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Journal of Policy Modeling 33 (2011) 328–346 Available online at www.sciencedirect.com Trade and poverty nexus: A case study of Sri Lanka Athula Naranpanawa , Jayatilleke S. Bandara, Saroja Selvanathan Department of Accounting, Finance and Economics, Griffith Business School, Griffith University, Queensland 4111, Australia Accepted 31 August 2010 Available online 6 September 2010 Abstract The link between trade liberalisation and poverty has become one of the most debated topics in recent years. There is a growing body of empirical literature on this topic and many studies provide mixed results. In this study, Sri Lanka is used as a detailed case study and a computable general equilibrium (CGE) approach is used as an analytical framework to examine the trade–poverty nexus. The results suggest that, liberalisation of the manufacturing industries is more pro-poor than that of the agricultural industries. Overall, this study suggests that trade reforms may widen the income gap between the rich and the poor creating uneven gains across different household groups in Sri Lanka. While short-term complementary policies are needed to compensate vulnerable income groups, long-term policies are needed to make gains from trade liberalisation more inclusive and equitable to maintain economic and political stability in Sri Lanka. Crown Copyright © 2010 Published by Elsevier Inc. on behalf of Society for Policy Modeling. All rights reserved. JEL classification: C68; F14; I32 Keywords: Poverty; Trade liberalisation; Computable general equilibrium model; Income distribution; South Asia; Sri Lanka 1. Introduction The link between trade liberalisation and poverty has become one of the most debated topics in international trade and development in recent years. The current debate on this topic centres on the question of how trade liberalisation affects poverty. Some argue that trade liberalisation is good for Corresponding author at: Department of Accounting, Finance and Economics, Gold Coast Campus, Griffith University, Queensland 4222, Australia. Tel.: +61 7 5552 8083. E-mail address: a.naranpanawa@griffith.edu.au (A. Naranpanawa). 0161-8938/$ – see front matter. Crown Copyright © 2010 Published by Elsevier Inc. on behalf of Society for Policy Modeling. All rights reserved. doi:10.1016/j.jpolmod.2010.08.008

Trade and poverty nexus: A case study of Sri Lanka

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Page 1: Trade and poverty nexus: A case study of Sri Lanka

Journal Identification = JPO Article Identification = 5873 Date: March 29, 2011 Time: 10:38 am

Journal of Policy Modeling 33 (2011) 328–346

Available online at www.sciencedirect.com

Trade and poverty nexus: A case study of Sri Lanka

Athula Naranpanawa ∗, Jayatilleke S. Bandara, Saroja SelvanathanDepartment of Accounting, Finance and Economics, Griffith Business School, Griffith University,

Queensland 4111, Australia

Accepted 31 August 2010Available online 6 September 2010

Abstract

The link between trade liberalisation and poverty has become one of the most debated topics in recentyears. There is a growing body of empirical literature on this topic and many studies provide mixed results.In this study, Sri Lanka is used as a detailed case study and a computable general equilibrium (CGE)approach is used as an analytical framework to examine the trade–poverty nexus. The results suggest that,liberalisation of the manufacturing industries is more pro-poor than that of the agricultural industries. Overall,this study suggests that trade reforms may widen the income gap between the rich and the poor creatinguneven gains across different household groups in Sri Lanka. While short-term complementary policies areneeded to compensate vulnerable income groups, long-term policies are needed to make gains from tradeliberalisation more inclusive and equitable to maintain economic and political stability in Sri Lanka.Crown Copyright © 2010 Published by Elsevier Inc. on behalf of Society for Policy Modeling. All rightsreserved.

JEL classification: C68; F14; I32

Keywords: Poverty; Trade liberalisation; Computable general equilibrium model; Income distribution; South Asia; SriLanka

1. Introduction

The link between trade liberalisation and poverty has become one of the most debated topics ininternational trade and development in recent years. The current debate on this topic centres on thequestion of how trade liberalisation affects poverty. Some argue that trade liberalisation is good for

∗ Corresponding author at: Department of Accounting, Finance and Economics, Gold Coast Campus, Griffith University,Queensland 4222, Australia. Tel.: +61 7 5552 8083.

E-mail address: [email protected] (A. Naranpanawa).

0161-8938/$ – see front matter. Crown Copyright © 2010 Published by Elsevier Inc. on behalf of Society for Policy Modeling. All rights reserved.

doi:10.1016/j.jpolmod.2010.08.008

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the poor; others, that it is not. In between there are some who argue its goodness if implementedwith correct complementary policies. This policy emphasis has spawned a large literature onthe trade liberalisation – poverty nexus in recent years (for example, Bussolo & Round, 2006;Dollar & Kraay, 2002, 2004; Hertel & Winters, 2006; Nissanke & Thorbecke, 2007; Harrison,2007). A number of literature reviews, including Bannister and Thugge (2001), Hertel and Reimer(2002), Goldberg and Pavcnik (2004), Winters, McCulloch, and McKay (2004), and Goldbergand Pavcnik (2007), have also emerged in recent years.

Given the nature of data availability, most of the empirical studies in the existing literaturehave used cross-country econometric models. There is a growing concern about limitations ofthis approach in providing a sound empirical basis for informing the policy debate (Srinivasan& Bhagwati, 2001; Ravallion, 2004; Nissanke & Thorbecke, 2007). Quite apart from generalmethodological flaws relating to model specification and econometric procedure (Deaton, 1989),there are few fundamental limitations that make results from any cross-country study on thissubject rather dubious (see for details, Srinivasan, 1994).

Moreover, the majority of empirical studies attempting to investigate the linkage betweentrade and poverty rely on partial equilibrium analytical framework. Chen and Ravallion (2004,p. 31) observe that “although partial equilibrium analysis requires little or no aggregation of theprimary household data, it misses potentially important indirect effects of prices and wages”. Inaddition, Coxhead (2003) further emphasised that complete picture of poverty changes can onlybe obtained using a general equilibrium framework as partial equilibrium assessments are solelybased on output prices, thus tend to be misleading.

