second factor explains of the variance 12.23% and it bears

Cronbach’s alpha coefficient 0.773. So, both of the factors have high inner

consistency and scale reliability. While affective image is the result of the

mean value of 2 affective attributes. These include the visitors feeling

about the recreational park as pleasant and relaxation. The attribute

(pleasant) shows the highest opinion (5.42) of the visitors and relaxation

shows the lowest one (5.34). It indicates the respondents’ affective image

towards the recreational park is very positive.

Similarly the cognitive image, the regression equation characteristics of

affective image indicates a good adjusted R² =0.887 (Table 6). This

indicates that near about 89% of the variation is explained by the equation

where the F-ratio of 1001.171 is significant. The regression analysis shows

that the affective image has statistically significant beta coefficients

(p.000). So, there are a positive relationship between these independent

variables and the dependent variable.

The independent variable (pleasant place to visit) show the results

(Standardized, β=0.542, t = 25.723) which indicates the positive

relationship of this factor with the affective image of the destination and

the proposed hypothesis is accepted. The descriptive study results show

that the visitors’ evaluation regarding the variables (price and food &

beverage) are not satisfactory. So, the park authority should give keen

attention in this regard to make the destination more pleasant among

visitors. The second factor of affective image is relaxing place to visit

and shows the results (Standardized, β=0.771, t = 36.615). These results

indicate that there is a positive relationship between the factor and

affective image of the destination. Thus, the hypothesis is accepted and

the respondents feel relax to visit this destination. The authority should


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continue the service quality of these variables within this factor and

should make more visible to develop the park image. The regression

model of affective image is described in below-

)2( 221102 eXXY

= 5.381+ 0.542X.542+0.771X0.772 + 0.063

= 5.381+0.294 +0.590+0.063

= 6.328

Where, Y2 = the independent variable affective image; β0 = the value

of regression coefficient; β1 X1 = Regression coefficient of pleasant

place to visit X beta value of it; β2 X2 = Regression coefficient of

relaxing place to visit X beta value of it; e = Random error

respectively.

(VI) Conclusion and Implications

Though destination image is the combination of multidimensional

constructs, the aims of the study are to explore the factors of cognitive and

affective images of the park and also to know the influence of these

factors on both of these images. The study conducted descriptive statistics,

EFA and regression analysis to attain the objectives. Overall of the study

results show 26 cognitive image variables which represent 6 cognitive

image factors. Whereas 9 affective image variables constitute 2 affective

image factors. The study also includes ten (10) cognitive and two (2)

affective images attribute which represent both of these images

independently. These cognitive and affective components which affect the

overall image of the park (Rashid & Ismail 2008; Baloglu 1996; Baloglu

& Mangaloglu 2001; Baloglu & McCleary 1999; Martin & Bosque, 2008;

Phillips & Jang 2008; Hosany et al. 2007) which is not tested yet

statistically. The results of the study will benefited for different

stakeholders in different ways. Firstly, the average opinion of the visitors

about the factor (i.e., natural and artificial environment) shows the highest

opinion ranging 5.95-5.42 for all variables. It indicates that the visitors are

very much satisfied and the authority is providing better services to

maintain the park environment properly for them. It is one of the most

important completive advantages for the park. Secondly, the average

opinion of visitors about the factor (i.e., transportation and safety) also


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shows moderately high opinion for all variables ranging 5.41-5.03 which

indicate the visitors feel safe and can easily access the park. It is also an

important strength for the park to attract more visitors. Thirdly, this study

elucidates different information for the visitors and authority relating to

the factors (i.e., entertainment & recreation and price charges) and all the

variables within these factors are not up to the mark. It ranges 4.02-3.35

for entertainment and recreation facilities available at the park which

indicates the number of different events should increase to satisfy and

attract more customers which will ultimately increase the image of this

park. The visitors opinion also express that the price for different services

at the park are not reasonable and it ranges 4.43-3.60 for the variables

within this factor. Though lower level income group people are interested

to visit this park, the authority should be rationale to charge for different

services at the park. In last but not for the least, the study indicates that all

the factors within cognitive image and affective image positively affect

both of the images of the park. The authority should provide keen

attention to maintain, improve and visible all the facilities available at the

park. Beside these, the park authority will also get valuable information

from this study and get insight to formulate development plan. It will also

enrich destination image and park related literatures which helps the

researchers, students and scholars etc. Destination’s marketers will also

get valuable insight from this study to attract more visitors and to satisfy

them. In addition to these, the recreational park visitors will get useful

information for visiting decision at the park and also can compare this

destination with other destinations considering available facilities.

Furthermore, it is noticeable by the study that there is more respondents’

professional background as students (49%). It is also observed that over

half of the respondents are single. The sample size may not so large in

amount for generalization and more than 80% of the respondents are from

Rajshahi Division. If the sample size may increase, it may provide more

satisfactory results into these issues. Therefore, our future research plan is

to test the data extensively to conduct CFA (Conformity Factor Analysis)

and develop a destination image model using Structural Equation

Modeling (SEM) to make the factor context more specific by following

the destination image theory. This will provide with reference for future

use.


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White, J.C. (2005), “Destination Image: See or not to See?” International

Journal of Contemporary Hospitality Management (Part II), Vol. 17,

No. 5, pp. 191-206.

Yew, L. & Malek, H. (2006), “Enhancing Miri’s image as a new tourist

destination through the media: the case of Malaysia” International

Review of Business Research Papers, Vol. 2, No. 4, pp. 69-84.


JBS-ISSN 2303-9884

Appendices Table: 1

Image components Factors Authors used in their studies

Cognitive/perception

Entertainment &

recreation

Baloglu, 1997; Baloglu & McCleary,

1999; Chen & Tsai, 2007; Ecthner &

Ritche, 1993; Rajesh, 2013; Yew &

Malek, 2006

Food & beverage Rajesh, 2013

Infrastructure Martin & Bosque, 2004; Rajesh, 2013;

Transportation &

Safety

Baloglu, 1997; Baloglu & McCleary,

1999; Kim & Yoon, 2008; Pike,2007;

Yew & Malek, 2006

Price charges Baloglu, 1997; Baloglu & McCleary,

1999; Pike, 2007; Rajesh, 2013; Yew &

Malek, 2006

Natural environment

Martin & Bosque, 2004; Rajesh, 2013

Affective

Pleasant place to visit Martin & Bosque, 2004

Relaxing place to visit Kim & Yoon, 2008; Rajesh, 2013


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Table 2: Profile of the Respondents

Demographic

Characteristics

Frequency Percent

(%)

Demographic

Characteristics

Frequency Percent

(%)

Age:

Less than 20 Years

21-30 Years

31-40 Years

41-50 Years

51-60 Years

60 Years or More

Occupation of

visitors:

Student

Govt. Employee

Private org. employee

Housewife

Businessman

Others

Educational

background:

SSC Level

HSC Level

Graduation Level

Post-Graduation Level

More

Division of visitors:

Rajshahi

Rangpur

Dhaka

Khulna

Chittagong

Barisal

Sylhet

56

127

52

20

1

1

126

43

45

22

10

11

38

51

118

49

1

209

9

19

11

4

2

3

21.8

49.4

20.2

7.8

.4

.4

49.0

16.7

17.5

8.6

3.9

4.3

14.8

19.8

45.9

19.1

0.4

81.3

3.5

7.4

4.3

1.6

.8

1.2

Monthly income:

>10,000 Tk.

10,001-20,000 Tk.

20,001-25,000 Tk.

25,001-30,000 Tk.

30,001-35,000 Tk.

35,001-40,000 Tk.

40,001-45,000 Tk.

45,001-50,000 Tk.

50,000 more

Gender:

Male

Female

Marital status:

Single

Married

Times of visiting:

First time

2-5 times

5 more times

Want to revisit:

Yes

Under consideration

No

58

74

48

15

24

10

10

5

13

168

89

140

117

44

84

129

224

27

6

22.6

28.8

18.7

5.8

9.3

4.0

3.9

1.9

5.1

65.4

34.6

54.5

45.5

17.1

32.7

50.2

87.2

10.5

2.3


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Table 3: Dimensions of cognitive destination image

Attributes F1 F2 F3 F4 F5 F6 Mean

Factor1: Entertainment &

recreation

Dance and jokey facility for

visitors

.791

3.70

Medical or first aid services

for visitors .771

3.91

Shopping facilities available

for visitors .700

3.35

3-d theater facility for visitors .679 3.89

Cultural programs arranged

occasionally .665

4.02

Factor 2: Food & beverage

facilities

Food preparation is hygienic .766 4.28

Pure drinking water available

for visitors .735

4.32

Preferable foods available for

visitors .715

4.00

Fast food facilities are

available for visitors .707

3.92

Restaurants and foods corner

for visitors .707

4.11

Factor 3: Transportation

and safety

Available local transport to

access the park

.699

5.36

Available public transport to

access the park .683

5.03

Safety of visitors to rides

different games .664

5.17

Security at park is up to the

mark .645

5.41

Interaction with local people is

possible .572

5.11


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Attributes F1 F2 F3 F4 F5 F6 Mean

Local people are friendly

enough .566

5.22

Factor 4: Infrastructure of

the park

It has available beautiful

garden

.790

4.93

The park surroundings is nice .669 5.32

The park has vast area .602 4.40

There is a nice lake at the park .587 4.89

Factor 5: Price charges at

the park

Ticket price is reasonable to

visitors

.748

4.43

Food price is reasonable to

visitors .685

4.07

Reasonable price of buying

different goods .630

3.60

Factor 6: Natural & artificial

environment

It is a nonsmoking area

.812

5.42

It is a sound , quite and

noiseless place .693

5.76

It is a neat and clean place .586 5.95

Eigenvalue 8.544 2.867 1.585 1.400 1.242 1.105

Variance (%) 14.68% 13.19% 11.93% 9.27% 8.07%

7.23

%

Cumulative variance (%) 14.68% 27.87% 39.81% 49.09% 57.16%

64.39

%

Cronbach’s alpha .865 .877 .796 .779 .716 .672


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Table 4: Dimensions of affective destination image

Attributes F1 F2 Mean

Factor 1: Pleasant Place to visit

Evaluation about price charges at the park .842

4.51

Evaluation about food and beverage at the park .814 4.61

Evaluation about visitor's facility services .645 5.39

Evaluation about rides facilities at the park .607 5.58

Evaluation about entertainment and recreation at the park .557 5.37

Factor 2: Relaxing place to visit

Evaluation about environmental scenery at park

.838

6.03

Evaluation about safety and security at the park .771 5.78

Evaluation about management services .674 5.50

Evaluation about transportation facilities at the park .552 5.63

Eigenvalue 4.24 1.10

Variance (%) 47.19% 12.23%

Cumulative variance (%) 47.19% 59.43%

Cronbach’s alpha 0.799 0.773

Model Unstandardized

Coefficients

Standardized

Coefficients

B Std. Error Beta t Sig.

1 (Constant) .605 .209 2.899 .004

Entertainment .074 .028 .126 2.631 .009

Food & Beverage .151 .033 .221 4.595 .000

Transportation & Security .411 .041 .433 9.954 .000

Infrastructure .073 .036 .090 2.005 .046

Price charges .104 .031 .143 3.325 .001

Natural & Artificial Environment .107 .030 .133 3.502 .001

a Dependent Variable: Cognitive Image

Table 5: Regression analysis for attributes affecting cognitive image

Multiple R = 0.841

Multiple R Square= 0.708

Adjusted R Square = 0.701

Standard error of estimates = 0.494

F value = 100.990

Significance F = 0.000


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Model Unstandardized

Coefficients

Standardized

Coefficients t Sig.

B Std.

Error Beta B

1 (Constant) 5.381 .021 255.802 .000

Pleasant place to visit .542 .021 .542 25.723 .000

Relaxing place to visit .772 .021 .771 36.615 .000

a Dependent Variable: Affective Image

Table 6: Regression analysis for attributes affecting affective image

Multiple R = 0.942

Multiple R Square= 0.887

Adjusted R Square = 0.887

Standard error of estimates = 0.337

F value = 1001.171

Significance F = 0.000


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Performance Evaluation of Selected NCBs and PCBs in

Bangladesh: An Empirical Study Dr. Mohammad Zahid Hossain1

Md. Fazle Fattah Hossain2

Abstract

There are several ways that the performance of a Bank can be measured. Among

these general business measures and profitability measures are important. In this

study, the performance of selected NCBs and PCBs have been evaluated within a

longer period (1996- 2013). In this study of NCBs and the PCBs, two from the

NCBs and three from the PCBs have been selected. This study has been

conducted on the basis of secondary data which have been collected from some

selected relevant papers. For the analysis of the data regression technique has

been applied. No significant development has been observed from expansion of

branches of Janata Bank Ltd , Rupali Bank Ltd as the NCBs and in terms of

operating profit to total deposit there is no significant achievement of Dutch

Bangla Bank Ltd. as the PCBs. On the other hand in case of Rupali Bank Ltd as

the NCBs no relation has been found in case of authorized capital. Similarly

lower profit has been found against total advance in case of Rupali Bank Ltd as

the NCBs and Jamuna bank Ltd.as the PCBs. From the findings of the analysis it

can be suggested that some parameters i.e. total no. of branches, authorized

capital should be improved for overall development of selected NCBs and the

PCBs.

Keywords: PCBs, NCBs, performance evaluation, general business measures,

regression analysis

(I) Introduction

inancial Institutes and the Non- Financial Institutes are common is all

capitalist form nations. Among these institutions Commercial banks

as a financial institute plays the important role in the country’s overall

development (Karim et al, 2013). Commercial Bank is one of the most

important financial institute. It is called the life blood of any economy.

1 Professor, Department of Finance, University of Rajshahi,

Email: [email protected] 2 Research Fellow, Department of Finance, University of Rajshahi

F


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These institutions circulate money in the economical functions. These

institutions canalize the money to the deficit unit from the surplus unit.

Thus country’s overall development depends on those financial

institutions. In this way these institutions affect the production of any

economy.

In our globalize world every bank is connected with each other. If one

bank fails to perform well then the effect will create anarchy in the rest

whole banking industry. This also may create a hotchpotch situation in the

country’s economic progress. So it is essential to the concerned

stakeholders in the country to be sincere in supervising and strengthening

the performance of these banks (Karim, 2013).

Generally bank is a collector of deposit and supplier of that deposited

money (Khan,2012).The depositor who want to deposit their hard earned

money can keep their money in return they are given interest or profit. On

the other hand the investors who want to invest in various productive

sectors can borrow fund from those Commercial Banks. So commercial

banks circulates the funds from the surplus unit to the deficit unit (Ahmed

et al; 2011).

