Do Regional Trade Pacts Benefit the Poor? An Illustration from Dominican Republic—Central American Free Trade Agreement in Nicaragua

World Development Vol. 37, No. 1, pp. 146–160, 2009 Ó 2008 Published by Elsevier Ltd. 0305-750X/$ - see front matter www.elsevier.com/locate/worlddev

doi:10.1016/j.worlddev.2008.02.005

Do Regional Trade Pacts Benefit the Poor? An Illustration from Dominican Republic—Central American Free Trade Agreement in Nicaragua MAURIZIO BUSSOLO and YOKO NIIMI * The World Bank, Washington, DC, USA Summary. — This paper provides an ex-ante assessment of the poverty and income distribution impacts of the Central American Free Trade Area agreement on Nicaragua. A general equilibrium macro model is used to simulate trade reform scenarios and estimate their price effects, while a micro module maps these price changes into real income changes at the individual household level. The final impact on poverty is not too large, but its dispersion across households is significant and should be considered when designing compensatory policies. A main policy message is that Nicaragua should consider enlarging its own liberalization to countries other than the United States to boost trade-induced poverty reductions. Ó 2008 Published by Elsevier Ltd. Key words — Central America, Nicaragua, trade, poverty, micro-simulation

(Hertel, Ivanic, Preckel, & Cranfield, 2004; Nicita, 2006). 3 The CGE model has the advantage of being a counterfactual analysis tool that can generate price effects which are directly and unequivocally linked to a trade reform. 4 The changes in relative factor prices (particularly between labor and capital remunerations, and between skilled and unskilled labor wages) and relative goods prices (such as between food and non-food items) are then linked to the household survey to generate income distribution effects. This methodology does not maintain full consistency between the micro data and the CGE results. By combining the two, however, it maps aggregate results from the CGE to the detailed information available in the household survey and provides a much more nuanced and useful analysis of poverty impact. This approach also allows decomposing the total effect on poverty into growth and redistribution components and, thus, it can show whether the trickle down assumption applies or not. The findings of this paper suggest that CAFTA may reduce poverty. This pro-poor effect is not large and its dispersion across households is significant due to their heterogeneity of factor endowments, inputs use, commodity production, and consumption preferences. This dispersion and the possibility that specific (low income) households may lose because of CAFTA need to be taken into account in designing compensatory policies. Our growth and redistribution decomposition analysis shows that, at least in the short to medium run, redistribution

1. INTRODUCTION The debate on the Dominican Republic—Central American Free Trade Agreement (DR-CAFTA) 1 between the United States, five Central American countries (Costa Rica, El Salvador, Guatemala, Honduras, and Nicaragua), and the Dominican Republic has been heated by the usual arguments surrounding trade deals. A seemingly persuasive argument against CAFTA is that although new jobs in Central America may be generated, this may be done at the expense of American jobs and to the detriment of local workers hired in jobs that do not comply with minimum labor standards. 2 This argument has limited validity because it considers only the static distributional effects of trade integration. Similarly, arguments in favor of CAFTA or other similar trade deals are also often partial for they normally assume that freer trade indisputably favors growth and that growth trickles down to the poor. A careful assessment of whether trade reform can be beneficial to poor people and what can be done (at least in the short to medium term) to correct potential anti-poor effects is needed to settle the debate, but it is also a difficult task. There are various channels through which trade liberalization affects the poor as discussed in conceptual terms by McCulloch, Winters, and Cirera (2001), Winters, McKay, and McCulloch (2004), although empirical evidence is rather thin, disparate, and piecemeal. In this study, a numerical simulation model—a computable general equilibrium (CGE) model—in conjunction with a non-behavioral micro-simulation module based on household survey data for Nicaragua is used to estimate ex-ante the effects of a CAFTA-like trade shock on poverty. Similar modeling frameworks have been applied to assess income distribution and poverty effects of China’s accessing the WTO (Ravallion & Chen, 2004), the consequences for poverty in Latin American countries signing free trade areas treaties (Bussolo, Lay, & van der Mensbrugghe, 2006; Ianchovichina, Nicita, & Soloaga, 2001), or to analyze the link between trade reform and poverty in other geographic areas

* The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors; they do not necessarily reflect the views of the World Bank, its Executive Directors, or the countries they represent. We are grateful to Jane Armitage, Carlos Felipe Jaramillo, Daniel Lederman, Hans Timmer, and participants of the Global Trade Analysis Project (GTAP) eighth annual international conference for helpful contributions, remarks, and suggestions. We also thank three anonymous referees for their useful comments. Final revision accepted: February 14, 2008. 146

DO REGIONAL TRADE PACTS BENEFIT THE POOR?

can be as important as growth. Further, given the already strong (pre-liberalization) import penetration of agricultural commodities coming from the United States and the initial higher tariffs that Nicaragua is granting to its agriculture, CAFTA may entail a liberalization that is not only geographically but also sectorally biased. A CAFTA-like agreement would thus likely trigger an inflow of cheaper agriculture commodities with a potential short-term unfavorable effect on farmers’ incomes and rural poverty. A key policy relevant message of this paper is therefore that Nicaragua should consider enlarging its own liberalization to countries other than the United States to avoid these negative effects on distribution and boost trade-induced poverty reductions. Although this message may not be valid for every country, it is consistent with one of the few robust results emerging from the empirical literature on preferential trade agreements between groups of countries, namely that this type of agreements outweighs its costs only if protection against non-member countries is low (Schiff & Winters, 2003). The paper is structured as follows: Section 2 presents the CGE model, the micro module, and the relevant data. The first part of Section 3 describes the general equilibrium results of the trade policy shocks, and the second part the poverty implications. The final section provides some concluding remarks. 2. MEASURING THE EFFECTS OF TRADE REFORMS ON POVERTY: LINKING A CGE MODEL TO HOUSEHOLD SURVEYS (a) The Nicaragua general equilibrium model and its data A 2000 Social Accounting Matrix (SAM) of Nicaragua represents the initial benchmark equilibrium for the CGE model. This SAM, which includes 39 sectors, 39 commodities, 3 factors (skilled and unskilled labor and one composite capital), an aggregate household account, and other accounts (government, savings and investment, and the Rest of the World), has been assembled from various sources incorporating data from the 2000 Input–Output Table and the 2001 Living Standards Measurement Survey (LSMS). The CGE model is based on a standard neoclassical general equilibrium model, that is, a model that combines the standard consumer and producer theories and the Heckscher–Ohlin– Samuelson trade theory with a compatible data set for a specific country; 5 the following subsections describe its main features. (i) Production Output results from nested CES (constant elasticity of substitution) functions that, at the top level, combine intermediate and value added aggregates. At the second level, the intermediate aggregates are obtained by combining all products in fixed proportions (Leontief structure), and the value added results are obtained by aggregating the primary factors. (ii) Income distribution and absorption Labor income and capital revenues are allocated to households according to a fixed coefficient distribution matrix derived from the original SAM. One of the main advantages of using the micro module is to enrich this rather crude macro distribution mechanism. Private consumption demand is obtained through maximization of household-specific utility function following the linear expenditure system (LES). Household utility is a function of consumption of different goods. Once their total value is determined, government and

