Effect of the membership in the World Trade Organization on Income Inequality

Effect of the membership in the World Trade Organization on Income Inequality | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effect of the membership in the World Trade Organization on Income Inequality Sena Kimm GNANGNON This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5079276/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper has investigated empirically the effect of the membership in the GATT/WTO on trade policy and income inequality. The analysis has revealed that the GATT/WTO membership has been associated with trade policy liberalization and lower income inequality, although the magnitudes of these effects vary across groups of countries. JEL Classification : F13; F14; O15. Macroeconomics International Economics Membership in the GATT/WTO Trade Policy Income Inequality 1. Introduction As successor of the General Agreement on Tariffs and Trade (GATT), the World Trade Organization (WTO) was created to promote - through trade policy liberalization - a smooth and predictable movement of goods and services across borders. Several empirical works have been undertaken on the effects of the GATT/WTO membership. Many works have shown that the GATT/WTO membership has helped promote international trade flows , and prevent trade wars (e.g., Bekkers and The, 2021 ; Hoekman, 2020 ). It has also helped foster economic growth (e.g., Brotto et al., 2021 ; Fan et al., 2021 ; Tang and Wei, 2009 ), the global welfare (e.g., Fan et al., 2021 ) and dampen economic growth volatility (e.g., Gnangnon, 2023 ). However, we are not aware of a study that has investigated empirically the effect of the GATT/WTO membership on within-country income inequality (henceforth referred to as “income inequality”) in developing countries. This is a major issue on the policy agenda of national policymakers and international organizations whose ultimate objective is to promote economic development. We argue that the WTO membership can affect income inequality through its effect on members’ trade policy. While it may seem a priori obvious that the GATT/WTO membership would promote trade policy liberalization, the landmark paper of Rose et al. (2004) has reported that the GATT/WTO membership has had almost no genuine significant impact on trade liberalization. On the other hand, the literature has reported a mixed evidence on the effect of trade liberalization on income inequality (e.g., Bourguignon, 2018 ; Goldberg and Pavcnik, 2007 ), reflecting different findings based on different datasets (panel countries or individual countries), econometric approaches, and time periods. For example, some studies have found that trade liberalization widens income inequality (e.g., Artuc et al., 2019 ; Harrison et al., 2011 ; Robertson, 2000 ; Rojas-Vallejos and Turnovsky, 2017 ), while other studies have reported a negative effect of trade policy liberalization on income inequality (e.g., Jaumotte et al., 2013 ; Reuveny and Li, 2003 ). On the other hand, some studies have found no significant effect of trade liberalization on income inequality (e.g., Dollar and Kraay, 2002 ; Edwards, 1997 ; Green et al., 2001 ). It is worth noting that in addition to its income distribution effect through trade policy, the GATT/WTO membership can affect income inequality through the international cooperation brought about by such membership. In fact, participation in international trade can widen income inequality by generating higher income uncertainty and volatility (e.g., Laursen et al., 2005 ; Huang et al., 2015 ; Rodrik, 1998 ). In this context, international cooperation on trade policies can help reduce risks and vulnerabilities, and cope with shocks (e.g., WTO, 2021: Chapter D), and consequently contribute to dampening income inequality. The present note aims to explore empirically effect of the GATT/WTO membership on member states’ trade policy, and within-country income inequality. The next section presents the empirical models, and the econometric approaches. Section 3 interprets empirical outcomes, and section 4 concludes. 2. Empirical strategy We use the entropy balancing (EB) approach, developed by Hainmueller ( 2012 ) (see also Neuenkirch and Neumeier, 2016 ), to investigate empirically the effect of the GATT/WTO membership on income inequality and trade policy (the channel-variable). The EB approach is a non-parametric multivariate reweighting approach for impact analysis, which essentially aims to reweight the control group to match the moments of the treatment group in order to make the control group data match the covariate moments of the treatment. It is particularly useful here because member states that join the WTO are not randomly selected. As a result, it is hard to determine the extent to which the average treatment effects of the membership in the GATT/WTO on trade policy and income inequality are attributed to the membership itself or to the characteristics of each member state. The EB approach helps mitigate endogeneity concerns by severely reducing the bias introduced by the selection into the treatment . Its utilization involves two steps. In the first step, we compute the weights to assign to the control units based on the pre-treatment characteristics of units under analysis. In the present analysis, the balancing requirements are achieved by using the mean (average) of covariates to ensure that countries in the control group (non-WTO Members) are as similar as possible to countries in the treatment group (i.e., WTO Members ). The weights are determined by using as observable pre-treatment characteristics of countries the macroeconomic determinants of accession to the GATT/WTO. Since we aim to ensure that the control group closely matches the treatment group, we follow for example Tang and Wei (2010) and Brotto et al. ( 2021 ), and do not include developed countries (at the time of accession) in our full sample. Thus, our full sample contains essentially developing countries , with the treatment group comprising GATT/WTO Members (taking into account the year of accession to the GATT/WTO), and the control group including WTO non-Members, that is, states that are either in the process of joining the WTO (i.e., WTO Observers) or those that have not even initiated the process of accession to the WTO. Drawing from the relevant literature (e.g., Copelovitch and Ohls, 2012 ; Davis and Wilf, 2017 ; Jones and Gai, 2013 ; Wong and Yu, 2015 ; Tang and Wei, 2009 ), the observable pre-treatment characteristics of countries include the applicant’s market (economic) size and wealth (e.g., Copelovitch and Ohls, 2012 ; Davis and Wilf, 2017 ; Wong and Yu, 2015 ), its economic growth (e.g., Wong and Yu, 2015 ) and it’s the level of democracy (which also acts as a proxy for institution and governance quality) (e.g., Jones and Gai, 2013 ; Mansfield et al., 2002 ) and the terms of trade. Following Neuenkirch and Neumeier ( 2016 ), we use the one-year lag of these variables. We measure the economic size by the real Gross Domestic Product (GDP) (in natural logarithm), and the wealth by the real GDP per capita (in natural logarithm). These two variables and the economic growth indicator are sourced from the World Development Indicators (WDI) of the World Bank. The institutional and governance quality indicator was computed as the first principal component (based on factor analysis) of the six indicators of governance and institutional quality developed by the World Bank Governance Indicators developed by Kaufmann et al. ( 2010 ). Table 1 presents the results of the sample means of the matching covariates after weighing. We observe that the reweighed means of covariates (see column [4]) are identical to the target values of the same covariates (column [1]). Moreover, the standardized difference between the target value and the balanced value is essentially zero for all variables (column [5]). Table 1 Covariates balance Target Value Unbalanced Balanced (1) (2) (3) (4) (5) Value Standardized difference Value Standardized difference GROWTH t−1 3.883 4.781 0.094 3.883 0 Log(GDP) t−1 24.000 23.287 -0.357 24.000 1.78e-15 Log(GDPC) t−1 7.809 7.536 -0.281 7.809 9.16e-16 POLITY t−1 2.431 -2.012 -0.712 2.431 -7.12e-17 TERMS 117.117 119.563 0.057 117.117 0 [Insert Table 1 , here] In the second step, we use the computed weights to construct the entropy-balanced sample, which is in turn, utilized to run regressions where the