Policy Research Working Paper 8825 Trading off the Income Gains and the Inequality Costs of Trade Policy Erhan Artuc Bob Rijkers Guido Porto Development Economics Development Research Group April 2019 Policy Research Working Paper 8825 Abstract This paper characterizes the trade-off between the income social welfare index. The analysis finds average income gains gains and the inequality costs of trade using survey data from import tariff liberalization in 45 countries and aver- for 54 developing countries. Tariff data on agricultural and age income losses in nine countries. Across countries in manufacturing goods are combined with household survey the sample, the gains from trade are 1.9 percent of real data on detailed income and expenditure patterns to esti- household expenditure on average. We find overwhelming mate the first-order effects of the elimination of import evidence of a trade-off between the income gains (losses) tariffs on household welfare. The paper assesses how these and the inequality costs (gains), which arise because trade welfare effects vary across the distribution by estimating tends to exacerbate income inequality: 45 countries face a impacts on the consumption of traded goods, wage income, trade-off, while only nine do not. The income gains typ- farm and non-farm family enterprise income, and govern- ically more than offset the increase in inequality. In the ment transfers. For each country, the income gains and the majority of developing countries, the prevailing tariff struc- inequality costs of trade liberalization are quantified and ture thus induces sizable welfare losses. the trade-offs between them are assessed using an Atkinson This paper is a product of the Development Research Group, Development Economics. 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 brijkers@worldbank.org, eartuc@worldbank.org, and guido.porto@depeco.unlp.edu.ar. 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 Trading off the Income Gains and the Inequality Costs of Trade Policy∗ Erhan Artuc† Guido Porto‡ Bob Rijkers§ The World Bank Dept. of Economics The World Bank DECTI UNLP DECTI Key words: Trade Policy, Poverty, Inequality, Households, Social Welfare, Trade Liberalization JEL classifications: D1, D6, F1 ∗ We thank R. Adao, I. Brambilla, A. Deaton, L. Gasparini, A. Mattoo, B. McCaig, A. Nicita and M. Olarreaga for comments and discussion, and C. Arkolakis and two anonymous referees for constructive feedback. J. Angbazo, S. Fernandez, W. Kassa, H. Liu, A. Luo and M. Saleh provided excellent research assistance. This research was supported by the World Bank’s Research Support Budget, the ILO-World Bank Research Program on Job Creation and Shared Prosperity, the Knowledge for Change Program, and the World Bank’s Umbrella Facility for Trade. 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 of Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the countries they represent. All errors are our responsibility. † Development Economics Research Group, Trade and Integration, The World Bank. email: eartuc@worldbank.org ‡ Universidad Nacional de La Plata, Departamento de Economia, Calle 6 e/ 47 y 48, 1900 La Plata, Argentina. email: guido.porto@depeco.econo.unlp.edu.ar § Development Economics Research Group, Trade and Integration, The World Bank. email: brijkers@worldbank.org 1 Introduction The recent wave of ‘new’ trade models has rekindled interest in the gains from trade. The results and theorems on the aggregate gains from trade in Dixit and Norman (1980, 1986) have been extended by Arkolakis, Costinot and Rodriguez-Clare (2012), Costinot, Donaldson and Komunjer (2012) and Costinot and Rodriguez-Clare (2014).1 Concurrently, there has also been a renewed interest in the distribution of those gains. These are the focus of Porto (2006), Fajgelbaum and Khandelwal (2015), Atkin and Donaldson (2015), and Atkin, Faber and Gonzalez-Navarro (2018).2 In this paper, we combine these two questions and assess the income gains relative to the inequality costs of trade policy. Using survey data for 54 developing countries, we explore the potential trade-off between the gains from trade and the distribution of those gains and we provide a quantification of the inequality-adjusted welfare gains from trade. The evaluation of this trade-off is important, especially because free trade is often opposed on inequality grounds. We develop a comprehensive model that describes how trade policy affects the real income of different households. Tariffs determine domestic prices which affect households both as consumers and as income earners. As consumers, households are affected through the cost of the entire bundle of traded consumption goods. Similarly, household income is affected through changes in the returns to household production activities, crop growing, family businesses, labor earnings, and government transfers. Our model encompasses all these mechanisms. Following Deaton (1989), we use a first-order approximation to measure how changes in tariffs impact real income. We then combine tariff data on various goods with household survey data on detailed income and expenditure patterns to estimate these first order welfare effects for 54 low and middle income countries. With estimates of the welfare effects of import tariff liberalization for each household, we study the aggregate gains from trade (as in Arkolakis, Costinot and Rodriguez-Clare, 2012) and the distribution of the gains from trade (as in Porto, 2006). Using 1 See also Artuc, Lederman and Porto (2015), Caliendo and Parro (2015), Melitz and Redding (2015), Arkolakis, Costinot, Donaldson and Rodriguez-Clare (2015), and Caliendo, Dvorkin and Parro (2018). 2 See also Nicita, Olarreaga and Porto (2014), Faber (2014), Goldberg and Pavcnic (2007), Topalova (2010), Kovak (2013), Autor, Dorn and Hanson (2013), Dix-Carneiro and Kovak (2017). 1 an Atkinson Social Welfare function (Atkinson, 1970), we assess the trade-off between the income gains and the inequality costs of trade. Our joint study of the gains from trade and their distribution across households contributes to an incipient strand of literature including Antras, de Gortari and Itskhoki (2017) and Galle, Rodriguez-Clare and Yi (2017). It is useful to put our methodological approach into context. Arkolakis, Costinot and Rodriguez-Clare (2012) quantify the aggregate gains from trade by deriving a sufficient statistic to compare autarky with the status quo. Subsequent literature has developed extensions allowing for imperfect competition (Arkolakis, Costinot, Donaldson and Rodriguez-Clare, 2015), labor market frictions (Caliendo, Dvorkin and Parro, 2016), and productivity advantages (Melitz and Redding, 2015). Work on the distributional effects identifies instead winners and losers from trade. Much of this literature builds on Deaton’s (1989) first-order effects approach. Porto (2006) studies the distribution of the household welfare effects across the income distribution, Nicita, Olarreaga and Porto (2014) explore the poverty bias of trade policy (the welfare effects of the poor relative to the welfare effects of the rich), and Atkin, Faber and Gonzalez-Navarro (2018) investigate the distribution of the household welfare effects from FDI. Another branch of the literature examines distributional effects in an Arkolakis, Costinot and Rodriguez-Clare (2012) setting. Fajgelbaum and Khandelwal (2015) introduce non-homothetic preferences and focus on expenditures only. Costinot, Donaldson and Komunjer (2012) and Galle, Rodriguez-Clare and Yi (2017) adopt a Ricardo-Roy model and focus on both expenditures and wages. A distinctive feature of this paper is that we merge these two approaches by looking at both average gains from trade and their distributional impacts. As is standard in the literature, we rely on first order approximations. We offer a flexible model with extensive household heterogeneity in incomes and expenditures. As in Nicita, Olarreaga and Porto (2014), we allow for a more comprehensive set of sources of income heterogeneity than in most other papers. As in Fajgelbaum and Khandelwal (2015), we allow for non-homothetic preferences and heterogeneity in expenditure patterns. To operationalize this flexible framework, we need to impose some structure on our model. Our setting is compatible with perfect competition, constant returns to scale, and homogeneous products. 2 We find average income gains from import tariff liberalization in 45 countries and average income losses in the remainder 9 countries. On average, the developing countries in our study enjoy gains from trade equivalent to 1.9 percent of real household expenditure. This is mostly because the consumption gains from lower prices dominate the income losses from reduced protection. The distributional impacts of import tariff liberalization are highly heterogeneous, across both countries and households. We find that the equality gains, the change in social welfare associated with these distributional impacts, are negatively correlated with the average income gains. Inequality costs arise primarily because trade exacerbates nominal income inequality, while the consumption gains tend to be more evenly spread. This creates trade-offs between the income gains and the equality gains in 45 of the 54 countries in our sample. The income gains typically more than offset the increase in inequality. In 39 countries, liberalization of import tariffs would result in inequality-adjusted welfare gains for a wide range of empirically plausible values of inequality aversion (between 1 and 2). In 9 countries that face trade-offs, protectionism would instead be welfare enhancing for plausible values of inequality aversion. Finally, there are 6 countries where the trade-offs are acute, in which the presence of welfare gains or losses depends crucially on the presumed level of inequality aversion and policy prescriptions are consequently more equivocal. These results imply that in the majority of developing countries in our study, the prevailing pattern of protection induces sizeable welfare losses. The rest of the paper is organized as follows. Section 2 sets up the model and derives the formulas for the welfare effects of trade policy. Section 3 uses the tariff data and the survey data to estimate those welfare effects in 54 countries. Section 4 discusses the gains from trade and their distribution. Section 5 evaluates and quantifies the trade-off between income gains and inequality costs of trade. It also decomposes equality gains into consumption equality gains and income equality gains, presents robustness tests, and assesses the trade-offs that would arise if countries were to undertake protectionist trade reforms instead of liberalizing. Section 6 concludes. 3 2 Tariffs and Household Welfare In this section, we develop a model to study the welfare effects of tariff changes. We adopt an extended agricultural household model to define household welfare (Singh, Squire and Strauss, 1986; Benjamin, 1993) and we derive the welfare effects using first order approximations (Deaton, 1989; Porto, 2006; Nicita, Olarreaga and Porto, 2014). We begin by discussing production decisions. We assume that households are endowed with a fixed amount of resources v h , which include land or capital, and labor Lh . There is no leisure choice but households can differ in the labor endowment because of differences in family size and composition. Assume for now that these factors can be allocated to the production of one (composite) agricultural good or to the labor market. The agricultural good i is produced with a constant return to scale production function Fi (v h , Lh ). The household takes goods prices pi and wages w as exogenous. There is no market for land or capital v , but labor can be traded, i.e., it can be hired in-farm, sold off-farm, or sold to the labor market. These different types of labor are perfect substitutes. Agricultural profit maximization requires using labor in-farm (own or hired) up to a point where pi ∂Fi /∂L = w. The profit function associated with this optimization problem is πi (pi , w, v ). In this h formulation, household income y h is the sum of maximized profits πi and the value of the labor endowment wLh . To simplify the exposition that follows, let wh be the labor income that household h derives only from the labor market and let π h be maximized profits defined net of hired off-farm labor only.3 Allowing for many goods (Singh, Squire and Strauss, 1986), total maximized household income y h is (1) y h (p, v h ) = wh + h πi (p) − T h + Ωh , i h where p is the vector of prices pi , πi are farm enterprise profits obtained from the sales of good i (such as cotton, tobacco, beans or maize), and T h are taxes paid to (or transfers 3 In particular, let Lhired be hired labor in-farm, Lmarket the supply of household labor to the labor market, and let Lf arm be the own-farm family labor. Then, the labor endowment is Lh = Lmarket + Lf arm , while farm employment is LD = Lf arm + Lhired . In addition, π h = pi qi − wLhired and household income is wh + π h . See Benjamin (1993) for a full derivation. 4 received from) the government. All other potential sources of income, such as remittances, gifts, other types of public transfers (e.g. pensions) and income from non-traded household enterprises are included in Ωh .4 Household h maximizes a utility function defined over a vector of consumer goods c, uh = u(c), subject to a vector of given prices p and total household income. Assuming households are price takers in consumer markets, in production and in the labor market, the optimization problem is recursive because production decisions are independent of consumption decisions. Thus, households maximize u(·), subject to p and maximized income y h . The solution to this optimization problem delivers a demand function for each good. Optimal consumption of good i is ch h i and, given required utility u , the household expenditure function is (2) e(p, uh ) = p i ch i (p). i To derive the welfare effects of trade, we use the concept of compensating variation CV h . For price changes, this is generally done using the expenditure function eh . In our case, we need to consider the fact that trade affects nominal income y h as well. Consequently, we follow Dixit and Norman (1980) and Anderson and Neary (1996) and use the trade expenditure function, V h : (3) V h (p, v h , uh ) = y h (p, v h ) − e(p, uh ), which depends on prices p via the maximized nominal income function y h (·) and the expenditure function e(·). Note that the trade expenditure function is usually defined in the Hicksian tradition as eh − y h but we work with (3) instead so as we can interpret the results as changes in real household income (Porto, 2006). We proceed in two steps. We first derive general welfare effects of price changes in Proposition 1. Then, in proposition 2, we derive estimable welfare effects of trade policy. In particular, we list additional assumptions that we need to impose in order to obtain 4 Because of data constraints, we do not deal with savings, debt, inventories, and other intertemporal considerations. Because very few surveys include detailed input expenditure data in the income modules, we do not deal with imported input prices (seeds, fertilizer) either. 5 estimates of the welfare effects that are compatible with our data. This proposition imposes restrictions on the price changes and on household impacts that we can accommodate in our data. Changes in these assumptions allow for different responses and different welfare effects. We discuss below some salient alternative model formulations, and present robustness tests in section 5.6. Proposition 1 Assume the household is a price taker in consumer, producer and labor markets. Given the income generating function y (p, v h ) in equation (1) and the expenditure function e(p, uh ) in equation (2), the impact of a price change on household welfare dVih (as a share of household initial expenditure eh ) is given by dVih ∂wh pi dT h (4) = (φh h h i − si ) + φw d ln pi − eh ∂pi wh eh where sh h i is the share of traded good i in the consumption bundle of household h, φi is the income share derived from the sales of good i, and φh w is the share of labor income. dVi h is the negative of the compensating variation CV h , the monetary transfer that would allow household h to attain the same utility uh before the trade shock and the price change. Equation (4) follows from taking the derivative of (3) with respect to pi and expressing the resulting expression in proportional terms. Thus, the proportional price change is d ln pi = dpi /pi . Hotelling’s Lemma (i.e., the envelope theorem applied to the profit function) implies h h that (∂πi /∂pi )dpi = qi dpi , where qi is the quantity produced of good i. Multiplying and h dividing by pi and expressing the result relative to total income y h gives (pi qi /y h )d ln pi = φh i d ln pi . From Shephard’s Lemma, the derivative of the expenditure function with respect to pi is the quantity consumed ch h h h h i so that (∂e/∂pi )dpi = ci dpi and (pi ci /e )d ln pi = si d ln pi . 5 This accounts for the first term within brackets in (4). The second term captures the labor income impacts, which are given by the product of the share of income derived from labor, 5 This assumes that, ex-ante, household expenditures equal household income. This rules out savings and intertemporal considerations. 6 φh h h w , and the wage elasticity with respect to the price (∂w /∂pi )/(pi /w ). This formulation allows for different assumptions about the functioning of labor markets that determine the nature of the labor income elasticities.6 The last term in (4) accounts for any impacts on government transfers received or on taxes paid by household h. Proposition 2 Let τi be the level of tariff protection in sector i. Assume: (i) goods are homogeneous; (ii) the country is small and thus faces exogenously given international prices p∗ i ; (iii) perfect price transmission from tariffs to domestic prices; (iv) labor is specific, that is, labor is perfectly immobile across sectors; (v) the loss of public revenue due to tariff cuts is compensated with increases in income taxes. Then, the estimable welfare effects are given by: dVih τi (5) h = − (φh h h i − si ) + φwi + Ψh i, e 1 + τi where now φh wi is the share of labor income derived only from wages in sector i (and not other sectors) and Ψh i is the increase in income tax accrued by the household. This expression is the welfare effect of a simulated full unilateral tariff liberalization. This means that the country reduced its own tariffs individually. Under full import tariff liberalization, so that dτi = −τi , the perfect pass-through assumption implies that (6) d ln pi = −τi /(1 + τi ). The unitary pass-through elasticity requires constant returns to scale in the production of the traded goods and perfect competition.7 This is a simplification of our analysis that is rooted in the lack of data needed to estimate the pass-through elasticities for a broad range of products and countries (see Nicita (2009), Ural Marchand (2012), Atkin, Faber and 6 Since we do not have information on input use, equation (4) omits the indirect effects of wages to hired in-farm labor, Lhired on farm profits. See Porto (2005) for a study of those effects in Moldova. 