147

investment demands 6 are disaggregated into sectoral demands according to fixed coefficient functions. (iii) International trade The model assumes imperfect substitution among goods originating in different geographical areas. 7 Import demand results from a CES aggregation function of domestic and imported goods. Export supply is symmetrically modeled as a Constant Elasticity of Transformation (CET) function. Producers allocate their output to domestic or foreign markets according to relative prices. Under the small country assumption, Nicaragua is unable to influence world prices and its imports and exports prices are treated as exogenous. 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. The balance of payments equilibrium is determined by the equality of foreign savings (which are exogenous) to the value of the current account. With fixed world prices and capital inflows, all adjustments are accommodated by changes in the real exchange rate. (iv) Factor markets The labor market specification is a key element of the model and an important driver of poverty and distributional results. Labor is divided into two categories: skilled and unskilled. These categories are considered imperfectly substitutable inputs in the production process. Moreover, some degree of market segmentation is assumed: composite capital is sector specific, and labor markets are segmented between agriculture and non-agriculture. Labor is assumed to be fully mobile within each of the two broad sectors, but fully immobile across them. 8 These restrictive conditions are imposed on the modeling framework so that it mimics in a simple and transparent way the short-term impact of trade reforms on the Nicaraguan economy. Finally, the version of the model with segmented labor markets also facilitates linking the macro results of the CGE model to the household survey micro-model, where households are not allowed to respond to price changes by migrating or increasing their human capital endowments (or even changing their consumption choices). (b) The micro module: linking household surveys to the CGE model Poverty effects of trade reforms are estimated using a topdown approach. Initially, the CGE model calculates the new equilibrium (i.e., new relative prices and quantities for factors and commodities) following a trade shock. Then prices are transferred to the micro module to estimate a new income distribution and poverty effects. No feedback from the micro module to the macro model is explicitly accounted for in this version. The household survey used for the micro module computations is the LSMS 2001 for Nicaragua. At the individual level, the active employed population aged more than 12 years is classified into skilled and unskilled according to their education level. The employed population is also classified into wage-workers and self-employed. For wage-workers, the entire factor-related income is either unskilled or skilled labor income. As for self-employed workers, their income is assumed to have both a labor and a capital component. To separate these two components, a wage for the self-employed is imputed based on a simple Mincerian wage equation that is estimated for the wage-workers separately for rural and urban areas. The difference between the reported income from self-

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characteristic—such as region of residence, employment status, gender, education, and age—that may strongly correlate with larger than average losses and then use this information in targeting compensatory measures. It should be noted that the approach undertaken in this paper only considers first order effects and excludes important second order mechanisms that may account for large income changes. It is better suited to estimating short run impacts, and it may overestimate the role of price adjustments to a trade shock, given that quantity adjustments and substitutions are ruled out. This latter assumption is perhaps excessively restrictive, especially for the case of labor markets in Nicaragua. According to our estimates based on the LSMS data, a relatively large sub-utilization of labor is observed in Nicaragua: unemployment rate is at 3.8% and underemployment rate is at 26.9%, 11 therefore assuming that labor markets clear exclusively through price adjustments may seem improbable. This issue is addressed as follows. Analysis of household micro data shows that poverty is lower for the unemployed than for the employed, and also that poverty does not differ greatly between the underemployed and the fully employed (suggesting that perhaps the observed sub-utilization of labor is partly voluntary in Nicaragua). This implies that our qualitative findings, at least in terms of poverty impact of trade reforms, will be marginally altered by accounting for quantity (i.e., for employment increases) changes. Therefore the cost, in terms of modeling complexity and data requirement, seems not justified. However, the CGE model is modified to accommodate changes in labor employment and simulation results under this less restrictive model setup are reported in the sensitivity section below. Acknowledging the above limitations, the main advantages of our approach are transparency, lower data requirements, and ease of implementation.

employment and the imputed wage is assumed to represent the capital component of self-employment income. In our micro module, the relative gains or losses as a result of trade liberalization for each household depend on (1) changes in prices for purchased goods and the initial share of expenditure on each good and (2) changes in factor returns (skilled and unskilled labor wages and returns to capital) and the shares of total initial income by source. Income by source is calculated for each member of the household and individual incomes are then aggregated at the household level. While fairly straightforward for urban workers, identifying sources of income is not obvious for the self-employed workers in the farm sector. For this large group, disaggregating income source can be a laborious and error prone procedure as the labor and capital components are not often easily separable. For farm households, the relative change in welfare is therefore represented by a change in consumption, by a change in explicit wage earnings, and by profit generated via the activity run by the household. This is estimated as the difference between sales (holding constant the quantity shares of the different goods sold) and input costs (again without changing the structure of input quantities). Finally, note that consumption of home production (autoconsumption hereafter), as well as income transfers (such as remittances, government transfers), has been explicitly excluded from the computations given that price changes—in the short run, and those of the order of magnitude considered here—do not affect them. 9 In the case of farm households, this means that final consumption excludes auto-consumption, and that input costs are net out of the costs related to production for auto-consumption. The main advantage of this approach is that it takes into account important sources of heterogeneity across households. A large literature on trade and poverty 10 has shown that changes in the distribution of income (or consumption) differ considerably across different groups of households and that predetermined groupings do not capture the whole spectrum of possible outcomes. Poor households themselves are poor for different reasons and designing compensatory policies that are targeted to the right recipients can be greatly facilitated by having a whole new counterfactual distribution. In the new distribution, households, as well as individuals, can be identified according to the full set of socio-economic characteristics recorded in the survey. It is thus easier to identify a specific

(c) Brief description of the pre-liberalization income distribution Given that the impact of liberalization-induced price changes on households depends on the relative importance of various income sources and on consumption patterns, it is worth examining how Nicaraguan households earn and spend their income. Figure 1 shows the factor income shares for Nicaragua when its whole population is ordered in income centiles. Some salient features of the Nicaraguan income distribution are highlighted by this graph. Auto-consumption

1.0 0.9 0.8

Income

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

0

10

20

30

40

50

60

70

80

90

Population percentile SKILL TRANSFER

UNSKILL AUTOCONSUMPTION

CAPITAL FOOD

Figure 1. Factor allocation (stacked area) and food share in consumption (line) by centile Source: Calculations based on Nicaragua LSMS 2001. Notes: The unskilled, skilled, and capital stacked areas measure the percent contribution of each factor to that part of income which is affected by factor price changes (auto-consumption and transfers are a large fraction of the total income of the poor but they are not affected by price changes in the market). The food line represents the (marketed) food consumption as a percentage of total (marketed) consumption. Both factor contributions and food percentages are measured for each centile in the population ordered by income levels.

DO REGIONAL TRADE PACTS BENEFIT THE POOR?

149

Table 1. Income by source, sector and location and food share (%) Self-employed Farm Skilled

Wages Non-farm

Farm

Autocons

Food (%)

Unskill

Capital

Skilled

Unskill

Capital

Unskill

Skilled

0.29 0.20 0.25 0.24 0.61

6.58 2.13 14.45 0.89 12.70

3.28 0.45 3.07 0.97 13.36

1.51 1.43 0.10 2.98 0.68

9.67 15.29 5.43 10.47 6.69

7.27 4.12 1.50 13.27 8.35

0.24 0.11 0.12 0.41 0.22

11.47 10.52 22.49 3.52 11.90

10.05 6.31 1.81 20.90 5.18

23.87 33.50 14.91 26.70 18.39

19.61 23.72 20.88 18.79 12.97

6.12 2.02 14.99 0.85 8.94

51.17 54.18 56.23 45.94 49.76

Income decile (all) 1 0.39 2 0.06 3 0.40 4 0.07 5 0.22 6 0.24 7 0.12 8 0.23 9 0.34 10 0.83

11.01 12.42 6.51 7.29 6.35 6.51 4.13 4.57 4.00 3.01

1.28 2.87 1.33 1.78 1.99 5.02 3.58 3.36 4.50 7.12

0.12 0.51 1.00 0.91 1.18 0.88 1.83 1.49 2.39 4.75

5.99 10.75 12.73 10.85 9.70 11.07 10.43 10.57 9.35 5.25

0.73 1.71 3.29 3.24 4.66 6.60 6.37 8.85 14.47 22.77

0.00 0.10 0.14 0.19 0.15 0.35 0.19 0.01 0.52 0.70

21.67 19.58 18.18 13.95 11.17 11.25 8.57 5.02 2.75 2.56

0.96 1.95 2.26 5.44 8.99 5.77 12.17 17.03 19.35 26.62

17.41 15.83 26.26 28.26 29.84 28.62 29.61 27.85 24.46 10.57

28.76 21.21 21.13 19.32 20.63 17.01 19.77 17.44 15.74 15.05

11.20 13.00 6.76 8.69 5.11 6.69 3.23 3.59 2.12 0.78

58.44 55.00 56.08 53.46 53.51 51.26 52.16 49.12 45.35 37.29

Income decile (urban) 1 0.34 2 0.10 3 0.09 4 0.29 5 0.03 6 0.03 7 0.33 8 0.14 9 0.05 10 0.84

