Abstract
This paper refines and tests the hypothesis that the impact of opening to trade on a country’s economic growth is affected by the inequality of its distribution of wealth. Analysis of panel data on 44 developing countries between 1960 and 2000 suggests that the difference in growth rates between the period an economy is open and the period it is closed depends inversely on the degree of wealth inequality prior to opening. There is evidence to suggest that access to credit and lack thereof may lie behind these results, thus highlighting a new aspect of the role of financial development.
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Notes
Inequality has also been found to affect growth through political economy, social and political instability and health channels, although this last one remains debated (see Deaton 2003).
These variables also control for changes in the savings rate between the two periods and thus make redundant the inclusion of the difference in investment rates between the two periods. As a sensitivity test, changes in investment will be included as an additional control, although doing so does not alter the results.
The countries included in the sample are: Argentina, Barbados, Bolivia, Brazil, Cameroon, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Ghana, Guatemala, Honduras, Indonesia, Israel, Jamaica, Jordan, Kenya, Malaysia, Mali, Mauritius, Mexico, Mozambique, Nepal, Nicaragua, Niger, Panama, Paraguay, Peru, Philippines, Romania, Singapore, South Africa, South Korea, Sri Lanka, Taiwan, Trinidad and Tobago, Tunisia, Turkey, Uganda, Uruguay, Venezuela and Zambia.
Using land inequality as a proxy for wealth inequality may be criticised on the grounds that, if land is heterogeneous in terms of productivity, a competitive land rental market and free entry will lead to land inequality but no wealth inequality because lower-productivity land will be concentrated into larger holdings (Lipton et al. 2009). While this problem can be solved by regressing land inequality on average farm size, from Vollrath (2007), and then using the residuals of such regression as the measure of inequality, in this sample no significant relationship between land inequality and average farm size is found.
Following the identification strategy by Acemoglu et al. (2001), the log of the early settlers’ mortality rate is also considered as a measure for the quality of institutions. The argument for using this latter measure is that Europeans adopted very different policies in different colonies, with different associated institutions, depending, at least partly, on whether Europeans could settle in the colony. In places where Europeans faced high mortality rates, they could not settle and they were more likely to set up worse (extractive) institutions. This legacy of early institutions has persisted to the present. However, early settlers’ mortality rates are time invariant and therefore cannot be used as a standalone variable, but can be used as an instrument for changes in the Civil Liberties Index. Results show that this is not an informative instrument and, therefore, all the regressions that include the early settlers’ mortality rate are not reported.
A dummy variable from this variable (dbmp) is constructed following the cutoff point considered by Sachs and Warner (1995), equal to 1 if a country in a certain year has a black market exchange rate premium <20% and 0 otherwise. Including this variable does not alter the results significantly when the OLS estimator is used, while the IV approach is not feasible because the coefficient estimated in a single regression of changes in dbmp between t and t − 1 on the level of dbmp at t − 2 is not statistically significant. For the same reason, the rate of inflation—a proxy for macroeconomic management, like the black market premium—cannot be used in the IV approach. Thus, these additional results are not reported.
Two other measures were used in additional regressions, private credit by deposit money banks to GDP and private credit by deposit money banks and other financial institutions to GDP, but the results do not differ significantly and are, therefore, not reported.
Casual evidence supports this claim since many Latin American countries with high levels of land inequality opened to trade after the 1980’s debt crisis hit the region (Goldberg and Pavcnik 2007).
Eliminating five or more years before and after liberalisation worth of data makes the sample too small to be able to make statistical inference.
Additional regressions, available upon request, have been run by replacing T n with a vector of dummy variables for regional categories. These regressions confirm that a country’s attitude towards trade liberalisation may be influenced by its neighbours, yet they do not show any correlation between the year of liberalisation and the level of land inequality.
The analysis of value chains is interesting insofar as they are driven by changes at the global level but have impacts at the local level in terms of employment and poverty.
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Acknowledgments
I thank Adrian Wood for many helpful comments and discussions. I am also grateful to Christopher Adam, Christopher Bowdler, Alasdair Smith, Tony Venables, Alan Woodland and seminar participants at the Gorman Workshop at the University of Oxford, at the ESRC Development Economics Conference at the University of Sussex and at the Conference on Poverty Traps at the University of Naples Parthenope for useful comments. I gratefully acknowledge that part of this research was financially supported by an Australian Research Council grant to Professor Alan Woodland. All errors are mine.
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Caselli, M. Does wealth inequality reduce the gains from trade?. Rev World Econ 148, 333–356 (2012). https://doi.org/10.1007/s10290-012-0119-0
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DOI: https://doi.org/10.1007/s10290-012-0119-0