treatment variable (the time - year - of membership in the GATT/WTO, defined as “Treat”) is the regressor, and the dependent variable is either the indicator of trade policy or income inequality. The coefficient of the treatment variable captures the average difference in the dependent (outcome) variable (trade policy, or income inequality) between GATT/WTO Members and NonWTO Members that is attributed to the membership in the GATT/WTO. Specifically, we estimate, on the one hand, the model specification that allows investigating the effect of the GATT/WTO membership on trade policy (see model (1)). On the other hand, we estimate the model specification that helps examine the effect of the GATT/WTO membership on income inequality (see model (2)). Building on the literature on the macroeconomic determinants of trade policy (e.g., Milner and Kubota, 2005 ; Rose, 2013 ; Svaleryd and Vlachos, 2002 ), we postulate model (1) as follows: $$\:{MATR}_{it}={\alpha\:}_{0}+{\alpha\:}_{1}{MATR}_{it-1}+{\alpha\:}_{2}{Treat}_{it}+\theta\:{X}_{it}+{\gamma\:}_{t}+{\mu\:}_{i}+{\epsilon\:}_{it}$$ 1 Building on the voluminous literature on the determinants of income inequality (e.g., Amponsah et al., 2023 ; Brei et al., 2023 ; Furceri and Ostry, 2019 ; Jaumotte et al., 2013 ; Liu et al., 2023 ), we postulate model (2) as follows: $$\:{GINI}_{it}={\beta\:}_{0}+{\beta\:}_{1}{GINI}_{it-1}+{\beta\:}_{2}{Treat}_{it}+\delta\:{Z}_{it}+{\gamma\:}_{t}+{\mu\:}_{i}+{\vartheta\:}_{it}$$ 1 In equations ( 1 ) and (2), i represents a country and t stands for a year in the unbalanced panel dataset, which is constructed on the basis of data availability. It contains 117 countries in the treatment group, and 15 countries in the control group, over the annual period from 1980 to 2021. The dependent variables “MATR” and “GINI” are respectively the indicators of trade policy, and income inequality. Trade policy is measured by the “Measure of Aggregate Trade Restrictions” developed by the International Monetary Fund (IMF) (see Estefania-Flores et al. 2022 ). It provides granular measures of different facets of trade protectionism, including tariffs, non-tariff barriers, and restrictions on requiring, obtaining, and using foreign exchange for current transactions (see Estefania-Flores et al., 2023: p747). It has many advantages over existing trade policy indicators, one of these advantages being its far larger time coverage. This indicator has now been utilized in literature because it is simple, and relies on sensible, plausible, trade policy inputs obtained from a transparent and reliable source, which is easily accessible (for example, Campos et al., 2023 ). Lower values of this index indicate a greater trade policy liberalization (Data is available online at: https://sites.google.com/view/m-atr/ ). Income inequality is measured by the market Gini income inequality, i.e., income inequality before taxes and transfers. The values of this indicator range from 0 to 100, with higher values reflecting a more unequal income distribution. Data on the income inequality indicator were extracted from the Standardized World Income Inequality Database (SWIID) - SWIID - Version 8.0, February 2019 (see Solt, 2019 - available online at: https://fsolt.org/swiid/ ). The one-year lag of the dependent variable is introduced in models (1) and (2) to take into account the inertia in these variables. \(\:{\alpha\:}_{0}\:\) to \(\:{\alpha\:}_{2};\:{\beta\:}_{0}\) to \(\:{\beta\:}_{2}\) as well as \(\:\theta\:\) and \(\:\delta\:\) are coefficients to be estimated. Specifically, \(\:{\alpha\:}_{2}\:\) represents the average treatment effect of the GATT/WTO membership on trade policy, and \(\:{\beta\:}_{2}\) is the average treatment effect of the GATT/WTO membership on income inequality. \(\:{\gamma\:}_{t}\) are temporal dummies, and \(\:{\mu\:}_{i}\) are countries' time invariant specific effects. \(\:{\epsilon\:}_{it}\) and \(\:{\vartheta\:}_{it}\:\) are two different well-behaving error terms. \(\:\theta\:\) and \(\:\delta\:\) are respectively two different vectors of parameters \(\:\beta\:\) that include coefficients relating respectively to the vector of control variables \(\:{X}_{it}\) and \(\:{Z}_{it}\) . Following Neuenkirch and Neumeier ( 2016 ), both \(\:{X}_{it}\) and \(\:{Z}_{it}\) include the variables used in the EB approach’s first step. \(\:{X}_{it}\) additionally includes the one-year lag of an indicator of financial development (see the literature on the macroeconomic determinants of trade policy), measured by the share of the domestic credit to private sector by banks in GDP. Data on this indicator were collected from the WDI. \(\:{Z}_{it}\) includes in addition to the indicator of financial development, the squared term of the natural logarithm of the real GDP per capita. This is to capture the existence of a non-linear - inverted U-curve - relationship between the real per capita GDP and the level of income inequality (Kuznets, 1955 ). We estimate models (1) and (2) and their different variants (see below) using the panel-corrected standard error (PCSE) estimator. This technique allows controlling for heteroscedasticity, first-order autocorrelation, and for contemporaneous correlation across individuals in the panel dataset (e.g., Beck and Katz, 1995 , 1996 ). We start by estimating models (1) and (2) over the full sample (see results in column [1] of Tables 2 and 3 ). We, then, examine the differentiated effects of the GATT/WTO membership on trade policy and income inequality in poorest countries (least developed countries – LDCs) versus NonLDCs in the sample (see results in column [2] of Tables 2 and 3 ). Next, we consider the differentiated effects of the GATT/WTO membership on trade policy and income inequality in African countries versus NonAfrican countries in the sample (see results in column [3] of Tables 2 and 3 ), given that African countries have the highest degrees of trade restrictiveness (see Estefania-Flores et al., 2022 ). Table 2 Effect of the GATT/WTO Membership on Trade Policy Estimator : PCSE with panel specific first order autocorrelation (AR1) Variables (1) (2) (3) (4) MATR t−1 0.0977*** 0.0968*** 0.0987*** 0.0963*** (0.0112) (0.0113) (0.0113) (0.0109) Treat -7.344*** -11.60*** -9.635*** -4.568*** (1.019) (1.431) (1.403) (1.599) Treat*LDC 7.650*** (1.304) LDC -4.771*** (1.519) Treat*Africa 4.843*** (1.174) Africa -1.178 (1.108) Treat*ART26 -0.244 (1.407) ART26 0.725 (1.337) Treat*NonART26 -10.19*** (1.585) NonART26 6.395*** (1.488) Observations-Countries 3,403 − 132 3,403 − 132 3,403 − 132 3,403 − 132 Pseudo-R 2 / R 2 0.971 0.973 0.971 0.972 Wald Chi2-Statistic (P-value) 23337.96 (0.0000) 27113.86 (0.0000) 23660.19 (0.0000) 28804.15 (0.0000) Note: *p-value < 0.1; **p-value < 0.05; ***p-value < 0.01. Robust standard errors are in parenthesis. Time dummies have been included in the regressions. The Pseudo R 2 is reported for the outcomes arising from the FGLS-based regressions, and was calculated as the correlation coefficient between the dependent variable and its predicted values. The R 2 is reported for regressions based on the PCSE estimator. Table 3 Effect of the GATT/WTO Membership on Income Inequality Estimator : PCSE with panel specific first order autocorrelation (AR1) Variables (1) (2) (3) (4) GINIM t−1 0.0299*** 0.0278*** 0.0298*** 0.0311*** (0.00491) (0.00476) (0.00468) (0.00474) Treat -6.810*** -13.35*** -11.31*** -9.033*** (1.593) (2.172) (2.099) (2.174) Treat*LDC 12.25*** (2.027) LDC -4.556** (1.895) Treat*Africa 7.686*** (1.825) Africa 2.001 (1.797) Treat*ART26 -1.033 (2.493) ART26 10.45*** (2.446) Treat*NonART26 -0.714 (2.531) NonART26 1.947 (2.408) Observations-Countries 2,788 − 127 2,788 − 127 2,788 − 127 2,788 − 127 Pseudo-R 2 / R 2 0.993 0.993 0.993 0.994 Wald Chi2-Statistic (P-value) 75093.96 (0.0000) 79016.53 (0.0000) 80160.50 (0.0000) 71790.26 (0.0000) Note: *p-value < 0.1; **p-value < 0.05; ***p-value < 0.01. Robust standard errors are in parenthesis. Time dummies have been included in the regressions. The Pseudo R 2 is reported for the outcomes arising from the FGLS-based regressions, and was calculated as the correlation coefficient between the dependent variable and its predicted values. The R 2 is reported for regressions based on the PCSE estimator. Finally, we examine whether there are differentiated effects of GATT/WTO membership on trade policy and income inequality depending on member states’ commitments to trade policy liberalization. To