At present there are four Nationalized Commercial Banks and almost

thirty nine Private Commercial Banks operating in Bangladesh (Activities

of Bank Insurance and Financial institutions. 2013-2014). These Banks are

playing a greater part in the country’s Development process (Changed

Bangladesh Bank, 2013). In 2007 all the NCBs have made limited

company so that these Banks can take active part in the country’s

development process. On the other hand PCBs are now flourishing. They

are now setting up upazilla level branches as per the directive of the

Bangladesh Bank.

In case of Nationalized Commercial Banks there are several important

aspects, because these organizations have the wide area coverage

including the root level branches. The common people can interact with

them very well. Although they are lacks in terms of customer satisfaction,

they play a good part in the National economy of Bangladesh. They have

to maintain numerous social responsibility, have to disburse agricultural


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loans to the poor people with a small interests so that they can uplift their

life standard, they have to deposit on a ten taka or hundred taka account

with a healthy interest given, they have to deposit to their school goers and

so on. On the other hand, the Private Commercial Banks are also

flourishing day by day. These organizations are increasing their coverage

according to the directives of the Bangladesh Bank. These banks are

becoming healthier in terms of deposit, advance profit and so on. Their

customer satisfaction and the employee satisfaction have also been

increasing day by day.

However, this research paper has also explored the performance of the

selected NCBs and the PCBs in Bangladesh through the use of regression

equation. With this equation the data have been used depending on

different parameters like deposit, advance, and profit and so on. The

findings have been scrutinized as to whether these selected banks are

performing well and helpful for the management to take corrective

measures. The academician, policy makers have also fore cast the trend of

these parameters so that effective suggestion and the recommendation can

be made more clearly.

Statement of the problem

Banking industry in Bangladesh is now facing numerous challenges. In

case of Nationalized Commercial Banks there are so many problems that

need to be addressed like problems incase of deposit collection, incase of

disbursing loan, in case of political influence and so on. On the other hand,

the private commercial banks are facing some problem regarding their

expansion of branches, their corruption into their internal management in

disbursing the loan, regarding their CSR and some other activities. So it is

time to un- earth the performance of those NCBs and the PCBs to see

whether it creates any negative impact in terms of profitability and in

general business measures.

General objective

To evaluate the performance of some selected NCBs and PCBs in terms of

General Business Measures and Profitability.


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Specific objectives

a) To depict the general picture of some selected NCBs and the PCBs

in Bangladesh.

b) To depict the growth rate of some selected NCBs and the PCBs in

terms of general business measures and profitability measures.

c) To evaluate the performance of some selected NCBs and the PCBs

in terms of General Business Measures and Profitability Measures.

(II) Methodology

A systematic research may be conducted through applying several study

methods like social survey, case study, observation and so on. But in this

study content analysis has been used as the progressive report and other

publication of Banks contain authentic data as because these reports are

based on the audited document. That is why only content analysis method

has been applied for getting the practical rigorous result of the topic.

Sources of data and data collection

For evaluating and measuring performance of the selected banks as per the

research objectives, secondary data from the different sources like Govt.

report, official record, various books, journal, and annual report of

respective banks have been consulted.

Sample and selection of sample

In any scientific study certain percentage of sample is usually collected for

conducting the study. But in this study the required percentage is not

maintained. However the finding of the study is representative and

trustworthy as the nature of the data is almost homogenous in nature. So

from the NCBs and the PCBs, two Banks from the NCBs and three Banks

from the PCBs have been taken. The NCBs are Janata Bank Ltd, Rupali

Bank Ltd. The PCBs are Islami Bank Ltd., Dutch Bangla Bank Ltd. and

Jamuna Bank Ltd. These Banks are generally called the first generation,

second generation and third generation commercial banks.


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Reference period

The secondary data from the above mentioned banks have been taken for

the period of 1996 to 2013.

Data analysis

Firstly data have been collected and tabulated. The growth rates of

different parameter have been calculated using excel program. So many

analysis techniques like ratio analysis, EVA model, AHP model, CAMEL

rating etc. can be used however considering suitability and easy applicable

in this field the performance has been evaluated using the regression

analysis on the basis of General Business Measures and the profitability

Measure. In this regard Eviews-8 has been uses. The above measure has

been used because it can depict the actual scenario of any Banks.

The model

y= ß1 + ß2 x + e

where y the dependent variable or the regressand indicates the trend in

years of the different parameters ß1 the intercept term ß2 is the slope

coefficient and x is the independent variable or the explanatory variable

indicates the performance indicator or parameter of the Banks like deposit,

advance, total assets etc.

(III) Review of Literature

An article titled “Performance Evaluation of Selected Private Commercial

Banks in Bangladesh” is an important article by Ahmed et. al. (2009).The

objective of the study is to synthesize the growth and development of

some private commercial banks. Secondary data from various sources

were used. The study used numerous statistical tools like growth

percentage, trend equation, square of correlation coefficient, correlation

matrix etc. From the analysis of the trend equation it was found that the

growth trend of branches, deposit, and the net income is significant.

Popa et. al. (2009) observed in their article titled “An Advance Methods

for the Performance Evaluation of Banks”. The Advanced Method for the

Performance Evaluation of Banks performance is called the EVA. The

authors tried to examine the Banks performance .In USA it were widely


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used to measure the Banking performance. The aim of the study is to

implement this technique in measuring the performance of the Banking

industry side by side in comparison of other technique. This method

usually includes the return on assets, return on equity, banking income etc.

The main advantages of the methods consist of in the management system,

in case of motivation, incase of measurement.

An article titled “Evaluating the performance of Commercial Banks in

India” by Malhotra et. al. (2011). The authors tried to examine the

different performance indicators like profitability, cost of intermediation,

efficiency and so on. Secondary data were used in the study. The study

used the descriptive statistics to measure the performance of Banks in the

different dimensions. The result showed that the enhancement of the net

interest margin, rising of the cost of intermediation and so on created

competition among the banking industry.

An article contains “Performance Evaluation of Scheduled Commercial

Banks in India” is an important article by Gurusamy and his co authors

(2013). The aim of the study was to measure the growth of the entire

schedule Commercial Banks and to make the comparison among the

different commercial banks. The study used descriptive statistics like

standard deviation, coefficient of variation; mean and so on the result

showed that the FBs were the highest mean among three groups. But in

case of NPA both of FBs and PSBs were experienced uniform services in

terms of the deposit, advance, income, interest income and so on. And also

FBs secured first place employee, profit per employee, and percentage of

wages to total expenditure.

Malhotra et. al. investigated the performance appraisal of banking sector

in their article titled “Performance Appraisal of Indian Public Sectors

Banks”. In this study the authors tried to evaluate the performance of the

public sectors Banks. The study used CAMEL rating technique to evaluate

the performance level of the Banks .The result found that Bank of Baroda

was excellent. On the other hand Bank of Andhra was average .Lastly the

Bank of Maharashtra and United Bank were positioned at the bottom.


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Kaur (2012) in his article titled “An Empirical Study on the Performance

Evaluation of Public Sector Banks in India” pointed out the performance

evaluation of Indian public sector Banks. In this study the authors tried to

emphasize the profitability measures of Public sectors Banks in India also

analyzed the non performing assets of the commercial Banks. The study

used coefficient of correlation, chi square test, median, etc .The result

revealed that there were high positive correlation between the profitability

and the interest earned, and there exists no significance difference between

the growth rates, total income, total expenditure, and the net profit of

PSCBs and SCB in India.

Sangmi studied the Financial Performance of Commercial Banks in India

in his article titled “Analyzing Financial Performance of Commercial

Banks in India: Application of CAMEL Model”. In this article the authors

tried to gain an insight into the performance of the two major banks using

the CAMEL rating technique. The study focused that the two banks were

remain in good position in terms of capital adequacy, asset quality, and

Management capability and so on. The study revealed that both the two

banks had been doing well in every aspect like capital adequacy, asset

quality, and management capability and so on.

A study conducted by Richard et al (2013) titled “The Determinant of

Financial Performance of Quoted Banks in Nigeria”. The study tried to

analyze the determinant that affects the financial performance of Banks.

The article used the secondary data from the various sources from the

three Banks. The statistical tools regression analysis had been used to

determine the trend of the different parameters. The study revealed that the

asset qualities growth trend was significant.

An article titled “AHP based Model for Bank Performance Evaluation and

Rating” is an important article by Hunjak et.al; (2001).The article used

Analytical Hierarchy Process. This process explains the internal and the

external factors of Banks. The Analytical Hierarchy process examines the

quantity and the quality based criteria .It is a multi criteria evaluation

models that enables the measurement of the subjective assessment of the

study.


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“An Evaluation of Financial Performance of Private Commercial Banks in

Bangladesh: Ratio Analysis” in an important study by Karim et. al; The

aim of the study was to find out the financial performance of the private

Commercial Banks using the ratio analysis. Economic Value Added had

been used to measure the liquidity ratio, return on assets, return on equity

and so on. The result showed that the Bank size, credit risk, operational

efficiency and the asset management were significant.

Raihan et. al. found the performance of State Owned Commercial Banks

in their article titled “Performance Evaluation and Competitive Analysis

of State Owned Commercial Banks in Bangladesh” The aim of the study

was to find the performance of the state owned commercial Banks. The

study used the secondary data for the analysis. The regression analysis was

used to un-earth the actual scenario of performance of the State Owned

Commercial banks in Bangladesh. The study found that these Banks were

not able to achieve the steady growth in terms of net profit, earning per

share, return on equity, and return on assets and so on. On the other hand it

achieved the stable growth in terms of general business measures.

Choong et. al. measured the performance of Islamic Commercial Banks in

Malaysia in their article titled “Performance of Islamic Commercial Banks

in Malaysia: An empirical study”. The study tried to gain an insight into

the most important performance indicators that is responsible for the

overall performance of the banks. The study used the regression model to

find the important indicator of the performance. The result revealed that

the credit risk was the important determinant of performance of Islamic

Commercial Banks in Malaysia.

From the above literature review from different sources it is evident that

there exist so many articles relating to the performance evaluation.

Different study uses different methods to un-earth the true picture of the

commercial Banks. The current study tries to determine the actual picture

of the Commercial Banks of Bangladesh using the longer study period and

also by exact statistical tools.


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(IV) Concept and Definitions

NCBs

NCBs: It is generally called the Nationalized Commercial Banks. There

are four NCBs in Bangladesh (Activities of Banks, Insurance and

Financial Institutions 2013-2014). These includes Sonali Bank Limited,

Janata Bank Ltd., Agrani Bank Ltd and Rupali Bank ltd,Basic Bank Ltd

and Bangladesh Development Bank Ltd. After the liberation war first four

Banks had been Nationalized by the then Govt. of Bangladesh. Prior to the

Nationalization, those Banks had been operating under different name and

are now operating under the Bank Company Act and direct control of the

government of Bangladesh.

PCBs

It is generally called the Private Commercial Banks. At present there are

almost thirty nine PCBs are operating in our country (Activities of Banks,

Insurance and Financial Institutions 2013-2014).These include the first

generation, second generation and the third generation Private

Commercial Banks. These Banks are also operating by the Bank Company

Act.

Janata Bank Limited

Janata Bank is one of the leading Nationalized Commercial Banks in

Bangladesh. Janata Bank was formed by merging United Bank Ltd and

Union Bank Ltd in 1972 according to President Order-26. In 2007 Janata

Bank started as a limited company. The Authorized capital amounted to

Tk 20000 million, Paid up capital amounted to Tk. 19140 million at the

end of December 2013.Janata Bank limited has 893 branches and the

employees is almost 15370. Bank participate in the socio economic

development of Bangladesh side by side the Bank provides loans in

various productive purposes and collects deposit from the depositors(

Activities of Banks , Insurance Companies and Financial institutions

2013-2014)

Rupali Bank Limited

The fourth Nationalized Commercial Banks in Bangladesh is the Rupali

Bank Limited. The Bank was formed by merging the Muslim Commercial


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Bank Limited, Standard Bank Limited and Australasia Bank Limited in

1972 according to the President Order-26. The Authorized capital

amounted to Tk. 7000 million, Paid up capital amounted to tk.1815

million at the end of December 2013.Rupali Bank limited has 532

branches and the employees is almost 5669. (Activities of Banks,

Insurance Companies and Financial institutions 2013-2014)

Islamic Bank Limited

The largest first generation Private Commercial Banks in Bangladesh is

the Islamic Bank Limited. The Bank was formed in 1983 as a public

limited company and started interest free Banking at the same year. The

major partnership of the Bank includes Islami Development Bank, some

of foreign Financial Institute, and Foreign entrepreneur. The Authorized

capital amounted to Tk 20000 million, Paid up capital amounted to tk.

14636 million at the end of December 2013.The Bank has almost 286

branches and the employees is almost 12000. Banks participate in the

socio economic development of Bangladesh and side by side the Bank

provides loans to various productive purposes and collects deposit from

the depositors. The Bank is the pioneer to launch Islami Banking activity

according to the Islami Sariah and engaged in different Musharaka

project. Also Bank has different Mudaraba savings scheme by which it

collects deposit from the investors (Activities of Banks, Insurance

Companies and Financial institutions 2013-2014)

Dutch Bangla Bank Limited

The largest second generation Private Commercial Banks in Bangladesh is

the Dutch Bangla Bank Limited. The Bank was formed in 1996 as a public

limited company and started its Banking business. The major partner of

the Bank includes the Nederland Development Finance Company and

Bangladeshi entrepreneurs. The Authorized capital amounted to Tk 4000

million, Paid up capital amounted to Tk 2000 million at the end of

December 2013.The Bank has almost 136 branches and the employees is

almost 4666. Bank participates in the socio economic development of

Bangladesh and side by side the Bank provides loans to various productive

purposes and collects deposit from the depositors. The Bank is the pioneer

to launching the mobile banking in Bangladesh; also initialize the


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Electronic Student Booth (Activities of Banks, Insurance Companies and

Financial institutions 2013-2014)

Jamuna Bank Limited

The third generation private Commercial Banks in Bangladesh is the

Jamuna Bank Limited. The Bank started its operation in 2001. The

Authorized capital amounted to Tk10000 million, Paid up capital

amounted to tk. 4488 million at the end of December 2013.Jamuna Bank

limited has 91 branches and the employees is almost 2100. Bank

participates in the socio economic development of Bangladesh and side by

side the Bank provides loans to various productive purposes and collects

deposit from the depositors. The Bank initialize the Islami Banking,

disbursing relief to the poor, set up swing training center, establish Jamuna

Bank Model village and so on(Activities of Banks , Insurance Companies

and Financial institutions 2013-2014)

Hypothesis of the Study

Ho = There is no difference between two types of banks performance.

H1 = There may have some differences between two types of banks in

terms of general business measures and profitability measures.

H2 = Let it be justified, measured and evaluated to what extent the

difference is existing.

