metadata: authors: 0: author: Artuc,Erhan
1: author: Rijkers,Bob
2: author: Porto,Guido
count: World
docna: 0: docna: Trading off the Income Gains and the
            Inequality Costs of Trade Policy
docty: Policy Research Working Paper
subtopic: International Trade and Trade Rules,Rules of Origin,Trade Policy,Trade and Multilateral Issues,Disability,Economic Assistance,Services & Transfers to Poor,Access of Poor to Social Services,Labor Markets,Rural Labor Markets
teratopic: International Economics and Trade,Macroeconomics and Economic Growth,Poverty Reduction,Social Protections and Labor,Rural Development
abstracts: cdata!: This paper characterizes the trade-off
            between the income gains and the inequality costs of trade
            using survey data for 54 developing countries. Tariff data
            on agricultural and manufacturing goods are combined with
            household survey data on detailed income and expenditure
            patterns to estimate the first-order effects of the
            elimination of import tariffs on household welfare. The
            paper assesses how these welfare effects vary across the
            distribution by estimating impacts on the consumption of
            traded goods, wage income, farm and non-farm family
            enterprise income, and government transfers. For each
            country, the income gains and the inequality costs of trade
            liberalization are quantified and the trade-offs between
            them are assessed using an Atkinson social welfare index.
            The analysis finds average income gains from import tariff
            liberalization in 45 countries and average income losses in
            nine countries. Across countries in the sample, the gains
            from trade are 1.9 percent of real household expenditure on
            average. We find overwhelming evidence of a trade-off
            between the income gains (losses) and the inequality costs
            (gains), which arise because trade tends to exacerbate
            income inequality: 45 countries face a trade-off, while only
            nine do not. The income gains typically more than offset the
            increase in inequality. In the majority of developing
            countries, the prevailing tariff structure thus induces
            sizable welfare losses.
repnme: repnme: Trading off the Income Gains and the
            Inequality Costs of Trade Policy
display_title: Trading off the Income Gains and the
            Inequality Costs of Trade Policy
listing_relative_url: /research/2019/04/30998890/trading-off-income-gains-inequality-costs-trade-policy
topicv3: Labor Markets m1326488 855,Trade m1326924 698,Social Assistance m1327201 1466,Trade Policy and Integration m1327407 775,Trade Policy m1327640 776
publishtoextweb_dt: 2019-04-22T00:00:00Z
historic_topic: International Economics and Trade,Macroeconomics and Economic Growth,Poverty Reduction,Social Protections and Labor,Rural Development
no_of_pages: 99
author_keys: 0: author_key: 1140434
1: author_key: 906035
2: author_key: 658883
model_extractions: 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 gains and the inequality costs of trade using survey data for 54 developing countries. Tariff data on agricultural and manufacturing goods are combined with household survey data on detailed income and expenditure patterns to estimate the first-order effects of the elimination of import tariffs on household welfare. The paper assesses how these welfare effects vary across the distribution by estimating impacts on the consumption of traded goods, wage income, farm and non-farm family enterprise income, and government transfers. For each country, the income gains and the inequality costs of trade liberalization are quantified and the trade-offs between them are assessed using an Atkinson social welfare index. The analysis finds average income gains from import tariff liberalization in 45 countries and average income losses in nine countries. Across countries in the sample, the gains from trade are 1.9 percent of real household expenditure on average. We find overwhelming evidence of a trade-off between the income gains (losses) and the inequality costs (gains), which arise because trade tends to exacerbate income inequality: 45 countries face a trade-off, while only nine do not. The income gains typically more than offset the increase in inequality. In the majority of developing countries, the prevailing tariff structure thus induces sizable welfare losses.

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<sup>_∗_</sup>

Erhan Artuc<sup>_†_</sup> Guido Porto<sup>_‡_</sup> Bob Rijkers<sup>_§_</sup> 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).<sup>1</sup> 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).<sup>2</sup> 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

> 1See 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).

> 2See 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 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_<sup>_h_</sup> , which include land or capital, and labor _L_<sup>_h_</sup> . 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_<sup>_h_</sup> _, L_<sup>_h_</sup> ). 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 formulation, household income _y_<sup>_h_</sup> is the sum of maximized profits _π_ � _i_<sup>_h_andthevalueofthe</sup> labor endowment _wL_<sup>_h_</sup> . To simplify the exposition that follows, let _w_<sup>_h_</sup> be the labor income that household _h_ derives _only_ from the labor market and let _π_<sup>_h_</sup> be maximized profits defined net of hired off-farm labor only.<sup>3</sup> Allowing for many goods (Singh, Squire and Strauss, 1986), total maximized household income _y_<sup>_h_</sup> is

> where **p** is the vector of prices _pi_ , _πi_<sup>_h_arefarmenterpriseprofitsobtainedfromthesalesof</sup> good _i_ (such as cotton, tobacco, beans or maize), and _T_<sup>_h_</sup> are taxes paid to (or transfers

> 3In particular, let _Lhired_ be hired labor in-farm, _Lmarket_ the supply of household labor to the labor market, and let _L_<sup>_farm_</sup> be the own-farm family labor. Then, the labor endowment is _L_<sup>_h_</sup> = _L_<sup>_market_</sup> + _L_<sup>_farm_</sup> , while farm employment is _L_<sup>_D_</sup> = _L_<sup>_farm_</sup> + _L_<sup>_hired_</sup> . In addition, _π_<sup>_h_</sup> = _piqi − wL_<sup>_hired_</sup> and household income is _w_<sup>_h_</sup> + _π_<sup>_h_</sup> . 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 Ω<sup>_h_</sup> .<sup>4</sup> Household _h_ maximizes a utility function defined over a vector of consumer goods **c** , _u_<sup>_h_</sup> = _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_<sup>_h_</sup> . The solution to this optimization problem delivers a demand function for each good. Optimal consumption of good _i_ is _c_<sup>_h_</sup> _i_<sup>and,givenrequiredutility</sup><sup>_uh_,thehouseholdexpenditurefunctionis</sup> To derive the welfare effects of trade, we use the concept of compensating variation _CV_<sup>_h_</sup> . For price changes, this is generally done using the expenditure function _e_<sup>_h_</sup> . In our case, we need to consider the fact that trade affects nominal income _y_<sup>_h_</sup> as well. Consequently, we follow Dixit and Norman (1980) and Anderson and Neary (1996) and use the _trade_ expenditure function, _V_<sup>_h_</sup> : which depends on prices **p** via the maximized nominal income function _y_<sup>_h_</sup> ( _·_ ) and the expenditure function _e_ ( _·_ ). Note that the trade expenditure function is usually defined in the Hicksian tradition as _e_<sup>_h_</sup> _− y_<sup>_h_</sup> 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 4Because 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_<sup>_h_</sup> ) _in equation (1) and the expenditure function e_ ( **p** _, u_<sup>_h_</sup> ) _in equation (2), the impact of a price change on household welfare dVi_<sup>_h(as_</sup> _a share of household initial expenditure e_<sup>_h_</sup> _) is given by_