3.55 2.01 1.89 1.15 1.60 0.60 1.45 0.41 0.46 0.69

0.55 0.64 0.10 0.53 0.90 0.36 0.91 0.51 0.66 2.48

0.63 2.12 1.56 1.63 1.09 2.53 1.89 2.93 4.72 4.55

13.72 19.77 14.90 12.43 13.57 12.98 11.37 11.18 9.42 4.51

1.92 4.53 4.76 5.20 7.67 7.60 8.39 11.02 21.03 24.34

0.00 0.00 0.18 0.27 0.32 0.33 0.01 0.25 1.07 0.50

16.99 7.21 11.68 6.17 8.32 4.69 3.18 0.76 2.33 1.67

2.61 2.52 8.43 11.87 8.15 15.59 20.48 22.73 26.98 31.73

27.82 34.76 35.22 35.86 37.22 31.75 31.14 31.99 17.51 10.61

27.88 24.73 18.88 23.57 19.70 22.57 19.40 17.30 15.54 17.94

3.18 1.62 2.33 1.04 1.43 0.96 1.45 0.78 0.22 0.17

55.90 54.05 54.46 52.54 51.91 51.06 48.79 44.90 45.04 33.47

Income decile (rural) 1 0.59 2 0.00 3 0.01 4 0.23 5 0.61 6 0.15 7 0.41 8 0.26 9 0.16 10 1.39

14.30 17.89 16.77 10.96 13.04 13.66 13.09 12.22 11.56 14.71

1.83 1.96 5.18 2.17 2.82 4.34 7.96 10.01 8.87 22.86

0.00 0.00 0.00 0.00 0.09 0.57 0.52 0.21 0.66 1.09

3.99 5.03 4.08 7.00 6.43 5.74 8.52 5.68 7.50 4.88

0.32 0.47 0.41 0.93 3.78 2.29 5.63 3.13 6.92 15.93

0.00 0.00 0.00 0.25 0.54 0.00 0.38 0.00 0.00 0.40

19.20 25.48 26.08 25.31 22.96 17.72 16.19 16.26 10.99 6.43

0.00 0.64 1.40 2.88 0.90 4.28 3.55 4.46 6.53 5.66

12.59 8.63 11.55 16.02 16.86 21.85 15.03 23.87 24.14 11.06

30.04 23.03 18.06 20.22 17.41 17.11 16.53 13.04 14.52 10.13

17.14 16.86 16.46 14.03 14.56 12.28 12.18 10.87 8.14 5.45

59.55 56.80 56.18 55.94 56.53 52.81 51.41 53.62 52.20 43.73

All Poor_urban Poor_rural NPoor_urban NPoor_Rural

Skilled

Transfer Non-farm Unskill

Source: Calculations based on Nicaragua LSMS 2001.

positive or negative. On the other hand, for households in the upper part of the distribution, unskilled labor, the most important source of income for the poorer, is replaced by skilled labor and capital revenues. Moreover, food-related ex-

and transfers represent a large share of income for the poorest household, up to 60%, implying that these households are less engaged in market transaction and would thus benefit or suffer less from the price shock depending on whether the shocks are 0.300

0.297

0.280 0.269

0.266

0.260

0.250

0.240 0.226

0.220

0.211

0.200

0.198

0.180 0.160 decile 1

0.178 0.165

decile 2

decile 3

decile 4

decile 5

decile 6

decile 7

decile 8

decile 9

0.171

decile 10

Figure 2. Herfindahl–Hirschman index of concentration of income sources (rural households by income deciles) Source: Calculations based on Nicaragua LSMS 2001.

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WORLD DEVELOPMENT

penses drop from about 60% of total consumption to about 40% as we move upwards along the income distribution. Table 1 expands the analysis of income distribution by grouping households according to income sources, sector of employment, and geographic location. For the population as a whole, non-farm unskilled wages are found to be the largest source of income, accounting for almost one-quarter of total income (see the first row of Table 1). However, the table also illustrates that there are substantial differences in the shares of various income sources depending on how well-off the household is and whether the household resides in the urban or rural sector. Figure 2 reports the Herfindahl–Hirschman index of concentration calculated as the sum of the squared shares of different income sources for the ten deciles of rural households. It shows an unambiguous downward trend as income rises, meaning that poorer households have income originating from a more concentrated set of sources. This strong dependence on a few income sources, especially for the poor households, may be a result of a rational choice, or it may reflect more limited employment opportunities due to limited skills and remote location. The implication of this is that it may trap these households in their poverty condition. Recent studies estimating households’ response to price incentives such as those induced by trade reforms indeed show that poor households respond less to these incentives. Deininger and Olinto (2000) showed that, for households in Zambia, the absence of key productive assets (e.g., draft animals, implements) was a major limitation in exploiting opportunities created by trade liberalization. Lo´pez, Nash, and Stanton (1995) found that the level of capital inputs was, on average, directly related to the responsiveness to price incentives across a sample of farm households in Mexico. These existing studies suggest that the micro analysis approach in this paper, where neither quantity response nor occupational/sectoral change is allowed, is appropriate and that its intrinsic bias in considering just first order effects may be less significant for the poor households that are our primary interest.

competitive sectors by requiring reciprocal opening. However, as clearly illustrated by the Chilean multi-pronged strategy of trade liberalization, 15 CAFTA is just one of the many trade policy options that Central American countries can pursue, and probably the best way to evaluate the opportunities offered by such a regional agreement is to compare it with the benchmark case of full liberalization. Two main scenarios are thereby considered: (1) a CAFTA type reciprocal liberalization, and (2) a full unilateral non-discriminatory trade liberalization. Although not being a realistic policy choice, the full unilateral liberalization provides a useful measure against which CAFTA can be evaluated. The potential advantages and disadvantages of the reciprocal liberalization are illustrated by further decomposing the CAFTA scenario into two separate unilateral liberalizations: first, Nicaragua liberalizes vis-a`-vis the United States, which does not reciprocate, and then the United States unilaterally liberalizes vis-a`-vis Nicaragua. In all the simulations, only tariffs are modified and they are completely eliminated in one step with no attempt to capture any sequencing across sectors or phase-out periods. As mentioned earlier, each of the simulations is based on a comparative static framework with no capital accumulation, no changes in labor supply or skill levels, and factor market segmentation. The short-term time horizon implicit in this CGE setup was assumed to focus on the immediate impact of trade shocks on poverty and to facilitate communication between the macro and micro modules where no substitution (i.e., no longterm behavior) is allowed. Additional simulations are carried out to examine the sensitivity of the results to specific assumptions and parameter values. In particular, a first additional simulation eliminates restrictions to inter-sectoral factor mobility, a second considers the case where the assumption of full employment is relaxed. Finally, elasticity values for the substitution between imports and domestic production are modified. However, in these cases, given the behavioral limitations imposed on the micro module, poverty and income distribution results will only be indirectly inferred from the macro results. (a) Macroeconomic results of trade reforms

3. DOES CAFTA HELP INDIVIDUALS TO ESCAPE FROM POVERTY? The DR-CAFTA is a recently negotiated free trade agreement between Central American countries, the Dominican Republic, and the United States. 12 Central American countries have already been enjoying the preferential market access to US markets through the Caribbean Basin Initiative (CBI) program. CAFTA will not only consolidate and formalize these existing benefits, but also provide greater market access by eliminating remaining tariffs on goods that have been exempted from the CBI preferences. The agreement also incorporates a gradual opening of Central American markets for US agricultural commodities. 13 Other main achievements of CAFTA include the greater flexibility of rules of origin for textiles and apparel, commitments to help producers meet sanitary and phytosanitary standards required for the entry into the United States, reciprocal commitments on access to service markets, and the institutional and legal framework to ease foreign direct investment (FDI) flows to the region. 14 Besides providing almost full free access to one of their major markets, the CAFTA agreement should assist the implementation of additional domestic market reforms, and produce significant efficiency gains due to resource reallocations toward more

In a general equilibrium model all relative prices and quantities are determined simultaneously. However, to disentangle the trade policy reform effects on the economy, it is helpful to describe the adjustment process as if it were sequential. First, reduction of tariffs increases import flows which, in turn, displace domestic production and generate resource reallocations. These shifts, by interacting with factor supply and demand, determine factor prices, and combined with new goods prices, affect household’s real income level. Then, changed household incomes feed back into the system through changes in consumption choices and the process continues until a new equilibrium is reached. Major advantages of this type of model are that it represents the whole economy in a consistent and theoretically sound framework and that the structural features of the country investigated strongly influence the final results. 16 Table 2 shows these features for Nicaragua in terms of sectoral shares of gross production, imports, exports, and private demand; the middle panel details the United States weight in total trade for each sector; the right panel shows Nicaraguan tariffs against the United States and other partners. Although the model is based on 28 sectors, the results for the aggregated sectors shown in the table are reported throughout this paper. 17 The initial import protection, both in its level and sectoral variability, is a crucial element determining the outcome of

DO REGIONAL TRADE PACTS BENEFIT THE POOR?