The problematic nature of cross-country studies has led the policy analysts to focus on moredetailed country-specific case studies based on the general equilibrium approach. Sri Lanka isan ideal candidate for such an in-depth study since it is the first country in South Asia to openthe economy by implementing a far-reaching economic reform process in 1977. Considering theexclusion of the Northern and Eastern provinces of the country in reaping economic benefits fromtrade by the three decades of war which ended in 2009, the Sri Lankan case study will also shedsome light on the need for inclusive economic policy making in the post-war period.

The main objective of this study, therefore, is to use a poverty-focussed CGE model of theSri Lankan economy to conduct policy simulations in examining the possible links betweentrade reforms on absolute and relative poverty at different household groups. The results of thesimulations will help us address some of the policy questions such as: Who benefits from tradeliberalisation and to what extent? What role can complementary policies play in reducing growingincome disparity between the rich and the poor as a result of trade policy reforms? Will people,who are rural poor depending on domestic agriculture or those who are affected by the war benefitfrom further trade policy reforms?

In order to meet the above objectives and to address policy issues the remainder of this paperis arranged as follows. Section 2 presents a brief background of the Sri Lankan trade reformsand poverty while Section 3 presents the structure of the Sri Lankan CGE model and the incomedistribution functions. Section 4 discusses the short run and long run effects of trade policy reformsusing simulation experiments and Section 5 presents some policy recommendations. The finalsection presents the concluding remarks.

2. Trade reforms and poverty trends in Sri Lanka: stylised facts

In 1977, Sri Lanka went through a radical policy shift towards a more liberal policy regime. Inthe initial phase of economic liberalisation, during 1977–1979, significant policy reforms were

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Fig. 1. Poverty headcount index (percentage) by sector.Source: Based on DCS (2008).

introduced such as trade liberalisation, promotion of foreign investments, interest rate reforms,limiting public sector involvement in the economy and exchange rate realignment. “Tariffication”of quantitative import restrictions has taken place retaining only 280 items under licence. Thistarrification has resulted in significant reduction in protection, which was the norm under theprevious trade regime. Subsequently, tariffs became the major policy instrument in shaping thetrade reform process.1

However, the liberalisation process experienced a set back in the mid-1980s, mainly due to theeruption of ethnic conflict which culminated into a civil war in the Northern and Eastern provincesof the country and the second rebellion in the south led by the Janatha Vimukthi Peramuna(JVP). The second phase of liberalisation and adjustment program was implemented in the early1990s which consisted of further tariff cuts and simplification of tariff structure; liberalisation ofthe current account; implementing a flexible exchange rate management; stringent privatisationprogram and fiscal restraint.

As noted by Bandara and Jayasuriya (2009, p. 418), “despite periods of slow progress andoccasional backsliding, the trend in overall policy has been towards progressive liberalization,and the country now perhaps the most open regime in South Asia”. Reviewing trade policy anddistortions to agricultural incentives since Sri Lanka gained independence in 1948, they furthernote that trade policy continues to protect the domestic agriculture sector, including rice and otherimport-competing food crops, while liberalising the export agriculture sector (tea, rubber andother agricultural crops).

Given the above background of the Sri Lankan trade policy regime and the selective nature ofprotection of domestic agriculture, there is growing concern among policy makers of Sri Lankaon the distributional and poverty implications of trade reform process. As is evident from Fig. 1,poverty in Sri Lanka is predominantly a rural2 phenomenon and the lowest poverty is recorded inthe urban3 sector. Several studies have shown that poor households are more likely to be found

1 For a detailed overview of trade reform process in Sri Lanka, see Athukorala and Rajapatirana (2000) and Athukoralaand Jayasuriya (2000).

2 Residential areas, which do not belong to urban sector or estate sector, are considered as rural sector.3 Area governed by either Municipal Council or Urban Council is considered as urban sector.

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Fig. 2. Trends in income distribution in Sri Lanka.Source: Central Bank of Sri Lanka, Consumer Finances and Socio-Economic Surveys.

in the rural than in urban areas due to working members being employed in agriculture andother primary production activities (Datt & Gunawardene, 1997). According to the Departmentof Census and Statistics (DCS) (2008), the headcount index values at national level shows asteady decline since 1995/1996 and the welcome drop of poverty incidence between 2002 and2006/2007. The headcount index reported in 2006/2007 (15.2%) is nearing the 50% mark (13.1%)of 26.1% reported in 1990/1991. At the sectoral level, the 36% drop of poverty incidence from24.7% in 2002 to 15.7% in 2006/2007 in the rural sector, to which 80% of the population of SriLanka belongs, is the main contributor to the drop of national poverty. However, poverty in theestate4 sector which is about 5.5% of the population of the country has reached a new high from30% in 2002 to 32% in 2006/2007 where almost one in every three persons suffers from poverty.

A careful observation of the trend in income inequality measured by the spending units basedGini coefficient, reveals a gradual increase in inequality from 0.35 in 1973 (under the protectionistpolicy regime) to 0.46 in 1986/1987 (towards the end of the first wave of liberalisation). This trendhas however, declined slightly towards 2006/2007. Fig. 25 presents the trends in Gini coefficientsbased on spending units.

This movement indicates that the gap between the rich and the poor has widened towards late1980s under the liberalised policy regime, resulting in an increase in relative poverty. The marginaldecline in the inequality observed towards 2006/2007 may be attributed to the long run positivedistributional effects emanating from the trade liberalisation process or other factors which mayhave influenced the income transfers to rural areas. For instance, Dunham and Edwards (1997)identified migrant remittances, particularly coming from Middle East migrant workers and incomecoming from armed force personnel engaged in the North and East conflict zone of Sri Lanka, astwo other vital factors that contribute to alleviating poverty among rural households.