_where s_<sup>_h_</sup> _i_<sup>_istheshareoftradedgoodiintheconsumptionbundleofhouseholdh,φh_</sup> _i_<sup>_isthe_</sup> _income share derived from the sales of good i, and φ_<sup>_h_</sup> _w_<sup>_istheshareoflaborincome.dV_</sup> _i_<sup>_h_</sup> _is the negative of the compensating variation CV_<sup>_h_</sup> _, the monetary transfer that would allow household h to attain the same utility u_<sup>_h_</sup> _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 that ( _∂πi/∂pi_ ) _dpi_ = _qi_<sup>_hdpi_,where</sup><sup>_q_</sup> _i_<sup>_h_isthequantityproducedofgood</sup><sup>_i_.Multiplyingand</sup> dividing by _pi_ and expressing the result relative to total income _y_<sup>_h_</sup> gives ( _piqi_<sup>_h/yh_)</sup><sup>_d_ln</sup><sup>_pi_=</sup> _φ_<sup>_h_</sup> _i_<sup>_d_ln</sup><sup>_pi_.FromShephard’sLemma,thederivativeoftheexpenditurefunctionwithrespect</sup> to _pi_ is the quantity consumed _c_<sup>_h_</sup> _i_<sup>so that (</sup><sup>_∂e/∂pi_)</sup><sup>_dpi_=</sup><sup>_ch_</sup> _i_<sup>_dpi_and (</sup><sup>_pich_</sup> _i_<sup>_/eh_)</sup><sup>_d_ln</sup><sup>_pi_=</sup><sup>_sh_</sup> _i_<sup>_d_ln</sup><sup>_pi_.5</sup> 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,

> 5This assumes that, ex-ante, household expenditures equal household income. This rules out savings and intertemporal considerations.

6
_φ_<sup>_h_</sup> _w_<sup>,andthewageelasticitywithrespecttotheprice(</sup><sup>_∂wh/∂pi_)</sup><sup>_/_(</sup><sup>_pi/wh_).Thisformulation</sup> allows for different assumptions about the functioning of labor markets that determine the nature of the labor income elasticities.<sup>6</sup> 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_<sup>_∗_</sup> _i_<sup>_;(iii)perfectpricetransmissionfromtariffstodomesticprices;(iv)laborisspecific,that_</sup> _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:_

_where now φ_<sup>_h_</sup> _wi_<sup>_istheshareoflaborincomederivedonlyfromwagesinsectori(andnot_</sup> _other sectors) and_ Ψ<sup>_h_</sup> _i_<sup>_istheincreaseinincometaxaccruedbythehousehold._</sup> 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 The unitary pass-through elasticity requires constant returns to scale in the production of the traded goods and perfect competition.<sup>7</sup> 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, _L_<sup>_hired_</sup> on farm profits. See Porto (2005) for a study of those effects in Moldova.

> 7See 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: 8
where _V_<sup>�</sup><sup>_h_</sup> 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 _x_<sup>_h_</sup> 0<sup>betheobserved,</sup> ex-ante level of real household income (from the data compiled in the household surveys). The counterfactual real income _x_ �<sup>_h_</sup> 1<sup>is</sup> 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: 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:

# **3.2 Expenditure and Income Shares**

To measure the first order welfare effects we retrieve the expenditure shares _s_<sup>_h_</sup> _i_<sup>andthe</sup> income shares _φ_<sup>_h_</sup> _i_<sup>and</sup><sup>_φh_</sup> _w_<sup>from the household surveys.Appendix Table A1 lists the countries</sup> 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).<sup>8</sup> Lastly, we need to derive the cost of tax payments needed to compensate for the tariff revenue loss, Ψ<sup>_h_</sup> _i_<sup>.We assume that the government imposes a proportional income tax to do so</sup> at the moment it liberalizes. Denoting import quantity by _mi_ , we can approximate the loss of tariff revenue as _dRi_ = _−τip_<sup>_∗_</sup> _i_<sup>_mi_(ignoringproductionandconsumptionresponses).With</sup> a proportional income tax, the change in income tax paid by household _h_ is _dT_<sup>_h_</sup> = _dψy_<sup>_h_</sup> , where _dψ_ is the compensatory change in the tax rate. Consequently, where _Mi_ = _p_<sup>_∗_</sup> _i_<sup>(1+</sup><sup>_τi_)</sup><sup>_mi_is the value of imports.In the robustness exercises, we consider two</sup> 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.

> 8In 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: where _V_<sup>�</sup><sup>_h_</sup> 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_<sup>�</sup><sup>_h_</sup> .

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 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, Cameroon, Cˆote d’Ivoire, the Arab Republic of Egypt, Ethiopia, Georgia, Guatemala, 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): where _W_ is social welfare and _ε_ = 1 is a parameter that measures the dislike for inequality.<sup>9</sup> 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

> 9For completeness, when _ε_ = 1, we define ln _W_ = (1 _/H_ )<sup>�</sup> _h_<sup>ln</sup><sup>_xh_</sup>

17
where _μ_ is mean income and 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: where _W_ 0 is the ex-ante social welfare, calculated with the observed ( _x_<sup>_h_</sup> 0<sup>) income distribution</sup> in the presence of trade protection and _W_ 1( _ε_ ) is the counterfactual social welfare under import tariff liberalization ( _x_ �<sup>_h_</sup> 1<sup>).Giventheinitialsituationandthepost-liberalizationsituation,we</sup> can compare _W_ 0 and _W_ 1 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 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 _I_ 1( _εa_ ) _> I_ 0( _εa_ ), then _G_ ( _εa_ ) incorporates a downward correction for these inequality costs. Conversely, if _I_ 1( _εb_ ) _< I_ 0( _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. _I_ <u>0(</u> _ε_ <u>)</u> _−I_ 1( _ε_ <u>)</u> when _G_ (0) and<sup>_<u>μ</u>_</sup> _μ_<sup><u>1</u></sup> 0 1 _−I_ 0( _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 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].<sup>10</sup> 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

