This paper presents background information to the assessment of competitiveness and exchange rate policy in India, as well as challenges to monetary policy from financial globalization. This paper discusses the role of communication in enhancing the effectiveness of monetary policy and strengthening the financial system in India. Currency derivatives can provide important benefits for financial systems. This paper aims to document the extent to which Indian growth has benefited the bottom of the income distribution and how India can achieve significantly better social outcomes.

Abstract

This paper presents background information to the assessment of competitiveness and exchange rate policy in India, as well as challenges to monetary policy from financial globalization. This paper discusses the role of communication in enhancing the effectiveness of monetary policy and strengthening the financial system in India. Currency derivatives can provide important benefits for financial systems. This paper aims to document the extent to which Indian growth has benefited the bottom of the income distribution and how India can achieve significantly better social outcomes.

VI. Inclusive Growth1

A. Introduction

1. “Faster and more inclusive growth” is the centerpiece of India’s 11th Plan (2007–2012). India is enjoying a period of unprecedented growth, with real GDP rising at over 8 percent per year for the past 4 years, making it one of the world’s fastest growing economies. Yet, India still has the largest concentration of poor people in the world. The extent to which India’s poor have been able to take up the opportunities provided by an expanding economy and contribute to its expansion is an important question for the well-being of millions. It is also at the heart of the current political debate in India. Decisive reforms are required to ensure continuing economic growth, yet the ability of the government to pass and sustain reform momentum depends on popular support. If large parts of the populations are left behind, even if only in relative terms, the viability of future reforms may be threatened.

2. This paper aims to document the extent to which Indian growth has benefited the bottom of the income distribution over the last two decades. Did the impressive growth performance translate into commensurate poverty reduction? How did growth and changes in inequality contribute to poverty reduction? Did the pattern of growth across the income distribution change as India’s economic expansion accelerated? Was the inclusiveness of growth across Indian states influenced by certain factors or policies such as financial development, education or labor legislation?

B. Growth, Poverty, and Inequality in the Last Two Decades

3. National income accounts and household survey data paint somewhat different pictures of the improvement in living standards over the last two decades. The annual growth rate of real GDP per capita accelerated from about 3 percent in 1983–1993/94 to an average of 4½ percent in the post reform period 1993/94–2004/05.2 A similar pattern is observed in private per capita consumption, as measured in national account statistics (NAS). Household survey data point to a substantially slower improvement in consumption per capita (Table VI.1).

Table VI.1.

India: Economic Growth in the 1980s and 1990s

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Source: IMF WEO, NSSO 38th, 55th, and 61st rounds; and Fund staff estimates.

In constant prices.

Converted in real terms using the official deflators of the Planning commission.

4. Over the same time period, the incidence of poverty fell by nearly 20 percentage points. As of 2004/05, 25¾ percent of people in urban areas and 28 percent of people in rural areas lived below the poverty line.3 Poverty depth decreased by more than 50 percent during this time period.

5. Overall consumption inequality increased in the 1990s, particularly in urban areas, and within almost all states according to a variety of measures. while inequality was stable (in urban India) and declining (in rural India) in the 1980s, this trend was reversed in the 1990s. As real consumption growth was significantly higher in urban areas, the urban-rural gap widened. The change in the distribution of consumption across households can explain the lower than expected poverty reduction. Despite the pick-up in consumption growth rate from the 1980s to the 1990s, the decline in poverty incidence remained roughly unchanged: the poverty rate fell by 9.4 percentage points (or 20.8 percent) in the 1983– 1993/94 period and 8.4 percentage points (or 23.4 percent) during the slightly longer 1993/94–004/05 period.

6. Anecdotal evidence suggests that wealth inequality may be even higher. Ahya and Sheth (2007) estimate that India has witnessed an increase in wealth of over 100 percent of GDP in the past four years from three key sources: the equity market, the residential property market, and gold (see also Purfield, 2007). With 4-7 percent of the population participating in the stock market, 47 percent of the population owning a ‘pucca’ house, and the top 34 percent of households holding 71 percent of the value of consumer durables (including gold and jewelry), it is likely that the bulk of wealth accretion was concentrated within a very small segment of the population (Ahya and Sheth, 2007).

