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Tariff Reduction and Income Inequality: Some Empirical Evidence

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Abstract

This paper explores the relationship between tariff reductions and income inequality for 37 countries over the period 1984 to 2010, a period of extensive trade liberalization. Using panel data techniques we find that a permanent reduction in the tariff rate will significantly increase short-run income inequality. To obtain further insight into how the distribution of income is affected, we also estimate the impact of tariffs on income shares by quintiles. We find that the relative income of the lowest quintile is the most adversely affected, while the greatest beneficiaries are the agents in the second richest quintile. By adopting a panel data approach, and including a wide range of control variables, we are confident that these findings reflect causal effects rather than merely reflecting spurious correlations. We also find that reducing tariffs will likely increase long-run income inequality, although these results are less conclusive. The empirical evidence provides some support for the proposition that the speed with which, and the initial level from which, the tariff is adjusted affects income inequality. Finally, our empirical analysis confirms the conventional result that tariff reductions have an expansionary effect on aggregate output. This suggests that tariff reduction involves at least a short-run tradeoff between increasing the level of economic activity coupled with more income inequality.

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Notes

  1. Heterogeneity is critical in studying inequality. While there are many sources of heterogeneity we focus on initial wealth endowments as most critical; see e.g. Piketty (2011).

  2. Rojas-Vallejos and Turnovsky (2015) also establish a theoretical result describing the impact of tariffs on wealth inequality. While the evolution of wealth inequality is a key driving force behind the evolution of income inequality, paucity of wealth inequality data prevent us from examining this relationship in the cross-country setting.

  3. Caselli and Ventura (2000) call this a “representative consumer theory of distribution”.

  4. The explanations for the negative relationship include: the political economy consequences of inequality, the negative impact of inequality on education, capital market imperfections and credit constraints. Explanations for the positive relationship include: the relative savings propensities of rich versus poor, investment indivisibilities, and incentives.

  5. Katsimi and Moutos (2010) provide evidence that supports channels of simultaneity between inequality and tariff rates. Hence, the potential endogeneity of the tariff should be addressed.

  6. To analyze the data correctly, correlations between observations from the same country need to be taken into account. If not, the standard errors of the estimates can be substantially downwardly biased, leading to potentially invalid inferences. The intra-class correlation (ICC) in our case for the Gini data is 0.92, while for the tariff rate is 0.44. This is a high ICC considering that a number above 0.02 may cause some problems.

  7. See González et al. (2005). In Section 5.2.2 below, we provide a brief description of this method.

  8. Trade openness is defined as the ratio of exports and imports of goods and services to GDP. These data are from the World Development Indicators (WDI) database.

  9. Applied tariff rate data are from the Data on Trade and Import Barriers database at the World Bank compiled by Francis K. T. Ng, consisting of 170 countries for the period 1981 to 2010.

  10. In testing this hypothesis we shall focus primarily on a debtor nation since more than 80% of the panel is formed by such economies. In addition, the few creditor nations were debtor nations in several of the years of the period analyzed.

  11. We try to perform instrumental variable PSTR but the results are insignificant and with different signs. This is due to the low quality of the available instruments.

  12. These results are presented in the Appendix. We choose not to discuss in detail this finding in the body of the article since there is a large literature on the topic. See Alesina and Rodrik (1994), Forbes (2000), Piketty and Saez (2014) and the references therein.

  13. External validity concerns are important in this respect because most of the literature on the relationship between growth and inequality provided different signs on this relationship depending upon methods or samples. This discrepancy in signs suggests that the effect must be highly heterogeneous rather than homogeneous which is the dominating assumption in most of the existing literature.

  14. This dataset is updated and improved in quality, coverage and comparability relative to the Deininger and Squire (1997) dataset.

  15. The SWIID and Lane and Milesi-Ferretti datasets are updated to 2013.

  16. Similar indices from the Democracy Barometer were employed but results did not change substantially, just the sample size was reduced. Hence, we opt for the Freedom House (2015) indices that cover a longer period of time.

