How do human rights violations affect poverty and income distribution?

Journal Pre-proof How do human rights violations affect poverty and income distribution? Nicholas Apergis, Arusha Cooray

PII:

S2110-7017(19)30115-5

DOI:

https://doi.org/10.1016/j.inteco.2019.11.003

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INTECO 221

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International Economics

Received Date: 6 May 2019 Revised Date:

6 November 2019

Accepted Date: 8 November 2019

Please cite this article as: Apergis, N., Cooray, A., How do human rights violations affect poverty and income distribution?, International Economics (2019), doi: https://doi.org/10.1016/j.inteco.2019.11.003. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V. on behalf of CEPII (Centre d'Etudes Prospectives et d'Informations Internationales), a center for research and expertise on the world economy.

How Do Human Rights Violations Affect Poverty and Income Distribution?

Abstract Employing data for 125 countries and data spanning the 1990-2014 period, we empirically examine the impact of human rights on income distribution and poverty. We also investigate how aid and trade can influence poverty and income distribution through human rights. The results suggest that stronger human rights records contribute to greater income equality, as well as to poverty reduction. The interaction of human rights with both ODA and trade show that as aid and trade flows increase, or alternatively as human rights records increase, ODA and trade flows reduce poverty and lead to greater equality in income distribution. Keywords: human rights; income inequality; poverty; global panel of countries JEL Classification: D63; I32; K38; O57; C33

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1. Introduction A country’s human rights record can influence its level of poverty and living conditions. This can occur through unequal access to resources, trade sanctions, the allocation of aid, conflicts, political violence, and repression. Human rights violations can aggravate conditions of poverty and the living conditions of the poorer segments of society which in turn can constrain growth and development. According to Amnesty International, the world has reached an all-time high for human rights in 2015 and ‘international systems are no longer adequate to cope’ (Withnall, 2016). Given the corresponding widening of income inequality globally, it is highly crucial to understand if a country’s human rights record has contributed to this widening inequality, and if so, what type of relationship exists between human rights violations and income inequality and poverty. The present study focuses on the direct influence of human rights conditions on poverty and inequality, and on trade and aid allocation as channels through which human rights violations may affect poverty. According to Donald and Mottershaw (2009), human rights provide a basis through which to pursue accountability for poverty and inequality, through an examination of the structural causes of poverty rather than merely the effects of poverty and outcomes of government measures taken. Many countries have begun to use economic coercion as an enforcement mechanism through which to promote human rights. Thus, Preferential Trading Agreements (PTAs) have increasingly begun to incorporate government compliance with human rights (Yap 2013, Hafner-Burton 2009, Hafner-Burton 2005) in particular, those by the European Union1. Several other countries including the U.S and Canada are also strong supporters of the incorporation of human rights clauses into trading agreements. For example, the participation of countries in the Generalized System of Preferences (GSP) Plus which grants developing countries preferential trading access to the 1

The human rights clause built into EU bilateral agreements is also called the 'democracy clause (Zamfir 2019)

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EU, is conditional on the human rights record of a country. Non-compliance has led to the withdrawal of countries from the GSP Plus for example, Belarus, Myanmar and Sri Lanka (Beke and Hachez 2015). Similarly, human rights concerns have led to the postponement of the introduction of free trade agreements in countries (Vietnam) and the suspension of aid for instance in Niger, Guinea Bissau, Sierra Leone, Togo, Cameroon, Haiti, Comoros, Côte d’Ivoire, Fiji, Liberia and Zimbabwe, Burundi (Yap 2013, Zamfir 2019). Accordingly, there has been debate amongst the international community on the degree to which countries should be punished for non-compliance. Despite the fact that these measures are meant to punish governments that violate human rights, studies show that they often adversely affect the vulnerable and marginalised groups in society (Weiss 1999). Some studies also show that economic sanctions lead to a greater degree of violation of physical integrity rights contributing to greater repression and increased economic instability (Peksen 2009, Wood 2008). However, the literature has only a limited understanding of the influence of human rights on poverty and income distribution and their effects through trade and aid flows. The higher the aid and trade flows into a country, the more inclined countries might be to conform to international human rights standards, which in turn, can lead to greater equality in income distribution, as well as to lower levels of poverty, or alternatively, non-compliance could lead to lower aid and trade flows which in turn affect poverty and income distribution2. Some observe that the World Trade Organization (WTO) or international organizations could play an important role in this regard, by using membership in the WTO or other organizations to advance the cause of democracy (Aaronson 2001, Subramanian and Wei 2007, Pevehouse 2005).

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Note that we do not investigate how these stipulations specifically affect trade and aid flows as it is difficult to measure the specific effects of stipulations. As non-compliance to human rights standards could lead to lower aid and trade flows, or alternatively, higher aid and trade flows could lead to higher compliance which in turn affect poverty and income distribution, we use aid and trade flows in our empirical analysis.

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Following from the above discussion, the contribution of the present study to the literature is twofold: (i) to investigate, the effect of human rights conditions on income distribution and poverty; and (ii) to investigate the impact of human rights conditions on income distribution and poverty through the channels of overseas development aid (ODA) and trade flows. Human rights in the present study is defined as physical integrity rights and civil and political rights. Accordingly, the Cingrenelli-Richards (CIRI) Physical Integrity Rights Index (Cingranelli and Richards 1999) and the Political Terror Scale (PTS) compiled by Gibney et al. (2019) which are the most commonly used indicators of state violations of citizens’ physical integrity rights are the two main measures used in the study. However, the civil liberties (CL) and political rights (PR) measures of Freedom House which measure civil and political rights are also used as additional measures of human rights. Greater detail on these measures are provided in the Data Section. Income distribution is measured by the Gini coefficient and poverty by the Headcount Poverty ratio, i.e. the percentage of the population falling below $1.90 a day. The Atkinson and Theil measures of income inequality are also used as part of a robustness check. The rest of this study is structured as follows. Section 2 discusses the literature. Section 3 describes the data. Section 4 presents the model and methodology. Section 5 discusses the empirical results and Section 6 concludes.

