PUMS99:1 UN IVERSITI MAL-AYSIA SABAH

I BORAN G PENGESAHAN STATUS TESrS@

.I UDUL. fl\,t(Jv~ _ V\~\I' \\,t ~~h~ O~ ~~ \.tIC p",A lh It, ~-\-,'.~ ,,, .tl '' ~ l~,).s ~ .. "'t",l+II\(<- \2t1lt 1111\( -

[J <\lAH' ~ . 9c.. (l\o~~ I \JM) 1 00~

SAYA ~~f !(OK- How SEs r PENGAJIAN:20 0, It ot>~ (HURUF BESAR)

mengaku membenarkaf] tes is (LPSrvVSarjanaiDoktor Falsafah) ini disimpan di Perpustakaan Universiti Malaysia Sabah dengan syarat-syaraL kegunaan seperti beriku t: -

i. Tesis adalah hakmili k Universiti Malaysia Sabah . 2. Perpustakaan Universiti Malaysia Sabah dibenarkan membuat sal inan unLuk tujuan pengajian

sa haja. 3 Perpustakaan dibenarkan membuat sali nan tesis ini sebagai bahan pertukaran antara institutsi

pengaj ian tinggi . 4. Si la tandakan ( / )

D SUUT (Mengandungi maklumat yang berdarjah keselamatan atau Kepentingan Malaysia seperti yang termaktub di dalam

D AKT A RAHSIA RASMI 1972)

TERHAD (Mengandungi makl umat TERHAD yang Lelah ditentukan

I /' I· TlDAK TERHAD _ .. -- oleh organisas ilbadan di mana R!!nyelidikan dijalankan) _"'"

Disahkan Oleh

~ (TANDAT ANGAN PENUUS) (TANDATANGAN PUSTAKAWAN)

Alamat TetaR: 4 ·1· ~ , \t\'1l tD\: _~NoYlil;lI\\

I

Mtw ",t,.. ~r ~ I 1 .. I~lO . A~A~\~~ t..,.i~trl )t l ,; 1 OVIJ k.'1~\'" L-rr. Nama Penyelia

Tarikh :~ ~ . ~{Jt Tarikh :l!..:. L " ,

CAT AT AN :- *Potong yang tidak berkenaan. ** Ji ka tesis ini SULIT atau TERHAD, sil a lampirkan sural daripada pihak berkuasa

! /organisasi berkenaan dengan menyatakan sekali sebab dan lempoh tesis ini perlu I

dikelaskan sebagai SUUT dan TERHAD . 1 @Tesis dimaksudkan sebagai tesis bagi lj azah Doktor Falsafah dan Sarjana secara I penyelidikan alau disertai bagi pengajian secara kerja kursus dan Laporan Proj ek Sarjana i

Muda (LPSM). - - ...

FACTORS AFFECTING THE PRICE OF RUBBER AND ITS CONTRIBUTION TOWARDS AGRICULTURE REVENUE .

YAP KOKHOW

THIS DISSERTATION IS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE DEGREE OF BACHELOR OF SCIENCE WITH

HONOURS

MATHEMATICS WITH ECONOMICS PROGRAMME SCHOOL OF SCIENCE AND TECHNOLOGY

UNIVERSITI MALAYSIA SABAH

April 2008

ii

DECLARATION

I hereby declare that this dissertation contains my original research work. Source of

findings reviewed herein have been duly acknowledge.

30 April 2008

YAP KOKHOW

HS 2005-4782

1ll

CERTIFIED BY

Signature

1. SUPERVISOR (pn. Noraini AbduUah)

2. EXAMINER 1

(Pn. Siti Rahayu Binti Mohd Hashim)

3. EXAMINER 2

(Cik Suriani Hassan)

4. DEAN )~~7-(Supt/KS. Assoc. Prof. Dr. Shariff A. Kadir S. Omang) .,r

IV

ACKNOWLEDGEMENT

Here, I would like to express my gratitude to my supervisor, Puan Noraini Abdullah.

From project 1 to project 2, she had given me a lot of guidance, encouragements and

concepts in order to help me to complete my research on time. Under her supervision, I

had learnt a lot of skills which were very useful and applicable in my future. I had also

gained lots of experiences which I believed will enrich my life.

