Modeling and Policy Impact Analysis(MPIA) Network

A paper presented during the 4th PEP Research Network General Meeting,June 13-17, 2005, Colombo, Sri Lanka.

Modeling and Policy Impact Analysis(MPIA) Network

Ganga Manjari TilakaratnaSri Lanka

Trade Liberalisationand Poverty in Sri Lanka:

An Analysis with a Poverty FocusedComputable General Equilibrium

(CGE) Model

Ganga Manjari TilakaratnaSri Lanka

Trade Liberalisationand Poverty in Sri Lanka:

An Analysis with a Poverty FocusedComputable General Equilibrium

(CGE) Model

1

Proposal 10467

Trade Liberalisation and Poverty in Sri Lanka:

An Analysis with a Poverty-Focused Computable

General Equilibrium (CGE) Model

A Research Proposal submitted to

The Modelling and Policy Impact Assessment (MPIA) Sub-network of

Poverty and Economic Policy (PEP) Network.

By

Ganga Tilakaratna (Team Leader) Institute of Policy Studies of Sri Lanka P.H. Thusitha Kumara Institute of Policy Studies of Sri Lanka Athula Naranpanawe Sri Lankan PhD Student, Griffith University J.S. Bandara (External Resource Person) Griffith University, Australia

15 February 2005

2

RESEARCH PRPOSAL

Trade Liberalisation and Poverty in Sri Lanka: An Analysis with a Poverty

Focused Computable General Equilibrium (CGE) model

1. Abstract: Many trade and development economists, policy makers and policy

analysts around the world believe that trade liberalisation plays a vital role in

reducing poverty in developing countries. However, understanding the links between

trade and poverty is not simple. These links are complex. There exists a large body of

theoretical and empirical literature on how trade liberalisation helps to promote

growth and reduce poverty. The critics of globalisation, however, argue that

developing countries’ integration into the world economy makes the poor poorer and

the rich richer in developing countries. The most common criticism of globalisation is

that it increases poverty and inequality. Much of the research related to the links

between openness, growth and poverty has been based on cross-country regressions.

In recent years, many critics have pointed out the limitations of these studies. These

limitations of cross-country studies have given rise to systematic case studies using

poverty-focused computable general equilibrium (CGE) models and the results of

these case studies at least complement the results of cross-country studies. Sri Lanka

provides another good case study as a first country in South Asia to open the

economy. It has also been the second hard-hit country by the recent tsunami. This

recent tragic event created an urgent need for a poverty-focused CGE model. The

purpose of this study, therefore, is to develop a poverty-focused CGE model for Sri

Lanka. It is expected that the results of this study will provide a better understanding

of the links between trade liberalisation and poverty that can make trade regime an

effective mechanism for poverty reduction in Sri Lanka.

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2. Main Research Questions and Core Research Objectives

In recent years, there have been serious concerns over the links between trade and

poverty. Dollar and Kraay (2000; 2001), using regression analysis, argue that growth

is pro-poor. Moreover, the study suggests that growth does not affect distribution and

poor as well as rich could benefit from it. Later, they demonstrated that openness to

international trade stimulates rapid growth; thus, linking trade liberalisation with

improvements in well-being of the poor. Several other cross-country studies have

demonstrated a positive relationship between trade openness and economic growth

(see for example Dollar, 1992; Sach and Warner, 1995 and Edward, 1998). In

contrast, Rodriguez and Rodrick (2001) questioned the measurements related to trade

openness in economic models, and suggested that generalisations cannot be made

regarding the relationship between trade openness and growth. Several other studies

have also criticised the pro-poor growth argument based upon the claim of weak

econometrics and have placed more focus on distributional aspect (see, for example,

Rodrick, 2000). Ultimately, openness and growth have therefore become an empirical

matter, and so has the relationship between trade and poverty.

These weaknesses of cross-country studies have led to a need for providing further

evidence from case studies. Various researchers have adopted different empirical

approaches to identify the complex link between globalisation and poverty. In

addition to Dollar and Kraay, several other studies have found pro poor effects on

trade reforms using partial equilibrium data-based analysis (see Case, 2000; Minot

and Goletti, 2000 and Dercon, 2001) and general equilibrium analysis (see Bautista

and Thomas,1997 and Ianchovichina et al 2001). In contrast, empirical evidence on

negative impacts on the poor has been observed by several other researchers using

partial equilibrium data-based analysis (see Ravallion and Walle, 1991) and general

equilibrium analysis (see Lofgren, 1999 and Harrison et al, 2000). Several others have

found mixed results (see Cockburn, 2001; Devarajan and Mensbrugghe, 2000; and

Cogneau and Robilliard, 2000 for general equilibrium treatments).

Recently many policy analysts have focused on case studies since systematic case

studies related to individual countries will at least complement cross-country studies

such as the study by Dollar and Kraay. As Chen and Ravallion (2004) argue,

4

aggregate inequality or poverty may not change with trade reform even though there

are gainers and losers at all levels of living. They further argue that policy analysis

that simply averages across diversities may miss important matters, which are critical

to the policy debate.

In this study we use Sri Lanka as a case study and seek to apply a poverty-focused

CGE model in our analysis. Sri Lanka is selected as an interesting case in point to

investigate this linkage for the following reasons:

• Although Sri Lanka was the first country in the South Asian region to

liberalise its trade substantially in the late seventies, it still has experienced an

incidence of poverty of a sizeable proportion that cannot totally be attributed

to the long standing civil conflict.

• Moreover, the trade-poverty linkage within the Sri Lankan context has hardly

received any attention.

• Multi-sectoral general equilibrium poverty analysis within Social Accounting

Matrix (SAM) based CGE model has never been attempted.

• It has also been one of the hard-hit countries by the Indian Ocean Tsunami and

the severe damage to its economy has caused increased vulnerability to

poverty.

The key questions that the study seeks to answer are:

• What is the relationship between trade and poverty in Sri Lanka?

• What is the potential role of trade liberalisation in poverty reduction in Sri

Lanka?

• Does trade liberalisation reduce or increase inequality?

• What lessons can we learn from the Sri Lankan experience?

• What are the economic effects of tsunami and the post-tsunami recovery

package?

In order to address the above questions, this study attempts to achieve the following

objectives more specifically:

• To develop a poverty-focussed CGE Model for the Sri Lankan economy to

examine the links between trade and poverty;

5

• To estimate empirically income distribution functional forms for different

household groups and link to the CGE model in "top down" mode to compute

a wide range of household level poverty and inequality measurements;

• To conduct a set of simulation experiments to identify the impacts of trade

liberalisation in manufacturing and agricultural industries on absolute and

relative poverty at household level.

• To conduct two sets of simulations to examine the economy-wide effects of

tsunami and the effects post-tsunami recovery package on the exchange rate,

the balance of payments and trade, productivity and growth, poverty, labour

supply etc.

3. Scientific Contribution of the Research

Computable General Equilibrium (CGE) models have widely been used to analyse a

wide variety of policy issues ranging from trade liberalisation to environmental

degradation both in developed and developing countries over the last three decades or

so. The use of these models has even become more popular among policy analysts in

developing countries, particularly in countries where adjustment policies have been

implemented in recent years. Recently, International Development Research Centre

(IRDC) initiated a project to analyse the micro impact of macro-economic and

adjustment policies (known as the MIMAP project). The Institute of Policy Studies

(IPS) has become the Sri Lankan partner of this project and carried out significant

research related to the impact of adjustment policies in Sri Lanka. Under this project,

however, CGE models have been developed for many countries of the project (such as

India, Pakistan, Bangladesh, Philippines, Vietnam and Nepal), except for Sri Lanka.