> 10For 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 (( _μ_ 1 _/μ_ 0)( _I_ 0( _ε_ ) _− I_ 1( _ε_ )) _/_ (1 _− I_ 0( _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.<sup>11</sup> 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_<sup>_∗_</sup> such that _G_ ( _ε_<sup>_∗_</sup> ) = 0. The cutoff _ε_<sup>_∗_</sup> , which we refer to as trade- _ε_<sup>_∗_</sup> , 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_<sup>_∗_</sup> 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.<sup>12</sup> A high value of _ε_<sup>_∗_</sup> implies a soft trade-off: a society

> 11See below for a more detailed discussion of some of these countries and their trade-off.

> 12Alternatively, 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_<sup>_∗_</sup> tends to infinity or when trade- _ε_<sup>_∗_</sup> 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_<sup>_∗_</sup> 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_<sup>_∗_</sup> 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_<sup>_∗_</sup> (column 1) and its 95% confidence interval (columns 2 and 3). Since the interpretation of the trade- _ε_<sup>_∗_</sup> 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_<sup>_∗_</sup> 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_<sup>_∗_</sup> 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_<sup>_∗_</sup> 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_<sup>_∗_</sup> , 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 ( _ε >_ 4 _._ 1), Niger ( _ε >_ 6 _._ 0), Nicaragua ( _ε >_ 6 _._ 3), Cˆote d’Ivoire ( _ε >_ 7 _._ 0), Georgia ( _ε >_ 7 _._ 1), 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_<sup>_∗_</sup> tend to be low. For instance, we get _ε_<sup>_∗_</sup> = 0 _._ 3 in Sri Lanka (at the first reversal) and _ε_<sup>_∗_</sup> = 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_<sup>_∗_</sup> thus implies a presumption in favor of lower tariffs, too. In Mauritania ( _ε_<sup>_∗_</sup> = 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 are equal to<sup>_<u>μ</u>_</sup> _μ_<sup><u>1</u></sup> 0 _I_ <u>01(</u> _ε−_ <u>)</u> _I−_ 0 _I_ (1 _ε_ <u>()</u> _ε_ <u>)</u><sup>).13</sup> 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

> 13Consumption 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.<sup>14</sup>

# **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).

> 14Of 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.<sup>15</sup>

# **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%.<sup>16</sup> 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.<sup>17</sup>

> 15See Appendix E for details.

> 16Following Ossa (2014), this is the level of tariffs that would prevail if countries did not fear retaliation from the rest of the world.

> 17These 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. In fact, such trade-offs arise in 45 out of 54 different impacts on prices and because the household heterogeneity noted above implies that the inequality implications of a positive welfare effect may be quite different from those of negative effects.

30
countries, and are primarily the result of trade exacerbating income inequality. By contrast, consumption gains tend to be more evenly spread across households.

While trade-offs are prevalent, our findings also suggest that liberalization would be welfare enhancing in the vast majority of countries in our study: in a large part of the developing world, the current structure of tariff protection is inducing sizable welfare losses. Explaining what drives these patterns is beyond the scope of this paper but an interesting avenue for future research.

# **References**

Anderson, J. and P. Neary (1996). “A New Approach to Evaluating Trade Policy,” The Review of Economic Studies, vol. 63(1), pp. 107–125.

Andrew Young School of Policy Studies. (2010). Andrew Young School World Tax Indicators (Volume 1).

Antras, P., A. de Gortari, and O. Itskhoki (2017). “Globalization, Inequality, and Welfare,” Journal of International Economics, 108, pp. 387-412.

Arkolakis, C., A. Costinot, and A. Rodriguez-Clare (2012). “New Trade Models, Same Old Gains?,” The American Economic Review, vol. 102(1), pp. 94–130.

Arkolakis, C., A. Costinot, D. Donaldson, and A. Rodriguez-Clare (2015). “The Elusive Pro-Competitive Effects of Trade,” mimeo.

Artuc, E., S. Chaudhuri, and J. McLaren (2010). “Trade Shocks and Labor Adjustment: A Structural Empirical Approach,” American Economic Review Economics, vol. 100(3), pp. 1008–45.

Artuc, E., D. Lederman, and G. Porto (2015). “A Mapping of Labor Mobility Costs in the Developing World,” Journal of International Economics, vol. 95(1), pp. 28–41.

Atkin, D. and D. Donaldson (2015). “Who’s Getting Globalized? The Size and Implications of Intranational Trade Costs,” mimeo.

31
Atkin, D., B. Faber and M. Gonzalez-Navarro (2016). “Retail Globalization and Household Welfare: Evidence from Mexico,”forthcoming, Journal of Political Economy.

Atkinson, A. (1970). “On the Measurement of Inequality,” Journal of Economic Theory, vol. 2(3), pp. 244–263.

Autor, D., D. Dorn, and G. Hanson (2013). “The China Syndrome: Local Labor Market Effects of Import Competition in the United States,” American Economic Review, 103(6), pp. 2121–2168. Benabou, R. and J. Tirole (2006). “Belief in a Just World and Redistributive Politics,” Quarterly Journal of Economics, vol. 121(2), pp. 699–746.

Benjamin, D. and A. Deaton (1993). “Household Welfare and the Pricing of Cocoa and Coffee in Cˆote d’Ivoire: Lessons from the Living Standards Surveys,” The World Bank Economic Review vol. 7, pp. 293–318.

Besley, T. and T. Persson (2013). “Taxation and Development,” Chapter 2, pp. 55–110 in Handbook of Public Economics, vol. 5. Edited by Alan J. Auerbach, Raj Chetty, Martin Feldstein and Emmanuel Saez.

Caliendo, L. and F. Parro (2015). “Estimates of the Trade and Welfare Effects of NAFTA,” The Review of Economic Studies, vol. 82(1), pp. 1–44.

Caliendo, L., M. Dvorkin, and F. Parro (2015). “Trade and Labor Market Dynamics,” NBER Working Paper No. 21149.