7 See Goldberg and Knetter (1997) for a exhaustive discussion of the conditions needed for perfect pass-through. Note also that there is no role for entry and exit in our model. 7 Gonzalez-Navarro (2018), for estimates of imperfect pass-through for selected countries). Note that by simulating cases of own unilateral tariff liberalization, we are not accounting for the effects of foreign tariff reductions and market access effects. The sector specificity of labor allows us to derive a simple wage-price elasticity, because with fixed labor d ln w = d ln pi for wages in sector i and d ln w = 0 for wages in all sectors j = i. It is in principle possible to accommodate different assumptions on how labor markets work. Under full labor mobility, for example, labor would reallocate until a new equilibrium is reached. In the literature, such a model has been estimated by Porto (2006), Nicita (2009) and Nicita, Olarreaga, and Porto (2014). In our model, furthermore, labor is homogeneous and, in particular, there is no skill differentiation. Nicita, Olarreaga and Porto (2014) estimate wage-price elasticities for skilled and unskilled labor separately. Another possibility is to assume imperfect labor mobility and equilibrium inter-industry wage differences. In this case, a price shock can trigger labor reallocation responses across sectors and, consequently, there can be sector-specific wages elasticities. Artuc, Lederman, and Porto (2015) provide estimates of such a model for a wide range of countries. In section 5.6 we assess how our results change if we do not account for labor market responses to tariff changes. The interpretation of equation (5) is straightforward. After a price change caused by tariff cuts d ln pi = −τi /(1 + τi ), the first order effects on real income can be well-approximated with the corresponding expenditure and income shares. In the language of Deaton (1989), because we are working with tariff cuts and price declines, net-consumers benefit while net-producers suffer. In our setting, the net position of a household is defined in an extended model including not only consumption and production of traded goods but also labor income and government transfers. As in Nicita, Olarreaga and Porto (2014), we want a measure of the welfare effects generated by the entire structure of tariff protection. To obtain it, we sum the changes in welfare in (5) over all traded goods i to get: dV h dVih (7) Vh = = , eh i eh 8 where V h is the proportional change in household real income. In the remainder of the paper, we estimate the different components of equation (7) and study them in detail. We also use equation (7) to build ex-post counterfactual distributions. Let xh 0 be the observed, ex-ante level of real household income (from the data compiled in the household surveys). The counterfactual real income xh 1 is (8) xh h h 1 = x0 (1 + V ). Much of what we do below hinges on the comparison of the ex-ante and ex-post distributions of income. Since our propositions are derived from an agricultural household model, which is not often used in the trade literature, it is important to bear in mind some of the key features of our approach when interpreting the results. If we take the standard models at face value, it is clear that several data limitations would prevent us from fully accurately estimating the welfare effects of trade stemming from those models. These limitations are inherent to household surveys. For example, information on returns to capital, especially corporate profits and so on, is often patchy and typically missing altogether in the household surveys. Consequently, it is very difficult to map the results from those standard models to our empirical results which are based on household surveys. There might therefore be some discrepancies between some of our results and standard trade theory. For example, Stolper-Samuelson effects resulting in differential impacts on the returns to capital vs labor, or to skilled vs unskilled labor, may not be captured by our analysis. However, most of the discussion about poverty, inequality and household welfare is (to a large extent) based on household surveys, which remain the dominant instrument for measuring household consumption and income portfolios. Such surveys are thus a natural starting point for analyzing the distributional impacts of trade policy. Our results also have interesting implications for trade theory. We argue that the most innovative feature of our approach is household heterogeneity, which is often missing in standard trade models. Heterogeneity takes two forms. One is consumption heterogeneity and non-homothetic preferences. In particular, since the poor and the rich allocate their 9 expenditure very differently across goods (most notably, food), this can have implications for the overall gains from trade as well as for the inequality costs of trade. These differences and their implications are not captured by standard models based on homothetic preferences. There are, of course, papers that rely on non-homothetic preferences, and the conclusions from those models are consistent with the conclusions from our paper. The other important dimension of heterogeneity is income heterogeneity. In our model, households earn income from various sources not limited to labor markets. In particular, income from sales of different agricultural products plays a crucial role in agrarian economies and these sales include returns to non-labor household factors such as land and assets such as tractors, ploughs, and more humble tools such as shovels, carts, and wheelbarrows. The “distribution” of these factors across households in the surveys is highly heterogeneous. Households thus have very different sources of incomes and this matters for welfare and inequality. This income heterogeneity is missing in many neoclassical models. 3 Estimating the Welfare Impacts of Trade Policy To estimate the welfare impacts of trade policy, we need to measure the different components of equations (5) and (7). The data needed to estimate impacts on consumption and production of traded goods, labor income and home enterprise income can be found in standard household surveys. Trade and trade policy data come from United Nations COMTRADE and UNCTAD TRAINS, which classify goods using the Harmonized System (HS) so that tariffs and imports are available at HS-6 level. Household surveys use different nomenclatures of goods produced and consumed. To match trade data and household survey data, we use and improve upon the templates and concordances developed by Nicita, Olarreaga and Porto (2014). In short, we first aggregate goods in the household surveys to 2-digit and 4-digit categories. We then aggregate tariff and trade data from COMTRADE to those categories. See Appendix A for details. 10 3.1 Trade Policy and Price Changes In the empirical application that follows, good i represents one of the product classifications from the expenditure, income and home-consumption templates modules of the household surveys. Each of these classifications includes many finer product groups from the HS classification. We compute weighted average tariff rates τi for each of our survey categories: mc,n (9) τi = τc,n , c,n∈i c,n∈i mc,n where n is an HS-category that belongs to survey-category i and mc,n are imports of good n from country c. The results are shown in Table 1. We report the average tariff for our 2-digit classification, Staple Agriculture, Non-Staple Agriculture, and Manufactures. Average tariffs are highest for non-staple agricultural goods (14.4 percent). They are lower for staple agricultural goods (10.8 percent) and manufactures (10.9 percent). These averages mask substantial variation in trade barriers across countries. Average tariffs on non-staple agricultural goods range from as high as 46.1 percent in Bhutan to as low as 1.9 percent in Indonesia. Countries with higher tariffs in agriculture (staple and non-staple) tend to have higher tariffs on manufactures as well. Using the full price transmission assumption (equation 6), we calculate the price changes induced by the elimination of tariffs as follows: p∗ ∗ i − pi (1 + τi ) τi (10) ∆ ln pi = ∗ =− . pi (1 + τi ) 1 + τi 3.2 Expenditure and Income Shares To measure the first order welfare effects we retrieve the expenditure shares sh i and the income shares φh h i and φw from the household surveys. Appendix Table A1 lists the countries included in the analysis, together with the corresponding household survey, the year of the survey and the sample size. Our analysis covers all low income countries for which appropriate household survey data were available, as well as the majority of lower middle 11 income countries. To minimize the role of measurement error, we exclude households in the top and bottom 0.5% of the status quo expenditure distribution in all our analyses. All statistics derived from household surveys presented in the remainder of the paper are weighted using survey weights. For the relatively few surveys for which survey weights are not available, we simply assume each household has the same weight. Expenditure shares are reported in Table 2. We show averages for six major expenditure aggregates, namely Staple Agriculture, Non-Staple Agriculture, Manufactured Goods, Non-Traded Goods, Other Goods, and Home Consumption. Expenditure on food is the dominant expenditure category, accounting on average for 45 percent of all household spending across countries, which is not surprising since the bulk of countries in our sample are low income countries with an average poverty rate of 35 percent (using national poverty lines) and an average GDP per capita of US$ 1879. This focus on poor countries also helps explain why home consumption is important, accounting for an average budget share of 17 percent across countries and for more than a third of all expenditure in Ethiopia, Madagascar, Mali and Uzbekistan. Spending on manufacturing goods on average accounts for 17 percent of overall household expenditure, and spending on non-tradables accounts for 15 percent. Average income shares for staple Agriculture income, Non-Staple Agricultural income, Wages, Family Enterprise Income, Other income, and Own Home Production are reported in Table 3. Wage income is the single most important source of income, accounting on average for 29 percent of household income across countries, and for 39 percent if we weigh countries by their GDP, suggesting that the importance of this source of income increases with development. The value of autoconsumption accounts for 23 percent of household income. Profits from running farms and other family businesses account for 17 and 13 percent of household income, respectively. These averages hide important heterogeneity across countries, which reflects differences in structural features of their economies and heterogeneity in survey design (including coverage of different sources of incomes and expenditures). 12 3.3 Labor Income and Transfers As established in Proposition 2, we assume that labor is sector specific. This is consistent with a short-run model, in which households do not adjust labor to the trade shock. To implement this assumption empirically, we consider 10 different sectors (see the Income Template in the Appendix). This is convenient because in this setting the changes in prices transmit one to one to nominal wages and the elasticity of the wage in sector i with respect to its own price pi is one, while the elasticities with respect to other prices j is zero. In robutsness tests, we also consider a model without labor income responses (as in Deaton, 1989).8 Lastly, we need to derive the cost of tax payments needed to compensate for the tariff revenue loss, Ψh i . We assume that the government imposes a proportional income tax to do so at the moment it liberalizes. Denoting import quantity by mi , we can approximate the loss of tariff revenue as dRi = −τi p∗ i mi (ignoring production and consumption responses). With a proportional income tax, the change in income tax paid by household h is dT h = dψy h , where dψ is the compensatory change in the tax rate. Consequently, τi Mi (11) Ψh i = − , 1 + τi h y h where Mi = p∗ i (1 + τi )mi is the value of imports. In the robustness exercises, we consider two additional cases, one where there is no compensation of the revenue losses via the income tax and another where there is progressivity in the income tax system. 4 Income Gains and Inequality Costs of Trade Policy In this section, we investigate the potential income gains (or losses) and the potential inequality costs (or gains) from import tariff liberalization. The next section (section 5) investigates the potential trade-off between the two. 8 In additional robustness exercises that are not reported here to conserve space we also estimate a model with perfectly mobile labor (as in Porto, 2006) as well as a model with imperfect labor mobility (as in Artuc, Chaudhuri and McLaren, 2010). The results are qualitatively consistent with those obtained using the baseline model with specific labor and the alternative model without labor income responses. 13 4.1 Income Gains from Trade To be consistent with the literature (e.g., Arkolakis, Costinot and Rodriguez-Clare, 2012), the gains from trade G are defined as the proportional change in aggregate household real expenditures, after import tariff liberalization: h h (x1 − xh 0) xh 0 (12) G = h = h V h, h x0 h h x0 where V h is the proportional change in real expenditures of household h which we estimate with equation (7). Thus, G is a weighted average of the welfare effects V h . Table 4 reports G for 45 countries with positive aggregate gains from trade (G > 0). On average, the net gain from import tariff liberalization is a 2.5 percentage point increase in real expenditures. The highest gains accrue to Cameroon and Zambia (6.9 and 5.9 percent of real expenditure, respectively). The smallest gains, for Bangladesh, Burundi, and Mongolia, are about 0.5, 0.4 and 0.1 percent of initial expenditures, respectively. Table 5 reports 10 countries in which import tariff liberalization causes losses (G < 0) which average –0.9 percent of real expenditures. In Cambodia, the country with the largest loss, households are estimated to lose 3.1 percentage points of real expenditure. There are also instances of very small, almost negligible, losses as in Rwanda. Across all countries in the sample, the average gain from trade liberalization is equal to 1.9 percent of real expenditures. The developing world seems to gain from trade. To establish the sources of the gains from trade, we decompose the average gains into different channels in columns 2-8 of Tables 4 and 5. Households gain on the expenditure side, but they lose on the income side. The consumption gains come from lower prices of tradables, which on average result in (gross) real income gains of 6.4 percent for the winners (Table 4) and 5.3 percent for the losers (Table 5). About two-thirds of these gains, on average, are due to lower prices of agricultural goods and one-third to lower prices of manufacturing goods. This is a consequence both of the higher expenditure shares on food items in developing countries, and the comparatively high tariffs on agricultural products. Households lose nominal income. Agricultural income losses account for average real income 14 declines of 1.5 percent across countries with gains and 2.0 percent across countries with losses. Wage income effects create losses of 0.6 percent in countries with gains and 1.1 percent in countries with losses. The reduction in income from enterprises producing tradable goods is small on average; –0.2 percent of income among winners and –0.1 percent among countries that lose. The biggest driver of income losses is the reduction in government revenue: this channel accounts for 1.6 of the 3.9 percentage points loss in income among winners and 3.2 of the 6.4 percentage point loss among losers. 