Applied Econometrics and International Development Vol- 8-2 (2008)

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1 Applied Econometrics and International Development Vol- 8- (008) TRADE LIBERALIZATION AND INCOME INEQUALITY IN INDIA: A POISSON DISTRIBUTED-LAG ANALYSIS AGARWAL, Vertica * PAELINCK, Jean H.P. REINERT, Kenneth A. STOUGH, Roger R. Abstract. This paper addresses the relationship between trade liberalization and income inequality among Indian states using a Poisson distributed-lag analysis. Two measures of income inequality are considered: the Gini coefficient and the coefficient of variation. Trade liberalization is proxied using export growth. Results indicate that there is a different pattern of responses in inequality to export growth between rural and urban households. Rural inequality tends to be more responsive in the hoped-for direction than urban inequality. JEL Classification: D63, F3, F4 Keywords: Trade Liberalization, Income Inequality, Poisson distributed-lag, India.. Introduction The relationship between trade liberalization and income inequality has not been fully sorted out at either the theoretical or empirical levels, with a number of factors (e.g., differences in skill levels, goods prices, technological differences and local factor abundance) all playing contributing roles. This ambiguity is reflected in the argument of Winters, McCulloch and McKay (004) that the impact of trade liberalization on income inequality is both country specific and asymmetric. The particular case of India has been controversial, with some economists arguing that trade reforms have not had a positive impact on economic growth and have not contributed to a reduction of income inequality (e.g, Jha, 000, Mundle and Tulasidhar, 998, and Patnaik, 997). Other observers (e.g., Panagariya, 006) argue that too much focus on inequality is a distraction from the real issue of poverty alleviation. In light of these ambiguities, this paper addresses the relationship between trade liberalization and income inequality among Indian states using a Poisson distributed-lag analysis. Two measures of income inequality are considered: the Gini coefficient and the coefficient of variation. Trade liberalization is proxied using export growth. The Poisson distribution is discrete, its lags being integer numbers, and has the advantage of both being flexible and requiring less a priori information. * Vertica Agarwal, Jean H.P. Paelinck, Kenneth A. Reinert, Roger R. Stough, School of Public Policy, George Mason University, Virginia, USA, Corresponding author: Kenneth A. Reinert, 340 North Fairfax Drive MS 3B, Arlington, VA 0 USA, kreinert@gmu.edu Acknowledgement: We would like to thank Sidhartha Das for helpful comments and discussions. See, for example, Friedrich (98) and Frick (986).

2 Applied Econometrics and International Development Vol- 8- (008). The Model We assess the impact of trade liberalization on income inequality using a distributed-lag model where a normalized Poisson distribution is introduced to the lag equation. This is described as follows: z t a3! 3! 3 a a ut a u t e a 3 = ut + aut ε t In this equation, zt is the growth rate (log difference) of the inequality indicator in period t as measured by either the Gini coefficient or the coefficient of variation. The variable u t is the growth rate of national exports as measured by either nominal national exports or exports as a percent of GDP. The coefficient a measures the effect of an increase in national exports on the inequality indicator. Our hypothesis is that the increased trade that occurs as a result of trade liberalization will be associated with reduced inequality. The coefficient a is the mean of the Poison distributed lag, and coefficient a 3 represents the influence of all non-trade factors on inequality. The data used for measuring income inequality come from the National Sample Survey Organization (NSSO), Ministry of Statistics and Program Implementation, Government of India. Out of 8 states and 7 union territories, the NSSO collects data on 5 states and 7 union territories, with the remaining states and union territories being grouped into 3 categories. Using the NSSO consumer expenditure data, we calculated the coefficient of variation and Gini coefficient measures using the income classes for rural and urban households from 974 to Table gives a sense of these data for India as a whole, and Table provides beginning and end of sample values for Indian states and territories. National exports and GDP data for these years come from the Economic Survey and Handbook of Statistics on the Indian economy. For some pertinent issues regarding the NSSO data, see Sundaram (00) and Datt and Ravallion (00). 3 In the case of the Gini coefficient, we have utilized the Brown (994) formula. 88