7. How much more or less poverty reduction might have been achieved had growth occurred without changes in the income distribution? To examine this, changes in poverty can be decomposed into the change attributable to “pure growth” (holding inequality constant) and the change attributable to the distributional component (holding the mean of consumption constant).4 To do so, we express the poverty rate at time t as a function of the level of consumption, mt, and the distribution of income or the Lorenz curve, lt, i.e. Pt = P(mt,lt). We adopt the methodology proposed by Dhongde (2007), which provides a path-independent and complete decomposition, by taking the average of the two growth components (with distribution kept fixed as in time t=0 and t=1), and the average of the two distribution components (with average consumption held fixed at t=0 and t=1), namely:

P11-P00=(P10-P00)+(P11-P01)2+(P10-P11)+(P00-P01)2

where P00 = P(m0,l0) and P11 = P(m1,l1). This simple decomposition abstracts from the fact that changes in the distribution of income may affect (and be affected by) the average growth rate (i.e. the observed growth rate may not have been the same had the distribution of income not changed).

8. The counterfactual simulation suggests that in the 1980s, changes in the distribution of income enhanced the effect of growth on poverty reduction (Table VI.4). In rural India, poverty reduction from “growth alone” would have been 27 percent lower had the distribution of income not changed in favor of the poor. In urban India, “growth alone” accounts for the entire poverty decline.

Table VI.2.

India: Evolution of Poverty

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Source: NSSO various rounds; and Fund staff estimates.

Defined as the share of the population below the poverty line.

Defined as the mean distance below the poverty line as a share of the poverty line.

At 93/94 prices in rural India.

Table VI.3.

India: Evolution of Inequality

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Source: NSSO various rounds; and Fund staff estimates.

Log of the ratio of the per capita expenditure of the 95th percentile relative to the 5th percentile.

Table VI.4.

India: Decomposing Changes in Poverty

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Source: NSSO various rounds; and Fund staff estimates.

9. In the period from 1993 to 2004/05, on the other hand, changes in the distribution of consumption moderated the extent to which growth reduced poverty. Distribution-neutral growth would have generated a poverty decline in rural India that was 22 percent higher; in urban areas, the decline in poverty would have been 76 percent higher. This finding suggests a marked change in the way the gains from growth were distributed across India’s households in the relatively new market-oriented framework governing India’s economic life.

C. Inclusiveness of Growth

10. To gain a fuller picture of how the absolute gains from growth accrue across the income distribution, we calculate “growth incidence curves” of real monthly per capita consumption (Ravallion and Chen, 2003). The growth incidence curve depicts how the growth rate for a given quantile varies across quantiles ranked by expenditure, thus succinctly describing how inclusive growth was. A growth incidence curve increasing over all quantiles implies rising inequality, while a downward-sloping curve characterizes growth that was biased towards the poor. Figure VI.1 presents the growth incidence curves for India, during the 1983–1993/94 and 1993/94–2004/05 period5. The top panel uses data for all India, the middle for urban India, and the bottom for rural areas. The annualized growth rate in the mean (horizontal solid line) and median (horizontal dashed line) incomes are also included as benchmarks.

Figure VI.1.
Figure VI.1.

India: Patterns of Real Consumption Growth

Citation: IMF Staff Country Reports 2008, 052; 10.5089/9781451947502.002.A006

Source: NSSO various rounds and Fund staff estimates.

11. The shift in the growth patterns of consumption across the income distribution is striking - in almost all states growth became less equalizing in the 1990s. From 1983 to 1993/94, growth in consumption at the bottom of the income distribution outpaced growth at the top, especially in rural India (Figure VI.2). In urban areas, growth was remarkably distribution-neutral. As India launched market-oriented reforms in 1991 and overall growth accelerated, the shape of the growth incidence curve reversed, with far faster growth at the top than the bottom. In fact, though aggregate growth was significantly higher in the 1990s (even when measured in NSS data), the bottom 50 percent of India’s population experienced faster consumption growth in the previous decade. Similar to the previous period, there was a substantial difference between the experience of urban and rural areas, with a stronger pro-rich bias of growth in urban areas.

Figure VI.2.
Figure VI.2.

