metadata: authors: 0: author: Maliszewska,Maryla
1: author: Osorio-Rodarte,Israel
2: author: Nichanametla Ramasubbaiah,Rakesh Gupta
count: World,Sri Lanka,China,India
docna: 0: docna: Ex-Ante Evaluation of Sub-National Labor
            Market Impacts of Trade Reforms
docty: Policy Research Working Paper
subtopic: International Trade and Trade Rules,Food Security,Food & Beverage Industry,Plastics & Rubber Industry,Textiles, Apparel & Leather Industry,Pulp & Paper Industry,Common Carriers Industry,Construction Industry,Business Cycles and Stabilization Policies,General Manufacturing,Labor Markets,Gender and Development
teratopic: Macroeconomics and Economic Growth,Industry,Agriculture,Social Protections and Labor,International Economics and Trade,Gender
abstracts: cdata!: A macro-micro simulation framework that
            links a computable general equilibrium model with the
            survey-based global income distribution dynamics model can
            be used to assess the economic and distributional effects of
            macroeconomic shocks and policies. The methodology is used
            to assess the economic and subnational labor market impacts
            of a series of stylized trade policy options for the Sri
            Lankan economy over a 10-year time period. The analysis
            focuses on the impact of unilateral para-tariff
            liberalization, free-trade agreements with China or India,
            and a full-reform scenario. The simulation results show that
            more ambitious trade reform can result in larger gains in
            gross domestic product, poverty reduction, and exports,
            particularly in sectors employing a higher proportion of
            women. In the absence of additional policies, growth is not
            equally distributed. In all the scenarios in which the Sri
            Lankan economy grows, the distribution of gains is
            regressive. Increasing labor demand for skilled workers
            translates into a larger skilled wage premium -- by as much
            as 1.1 percent with respect to the baseline. Implementation
            of full trade reform accelerates the concentration of
            economic activity in the western regions of Colombo,
            Gampaha, and Kalutara. Net employment gains in the western
            regions would increase from 111,000 to 136,000 in the full
            reform scenario by 2028 and with respect to baseline conditions.
geo_reg_key: 517198,119225,517191,517212
repnme: repnme: Ex-Ante Evaluation of Sub-National Labor
            Market Impacts of Trade Reforms
display_title: Ex-Ante Evaluation of Sub-National
            Labor Market Impacts of Trade Reforms
listing_relative_url: /research/2020/11/32602590/ex-ante-evaluation-sub-national-labor-market-impacts-trade-reforms
topicv3: Agriculture and Food Security m1326205 435,Manufacturing Sectors m1326521 1281,Trade m1326924 698,Gender m1327621 615,Jobs m1503931 2670
publishtoextweb_dt: 2020-11-23T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Industry,Agriculture,Social Protections and Labor,International Economics and Trade,Gender
origu: EFI-MTI-TIC-Trade and RI (ETIRI)
no_of_pages: 57
author_keys: 0: author_key: 545393
1: author_key: 903799
2: author_key: 1538071
model_extractions: Policy Research Working Paper 9478 Ex-Ante Evaluation of Sub-National Labor Market Impacts of Trade Reforms _Maryla Maliszewska Israel Osorio-Rodarte Rakesh Gupta_ Macroeconomics, Trade and Investment Global Practice November 2020
Policy Research Working Paper 9478

# **Abstract**

A macro-micro simulation framework that links a computable general equilibrium model with the survey-based global income distribution dynamics model can be used to assess the economic and distributional effects of macroeconomic shocks and policies. The methodology is used to assess the economic and subnational labor market impacts of a series of stylized trade policy options for the Sri Lankan economy over a 10-year time period. The analysis focuses on the impact of unilateral para-tariff liberalization, freetrade agreements with China or India, and a full-reform scenario. The simulation results show that more ambitious trade reform can result in larger gains in gross domestic product, poverty reduction, and exports, particularly in sectors employing a higher proportion of women. In the absence of additional policies, growth is not equally distributed. In all the scenarios in which the Sri Lankan economy grows, the distribution of gains is regressive. Increasing labor demand for skilled workers translates into a larger skilled wage premium—by as much as 1.1 percent with respect to the baseline. Implementation of full trade reform accelerates the concentration of economic activity in the western regions of Colombo, Gampaha, and Kalutara. Net employment gains in the western regions would increase from 111,000 to 136,000 in the full reform scenario by 2028 and with respect to baseline conditions.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mmaliszewska@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Ex-Ante Evaluation of Sub-National Labor Market Impacts of Trade Reforms

Maryla Maliszewska, Israel Osorio-Rodarte and Rakesh Gupta<sup>1,2</sup>

**Keywords** : economic growth of open economies; measurement and analysis of poverty; computable general equilibrium modeling; distributional impacts of trade; trade agreements; Sri Lanka

**JEL Classification** : F16, I32, F61, F63, O53, C68

> 1 Maryla Maliszewska, Israel Osorio-Rodarte, and Rakesh Gupta: The Word Bank. The paper is part of the background analysis for the World Bank Report, _Distributional Impacts of Trade (2020)_ . It was generated under the guidance of Caroline Freund, Antonio Nucifora and Carolina Sanchez-Paramo. We received comments and suggestions from Erhan Artuc, Paulo Bastos, Paul Brenton, Maurizio Bussolo, Erik William Churchill, Jakob Engel, Carmen Estrades, Michael Ferrantino, Sanjay Kathuria, Deeksha Kokas,  Tae Hyun Lee, Gladys Lopez-Acevedo, Mariem Malouche, Ambar Narayan, David Newhouse, Bob Rijkers, Raymond Robertson, Carlos Rodriguez-Castelan, Sebastian Saez, Hans Timmer and participants of the various World Bank seminars and trainings where this paper was presented.

> 2 This paper is a product of the staff of the International Bank for Reconstruction and Development/the World Bank. The findings, interpretations, and conclusions expressed in this paper do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. This material should not be reproduced or distributed without the World Bank's prior consent.
# 1. Introduction

**Trade reform inevitably produces winners and losers** . Most economists accept that, in the long run, economies open to international trade perform better than do closed ones, and that relatively open trade policies contribute to sustained economic development. The long-term gains from trade openness have been discussed extensively.<sup>3</sup> By providing incentives to reallocate resources, openness to trade aligns a country’s economic activity with its comparative advantage. Of concern, however, is that trade liberalization could harm disadvantaged groups and that even in the long run, successful open economies may leave some vulnerable people permanently behind.

**International trade operates in complex economic and political contexts** . By creating winners, losers and a potentially large pool of resources to reallocate, trade reform has inherent political economy repercussions. During recent decades, the regional integration agenda was motivated by the need to modernize economies and integrate into global value chains as well as a tool to dismantle monopolies, rentseeking protectionist schemes, or obsolete labor arrangements. The political economy consequences of trade were recognized early in the academic literature (Hillman 1982; Cassing and Hillman 1985; Baldwin 1989; Rodrik 1995). It has been understood that assessing the economic and distributional consequences of trade policies is key in designing adequate policy implementation and balancing possible options for advancing reform.

**The assessment of the distributional aspects of trade reform requires a systematic approach** . Studying the poverty and distributional effects of trade reform or large trade shocks requires a methodology that accounts on the one hand for the aggregate nature of the policy or shock being studied and, on the other hand, the heterogeneity of the overall effects among individuals or households. The traditional randomized trial offers only limited scope for measuring the impact. As noted by Bourguignon and Bussolo (2013) in the context of macroeconomic policies, ex-post evaluation methodologies based on the comparison between individuals exposed and not exposed to the policy or shock are not applicable since, by definition, all individuals are affected by a policy with macroeconomic dimensions. The evaluation methodology thus needs to rely not only on micro data but also on the creation of a macro counterfactual within a general equilibrium setting.

**This paper discusses a simulation framework used for the ex-ante assessment of the distributional aspects of trade** . The methodology is applied to Sri Lanka for the evaluation of relevant trade policy simulations, such as potential impacts of the unilateral tariff and para-tariff liberalization and implementing a series of free-trade agreements (FTAs) with China and India. This methodology combines a computable general equilibrium (CGE) model linked to a microsimulation in a top-down approach. This modeling framework allows for the incorporation of the complex interactions across agents (consumers, producers and government) at the country, sector and factor levels. It captures changes in comparative advantage and trade flows following trade liberalization and shifts in demand as income rises. The analysis includes 17 sectors and 35 trading partners (see Table A1) and simulates the impacts of policy changes up to 2028.