In addition, I would like to extend my appreciation to Dr. Caroline Geetha of

School of Business and Economics who had supervised me in Project I, our course's tutor

Miss Khunes and all of the lecturers of Mathematics with Economics Programme who

had given me lots of priceless ideas and supports.

v

ABSTRAK

Kajian ini mempunyai enam objektif iaitu meneari kuantiti optimum pengeluaran getah,

pendapatan maksimum pengeluaran getah, keuntungan maksimum pengeluaran getah,

sumbangan komoditi pertanian terhadap pendapatan keseluruhan pertanian, faktor-faktor

yang mempengaruhi harga getah dan perangkaan model ramalan bagi harga getah.

Kuantiti optimum pengeluaran getah dapat dieari dengan melukiskan graf marginal kos

dengan marginal pendapatan. Pendapatan maksimum pengeluaran getah akan diperoleh

dengan mendarabkan kuantiti optimum pengeluaran getah dengan harga optimal getah.

Hasil tolak pendapatan maksimum pengeluaran getah dengan jumlah kos pengeluaran

getah akan mendapat keuntungan maksimum pengeluaran getah. Faktor-faktor yang

menpengaruhi harga getah dieari dengan menggunakan model regressi. Satu model yang

terbaik akan dipilih pada akhimya dan pembolehubah-pembolehubah yang terdapat

dalarn model terbaik itu mewakili faktor-faktor yang menpengaruhi harga getah. Harga

ramal an boleh didapati dengan menggantikan pembolehubah-pembolehubah yang

berkenaan ke dalam model terbaik itu. Sumbangan komoditi pertanian terhadap

pendapatan keseluruhan pertanian juga dapat dieari dengan menggunakan analisasi

regressi. Satu model akhir yang terbaik sekali akan dipilih dan pembolebubah­

pembolehubah yang terdapat di dalam model terse but adalah faktor-faktor yang

menyumbang kepada pendapatan keseluruhan pertanian.

VI

ABSTRACT

There are six objectives in this research. They are to determine the optimal quantity of

rubber production, the maximum revenue of rubber, maximum profit of rubber, looking

into the contribution of agricultural commodity towards agriculture revenue, factors that

influence the price of rubber and obtain a forecasting model of the rubber's price. Firstly,

the optimal quantity of rubber production is determined by plotting the graph of marginal

cost and marginal revenue. Maximum revenue will be obtained by multiplying the

optimal quantity of rubber with the rubber's optimal price. This is followed by the

substracting the total cost of rubber production from maximum revenue in order to fmd

the maximum profit. Secondly, the factors that influence the price of rubber are found by

doing multiple regression. Eventually, a final best model will be acquired and the

variables on the fmal best model are the factors that affect the price of rubber. By

substituting the data into variables of the final best model, the price of rubber can be

predicted. Lastly, the contribution of agriculture commodity toward agriculture revenue is

also determined through multiple regression analysis. The models will be examined and a

final best model will be obtained. Factors that contribute towards the agriculture revenue

will be in the final best model.