(V) Analysis and Findings

General Business Measures

Growth of total investment of the selected NCBs and PCBs

From the table-1 it has been found that there is stable growth rate in case

of Janata Bank except in 2003 and 2006.In case of Rupali Bank growth

rate is positive except in 2004,05 and06.Incase of all the years growth rate

is positive except in 2005 and 2008.In case of DBBL the growth rate is

positive except in 2003,2004 and 2011.Lastly in case of Jamuna Bank

growth rate is positive except in 2002,2003 and 2013.


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r-square value total investment of selected NCBs and PCBs

From the table- 2 it has been found that highest r-square value is for

Dutch Bangla Bank Ltd (0.885) as the PCBs and the lowest value is for

Islami Bank Bangladesh Ltd (0.572) as also PCBs .So the performance of

PCBs has been better than NCBs in case of total investment activities.

Growth of total deposit of the selected NCBs and PCBs

From the table-3 and from the growth trend it has been found that there is

stable growth rate in case of Janata Bank except in 2003.In case of Rupali

Bank growth rate is positive except in 1997 and 2008.In case of Islami

bank Ltd all the years growth rate if positive. In case of DBBL all the

year’s growth rate is positive. Lastly in case of Jamuna Banks Ltd. deposit

growth rate is positive.

r-square value of total deposit of selected NCBs and PCBs

From the table- 4 it has been found that the highest value of r-square value

in case of Islami Bank Bangladesh Ltd (0.904) as the PCBs and lowest

value has been found for Rupali Bank Ltd. (0.777)as the NCBs. Hence it is

clear that performance of PCBs is better than performance of NCBs.

Growth of total branches of the selected NCBs and PCBs


of Janata Bank except in 2002 and 2003.In case of Rupali Bank growth

rate in 1998.1999., 2002, and 2003 is negative. Incase of Islami Bank Ltd

all the years’ growth rate is positive .In case of DBBL in 2003 growth rate

is zero.

r-square value of total branches of selected NCBs and PCBs

From the table- 6 it has been found that the higher r-square value in case

of Jamuna Bank Ltd (0.968) as the PCBs and the lowest value has been for

Janata Bank Ltd (0.191) as the NCBs. This shows that PCBs has been able

to maintain the required growth in case of expansion of branches on the

other hand NCBs has not been able to maintain the required growth.


JBS-ISSN 2303-9884

Growth of total operating profit of the selected NCBs and PCBs


of Janata Bank is positive except in 1998, 2002, 2012, and 2013(according

to June 2013).In case of Rupali Bank growth rate is not satisfactory. In

case of Islami Bank the growth rate if positive except in 1997, 1998,

2000,2003,2004,2006 and 2013 and 2013.In case of DBBL the growth rate

is satisfactory except 1997,1998,2003,2009, and 2013. Lastly in case of

DBBL in 2013 is not satisfactory and lastly Jamuna Bank growth rate is

satisfactory.

r-square value of total operating profit of selected NCBs and PCBs

The higher r-square value from the table- 8 has been found in case of

Jamuna Bank Ltd(0.884) as the PCBs and the lower value has been found

for Rupali Bank Ltd. (0.656) as the NCBs .Hence we can conclude that

PCBs has been in healthier position in terms of the operating profit than

NCBs.

Growth of manpower of the selected NCBs and PCBs

From the table-9 it has been found that the growth rate of Manpower is not

satisfactory in case of Janata Bank. In case of Rupali Bank growth rate is

also not satisfactory. Incase of Islami Bank all the years growth rate if

positive. In case of DBBL the growth rate is negative in 2004 and 2013

and the Jamuna Banks Manpower growth rate is satisfactory.

r-square value of manpower of selected NCBs and PCBs

From the table10 it has been found that the highest r-square value is for

Jamuna Bank Ltd.(0.943) as the PCBs and the lowest value has been

found for Rupali bank Lid.(0.424) as the NCBs. This means that PCBs

have been well ahead in case of recruiting of manpower than the NCBs.

Growth of total advance of the selected NCBs and PCBs

From the table-11 it has been found that there the growth rate in 1998 and

2007 is negative in case of Janata Bank Ltd. In case of Rupali Bank

growth rate is positive except in 2005. Incase of all the three PCBs the

growth rate is positive.


JBS-ISSN 2303-9884

r-square value of total advance of selected NCBs and PCBs

From the table- 12 higher r-square value has been found for Jamuna

Bank Ltd. (0.978) as the PCBs and lower value has been found for Rupali

bank Ltd.(0.823) as the NCBs.Hence we can conclude that PCBs

performance is better than NCBs performance in terms of the advance.

Growth of inland remittance of the selected NCBs and PCBs

From the table-13 it has been found that the growth rate is negative in

1999,2003,2010,2011 and in case of JBL. In case of Rupali Bank growth

rate is negative in 1997, 1999, 2001, 2010, 2013. Incase of Islami Bank all

the years growth rate is positive except in 2000and 2013. In case of DBBL

the growth rate is negative in 1999,2000,2003,2005 and .In case of

Jamuna Bank Ltd. (JBL) the growth rate is negative in 2009, 2010 and.

r-square value of inland remittance of selected NCBs and PCBs

From the table- 14 it has been found that the maximum r-square value for

Janata Bank Ltd.(0.863) as the NCBs and the lowest for Dutch Bangla

Bank Ltd.(0.734) as the PCBs. So NCBs performance is better than PCBs

performance in case of inland remittance.

Growth of total assets of the selected NCBs and PCBs

From the table-15 it has been found that in case of JBL the growth rate in

2003 is negative and in case of RBL the growth rate in 2000 is negative. In

case of all the PCBs growth rate of total Assets is satisfactory.

r-square value of total assets of selected NCBs and PCBs

From the table- 16 the r-square value of Jamuna Bank Ltd has been found

higher (0.875) as the PCBs and lower value has been found for Rupali

Bank Ltd.(0.739) as the NCBs. So PCBs performance has been better in

comparison to the NCBs performance.

Growth of paid up capital of the selected NCBs and PCBs

From the table-17 it has been found that there exists no steady growth in

case of Paid up Capital except the Islami Banks and Jamuna Banks.


JBS-ISSN 2303-9884

r-square value of paid up capital of selected NCBs and PCBs

From the table- 18 the r-square value has been maximum for jamuna Bank

ltd.(0.835) as the PCBs and the minimum value has been for Rupali Bank

Ltd.( 0.357) as the NCBs . It shows PCBs performance has been better

regarding the paid up capital.

Growth of total authorized capital of the selected NCBs and PCBs

From the table-19 it has been found that there exists no stable growth rate

in all the five NCBs and the PCBs

r-square value of total authorized capital of selected NCBs and PCBs

The r-square value from the table- 20 Islami Bank Bangladesh Ltd as the

PCBs has the highest value (0.808) and no value has been found for

Rupali Bank Ltd as the NCBs. So PCBs performed well in comparison to

the NCBs in case of authorized capital.

Growth of total import of the selected NCBs and PCBs

From the table-21 it has been found that the growth rate in 1997, 1999,

2005,2007,2009,2012 and 2013 shows the negative trend. In case of RBL

the growth rate 1999, 2002, 2005, 2006, 2012 and shows the negative

trend. In case of Islami Bank the growth rate in 1997, 2000, 2009, 2012,

and also shows the negative trend. In case of DBBL the growth rate in

2011 and 2013 is negative .Finally in case of Jamuna bank ltd the growth

rate in 2011 and 2013 is negative.

r-square value of total import of selected NCBs and PCBs

From the table- 22 the r-square value for Jamuna Bank has been found

maximum (0.910) as the PCBs and minimum value (0.629) has been

found for Rupali bank Ltd as the NCBs. So PCBs performed well in case

of import activities.

Growth of total export of the selected NCBs and PCBs

From the table-23 it has been found that the growth rate in case of JBL in

1998 is negative. In case of RBL the growth rate in 2001, 2002, 2004,

2005, 2007, 2009, and 2013 is negative .In case of IBBL in 2002, 2013 the


JBS-ISSN 2303-9884

growth rate is negative .In case of DBBL and the Jamuna Bank Ltd the

growth rate is negative in 2012

r-square value of total export of selected NCBs and PCBs

The r-square value from the table- 24 it has been found maximum value

for Janata bank Ltd.(0.891) as the NCBs and minimum value has been for

Rupali Bank Ltd(0.507). This shows the better position of NCBs in case of

export business.

Growth of gross income of the selected NCBs and PCBs

From the table-25 it has been found that in case of JBL the growth rate is

negative in 1998, 2004.In case of RBL the growth rate is negative in 2004,

2008.

r-square value of gross income of selected NCBs and PCBs

the r-square value from the table- 26 it has been found maximum for

Jamuna Bank Ltd.(0.885) as the PCBs and minimum value has been found

for Rupali Bank Ltd.(0.736) as the NCBs. Hence PCBs generate higher

gross income in comparison to the NCBs.

Growth of operating expenditure of the selected NCBs and PCBs

From the table-27 it has been found that the growth rate is shows the

negative trend in the year 2001, 2003, 2004 in case if JBL. In case of RBL

the growth rate in negative in 2002, 2004, 2008.

r-square value of operating expenditure of selected NCBs and PCBs

From the table- 28 the r-square value has been found for Jamuna Bank

Ltd.(0.885) as the PCBs and the lowest value has been for Rupali Bank

ltd(0.624) as the NCBs.So PCBs expenditure has been higher than the

NCBs expenditure.

Profitability Measures

Growth of total operating profit to total advance of the selected NCBs

and PCBs

From the table-29 it has been found that there exists no stable growth in all

the five NCBs and the PCBs.


JBS-ISSN 2303-9884

r-square value of total operating profit to total advance of selected

NCBs and PCBs

From the table- 30 r-square has been found in case of Islami Bank

Ltd.(0.871) as the PCBs and lower value has been found for Jamuna Bank

Ltd.(0.216).So it has been observed that PCBs performance is better than

NCBs performance.

Growth of total operating profit to total deposit of the selected NCBs

and PCBs

From the table-31 it has been found that there exists no stable growth in all

the five NCBs and the PCBs.

r-square value of total operating profit to total deposit of selected

NCBs and PCBs

The r-square value from the table- 32 it has been observed that higher

value for Janata Bank Ltd.(0.789) and lower value for Dutch Bangla Bank

Ltd.(0.270). Hence it has been concluded that NCBs performance is better

than PCBs performance.

(VI) Conclusion

The performance evaluation of Banks means how and what level the

Banks performance exists. If the Banks performs well, the Bank has to do

well in every parameter. The parameter means deposit, advance, profit,

investment, assets, import, export, and so on. All these parameter are the

raw material to evaluate the level of performance. In this research that has

considered the general business measures we see that growth rate of total

investment, total no. of Branches, and total no. of Manpower, Inland

remittance, total import, total export, operating expenditure, total advance

and operating profit of the NCBs are not satisfactory. In the case of PCBs

Inland remittance and Paid up Capital for Dutch Bangla Bank Ltd. ,

Authorized capital for all the selected PCBs, Total import for Islami Bank

Bangladesh Bank Ltd., total operating profit to Total advance for all the

selected PCBs are not satisfactory. Again it is clear that there exists no

significant trend in case of Janata Bank Ltd and Rupali Bank Ltd. as the

NCBs in terms of branches. Higher and significant r-square value has been

found in case of total investment, Deposit, Expansion of Branches,


JBS-ISSN 2303-9884

Operating Profit, Manpower, Advance, total Assets, Paid up Capital,

Authorized Capital, Import, Gross income of PCBs. On the other hand

Higher and significant R-square value has been found in case of Inland

Remittance, Export and lowest value of operating expenditure of NCBs.

Hence we can conclude that for considering most of the parameter of

general business measures PCBs performance has been better than the

NCBs performance.

Recommendations

From the analysis the Banks should take the following steps to enhance

their healthy business or the profitability:

a) The PCBs should collect deposit from the various sources in the

name of different scheme.

b) The NCBs should set up more branches.

c) The RBL as NCBs should recruit more employees for enhancing

profitability.

d) The NCBs should improve the condition of the paid up capital and

the authorized capital.

e) The DBBL and the Jamuna Bank Ltd as PCBs should improve the

condition of advance.

Limitation of the study

a) The study depends only on secondary data from various sources

and the whole study is based on the accuracy of those data.

b) Limited number of selected Banks for unveiling the actual scenario

of Banking Industry in Bangladesh


JBS-ISSN 2303-9884

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JBS-ISSN 2303-9884

Appendices- 1

Table : 1 Trend of Total Investment of the Selected NCBs and PCBs Amount in Million

Year

Janata Bank

Ltd.

Rupali Bank

Ltd.

Islami Bank BD.

Ltd

Dutch Bangla

Bd. Ltd.

Jamuna Bank

Ltd.

Amount AGR% Amount AGR% Amount AGR% Amount AGR% Amount AGR%

1996 15979 Na 8426 Na Na Na na na Na Na

1997 19058 19.27 7148 -15.17 Na 229 Na Na

1998 18065 -5.21 7963 11.40 Na 341 48.91 Na Na

1999 19223 6.41 8652 8.65 351 2.93 Na Na

2000 20368 5.96 9640 11.42 34 742 111.40 Na Na

2001 20456 0.43 10229 6.11 34 0 752 1.35 2963 Na

2002 29719 45.28 12108 18.37 34 0 3292 337.77 2052 -30.75

2003 22821 -23.21 13997 15.60 34 0 2538 -22.90 936 -54.39

2004 28375 24.34 13203 -5.67 3536 10300 2035 -19.82 1164 24.36

2005 29168 2.79 12903 -2.27 3534 -0.06 3440 69.04 2038 75.09

2006 24785 -15.03 12068 -6.47 3558 0.68 5877 70.84 2553 25.27

2007 55821 125.22 14091 16.76 20058 463.74 5909 0.54 4239 66.04

2008 57824 3.59 12546 -10.96 7533 -62.44 5955 0.78 5390 27.15

2009 72533 25.44 14303 14.00 11137 47.84 9670 62.38 8503 57.76

2010 57514 -20.71 15717 9.89 12269 10.16 11002 13.77 10891 28.08

2011 95257 65.62 23611 50.23 16932 38.01 10898 -0.95 16315 49.80

2012 104046 9.23 26572 12.54 27010 59.52 13429 23.22 39119 139.77

2013 193270 85.75 39120 47.22 67211 148.84 17442 29.88 31392 -19.75

Source: Bank abong Arthik Protisthan er Karzaboli


JBS-ISSN 2303-9884

Table : 2 Trend Equation and r –Square of Total Investment of Selected NCBs and

PCBs

SL No Name of Banks Y=a+bx t- statistic r-square

1 Janata Bank Ltd. Y=1999.836+9.49Ex 5.390 0.644

2 Rupali Bank Ltd. Y=1996.598+.000542x 5.416 0.647

3 Islami Bank Bangladesh Ltd. Y=2004.316+.000177x 4.011 0.572

4 Dutch Bangla Bank Ltd. Y=1999.988+0.000907x 10.769 0.885

5 Jamuna Bank Ltd Y=2004.471+0.00258x 4.566 0.654

Source : Own study

Table :3 Trend of Total Deposit of the Selected NCBs and PCBs Amount in Million

Year Janata Bank

Ltd.