Real Consumption Growth of the Top and Bottom 30 Percentile of the Population Across India’s States

Citation: IMF Staff Country Reports 2008, 052; 10.5089/9781451947502.002.A006

Source: NSSO various rounds; and Fund staff estimates.

12. while growth incidence curves describe distributional changes well, a simple summary statistic is useful for making comparisons over time and across states, and for statistical analysis. How might the inclusiveness of growth be defined? The authorities’ definition, “a growth process in which people in different walks in life… feel that they too benefit significantly from the process,”6 suggests using a measure that captures the unevenness in consumption growth rates across households. We therefore define inclusiveness, or “pro-poor bias of growth” as the difference between the consumption growth rate of the poorest 30 percent and richest 30 percent of the population.7, 8

13. We use variation across India’s states and over time to establish the relationship between the inclusiveness of growth, the growth rate and its sectoral composition. Specifically, we compute for each of the 15 large states in India and for each of the following periods, 1983–1987/88, 1987/88–1993/94 and 1993/94–2004/05, the inclusiveness of growth as defined above, the average annual growth rate of real state GDP per capita, as well as the per capita growth rates of the agricultural, industrial and service sector. We then regress the inclusiveness of growth on per capita growth rates (Table VI.5). We include period fixed-effects to control for economy-wide changes, and state fixed-effects to control for time-invariant heterogeneity across states.

Table VI.5.

Growth Inclusiveness and Sectoral Composition of Growth

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Note: All regressions include state and period fixed effects and are weighted by the square root of the number of observations within a state. Robust standard errors in parenthesis. Data are from Schedule 1 of NSS 38th, 43rd, 50th, and 61st rounds.

14. There is no evidence of correlation between the speed of growth and its inclusiveness, however the sectoral composition matters. Faster growth in services is associated with a larger gap between the consumption growth of the poor and the rich, in favor of the rich. The service sector includes a mix of activities that may be of varying importance to the bottom as well as the top of the income distribution. A finer disaggregation of the services sector reveals that, just as expected, the observed negative correlation is driven by the expansion of the banking and insurance sector within the services sector, which employs predominantly highly educated people. Thus, while service growth is associated with absolute gains by the poor, the results indicate that it is associated with even more benefits to the people at the top of the income distribution. For broader definitions of inclusiveness of growth, such as the difference in growth below and above the median, higher growth of the secondary/industrial sector is correlated with a larger pro-poor bias of consumption growth, presumably because of the lower threshold of skills and education for employment in manufacturing and construction.

D. The Role of Policy for Inclusiveness of Growth

15. Why has economic growth been less inclusive in some states than others? Do economic policies affect how the benefits of growth are distributed across households? A large literature has analyzed the heterogeneity of the Indian experience to examine why and how certain Indian states have experienced faster growth and poverty reduction than others (for a survey, see Besley, Burgess, and Esteve-Volart, 2007). Few have explored what affects the distribution of growth across households. Building on the previous work, we consider here whether factors that have been shown to be associated with the growth and poverty reduction experiences of India’s states are also related to the distributional impact of growth. More importantly, these are all policies that have been highlighted as crucial for making growth faster and more inclusive in the Approach Towards the 11th Plan Paper of the Planning Commission of India.

16. Numerous studies have argued that labor regulations are an important determinant of the investment climate in India. The Industrial Disputes Act, which governs hiring and firing of labor in manufacturing was initially passed at the central level, but state governments were given authority to amend it. Besley and Burgess (2004) classify these amendments as pro-worker, pro-employer or neutral and demonstrate that labor regulations significantly affect manufacturing performance across Indian states. In particular, additional labor protection led to lower growth in manufacturing employment. A similar conclusion is reached by Ahsan and Pages (2007), who find that laws that increase job security or increase the cost of labor disputes substantially reduce registered sector employment, without increasing the labor share.

17. A second factor that may play an important role is access to finance. Credit may enable people to move out of agriculture into higher-earning activities, such as organized manufacturing or certain types of self-employment. In India, Burgess and Pande (2005) found that the rural bank branch expansion program of 1977–90 significantly lowered rural poverty and increased nonagricultural output. While financial development may boost growth, its effect on the distribution of growth across households is less clear. We measure financial development of Indian states as the log of total real credit per capita.