address this question, we consider three groups of members states with different degrees of liberalization commitments. The first group includes countries that were essentially former colonies, and had neither undergone long negotiation processes, nor undertaken extensive policy reforms commitments when joining the GATT. This group is, henceforth, referred to as Article26 member states (“ART26”). The second group of countries comprises countries that did not invoke GATT Article XXVI 5(c) when joining the GATT. These countries (referred to as "NonArticle 26 member states") underwent long negotiation processes, and carried extensive reforms. The third and last group of countries are those that did not join the GATT, but joined the WTO, especially under Article XII of the Marrakesh Agreement establishing the WTO. This set of countries (referred to as Article12 countries - defined as “ART12”) underwent more stringent procedures than the ones undergone by original WTO Members (i.e., contracting parties of the GATT) (e.g., Drabek and Bacchetta, 2004 ). To address empirically our question, we create two dummy variables, one for Article26 member states, and another for NonArt26 members states, and introduce them in models (1) and (2) along with their interaction with the variable “Treat”. Therefore, the reference group in the regressions is Article 12 member states. Results of the estimation of this model specification are reported in column [4] of Tables 2 and 3 . 3. Results’ interpretation Outcomes in Tables 2 and 3 suggest that trade policy and income inequality display a path dependence (the coefficients of “MATR t−1 ” and “GINIM t−1 ” are positive and significant at the 1% level). Outcomes in column [1] of Table 2 show that at the 1% level, the membership in the GATT/WTO is associated with a greater trade policy liberalization over the full sample. The GATT/WTO membership has led to 7.344 points decline in the values of the MATR index over the full sample. Estimates reported in column [2] of Table 2 show that the GATT/WTO membership has exerted a less greater trade policy liberalization effect on LDCs than on other countries in the full sample (see the positive and significant coefficient of the interaction variable “Treat*LDC”). The net effects of the GATT/WTO membership on trade liberalization in LDCs and NonLDCs amount respectively to -3.95 (= -11.60 + 7.650) and − 11.60. The same conclusion applies to African countries versus NonAfrican countries in the full sample (see column [3]): the net effects of the GATT/WTO membership on trade liberalization in African countries and NonAfrican countries amount respectively to -4.792 (= -9.635 + 4.843) and − 9.635. The membership in the GATT/WTO has led to the same extent of trade policy liberalization in both Article12 Members and Article26 members (the coefficient of “Treat” (-4.568) is significant at the 1% level, but the interaction term of “Treat*ART26” is not significant at the 10% level) (see column [4]). In the meantime, the GATT/WTO membership has been associated with a greater trade policy liberalization in NonArticle26 Members than that in Article12 Members and Article26 Members (the net effect amounting to -14.758 (= -4.568–10.19) for NonArticle26 Members. [Insert Table 2 , here] [Insert Table 3 , here] Estimates in Table 3 suggest that at the 1% level, the GATT/WTO membership has resulted in the decline in income inequality over the full sample (see column [1]): this is 6.81-point decline in the index of market-based income inequality in WTO Members compared to NonWTO Members. LDCs enjoy yet a negative income inequality effect of their membership in the GATT/WTO, but to a far lesser extent than NonLDCs in the full sample (see column [3]): income inequality has decreased by -1.1 (= -13.35 + 12.25) in LDCs and by -13.35-point in NonLDCs, thanks to these countries’ membership in the GATT/WTO. Finally, outcomes in column [4] of Table 3 show that the membership in the GATT/WTO has led to a lower income inequality in Article12 Members, Article26 Members and NonArticle26 Members alike. The net effect on income inequality in these three groups of countries amount to -9.033. Overall, the findings indicate that the membership in the GATT/WTO has been associated with a greater trade policy liberalization, and a fall in income inequality, although the magnitudes of these negative effects vary across sub-samples. It is important to note that these findings do not change substantially when we used the FGLS estimator of Zellner ( 1962 ) as an alternative estimator, or when we used the income disposable inequality (i.e., income inequality after tax and transfers) instead of the market-based income inequality. 4. Conclusion This paper has investigated empirically the effect of the GATT/WTO membership on trade policy and income inequality. The analysis has revealed that the GATT/WTO membership has been associated with a greater trade policy liberalization and lower income inequality, although the magnitudes of these effects vary across groups of countries. 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Markets for Risk and Openness to Trade: How are they Related? Journal of International Economics, 57(2), 369-395. Tang, M.-K., and Wei, S.-J. (2009). The value of making commitments externally: Evidence from WTO accessions. Journal of International Economics, 78(2), 216-229. Wong, K., and Yu, M. (2015). Democracy and Accession to GATT/WTO. Review of Development Economics, 19(4), 843-859. WTO (World Trade Organization) (2021). World Trade Report 2021: Economic resilience and trade. Geneva, Switzerland. Zellner, A. (1962). An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias. Journal of the American Statistical Association, 57(298), 348-368. Footnotes The World Bank. E-mail for correspondence: [email protected] See for example the literature survey by Koopman et al. ( 2020 ). See Neuenkirch and Neumeier ( 2016 ) for the list of advantages associated with the EB approach compared to other matching methods. Data on the GATT/WTO Membership are extracted from the WTO's website ( https://www.wto.org/english/thewto_e/whatis_e/tif_e/org6_e.htm ). These are essentially founding Members of the GATT, also referred to as “old-industrialized countries”. There are no WTO definitions of “developed” and “developing” countries. Countries self-designate as “developed” or “developing” countries when acceding to the WTO ( https://www.wto.org/english/tratop_e/devel_e/d1who_e.htm ). The category of “developing countries” used in the present analysis is close to what the existing category of “developing countries” at the WTO based on countries’ self-designation as developed or developing countries. A similar control group was used by Tang and Wei ( 2009 ) and Brotto et al. ( 2021 ). The group of LDCs enjoys many flexibilities in WTO Agreements that may limit the extent of their trade policy liberalization. This refers to GATT Article XXVI 5(c) reads as follows: If any of the customs territories, in respect of which a contracting party has accepted this Agreement, possesses or acquires full autonomy in the conduct of its external commercial relations and of the other matters provided for in this Agreement, such territory shall, upon sponsorship through a declaration by the responsible contracting party establishing the above-mentioned fact, be deemed to be a contracting party. GATT Article XXVI 5(c) is accessible online at: https://www.wto.org/english/res_e/publications_e/ai17_e/gatt1994_art26_gatt47.pdf Article XII of the Marrakesh Agreement establishing the WTO provides that " Any State or separate customs territory possessing full autonomy in the conduct of its external commercial relations and of the other matters provided for in this Agreement and the Multilateral Trade Agreements may accede to this Agreement, on terms to be agreed between it and the WTO. Such accession shall apply to this Agreement and the Multilateral Trade Agreements annexed thereto " (see Article XII.1). Further information on Article XII is available online at: https://www.wto.org/english/docs_e/legal_e/04-wto.pdf and https://www.wto.org/english/thewto_e/acc_e/acces_e.htm Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5079276","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":353254599,"identity":"4916bb01-ff7c-4190-978c-a8d84887fb00","order_by":0,"name":"Sena Kimm GNANGNON","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYBACCTiLvYGBGUoTq4XnAEQLkCZWi0QCkVok+9ce/Fzw646cueQbw88FFTYMPNIE9EhLvEuWntn3zNhydo6x9IwzaQw8fAn4tchJnDGQ5u05nLjhdg6Q0XaYwZ6HgMOAWox/g7XcBDGAWngIaZHm7zGT5vkB1HKDx0yaKC2SM3jMrHkbDhsbnEkrs+Y5k8ZDUIvE+TPGt3n+HJYzOH54822eChs5glpA0cHA2AZicRiASIIaGBj4DwCJPyAW+wPCqkfBKBgFo2BEAgC0Oz8Vqq/vEwAAAABJRU5ErkJggg==","orcid":"","institution":"World Bank","correspondingAuthor":true,"prefix":"","firstName":"Sena","middleName":"Kimm","lastName":"GNANGNON","suffix":""}],"badges":[],"createdAt":"2024-09-12 17:11:19","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5079276/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5079276/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64578435,"identity":"899e0709-2f97-410c-963b-344afa9178ba","added_by":"auto","created_at":"2024-09-16 04:57:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":539059,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5079276/v1/bc1721bd-8cfd-4576-8654-04bbaedbbc63.