151

Table 2. Nicaragua’s economic structure in the year 2000: aggregate sectors averages (%) Sectoral shares

Agriculture Food processing Mining and energy Other manufacturing Services

US weight

Tariffs

Xp

M

Ex

Xc

M US

Ex US

Nic-US

Nic-ROW

17 15 4 12 53

4 12 10 68 6

32 36 4 20 8

13 29 3 19 37

41 18 1 28 24

28 54 64 14 40

20 8 2 3 0

6 4 0 3 0

Source: Nicaragua SAM estimated by the author. Notes: In the left panel, Xp represents the sectoral output as a percentage of total output; M, the sectoral total imports; Ex, the exports shares; Xc, the private consumption shares, In the middle panel; M US, the initial share of imports coming from the United States over total imports; Ex US, the initial share of exports going to the United States. In the right panel, tariff rates are shown: Nic-US and Nic-ROW are Nicaraguan tariffs against the United States and other partners’ imports, respectively.

would lead to an increase of competition in the agricultural sectors with a potential initial negative shock for households strongly dependent on farming incomes. The level of sector aggregation used in the model may exacerbate this potentially negative outcome. It may be that at finer sectoral levels, imports and domestic products are complements rather than substitutes. For example, lower feed costs could stimulate livestock and poultry productions. However, agricultural products are normally fairly homogeneous and thus substitutable, and the risk of negative impacts should not be completely ruled out.

the simulated trade reforms. Three features are highlighted by the tariff data: (1) the overall trade-weighted protection rate is rather low, (2) its dispersion is high with a clear bias against agricultural imports, and (3) tariffs against the United States are generally above the trade-weighted average of tariffs against the Rest of the World. Table 2 also shows that domestic Nicaraguan agricultural producers may be facing strong competition from US imports, which are 41% of total imports of agricultural commodities. It is likely that a liberalization of US imports, which reduces an anti-agricultural import bias,

Table 3. Sectoral effects of full unilateral trade liberalization Full unilateral trade liberalization Imports and local sale

Agriculture Food processing Mining and energy Other manufacturing Services

tm

DM

M/D

12 5 0 3 0

23 6 4 2 4

7 21 48 57 3

Exports and production DS

DPd

0.4 0.7 0.8 2.9 0.2

Ex/Xp

DEx

2.9 2.3 1.6 1.3 0.8

7 6 3 5 5

23 28 12 21 2

DXp

DPx

1.1 1.1 0.4 1.6 0.1

2.5 1.8 1.5 1 0.8

CAFTA agreements Imports and local sales

Agriculture Food processing Mining and energy Other manufacturing Services

tm US

DM

W/D

21 8 2 3 0

22 3 0 1 1

7 21 48 57 3

Exports and production DS

DPd

0.1 0.9 0.5 0.4 0.2 0.3 0.8 0.1 0.1 0.4 Decomposition of CAFTA

5 6 2 1 1

Nicaragua unilateral liberalization Imports and local sales DM

DS

23 28 12 21 2

DXp

DPx

1.0 1.9 0.4 0.5 0.1

0.7 0.0 0.3 0.0 0.4

US unilateral liberalization

Exports and production DXp

Ex/Xp

DEx

Imports and local sales DS

DPd

Exports and production

DPd

DEx

DPx

DM

Agriculture Food processing Mining and energy Other manufacturing Services

19 1 0 1 1

0.1 0.5 0.2 0.7 0.1

1.9 1.1 0.2 0.4 0.1

5 3 0 1 0

1.0 1.3 0.2 0.3 0.1

1.7 0.9 0.2 0.3 0.1

3 1 0 0 2

0.2 0.1 0.0 0.2 0.1

1.0 0.7 0.5 0.3 0.5

DEx 0 3 2 0 2

DXp 0.1 0.6 0.2 0.2 0.0

DPx 1.0 0.9 0.5 0.3 0.5

Total

1

0.1

0.6

3

0.3

0.5

1

0.0

0.6

1

0.1

0.6

Source: Calculations based on model results. Notes: tm represents initial tariff rates, DM, the percent variation in total import volumes with respect to the initial levels; M/D, the ratio of imports to domestic demand (the sectoral import dependency, calculated using pre-liberalization levels); DS, the percent variation in the volumes of domestic sales of domestic output; DPd, the percent variation in domestic prices for local sales; DEx, the percent variation in the volumes of exports; Ex/Xp, the ratio of exports to domestic output (the sectoral export orientation); DXp, the percent change of domestic output; and DPx, the percent change of output prices.

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(i) Unilateral liberalization with respect to all trading partners As outlined above, the adjustment process caused by this reform is initially described in terms of sectoral demand and supply changes as shown in Table 3. Consider first the demand/imports side. Initial tariff rates tm 18 are highest in Agriculture and Food Processing sectors—in particular in Basic Grains, Meat and Fish Products, Sugar Products, and Dairy—and accordingly these sectors experience the largest increases in imports once protection is removed. Import volumes increase (DM) by 23% for Agriculture and 6% for Food Processing from their pre-liberalization levels. These increases can be compared with the average 2% increase for total imports. However, imports do not represent a large share of local demand (M/D) in Agriculture and account for just a moderate one in Food Processing. Thus, even with a high elasticity of substitution between local production and imports (set equal to 3), the impact of increased imports on sales of domestic goods (DS) for Agriculture and Food Processing is very low, 0.4% and 0.7%, respectively. Compared to these sectors, Other Manufacturing suffers from larger domestic sales contractions ( 2.9%) due to its larger initial share of import dependency despite its lower initial level of protection. For the economy as a whole, these low or moderate domestic market share losses are reflected in small declines of producer prices for local sales (DPd). Some of these effects are larger when disaggregated sectors are examined, and complementary analyses considering more disaggregated sectors of production may be needed to identify specific sensitive commodities. 19 These demand/imports effects are linked to the supply response to which we now turn. For producers of exportable goods, the reduction of prices in local markets (DPd) combined with unchanged export prices creates incentives to increase the share of sales destined to foreign markets. This export response (DEx) varies across sectors and is linked to the pattern of Nicaragua’s comparative advantage, which is found within three main sectors: Coffee, Meat and Fish Products, and Textiles and Clothing. For these sectors, rising export sales more than offset the reduction of domestic sales and lead to an overall increase in sectoral production (DXp). In other sectors, 20 with lower export orientation, the change in sectoral

production is roughly equal to the change in local sales (DS). Sectors enjoying export-led growth experience reductions of output price (DPx) smaller than the reductions of domestic sales prices (DPd) because output prices are a combination (CES prices) of fixed export prices and domestic prices. In summary, trade liberalization, even if it consists of eliminating relatively low economy-wide protection (3%), entails considerable structural adjustment. 21 Within agriculture, Basic Grains is the only sector registering a contraction due to its high tariffs and low export orientation. Among other sectors, Coffee and Other Agricultural Products enjoy significant export-led growth. Similarly, in the non-farm portion of the economy, import competing sectors contract and release resources that move toward sectors that were less protected or produced for foreign markets. Considering the aggregate averages, the macro-sector Food processing records positive output changes, whereas the other non-farm macro sectors experience moderate contractions. Changes in factor remunerations, shown in Table 4, are another important aspect of the structural adjustment caused by trade reform. Changes in wages and capital returns are linked to changes of goods prices through the production technology and the functioning of the factor markets. In the farm segment (which corresponds to the macro-sector Agriculture in the previous tables), capital (including land) has a positive real price change (5.1%), and skilled and unskilled labor experience wage reductions ( 1.6% and 3.9%, respectively). The expanding agricultural sectors are those which use capital relatively more intensively than Basic Grains, which is contracting. The largest output gainers—Coffee, Other Agricultural Products, and Livestock sectors—use together almost 70% of the total farm capital value added. Turning to the non-farm segment, it is easy to see that Food Processing, the sector with the largest output expansion, is relatively intensive in the use of capital. In terms of number of workers (rather than value added which includes wage differential biases), this sector also uses most intensively unskilled workers. Other Manufacturing, the sector experiencing the largest contraction, uses unskilled labor to a large extent but not as intensively as Food Processing. The combination of the trade shock with this production structure explains why