This research gap has created a demand for developing a CGE model for Sri Lanka in

order to analyse the micro impact of macroeconomic policies and impact of reforms

on poverty.

6

4. Policy Relevance

Sri Lankan Economy and Trade liberalization

Sri Lanka is an open economy with its total trade equivalent to over 65 per cent of the

country’s GDP. It had an average GDP growth rate of over 5 per cent during the last

decade. The service sector is the dominant sector in the economy, accounting for

about 55 per cent of the GDP and 43 per cent of employment (2003). With a growth

rate of about 7.7 per cent, it has contributed about 70 per cent to the economic growth

in 2003.1 Moreover, the Industrial sector2 accounted for approximately 26 per cent of

the GDP and 22 per cent of the employment. However, the relative importance of the

agricultural sector has declined during the last decade with a lower contribution to

GDP (19 per cent in 2003) and to the economic growth (only 5 per cent in 2003).

Nevertheless, the share of employment in the agricultural sector is still high –

accounting for nearly one third of the total employment in country. 3

Trade liberalization was an important component of the economic liberalization

policy package introduced in 1977. Since then, the tariff structure in the country has

undergone a major reduction of levels and compression of tariff bands, indicating the

country’s commitment to trade liberalization. In 1977, quantitative restrictions on

imports, which were near universal, were replaced by a revised tariff system, retaining

only 280 items under license. Moreover, under the ‘second wave of liberalization

initiated in late 1980s, import tariffs were further reduced with the aim of moving

towards a three-band tariff system (with rates of 10, 20 and 35 per cent). The

government further reduced the maximum tariff rate to 30 per cent in 1998, and then

again to 25 per cent (in 1999) except for few agricultural products. By 1998r, over 50

per cent of the tariff lines in Sri Lanka’s tariff schedule carried tariff rates of 5 per

cent or below and about 73 per cent of the tariff lines contained rates of 10 per cent or

below. In addition, approximately one fifth of the tariff lines carried zero rates.

1 The high growth momentum in trade, transport, telecommunication, tourism and financial sub-sectors

has helped to achieve this high growth in the service sector. 2 The industrial sector is defined to include mining and quarrying, Manufacturing, Construction and

electricity, gas, water and sanitary services.

3 Figures taken from the Central Bank of Sri Lanka, Annual Report 2003

7

However, since 2001, there were some setbacks in the trade liberalization process due

to the economic down turn in 20014 and the fiscal constraints resulting from the civil

war of the country. A temporary surcharge of 40 per cent (subsequently reduced to 20

per cent) was introduced in 2001 across the board, except for very few items. The

tariff system took a backward step when the government introduced new tariff bands

of 2, 15 and 20 per cent (in addition to the bands of 5, 10, and 25 per cent that were

already in place) in 2002. Moreover, specific duties were re-introduced in place of ad

valerom rates for some selected agricultural and industrial goods. With the 2004

budget, the nominal level of protection has increased due to changes in the tariff rates

and the calculation of the total tax base. The budget brought down the import

surcharge to 10 per cent and introduced other taxes like Port and Aviation Levy

(PAL) and Education tax (Para taxes) which increased the level of protection.

Despite some policy reversals in the area of trade liberalization in recent years, Sri

Lanka has achieved considerable progress in liberalizing its trade regime during the

last decades. The figure 1 below clearly depicts how, the tariff lines have moved

towards lower tariff bands over the last decade.

Free

2%5%10

%15

%20

%25

%30

%35

%45

%50

%O

ther

19942000

0%5%10%15%20%25%30%35%40%45%50%

% of traiff lines

Tariff rate bands

Year

Figure1 : Changes in the Tariff Structure

1994 1998

2000 2002

Source: Various government gazettes and tariff advisory council.

4 Year 2001 was considered a “bad’ year for Sri Lanka with a negative economic growth for the first

time in the history after its independence in 1948.The budget deficit (as a % of GDP) rose to a double-digit number (10.8%).

8

The total tax revenue (as percentage of GDP) declined from 17.2 per cent in 1994 to

about 13.2 per cent in 2003. One of the major contributions to this decline was the

import duty, which reduced from 3.9 per cent of GDP in 1994 to 1.9 per cent in

2003.5 Moreover, as a share of total tax revenue, import duty has fallen to about 14.7

per cent by 2003 from approximately 22.7 per cent in 1994.

In the case of economic cooperation in the area of trade with the rest of the world, Sri

Lanka is a signatory to the SAARC Preferential Trading Arrangement (SAPTA),6

Framework Agreement on SAARC Free Trade Arrangement (SAFTA), Bangkok

Agreement, Generalized Scheme of Preferences (GSP), and Global System of Trade

Preferences (GSTP). The progress of these trading arrangements has been less than

satisfactory. The Indo-Sri Lanka Free Trade Agreement (ISFTA) was signed in

December 1998, and came into effect from 1st of March 2000. In order to include

service sector as well, the framework agreement - Comprehensive Economic

Partnership Agreement (CEPA) was signed between India and Sri Lanka in 2003. In

addition, Sri Lanka, signed a Free Trade Agreement with Pakistan in February 2005,

which will come in to effect in near future.

To conclude this section it is important to note that the devastating effects of the

tsunami that hit the country on 26 December 2004. This has been the worst natural

disaster Sri Lanka experienced in its more than 2500 years of history. Around 39, 000

people were killed and approximately 443,000 people have been displaced. Of those

killed, 27,000 belonged to fishing families and 65 percent of the country’s fishing

fleet has been completely destroyed or damaged. The tourism sector has been badly

affected. In addition to these sectors, physical and social infrastructure has been

severely damaged. According to estimates the overall damage to the economy is

around $1 billion (or 4.5 percent of GDP) and country needs around $1.5 to 1.6 billion

to rebuild the affected sectors and infrastructure. The tsunami had an impact on a

large poor and vulnerable people in the country. The economic growth rate in 2005 is

expected to be 1 percent slower than the expected rate of 6 percent. As noted before,

5 Sri Lanka has eliminated export duties, but cesses on tea, coconuts and some other items still apply.

6 Sri Lanka is a member of the South Asian Association for Regional Cooperation (SAARC)

9

it is important to examine the effects of tsunami and the post-tsunami recover package

in this study.

Poverty and inequality in Sri Lanka

There is a growing concern among policy makers of Sri Lanka on the distributional

and poverty implications of trade reform process. As per the Official Poverty Line for

Sri Lanka,7 using the Household Income and Expenditure Survey (HIES) of the

Department of Census & Statistics (DCS), approximately 22.7 per cent of the

population is identified as poor (in 2002). Moreover, the figures show a decline in the

aggregate poverty levels during the 1990-2002 period. The fall in poverty is

significant both in the urban and rural sectors. Particularly in the urban sector, the

percentage of poor has more than halved during the last decade. On the contrary, the

estate sector has recorded an increase in poverty levels from 20.5 per cent in 1990/91

to 30 per cent in 2002 (in 1995/96, incidence of poverty in this sector even increased

to 38.4 per cent). Furthermore, it should be noted that despite the declining trend in

poverty in the rural sector, poverty in Sri Lanka is predominantly a rural phenomenon.

(See Gunewardene, 2000 and Kelegama, 2001).