Vll

CONTENTS

Page Number

DECLARATION II

CERTIFICATION III

ACKNOWLEDGEMENT IV

ABSTRAK V

ABSTRACT VI

LIST OF CONTENTS Vll

LIST OF TABLES Xl

LIST OF FIGURES xii

LIST OF SYMBOLS xiv

CHAPTER 1 INTRODUCTION 1

1.1 Introduction 1

1.1.1 Marketing

1.1.2 Price 2

1.1.3 Labor 3

1.1.4 Production 3

1.1.5 Land 4

1.1.6 Rainfall 4

1.1.7 Population 5

1.1.8 GDP of Agriculture 5

1.1.9 GDP 6

1.2 Background 6

1.3 Problem Statement 8

1.4 Research Objectives 8

1.5 Scope Of Study 9

1.6 Significance Of Study 10

1.7 Organisation Of Study 10

1.8 Assumptions And Limitations 11 .

1.9 Definition Of Terms 12

Vlll

1.9.1 Short Run 12

1.9.2 LongRun 12

1.9.3 Overstocking 12

1.9.4 Understocking 13

1.9.5 Marginal Cost 13

1.9.6 Marginal Revenue 13

1.9.7 Profit 14

1.9.8 Opportunity Cost ]4

CHAPTER 2 Literature Review 15

2.1 Introduction 15

2.2 Literature Review 15

2.2.1 Perfect Competition 15

2.2.2 Short Run And Long Run Under Perfect Competition 18

2.2.3 Price And Ouput Determination Under Perfect Competition 22

2.3 Previous Research 23

CHAPTER 3 Methodology 30

3.1 Data 30

3.2 Model Specification 30

3.2.1 Model I And Model n 30

3.2.2 Optimal Price, Optimal Quantity And Maximum Revenue 32

3.2.3 Simultaneous Equation 33

3.2.4 Variables 34

3.3 Descriptive Analysis 34

3.4 Multiple Regression 36

3.5 Ordinary Least Square Method COLS) 44

3.6 Multicollinearity 45

3.7 R Square (R2) and Adjusted R Square Method C R2) 47 3.8 Model Selection Criteria 48

3.9 Enhancement of best model selected 50

IX

3.9.1 The Wald Test 50

3.10 Randomness Test 52

3.11 Forecasting 54

CHAPTER 4 Data Analyze And Empirical Results 55

4.1 Overview 55

4.2 Modell 56

4.2.1 Assumption Testing 56

a. Multicollinearity Test 56

b. Remedy of Multicollinearity Effect 59

c. Normality Test 61

d. Model Transformation 62

4.2.2 Multiple Regression Analysis 64

a. Individual t-Test 64

b. Global F-Test 66

c. Best Model Selection 68

d. Randomness Test 69

e. Forecasting 72

4.3 Model II 73

4.3.1 Assumption Testing 73

a. Normality Test 73

b. Model Transformation 75

c. Test of Multicollinearity 76

4.3.2 Multiple Regression Analysis 78

a. Individual t-Test 78

b. Global F-Test 80

c. Best Model Selection 81

d. Randomness Test 82

4.4 Optimal Quantity Of Rubber Production 85

CHAPTER 5 Discussion And Conclusion

5.1

5.2

5.3

5.4

5.5

5.6

5.7

Overview

Modell

Model II

Optimal Quantity, Maximum Revenue and Maximum Profit

Limitations

Suggestions For Future Works

Conclusions

REFERENCE

APPENDIX

88

88

88

90

91

92

93

94

96

100

x

LIST OF TABLES

No. Table Page Number

3.1

3.2

Calculation Of Combination Model For Six Dependent Variables.

Existence Of Multicollinearity

40

46

Xl

XlI

LIST OF FIGURES

No. Figure Page Number

2.1 Marginal Cost Curve (MC) Intersects Demand Curve 17

2.2 The Demand Curve Of A Perfectly Competitive Firm 18

2.3 SMC, SATC And SA VC Curves 19

2.4 LMC And LAC Curves 21

2.5 TC, TVC And TFC Curves 23

3.1 Scatterplots Of Residuals 37

3.2 Examples OfHomoscedasticity And Heteroscedasticity 39

3.3 The Rejection Region For Student-t Distribution 53

4.1 Multicollinearity test of Model I 58

4.2 Multicollinearity test on transformed model of Model I 61

4.3 K-S Test on Model I after model minimization 62

4.4 K-S Test after model transformation for Model I 64

4.5 Detrended Normal Q-Q Plot of X B 65

4.6 Coefficient table of model 3.1 of Model I 66

4.7 ANOVA table of model 12.7 of Model I 68

4.8 K-S Test on residuals 72

4.9 K-S Test on Model II 75

4.10 K-S Test for Model II after model transformation 77

4.11 Test of multicollinearity for Model 11 after mode transformation 78

4.12 Coefficient table for model 24.2 of Model II 79

4.13 ANOVA table for model 24.2 of Model II

4.14 K-S Test on residuals

4.15 Marginal cost and marginal revenue curve

81

85

86

Xlll

XIV

LIST OF SYMBOLS

< less than

> greater than

~ less than or equal

~ greater than or equal

= equal

;I; not equal

+ addition

subtraction

x multiplication

division

L summation

n number of observations

k number of parameters

Chapter 1

INTRODUCTION

1.1 INTRODUCTION

1.1.1 Marketing

Marketing exists in both profit and non-profit organization. Kotler (2005) stated that a

market-focused or customer-focused organization first determines what its potential

customers' desires and then builds the product or service. Marketing theory and practice

is justified in the belief that customers use a product and service because they have a need,

or because a product or service provides a perceived benefit. Two major factors of

marketing are the recruitment of new customers and the retention and expansion of

relationships with existing customers.