Rupali Bank

Ltd.

Islami Bank

BD.Ltd

Dutch Bangla

Bd.Ltd.

Jamuna Bank

Ltd.


1996 76368 na 32936 Na Na Na na Na Na Na

1997 87102 14.06 32545 -1.19 Na Na 1083 Na Na Na

1998 88489 1.59 36085 10.88 20385 Na 1874 73.04 Na Na

1999 92479 4.51 39671 9.94 25500 25.092 3464 84.85 Na Na

02000 104678 13.19 44557 12.32 32113 25.933 6119 76.65 Na Na

2001 124122 18.58 49227 10.48 41641 29.67 11458 87.25 3794 Na

2002 138893 11.90 57169 16.13 55462 33.191 15975 39.42 4752 25.25

2003 138597 -0.21 59466 4.02 69655 25.59 17134 7.255 6614 39.18

2004 151035 8.97 63674 7.08 87721 25.936 21067 22.95 10265 55.20

2005 168895 11.83 66871 5.02 107788 22.876 27241 29.31 14454 40.81

2006 182946 8.32 67832 1.44 132419 22.851 40112 47.25 17285 19.59

2007 196755 7.55 72809 7.34 166325 25.605 42110 4.981 20924 21.05

2008 218902 11.26 71394 -1.94 200343 20.453 51576 22.48 27308 30.51

2009 240919 10.06 72985 2.23 244292 21.937 67789 31.44 42356 55.10

2010 286566 18.95 91124 24.85 291635 19.38 83245 22.8 60674 43.25

2011 361677 26.21 107234 17.68 342238 17.351 100711 20.98 70508 16.21

2012 409767 13.30 136599 27.38 417844 22.092 125439 24.55 79624 12.93

2013 478536 16.78 177950 30.27 473141 13.23 145230 15.78 97485 22.43



JBS-ISSN 2303-9884

Table : 4 Trend Equation and r –Square of Total Deposit of Selected NCBs and PCBs

SL No Name of Banks Y=a+bx t-statistic r-square

1 Janata Bank Ltd. Y=1996.249+4.19Ex 9.5617 0.851

2 Rupali Bank Ltd. Y=1995.623+0.000125x 7.473 0.777

3 Islami Bank Bangladesh Ltd. Y=2000.237+3.11Ex 11.5377 0.904



Source : Own Study

Table : 5 Trend of Branches of the Selected NCBs and PCBs Amount in Million

Year Janata Bank

Ltd.

Rupali Bank

Ltd.

Islami Bank

BD.Ltd

Dutch Bangla

Bd.Ltd. Jamuna Bank

Ltd.


1996 897 Na 516 Na 95 Na Na Na Na Na

1997 897 0 516 0 100 5.26 1 Na Na Na

1998 897 0 514 -0.39 105 5.00 4 300 Na Na

1999 898 0.11 512 -0.39 110 4.76 6 50 Na Na

2000 898 0.00 512 0 116 5.45 9 50 Na Na

2001 900 0.22 514 0.39 121 4.31 11 22.222 3 Na

2002 870 -3.33 506 -1.56 128 5.79 17 54.545 8 166.7

2003 847 -2.64 493 -2.57 141 10.16 17 0 15 87.5

2004 847 0 493 0 151 7.09 19 11.765 19 26.7

2005 847 0 492 -0.20 169 11.92 28 47.368 23 21.1

2006 848 0.12 492 0 176 4.14 39 39.286 27 17.4

2007 848 0 492 0 186 5.68 49 25.641 35 29.6

2008 849 0.12 492 0 196 5.38 64 30.612 39 11.4

2009 851 0.24 492 0 231 17.86 79 23.438 54 38.5

2010 861 1.18 492 0 251 8.66 96 21.519 66 22.2

2011 873 1.39 503 2.24 266 5.98 111 15.625 73 10.6

2012 888 1.72 506 0.60 276 3.76 126 13.514 83 13.7

2013 893 0.56 532 5.13 286 3.62 136 7.93 91 9.63



JBS-ISSN 2303-9884

Table : 6 Trend Equation and r –Square of Branches of Selected NCBs and PCBs


1 Janata Bank Ltd. Y=2093.982-0.102532x -1.946 0.1914

2 Rupali Bank Ltd. Y=2069.520-0.129052x -1.230 0.0864

3 Islami Bank Bangladesh Ltd. Y=1990.663+0.08024x 17.932 0.9526


5 Jamuna Bank Ltd Y= 2001.614+0.13063x 18.5366 0.968

Source : Own study

Table: 7 Trend of Operating Profit of the Selected NCBs and PCBs Amount in Million

Year Janata Bank

Ltd.

Rupali Bank

Ltd.

Islami Bank

BD.Ltd

Dutch Bangla

Bd.Ltd.

Jamuna Bank

Ltd.


1996 947 371 284 Na Na

1997 1100 16.16 144 -61.19 243 -14.44 9 Na Na

1998 34 -96.91 -71 -149.31 148 -39.09 21 133.33 Na Na

1999 240 605.88 -308 333.80 167 12.84 86 309.52 Na Na

2000 831 246.25 102 -133.12 330 97.60 239 177.91 Na Na

2001 1597 92.18 287 181.37 577 74.85 397 66.11 0 Na

2002 1231 -22.92 622 116.72 994 72.27 423 6.55 15 Na

2003 2121 72.30 553 -11.09 803 -19.22 454 7.33 129 760

2004 2313 9.05 513 -7.23 1225 52.55 632 39.21 308 138.76

2005 3301 42.72 811 58.09 2163 76.57 940 48.73 420 36.36

2006 4213 27.63 255 -68.56 2908 34.44 1080 14.89 701 66.90

2007 4962 17.78 363 42.35 3781 30.02 1438 33.15 824 17.55

2008 7003 41.13 1145 215.43 6834 80.75 1936 34.63 1041 26.33

2009 8579 22.50 2099 83.32 6518 -4.62 2695 39.20 1914 83.86

2010 12108 41.14 2447 16.58 8455 29.72 4197 55.73 2406 25.71

2011 16013 32.25 3603 47.24 12731 50.57 4779 13.87 2807 16.67

2012 14734 -7.99 3674 1.97 15608 22.60 5206 8.93 3160 12.58

2013 12127 -17.69 1803 -50.93 14104 -9.64 2954 -43.25 4584 45.06



JBS-ISSN 2303-9884

Table : 8 Trend Equation and r –Square of Operating Profit of Selected NCBs and

PCBs


1 Janata Bank Ltd. Y= 1999.762+0.00091x 8.421 0.815


3 Islami Bank Bangladesh Ltd. Y= 2000.542+0.00091x 7.848 0.793

4 Dutch Bangla Bank Ltd. Y= 2000.82+0.002583x 7.397 0.784

5 Jamuna Bank Ltd Y= 200.431+0.002534x 9.1968 0.884

Source : Own Study

Table :9 Trend of Total Manpower of the Selected NCBs and PCBs Amount in Million

Year

Janata Bank

Ltd.

Rupali Bank

Ltd.

Islami Bank

BD. Ltd

Dutch Bangla

Bd. Ltd.

Jamuna Bank

Ltd.


1996 17351 Na 6179 Na 1774 Na Na

1997 17113 -1.37 6007 -2.78 1903 7.27 55 Na Na

1998 17451 1.98 6084 1.28 2171 14.08 99 80 Na Na

1999 17138 -1.79 5885 -3.27 2302 6.03 176 77.78 Na Na

2000 16947 -1.11 5778 -1.82 2685 16.64 248 40.91 115

2001 16692 -1.50 5824 0.80 3060 13.97 317 27.82 140 21.739

2002 16330 -2.17 5628 -3.37 3297 7.75 409 29.02 153 9.29

2003 15993 -2.06 5412 -3.84 3752 13.80 437 6.85 253 65.359

2004 15705 -1.80 5225 -3.46 4261 13.57 431 -1.37 314 24.111

2005 15321 -2.45 5008 -4.15 5884 38.09 548 27.15 438 39.49

2006 14772 -3.58 4753 -5.09 7133 21.23 684 24.82 670 52.968

2007 13860 -6.17 4430 -6.80 8083 13.32 789 15.35 861 28.507

2008 13379 -3.47 4269 -3.63 9397 16.26 1229 55.77 938 8.9431

2009 13122 -1.92 4529 6.09 9588 2.03 1785 45.24 1215 29.531

2010 12826 -2.26 4503 -0.57 10349 7.94 2763 54.79 1511 24.362

2011 15020 17.11 4982 10.64 11465 10.78 4015 45.31 1786 18.2

2012 15071 0.34 5645 13.31 12188 6.31 5268 31.21 2050 14.782

2013 15370 1.98 5669 0.42 12980 6.49 4666 -11.42 2206 7.60



JBS-ISSN 2303-9884

Table : 10 Trend Equation and r –Square of Total Manpower of Selected NCBs and

PCBs


1 Janata Bank Ltd. Y=2048.924-0.002861x -5.354 0.641

2 Rupali Bank Ltd. Y=2034.139-0.005568x -3.438 0.424


4 Dutch Bangla Bank Ltd. Y= 2001.427+0.002539x 6.436 0.734


Source : Own study

Table : 11 Trend of Total Advance of the Selected NCBs and PCBs Amount in Million

Year Janata Bank

Ltd.

Rupali Bank

Ltd.

Islami Bank

BD. Ltd

Dutch Bangla

Bd. Ltd.

Jamuna Bank

Ltd.


1996 53159 Na 23514 Na 13519 Na na Na Na Na

1997 61928 16.50 23650 0.58 13075 372 Na Na Na

1998 57330 -7.42 24846 5.06 13436 972 161.29 Na Na

1999 61229 6.80 31254 25.79 18113 34.81 2259 132.41 Na Na

2000 80952 32.21 33783 8.09 27437 51.48 4588 103.10 Na Na

2001 89862 11.01 38209 13.10 35238 28.43 8044 75.33 349 Na

2002 99749 11.00 41608 8.90 46281 31.34 9392 16.76 1512 333.24

2003 101462 1.72 42110 1.21 58973 27.42 11431 21.71 3240 114.29

2004 107786 6.23 45345 7.68 76826 30.27 14976 31.01 6723 107.50

2005 123546 14.62 44921 -0.94 93644 21.89 20349 35.88 11012 63.80

2006 138492 12.10 45710 1.76 113575 21.28 28325 39.20 12797 16.21

2007 121204 -12.48 47080 3.00 144921 27.60 29403 3.81 16617 29.85

2008 144678 19.37 49030 4.14 191230 31.95 41698 41.82 21037 26.60

2009 166359 14.99 52344 6.76 214616 12.23 48411 16.10 32288 53.48

2010 225732 35.69 66049 26.18 263225 22.65 67658 39.76 49430 53.09

2011 257801 14.21 76525 15.86 305840 16.19 79248 17.13 56612 14.53

2012 295340 14.56 90642 18.45 361168 18.09 91649 15.65 54826 -3.15

2013 285748 -3.25 107426 18.52 406805 12.64 106423 16.12 67669 23.43



JBS-ISSN 2303-9884

Table: 12 Trend Equation and r –Square of Total Advance of Selected NCBs and PCBs


1 Janata Bank Ltd. Y=1995.628+(6.51E-05)x 9.023 0.851

2 Rupali Bank Ltd. Y=1994.098+0.000212x 8.631s 0.823

3 Islami Bank Bangladesh Ltd. Y=1999.282+3.92Ex 11.012 0.883



Source : Own Study

Table : 13 Trend of Inland Remittance of the Selected NCBs and PCBs Amount in Million

Year

Janata Bank

Ltd.

Rupali Bank

Ltd.

Islami Bank

BD.Ltd

Dutch Bangla

Bd.Ltd.

Jamuna Bank

Ltd.


1996 7325 Na 1370 Na 3328 Na Na Na Na

1997 9640 31.60 1080 -21.17 4825 44.98 286 Na Na Na

1998 9850 2.18 1980 83.33 6361 31.83 374 30.77 Na Na

1999 9770 -0.81 1830 -7.58 8415 32.29 227 -39.30 Na Na

2000 10973 12.31 2040 11.48 7644 -9.16 213 -6.17 Na Na

2001 12885 17.42 1630 -20.10 9879 29.24 384 80.28 0.6 Na

2002 21880 69.81 8005 391.10 14670 48.50 833 116.93 0.69 15

2003 21384 -2.27 10203 27.46 16668 13.62 749 -10.08 96 13813

2004 24331 13.78 11340 11.14 23669 42.00 1118 49.27 174 81.25

2005 26573 9.21 13641 20.29 36948 56.10 838 -25.04 603 246.55

2006 29267 10.14 18050 32.32 53819 45.66 1556 85.68 2262 275.12

2007 36788 25.70 18895 4.68 67113 24.70 4884 213.88 2506 10.787

2008 45924 24.83 21643 14.54 140404 109.21 5172 5.90 3165 26.297

2009 56190 22.35 22312 3.09 194716 38.68 6760 30.70 2658 -16.02

2010 52640 -6.32 19851 -11.03 214629 10.23 7375 9.10 1594 -40.03

2011 72285 37.32 21140 6.49 236607 10.24 11731 59.06 3360 110.79

2012 100089 38.46 24764 17.14 300915 27.18 16332 39.22 4029 19.911

2013 103982 3.89 10875 -56.09 286956 -4.64 19624 20.16 6859 70.241



JBS-ISSN 2303-9884

Table : 14 Trend Equation and r –Square of Inland Remittance of Selected NCBs and

PCBs


1 Janata Bank Ltd. Y=1998.657+0.000161x 9.038 0.836


3 Islami Bank Bangladesh Ltd. Y=2000.4+(4.48E-05)x 8.232 0.809



Source : Own study

Table :15 Trend of Total Assets of the Selected NCBs and PCBs Amount in Million

Year Janata Bank Ltd. Rupali Bank Ltd.

Islami Bank BD.

Ltd

Dutch Bangla

Bd. Ltd.

Jamuna Bank

Ltd.


