The Impact of Trade Openness on Regional Inequality: The Cases of India and Brazil

Abstract

Regional inequalities are large in India and Brazil and represent a development challenge. This article aims to determine whether regional inequalities are linked to a country's trade openness. An annual indicator of regional inequalities is constructed for India for the period 1980–2004 and for Brazil from 1985–2004. Results from time series regressions show that Brazil's trade openness contributes to a reduction in regional inequalities. The opposite result is found for India. India's trade openness is an important factor aggravating income inequality among Indian states. In both countries, inflows of foreign direct investment are found to increase regional inequalities.

Keywords:

• trade openness
• regional inequality
• India
• Brazil
• time series regression

Notes

¹ Source: The Hindu, national newspaper, August 15, 2007.

² Source: http://www.brazil.org.uk/press/speeches-files/20061031.html.

³ Two I(1) time series (Xt and Yt) are cointegrated if the series of residuals collected from the regression of the dependent variable Yt on the explanatory variable Xt is stationary.

⁴ The Durbin-Watson test finds no autocorrelation in the estimations of Tables 3 and 4. The Breusch-Godfrey test that must be used in estimations with lagged dependent variables among the explanatory variables finds autocorrelation in all estimations of Tables A1, A2, and A3, excepted in column 3 of Table A2. According to Keele and Kelly (2006), OLS estimation does relatively well when autocorrelation is low, inferior to 0.50 in absolute value. The degree of autocorrelation, φ, is calculated and reported for each estimation of Tables A1, A2, and A3. Results show that autocorrelation is low in Table A2 and shouldn't affect the results on India. Autocorrelation is higher for Brazil. Nevertheless, OLS estimation in column 4 of Table A3 reports a autocorrelation equal to 0.51, just at the limit. This estimation confirms the results on Brazil and reports a significant and negative coefficient on the trade openness variable.

⁵ The calculation is the following: The Gini index of regional inequality in log will respond by decreasing a total of −0.19 unit (column 1 of Table 3) at a rate of 69% per year (column 2 of Table 3). The log of Gini index will decrease at 0.13 (0.19*0.69) at year t, then another 0.04 (0.06*0.69) at year t+1, then another 0.013 at year t+2, until the change in trade openness at t-1 has no effect on regional inequality.

⁶ Source: Banco Central do Brazil, http://www.bcb.gov.br/rex/censo1995/ingl/980527/hics0400.asp.

⁷ Source: http://www.indiabudget.nic.in/es2003-04/chapt2004/chap76.pdf8.

⁸ “[W]ith development and the passage of time, a country's economic geography approximates a natural balance that equalizes welfare between urban and rural residents,” Chapter 1, “Density” (World Bank 2009).

⁹ Growth equations are estimated using panel data, including indicators of infrastructure as explanatory variables.