Have the Lower Income States Caught Up?

Transcription

1 Convergence Have the Lower Income States Caught Up? C. Rangarajan, Padma Iyer Kaul and Vibeesh E.M. i c r a b u l l e t i n Abstract Balanced regional growth is an important development objective in India. In this paper an attempt is made to answer the question whether there is any convergence among states by examining the per capita income data for the three periods to , to and to Strictly speaking, the three periods do not show convergence. Absolute convergence in the sense of the per capita incomes of the different states converging to a single number has not happened. However, in the recent period, there is strong evidence of catching up by the lower income states. States with lower per capita income in like Assam, Madhya Pradesh, Uttar Pradesh and Bihar have made significant gains in growth rates in the recent period to Notwithstanding these gains, convergence did not happen mainly because some states, especially Maharashtra, Gujarat, Kerala, Tamil Nadu and Haryana, which had high initial per capita incomes, also posted high growth rates (in the range of 7 per cent and above). The declining trend in the positive values of the coefficients of divergence points towards increased catching up and greater convergence. Introduction Balanced regional growth is an important development objective in India. More importantly, an inclusive growth process is essential to promoting social harmony and political stability. Differential natural resource endowments and skilled human resources result in wide variations in growth rates and hence incomes across states. While initially inter-regional disparity may increase in the process of development, it is expected that as growth gathers momentum and with specific policy initiatives, this divergence would attenuate. C. Rangarajan is Chairman of the Economic Advisory Council to the Prime Minister. He may be reached at c.rangarajan@nic.in. Padma Iyer Kaul is Communication Controller of Accounts, Department of Telecommunications, and Vibeesh E.M. is Senior Research Officer in the Economic Advisory Council to the Prime Minister. The views expressed are personal. While initially inter-regional disparity may increase in the process of development, it is expected that as growth gathers momentum and with specific policy initiatives, this divergence would attenuate. 43

2 44 When the dispersion of real per capita income across a group of economies falls over time, there is sigma convergence and when the correlation between growth in income over time and its initial level is negative, there is beta convergence. Convergence in per capita incomes (PCIs) will be driven by diminishing returns to capital, that is, states with a high initial income and presumably higher capital-labour ratio will, over time, have lower per capita income growth rates and vice versa. This process of convergence will be aided by redistribution of incomes in favour of poorer states through mechanisms such as the Commission and grants from the Centre to the states (Cashin and Sahay, 1996). Empirical evidence on convergence, largely based on per capita income time series, has been the subject of a lot of debate and study. In this paper we attempt to address this issue by examining the per capita income data for the period to The article is divided into four sections. Section I presents a brief literature survey on the empirical evidence and conclusions on inter-state disparities and convergence. Section II details the methodology underlying the study, while Section III analyses the GSDP and NSDP data to examine the movements in inter-regional disparities in the period to and the more recent period, to Section IV uses some simple inequality measures and the Gini coefficient to examine the trends in inter-state inequality. Section V tests for absolute convergence using the technique of simple regression, taking the initial per capita income and the growth rates of states. Section VI makes some concluding remarks. I. Literature Review One can distinguish between two types of convergence in growth empirics: sigma convergence and beta convergence. When the dispersion of real per capita income across a group of economies falls over time, there is sigma convergence and when the correlation between growth in income over time and its initial level is negative, there is beta convergence (Young, 2007). Beta convergence literature divides itself into two broad sets of studies. The first category pertains to absolute convergence, which tests the hypothesis that states with low initial incomes experience higher growth rates while states with high initial incomes experience some slowing down. The assumption here is that heterogeneity among states does not make a difference to this process. The second category pertains to conditional convergence, which tests for the same process but controls for heterogeneity using variables like savings/investments (Aiyar, 2001), infrastructure (Nagaraj, 1998), and income redistribution (Cashin and Sahay, 1996). A large number of studies have documented beta convergence of per capita state incomes in the pre-1990s. Cashin and Sahay (1996) find absolute and conditional convergence for the period 1961 to Purfield (2006) finds absolute convergence for the period and estimates the rate of convergence at 1.5 per cent per annum though the post-1991 pace of convergence was slower than that observed over the full sample period. The study also finds evidence for conditional convergence once controls that proxy for differences in the policies and