Table 1 :Incidence of Poverty (head count ratio) by Sector 1990 – 2002

Survey period Sector

1990-91 (%) 1995-96 (%) 2002 (%)

Sri Lanka8 26.1 28.8 22.7

Urban 16.3 14.0 7.9

Rural 29.4 30.9 24.7

Estate 20.5 38.4 30.0

Source: Department of Census & Statistics (DCS); estimates based on HIES 1990/91, 1995/96 and 2002.

7 The Official Poverty Line for Sri Lanka was introduced in June 2004. According to the Official Poverty Line, the

persons living in the households whose real per capita monthly total consumption expenditure is below Rs 1423 in the year 2002 in Sri Lanka are considered poor. It takes into account both the food and non-food consumption expenditure. The poverty line for 2002 has been adjusted by using the Colombo Consumer Price Index (CCPI) to obtain poverty lines for 1995/96 and 1990/91.

8 Household Income and Expenditure Surveys based on which the poverty levels have been estimated, have excluded the North and East (conflict areas) from their surveys. However, it is expected that exclusion of these areas have led to underestimation of the level of poverty in the country, considering that over 800,000 people have been internally displaced, and that the majority of the inhabitants in these areas and their properties have been affected by the civil war that lasted for nearly 20 years.

10

In spite of the long standing welfare policies adopted in Sri Lanka, a massive

government poverty reduction program (the Janasaviya Program) was initiated in

1989 and another version of it (the Samurdhi Program) is in operation since 1995.

However, these poverty alleviation programs have attracted wide criticisms for their

poor targeting and mismanagement due to political bias (World Bank, 2000).

A careful observation of the trend in income inequality measured by the income-based

Gini coefficient, reveals a gradual increase in inequality from 0.41 in 1973 (under the

protectionist policy regime) to 0.52 in 1986/87 (towards the end of the first wave of

liberalisation). This trend has however, declined slightly towards 1996/97. Figure 29

presents the trends in Gini coefficients based on both income receivers and spending

units. This movement indicates that the gap between rich and poor in the society has

widened towards late 1980's under the liberalised policy regime, resulting in an

increase in relative poverty. The marginal decline in the inequality observed towards

1996/97 may be attributed to the long run positive distributional effects emanating

from the trade liberalisation process or other factors which may have influenced the

income transfers to rural areas. For instance, Dunham and Edwards (1997) identified

migrant remittances, particularly coming from Middle East migrant workers and

income coming from armed force personnel engaged in the North and East conflict

zone of Sri Lanka, as two other vital factors that contribute to alleviating poverty

among rural households.

9 Source: Central Bank of Sri Lanka, Consumer Finances and Socio-Economic Surveys, various issues, Colombo.

00.10.20.30.40.50.6

Gin

i coe

ffic

ient

1973 1978/79 1981/82 1986/87 1996/97

Year

Gini C o e ffic ie nt (Inc o m e R e c e ive rs ) Gini C o e ffic ie nt (S pe nding Units )

Figure 2: Trends in income distribution in Sri Lanka

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5. Methodology

Literature Survey on Methodology

The majority of empirical studies attempting to investigate the linkage between trade

and poverty rely on partial equilibrium analytical framework. Partial equilibrium

framework, however, ignores the mutual relationships between prices, outputs of

various goods and factors. Trade impulse transmission mechanism that operates

within many channels, together with the difficulty in isolating the impacts of

numerous other policy-induced or natural shocks that causes poverty, demand a better

experimental design to trace this linkage.

Counterfactual analysis within a general equilibrium framework, therefore, provides

an ideal experimental environment to investigate this relationship. The general

equilibrium framework not only allows analysts to capture the direct, as well as the

indirect interactions among different agents and markets, but also provides a

convenient framework in carrying out controlled policy experiments where the impact

of trade reforms could be isolated from other shocks by fixing their impacts. These

models have long been used to analyse poverty and income distributional issues.

Savard (2003) provides a review of the methods used in these studies.

The traditional CGE models that focussed on the distributional consequences of

policy induced or other shocks were mainly based on representative households

(RHs). Two main approaches in specifying representative households can be

identified in the CGE literature. In the first approach, the household sector was

disaggregated into different representative household groups, assuming that

representative agent from each group could well be representing the economic

behavior of the whole group. These representative agents, in other words, correspond

to the mean values of variables such as income and expenditure of different household

groups in a household income or consumption survey10.

The second approach attempts to partially endogenise the within group income

distribution using more realistic distribution functions such as lognormal, pareto and

more flexible beta distribution. These models further assume that the mean of the

10 See Coxhead, et al, 1995; Horridge, et al, 1995; Bautista, et al, 1997; Maio, et al, 1999; Lofgren, 1999 and Storm, 2001.

12

income distribution could well be determined endogenously while its variance

remains fixed or determined exogenously. The main advantage of this approach over

the previous approach is the specification of income distribution using more

disaggregated household survey data. These representative household distributions are

estimated using the base year household survey data. Once a comparative static

simulation has been carried out, a new mean income for each representative

household would be generated within the CGE model. However, the variation of the

distribution has been assumed to be constant (or determined exogenously). Therefore,

the household income distribution would shift to right or left depending on the change

in mean income while maintaining the same shape of the distribution as the variance

is fixed. Unlike the first approach, the latter approach partially captures the intra-

group income distribution as well as the inter group income distribution. Thus, they

could be used effectively in capturing absolute poverty. Pioneering attempts to

capture the intra-group income distribution goes back to the work of Adelman and

Robinson (1979) in which they used the lognormal distribution function in specifying

income distribution of various representative household groups in Korea. Later, de

Jenvry et al (1991) and Chia et al (1991) used Pareto and lognormal distribution

functions, respectively, in specifying the income distribution of representative

household groups.

Despite the simplicity, the second approach noted above suffers from various

limitations. As Decaluwe et al (2001) points out, non-of those studies satisfactorily

justify why distribution functions such as lognormal and Pareto were preferred to

other more flexible functional forms, such as the beta function. As the beta function

could adopt fairly well under asymmetric situations by generating skewed

distributions, it is more flexible in terms of utilizing socio-economic information in

forming household income distributions. Furthermore, in those studies, the poverty

line, which is used as a reference point in measuring poverty, was determined

exogenously. As prices of different commodities are generated from the model,

treating poverty line (which is determined based on the prices of a distinctive basket

of commodities) as exogenous is somewhat misleading.

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In recent years, a number of innovative approaches have been used to analyse poverty

issues using CGE models. An innovative development in specifying intra-group

income distribution has been proposed by Decaluwe et al (1998) for an archetype

African country. In this model, the income distributions of different household groups

have been estimated using the beta functional form. Moreover, the poverty line has

been determined endogenously. Hence, once a simulation has been conducted, the

model would generate a new set of mean incomes for each household group and also

generate a new value for the poverty line based on the changes in commodity prices.

However, this model also assumes that the within group variation is fixed, thus

depending on the direction and the magnitude of the income change, the mean of the

distribution remains constant or shifts to the right or the left while keeping the shape

of the distribution unaffected. Additionally, the poverty line too shifts to either

direction depending on the change. This process, therefore, would capture the changes

in the poverty line as well as the income in computing poverty measurements.

Furthermore, Deacaluwe et al (1998) used the Foster, Greer and Thorbecke (FGT)

poverty measure to compute the headcount ratio and income poverty gap (Foster et al,

1984). Although, this approach maintains the assumption of RHs, it turned out to be a

better framework in capturing poverty compared to previous models. More

explanations on the methodology and archetype applications of this approach could be

found in Decaluwe et al (1999a, 1999b, 2001); Thorbecke, 2001; and Boccanfuso et al

(2003). Several other researches have adopted this approach in their applications

within the LDC context (Colatei and Round, 2000; Pradhan and Sahoo, 2000 and

Croser, 2002).