**This paper is organized as follows** . Section 2 presents the methodology used in this paper for the exante evaluation of distributional impacts of trade. Section 3 elaborates on a set of policy-relevant scenarios for the case of Sri Lanka. Section 5 presents the simulation results at the macro, sectoral, and district levels. Lastly, section 6 concludes with recommendations for further development.

> 3 Initially, the openness to trade discussion circled around three pillars: does trade liberalization stimulate growth and relieve poverty? does it boost productivity? are open economies less stable? An early review of the literature is presented in Winters, McCulloch, and McKay (2004). For a recent review of the literature, see (Kokas and Engel, 2020.).

2
2. Macro-micro methodologies for ex-ante evaluation of the distributional impacts of trade

**The importance of assessing the distributional gains from trade has been long recognized in economic thinking.** The origin of dynamic microsimulation can be traced back to the 1950s seminal work of Orcutt (1957) whose contributions aimed at overcoming the limitations of models available at that time. Orcutt observed that those earlier models could be used to predict the aggregate impact but could not describe the distributional impacts of policy reforms nor the effects on inequality of long-term policy options or shocks. Data availability and modeling have significantly advanced since then, yet dynamic microsimulations remain the main tool to study distributional change and to provide the unique perspective of projecting samples of population forward in time.

**Traditionally, tools for policy analysis concerned with distributional issues tend to be either exclusively micro or macro** . Microsimulation models based on household samples are used for studying reforms of a variety of policy choices, such as tax-benefit systems, impact of delivering public goods like education or health care, or changes in labor market regulations. Macro modeling, on the other hand, is typically used to measure the impact of reforms in tax, trade, finance or monetary policies on the sectoral structure of the economy, its level of employment and wages. From the aggregate information, it is possible to inform which groups in the population will benefit or lose from the reform being analyzed.

**Aggregate outcomes from macro models are insufficient to assess how individual households might be affected by the policy being analyzed** . Likewise, seemingly micro-oriented policies like a reform in cash transfers or in the pension system may have important macro effects through labor supply and savings, in turn, these macro effects of micro-oriented policies will impact wages, employment and returns to wealth affecting the income distribution within a population. Hence, the need to combine macro and micro approaches to policy analysis is obvious.

**Several approaches have been proposed to integrate macro-micro frameworks that allow for the simultaneous study of the distributional and macro impact of policy reforms** (Bourguignon and Bussolo 2013). The usual problem of data availability and quality is, in the case of a macro-micro model, compounded because data from national accounts and from household surveys – the two most common sources of macro and micro data – need to be reconciled, let alone the economic treatment of their respective sources. In this context, the modeling framework used in this paper represents a departure from the static simple top-down approach. It incorporates a macro-micro framework operating in a dynamic setting.

**This paper discusses the CGE-GIDD macro-micro methodology that can be used for the ex-ante assessment of trade policy and shocks.** This paper uses the Global Income Distribution Dynamics (GIDD) microsimulation framework in combination with the global computable general equilibrium (CGE) model called LINKAGE/ENVISAGE. CGE-GIDD is a “top-down” macro-micro simulation framework that exploits heterogeneity observed in household surveys to distribute macroeconomic shocks. CGE-GIDD was developed by the World Bank (Bussolo, De Hoyos, and Medvedev 2010), inspired by previous efforts involving simulation exercises (Bourguignon, Ferreira, and Leite 2008; Bourguignon, Bussolo, and Pereira da Silva 2008; Davies 2009). The full technical documentation of the LINKAGE/ENVISAGE model is provided by van der Mensbrugghe (2013; 2020); the following sub-section briefly describes the main structure of the models.

3
<!-- Start of picture text --> Population Projection by Education Projection<br>Age Groups (Semi-exogenous)<br>(Exogenous)<br>Household Survey<br>(New sampling weights<br>by age and education)<br>CGE<br>(Growth, new wages,<br>sectoral reallocation)<br>Simulated Distribution<br><!-- End of picture text -->
**The CGE model relies on neoclassical economic theory to model macroeconomic behavior.** Production is modeled using nested Constant Elasticity of Substitution (CES) functions that combine at various levels, with different substitution elasticities, intermediates and primary factors.<sup>6</sup> Households’ consumption demand is derived from the maximization of household-specific utility function following the Linear Expenditure System (LES). International trade is modeled assuming imperfect substitution among goods originating in different geographical areas. Demand for foreign good result from a CES aggregation function of domestic and imported goods. Export supply is symmetrically modeled as a Constant Elasticity of Transformation (CET) function. Producers decide to allocate their output to domestic or foreign markets responding to relative prices. The assumptions of imperfect substitution and imperfect transformability grant a certain degree of autonomy of domestic prices with respect to foreign prices and prevent the model from generating corner solutions.<sup>7</sup>

**The labor market specification is a key element of LINKAGE and an important driver of the distributional results** . Therefore, its specification calls for some clarification and justification. Two types of labor are distinguished, skilled and unskilled.<sup>8</sup> These categories are considered imperfectly substitutable inputs in the production process. Moreover, some degree of factor market segmentation is assumed: skilled workers are perfectly mobile across sectors, whereas, in developing countries, the labor market for the unskilled is divided into agriculture and non-agriculture segments. The labor market segmentation by skill level has become a standard assumption in CGE modeling. The imperfect substitution in the production process for workers with different skills is likely to persist for the medium-term time horizon, as unskilled workers cannot be ‘transformed’ into skilled ones, even with increased on-the-job training.

**The CGE can incorporate several assumptions on the structure of labor markets and complementarities between workers and technology** . In the standard setting, the market for unskilled labor can be segmented into agricultural and non-agricultural activities. This can be controversial. However, econometric analysis indicates that a gap in remunerations between these two segments remains even after controlling for education, gender, experience and other variables including cost of living differentials (between rural areas, where agricultural activities are predominantly located, and urban ones). Some barriers to mobility – land ownership providing economic security to farmers, specificity to human capital acquired in agriculture, or others – must exist and hinder equalization of wages across the two segments. In the model, this segmentation is implemented with some flexibility. Using a Harris-Todaro specification,

6 At the top of the nest, a value-added bundle is combined with an intermediate-inputs bundle under the Leontief technology assumption. The intermediate inputs bundle is combined with different inputs, with an Armington assumption applied to specific inputs. That is, for a given type of intermediate input, there is substitutability between domestic and imported inputs, and then again between imported inputs from different source countries. The value-added bundle is made up of unskilled labor being slightly substitutable with a capital and skilled labor bundle. The choice of elasticity often depends on the structure of the model and the level of aggregation. We find that an elasticity of 2 between capital and skilled labor and the same value between skilled and unskilled labor work well for the level of disaggregation in the paper and from previous testing and applications by other researchers (such as Ahmed et al. (2016), Devarajan et al. (2015), and Francois Bourguignon, Bussolo, and Pereira da Silva (2008)). A lower elasticity would be appropriate the more aggregated the structure of the economy and its factor markets. At the extreme, when the economy is one sector with one labor and capital (a Solow growth model), the elasticity between labor and capital traditionally takes the value of one (a Cobb-Douglas function in Solow (1956)) or less than one (a CES function) in more recent findings (e.g. León-Ledesma, McAdam, and Willman (2010) or La Grandville (2017). The same tendency appears to be the case with trade related elasticities and the level of commodity specificity (e.g., Hillberry and Hummels (2013) and Devarajan, Go, and Li (1999)).

> 7 However, even if these assumptions attenuate factor prices equalization when countries trade with each other, trade is still an important channel through which factor prices are influenced.

> 8 Recent applications distinguish work by gender.