Carlsson, F., D. Daruvala, and O. Johansson-Stenman (2005). “Are People Inequality-Averse or Just Risk-Averse,” Economica, vol. 72(287), pp. 375–396.

Costinot, A., D. Donaldson, and I. Komunjer (2012). “What Goods Do Countries Trade? A Quantitative Exploration of Ricardo’s Ideas,” The Review of Economic Studies, vol. 79(2), pp. 581–608.

32
Costinot, A., A. Rodriguez-Clare (2014). “Trade Theory with Numbers: Quantifying the Consequences of Globalization,” Handbook of International Economics, Edited by G. Gopinath, E. Helpman and K. Rogoff, Chapter 4, pp. 197–262.

Deaton, A. (1989). “Rice Prices and Income Distribution in Thailand: a Non-parametric Analysis,” Economic Journal, 99 (Supplement), pp. 1–37.

Deaton, A. (1997). _The Analysis of Household Surveys - A Microeconometric Approach to Development Policy_ . Baltimore: Johns Hopkins Press.

Dix-Carneiro, R. (2014). “Trade Liberalization and Labor Market Dynamics,” Econometrica, vol. 82(3), pp. 825–885.

Dix-Carneiro, R., and B. Kovak (2017). “Trade Reform and Regional Dynamics: Evidence from 25 Years of Brazilian Matched Employer–Employee Data,” forthcoming The American Economic Review.

Dixit, A. and V. Norman (1980). Theory of International Trade: A Dual, General Equilibrium Approach, Cambridge University Press.

Dixit, A. and V. Norman (1986). “Gains from Trade without Lump-Sum Compensation,” Journal of International Economics, vol. 21, pp. 111–122.

Faber, B. (2014). “Trade Liberalization, the Price of Quality, and Inequality: Evidence from Mexican Store Prices,” mimeo, Department of Economics, University of California at Berkeley.

Fajgelbaum, P. and A. Khandelwal (2016). “Measuring the Unequal Gains from Trade,” Quarterly Journal of Economics, vol. 131, pp. 1113–1180.

Galle, S., A. Rodriguez-Clare, and M. Yi (2017). “Slicing the Pie: Quantifying the Aggregate and Distributional Effects of Trade,” mimeo Berkeley University.

Grossman, G. and E. Helpman (1994). “Protection for Sale,” American Economic Review, vol. 84(4), pp. 833–850.

33
Goldberg, P. and M. Knetter (1997). “Goods Prices and Exchange Rates: What Have We Learned?”. Journal of Economic Literature, vol. XXXV, pp. 1243-1272.

Goldberg, P. and N. Pavcnick (2007). “Distributional Effects of Globalization in Developing Countries,” Journal of Economic Literature, vol. 45(1), pp. 39–82.

Kovak, B. (2013). “Regional Efects of Trade Reform: What Is the Correct Measure of Liberalization?,” American Economic Review, 103(5),pp. 1960–1976.

Kruger, A. (1974). “The Political Economy of the Rent-Seeking Society,” American Economic Review, vol. 64(3), pp. 291–303.

Layard, R., G. Mayraz, and S. Nickell (2008). “The Marginal Utility of Income,” Journal of Public Economics, 92(8), pp. 1846–1857.

Nicita, A. (2009). “The Price Effect of Trade Liberalization: Measuring the Impacts on Household welfare,” Journal of Development Economics, vol. 89(1), pp. 19–27.

Nicita, A., M. Olarreaga, and G. Porto (2014). “Pro-Poor Trade Policy in Sub-Saharan Africa,” Journal of International Economics, Vol. 92(2), pp. 252–265.

Melitz, M. and S. Redding (2015). “New Trade Models, New Welfare Implications,” American Economic Review vol. 105(3), pp 1105–1146.

Piketty, T. (1995). “Social Mobility and Redistributive Politics,” Quarterly Journal of Economics, 110, pp. 551–583.

Porto, G. (2005). “Informal Export Barriers and Poverty,” Journal of International Economics vol. 66, pp. 447–470.

Porto, G. (2006). “Using Survey Data to Assess the Distributional Effects of Trade Policy,” Journal of International Economics 70, pp. 140–160.

Ural Marchand, B. (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

<!-- Start of picture text --> 6 8 10 12<br>log per capita expenditure<br>(b) Mauritania<br>7 8 9 10 11 12<br>log per capita expenditure<br>20<br>10<br>0<br>welfare effects<br>-10<br>-20<br>5<br>0<br>-5<br>welfare effects<br>-10<br>-15<br><!-- End of picture text --> 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 <!-- Start of picture text --> (a) Uzbekistan (b) Togo<br>8 9 10 11 12 6 8 10 12 14<br>log per capita expenditure log per capita expenditure<br>(c) Ghana<br>8 10 12 14 16<br>log per capita expenditure<br>15<br>10<br>10<br>5<br>5<br>0<br>0<br>welfare effects welfare effects -5<br>-5 -10<br>-10 -15<br>10<br>5<br>0<br>welfare effects -5<br>-10<br>-15<br><!-- End of picture text --> 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

<!-- Start of picture text --> 0 2 4 6 8 10<br>inequality aversion, ε<br>8 8<br>7 7<br>)ε 6 6<br>5 5<br>4 4<br>3 3<br>2 2<br>inequality adjusted gains, G(<br>1 1<br>0 0<br><!-- End of picture text --> 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

<!-- Start of picture text --> 0 2 4 6 8 10<br>inequality aversion, ε<br>0 0<br>-1 -1<br>)ε<br>-2 -2<br>-3 -3<br>-4 -4<br>-5 -5<br>inequality adjusted gains, G(<br>-6 -6<br>-7 -7<br><!-- End of picture text --> 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

<!-- Start of picture text --> 0 2 4 6 8 10<br>inequality aversion, ε<br>4 4<br>)ε 3 3<br>2 2<br>1 1<br>inequality adjusted gains, G(<br>0 0<br><!-- End of picture text --> 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

<!-- Start of picture text --> 0 2 4 6 8 10<br>inequality aversion, ε<br>3 3<br>2 2<br>) 1 1<br>ε<br>0 0<br>-1 -1<br>-2 -2<br>-3 -3<br>inequality adjusted gains, G(<br>-4 -4<br>-5 -5<br>-6 -6<br><!-- End of picture text --> 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