1996 83529 Na 44365 16990 Na Na Na Na Na

1997 95729 14.61 50480 13.78 20017 17.82 1445 Na Na Na

1998 96557 0.86 52176 3.36 23443 17.12 2945 103.81 Na Na

1999 101010 4.61 54550 4.55 28820 22.94 5692 93.28 Na Na

2000 128568 27.28 46552 -14.66 39362 36.58 6966 22.38 Na Na

2001 151862 18.12 52564 12.91 49552 25.89 13463 93.27 4883 Na

2002 168234 10.78 58931 12.11 65081 31.34 17866 32.70 6794 39.14

2003 156092 -7.22 67244 14.11 81615 25.41 19966 11.75 12096 78.04

2004 169030 8.29 71580 6.45 102128 25.13 24561 23.01 16395 35.54

2005 188166 11.32 75120 4.95 122880 20.32 32279 31.42 16864 2.86

2006 212664 13.02 76241 1.49 150253 22.28 45493 40.94 20157 19.53

2007 243088 14.31 81923 7.45 191362 27.36 49371 8.52 26405 31.00

2008 267157 9.90 82312 0.47 230879 20.65 60682 22.91 31647 19.85

2009 296894 11.13 87574 6.39 278303 20.54 81481 34.28 48731 53.98

2010 345438 16.35 124434 42.09 330586 18.79 101181 24.18 70753 45.19

2011 448160 29.74 144836 16.40 389192 17.73 122854 21.42 87065 23.05

2012 508193 13.40 176469 21.84 482536 23.98 155918 26.91 109679 25.97

2013 586083 15.33 215310 22.01 550839 14.16 185537 18.99 115682 5.47



JBS-ISSN 2303-9884

Table :16 Trend Equation and r –Square of Total Assets of Selected NCBs and PCBs



2 Rupali Bank Ltd. Y=1996.5+(9.56E-05)x 6.737 0.739


4 Dutch Bangla Bank Ltd. Y=2000.50+8.25E-05x 9.164 0.848

5 Jamuna Bank Ltd Y=2002.951+9.28E-05x 8.806 0.875

Source : Own Study

Table : 17 Trend of Total Paid up Capital of the Selected NCBs and PCBs Amount in Million

Year Janata Bank

Ltd. Rupali Bank

Ltd. Islami Bank BD.

Ltd Dutch Bangla

Bd. Ltd. Jamuna Bank Ltd.


1996 2594 Na 1250 Na 316 Na Na Na Na Na

1997 2594 0 1250 0 318 100 Na Na

1998 2594 0 1250 0 320 180 Na Na

1999 2594 0 1250 0 320 180 Na Na

2000 2594 0 1250 0 320 180 Na Na

2001 2594 0 1250 0 640 100 202 12.222 390

2002 2594 0 1250 0 640 0 202 0 390 0

2003 2594 0 1250 0 1920 200 202 0 390 0

2004 2594 0 1250 0 2304 20 202 0 429 10

2005 2594 0 1250 0 2765 20.01 202 0 429 0

2006 2594 0 1250 0 3456 24.99 202 0 1073 150.12

2007 2594 0 1250 0 3802 10.01 202 0 1226 14.259

2008 2594 0 1250 0 4752 24.99 1000 395.05 1313 7.0962

2009 5000 92.75 1250 0 6178 30.01 1500 50 1622 23.534

2010 5000 0 1250 0 7413 19.99 2000 33.333 2230 37.485

2011 11000 120 1375 10 10008 35.01 2000 0 3648 63.587

2012 11000 0 1650 20 12510 25 2000 0 4488 23.026

2013 19140 74 1815 10 14636 16.99 2000 0 4488 0



JBS-ISSN 2303-9884

Table : 18 Trend Equation and r –Square of Total Paid Up Capital of Selected NCBs and

PCBs







Source : Own study

Table 19 Trend of Total Authorized Capital of the Selected NCBs and PCBs Amount in Mill.

Year Janata Bank

Ltd.

Rupali Bank

Ltd.

Islami Bank

BD. Ltd

Dutch Bangla

Bd. Ltd.

Jamuna Bank

Ltd.


1996 8000 Na 7000 Na 500 Na Na Na Na Na

1997 8000 7000 500 400 Na Na Na

1998 8000 7000 500 400 Na Na Na

1999 8000 7000 500 400 Na Na Na

2000 8000 7000 1000 400 Na Na Na

2001 8000 0 7000 0 1000 0 400 0 1600 Na

2002 8000 0 7000 0 1000 0 400 0 1600 0

2003 8000 0 7000 0 3000 200 400 0 1600 0

2004 8000 0 7000 0 3000 0 400 0 1600 0

2005 8000 0 7000 0 5000 66.667 400 0 1600 0

2006 8000 0 7000 0 5000 0 400 0 1600 0

2007 8000 0 7000 0 5000 0 400 0 4000 150

2008 8000 0 7000 0 10000 100 1000 150 4000 0

2009 20000 150 7000 0 10000 0 1000 0 4000 0

2010 20000 0 7000 0 10000 0 4000 300 10000 150

2011 20000 0 7000 0 20000 100 4000 0 10000 0

2012 20000 0 7000 0 20000 0 4000 0 10000 0

2013 20000 7000 0 20000 0 4000 0 10000 0



JBS-ISSN 2303-9884

Table: 20 Trend Equation and r –Square of Total Authorized Capital of Selected NCBs

and PCBs



2 Rupali Bank Ltd. - - -




Source : Own Study

Table : 21 Trend of Total Import of the Selected NCBs and PCBs Amount in Million

Year

Janata Bank

Ltd.

Rupali Bank

Ltd.

Islami Bank

BD.Ltd

Dutch Bangla

Bd.Ltd.

Jamuna Bank

Ltd.


1996 37860 na 12312 Na 17875 Na na na Na Na

1997 36938 -2.44 14500 17.77 17370 -2.83 598 na Na Na

1998 45401 22.91 21360 47.31 20238 16.51 1634 173.24 Na Na

1999 43250 -4.74 13722 -35.76 20396 0.78 4413 170.07 Na Na

2000 48005 10.99 21120 53.91 16889 -17.19 8329 88.74 Na Na

2001 54666 13.88 20637 -2.29 25907 53.40 11215 34.65 125 Na

2002 58910 7.76 17044 -17.41 33788 30.42 11856 5.72 1449 1059.2

2003 60476 2.66 19849 16.46 46237 36.84 17550 48.03 3081 112.63

2004 74920 23.88 24424 23.05 59804 29.34 25974 48.00 7923 157.16

2005 72912 -2.68 21654 -11.34 74525 24.62 26029 0.21 11152 40.755

2006 128809 76.66 14840 -31.47 96870 29.98 32068 23.20 15458 38.612

2007 84065 -34.74 19857 33.81 103293 6.63 35667 11.22 22192 43.563

2008 129413 53.94 20590 3.69 168329 62.96 43999 23.36 30312 36.59

2009 118525 -8.41 55033 167.28 161230 -4.22 53089 20.66 46685 54.015

2010 183744 55.03 60245 9.47 246281 52.75 87663 65.12 61035 30.738

2011 197285 7.37 69263 14.97 301207 22.30 83434 -4.82 55907 -8.402

2012 188283 -4.56 45108 -34.87 284587 -5.52 108878 30.50 57705 3.2161

2013 176671 -6.17 65165 44.46 285890 0.46 108259 -0.57 52751 -8.58



JBS-ISSN 2303-9884

Table : 22 Trend Equation and r –Square of Total Import of Selected NCBs and PCBs







Source : Own study

Table 23 Trend of Total Export of the Selected NCBs and PCBs Amount in Million

Year

Janata Bank

Ltd.

Rupali Bank

Ltd.

Islami Bank

BD.Ltd

Dutch Bangla

Bd.Ltd.

Jamuna Bank

Ltd.


1996 20566 Na 3974 Na 11766 Na Na Na Na Na

1997 22969 11.68 5400 35.88 14440 22.73 31 Na Na Na

1998 21350 -7.05 6110 13.15 14894 3.14 111 Na Na Na

1999 21596 1.15 7191 17.69 14798 -0.64 1177 Na Na Na

2000 30780 42.53 7200 0.13 25327 71.15 3434 191.76 Na Na

2001 32390 5.23 6809 -5.43 25907 2.29 4801 39.808 90 Na

2002 34450 6.36 6428 -5.60 16673 -35.64 5016 4.4782 1133 1158.9

2003 42865 24.43 7324 13.94 21738 30.38 7659 52.691 3069 170.87

2004 54623 27.43 6800 -7.15 29151 34.10 13582 77.334 4791 56.109

2005 58395 6.91 6118 -10.03 36169 24.07 22144 63.039 6522 36.13

2006 70896 21.41 6959 13.75 51133 41.37 33345 50.583 11584 77.614

2007 71855 1.35 6399 -8.05 59097 15.58 34060 2.1442 13990 20.77

2008 85418 18.88 7184 12.27 93962 59.00 40083 17.683 18617 33.074

2009 88653 3.79 5143 -28.41 106424 13.26 41163 2.6944 21407 14.986

2010 118515 33.68 8490 65.08 148421 39.46 73500 78.558 41860 95.544

2011 153756 29.74 13513 59.16 178244 20.09 92412 25.731 57929 38.387

2012 156525 1.80 15506 14.75 197095 10.58 104306 12.871 68844 18.842

2013 153252 -2.09 18170 17.18 205269 4.15 118045 13.17 64250 -61.76



JBS-ISSN 2303-9884

Table: 24 Trend Equation and r –Square of Total Export of Selected NCBs and PCBs







Source : Own Study

Table : 25 Trend of Bross Income of the Selected NCBs and PCBs Amount in Million

Year

Janata Bank

Ltd.

Rupali Bank

Ltd.

Islami Bank

BD. Ltd

Dutch Bangla

Bd. Ltd.

Jamuna Bank

Ltd.


1996 6232 2731 Na 1232 Na Na Na Na Na

1997 7593 21.84 2896 6.04 1361 10.47 91 Na Na Na

1998 7304 -3.81 2926 1.04 1629 19.69 193 112.09 Na Na

1999 8251 12.97 3245 10.90 1860 14.18 416 115.54 Na Na

2000 9296 12.67 3745 15.41 3208 72.47 767 84.38 Na Na

2001 10013 7.71 4232 13.00 4260 32.79 1299 69.36 229 Na

2002 10858 8.44 4304 1.70 5234 22.86 1897 46.04 391 70.74

2003 11518 6.08 4593 6.71 6841 30.70 2116 11.54 847 116.62

2004 10935 -5.06 4372 -4.81 8400 22.79 2367 11.86 1397 64.94

2005 13148 20.24 4759 8.85 10587 26.04 3435 45.12 1727 23.62

2006 16272 23.76 4838 1.66 14038 32.60 5181 50.83 2750 59.24

2007 18522 13.83 10732 121.83 17699 26.08 6367 22.89 3103 12.84

2008 20922 12.96 5850 -45.49 24230 36.90 7276 14.28 4075 31.32

2009 24074 15.07 7242 23.79 25404 4.85 8914 22.51 5817 42.75

2010 30683 27.45 8254 13.97 30129 18.60 10604 18.96 7467 28.37

2011 40926 33.38 12462 50.98 38401 27.46 14114 33.10 11542 54.57

2012 49714 21.47 15422 23.75 50346 31.11 18213 29.04 13073 13.26

2013 55072 10.77 17016 10.33 56118 11.46 20051 10.09 14388 10.06



JBS-ISSN 2303-9884

Table : 26 Trend Equation and r –Square of Gross Income of Selected NCBs and PCBs

SL No Name of Banks Y=a+bx t-statistics r-square






Source : Own study

Table :27 Trend of operating expenditure of the Selected NCBs and PCBs Amount in Million

Year Janata Bank

Ltd.

Rupali Bank

Ltd.

Islami Bank

BD. Ltd

Dutch Bangla

Bd. Ltd.

Jamuna Bank

Ltd.


1996 5285 Na 2360 Na 948 Na Na Na Na Na

1997 6493 22.86 2752 16.61 1118 17.93 82 Na Na Na

1998 7270 11.97 2997 8.90 1481 32.47 172 109.76 Na Na

1999 8011 10.19 3553 18.55 1693 14.31 330 91.86 Na Na

2000 8465 5.67 3643 2.53 2878 69.99 528 60.00 Na Na

2001 8416 -0.58 3945 8.29 3683 27.97 902 70.83 229 Na

2002 9627 14.39 3682 -6.67 4240 15.12 1474 63.41 376 64.19

2003 9397 -2.39 4040 9.72 6038 42.41 1662 12.75 718 90.96

2004 8622 -8.25 3859 -4.48 7175 18.83 1735 4.39 1089 51.67

2005 9847 14.21 3948 2.31 8424 17.41 2495 43.80 1307 20.02

2006 12059 22.46 4583 16.08 11130 32.12 4101 64.37 2049 56.77

2007 13560 12.45 10369 126.25 13918 25.05 4929 20.19 2279 11.22

2008 13919 2.65 4705 -54.62 17396 24.99 5340 8.34 3034 33.13

2009 15495 11.32 5143 9.31 18886 8.57 6219 16.46 3903 28.64

2010 18575 19.88 5807 12.91 21674 14.76 6407 3.02 5061 29.67

2011 24913 34.12 8859 52.56 25670 18.44 9335 45.70 8735 72.59

2012 34980 40.41 11748 32.61 34738 35.33 13007 39.34 9913 13.49

2013 42945 22.77 15213 29.49 42014 20.95 15467 18.91 11442 15.42



JBS-ISSN 2303-9884

Table : 28 Trend Equation and r –Square of operating expenditure of Selected NCBs

and PCBs







Source : Own Study

Table : 29 Trend of Profit to Total Advance of the Selected NCBs and PCBs Amount in Mill.

Year Janata Bank

Ltd.

Rupali Bank Ltd. Islami Bank BD.

Ltd

Dutch Bangla

Bd. Ltd.

Jamuna Bank

Ltd.