4.2 The Distributional Effects of Trade We now turn to the distribution of the gains from the trade, which have been the focus of Porto (2006), Nicita, Olarreaga and Porto (2014), Fajgelbaum and Khandelwal (2016), Atkin, Faber and Gonzalez-Navarro (2016), Faber (2014) and Atkin and Donaldson (2015). Indeed, the average impacts just discussed mask significant heterogeneity across households. This is because the net welfare impact is determined by a combination of initial tariffs as well as income and consumption portfolios. We combine two techniques to explore the distributional effects. We estimate kernel averages of the gains from trade, conditional on household initial well-being (per capita expenditure), and we estimate bivariate kernel densities of the joint distribution of the gains from trade and household per capita expenditure. For the sake of exposition, we divide countries into two groups using the pro-poor index of Nicita, Olarreaga and Porto (2014). In our application, the pro-poor index is the difference between the average gains for the poor—the bottom 20 percent of the income distribution—and the rich—the top 20 percent. If the index is positive the poor gain proportionately more (or lose proportionately less) than the rich, while the opposite happens when the index is negative. According to this classification, import tariff liberalization would be pro-poor in 17 countries, while it would be pro-rich in the remaining 37 countries. We illustrate the case of pro-poor bias in Figure 1 for the cases of the Central African Republic (panel (a)) and Mauritania (panel (b)). Appendix B provides figures for all countries. In the Central African Republic, the kernel average is positive everywhere, so that there are average gains from trade across the income distribution, but the slope of the 15 kernel regression is negative (so that the poor gain proportionately more than the rich). This pro-poor bias with positive kernel average gains also appears in Azerbaijan, Central African Republic, Ecuador, Indonesia, Moldova, Nepal, Pakistan, Papua New Guinea, Rwanda, the Republic of Yemen and Zambia (see Appendix B). In these countries, import tariff liberalization raises incomes across the income distribution and may reduce inequality. Liberalization would not be Pareto improving, however. The bivariate density of the welfare effects and initial income illustrates the dispersion in the welfare effects and the existence of winners and losers in all segments of the per capita expenditure spectrum. A more extreme version of this pattern is shown in panel (b) for the case of Mauritania, where there are average gains for the poor but average losses for the rich. This implies a very strong pro-poor bias. Similar patterns are also observed in Guinea-Bissau, Mali, Mongolia and Sri Lanka (see Appendix B). In all these countries, import tariff liberalization raises average income and may reduce inequality significantly. We illustrate the pro-rich bias in Figure 2. In Uzbekistan (panel (a)), the kernel average is always positive at all levels of per capita expenditure, and the slope of the kernel regression is positive, indicating that, on average, the rich gain proportionately more than the poor. Again, some individual households stand to lose, as the underlying bivariate kernel density graph shows. Import tariff liberalization lifts incomes throughout the income distribution, but at the expense of potentially higher inequality. This pattern is found in Armenia, Bolivia, ote d’Ivoire, the Arab Republic of Egypt, Ethiopia, Georgia, Guatemala, Cameroon, Cˆ Guinea, Iraq, Kyrgyz Republic, Liberia, Malawi, Mozambique, Nicaragua, Niger, Sierra Leone, South Africa, Tajikistan, Tanzania, Uganda, and Ukraine (see Appendix B). The case of pro-rich bias with average gains for the richest households and losses for the poorest is illustrated in panel (b) for Togo. Import tariff liberalization is strongly pro-rich and inequality significantly exacerbated. Similar patterns arise in Bangladesh, Benin, Burkina Faso, Burundi, The Gambia, Kenya, Nigeria, and Vietnam. In panel (c), we show the case of average losses and a pro-rich bias for Ghana. In this country, as well as in Bhutan, Cambodia, Comoros and Madagascar, the poor lose proportionately more than the rich. This is a scenario with average losses as well as increased inequality. 16 5 The Trade-Off between Income Gains and Inequality Costs Given the patterns of gains from trade and of the distribution of those gains, we now assess whether there is a trade-off between the income gains and the inequality costs of import tariff liberalization. This necessarily involves value judgments because different societies, individuals or policy makers may value the gains or losses of some households differently. A tool to describe the trade-off between income inequality and average incomes is the Atkinson social welfare function (Atkinson, 1970): 1−ε 1 xh (13) W = , H h 1−ε where W is social welfare and ε = 1 is a parameter that measures the dislike for inequality.9 When ε=0, every household counts the same and social welfare is just the sum (average) of per capita expenditures. As ε increases, the weight attached to the well-being of poorer households increases. In the limit, as ε approaches infinity social welfare is determined by the well-being of the very poorest household (as in a Rawlsian social welfare function). It is very important to interpret the Atkinson social welfare function correctly. As Deaton (1997) explains, W in (13) is not necessarily (and more precisely, it seldom is) the object that policy makers maximize when choosing among policy options. Rather, it provides a means of quantifying potential tensions between mean income and its distribution across households. An important property of the Atkinson social welfare function is that it can be decomposed in a way that is conducive to the assessment of this trade-off. Concretely, we can write (14) W = µ ∗ (1 − I ), 9 For completeness, when ε = 1, we define ln W = (1/H ) h ln xh 17 where µ is mean income and H 1/(1−ε) 1 (15) I = 1 − (xh /µ)1−ε , H h=1 is an implicit measure of income inequality. Social welfare thus depends on average income µ and on the aggregate level of “equality” (1 − I (ε)). This measure of inequality I (ε) (or the measure of equality (1 − I (ε))) depends on ε and nests a whole family of inequality measures. Using W (ε), we can define a measure of the gains from trade that includes a correction for the inequality costs: W1 (ε) − W0 (ε) (16) G(ε) = , W0 (ε) where W0 is the ex-ante social welfare, calculated with the observed (xh 0 ) income distribution in the presence of trade protection and W1 (ε) is the counterfactual social welfare under import tariff liberalization (xh 1 ). Given the initial situation and the post-liberalization situation, we can compare W0 and W1 using (16). For ε = 0, this is a comparison of mean income, that is, the calculation of the gains from trade (Arkolakis, Costinot and Rodriguez Clare, 2012; Costinot, Donaldson and Komunjer, 2012; Costinot and Rodriguez-Clare, 2014; Artuc, Lederman and Porto, 2015; Caliendo and Parro, 2015; Melitz and Redding, 2015; Arkolakis, Costinot, Donaldson and Rodriguez-Clare, 2015; and Caliendo, Dvorkin and Parro, 2015). For ε > 0, this comparison involves the calculation of the gains from trade with an implicit correction for inequality (Antras, de Gortari, Itskhoki, 2017; and Galle, Rodriguez-Clare, and Yi, 2017). With estimates of G(ε) for different ε, we can establish whether there is a trade-off between the gains in average incomes and the costs of inequality in its distribution, we can quantify this trade-off, and we can assess it. For the discussion that follows, we exploit the decomposition of W in equation (14). Note that we can write µ1 I0 (ε) − I1 (ε) (17) G(ε) = G(0) + . µ0 1 − I0 (ε) 18 The inequality-adjusted gains from trade are thus equal to the income gains from trade G(0) plus a correction for changes in inequality, which we will refer to as equality gains. The gains from trade G(0) can be positive or negative, as shown in section 4.1. The correction for inequality is governed by the Atkinson inequality index I (ε), which may depend non-monotonically on ε. If inequality increases for some εa > 0 so that I1 (εa ) > I0 (εa ), then G(εa ) incorporates a downward correction for these inequality costs. Conversely, if I1 (εb ) < I0 (εb ) at some εb , then the gains from trade are amplified. Note that G(ε) > 0 does not imply no inequality costs per se but rather that their welfare impacts are dominated by the income gains. Trade-offs arise when income gains and equality gains move in opposite directions, i.e. µ1 I0 (ε)−I1 (ε) when G(0) and µ0 1−I0 (ε) have opposite signs. This is the case in countries where trade exacerbates inequality but improves average income, and in countries where it reduces inequality at the expense of lowering mean income. In some countries, these trade-offs can even result in reversals of trade policy preferences, in the sense that for certain levels of inequality aversion ε, the inequality adjusted gains from trade may be negative (positive) even though import tariff liberalization leads to an increase (reduction) in average income. Since the sign and magnitude of the equality gains can vary with ε both the existence and acuteness of the trade-offs depend on the level of inequality aversion. No trade-offs occur in countries where import tariff liberalization leads to both income and equality gains (for all ε) or in countries where it leads to lower income and higher inequality (for all ε). One of the main findings of our paper is the high prevalence of trade policy trade-offs between average incomes and income inequality in the developing world. Among the 54 countries in our sample, 45 face a trade-off and only 9 do not. In 27 of the 45 countries the trade-offs can be severe enough to generate (potential) reversals in the ranking of trade policy preferences. We present countries without trade-offs (section 5.1), countries with trade-offs but without trade policy preference reversals (section 5.2) and with reversals (section 5.3). Sections 5.4 and 5.5 evaluate such trade-offs and some of the underlying factors. Finally, we run robustness tests in section 5.6 and we explore protectionists scenarios in section 5.7. 19 5.1 No Trade-off Countries When average income gains emerge together with equality gains, there is no trade-off. The case of the Central African Republic is shown in Figure 3, which plots G(ε) for ε ∈ [0, 10].10 To obtain confidence intervals for G(ε) we resample from the observed distribution and bootstrap using 1000 replications. In the Central African Republic, import tariff liberalization leads to average welfare gains with a pro-poor bias (see Figure 1). The gains in average incomes of 4.2 percent are independent of ε and the pro-poor bias implies that liberalization also leads to equality gains. As ε increases and more weight is put on the poor, these equality gains actually get bigger. As a result, the inequality adjusted welfare gains are increasing in the inequality aversion parameter ε, and exceed 6 percent for large ε. Other countries in which import tariff liberalization yields both equality gains and lifts average incomes are Guinea-Bissau, Jordan and Yemen. In these countries, import tariff liberalization is unambiguously social welfare enhancing. At the other end of the spectrum lie 4 countries, Comoros, Ghana, Madagascar and Rwanda, which are characterized by average income losses and inequality costs for all ε. In these countries import tariff liberalization would be unambiguously social welfare depressing. Figure 4 illustrates the case of Ghana. Since income losses are disproportionately borne by the poor, the inequality adjusted gains from trade are negative and decreasing with ε. For instance, the aggregate losses of –1.9 percent (for ε = 0) are augmented to –4.3 percent when inequality aversion is high. 5.2 Trade-off Countries without Trade Policy Preference Reversals There are 45 countries with evidence of a trade-off. In 18 countries, this trade-off is not strong enough to generate reversals of trade policy preferences because import tariff liberalization dominates protection at all levels of inequality aversion (in 16 countries) or because protection dominates liberalization (in only 2 countries, notably Bhutan and Cambodia). Figure 5 10 For presentational purposes, we examine G(ε) for a limited range of ε ∈ [0, 10] but the results hold more generally. Results are available upon request but omitted to conserve space. 20 illustrates the case of Uzbekistan, where liberalization creates average income gains at the expense of inequality costs. In Uzbekistan inequality increases smoothly with ε and, as a consequence, the inequality adjusted welfare gains G(ε) decrease as inequality aversion rises. The gains from trade are G(0) = 3.5 percent, while the inequality-adjusted gains for large ε can go down to about 1.1 percent. Other countries exhibiting a similar pattern are Armenia, Azerbaijan, Cameroon, the Arab Republic of Egypt, Guinea, Indonesia, Iraq, the Kyrgyz Republic, Moldova, Pakistan, Tajikistan, Uganda, Ukraine, Uzbekistan, South Africa, and Zambia. Since G(ε) is positive and statistically significant for all ε, import tariff liberalization would unambiguously lead to higher social welfare. Plots of G(ε) for all these countries are given in Appendix C. We summarize the information contained in Figures 3, 4 and 5 in Table 6, which reports the income gains from trade G(0) (column 1) as well as the equality gains ((µ1 /µ0 )(I0 (ε) − I1 (ε))/(1 − I0 (ε)) for several values of inequality aversion ε. To illustrate, consider the case of Guinea-Bissau (a country without a trade-off) in panel (a). The gains from trade are 2.0 percent (column 1). Because inequality declines with import tariff liberalization, the equality gains increase with ε. The correction is thus positive and increasing with ε. At ε = 0.5, for instance, the correction is 0.5 percent and the inequality-adjusted gains are 2.5 percent. At ε = 1 (ε=10), the correction is 0.7 (0.8) percent and the total inequality-adjusted gains go up to 2.7 (2.8) percent. Another interesting example is Madagascar, where there are losses from trade of −1.1 percent and increases in inequality costs so that, at ε = 1, the inequality-adjusted losses from trade drop to −1.8 percent and at ε = 10, to −3.4 percent. To illustrate a country with trade-offs (Panel (b)), consider Ukraine. The gains from trade are 3.2 percent, but inequality increases and consequently there is a downward correction to G. At ε = 1, this correction is very small, –0.1 percent and the inequality adjusted gains drop to 3.2; at ε = 10, the correction is –0.7 percent and the inequality adjusted total gains are 2.5 percent. The table reports many other interesting patterns. 21 5.3 Trade-off Countries with Trade Policy Preference Reversals In the remaining 27 countries in our sample, we find evidence of a stronger trade-off which may induce a potential reversal of the ranking of trade policy preferences. This reversal occurs when G(ε) changes sign, going from positive to negative or from negative to positive, as ε increases. This means that, depending on the value judgement parameter ε, the social welfare function points to welfare gains associated with import tariff liberalization or with trade protection. Figures 6 and 7 show two examples of the existence of such trade-offs. In Benin (Figure 6), there are significant average income gains of 2.2 percent so that G(0) > 0. However, as ε increases, import tariff liberalization creates larger and larger inequality costs so that, eventually, G(ε) becomes significantly negative. At very large ε, the inequality-adjusted gains are −4.4 percent. It follows that free trade dominates protection when ε is low, whereas protection dominates free trade when ε is high. Other countries that exhibit similar trade-offs are Bangladesh, Burkina Faso, Burundi, Ethiopia, The Gambia, Guatemala, Kenya, Liberia, Malawi, Mozambique, Nigeria, Papua New Guinea, Togo, and Vietnam.11 Mali (Figure 7) exhibits the opposite pattern; There are small but statistically significant average losses from trade (G(0) = −0.3) but, as ε increases, the equality gains from liberalization end up strictly dominating those losses and the inequality-adjusted gains G(ε) approach 3 percent. Consequently, protection dominates free trade at low ε, while free trade dominates protection at high ε. This also happens in Mauritania and Sri Lanka. To quantify these policy preference reversals, we define the cutoff value ε∗ such that G(ε∗ ) = 0. The cutoff ε∗ , which we refer to as trade-ε∗ , is a measure of the inequality aversion to import tariff liberalization. It is a sufficient statistic to describe the trade-off between mean income and inequality in the presence of trade policy preference reversals. Defining the trade-off in terms of the gains, the value of ε∗ shows how intolerant towards inequality a society would have to be in order to make the gains from trade not worthwhile from a social welfare perspective.12 A high value of ε∗ implies a soft trade-off: a society 11 See below for a more detailed discussion of some of these countries and their trade-off. 12 Alternatively, the ε∗ shows how much a society would have to value equality to forgo the average gains from trade. 22 needs to put a heavy weight on the cost of higher inequality to be willing to forgo the gains (always in a social welfare function sense). In the limit case, when trade-ε∗ tends to infinity or when trade-ε∗ does not exist (as in the countries discussed in sections 5.1 and 5.2), there is no reversal in trade policy preference rankings and, given gains from trade, import tariff liberalization leads to higher social welfare for any ε. By contrast, a low trade-ε∗ implies a very hard trade-off because relatively light weights on the inequality costs are enough to offset the gains from trade. It is important to note that while the value of ε∗ describes the nature of the trade-off, it is silent about whether this trade-off is socially acceptable. Table 7 presents estimates of the trade-ε∗ (column 1) and its 95% confidence interval (columns 2 and 3). Since the interpretation of the trade-ε∗ depends on the sign of the gains, we report results separately for countries that enjoy income gains in panel (a) and countries that suffer income losses in panel (b). The select few countries characterized by multiple (potential) reversals are presented in panel (c). Among the countries with gains (Panel (a)), the trade-ε∗ vary a lot. In some cases, the cutoff can be as low as 0.1 (Burundi), or 0.57 (Burkina Faso and Bangladesh). In other cases, it can be much larger, as in Malawi (7.1) or Guatemala (7.0). To put these numbers in perspective, we canvassed the literature for guidance on what a reasonable value for ε is. Deaton (1997) recommends exploring values of ε ∈ [0, 2] when doing policy evaluations. Using experiments, Carlsson, Daruvala and Johansson-Stenman (2005) estimate ε ∈ [1, 2] and Layard, Mayraz and Nickell (2008) estimate a value of ε of 1.3. A high ε∗ consequently suggests that the trade-offs are soft in the sense that one would have to be implausibly inequality averse in order not to prefer liberalization. This implies a strong presumption in favor of lower tariffs. By contrast, in Burundi, Bangladesh or Burkina Faso, the trade-off would be quite stark. Since the gains from trade are positive but very small, even at low levels of inequality aversion one would prefer protection. In the remaining countries, the trade-off appears to be more moderate, with a substantial number of the estimates of trade-ε∗ lying in the [1,2] interval (1.2 in The Gambia, 1.2 in Togo, 1.5 in Benin, 1.9 in Nigeria, and 2.0 in Vietnam). Kenya (2.5), Ethiopia (3.1), Mozambique (3.5), Papua New Guinea (4.4), and Liberia (4.5) are countries with relatively high trade-ε∗ , but not quite as extreme as Malawi 23 or Guatemala. There are countries with trade-offs where the evidence on trade policy reversals is not so compelling. This occurs when the inequality-adjusted gains from trade are not statistically indistinguishable from zero, that is the null hypothesis that G(ε) = 0 cannot be rejected for a range of ε. We report these cases in columns 4 and 5 of Table 7. In Sierra Leone, for instance, for ε > 4.0, there are inequality adjusted gains from trade (G(ε) > 0) that are not statistically different from 0. Consequently, we cannot rule out a potential reversal (from preferring liberalization to preferring protection). Similar scenarios emerge in Bolivia ote d’Ivoire (ε > 7.0), Georgia (ε > 7.1), (ε > 4.1), Niger (ε > 6.0), Nicaragua (ε > 6.3), Cˆ Nepal (ε > 9.3), Tanzania (ε > 8.9), and Ecuador (ε > 9.9). In all these countries, however, the trade policy preference reversal would come about only for levels of inequality aversion that are arguably implausibly large. Among the countries with aggregate losses (Panel (b)) of Table 7), the estimated trade-ε∗ tend to be low. For instance, we get ε∗ = 0.3 in Sri Lanka (at the first reversal) and ε∗ = 0.4 in Mali. Note that the interpretation in these cases is different because for low ε, trade protection is preferred to liberalization, and, conversely, liberalization is preferred to protection for higher ε. In these countries, a low ε∗ thus implies a presumption in favor of lower tariffs, too. In Mauritania (ε∗ = 1.6), trade protection would be preferred under more moderate values of inequality aversion making it harder to infer trade policy prescriptions. 