3. A CGE model for the Sri Lankan economy

There is a long tradition in applying CGE models to analyse policy issues in Sri Lanka (seeBandara, 1991 and Naranpanawa, 2005 for details). However, none of the studies have carried out

4 Plantation areas, which are more than 20 acres in extent and having not less than 10 residential labourers, are consideredas estate sector.

5 Source: Central Bank of Sri Lanka (2010). Annual Report 2009, Special Statistical Appendix, Colombo.

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Diagram 1. Major components and the linking process of the SLGEM–P model with income distribution functions.

a comprehensive CGE modelling to capture the trade – poverty link. In this study, a comparativestatic multi-sectoral SAM based CGE model (Sri Lankan Poverty-Focussed Computable GeneralEquilibrium Model – SLPFCGEM) developed in Naranpanawa (2005) by adopting the approachproposed by Decaluwe, Patry, & Savard (1998) is used to capture the link between trade reformsand absolute poverty within the Sri Lankan context. Empirically estimated income distributionfunctions by Naranpanawa (2005) have been linked to the SAM based CGE model using the ‘topdown’ approach to estimate absolute and relative poverty (see Diagram 1 for an illustration of thelinking process).

The theoretical structure of the core model closely follows the ORANI model (Dixon,Parmenter, Sutton, & Vincent, 1982) with an extension to incorporate the social accountingmatrix (SAM) following Horridge et al. (1995). A SAM constructed by Naranpanawa (2005)for the year 1995 was used as the database in the study. There are five household groups in themodel, namely, urban low-income households, rural low-income households, estate low-incomehouseholds, urban high-income households and rural high-income households, based on theirgeographical location and income levels. For the details of full model see Naranpanawa (2005).

In this study, an attempt was made to measure income poverty within the five householdgroups. As general equilibrium framework permits one to endogenise prices, the change in mon-etary poverty line during a simulation can be endogenised within the CGE model. A recentdevelopment introduced by Decaluwe et al. (2001) demonstrates that a basket of commodities(volume) which reflects the basic needs of average households can be defined within the CGEmodel. The percentage change in prices of these commodities in turn provides the percentagechange in monetary poverty line for different household groups. This study adopts a variant ofthe above approach while keeping the conceptual basis intact.6

A majority of studies which adopt CGE models in analyzing income distribution or povertyrely on either a uniform distribution or few other popular distribution functions such as lognormalor Pareto in explaining the distribution of income of different groups. As Boccanfuso, Decaluwe,& Savard (2003) emphasised, within the context of CGE models, previous studies have paid littleattention in justifying the selection of a particular functional form for the income distribution.Moreover, they suggested that a single functional form may not always be suitable for all house-hold groups and the best fit functional forms should be selected by giving due considerations tocharacteristics of sample and subgroups.

Naranpanawa (2005) has empirically estimated income distribution functional forms for vari-ous household income groups in Sri Lanka. In that study, Naranpanawa has fitted nine functional

6 For a detailed discussion of the method see Naranpanawa (2005).

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forms7 for the five household groups described in this CGE model and ranked beta general distribu-tion as the best fit functional form in describing the income distribution for low-income householdgroups. Hence, in this analysis, we link the CGE model with those empirically estimated betaincome distribution functional forms to estimate poverty measures.

In order to compare the pre- and post-simulation absolute poverty, a range of poverty mea-surements is considered. The main measurements are the most popular money metric povertyindices of Foster, Greer and Thorbecke (FGT) (Foster, Greer, & Thorbecke, 1984). To verify therobustness of the poverty indicators, a range of other poverty measures were computed using thepercentage change in income and the poverty line generated by the CGE model, i.e., Watts index,single parameter Gini index, Sen index and Clark, Hemming and Ulph’s (CHU) index.8 Similarly,a range of inequality measures was also estimated to test the robustness of inequality indicators.Inequality measures such as the Atkinson index, S-Gini index, Atkinson–Gini index and Entropyindex were estimated at household level for the aggregated sample as well as for the aggregatedlow-income groups separately.

4. Effects of trade policy changes: simulation results

Simulation experiments were carried out to identify the short run and long run impact ofthe tariff cuts on macro-variables, industry level variables and the household level absolute andrelative poverty. Three simulation experiments were carried out with the intention to quantify thedirection and the magnitude of the short run and long run impacts of the following tariff reforms(namely, a 100% reduction in prevailing tariffs in the manufacturing industries; a 100% reductionin prevailing tariffs in the agricultural industries and a 100% reduction in prevailing tariffs acrossthe board).

These experiments were carried out within two different macro-environments (or closures),representing the short run and long run effects. In the short run closure,9 all sectoral capitalis exogenised and as we assume slack labour market, the total employment is endogenised.Furthermore, balance of trade, sectoral rates of return, and other variables are also consideredendogenous.

In the long run closure, we assume full employment, thus the aggregate employment is exo-genised and allow real wages to be determined within the model. Similarly, we allow sectoralcapital to be mobile thus allowing sectoral rate of return to be exogenised. In both scenarios thenominal exchange rate, which is exogenous, is considered as the numeraire. The CGE model wassolved using the GEMPACK software suite (Harrison & Pearson, 1998).

7 Such as beta general, lognormal, chi-square, Pareto, exponential, inverse Gaussian, triangular, uniform and Pearsontype 5.

8 These measurements were computed using the DAD/distributional analysis software (Duclos & Araar, 2002) specifi-cally developed for poverty and inequality estimations.

9 Following variable are assumed to be exogenous: agricultural land, all technological change, real wages, real investmentexpenditure, real private consumption, other real demands, demand for inventories by commodity, demand for traditionalexports, demand for non-traditional exports, all sales tax rates except export tax shifters and commodity specific shifters,foreign prices of imports, number of households and their consumption preferences, real unit cost of ‘other cost tickets’(production subsidies etc.) and all shift variables for the determination of sectoral investment.