  17. See also Bergh and Nilsson (2010), Milanovic and Squire (2005) and the references therein.

  18. See Forbes (2000), Voitchovsky (2005), Bergh and Nilsson (2010) and Jaumotte et al. (2013).

  19. The point is that unemployment falls more on low-income workers. This is discussed by Galbraith (1998) and Carpenter and Rodgers (2004).

  20. The Box-Cox transform suggests the functional forms adopted for the different variables. For more discussion of this issue see Box and Cox (1964) and Aneuryn-Evans and Deaton (1980).

  21. Some of the most significant ones include: the early 1980s recession, the collapse of the Soviet Union in the early 1990s, the Asian Crisis in 1997 and the Great Recession starting in 2008.

  22. For a more extensive discussion on this see Griliches and Hausman (1986) and Bound and Krueger (1991).

  23. Fixed effects are preferred to the random effects specification due to the nature of the problem and the fact that the Hausman test strongly suggests the use of the former.

  24. See also Granger and Teräsvirta (1993).

  25. We also estimate a naïve regression of only tariffs on inequality and we obtain a negative sign that is not statistically significant, but the order of magnitude is similar to the ones reported in Table 1.

  26. The major problem is that for a given country some variables are measured during a given period but others are not. This excludes all other observations for that period, which reduces the overall number of observations.

  27. We use the hypothesis tests developed by Kleibergen and Paap (2006) and by Hansen (1982), respectively.

  28. In the case of a creditor economy, they find that following its temporary decline, income inequality takes a few years to increase by approximately 2%.

  29. This number is obtained multiplying the parameter estimate by the 10-percentage points tariff reduction and dividing that by the mean share of income of this quintile.

  30. According to González et al. (2005) it is usually sufficient to perform the analysis with m ∈ {1, 2}.

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Acknowledgements

Rojas-Vallejos’s research was supported in part by the BecasChile Scholarship Program of the Chilean Government and the Grover and Creta Ensley Fellowship of the University of Washington. Turnovsky’s research was supported in part by the Van Voorhis endowment at the University of Washington. We thank Chris Papageorgiou for his constructive suggestions. Also, comments received at the 2014 Conference of the Association of Public Economic Theory, the 2014 Annual Conference of the Chilean Economic Association, the 4th International Economic Conference of the Turkish Economic Association and the 2015 Society for Computational Economics conference are gratefully acknowledged.

Author information

Authors and Affiliations

  1. Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

    Jorge Rojas-Vallejos

  2. University of Washington, Seattle, WA, USA

    Stephen J. Turnovsky

Authors
  1. Jorge Rojas-Vallejos
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  2. Stephen J. Turnovsky
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Correspondence to Stephen J. Turnovsky.

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Appendices

Data Appendix

A.1 Inequality

The income Gini data come from the All The Ginis (ATG) dataset compiled by Milanovic (2014). These consist only of the Gini coefficients that have been calculated from actual households surveys and it contains Gini’s estimated from expenditures surveys and income surveys at the national level. It includes no Gini estimates produced by regressions or imputations. Milanovic compiles these data from nine different sources: the Luxembourg Income Study (LIS), the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), the Survey of Income and Living Condition (SILC), the World Bank’s Eastern Europe and Central Asia (ECA), the World Income Distribution (WYD), the PovcalNet from the World Bank, the World Institute for Development Research (WIDER), the Economic Commission for Latin America and the Caribbean (CEPAL), and Individual data sets (INDIE). Notice that he excludes data from (Deininger and Squire 1997) because they have been either superseded or included in WIDER. In order to analyze income transfers between different groups, we also collect data on income shares by quintiles. We use the World Income Inequality Dataset (WIID) provided by the United Nations, version 3.0.b. As a robustness check of our findings we also use the dataset provided by Solt (2009) that uses imputed data standardized with the LIS methodology.