Another major development in specifying households within poverty-focussed CGE

models is the integration of individual households into a CGE model in the form of a

linked micro-simulation (MS) model. As individual households are taken into

account, these models are more reliable in capturing the individual heterogeneity.

Moreover, these models allow researchers to completely endogenise the within group

income distribution together with the within group variation (see Cogneau and

Robillard 2000, Cockburn 2001 and Cororaton and Cockburn 2004)11. This is a two

steps (or open loop) approach. A CGE model with a single representative agent is

11 Pioneering work on incorporating micro simulation models into CGE framework was carried out by Meagher, (1993). Since then few others have adopted micro simulation approach by attempting to fully integrate individual households into a CGE model (see Cogneau and Robillard, 2000 and Cockburn, 2001).

14

implemented to obtain the estimated price changes from a policy shock as the first

step and these price changes are then fed into a micro-simulation model as the second

step. In this approach, however, the causality usually runs from the CGE model to the

MS model, not the other way around. To avoid this limitation, more recently new

approaches have been adopted by several researchers.

Firstly, Savard (2003) raised the above limitation and linked both models by running

them in a repeated sequence of CGE-MS model runs. Following similar approach

Ferreira-Fiho, et al, (2004) have used a CGE model linked to a MS model with bi-

directional linkages to analyse the effects of economic integration on poverty and

regional inequality in Brazil. Secondly, Rutherford, et al (2004) have linked 54,000

plus households as agents in their CGE model of the Russian economy to examine the

households and poverty effects from Russia’s accession to the WTO. These authors

claim that this is a methodological breakthrough in this area of research.

Proposed Method in This Study

Sri Lanka has a long history of applying CGE models in analysing various issues

related to the economy. The availability of quality data has been one of the main

reasons for this trend. In fact, Sri Lanka is the first developing country for which a

Social Accounting Matrix (SAM) was developed in the early 1970’s (Pyatt and Roe,

1977). This has influenced the wide adoption of CGE framework in economic policy

analysis of the country. De Melo (1978) developed the pioneering CGE model for Sri

Lanka. Since then, several studies have developed CGE models for the Sri Lankan

economy12. Bandara (1989) developed the first CGE model of the Johansen class

(with linearized system of equations) following the ORANI model (Dixon et al, 1982)

of the Australian economy. This model contained a limited income distribution

component. However, no study has attempted to build poverty-focussed, multi-

sectoral, SAM based CGE model to analyse absolute poverty within the general

equilibrium framework for Sri Lanka. One of the most surprising facts is that Sri

Lanka is the only country for which a CGE model has not been developed so far

12 See Blitzer & Eckaus (1986); Jayawardena et al, (1987); Bandara (1989); CIE (1992); Herath (1994); Somaratne (1998); Bandara & Coxhead (1999); Kandiah (1999). For a comprehensive survey of CGE applications for the Sri Lankan economy see Bandara (1990).

15

under a MIMAP or PEP project. This is itself a justification for our attempt to develop

a poverty-focused CGE model under this proposal.

In this study a comparative static multi-sectoral SAM based poverty focused CGE

model will be developed by adopting the approach proposed by Decaluwe et al (1998)

to capture the link between trade reforms and absolute poverty within the Sri Lankan

context. Income distribution functions are empirically estimated and are linked to the

SAM based CGE model using the ‘top down’ approach to estimate absolute and

relative poverty. This is the initial stage of our project to begin the poverty-focused

CGE modelling approach in Sri Lanka. It is expected to develop a fully integrated

CGE and MS modelling framework depending on the outcome of this initial stage.

As noted in earlier, economy-wide CGE models have been used to investigate the

trade and poverty link in recent years. This first attempt of incorporating poverty into

a CGE framework in Sri Lankan is an extension of previous Sri Lankan CGE

modelling work led by one of the team members (for example, Bandara, 1989;

Bandara and Coxhead, 1999 and Bandara, et al, 2001). The core component of the

proposed Sri Lankan CGE model will follow the previous models, which were based

on the Australian ORANI model (Dixon, et al, 1982). Similar to its predecessors,

most of the behavioural equations of the core model of the proposed Sri Lankan

model are derived on the basis of neo-classical utility maximisation and profit

maximisation assumptions. They:

• Describe household and other final demands for commodities;

• Describe industry demand for primary factors and for intermediate inputs

from domestic and imported sources;

• Ensure zero-pure profit conditions, that is, the prices of commodities reflect

costs of production;

• Ensure market clearance;

• Relate producer prices paid by purchasers;

• Describe income distribution of households;

• Describe government income and expenditure sides and

• Define key macroeconomic identities.

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All equations in the model can be grouped into a number of blocks as shown in Table

2.

Table 2 Main blocks of Equations in the Model

Block Equations Block 1:

Demands Industry Inputs

Intermediate inputs (domestic and imported) Primary factors Labour by occupation Production subsidies Block 2:

Final Demands for Commodities

Demand for capital creation Household demands Exports Government demand Block 3:

Demand for Margins

Block 4:

Zero Pure Profits Conditions

Production Capital creation Importing Exporting Distribution Block 5:

Investment Allocation

Distribution of investment Investment budget constraint Block 6:

Market-Clearing Equations

Domestically produced commodities Imported commodities Primary factors Block 7:

Balance of Trade

Imports Exports Balance of trade Block 8:

Income Distribution

Firm’s income Household income Government income Block 9:

Miscellaneous Equations

17

The equation system of the model closely follows the Australian ORANI model

which belongs to the well-known Johansen class (Johansen, 1960). The full system of

equations, variables and exogenous variables are shown in Tables 1, 2, and 3 of the

appendix (NOTE: this system of equations and variables may be modified to

accommodate the trade-poverty linkage). Following the Johansen method, all

variables in the model are shown in percentage change forms.

6. Data Requirements and Sources

Two types of data sets are required to implement the proposed model in this study.

They are;

• A recently compile SAM with a detailed input-output database; and

• Household survey data on expenditure and income.

The main problem in developing a CGE model for the Sri Lankan economy has been

the lack of recently compiled SAM (see Bandara and Kelegama, forthcoming). This

must probably explain why a CGE model has not been developed for Sri Lanka under

MIMAP projects, although there are CGE models for many other developing

countries under the MIMAP projects. Fortunately, two of the team members of this

proposal (Bandara and Naranpanawa) are currently undertaking a research project in

developing a Sri Lankan SAM for a recent year under an IPS funded project. This

database can easily be used in the proposed project. Moreover, Sri Lanka has two

recent household survey data sets: (i.) Household Income and Expenditure Survey

(HIES)-2002 of the Department of Census and Statistics and, (ii.) Consumer Finance

and Socio-Economic Survey- 2003/2004 of the Central Bank of Sri Lanka, that can be

used in this study.

7. Dissemination of Results of the Study:

The results of this study will be disseminated in many ways:

1. The findings of the study will be presented at a workshop organised in

Colombo to a broad group of policy makers including officials from the

Ministry of Samurdhi and Poverty Alleviation (Government arm for poverty

alleviation), Ministry of Finance, Ministry of Trade, Commerce and Consumer

Affairs and various NGOs engaged in poverty alleviation projects at local

levels, Researchers and Academics.