1996 1.78 1.58 2.10 Na Na Na Na Na

1997 1.78 -0.29 0.61 -61.41 1.86 -11.53 2.42 Na Na Na

1998 0.06 -96.66 -0.29 -146.93 1.10 -40.73 2.16 -10.70 Na Na

1999 0.39 560.93 -0.99 244.86 0.92 -16.30 3.81 76.21 Na Na

2000 1.03 161.89 0.30 -130.64 1.20 30.45 5.21 36.83 Na Na

2001 1.78 73.12 0.75 148.78 1.64 36.14 4.94 -5.26 0 Na

2002 1.23 -30.56 1.49 99.02 2.15 31.17 4.50 -8.74 0.99 Na

2003 2.09 69.39 1.31 -12.15 1.36 -36.60 3.97 -11.82 3.98 301.33

2004 2.15 2.65 1.13 -13.85 1.59 17.10 4.22 6.26 4.58 15.06

2005 2.67 24.51 1.81 59.58 2.31 44.86 4.62 9.46 3.81 -16.75

2006 3.04 13.85 0.56 -69.10 2.56 10.85 3.81 -17.46 5.48 43.62

2007 4.09 34.58 0.77 38.21 2.61 1.90 4.89 28.27 4.96 -9.48

2008 4.84 18.23 2.34 202.88 3.57 36.98 4.64 -5.07 4.95 -0.21

2009 5.16 6.54 4.01 71.71 3.04 -15.02 5.57 19.90 5.93 19.79

2010 5.36 4.01 3.70 -7.61 3.21 5.76 6.20 11.43 4.87 -17.89

2011 6.21 15.80 4.71 27.08 4.16 29.59 6.03 -2.79 4.96 1.87

2012 4.99 -19.68 4.05 -13.91 4.32 3.82 5.68 -5.81 5.76 16.24

2013 4.24 -15.01 1.67 -58.80 3.46 -19.94 2.77 -51.24 6.77 17.46



JBS-ISSN 2303-9884

Table : 30 Trend Equation and r –Square of Profit to Total Advance of Selected NCBs

and PCBs







Source : Own study Table :31 Trend of Profit to Total Deposit of the Selected NCBs and PCBs (Amount in Million)

Year

Janata Bank

Ltd.

Rupali Bank

Ltd.

Islami Bank

BD. Ltd

Dutch Bangla

Bd. Ltd.

Jamuna Bank

Ltd.


1996 1.24 1.13 Na Na Na Na Na Na

1997 1.26 1.84 0.44 -60.72 Na Na 0.83 Na Na Na

1998 0.04 -96.96 -0.20 -144.47 0.73 Na 1.12 34.85 Na Na

1999 0.26 575.43 -0.78 294.59 0.65 -9.80 2.48 121.55 Na Na

2000 0.79 205.90 0.23 -129.49 1.03 56.91 3.91 57.32 Na Na

2001 1.29 62.07 0.58 154.68 1.39 34.84 3.46 -11.29 0 Na

2002 0.89 -31.12 1.09 86.62 1.79 29.34 2.65 -23.58 0.32 Na

2003 1.53 72.67 0.93 -14.53 1.15 -35.68 2.65 0.07 1.95 517.8893

2004 1.53 0.07 0.81 -13.36 1.40 21.13 3.00 13.22 3.00 53.84

2005 1.95 27.62 1.21 50.53 2.01 43.70 3.45 15.02 2.91 -3.16

2006 2.30 17.83 0.38 -69.00 2.20 9.44 2.69 -21.97 4.06 39.57

2007 2.52 9.512 0.50 32.62 2.27 3.52 3.41 26.83 3.94 -2.90

2008 3.20 26.85 1.60 221.68 3.41 50.06 3.75 9.92 3.81 -3.20

2009 3.56 11.31 2.88 79.32 2.67 -21.78 3.98 5.91 4.52 18.54

2010 4.23 18.65 2.69 -6.63 2.90 8.66 5.04 26.82 3.97 -12.25

2011 4.43 4.79 3.36 25.12 3.72 28.31 4.75 -5.88 3.98 0.39

2012 3.60 -18.79 2.69 -19.95 3.74 0.42 4.15 -12.54 3.97 -0.31

2013 2.53 -29.64 1.01 -62.45 2.98 -32.26 2.03 -51.09 0.47 -93.95



JBS-ISSN 2303-9884

Table : 32 Trend Equation and r –Square of Profit to Total Deposit of Selected NCBs and PCBs







Source : Own Study


JBS-ISSN 2303-9884

Succession Plan in Second or Subsequent Generation Family

Owned Firms in Bangladesh- a Study on Rajshahi Division Md. Shariful Islam1

Dr. Md. Amzad Hossain 2

Abstract

The study aims at exploring the existence and nature of succession plan in family

owned firms in Bangladesh. A survey has been conducted on second or

subsequent generation family owned firms in eight districts of Rajshahi Division.

Incumbents of 229 family owned firms have been interviewed using a

questionnaire. The study finds lack of existence of formal and written succession

plan. In most of the cases successors have not been identified. Siblings of the

incumbents play a major role in family owned firms. Incumbents mastermind the

succession plan, where exist, consulting with other family members. The study

also finds evidences of incomplete succession in a large number of cases even

after transfer of responsibilities of the firms to incumbents.

Keywords: Succession plan, family owned firms, generation of business

(I) Introduction

n the new global economy family owned firms play significantly

important role (Ibrahim, Soufani & Lam, 2001). Family owned firms

account for the majority of the whole businesses and contribute strongly in

the growth of the national economy of different countries (Nordqvist,

2005; Chrisman, Chua & Steier, 2005; Poutziouris, O‟Sullivan, &

Nicolescu, 1997; Gallo, 1995; Poza, 1995; Ibrahim & Ellis, 1994; Lank,

Owens, Martinez, Reidel, deVisscher, & Bruel, 1994). Succession is a

common phenomenon in every small and medium sized family owned

firm. It is considered as the most critical issue that is commonly faced by

the firms (Islam, Aleem, & Chowdhury, 2014; Ibrahim, Soufani, & Lam,

2001). Succession is very complex in family owned businesses (Miller,

Steier, & Le Breton-Miller, 2003; Lansberg, 1999; Dyer, 1986). Studies in

1 Associate Professor, Institute of Business Administration, University of Rajshahi Email: [email protected] 2 Professor, Department of Finance, University of Rajshahi


I


JBS-ISSN 2303-9884

the area of family business also show that such family owned firms strive

for continuity in ensuring competent leadership across generations for the

continuity of the business itself (Le Breton-Miller, Miller, & Steier, 2004).

Though the owners of family owned firms consider longevity of the

business as the most important concern (Nutek, 2004), evidence from

previous studies suggest that 30% of family businesses survive into the

second generation and only 10% or 15% make it to the third generation

(Aronoff, 1999; Kets de Vries 1993; Ward 1987). This higher rate of

failure of the family owned firms has become a concern of the researchers

(Aronoff, 1999; Lansberg, 1999; Handler 1990; Kepner, 1983). Therefore,

a proper succession planning and process is important in the sense that it

affords the family owned firms to select the most appropriate future

leaders to carry forward the business successfully (Islam, et al., 2014;

Ibrahim, McGuire, Ismail, & Dumas, 1999; Ward, 1987). Researchers

believe that there is need for formal succession plan in the family owned

firms and the plan should be long term (Kets de Vries, 1993; Ward &

Aronoff, 1992; Williams, 1992; Ward, 1987; Danco, 1982).

This paper is focused on presenting information related to succession plan

in family owned firms in Bangladesh. These information will be used in

future studies for advanced analysis to explore more specific issues related

to succession planning in family owned firms in Bangladesh.

(II) Method and Data

In this study a survey has been conducted on the family owned firms

located in eight districts under Rajshahi Division. Districts covered in this

research are (i) Rajshahi, (ii) Chapai Nawabgonj, (iii) Naogaon, (iv)

Natore, (v) Bogra, (vi) Jaipurhat, (vii) Pabna, and (viii) Sirajgonj. Family

owned firms from these districts have been selected using convenient

sampling method.

During selection of sample, the study considers factors such as control of

the family in the business in terms of ownership and generation(s)

involved. As there is no previously constructed database of family owned

firms in Bangladesh, the study did not have idea about the population. The

study collected list of the firms in each district from respective Chamber

of Commerce. Lists of firms, which are in operation at the BSCIC areas,


JBS-ISSN 2303-9884

have also been collected from BSCIC offices. Afterwards the survey team

went outside BSCIC and prepared a sample frame of family owned firms

of the respective districts. In the first phase of the survey, the study

surveyed each of the enlisted firms to identify the family businesses.

During identifying family businesses, special care has been taken so that

the criteria of family business considered in the present study are fulfilled.

The study considers firms with the following criteria as family owned

firms:

(i) If the founder (or the founders of the same family, in case of multiple

founders) or descendant(s) of the founder (or founders of the same family)

or their spouses hold more than 50% of ownership of the business;

And/or

(ii) If the founder (or any of the founders, in case of multiple founders) or

descendant(s) of the founder(s) or their spouses serves as Chief Executive

Officer (CEO) or Chief of the Business (COB).

A formal questionnaire has been used for conducting the survey. The

questionnaire was tested through pilot survey conducted on 15 family

owned firms located in Rajshahi metropolitan city. Expert opinions have

also been invited after preparing the questionnaire. On the basis of pilot

survey and experts‟ opinions, several modifications and corrections have

been made. The revised and improved questionnaire has been used for

final survey.

From the first phase of the survey the study identifies 1489 family owned

firms out of which 865 firms fulfill criteria for the study and the family

business definition of the study. The study approached with the

questionnaire for data collection while 743 firms responded and 122

declined to participate in the interview. 514 family owned firms out of

total 743 are in first generation while the rest 229 family businesses are

identified as second generation or subsequent generations. The study has

approached with the interview questionnaire to those 229 second or

subsequent generation family owned firms with direct interviewing

method. Here, it should be noted that „generation‟ means the generation of

business, not the generation of family.


JBS-ISSN 2303-9884

As presented in table 1, out of total 229 family owned firms of the study,

63 firms (27.51%) are in Rajshahi followed by in Nawabganj 30 (13.10%),

Bogra 30 (13.10%), Natore 30 (13.10%), Jaipurhat 17 (7.42%), Naogaon

28 (12.23%), Pabna 10 (4.37%), and Sirajganj 21 (9.17%) firms. Highest

number of family owned firms have been interviewed from Rajshahi

district (n=63; 27.51%). On the other hand lowest number of firms (n=10;

4.37%) have been interviewed from Pabna.

Table 1: District wise distribution of surveyed family owned firms

Districts Frequency (n) Percentage (%)

Rajshahi 63 27.51

Nawabganj 30 13.10

Bogra 30 13.10

Natore 30 13.10

Jaipurhat 17 7.42

Naogaon 28 12.23

Pabna 10 4.37

Sirajganj 21 9.17

Total (N) 229 100

(III) Results

Types and Generations of Firms

The study attempts to classify the family owned firms in terms of nature of

business that they are involved in. Three categories of family owned firms

have been identified such as (i) Manufacturing firms, (ii) Merchandising

firms, and (iii) Service providing firms. From table 2 it is observed that

most of the family owned firms under this study are involved in

merchandising business (n=166; 72.49%) followed by manufacturing

business (n=51; 22.27%) and service providing business (n=12; 5.24%).


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In terms of ownership of the firms, the study classifies family owned firms

in three categories such as, (i) Sole proprietorship firms, (ii) Partnership

firms, and (iii) Joint stock companies. It is observed in the study (Table 3)

that family owned firms are mainly dominated by sole proprietorship

forms of ownership (n=170; 74.24%) followed by Partnership (n=57;

24.89%) and Joint stock companies (n=2; 0.87%) which are private

limited in type.

Table 2: Types of business operations

Business operations Frequency (n) Percentage (%)

Manufacturing 51 22.27

Merchandising 166 72.49

Service 12 5.24

Total (N) 229 100

Table 3: Forms of ownership

Forms of ownership Frequency (n) Percentage (%)

Sole Proprietorship 170 74.24

Partnership 57 24.89

Joint Stock Companies 2 0.87

Total (N) 229 100

The present study also attempts to explore the present generation of the

family owned firms. Family owned firms which are in first generation

have been excluded from the study. Therefore, all the firms considered in

this phase of the study are in second or subsequent generation. It is

observed from table 4 that most of the family owned firms under

observation are in second generation (n=187; 81.66%) followed by third

generation (n=33; 14.41%) and subsequent generations (n=9; 3.93%). This

indicates the facts that family owned firms in Bangladesh suffer set back

in subsequent generations and thus fail to survive in long run.


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Table 4: Present generation(s) of the business

Generation of the business Frequency (n) Percentage (%)

Second Generation 187 81.66

Third Generation 33 14.41

Subsequent Generation 9 3.93

Total (N) 229 100

Predecessors

It is observed from the study (Table 5) that most of the family owned

firms have been established by the parents of the incumbents (n=167;

75.23%) followed by grandfather or grandmother of the incumbents

(n=33; 14.86%), parents of the incumbent's spouse (n=5; 2.25%) and other

cases (n=17; 7.66%). The study attempts to explore whether the

predecessor of the incumbent of the firm has involvement in the family

owned firms or not. It has been observed from table 6 that most of the

predecessors or previous owners (n=153; 66.81%) are not active in the

business while a small but significant number of previous owners (n=76;

33.19%) are somehow active in the business. This indicates that there are a

large number of cases of incomplete succession in family owned firms in

Bangladesh.

Table 5: Founder of the business

Founder Frequency (n) Percentage (%)

Parents of the incumbent 167 75.23

Grandfather or grandmother of the

incumbent

33 14.86

Parents of the incumbent's spouse 5 2.25

Others 17 7.66

Total (N) 222 100


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Table 6: Status of the previous owner

Status of the previous owner Frequency (n) Percentage (%)

Active 76 33.19

Inactive 153 66.81

Total (N) 229 100

Existence and Nature of Succession Plan

The study attempts to explore the existence and nature of succession plan

in the family owned firms. Table 7 shows that in most of the cases (n=167;

72.93%) there is no succession plan. In 62 (27.07%) cases there is

succession plan out of which in 5 (2.18%) cases succession plan is written

and formal while in 57 (24.89%) cases the nature of succession plan is

informal and not in written form.

Table 7: Existence and nature of succession plan

Existence Frequency (n) Percentage (%)

Formal written succession plan 5 2.18

Informal/not written succession

plan

57 24.89

No succession plan 167 72.93

Total (N) 229 100

Involvement of Family Members in Succession Planning

The implementation of a succession plan depends much on how strong is

the understanding among the family members regarding that plan. The

study identifies the involved members in the succession planning process

where it exists. It has been observed from table 8 that out of 62 family

owned firms where there is succession plan, the incumbent alone is the

mastermind of the succession plan in 10 (16.13%) cases, while in 50

(80.64%) cases the incumbent has masterminded the succession plan

consulting with other members of the family. In 2 cases (3.23%) of 62


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firms where there is succession plan, it was not possible to know the

persons who were involved in the succession planning process.