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEffect of the membership in the World Trade Organization on Income Inequality\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAs successor of the General Agreement on Tariffs and Trade (GATT), the World Trade Organization (WTO) was created to promote - through trade policy liberalization - a smooth and predictable movement of goods and services across borders. Several empirical works have been undertaken on the effects of the GATT/WTO membership. Many works have shown that the GATT/WTO membership has helped promote international trade flows\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e, and prevent trade wars (e.g., Bekkers and The, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hoekman, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It has also helped foster economic growth (e.g., Brotto et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tang and Wei, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), the global welfare (e.g., Fan et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and dampen economic growth volatility (e.g., Gnangnon, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, we are not aware of a study that has investigated empirically the effect of the GATT/WTO membership on within-country income inequality (henceforth referred to as \u0026ldquo;income inequality\u0026rdquo;) in developing countries. This is a major issue on the policy agenda of national policymakers and international organizations whose ultimate objective is to promote economic development.\u003c/p\u003e \u003cp\u003eWe argue that the WTO membership can affect income inequality through its effect on members\u0026rsquo; trade policy. While it may seem a priori obvious that the GATT/WTO membership would promote trade policy liberalization, the landmark paper of Rose et al. (2004) has reported that the GATT/WTO membership has had almost no genuine significant impact on trade liberalization. On the other hand, the literature has reported a mixed evidence on the effect of trade liberalization on income inequality (e.g., Bourguignon, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Goldberg and Pavcnik, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), reflecting different findings based on different datasets (panel countries or individual countries), econometric approaches, and time periods. For example, some studies have found that trade liberalization widens income inequality (e.g., Artuc et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Harrison et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Robertson, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Rojas-Vallejos and Turnovsky, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), while other studies have reported a negative effect of trade policy liberalization on income inequality (e.g., Jaumotte et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Reuveny and Li, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). On the other hand, some studies have found no significant effect of trade liberalization on income inequality (e.g., Dollar and Kraay, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Edwards, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Green et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). It is worth noting that in addition to its income distribution effect through trade policy, the GATT/WTO membership can affect income inequality through the international cooperation brought about by such membership. In fact, participation in international trade can widen income inequality by generating higher income uncertainty and volatility (e.g., Laursen et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rodrik, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). In this context, international cooperation on trade policies can help reduce risks and vulnerabilities, and cope with shocks (e.g., WTO, 2021: Chapter D), and consequently contribute to dampening income inequality.\u003c/p\u003e \u003cp\u003eThe present note aims to explore empirically effect of the GATT/WTO membership on member states\u0026rsquo; trade policy, and within-country income inequality. The next section presents the empirical models, and the econometric approaches. Section 3 interprets empirical outcomes, and section 4 concludes.\u003c/p\u003e"},{"header":"2. Empirical strategy","content":"\u003cp\u003eWe use the entropy balancing (EB) approach, developed by Hainmueller (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) (see also Neuenkirch and Neumeier, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), to investigate empirically the effect of the GATT/WTO membership on income inequality and trade policy (the channel-variable). The EB approach is a non-parametric multivariate reweighting approach for impact analysis, which essentially aims to reweight the control group to match the moments of the treatment group in order to make the control group data match the covariate moments of the treatment. It is particularly useful here because member states that join the WTO are not randomly selected. As a result, it is hard to determine the extent to which the average treatment effects of the membership in the GATT/WTO on trade policy and income inequality are attributed to the membership itself or to the characteristics of each member state. The EB approach helps mitigate endogeneity concerns by severely reducing the bias introduced by the selection into the treatment\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e. Its utilization involves two steps. In the first step, we compute the weights to assign to the control units based on the pre-treatment characteristics of units under analysis. In the present analysis, the balancing requirements are achieved by using the mean (average) of covariates to ensure that countries in the control group (non-WTO Members) are as similar as possible to countries in the treatment group (i.e., WTO Members\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e). The weights are determined by using as observable pre-treatment characteristics of countries the macroeconomic determinants of accession to the GATT/WTO. Since we aim to ensure that the control group closely matches the treatment group, we follow for example Tang and Wei (2010) and Brotto et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and do not include developed countries\u003ca class=\"FNLink\" href=\"#Fn4\" id=\"#FNLinkFn4\"\u003e\u003c/a\u003e (at the time of accession) in our full sample. Thus, our full sample contains essentially developing countries\u003ca class=\"FNLink\" href=\"#Fn5\" id=\"#FNLinkFn5\"\u003e\u003c/a\u003e, with the treatment group comprising GATT/WTO Members (taking into account the year of accession to the GATT/WTO), and the control group including WTO non-Members, that is, states that are either in the process of joining the WTO (i.e., WTO Observers) or those that have not even initiated the process of accession to the WTO.