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Table 1Projections of percentage change in macro-variables under different policy experiments.

Macro-variable Simulation 1100% cut inprevailing tariffsin manufacturingindustries

Simulation 2100% cut inprevailing tariffsin agriculturalindustries

Simulation 3100% cut inprevailing tariffsin all sectors

SR LR SR LR SR LR

Aggregate employment 1.66 – 0.3 – 1.98 –Real gross domestic product 0.56 5.66 0.1 0.01 0.66 5.7(Balance of trade)/GDP 0.003 0.06 0.001 0.000 0.004 0.06Consumer price index −1.91 −1.84 −0.5 −0.43 −2.39 −2.26Poverty line −1.37 −1.41 −0.56 −0.52 −1.91 −1.92Import volume index, CIF weights 4.21 18.27 0.2 0.22 4.43 18.67Export volume index 7.3 42.82 0.52 0.33 7.86 43.41

SR = short run effects and LR = long run effects.

4.1. Macroeconomic and industry effects

The percentage change results of important macro-variables and the poverty line over the baseyear values for above simulation experiments are summarised in Table 1.

All three simulation results yield a positive increase in real GDP and aggregate employment10

which suggest that trade liberalisation stimulates growth as found in many empirical studies in theliterature. However, it can be seen that the effects are more pronounced in the long run comparedto that in the short run. The tariff reduction increases the output of industries which are heavilydependant on imported inputs thus adjusting the relative prices favourable to those industries.This would in turn attract resources from other unfavourable sectors and grow even further in thelong run generating higher GDP. Moreover, the results indicate that, both in the short run and longrun, trade liberalisation in the manufacturing industries stimulates the economy more positivelythan that in the agricultural industries. This suggests that agricultural trade liberalisation play aless important role in achieving growth objectives in Sri Lanka.

In all three simulations, balance of trade as a proportion of GDP has improved marginally.Though trade liberalisation mainly stimulated exports, it promoted imports as well. Interestingly,it is revealed that trade liberalisation reduces inflation in the economy which could be observed bythe percentage change of consumer price index as well as that of the poverty line. Tariff cut leadsto a reduction in prices of imported inputs and that may well reflect in certain output prices. Whenthe prices of basic needs commodities, especially the manufactured food products, petroleumproducts, garments and other agricultural imports, are reduced due to tariff cuts, money metricpoverty line shifts to the left thus making the basic needs commodities cheaper in the market.

Under the second simulation, which implemented a 100% tariff cut in the agricultural industries,a majority of industries were unaffected or marginally benefited while import-competing domesticagricultural industries (mainly rural agriculture) including paddy, other agriculture and millingindustries were contracted both in the short run and long run. These results are consistent withthe proposition that previously heavily protected industries suffer when trade is liberalised.

10 Aggregate employment is fixed in the long run.

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Table 2Projections of percentage change in aggregate employment (persons) among different occupational groups.

Occupational group Simulation 1100% cut inprevailing tariffsin manufacturingindustries

Simulation 2100% cut inprevailing tariffsin agriculturalindustries

Simulation 3100% cut inprevailing tariffsin all sectors

SR LR SR LR SR LR

Professional, technical andrelated workers

0.51 −0.31 0.06 −0.04 0.60 −0.36

Administrative andmanagerial workers

0.75 1.11 0.13 −0.02 0.93 1.08

Clerical and related workers 0.34 −1.64 0.30 0.14 0.69 −1.46Sales workers 1.68 −0.06 0.33 −0.01 2.03 −0.06Service workers 0.75 −1.15 0.13 −0.01 0.93 −1.15Agricultural, animal

husbandry, fisheries andforestry workers

1.56 −0.34 0.12 −0.02 1.69 −0.36

Production and relatedtransport equipmentoperators and labourers

8.22 14.60 0.30 0.04 8.54 14.28

Other workers 0.59 −3.09 0.09 −0.05 0.71 −3.20

SR = short run effects and LR = long run effects.

The third simulation of a 100% across the board tariff cut (both in the short run and thelong run) revealed more or less similar results as the first simulation. However, previously pro-tected paddy sector and the milling sector have shown a contraction. The short run and longrun impact on a number of individual industries’ output level and employment are presented inTable A1 in Appendix A.

4.2. Household level effects

The impact of trade policy shocks at the household level could be traced from the CGE results,whereas the effects on poverty and inequality could be tracked from income distribution models.The CGE model captures the changes that occur among the occupational labour categories throughdifferential impacts observed at industry level and associated derived demand for occupationallabour categories. Similarly, the household income flows are determined by taking into accountthe changes of wage income, government transfers, other transfers, gross operating surplus andother sources of household income. Tables 2 and 3 present the projection of aggregate employment(persons) among different occupational groups and post-tax income among different householdgroups, respectively.

As can be seen from Table 2, the short run results of simulations 1 and 3 indicate an overallincrease in derived demand for occupational labour categories following industry expansion dueto a slack labour market. However, it reveals an increase of 8.22% and 8.54% of production andrelated transport equipment operators and labourers who form the main labour category in themanufactured product industries under simulations 1 and 3, respectively. The long run resultsreveal that unskilled labour has markedly benefited. However, unskilled agricultural labour has

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Table 3Projections of percentage change in post-tax income among different household groups.

Household group

Simulation 1100% cut inprevailing tariffsin manufacturingindustries

Simulation 2100% cut inprevailing tariffsin agriculturalindustries

Simulation 3100% cut inprevailing tariffsin all sectors

SR LR SR LR SR LR

Urban low-income households −0.71 11.22 −0.25 −0.03 −0.94 11.30Rural low-income households −1.87 3.35 −0.32 −0.27 −2.16 3.13Estate low-income households −0.69 8.96 −0.25 −0.08 −0.92 8.94Urban high-income households 1.25 5.21 −0.05 −0.06 1.26 5.20Rural high-income households 0.79 6.24 −0.07 −0.05 0.76 6.23

SR = short run effects and LR = long run effects.

contracted due to the migration of labour from backward domestic agricultural industries intoexpanded manufacturing industries. In contrast, the trade reforms in agricultural imports (simu-lation 2) reveal an insignificant increase in derived demand for occupational labour categories asthis shock yields a relatively low stimulus at the industry level.