Table 8: Involved members in succession planning

Mastermind Frequency (n) Percentage (%)

Present incumbent only 10 16.13

Incumbent consultation with other

members of family

50 80.64

No reply 2 3.23

Total (N) 62 100

Status of Successors

The identification of next business leader is one of the most important

tasks in a succession planning process. Therefore, the present study tries to

find out whether the family owned firms have identified next business

leader or not. From table 9 it is observed that 62 firms (27.07%) have

identified concerned successor in advance while in most of the cases

(n=164; 71.62%) successor has not been identified. From table 10 it is

observed that out of 62 family owned firms where successor has been

identified, the selected one is the son of the incumbent in 34 cases

(54.84%), sibling in 21 cases (33.87%) and spouse in 1 (1.61%) case. In 6

cases the relationship of the successor with the predecessor (incumbent)

could not be identified. Out of 62 family owned firms where successor has

been identified, the successor is involved in the business in 56 (90.32%)

cases while in the rest cases (n=6; 9.68%) the successor is not involved

(table 11). The successor is involved in top level management in 34

(54.84%) cases, in middle management in 14 (22.58%) cases and in

operational level in 8 (12.90%) cases.


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Table 9: Identification of successors

Status of identification Frequency (n) Percentage (%)

Identified 62 27.07

Not identified 164 71.62

No reply 3 1.31

Total (N) 229 100

Table 10: Relationship of the successor with the incumbent

Relationship Frequency (n) Percentage (%)

Son 34 54.84

Sibling 21 33.87

Spouse 1 1.61

Not specified 6 9.68

Total (N) 62 100

Table 11: Involvement of the successor in business

Involvement and positions Frequency (n) Percentage (%)

Involved in top management 34 54.84

Involved in middle management 14 22.58

Involved in operation level 8 12.90

Not involved 6 9.68

Total (N) 62 100

Professional Managers and Other Family Members in the Business

The study has attempted to explore the scenario related to involvement of

professional managers, who are not member(s) of the respective business

family but are working in the management of that business. It has been

observed from table 12 that, 214 firms (93.45%) don‟t take service of

professional managers at all while professional managers are working in


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only 15 (6.55%) firms. Table 13 shows that out of these 15 firms, 6

(2.62%) firms have employed professional managers as chief executive

officers (CEO) while in 223 (97.38%) cases CEOs have been selected

from within the family. This reveals the fact that there is lack of use of

professional managers, especially in the top level, in the family owned

firms in Bangladesh.

Table 12: Involvement of professional managers

Professional managers Frequency (n) Percentage (%)

Involved 15 6.55

Not involved 214 93.45

Total (N) 229 100

Table 13: Professional manager in the CEO position

Type of CEO Frequency (n) Percentage (%)

Professional manager as CEO 6 2.62

CEO from the family 223 97.38

Total (N) 229 100

As there is lack of use of professional manager in the family owned firms

in Bangladesh, it is usual that family members have involvement in the

operations of the family owned firms. As involvement of more members

of the family presumably facilitates successful succession of the business

within the family, present study intended to explore the involvement of

other family members in the business. It is observed from table 14 that in

case of 149 family owned firms (65.07%) there is involvement of other

members of the respective families in the management while in case of 80

family owned firms (34.93%), other members of the family are not

involved.

From table 15 it is observed that out of 149 family owned firms where

other members of the family are involved, siblings are in leading position

next to incumbents in 109 firms (73.15%), sons in 15 firms (10.07%), and


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spouse in 1 firm (0.67%). Respondents of 24 (16.11%) firms did not

specify the involved family member in the firm. These findings indicate

that the family owned firms in Bangladesh may have influence of socio

cultural factors. In most of the cases the present owner has to bring his or

her siblings instead of his or her offspring as the leading member in the

family owned firms.

Table 14: Involvement of other family members in the business

Other family members Frequency (n) Percentage (%)

Involved 149 65.07

Not involved 80 34.93

Total (N) 229 100

Table 15: Leading involved family member next to incumbent

Family member next to

incumbent

Frequency (n) Percentage (%)

Sibling 109 73.15

Son 15 10.07

Spouse 1 0.67

No reply 24 16.11

Total (N) 149 100

(IV) Conclusion

The study has a number of observations. It is observed that most of the

family owned firms under observation are in second generation followed

by third generation and subsequent generation. This result shows that

fewer number of family owned firms in the sample are in third and

subsequent generations compared to second generations. The study did not

intend to identify causes of this outcome. This outcome may be caused by

multiple number of factors. Without indicating those factor(s), the study

infers that fewer number of family owned firms here in Bangladesh


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survive in third and subsequent generations. The study finds lack of

existence of succession plan in the family owned firms in Bangladesh. In

most of the cases there is no succession plan. Out of the firms that have

succession plan, only a few of them have written succession plan while in

most of the firms succession plan is informal and not written. Therefore,

existence of formal written succession plan is pretty low in the family

owned firms in Bangladesh.

It is also observed that, there is high involvement of other family members

in the firms in Bangladesh where siblings of the incumbents are in leading

position next to him. Siblings are selected as successors in 33.87% of

those cases where successors have been identified. Therefore, though sons

of the incumbents are the leading selected successors, siblings are

important part in the succession of family owned firms in Bangladesh. It

has been observed from the study that a significant number of previous

owners are somehow active in the business. As involvement of the

previous owner in the business indicates incomplete succession, this

finding indicates that there are a large number of cases of incomplete

succession in family owned firms in Bangladesh.

As in Bangladesh very small number of studies has been conducted on

succession planning in family owned firms, the present study recommends

further in depth study on similar issues on family owned firms in

Bangladesh. The study hopes that findings of the study will provide

guidelines to the incumbents of family owned firms and academicians of

Bangladesh. The study also hopes that it will create a new trend of

research in business administration in Bangladesh and that will help in

long run survival of family owned firms and sustainable economic growth

of the country.


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Impact of Remittances to the Economic Development of

Bangladesh

Md. Omar Faruque 1

Udayshankar Sarkar 2

Abstract

examine the effect of workers’ remittance on Bangladesh economy.

To illustrate the effect of remittance, the paper uses the same national income

accounting framework as considered by Amjad R. (1986). Findings suggest that

the inflow of remittances increased from $0.2 billion in 1980 to $1.7 billion in

1999 that is about $1.5 billion increase over the 18 years. In the year of 1996-97,

remittances contributed almost 53.34% to overall balance of payment for

Bangladesh. Moreover, remittance contributed the highest of 62.12% in the year

1998. As remittances, GNP and remittance as percentage of GNP shows similar

trend in growth rate, this indicates that inflow of remittances positively

contributes to GNP. Furthermore, remittance earnings also positively contribute

to the Balance of Payments (BOP).

Keywords: Remittance, Balance of Payment, GDP, GNP

(I) Introduction

emittances to Bangladesh have been growing steadily over the last

decade. Since its independence in 1971, more than 3 million

Bangladeshis have left the country in search of employment. The central

bank estimates their cumulative remittances during 1976-2003 at round

US$22 billion (Amjad, 1989). Recognizing their economic importance,

the government for years has had legislation, policies, and an institutional

structure in place to facilitate the migration of its citizens. Now the

question is why sudden importance is put into the perception of

remittances? The fact is that the absolute and the relative volumes of

workers’ remittances are increasing. They have shown a steady increase

over the last decade. The amount of remittance flows to developing

countries already surpassed that of official resource inflows. Since 1999,

1 Assistant Professor, Department of Finance, Jagannath University,

Email: [email protected] 2 Department of Finance, Jagannath University,


R







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workers’ remittances have been the second largest resource flowing into

developing countries after foreign direct investment (FDI). In addition,

workers’ remittances are not liabilities but cash transfers from overseas,

which in principle, they do not cost any to recipient countries. As there has

been much debate about external debt and its negative effect on growth,

this feature is very attractive force. Despite the growing interest in

workers’ remittances, the role of remittance in development and economic

growth in general is not clearly understood. For example, studies based on

a country’s time-series data tend to find positive impacts of remittances on

growth, but a cross-country/panel data study by Hear and Sorensen (2003)

shows the opposite outcome. This is still one of the least studied areas of

research in migration literature. Despite the expanding literature on the

subject, there remains an inadequate understanding of a number of issues

related to the flow and use of remittances. Thus, there has been little work

on the impact of remittances on the overall economy.

The major labor exporting countries follow different conventions on

whether to include remittances from overseas workers as a part of the net

factor income in national income accounts. The resulting GNP estimates

(GNP= GDP + net factor income from abroad) therefore are not

comparable. Amongst the major Asian labor exporting countries, GNP

estimates published by governments in India, Sri Lanka, and Thailand

exclude workers’ remittances while Bangladesh, Pakistan and Philippines

include them.

In this , an attempt has been made to clarify concepts relating to the

affect of workers’ remittances on the overall economy of Bangladesh. As

Bangladesh is among the few countries that include workers’ remittances

separately in their gross national income estimates, it is important to

identify the impact of remittance on the national economy. In order to

understand the effect, this paper integrates remittance in the national

income accounting framework.

(II) Statement of the Problem

I bal and Sorensen (2003) have conducted a research on the impact of

workers’ remittance from the Middle East on Pakistan’s economy. The

research is based on the concept that inflow of remittance can have a

profound effect on Pakistan economy. The study reveals that significant


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inflow of remittance will add to the society’s resources; ease the balance

of payment constraint, positively contribute to the Gross National Product

(GNP) and help gross national savings to increase. There is another study

conducted by Bruyn & Kuddus (2005) on ―Dynamics of Remittance

Utilization in Bangladesh‖. The study reveals that remittance has strong

impact on the national economy. In the current study use national

income accounting identity to analyze such effects of remittance on

Bangladesh economy. In this current research, to analyze the

effect of remittance on Bangladesh economy.

(III) Review of Literature

Remittance

When migrants send home part of their earnings in the form of either cash

or goods to support their families, these transfers are known as workers’ or

migrant remittances. Remittances have been growing rapidly in the past

few years and now represent the largest source of foreign income for many

developing countries. The official data on the inflow of remittances into

Bangladesh refers to the transfer of funds made by migrant workers

through the banking channel (and through post offices) (Mahmud, 1989).

The records of such transfers can be easily separated from other foreign

exchange transactions since these take place under what is known as the

Wage Earners’ Scheme (WES).

According to Ratha (2005), it is hard to estimate the exact size of

remittance flows because many transfers take place through unofficial

channels. Worldwide, officially recorded international migrant remittances

are projected to exceed $232 billion in 2005, with $167 billion flowing to

developing countries. These flows are recorded in the balance of

payments; an international technical group is reviewing exactly how to

record them. Unrecorded flows through informal channels are believed to

be at least 50 percent larger than recorded flows. Not only are remittances

large but they are also more evenly distributed among developing

countries than capital flows, including foreign direct investment, most of

which goes to a few big emerging markets. In fact, remittances are

especially important for low-income countries. Remittances are typically

transfers from a well-meaning individual or family member to another


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individual or household. They are targeted to meet specific needs of the

recipients and thus, tend to reduce poverty. In fact, World Bank studies,

based on household surveys conducted in the 1990s, suggest that

international remittance receipts helped lower poverty (measured by the

proportion of the population below the poverty line) by nearly 11

percentage points in Uganda, 6 percentage points in Bangladesh, and 5

percentage points in Ghana. In poorer households, remittance may finance

the purchase of basic consumption goods, housing, and children's

education and health care. In richer households, they may provide capital

for small businesses and entrepreneurial activities. They also help pay for

imports and external debt service, and in some countries, banks have been

able to rise overseas financing using future remittances as collateral.

Remittance flows tend to be more stable than capital flows, and they also

tend to be counter-cyclical—increasing during economic downturns or

after a natural disaster in the migrants' home countries, when private

capital flows tend to decrease. In countries affected by political conflict,

they often provide an economic lifeline to the poor. The World Bank

estimates that in Haiti they represented about 17 percent of GDP in 2005,

while in some areas of Somalia, they accounted for up to 40 percent of

GDP in the late 1990s. There are a number of potential costs associated

with remittances. Countries receiving migrants' remittances incur costs if

the emigrating workers are highly skilled, or if their departure creates

labor shortages. In addition, if remittances are large, the recipient country

could face an appreciation of the real exchange rate that may make its

economy less competitive internationally. Some argue that remittances can

also create dependency, undercutting recipients' incentives to work, and

thus slowing economic growth. But others argue that the negative

relationship between remittances and growth observed in some empirical

studies may simply reflect the counter-cyclical nature of remittances—that

is, the influence of growth on remittances rather than vice-versa.

Remittances may also have human costs. Migrants sometimes make

significant sacrifices—often including separation from family—and incur

risks to find work in another country. And they may have to work

extremely hard to save enough to send remittances.


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According to Rahman (2001), substantial proportion of remittances is

utilized by the migrants on the consumer durable items. To sum up, we

can say that migrants’ families enjoy a higher standard of living and status

than what it was before migration (Rahman, 2001).

Impact of Remittance on balance of payment, investment, and

national savings

It is clear, indeed obvious that the most important macro-economic impact

of financial flow arising from international labor migration is on the

balance –of –payments and through that on the economy as a whole.

A major benefit of labor export is the balance of payments support

provided by remittance (Rahman, 2001). He also stated that, in a situation

of chronic foreign exchange shortage, remittance inflows could promote

investment and capacity utilization if most of the remitted foreign

exchange is used for importing capital goods and essential inputs.

Alternatively, increased foreign exchange availability may lead to a

relaxation of controls on luxury imports. It may also lead the government

to choose the easier short-run options instead of taking measures designed

to strengthen the economy’s structure and reduce its import dependence in

the longer run.

A precarious balance of payments has always been a major constraint to

development efforts in Bangladesh. The country became heavily

dependent on foreign aid immediately after Independence, Particularly

because of the disastrous fall in terms of trade in the early seventies and

the sluggish growth in exports ever since. However, since the beginning of

the eighties, the external aid inflow in real terms has stagnated or even

declined. Against his background, the huge upsurge in the f of

remittances inevitably had a salutary effect on the country’s capacity to

import. The role of remittances in compensating for the sluggish growth in

real export earnings particularly since the beginning of the eighties is quite

evident.

Turning to the balance-of payments (BOP) issue, while it is widely

recognized that the remittance flows from the migrants provided a

dramatic boost to the BOP, the precise position is not clear (Rahman,

2001). In part, this is on account of the absence of appropriately and


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accurately recorded data and some other problems, like the leakage or

diversion of the remittances into imports. The financial flows triggered by

international migration have had a dominant impact on the balance of

payments of all the labor exporting countries. At a time when massive

increase in oil imports and international recession put severe pressure on

the country’s balance of payments, remittances offered much needed relief

(Amjad, 1989).

(Rahman, 2001) estimated that during the late 1970s a 10 per cent increase

in remittances led to a 0.32 per cent increase in private consumption in the

long run and fixed investment by .053 per cent. GDP increased by 0.22 per

cent and GNP by 0.24 per cent. Hyun also estimates that a 10 per cent

increase in remittance leads to a decrease in the ratio on the current

account deficit to GNP by 0.40 percent in the long run. He however argues

that the immediate effect of increase in remittances is too adversely affect

exports due to increase in prices and wages but the net effect in the long

run would be positive.