18

2. The results will also be presented at the Institute of Policy Studies (IPS)

seminar series in Colombo and make them available for Sri Lankan policy

makers and academics through IPS Working Paper series.

3. The results will be presented at international workshops and conferences

organised by the PEP and other modelling conferences such as Annual Global

Economic Analysis conferences.

4. Revised papers on the basis of the comments made by policy analysts and

policy makers and participants of workshops and conferences will be

submitted to international refereed journals.

8. List of Team Members and prior training and experience in the issues and

techniques involved.

The research team consists of one senior researcher (“external resource person”) and

three young Sri Lankan researchers. The team is led by Ms Ganga Tilakaratna

(Female, 28) is a Research Economist at the Institute of Policy Studies of Sri Lanka

(IPS). As the researcher in-charge of the Poverty and Social Welfare Policy Unit, she

has been actively involved in research, policy analysis and formulation in the areas of

poverty, social policy and microfinance. Currently she is the Project Leader of the

MIMAP- Sri Lanka (Phase II) funded by the IDRC and has been actively participating

in the MIMAP/PEP meetings and trainings since 2002. Among several other projects,

she has carried out a research on trade and poverty linkage in Sri Lanka (with special

reference to the rice sector) using the partial equilibrium analysis (a study funded by

the North-South Institute, Canada). This proposed project will undoubtedly help her

to strengthen her knowledge and skills on poverty analysis within a general

equilibrium framework.

Dr Bandara (as an “external resource person” in the team) has extensive research

experience in CGE modelling in general and Sri Lanka in particular. He is the first Sri

Lankan national to develop a CGE model for the Sri Lankan economy in the late

1980s. Since then he has been working in CGE modelling projects related to the Sri

Lankan economy although he is an academic at Griffith University in Australia.

Currently he is working at the Institute of Policy Studies of Sri Lanka, to complete a

project on developing a SAM database for Sri Lanka and a general purpose CGE

model for the Sri Lankan. The proposed project will provide an excellent opportunity

19

for him to interact with young Sri Lankan policy analysts and train them in CGE

modelling area where there is a lack of local expertise. He has also actively involved

in Global Trade Analysis Project (GTAP) at Purdue University and he contributed the

Sri Lankan, Vietnamese and South African input-output tables to the version 4 GTAP

database in 1997 when he was on sabbatical leave at Purdue University. He has

presented papers at international conferences and published articles in international

journals such as the Journal of Policy Modelling and the World Economy (please see

his CV for details).

Mr Naranpanawa is a young Sri Lankan PhD candidate who is very close to complete

his PhD thesis at Griffith University in Australia. In recent years he has been working

with Dr Bandara as a PhD candidate in CGE modelling. The proposed project will

provide him with an excellent opportunity to continue his modelling work related to

the Sri Lankan economy.

Mr Thusitha Kumara (Male, 27) is working as a Research Assistant at the Institute of

Policy Studies of Sri Lanka. He is also currently pursuing an MPhil degree in

Economics at the University of Peradeniya (Sri Lanka). He has been involved in

number of research projects in the areas of poverty and microfinance.

9. Description of Research Capacities that Team Members and Their Institutions

are Expected to Build

Although there is a long history of CGE modelling in Sri Lanka there is significant

research gap in the area at present (as noted previously). Several attempts were made

by one of the team members of this proposed study (see Bandera’s CV). However,

there is no continuity of CGE modelling, particularly by local policy analysts.

Therefore, it is important to have a detailed research plan in capacity building. In this

proposal we propose a detailed research plan to build research capacities of IPS

researchers and other local interested researchers. We treat this research proposal as a

component of a detailed integrated IPS research agenda in the area of economy-wide

modelling.

20

The proposed research agenda consists of several stages, of increasing length and

weight as outlined below. Some initial stages have already been completed or

undertaken under different research projects:

1. To update our knowledge of the CGE modelling experience and economy-

wide data bases such as input-output (IO) tables and Social Accounting

Matrices (SAMs) in Sri Lanka (this has already been done);

2. To compile a Sri Lankan SAM for a recent year to lay the foundation for the

proposed CGE model on the basis of unpublished input-output tables, National

Accounts and other primary data (this is currently undertaken by two members

of this team);

3. To develop a poverty-focused CGE model using a Sri Lankan SAM and other

relevant data such as household surveys;

4. To undertake some preliminary policy experiments with the model;

5. To document the model properly to demonstrate the basic functions of the

model and its usefulness in policy analysis; and

6. To train local policy analysts at Ministries and government and non-

governmental organisations that are involved in poverty alleviation programs,

in running policy experiments with the model and interpreting results with a

view of capacity building through the project.

10. Any Ethical, Social, gender or environmental issues or risks which should be noted - None

11. List of Past and Current and Pending Projects in Related Areas Involving Team Members

• Project Title: Promoting Empowerment through Microfinance Programs

(on-going project) Funding Institution: South Asia Centre for Policy Studies (SACEPS) Team Members: Ganga Tilakaratna (team leader), Thusitha Kumara, Ayodya Galappatige of IPS

• Project Title: Impact of Trade Liberalization on Poverty in Sri Lanka (2004)

Funding Institute: North-South Institute (NSI), Canada Project Team: Ganga Tilakaratna, Sanath Jayanetti (IPS)

21

• Project Title: Microfinance for Poverty Alleviation in Sri Lanka: Current Status and Future Options ( to be completed by February 2005) Funding Institution: MIMAP- IDRC, Canada Team Members: Ganga Tilakaratna (IPS) Upali Wickremasignhe (University of Jayawardanapura), Thusitha Kumara (IPS)

• Project Title: Upgrading Educational Opportunities for the Poor (on-going project)

Funding Institution: South Asia Centre for Policy Studies (SACEPS) Team Members: Ganga Tilakaratna (team leader), Ruwan Jayatileka, Ayodya Galappatige of IPS

• Project Title: Community-based Poverty Monitoring Systems (to be completed by February 2005) Funding Institution: MIMAP- IDRC, Canada Team Members: S.T. Hettige, Markus Mayer ( University of Colombo), Ganga Tilakaratna (IPS)

• Project Title: Country Paper for the preparation of SAARC Development

Goals (2004) Funding Institution: South Asian Association for Regional Cooperation (SAARC) Team Members: Ganga Tilakaratna

• Project Title: Complementarities and Competition Between the Australian and South African Economies ( 2001)

Funding Institution: Griffith University Research Grant (GURG) Team Members: J.S. Bandara (team leader), D.T. Nguyen

• Project Title: A Survey on GTAP Applications ( 1999-2000) Funding Institution: Global Trade Analysis Project (GTAP)Grant.

Team Members: J.S. Bandara

• Project Title: South Asia and its Integration into the World Economy: Implications for Australia. (1999)

Funding Institution: GURG Team Members: J.S. Bandara (team leader), C. Smith

• Project Title: Optimal Land Use in Sri Lanka with Particular Application to Land Degradation and the Plantation Industries (1996-2000)

Funding Institution:Australian Centre for International Agricultural Research (ACIAR)

Team Members: J.S. Bandara and a team from La Trobe University and the Ministry of Plantation Industries in Sri Lanka

• Project Title: Economic Reforms in Vietnam and Implications for Australia ( 1995-96)

Funding Institution: Large Australian Research Council grant

22

Team Members: J.S. Bandara (team leader), D.T. Nguyen

• Project Title: Construction of a Computable General Equilibrium (CGE) Model for the Sri Lankan Economy to Analyse Policy Issues (2003-2004)

Funding Institution: IPS Team Members: J.S. Bandara and A. Naranpanawa.