<!-- Start of picture text --> 0 2 4 6 8 10<br>inequality aversion, ε<br>5 5<br>)ε<br>3 3<br>1 1<br>-1 -1<br>inequality adjusted gains, G(<br>-3 -3<br>-5 -5<br><!-- End of picture text --> 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 <!-- Start of picture text --> (a) ε = 1 . 5<br>Zambia<br>Central African Republic Cameroon<br>Jordan<br>Pakistan YemenEcuadorUkraine Tanzania<br>Guinea BissauAzerbaijan BoliviaEgypt Cote d'IvoireUzbekistanSierra Leone<br>P.N. GuineaNepalIndonesiaNicaraguaIraqArmeniaGuatemalaTajikistanSouth AfricaGuineaKenyaNigeriaMozambique<br>Mali Uganda<br>Sri LankaMongolia MoldovaGeorgiaKyrgyz Rep.LiberiaVietnam NigerEthiopiaMalawi<br>Mauritania Benin<br>Rwanda<br>Comoros Bangladesh GambiaTogo<br>Burkina Faso<br>Bhutan<br>Madagascar<br>Burundi<br>Cambodia Ghana<br>-5 0 5 10<br>G(0)<br>10 10<br>5 5<br>G(1.5)<br>0 0<br>-5 -5<br><!-- End of picture text --> 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

<!-- Start of picture text --> Zambia Cameroon<br>Central African Republic<br>Jordan<br>Guinea BissauAzerbaijanPakistanYemenEcuadorEgypt BoliviaUkraineNigeriaCote d'IvoireUzbekistanTanzaniaSierra Leone<br>P.N. GuineaNepalLiberiaIndonesiaNicaraguaTajikistanIraq NigerGuatemalaUgandaSouth AfricaEthiopiaMalawiArmeniaGuineaKenya Mozambique<br>MaliMongoliaSri LankaKyrgyz Rep.Moldova GeorgiaVietnam GambiaTogoBenin<br>Mauritania Rwanda Comoros Bangladesh Burkina Faso<br>Bhutan<br>Madagascar<br>Burundi<br>Ghana<br>Cambodia<br>-5 0 5 10<br>G(0)<br>10 10<br>5 5<br>G(1)<br>0 0<br>-5 -5<br><!-- End of picture text --> (b) _ε_ = 2 <!-- Start of picture text --> Central African Republic Zambia<br>Jordan<br>Cameroon<br>Guinea BissauPakistanAzerbaijanYemenEcuador Ukraine Tanzania<br>Mauritania Sri LankaMaliMongolia GeorgiaKyrgyz Rep.MoldovaP.N. GuineaNepalGuatemalaIndonesiaNicaraguaLiberiaIraq TajikistanNigerUgandaGuineaEthiopiaMalawiSouth AfricaArmeniaBoliviaEgyptKenya Cote d'IvoireUzbekistanMozambiqueSierra Leone<br>Vietnam<br>ComorosRwanda Bangladesh Benin Nigeria<br>Burkina Faso<br>GambiaTogo<br>MadagascarBhutan Burundi<br>Cambodia Ghana<br>-5 0 5 10<br>G(0)<br>10 10<br>5 5<br>G(2)<br>0 0<br>-5 -5<br><!-- End of picture text --> Notes: scatter plot of the inequality-adjusted gains from trade _G_ ( _ε_ ) against the gains from trade _G_ (0), for _ε_ = 1 (panel44a) and _ε_ = 2 (panel b). 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.
|Manufactures<br>6.7<br>18.8<br>23.5<br>10.1<br>6.1<br>5.0<br>8.3<br>4.0<br>4.9<br>13.9<br>17.4<br>0.9<br>15.3<br>8.3<br>8.5<br>9.8<br>7.7<br>10.4<br>0.5<br>3.3<br>4.8<br>15.1<br>14.0<br>7.4<br>9.1<br>10.9<br>11.8<br>10.9<br>tage points.|
|---|
|Non-Staple<br>Agric.<br>7.3<br>4.9<br>46.1<br>6.4<br>1.9<br>5.0<br>18.6<br>6.1<br>6.5<br>11.7<br>8.1<br>12.4<br>16.3<br>5.8<br>11.4<br>6.3<br>7.6<br>4.0<br>6.4<br>10.7<br>5.1<br>12.6<br>15.4<br>10.2<br>9.8<br>14.4<br>12.1<br>10.2<br>xpressed in percen|
|Staple<br>Agric.<br>6.9<br>7.4<br>43.7<br>13.0<br>6.0<br>5.0<br>7.9<br>6.1<br>5.3<br>9.0<br>3.7<br>4.7<br>7.8<br>7.4<br>14.8<br>11.1<br>4.4<br>5.7<br>6.0<br>7.9<br>4.8<br>11.0<br>14.4<br>10.3<br>12.1<br>10.8<br>9.0<br>8.1<br>e tarif is e|
|Country<br>Armenia<br>Bangladesh<br>Bhutan<br>Cambodia<br>Indonesia<br>Iraq<br>Jordan<br>Kyrgyz Republic<br>Mongolia<br>Nepal<br>Pakistan<br>Papua New Guinea<br>Sri Lanka<br>Tajikistan<br>Uzbekistan<br>Vietnam<br>Yemen, Rep.<br>Azerbaijan<br>Georgia<br>Moldova<br>Ukraine<br>Bolivia<br>Ecuador<br>Guatemala<br>Nicaragua<br>Average<br>Pop. weighted average<br>GDP weighted average<br>TAD TRAINS data. The averag|
|Manufactures<br>10.8<br>9.3<br>10.8<br>23.0<br>21.8<br>8.9<br>9.2<br>18.0<br>12.4<br>13.9<br>14.3<br>9.5<br>12.8<br>11.0<br>16.4<br>14.8<br>9.3<br>8.8<br>15.9<br>7.4<br>9.3<br>11.0<br>11.0<br>9.7<br>16.8<br>10.7<br>9.5<br>10.0<br>6.8<br>OMTRADE and UNC|
|Non-Staple<br>Agric.<br>16.9<br>18.3<br>21.6<br>22.5<br>23.7<br>10.4<br>10.2<br>28.0<br>13.3<br>13.5<br>11.6<br>18.9<br>15.7<br>25.1<br>5.6<br>9.6<br>22.0<br>16.8<br>14.8<br>13.9<br>17.6<br>19.8<br>30.1<br>16.2<br>6.4<br>29.1<br>18.6<br>29.7<br>19.7<br>United Nations C|
|Staple<br>Agric.<br>12.2<br>12.0<br>23.8<br>13.8<br>16.6<br>1.8<br>10.4<br>7.1<br>10.1<br>6.6<br>16.4<br>13.9<br>13.5<br>18.7<br>6.3<br>8.3<br>8.2<br>11.2<br>9.2<br>8.8<br>12.2<br>11.3<br>21.0<br>11.8<br>7.1<br>12.6<br>11.6<br>11.4<br>17.1<br>ns based on|
|Country<br>Benin<br>Burkina Faso<br>Burundi<br>Cameroon<br>Central African Rep.<br>Comoros<br>Cˆote d’Ivoire<br>Egypt, Arab Rep.<br>Ethiopia<br>Gambia, The<br>Ghana<br>Guinea<br>Guinea-Bissau<br>Kenya<br>Liberia<br>Madagascar<br>Malawi<br>Mali<br>Mauritania<br>Mozambique<br>Niger<br>Nigeria<br>Rwanda<br>Sierra Leone<br>South Africa<br>Tanzania<br>Togo<br>Uganda<br>Zambia<br>Notes: Authors’ calculatio|