5
a certain number of unskilled workers migrate from one segment to the other in response to changes of the wage differentials across the segments.<sup>9</sup>

**This rich set-up can capture the changes in wages for different types of workers** . Since skilled-unskilled and agriculture–non-agriculture wage gaps represent important drivers of inequality, the set-up enables the delineation of changes in the income distribution of each economy. The evolution of the labor force is in line with the change of working age population.<sup>10</sup> The growth of supply of skilled and unskilled workers is consistent with this overall growth of the labor force present in the microsimulation model.

**Capital accumulation, labor expansion, and productivity changes determine the growth dynamics in the model** . In each period, capital stock is equal to the depreciated capital stock from the previous period augmented with the investment. Investment is driven by savings which, in turn, depend on per capita income growth and the population youth and elderly dependency ratios. In contrast to national savings, foreign savings are exogenous.

**The model assumes heterogeneity of productivity trends across sectors** . Productivity in agriculture is assumed to be factor-neutral and exogenous and is set to estimates from empirical studies (e.g., Martin and Mitra (1999)). Productivity in manufacturing and services is labor-augmenting; it is skill-neutral but sector-biased. In the case of agriculture, productivity growth averages 2.5% per annum for all countries. In manufacturing and services changes in productivity are country-specific and based on past trends. Following the broad findings of Bosworth and Collins (2003) productivity growth in manufacturing is assumed to grow about three percentage points faster than in services.<sup>11</sup>

**This model calculates wages of skilled and unskilled workers by combining firms’ demand for labor with the aggregate change in supply of these two types of workers** . In addition to the changes in rewards, the computable general equilibrium model also calculates the overall economic growth and sectoral reallocation of labor.<sup>12</sup> As already described above, changes of these variables – the Linkage Aggregate Variables (LAVs) – are used as inputs in the final step of the microsimulation.

9 We do not distinguish agricultural sector and rural regions, which may neglect the presence of rural non-farm activities. See, for example, Reardon, Stamoulis, and Pingali (2007).

10 This means that participation rates are constant. This is a conservative assumption given that the overall labor force participation rates are likely to increase as more women enter the labor force in developing countries or due to the changes in the retirement age in high income countries. The United Nations population projections are disaggregated by age groups and gender. They include net migrations per 1,000 people by country/region. We do not provide alternative migration scenarios, which can be complex and beyond the scope of the paper. However, migration can certainly be handled by the methodology, see for example, Ahmed et al. (2016).

11 The dynamic calibration allows the model to reproduce, in its baseline scenario, GDP growth rate trends that are in line with available long-term projections such as, for example, the growth projections under the Shared Socio-Economic Pathway Scenario 2 of Dellink et al. (2017). In fact, during the dynamic calibration the model targets these exogenous growth rates of GDP by endogenously determining the labor augmenting productivity factor described in the text. In the counterfactual scenarios, the real GDP growth rate is endogenized, and the uniform productivity variable is exogenized, and simulated to grow at the rate determined in the baseline scenario. The real growth rate can thus change depending on the other shocks considered in the scenario.

12 In the GIDD microsimulations, workers move across sectors to achieve the proportions of employment in agriculture and non-agriculture calculated by the CGE model. Note that these inter-sectoral movements are net of the sectoral shifts already generated by the reweighting procedure. The microsimulation procedure to select which specific worker moves is based on a probabilistic model described in detail in (Bussolo, De Hoyos, and Medvedev 2010).

6
# 3.2.The Global Income Distribution Dynamics Model

**The first step in the microsimulation exercise is to implement a set of changes in the household surveys’ demographic structure** . The population growth adjustment is particularly important in countries with rapid demographic changes. In practical terms, the adjustment for population growth allows the analysis to explicitly take into account the changes in the size of the working-age population.<sup>13</sup> We perform population and education projections during the first stage of the microsimulation model and in creating the baseline scenario for the CGE model. For each country, we construct the demographic profile in two steps. First, the age and gender composition are exogenously determined following medium variant estimates from the World Population Prospects (UN DESA 2019). In the second step, following Bourguignon and Bussolo (2013), country-specific educational profiles are constructed using initial educational achievement levels observed in the household surveys with some conservative yet simple assumptions about educational progress.

**The microsimulations’ second step adjusts individual factor returns by skill and sector in accordance with the results of the CGE model** . The GIDD imposes an entirely new vector of earnings on each worker, conditional on each worker’s individual characteristics. In a basic setup, the CGE and GIDD can be linked with two sectors (agricultural and non-agricultural) and two types of workers (skilled and unskilled) but depending on the quality of the data and the scope of the study, this restriction can be relaxed.<sup>14</sup> The GIDD reallocates workers moving them out from shrinking into expanding sectors. The sectoral reallocation process estimates the probability of each worker to be reallocated into new sectors, based on individual characteristics. Once workers are reallocated, a new vector of earnings is generated using estimates from a set of Mincer equations. To account for unobserved characteristics, each individual residual is brought into the new sectors and scaled accordingly. This latter process assures that an individual switching sectors can carry his unobservable personal characteristics - beyond age, experience and years of schooling.

**The third step adjusts average wages between groups of workers and sectors** . While the second step operates at the individual level, the third step operates at the group level scaling the average wages for each type of worker and sector. In practical terms, one group is selected as numeraire, i.e. unskilled agricultural, and average wages for each group are scaled relative to the numeraire. Within a group, all earnings are scaled with respect to the numeraire group. Operating through changes in relative wages guarantees internal consistency between macro and micro results considering that in the CGE-GIDD linkage, initial relative wages were obtained from the micro data and feed into the global CGE model. It is important to highlight that until this point the microsimulation has operated only in relative terms.

**Lastly, GIDD adjusts the average income/consumption per capita to guarantee that it changes exactly in line with the CGE results** . After creating new earnings for workers, a vector of per capita household income is constructed considering new earnings and household size. When information about the relationship between incomes and savings exists, if not, a one-to-one passthrough from per capita household income to consumption is assumed. In this regard, **GIDD constructs a household-specific deflator to adjust for changes in relative prices** . The price deflator is constructed using initial and final price indexes of food versus non-food expenditure from the macro model and household-specific budget consumption shares for food and non-food expenditure observed in the micro data. Food and non-food shares can be adjusted estimating a Lorenz’s curve of budget share expenditure on food and total per capita household consumption. These steps are explained in detail in the annex of this paper.

> 13 For the case of Sri Lanka, as will be shown later, working age population is projected to diminish by approximately 100,000 individuals by 2030.

> 14 See for instance, World Bank (n.d.).

7
# 4. Applying the Macro-Micro Simulations to Potential Trade Policy Reforms in Sri Lanka

**To illustrate the use of this methodology, the CGE-GIDD approach is used to assess a set of ex-ante trade policy simulations for the case of Sri Lanka.** Sri Lanka is a country that has some key characteristics that make the country a good candidate to showcase the methodology. First, it is a small open economy and effectively a price taker in international markets. Second, as other developing countries, its export basket is dominated by a primary commodity – in this case exports of tea. Lastly, after a period of political instability that ended in 2009, the economy grew at an average 5.8 percent during the period of 2010-2017. This reflects a determined policy momentum towards reconstruction and rapid structural transformation, despite signs of a slowdown in the last few years. This paper studies the impacts of stylized policy scenarios including assessments of unilateral para-tariff liberalization, free trade agreements with China and India, and a full reform scenario (para-tariffs + FTAs). Section 3.1 discusses the current structure of the labor market in Sri Lanka while Section 3.2 describes the details of the baseline and trade policy scenarios. Simulation results are discussed in Section 4, first focusing on the sectoral level and then on the sub-national distribution of those effects.

**The model can be used to assess the sub-national, localized effects of trade** . The impact of reforms is differentiated across types of households and workers. Such heterogeneity is key in determining the welfare (consumption, poverty, inequality, etc.) and distributional impacts of any reforms to be undertaken. The GIDD distributes the macroeconomic results of the CGE model to households in line with the Sri Lanka Household Income and Expenditure Survey (HIES 2016). The microeconomic model distributes shocks while keeping the consistency with the aggregate results obtained from the macro CGE model.