2. Literature review An increasing number of PTAs have begun to incorporate human rights clauses whereby the economic incentive of increased market access is used as a tool to promote compliance with international human rights norms (Yap 2013, Hafner-Burton 2005, Hafner-Burton 2009). Studies show that PTAs are more effective compared to human rights agreements (HRAs) in

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changing repressive government behaviours because they influence a governments human rights practices through coercion rather than persuasion (Hafner-Burton 2005, Hafner-Burton 2009). According to these studies, HRAs attempt to influence a governments' human rights practices through persuasion and therefore may not lead to desired results, whereas PTAs linked to market benefits tend to lead to better outcomes in terms of human rights. In a study of European withdrawal of the GSP+ programme from Sri Lanka’s garment industry due to human rights problems, Yap (2013) notes that the economic incentive for market access could encourage the garment industry to take a leadership role in helping the government to take measures to improve its human rights record. Similar views are put

forward by Harrelson-Stephens and Callaway (2003) who note that it is common practice for Western countries to use trade policies to discourage human rights violations in developing countries. They show using cross sectional data for several developing countries over 1976-1996, that trade is negatively related to human rights violations. In a study of the effect of human rights treaties on the behaviour of governments in the context of the International Covenant on Civil and Political Rights (ICCPR), the Convention against Torture and other Cruel, Inhuman or Degrading Treatment or Punishment (CAT), and the Convention on the Elimination of All Forms of Discrimination against Women (CEDAW) in 165 countries from 1976 to 2006, Hill (2010) finds evidence in favour of the CEDAW leading to an improvement in state behaviour, the CAT to a lesser extent, and the ICCPR associated with worse practices. According to him, this perhaps might be explained by state ratification of human rights treaties by countries to cover worsening practices. Some studies show that economic sanctions are a counter-productive measure even if they are enforced with the specific objective of improving human rights. Peksen (2009) utilizing time-series data over 1981–2000, shows that extensive sanctions are more damaging to human rights compared to partial sanctions, leading to a deterioration of government 5

respect for physical integrity rights, including freedom from disappearances, extra-judicial killings, torture, and political imprisonment. These views are supported by Wood (2008) who argues that the imposition of economic sanctions has adverse effects on human rights conditions in a state by encouraging increased repression and instability. Sanctions by multiple nations are found to have greater detrimental effects on the target state. Studies also show that international organizations play an important role in the transition to a democracy as these organizations have influence over powerful actors in a society (Pevehouse 2005). This view is supported by Aaronson (2001) and Subramanian and Wei (2007) who suggest that accession to the WTO can enforce countries to improve their human rights records to appease donor states, and failure to do so, can lead to the imposition of trade sanctions. Compliance however, may pose a problem due to several reasons. One, is the absence of proper enforcement mechanisms. In some cases, leaders may have a strong desire to improve human rights practices, but face obstructions at the implementation stage. An absence of compliance will also arise in the case where repressive governments have leaders who have no desire to promote human rights. It should also be noted that committees set up to determine whether states comply with human rights standards are set up by the very same governments that are violating human rights and therefore will have no incentive to report their own transgressions. Critics point out that employing trade sanctions to ensure the maintenance of labour standards do not necessarily lead to improved trade or labour standards (Brown, 2001), but could aggravate conditions of poverty in poorer nations. Bhagwati (1995) notes that the rules set out by the WTO under which sanctions are imposed against countries are biased against poor countries. That is, developed countries may not be found to be in violation of the very same issues that poor countries are found to be in violation of, which lead to the enforcement of sanctions. Similar views are expressed by Srinivasan (1998). 6

Some studies show that countries ratify human rights agreements due to fear of sanctions. Spence (2014) in a study of 152 aid receiving countries over 1981-2010 argues that countries ratify human rights agreements not because they expect to receive larger aid flows, but due to fear of the imposition of aid sanctions. The ratification of these agreements act as a cover for human rights violations, making it easier for donors to continue providing aid to countries that violate human rights. While some studies document that human rights violations reduce aid flows into countries (Neumayer, 2003 a,b; Cingranelli and Pasquarello, 1985) which can adversely affect conditions of poverty, others show the opposite, that is, aid flows into countries are not influenced by human rights records (McCormick and Mitchell, 1988; Carleton and Stohl, 1985; Schoultz, 1981; Chomsky and Herman, 1979). Neumayer (2003a, b) notes that the respect for civil/political rights play a statistically significant role for most donors at the aid entitlement stage. At the aid granting stage, however, most donors do not promote the respect for human rights consistently across aid recipients and tend to grant more aid to countries with a poor record on the basis of either civil/political or personal integrity rights. If this were the case, poor human rights records could lead to an improvement in living standards of a country through foreign aid. This perhaps is explained by the fact that aid is influenced, to a great degree, by strategic considerations and political motives of donor countries (Alesina and Dollar, 2000, Lebovic and Voeten 2009, Richards et al. 2001). The proponents of dependency theory argue that foreign aid is a channel through which core-periphery relations are reinforced (Richards et al., 2001). The respect for human rights could additionally indirectly influence ODA receipts into a country by enhancing the effectiveness of public expenditure programmes (Roberts, 2003), thereby, generating higher equality in income distribution.

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Thus, according to supporters of free trade, trade policy can be an important measure for improving a country’s human rights practices, which, in turn, can create conditions conducive for growth and the promotion of greater equality in income distribution and a fall in poverty.