\u003c/p\u003e \u003cp\u003eDrawing from the relevant literature (e.g., Copelovitch and Ohls, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Davis and Wilf, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jones and Gai, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wong and Yu, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tang and Wei, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), the observable pre-treatment characteristics of countries include the applicant\u0026rsquo;s market (economic) size and wealth (e.g., Copelovitch and Ohls, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Davis and Wilf, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wong and Yu, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), its economic growth (e.g., Wong and Yu, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and it\u0026rsquo;s the level of democracy (which also acts as a proxy for institution and governance quality) (e.g., Jones and Gai, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mansfield et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and the terms of trade. Following Neuenkirch and Neumeier (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), we use the one-year lag of these variables. We measure the economic size by the real Gross Domestic Product (GDP) (in natural logarithm), and the wealth by the real GDP per capita (in natural logarithm). These two variables and the economic growth indicator are sourced from the World Development Indicators (WDI) of the World Bank. The institutional and governance quality indicator was computed as the first principal component (based on factor analysis) of the six indicators of governance and institutional quality developed by the World Bank Governance Indicators developed by Kaufmann et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the results of the sample means of the matching covariates after weighing. We observe that the reweighed means of covariates (see column [4]) are identical to the target values of the same covariates (column [1]). Moreover, the standardized difference between the target value and the balanced value is essentially zero for all variables (column [5]).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCovariates balance\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarget Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnbalanced\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eBalanced\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized difference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStandardized difference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGROWTH\u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog(GDP)\u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.78e-15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog(GDPC)\u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.16e-16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOLITY\u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-7.12e-17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTERMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e117.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e[Insert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cb\u003ehere]\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn the second step, we use the computed weights to construct the entropy-balanced sample, which is in turn, utilized to run regressions where the treatment variable (the time - year - of membership in the GATT/WTO, defined as \u0026ldquo;Treat\u0026rdquo;) is the regressor, and the dependent variable is either the indicator of trade policy or income inequality. The coefficient of the treatment variable captures the average difference in the dependent (outcome) variable (trade policy, or income inequality) between GATT/WTO Members and NonWTO Members that is attributed to the membership in the GATT/WTO. Specifically, we estimate, on the one hand, the model specification that allows investigating the effect of the GATT/WTO membership on trade policy (see model (1)). On the other hand, we estimate the model specification that helps examine the effect of the GATT/WTO membership on income inequality (see model (2)).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eBuilding on the literature on the macroeconomic determinants of trade policy (e.g., Milner and Kubota, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Rose, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Svaleryd and Vlachos, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), we postulate model (1) as follows:\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{MATR}_{it}={\\alpha\\:}_{0}+{\\alpha\\:}_{1}{MATR}_{it-1}+{\\alpha\\:}_{2}{Treat}_{it}+\\theta\\:{X}_{it}+{\\gamma\\:}_{t}+{\\mu\\:}_{i}+{\\epsilon\\:}_{it}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBuilding on the voluminous literature on the determinants of income inequality (e.g., Amponsah et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Brei et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Furceri and Ostry, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jaumotte et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), we postulate model (2) as follows:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{GINI}_{it}={\\beta\\:}_{0}+{\\beta\\:}_{1}{GINI}_{it-1}+{\\beta\\:}_{2}{Treat}_{it}+\\delta\\:{Z}_{it}+{\\gamma\\:}_{t}+{\\mu\\:}_{i}+{\\vartheta\\:}_{it}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn equations (\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and (2), \u003cem\u003ei\u003c/em\u003e represents a country and \u003cem\u003et\u003c/em\u003e stands for a year in the unbalanced panel dataset, which is constructed on the basis of data availability. It contains 117 countries in the treatment group, and 15 countries\u003ca class=\"FNLink\" href=\"#Fn6\" id=\"#FNLinkFn6\"\u003e\u003c/a\u003e in the control group, over the annual period from 1980 to 2021. The dependent variables \u0026ldquo;MATR\u0026rdquo; and \u0026ldquo;GINI\u0026rdquo; are respectively the indicators of trade policy, and income inequality. Trade policy is measured by the \u0026ldquo;Measure of Aggregate Trade Restrictions\u0026rdquo; developed by the International Monetary Fund (IMF) (see Estefania-Flores et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It provides granular measures of different facets of trade protectionism, including tariffs, non-tariff barriers, and restrictions on requiring, obtaining, and using foreign exchange for current transactions (see Estefania-Flores et al., 2023: p747). It has many advantages over existing trade policy indicators, one of these advantages being its far larger time coverage. This indicator has now been utilized in literature because it is simple, and relies on sensible, plausible, trade policy inputs obtained from a transparent and reliable source, which is easily accessible (for example, Campos et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Lower values of this index indicate a greater trade policy liberalization (Data is available online at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sites.google.com/view/m-atr/\u003c/span\u003e\u003cspan address=\"https://sites.google.com/view/m-atr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Income inequality is measured by the market Gini income inequality, i.e., income inequality before taxes and transfers. The values of this indicator range from 0 to 100, with higher values reflecting a more unequal income distribution. Data on the income inequality indicator were extracted from the Standardized World Income Inequality Database (SWIID) - SWIID - Version 8.0, February 2019 (see Solt, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e - available online at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fsolt.org/swiid/\u003c/span\u003e\u003cspan address=\"https://fsolt.org/swiid/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ).\u003c/p\u003e \u003cp\u003eThe one-year lag of the dependent variable is introduced in models (1) and (2) to take into account the inertia in these variables. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{0}\\:\\)\u003c/span\u003e\u003c/span\u003eto \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{2};\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{2}\\)\u003c/span\u003e\u003c/span\u003e as well as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\theta\\:\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\delta\\:\\)\u003c/span\u003e\u003c/span\u003e are coefficients to be estimated. Specifically, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{2}\\:\\)\u003c/span\u003e\u003c/span\u003erepresents the average treatment effect of the GATT/WTO membership on trade policy, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{2}\\)\u003c/span\u003e\u003c/span\u003e is the average treatment effect of the GATT/WTO membership on income inequality. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e are temporal dummies, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e are countries' time invariant specific effects. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{it}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\vartheta\\:}_{it}\\:\\)\u003c/span\u003e\u003c/span\u003eare two different well-behaving error terms.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\theta\\:\\)\u003c/span\u003e \u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\delta\\:\\)\u003c/span\u003e\u003c/span\u003e are respectively two different vectors of parameters \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e that include coefficients relating respectively to the vector of control variables \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{it}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Z}_{it}\\)\u003c/span\u003e\u003c/span\u003e. Following Neuenkirch and Neumeier (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), both \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{it}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Z}_{it}\\)\u003c/span\u003e\u003c/span\u003e include the variables used in the EB approach\u0026rsquo;s first step. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{it}\\)\u003c/span\u003e\u003c/span\u003e additionally includes the one-year lag of an indicator of financial development (see the literature on the macroeconomic determinants of trade policy), measured by the share of the domestic credit to private sector by banks in GDP. Data on this indicator were collected from the WDI.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{Z}_{it}\\)\u003c/span\u003e \u003c/span\u003e includes in addition to the indicator of financial development, the squared term of the natural logarithm of the real GDP per capita. This is to capture the existence of a non-linear - inverted U-curve - relationship between the real per capita GDP and the level of income inequality (Kuznets, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1955\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe estimate models (1) and (2) and their different variants (see below) using the panel-corrected standard error (PCSE) estimator. This technique allows controlling for heteroscedasticity, first-order autocorrelation, and for contemporaneous correlation across individuals in the panel dataset (e.g., Beck and Katz, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1995\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe start by estimating models (1) and (2) over the full sample (see results in column [1] of Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We, then, examine the differentiated effects of the GATT/WTO membership on trade policy and income inequality in poorest countries (least developed countries\u003ca class=\"FNLink\" href=\"#Fn7\" id=\"#FNLinkFn7\"\u003e\u003c/a\u003e \u0026ndash; LDCs) versus NonLDCs in the sample (see results in column [2] of Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Next, we consider the differentiated effects of the GATT/WTO membership on trade policy and income inequality in African countries versus NonAfrican countries in the sample (see results in column [3] of Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), given that African countries have the highest degrees of trade restrictiveness (see Estefania-Flores et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of the GATT/WTO Membership on Trade Policy \u003cb\u003eEstimator\u003c/b\u003e: PCSE with panel specific first order autocorrelation (AR1)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMATR\u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0977***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0968***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0987***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0963***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0109)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-7.344***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-11.60***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-9.635***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-4.568***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e(1.019)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e(1.431)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e(1.403)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e(1.599)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreat*LDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e7.650***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e(1.304)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.771***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.519)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreat*Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4.843***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e(1.174)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfrica\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreat*ART26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.244\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e(1.407)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eART26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.337)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreat*NonART26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-10.19***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e(1.585)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonART26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.395***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.488)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations-Countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,403\u0026thinsp;\u0026minus;\u0026thinsp;132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,403\u0026thinsp;\u0026minus;\u0026thinsp;132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,403\u0026thinsp;\u0026minus;\u0026thinsp;132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,403\u0026thinsp;\u0026minus;\u0026thinsp;132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo-R\u003csup\u003e2\u003c/sup\u003e / R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWald Chi2-Statistic (P-value)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23337.96 (0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27113.86 (0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23660.19 (0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28804.15 (0.0000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote: *p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1; **p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ***p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Robust standard errors are in parenthesis. Time dummies have been included in the regressions. The Pseudo R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eis reported for the outcomes arising from the FGLS-based regressions, and was calculated as the correlation coefficient between the dependent variable and its predicted values. The R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eis reported for regressions based on the PCSE estimator.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of the GATT/WTO Membership on Income Inequality \u003cb\u003eEstimator\u003c/b\u003e: PCSE with panel specific first order autocorrelation (AR1)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGINIM\u003csub\u003et\u0026minus;1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0299***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0278***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0298***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0311***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.00491)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00476)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.00468)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.00474)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-6.810***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-13.35***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-11.31***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-9.033***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e(1.593)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e(2.172)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e(2.099)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e(2.174)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreat*LDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e12.25***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e(2.027)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.556**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.895)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreat*Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e7.686***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e(1.825)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfrica\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.797)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreat*ART26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-1.