# 4.1.Employment and skills profile

**The Sri Lanka Household Income and Expenditure Survey (HIES) 2016 was utilized to examine the characteristics of workers at the sectoral and district levels.**<sup>15</sup> The HIES 2016 data set consists of approximately 21,755 households and 82,935 individuals found within these households. We focus on profiling workers in each sector according to the localized Sri Lanka official version of the 1-digit and 4-digit International Standard Industrial Classification (ISIC),<sup>16,17</sup> the province in which they live, median wage and skill level proxied by educational attainment.

**Women account for one-third of employment of 7.7 million workers and they are primarily employed in the agriculture (incl. vegetables and fruits) sector.** Male workers comprise 5.07 million (66 percent) and female workers comprise of 2.63 million (34 percent). Agriculture (25 percent), manufacturing (18 percent), trade and commerce (18 percent), and social services and others (12 percent) sectors are the four largest employers in Sri Lanka according to estimates from HIES 2016 (Table 1).<sup>18</sup>

> 15The survey was conducted by the Department of Census and Statistics (DCS) under the National Household Survey Programme (NHSP) of Sri Lanka. The survey is nationally representative, and the unit of analysis is the household with details on all the individuals comprising the household. The sampling frame consists of two-stage stratified sampling – area of residence (urban and rural) and the estate sectors in each district of the country were the selection domains. This makes our results representative at the district level too, which is essential for the results of the CGE-GIDD at the regional level.

> 16 <u>http://www.statistics.gov.lk/industry/ASI_2016_Report.pdf.</u>

> 17 Sri Lanka HIES 2016 is utilized for the descriptive analysis of workers section, despite that wage information is more reliable in the Sri Lanka Labor Force Survey (LFS 2015).

> 18 Analysis presented in this section relies on the 1-digit ISIC industry classification (10 sectors).

8
**Women in Sri Lanka are mainly employed in the agriculture sector** . Around 28 percent (approximately 740,000) of women are employed in agriculture, compared with 25 percent in the manufacturing sector – approximately 650,000, social services and other sectors – 18 percent (approximately 470,000), and commerce sector – 12 percent (approximately 325,000) of the total jobs for women. These sectors are also the four largest employers of women (Figure 2 and Table 1). On the other hand, more than 50 percent of men are employed either in agriculture, manufacturing or trade-related activities.

**Table 1: Estimated number of workers, by sector and gender**

||**Total**||**Men**|||**Women**||
|---|---|---|---|---|---|---|---|
|**Sector**|**Estimated**<br>**workers**|**Obs.**|**Estimated**<br>**workers**|**% of total**|**Obs.**|**Estimated**<br>**workers**|**% of total**|
|Agriculture|1,913,073|5,066|1,175,861|61.5|2,986|737,212|38.5|
|Mining|51,798|186|48,475|93.5|15|3322.562|6.4|
|Manufacturing|1,404,570|2,879|754,379|53.7|2,547|650,191|46.3|
|Public utilities|39,633|133|32,441|81.9|31|7191.704|18.2|
|Construction|607,792|2,378|586,746|96.5|83|21,046|3.5|
|Commerce|1,063,299|2,869|738,379|69.4|1,247|324,920|30.6|
|Transports &<br>communications|737,296|2,532|648,494|88.0|350|88,802|12.0|
|Financial &<br>business services|356,071|906|237,835|66.8|449|118,236|33.2|
|Public<br>administration|575,378|1,463|360,764|62.7|872|214,613|37.3|
|Other services,<br>Unspecified|960,634|1,891|492,266|51.2|1,804|468,368|48.8|
|Total|7,709,543|20,303|5,075,641|65.8|10,384|2,633,903|34.2|

Source: Staff estimates based on Sri Lanka HIES 2016; confidence intervals of estimated workers can be requested from the authors.

9
**Figure 2: Sector-level concentration of jobs, by gender**

<!-- Start of picture text --> Sector composition (%) by sex<br>Agriculture 38.5<br>61.5<br>Mining 6.4<br>93.6<br>Manufacturing 46.3<br>53.7<br>Public 18.1<br>utilities 81.9<br>Construction 3.5<br>96.5<br>Commerce 30.6<br>69.4<br>Transport & 12.0<br>communications 88.0<br>Finance & 33.2<br>Business 66.8<br>Public 37.3<br>administration 62.7<br>Other 48.7<br>services 51.3<br>0 20 40 60 80 100<br>Percent<br>Female Male<br><!-- End of picture text --> Source: Staff estimates based on Sri Lanka HIES 2016; confidence intervals of estimated workers can be requested from the authors.

**Manufacturing jobs are concentrated in some western and northern provinces** . Manufacturing jobs are concentrated in the Colombo, Gampaha and Kalutara in the Western Province; Kurunegala and Puttalam in the North Western province and Kandy in Central province (Figure 3). The jobs in the textiles and wearing apparel sectors are similarly distributed as the manufacturing sector, which tend to demand similar infrastructure. The level of urbanization is higher in these districts compared to the other districts or provinces in Sri Lanka. Trade and transportation related jobs are rather evenly spread out by districts, except for Colombo and Gampaha districts that employ a disproportionately higher share of jobs in this sector. A similar profile with the social services sector (education, health care and domestic personnel) is observable with jobs evenly distributed across Sri Lanka. However, most jobs in the agriculture sector are found in districts outside the Western, North Western and Central provinces (Figure A32-Figure A35 in Annex).

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<!-- Start of picture text --> Geographic concentration of sectors<br>percent by districts<br>0 20 40 60 80 100<br>2)100 ee|<br>8 ee eS ee<br>2 ee<br>FE 60 eae 1 =<br>‘A Cl a = a<br> ee ee I<br>‘Ti=n l Ul i<br>35 £ e 3 28 2 f£ SE Bes sg Ee gessesg wg<br>2 § § 83 6 £25 82 § £22282 §<br>z = ze =<br>(mmm Transports& communications sm" Commerce @amms Construction<br>je Public utilities "mm Manufacturing Sas Mining<br>meee Agriculture<br>Source: Sri Lanka HIES 2016<br><!-- End of picture text -->
<!-- Start of picture text --> Average (median and mean) total monthly wage<br>8 27,637.1<br>22,025.9 24,000.0<br>aS<br>46,000.0<br>84<br>s<br>°<br>Female Male<br><!-- End of picture text --> <!-- Start of picture text --> Log total monthly wage<br>2<br>:<br>vel<br>|<br>© {<br>x i f /\ |<br>Se } j \ i \ |<br>E / | \<br>\<br>oe —_—= . = —<br>6 8 10 12 14<br><!-- End of picture text -->
**Figure 6: Median wages by gender & sectors**

<!-- Start of picture text --> Average (median) total monthly wage<br>Public<br>administration<br>Public<br>utilities<br>Finance &<br>Business<br>Other<br>services<br>Construction<br>Transport &<br>Communications<br>Manufacturing<br>Commerce<br>Mining<br>Agriculture<br>0 10,000 20,000 30,000 40,000<br>Female Male<br><!-- End of picture text --> Source: Staff estimates based on HIES 2016 13
**Figure 7: Years of education, by gender and location**

<!-- Start of picture text --> 1940 1950 1960 1970 1980 1990<br>Women/Rural Women/Urban Men/Rural Men/Urban<br>12<br>10<br>Years of education 8<br>6<br><!-- End of picture text --> Source: Staff estimates based on HIES 2016.

Note: 1990s cohort refer to individuals born between 1990 and 1995. Average mean years of education by decade-cohort of individual’s year of birth; individuals in 1990 cohort may not have fully completed education at the time of survey (2015), and hence these individuals could attain higher mean years of education.

# 4.2.Defining Trade Policy Scenarios<sup>21</sup>

# 4.2.1. Baseline

**As mentioned above, an ex-ante evaluation requires the creation of a counterfactual simulation that will serve as a business-as-usual scenario with no policy change.** The model relies on the GTAP version 9 database benchmarked to 2011. The CGE model runs up to 2028, replicating the key historical macroeconomic aggregates from the World Bank World Development Indicators (World Bank, n.d.). Productivity growth in the baseline scenario is calibrated to achieve the historical and forecasted GDP growth rates up to 2021. Productivity growth is adjusted thereafter to be consistent with historical trends.<sup>22</sup> The baseline scenario also incorporates tariff reductions under existing FTAs. These are based on the data set provided by the International Trade Center, including all TPP members’ FTA commitments up to 2027 (International Trade Centre 2016). We also assume that Sri Lanka enjoys the Generalized System of Preferences (GSP) preferences on its exports to EU markets.