• Project Title: Estimation of Multipliers for the Northern Territory Economy (2000)

Funding Institution: KPMG Consultants Team Members: J.S. Bandara

• Project Title: Economic Structural Changes and International Migration Pressures in Vietnam (1995)

Funding Institution: International Labour Organisation Team Members: J.S. Bandara, D.T. Nguyen

• Project Title: An Assessment of the Economic and Social Impact of Defence Activity on the Darwin Region for the Darwin Committee as part of Wran Committee of Inquiry into Future of Darwin Region,Economic Structural Changes and International Migration Pressures in Vietnam Economy (1994)

Funding Institution: Federal Government of Australia Team Members: J.S. Bandara, Ciaran O’Faircheallaigh, Christine Smith

23

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27

Table A1: Equations of the Model (in percentage changes)

No. Equation Subscripts Range

Number Description

1.

Industry Inputs (Block 1)

[ ]x (is)j

(1) = −

−=∑

x

p S p

j ij

is j iw j iw jw

( )( ) ( )

( )( ) ( )

( )( )( )

10 1

1 1 11

2

σ

i=1,...,n

j=1...,n

s=1,2

2n2 Demands for intermediate inputsof good i from source s to industry j for current production.

2. xn+2,j(1) = x j( )

( )1

0 j=1,...,n n Demands for production subsidies

3. p((n+1,1)j(1) = + +=∑ S pn q j n q jq ( , , )

( )( , , )( )

111

1 11

13 j=1,...,n n Price of labour in

general

4.

[ ]x j x

p S p

n q n j n j

n q j n m j n m jm

( , , )( )

( , )( )

( , )( )

( , , )( )

( , , )( )

( , , )( )

+ + +

+ + +=

= −

−∑1 1

11 1

11 1

1

1 11

1 11

111

13

σ

j=1,...,n

q=1,2,3

3n Demands for labourby occupational groups

5.

[ ]x j x

p S p

n v j n j

n v j n w j iw jw j

( , )( )

( )( )

,( )

( , )( )

( , )( )

( )( )

( )

+ +

+ +=

= −

−∑1

11

01

1

11

11 1

12

σ

j=1,...,n

v=1,2

2n Demands for primary factors.

Final Demands (Block 2)

6. [ ]x y p

S p

is r r ir is r

iw r iw rw

( )( ) ( )

( )

( )( )

( )( )

2 2 2

2 21

2

= −

−=∑

σ

i=1,...,n

s=1,2

r=1,2,3,4

8n Demands for inputsto capital creation

7. y S yr j jjer= ∑ ( )2 r=1,2,3,4 4 Aggregation of industry wise capital creation intosectors

8. [ ]x x p S pis h i h ih is h iw h iw hw( )( )

( )( )

( ) ( )( )

( )( )3 3 3 3 3 3

12= − −=∑σ

i=1,...,n

s=1,2

h=1,2,3

6n Households demands for commodities by different sources.

9. p S pi h is h is hs( )( )

( )( )

( )( )3 3 3

12−=∑ i=1,...,n

h=1,2,3

3n Price of ‘effective commodities’

28

10 x q p

c qi h h ik k hk

n

i h h h

h( )( )

( )( )

( ) ( )

3 31− =

+ −=∑ η

ε

i=1,...,n 3n Households demands for ‘effective commodities’

11. p x fie

i ieei( ) ( )

( )( )1 1

41= − +γ i=1,...,n n Export demands

12. x h c fis is R is( )( )

( )( )

( )5 5 5= + i=1,...,n

s=1,2

2n Government demands for commodities by different sources

13. c cR = − ξ( )3 1 Real household expenditure

Demands for Margins (Block 3)

14. x xis jis j( , )

( ) ( )( )( )

23 11 1= i=1,...,n

j=1,...,n

s=1,2

23=margin

2n2 Demands for margins current production

15. x xis ris r( , )

( ) ( )( )( )

23 12 2= i=1,...,n

s=1,2

r=1,2,3,4

23=margin

8n Demands for margins - capital creation

16. x xis his h( , )

( ) ( )( )( )

23 13 3= i=1,...,n

s=1,2

h=1,2,3

23=margin

6n Demands for margins - household consumption

17. x xii( , )

( )( )( )( )

23 11 4

14= i=1,...,n

23=margin

n Demands for margins - exports

18. x xisis( , )

( )( )( )( )

23 15 5= i=1,...,n

s=1,2

23=margins

2n Demands for margins - goverment consumption

Zero Pure Profits (Block 4)

29

19. p H p

H p

H p

H p

i in

s is j is j

q n q j n q j

n j n j

n j n j

( )( )

( )( )

( )( )

( , , )( )

( , , )( )

( , )( )

( , )( )

,( )

,( )

10

1 12 1 1

13

1 11

1 11

1 21

1 21

21

21

=

+

+

= =

= + +

+ +

+ +

∑ ∑∑

I = 1,...,n n Zero pure profits in production

20. π r in

s is r is rH p== =∑ ∑1 1

2 2 2( )( )

( )( ) r=1,2,3,4 4 Zero pure profits in

capital creation

21. p p ti im

i( )( )

( ) ( , )20

2 2 0= + +φ i=1,...,n n Zero pure profits in importing

22. p t Q p

Q p

ie

i i i

i

( )( )

( , ) ( , ) ( )( )

( , ) ( , )( )

1 1 4 1 1 4 10

2 1 4 23 10

+ + =

+

φ

i=1,...,n

23=margin

n Zero pure profits in exporting

23. ( )p Q Q p

Q t Q p

is j is j is j is

is j is j is j

( )( )

( )( )

( )( )

( )( )

( )( )

( )( )

( )( )

( , )( )

111

21 0

11 1

31

23 10

= +

+

i=1,...,n

j=1,...,n

s=1,2

23=margin

2n2 Zero pure profits in distribution of goods to users of current production.

24. p Q p Q pis r is r is is r( )( )

( )( )

( )( )

( )( )

( , )( )2

12 0

22

23 10= + i=1,...,n

s=1,2

r=1,2,3,4

23=margin

8n Zero pure profits in distribution of goods to users of capital creation

25. ( )p Q Q p Q

t Q p

is h is h is h is is h

is h is h

( )( )

( )( )

( )( )

( )( )

( )( )

( )( )

( )( )

( , )( )

313

23 0

23

333

23 10

= + +

+

i=1,...,n

s=1,2

h=1,2,3

23=margin

6n Zero pure profits in distribution of goods to government

26 p Q p Q pis is is is( )( )

( )( )

( )( )

( )( )

( , )( )5

15 0

25

23 10= + i=1,...,n

s=1,2

23=margin

2n Zero pure profits in distribution of goods to government

Investment Allocations (Block 5)

27. [ ]r Q pj j n j j( ) ( , )( )0 1 22= −+ π j=1,...,n

jεr

n Sectoral rates of return

28. ( )y k r wj j j j= + −( ) ( )*0 0β j=1,...,n n Sectoral Investment

30

29. i yjn

r j j= +=∑ 1 ( )π γ jεr 1 Aggregate

investment

30. i iR = − ξ( )2 1 Real Investment

31. f c iR R R= − 1 Ratio of real consumption expenditure to investment

32. r r fj jR( )0 = + j=1,...,n n Relationship

between rates of return in individual industries and the average annual rate of return

33. r w f w= + 1 Average rate of change of the net rate of return

Market-Clearing (Block 6)