45
|Home|8.2<br>11.0<br>12.3<br>13.0<br>11.3<br>0.8<br>0.2<br>9.7<br>19.3<br>23.9<br>21.3<br>27.1<br>6.7<br>13.9<br>38.5<br>10.7<br>3.1<br>9.0<br>2.1<br>27.1<br>7.4<br>6.4<br>7.2<br>14.6<br>17.7<br>16.6<br>16.6<br>11.7|
|---|---|
|Other<br>Cons.|0.0<br>4.4<br>12.4<br>8.5<br>13.5<br>3.4<br>1.2<br>3.4<br>1.1<br>4.7<br>6.6<br>13.7<br>21.7<br>3.2<br>1.9<br>10.6<br>4.4<br>1.6<br>4.7<br>7.0<br>0.1<br>1.3<br>8.5<br>4.8<br>1.0<br>5.8<br>6.6<br>7.5|
|Non-<br>Traded|21.2<br>16.2<br>15.8<br>18.8<br>22.8<br>23.0<br>29.1<br>13.6<br>8.8<br>27.6<br>12.9<br>5.0<br>19.4<br>14.8<br>10.5<br>15.3<br>15.5<br>11.6<br>27.6<br>15.3<br>16.3<br>23.9<br>21.5<br>17.8<br>19.1<br>15.1<br>15.9<br>18.3|
|Manuf.|7.1<br>14.1<br>25.5<br>16.0<br>11.4<br>35.2<br>19.1<br>25.5<br>14.3<br>11.7<br>23.4<br>5.8<br>9.4<br>24.8<br>7.5<br>19.6<br>17.5<br>20.9<br>23.7<br>32.1<br>20.0<br>16.8<br>16.8<br>19.8<br>16.6<br>17.4<br>17.5<br>18.5<br>ints.|
|Non-<br>Staple<br>Agric.|8.0<br>9.0<br>7.2<br>12.4<br>11.7<br>5.2<br>15.2<br>5.5<br>8.9<br>4.8<br>7.7<br>12.2<br>10.2<br>5.5<br>5.1<br>6.5<br>20.4<br>5.9<br>7.8<br>2.2<br>11.4<br>7.7<br>3.9<br>5.3<br>4.9<br>8.0<br>7.9<br>8.0<br>entage po|
|Staple<br>Agric.|55.5<br>45.3<br>26.9<br>31.2<br>29.3<br>32.3<br>35.1<br>42.3<br>47.6<br>27.3<br>28.2<br>36.2<br>32.6<br>37.8<br>36.5<br>37.3<br>39.2<br>51.1<br>34.1<br>16.4<br>44.8<br>44.0<br>42.2<br>37.7<br>40.8<br>37.0<br>35.4<br>35.9<br>sed in perc|
||ublic<br> Guinea<br>.<br>ed av.<br>ted av.<br>e expres|
|Country|Armenia<br>Bangladesh<br>Bhutan<br>Cambodia<br>Indonesia<br>Iraq<br>Jordan<br>Kyrgyz Rep<br>Mongolia<br>Nepal<br>Pakistan<br>Papua New <br>Sri Lanka<br>Tajikistan<br>Uzbekistan<br>Vietnam<br>Yemen, Rep<br>Azerbaijan<br>Georgia<br>Moldova<br>Ukraine<br>Bolivia<br>Ecuador<br>Guatemala<br>Nicaragua<br>Average<br>Pop. weight<br>GDP weigh<br>ture shares ar|
|Home|21.6<br>30.1<br>4.6<br>9.4<br>11.7<br>9.2<br>11.3<br>2.4<br>37.7<br>9.6<br>11.5<br>19.2<br>24.7<br>9.4<br>15.6<br>39.4<br>31.8<br>57.1<br>19.3<br>29.9<br>21.7<br>20.6<br>22.2<br>15.8<br>0.1<br>28.8<br>6.1<br>31.9<br>21.8<br> expendi|
|Other<br>Cons.|6.1<br>8.3<br>10.8<br>5.9<br>0.2<br>5.2<br>6.5<br>2.0<br>10.1<br>10.4<br>15.5<br>5.0<br>4.2<br>2.4<br>2.5<br>0.7<br>0.7<br>0.5<br>0.7<br>1.5<br>10.2<br>0.5<br>29.0<br>4.4<br>11.8<br>6.2<br>5.8<br>2.0<br>0.6<br>e average|
|Non-<br>Traded|10.7<br>8.8<br>12.7<br>14.7<br>7.9<br>17.3<br>20.5<br>31.4<br>2.9<br>12.0<br>33.0<br>12.6<br>7.4<br>24.9<br>15.2<br>3.6<br>6.9<br>4.9<br>6.7<br>3.9<br>6.5<br>9.4<br>9.0<br>10.8<br>16.4<br>9.9<br>26.2<br>17.9<br>10.1<br>ey data. Th|
|Manuf.|23.3<br>16.1<br>20.2<br>17.1<br>21.3<br>10.8<br>22.2<br>13.8<br>17.0<br>11.3<br>30.8<br>18.3<br>6.6<br>23.4<br>12.4<br>12.0<br>29.1<br>4.2<br>14.6<br>14.7<br>17.1<br>18.0<br>10.6<br>12.4<br>31.8<br>19.1<br>15.1<br>16.4<br>6.3<br>ehold surv|
|Non-<br>Staple<br>Agric.|3.8<br>12.3<br>9.9<br>6.1<br>18.5<br>9.5<br>3.9<br>4.9<br>9.1<br>11.5<br>1.4<br>11.9<br>6.3<br>9.7<br>7.2<br>7.2<br>5.7<br>7.6<br>11.5<br>5.3<br>8.8<br>3.6<br>4.9<br>10.4<br>8.3<br>6.6<br>7.8<br>7.6<br>4.8<br>ed on hous|
|Country<br>Staple<br>Agric.|Benin<br>34.4<br>Burkina Faso<br>24.4<br>Burundi<br>41.8<br>Cameroon<br>46.8<br>Central African Rep.<br>40.4<br>Comoros<br>48.1<br>Cˆote d’Ivoire<br>35.7<br>Egypt, Arab Rep.<br>45.5<br>Ethiopia<br>23.1<br>Gambia, The<br>45.3<br>Ghana<br>7.7<br>Guinea<br>33.0<br>Guinea-Bissau<br>50.7<br>Kenya<br>30.2<br>Liberia<br>47.1<br>Madagascar<br>37.2<br>Malawi<br>25.8<br>Mali<br>25.6<br>Mauritania<br>47.2<br>Mozambique<br>44.7<br>Niger<br>35.7<br>Nigeria<br>47.9<br>Rwanda<br>24.3<br>Sierra Leone<br>46.2<br>South Africa<br>31.6<br>Tanzania<br>29.4<br>Togo<br>39.0<br>Uganda<br>24.2<br>Zambia<br>53.5<br>Notes: Authors’ calculations bas|