3. Data Annual data, spanning the period 1990-2014 for 125 countries is used. The sample constitutes a representative panel of the regions covering Eastern Europe and Central Asia, the Middle East and North Africa, Latin America and the Caribbean, East Asia and the Pacific, South Asia, Africa, and the high income OECD countries (see the Appendix for the full list of countries included). The selection was totally based on data availability for the entire set of variables included in the analysis. The dependent variables in the study are: income distribution, measured by the Gini coefficient (GINI), which can vary from 0 (perfect income equality) to 1 (perfect income inequality). Poverty (POV) is measured by the Headcount Poverty Ratio, i.e. the percentage of the population living on less than $1.90 a day, with data being obtained from the World Bank database. We also use the Atkinson (1975) and Theil measures of income distribution for robustness checks. The Atkinson index is sensitive to inequalities in different parts of the income distribution. That is, it gives different parts of the income distribution different weights which the Gini coefficient does not. The Atkinson index further takes into account social judgements and is used to calculate the proportion of total income that is needed to achieve an equal level of social welfare if income were equally distributed (De Maio 2007). The Theil Index can be separated by subgroups, that is, the relative importance in the total of inequality within the groups for each of the groups and inequality between the groups can be identified (Theil 1967, McKay 2002). The data for the Atkinson and Theil measures are taken from the University of Texas Inequality Project. 8

The main independent variables of interest are the human rights index (HRI), the HRI interacted with ODA (disbursements of loans made on concessional terms -net of repayments of principal- and grants by official agencies of the members of the Development Assistance Committee–DAC, by multilateral institutions, and by non-DAC countries, measured as percentage of gross national income) and the HRI interacted with trade - TR (defined as net trade in goods, i.e. the difference between exports and imports of goods divided by GDP and expressed in percentage terms, with trade in services not being included, while data are in current U.S. dollars). The two main measures of human rights used in the empirical study are: (i) the Cingrenelli-Richards (CIRI) Physical Integrity Rights Index (HRI1 in the empirical analysis), which is an additive index constructed from the Torture, Extrajudicial Killing, Political Imprisonment, and Disappearance indicators of the CIRI database, and ranges from 0 (no government respect for these four rights) to 8 (full government respect for these four rights) (Cingranelli et al., 1999), and (ii) the Political Terror Scale (PTS) (HRI2) measure of Gibney et al. (2019), which measures the level of political violence and terror that a country faces in a given year on a ‘terror scale’ of 1-5, originally developed by Freedom House, where 1 represents the best case and 5 the worst; the index has been reversed so that 1 stands for the worst case and 5 the best, while it is consistent with the Cingranelli et al. (1999) index. The data used in compiling the PTS index come from three different sources: the yearly country reports of Amnesty International, the U.S. State Department Country Reports on Human Rights Practices, and the Human Rights Watch’s World Reports (Gibney et al., 2019). A number of other control variables are also used: per capita income (PCI) and the gross secondary enrolment ratio (ENROLL) are used to measure the level of development of a country. This data are taken from the World Bank (2019). The analysis also considers a number of political/institutional-related indexes to capture civil and political rights including 9

the Polity Index (POL) from the Polity IV database by Marshall and Jaggers (2015) which is used to capture the quality of institutions in a country (Rodrik, 1996). The index ranges from -10 (pure autocracies) to 10 (pure democracies); the Political Rights index (PR), and the Civil Liberties index (CL) from Freedom House where both indices are measured on a oneto-seven scale, with one representing the highest degree of Freedom and seven the lowest. Corruption (COR) can adversely affect an economy (Mauro, 1997; Rose-Ackerman, 1999; among others) by increasing rent seeking with no corresponding quid pro quo to the rest of the society. To this end, the ICRG (2015) corruption index is used. This index ranges from 0 (totally corrupt) to 6 (not corrupt) and has been reversed so that 0 stands for not corrupt and 6 stands for totally corrupt. Population (POP) is also included as a control variable to account for the size of the country (Neumayer, 2003a,b). The larger the population of a country, the greater might be the need for aid and trade which will affect income distribution. Studies also illustrate that the success of achieving developmental objectives through aid depends on the effectiveness of government expenditure programs (Roberts, 2003). Therefore, government expenditure as percentages of GDP (GOV) is also included in the empirical analysis as an additional control variable. Data for population and government expenditure are taken from the World Bank (2019). Trade sanctions can be imposed on a country which violates human rights to encourage changes in human rights policy (Sykes, 2003) which, in turn, are expected to affect the poor groups of the population. Thus, a dummy variable (DSAN) is created for trade sanctions. This dummy variable is coded 1 if trade sanctions are imposed on a country, and 0, otherwise. We also interact this term with the HRI to investigate how trade sanctions affect poverty and income distribution through human rights practices. Information on trade sanctions is obtained from the Threat and Imposition of Economic Sanctions (TIES) dataset by Morgan et al. (2014) and the U.S. Department of the Treasury (2015) Sanctions Programs and Country Information. The TIES dataset spans the period 1945-2011, and has data on

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economic sanctions, as well as on threats of sanctions3. Economic sanctions take many forms, including actions such as tariffs, export controls, embargoes, import bans, travel bans, freezing assets, cutting foreign aid, and/or blockades. As the present study covers the period up until 2014, the TIES data are supplemented by the U.S. Department of the Treasury (2015) Sanctions Programs and Country Information. Studies also show that the accession to the WTO can improve human rights records (Aaronson, 2001; Subramanian and Wei, 2007), which in turn can improve trade that reduces inequality in income distribution. Therefore, a variable (DTWO) is included to capture WTO membership, as well as an interaction term for HRI x WTO to examine whether WTO membership leads to the reduction in income inequality and poverty through human rights practices. Data on WTO membership are obtained from the WTO (2016) website. Countries are coded 1 from the year of membership in the WTO and 0, otherwise. Table 1 provides some summary statistics. [Insert Table 1 about here]

4. The Model and Methodology The empirical panel data model is described as follows:

q1 q2 q3 q4 GINIit or POVit = αi + Σ β1j HRIit-j + Σ β2j PCIit-j + Σ β3j ENROLLit-j + Σ β4j POLit-j + j=0 j=0 j=0 j=0

q5 q6 q7 q10 Σ β5j CORit-j + Σ β6j POPit-j + Σβ7j GOVit-j + β8 DSAN + β9 DWTO + Σ β10j HRIit-j x TRit-j j=0 j=0 j=0 j=0

3

See Morgan et al. (2014) for greater detail.