2

However, marketing exists at micro-level which relates to customers and the

organizations that serve them. While macro-level of marketing connects the entire

production-distribution system.

The marketing concept implies that an organization aims all of its efforts in a

coordinated and integrated manner, and simultaneously satisfying its customer and

achieving its corporate goals.

Kotler (2005) also emphasized that marketing strategies are often designed to

influence consumer decision making and lead to profitable exchanges. After all, variables

in marketing mix can be categorized into just four fundamental elements: product, price,

place and promotion that are known as the 4Ps in marketing. For a marketing plan to be

successful, the mix of the four Ps must reflect the wants and desires of the consumers in

the target market.

1.1.2 Price

Peter and Donnelly (2004) defined price as the amount of money charged for a product or

service. Pricing is one of the marketing mix strategies that is used to achieve company's

goal. A company's pricing decisions are affected by both internal company factors and

external environmental factors. Internal factors affecting pricing include the company's

marketing objectives, marketing mix strategy, costs, and organizational considerations.

3

Meanwhile, nature of the market, demand, competition and other environmental factors

such as economy, resellers and government are among the external factors.

Price decisions must be coordinated with product design, distribution, and

promotion to fonn an effective marketing mix which is known as the "4P" of the

marketing mix.

1.1.3 Labor (quantity of workers)

Begg et al. (2003) argued that all inputs of a firm can be adjusted in the long run. In

producing any particular output by the cheapest available technique, a rise in the price of

labour relative to capital makes the finn switch to more capital-intensive technique and

vIce-versa.

In the short run, the firm has fixed factors of production and fixed production

techniques. The firm can vary output by varying its input such as labor. Thus, a rise in the

price of one factor not merely changes factor intensity at a given output but also changes

the profit-maximizing level of output.

1.1.4 Production (quantity of production)

Case and Fair (2007) said that production is the process through which inputs are

combined and transformed into output. In this research, the number of production of

rubber was focused. Quantity of production can be affected by many reasons such as

4

amount of land management, labor, technology, cost of production and other inputs. For

instance, a firm with a plentiful supply of inexpensive labor but not much capital, the

optimal method of production will involve labor-intensive technique. In contrast, capital­

intensive technique is appropriate for a finn with high labor cost.

1.1.5 Land (total area of planted bectareage)

Case and Fair (2007) also stated that land is fixed in supply and its price is demand

determined. Price of land is determined by households and firms in other words. In this

research, the total area of the planted hectareage of rubber is varies due to many reasons.

According to Tarnin (1992), the factors could be the increasing cost of production, the

sluggish rate of productivity and the problems in structural change from primary

commodity sector to modem structure.

1.1.6 Rainfall

The sustainability of food supply could also be affected by climate change. Moreover,

increases in temperature and changes in rainfall pattern could affect yields directly. They

could also fasten the spread of fungus and diseases directly or indirectly, thus affecting

yield. The most vulnerable to these changes are the northern Peninsular Malaysia as well

as the coastal areas of Sabah and Sarawak. Changing climate can affect the agriculture

industry. Based on agricultural cycle, increasing in rainfall is not good for rubber. Rubber

plantations could suffer due to lost of tapping days and crop washouts (Tamin, 1992).

5

1.1.7 Population

Mankiw (2007) expressed that the most direct effect is on the size of the labor force. A

large population means more workers to produce goods and services. At the same time, it

means more people to consume those goods and services and vice versa. Applying into

this research, large population means more labor forces to work in rubber estates and thus

increases in productivity. Furthermore, there will be more customers consuming rubber's

products.

1.1.8 Gross Domestic Product (GDP) Of Agriculture Sector

Agriculture remains an important sector of Malaysia'S economy. It contributed 12 percent

to the national GDP and provided employment for 16 percent of the population. The three

main plantation: rubber, palm oil, and cocoa had dominated agricultural exports

(Yearbook of Statistic Malaysia, 2006).

Nonetheless, these three plantations had declined steadily during the last two

decades. In this research, the relationship between these three plantations and the

agriculture's GDP would be determined. Moreover, GDP of Malaysia was also embraced

into the research model.

