Finance and inequality: a study of Indian states

Abstract

Although a large literature exists on finance and economic growth, few studies have empirically examined the relationship between finance and inequality. Using grouped national household sample survey data on monthly household consumption expenditure at the sub-national level for the years 1999–2000 to 2006–2007, we examine the relationship between Financial Development (FD) and rural and urban inequality in India. The results indicate that FD is associated with a reduction in inequality, but only in the urban areas. Further, inequality is found to be higher in the richer states compared to less developed and low income states, and as state income increases, inequality also increases both in the rural and urban areas. Finally, our results show that increase in population per bank branch leads to higher inequality in urban areas but decline in rural areas.

Keywords:

• financial development
• inequality
• India
• sub-national level

JEL Classification:

• G28
• G21
• O16
• O53

Acknowledgements

This article was presented in the 2010 Global Development Finance Conference (Valuing Growth Trends in Development Finance), November 24-26, 2010 organized by Africa Growth Institute held at Spier Wine Estate, Stellenbosch, Cape Town, South Africa. The useful feedback received from the conference participants is gratefully acknowledged. I gratefully thank Nicholas Rohde for the statistical inputs provided by him. This study, in its present form, would not have been possible without his help. I also thank Tom Nguyen for his comments on the earlier version of this article. Any errors are solely mine.

Notes

¹ Some of the older literature which discussed the role of finance in development are Gurley and Shaw (1955), Gerschenkron (1962), Patrick (1966) and Goldsmith (1969).

² Although several other factors could be contributing to income inequality, our study focuses on financial factors only. Also in this study we refer to only banks as they are the major financial intermediaries in the country.

³ The institutional sources of credit are government, cooperative societies and banks; among noninstitutional sources are landlords, moneylenders and friends and relatives.

⁴Until 1997, M ₃ was the intermediate target of the monetary policy. However, with financial liberalization, innovations and the reforms, the monetary target was increasingly getting ineffective hence a multiple indicator approach besides M ₃ was adopted in 1997.

⁵ The observations for 2003 are linearly interpolated.

⁶ A number of different techniques were applied to the data including fixed and random effects models and Seemingly Unrelated Regression (SUR). The pooled estimation approach presented, however, yielded the best results. Longitudinal fixed and random effects models were redundant, while cross-sectional fixed effects gave mostly insignificant regressors. Cross-sectional random effects models did not pass Hausman test for convergence to fixed effects estimates.