The decline of rural earnings inequality
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Ashok Kotwal, Bharat Ramaswami and Wilima Wadhwa, in their paper Economic Liberalization And Indian Economic Growth: What’s The Evidence?, point to the existence of two Indias: “One of educated managers and engineers who have been able to take advantage of the opportunities made available through globalization and the other—a huge mass of undereducated people who are making a living in low-productivity jobs in the informal sector—the largest of which is still agriculture.”
This column is about the second India that resides mainly in its rural parts where agriculture is still the mainstay. In 2011, the employment shares of agriculture, industry and services were 49%, 24% and 27%, respectively, whereas their shares in gross value added were 19%, 33%, and 48%, respectively. And between 2004-05 and 2011-12, real gross domestic product (GDP) in these sectors grew at 4.2%, 8.5% and 9.6% per annum, respectively. Given these figures, we examine to what extent those at the bottom in rural India have benefited from the high overall GDP growth.
We use 2004-05 and 2011-12 data from the nationally representative Employment Unemployment Surveys conducted by the National Sample Survey Office (NSSO) to study wage earners between the ages of 15 and 64 living in rural areas. In both years, they constituted a quarter of the rural working-age population; about 104 and 118 million paid workers in 2004-05 and 2011-12, respectively.
Over the seven-year period, the earnings distribution shifted to the right and became less dispersed. The average real (2004-05 prices) weekly earnings increased from Rs391 to about Rs604. For 2004-05, the all-India official rural poverty line was Rs447 per capita per month. Thus, in 2004-05, the average real monthly earning was 3.5 times the poverty line, and in 2011-12, it was 5.4 times this value.
The accompanying chart plots the percentage increase in real weekly earnings at each percentile over the seven-year period as the black bold line (total change). As can be seen, percentage increases were greater at the lower end of the distribution, revealing that earnings inequality declined over this period. The Gini coefficient for real weekly earnings also captures this decline, from 0.462 to 0.396.
The figure also shows the results of the decomposition of the change in the (log) real earnings at various percentiles. A decomposition essentially divides the observed change into two parts by constructing an artificial earnings distribution that combines worker characteristics (such as the share of male workers, and the shares of workers with different levels of education), as observed in 2004-05, with the rates of return (such as how the labour market rewards males versus females, or how it rewards illiterates, high- schoolers and college graduates) as observed in 2011-12.
Consequently, the difference between the 2004-05 distribution and the artificial distribution gives us the structure effect, that is the part arising due to changes in the rates of return keeping the distribution of characteristics fixed at 2004-05 levels; while the difference between the artificial distribution and the 2011-12 distribution gives the composition effect, that is the part due to changes in the distribution of worker characteristics keeping the rates of return fixed at 2011-12 rates.
In the figure, the dashed line representing the structure effect closely follows the bold total change line, is in the positive domain, and is downward sloping. From this one can conclude that most of the decline in inequality occurred because the returns improved a lot more for low earners (typically low-skilled, women, illiterates) than for high earners (typically high-skilled, men, college graduates). On the other hand, although changing characteristics did lead to an improvement in real earnings at all percentiles, they had an inequality-increasing effect (the dotted line is in the positive domain but is upward sloping at higher percentiles). Thus, if the “wage structure” had been held constant over the period, earnings inequality would have risen due to the change in worker characteristics.
Deeper analysis of the composition effect reveals that the inequality-increasing effect was driven mainly by changes in the distribution of education among paid workers: Over the seven-year period, the share of illiterates decreased from 45% to 35.6%, while shares of all other levels of education, ranging from primary to college and beyond, increased. On the other hand, the change in the industrial composition, arising mainly from a shift from agriculture to construction, led to decreased earnings inequality. Further exploration of the structure effect reveals that the inequality- decreasing effect was driven by lower returns to higher levels of education for workers at the top end of the earnings distribution.
To sum up, for wage earners in rural India, we find that real earnings increased at all percentiles between 2004-05 and 2011-12. Our analysis also reveals that inequality among them fell over this seven-year period. Regardless of the underlying causes of the recent decline in earnings inequality in rural India, volatility in global crop prices and the drought conditions recently experienced by large parts of the country owing to two consecutive weak monsoons are important reminders that policies designed to foster the employment opportunities and wage growth of unskilled workers outside agriculture are crucial for improving the economic well-being of the rural workforce in India.
This research received funding from the European Community’s Seventh Framework Programme (FP7/2007–13) under grant agreement No. 290752. The views expressed do not reflect those of Statistics Canada or other institutions that the authors are affiliated to.
Published with permission from Ideas For India (Ideasforindia.in), an economics and policy portal.
Shantanu Khanna, Deepti Goel AND René Morissette are, respectively, a PhD student at the University of California, Irvine; assistant professor at the Delhi School of Economics, and senior labour economist at Statistics Canada.