6

1.1.9 Gross Domestic Product (GDP)

Mankiw (2007) defined gross domestic product (GOP) as the market value of all final

goods and services produced within a country in a given period of time. Gross domestic

product of Malaysia is being considered in this particular research.

Basically, gross domestic product's components include consumption by

households, investment by household, government purchases and country's net exports.

However, gross domestic product is not a perfect measure of economic well-being. For

example, it omits goods and services that have never been brought into the market such as

illegal drugs and vegetables that are produced and consumed by households themselves

(Mankiw, 2007).

1.2 BACKGROUND

Rubber's hectarages experienced a rapid decline from 1991600 hectarages in year 1982 to

1348400 hectarages in year 2003. This was indicative of massive shift from rubber to

other agricultural plantations, a phenomenon which has been dragged by relative

profitability. The rapidity and the magnitude of the shift may result a massive change in

the structure of rubber industry and may exert an adverse effect to the growth of the

industry as a whole (Tamin, 1992).

7

However, according to Yearbook of Statistic Malaysia (2006), Malaysia was the

third biggest producer of natural rubber in the world, and its well-established and superior

quality was widely used as a benchmark in the international market. The Malaysian

rubber industry produced a broad range of products from natural rubber as well as

rubberwood products. Malaysia has also successfully established itself as a major

producer and exporter of rubber and rubber products globally. Main importers of

Malaysian rubber and rubber products were the United States of America, United

Kingdom, Japan, Germany and Italy. In 2001, Malaysia's total exports of rubber were

valued at RM 4.05 billion.

Finished rubber products included rubber gloves, footwear, tyres, condoms and

prophylactic sheaths. Malaysian rubber was also employed in the building, contraction

and automotive industry for a variety of uses. Malaysia's biggest export product was

rubber gloves, whereby Malaysia was estimated to control over 60% of the global rubber

glove market.

Malaysia invested heavily in research and development in the rubber industry in

order to develop innovative and new uses for its rubber. In 2004, the rubber products

sector contributed RM 19.6 billion to the country's export earnings, comprising RM 5.2

billion of natural rubber, RM 7.9 billion of manufactured goods and RM 6.5 billion of

rubberwood products.

8

1.3 PROBLEM STATEMENT

In certain industry, especially industries where seasonal factors or trend play an important

role in deciding accurate demand or supply is vital in deciding the price of a product.

Accuracy in measuring demand and supply is vital because we need to make cost

effective order quantities. Uncertainty can take place in agriculture goods due to supply

yields, climate, transportation delay, shipment accidents and so on. Thus, buyers opt for

either overstocking or understocking. Overstocking can lead a buyer to eventually dispose

the stock at a lower price while understocking can lead to lost of sales through increase in

price. Due to the high amount of uncertainty, buyers feel it would be appropriate to find

the optimum price and quantity of an agriculture good, such as rubber to maximize

revenue and eventually the profit. Since agriculture goods belong to a perfect competition,

the condition to obtain optimal pricing, quantity and revenue are MR = MC = P where

MR equal to marginal revenue, MC equal to marginal cost and P equal to the price of

product. The aim of the study is to determine the optimum price of rubber, quantity of

rubber and revenue of rubber.

1.4 RESEARCH OBJECTIVES

i.) To determine for the optimal quantity of rubber.

ii.) To determine for the maximum revenue of rubber.

iii.) To look for the maximum profit of rubber.

iv.) To determine the factors influence the price of good.

9

v.) To create a forecasting model of rubber's price.

vi.) To look into the contribution of agriculture commodity toward agriculture

revenue.

1.5 SCOPE OF STUDY

The research was made on Malaysia's rubber. The compilation of rubber data for

reference from year 1982 until year 2003 were taken from the Annual Census of Rubber

Estates in Malaysia except for years 1993, 1994, 1996, 1997, 1999,2001 and 2003 for

which the data were obtained from Monthly Rubber Censuses. The data of rubber

includes the price of rubber, the number of workers, the salaries of workers, the

production of rubber and the planted hectareages of rubber (Yearbook of Statistic

Malaysia, 2006).

The population data were obtained from years 1982-2003 in Malaysia. For years

1991-1999, the data were compiled based on Population Census data. For the period

2000-2003, the data were compiled based on the 2000 Population Census data.