Table 4. Factor Price changes due to full trade liberalization Full unilateral liberalization DP

D(P/CPI)

CAFTA liberalization DP

D(P/CPI)

% of Full liberalization

Non-farm segment Skilled labor Unskilled labor Capital Sk/Unsk wage gap

0.3 0.6 0.6

2.1 2.9 1.8 0.9

0.7 1.0 0.9

1.2 1.6 1.5 0.4

60 55 85

Fam segment Skilled labor Unskilled labor Capital Sk/Unsk wage gap

4.0 6.3 2.7

1.6 3.9 5.1 2.5

2.0 4.5 4.1

1.4 3.9 4.7 2.6

87 100 92

Price indexes Food price index Non-food price index CPI

3.6 1.5 2.4

1.4 0.0 0.6

39 1 24

Sources: Calculations based on model results. Notes: The first column, DP, represents the percent variation of the price of each factor with respect to the initial levels, and D(P/CPI) is the percent variation of the price deflated by the consumer price index. The column, % of Full lib., shows the percent ratio of the real price changes in the CAFTA scenario with respect to the unilateral non-discriminatory full liberalization case.

DO REGIONAL TRADE PACTS BENEFIT THE POOR?

unskilled labor is the largest gainer in the non-farm segment (2.9%), followed by skilled labor and capital (2.1% and 1.8%, respectively) as shown in Table 4. These results are consistent with the comparative advantage of Nicaragua, a country with abundant unskilled labor that specializes in the production of agriculture-derived products. Nicaragua is also import dependent for capital goods and intermediates, which are normally produced by sectors with an intensive use of skilled workers. Even with segmented labor markets, the farm and non-farm parts of the economy have strong interconnections that determine the final results, particularly between the Agricultural and the Food Processing sectors. 22 While both sectors face the largest average drops in tariff protection and large inflows of imports, they also have the largest aggregate output gains. This is achieved by significant structural shifts that are qualitatively different between these two sectors. For Agriculture, the main adjustment consists of a reduction of one single sub-sector and a specialization toward export-oriented sectors. Increased competitiveness is achieved by a reduction of domestic production costs. This, in turn, is obtained through a contraction of factor costs (mainly a wage reduction) rather than through cost savings from cheaper imported intermediates goods. This latter type of cost savings is limited given the moderate use of intermediates (35% of total input value) in agriculture. For Food Processing, the inflow of imports does not entail large sectoral contractions because producers can still compete in domestic markets by enjoying reduced production costs. This is due to their use of cheaper intermediates, which represent, on average, almost three-quarters of total input value. In fact, most of these intermediate inputs come from agriculture whose prices are reduced following the trade shock. (ii) CAFTA bilateral trade liberalization The full unilateral trade liberalization serves as a benchmark against which the CAFTA regional agreement can be compared. Table 3 above reports sectoral results for the simulation of this regional free trade agreement. This policy discriminates between import origins and has trade diverting effects that may not be compensated by trade creation. However, as shown below, this geographic discrimination is not the most relevant aspect of this policy option. Nicaragua’s liberalization of US imports affects just onequarter of total imports (as shown in Table 2) and, thus, has a smaller aggregate impact than full unilateral trade liberalization. Nonetheless, the overall structural adjustment and intersectoral resource reallocations are quite significant given the large US weight in some critical sectors—such as the 72% of imports of Basic Grains and the 26% of exports of two of the largest exporting sectors in Nicaragua, Coffee, and Meat and Fish Products. The CAFTA agreement includes increased market access for Nicaraguan products in the US market. As shown more clearly in the next subsection, however, this reciprocal liberalization amounts to a positive but rather small shock. A preferential bilateral agreement with the United States shows some relevant divergences from a full liberalization, especially with respect to factor price changes. Firstly, the overall price deflation resulting from partial trade reform is roughly equal to one-quarter of the deflation induced by complete tariff abatement ( 0.6% as opposed to 2.4% as shown in the bottom panel of Table 4). Secondly, a CAFTA agreement entails a liberalization that is not only geographically biased but also sectorally distortionary. Consider again the shares of imports originating from the United States in Table

153

2. The economy-wide average share is 24%, but the imports of the US agricultural goods represent more than 40% of total imports in that macro sector, with peaks of 72% for Basic Grains, which is also the most protected sector. Additionally, tariffs against US imports are slightly higher than those against other partners. As a result, the CAFTA agreement-induced imports surge of agricultural goods is equal to 94% of that induced by a full unilateral liberalization, whereas the economy-wide average imports surge stands at 76%. These sectoral distortions explain why factor returns in the farm segment undergo changes that are very close to those experienced in a full liberalization scenario. The unskilled labor real wage contraction is in fact the same in the two cases, whereas factor returns in the non-farm sector record a smaller percentage of the full liberalization shock. In summary, the impact on factor remunerations of the examined trade reforms, namely full liberalization and CAFTA, should be positive for urban workers, both wage-workers and self-employed owners of physical capital. However, it may, at least temporary, be negative for wage-workers of the rural sector. Besides, the potential negative effects of inequality derived from these factors’ price trends are exacerbated in the case of the regional agreement. (iii) Is reciprocity important? In order to distinguish the effects of market access from those of own tariff unilateral abatement, the simulated reciprocal CAFTA trade agreement has been decomposed into two separate reforms: in the first, Nicaragua unilaterally eliminates all tariffs against US imports and, in the second, the United States unilaterally responds, that is, it preferentially liberalizes imports from Nicaragua. 23 As anticipated, the opening up of the Nicaraguan market accounts for almost three-quarters of the variation in imports, exports, and domestic output recorded by the reciprocal CAFTA case. As shown in Table 3, in the case of unilateral US liberalization, effects on imports and local sales are more or less muted, and the most visible effects consist of some additional specialization in exports of food processing products. The two unilateral liberalizations are consistent in their sectoral output effects. Both induce additional growth of agricultural and food processing sectors and, in this sense, help Nicaragua exploit its comparative advantage. Although the United States has already granted preferential access to Nicaraguan exports in the past, the remaining US tariffs seem to inhibit potential growth in some key sectors in Nicaragua, and obtaining full access to the US markets may thus bring some advantages. As far as factor market effects are concerned, the non-reciprocal removal of Nicaragua’s tariffs causes factor prices of the non-farm segment to vary almost as much as with the full CAFTA scenario. Interestingly, the two unilateral liberalizations have contrasting price effects for factors in the farm segment. In the case of the US liberalizing its tariffs, factor prices go up due to the increased export demand and this inflationary effect is not counterbalanced by inflows of cheaper imports. However, these inflows explain why factor prices tend to contract with the unilateral liberalization of Nicaragua. Increasing market access thus mitigates the potentially negative shocks to farm incomes associated with the liberalization of Nicaraguan agricultural markets. (iv) Sensitivity analysis The model used to evaluate trade policy reform is a static model, which only accounts for the allocative efficiency gains originating from trade liberalization. It does not measure the potentially vastly larger dynamic gains. For instance, in the