**The baseline contemplates continuation of recent trends using standard closure rules for macroeconomic accounting.** The scenarios incorporate three closure rules. First, government expenditures are held constant as a share of GDP, fiscal balance is exogenous while direct taxes adjust to cover any

> 21 This section was largely was developed by Guillermo Arenas (Economist, World Bank Group) using the Tariff Reform Impact Simulations Tool.

> 22 These productivity assumptions are not altered in in the counterfactual scenarios.

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changes in the revenues to keep the fiscal balance at the exogenous level. The second closure rule determines the investment-savings balance. Households save a portion of their income, with the average propensity to save influenced by elderly and youth dependency rates, as well as GDP per capita growth rates. The savings function specification follows Loayza, Schmidt-Hebbel, and Servén (2000) with different coefficients for developed and developing countries. Since government and foreign savings are exogenous, investment is savings driven. The last closure determines the external balance. We fix the foreign savings and therefore the trade balance, hence changes in trade flows result in shifts in the real exchange rate.

# 4.2.2. Para-tariff unilateral liberalization

**Over the last two decades, Sri Lanka introduced several additional taxes on imports, commonly referred to as “para-tariffs”.** Ad hoc introduction of these taxes and frequent revisions to their rates make the import tax structure complex and unpredictable. Although custom duty rates have largely been kept intact and at a lower rate, the introduction of para-tariffs reversed previous efforts aimed to further simplify and reduce taxes on imports. While the number of para-tariffs has been reduced from four to two over the last decade,<sup>23</sup> the remaining para-tariffs affect a significant percentage of imports and, in some cases, grant high protection levels to selected products. Currently, the Ports and Airports Development Levy (PAL)<sup>24</sup> and the Export Development Import Cess (EIC)<sup>25</sup> are the two para-tariffs active in Sri Lanka.

**A third of the revenue obtained from the Ports and Airports Development Levy will be lost due to exemptions** . Since January 1, 2016, the PAL rate is 7.5 percent of the CIF value (5 percent from January 1, 2011 to December 31, 2015). In theory, the levy should be imposed on every article imported into Sri Lanka, but the law allows for several exemptions (e.g. goods used for manufacturing of exports, staple food items, etc.). The PAL Act also grants the Director-General of Customs and the Minister of Finance the power to partially or totally exempt any article from this levy. In practice, about a quarter of tariff lines benefit from reduced or zero rates in 2016 (1,138 tariff lines paying zero and 638 tariff lines paying 2.5% PAL). About a third of PAL revenue is lost due to exemptions.

**The Export Development Import Cess protects a wide range of sectors, particularly footwear, animal and agricultural products** . The EIC was applied on 1,937 tariff lines (TLs) in 2016 (roughly 30% of TLs). Ad valorem rates were applied on 598 TLs, specific rates on 787 TLs, and mixed rates on 552 TLs. As with the PAL, there are several exemptions granted to either products or specific industries that reduce or eliminate Cess liabilities. About half of EIC revenue was lost due to exemptions in 2015. But unlike PAL, which has a low rate and is uniformly applied, EIC significantly increases the protection rate for products in some sectors. Most ad-valorem equivalents of EIC specific rates are in the double digits with a sizeable share of them above 50 percent - especially those applied on animal and agricultural products. The EIC is also applied to more than half of tariff lines in several sectors like footwear, vegetables, and apparel and textiles. To illustrate, Figure 8 shows the percentage of tariff lines under Cess in each import sector and the weighted average protection granted by the Cess in the y-axis.

> 23 Four para-tariffs were active in the 2000s: Import surcharge; Regional Infrastructure Development Levy (RIDL); Port and Airport Development Levy (PAL); and Import Cess. The Import surcharge and the RIDL were eliminated in 2010 and 2011, respectively.

> 24 PAL was imposed by the Ports and Airports Development Levy Act, No. 18 of 2011.

> 25 EIC was imposed by special orders under Section 14 (Import Cess) of the Sri Lanka Export Development Act No. 40 of 1979.

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<!-- Start of picture text --> 45<br>» 40 ® Foodstutt ® Footwear<br>a @ Glass<br>4 35<br>3 30<br>€ ® Vegetable<br>= 25 e Animal © Fides<br>3 Chemical @ Miscellaneous<br>= 20 emicals<br>=*-<br>5<br>o ” Transportation ® Metals © Mastics<br>pW ® Mineral ® Textiles& Apparel<br>z 5 ® Machinery ® Wood<br>i]<br>st] 10 20 30 40 50 60 70 £0 90 100<br>6 Tariff lines under Cess<br><!-- End of picture text -->
<!-- Start of picture text --> Duties<br>Rice OEE<br>Vegetables and Fruits TM<br>OtherCrops<br>Fishing IF<br>Energy =<br>OO<br>MPs E————_{z*«=~—~h—eO<—_—_<br>Other Foo’<br>Textiles TM<br>Wearing Apparel =="<br>Chemicals Rubber and Plastics ="<br>Motor Vehicles HTM<br>Other Machinery =<br>Other Manufacture =<br>0 50 100<br>@ 2018 ® 2028<br><!-- End of picture text -->
<!-- Start of picture text --> Duties<br>Vegetables and Fruits FTM<br>OtherCrops #<br>Energy Henne =<br>nd —<br>Other Food ——<br>Textiles =<br>Wearing Appare)<br>Chemicals Rubber and Plastics<br>Motor Vehicles a<br>Other Machinery |<br>Other Manufacture aa<br>0 20 40<br>TM 2018 ® 2028<br><!-- End of picture text -->

<!-- Start of picture text --> Duties<br>OtherCropsee<br>Fishing ee<br>Energy —_<br>Other Food ee<br>Textiles/_<br>Wearingi Apparel re<br>Chemicals: Rubber and Plasticsoc rs<br>Other Machinery. es<br>Other ManufactureeC<br>ie) 5 10 15<br>TM 2018 ® 2028<br><!-- End of picture text -->
Duties <!-- Start of picture text --> Rice<br>Vegetables and Fruits TM<br>OtherCrops F<br>Fishing<br>Energy<br>Food ="<br>Other Food =<br>Textiles TM<br>Wearing Apparel<br>Chemicals Rubber and Plastics ="<br>Motor Vehicles TM<br>Other Machinery P=<br>Other Manufacture P="<br>0 50 100<br>@® 2018 ® 2028<br><!-- End of picture text -->
Duties <!-- Start of picture text --> Vegetables and Fruits |<br>CtherC op $<br>Fishing | !<br>Energy<br>Foo =—<br>Other Food| ©<br>Tetleg<br>Wearing Apparel<br>Chemicals Rubber and Plasticg<br>Motor Vehicles | '<br>Other Machinery<br>Other Manufacture!<br>0 10 20 30<br>#2018 & 2028<br><!-- End of picture text -->
## **Figure 14: Sri Lanka tariffs (percentage) in the full reform scenario with FTAs to China and India (difference between tariffs in 2028 and 2017)**

<!-- Start of picture text --> Sri Lanka tariffs on China Sri Lanka tariffs on India<br>Vegetables and... Rice<br>Other Crops<br>Other Crops<br>Other Agric<br>Energy Other Agric<br>Food<br>Food<br>Other Food<br>Textiles Other Food<br>Wearing Apparel<br>Chemicals Rubber... Wearing Apparel<br>Motor Vehicles Other Machinery<br>Other Machinery<br>Other... Other Manufacturing<br>0 50 100 150<br>0 20 40 60<br>2017 2028 2017 2028<br><!-- End of picture text --> Source: Staff estimates based on LINKAGE CGE Simulations

**Table 2: Sri Lanka tariffs reductions (in percentage) in the combined FTAs to China and India (difference between tariffs in 2028 and 2017)**

|Item|China|India|Item|China|India|
|---|---|---|---|---|---|
|Rice||-7|Other Food|-5|-18|
|Vegetables and fruits|-2|-2|Textiles|-4|-4|
|Other Crops|-1|-1|Wearing Apparel|-27|-12|
|Fishing||-3|Chem., Rubber, Plastic|-8|-10|
|Other Agriculture|-7|-80|Motor Vehicles|-3|-2|
|Energy|-7|-6|Other Machinery|-5|-7|
|Food|-7|-3|Other Manufacturing|-8|-9|
|Milk Products||-61||||

Source: Staff estimates based on LINKAGE simulations

# 5. Simulation results

**With the reduction of barriers to trade, all liberalization scenarios are expected to result in faster expansion of GDP and international trade compared to the baseline** . Our results indicate that 10 years after liberalization, the volume of GDP would be expected to be around 2 percent higher than in the baseline (Table 3). Exports expand under all scenarios, even when only import duties are reduced with no better access to other markets under the unilateral liberalization scenario. This is due to the availability of cheaper imported inputs, but also because of our assumption of fixed trade balance (as a share of GDP) that leads to adjustments in real exchange rate. When import duties decline and imports increase, the real exchange rate depreciates, and exports expand to keep the trade balance a fixed share of GDP. Overall, the

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higher the degree of own liberalization and improved market access the higher the gains in GDP, exports and imports, but also the higher the sectoral reallocation and adjustment costs.