The important point to grasp is that the increase in income attributable to

remittances enables the economy to realize an excess of investment over

domestic savings through a corresponding excess of imports over exports

with a smaller withdrawal on external resources than would otherwise be

the case (Amjad, 1989).

(Amjad, 1989) explains, as a result of remittance financed investments it

―may appear to be paradoxical – but it is gross national savings rather than

gross domestic savings that would rise and the economy would be able to

realize an excess of investment over the latter.‖ What this means is that the

effective savings constraint on investment is not domestic savings but

national savings, which take into account remittances.

According to (Amjad, 1989) in a situation where the departure of migrants

does not reduce domestic output, remittance inflows should increase

national income. He also stated in his research paper that, the increase in

income attributable to remittances might enable the economy to realize an

excess of investment over savings, through a corresponding excess of

imports over exports, with a smaller drawl on external resources than


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would otherwise be the case. Unless the marginal propensity to absorb out

foreign incomes exceeds unity, remittance inflows should always improve

the balance of payments position or prevent it from deteriorating as much

as it otherwise would.

The increased inflow of remittances significantly improved the balance of

payment position of Pakistan’s economy during the second half of the

seventies and early eighties. The foreign exchange made available because

of workers’ remittances also reduced the external debt, improved debt

servicing ability, and decreased the nee for additional foreign loans

(Nikos, 2005).

(IV) Methodology

Keynes National Income Accounting framework is used to determine the

effect of remittance on the economy of Bangladesh. This will provide an

important insight how workers remittances affect the economy. To

illustrate the effect of remittance, this paper uses the same national income

accounting framework that was used by Amjad R. (1986), in his paper

―Impact of Workers’ Remittances from Middle East on Pakistan

Economy: Some Selected Issues- the Pakistan Development Review

(1986)‖

To analyze the significance of migrant workers’ earning at the aggregate

level we will review data on imports, exports, workers’ remittances,

national and domestic savings, GNP, GDP, Net Income, Net Current

Transfers, Trade Balance, debt payment and investment. These data

accompanied with some other related data will be inserted in the MS Excel

software to run the analysis.

The Data

The data for the study is obtained from World Development Indicator (CD

ROM-2008-2009). Gross National Product (GNP), Gross Domestic

Product (GDP), Workers’ Remittance, Domestic and National Savings,

Capital Investment, Export and import of goods and services are collected

in current US$. The GDP Deflator (2005=100) is also obtained from the

WDI CD-ROM. All the variables are expressed in real terms by deflating

the data using GDP deflator (2005=100). The GNP figures are expressed


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in real terms by deflating them by the GDP deflator. Deflating them by

GDP deflators eliminates the effects of exchange rate fluctuations.

(V) Analysis and Discussion on findings

In terms of national income accounting identities after including workers

remittances in net factor income from abroad the basic relationships that

this paper is using are:

Net Factor Income ≡Net income + Net Current Transfer---1

External Resources Balance ≡Exports– Imports + Foreign loans and grants

+ NFI---2

Total Investment* ≡ Gross Domestic Savings + External Resource

Balance ----3

Gross Domestic Savings ≡Total Investment – External Resource Balance

(as % of GDP) ---4

(As % of GDP) Gross National savings ≡Gross Domestic Savings + Net

Factor Income (NFI) --5

(As % of GDP)

Source: Amjad R. (1986)

Now, by using the Keynes national accounting this paper will try to

examine in detail how remittances effect on Bangladesh’s economy.

Specifically, this paper will try to focus on affect that remittances have on

balance of payment, investment, and savings (nationally). For the above

analysis, this paper uses two important identities:

Net Factor Income (NFI) and External Resource Balance (ERB).

Remittance to Bangladesh has increased from $0.2 billion in the 1990 to

$1.7 billion in 2009(Appendix, Table 1). That is about $1.5 billion has

increased over 18 years of time. This is a significant increase for

Bangladesh, and is one of the largest sources of foreign exchange earnings

for it. The growth rate of workers’ remittance is also quite interesting to

observe. From Figure 1 & Table 1 it is evident that the growth rate of

remittance significantly increased from the year 1990 to 2000. On the

other hand, it has drastically decreased in the year 2000 and onwards.


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Than from the year 2006 it again started to increase. The growth rate was

highest in the year 2000, about 1.11 %.

Figure 1: Growth rate of Workers’ remittances in Bangladesh

Remittances, as a percentage of GNP, have also gone up approximately

more than two fold for Bangladesh from 1990 to 2009(Appendix, Table

2). From Figure 2 it quite evident that the trend of growth rate of GNP

(Gross National Product) and the contribution of remittance to GNP is

quite similar. Thus, it can be said that remittances contribute positively to

GNP.

Figure 2: Growth Rate Of Remittance (%), GNP (%) And Remittance As

Percentage Of GNP

Table 3 to 6(Appendix) will allow us to examine the impact of remittance

in terms of balance of payments support. Table 3 and 3.1(Appendix) bring


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out the contribution of remittance to the balance of payments and for debt

and services payments for Bangladesh. The data provides quite impressive

results for Bangladesh. Remittance as percentage of trade balance has

increased for Bangladesh from 33.737 in 1990 to 57.668 in 2009. This

indicates that remittance is quiet significantly contributing to meet the

trade imbalance of the country (Appendix, Table 3). For the peak year

2006-2007, remittances contributed almost 53.34% to overall balance of

payment for Bangladesh. This continued to increase as the year passed.

For example, remittance contributed the highest of 62.12% in the year

2008. From table 3 it is also evident that remittance has quite significant

role in the export earnings. Remittances consist on average about 20% of

the export earnings, during the survey period. The more impressive picture

is that over the years, debt service payments as a percentage of remittance

has an interesting trend in the scenario for Bangladesh. Remittance that

contributed in debt payment of the country increased from 49.45% in the

year 1990 to 83.19% in 2000(Appendix, Table 3.1). During the same

period, remittance was not much as it was in the years of 2005 and

onwards. Still remittance contributed a significant amount. When the debt

payment started to decline after 2005 the contribution of remittance also

declined, at the same time the inflow of remittance was increasing. Thus it

shows, whether the inflow of remittance is not significant or not, it

contributes an impressive amount in the debt payment. Therefore, it can be

suggesting that remittance plays a positive role in debt payment by

contributing a significant amount in it. Growth rate of debt payment as

percentage of remittance (Figure 3) shows that after 2007 the remittance

started to contribute more on the debt payment. If this trend continues than

it can be inferred that as years passes remittance is going to contribute

more to the repayment of debt of the country.


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Figure 3: Growth Rate of Debt Payments as Percentage of Remittance

Furthermore, remittance financed about 17% on average of imported

goods and services for Bangladesh (Appendix, Table 4). The aggregate

import of goods and services has gone up for Bangladesh almost twice

over between 1990 and 2009; also, remittance earnings have also

increased for Bangladesh during the period under study. Here one can

argue that the remittance earnings have forced the demand for imported

consumer goods. However, for Bangladesh, this argument will not be

valid. Level of aggregate investment and investment as a percentage of

GNP (Appendix, Table 5) has gone up hand-to hand for Bangladesh for

the period under review. This would suggest that income from abroad,

including remittance earnings, has contributed to domestic investment.

The growth rate of the Figures 4 and 5 indicates that the import and

investment has quite similar pattern. In the year from 1995 to 2000, the

growth rate was the highest. Import grew from 18% to 61%. At the same

time, investment also shows an impressive growth. Investment also grew

from about 5% to 16%. Thus, the growth rate also supports the aggregate

statistics.


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Figure 4: Growth Rate of Import and Workers’ Remittance (%)

Figure 5: Growth Rate Of Capital Investment And Investment As

Percentage of GNP

It has been stated by Brown (1994): When NFI is not significant and the

economy is running into large deficit, ―foreign loans and grants are

considered to finance the excess of import over export and along with the

domestic savings, foreign grants and loans, finance the total investment

(equation 3). On the other hand, when the NFI is significant, ―the external

resource balance reflects financing the deficit, both foreign loan and grants

and workers remittance which are available in foreign exchange.‖ Here the


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foreign exchange, which is available to the domestic economy, will

enhance the national savings. According to Amjad.R (1986), the earlier

identity states that when the NFI (which includes the workers remittances)

is significant, reliance on sources like foreign loans and grants to finance

the investment decreases as national savings increases (as NFI increases;

NS=DS+ NFI) However, period under review for Bangladesh NFI (NFI,

equation 1) is significant (Appendix, Table 6 and Figure 6). Thus, it can be

said that the external resource balance (ERB, equation 2) Table

6(Appendix) and figure 6, in terms of financing the deficit, both foreign

loans and grants and workers remittance are used.

Figure 6: Time-Trend of Net Factor Income and External Resource

Balance

To know how much ERB is being financed by NFI, take the difference

between domestic and national savings (National savings= domestic

savings + NFI). Table 7 shows gross national savings has gone up for

Bangladesh; therefore, there is a positive contribution to ERB (i.e.

financed by workers’ remittance earnings). Therefore, as NFI is

significant, it helps to lessen the dependence on sources like foreign loans

and grants to finance the investment. About savings, Table 7(Appendix)

presents gross domestic savings, as a percentage of GDP, did not change


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much between 2000 and 2009. Gross domestic savings is quite stable

compared to gross national savings. Gross national savings has increased

substantially during the survey period for Bangladesh. This reads as

increase in workers, remittances (plus other variables of NFI) substantially

decreased the dependence on foreign borrowings to finance investment.

The stable pattern of domestic savings for the economy illustrates that a

proportion of domestic consumption is being boosted due to increase in

foreign remittance earnings. For example, in the peak year of 2006 to 2007

remittance increased from 1.22 (billion US$) to 1.48 (billion US $) and at

the same time total consumption also increased from 32.92(billion US $)

to 34.36 (billion US $), which is quiet significant. On the other hand,

domestic savings only increased 1.17(billion US $) between those periods.

Figure 7: Trend of NFI, Gross Domestic Savings and Gross National

Savings


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Figure 8: Growth Rate Of National Savings (GNS), Gross Domestic

Savings (GDS) And Net Factor Income

The growth rate of NFI is quite impressive comparing to the GDS (Figure

8). Thus, the high growth rate of GNS is due to NFI rather than GDS.

Thus increase in NFI also increases the national savings.

Figure 9: Comparing remittance with some selected economic indicators


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Finally, when considering Table 8(Appendix) and Figure 9, remittances

significantly exceed foreign direct investment and foreign aid for

Bangladesh. From 2006 onwards, remittance earnings outweigh foreign

aid for Bangladesh. Incase of Bangladesh, remittance earnings partially

cover its trade deficit. One thing the reader should keep in mind that

remittance may finance consumer durable goods, but this will never

worsen the trade deficit. For example, if the remittance earning is $5,

import of goods cannot exceed $5, which is covered by the foreign

earnings the remittent sends. Now the only concern is if total remittance is

spent on consumption, it can lead to inflation in the economy but this

heavily depends on the elasticity of the goods, which are in high demand.

If inflow of remittances leads to increase in inflation, it might hurt others

welfare. (A micro level study is required to investigate this; a micro level

study can provide deeper insight into this problem. Considering viable

evidence from the countries under review, it is quite clear that remittance

earnings do not lead to inflation. As we already saw in our earlier

discussion, a fall in domestic savings is quite insignificant for Bangladesh

for the period under review). Consider the national income accounts, GNP

= GDP + NFI. From the above discussion, it has been found that NFI

(which includes workers’ remittance) is significant for Bangladesh in the

survey timeframe. Thus, it indicates that it also has quiet a significant

effect on the GNP.

(VI) Conclusion

Remittances to Bangladesh have been growing steadily over the past

decade. The study shows that the inflow of remittance in Bangladesh has

worked as a catalyst to restore the balance of payments deficits. The

inflow of remittances uniformly proved to be invaluable for Bangladesh,

by reducing the burden of debt payment, providing scarce foreign

exchange and finally boosting the national savings. From the analysis, the

economic benefits of inflow of remittance are clear. The finding of this

study, where remittance was integrated in the national income accounting

framework, brings out the importance of inflow of remittance in the

economy. The positive impact of remittances in economy would be much

clear, if further empirical study, using an econometric model is run.


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References

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Appendix

Table 1: Time-Trend and Growth Rate of Workers' Remittance (Billion

US$)

Table 2: Growth Rate of Remittance (%), GNP (%) and Remittance as

percentage of GNP and time-trend of Remittance as percentage of GNP

(in billion US$)


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Table 3: Remittances as percentage of trade balance, export and import

Table 3.1: Trend and Growth rate of Debt Payment as percentage of

remittance (in billion US$)


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Table 4: Growth Rate of Import and Workers' Remittance (%) (in billion

US$)

Table 5: Aggregate amount of Investment and investment as percentage of

GNP ( in billion US$)


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Table 5.1: Growth Rate of Capital Investment and Investment as

percentage of GNP (in billion US$)

Table 6: Time-Trend of Net Factor Income (NFI) and External Resource

Balance (ERB) (in billion US$)


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Table 7: Data on Gross Domestic Savings, Gross National Savings and

Net Factor Income (billion US$)

Table 7.1: Growth Rate of Gross National Savings (GNS), Gross

Domestic Savings (GDS), and Net Factor Income (NFI)


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Table 8: Comparing remittance with some selected economic indicators

Contributors

Md. Ataul Gani OsmaniMd. Elias HossainAgricultural Commercialization in Bangladesh: Are Smallholder Farmers Market Oriented?

Dr. A S M KamruzzamanFactors affecting the choices for off-farm activities in Bangladesh: A study in Rajshahi District

Mahmud Hossain RiaziThe Economics of Price Volatility in Commodity Futures Markets: A Survey

Rakibul IslamImpact of Market Size and Foreign Trading on FDI Inflow in Bangladesh: A VEC Approach

Md. Abdul AlimRudrendu RayDr. Md Enayet HossainVisitors’ Perception towards Tour Destinations: A Study on Padma Garden

Ajit Kumar GhoseMd. Solaiman ChowdhuryDeterminants of Share Prices in Bangladesh: Evidence from Pharmaceuticals industry

Md. Ikbal HossainRebeka Sultana RekhaDr. Md. Enayet HossainInfluence of Cognitive and Affective Image on a Recreational Park: An Empirical Study

Mohammad Zahid Hossain, Ph.DMd. Fazle Fattah HossainPerformance Evaluation of Selected NCBs and PCBs in Bangladesh: An Empirical Study

Md. Shariful IslamProfessor Dr. Md. Amzad HossainSuccession Plan in Second or Subsequent Generation Family Owned Firms in Bangladesh- a Study in Rajshahi Division.

Md. Omar FaruqueUdayshankar SarkarImpact of Remittances to the Economic Development of Bangladesh

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