5.4 Assessment While our results attest to highly heterogeneous welfare impacts of trade liberalization across households and countries, overall the analysis provides overwhelming evidence of a trade-off between income gains and inequality costs of trade policy. In most cases, however, the income gains outweigh the inequality costs, suggesting countries are better off with freer trade. We summarize these observations and results in Figure 8. We plot the value of the inequality-adjust gains from trade G(ε) against the gains from trade G(0). For our assessment, we use ε = 1.5 because it is in the middle of the empirically plausible interval [1, 2] and because it yields a measure of the Atkison inequality index I that is, in general, 24 close to the Gini coefficient. Since the Gini is often used in discussions about inequality, this is a useful benchmark. If there were only small corrections for inequality, then the pairs (G(1.5), G(0)) would lie along the 45 degree line, with larger corrections for those pairs further away. Orthant I hosts countries with average gains as well as gains after inequality corrections; orthant III hosts countries with average losses with and without inequality corrections. In orthant II, we see countries with losses from trade that turn into gains after the inequality adjustments, and, in orthant IV, those countries with gains from trade that turn into losses with inequality considerations. For an inequality aversion parameter of ε = 1.5, 17 countries would not face a trade-off. Eleven of them would unambiguously benefit from liberalization as they enjoy both income and inequality gains. These countries, which lie in orthant I, above the 45 degree line, are Azerbaijan, Central African Republic, Ecuador, Guinea-Bissau, Indonesia, Jordan, Mongolia, Pakistan, Papua New Guinea, Nepal, and Yemen. The remaining six countries, Bhutan, Cambodia, Comoros, Ghana, Madagascar and Rwanda, would unambiguously prefer protectionism as trade liberalization leads to both income losses and inequality costs (they lie in orthant III, below the 45 degree line). A total of 37 countries would exhibit trade-offs (for ε = 1.5). In 30 of these countries, the trade-off is resolved in favor of liberalization. Twenty-eight countries would show income gains and inequality costs, but inequality-adjusted gains from trade liberalization. These are the countries in orthant I, below the 45 degree line. Two countries (Mali and Sri Lanka) would show instead income losses but sufficiently high equality gains so that there are inequality-adjusted gains from trade in the end (for ε = 1.5). These are the countries in orthant II. In 7 countries the trade-off is instead resolved in favor of protection because tariffs lead to higher inequality-adjusted welfare. One country, Mauritania (orthant III, above the 45 degree line), would face income losses and equality gains, which, for ε = 1.5, are not enough to compensate for those losses. In six countries, Bangladesh, Benin, Burkina Faso, Burundi, The Gambia and Togo, the inequality costs dominate the income gains. These are in orthant IV. 25 It turns out that the resolution of the trade-off is very stable for different values of plausible inequality aversion. In Figure 9, we reproduce Figure 8 for ε = 1 (panel (a)) and ε = 2 (panel (b)). As it can be seen, there are only a few countries where the trade policy prescriptions are more equivocal. For ε = 1, Benin, The Gambia, and Togo jump from orthant IV to orthant I (thus preferring liberalization instead of protection). For ε = 2, Vietnam and Nigeria jump from orthant I to orthant IV (thus preferring protection) while Mauritania jumps from orthant III to orthant II (thus preferring liberalization). A fundamental conclusion of this analysis is therefore that many of the countries that face a trade-off between mean income and its distribution are better off with lower tariffs (liberalization) than with higher tariffs (protection). Concretely, for empirically plausible levels of inequality aversion, liberalization is expected to enhance welfare in 39 countries and to reduce it in 9 countries. Only in the remaining 6 countries are the policy implications more equivocal. These results raise questions about why these countries protect their economies. Perhaps countries are not maximizing the Atkinson social welfare function when setting tariffs. Alternatively, they may be maximizing this function, but subject to constraints. These could include political economy considerations such as rent re-distribution to non-labor income (capital) and tariff revenue capture (Grossman and Helpman, 1994; Krueger, 1974). In addition, tariffs may be an appealing means of collecting revenue in a context in which income (and other) taxes are difficult to collect (Besley and Persson, 2013). Theories of social mobility and redistribution that combine psychology and political economy may also rationalize the political constraints faced by policy makers (Piketty, 1995; Benabou and Tirole, 2006). Finally, there may be dynamic considerations. Whatever the reason for it, our findings show that protection can be very costly in terms of the social welfare aggregator W. 26 5.5 Underlying Factors: Expenditure and Income Household Heterogeneity How do the gains from trade and trade-offs described above emerge? We argue that a distinctive element of our approach is the vast heterogeneity in household expenditures and incomes. We showed in section 4.1 that this heterogeneity helps explain the gains from trade. Across-household differences in the consumption gains and in the income losses of the elimination of tariffs show that countries are more likely to benefit from liberalization if food expenditure shares are large, relative to agricultural production income shares. Household heterogeneity underlies the patterns of trade-offs as well. In Table 8, we decompose the equality gains (or losses) into consumption equality gains and (nominal) income equality gains. To calculate these, we estimate two counterfactual scenarios; one in which liberalization solely impacts consumption (and does not impact income), and one in which it solely impacts income (and does not impact consumption). We compute the consumption and income equality components using equation (17) (recall that equality gains µ1 I0 (ε)−I1 (ε) 13 are equal to µ0 1−I0 (ε) ). As much as there is heterogeneity in the trade-offs, there is a marked heterogeneity in the income and consumption equality components. Note, however, that the consumption equality gains are positive yet small in the majority of countries. As consumers, the poor seem to benefit disproportionately from liberalization, in part because they spend a larger share of their budget on food items, which are subject to comparatively high tariffs. By contrast, the income component is overwhelmingly negative across countries, reflecting the fact that trade liberalization creates income inequality costs that are disproportionately borne by poorer households. Whereas the consumption equality gains on average increase only slightly as inequality aversion rises, the average income inequality costs tend to increase sharply (i.e., become more negative) with ε. The trade-offs between the aggregate gains and aggregate 13 Consumption and income impacts may interact, such that the total equality gains from trade are not simply equal to the sum of the consumption equality gains and income equality gains; to assess the importance of these types of interaction effects, we calculated “residual” equality gains as the difference between total equality gains and the sum of income and consumption equality gains. The residual was typically very small and is therefore not presented here. 27 inequality costs are thus predominantly driven by nominal income inequality. This finding shows that the income losses associated with trade liberalization are borne disproportionately by the poorer segment of the income distribution, whereas the consumption gains are more widely spread. The role of household heterogeneity is typically underplayed in much of trade theory. There are theories that postulate non-homothetic preferences and expenditure heterogeneity, but income heterogeneity is often ignored. This can help rationalize potential discrepancies between our results and the intuitions regarding the gains from trade and their distributive impacts that stem from many trade models. For example, the Stolper-Samuelson result could imply a reduction in inequality for low-income, unskilled intensive countries that integrate with the world. Our model shows that this effect can be offset and fully dominated by impacts on other sources of income also affected by trade.14 5.6 Robustness To assess the robustness of our results, we use two different permutations of our model. To start with, we show results of a model that does not allow for labor market responses to tariff changes. With this alternative model, we find income gains in 50 countries and losses in 4 countries. The estimated average income gains from trade are 2.5%. Yet, the estimated pattern of inequality adjusted gains is very similar. Gains from trade are negatively correlated with equality gains, which tend to become lower (i.e. more negative) as inequality aversion increases. Nonetheless, in spite of the widespread prevalence of trade-offs, countries are typically better off when pursuing free trade policies. Our results are thus robust to using this alternative model. Second, we re-evaluate our model using two alternative assumptions about tariff redistribution, notably (i) that no tax response is observed and (ii) that the government makes up for lost revenue by imposing additional personal income taxes which respect the progressivity of the existing personal income tax system (see appendix E for details). 14 Of course, the heterogeneity at the household level is in itself a consequence of additional underlying factors, such as endowments, policies, institutions and so on. However, the analysis of these factors is beyond the scope of our paper. 28 Excluding the loss of tariff revenue from the model nearly doubles the estimated average income gains from trade to 3.7% on average across countries (relative to 1.9% in the main model). Moreover, income gains are now positive in 53 out of the 54 countries. Yet, equality gains continue to be negatively correlated with income gains and trade-offs remain widespread. If instead of assuming away tariff redistribution we assume that the government makes up for the loss in tariff revenue by imposing progressive taxes (calibrated using the World Tax Indicators (WTI) database (Andrew Young School of Policy Studies, 2010)), the inequality costs of trade liberalization show some minor difference, but the overall pattern of results is not impacted. Income gains are negatively correlated with equality gains. Yet, the latter typically are dominated by the former such that countries facing trade-offs are typically better of with freer trade.15 5.7 Protectionist Scenarios Thus far, we have analyzed the inequality-adjusted gains from liberalization, but our framework also lends itself to evaluating trade-offs that might arise if countries become more protectionist. To explore this, we evaluate the welfare impacts associated with three protectionist scenarios that move the economy closer to autarky: (i) a uniform increase in tariffs of 10 percentage points (i.e. adding 10% to all existing tariffs); (ii) a relative increase in tariffs of 10 percent (i.e. multiplying all pre-existing tariffs by 1.1); and (iii) increasing all tariffs to 62.4%.16 The aim of this exercise is to establish whether the trade-offs that may arise under protectionist scenarios are in general consistent with those derived when countries liberalize. Accordingly, we summarize the results in Table 10 which presents estimates of the (inequality adjusted) gains from trade, the number of countries that exhibit trade-offs, and the trade policy reversals. Country-specific estimates of the gains from trade are presented in Appendix F.17 15 See Appendix E for details. 16 Following Ossa (2014), this is the level of tariffs that would prevail if countries did not fear retaliation from the rest of the world. 17 These country-specific losses/gains from trade under these various scenarios are not necessarily the mirror image of the results obtained when liberalizing. This is because the autarky scenarios may have 29 As expected, the average gains from these protectionist trade reforms are now negative in the vast majority of countries. While there are 45 winners under liberalization, we find between 43 and 48 losers with increased protection. The estimated average income gains (G(0)) across countries in the three scenarios are, respectively –0.2%, –1.3% and –5.7% (Panel A of Table 10). More importantly, the prevalence of trade-offs is widespread (Panel B). There are only 8-10 countries without trade-offs and, of these, between 6 and 9 prefer the status quo to more protection. There are between 44 and 46 countries with trade-offs. The resolution of these trade-offs is fairly stable in favor of the status quo for plausible levels of ε (1, 1.5 or 2). That is, in all three protectionist scenarios the vast majority of countries attain higher levels of welfare with the pre-existing structure of trade protection rather than with more protection. Most countries would be hurt by protectionist trade reforms, even after inequality impacts are taken into consideration. 6 Conclusion Using household survey data for 54 low and middle income countries harmonized with trade and tariff data, this paper offers a quantitative assessment of the income gains and inequality costs of trade liberalization and the potential trade-off between them. A stylized yet comprehensive model that allows for a rich range of first-order effects on household consumption and income is used to quantify welfare gains or losses for households in different parts of the expenditure distribution. These welfare impacts are subsequently explored by deploying the Atkinson social welfare function that allows us to decompose inequality adjusted gains into aggregate gains and equality (distributional) gains. Liberalization is estimated to lead to income gains in 45 countries in our study, and to income losses in 9 countries. The developing world as a whole would enjoy gains of about 1.9 percent of real household expenditures, on average. These income gains are negatively correlated with equality gains, such that liberalization typically entails a trade-off between average incomes and income inequality. 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(2012). “Tariff Pass-Through and the Distributional Effects of Trade Liberalization,” Journal of Development Economics, 99(2), pp. 265–281. 34 Topalova, P. (2010). “Factor Immobility and Regional Impacts of Trade Liberalization: Evidence on Poverty from India,” American Economic Journal: Applied Economics, vol. 2(4), pp. 1–41. 35 Figure 1 Patterns of Distributional Impacts Pro-Poor Bias (a) Central African Republic 20 10 welfare effects 0-10 -20 6 8 10 12 log per capita expenditure (b) Mauritania 5 0 welfare effects -5 -10 -15 7 8 9 10 11 12 log per capita expenditure Notes: The solid curve is the non-parametric kernel regression of the welfare effects and the initial level of per capita household expenditure. The dotted curves are the corresponding 95% confidence bands. The contour lines are level curves of the non-parametric kernel bivariate density of these two variables. Liberalization is classified as having a pro-poor bias if the average proportional real income gains accruing to households in the the bottom 20% of the pre-liberalization income distribution exceed the average proportional real income gains accruing to households in the top 20% of the pre-liberalization real income income distribution. 36 Figure 2 Patterns of Distributional Impacts Pro-Rich Bias (a) Uzbekistan (b) Togo 15 10 10 5 welfare effects welfare effects 5 0 0 -5 -5 -10 -10 -15 8 9 10 11 12 6 8 10 12 14 log per capita expenditure log per capita expenditure (c) Ghana 10 5 welfare effects -5 -10 -15 0 8 10 12 14 16 log per capita expenditure Notes: The solid curve is the non-parametric kernel regression of the welfare effects and the initial level of per capita household expenditure. The dotted curves are the 95% confidence bands. The contour lines are level curves of the non-parametric kernel bivariate density of these two variables. Liberalization is classified as having a pro-rich bias if the average proportional real income gains accruing to households in the the top 20% of the pre-liberalization income distribution exceed the average proportional real income gains accruing to households in the bottom 20% of the pre-liberalization real income income distribution. 37 Figure 3 No Trade-off Income Gains and Equality Gains (a) Central African Republic 8 8 7 7 inequality adjusted gains, G(ε) 6 6 5 5 4 4 3 3 2 2 1 1 0 0 0 2 4 6 8 10 inequality aversion, ε Notes: The solid line depicts how the inequality adjusted welfare gains associated with liberalization G(ε) vary with inequality aversion ε. The dotted lines represent 95% confidence intervals based on 1000 bootstrap replications. 38 Figure 4 No Trade-off Income Losses and Inequality Costs (a) Ghana 0 0 -1 -1 inequality adjusted gains, G(ε) -2 -2 -3 -3 -4 -4 -5 -5 -6 -6 -7 -7 0 2 4 6 8 10 inequality aversion, ε Notes: The solid line depicts how the inequality adjusted welfare gains associated with liberalization G(ε) vary with inequality aversion ε. The dotted lines represent 95% confidence intervals based on 1000 bootstrap replications. 