**Table 3: Key macro implications of simulated scenarios, percentage deviations from the baseline**

||**Para-tariff**<br>**liberalizati**|<br>**on**|**China F**|**TA**|**India FTA**||**Full refor**|**m**|
|---|---|---|---|---|---|---|---|---|
||2023|2028|2023|2028|2023|2028|2023|2028|
|GDP|0.77|1.97|0.83|1.37|0.94|2.18|1.37|2.81|
|Exports|7.6|18.3|3.3|7.52|3.8|19.3|7.52|24.23|
|Imports|5.4|9.8|4.2|11.11|5.1|11.6|11.11|18.86|

Source: Staff estimates based on LINKAGE CGE simulations.

**The sectoral impacts of scenarios are driven by the relative liberalization of imports and exports and initial trade shares with integrating countries** . Sectors where products are exposed to tougher competition as a result of the reduction of imports duties, might be crowded out by imports. At the same time if they constitute important inputs to domestic production of the same or other sectors, the domestic output of sectors which use those inputs intensively might expand. With better access to foreign markets certain domestic products become more competitive abroad and enjoy higher sales on foreign markets.

**Under the unilateral para-tariffs liberalization scenario the key sectors that benefit from the lower import duties are wearing apparel and textiles, services, trade and transport.** For some these sectors, demand is being stimulated either by their use in trade (trade and transport) or by higher spending power of consumers due to faster growth of income. As resources are attracted into sectors which become more competitive, selected sectors are bound to see their output declining due to finite availability of capital and labor. A few sectors experience small declines of domestic output, while they are being replaced by more competitive imports. Those negatively affected include other agricultural products, milk and food products. Under unilateral liberalization scenario exports of wearing apparel expand mostly to the United States and rest of the world, while additional exports of machinery and other manufacturing products are mostly directed to India, rest of Asia, the United States and rest of the world (Figure 15).

**Under the FTA with China the results are also largely driven by Sri Lanka’s unilateral liberalization of para-tariffs with the decline of the same sensitive sectors as in the previous scenarios** (Figure 11). In this scenario, the main sectors experiencing expansion of output include textiles and wearing apparel, whose output gains are driven solely by exports to China (Figure 12). Contrary to previous scenarios sectors experiencing gains in output and exports to China include also other crops, fishing products, chemicals, rubber and plastic products and other manufacturing goods.

**Under the FTA with India the output of wearing apparel and textiles as well as other crops expand the most** (Figure 9). The expansion of those sectors is mostly driven by a boost to exports to India, with exports of other machinery and other manufacturing goods also expand to the rest of the world and the rest of Asia (Figure 10). Similarly, to unilateral scenario, several sectors would be expected to see their output decrease slightly relative to the baseline 2028 level (their level is still above their current values). Sectors experiencing negative impacts include vegetables and fruits, other agricultural goods, food and milk products. Those results are mostly driven by the unilateral liberalization of para-tariffs in those sectors and not by specific commitments in their FTA with India.

**In the full reform scenario, the results are more pronounced, with an increase of exports of 24.2 percent relative to the baseline, compared with 18.3 percent under para-tariff liberalization alone** . Sectors like wearing apparel, which has one of the highest increases of production, are benefiting from cheaper imported inputs derived by an increase of imports due to lower tariffs, and therefore becoming