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q11

q12

q13

Σβ11j HRIit-j x ODAit-j + Σβ12j HRIit-j x DWTO + Σβ13j HRIit-j x DSAN + j=0

j=0

q14

j=0

q15

Σ β14j PRit-j + Σ β15j CLit-j + εit j=0

j=0

where t denotes time, i country and j the time lag; GINI is the Gini coefficient, POV denotes poverty, HRI stands for human rights, measured by the CIRI and PTS indices. ODA measures official development assistance, TR proxies net trade in goods, PCI is per capita income, ENROLL is the enrolment ratio (ENROLL), POL denotes the Polity index (POL), COR measures corruption, POP measures population (POP), GOV is government expenditure, DSAN denotes a dummy variable for trade sanctions (is coded 1 if trade sanctions are imposed on a country, and 0, otherwise), DWTO shows a dummy variable for WTO membership (1 for the year of membership in WTO and 0, otherwise), PR stands for Political Rights, and CL stands for Civil Liberties. αi captures country fixed effects and, finally, ε denotes the error term. We have incorporated interaction terms for human rights x ODA (HRI x ODA) and human rights x trade (HRI x TR), to examine the effect of human rights on income distribution and poverty through aid and trade. We have also included interaction terms for HRI x DWTO, and HRI x DSAN, to investigate how accession to the WTO and trade sanctions affect poverty and income distribution through their influence on human rights. In the first step of the empirical analysis, we examine the unit root properties of the data through advanced panel unit root tests. Panel unit root tests of the first-generation can lead to spurious results, if significant degrees of positive residual cross-section dependence exist and are ignored. Consequently, the implementation of second-generation panel unit root

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tests is desirable only when it has been established that the panel is subject to a significant degree of residual cross-section dependence. In cases where cross-sectional dependence is not sufficiently high, a loss of power might result if second-generation panel unit root tests that allow for cross-section dependence are employed. Therefore, before selecting the appropriate panel unit root test, it is crucial to provide some evidence on the degree of residual crosssectional dependence. The cross-sectional dependence (CD) statistic by Pesaran (2004) is based on a simple average of all pair-wise correlation coefficients of the OLS residuals obtained from standard augmented Dickey-Fuller regressions for each variable in the panel. Under the null hypothesis of cross-sectional independence, the CD test statistic follows asymptotically a two-tailed standard normal distribution. The results are reported in Table 2 and they uniformly reject the null hypothesis of cross-section independence, providing evidence of cross-sectional dependence in the data given the statistical significance of the CD statistics regardless of the number of lags (from 1 to 4) included in the ADF regressions. [Insert Table 2 about here] Two second-generation panel unit root tests are employed to determine the degree of integration in the respective variables. The Pesaran (2007) panel unit root test does not require the estimation of factor loading to eliminate cross-sectional dependence. Specifically, the usual ADF regression is augmented to include the lagged cross-sectional mean and its first difference to capture the cross-sectional dependence that arises through a single-factor model. The null hypothesis is a unit root for the Pesaran (2007) test. The bootstrap panel unit root tests by Smith et al. (2004) utilize a sieve sampling scheme to account for both the time series and cross-sectional dependence in the data through bootstrap blocks. All four tests by Smith et al. (2004) are constructed with a unit root under the null hypothesis and

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heterogeneous autoregressive roots under the alternative hypothesis. The results of these panel unit root tests are reported in Table 3 and support of the presence of a unit root across all variables under consideration, except in the case of the Gini coefficient, Poverty measure, and the quality of political institutions variables. [Insert Table 3 about here] The empirical analysis is carried out through the two-step panel GMM approach. The GMM methodology avoids potential endogeneity and is based on the approach recommended by Arrelano and Bover (1995) and Blundell and Bond (1998). The Hansen test for overidentification can be used to check the validity of instruments. The two-step system GMM provides more efficient estimators over one-step system GMM. Moreover, two-step GMM gives robust Hansen J-test for over-identification. The GMM methodology uses as instruments a constant term plus a number of lags coming from both the dependent and the independent variables.

5. Empirical Results The empirical findings for the full sample are reported in Table 4. The first two columns report results for income distribution using the Gini coefficient as the dependent variable and the second two columns report results for poverty as the dependent variable with HRI1 and HRI2 representing the CIRI and PTS human rights indices respectively. If we focus on the primary variable of interest, the human rights index, the results document that both definitions of the human rights index lead to a better income equality, as well as to poverty reduction. Moreover, according to the interaction terms of these two indexes with both the official development assistance flows (aid flows) and trade flows, as both aid and trade flows rise, the human rights indexes have a negative effect on both income inequality 14

and poverty. The results indicate that increased levels of aid and trade are related with improved human rights conditions in a country, consistent with the studies of HarrelsonStephens and Callaway (2003) and Neumayer (2003a, 2003b), or alternatively, that better human rights records lead to higher levels of trade and aid, which in turn lead to a fall in poverty and income inequality. This suggests that human rights clauses in PTAs and trade policy can be important measures for improving a country’s human rights practices which in turn can create conditions conducive for growth and lower levels of poverty and promotion of greater equality in income distribution. In terms of the remaining control variables, the results illustrate that income per capita leads to more income equality and less poverty, while the same holds for the case of school enrollment. Similarly, higher government expenses lead to the same results suggesting that public expenditure programmes lead to greater equality in income distribution and lower levels of poverty (Roberts 2003). Higher corruption scores lead to both a worse income distribution and a reduced poverty score, while higher measures of population worsen both income equality and poverty consistent with the findings of Gupta et al. (2002). An improved quality in the political regime (i.e., movements towards democracy) leads to a better income distribution, as well as to improved poverty scores. In terms of the political environment variables, the findings illustrate that there is a negative relation between the Polity Index that captures the quality of institutions and both income inequality and poverty; by contrast, both the Political Rights index the Civil Liberties index are positively associated with income inequality and poverty, indicating that higher political freedom and property rights lead to lower inequality and poverty. In terms of the dummy variables included, the presence of sanctions (DSAN) worsens both

income distribution and poverty as argued by Brown

(2001), while membership in the WTO regime reverses that picture (the latter is also confirmed by the simultaneous effect –the interaction term- between WTO membership and 15