39 Figure 5 Trade-off Without Policy Preference Reversal Income Gains and Inequality Costs (a) Uzbekistan 4 4 inequality adjusted gains, G(ε) 3 3 2 2 1 1 0 0 0 2 4 6 8 10 inequality aversion, ε Notes: The solid line depicts how the inequality adjusted welfare gains associated with liberalization G(ε) vary with inequality aversion ε. The dotted lines represent 95% confidence intervals based on 1000 bootstrap replications. 40 Figure 6 Trade-off with Trade Policy Preference Reversal Income Gains and Inequality Costs (a) Benin 3 3 2 2 1 1 inequality adjusted gains, G(ε) 0 0 -1 -1 -2 -2 -3 -3 -4 -4 -5 -5 -6 -6 0 2 4 6 8 10 inequality aversion, ε Notes: The solid line depicts how the inequality adjusted welfare gains associated with liberalization G(ε) vary with inequality aversion ε. The dotted lines represent 95% confidence intervals based on 1000 bootstrap replications. 41 Figure 7 Trade-off with Trade Policy Preference Reversal Income Losses and Equality Gains (a) Mali 5 5 inequality adjusted gains, G(ε) 3 3 1 1 -1 -1 -3 -3 -5 -5 0 2 4 6 8 10 inequality aversion, ε Notes: The solid line depicts how the inequality adjusted welfare gains associated with liberalization G(ε) vary with inequality aversion ε. The dotted lines represent 95% confidence intervals based on 1000 bootstrap replications. 42 Figure 8 Trade-Off Resolution (a) ε = 1.5 10 10 Zambia Central African Republic Cameroon 5 5 Jordan Ecuador Ukraine Tanzania Pakistan Yemen G(1.5) Guinea BissauAzerbaijan Egypt Cote d'Ivoire Sierra Leone Bolivia Uzbekistan Guinea Indonesia Armenia Nicaragua P.N. Guinea South Africa Iraq Tajikistan Kenya Nepal Guatemala Mozambique Nigeria Mali Uganda Liberia Niger Malawi Sri Lanka Georgia Moldova Ethiopia Kyrgyz Rep. Mongolia Vietnam 0 0 Mauritania Benin Rwanda Bangladesh Togo Comoros Gambia Burkina Faso Bhutan Madagascar Burundi Ghana Cambodia -5 -5 -5 0 5 10 G(0) Notes: scatter plot of the inequality-adjusted gains from trade G(ε), at ε = 1.5, against the gains from trade G(0). The symbols represent the trade-off resolution: : no trade-off, liberalize; N: soft trade-off, liberalize; I: policy reversal, liberalize; : no trade-off, protect; L: policy reversal, protect; G: soft trade-off, protect. 43 Figure 9 Trade-Off Resolution (a) ε = 1 10 10 Zambia Cameroon Central African Republic 5 5 Jordan Ecuador Tanzania Ukraine Yemen Sierra Leone Cote Pakistan Egypt Bolivia Nigeria d'Ivoire Uzbekistan Guinea BissauAzerbaijan G(1) Kenya Guinea Mozambique Nicaragua Armenia Indonesia South Africa P.N. Guinea Tajikistan Guatemala Iraq Uganda Nepal Liberia Malawi Niger Ethiopia Sri Lanka Georgia Mali Moldova Vietnam Benin Kyrgyz Rep. Mongolia Togo Gambia 0 0 Bangladesh Rwanda Mauritania Burkina Faso Comoros Bhutan Madagascar Burundi Ghana Cambodia -5 -5 -5 0 5 10 G(0) (b) ε = 2 10 10 Central African Republic Zambia Jordan 5 5 Cameroon Pakistan Ecuador Guinea Bissau Ukraine Yemen Tanzania Azerbaijan G(2) Egypt Uzbekistan Indonesia Armenia Sierra Leone Bolivia Cote d'Ivoire Mali P.N. Nicaragua Guinea Guinea Nepal South Africa Iraq Tajikistan Uganda Guatemala Sri Lanka Georgia Kenya Mozambique Niger Mongolia Moldova Liberia Malawi Kyrgyz Rep. Ethiopia Mauritania 0 0 Vietnam Rwanda Nigeria Benin Comoros Bangladesh Burkina Faso Togo Gambia Madagascar Bhutan Burundi Cambodia Ghana -5 -5 -5 0 5 10 G(0) Notes: scatter plot of the inequality-adjusted gains from trade G(ε) against the gains from trade G(0), for ε = 1 (panel a) and ε = 2 (panel b). The symbols 44 represent the trade-off resolution: : no trade-off, liberalize; N: soft trade-off, liberalize; I: policy reversal, liberalize; : no trade-off, protect; L: policy reversal, protect; G: soft trade-off, protect. Table 1 Average Tariffs Country Staple Non-Staple Manufactures Country Staple Non-Staple Manufactures Agric. Agric. Agric. Agric. Benin 12.2 16.9 10.8 Armenia 6.9 7.3 6.7 Burkina Faso 12.0 18.3 9.3 Bangladesh 7.4 4.9 18.8 Burundi 23.8 21.6 10.8 Bhutan 43.7 46.1 23.5 Cameroon 13.8 22.5 23.0 Cambodia 13.0 6.4 10.1 Central African Rep. 16.6 23.7 21.8 Indonesia 6.0 1.9 6.1 Comoros 1.8 10.4 8.9 Iraq 5.0 5.0 5.0 Cˆote d’Ivoire 10.4 10.2 9.2 Jordan 7.9 18.6 8.3 Egypt, Arab Rep. 7.1 28.0 18.0 Kyrgyz Republic 6.1 6.1 4.0 Ethiopia 10.1 13.3 12.4 Mongolia 5.3 6.5 4.9 Gambia, The 6.6 13.5 13.9 Nepal 9.0 11.7 13.9 Ghana 16.4 11.6 14.3 Pakistan 3.7 8.1 17.4 Guinea 13.9 18.9 9.5 Papua New Guinea 4.7 12.4 0.9 45 Guinea-Bissau 13.5 15.7 12.8 Sri Lanka 7.8 16.3 15.3 Kenya 18.7 25.1 11.0 Tajikistan 7.4 5.8 8.3 Liberia 6.3 5.6 16.4 Uzbekistan 14.8 11.4 8.5 Madagascar 8.3 9.6 14.8 Vietnam 11.1 6.3 9.8 Malawi 8.2 22.0 9.3 Yemen, Rep. 4.4 7.6 7.7 Mali 11.2 16.8 8.8 Mauritania 9.2 14.8 15.9 Azerbaijan 5.7 4.0 10.4 Mozambique 8.8 13.9 7.4 Georgia 6.0 6.4 0.5 Niger 12.2 17.6 9.3 Moldova 7.9 10.7 3.3 Nigeria 11.3 19.8 11.0 Ukraine 4.8 5.1 4.8 Rwanda 21.0 30.1 11.0 Sierra Leone 11.8 16.2 9.7 Bolivia 11.0 12.6 15.1 South Africa 7.1 6.4 16.8 Ecuador 14.4 15.4 14.0 Tanzania 12.6 29.1 10.7 Guatemala 10.3 10.2 7.4 Togo 11.6 18.6 9.5 Nicaragua 12.1 9.8 9.1 Uganda 11.4 29.7 10.0 Zambia 17.1 19.7 6.8 Average 10.8 14.4 10.9 Pop. weighted average 9.0 12.1 11.8 GDP weighted average 8.1 10.2 10.9 Notes: Authors’ calculations based on United Nations COMTRADE and UNCTAD TRAINS data. The average tariff is expressed in percentage points. Table 2 Expenditure Shares Country Staple Non- Manuf. Non- Other Home Country Staple Non- Manuf. Non- Other Home Agric. Staple Traded Cons. Agric. Staple Traded Cons. Agric. Agric. Benin 34.4 3.8 23.3 10.7 6.1 21.6 Armenia 55.5 8.0 7.1 21.2 0.0 8.2 Burkina Faso 24.4 12.3 16.1 8.8 8.3 30.1 Bangladesh 45.3 9.0 14.1 16.2 4.4 11.0 Burundi 41.8 9.9 20.2 12.7 10.8 4.6 Bhutan 26.9 7.2 25.5 15.8 12.4 12.3 Cameroon 46.8 6.1 17.1 14.7 5.9 9.4 Cambodia 31.2 12.4 16.0 18.8 8.5 13.0 Central African Rep. 40.4 18.5 21.3 7.9 0.2 11.7 Indonesia 29.3 11.7 11.4 22.8 13.5 11.3 Comoros 48.1 9.5 10.8 17.3 5.2 9.2 Iraq 32.3 5.2 35.2 23.0 3.4 0.8 Cˆote d’Ivoire 35.7 3.9 22.2 20.5 6.5 11.3 Jordan 35.1 15.2 19.1 29.1 1.2 0.2 Egypt, Arab Rep. 45.5 4.9 13.8 31.4 2.0 2.4 Kyrgyz Republic 42.3 5.5 25.5 13.6 3.4 9.7 Ethiopia 23.1 9.1 17.0 2.9 10.1 37.7 Mongolia 47.6 8.9 14.3 8.8 1.1 19.3 Gambia, The 45.3 11.5 11.3 12.0 10.4 9.6 Nepal 27.3 4.8 11.7 27.6 4.7 23.9 Ghana 7.7 1.4 30.8 33.0 15.5 11.5 Pakistan 28.2 7.7 23.4 12.9 6.6 21.3 Guinea 33.0 11.9 18.3 12.6 5.0 19.2 Papua New Guinea 36.2 12.2 5.8 5.0 13.7 27.1 Guinea-Bissau 50.7 6.3 6.6 7.4 4.2 24.7 Sri Lanka 32.6 10.2 9.4 19.4 21.7 6.7 46 Kenya 30.2 9.7 23.4 24.9 2.4 9.4 Tajikistan 37.8 5.5 24.8 14.8 3.2 13.9 Liberia 47.1 7.2 12.4 15.2 2.5 15.6 Uzbekistan 36.5 5.1 7.5 10.5 1.9 38.5 Madagascar 37.2 7.2 12.0 3.6 0.7 39.4 Vietnam 37.3 6.5 19.6 15.3 10.6 10.7 Malawi 25.8 5.7 29.1 6.9 0.7 31.8 Yemen, Rep. 39.2 20.4 17.5 15.5 4.4 3.1 Mali 25.6 7.6 4.2 4.9 0.5 57.1 Mauritania 47.2 11.5 14.6 6.7 0.7 19.3 Azerbaijan 51.1 5.9 20.9 11.6 1.6 9.0 Mozambique 44.7 5.3 14.7 3.9 1.5 29.9 Georgia 34.1 7.8 23.7 27.6 4.7 2.1 Niger 35.7 8.8 17.1 6.5 10.2 21.7 Moldova 16.4 2.2 32.1 15.3 7.0 27.1 Nigeria 47.9 3.6 18.0 9.4 0.5 20.6 Ukraine 44.8 11.4 20.0 16.3 0.1 7.4 Rwanda 24.3 4.9 10.6 9.0 29.0 22.2 Sierra Leone 46.2 10.4 12.4 10.8 4.4 15.8 Bolivia 44.0 7.7 16.8 23.9 1.3 6.4 South Africa 31.6 8.3 31.8 16.4 11.8 0.1 Ecuador 42.2 3.9 16.8 21.5 8.5 7.2 Tanzania 29.4 6.6 19.1 9.9 6.2 28.8 Guatemala 37.7 5.3 19.8 17.8 4.8 14.6 Togo 39.0 7.8 15.1 26.2 5.8 6.1 Nicaragua 40.8 4.9 16.6 19.1 1.0 17.7 Uganda 24.2 7.6 16.4 17.9 2.0 31.9 Zambia 53.5 4.8 6.3 10.1 0.6 21.8 Average 37.0 8.0 17.4 15.1 5.8 16.6 Pop. weighted av. 35.4 7.9 17.5 15.9 6.6 16.6 GDP weighted av. 35.9 8.0 18.5 18.3 7.5 11.7 Notes: Authors’ calculations based on household survey data. The average expenditure shares are expressed in percentage points. Table 3 Income Shares Country Staple Non- Wages Family Other Home Country Staple Non- Wages Family Other Home Agric. Staple Enterp. Cons. Agric. Staple Enterp. Cons. Agric. Agric. Benin 14.2 10.0 13.1 0.0 40.9 21.8 Armenia 9.2 0.1 35.1 6.5 40.6 8.5 Burkina Faso 19.1 2.9 13.4 17.7 12.9 34.0 Bangladesh 33.0 2.1 31.4 14.3 12.1 7.1 Burundi 39.5 29.4 8.1 7.5 11.0 4.5 Bhutan 12.9 0.0 44.2 9.3 8.6 25.1 Cameroon 15.4 0.1 27.3 23.1 0.0 34.1 Cambodia 24.2 0.5 30.8 23.6 5.3 15.6 Central African Rep, 42.5 9.3 2.4 3.4 4.3 38.1 Indonesia 4.6 1.2 38.2 0.6 20.9 34.4 Comoros 24.2 3.7 26.7 16.4 11.1 17.8 Iraq 8.1 1.6 49.2 11.9 28.4 0.8 Cˆote d’Ivoire 7.1 13.7 17.0 28.4 15.7 18.2 Jordan 1.7 2.1 45.2 8.9 41.0 1.0 Egypt, Arab Rep. 6.9 6.9 41.1 15.1 29.8 0.2 Kyrgyz Republic 12.2 1.4 40.4 12.0 27.4 6.6 Ethiopia 14.3 0.5 5.2 24.3 10.9 44.8 Mongolia 10.1 0.3 38.1 8.7 31.6 11.1 Gambia, The 2.7 6.7 45.7 21.9 8.6 14.4 Nepal 4.1 1.2 25.8 11.1 22.1 35.7 Ghana 8.5 5.5 58.5 0.0 12.0 15.6 Pakistan 7.6 3.1 45.9 12.1 13.8 17.5 Guinea 17.5 3.2 7.0 18.2 13.5 40.5 Papua New Guinea 13.8 6.5 14.8 9.6 17.9 37.2 Guinea-Bissau 5.5 21.9 21.7 7.8 10.4 32.8 Sri Lanka 13.1 4.6 48.8 19.3 0.0 14.2 47 Kenya 21.8 3.1 35.4 5.3 17.8 16.6 Tajikistan 0.9 1.5 38.7 8.5 22.4 28.0 Liberia 10.2 3.4 22.2 29.2 9.9 25.1 Uzbekistan 7.4 0.2 20.3 11.2 20.7 40.2 Madagascar 27.1 3.0 23.0 13.1 5.1 28.8 Vietnam 21.1 3.5 35.3 19.6 13.1 7.4 Malawi 18.4 4.6 21.4 12.7 3.8 39.0 Yemen, Rep. 7.8 9.2 43.7 15.3 21.2 2.8 Mali 8.7 2.8 8.3 10.5 15.4 54.3 Mauritania 13.4 0.0 3.7 10.1 30.8 42.0 Azerbaijan 28.8 1.9 26.1 2.9 26.2 14.1 Mozambique 10.4 7.1 15.1 10.4 10.0 46.9 Georgia 7.3 1.9 29.2 7.8 51.9 1.9 Niger 17.3 3.1 4.0 1.5 38.2 35.9 Moldova 5.5 2.1 30.4 1.7 26.3 33.9 Nigeria 15.5 5.6 33.2 10.3 4.6 30.8 Ukraine 2.8 0.0 43.5 0.1 48.0 5.6 Rwanda 10.5 3.7 24.7 2.5 11.8 46.7 Sierra Leone 18.7 4.6 11.0 13.3 19.6 32.7 Bolivia 6.3 7.6 36.1 27.3 16.2 6.5 South Africa 0.6 0.0 54.9 0.0 43.6 0.8 Ecuador 10.6 1.1 48.4 16.7 17.3 5.8 Tanzania 10.9 3.0 23.0 5.5 11.6 46.0 Guatemala 6.4 2.9 45.2 18.0 14.1 13.4 Togo 8.7 6.5 30.2 37.2 9.7 7.7 Nicaragua 10.9 2.8 40.4 18.4 13.5 14.0 Uganda 9.7 2.9 21.6 18.7 13.9 33.1 Zambia 5.7 1.7 21.0 13.3 18.8 39.5 Average 12.9 4.2 29.0 12.5 18.6 22.8 Pop. weighted av. 12.8 3.3 33.0 11.2 17.0 22.7 GDP weighted av. 9.4 2.7 39.0 8.1 21.6 19.3 Notes: Authors’ calculations based on household survey data. The average income shares are expressed in percentage points. Table 4 Gains from Trade - Winners Gains Expenditure Income agric. manuf. total agric. wage enter. rev. total Cameroon 6.9 8.9 3.6 12.5 -1.7 -1.3 -0.7 -1.9 -5.6 Zambia 5.9 7.8 1.2 9.0 -1.0 -0.5 -0.7 -0.8 -3.0 Sierra Leone 4.3 5.8 1.7 7.4 -1.5 -0.1 -0.1 -1.5 -3.1 Tanzania 4.2 4.7 4.1 8.8 -1.7 -1.1 -0.2 -1.7 -4.6 Central African Rep, 4.2 7.1 3.7 10.8 -4.5 -0.0 0.0 -2.1 -6.6 Jordan 4.0 6.2 2.1 8.3 -0.3 -0.4 -0.1 -3.4 -4.2 Mozambique 3.7 6.0 1.2 7.2 -1.1 -0.3 -0.1 -2.0 -3.6 Uzbekistan 3.5 5.0 1.9 7.0 -0.7 -1.1 -0.4 -1.2 -3.4 Cˆote d’Ivoire 3.4 4.6 2.6 7.2 -1.7 -0.6 -0.4 -1.1 -3.8 Nigeria 3.3 6.2 2.2 8.3 -1.6 -1.8 -0.3 -1.3 -5.0 Ukraine 3.2 3.7 0.9 4.6 -0.2 -0.2 -0.0 -0.9 -1.3 Ecuador 3.0 5.9 1.5 7.3 -1.6 -1.4 -0.3 -1.0 -4.4 Kenya 2.9 6.1 2.5 8.6 -2.8 -1.2 -0.1 -1.6 -5.7 Egypt, Arab Rep. 2.9 4.7 1.9 6.6 -1.3 -1.1 -0.4 -1.0 -3.7 Guinea 2.8 4.9 2.9 7.8 -1.9 -0.1 -0.2 -2.9 -5.0 Bolivia 2.8 4.2 2.3 6.5 -1.2 -0.6 -0.6 -1.3 -3.7 Yemen, Rep. 2.7 4.1 1.3 5.4 -0.8 -0.4 -0.1 -1.5 -2.8 Azerbaijan 2.5 3.9 2.3 6.2 -2.3 -0.1 0.0 -1.3 -3.7 Armenia 2.5 3.8 0.4 4.1 -0.5 -0.3 -0.0 -0.9 -1.7 South Africa 2.5 1.2 2.9 4.2 -0.0 -1.0 0.0 -0.7 -1.7 Malawi 2.4 4.1 2.8 6.9 -2.4 -0.6 -0.3 -1.2 -4.5 Pakistan 2.4 2.0 3.7 5.7 -1.4 -0.8 -0.3 -0.8 -3.3 Benin 2.3 4.8 2.9 7.7 -2.0 -0.2 -0.0 -3.2 -5.4 Ethiopia 2.2 3.7 3.6 7.3 -2.9 -0.0 -0.7 -1.4 -5.1 Togo 2.1 5.3 1.8 7.1 -0.9 -1.0 -0.9 -2.3 -5.0 Guinea-Bissau 2.0 4.7 0.8 5.5 -0.9 -0.3 -0.0 -2.3 -3.5 Tajikistan 2.0 3.3 1.4 4.7 -0.2 -0.6 -0.0 -2.0 -2.8 Uganda 2.0 5.4 1.1 6.6 -2.0 -1.0 -0.3 -1.4 -4.6 Niger 1.9 4.3 2.0 6.3 -2.4 -0.0 -0.0 -1.9 -4.4 Nicaragua 1.9 5.0 1.1 6.1 -1.7 -0.9 -0.3 -1.3 -4.1 Gambia, The 1.9 6.5 1.5 8.0 -0.7 -1.1 -0.6 -3.7 -6.1 Guatemala 1.9 3.6 1.2 4.8 -0.8 -1.0 -0.2 -0.9 -2.9 Indonesia 1.9 2.8 0.5 3.2 -0.2 -0.6 -0.0 -0.6 -1.4 Papua New Guinea 1.7 4.3 0.4 4.7 -2.3 -0.2 -0.0 -0.5 -3.0 Iraq 1.6 1.5 2.0 3.5 -0.3 -0.3 -0.1 -1.2 -1.8 Liberia 1.6 3.3 1.3 4.6 -0.8 -0.4 -0.5 -1.3 -3.0 Nepal 1.4 2.7 1.6 4.4 -0.5 -0.3 -0.1 -2.0 -3.0 Vietnam 1.1 5.1 2.0 7.1 -2.8 -1.0 -0.4 -1.8 -5.9 Georgia 1.0 2.1 0.1 2.2 -0.6 -0.0 -0.0 -0.6 -1.2 Moldova 0.7 1.4 1.5 2.9 -0.6 -0.1 -0.0 -1.4 -2.1 Burkina Faso 0.7 3.8 2.3 6.1 -2.4 -0.6 -0.5 -1.9 -5.4 Kyrgyz Republic 0.6 1.8 1.4 3.2 -0.7 -0.2 -0.0 -1.6 -2.6 Bangladesh 0.5 4.9 2.3 7.2 -3.8 -1.5 -0.1 -1.3 -6.7 Burundi 0.4 6.9 2.2 9.0 -6.2 -0.5 -0.0 -1.8 -8.6 Mongolia 0.1 2.7 0.6 3.4 -0.6 -0.2 -0.1 -2.4 -3.3 Average 2.4 4.5 1.9 6.4 -1.5 -0.6 -0.2 -1.6 -3.9 Pop. weighted av. 2.3 4.1 2.1 6.2 -1.6 -0.9 -0.2 -1.2 -3.9 GDP weighted av. 2.4 3.7 1.8 5.4 -1.0 -0.9 -0.2 -1.0 -3.1 48 Notes: Authors’ calculations. The gain from trade, expressed in percentage points, is the population weighted average of the proportional change in household real expenditure. Table 5 Gains from Trade - Losers Gains Expenditure Income agric. manuf. total agric. wage enter. rev. total Cambodia -3.1 4.4 0.9 5.4 -4.5 -0.8 0.0 -3.1 -8.4 Ghana -1.9 1.0 2.9 3.9 -1.2 -2.8 0.0 -1.8 -5.8 Mauritania -1.3 4.5 1.8 6.3 -1.1 -0.1 -0.0 -6.5 -7.6 Madagascar -1.1 3.2 0.8 3.9 -2.3 -0.9 -0.1 -1.7 -5.0 Bhutan -0.8 8.5 5.3 13.8 -3.1 -2.8 0.0 -8.7 -14.6 Mali -0.3 2.4 0.2 2.6 -0.9 -0.0 -0.0 -2.0 -3.0 Sri Lanka -0.3 3.3 0.8 4.1 -1.4 -1.2 -0.7 -1.0 -4.4 Comoros -0.3 1.6 1.3 3.0 -0.6 -0.3 -0.3 -2.0 -3.2 Rwanda -0.1 3.7 1.4 5.1 -2.5 -1.1 -0.0 -1.6 -5.2 Average -1.0 3.6 1.7 5.3 -2.0 -1.1 -0.1 -3.2 -6.4 Pop.weighted av. -1.2 2.9 1.3 4.2 -1.9 -1.2 -0.2 -2.0 -5.4 GDP weighted av. -1.0 2.8 1.4 4.2 -1.7 -1.5 -0.3 -1.8 -5.3 Notes: Authors’ calculations. The gain from trade, expressed in percentage points, is the population weighted average of the proportional change in household real expenditure. 49 Table 6 Income Gains and Inequality Costs without Trade Policy Preference Reversals Income Gains Equality Gains µ1 −µ0 µ1 I0 (ε)−I1 (ε) µ0 µ0 1−I0 (ε) ε = 0.5 ε=1 ε = 10 A) Countries without Trade-offs Guinea-Bissau 2.0 0.5 0.7 0.8 Central African Republic 4.2 0.4 0.8 2.1 Jordan 4.1 0.4 0.7 0.3 Yemen, Rep. 2.7 0.2 0.3 0.1 Mongolia 0.1 0.1 0.2 0.5 Comoros -0.3 -0.1 -0.2 -1.8 Rwanda -0.2 -0.2 -0.2 -4.9 Madagascar -1.1 -0.3 -0.7 -2.4 Ghana -1.9 -0.4 -0.8 -2.3 B) Countries with Trade-offs Indonesia 1.9 0.1 0.2 -0.4 Pakistan 2.4 0.0 0.3 -0.2 Azerbaijan 2.5 0.0 0.0 -0.6 Zambia 5.9 -0.0 -0.1 -0.0 Moldova 0.7 -0.0 -0.0 0.6 Kyrgyz Republic 0.6 -0.0 -0.1 -0.0 Ukraine 3.2 -0.0 -0.1 -0.7 Egypt, Arab Rep. 2.9 -0.1 -0.2 -0.8 Tajikistan 1.9 -0.1 -0.2 -0.6 Iraq 1.6 -0.1 -0.1 -0.3 Armenia 2.5 -0.1 -0.2 -1.0 South Africa 2.5 -0.1 -0.4 -1.2 Guinea 2.8 -0.3 -0.6 -0.8 Uganda 1.9 -0.3 -0.5 0.3 Uzbekistan 3.5 -0.4 -0.7 -2.4 Cameroon 6.8 -0.5 -1.1 -5.0 Cambodia -3.0 0.0 -0.1 -0.8 Bhutan -0.8 -0.1 -0.5 -7.5 Notes: Authors’ calculations. The table presents the decomposition of the inequality-adjusted gains from trade G(ε). The first column reports the average income gains from trade (the proportional change in real household expenditures). The three remaining columns show the equality gains (due to changes in inequality) for different values of inequality aversion (ε = 0.5,ε = 1, and ε = 10). The inequality-adjusted gains from trade is the sum of the income gains and the equality gains. 50 Table 7 Income Gains and Inequality Costs with Trade Policy Preference Reversals Trade Policy Preference Reversals Potential Reversals ε∗ Lower Bound Upper bound Lower Bound Upper Bound A) Countries with Income Gains Burkina Faso 0.6 0.5 0.7 Bangladesh 0.6 0.5 0.7 Gambia, The 1.2 1.1 1.3 Togo 1.2 1.2 1.3 Benin 1.5 1.4 1.6 Nigeria 1.9 1.8 2.0 Vietnam 2.0 1.8 2.1 Kenya 2.5 2.4 2.7 Ethiopia 3.1 2.7 3.6 Mozambique 3.5 2.9 8.5 Papua New Guinea 4.4 3.1 – Liberia 4.5 3.6 – Guatemala 7.0 5.2 – Malawi 7.1 4.1 – Sierra Leone 4.0 – Bolivia 4.1 – Niger 6.0 – Nicaragua 6.3 – Cˆote d’Ivoire 7.0 – Georgia 7.1 – Tanzania 8.9 – Nepal 9.3 – Ecuador 9.9 – B) Countries with Income Losses Mali 0.4 0.2 0.5 Mauritania 1.6 1.5 1.8 C) Countries with multiple (potential) reversals Countries with Income Gains Burundi 0.1 0 0.2 5.6 7.1 Countries with Income Losses Sri Lanka 0.3 0.2 0.4 8.9 7.2 – Notes: Authors’ calculations. The table presents estimates of the trade-ε∗ , the cut-off value of inequality aversion at which there is a reversal of trade policy preference in terms of social welfare. The standard errors are bootstrapped using 1000 replications. 51 Table 8 Decomposing Equality Gains Consumption Equality Gains Income Equality Gains ε = 0.5 ε = 1 ε = 10 ε = 0.5 ε = 1 ε = 10 Benin 0.9 1.4 2.9 -0.4 -0.7 -1.1 Burkina Faso 0.8 1.4 2.8 -0.3 -0.6 -3.0 Burundi 0.6 1.3 6.7 -0.1 -0.3 -3.5 Cameroon 0.5 1.0 3.3 -0.1 -0.1 0.7 Central African Republic 0.3 0.5 0.2 0.1 0.2 0.6 Comoros 0.4 0.8 0.8 -0.0 -0.1 -0.5 Cˆote d’Ivoire 0.3 0.6 1.4 -0.2 -0.3 -1.2 Egypt, Arab Rep. 0.1 0.2 0.8 0.0 0.0 -0.4 Ethiopia 0.6 1.0 1.6 -0.4 -0.9 -4.4 Gambia, The 0.2 0.4 0.3 -0.1 -0.2 -0.7 Ghana 0.2 0.4 1.0 -0.1 -0.3 -2.2 Guinea 0.2 0.4 0.6 -0.1 -0.3 -9.4 Guinea-Bissau 0.3 0.5 0.3 -0.2 -0.4 -2.4 Kenya 0.2 0.6 1.8 -0.2 -0.3 -2.0 Liberia 0.4 0.6 0.5 -0.3 -0.6 -1.3 Madagascar 0.2 0.3 0.6 -0.1 -0.3 -1.2 Malawi 0.0 0.0 -0.3 -0.0 -0.1 -0.5 Mali -0.0 -0.0 0.4 -0.0 -0.0 0.2 Mauritania 0.1 0.2 0.5 -0.1 -0.3 -0.7 Mozambique -0.0 -0.0 -0.1 -0.0 -0.0 0.1 Niger -0.0 -0.0 -0.3 -0.0 -0.0 -0.4 Nigeria 0.2 0.4 1.0 -0.3 -0.5 -1.8 Rwanda -0.0 -0.0 -0.2 -0.1 -0.1 -0.4 Sierra Leone 0.2 0.4 -0.1 -0.3 -0.6 -2.7 South Africa -0.0 0.0 0.4 -0.1 -0.1 -0.7 Tanzania -0.0 -0.1 -1.0 -0.0 -0.1 0.0 Togo 0.1 0.1 -0.4 -0.2 -0.3 -1.4 Uganda 0.9 1.5 2.0 -1.0 -2.0 -11.7 Zambia -0.0 -0.2 -1.5 -0.1 -0.2 0.3 Armenia -0.1 -0.2 -0.2 -0.1 -0.2 -1.8 Notes: The table presents the decomposition of the equality gains from trade G(ε) − G(0). The first three column report the average consumption equality gains from trade for different values of inequality aversion (ε = 0.5, ε = 1, and ε = 10). These consumption equality gains are calculated by assuming that liberalization only impacts consumption and not income. The three remaining columns report the income equality gains from trade for different values of inequality aversion (ε = 0.5, ε = 1, and ε = 10), calculated by assuming that liberalization only impacts income but not consumption. 