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Trade, Politics, and the Poor: Is Sen Right and Bhagwati Wrong?

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An Erratum to this article was published on 17 January 2017

Abstract

The current debate between two of the world’s finest economists—Amartya Sen and Jagdish Bhagwati—has not only roiled India but also attracted global attention. Is trade liberalization associated with improved welfare outcomes for the poor, as Bhagwati contends? Or is Sen correct that policymakers in liberalizing economies need to change their governance priorities to focus on redistribution? This analysis draws on existing literature to develop testable hypotheses that attempt to resolve the Sen–Bhagwati divide. Using fixed effect panel regressions and simultaneous equation models, we find that although there is empirical support for both arguments, the results on balance favor Sen: The positive relationship between trade and improved poverty is conditional upon more equitable distributions of income. In effect, Bhagwati’s predictions about the beneficial impacts of openness on social welfare occur only in a subset of developing nations, findings which have very different implications for the poor in developing countries.

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Notes

  1. New York Times “Rival Economists in Public Battle Over Cure for India’s Poverty” August 21, 2013.

  2. Amartya Sen refers to this as creating “two Indias,” one for the rich and one for the poor. See “Two Indias” New York Times. September 6, 2013.

  3. Aisbett (2007, p. 40) persuasively argues that cross-country comparisons are useful for examining the trade-poverty relationship because “it is very difficult to prove in the case of an individual country exactly which factor or combination of factors was responsible for its success or lack thereof.”

  4. Both infant mortality and life expectancy are used as broad measures of poverty (e.g., Ross 2006; McGuire 2010); they serve as proxies for the quality and presence of health and welfare programs, poverty and socioeconomic status, availability of clean drinking water, and environmental and social barriers that prevent access to basic health services.

  5. Some development scholars argue that the standard measures can improve without having any meaningful impact on the livelihoods (and freedoms) of the poor (Nussbaum and Sen 1993; Sen 1999).

  6. See Acemoglu and Robinson’s (2013) discussion of how various economists consider that good economic policies will overcome and often eliminate government inefficiencies. For example, Bhagwati and Panagariya (2013) argue that the policy focus should be on reducing industrial tariffs, agricultural protections, etc. Once overall productivity improves government can play a small but efficient role in directing social policies.

  7. The bottom line is that the recent trend of inequality research has focused on institutions that privilege power rather than emphasizing supply and demand. Even Piketty has recently backtracked on his central thesis in Capital in the Twenty-First Century to argue that “institutional changes and political shocks—which to a large extent can be viewed as endogenous to the inequality and development process itself—played a major role in the past and it will probably be the same in the future” (Piketty 2015, p. 48).

  8. Their logic is supported by two strands of empirical work in political economy: (1) studies finding that public goods are undersupplied when inequality is high (Easterly 2007; Perotti 1993; 1996; Waldmann 1992); (2) scholars providing evidence that high inequality is associated with the persistence of weak conflict-mediating institutions such as an ineffective (or absent) judiciary, corrupt bureaucracy, unbalanced media, and civil liberties and political rights which are arbitrarily administered (Easterly et al. 2006; Rodrik 1999).

  9. See Chandra (2002) and Blouin et al. (2007) for examples. Their analyses suggest that trade hurts the poor as knowledge spillovers in health lead to demands for specialized health care. This occurs through greater imports and domestic reproduction of health supplies, pharmaceuticals, equipment, insurance, and direct health services such as cross-border telehealth services (e.g., telediagnostics), movement of consumers to countries providing diagnosis and treatment, and cross-border movement of health care providers.

  10. We thank the authors for providing their dataset and replication files.

  11. Unless noted otherwise, the variables used in the analysis come from the Owen and Wu (2007) dataset. We updated this dataset for the years 2000 and 2005 by using data from Penn World Table 7.1 (Heston et al. 2012), from which the original data were also obtained.