the human rights index). This result suggests that accession to the WTO can cause countries to conform to international human rights records (Aaronson 2001, Subramanian and Wei 2007, Pevehouse 2005) which in turn will lead to a fall in poverty and income inequality. Finally, the interaction term between the human rights index and the dummy of trade sanctions seems to impose a positive impact on both income inequality and on poverty. These findings clearly illustrate that although human right conditions are improved, the presence of trade sanctions leads to lower income equality and more poverty. All the relevant diagnostics are reported at the bottom of Table 4. For the validity of the instruments, the results need to reject the test for second-order autocorrelation, AR(2), in the error variances. Moreover, they need to reject the null hypothesis of difference-in-Hansen tests of the exogeneity of instruments. It is evident that both the test for AR(2) of disturbances and the difference-in-Hansen tests fail to reject the respective nulls. Thus, these tests support the validity of the instruments used, while difference-in-Hansen tests imply the exogeneity of the instruments employed. The table also reports the Hansen test for overidentifying restrictions. In the estimation process, 28 instruments have been used. These instruments were generated as we used two lags for levels and three lags for difference in the regressors. As the number of instruments was by far lower than the number of observations, it did not create any identification problem, as reflected by the Hansen test. Reported Hansen test results also fail to detect any problem in the validity of the instruments used in the estimation approach. Finally, the explanatory power of models, through the R-squared metrics, is highlighted to be strong enough across all four modeling specifications. [Insert Table 4 about here] Finally, for robustness purposes and based on the argument that a limitation of the Gini coefficient as a measure of inequality is that it is most sensitive to the middle part of income

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distribution than to that of extremes, provided that it depends on the rank order weights of income recipients, as well as on the number of recipients within a given range, this part of the analysis makes use of alternative measures of income inequality (recommended by Frank, 2014). These measures include the Atkinson inequality measure and the Theil index. The new results, in both measurement cases, are reported in Table 5 and they provide strong empirical support to those in Table 4 once again suggesting that both improvements on the CIRI and PTS measures lead higher income equality. Similarly, the interaction terms on HRI x TR and HRI x ODA indicate that as both trade and aid flows increase, that human rights lead to a fall in income inequality and poverty, or that as human rights records increase, that aid and trade flows lead to greater income equality and lower levels of poverty. The results also suggest that stronger Political Rights and Civil Liberties lead to lower levels of inequality. The results additionally indicate that economic sanctions increase income inequality through human rights consistent with the conclusions of Wood (2008) and Peksen (2009), while WTO membership reduces income inequality through better human rights. The signs on the rest of the control variables are in general similar to those obtained in Table 4. [Insert Table 5 about here]

6. Conclusion Despite the literature focusing on how inequality in income distribution affects human rights, little attention has been given to how human rights affect income distribution. Similarly, little is known of the interactive effects of trade openness and aid on income inequality and poverty through human rights. This is a critical issue given the recent widening of income inequality and corresponding increase in human rights violations. The central objective of this paper therefore, has been to empirically examine the impact of human rights on income

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distribution and poverty, and the effects of human rights on poverty and income distribution through aid and trade. The results suggest that stronger human rights records contribute to greater income equality, as well as to poverty reduction. The interaction of human rights with both ODA and trade show that as aid and trade flows increase that human rights records lead to a fall in both income inequality and poverty, or alternatively, that stronger human rights records will lead to an increase in aid and trade flows, leading to a fall in income inequality and poverty. These findings have important policy implications. Our results tend to suggest an important complementary role for stronger human rights records and greater income equality, and trade and aid in enhancing income equality and reducing poverty through human rights. Opening up a country to trade and higher aid flows will reduce poverty, only if human rights records are strong. Thus, policy-makers should focus on improving institutional quality to enhance human rights records. Aid and trade policy can similarly be used for improving a country’s human rights practices and reducing poverty.

Appendix: List of countries: Europe (40) = Albania, Armenia, Austria, Azerbaijan, Belarus, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Hungary, Ireland, Italy, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Luxembourg, Moldova, Netherlands, Norway, Poland, Portugal, Romania, Russia, Slovakia Republic, Slovenia, Spain, Sweden, Switzerland, Tajikistan, Turkmenistan, U.K., Ukraine. Africa (29) = Algeria, Angola, Botswana, Cameroon, Central African Republic, Chad, Congo Democratic Republic, Egypt, Ethiopia, Gambia, Ghana, Kenya, Liberia, Libya,

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Mauritania, Morocco, Mozambique, Niger, Nigeria, Rwanda, Senegal, South Africa, Sudan, Tanzania, Togo, Tunisia, Uganda, Zambia, Zimbabwe. America (24) = Argentina, Bahamas, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominican Republic, El Salvador, Equator, Guatemala, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Trinidad and Tobago, Uruguay, U.S., Venezuela. Asia (30) = Bangladesh, Brunei, Bhutan, Cambodia, China, Hong-Kong, India, Indonesia, Iran, Israel, Japan, Jordan, Kuwait, Laos, Malaysia, Nepal, Oman, Pakistan, Philippines, Qatar, Singapore, Saudi Arabia, South Arabia, South Korea, Sri Lanka, Syria, Thailand, Taiwan, Turkey, Vietnam. Pacific (2) = Australia, New Zealand.