52 Table 8 Decomposing Equality Gains (Continued) Consumption Equality Gains Income Equality Gains ε = 0.5 ε = 1 ε = 10 ε = 0.5 ε = 1 ε = 10 Bangladesh 0.1 0.2 0.0 -0.3 -0.5 -2.2 Bhutan 0.3 0.7 1.7 -0.4 -0.9 -6.7 Cambodia 1.0 1.6 -0.2 -1.2 -2.3 -9.7 Indonesia 0.1 0.2 0.7 -0.3 -0.7 -3.8 Iraq 0.2 0.4 -0.6 -0.5 -0.9 -2.9 Jordan -0.1 -0.3 -4.3 -0.2 -0.5 -5.0 Kyrgyz Republic -0.1 -0.1 -0.3 -0.2 -0.4 -0.7 Mongolia -0.0 -0.1 1.4 -0.3 -0.5 -2.5 Nepal -0.1 -0.1 2.3 -0.2 -0.4 -2.4 Pakistan -0.1 -0.2 0.3 -0.2 -0.5 -2.8 Papua New Guinea -0.4 -0.8 -2.6 0.0 0.0 -0.3 Sri Lanka 0.0 -0.0 -0.9 -0.4 -0.8 -2.8 Tajikistan 0.2 0.4 0.7 -0.6 -1.3 -3.2 Uzbekistan 0.1 0.1 -0.1 -0.6 -1.0 -3.7 Vietnam 0.2 0.3 0.1 -0.7 -1.4 -6.1 Yemen, Rep. -0.4 -0.7 -1.2 -0.2 -0.4 -1.5 Azerbaijan -0.0 -0.2 -0.5 -0.6 -1.3 -6.4 Georgia -0.2 -0.4 -1.1 -0.4 -0.7 -3.3 Moldova -0.3 -0.5 -1.9 -0.4 -0.7 -2.4 Ukraine -0.1 -0.2 -4.3 -0.5 -1.1 -0.5 Bolivia -0.5 -1.1 -3.8 -0.2 -0.4 -2.7 Ecuador 0.1 0.1 -0.7 -0.8 -1.8 -5.3 Guatemala -0.4 -0.7 -3.3 -0.4 -0.8 -3.4 Nicaragua 0.9 1.9 0.5 -2.6 -4.5 -9.4 Average 0.2 0.3 0.1 -0.3 -0.6 -2.6 Pop. weighted av. 0.1 0.2 0.4 -0.3 -0.6 -2.8 GDP weighted av. 0.1 0.2 0.2 -0.3 -0.6 -2.7 Notes: The table presents the decomposition of the equality gains from trade G(ε) − G(0). The first three column report the average consumption equality gains from trade for different values of inequality aversion (ε = 0.5, ε = 1, and ε = 10). These consumption equality gains are calculated by assuming that liberalization only impacts consumption and not income. The three remaining columns report the income equality gains from trade for different values of inequality aversion (ε = 0.5, ε = 1, and ε = 10), calculated by assuming that liberalization only impacts income but not consumption. 53 Table 9 Robustness Tests No Wage No tariff Alternative Baseline Response redistribution redistribution N winners 45 50 53 45 N losers 9 4 1 9 Average (Inequality Adjusted) Gains from trade reform G(0) 1.9 % 2.5 % 3.7 % 1.9 % G(1) 1.5 % 2.4 % 3.3 % 2.3 % G(1.5) 1.3 % 2.2 % 3.1 % 2.2 % G(2) 1.1 % 2.1 % 3.0 % 2.1 % G(10) -0.4 % 0.9 % 1.5 % 0.7 % Countries without tradeoffs 11 11 8 12 of which prefer freer trade 5 10 8 12 Countries with tradeoffs 43 43 46 42 of which prefer freer trade =1 39 39 42 38 = 1.5 36 39 42 37 =2 35 38 42 35 = 10 27 27 30 23 Total number of countries that prefer freer trade =0 45 50 53 45 =1 44 49 50 50 = 1.5 41 49 50 49 =2 40 48 50 47 = 10 32 37 38 35 Notes: The table summarizes the results of various alternative models to assess the robustness of the results obtained using our baseline model present in column 1. The alternative model presented in column 2 does not allow for labor market responses. Column 3 shows the results of a model without tariff redistribution, i.e. in which governments do not increase taxes to make up for the loss of government revenue. Column 4 shows the results of model in which the government makes up the loss in tariff revenue by means of progressive taxes. Both of these last two models are discussed in greater detail in the Appendix, which also presents country specific results for all these models. 54 Table 10 Protectionist Scenarios Baseline 10% relative 10% absolute Increase (Liberalization) increase increase to 62.4% N winners 45 6 11 11 N losers 9 48 43 43 Average (Inequality Adjusted) Gains from trade reform G(0) 1.9 % -0.2 % -1.3 % -5.7 % G(1) 1.5 % -0.2 % -1.3 % -7.2 % G(1.5) 1.3 % -0.2 % -1.3 % -7.8 % G(2) 1.1 % -0.2 % -1.3 % -8.3 % G(10) -0.4 % -0.2 % -1.7 % -17.9 % Countries without tradeoffs 11 8 9 10 of which prefer freer trade 5 6 7 9 Countries with tradeoffs 43 46 45 44 of which prefer freer trade =1 39 42 39 37 = 1.5 36 43 38 37 =2 35 41 36 37 = 10 27 33 36 42 Total number of countries that prefer freer trade =0 45 48 43 43 =1 44 48 46 46 = 1.5 41 49 45 46 =2 40 47 43 46 = 10 32 39 43 51 Notes: The table summarizes the results of three alternative protectionist trade reforms. Column 1 replicates the results of our baseline exercise in which a country eliminates its own import tariffs as a benchmark. Column 2 shows what would happen if countries were to increase their tariffs by 10% in relative terms, i.e. if all tariffs were multiplied by 1.1. Column 3 shows what would happen if all countries were to increase all of their import tariffs by 10 percentage points. Column 4 demonstrates the gains from trade and trade-offs between income gains and inequality costs if countries were to increase all of their tariffs to 62.4%, or leave them at their pre-existing levels in case tariffs were already in excess of 62.4%. See the Appendix for country specific results. 55 Appendix A: Household Surveys and Data Harmonization Table A1 displays basic information on the household surveys used in the analysis. We report the name of the survey, the year when the data were collected and sample sizes (number of households). The harmonization of the household surveys and the trade and trade policy data was done in two steps. First, all household surveys product and income sources were standardized to common templates, which are shown in Figures A1-A3 below. Second, these harmonized household survey data were merged with HS6 tariff and trade data using custom-made concordances. Table A1 Household Surveys Country Year Obs Survey Benin 2003 5296 Questionnaire Unifi´ ˆ e sur les Indicateurs de Base du Bien-Etre Burkina Faso 2003 8413 Enquˆ ete sur les Conditions de Vie des M´ enages Burundi 1998 6585 Enquˆ ete Prioritaire, Etude Nationale sur les Conditions de Vie des Populations Cameroon 2001-2002 10881 Deuxi` eme Enquˆ ete Camerounaise Aupr` es des M´ enages Central African Republic 2008 6828 Enquˆ ete Centrafricaine pour le Suivi-Evaluation du Bien-ˆ etre Comoros 2004 2929 Enquˆ egrale aupr` ete Int´ es des M´enages Cˆote d’Ivoire 2008 12471 Enquˆ ete sur le Niveau de Vie des M´ enages Egypt, Arab Rep. 2008-2009 23193 Household Income, Expenditure and Consumption Survey Ethiopia 1999-2000 16505 Household Income, Consumption and Expenditure Survey The Gambia 1998 1952 Household Poverty Survey Ghana 2005-2006 8599 Living Standards Survey V Guinea 2012 7423 Enquˆ ete L´ ere pour l’Evaluation de la Pauvret´ eg` e Guinea-Bissau 2010 3141 Inquerito Ligeiro para a Avalic˜ ao da Pobreza Kenya 2005 13026 Integrated Household Budget Survey Liberia 2014-2015 4063 Household Income and Expenditure Survey Madagascar 2005 11661 Permanent Survey of Households Malawi 2004-2005 11167 Second Integrated Household Survey Mali 2006 4449 Enquˆ ete L´eg` ere Int´ ee aupr` egr´ es des M´enages Mauritania 2004 9272 Enquˆ ete Permanente sur les Conditions de Vie des M´ enages Mozambique 2008-2009 10696 Inqu´erito sobre Or¸ camento Familiar Niger 2005 6621 Enquˆ ete Nationale sur les Conditions de Vie des M´ enages Nigeria 2003-2004 18603 Living Standards Survey Rwanda 1998 6355 Integrated Household Living Conditions Survey Sierra Leone 2011 6692 Integrated Household Survey South Africa 2000 25491 General Household Survey Tanzania 2008 3232 Household Budget Survey Togo 2011 5464 Questionnaire des Indicateurs de Base du Bien-ˆ etre Uganda 2005-2006 7350 National Household Survey Zambia 2004 7563 Living Conditions Monitoring Survey IV 56 Table A1 (Continued) Household Surveys (Continued) Country Year Obs Survey Armenia 2014 5124 Integrated Living Conditions Survey Bangladesh 2010 12117 Household Income and Expenditure Survey Bhutan 2012 8879 Living Standards Survey Cambodia 2013 3801 Socio-Economic Survey Indonesia 2007 12876 Indonesian Family Life Survey Iraq 2012 24895 Household Socio-Economic Survey Jordan 2010 11110 Household Expenditure and Income Survey Krygyz Republic 2012 4962 Intergrated Sample Household Budget and Labor Survey Mongolia 2011 11089 Household Socio-Economic Survey Nepal 2010-2011 5929 Living Standards Survey Pakistan 2010-2011 16178 Social and Living Standards Measurement Survey Papua New Guinea 2009 3776 Household Income and Expenditure Survey Sri Lanka 2012-2013 20335 Household Income and Expenditure Survey Tajikistan 2009 1488 Tajikistan Panel Survey Uzbekistan 2003 9419 Household Budget Survey Vietnam 2012 9306 Household Living Standard Survey Yemen, Rep. 2005-2006 12998 Household Budget Survey Azerbaijan 2005 4797 Household Budget Survey Georgia 2014 10959 Household Integrated Survey Moldova 2014 4836 Household Budget Survey Ukraine 2012 10394 Sampling Survey of the Conditions of Life of Ukraine’s Households Bolivia 2008 3900 Encuesta de Hogares Ecuador 2013-2014 28680 Encuesta de Condiciones de Vida Guatemala 2014 11420 Encuesta Nacional de Condiciones de Vida Nicaragua 2009 on de Niveles de Vida 6450 Nicaragua - Encuesta Nacional de Hogares sobre Medici´ 57 Figure A1 Expenditure Template Expenditure 1. Agriculture/Food 11. Staple Food 111. Cereals 112. Legumens 113. Fruits 114. Vegetables 115. Oils/Fats 116. Fish 117. Meat/Livestock 118. Dairy/Eggs 119. Other staple food 1111. Corn 1121. Beans 1131. Banana 1141. Tomato 1151. Vegetable Oils 1161. Fish 1171. Pork (Pig) 1181. Milk 1191. Other staple food 1112. Wheat 1122. Other 1132. Grapes 1142. Potato 1152. Animal Fats 1162. Shrimp 1172. Beef (Cattle) 1182. Eggs 1192. Other processed food 1113. Rice 1133. Citrus 1143. Greens 1153. Other oils/fats 1163. Other Crustacean 1173. Poultry (Chicken) 1183. Cheese 1114. Other Cereals 1134. Apples 1144. Other 1174. Other meat/animals 1184. Other Dairy 1135. Other Fruits Vegetables 12. Non Staple 121. Alcohol 122. Tobacco 123. Oil seeds 124. Spices/herbs 125. Coffee/tea/cocoa 126. Nuts 127. Cotton 128. Other non-staple food 1211. Wine 1221. Cigarettes 1231. Soya 1241. Cloves 1251. Coffee 1261. Cashew 127. Cotton 1281. Sugar (any kind) 1212. Beer 1222. Other tobacco 1232. Other oil seeds 1242. Pepper 1252. Tea 1262. Coconut 1282. Other non-staple 1213. Other alcohol 1243. Vanilla 1253. Cocoa 1263. Other nuts 1244. Saffron 1245. Qat (chat) 1246. Other spices 2. Manufacturing/Household Items 21. Energy 22. Textiles/Apparel 23. Electric/Electronics 24. Household items/Furniture 25. Other physical goods 3. Services 31. Transportation 32. Health 33. Education 34. Communication 35. Other Services 4. Other Expenditures 41. Remittances/transfers given 42. Investment of any sort 43. Festivities 44. Other Disbursement Figure A2 Auto-Consumption Template Autoconsumption 1. Agriculture/Food 11. Staple Food 111. Cereals 112. Legumens 113. Fruits 114. Vegetables 115. Oils/Fats 116. Fish 117. Meat/Livestock 118. Dairy/Eggs 119. Other staple food 1111. Corn 1121. Beans 1131. Banana 1141. Tomato 1151. Vegetable Oils 1161. Fish 1171. Pork (Pig) 1181. Milk 1191. Other staple food 1112. Wheat 1122. Other 1132. Grapes 1142. Potato 1152. Animal Fats 1162. Shrimp 1172. Beef (Cattle) 1182. Eggs 1192. Other processed food 1113. Rice 1133. Citrus 1143. Greens 1153. Other oils/fats 1163. Other Crustacean 1173. Poultry (Chicken) 1183. Cheese 1114. Other Cereals 1134. Apples 1144. Other 1174. Other meat/animals 1184. Other Dairy 1135. Other Fruits Vegetables 12. Non Staple 121. Alcohol 122. Tobacco 123. Oil seeds 124. Spices/herbs 125. Coffee/tea/cocoa 126. Nuts 127. Cotton 128. Other non-staple food 1211. Wine 1221. Cigarettes 1231. Soya 1241. Cloves 1251. Coffee 1261. Cashew 127. Cotton 1281. Sugar (any kind) 1212. Beer 1222. Other tobacco 1232. Other oil seeds 1242. Pepper 1252. Tea 1262. Coconut 1282. Other non-staple 1213. Other alcohol 1243. Vanilla 1253. Cocoa 1263. Other nuts 1244. Saffron 1245. Qat (chat) 1246. Other spices 2. Other goods 21. Energy (wood, coal) 22. Gathering (forest, mushrooms, berries, etc.) 23. Other goods collected for free 24. Other goods produced and consumed within the household 58 Figure A3 Income Template Income 1. Agriculture/Food 11. Staple Food 111. Cereals 112. Legumens 113. Fruits 114. Vegetables 115. Oils/Fats 116. Fish 117. Meat/Livestock 118. Dairy/Eggs 119. Other staple food 1111. Corn 1121. Beans 1131. Banana 1141. Tomato 1151. Vegetable Oils 1161. Fish 1171. Pork (Pig) 1181. Milk 1191. Other staple food 1112. Wheat 1122. Other 1132. Grapes 1142. Potato 1152. Animal Fats 1162. Shrimp 1172. Beef (Cattle) 1182. Eggs 1192. Other processed food 1113. Rice 1133. Citrus 1143. Greens 1153. Other oils/fats 1163. Other Crustacean 1173. Poultry (Chicken) 1183. Cheese 1114. Other Cereals 1134. Apples 1144. Other 1174. Other meat/animals 1184. Other Dairy 1135. Other Fruits Vegetables 12. Non Staple 121. Alcohol 122. Tobacco 123. Oil seeds 124. Spices/herbs 125. Coffee/tea/cocoa 126. Nuts 127. Cotton 128. Other non-staple food 1211. Wine 1221. Cigarettes 1231. Soya 1241. Cloves 1251. Coffee 1261. Cashew 127. Cotton 1281. Sugar (any kind) 1212. Beer 1222. Other tobacco 1232. Other oil seeds 1242. Pepper 1252. Tea 1262. Coconut 1282. Other non-staple 1213. Other alcohol 1243. Vanilla 1253. Cocoa 1263. Other nuts 1244. Saffron 1245. Qat (chat) 1246. Other spices 2. Wages 20. Agriculture, forestry, and fishing 21. Mining, oil, and gas extraction 22. Manufacturing 23. Construction 24. Transportation, communications, electric, gas, and sanitary services 25. Wholesale and retail trade 26. Finance, insurance, and real estate 27. Entertainment Services (Restaurant, entertainment, hotels, etc.) 28. Professional Services (Education, health, other professional occupations) 29. Public Administration 3. Sales of Goods/Services 30. Agriculture, forestry, and fishing (n.e.c.) 31. Mining, oil, and gas extraction 32. Manufacturing 33. Construction 34. Transportation, communications, electric, gas, and sanitary services 35. Wholesale and retail trade 36. Finance, insurance, and real estate 37. Entertainment Services (Restaurant, entertainment, hotels, etc.) 38. Professional Services (Education, health, other professional occupations) 39. Public Administration 4. Transfers 41. Remittances/transfers received (friend, relative) 42. Profits of investment (rent, interests) 43. Government transfers 44. Non-governmental transfers 45. Other 59 Appendix B: Distributional Effects This Appendix includes plots of the distributional effects (kernel regressions and bivariate kernel densities) for each of the 54 countries. We first report 17 cases with a pro-poor bias (Figures B1 to B3), then show another 37 cases with a pro-rich bias (Figures B4 to B10). 60 Figure B1 Pro-Poor Bias (a) Azerbaijan (b) Central African Republic 10 20 10 5 welfare effects welfare effects 0 0 -10 -5 -10 -20 11.5 12 12.5 13 13.5 14 6 8 10 12 log per capita expenditure log per capita expenditure (c) Ecuador 10 (d) Guinea-Bissau 10 5 0 welfare effects welfare effects -10 0 -5 -20 -10 -30 3 4 5 6 7 8 6 8 10 12 14 log per capita expenditure log per capita expenditure (e) Indonesia (f) Jordan 15 20 10 10 welfare effects welfare effects 5 0 0 -10 -5 4 5 6 7 8 9 3 4 5 6 7 log per capita expenditure log per capita expenditure Notes: The red curve is the non-parametric kernel regression of the welfare effects and the initial level of per capita household expenditure. The contour lines are level curves of the non-parametric kernel bivariate density of these two variables. Liberalization is classified as having a pro-poor bias if the average proportional real income gains accruing to households in the the bottom 20% of the pre-liberalization income distribution exceed the average proportional real income gains accruing to households in the top 20% of the pre-liberalization real income income distribution. 61 Figure B2 Pro-Poor Bias (a) Mali (b) Mauritania 5 10 0 5 welfare effects welfare effects -5 0 -10 -5 -10 -15 8 9 10 11 12 13 7 8 9 10 11 12 log per capita expenditure log per capita expenditure (c) Moldova (d) Mongolia 10 5 5 0 welfare effects welfare effects 0 -5 -5 -10 -10 -15 -15 6 7 8 9 9 10 11 12 13 log per capita expenditure log per capita expenditure (e) Nepal (f) Pakistan 10 10 5 5 welfare effects welfare effects 0 0 -5 -5 -10 -10 7 8 9 10 11 6 8 10 12 log per capita expenditure log per capita expenditure Notes: The red curve is the non-parametric kernel regression of the welfare effects and the initial level of per capita household expenditure. The contour lines are level curves of the non-parametric kernel bivariate density of these two variables. Liberalization is classified as having a pro-poor bias if the average proportional real income gains accruing to households in the the bottom 20% of the pre-liberalization income distribution exceed the average proportional real income gains accruing to households in the top 20% of the pre-liberalization real income income distribution. 62 Figure B3 Pro-Poor Bias (a) Papua New Guinea (b) Rwanda 20 20 10 10 welfare effects welfare effects 0 0 -10 -10 -20 -20 2 4 6 8 6 8 10 12 log per capita expenditure log per capita expenditure (c) Sri Lanka (d) Yemen, Rep. 20 10 5 10 welfare effects welfare effects 0 0 -5 -10 -10 -20 -15 7 8 9 10 11 12 7 8 9 10 11 12 log per capita expenditure log per capita expenditure (e) Zambia 15 10 welfare effects 0 -5 -10 5 9 10 11 12 13 log per capita expenditure Notes: The red curve is the non-parametric kernel regression of the welfare effects and the initial level of per capita household expenditure. The contour lines are level curves of the non-parametric kernel bivariate density of these two variables. Liberalization is classified as having a pro-poor bias if the average proportional real income gains accruing to households in the the bottom 20% of the pre-liberalization income distribution exceed the average proportional real income gains accruing to households in the top 20% of the pre-liberalization real income income distribution. 63 Figure B4 Pro-Rich Bias (a) Armenia (b) Bangladesh 10 5 5 0 welfare effects welfare effects -5 0 -5 -10 -15 -10 9 10 11 12 13 -1 0 1 2 3 log per capita expenditure log per capita expenditure (c) Benin 20 (d) Bhutan 10 10 5 welfare effects welfare effects 0 0 -5 -10 -10 -20 -15 -30 7 8 9 10 11 12 7 8 9 10 11 log per capita expenditure log per capita expenditure (e) Bolivia (f) Burkina Faso 10 10 5 5 welfare effects welfare effects 0 0 -5 -5 -10 -10 -15 4 6 8 10 7 8 9 10 11 12 log per capita expenditure log per capita expenditure Notes: The red curve is the non-parametric kernel regression of the welfare effects and the initial level of per capita household expenditure. The contour lines are level curves of the non-parametric kernel bivariate density of these two variables. Liberalization is classified as having a pro-rich bias if the average proportional real income gains accruing to households in the the top 20% of the pre-liberalization income distribution exceed the average proportional real income gains accruing to households in the bottom 20% of the pre-liberalization real income income distribution. 64 Figure B5 Pro-Rich Bias (a) Burundi (b) Cambodia 20 10 10 0 welfare effects welfare effects -10 0 -10 -20 -20 -30 4 6 8 10 12 4 5 6 7 8 9 log per capita expenditure log per capita expenditure (c) Cameroon (d) Comoros 20 10 10 5 welfare effects welfare effects 0 0 -10 -5 -20 -10 8 9 10 11 12 13 6 8 10 12 14 log per capita expenditure log per capita expenditure ote d’Ivoire (e) Cˆ (f) Egypt, Arab Rep. 15 10 10 5 welfare effects welfare effects 5 0 0 -5 -5 -10 -15 -10 6 8 10 12 14 4 5 6 7 8 log per capita expenditure log per capita expenditure Notes: The red curve is the non-parametric kernel regression of the welfare effects and the initial level of per capita household expenditure. The contour lines are level curves of the non-parametric kernel bivariate density of these two variables. Liberalization is classified as having a pro-rich bias if the average proportional real income gains accruing to households in the the top 20% of the pre-liberalization income distribution exceed the average proportional real income gains accruing to households in the bottom 20% of the pre-liberalization real income income distribution. 