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<!-- Start of picture text --> Rice<br>Vegetables and Fruits !<br>Other Crops @<br>Fishing<br>Energy<br>Food<br>Milk Products |<br>Other Food i=!<br>Textiles<br>Wearing Apparel<br>Chemicals Rubber and Plastics<br>Motor Vehicles<br>Other Machinery =<br>Other Manufacturing<br>Trade and Transport Margins |<br>Sea Transport Mm<br>Communications |<br>Business Services ll<br>0 5 4 1.45<br>millions of US$<br>@ India m= China MM Singapore M@@ Rest of Asia<br>@= Bangladesh mm USA Pakistan Rest of World<br><!-- End of picture text -->
<!-- Start of picture text --> Rice |<br>Vegetables and Fruits!<br>Other Crops<br>Fishing i<br>Energy er<br>Food |<br>Milk Products |<br>Other Food a<br>Textiles DA<br>Wearing Apparel<br>Chemicals Rubber and Plastics D7<br>Motor Vehicles Ml<br>Other Machiney<br>Other Manufactuing<br>Trade and Transport Margins |<br>SeaTransport<br>Communications Jt<br>Business Services =<br>0 5 1 1.5<br>millions of US$<br>@ India mTM China MM Singapore MM Rest of Asia<br>@®§ Bangladesh mm USA lM Pakistan Rest of World<br><!-- End of picture text -->
<!-- Start of picture text --> 80% | 5,000<br>H 200,000<br>=9 70%- I O 1 400,000600,000<br>o Textiles Sbo.000<br>8 ,<br>Ww Wearing apparel 21,000,000<br>Ww =60%- ‘<br>=<br>Ss I<br>= 50% -- = = 5 oo nnn nnn nn nnn nn nnm Bann penn nnn npn nnn nn<br>2 Social services<br>o Veg Fruit<br>E 40%- L)<br>2 Finance & business services<br>E 30%- Agriculture 1 Chemicals<br>©E 4 {Machinery\ }<br>$ 20% UtilitiesWe oeOther manufacturing<br>+ 10%- o<br>wn<br>£ 0%- MiningO a G) Transport equipment<br>i]<br>-40% -30% -20% -10% 0% 10% 20% 30% 40%<br>Baseline labor variation (%, 2018-2028)<br><!-- End of picture text -->
<!-- Start of picture text --> 80% { 5,000<br>se { Textiles 200,000<br>70% ‘i 400600 ,000<br>g { 800,000<br>4] i Wearing apparel eaeED),000<br>uw 60%- ‘<br>=<br>< 1<br>re 50% -- ~~ = mm nnn nn nnn Coea een menemaars<br>5 =~ Social services<br>= 40%- Veg Fr G3) FAnance & business services<br>t Agriculture (<br>xbe 30%; Chemicals CF<< . ade .& Transport<br>5 20%- \ 4)<br>= Uti ities Qt) (O)\Machinery<br>S \— Other manufacturing<br>= 10%- + Tra t ‘<br>cv) nt ry EN Transport equipment<br>x MiningC(¥ ;¢ \<br>“ { ' )construction<br>0%- NY<br>-40% -30% -20% -10% 0% 10% 20% 30% 40%<br>Labor variation (%, 2028): Para-tariffs scenario w.r.t. baseline<br><!-- End of picture text -->
<!-- Start of picture text --> 80% { 5,000<br>i Textiles 400,000<br>Ss i 200,000<br>a 70% i 600,000<br>g t 800,000<br>4) {Wearing apparel 21,000,000<br>Wi 60%:<br>=<br>><br>& 50%----------------------------7 i Ss eR nnen annnnns mnOaneee<br>5 Veg Fruit { Social services<br>E 40%- Cc) ()) 4nce& business services<br>2 Agriculture /<br>E 11<br>G 30%- ; aA<br>o Chemicals \ yj<br>E 20%- —<br>2-UtilitiesQ (/\\)CO)achinery<br>3Other manufacturing\_/; Transport oquiornent<br>x 10%- 4 _ofransport equipment<br>a ~ ( \ fo astruction<br>0%6- Mining \NE,!1 }<br>-40% -30% -20% -10% 0% 10% 20% 30% 40%<br>Labor variation (%, 2028): Full scenario w.r.t. baseline<br><!-- End of picture text -->
<!-- Start of picture text --> Agriculture % change, bau, 2018-23 FP<br>% change, bau, 2023-28 pt<br>Chemicals % change, bau, 2018-23 Pe<br>% change, bau, 2023-28 |<br>Construction % change, bau, 2018-23 |<br>% change, bau, 2023-28 |<br>Finance & business services % change, bau, 2018-23 7<br>% change, bau, 2023-28 |<br>Machinery % change, bau, 2018-23 FC<br>% change, bau, 2023-28 iz<br>Mining % change, bau, 2018-23 P|<br>% change, bau, 2023-28 es<br>Other manufacturing % change, bau, 2018-23 Pe<br>% change, bau, 2023-28 |<br>Social services % change, bau, 2018-23 | |<br>% change, bau, 2023-28 |_|<br>Textiles % change, bau, 2018-23 iz<br>% change, bau, 2023-28 | |<br>Trade & Transport % change, bau, 2018-23 CC<br>% change, bau, 2023-28 _<br>Transport equipment % change, bau, 2018-23 eC<br>% change, bau, 2023-28 _<br>Utilities % change, bau, 2018-23 ;<br>% change, bau, 2023-28 |<br>Veg Fruit % change, bau, 2018-23 PC<br>% change, bau, 2023-28 ee<br>Wearing apparel % change, bau, 2018-23 |<br>% change, bau, 2023-28 | | | L_| | | | |<br>-20% -15% -10% -5% 0% 5% 10% 15% 20%<br>Labor demand, baseline scenario (%)<br><!-- End of picture text -->
<!-- Start of picture text --> Agriculture % change, para-tariffs, 2018-23 =<br>% change, para-tariffs, 2023-28 |<br>Chemicals % change, para-tariffs, 2018-23 ma<br>% change, para-tariffs, 2023-28 =<br>Construction % change, para-tariffs, 2018-23 I<br>% change, para-tariffs, 2023-28 I<br>Finance & business services % chango, para-tariffs, 2018-23<br>% change, para-tariffs, 2023-28 |<br>Machinery % change, para-tariffs, 2018-23 |<br>% change, para-tariffs, 2023-28 Le<br>Mining % change, para-tariffs, 2018-23 =<br>% change, para-tariffs, 2023-28 az<br>Other manufacturing % change, para-tariffs, 2018-23 | |<br>% change, para-tariffs, 2023-28 a<br>Social services % change, para-tariffs, 2018-23 |<br>Toxtiles %% change, change, para-tariffs,para-tariffs, 2023-282018-23 IYe<br>% change, para-tariffs, 2023-28 PC<br>Trade & Transport % change, para-tariffs, 2018-23 i]<br>% change, para-tariffs, 2023-28 I<br>Transport equipment % change, para-tariffs, 2018-23 ||<br>% change, para-tariffs, 2023-28 PC<br>Utilities % change, para-tariffs, 2018-23 =<br>% change, para-tariffs, 2023-28 |<br>Veg Fruit % change, para-tariffs, 2018-23 a<br>% change, para-tariffs, 2023-28 tz<br>Wearing apparel % chango, para-tariffs, 2018-23 a<br>% change, para-tariffs, 2023-28 | | | | es | |<br>-20% -15% -10% -5% 0% 5% 10% 15% 20%<br>Labor demand, para-tariffs scenario w.r.t. baseline (%)<br>Agriculture % change, full, 2018-23 =<br>Chemicals %% change, change, full,full, 2023-282018-23 a;<br>% change, full, 2023-28 a<br>Construction % change, full, 2018-23 |<br>% change, full, 2023-28 a<br>Finance & business services % change, full, 2018-23 I<br>% change, full, 2023-28<br>Machinery % change, full, 2018-23<br>% change, full, 2023-28 Pe<br>Mining % change, full, 2018-23 a<br>% change, full, 2023-28 De<br>Other manufacturing % change, full, 2018-23 a<br>% change, full, 2023-28 mz<br>Social services % change, full, 2018-23 a<br>% change, full, 2023-28 |<br>Textiles % change, full, 2018-23 a<br>% change, full, 2023-28 eC<br>Trade & Transport % change, full, 2018-23 |<br>% change, full, 2023-28 I<br>Transport equipment % change, full, 2018-23 be<br>% change, full, 2023-28 ||<br>Utilities % change, full, 2018-23 a<br>% change, full, 2023-28 |<br>Veg Fruit % change, full, 2018-23 fe<br>% change, full, 2023-28 pl<br>Wearing apparel % change, full, 2018-23 a<br>% change, full, 2023-28 | | | es | |<br>-20% -15% -10% -5% 0% 5% 10% 15% 20%<br>Labor demand, full scenario w.r.t. baseline (%)<br><!-- End of picture text -->
# 5.2.Sub-national labor market effects

**In the baseline scenario, employment in agriculture and related sectors is shrinking over time across all districts in Sri Lanka** . The job losses range from 15 percent to 30 percent in different districts. However, since some districts are predominantly agriculture dependent, the job losses range from approximately 2,000 jobs (Jaffna, Mannar, Vavuniya, Mullaitivu, and Kilinochchi) to approximately over 20,000 jobs (Nuwara-eliya, Kurunegala, Badulla, and Ratnapura). Manufacturing and services related sectors are generally expected to grow and benefit already industrialized and wealthier districts – between 5,000 and 25,000 jobs approximately – in districts like Colombo, Gampaha, Kalutara, Kandy, Galle, Kurunegala, and Kegalle. These districts are also expected to attract and absorb skilled workers from agricultural districts which experience job losses (See Table A2 in Annex).

**Some districts can expect protracted effects reflecting a large internal migration towards more urbanized metropolitan areas** . For example, in the district of Nuwara-eliya job losses under baseline conditions are expected across all sectors **.** On the other hand, Kurunegala district is estimated to experience job losses in agriculture, but job creation across almost all other sectors in the long-term horizon. These differentiated results by sectors also aggregate to net job losses and gains respectively in each district. This is something to consider when designing policy reforms – similar to a stock and flow concept - some sectors may perform significantly better (worse), but the size of the sector employment and the associated changes in labor demand is crucial to understand the absolute numbers of job losses (gains) in each district. Relatedly, the net change in employment by district or by sector are other metrics to consider for a policy maker (See Table A2 in Annex).

**The sectoral reconfiguration observed in para-tariffs unilateral liberalization and the full-reform scenario, especially in wearing apparel, leads to larger concentration in urban areas.** In the full reform scenario, a similar trend in employment changes is estimated across all districts as in the baseline scenario. Textiles, wearing apparel, and trade and transportation sectors expand with respect to baseline, from 9 to 17 percent. Concurrently, the higher wages in textiles and wearing apparel would attract workers from chemicals and some manufacturing sectors (in Annex). Most employment gains are projected in the western regions of Colombo, Gampaha and Kalutara with a net increase in employment of 111,000 from 2018 to 2028 – amid the rest of the country experiencing a net employment decline of 216,000 jobs. Implementation of full-trade reform accelerates the concentration of employment in the western regions, going from 111,000 to 134,000, or 20 percent increase in net employment gains by 2028.