34. x B x

B x

B x

B x

B x

k jn

k j k j

r k r k r

h k h k

k k

k k

( )( )

( )( )

( )( )

( )( )

( )( )

( )( )

( )( )

( )( )

( )( )

( )( )

( )( )

10

1 11

11

14

12

12

13

13

13

14

14

15

15

=

+

+

+

+

=

=

=

∑

∑∑

+

+

+

+

+

= = =

= = =

= = =

=

= =

∑ ∑ ∑

∑ ∑ ∑∑ ∑ ∑∑∑ ∑

δk in

jn

s kis j

kis j

in

r s kis r

kis r

in

h s kis h

kis h

in

ki

ki

in

s kis

kis

B x

B x

B x

B x

B x

[

]

( ) ( ) ( ) ( )

( )( ) ( )

( )( ) ( )

( )( ) ( )

( )( ) ( )

( )( )( )

( )( )( )

( )( )( )

( )( )( )

1 1 12

11

11

1 14

12

12

12

1 13

12

13

13

1 11 4

11 4

1 12

15

15

δ

δk

k

if kif k

= =

= ≠

1 230 23

k=1,...,

n

n Demand equals supply for dometically produced commodities

35. l xm jn

n m j n m j== + +∑ 1 11

11 1

1B( , , )( )

( , , )( ) m=1,2,

3

3 Demand equals supply for labour of type m

31

36. k xj n j( ) ( , )( )0 1 21= + j=1,...,n n Demand equals

supply of capital

Balance of trade (Block 7)

37. x B x

B x B x

B x

k jn

k k j

r k r k r h k h k h

k k

( )( )

( )( )

( )( )

( )( )

( )( )

( )( )

( )( )

( )( )

( )( )

20

1 21

21

14

22

22

13

23

23

25

25

=

+ +

+

=

= =

∑

∑ ∑

k=1,...,

n

n Import volumes

38. [ ]m M p xkn

k km

k= +=∑ 1 2 2 2

0( ) ( ) ( )

( ) 1 Foreign currency value of imports

39. [ ]e E p xkn

k ke

k= +=∑ 1 1 1 1

4( ) ( ) ( )

( ) 1 Foreign currency value of exports

40. 100∆B Ee Mm= − 1 Balance of trade

Income Distribution (Block 8)

41. yYY

yFjn j

F

F jF=

=∑ 1 1 Total firms’ income

42. ( )

( )

y S p x

TT

S S t S d

jF

jF

n j n j

jF

jF j

Fj

FjF

jF

j

= + +

−

−

+

+

+ +1 1 2 1 2

1 2 21

, ( , ) , )

, , ,

j=1,...,n n Firms’ sectoral

income

43. y Q d Q nf

Q u

jF

m j hF

j hF

A jF

A jF

F jF

jF

= +

+

=∑ 13

1 2

3

( , ) , ( , ) ,

( , )

j=1,...,n n Undistributed

profits

44. d yj hF

jF

, = j=1,...,n 3n Household dividends

45. uf yjF

jF= j=1,...,n n Profits and

dividends remitted overseas

32

46. y S

x

p

S d s tr

TT

t Q tr Q

h jn

jH n h j

n h j

jn

jF

j hF

hH

hA

h

hh

HhG H

=+

+ +

+−−

+

=

+

+

=

∑

∑

[

]

,( , , )( )

( , , )( )

, , ,

1 11 1

1

1 11

1 2 3

1 21

h=1,2,3 3 Household income

47. y p xT

Tt G

p xT

Tt G

G G

G G p

Gin

im

ii

ii i

in

ie

ii

ii i

in

i i

in

s jn

is j h is h is

i

= + +−

+ +−

+ +

+ +

+

=

=

=

= = = =

=

∑

∑

∑∑ ∑ ∑ ∑

[ ]

[ ]

[ ]

( ) ( )( ) ( , )

( , )( , )

( ) ( )( ) ( , )

( , )( , ) ,

, ,

( ) ( ) ( )

( )

1 2 20 2 0

2 02 0 1

1 1 14 1 4

1 41 4 2

1 1 2

1 12

1 3 13

40

1

1

1

φ

( )ns i

nis j is j is jx t G∑ ∑ ∑= =

+12

11 1

3( )( )

( )( )

( )

( )( )( )[ ]{ }

( )

+ +

+ +

+ + − +

+ + +

= = =

= + +

= + +

= = + +

=

∑ ∑ ∑∑

∑

∑ ∑

∑

in

s h is h is h is h

jn

n j n j j

jn

n j n j jF

j jF

jF

h jn

n h j n h j j hH

jn

j hF

x t G

p x G

x p W d W t

p x W

d

1 12

13 3 3

4

1 21

21

5

1 1 21

1 21

1 2

13

1 1 11

1 11

1

1

( )( )

( )( )

( )

( , )( )

( ,( )

,

( , )( )

( , ) , ,

( , , )( )

( , , )( )

( , )

,

{[W tr W t G

tr G

j hF

hA

hH

h h

h hH

s h

2 3 7

13

( , ) , ,

,

] }+ +

−=∑

1 Government income

48. Miscellaneous Equations (Block 9)

ξ( )( )( )

( )( )3

1 12

13 3 3=

= = =∑ ∑ ∑in

s h is h is hW p

1 Consumer price

index

49. ξh in

s is h is hV p( )( )( )

( )( )3

1 12 3 3=

= =∑ ∑ h=1,2,3 3 Household consumer price indices

50. ξ γ π( )21

4==∑r r r 1 Capita good price

index

33

51. p h

f f

f f

n m j n m j

n n j

n j n m j

( , , )( )

( , , )( ) ( )

( , )( )

( , )( )

( , )( )

( , , )( )

+ +

+ +

+ +

=

+ +

+ +

111

1 11 3

1 11

1 11

1 11

1 11

ξ

j=1,...,n m=1,2,3

3n Wage indexatiion

52. p h fn j n j n j+ + += +21

21 3

21

,( )

,( ) ( ) ( )ξ j=1,...,n n Price of production

subsidies

53. l lm m m==∑ 1

3 ψ 1 Aggregate employment

54. k kjn

j j( ) ( )0 01==∑ χ 1 Aggregate capital

stock

55. c y fh h hC= + h=1,2,3 3 Household

expenditure

56. c c Wh h h==∑ 1

3 1 Aggregate household expenditure

57. f f fhC C

hC= + * h=1,2,3 3 Shift variable for

consumption

58. y yhR

h h= − ξ( )3 h=1,2,3 3 Real household income

Total 6n2+82n+41

34

Table A2: Variables of the Model (in percentage changes)

Variable Subscript Range Number Description

x is j( )( )1 i,j=1,...,n

s=1,2 2n2 Demands for inputs from

domestic and foreign sources for current production

x is r( )( )2 i=1,...,n

s=1,2 r=1,2,3,4

8n Demands for inputs from domestic and foreign sources for capital creation

x n q j( , , )( )+1 1

1 j=1,...,n q=1,2,3

3n Demands for labour by occupational type and industry

x n v j( , )( )+1

1 j=1,...,n v=1,2

2n Demands for labour in general and capital by industry

xn j+21

,( ) j=1,...n n Demands for production

subsidies

x i h( .)( )3 i=1,...,n 3n Demands for effective

commodities by different household groups

x is h( )( )3 i=1,...,n

s=1,2 h=1,2,3

6n Demands for commodities in different sources by different household groups

x i( )( )