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Frank MW (2014) A New State-Level Panel of Annual Inequality Measures over the Period 1916-2005, Journal of Business Strategies, 31, 241-263. Gibney, M. Cornett L, Wood R., Haschke P., Arnon D., Pisanò A., and Barrett G. (2019) The Political Terror Scale 1976-2018. http://www.politicalterrorscale.org/(retrieved July 2015) Gupta, S., Davoodi, H. and Alonso-Terme, R. (2002). Does Corruption affect Income Inequality and Poverty? Economics of Governance, 23-45. Hafner-Burton, Emilie M (2009) "The Power Politics of Regime Complexity: Human Rights Trade Conditionality in Europe, Perspectives on Politics, 7, 33-37. Hafner-Burton, Emilie M (2005) Trading human rights: How preferential trade agreements influence government repression, International Organization, 59, 593-629. Harrelson-Stephens J and Callaway R (2003) Does Trade Openness Promote Security Rights in Developing Countries? Examining the Liberal Perspective, International Interactions, 29, 143-158. Hill Jr., Daniel W. and Jones Z M (2014) An Empirical Evaluation of Explanations for State Repression.” American Political Science Review, 108, 661-687. Hill, Daniel W (2010) Estimating the Effects of Human Rights Treaties on State Behavior, Journal of Politics, 72,1161–1174. ICRG Report (2015) ICRG report on methodology, ICRG: London: https://www.prsgroup.com/?pdf_file=http://www.prsgroup.com/wpcontent/uploads/201 5/icrgmethodology.pdf (retrieved July 2015) Lebovic J and Voeten E (2009) The Cost of Shame: International Organizations and Foreign Aid in the Punishing of Human Rights Violators, Journal of Peace Research, 46, 79-97. Marshall M, Jaggers K (2015) Polity IV Project, available at: http://www.systemicpeace.org/polity/polity15.htm#nam (retrieved June 2013) Mauro, P. (1997) The Effects of Corruption on Growth, Investment, and Government Expenditure: A Cross–CountryAnalysis. In: Elliott, K.-A. (ed.) Corruption and the Global Economy, pp.83–107. Institute for International Economics,Washington D.C. McCormick J and Mitchell N (1988) Is U.S. Aid Really Linked to Human Rights in Latin America?, American Journal of Political Science, 32, 231-239. McKay A (2002) Defining and Measuring Inequality, Inequality Briefing, Overseas Development Institute and University of Nottingham, Briefing Paper No. 1, March 2002. Morgan T, Bapat N, and Kobayashi Y (2014) The Threat and Imposition of Sanctions: Updating the TIES dataset, Conflict Management and Peace Science, 31, 541-558. 21

Neumayer E (2003a) Do Human Rights Matter in Bilateral Aid Allocation? A Quantitative Analysis of 21 Donor Countries, Social Science Quarterly, 84, 650-666. Neumayer E (2003b) Is Respect for Human Rights Rewarded? An Analysis of Total Bilateral and Multilateral Trade Flows, Human Rights Quarterly, 25, 510-527. Peksen D (2009) Better or Worse? The Effect of Economic Sanctions on Human Rights, Journal of Peace Research, 46, 59-77. Pesaran M (2007) A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics 22: 265-312. Pesaran MH (2004) General diagnostic tests for cross section dependence in panels. Cambridge Working Papers in Economics, No. 435 and CESifo Working Paper, No. 1229. Pevehouse J (2005) Democracy from Above: Regional Organizations and Democratization, Cambridge University Press. Richards D, Gelleny R and Sacko D (2001) Money with a Mean Streak? Foreign Economic Penetration and Government Respect for Human Rights in Developing Countries, International Studies Quarterly, 45, 219-239. Roberts J (2003) Poverty Reduction Outcomes in Education and Health Public Expenditure and Aid. Overseas Development Institute working paper 210. Rodrik D (1996). Labor standards in international trade: Do they matter and what do we do about them? In R. Z. Lawrence, D. Rodrik, & J. Whalley (Eds.), Emerging agenda for global trade: High stakes for developing countries (pp. 35-80). Washington, DC: Johns Hopkins University Press. Rose-Ackerman S (1999) Corruption and Government: Causes, Consequences and Reform, Cambridge University Press, Cambridge. Schoultz L 1981. U.S. foreign policy and human rights violations in Latin America: A comparative analysis of foreign aid distributions. Comparative Politics, 13, 149-70. Smith V, Leybourne S, Kim TH (2004) More powerful panel unit root tests with an application to the mean reversion in real exchange rates. Journal of Applied Econometrics 19: 147-170. Spar D (1998) The Spotlight and the Bottom Line: How Multinationals Export Human Rights. Foreign Affairs 77, 7–12. Spence D H (2014) Foreign Aid and Human Rights Treaty Ratification: Moving Beyond the Rewards Thesis, International Journal of Human Rights, 18, 414-432. Srinivasan, T.N. (1998) “Trade and Human Rights,” in Constituent Interests and U.S. Trade Policy, A.V. Deardorff and R. M. Stern (eds.), Ann Arbor: University of Michigan Press.

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Subramanian A and Wei S (2007) The WTO Promotes Trade, Strongly but Unevenly, Journal of International Economics, 72, 151-175. Sykes A (2003) International Trade and Human Rights: An Economic Perspective, University of Chicago Working Paper Series Law School, Working Paper No. 188. Theil, H. (1967). Economics and Information Theory. Amsterdam: North Holland. U.S. Department of the Treasury (2015) Sanctions Programs and Country Information: https://www.treasury.gov/resource-center/sanctions/Programs/Pages/Programs.aspx Weiss T (1999) Sanctions as a Foreign Policy Tool: Weighing Humanitarian Impulses, Journal of Peace Research. 36, 99-510. Wood R M. (2008) “A Hand Upon the Throat of the Nation”: Economic Sanctions and State Repression, 1976-2001.” International Studies Quarterly, 52, 489-513. World Bank (2015) World Development Indicators, World Bank. World Trade Organization (2016) Understanding the WTO: The Organization https://www.wto.org/english/thewto_e/whatis_e/tif_e/org6_e.htm Yap, J (2013) Beyond ‘Don’t Be Evil:’ The European Union GSP+ Trade Preference Scheme and the Incentivisation of the Sri Lankan Garment Industry to Foster Human Rights, European Law Journal 19, 283–301. Zamfir I (2019) Human Rights in EU Trade Agreements: The Human Rights Clause and its Application, Briefing European Parliamentary Research Service, July 2019.

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Table 1. Summary statistics ___________________________________________________________________________ Variables

Mean

S.D.