65 Figure B6 Pro-Rich Bias (a) Ethiopia (b) The Gambia 20 10 10 5 welfare effects welfare effects 0 0 -5 -10 -10 -20 -15 3 4 5 6 7 2 4 6 8 log per capita expenditure log per capita expenditure (c) Georgia 10 (d) Ghana 5 5 welfare effects welfare effects 0 -5 0 -5 -10 -15 -10 -4 -3 -2 -1 0 8 10 12 14 16 log per capita expenditure log per capita expenditure (e) Guatemala (f) Guinea 10 10 5 5 welfare effects welfare effects 0 0 -5 -5 -10 -10 5 6 7 8 9 4 5 6 7 8 log per capita expenditure log per capita expenditure Notes: The red curve is the non-parametric kernel regression of the welfare effects and the initial level of per capita household expenditure. The contour lines are level curves of the non-parametric kernel bivariate density of these two variables. Liberalization is classified as having a pro-rich bias if the average proportional real income gains accruing to households in the the top 20% of the pre-liberalization income distribution exceed the average proportional real income gains accruing to households in the bottom 20% of the pre-liberalization real income income distribution. 66 Figure B7 Pro-Rich Bias (a) Iraq (b) Kenya 4 20 10 2 welfare effects welfare effects 0 0 -10 -2 -20 -4 10 11 12 13 14 15 4 6 8 10 12 log per capita expenditure log per capita expenditure (c) Kyrgyz Republic (d) Liberia 4 5 2 0 welfare effects welfare effects 0 -5 -2 -10 -4 -15 -6 6 7 8 9 10 7 8 9 10 11 log per capita expenditure log per capita expenditure (e) Madagascar (f) Malawi 20 10 5 10 welfare effects welfare effects 0 0 -5 -10 -10 -15 -20 8 9 10 11 12 5 6 7 8 9 10 log per capita expenditure log per capita expenditure Notes: The red curve is the non-parametric kernel regression of the welfare effects and the initial level of per capita household expenditure. The contour lines are level curves of the non-parametric kernel bivariate density of these two variables. Liberalization is classified as having a pro-rich bias if the average proportional real income gains accruing to households in the the top 20% of the pre-liberalization income distribution exceed the average proportional real income gains accruing to households in the bottom 20% of the pre-liberalization real income income distribution. 67 Figure B8 Pro-Rich Bias (a) Mozambique (b) Nicaragua 20 10 10 5 welfare effects welfare effects 0 0 -5 -10 -10 -15 -20 4 6 8 10 5 6 7 8 9 log per capita expenditure log per capita expenditure (c) Niger 20 (d) Nigeria 10 10 welfare effects welfare effects 0 0 -10 -10 -20 -20 7 8 9 10 11 12 2 4 6 8 10 log per capita expenditure log per capita expenditure (e) Sierra Leone (f) South Africa 15 10 10 5 welfare effects welfare effects 5 0 0 -5 -5 -10 -10 -15 8 10 12 14 16 2 4 6 8 10 log per capita expenditure log per capita expenditure Notes: The red curve is the non-parametric kernel regression of the welfare effects and the initial level of per capita household expenditure. The contour lines are level curves of the non-parametric kernel bivariate density of these two variables. Liberalization is classified as having a pro-rich bias if the average proportional real income gains accruing to households in the the top 20% of the pre-liberalization income distribution exceed the average proportional real income gains accruing to households in the bottom 20% of the pre-liberalization real income income distribution. 68 Figure B9 Pro-Rich Bias (a) Tajikistan (b) Tanzania 20 6 10 4 welfare effects welfare effects 2 0 0 -10 -2 -20 -4 -30 4 5 6 7 8 8 10 12 14 log per capita expenditure log per capita expenditure (c) Togo 20 (d) Uganda 10 10 5 welfare effects welfare effects 0 0 -5 -10 -10 -15 -20 6 8 10 12 14 8 9 10 11 12 13 log per capita expenditure log per capita expenditure (e) Ukraine (f) Uzbekistan 15 6 10 4 welfare effects welfare effects 5 2 0 0 -5 -2 -10 6 6.5 7 7.5 8 8.5 8 9 10 11 12 log per capita expenditure log per capita expenditure Notes: The red curve is the non-parametric kernel regression of the welfare effects and the initial level of per capita household expenditure. The contour lines are level curves of the non-parametric kernel bivariate density of these two variables. Liberalization is classified as having a pro-rich bias if the average proportional real income gains accruing to households in the the top 20% of the pre-liberalization income distribution exceed the average proportional real income gains accruing to households in the bottom 20% of the pre-liberalization real income income distribution. 69 Figure B10 Pro-Rich Bias (a) Vietnam 20 10 welfare effects 0-10 -20 5 6 7 8 9 log per capita expenditure Notes: The red curve is the non-parametric kernel regression of the welfare effects and the initial level of per capita household expenditure. The contour lines are level curves of the non-parametric kernel bivariate density of these two variables. Liberalization is classified as having a pro-rich bias if the average proportional real income gains accruing to households in the the top 20% of the pre-liberalization income distribution exceed the average proportional real income gains accruing to households in the bottom 20% of the pre-liberalization real income income distribution. 70 Appendix C: Inequality Adjusted Welfare Gains Figure C1 No Trade-off Income Gains and Equality Gains No Trade Policy Preference Ranking Reversals (a) Central African Republic (b) Guinea-Bissau 8 8 7 7 7 7 6 6 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) 6 6 5 5 5 5 4 4 4 4 3 3 3 3 2 2 2 2 1 1 1 1 0 0 0 0 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε (c) Jordan (d) Mongolia 6 6 1 1 5 5 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) 4 4 3 3 2 2 1 1 0 0 0 0 2 4 6 8 10 0 2 4 6 8 0 10 inequality aversion, ε inequality aversion, ε (e) Yemen, Rep. 4 4 inequality adjusted gains, G(ε) 3 3 2 2 1 1 0 0 0 2 4 6 8 10 inequality aversion, ε Notes: The solid red line depicts how the inequality adjusted welfare gains associated with liberalization G(ε) vary with inequality aversion ε. The dotted blue lines represent 95% confidence intervals based on 1000 bootstrap replications. 71 Figure C2 No Trade-off Income Losses and Inequality Costs No Trade Policy Preference Ranking Reversals (a) Comoros (b) Ghana 0 0 0 0 -1 -1 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) -1 -1 -2 -2 -3 -3 -2 -2 -4 -4 -5 -5 -3 -3 -6 -6 -4 -4 -7 -7 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε (c) Madagascar (d) Rwanda 0 0 1 1 0 0 -1 -1 -1 -1 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) -2 -2 -2 -2 -3 -3 -4 -4 -3 -3 -5 -5 -6 -6 -4 -4 -7 -7 -5 -5 -8 -8 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε Notes: The solid red line depicts how the inequality adjusted welfare gains associated with liberalization G(ε) vary with inequality aversion ε. The dotted blue lines represent 95% confidence intervals based on 1000 bootstrap replications. 72 Figure C3-A Trade-offs Income Gains and Inequality Costs No Trade Policy Preference Ranking Reversals (a) Armenia (b) Azerbaijan 3 3 3 3 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) 2 2 2 2 1 1 1 1 0 0 0 0 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε (c) Cameroon (d) Egypt, Arab Rep. 8 8 3 3 7 7 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) 6 6 2 2 5 5 4 4 3 3 1 1 2 2 1 1 0 0 0 0 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε (e) Guinea (f) Indonesia 3 3 3 3 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) 2 2 2 2 1 1 1 1 0 0 0 0 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε Notes: The solid red line depicts how the inequality adjusted welfare gains associated with liberalization G(ε) vary with inequality aversion ε. The dotted blue lines represent 95% confidence intervals based on 1000 bootstrap replications. 73 Figure C3-B Trade-offs Income Gains and Inequality Costs No Trade Policy Preference Ranking Reversals (a) Iraq (b) Kyrgyz Republic 2 2 1 1 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) 1 1 0 0 0 0 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε (c) Moldova (d) Pakistan 2 2 4 4 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) 3 3 1 1 2 2 1 1 0 0 0 0 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε (e) South Africa (f) Tajikistan 3 3 3 3 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) 2 2 2 2 1 1 1 1 0 0 0 0 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε Notes: The solid red line depicts how the inequality adjusted welfare gains associated with liberalization G(ε) vary with inequality 74 based on 1000 bootstrap replications. aversion ε. The dotted blue lines represent 95% confidence intervals Figure C3-C Trade-offs Income Gains and Inequality Costs No Trade Policy Preference Ranking Reversals (a) Uganda (b) Ukraine 4 4 4 4 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) 3 3 3 3 2 2 2 2 1 1 1 1 0 0 0 0 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε (c) Uzbekistan (d) Zambia 4 4 8 8 7 7 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) 3 3 6 6 5 5 2 2 4 4 3 3 1 1 2 2 1 1 0 0 0 0 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε Notes: The solid red line depicts how the inequality adjusted welfare gains associated with liberalization G(ε) vary with inequality aversion ε. The dotted blue lines represent 95% confidence intervals based on 1000 bootstrap replications. 75 Figure C4-A Trade-offs Income Gains and Inequality Costs Trade Policy Preference Ranking Reversals (a) Bangladesh (b) Benin 1 1 3 3 2 2 0 0 1 1 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) 0 0 -1 -1 -1 -1 -2 -2 -2 -2 -3 -3 -4 -4 -3 -3 -5 -5 -4 -4 -6 -6 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε (c) Burkina Faso (d) Burundi 1 1 1 1 0 0 0 0 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) -1 -1 -1 -1 -2 -2 -2 -2 -3 -3 -3 -3 -4 -4 -4 -4 -5 -5 -5 -5 -6 -6 -6 -6 -7 -7 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε (e) Ethiopia (f) The Gambia 3 3 3 3 2 2 2 2 1 1 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) 1 1 0 0 -1 -1 0 0 -2 -2 -1 -1 -3 -3 -4 -4 -2 -2 -5 -5 -3 -3 -6 -6 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε Notes: The solid red line depicts how the inequality adjusted welfare gains associated with liberalization G(ε) vary with inequality aversion ε. The dotted blue lines represent 95% confidence intervals based on 1000 bootstrap replications. 76 Figure C4-B Trade-offs Income Gains and Inequality Costs Trade Policy Preference Ranking Reversals (a) Guatemala (b) Kenya 2 2 3 3 1 1 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) 1 1 -1 -1 -3 -3 -5 -5 0 0 -7 -7 -9 -9 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε (c) Liberia (d) Mozambique 2 2 4 4 3 3 2 2 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) 1 1 1 1 0 0 0 0 -1 -1 -2 -2 -1 -1 -3 -3 -4 -4 -2 -2 -5 -5 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε (e) Malawi (f) Nigeria 5 5 3 3 2 2 2 2 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) 1 1 -1 -1 0 0 -4 -4 -1 -1 -2 -2 -7 -7 -3 -3 -10 -10 -4 -4 -13 -13 -5 -5 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε Notes: The solid red line depicts how the inequality adjusted welfare gains associated with liberalization G(ε) vary with inequality 77 based on 1000 bootstrap replications. aversion ε. The dotted blue lines represent 95% confidence intervals Figure C4-C Trade-offs Income Gains and Inequality Costs Trade Policy Preference Ranking Reversals (a) Papua New Guinea (b) Togo 3 3 4 4 2 2 1 1 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) 1 1 -2 -2 0 0 -1 -1 -5 -5 -2 -2 -8 -8 -3 -3 -11 -11 -4 -4 -14 -14 -5 -5 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε (c) Vietnam 2 2 1 1 inequality adjusted gains, G(ε) 0 0 -1 -1 -2 -2 -3 -3 -4 -4 0 2 4 6 8 10 inequality aversion, ε Notes: The solid red line depicts how the inequality adjusted welfare gains associated with liberalization G(ε) vary with inequality aversion ε. The dotted blue lines represent 95% confidence intervals based on 1000 bootstrap replications. 78 Figure C5-A Trade-offs Income Gains and Inequality Costs Potential Trade Policy Preference Ranking Reversals (a) Bolivia ote d’Ivoire (b) Cˆ 4 4 4 4 3 3 3 3 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) 2 2 1 1 2 2 0 0 1 1 -1 -1 -2 -2 0 0 -3 -3 -4 -4 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε (c) Georgia (d) Ecuador 2 2 4 4 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) 3 3 1 1 2 2 1 1 0 0 0 0 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε (e) Nepal (f) Nicaragua 2 2 3 3 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) 2 2 1 1 1 1 0 0 0 0 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε Notes: The solid red line depicts how the inequality adjusted welfare gains associated with liberalization G(ε) vary with inequality 79 based on 1000 bootstrap replications. aversion ε. The dotted blue lines represent 95% confidence intervals Figure C5-B Trade-offs Income Gains and Inequality Costs Potential Trade Policy Preference Ranking Reversals (a) Niger (b) Sierra Leone 3 3 6 6 2 2 5 5 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) 1 1 4 4 0 0 3 3 -1 -1 2 2 -2 -2 1 1 -3 -3 0 0 -4 -4 -1 -1 -5 -5 -2 -2 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε (c) Tanzania 5 5 4 4 inequality adjusted gains, G(ε) 3 3 2 2 1 1 0 0 0 2 4 6 8 10 inequality aversion, ε Notes: The solid red line depicts how the inequality adjusted welfare gains associated with liberalization G(ε) vary with inequality aversion ε. The dotted blue lines represent 95% confidence intervals based on 1000 bootstrap replications. 80 Figure C6 Trade-offs Income Losses and Equality Gains Trade Policy Preference Ranking Reversals (a) Mali (b) Mauritania 4 4 5 5 3 3 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) 3 3 2 2 1 1 1 1 -1 -1 0 0 -3 -3 -1 -1 -5 -5 -2 -2 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε (c) Sri Lanka 1 1 inequality adjusted gains, G(ε) -2 -2 -5 -5 -8 -8 -11 -11 -14 -14 0 2 4 6 8 10 inequality aversion, ε Notes: The solid red line depicts how the inequality adjusted welfare gains associated with liberalization G(ε) vary with inequality aversion ε. The dotted blue lines represent 95% confidence intervals based on 1000 bootstrap replications. 81 Figure C7 Trade-offs Income Losses and Equality Gains No Trade Policy Preference Ranking Reversals (a) Bhutan (b) Cambodia 0 0 0 0 -2 -2 -1 -1 inequality adjusted gains, G(ε) inequality adjusted gains, G(ε) -4 -4 -2 -2 -6 -6 -3 -3 -8 -8 -4 -4 -10 -10 -5 -5 0 2 4 6 8 10 0 2 4 6 8 10 inequality aversion, ε inequality aversion, ε Notes: The solid red line depicts how the inequality adjusted welfare gains associated with liberalization G(ε) vary with inequality aversion ε. The dotted blue lines represent 95% confidence intervals based on 1000 bootstrap replications. 82 Appendix D: Alternative Model - No Labor Markets Table D1 Inequality Adjusted Gains from Trade and trade No Wage Responses (Table continues on the next page) G(0) G(0.5) G(1) G(1.5) G(2) G(10) Countries without trade-offs Jordan 4.4 4.8 5.2 5.5 5.7 4.8 Central Afr. Rep. 4.2 4.6 5.0 5.3 5.6 6.3 Pakistan 3.2 3.5 4.0 4.5 4.8 4.5 Yemen, Rep. 3.1 3.3 3.4 3.5 3.6 3.5 Nicaragua 2.8 3.1 3.3 3.5 3.7 3.3 Indonesia 2.4 2.6 2.8 2.8 2.9 2.6 Guinea-Bissau 2.3 2.9 3.2 3.3 3.4 3.9 Nepal 1.7 1.9 2.1 2.2 2.3 2.7 Sri Lanka 1.0 1.8 2.6 3.1 3.5 4.3 Mongolia 0.3 0.4 0.6 0.7 0.8 0.8 Madagascar -0.2 -0.4 -0.7 -0.9 -1.1 -2.5 Countries with trade-offs Nigeria 5.1 4.9 4.4 3.2 1.1 -5.0 2.25 Kenya 4.2 4.1 3.6 3.0 2.2 -6.1 2.96 Mozambique 4.0 3.3 2.5 1.8 1.2 -2.2 4.20 Togo 3.1 2.8 2.3 1.8 1.2 -0.6 3.84 Gambia, The 3.0 2.5 1.8 1.0 0.3 -0.9 2.21 Benin 2.5 1.7 0.9 0.3 -0.4 -4.1 1.71 Ethiopia 2.2 1.6 1.1 0.7 0.4 -1.5 3.06 Vietnam 2.1 1.9 1.7 1.4 1.1 -1.9 4.09 Bhutan 2.0 2.3 2.3 2.0 1.7 -4.6 4.11 Bangladesh 2.0 1.7 1.4 1.1 0.9 -1.1 4.24 Papua New Guinea 1.9 2.0 2.0 1.9 1.8 -8.5 4.49 B. Faso 1.3 0.9 0.4 0.2 -0.1 -2.6 1.86 Burundi 0.9 -0.9 -1.9 -2.2 -2.3 -4.5 0.25 Ghana 0.9 0.9 0.8 0.7 0.5 -1.5 2.90 Comoros 0.1 -0.1 -0.2 -0.4 -0.6 -1.9 0.30 Mali -0.3 0.1 0.7 1.3 1.9 3.0 0.37 Mauritania -1.2 -0.8 -0.4 -0.0 0.3 2.7 1.52 Notes: this table shows the gains from trade G(0), the inequality adjusted gains from trade, G(0.5), G(1), G(1.5), G(2) and G(10), and trade- when wages do not respond to the tariff liberalization. 83 Table D1 Inequality Adjusted Gains from Trade and trade No Wage Responses (continued from previous page) G(0) G(0.5) G(1) G(1.5) G(2) G(10) Countries with trade-offs (continued) Cameroon 8.1 7.8 7.5 7.2 6.9 4.7 Zambia 6.4 6.4 6.4 6.4 6.4 6.3 Tanzania 5.3 5.1 5.0 5.0 5.1 5.1 Uzbekistan 4.6 4.2 3.8 3.5 3.2 1.8 Ecuador 4.4 4.7 5.0 5.2 5.3 3.5 Sierra Leone 4.3 3.7 3.0 2.6 2.2 0.6 Cˆote d’Ivoire 4.0 3.7 3.3 2.9 2.5 0.9 Egypt, Arab Rep. 3.9 3.9 3.8 3.7 3.6 3.1 Ukraine 3.5 3.4 3.4 3.4 3.3 2.9 South Africa 3.5 3.4 3.3 3.0 2.7 1.8 Bolivia 3.4 3.5 3.4 3.1 2.8 0.3 Malawi 3.0 2.3 1.8 1.5 1.2 0.5 Guinea 2.9 2.7 2.4 2.1 2.0 2.0 Uganda 2.9 2.7 2.7 2.8 3.0 6.0 Guatamala 2.9 2.9 2.9 2.9 2.9 2.8 Armenia 2.7 2.6 2.5 2.4 2.4 1.9 Azerbaijan 2.6 2.7 2.7 2.7 2.7 2.1 Tajikistan 2.5 2.5 2.5 2.5 2.4 2.1 Liberia 2.0 1.9 1.7 1.5 1.3 0.3 Niger 2.0 1.7 1.3 1.1 0.9 1.2 Iraq 1.9 1.8 1.8 1.7 1.7 1.5 Georgia 1.1 1.0 1.0 1.0 0.9 0.5 Rwanda 0.9 1.1 1.5 2.0 2.6 4.0 Kyrgyz Republic 0.9 0.8 0.8 0.8 0.8 0.8 Moldova 0.8 0.8 0.8 0.8 0.8 1.4 Cambodia -2.2 -2.1 -2.1 -2.2 -2.3 -2.6 Average 2.5 2.5 2.4 2.2 2.1 0.9 2.61 Population weighted average 3.0 2.9 2.8 2.6 2.4 0.8 3.14 GDP weighted average 3.1 3.1 3.1 2.9 2.6 1.2 2.95 Notes: this table shows the gains from trade G(0), the inequality adjusted gains from trade, G(0.5), G(1), G(1.5), G(2) and G(10), and trade- when wages do not respond to the tariff liberalization. 