14 i=1,...,n n Export volumes

x is( )( )5 i=1,...,n

s=1,2 2n Government demands for

commodities classified by sources

x is j( , )( ) ( )23 1

1 i,j=1,...,n s=1,2 23=margin

2n2 Demands for margins associated with commodity flows to current production

x is r( , )( ) ( )23 1

2 i=1,...,n s=1,2 r=1,2,3,4 23=margin

8n Demands for margins associated with commodity flows to capital creation

x is h( , )( ) ( )23 1

3 i=1,...,n s=1,2 r=1,2,3,4 23=margin

6n Demands for margins associated with commodity flows to household consumption

x il( , )( )( )23 1

4 i=1,...,n 23=margin

n Demands for margins associated with commodity flows to domestic ports for exports

x is( , )( )( )23 1

5 i=1,...,n s=1,2 23=margin

2n Demands for margins associated with commodity flows to government consumption

35

x k( )( )

10 k=1,...,n n Supplies of domestic

commodities

x k( )( )

20 k=1,...,n n Supplies of imported

commodities

yr r=1,2,3,4 4 Capital creation by investing sectors

y j j=1,...,n n Capital creation by industries

p is j( )( )1 i,j=1,...,n

s=1,2 2n2 Purchasers’ prices of produced

inputs for current production

p n q j( , , )( )+1 1

1 j=1,...,n q=1,2,3

3n Prices of different occupational labour paid by industries

p n v j( , )( )+1

1 j=1,...,n v=1,2

2n Prices of labour in general and capital paid by industries

p is r( )( )2 i=1,...,n

s=1,2 r=1,2,3,4

8n Purchasers’ prices of produced inputs for capital creation

p i h( .)( )3 i=1,...,n

h=1,2,3 3n Purchasers’ prices of effective

commodities of household consumption

p is h( )( )3 i=1,...,n

s=1,2 h=1,2,3,4

6n Purchasers’ prices of commodities for household consumption from different sources

p ie

( )( )

1 i=1,...,n n Foreign currency prices of exports in f.o.b. term

p is( )( )5 i=1,...,n

s=1,2

2n Purchasers’ prices of commodities for government consumption from differenct sources

p is( )( )0 i=1,...,n

s=1,2 2n Basic prices of domestically

produced and imported goods

p im

( )( )

2 i=1,...,n n c.i.f. prices of imports in foreign currency

pn j+21

,( ) j=1,...,n n Price of production subsidies

πr r=1,2,3,4 4 Costs of units of capital

φ 1 The exchange rate

qh h=1,2,3 3 Number of households in each household group

kj j=1,...,n n Current capital stock in each industry

36

rj(0) j=1,...,n n Current rate of return on fixed capital

ω 1 Economy-wide expected rate of return

r 1 The average growth rate of return

iR 1 Aggregate real invetment

i 1 Aggregate investment

k(0) 1 Aggregate capital stoc

l 1 Aggregate employment

lm m=1,2,3 3 Employment by occupational group

m 1 Value of imports in foreign currency

3 1 Value of exports in foreign currency

∆B 1 The balance of trade

ch h=1,2,3 3 Household consumption

yh h=1,2,3 3 Household nominal income

y hR

( )( ) h=1,2,3 3 Household real income

cR 1 Real aggregate household expenditure

c 1 Aggregate household expenditure

ξ(3) 1 Aggregate consumer price index

ξh( )3 h=1,2,3 3 Consumer price indices for

different households

ξ(2) 1 Capital-good price index

fR 1 The ratio of real invetment expenditure to real household consumption expenditure

f ie( )1 i=1,...,n n Shifts in foreign export

demands

f is( )( )5 i=1,...,n

s=1,2

2n Shifts terms for government demand

fn j+21

,( ) j=1,...,n n Shift terms for prices of

production subsidies

f n( , )( )+11

1 1 General wage shift variable

37

f n j( , )( )+11

1 j=1,...,n n Variable which can be used to simulate the effects of changes in wages payable by an industry relative to other industries

f n m( , , )( )+1 1

1 m=1,2,3 3 Variables which can be used to simulate the effects of changes in occupational wage relativities

f n m j( , , )( )+1 1

1 i=1,...,m

m=1,2,3

3n Variables allowing changes in both occupational and industrial wage relativities

fω 1 Shift variable allowing divergencies between r and w

fjr j=1,...,n n Shift variable allowing the

introductin of changes in relative rates of return across industries

fC 1 Shift variable fr aggregate consumption

fhC h=1,2,3 3 Marginal propensity to consume

fhC* h=1,2,3 3 Shift variables for marginal

propensity to consume

t(i2,0) i=1,...,n n One plus the ad valorem tariff rates on imports

t(i1,4) i=1,...,n n One plus the ad valorem rates of export subsidies or taxes

t is j( )( )1 i,j=1,...,n

s=1,2

2n2 Sales tax rates on produced inpouts for current production

t is h( )( )3 i=1,...,n

s=1,2

h=1,2,3

6n Sales tax rates on household consumption

yF 1 Firms’ income

y jF j=1,...,n n Sectoral firms’ income

t jF j=1,...,n n Taxes on sectoral profits

d j j=1,...,n n Sectoral depreciation

d j hF, n=1,...,n

h=1,2,3

3n Household dividends

nfjF j=1,...,n n Profits and dividends remitted

38

overseas

u jF j=1,...,n n Undistributed profits

trhG h=1,2,3 3 Government transfers to

households

trhA h=1,2,3 3 Transfers to households from

abroad

thD h=1,2,3 3 Direct income taxes on

household income

yG 1 Government income

Total 8n2+102n+64

39

Table A3: A Possible List of Exogenous Variables

Variable Subscript Range Number Description

p i jm( )2 i=1,...,n n Foreign currency import prices

in c.i.f. terM

t(i2,0) i=1,...,n n One plus the ad valorum tariff rates

t(i1,4) iεZ n Choice of export tax (subsidy) terms

x il( )( )4 i∉Z Complementary selection of

export volumes

kj(0) j=1,...,n n Current specific capita stocks

t is j( )( )1 i,j=1,...,n

s=1,2

2n2 Sales tax rates on intermediate inputs

t is h( )( )3 i=1,...,n

s=1,2

h=1,2,3

6n Sales tax rates on consumer goods

t jF( ) j=1,...,n n Income tax rates on sectoral

profits

th h=1,2,3 3 Tax rates on household income

trhA h=1,2,3 3 Transfers to household from

abroad

trhg h=1,2,3 3 Transfers to household from

government

dj j=1,...,n n Sectoral depreciation

fn j+21

,( ) j=1,...,n n Shifts variables for the price of

production subsidies

f ie( )1 i=1,...,n n Shift variables for foreign

export demands

f is( )( )5 i=1,...,n

s=1,2

2n Shift variables in government demands

l 1 Aggregate employment

f n m( , , )( )+1 1

1 m=1,2,3 3 Shift variables which can be used to simulate the effects of change in occupational wage relativities

f n j( , )( )+11

1 j=1,...,n n Shift variables which can be used to simulate the effects of

40

changes in industry wage relativities

f n m j( , , )( )+1 1

1 j=1,...,n

m=1,2,3

3n Shift variables which can be used to simulate the effects of change in both occupational and industry wage relativities

qh h=1,2,3 3 Number of households in different household groups

r 1 Average growth rate of rates of return

fR 1 Ratio of real investment to real consumption expenditure

∆B 1 Balance of trade

fhC− h=1,2,3 3 Shift variables for marginal

propensity to consume

φ 1 Exchange rate

Total 2n2+20n+23