3 Table. Urban and Rural Inequality in India Gini Coefficient Coefficient of Variation Period Rural Urban Rural Urban a b a Due to data limitations, the actual period is July 99 to December 99. b Due to data limitations, the actual period is January 99 to December 99. Due to the intractability of the above equation, we approach estimation using a two-step procedure. We first calculate a in a constrained optimization procedure with a non-negativity constraint and then estimate the full equation using a standard OLS regression approach. Our focus then turns to hypothesis testing for coefficient a. 89

4 Applied Econometrics and International Development Vol- 8- (008) Table. Urban and Rural Inequality in Indian States Gini Coefficient Coefficient of Variation Rural Urban Rural Urban State Andhra Assam Bihar Gujarat Haryana Karnataka Kerala Madhya Maharashtra Orissa Punjab Rajasthan Tamil Nadu Uttar West Bengal North Eastern NW+GUT All India Estimation Results A summary of estimated results for the effect of nominal export growth on income inequality in Indian states, given the optimum Poisson lag, is presented in Table 3. The table considers this for both the Gini coefficient and the coefficient of variation measures, and for rural vs. urban India. Mean Poisson lags range from zero to. for the Gini coefficient and from zero to.3 for the coefficient of variation. As can be seen in the table, in most cases, the coefficient a is between and, indicating that exports in previous years had the greatest impact on the inequality measures. Also, there is a tendency for the optimal lag to be smaller for the urban inequality indicators, with more of the lags taking on the value of zero. 90

5 Table 3. The Estimated Effects of Exports on Inequality Measures in Indian States Gini Coefficient Coefficient of Variation Rural Urban Rural Urban States Effect Lag Effect Lag Effect Lag Effect Lag Andhra -.743* *.49* Assam Bihar Gujarat ** 3.06** Haryana **.894** Karnataka * Kerala -.703* ** Madhya * Maharashtra * Orissa -.857* * Punjab * * Rajasthan * Tamil Nadu * Uttar West Bengal North Eastern -.30* * NW+GUT * All India -.374* * *.8 Notes: * denotes significance at the 5 percent level, and ** denotes significance at the percent level. The hypothesis that trade liberalization has reduced income inequality in India will be accepted if the a coefficient is both negative and statistically significantly. The first thing to notice with regard to the results of Table 3 is that for nearly every Indian state, the estimated sign of the coefficient a is negative for rural India for both the Gini coefficient and coefficient of variation measures of income inequality. Only rural Uttar under the Gini coefficient measure and rural West Bengal under the coefficient of variation measure have positive a coefficients. While most of these negative coefficients are not statistically significant, and we therefore cannot accept the hypothesis in these cases, the robustness of the negative coefficient results for rural Indian states across both inequality measures is notable. 9

6 Applied Econometrics and International Development Vol- 8- (008) Results for urban Indian states are significantly more mixed. Under the Gini coefficient measure, most Indian states have positive a coefficients. Under the coefficient of variation measure, most Indian states have negative a coefficients, although only three of these are statistically significant. Clearly, there is evidence here of rural and urban Indian states responding differently to export expansion. Table 4 repeats the exercise of Table 3 using exports as a percent of GDP rather than nominal exports. Mean Poisson lags range from zero to 4.9 for the Gini coefficient and from zero to 5. for the coefficient of variation. Again the coefficient a tends to be between and and to be smaller for urban inequality indicators. Table 4. The Estimated Effects of Exports as a Percentage of GDP on Inequality Measures in Indian States Gini Coefficient Coefficient of Variation Rural Urban Rural Urban States Effect Lag Effect Lag Effect Lag Effect Lag Andhra Assam 0.47** ** Bihar Gujarat Haryana Karnataka Kerala Madhya Maharashtra Orissa ** ** Punjab * Rajasthan * Tamil Nadu Uttar ** 0.373** West Bengal North Eastern NW+GUT All India Notes: * denotes significance at the 5 percent level, and ** denotes significance at the percent level. 9