Min

Max

___________________________________________________________________________ GINI

0.41

0.09

0.16

0.66

POV

6.23

9.58

0.01

62.96

HRI1

5.79

1.96

3.00

7.00

HRI2

3.83

1.65

2.00

5.00

ODA

7.72

11.12

-0.69

181.19

TR

80.55

40.96

0.31

531.74

PCI

3548.42

6934.98

50.04

82192.93

ENROLL

63.27

28.64

5.16

119.72

POL

2.30

6.21

-8.74

9.28

COR

3.49

0.97

1.00

6.00

POP

36,938,675.71

1.43E+08

417,784

1.35E+09

GOV

14.92

6.39

2.06

69.54

PR

3.369

2.165

1

7

CL

3.313

1.910

1

7

___________________________________________________________________________ Note: S.D. stands for standard deviation.

24

Table 2. Cross-section dependence (CD) tests Lags Variables

1

2

3

4

HR1

[0.00]***

[0.00]***

[0.00]***

[0.01]***

HR2

[0.00]***

[0.00]***

[0.01]***

[0.00]***

POV

[0.00]***

[0.00]***

[0.00]***

[0.01]***

PCI

[0.00]***

[0.00]***

[0.00]***

[0.00]***

ENROLL

[0.00]***

[0.00]***

[0.00]***

[0.00]***

GOV

[0.00]***

[0.00]***

[0.00]***

[0.01]***

TR

[0.00]***

[0.00]***

[0.01]***

[0.01]***

ODA

[0.00]***

[0.00]***

[0.00]***

[0.00]***

POL

[0.00]***

[0.01]***

[0.02]**

[0.03]**

COR

[0.00]***

[0.00]***

[0.00]***

[0.02]**

PR

[0.00]***

[0.00]***

[0.00]***

[0.01]***

CL

[0.00]***

[0.00]***

[0.00]***

[0.00]***

Under the null hypothesis of cross-sectional independence the CD statistic is distributed as a two-tailed standard normal. Results are based on the test of Pesaran (2004). Figures in brackets denote p-values. Significance levels: ***: p≤0.01, **: p≤0.05.

25

Table 3. Panel unit root tests Pesaran

Pesaran

CIPS GINI

CIPS*

Smith et al. t-test

Smith et al. LM-test

Smith et al. max-test

Smith et al. min-test

-5.36***

-5.59***

-5.82***

23.15***

-6.14***

6.25***

POV

-5.25***

-5.48***

-5.34***

20.53***

-6.62***

6.81***

HR1

-1.27

-1.35

-1.31

3.14

-1.40

1.29

∆HR1

-5.42***

-5.64***

-6.35***

20.42***

-7.09***

7.16***

HR2

-1.31

-1.37

-1.33

3.10

-1.38

1.34

∆HR2

-5.46***

-5.59***

-6.30***

20.64***

-6.98***

7.11***

PCI

-1.28

-1.36

-1.31

3.03

-1.42

1.39

∆PCI

-5.62***

-5.83***

-6.19***

20.84***

-6.57***

6.91***

ENROLL

-1.30

-1.38

-1.36

2.91

-1.39

1.43

∆ENROLL

-5.53***

-5.77***

-5.60***

21.16***

-6.84***

-7.11***

GOV

-1.27

-1.36

-1.33

3.01

-1.38

1.45

∆GOV

-5.81***

-6.03***

-5.97***

21.79***

-1.40***

1.44***

TR

-1.38

-1.46

-1.42

2.95

-1.42

1.47

∆TR

-5.94***

-6.22***

-6.14***

22.65***

-6.15***

6.63***

ODA

-1.38

-1.50

-1.46

2.98

-1.47

1.49

∆ODA

-5.61***

-5.84***

-5.72***

20.74***

-5.69***

5.93***

POL

-5.48***

-5.61***

-5.55***

20.52***

-5.38***

5.41***

COR

-1.36

-1.42

-1.40

3.04

-1.39

1.43

∆COR

-5.62***

-5.79***

-5.73***

20.18***

-5.58***

5.74***

PR

-1.29

-1.37

-1.34

2.98

-1.30

1.38

∆PR

-5.44***

-5.62***

-5.51***

20.49***

-5.26***

5.41***

CL

-1.35

-1.41

-1.39

2.81

-1.38

1.42

∆CL

-5.39***

-5.52***

-5.48***

19.96***

-5.15***

5.63***

Variable

∆ denotes first differences. A constant is included in the Pesaran (2007) tests. Rejection of the null hypothesis indicates stationarity in at least one country. CIPS* = truncated CIPS test. Critical values for the Pesaran (2007)

26

test are -2.40 at 1%, -2.22 at 5%, and -2.14 at 10%, respectively. Both a constant and a time trend are included in the Smith et al. (2004) tests. Rejection of the null hypothesis indicates stationarity in at least one country. For both tests the results are reported at lag = 4. The null hypothesis is that of a unit root. ***: p≤0.01.