18. A third factor considered is secondary education. In the approach to the 11th Plan, the Planning Commission envisions a stronger focus on secondary, higher and technical education to promote faster and more inclusiveness growth. Empirical evidence of the importance of human capital for economic growth across the world abounds. In the Indian context, Trivedi (2002) finds that for the period 1965–1992, secondary school enrollment rates are positively and significantly related to economic growth across Indian states. We thus look at the share of a state population with secondary education and above as a measure of human capital.

19. Access to infrastructure is also considered one of the key constraints to growth. Kochhar et al. (2006) show that states with higher quality infrastructure enjoy higher GDP growth and faster growth in industrial sectors. However, they do not consider whether infrastructure affects the distribution of income. Infrastructure at the state level is measured as the main factor from a principal component analysis of installed electricity capacity per capita, kilometers of surfaced roads per state area, and share of households with access to drinking water.

20. Finally we verify whether states’ revenue expenditures on social services are associated with more inclusive growth. Social services include health, education, water supply, housing, urban development, nutrition and various welfare schemes for economically disadvantaged groups. As the majority of these services are targeted to poorer households, one might expect the inclusiveness of growth to be positively correlated with states’ social spending.

21. A panel regression framework is adopted, using measures of the inclusiveness of growth across several time periods for each state, to investigate whether the above variables are correlated with the distributional patterns of growth. By exploiting the variation both across states and over time, the framework controls for any time invariant state characteristic, such as preferences for equality, natural resource endowment etc. that may be somehow correlated with both policies and patterns of growth and thus obfuscate cross sectional studies.9 We thus estimate:

yt,t-1,s = α + βXt-1,s + γZt-1,s + τt + Ss + εt,s

where yt, t-1, s is a measure of the inclusiveness, or pro-poor bias, of growth in state s between year t and t-1 (The three periods considered are 1983–1987/88, 1987/88–1993/94 and 1993/94–2004/05 periods). Inclusiveness is measured as the difference between the consumption growth rate of the bottom 30 and the top 30 percent of the population (different cutoffs are also considered). Xt-1,s is a vector of state-level policy variables described above at the beginning of the time period. Since many of these variables could potentially be correlated with the overall level of development of a state, we control for a set of initial characteristics Zt-1,s: the log of income per capita (measured as the real net state domestic product per capita) and the number of people involved in agriculture as a share of the total workforce. Finally, τt and Ss represent period and state fixed effects. The necessary data are available for 15 major states in India (comprising 95 percent of India’s population in 2004).

22. Several interesting relationships emerge from the data (Table VI.6).

  • Higher financial development is significantly associated with more pro-poor growth. This relation is consistent with the idea that better access to credit enables people in the bottom of the income distribution to move out of agriculture into higher-earning activities, such as organized manufacturing or certain types of self-employment.

  • There is some evidence that labor regulations, intended to protect workers from exploitation by factory owners, in fact reduced the relative gains of the poor. While the point estimate is not statistically significant in column (2), once other policy measures are controlled for, the absolute value of the coefficient increases and it is consistently statistically significant. As states amend their regulations towards greater flexibility for the employer, the poor seem to benefit more in terms of consumption growth.

  • As a larger share of the population completes secondary education, growth becomes relatively more pro-poor. The correlation may stem from the fact that a larger supply of skilled labor eases the pressure on wages at the top of the income distribution.

  • There is also evidence that better infrastructure is associated with more inclusive growth. There does not appear to be a statistically significant correlation between state expenditures for socioeconomic purposes (such as health, education etc.) and the distribution of growth rates across households.

Table VI.6.

Growth Inclusiveness and Economic Policy

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Note: All regressions include state and period fixed effects and are weighted by the square root of the number of observations within a state. Robust standard errors in parenthesis. Data are from Schedule 1 of NSS 38th, 43rd, 50th, and 61st rounds.