  12. Owen and Wu utilize life expectancy by sex, while we use a combined measure.

  13. There may be some question as to how representative national life expectancy rates are of the health of the poor since the national rates reflect the entire population, including all income groups. Since poor countries are populated primarily by low-income individuals, however, the national rates should fairly closely mirror the actual rate among the nation’s poor.

  14. Bhagawati and Panagariya (2013, p. 183) argue the best way to improve the poor’s health conditions is to make cash transfers to senior female household members.

  15. Available at http://www.barrolee.com/. Accessed July 24, 2009.

  16. We multiply Ethnic Fractionalization by 100 to generate an index between 0 and 100.

  17. An effective instrument must be correlated with the endogenous variable but have no direct relationship with the dependent variable (Savun and Tirone 2011, p. 239). Electricity production satisfies the first criterion, as it is has been widely shown to be highly correlated with economic growth, even if the causal relationship between the two is still under analysis (Payne 2010). Although the second consideration, the exclusion restriction, is inherently untestable, existing research indicates that electricity consumption satisfies this requirement by having a direct impact on economic growth and an indirect impact on social development (see Leung and Meisen 2005).

  18. This data includes interpolated observations of income distribution for some of the years with missing observations, which is not uncommon in studies utilizing inequality data (see, for example, Rueda and Pontusson 2000). Missing observations for the period up to and including 1995 are interpolated based on the assumption that any changes between two observations are evenly distributed across the interval separating the two. Observations after 1995 use averages for years 2000 and 2005 incorporating all available observations in the preceding 5 years.

  19. The SERF index captures the degree to which a country has achieved the highest level of social and economic rights possible given its capacity. It does so by comparing estimates of what a country should be able to achieve (in the form of an estimated “Achievement Possibility Frontier”) to its observed values in each area. The difference between the two values is a reflection of the inclusiveness and effectiveness of the state’s policies and institutions. Available at http://www.serfindex.org/overview, accessed March 2, 2015.

  20. For a description of the SERF Index methodology, see Fukuda-Parr et al. (2009) and Randolph et al. (2001).

  21. As a measure which is slow to change over time, income inequality is highly correlated with both country and year fixed effects. The use of both of these fixed effects, as we do in models 1–1 through 2–2, would therefore risk type I bias by artificially attenuating the estimated coefficient on income inequality. For models 3–1 and beyond, we therefore drop the year fixed effects while maintaining those for individual countries.

  22. Due to the small sample sizes (75 observations) and the fact that most countries only have one observation, we drop country fixed effects from the models. We also omit the top and bottom 5 % of the distribution of the full SERF dataset to reduce the influence of outliers of the estimates, although the observed trends are consistent when these observations are included.

  23. We also confirm that the estimated conditional marginal effects are statistically different from one another by using post-estimation testing.

  24. The two sets of estimations differ upon whether trade can actual be harmful; openness does not have negative effects on poverty outcomes in the inequality models but appears to have detrimental effects on well-being when SERF is low.

  25. Narendra Modi unveils broad policy priorities, asks ministers to set 100-day agenda. (2014, May 29). The Hindustan Times. Retrieved from http://search.proquest.com/docview/1529942160?accountid=11091.

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Authors and Affiliations

  1. Department of Government, Georgetown University, Intercultural Center (ICC) 594, Washington, DC, 20057, USA

    Nita Rudra

  2. Department of Political Science, Louisiana State University, 240 Stubbs Hall, Baton Rouge, LA, 70803, USA

    Daniel C. Tirone

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  1. Nita Rudra
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Correspondence to Nita Rudra.

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An erratum to this article is available at http://dx.doi.org/10.1007/s12116-017-9234-1.

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Rudra, N., Tirone, D.C. Trade, Politics, and the Poor: Is Sen Right and Bhagwati Wrong?. St Comp Int Dev 52, 1–22 (2017). https://doi.org/10.1007/s12116-016-9231-9

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