A.2 Trade

Data on tariff rates are obtained from the Data on Trade and Import Barriers database at the World Bank (2015) compiled by Francis K.T. Ng consisting on 170 countries for the period 1981 to 2010.

A.3 International Finance

We use the data provided by Lane and Milesi-Ferretti (2007).

A.4 Financial Development

We use the data provided by the World Bank in the Global Financial Development Database (GFDD) by Cinak et al. (2012).

A.5 Education

The data on educational attainment are obtained from Barro and Lee (2001, 2013). We pay particular attention to the impact on inequality of the share of the population with at least secondary and the average years of education as has been widely discussed in Jaumotte et al. (2013) and Li et al. (1998).

A.6 Political System

The information about the political system is obtained from two different sources. We obtain data on political rights and civil liberties from the Freedom House (2015). The Freedom House scale ranges from 1.0 (free) to 7.0 (not free).

A.7 Other Macroeconomic Variables

Data on unemployment and share of employment by sectors and added value by sectors are obtained from the World Development Indicators (WDI) at the World Bank. Relative labor productivity is computed using the standard definitions. Data on the contribution of Information and Communications Technologies (ICTs) capital services to GDP growth are obtained from Jorgenson and Vu (2005, 2007).

A.8 Countries

We use the division of territories and income provided by the World Bank. The world is divided in eight regions: Latin America and the Caribbean, Sub-Saharan Africa, Central and Eastern Europe, Commonwealth of Independent States, Developing Asia, Middle East and North Africa, North America, and Western Europe. Income groups are divided in four groups: low income, $610 or less (L); low-middle income, $611-$2,465 (LM); upper-middle income, $2,466-$7,620 (UM); and high income, $7,621 or more (H). We use the income classification assigned by the World Bank in year 1990 in dollars of that year.

The countries involved in this empirical study are the following. The number of observations available on income inequality and tariffs are given in the parentheses. Algeria (1), Angola (1), Argentina (23), Australia (9), Azerbaijan (3), Barbados (1), Belarus (2), Belize (2), Bolivia (14), Brazil (27), Bulgaria (12), Canada (9), Chile (19), China (21), Colombia (21), Costa Rica (18), Croatia (2), Cyprus (2), Czech Republic (8), Dominican Republic (11), Ecuador (15), Egypt (1), El Salvador (18), Estonia (9), Gambia (1), Guatemala (8), Guyana (1), Haiti (1), Honduras (12), Hungary (11), Iceland (4), Israel (7), Jamaica (5), Japan (7), Jordan (1), South Korea (8), Latvia (6), Lithuania (2), Macedonia (1), Malaysia (6), Mauritania (1), Mexico (13), Namibia (1), Nepal (1), New Zealand (4), Nicaragua (4), Nigeria (1), Norway (9), Panama (11), Paraguay (14), Peru (16), Poland (15), Romania (8), Russia (14), Serbia (4), Singapore (7), Slovak Republic (7), Slovenia (4), South Africa (3), Sri Lanka (2), Suriname (1), Switzerland (4), Taiwan (16), Tanzania (1), Trinidad and Tobago (2), Tunisia (1), Turkey (2), Uganda (1), Ukraine (1), United States (22), Uruguay (17), Uzbekistan (2), and Venezuela (18).

Descriptive Statistics

Table 5 Income inequality and tariff reduction panel regressions Dependent Variable: Logarithm of Income Gini
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Table 6 GMM estimation. Dependent variable: logarithm of income Gini
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Table 7 The effect of income inequality on output. Dependent variable: logarithm of income per-capita
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Table 8 Output panel regressions with no inequality. Dependent variable: logarithm of income per-capita
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Table 9 Summary descriptive statistics
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Table 10 Summary for income share by quintiles
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Rojas-Vallejos, J., Turnovsky, S.J. Tariff Reduction and Income Inequality: Some Empirical Evidence. Open Econ Rev 28, 603–631 (2017). https://doi.org/10.1007/s11079-017-9439-y

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