23. The above exercise points to the ability of economic policy to influence how the benefits of growth are distributed across the income distribution, though the results should be interpreted with caution. As in most macro-level analyses that lack exogenous variation, it is only possible to identify conditional correlations, rather than causal relationships. Additionally, there could be unobserved, state-specific time varying factors that affect both the pattern of growth and the policies or outcomes we identify. The causality could run in both directions: for example socioeconomic spending may be particularly high in some states because growth is not inclusive.

E. Conclusion

24. while many have celebrated India’s accelerating economic growth, others have expressed concern about the distributional impacts of the growth process. Cognizant of the vulnerability of its large population below poverty, India’s authorities have made faster and more inclusive economic growth the primary goal of their development strategy. Decades of rapid growth have led to a dramatic reduction in poverty in rural and urban India, with millions of households escaping from poverty, and similarly dramatic declines in measured poverty depth. There is every reason to believe that economic growth will continue to lead to declines in poverty.

25. As India adopted a market-oriented model of development, there was a marked shift in the way the benefits of growth were distributed across the income distribution. In the 1980s, the growth rate of consumption of the bottom of the income distribution was substantially higher than that of the top. In contrast, in the 1990s, the top of the population enjoyed a substantially larger share of the gains from economic growth compared to the previous decade. This had significant effects on income inequality, which grew within states, across states, and between rural and urban areas.

26. There is indicative evidence that economic policies can influence how the benefits of growth are distributed. States with higher financial development, more flexible labor markets and higher human capital raised the ability of the poor to gain from the growth process. Improving infrastructure may also lead to a growth process that is more inclusive of the poor.

27. Should the government be concerned that inequality is increasing? It is certainly true that the entire population, rich and poor alike, are significantly better off now than ten or twenty years ago. What are the costs or benefits associated with higher inequality? Providing a definitive answer to this question is beyond the scope of this paper. Nevertheless, it, may be useful to consider the characterization of Chaudhari and Ravallion (2006), who argue that there are two types of inequality. “Bad inequalities,” typically rooted in market and government failures, are those that prevent individuals from connecting to markets, and limit investment and accumulation of human and physical capital, such as geographic poverty traps, patterns of social exclusion, lack of access to credit and insurance, etc. “Good inequalities,” on the other hand, reflect the role of economic incentives. Widening income gaps, arising from an increase in the skill premium, increases the incentive for investment in education and may eventually narrow over time as the younger generation invests more in their human capital. Thus, policy makers should focus on how to increase access to and quality of schooling, and remove potential sources of “bad inequalities.”

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1

Prepared by Petia Topalova.

2

These particular periods were chosen based on availability of household survey data. NSS quinquennial rounds were conducted in 1983, 1987/88, 1993/94, 1999/00, and 2004/05. Due to the substantial differences in the measurement of per capita expenditure in the 1999/00 round, most of the analysis will not rely on data from this round.

3

The poverty line is defined in India as the minimum subsistence income that can support the consumption of 2400 calories in rural areas and 2100 calories per person in urban areas.

4

For a similar decomposition for earlier time periods, see Jain and Tendulkar (1990), Datt and Ravallion (1992), Deaton and Dreze (2002), Bhanumurty and Mitra (2004) and Dhongde (2007).

5

These calculations use the disaggregate household survey data from 1983, 1993/94 and 2004/05, with household expenditures adjusted to be comparable across states, and rural and urban areas and deflated to 1993/94 values using the official deflators of the Planning Commission.

6

Ahluwalia, Montek, Deputy Chairman of the Planning Commission. Business Standard June 29, 2007.

7

Given the nature of the policy debate in India, this seems to be a more appropriate definition than for example the standard deviation of growth rates, or other measures that describe the unevenness of growth.

8

The choice of 30 percent is arbitrary, and as a robustness check, we analyze the difference in the growth rates between the bottom and top 10 percent, 20 percent, and below and above the median.

9

This is in contrast to previous studies that have focused on explaining the relationship between policies and growth elasticity of poverty reduction across states in a single cross-section (Besley, Burgess and Esteve-Volart, 2007) or looked at the correlation of this elasticity with initial state characteristics (Datt and Ravallion, 2002).

India: Selected Issues
Author: International Monetary Fund
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    India: Patterns of Real Consumption Growth

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    Real Consumption Growth of the Top and Bottom 30 Percentile of the Population Across India’s States