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current setup, market access does not appear to provide large benefits. This is because increased capital flows, productivity gains, locking up of important domestic reforms, and other dynamic links between trade regimes and growth are not accounted for. In addition to its static nature, the current model imposes extra restrictions: factors’ mobility is limited and factor supplies are fixed. These two latter assumptions intensify sectoral price responses to tariff abatement. Factors ‘‘trapped” in sectors hit by imports surges experience decreasing demand and lower returns. Similarly, the full employment assumption rules out quantity adjustments and the new equilibrium is reached only by changes of wages and rental rates. The initial specification (with low mobility and no change of factor supplies) can be very useful to highlight potential losers and inform compensatory policies. However, since factors move from one sector to another in the longer run, and given the observed high level of underemployment in Nicaragua, it seems desirable to test the sensitivity of results to these assumptions. The CGE literature 24 has also emphasized that the elasticity of substitution between imported and domestically produced commodities in demand (the Armington elasticity) is among the most important parameter determining the response of applied models to trade policy experiments. To conclude the macro analysis, therefore, the trade reform scenarios are replicated with high and low value cases for the Armington elasticity. For the sake of brevity, only the full unilateral liberalization scenario is analyzed in this sensitivity section, but the main findings can be easily extended to the CAFTA case. In the version with perfect factors’ mobility (but still with no change in their supplies) sectoral specialization is stronger and Nicaragua exploits more fully its static comparative advantage in producing agricultural goods. Agriculture, which accounts for almost 20% of total output, records the largest expansion. In the full liberalization case, unskilled workers experience an economy-wide 0.4% increase in their real wages. In fact, this increment is approximately the average of the real wage changes recorded in the segmented markets case. In that setup, non-farm unskilled workers were experiencing a 2.9% raise, but farm workers were facing a 3.9% cut in their wages. Overall, assuming flexible factor markets results in stronger specialization and in a slightly higher positive change in real GDP. In the full liberalization case, real GDP increases by about 1.1% with perfect mobility and by about 0.6% with segmented factor markets. In the version with flexible labor supply 25 (but with segmentation between the farm and non-farm segments of the economy), changes in both quantity and price contribute to reaching a new equilibrium after the shock. For this reason, changes of real wage are smaller in comparison with the full employment case. In the full liberalization scenario, real wages for rural unskilled workers decrease by 1.3%, quite less than the reduction of 3.9% in the initial specification. However, in the current case, labor supply contracts by 1.8%. This effect explains why agriculture expand less than in the fixed factor case. Opposite trends are observed for the non-agricultural segment of the economy. In the initial specification with fixed labor supply, real wages for unskilled workers were increasing by 2.9% whereas in the current setup they only go up by 1.6%, but their employment increases by more than 1%. And this is the reason why non-agriculture sectors expand (services, the largest of these sectors, expand its output by 0.5% whereas before it was contracting by 0.1%). In summary, with flexible labor supply, the trade reform shock is absorbed by changes in both employment levels and wages; however, the qualitative results, such as the potential losses (in this version, in terms

of both wage reduction and unemployment) for poorer unskilled workers in agriculture, remain the same. One response to address the uncertainty regarding elasticity values has been to undertake ‘‘systematic sensitivity analysis,” where a range of elasticity values are used and results are presented with corresponding confidence intervals. However, this approach is computationally very intensive and generally tends to support the view that simulation results are robust with respect to a broad range of elasticity values (see Harrison, Rutherford, & Tarr, 2003). A simpler approach is adopted here and the full unilateral liberalization scenario (under the specification of fixed labor supply and low inter-sectoral mobility) has been replicated with high and low levels of the key Armington elasticities. 26 Compared to the key results of Table 4, the higher elasticity experiment generates stronger price reactions: unskilled workers in the agriculture segment experience a real wage reduction of 7.0%, compared with the central elasticity case of 3.9%; in the non-agriculture segment, the same type of workers enjoy a 3.7% increase in their real wages versus the previous 2.9% increase. Higher Armington elasticities induce stronger substitution of (post-liberalization) cheaper imports, and thus contribute to these factor returns’ effects. Similarly lower elasticity values, by reducing the substitutability between imports and domestic output, generate lower factor price responses. Although more precise estimates for some of the key elasticities used in the current CGE model would be desirable, as with the previous sensitivity cases, the qualitative results do not seem to be affected by the uncertainty surrounding the values used in the central case. The macro results presented above are broadly consistent with those obtained in existing CGE studies on CAFTA, which illustrate the overall positive impact of CAFTA on Central American economies (e.g., Brown, Kyota, & Stern, 2005; Francois et al., 2005; Hilaire & Yang, 2004). Francois et al. (2005), for instance, illustrate that the further export specialization on the textile and apparel sector shifts resources away from agricultural sectors, which can generate significant adjustment strains. Besides, given the increasing competition from China in the textile and apparel sector, they argue for the importance of implementing policies aimed at diversifying exports and increasing agricultural competitiveness. Brown et al. (2005) also find that Central American countries would enjoy a larger welfare gain from unilateral free trade than from a CAFTA case. A limitation of these studies is that Central American countries are treated as one entity and the differences in the economic structures and endowments within the region are not taken into account. In fact, the poverty rates vary from 20% in Costa Rica to 60% in Nicaragua (Francois et al., 2005) and the Gross National Income per capita of Nicaragua at US$790 in 2004 is only 17% of that of Costa Rica. 27 The latter also has a larger fraction of industrial exports classified as high-technology products unlike the rest of the member countries (Francois et al., 2005). These differences highlight the need to evaluate the impact of CAFTA on Central American economies individually, particularly from the poverty perspective. The present analysis tries to fill this gap by examining the specific case of Nicaragua. (b) Microeconomic results: the poverty effects of trade reforms The above aggregate macro results are used to shock the micro data to produce a new income distribution. A reform of trade policy results in different levels of aggregate income or GDP—the ‘‘growth” impact—and in significant changes of factors’ and goods’ prices—the distributive or ‘‘inequality” im-

DO REGIONAL TRADE PACTS BENEFIT THE POOR?

pact. In order to appreciate the relevance of the distributive effect, we calculate two counterfactual distributions. The first, labeled the ‘‘full distribution,” is obtained by mapping the aggregate factor and goods price changes from the CGE to the relevant income sources and consumption patterns of the micro data. In the second counterfactual distribution incomes for all households are shifted according to the average growth effect, irrespective of their specific structure of income or pattern of consumption. This second simulated distribution has almost the exact same ‘‘shape” as the initial one since almost no redistribution has occurred. The qualification is needed because under this method—labeled as the ‘‘distribution neutral” approach—location-specific growth rates are separately applied for rural and urban households. A straightforward comparison between the full distribution and the distribution neutral results provides an assessment of the distributive effect due to changes in relative prices. For the trade reform scenarios analyzed here, the aggregate income change rates—estimated by aggregation from the micro data shocked with the factor and goods prices changes— are shown in Table 5. The table highlights that trade liberaliza-

155

tion is positive overall, however it tends to favor urban households more than rural ones. 28 Table 6 shows the poverty effects estimated using the full distribution and the distributionally neutral approaches for the simulated unilateral non-discriminatory liberalization and CAFTA agreement. 29 The initial poverty conditions in Nicaragua, as depicted by the indicators shown in the top panel of Table 6, are quite worrisome. Nicaragua is one of the poorest countries in Latin America with about half the population being poor and more than 20% extremely poor. The situation looks even worse for the rural area where almost twothirds of the population is poor and one-third is extremely poor. The poverty gap (PG) suggests that, for the whole sample, the perfectly targeted cash transfer needed to lift every poor person out of poverty is 21% of the poverty line, with considerable variations among the rural and urban portions of the sample. Inequality among the poor, measured by the severity index (P2), seems to be fairly high for the rural population. Overall, both liberalization scenarios marginally reduce poverty in Nicaragua, with the non-discriminatory one being

Table 5. Real income change at the aggregate level (%) All

Urban

Rural

Full unilateral liberalization

1.48

1.73

0.82

CAFTA bilateral liberalization Nicaragua and US liberalization Nicaragua liberalization only US liberalization only

0.91 0.69 0.22

1.05 0.88 0.16

0.55 0.18 0.37

Source: Calculations based on model results and Nicaragua LSMS 2001. Note: The figures show the percentage change in real incomes at the aggregate (national) level.

Table 6. Initial poverty levels and percent changes due to trade reforms (%) Initial distribution H

PG

P2

Absolute levels Initial levels

All

PovLine ExtremePL PovLine ExtremePL PovLine ExtremePL

Urban Rural

49.8 21.0 39.8 12.5 63.9 32.9

21.0 6.9 14.9 4.1 29.5 10.9

Full distribution H

PG

1.6 4.4 2.5 7.0 0.9 3.1 0.3 0.9 0.4 1.0 0.2 0.8

3.7 6.9 6.3 11.3 1.8 4.6 1.2 1.8 2.3 3.4 0.4 1.0

11.6 3.3 7.6 1.9 17.1 5.3

Neutral distribution P2

H

PG

P2

Percent changes from initial levels Full unilateral liberalization

All Urban Rural

CAFTA bilateral liberalization

All Urban Rural

PovLine ExtremePL PovLine ExtremePL PovLine ExtremePL PovLine ExtremePL PovLine ExtremePL PovLine ExtremePL

Source: Calculations based on model results and Nicaragua LSMS 2001.