The gross domestic product (GDP) in Malaysia's agriculture sector was taken

from years 1982-2003. For years 1982-1986, the agriculture' s GDP had mixed with other

sectors like livestock, forestry and fishing.

96

References

B gg Da id. Fischer, Stanley. & Dombusc~ Rudiger. 2003. Economics. Ed 7th•

McGraw-Hill, Inc., Singapore.

ase K. E. & Fair, R. C. 2007. Principles of Microeconomics. Ed 8th• Prentice Hall, New

Jersey.

oakes S.1.. 2005. SPSS version 12.0 for windows. John Wiley & Sons, Sydney.

Department of Statistics. 2006. Yearbook of Statistics 2006. Malaysia.

ng Duncan. K. H. & DeSarbo, Wayne. S. 2007. A Bayesian methodology for

simultaneously detecting and estimating regime change points and variable

selection in multiple regression models for marketing research. Quantitative

Markel Economy.

~ Robert H. 1997. Microeconomics And Behaviour. Ed 3cd• McGraw-Hill Inc.,

United States.

Giordano Frank R., Weir, Maurice D. & Fox, William P .. 2003. Mathemathical

Modeling. Ed 3cd• Thomson Learning Inc., United States.

97

Gujarati Damodar N. ] 995. Basic Econometrics. Ed 3rd• McGraw Hill Inc, United States.

Ismail, B. M. 2007. Unimodality tests for globalization of single variable function using

statistical method. Malaysian Journal of Mathematical Sciences. 1 (2): 1- 11.

Jones, Thomas. A. 1972. Multiple regression with correlated independent variables.

Mathematical Geology. 4 (3): 203-218.

Kotler Philip. 2005. Marketing Management. Ed 12th. Prentice Hall, New Jersey.

Kwek, Kian Teng. 2001. Macroeconomics Indicators In Malaysia. McGraw Hill, Kuala

Lumpur.

Mankiw, N. Gregory. 2007. Principles Of Macroeconomics. Thomson South-Western,

United States.

McCarthy E. Jerome, Perreault, William D. Jr & Quester, Pascale G .. 2005. Basic

Marketing: A Managerial Approach. Ed. 2nd .• McGraw-Hill Inc, Sydney.

Peter J. Paul & Donnelly, James H. Jr. 2004. Marketing Management: Knowledge And

Slcills. Thomson South-Western, United States.

Ramanathan, Ramu. 1997. Statistical Methods In Econometrics. Academic Press Inc

United States.

Ramanathan, R. 2002. Introductory Econometrics with Applications. Ed. 5th. South

Western, Thomson Leaming, Ohio, United States.

98

,

Salvatore, Dominick. 1992. Schaum's Outline Of Theory And Problems Of

Microeconomic Theory. Ed 3rd• McGraw-Hill Inc., Singapore.

Seitz, Wesley D., Nelson, Gerald C., Halcrow, Harold G. 1994. Economics Of Resources,

Agriculture, And Food. McGraw Hill Inc., United States.

Singh, R.S. 1985. Empirical Bayes estimation in a multiple linear regression model.

Annals Institute of Statistics Mathematics. 37(A): 71-86

Spencer David E. 1993. Microeconomics. Ed 2nd. Addison-Wesley Publishing, United

States.

StigJer, 1. George. 1966. The Theory of Price. Ed. 4th. SAGE Publication, Newbury Park,

California

99

Strauss, Monty 1., Bradley, Gerald L. & Smith, Karl J .. 2002. Calculus. Ed 3rd• Prentice­

Hall Inc., United States.

Tamin, Mokhtar., Teh Hoe Yoke & Goh Kim Leng (pnys.). 1992. Malaysia 's Economic

Vision. Pelanduk Publication, Malaysia.

Teeravaraprug,1. 2004. Detennining optimal process mean of two-market products.

Institute of J Advanced Manufacturing Technology. 25: 1248-1253

Varian, Hal R. 1986. Intermediate Microeconomics: A Modern Approach. W.W.Norton

& Company, New York.

Worsam, Mike & Wright, D.BerkeJey. 1995. Marketing In Management: Basic Principle.

Prentice Hall, New Jersey.

Zhang, lin. & Wang, Xueren. 1997. Selecting the best equation via the p-value ofF-test.

Department DfStatistics, Yunnan University of China . 46 : 33-40.