The industry expansions and contractions affect the derived demand for primary factor inputsso does the factor income. As can be seen from Table 3, the short run results of simulations 1 and3 revealed contraction of nominal post-tax income in all low-income household groups. It furthershows a comparatively marked decline in income of rural low-income households. The main rea-son for this decline in income is the reduction of government transfer payments to low-incomehouseholds following the reduction of government revenue as a result of tariff cut. Although, theimport tariff rates of manufactured products were moderate in size, the total revenue loss wassignificant due to high import volume. Interestingly, the rural low-income households, who suf-fered most under these shocks, receive approximately 83% of government transfer payments. Thehigh-income households, however, have benefited as the expansion of manufacturing industrieshas contributed in expanding the gross operating surplus of those industries, and more impor-tantly, service industries such as retail and wholesale trade and transport which accounted fora sizeable portion of gross operating surplus, form a significant income source of high-incomegroups. In contrast, long run effects of these shocks benefited low-income groups, particularlythe urban low-income households, as they form the main labour component of the manufacturedproduct industries. However, it is revealed that the high-income groups too receive moderatelyhigh benefits in terms of income mainly due to expansion of service industries as discussed underthe short run scenario.

Simulation 2 revealed rather neutral effect on income with respect to high-income groups inboth the short run and long run. This may be due to less involvement of high-income groupsin the activities of heavily protected domestic agricultural industries. In contrast to the previoussimulations, in both the short run and long run, low-income groups experienced a marginal negativeeffect, suggesting that agricultural trade reforms are moderately income neutral compared to thatof manufacturing industries.

As can be seen from Table 1, we observe a decline in post-simulation poverty line under all threesimulation experiments. When the prices of basic needs commodities, especially manufactured

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food products, petroleum products, garments and other agricultural imports are reduced due totariff cuts, money metric poverty line declines thus making basic needs commodities cheaper inthe market. With the changes in post-tax nominal household income distribution and the newmoney metric poverty line, the beta income distribution models for different household groupswould generate income poverty measures based on the FGT P� indices. The FGT P� results of allthree simulations under short run and long run cases are presented in Tables 4 and 5, respectively.

The FGT indicators are estimated and compared with the base case and their percentage changesfrom the base case are reported in the tables. Thus, negative change of FGT index denotes areduction in absolute poverty. Furthermore, three types FGT indices that capture headcount ratio,poverty gap and poverty severity are reported. As can be seen from Table 4, in the short run case,under the simulations 1 and 3, the headcount ratio, poverty gap and the poverty severity havedecreased among all household groups (except the rural low-income households). As discussedearlier, reduction in government transfers following government revenue loss due to tariff cuts,particularly import tariffs of manufactured products, seems to push the rural low-income house-holds into poverty in the short run. In contrast, under simulation 2, all indicators show a reductionwithin all groups suggesting that agricultural trade reforms are comparatively poverty neutral inthe short run.

From the results presented in Table 5, in the long run, under all three simulations, all householdgroups show a reduction in absolute poverty in terms of headcount ratio, poverty gap and thepoverty severity. However, it is striking to note that simulations 1 and 3 substantially reducepoverty among all household groups. Trade liberalisation in manufacturing industries remarkablystimulated the manufactured product industries and attracted labour from the low productivesectors, which would ultimately improve the nominal income of a sizeable portion of low-incomehouseholds, particularly the urban low-income group. This in turn, along with the decline inpoverty line has resulted in a substantial drop in absolute poverty among all income groups inthe long run. In contrast, agricultural trade liberalisation (simulation 2) demonstrates a marginalimprovement in absolute poverty in all household groups suggesting that trade reform in themanufacturing industries are pro-poor than that of agricultural industries, at least in the longrun.

In order to evaluate the robustness of the FGT poverty measures, a range of popular povertymeasures i.e., Watts index, S-Gini index, Sen index and CHU index, are computed based onthe changes in nominal income distributions and poverty lines generated from the CGE model.Interestingly, both the short run and long run results are consistent with the FGT measures (seeAppendix B, Table B1). Furthermore, the conclusions drawn from the poverty results generatedunder the present study are consistent with the previous CGE analysis carried out for Nepal byCockburn (2001) in which poverty reduces among urban household groups while it increases inthe rural areas.

In order to evaluate the inequality or relative poverty among total households, a range ofinequality measures, i.e., Atkinson index, S-Gini index, Atkinson–Gini index and Entropy index,are computed based on the changes in nominal income distributions generated from the CGEmodel. Interestingly, both the short run and long run results revealed an increase in inequalityestimates under all three simulations. Contrary to the Stolper–Samuelson theory, which postu-lates that most abundant factors would be benefited by trade reforms, inequality has increasedsuggesting the gap between poor and rich households has widened.

Despite the benefits accruing to the most abundant factor in Sri Lanka – labour – the increase ingross operating surplus in the light of expansion of manufacturing industries and the linked servicessectors, accrued benefits to high-income households. One possible reason for this outcome could

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Table 4FGT poverty indices (%) under different policy simulations (short run results).