4.9 9.6 8.4 15.5 2.8 6.7 1.4 2.5 2.8 4.4 0.5 1.5

1.3 2.5 1.6 2.5 0.4 1.0 0.7 1.1 1.2 1.3 0.3 0.7

2.0 3.0 2.8 3.5 1.0 1.6 1.2 1.8 1.7 2.1 0.6 1.1

2.4 3.1 3.2 3.8 1.2 1.7 1.5 2.0 2.0 2.4 0.8 1.2

WORLD DEVELOPMENT

2 1

% net gain or loss

6 4 0

0

2

% net gain or loss

8

3

10

4

156

0

20

40

60

80

100

0

20

% of pop. ranked by per capita income

40

60

80

100

% of pop. ranked by per capita income

Figure 3. Mean percentage gains by per capita income percentile: full liberalization (left graph), CAFTA liberalization (right graph). Source: Calculations based on Nicaragua LSMS 2001.

more pro-poor than CAFTA. A positive aspect is that percentage reductions are larger for the extreme poverty headcount than for the standard poverty headcount. Both trade liberalization scenarios induce larger poverty reductions for the urban than the rural population. However, the two scenarios differ in their distributive impacts. For the non-discriminatory liberalization, poverty reductions (across all categories) are actually higher in the full distribution approach than in the distribution neutral one, suggesting that this type of trade reform induces a pro-poor distributional shift. The opposite happens in the CAFTA

scenario. Notice that comparing the full and the neutral distributional approaches accounts only for the distributive shift that is left after the different ‘‘growth” effects are applied to the urban and rural areas. This net distributive shift is due to what happens within the urban and rural areas and is caused by anti- or pro-poor changes in prices of factors, inputs and/or outputs. In the case of the full unilateral liberalization experiment, as shown in Table 4, unskilled labor wages in the farm segment are declining more steeply than returns of the other factors; however they are increasing more rapidly in the non-farm segment. In the CAFTA sce-

Table 7. Repression results for logarithms of income changes Full liberalization Coef. Household head’s characteristics Age Age squared Female Education (no education) Primary or less Secondary Technical University or above Demographic variables Logarithm of hhsize Ratio of no. of infants (0-5) to hhsize Ratio of no. of children (6–12) to hhsize Geographic variables Urban regions (Managua) Pacific Central Atlantic

**

3.33 0.02 * 16.31

*

***

63.19 100.11 ** 171.33 *** 193.12 ***

***

74.35 41.70 17.41

***

R

2

No. of observations

1.38 0.01 8.99

8.35 25.52 87.73 58.33 10.16 32.95 32.33

68.56

7.88

85.23 105.32 *** 83.76

12.74 12.52 13.25

10.11

32.78

168.29

32.51

*** ***

Employment variables Ratio of skilled workers to total workers Constant

Robust Std. Err.

***

0.19 4169

CAFTA Coef. *** *** ***

5.70 0.04 22.13

***

42.69 34.30 26.54 *** 334.02 **

***

49.62 19.04 20.92

***

*** *** ***

Robust Std. Err. 1.61 0.01 8.39

6.27 14.66 18.96 104.85 16.19 22.45 20.73

26.82

6.30

53.71 61.58 32.75

13.79 12.85 12.14

***

66.40

13.79

15.86

25.66

0.17 4169

Note: The dependent variable is the change from the initial income (in cordoba). Significance levels of 1%, 5% and 10% are indicated by respectively. Source: Calculations based on model results and Nicaragua LSMS 2001.

*** **

,

and *,

DO REGIONAL TRADE PACTS BENEFIT THE POOR?

nario, the farm unskilled wages contraction is about the same as in the full liberalization (see the last column of Table 4) and, for the same type of labor, this is not compensated by a higher increase in the non-farm segment. Besides in the farm sectors, the ratio of output to input prices tends to change more favorably for the self-employed farmers in the non-discriminatory liberalization than in the CAFTA case. The complete distributional effect can be seen in Figure 3, which plots incidence curves for the two trade policy reforms, and for the total, urban, and rural populations. These graphs confirm the observations based on the more aggregate indicators of the previous table: the urban population enjoys larger gains and the full liberalization scenario is more progressive than CAFTA. Having identified some variation in the effects of trade liberalization across households, it would be interesting to see how these effects vary with observed household characteristics; identifying strong correlates should help in designing compensatory policies. To do that, the change in per capita incomes 30 under different scenarios considered above is regressed on the age, age squared, gender and education level of household head, the logarithm of household size, the ratios of number of infants (age 0–5) and children (6–12) to household size, urban and regional dummies, the ratios of number of skilled workers to the total number of workers in the household. 31 They are estimated by ordinary least squares and the results are summarized in Table 7. As expected, the two scenarios display similar results and most of the coefficients have the expected sign. The coefficients on age and age squared indicate that the gain from trade reform first increases and then decrease with age. Education variables unambiguously show that the more educated the household head, the larger the gains of the household, which seems to coincide with the positive coefficient on the ratio of skilled workers. This can be partly explained by the fact that more unfavorable effects on wage were observed for unskilled labor in the farm sector under both scenarios (see Table 4). Impacts of the reforms also show some geographical variation. The significant positive effect of residing in the urban sector reflects the more favorable effect of trade liberalization on the non-farm sector. Similarly, the coefficients on the regional dummies show that households in the capital Managua (the largest urban center) tend to gain more from trade liberalization. These results imply that agricultural households in the rural sector may face short-term adverse shocks from the reform, and some compensatory measures should be formulated to ease any anti-poor effect. The negative coefficient on the logarithm of household size is not very intuitive given the decline of food prices. Some of the counter-intuitive results from these regressions emphasize the importance of using the full distribution of income, as opposed to just average poverty and inequality indicators, to fully understand what drives poverty dynamics and prepare government responses to possible anti-poor impacts.

4. CONCLUSIONS This paper analyzes the income distribution and poverty impacts of various trade options currently under the scrutiny of Central American policy makers. In particular, it assesses for Nicaragua the poverty effects of the DR-CAFTA recently signed between five Central American countries plus the

157

Dominican Republic and the United States. The methodology adopted here relies on a general equilibrium macro model used to simulate various trade reform scenarios and to estimate the price effects of these scenarios, and a micro module which maps the aggregate general equilibrium price changes onto variations of real incomes at the individual household levels. Among the various assumptions that make this complex analysis treatable, the main one consists of taking into account just the short-term impacts. In fact, the simulations carried out in this analysis do no consider the gradual multi-year phasing in of the liberalization scenarios. The ‘‘big-bang” implementation of the scenarios, where tariffs are eliminated in a single step, implicitly assumes that negatively affected parties have to adjust instantaneously to the new situation. This is useful because it permits highlighting potential problems linked to the new commercial policies. These potential problems may not materialize in the real world given that trade reforms, such as the current DR-CAFTA agreement, envision gradual reductions of tariffs and other protection measures over a 15–20 year time span. In such a time horizon, growth effects derived from investments in physical and human capital and increased flows of foreign direct investment (a potentially important indirect benefit of trade reforms), as well as large consumption shifts toward services due to higher incomes per capita, can strongly enhance the adjustment capacity of economic agents and can even reverse initial negative results. Notwithstanding these simplifications, important economic policy messages can be inferred from the analysis in this paper. The first is that CAFTA may imply some short-term risks for Nicaraguan unskilled poor working in agriculture. Due to the sectoral concentration of imports coming from the United States and the initial tariff structure, which is biased in favor of Basic Grains, a strong agricultural import inflow entailed by a regional agreement with the United States seems to raise some concerns for a short-term potentially unfavorable rural poverty effect in a big-bang scenario. This occurs as cheaper grains from the United States enter the Nicaraguan market and depress prices and factor returns of farmers. Given that farmers, and especially small poor farmers, cannot easily switch occupations or even crops they cultivate, adoption of a (‘‘big-bang”) regional liberalization should be counterbalanced by two additional policy interventions in the short term. A further non-discriminatory trade liberalization and compensatory income support policy targeted toward poor farmers. Through the first, as shown in the unilateral full trade liberalization simulation, the sectoral imbalance in import inflows should be corrected and thus also the price bias against the rural factors. And the second transfer policy should temporarily assist farmers in their adjustment toward expanding sectors. This advice is consistent with a key policy recommendation that emerges from the literature on regional trade agreements, 32 specifically that benefits of regional agreements normally outweigh their costs when protection against outsiders is low. The poverty results produced in this paper appear to reinforce this view: to boost trade-induced poverty reductions, Nicaragua should consider enlarging its own liberalization to countries other than the United States. Another important conclusion is that factor price changes appear to have a more important role than goods price changes across the various transmission channels between trade reform and poverty. Therefore subsidizing consumption may be less effective than subsidizing employment or directly supporting income of the poorer, though this should only be temporary and specifically targeted.