84 Appendix E: Robustness to Alternative Tariff Redistribution Schemes To assess the robustness of our results we consider two alternative tariff redistribution scenarios: (1) the government does not make up the budget loss (i.e. tariff redistribution is ignored) and (2) the government makes up for lost revenue by imposing additional personal income taxes which respect the progressivity of the existing personal income tax system. To proxy the progressivity of taxes we use the World Tax Indicator (WTI) database (Andrew Young School of Policy Studies 2010). This is the most comprehensive and comparable measure of tax progressivity available (see e.g. Heathcote et al., 2018). More specifically, it offers measures of the average tax rates faced by households in different segments of the income distribution.18 For each country we calculate the increase in segment j specific tax rates τj required to balance the budget to compensate for the revenue loss resulting from liberalization. To do so, we first calculate the scaling factor λ J N (18) dT = λ ∗ Iij yi rj j =1 i=1 where dT is the anticipated tariff revenue loss associated with the liberalization, Iij is a dummy variable that takes the value 1 if household i belongs to tax segment j and 0 otherwise, yi is a measure of (pre- additional tax) income, and rj is the average tax rate paid by households in segment j . After solving for λ we can calculate the “segment” specific tax increase as τj = λ ∗ rj . These tax increases in turn are part of the income losses associated with liberalization imposing that the progressivity of the tax system is respected.19 . Tables E1 and E2 presents estimates of the inequality adjusted gains from trade using 18 These measures are adjusted for allowances/deductions, tax credits, significant local taxes and other main rules of the tax code. They are not, however, adjusted for deductions, exemptions, and credits that depend on taxpayer specific characteristics (for example, no adjustment is made for child credits). They also do not account for evasion and/or avoidance. 19 This formula can be thought of as a crude approximation to the tax function T (y ) = y − λy 1−τ . 85 these alternative tariff redistribution scenarios. Not considering tariff revenue losses almost doubles the average income gains from trade which are now 3.7% on average across countries (as opposed to 1.9% in our main model). Moreover, only 1 of the 54 countries (Ghana) experiences (very modest) income losses when trade is liberalized. Yet, trade-offs remain widespread, as they are prevalent in 45 countries. However, in the vast majority of cases social welfare would be higher in case a country would liberalize its own tariffs than in the status quo. When imposing that the tax hikes introduced to compensate for the tariff revenue loss respect the progressivity of the existing income tax system, the estimated income gains from trade do not change of course. Yet, as expected, inequality costs are somewhat lower when using proportional income taxes, as these tend to reduce inequality. Crucially, however, the income and equality gains of trade continue to be negatively correlated. Yet, income gains tend to dominate inequality costs for plausible levels of inequality aversion. Put differently, our main conclusions appear robust to different ways of modeling the response to the loss of tariff revenue that results from trade liberalization. References Andrew Young School of Policy Studies. (2010). Andrew Young School World Tax Indicators (Volume 1). Heathcote, J., K. Storesletten, and G. Violante (2017). “Optimal Tax Progressivity: An Analytical Framework” Quarterly Journal of Economics, 132(4),1693-1754. 86 Table E1 Inequality Adjusted Gains from Trade and trade No Tariff Redistribution (Table continues on the next page) G(0) G(0.5) G(1) G(1.5) G(2) G(10) Countries without trade-offs Jordan 7.5 7.9 8.2 8.5 8.7 7.8 Central Afr. Rep. 6.3 6.7 7.1 7.4 7.7 8.4 Mauritania 5.2 5.6 6.1 6.4 6.8 9.2 Guinea-Bissau 4.3 4.8 5.1 5.1 5.2 5.2 Yemen, Rep. 4.1 4.3 4.4 4.4 4.5 4.2 Mongolia 2.5 2.6 2.7 2.8 2.9 3.0 Mali 1.7 2.1 2.7 3.3 3.9 5.0 Sri Lanka 0.7 1.2 1.6 1.8 2.0 0.8 Ghana -0.1 -0.5 -0.9 -1.3 -1.6 -2.4 Countries with trade-offs Mozambique 5.6 5.0 4.2 3.5 3.0 -0.5 8.5 Benin 5.5 4.7 3.9 3.2 2.5 -1.1 5.0 Nigeria 4.6 4.3 3.8 2.7 0.7 -5.5 2.2 Kenya 4.5 4.3 3.8 3.2 2.5 -4.7 3.3 Togo 4.4 3.7 2.7 1.8 0.9 -1.3 2.6 Ethiopia 3.7 3.0 2.5 2.1 1.9 -0.0 9.9 Vietnam 2.9 2.7 2.4 2.1 1.8 -0.6 6.8 Burkina Faso 2.6 1.9 1.4 1.0 0.7 -1.9 3.9 Papua New Guinea 2.3 2.4 2.4 2.3 2.2 -8.0 4.8 Burundi 2.2 0.6 -0.4 -0.7 -0.6 -2.6 0.8 Bangladesh 1.7 1.3 0.9 0.6 0.3 -1.8 2.5 Comoros 1.7 1.6 1.5 1.3 1.1 -0.1 8.7 Rwanda 1.5 1.3 1.3 1.3 1.2 -3.4 4.1 Madagascar 0.7 0.3 -0.0 -0.3 -0.5 -1.7 1.0 Cambodia 0.0 0.1 -0.0 -0.1 -0.3 -0.7 0.9 Notes: this table shows the gains from trade G(0), the inequality adjusted gains from trade, G(0.5), G(1), G(1.5), G(2) and G(10), and trade- when the loss in tariff revenue incurred when countries liberalize and the welfare consequences of the attendant loss in tariff revenue is not taken into consideration. 87 Table E1 Inequality Adjusted Gains from Trade and trade No Tariff Redistribution (continued from previous page) G(0) G(0.5) G(1) G(1.5) G(2) G(10) Countries with trade-offs (continued) Cameroon 8.7 8.2 7.6 7.1 6.7 3.8 Bhutan 7.9 7.9 7.5 6.9 6.3 0.8 Zambia 6.7 6.7 6.7 6.6 6.6 6.7 Tanzania 5.8 5.4 5.0 4.7 4.5 2.9 Sierra Leone 5.7 5.1 4.5 4.0 3.6 2.1 Guinea 5.7 5.4 5.1 4.9 4.7 4.9 Gambia, The 5.7 5.0 4.1 3.1 2.1 0.4 Uzbekistan 4.8 4.4 4.0 3.8 3.6 2.3 Cˆote d’Ivoire 4.5 4.3 4.0 3.6 3.2 1.7 Bolivia 4.2 4.1 3.9 3.7 3.4 1.5 Ukraine 4.2 4.1 4.1 4.0 4.0 3.5 Ecuador 4.0 4.2 4.2 4.2 4.1 1.8 Tajikistan 3.9 3.9 3.8 3.7 3.6 3.4 Egypt, Arab Rep. 3.9 3.8 3.7 3.6 3.5 3.1 Niger 3.8 3.5 3.2 2.9 2.7 3.1 Azerbaijan 3.8 3.8 3.8 3.8 3.8 3.2 Malawi 3.6 3.0 2.5 2.1 1.9 1.1 Nepal 3.4 3.5 3.6 3.5 3.5 2.4 Armenia 3.4 3.3 3.2 3.1 3.0 2.4 Uganda 3.3 3.0 2.8 2.7 2.7 3.6 Nicaragua 3.3 3.3 3.3 3.3 3.1 1.5 Pakistan 3.2 3.2 3.4 3.7 3.9 3.0 South Africa 3.1 3.0 2.8 2.5 2.4 1.9 Liberia 2.9 2.8 2.6 2.4 2.2 1.0 Iraq 2.8 2.7 2.7 2.6 2.6 2.5 Guatamala 2.8 2.6 2.4 2.2 2.0 0.7 Indonesia 2.5 2.6 2.7 2.7 2.7 2.1 Kyrgyz Republic 2.2 2.2 2.2 2.2 2.2 2.2 Moldova 2.2 2.1 2.1 2.1 2.1 2.7 Georgia 1.6 1.6 1.5 1.5 1.4 0.8 Average 3.7 3.5 3.3 3.1 3.0 1.5 4.3 Population weighted average 3.4 3.2 3.0 2.8 2.5 0.7 4.3 GDP weighted average 3.3 3.2 3.1 2.9 2.6 1.0 3.5 Notes: this table shows the gains from trade G(0), the inequality adjusted gains from trade, G(0.5), G(1), G(1.5), G(2) and G(10), and trade- when the loss in tariff revenue incurred when countries liberalize and the welfare consequences of the attendant loss in tariff revenue is not taken into consideration. 88 Table E2 Inequality Adjusted Gains from Trade and trade Alternative Tariff Redistribution Scheme (Table continues on the next page) G(0) G(0.5) G(1) G(1.5) G(2) G(10) Countries without trade-offs Zambia 6.0 6.2 6.4 6.5 6.5 6.7 Central Afr. Rep. 4.2 5.5 6.6 7.2 7.6 8.4 Jordan 4.1 4.7 5.3 5.7 6.1 5.3 Guinea 2.8 3.1 3.2 3.1 3.1 3.4 Yemen, Rep. 2.7 3.0 3.2 3.4 3.4 3.2 Pakistan 2.4 2.8 3.3 3.7 3.9 3.0 Guinea-Bissau 2.0 3.7 4.6 4.9 5.1 5.2 Uganda 2.0 2.3 2.5 2.6 2.7 3.6 Iraq 1.6 1.7 1.8 1.8 1.8 1.7 Nepal 1.4 2.5 3.1 3.4 3.5 2.4 Moldova 0.7 0.8 0.9 1.0 1.0 1.7 Mongolia 0.1 0.4 0.7 0.9 1.0 1.2 Ghana -1.9 -2.0 -2.2 -2.4 -2.7 -3.5 Countries with trade-offs Mozambique 3.6 3.0 2.2 1.5 1.0 -2.5 3.5 Nigeria 3.3 3.2 2.7 1.7 -0.2 -6.4 2.0 Kenya 2.9 2.8 2.4 1.9 1.1 -6.1 2.6 Benin 2.3 2.7 2.8 2.6 2.3 -1.1 5.0 Ethiopia 2.2 2.1 2.0 1.9 1.7 -0.0 9.9 Togo 2.1 2.3 2.1 1.5 0.8 -1.3 2.6 Gambia, The 1.9 2.1 1.8 1.1 0.3 -1.3 2.2 Papua New Guinea 1.7 2.1 2.2 2.3 2.2 -8.0 4.8 Liberia 1.6 1.5 1.3 1.1 0.9 -0.4 4.5 Vietnam 1.1 1.2 1.2 1.0 0.8 -1.5 3.8 Burkina Faso 0.7 0.5 0.1 -0.1 -0.4 -2.9 1.28 Bangladesh 0.5 0.7 0.6 0.4 0.2 -1.8 2.4 Burundi 0.4 -0.3 -0.7 -0.8 -0.7 -2.6 0.3 Rwanda -0.1 0.3 0.8 1.1 1.2 -3.4 0.2 Comoros -0.2 -0.0 0.1 0.1 -0.0 -1.2 0.5 Sri Lanka -0.3 0.8 1.4 1.8 2.0 0.8 0.1 Mali -0.3 0.8 1.8 2.7 3.5 4.8 0.2 Bhutan -0.7 1.6 2.8 3.1 2.8 -2.5 0.1 Mauritania -1.3 0.2 1.4 2.1 2.7 5.4 0.5 Notes: this table shows the gains from trade G(0), the inequality adjusted gains from trade, G(0.5), G(1), G(1.5), G(2) and G(10), and trade- when the loss in tariff revenue incurred when countries liberalize is compensated for by an increase in income taxes that respects the progressivity of the existing labor income tax system. 89 Table E2 Inequality Adjusted Gains from Trade and trade Alternative Tariff Redistribution Scheme (continued from previous page) G(0) G(0.5) G(1) G(1.5) G(2) G(10) Countries with trade-offs (continued) Cameroon 6.9 6.7 6.3 5.9 5.6 2.7 Sierra Leone 4.3 4.5 4.3 3.9 3.6 2.1 Tanzania 4.2 4.6 4.7 4.6 4.5 2.9 Uzbekistan 3.5 3.2 2.9 2.7 2.5 1.2 Cˆote d’Ivoire 3.4 3.4 3.3 3.0 2.7 1.2 Ukraine 3.2 3.2 3.1 3.1 3.1 2.5 Ecuador 3.0 3.6 4.0 4.1 4.1 1.8 Egypt, Arab Rep. 2.9 3.0 3.0 2.9 2.8 2.5 Bolivia 2.8 3.4 3.6 3.5 3.3 1.5 Azerbaijan 2.5 2.6 2.6 2.7 2.7 2.1 Armenia 2.5 2.4 2.3 2.2 2.1 1.5 South Africa 2.5 2.6 2.6 2.5 2.3 1.9 Malawi 2.4 2.3 2.1 2.0 1.8 1.1 Nicaragua 2.0 2.6 3.0 3.1 3.1 1.5 Tajikistan 1.9 1.9 1.9 1.8 1.7 1.5 Niger 1.9 1.7 1.4 1.1 0.9 1.3 Indonesia 1.9 2.0 2.1 2.2 2.2 1.5 Guatamala 1.9 1.9 1.8 1.6 1.5 0.2 Georgia 1.0 1.0 1.0 0.9 0.8 0.2 Kyrgyz Republic 0.6 0.6 0.6 0.6 0.6 0.7 Madagascar -1.0 -0.7 -0.5 -0.6 -0.7 -1.7 Cambodia -3.0 -2.3 -2.0 -1.9 -1.9 -2.3 Average 1.9 2.2 2.3 2.2 2.1 0.7 2.4 Population weighted average 2.1 2.3 2.3 2.2 1.9 0.1 3.4 GDP weighted average 2.2 2.4 2.4 2.3 2.0 0.4 2.6 Notes: this table shows the gains from trade G(0), the inequality adjusted gains from trade, G(0.5), G(1), G(1.5), G(2) and G(10), and trade- when the loss in tariff revenue incurred when countries liberalize is compensated for by an increase in income taxes that respects the progressivity of the existing labor income tax system. 90 Appendix F: Protectionist Scenarios Table F1 Inequality Adjusted Gains from Trade and trade 10% Absolute Increase in Tariffs (Table continues on the next page) G(0) G(0.5) G(1) G(1.5) G(2) G(10) Countries without trade-offs Vietnam 0.9 1.0 1.2 1.3 1.5 2.0 Burundi 0.2 0.8 1.2 1.4 1.5 1.6 Rwanda -0.2 -0.4 -0.8 -1.1 -1.4 -2.4 Jordan -0.4 -0.6 -0.7 -0.9 -1.0 -1.9 Kyrgyz Republic -0.7 -0.8 -0.8 -0.9 -1.0 -1.7 Nicaragua -0.8 -0.9 -1.0 -1.1 -1.2 -1.5 Tanzania -0.8 -0.8 -0.8 -0.8 -0.9 -1.3 Bangladesh -1.2 -1.3 -1.3 -1.4 -1.5 -2.2 Nepal -1.2 -1.4 -1.6 -1.7 -1.9 -2.5 Indonesia -1.6 -1.9 -2.2 -2.4 -2.6 -3.6 Ecuador -1.7 -1.9 -2.0 -2.2 -2.3 -2.4 Georgia -1.8 -1.8 -1.9 -2.0 -2.0 -2.7 Yemen, Rep. -1.8 -2.1 -2.4 -2.5 -2.6 -3.2 Guatamala -1.9 -2.1 -2.2 -2.3 -2.4 -3.5 Guinea-Bissau -1.9 -2.5 -2.9 -3.0 -3.2 -5.2 Azerbaijan -2.0 -2.2 -2.3 -2.5 -2.6 -3.6 Armenia -2.3 -2.3 -2.4 -2.4 -2.4 -3.2 South Africa -2.5 -3.4 -4.6 -5.5 -6.1 -6.8 Zambia -3.0 -3.0 -3.0 -3.0 -3.1 -3.7 Tajikistan -3.0 -3.1 -3.1 -3.2 -3.2 -3.1 Notes: this table shows the gains from trade G(0), the inequality adjusted gains from trade, G(0.5), G(1), G(1.5), G(2) and G(10), and trade- when all tariffs are increased by 10% in absolute terms terms. 91 Table F1 Inequality Adjusted Gains from Trade and trade 10% Absolute Increase in Tariffs (continued from previous page) G(0) G(0.5) G(1) G(1.5) G(2) G(10) Countries with trade-offs Mauritania 1.0 0.6 0.1 -0.2 -0.5 -2.6 1.2 Sri Lanka 0.9 0.3 -0.2 -0.6 -1.0 -3.0 0.8 Mali 0.8 0.4 -0.1 -0.7 -1.2 -2.6 0.9 Moldova 0.4 0.4 0.4 0.4 0.4 -0.4 6.4 Ghana 0.4 0.4 0.3 0.3 0.3 -0.4 4.2 Mongolia 0.3 -0.0 -0.3 -0.6 -0.8 -1.5 0.5 Madagascar 0.0 0.1 0.1 0.2 0.2 -1.2 4.6 Papua New Guinea -0.1 -0.1 -0.1 -0.0 0.1 0.7 1.6 Burkina Faso -0.6 -0.3 -0.1 0.0 0.1 -0.1 1.3 Central Afr. Rep. -0.8 -0.6 -0.4 -0.2 -0.0 0.1 2.3 Togo -0.9 -0.7 -0.4 -0.2 0.1 0.1 1.8 Benin -1.1 -0.7 -0.3 0.1 0.5 1.6 1.4 Kenya -1.3 -1.4 -1.3 -1.1 -0.8 2.1 2.8 Comoros -1.4 -1.2 -1.0 -0.7 -0.4 0.3 2.9 Gambia, The -1.5 -1.6 -1.4 -1.1 -0.7 0.4 3.1 Bhutan 1.2 1.2 1.2 1.2 1.2 1.3 Cambodia 1.1 1.2 1.2 1.3 1.3 0.8 Uganda -0.6 -0.7 -0.8 -0.9 -1.1 -3.8 Uzbekistan -0.9 -0.8 -0.7 -0.7 -0.7 -2.1 Ethiopia -1.3 -1.0 -0.8 -0.6 -0.5 -0.2 Guinea -1.5 -1.3 -1.2 -1.1 -1.0 -1.7 Cˆote d’Ivoire -1.5 -1.6 -1.5 -1.4 -1.3 -2.5 Malawi -1.8 -1.6 -1.4 -1.3 -1.3 -0.6 Egypt, Arab Rep. -1.9 -2.0 -2.0 -1.9 -1.9 -2.4 Mozambique -2.2 -1.7 -1.1 -0.7 -0.4 -0.4 Niger -2.3 -2.2 -2.0 -1.8 -1.6 -2.3 Bolivia -2.4 -2.4 -2.4 -2.2 -2.0 -1.2 Ukraine -2.5 -2.5 -2.5 -2.5 -2.5 -2.3 Pakistan -2.8 -2.7 -2.9 -3.2 -3.5 -4.3 Nigeria -3.0 -2.9 -2.6 -2.0 -1.2 -4.0 Cameroon -3.1 -3.1 -3.0 -2.9 -2.9 -3.0 Iraq -3.2 -3.1 -3.0 -3.0 -3.0 -3.2 Sierra Leone -3.4 -3.0 -2.8 -2.6 -2.4 -1.1 Liberia -3.7 -3.6 -3.5 -3.3 -3.2 -2.7 Average -1.3 -1.3 -1.3 -1.3 -1.3 -1.7 2.4 Population weighted average -1.6 -1.6 -1.7 -1.7 -1.7 -2.4 2.5 GDP weighted average -1.8 -2.0 -2.2 -2.3 -2.3 -3.2 2.2 Notes: this table shows the gains from trade G(0), the inequality adjusted gains from trade, G(0.5), G(1), G(1.5), G(2) and G(10), and trade- when all tariffs are increased by 10% in absolute terms terms. 92 Table F2 Inequality Adjusted Gains from Trade and trade 10% Relative Increase in Tariffs (Table continues on the next page) G(0) G(0.5) G(1) G(1.5) G(2) G(10) Countries without trade-offs Madagascar 0.1 0.1 0.1 0.1 0.1 0.1 Comoros 0.0 0.0 0.0 0.0 0.1 0.2 Mongolia -0.0 -0.0 -0.0 -0.0 -0.1 -0.1 Sri Lanka -0.0 -0.1 -0.1 -0.2 -0.2 -0.3 Rwanda -0.1 -0.1 -0.1 -0.2 -0.3 -0.5 Bhutan -0.1 -0.1 -0.2 -0.2 -0.2 -0.1 Nepal -0.2 -0.2 -0.2 -0.2 -0.2 -0.3 Indonesia -0.2 -0.2 -0.2 -0.2 -0.3 -0.2 Guinea-Bissau -0.2 -0.3 -0.3 -0.3 -0.4 -0.6 Nicaragua -0.2 -0.3 -0.3 -0.3 -0.3 -0.4 Azerbaijan -0.2 -0.2 -0.3 -0.3 -0.3 -0.3 Guatamala -0.2 -0.3 -0.3 -0.3 -0.3 -0.3 Yemen, Rep. -0.3 -0.3 -0.3 -0.3 -0.4 -0.4 Ecuador -0.4 -0.4 -0.4 -0.5 -0.5 -0.5 Jordan -0.4 -0.4 -0.5 -0.5 -0.5 -0.6 Central Afr. Rep. -0.4 -0.5 -0.5 -0.6 -0.6 -0.7 Zambia -0.6 -0.6 -0.6 -0.6 -0.6 -0.7 Countries with trade-offs Mauritania 0.1 0.1 0.0 -0.0 -0.0 -0.3 1.4 Mali 0.0 -0.0 -0.1 -0.1 -0.2 -0.4 0.4 Burundi -0.1 0.1 0.2 0.2 0.2 0.2 0.2 Bangladesh -0.1 -0.1 -0.0 -0.0 -0.0 0.0 2.6 Burkina Faso -0.1 -0.1 -0.0 -0.0 0.0 0.1 1.7 Vietnam -0.1 -0.1 -0.1 -0.1 -0.1 0.0 5.3 Papua New Guinea -0.2 -0.2 -0.2 -0.2 -0.2 0.3 6.7 Ethiopia -0.2 -0.2 -0.1 -0.1 -0.1 0.1 5.1 Benin -0.2 -0.2 -0.1 -0.0 0.0 0.3 1.9 Gambia, The -0.2 -0.2 -0.1 -0.1 -0.0 0.1 2.2 Togo -0.3 -0.3 -0.2 -0.2 -0.1 -0.0 5.3 Mozambique -0.4 -0.3 -0.2 -0.2 -0.1 0.1 6.8 Kenya -0.4 -0.4 -0.4 -0.3 -0.3 0.3 3.5 Nigeria -0.4 -0.4 -0.4 -0.3 -0.1 0.4 2.3 Notes: this table shows the gains from trade G(0), the inequality adjusted gains from trade, G(0.5), G(1), G(1.5), G(2) and G(10), and trade- when all tariffs are increased by 10% in relative terms (i.e. then tariffs are pre-multiplied by 1.1) 93 Table F2 Inequality Adjusted Gains from Trade and trade 10% Relative Increase in Tariffs (continued from previous page) G(0) G(0.5) G(1) G(1.5) G(2) G(10) Countries with trade-offs (continued) Cambodia 0.3 0.3 0.3 0.3 0.3 0.2 Ghana 0.0 0.0 0.0 0.0 0.0 0.0 Kyrgyz Republic -0.1 -0.1 -0.1 -0.1 -0.1 -0.1 Moldova -0.1 -0.1 -0.1 -0.1 -0.1 -0.1 Georgia -0.1 -0.1 -0.1 -0.1 -0.1 -0.1 Iraq -0.2 -0.2 -0.1 -0.1 -0.1 -0.1 Liberia -0.2 -0.2 -0.1 -0.1 -0.1 -0.1 Niger -0.2 -0.2 -0.1 -0.1 -0.1 -0.3 Tajikistan -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 Armenia -0.3 -0.2 -0.2 -0.2 -0.2 -0.2 South Africa -0.3 -0.2 -0.2 -0.2 -0.2 -0.2 Malawi -0.3 -0.2 -0.2 -0.1 -0.1 -0.1 Pakistan -0.3 -0.3 -0.3 -0.4 -0.4 -0.4 Uganda -0.3 -0.3 -0.3 -0.3 -0.3 -0.7 Guinea -0.3 -0.3 -0.2 -0.2 -0.2 -0.3 Bolivia -0.3 -0.3 -0.3 -0.3 -0.2 -0.1 Egypt, Arap Rep. -0.3 -0.3 -0.3 -0.3 -0.3 -0.3 Uzbekistan -0.3 -0.3 -0.3 -0.3 -0.2 -0.2 Ukraine -0.3 -0.3 -0.3 -0.3 -0.3 -0.3 Cˆote d’Ivoire -0.4 -0.3 -0.3 -0.3 -0.3 -0.3 Sierra Leone -0.4 -0.4 -0.3 -0.3 -0.2 -0.2 Tanzania -0.5 -0.5 -0.5 -0.5 -0.5 -0.7 Cameroon -0.8 -0.7 -0.7 -0.7 -0.7 -0.7 Average -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 3.2 Population weighted average -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 3.4 GDP weighted average -0.3 -0.3 -0.3 -0.2 -0.2 -0.2 3.1 Notes: this table shows the gains from trade G(0), the inequality adjusted gains from trade, G(0.5), G(1), G(1.5), G(2) and G(10), and trade- when all tariffs are increased by 10% in relative terms (i.e. then tariffs are pre-multiplied by 1.1). 94 Table F3 Inequality Adjusted Gains from Trade and trade Tariffs increase to 62.4% (Table continues on the next page) G(0) G(0.5) G(1) G(1.5) G(2) G(10) Countries without trade-offs Bhutan 8.0 8.2 8.4 8.5 8.7 8.5 Jordan -0.2 -1.3 -2.4 -3.4 -4.3 -11.6 Tanzania -0.2 -0.7 -1.1 -1.3 -1.6 -6.3 Madagascar -0.5 -1.3 -2.3 -3.3 -4.5 -26.9 Rwanda -0.8 -2.1 -3.9 -5.7 -7.4 -13.1 Uganda -1.2 -1.8 -2.8 -4.1 -5.6 -22.5 Uzbekistan -2.1 -2.4 -2.8 -3.2 -3.7 -27.8 Nicaragua -2.5 -3.5 -4.6 -5.5 -6.5 -15.1 Kyrgyz Republic -3.7 -4.8 -5.8 -6.7 -7.5 -15.9 Cˆote d’Ivoire -5.9 -6.9 -7.5 -7.9 -8.3 -24.3 Nepal -5.9 -7.2 -8.3 -9.2 -10.0 -18.4 Bangladesh -6.5 -7.9 -9.4 -10.8 -12.2 -27.6 Ecuador -7.0 -8.1 -9.2 -10.1 -10.9 -15.8 Indonesia -8.1 -10.5 -12.7 -14.6 -16.3 -30.8 Yemen, Rep. -8.4 -10.6 -12.4 -13.7 -14.8 -24.0 Egypt, Arab Rep. -9.3 -9.7 -10.0 -10.3 -10.5 -17.2 Guatamala -9.3 -10.8 -12.1 -13.2 -14.1 -25.8 Guinea-Bissau -9.8 -13.4 -15.6 -17.0 -18.3 -30.5 Georgia -9.9 -11.0 -12.1 -13.1 -14.1 -23.8 Azerbaijan -10.3 -11.8 -13.4 -14.9 -16.4 -27.9 Armenia -11.8 -12.7 -13.5 -14.3 -15.2 -24.0 Ukraine -12.1 -12.6 -13.1 -13.4 -13.7 -16.6 Zambia -12.3 -12.8 -13.3 -13.8 -14.3 -21.7 South Africa -12.8 -19.6 -28.0 -34.0 -37.6 -44.4 Tajikistan -16.5 -17.4 -18.0 -18.4 -18.7 -21.4 Countries with trade-offs Sri Lanka 5.4 1.9 -1.7 -4.7 -7.4 -23.6 0.8 Mauritania 5.2 2.2 -0.5 -2.6 -4.4 -14.7 0.9 Mali 4.7 2.1 -1.4 -5.0 -8.7 -27.3 0.8 Cambodia 4.0 4.1 4.2 4.1 4.0 -2.3 7.1 Moldova 3.3 3.1 2.8 2.5 2.1 -6.2 4.3 Ghana 2.1 1.5 1.0 0.5 -0.2 -18.4 1.9 Mongolia 1.8 -1.3 -4.1 -6.4 -8.4 -15.0 0.30 Burundi 1.6 3.6 4.8 5.2 5.2 -11.1 5.5 Papua New Guinea 1.1 0.6 0.6 0.6 0.6 -10.7 2.7 Central Afr. Rep. -0.7 0.2 1.5 2.5 3.0 -3.3 0.4 Togo -2.9 -2.5 -1.7 -0.9 -0.1 -6.0 2.1 Kenya -4.5 -5.1 -5.1 -4.5 -3.4 4.2 3.0 Benin -4.7 -3.5 -2.2 -1.0 0.1 -4.8 2.0 Notes: this table shows the gains from trade G(0), the inequality adjusted gains from trade, G(0.5), G(1), G(1.5), G(2) and G(10), and trade- when all tariffs are increased to 62.4%, with tariffs already in excess of 62.4% left unaltered. 95 Table F3 Inequality Adjusted Gains from Trade and trade Tariffs increase to 62.4% (continued from previous page) G(0) G(0.5) G(1) G(1.5) G(2) G(10) Countries with trade-offs (continued) Vietnam 6.8 6.9 7.1 7.4 7.7 4.6 Burkina Faso -2.4 -1.8 -1.5 -1.6 -2.0 -14.4 Ethiopia -5.7 -5.1 -4.6 -4.4 -4.4 -14.8 Guinea -6.3 -6.0 -5.6 -5.4 -5.3 -15.0 Gambia, The -7.0 -8.1 -8.2 -7.5 -6.2 -0.5 Malawi -8.8 -8.5 -8.4 -8.6 -8.9 -17.7 Comoros -9.0 -9.2 -9.1 -8.9 -8.8 -29.3 Mozambique -10.0 -8.2 -6.2 -4.8 -4.1 -20.2 Cameroon -11.9 -11.8 -11.7 -11.6 -11.6 -15.4 Bolivia -12.4 -13.0 -13.5 -13.6 -13.4 -23.8 Niger -12.7 -12.6 -12.1 -11.6 -11.1 -20.7 Nigeria -14.1 -14.4 -14.0 -12.6 -10.7 -43.6 Pakistan -15.0 -15.0 -16.4 -18.0 -19.6 -27.0 Sierra Leone -16.7 -15.7 -15.4 -15.4 -15.6 -17.2 Iraq -18.5 -18.3 -18.4 -18.6 -19.0 -23.9 Liberia -21.2 -21.4 -21.4 -21.4 -21.3 -26.1 Average -5.7 -6.4 -7.2 -7.8 -8.3 -17.9 2.4 Population weighted average -7.5 -8.4 -9.4 -10.2 -10.8 -23.1 2.6 GDP weighted average -9.0 -10.7 -12.5 -13.8 -14.8 -27.3 2.1 Notes: this table shows the gains from trade G(0), the inequality adjusted gains from trade, G(0.5), G(1), G(1.5), G(2) and G(10), and trade- , when all tariffs are increased to 62.4%, with tariffs already in excess of 62.4% left unaltered. 96