Policy Experiment Urban low-income households Rural low-income households Estate low-income households

Before After % Change Before After % Change Before After % Change

Simulation 1Head count ratio (α = 0) 35.70 35.47 −0.6443 45.42 45.64 0.4844 79.17 78.75 −0.5305Poverty gap (α = 1) 17.24 17.11 −0.7541 22.23 22.35 0.5398 36.58 36.29 −0.7928Poverty severity (α = 2) 10.99 10.90 −0.8189 14.37 14.45 0.5567 21.58 21.37 −0.9731

Simulation 2Head count ratio (α = 0) 35.70 35.58 −0.3361 45.42 45.32 −0.2202 79.17 78.98 −0.2400Poverty gap (α = 1) 17.24 17.18 −0.3480 22.23 22.17 −0.2699 36.58 36.45 −0.3554

Poverty severity (α = 2) 10.99 10.95 −0.3640 14.37 14.33 −0.2784 21.58 21.49 −0.4171Simulation 3

Head count ratio (α = 0) 35.70 35.35 −0.9804 45.42 45.53 0.2422 79.17 78.56 −0.7705Poverty gap (α = 1) 17.24 17.05 −1.1021 22.23 22.29 0.2699 36.58 36.15 −1.1755Poverty severity (α = 2) 10.99 10.86 −1.1829 14.37 14.41 0.2784 21.58 21.28 −1.3902

Base poverty line = Rs. 3649.6/month/household. Percentage change in poverty line under different simulation experiments: simulation 1 = −1.37,simulation 2 = −0.56, simulation3 = −1.91.

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Table 5FGT poverty indices (%) under different policy experiments (long run results).

Policy Experiment Urban low-income households Rural low-income households Estate low-income households

Before After % Change Before After % Change Before After % Change

Simulation 1Head count ratio (α = 0) 35.70 31.66 −11.317 45.42 43.42 −4.403 79.17 72.97 −7.831Poverty gap (α = 1) 17.24 15.13 −12.239 22.23 21.16 −4.813 36.58 32.42 −11.372

Poverty severity (α = 2) 10.99 9.56 −13.012 14.37 13.64 −5.080 21.58 18.71 −13.299Simulation 2

Head count ratio (α = 0) 35.70 35.52 −0.504 45.42 45.31 −0.242 79.17 78.90 −0.341Poverty gap (α = 1) 17.24 17.14 −0.580 22.23 22.17 −0.270 36.58 36.39 −0.519Poverty severity (α = 2) 10.99 10.92 −0.637 14.37 14.33 −0.278 21.58 21.45 −0.602

Simulation 3Head count ratio (α = 0) 35.70 31.47 −11.849 45.42 43.30 −4.668 79.17 72.65 −8.235Poverty gap (α = 1) 17.24 15.03 −12.819 22.23 21.09 −5.128 36.58 32.21 −11.946Poverty severity (α = 2) 10.99 9.50 −13.558 14.37 13.60 −5.358 21.58 18.57 −13.948

Base poverty line = Rs. 3649.6/month/household. Percentage change in poverty line under different simulation experiments: simulation 1 = −1.41,simulation 2 = −0.52, simulation3 = −1.92.

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be the expansion observed in service industries such as retail and wholesale trade and transport,which generate a notable amount of gross operating surplus that form a vital source of income forhigh-income groups. This evidence on the widening of gap between the rich and the poor undertrade reforms is consistent with a previous CGE analysis carried out for Indonesia (Croser, 2002).The short run and long run estimates of inequality indicators among total households and totallow-income households are given in Table B2 of Appendix B.

5. Overall policy implications

Despite the overall gains and reduction in poverty as a result of further trade liberalisation, theresults of our policy simulations clearly suggest that the benefits of trade liberalisation are unevenacross different household groups, particularly in the short run, favouring high-income groups inthe rural and urban sectors of the economy. Furthermore, the industry results demonstrate that theimport-competing agricultural sectors which dominate the rural economy are expected to sufferfrom further liberalisation. The rural low-income group concentrated on these agricultural sectorsdominates many rural provinces in Sri Lanka including the war-affected Northern and Easternprovinces. The results also demonstrate that the reduction in poverty in the rural and estate sectorsis also slow compared with the urban sector.

Furthermore, according to a latest report prepared for the International Labour Organizationon “Sri Lanka’s working poor” (Gunatilaka, 2010), about a million employed Sri Lankans arepoor and the majority of them are employed in agricultural sectors. It is, therefore, paramount forSri Lanka to make economic policies more inclusive particularly during this post-war period inorder to maintain the stability in the economy and achieve long-term peace and reconciliation.

At present, manufacturing industries are concentrated in and around of the capital city,Colombo, in the Western province. Mostly the people involved in these industries will be benefitedfrom further liberalisation of manufacturing trade and rural and estate low-income householdswill not be benefited much from further liberalisation. This will be a policy challenge to thegovernment. As attempted previously by the Sri Lankan government in the 1990s, it is importantto establish manufacturing and services bases in regional areas in order to absorb unemployedyouth in the North and the South to avoid future arm conflicts (similar to the war in the Northbetween 1976 and 2009 and the arm rebellion in the South in the 1980s, known as “twin wars”(Abeyratne, 2004)). For this purpose a number of complementary policies are needed. One ofthe most important complementary policies is the development of infrastructure. Currently theSri Lankan government has undertaken massive infrastructure projects in the Southern provincecentred on the development of Hambanthota harbour to attract investors. This concept needs to beextended to the war-affected Northern and Eastern provinces in order to bring benefits from tradeto the minority Tamils and maintain long-term peace in Sri Lanka. Some Southern manufacturershave already volunteered to establish manufacturing units in the North to absorb unemployedyoung Tamils (Ferdiando, 2010). While exploring and finding a long-lasting political solutionto the conflict during the post-war period, it is equally important for the government to makeeconomic policies to spread economic benefits to the Northern and Eastern provinces which havebeen neglected nearly three decades due to the war. In order to encourage manufacturing firms tomove to the North and the East, the government needs to implement complementary policies suchas investing in physical infrastructure and improving the production capacity through investingin education and training.

The overall results suggest that it would be necessary to implement complementary policies thatwould ease out the adjustment costs of trade liberalisation for low-income groups in the short run as

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well as in the long run. These complementary policies should include targeted transfer paymentsto low-income groups (such as the current program of Samurdhi) as short-term measures andinvesting in physical and human capital.