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e(2.493)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eART26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.45***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.446)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreat*NonART26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.714\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e(2.531)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonART26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.947\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.408)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations-Countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,788\u0026thinsp;\u0026minus;\u0026thinsp;127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,788\u0026thinsp;\u0026minus;\u0026thinsp;127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,788\u0026thinsp;\u0026minus;\u0026thinsp;127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,788\u0026thinsp;\u0026minus;\u0026thinsp;127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo-R\u003csup\u003e2\u003c/sup\u003e / R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWald Chi2-Statistic (P-value)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75093.96 (0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79016.53 (0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80160.50 (0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71790.26 (0.0000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote: *p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1; **p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ***p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Robust standard errors are in parenthesis. Time dummies have been included in the regressions. The Pseudo R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eis reported for the outcomes arising from the FGLS-based regressions, and was calculated as the correlation coefficient between the dependent variable and its predicted values. The R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eis reported for regressions based on the PCSE estimator.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFinally, we examine whether there are differentiated effects of GATT/WTO membership on trade policy and income inequality depending on member states\u0026rsquo; commitments to trade policy liberalization. To address this question, we consider three groups of members states with different degrees of liberalization commitments. The first group includes countries that were essentially former colonies, and had neither undergone long negotiation processes, nor undertaken extensive policy reforms commitments when joining the GATT. This group is, henceforth, referred to as Article26\u003ca class=\"FNLink\" href=\"#Fn8\" id=\"#FNLinkFn8\"\u003e\u003c/a\u003e member states (\u0026ldquo;ART26\u0026rdquo;). The second group of countries comprises countries that did not invoke GATT Article XXVI 5(c) when joining the GATT. These countries (referred to as \"NonArticle 26 member states\") underwent long negotiation processes, and carried extensive reforms. The third and last group of countries are those that did not join the GATT, but joined the WTO, especially under Article XII\u003ca class=\"FNLink\" href=\"#Fn9\" id=\"#FNLinkFn9\"\u003e\u003c/a\u003e of the Marrakesh Agreement establishing the WTO. This set of countries (referred to as Article12 countries - defined as \u0026ldquo;ART12\u0026rdquo;) underwent more stringent procedures than the ones undergone by original WTO Members (i.e., contracting parties of the GATT) (e.g., Drabek and Bacchetta, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). To address empirically our question, we create two dummy variables, one for Article26 member states, and another for NonArt26 members states, and introduce them in models (1) and (2) along with their interaction with the variable \u0026ldquo;Treat\u0026rdquo;. Therefore, the reference group in the regressions is Article 12 member states. Results of the estimation of this model specification are reported in column [4] of Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e"},{"header":"3. Results’ interpretation","content":"\u003cp\u003eOutcomes in Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e suggest that trade policy and income inequality display a path dependence (the coefficients of \u0026ldquo;MATR\u003csub\u003et\u0026minus;1\u003c/sub\u003e\u0026rdquo; and \u0026ldquo;GINIM\u003csub\u003et\u0026minus;1\u003c/sub\u003e\u0026rdquo; are positive and significant at the 1% level). Outcomes in column [1] of Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show that at the 1% level, the membership in the GATT/WTO is associated with a greater trade policy liberalization over the full sample. The GATT/WTO membership has led to 7.344 points decline in the values of the MATR index over the full sample. Estimates reported in column [2] of Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show that the GATT/WTO membership has exerted a less greater trade policy liberalization effect on LDCs than on other countries in the full sample (see the positive and significant coefficient of the interaction variable \u0026ldquo;Treat*LDC\u0026rdquo;). The net effects of the GATT/WTO membership on trade liberalization in LDCs and NonLDCs amount respectively to -3.95 (= -11.60\u0026thinsp;+\u0026thinsp;7.650) and \u0026minus;\u0026thinsp;11.60. The same conclusion applies to African countries versus NonAfrican countries in the full sample (see column [3]): the net effects of the GATT/WTO membership on trade liberalization in African countries and NonAfrican countries amount respectively to -4.792 (= -9.635\u0026thinsp;+\u0026thinsp;4.843) and \u0026minus;\u0026thinsp;9.635. The membership in the GATT/WTO has led to the same extent of trade policy liberalization in both Article12 Members and Article26 members (the coefficient of \u0026ldquo;Treat\u0026rdquo; (-4.568) is significant at the 1% level, but the interaction term of \u0026ldquo;Treat*ART26\u0026rdquo; is not significant at the 10% level) (see column [4]). In the meantime, the GATT/WTO membership has been associated with a greater trade policy liberalization in NonArticle26 Members than that in Article12 Members and Article26 Members (the net effect amounting to -14.758 (= -4.568\u0026ndash;10.19) for NonArticle26 Members.\u003c/p\u003e \u003cp\u003e \u003cb\u003e[Insert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003ehere]\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003e[Insert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cb\u003ehere]\u003c/b\u003e\u003c/p\u003e \u003cp\u003eEstimates in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e suggest that at the 1% level, the GATT/WTO membership has resulted in the decline in income inequality over the full sample (see column [1]): this is 6.81-point decline in the index of market-based income inequality in WTO Members compared to NonWTO Members. LDCs enjoy yet a negative income inequality effect of their membership in the GATT/WTO, but to a far lesser extent than NonLDCs in the full sample (see column [3]): income inequality has decreased by -1.1 (= -13.35\u0026thinsp;+\u0026thinsp;12.25) in LDCs and by -13.35-point in NonLDCs, thanks to these countries\u0026rsquo; membership in the GATT/WTO. Finally, outcomes in column [4] of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show that the membership in the GATT/WTO has led to a lower income inequality in Article12 Members, Article26 Members and NonArticle26 Members alike. The net effect on income inequality in these three groups of countries amount to -9.033.