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<!-- Start of picture text --> »<br>) ‘ |-<br>’~<br>Gampaha<br>+ Trade+Social services& transport 4 d<br>Colombo<br>+ Social services<br>+ Trade& transport<br>Kalutara<br>+ Trade& transport<br>© 2019 Mapbox © OpenStreetMap<br>,<br>-40,000 120,000<br>»<br>zy ~ \<br>' 7 vavu =»Trincomalee<br>+WearingApp % ag<br>- Trade & transport ne<br>~TradeColombo& transpo )[nee<br>+ Wearing App lj d<br>~TradeKalutara& transpo et<br>+ WesringApp Vian\ Gaile SS<br>©2019 Mapbox © OpenStreetMap<br>ad<br>5,000 10,000<br><!-- End of picture text -->
<!-- Start of picture text --> ,<br>SRA,<br>e. SN<br>b ZS> A,<br>p<br>Trincomalee<br>D) r #<br>pyreVe polonear q<br>B tr aloa<br>" Kurunegala f Q<br>+Wearing v ay<br>+WearingApp {] de & transpor g<br>- Trade & transport bua<br>- Trade+ Wearing‘Colombo& transpokalutaraApp \<br>Trade+ & transpo<br>Wearing App Y f<br>(© 2019 Mapbox © OpenStreetMap_<br>.-5,000 10,000<br><!-- End of picture text -->
<!-- Start of picture text --> Sri Lanka: Poverty Headcount (%)<br>°<br>FS] 40"<br>°<br>8<br>3<br>S 21,<br>N<br>13.4<br>° 12.8<br>S<br>2015 2020 2025 2030<br>— <= Baseline === Full Reform<br><!-- End of picture text --> <!-- Start of picture text --> Growth Incidence Curve with respect to Baseline, 2028<br>ite)<br>ov<br>£<br>3<br>Bo<br>3<br>a<br>6Sg<br>ee<br>oO<br>0 20 40 60 80 100<br>Percentiles of Per Capita Income<br>= Paratariffs = CHN = IND = Full<br><!-- End of picture text -->
<!-- Start of picture text --> Colombo Gampaha Kalutara Kandy Matale<br>90 17.4 28.5 35.3 344<br>29 53 10.0 145 129<br>28 5.2 91 13.8 121<br>Nuwara-eliya Galle Matara Hambantota Jaffna<br>452 31.3 4u1 279 487<br>17.6 m4 17.2 10.3 23.3<br>16.0 10.9 163 10.2 215<br>Mannar Vavuniya Mullaitivu Kilinochchi Batticaloa<br>422 28.9 66.8 75.9 58.3<br>12.4 14 377 46.2 293<br>125 10.3 344 444 28.6<br>Ampara Tricomalee Kurunegala Puttlam Anuradhapura<br>77 55.4 30.9 28.3 32.4<br>146 23.9 19 94 13.0<br>140 22.0 12.1 94 129<br>Polonnaruwa Badulla Moneragala Ratnapura Kegalle<br>35.3 513 513 49.2 378<br>13.5 26.0 20.4 19.6 15.4<br>124 240 20.2 17.9 146<br>0 20 40 60 80 0 20 40 60 80 te) 20 40 60 80 te) 20 40 60 80 te) 20 40 60 80<br>Poverty headcount PPP$5.50/day<br>TM@ Baseline; ' '18 & Baseline'28; ' & Full-reform ''28<br><!-- End of picture text -->
**Limitations associated with the use of microdata and the reconciliation with macroeconomic statistics should be considered** . Nationally representative household surveys are incorporated in the CGE modeling framework to provide information related to the contribution of labor to value added, disaggregated by sector and type of worker. To incorporate this information, one must reconcile macro and microdata sources. This reconciliation must deal with the fact that (1) the aggregates obtained from microeconomic data do not add up to the aggregate statistics in national statistics; and (2) microeconomic data may not provide accurate information about some very small sectors.

# 6. Concluding remarks

**This paper discussed a simulation framework used for the ex-ante assessment of the distributional aspects of trade, at the sectoral and sub-national district levels.** In this regard, the assessment of the distributional aspects needs to operate within a systematic framework. The CGE-GIDD is a macro and microeconomic model linked through a set of linked aggregate variables, including labor supply, skill formation, and real earnings. The model operates in a top-down fashion. The CGE model relies heavily on economic theory and provides aggregate and complex interactions within consistent market-clearing mechanisms. The microsimulation can project the behavior of consumers and workers at a very disaggregated level. The CGE-GIDD allows us to track patterns from policy shocks, to macro and sectoral outputs, and finally to disaggregated effects on labor at a sub-national sectoral level. On a technical level, the labor market specification is a key element that drives the distributional results. In such circumstances, the assumptions used to model and calibrate the labor market and the complementarities between workers and capital have a great influence on the results.

**A set of relevant trade policy simulations were assessed for the case of Sri Lanka** . An ex-ante evaluation requires the creation of a counterfactual simulation that serves as a comparison. The baseline follows historical trends and is key in understanding the net effect of the alternative policy scenarios. The alternative scenarios contemplate i. a unilateral para-tariff liberalization, ii. FTA with China, iii. FTA with India, and lastly, iv. a full-reform scenario that incorporates the previous three elements plus implementation of the WTO Trade Facilitation Agreement. In practice, the level of protection with tariffs is small for open economies and most of the effective gains from trade would come from reduction in NTMs and extra forms of taxation, such as para-tariffs. Macro simulation results show strong complimentary between the policy choices. As a result, and for brevity, only the para-tariff unilateral liberalization and the full-reform scenario were discussed in the section dealing with the distributional assessment.

**Reductions of trade barriers result in faster expansion of GDP and trade, accompanied by greater economic activity in urban areas, shifts toward export competitive sectors and increases in wage inequality** . With respect to baseline and by 2028, implementation of full trade reform would help in reducing poverty (PPP$5.50/day) 0.66 additional percentage points. Our analysis shows that despite these positive effects on poverty reduction and in the absence of additional policies, the economic gains would be in favor of skilled workers – which are located in the top 40 percent of the income distribution. In the realm of scenario analysis, it is important to highlight that these results are not forecasts; rather, these results can be best understood as mechanical responses only occurring when the economy operates under a narrow set of assumptions. Policy creates a new set of incentives for economic actors and the framework presented here can assess the magnitude and direction of those incentives within a complex, systematic, and detailed framework.

**The distributional gains from trade have long been recognized as an important component for economic theory and subsequently for trade policy design and implementation** . Recent advances in computational power, better standards for collecting macro and micro data and increases in the technical capacity of governmental bodies (particularly national statistical offices, and finance ministries) increase the accessibility these methodologies in developing countries. This paper aims to demonstrate how this

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method can be leveraged in the context of a small, open economy. There are a set of possible extensions that have been implemented in other contexts, particularly promising is incorporating a higher level of granularity in the “down” part of the framework relying on firm-level, census, or new databases that use high-frequency data.

**Policy implications should aim to distribute the gains with more equity** . By understanding the varied impacts across regions and sectors, policy makers can design policies to mitigate negative impacts on selected groups of workers and facilitate the transition of workers across regions and sectors. In doing so, the gains from trade could be distributed more equally. Possible specific policies might include:

• **Skills for migrant workers** : Increasing economic activity in urban areas is expected with a major reallocation of labor from districts with large agriculture and vegetables/fruit sectors into urban areas that host emerging sectors (chemicals, textile and apparel, and other manufacturing). Policies that promote the acquisition of job-related skills will benefit both workers and firms. While workers’ technical knowledge and expertise are critically important, the emphasis on managerial and administrative capacities should be underscored, particularly for firms engaging in exporting activities.

• **Better mobility and communication** . Some non-tradable sectors, such as social services, construction, trade and transport sectors which are essentially in highly urbanized districts experience positive effects on the extensive and intensive margins of labor demand. Still, geographic labor mobility in Sri Lanka is very limited. Infrastructure and communication investment policies can help to reduce the gap between the less connected, isolated and job loss prone locations with the expanding urbanized districts. Domestic labor mobility could also be promoted with security and socio-political integration, which is crucial in the aftermath of a prolonged civil war.

**• Enforce progressive labor standards and policies that protect vulnerable workers.** Textiles and wearing apparel experience the highest rate of labor demand expansion with large increases in employment of women. Policies that prevent the exploitation of vulnerable workers, particularly migrant unskilled women, can help to guarantee the long-term viability of the expanding sectors and expanding urban communities. Policies that protect the safety in the workplace and that promote that workers obtain a fair share from their labor guarantee the long-term sustainability of the sector, and the economic viability of the new urban areas that they will populate.

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