Table 4. GMM estimates ___________________________________________________________________________ Gini Poverty Variables HRI1 HRI2 HRI1 HRI2 __________________________________________________________________________________________ ∆HRI -0.062*** -0.068*** -0.045*** -0.054*** [0.00] [0.00] [0.01] [0.00] ∆HRI x ODA -0.057*** -0.059*** -0.036** -0.042*** [0.00] [0.00] [0.02] [0.00] ∆HRI x TR -0.075*** -0.081*** -0.042*** -0.047*** [0.00] [0.00] [0.01] [0.00] ∆PCI -0.055*** -0.059*** -0.076*** -0.087*** [0.00] [0.00] [0.00] [0.00] ∆PCI(-1) -0.025*** -0.030*** -0.024* -0.028* [0.01] [0.01] [0.09] [0.08] ∆ENROLL -0.050*** -0.055*** -0.057*** -0.062*** [0.00] [0.00] [0.00] [0.00] POL -0.046*** -0.053*** -0.077*** -0.085*** [0.00] [0.00] [0.00] [0.00] ∆PR 0.071*** 0.078*** 0.059*** 0.073*** [0.00] [0.00] [0.00] [0.00] ∆CL 0.063*** 0.071*** 0.069*** 0.086*** [0.00] [0.00] [0.00] [0.00] ∆COR 0.039*** 0.046*** 0.085*** 0.094*** [0.01] [0.00] [0.00] [0.00] ∆COR(-1) 0.021* 0.027** 0.039*** 0.047*** [0.06] [0.05] [0.01] [0.00] ∆POP -0.035*** -0.040*** 0.037** 0.046*** [0.01] [0.00] [0.02] [0.00] ∆POP(-1) -0.020** -0.027*** 0.018* 0.024** [0.03] [0.01] [0.06] [0.05] ∆GOV -0.079*** -0.087*** -0.070*** -0.078*** [0.00] [0.00] [0.00] [0.00] ∆GOV(-1) -0.037*** -0.045*** -0.049*** -0.057*** [0.01] [0.00] [0.00] [0.00] ∆GOV(-2) -0.021** -0.026** -0.038** -0.044*** [0.03] [0.02] [0.02] [0.01] DSAN 0.018** 0.023** 0.030*** 0.038*** [0.04] [0.03] [0.01] [0.00] DWTO -0.029*** -0.037*** -0.047*** -0.053*** [0.01] [0.00] [0.00] [0.00] ∆HRI x DWTO -0.027** -0.032** -0.022** -0.028** [0.04] [0.03] [0.03] [0.02] ∆HRI x DSAN 0.051*** 0.059*** 0.066*** 0.078*** [0.00] [0.00] [0.00] [0.00] Diagnostics R2 0.58 0.65 0.63 0.71 AR(1) [0.00] [0.00] [0.00] [0.00] AR(2) [0.40] [0.52] [0.35] [0.31] Hansen test [0.54] [0.62] [0.48] [0.54] Difference Hansen test [0.81] [0.86] [0.58] [0.75] No. Of observations 3,125 3,125 3,125 3,125

27

___________________________________________________________________________ Notes: HRI1 is the Cingrenelli-Richards (CIRI) Physical Integrity Rights Index, while HRI2 is the Political Terror Scale (PTS) measure by Wood and Gibney (2010). AR(1) is the first-order test for residual autocorrelation. AR(2) is the test for autocorrelation of order 2. Hansen is the test for the overidentification check for the validity of instruments. The difference-in-Hansen test checks the exogeneity of the instruments. Figures in parentheses denote p-values. *: p≤0.01; **: p≤0.05; ***: p≤0.01. All estimations were performed with time dummies and coefficients are not reported.

28

Table 5. GMM estimates: Income inequality measured by the Atkinson and the Theil indexes. ___________________________________________________________________________ Atkinson Theil Variables HRI1 HRI2 HRI1 HRI2 __________________________________________________________________________________ ∆HRI(-1) -0.069*** -0.078*** -0.074*** -0.085*** [0.00] [0.00] [0.00] [0.00] ∆HRI x ODA -0.051*** -0.063*** -0.056*** -0.067*** [0.00] [0.00] [0.00] [0.00] ∆HRI x TR -0.069*** -0.076*** -0.084*** -0.095*** [0.00] [0.00] [0.00] [0.00] ∆PCI -0.060*** -0.076*** -0.093*** -0.119*** [0.00] [0.00] [0.00] [0.00] ∆PCI(-1) -0.043*** -0.049*** -0.054*** -0.068*** [0.00] [0.00] [0.00] [0.00] ∆ENROLL -0.061*** -0.068*** -0.075*** -0.087*** [0.00] [0.00] [0.00] [0.00] POL -0.055*** -0.064*** -0.083*** -0.095*** [0.00] [0.00] [0.00] [0.00] ∆PR 0.060*** 0.074*** 0.068*** 0.086*** [0.00] [0.00] [0.00] [0.00] ∆CL 0.053*** 0.062*** 0.065*** 0.089*** [0.00] [0.00] [0.00] [0.00] ∆COR 0.047*** 0.061*** 0.089*** 0.120*** [0.00] [0.00] [0.00] [0.00] ∆COR(-1) 0.034** 0.045*** 0.066*** 0.090*** [0.02] [0.00] [0.00] [0.00] ∆POP -0.045*** -0.059*** 0.058*** 0.075*** [0.00] [0.00] [0.01] [0.00] ∆POP(-1) -0.032** -0.045*** 0.039** 0.046*** [0.02] [0.01] [0.02] [0.01] ∆GOV -0.096*** -0.110*** -0.104*** -0.122*** [0.00] [0.00] [0.00] [0.00] ∆GOV(-1) -0.062*** -0.078*** -0.079*** -0.094*** [0.00] [0.00] [0.00] [0.00] ∆GOV(-2) -0.036** -0.045*** -0.057*** -0.074*** [0.02] [0.01] [0.00] [0.00] DSAN 0.029** 0.038** 0.052*** 0.065*** [0.02] [0.02] [0.00] [0.00] DWTO -0.039*** -0.046*** -0.065*** -0.080*** [0.00] [0.00] [0.00] [0.00] ∆HR x DWTO -0.024** -0.038** -0.048*** -0.059*** [0.05] [0.03] [0.00] [0.00] ∆HR x DSAN 0.046*** 0.061*** 0.064*** 0.076*** [0.00] [0.00] [0.00] [0.00] Diagnostics R2 0.53 0.60 0.66 0.72 AR(1) [0.00] [0.00] [0.00] [0.00] AR(2) [0.36] [0.49] [0.37] [0.34] Hansen test [0.48] [0.55] [0.52] [0.56] Difference Hansen test [0.63] [0.69] [0.56] [0.65] Fixed Effects YES YES YES YES

No. Of observations 3,125 3,125 3,125 3,125 __________________________________________________________________________________ Notes: Similar to those in Table 4.

29

Dear Editors of International Economics: May 6, 2019

Please, be informed that in relevance to my submission entitled:

How do human rights violations affect poverty and income distribution?

to be potentially considered for publication by International Economics, the authors confirm that there are no conflicts of interest.

Yours sincerely,

Dr. Nicholas Apergis Professor of MacroFinance University of Derby, UK [email protected]