6. Concluding remarks

In this study, we have used a poverty-focused CGE model to undertake a detailed Sri Lankancountry study to examine the link between trade and poverty and answer some policy questionssuch as: Will further trade liberalisation reduce poverty in Sri Lanka? Who benefits from furthertrade liberalisation? How can trade liberalisation make more inclusive in the post-conflict period?

The results suggest that in the short run, trade liberalisation of manufacturing industries tendto increase the economic growth and reduce absolute poverty in low-income household groupsexcept the rural low-income group. However, long run results indicate that trade liberalisationreduces absolute poverty in all groups. It further reveals that, in the long run, trade liberalisation ofmanufacturing industries are pro-poor than that of agricultural industries creating uneven benefitsacross different household groups. The overall results suggest that trade reforms may widen theincome gap between rich and poor thus promoting relative poverty. This may warrant implement-ing complementary policies (such as targeted transfers) that would ease out the adjustment costsof trade liberalisation for low-income groups in the short run and investing physical infrastructureand human capital in the long run.

Acknowledgement

The principle author would like to acknowledge the financial assistance provided by GriffithUniversity for this research.

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328–346Appendix A.Table A1 Projections of percentage change in industry effects under different policy simulations.

Simulation 1 Simulation 2 Simulation 3

Industry 100% cut in prevailing tariffs in manufacturing industries 100% cut in prevailing tariffs in agricultural industries 100% cut in prevailing tariffs in all industries

Output level Employment Output level Employment Output level Employment

Short run Long run Short run Long run Short run Long run Short run Long run Short run Long run Short run Long run

Tea growing 0.78 0.31 1.48 0.3 0.18 0.13 0.34 0.22 0.94 0.45 1.8 0.53Rubber growing 0.43 0.4 1.27 0.53 0.13 0.07 0.37 0.18 0.55 0.45 1.63 0.66Coconut growing 0.26 −0.4 0.79 −1.26 0.09 0.06 0.28 0.15 0.35 −0.37 1.07 −1.17Paddy 0.06 −0.25 0.24 −1.45 −0.15 −0.2 −0.59 −0.43 −0.08 −0.42 −0.34 −1.81Minor export crops 0.05 0.48 1.08 −0.46 0.01 0.01 0.27 0.09 0.07 0.47 1.35 −0.39Tobacco 0.23 −0.81 1.42 −6.29 1.49 4.21 9.59 12.05 1.7 3.4 11.07 4.89Other agriculture 0.07 −0.02 0.7 −1.43 −0.02 −0.13 −0.19 −0.32 0.06 −0.15 0.54 −1.73Livestock 0.24 1.02 2.17 3.07 0.03 0.04 0.28 0.21 0.27 1 2.45 3.05Firewood 0.02 −0.56 0.14 −4.51 0.01 0.01 0.08 0.04 0.04 −0.55 0.23 −4.49Forestry 0.2 1.66 0.81 −1.01 0.06 −0.03 0.23 −0.05 0.26 1.58 1.04 −1.22Fisheries 0.09 0.56 1.15 0.78 0.02 0.02 0.3 0.13 0.11 0.57 1.45 0.89Mining and quarrying 0.44 4.69 1.49 7.15 0.11 −0.02 0.35 −0.05 0.55 4.87 1.85 7.52Tea processing 1.07 0.88 5.29 −0.75 0.18 0.13 0.88 0.2 1.25 1.02 6.21 −0.53Rubber processing 0.81 −1.18 3.05 −2.78 0.13 0.05 0.49 0.12 0.94 −1.13 3.56 −2.66Coconut processing 0.98 2.11 2.39 0.88 0.21 0.14 0.49 0.19 1.18 2.17 2.88 1Milling 0.06 −0.25 0.38 −10.2 −0.15 −0.2 −0.94 −0.22 −0.08 −0.42 −0.53 −10.4Food, beverages and other −0.04 −1.94 −0.46 −8.15 0.06 0.41 0.64 0.39 0.02 −1.49 0.2 −7.78Textiles 1.55 8.32 4.44 3.42 0.16 0.15 0.46 0.13 1.71 8.19 4.91 3.24Garments 1.65 7.75 4.94 2.95 0.14 0.01 0.4 −0.01 1.78 7.37 5.35 2.53Wood and wood products 0.15 3.36 0.87 −3.26 0.05 −0.05 0.32 −0.07 0.2 3.23 1.19 −3.45Paper and paper products 18.73 63.43 85.77 56.73 0.07 −0.05 0.21 −0.07 18.81 63.16 86.35 56.4Chemicals and fertilisers 3.2 11.08 21.72 6.63 1.12 2.41 6.81 2.4 4.18 15.75 29.97 11.09Petroleum 1.87 145.2 37.54 121.2 0.03 0 0.37 −0.01 1.89 143.7 37.98 119.7Plastic and rubber products 0.54 30.01 4.42 24.39 0.05 0.41 0.37 0.4 0.58 30.2 4.78 24.54Non-metallic and other mineral products 0.83 9.22 4.36 3.02 0.07 −0.02 0.33 −0.04 0.89 8.87 4.72 2.62Basic metal products 39.26 60.24 39.26 60.24 0.07 −0.01 0.07 −0.01 39.39 59.96 39.39 59.96Fabricated metal products 2.3 88.74 12.66 78.41 0.07 0.04 0.33 0.02 2.36 89.37 13.03 78.92Other manufacturing 0.88 19.22 3.43 10.72 0.15 0.62 0.57 0.6 1.51 28.03 5.99 18.82Electricity, gas and water 0.2 4.71 0.97 0 0.03 0.01 0.16 0 0.23 4.71 1.14 −0.02Construction −0.03 0.16 −0.04 −1.97 −0.01 −0.01 −0.01 −0.02 −0.04 0.13 −0.06 −2.02Wholesale and retail trade 0.62 13.88 1.87 2.93 0.13 0.01 0.37 0 0.75 13.92 2.25 2.94Hotels and restaurants 0.53 0.32 0.82 −3.21 0.08 0.06 0.12 0.06 0.62 0.39 0.95 −3.14Transport 0.53 7.91 1.83 3.48 0.1 −0.03 0.33 −0.04 0.63 7.81 2.16 3.35Post and communication −0.24 1.85 −0.24 1.85 0.04 −0.09 0.04 −0.09 −0.2 1.72 −0.2 1.72Banking insurance and real estate 2.49 −4.99 3.29 −7.06 0.64 0 0.85 0 3.14 −5.06 4.14 −7.13Ownership of dwellings 0 −0.57 0 0 0 −0.13 0 0 0 −0.73 0 0Public administration and defence 0.75 −0.55 0.75 −0.55 0.13 0 0.13 0 0.93 −0.55 0.93 −0.55Other personal services 0.23 −0.28 0.27 −1.41 0 −0.11 0 −0.11 0.23 −0.4 0.27 −1.54