\u003c/p\u003e \u003cp\u003eOverall, the findings indicate that the membership in the GATT/WTO has been associated with a greater trade policy liberalization, and a fall in income inequality, although the magnitudes of these negative effects vary across sub-samples. It is important to note that these findings do not change substantially when we used the FGLS estimator of Zellner (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1962\u003c/span\u003e) as an alternative estimator, or when we used the income disposable inequality (i.e., income inequality after tax and transfers) instead of the market-based income inequality.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis paper has investigated empirically the effect of the GATT/WTO membership on trade policy and income inequality. The analysis has revealed that the GATT/WTO membership has been associated with a greater trade policy liberalization and lower income inequality, although the magnitudes of these effects vary across groups of countries.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDISCLAIMER\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a working paper, which represents the personal opinions of individual staff members of the World Bank and is not meant to represent the position or opinions of the World Bank Group, nor the official position of any staff members. Any errors or omissions are the fault of the author.\u0026nbsp;The author declares no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAmponsah, M., Agbola, F.W., and Mahmood, A. (2023). The relationship between poverty, income inequality and inclusive growth in Sub-Saharan Africa. 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Joining the WTO: Why Does It Take So Long? Open Economies Review, 24(4), 695-716.\u003c/li\u003e\n \u003cli\u003eKaufmann, D., Kraay, A., and Mastruzzi, M. (2010). The Worldwide Governance Indicators Methodology and Analytical Issues. World Bank Policy Research N\u0026deg; 5430 (WPS5430). The World Bank, Washington, D.C.\u003c/li\u003e\n \u003cli\u003eKoopman, R., Hancock, J., Piermartini, R., and Bekkers, E. (2020). The Value of the WTO. Journal of Policy Modeling, 42(4), 829-849.\u003c/li\u003e\n \u003cli\u003eKuznets, S. (1955). Economic Growth and Income Inequality. American Economic Review, 45(1), 1-28.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLaursen T, Mahajan S. Volatility, Income Distribution, and Poverty. In: Aizenman J, Pinto B, eds. Managing Economic Volatility and Crises: A Practitioner\u0026rsquo;s Guide. Cambridge University Press; 2005, 101-136.\u003c/li\u003e\n \u003cli\u003eLiu, Z., Spiegel, M.M., and Zhang, J. (2023). Capital flows and income inequality. 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Markets for Risk and Openness to Trade: How are they Related? Journal of International Economics, 57(2), 369-395.\u003c/li\u003e\n \u003cli\u003eTang, M.-K., and Wei, S.-J. (2009). The value of making commitments externally: Evidence from WTO accessions. Journal of International Economics, 78(2), 216-229.\u003c/li\u003e\n \u003cli\u003eWong, K., and Yu, M. (2015). Democracy and Accession to GATT/WTO. Review of Development Economics, 19(4), 843-859.\u003c/li\u003e\n \u003cli\u003eWTO (World Trade Organization) (2021). World Trade Report 2021: Economic resilience and trade. Geneva, Switzerland.\u003c/li\u003e\n \u003cli\u003eZellner, A. (1962). An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias. Journal of the American Statistical Association, 57(298), 348-368.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\n \u003cli\u003e\u0026nbsp;The World Bank. E-mail for correspondence: [email protected]\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;See for example the literature survey by Koopman et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;See Neuenkirch and Neumeier (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) for the list of advantages associated with the EB approach compared to other matching methods.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;Data on the GATT/WTO Membership are extracted from the WTO\u0026apos;s website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wto.org/english/thewto_e/whatis_e/tif_e/org6_e.htm\u003c/span\u003e\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;These are essentially founding Members of the GATT, also referred to as \u0026ldquo;old-industrialized countries\u0026rdquo;.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;There are no WTO definitions of \u0026ldquo;developed\u0026rdquo; and \u0026ldquo;developing\u0026rdquo; countries. Countries self-designate as \u0026ldquo;developed\u0026rdquo; or \u0026ldquo;developing\u0026rdquo; countries when acceding to the WTO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wto.org/english/tratop_e/devel_e/d1who_e.htm\u003c/span\u003e\u003c/span\u003e). The category of \u0026ldquo;developing countries\u0026rdquo; used in the present analysis is close to what the existing category of \u0026ldquo;developing countries\u0026rdquo; at the WTO based on countries\u0026rsquo; self-designation as developed or developing countries.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;A similar control group was used by Tang and Wei (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) and Brotto et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;The group of LDCs enjoys many flexibilities in WTO Agreements that may limit the extent of their trade policy liberalization.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;This refers to GATT Article XXVI 5(c) reads as follows: If any of the customs territories, in respect of which a contracting party has accepted this Agreement, possesses or acquires full autonomy in the conduct of its external commercial relations and of the other matters provided for in this Agreement, such territory shall, upon sponsorship through a declaration by the responsible contracting party establishing the above-mentioned fact, be deemed to be a contracting party. GATT Article XXVI 5(c) is accessible online at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wto.org/english/res_e/publications_e/ai17_e/gatt1994_art26_gatt47.pdf\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003e\u0026nbsp;Article XII of the Marrakesh Agreement establishing the WTO provides that \u0026quot;\u003cem\u003eAny State or separate customs territory possessing full autonomy in the conduct of its external commercial relations and of the other matters provided for in this Agreement and the Multilateral Trade Agreements may accede to this Agreement, on terms to be agreed between it and the WTO. Such accession shall apply to this Agreement and the Multilateral Trade Agreements annexed thereto\u003c/em\u003e\u0026quot; (see Article XII.1). Further information on Article XII is available online at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wto.org/english/docs_e/legal_e/04-wto.pdf\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wto.org/english/thewto_e/acc_e/acces_e.htm\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"World Bank Group","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Membership in the GATT/WTO, Trade Policy, Income Inequality","lastPublishedDoi":"10.21203/rs.3.rs-5079276/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5079276/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper has investigated empirically the effect of the membership in the GATT/WTO on trade policy and income inequality. The analysis has revealed that the GATT/WTO membership has been associated with trade policy liberalization and lower income inequality, although the magnitudes of these effects vary across groups of countries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification\u003c/strong\u003e: F13; F14; O15.\u003c/p\u003e","manuscriptTitle":"Effect of the membership in the World Trade Organization on Income Inequality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-16 04:49:12","doi":"10.21203/rs.3.rs-5079276/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6a25a8e3-46ce-46a4-af59-e67cb8276e35","owner":[],"postedDate":"September 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":37532281,"name":"Macroeconomics"},{"id":37532282,"name":"International Economics"}],"tags":[],"updatedAt":"2024-09-16T04:49:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-16 04:49:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5079276","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5079276","identity":"rs-5079276","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}