46
|Home<br>Cons.|8.5<br>7.1<br>25.1<br>15.6<br>34.4<br>0.8<br>1.0<br>6.6<br>11.1<br>35.7<br>17.5<br>37.2<br>14.2<br>28.0<br>40.2<br>7.4<br>2.8<br>14.1<br>1.9<br>33.9<br>5.6<br>6.5<br>5.8<br>13.4<br>14.0<br>22.8<br>22.7<br>19.3|
|---|---|
|Other|40.6<br>12.1<br>8.6<br>5.3<br>20.9<br>28.4<br>41.0<br>27.4<br>31.6<br>22.1<br>13.8<br>17.9<br>0.0<br>22.4<br>20.7<br>13.1<br>21.2<br>26.2<br>51.9<br>26.3<br>48.0<br>16.2<br>17.3<br>14.1<br>13.5<br>18.6<br>17.0<br>21.6|
|Family<br>Enterp.|6.5<br>14.3<br>9.3<br>23.6<br>0.6<br>11.9<br>8.9<br>12.0<br>8.7<br>11.1<br>12.1<br>9.6<br>19.3<br>8.5<br>11.2<br>19.6<br>15.3<br>2.9<br>7.8<br>1.7<br>0.1<br>27.3<br>16.7<br>18.0<br>18.4<br>12.5<br>11.2<br>8.1|
|Wages|35.1<br>31.4<br>44.2<br>30.8<br>38.2<br>49.2<br>45.2<br>40.4<br>38.1<br>25.8<br>45.9<br>14.8<br>48.8<br>38.7<br>20.3<br>35.3<br>43.7<br>26.1<br>29.2<br>30.4<br>43.5<br>36.1<br>48.4<br>45.2<br>40.4<br>29.0<br>33.0<br>39.0<br>age points.|
|Non-<br>Staple<br>Agric.|0.1<br>2.1<br>0.0<br>0.5<br>1.2<br>1.6<br>2.1<br>1.4<br>0.3<br>1.2<br>3.1<br>6.5<br>4.6<br>1.5<br>0.2<br>3.5<br>9.2<br>1.9<br>1.9<br>2.1<br>0.0<br>7.6<br>1.1<br>2.9<br>2.8<br>4.2<br>3.3<br>2.7<br>n percent|
|Staple<br>Agric.|9.2<br>33.0<br>12.9<br>24.2<br>4.6<br>8.1<br>1.7<br>12.2<br>10.1<br>4.1<br>7.6<br>13.8<br>13.1<br>0.9<br>7.4<br>21.1<br>7.8<br>28.8<br>7.3<br>5.5<br>2.8<br>6.3<br>10.6<br>6.4<br>10.9<br>12.9<br>12.8<br>9.4<br> expressed i|
|Country|Armenia<br>Bangladesh<br>Bhutan<br>Cambodia<br>Indonesia<br>Iraq<br>Jordan<br>Kyrgyz Republic<br>Mongolia<br>Nepal<br>Pakistan<br>Papua New Guinea<br>Sri Lanka<br>Tajikistan<br>Uzbekistan<br>Vietnam<br>Yemen, Rep.<br>Azerbaijan<br>Georgia<br>Moldova<br>Ukraine<br>Bolivia<br>Ecuador<br>Guatemala<br>Nicaragua<br>Average<br>Pop. weighted av.<br>GDP weighted av.<br>age income shares are|
|Home<br>Cons.|21.8<br>34.0<br>4.5<br>34.1<br>38.1<br>17.8<br>18.2<br>0.2<br>44.8<br>14.4<br>15.6<br>40.5<br>32.8<br>16.6<br>25.1<br>28.8<br>39.0<br>54.3<br>42.0<br>46.9<br>35.9<br>30.8<br>46.7<br>32.7<br>0.8<br>46.0<br>7.7<br>33.1<br>39.5<br>The aver|
|Other|40.9<br>12.9<br>11.0<br>0.0<br>4.3<br>11.1<br>15.7<br>29.8<br>10.9<br>8.6<br>12.0<br>13.5<br>10.4<br>17.8<br>9.9<br>5.1<br>3.8<br>15.4<br>30.8<br>10.0<br>38.2<br>4.6<br>11.8<br>19.6<br>43.6<br>11.6<br>9.7<br>13.9<br>18.8<br>ey data.|
|Family<br>Enterp.|0.0<br>17.7<br>7.5<br>23.1<br>3.4<br>16.4<br>28.4<br>15.1<br>24.3<br>21.9<br>0.0<br>18.2<br>7.8<br>5.3<br>29.2<br>13.1<br>12.7<br>10.5<br>10.1<br>10.4<br>1.5<br>10.3<br>2.5<br>13.3<br>0.0<br>5.5<br>37.2<br>18.7<br>13.3<br>usehold surv|
|Wages|13.1<br>13.4<br>8.1<br>27.3<br>2.4<br>26.7<br>17.0<br>41.1<br>5.2<br>45.7<br>58.5<br>7.0<br>21.7<br>35.4<br>22.2<br>23.0<br>21.4<br>8.3<br>3.7<br>15.1<br>4.0<br>33.2<br>24.7<br>11.0<br>54.9<br>23.0<br>30.2<br>21.6<br>21.0<br>sed on ho|
|Non-<br>Staple<br>Agric.|10.0<br>2.9<br>29.4<br>0.1<br>9.3<br>3.7<br>13.7<br>6.9<br>0.5<br>6.7<br>5.5<br>3.2<br>21.9<br>3.1<br>3.4<br>3.0<br>4.6<br>2.8<br>0.0<br>7.1<br>3.1<br>5.6<br>3.7<br>4.6<br>0.0<br>3.0<br>6.5<br>2.9<br>1.7<br>lations ba|
|Country<br>Staple<br>Agric.|Benin<br>14.2<br>Burkina Faso<br>19.1<br>Burundi<br>39.5<br>Cameroon<br>15.4<br>Central African Rep,<br>42.5<br>Comoros<br>24.2<br>Cˆote d’Ivoire<br>7.1<br>Egypt, Arab Rep.<br>6.9<br>Ethiopia<br>14.3<br>Gambia, The<br>2.7<br>Ghana<br>8.5<br>Guinea<br>17.5<br>Guinea-Bissau<br>5.5<br>Kenya<br>21.8<br>Liberia<br>10.2<br>Madagascar<br>27.1<br>Malawi<br>18.4<br>Mali<br>8.7<br>Mauritania<br>13.4<br>Mozambique<br>10.4<br>Niger<br>17.3<br>Nigeria<br>15.5<br>Rwanda<br>10.5<br>Sierra Leone<br>18.7<br>South Africa<br>0.6<br>Tanzania<br>10.9<br>Togo<br>8.7<br>Uganda<br>9.7<br>Zambia<br>5.7<br>Notes: Authors’ calcu|