Notes: projections are in percentage changes from the base solution.

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Appendix B.

Table B1 Poverty indices (other than FGT) under different policy simulations (short run results).

Urban low-income households Rural low-income households Estate income households

Policy experiment Before After % Change Before After % Change Before After % Change

Simulation 1Watts index 0.2908 0.2881 −0.92847 0.3854 0.3880 0.674624 0.6102 0.6044 −0.95051S-Gini index (ρ = 2.0) 0.2905 0.2882 −0.79174 0.3649 0.3669 0.548095 0.5261 0.5230 −0.58924Sen index 0.2281 0.2252 −1.27137 0.2982 0.3009 0.905433 0.4844 0.4786 −1.19736CHU index (ε = 0.5) 47.426 46.314 −2.34471 60.535 60.836 0.497233 100.594 98.566 −2.01602

Simulation 2Watts index 0.2908 0.2895 −0.44704 0.3854 0.3842 −0.31136 0.6102 0.6076 −0.42609S-Gini index (ρ = 2.0) 0.2905 0.2894 −0.37866 0.3649 0.3639 −0.27405 0.5261 0.5247 −0.26611Sen index 0.2281 0.2275 −0.26304 0.2982 0.2963 −0.63716 0.4844 0.4833 −0.22709

CHU index (ε = 0.5) 47.426 47.290 −0.28676 60.535 59.759 −1.2819 100.594 100.306 −0.2863Simulation 3

Watts index 0.2908 0.2869 −1.34113 0.3854 0.3867 0.337312 0.6102 0.6018 −1.3766S-Gini index (ρ = 2.0) 0.2905 0.2871 −1.1704 0.3649 0.3659 0.274048 0.5261 0.5216 −0.85535Sen index 0.2281 0.2247 −1.49057 0.2982 0.2996 0.469484 0.4844 0.4774 −1.44509CHU index (ε = 0.5) 47.426 46.183 −2.62093 60.535 60.307 −0.37664 100.594 98.289 −2.29139

Base poverty line = Rs. 3649.6/month/household. Percentage change in poverty line under different simulation experiments: simulation 1 = −1.37, simulation 2 = −0.56,simulation 3 = −1.91.

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Table B2 Inequality measures under different policy simulations.

Policy Experiment Total households Total low-income households

Short run Long run Short run Long run

Before After % Change Before After % Change Before After % Change Before After % Change

Simulation 1Atkinson index (ε = 0.5) 0.2281 0.2317 1.57826 0.2281 0.2287 0.26304 0.1299 0.1311 0.923788 0.1299 0.1297 −0.15396S-Gini index (ρ = 2.0) 0.5306 0.5331 0.47116 0.5306 0.5307 0.01885 0.3889 0.3914 0.642839 0.3889 0.3925 0.925688Atkinson–Gini index (ε = 0.5, ρ = 2.0) 0.6101 0.6185 1.37682 0.6101 0.6107 0.09834 0.4689 0.4707 0.383877 0.4689 0.4695 0.127959Entropy index (θ = l.0) 0.4900 0.4903 0.06122 0.4900 0.4911 0.22449 0.2481 0.2512 1.249496 0.2481 0.2503 0.886739

Simulation 2Atkinson index (ε = 0.5) 0.2281 0.2297 0.70145 0.2281 0.2365 3.6826 0.1299 0.1259 −3.07929 0.1299 0.1309 0.769823S-Gini index (ρ = 2.0) 0.5306 0.5312 0.11308 0.5306 0.5383 1.45119 0.3889 0.3870 −0.48856 0.3889 0.3932 1.105683Atkinson–Gini index (ε = 0.5, ρ = 2.0) 0.6101 0.6148 0.77037 0.6101 0.6236 2.21275 0.4689 0.4591 −2.09 0.4689 0.4710 0.447857Entropy index (θ = l.0) 0.4900 0.4878 −0.44898 0.4900 0.5021 2.46939 0.2481 0.2459 −0.88674 0.2481 0.2525 1.773478

Simulation 3Atkinson index (ε = 0.5) 0.2281 0.2368 3.81412 0.2281 0.2361 3.50723 0.1299 0.1217 −6.31255 0.1299 0.1226 −5.61971S-Gini index (ρ = 2.0) 0.5306 0.5406 1.88466 0.5306 0.5403 1.82812 0.3889 0.3807 −2.10851 0.3889 0.3787 −2.62278Atkinson–Gini index (ε = 0.5, ρ = 2.0) 0.6101 0.6245 2.36027 0.6101 0.6267 2.72087 0.4689 0.4521 −3.58285 0.4689 0.4536 −3.26296Entropy index (θ = l.0) 0.4900 0.5043 2.91837 0.4900 0.5033 2.71429 0.2481 0.2365 −4.67553 0.2481 0.2357 −4.99798

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