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WORLD DEVELOPMENT

NOTES 1. For brevity, instead of the full acronym DR-CAFTA, the shorter version CAFTA will be used in the remainder of this paper. 2. See Elizabeth Becker’s CAFTA article on the New York Times article, April 6th, 2004.

13. DR-CAFTA, however, exempts from further liberalization some sensitive agricultural products including sugar imports to the United States, white maize imports to four Central American countries (El Salvador, Guatemala, Honduras and Nicaragua), and potatoes and onions to Costa Rica.

3. An excellent collection of general equilibrium studies is found in Hertel and Winters (2006).

14. For more details see Francois, Rivera, and Rojas-Romagosa (2005) and World Bank (2005).

4. One of the main advantages of a simulation model (such as the one used in this paper) is that it can provide an ‘‘experiment lab” environment where one can control which shocks hit the system. Although they have many advantages, identification issues represent a major difficulty for econometric models: in most cases trade policy reform is simultaneous to many other shocks and identifying direct trade policy-related price changes can be a challenge.

15. Chile started liberalizing trade unilaterally toward the end of the 1970s and then moved toward signing trade agreements in the early 1990s, the idea being that it could get useful concessions from trade partners that were not available with unilateral tariff reductions. Chile has signed Agreements with most economies in South America: Bolivia, Colombia, Cuba, Ecuador, Peru, Venezuela, and Mercosur. Chile has also signed standard FTA’s with: the European Union, Canada, Mexico, the United States, EFTA, Central America, and a recently ratified agreement with South Korea. Besides, Chile enjoys full membership of the WTO and benefits from the multilateral rounds of trade liberalization under the WTO’s auspices. Through this strategy, Chile has achieved almost fully free access to most OECD markets and the other developing countries relevant to its trade. For a balanced and perhaps more nuanced review of trade policy in Chile see Lederman (2005).

5. A more detailed description of the CGE model is available in Bussolo and Niimi (2006). The complete list of equations is available upon request. 6. Aggregate investment is set equal to aggregate savings, while aggregate government expenditures are exogenously fixed. 7. See Armington (1969) for details. 8. The labor market skill segmentation has become a standard assumption in CGE modeling and it is easily justifiable for the case of Nicaragua, where inequalities in educational endowments and access to education support this assumption. The assumption that the market for labor is further segmented into agricultural and nonagricultural activities is more controversial. To test its validity, we check whether incomes in agriculture are still below incomes in other sectors once the following wage determinants are controlled for education, experience, gender, and employment-status variables such as self-employment. Additionally, to account for price differentials across space, geographical variables capturing differences among Nicaraguan regions are included in the wage estimation. The regression analysis shows that agricultural individual labor incomes are significantly below a reference group and the gap widens between unskilled and skilled workers (detailed regression results are available in Bussolo & Niimi, 2006). 9. Notice that this is equivalent to assume that transfers are kept constant in real terms, that is, that increasing prices do not change the real value (in purchasing power terms) of these transfers. Similarly, price changes do not affect the real value (in utility terms) of auto-consumption. Alternative assumptions have been tested and, although some numeric results vary, the qualitative findings are unchanged. 10. See Winters et al. (2004) for an excellent survey. 11. Note that the unemployment is calculated on the notion that those who are in the informal sector (e.g., employed without payment) are considered to be in employment. Hence this may be underestimate the unemployment rate. As for the underemployment, we regard those who work less than 40 h a week as underemployed. 12. DR-CAFTA was approved by the US Senate and House of Representatives in June–July 2005 and it has also been approved by the legislatures in El Salvador, Guatemala, the Dominican Republic, Nicaragua and Honduras, while its approval is pending in Costa Rica, as of writing this paper.

16. Three main elements determine the position—that is, the values of the endogenous variables—of the new equilibrium: (1) the starting level of some key variables in the initial equilibrium, that is, the prices and quantities implicit in the initial SAM; (2) the functional forms of the model’s behavioral equations; and (3) some key parameters, namely substitution elasticities among factors in the production process and, for a trade reform analysis, the elasticities of substitution in demand between domestic and imported commodities and the elasticity of transformation in supply between domestic and foreign markets. The bottom line is that results presented here are indicative of a likely response to the analyzed shocks. In most cases, the sign and relative, if not absolute, magnitude of the model’s results—for example, a finding that gains for unskilled labor are larger than those for skilled labor—should be reliable. 17. For more disaggregated sectoral results, see Bussolo and Niimi (2006). 18. Note that column tm in Table 3 is the trade-weighted average of the Nicaraguan tariffs against United States and the Rest of the World (which are separately shown in Table 2). 19. These analyses usually consider data at the tariff line level, that is, at a very fine degree of disaggregation. Trade data at this level may be available, but production, consumption, and other important data needed to calibrate CGE models are normally available at a much more aggregate level. Therefore tariff lines analyses are normally partial equilibrium analyses and they should be considered in conjunction with the general equilibrium analysis presented here. 20. Due to the sectoral classification used in this model, some sectors, notably Tobacco and Machinery and Equipment, appear to be both import and export intensive. However, the apparent export intensity in these sectors results from dividing low levels of exports (probably re-exports) by even lower levels of domestic production. Exports of Tobacco and Machinery and Equipment jointly account for just 2% of total exports. 21. Due to the closure rule of the external account, namely the fixing of foreign savings and the full employment assumption, the larger expansion of the volumes of exports with respect to import volumes is compensated with a real exchange rate depreciation which originates from falling

DO REGIONAL TRADE PACTS BENEFIT THE POOR? domestic resource costs. In other words, exporting sectors expand by employing resources whose relative prices have declined because of their falling demand from the contracting import competing sectors. 22. These two sectors account for a third of total production and for almost 40% of total employment. 23. This decomposition is not exact given that the sequence in which these reforms are carried out matters for the final results. However, in this particular case, given that the magnitude of the shocks, especially the reduction of the US tariffs against Nicaraguan products is not too large; the order in which the two simulations are carried out is almost indifferent. 24. See Harrison, Jones, Kimbell, and Wigle (1993) Hertel, Hummels, Ivanic, and Keeney (2007). 25. In this version, a ‘‘wage-curve” specification is implemented: labor supply expands in response to rising real wages. 26. The choice of any particular point elasticity value is somewhat arbitrary, as estimates vary with countries, time periods, level of disaggregation, and methods of estimation. At the same time, most elasticity estimates fall within a certain range and, as shown in the text, moderate variations in these parameter values do not qualitatively affect the results in well-behaved models. Inter-sectoral simple average for the Armington elasticity values of the first nest (substitution between domestic and imported commodities) are around 3.0, whereas those for the second nest (substitution between import varieties originating from different geographic suppliers) are around 5.0. A detailed sector by sector table of elasticity values can be provided upon request.

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27. Based on data from World Development Indicators 2005. 28. The figures in Table 5 are not fully consistent with those obtained from the CGE model. In fact even by modifying the SAM in accordance with the household survey, some discrepancies between the macro and micro databases remain. Besides, as already outlined, despite the segmentation in the factor markets of the CGE model, intra segment factors’ mobility is allowed, and substitution across goods in consumption is permitted, whereas this is not case for the micro data. At the micro level, individuals never switch jobs and households’ consumption patterns do not change. 29. Although informative, the rural and urban poverty measurement obtained using a single national poverty line may be to some extent misleading. Price levels and the corresponding purchasing powers in the rural and urban sectors of the economy can be quite different. As a result the zone-specific poverty lines should be used to guarantee more accurate estimates. 30. These incomes are computed by separating out auto-consumption and using household-specific price indices. In addition, the simulated incomes are calculated under the full distribution approach. 31. A similar regression analysis is found in Chen and Ravallion (2003). 32. See for excellent surveys World Bank (2000, 2004) and Schiff and Winters (2003).

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