47
Table 4 Gains from Trade - Winners

||Gains|E|xpenditur|e|||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|
|GDPihtd|24|37|18|54|10|09|02|10|31|
|wege av.<br>’|.<br>|.<br>|.<br><br>4|.<br><br>8|-.<br>|-.<br>|-.<br>|-.<br>|-.<br>|

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|E|xpenditur|e|||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|Equ|ality G<br>|ains<br>|
|---|---|---|---|---|
||_μ_1_−μ_0<br>_μ_0|_μ_1<br>_μ_0|_I_0(_ε_)_−I_1<br>1_−I_0(_ε_|_ε_)<br>)|
|||_ε_= 0_._5|_ε_= 1|_ε_= 10|
|A) Countries without Trade-ofs|||||
|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-ofs|||||
|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

|Trad<br>_ε∗_|e Policy Prefer<br>Lower Bound|ence Reversals<br>Upper bound|Potential <br>Lower Bound|Reversals<br>Upper Bound|
|---|---|---|---|---|
|A) Countries with Income Gains|||||
|Burkina Faso<br>0.6|0.5|0.7|||
|Bangladesh<br>0.6|0.5|0.7|||
|Gambia, The<br>1.2|1.1|1.3|||
|Togo<br>1.2|1.2|1.3|||
|Benin<br>1.5|1.4|1.6|||
|Nigeria<br>1.9|1.8|2.0|||
|Vietnam<br>2.0|1.8|2.1|||
|Kenya<br>2.5|2.4|2.7|||
|Ethiopia<br>3.1|2.7|3.6|||
|Mozambique<br>3.5|2.9|8.5|||
|Papua New Guinea<br>4.4|3.1|–|||
|Liberia<br>4.5|3.6|–|||
|Guatemala<br>7.0|5.2|–|||
|Malawi<br>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<br>0.4|0.2|0.5|||
|Mauritania<br>1.6|1.5|1.8|||
|C) Countries with multiple (potentia|l) reversals||||
|Countries with Income Gains|||||
|Burundi<br>0.1|0|0.2|5.6|7.1|
|Countries with Income Losses|||||
|Sri Lanka<br>0.3|0.2|0.4|||
|8.9|7.2|–|||

Notes: Authors’ calculations. The table presents estimates of the trade- _ε_<sup>_∗_</sup> , 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

||Consum|ption Eq|uality Gains|Income|Equalit|y 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)

||Consum|ption Eq|uality 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

|B|aseline|No Wage<br>Response|No tarif<br>redistribution|Alternative<br>redistribution|
|---|---|---|---|---|
|N winners|45|50|53|45|
|N losers|9|4|1|9|
|Average (Inequality Adjusted)|Gains fr|om trade re|form||
|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 tradeofs|11|11|8|12|
|of which prefer freer trade|5|10|8|12|
|Countries with tradeofs|43|43|46|42|
|of which prefer freer trade|||||
|_ε_= 1|39|39|42|38|
|_ε_= 1_._5|36|39|42|37|
|_ε_= 2|35|38|42|35|
|_ε_= 10|27|27|30|23|
|Total number of countries that|prefer f|reer trade|||
|_ε_= 0|45|50|53|45|
|_ε_= 1|44|49|50|50|
|_ε_= 1_._5|41|49|50|49|
|_ε_= 2|40|48|50|47|
|_ε_= 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|
|---|---|---|---|---|
|(|Liberalizati|on)<br>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 tradeofs|11|8|9|10|
|of which prefer freer trade|5|6|7|9|
|Countries with tradeofs|43|46|45|44|
|of which prefer freer trade|||||
|_ε_= 1|39|42|39|37|
|_ε_= 1_._5|36|43|38|37|
|_ε_= 2|35|41|36|37|
|_ε_= 10|27|33|36|42|
|Total number of countries that|prefer freer|trade|||
|_ε_= 0|45|48|43|43|
|_ε_= 1|44|48|46|46|
|_ε_= 1_._5|41|49|45|46|
|_ε_= 2|40|47|43|46|
|_ε_= 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