7 As in Table 3, the estimated sign of the coefficient a in Table 4 is usually negative for both the Gini coefficient and the coefficient of variation measures of inequality. However, there are fewer instances of statistical significance using exports as a percent of GDP. Interestingly, using this measure of export growth, it is more likely for the a coefficient to be negative than is the case in Table 3 (particularly for the Gini coefficient measure), although again there is a distinct lack of statistical significance. In interpreting the results of Tables 3 and 4, it is important to keep in mind that not all economic activity in the rural segments of Indian states is strictly agricultural. It is possible that the rural non-farm sector (e.g., Reinert 998) is also involved in the response of inequality to export growth. The results of Ravallion and Datt (00) confirm that, in some instances at least, the rural non-farm sector can play a role in poverty outcomes across Indian states. It would not be a surprise if this were also the case for inequality outcomes as measured here. 4. Conclusion Variations in the effects of trade liberalization across Indian states remain a key topic of research. For example, Srinivasan and Tendulkar (003) comment that disparities among states could increase and threaten the stability of India as a federal nation (p. 40). Reasons cited for disparities among Indian states in being able to take advantage of reforms draw attention to initial conditions such as skilled labor, infrastructure, and good governance. Whatever the reason, the success of India s integration with the global economy has become dependent on the states. There is also an emerging consensus that India is in need of further reforms. This has been argued, for example, by Delong (003), Srinivasan and Tendulkar (003) and Panagariya (forthcoming). Given the need for further reforms and the crucial role of state experience in the political economy of the Indian federation, it is worthwhile to examine the impacts of past reforms, and the trade responses they entail, on state-level inequality. This paper has done so using the Poisson distributed-lag methodology. Our results indicate that there tends to be a different pattern of responses in inequality to export growth between rural and urban households, both as measured by the Gini coefficient and the coefficient of variation. Rural inequality tends to be more responsive in the hoped-for direction than urban inequality. Given concerns with poverty in the rural areas of India, this might be welcomed, although the political economy of the urban populations and their attitudes toward reform is also important. The Poisson distributed-lag methodology has not been widely employed in the trade and development literature, and this paper suggests that it might indeed have applicability in sorting out some dynamics of interest. References Brown, C.M. (994) Using Gini-style indices to evaluate the spatial patterns of health practitioners: Theoretical considerations and an application based on Alberta data, Social Science and Medicine, 38,

8 Applied Econometrics and International Development Vol- 8- (008) Datt, G. and M. Ravallion (00) Is India s economic growth leaving the poor behind? Journal of Economic Perspectives, 6, Delong, J.B. (003) India since independence, in In Search of Prosperity: Analytic Narratives on Economic Growth (Ed.) D. Rodrik, Princeton University Press, Frick, H. (986) A note on a dynamic adjustment equation for a Poisson distributed lag model, Empirical Economics,, Friedrich, D. (98) A dynamic adjustment equation for a Poisson distributed lag model, Empirical Economics, 7, Jha, R. (000) Reducing Poverty and Inequality in India: Has Liberalization Helped? World Institute for Development Economics Research. Mundle, S. and V.B. Tulasidhar (998) Adjustment and Distribution: The Indian Experience, Asian Development Bank. Panagariya (006) The pursuit of equity threatens poverty alleviation, Financial Times, 3 May 006. Panagariya, A. (forthcoming) India: An Emerging Giant, Oxford University Press. Patnaik, P. (997) The context and consequences of economic liberalization in India, Journal of International Trade and Economic Development, 6, Ravallion, M. and G. Datt (00) Why has economic growth been more pro-poor in some states of India than others? Journal of Development Economics, 68, Reinert, K.A. (998) Rural nonfarm development: A trade theoretic view, Journal of International Trade and Economic Development, 7, Srinivasan, T.N. and S.D. Tendulkar (003) Reintegrating India with the World Economy, Institute for International Economics. Sundaram, K. (00) Employment and poverty in the 990s: Further results from NSS 55 th round employment-unemployment survey, , Economic and Political Weekly, 36, August -7, Winters, L.A., N. McCulloch and A. McKay (004) Trade liberalization and poverty: The evidence so far, Journal of Economic Literature, 4, 7-5. Journal published by the EAAEDS: 94