3 economic structure are held constant. Bandyopadhyay (2006) finds twin peaked dynamics over the period , that is, the Indian inter-state distribution has polarized into two income convergence clubs. He also finds that the association between infrastructural indicators (roads/power consumption in industrial sector) and economic growth is a significant one, especially for the lower income states. Nagaraj et al (1998) find evidence of conditional convergence across states for the period and observe that disparities are accounted for by differences in the structure of production, infrastructure endowments and state specific factors. Ahluwalia (2000) found that the Gini coefficient, a measure of dispersion, was fairly stable up to but rose subsequently, indicating an increase in inter-state inequalities in the 1990s. This is however not inconsistent with the conditional convergence results of others. Ghosh (1998) records absolute divergence for the period to , but unlike others, rejects the need for conditional convergence stating that it is enough to look at the above regression as the states within a geopolitical boundary do share common characteristics. Interesting point is how divergence appears in such homogeneous environment. Rao et al (1999) find the existence of absolute and conditional divergence during 1965 to 1995 among 14 major states. They identify unequal private investment as contributing to divergence. Sachs et al (2002) confirmed the above finding and found geographical variables to be responsible for this diverging trend. There is similar lack of consensus for the more recent period. Ahluwalia (2011) finds the growth rates of the poorer states, notably the BIMARU 1 states, accelerated after and the process of catch-up was clearly visible. However, he does not make any comments on convergence. Bhalla (2011) has shown that the extent of acceleration in growth in the post-reform period is negatively correlated with the level of per capita GSDP, that is, the poorer states have actually experienced faster growth rates relative to the richer states. 2 This is stated as being consistent with what one would expect under conditional convergence. The mid-term appraisal of the 11 th Five-Year Plan finds similar results and notes: Although growth rates continue to differ across states the variation has tended to decline. Arvind Subramanian (2012) explores two periods, and , and finds evidence of divergence. However, the author does not share the regression results and is silent on the coefficients. The rich literature on convergence with significant variations in conclusions, often for similar periods, could probably be explained i c r a b u l l e t i n Bhalla has shown that the extent of acceleration in growth in the post-reform period is negatively correlated with the level of per capita GSDP, that is, the poorer states have actually experienced faster growth rates relative to the richer states. 1 Acronym for Bihar, Madhya Pradesh, Rajasthan, and Uttar Pradesh. 2 Bhalla comments on two periods, and , to draw this conclusion. However, instead of using initial income he uses initial growth rate and the difference between growth rates in the two periods to comment on convergence. It is important to note that states with low growth rates in were not necessarily the ones with low initial incomes, e.g. Punjab and Haryana. 45

4 46 We analyse the GSDP and NSDP data for catching up using growth rates and dispersion indicators like max-min ratios, coefficient of variation and Gini coefficient. We also test for absolute convergence using initial income and growth rates for different periods. by the fact that the studies vary across various dimensions: sample period, coverage of states, data sources and estimation methodology. Ghosh (1998) uses a sample of 26 states, while a number of others use a sample of only 14 states. Some authors use annual growth rates directly, while others use compounded annual growth rates (CAGRs). The deflators used also vary between different studies; for instance, Ghosh (1998) uses state-wise Consumer Price Index (AL) to deflate nominal per capita NSDP while others use all-india deflators. There are variations in the control variables used, which range from sector shares, infrastructure improvements, and credit growth, to migration and literacy (Kalra, 2010). II. Methodology Some of the recent literature has used the terms catching up and convergence synonymously, but this is erroneous and a distinction needs to be made. Catching up is a broad term. Loosely defined, it can mean that lower income states are growing faster than earlier so that the gap between their growth rates and those of the richer states is reduced. Convergence on the other hand would mean that the lower income states have caught up and the per capita incomes of the states are converging towards the national mean. In other words, convergence can simply mean the successful culmination of the process of catching up by the lower income states. We analyse the GSDP and NSDP data for catching up using growth rates and dispersion indicators like max-min ratios, coefficient of variation and Gini coefficient. We also test for absolute convergence using initial income and growth rates for different periods. We use per capita GSDP and NSDP time series data from to for 15 states: four in the south, viz. Tamil Nadu, Karnataka, Andhra Pradesh and Kerala; three in the west, viz. Maharashtra, Gujarat and Rajasthan; three in the north, viz. Punjab, Haryana and Uttar Pradesh; one in the centre, Madhya Pradesh; and four in the east, viz. Bihar, West Bengal, Assam and Odisha. Together these account for 95 per cent of the Indian population. 3 One of the major limitations of this data set is that it is not a single series. GSDP/ NSDP data from the Central Statistics Office (CSO) is available in different series with different base periods, , , and We use the unspliced NSDP data series for the growth and convergence analysis and spliced GSDP series at constant price ( ) for calculating inequality measures. To examine the growth performance of these major states, we divide the per capita NSDP data series into three periods, namely, to , to , and to , in line with the different series provided by the CSO. The state-wise Annual Trend Growth Rates 3 In the case of Bihar, Uttar Pradesh and Madhya Pradesh, in the post-bifurcation period we have added the GSDP/NSDP of Jharkhand, Uttarakhand and Chhattisgarh to keep the data comparable.

5 (ATGRs) of per capita NSDP (PCI) for these periods were estimated by fitting the exponential trend curves for each state. III. Analysing GSDP Data Chart 1 compares the growth performance of states for the latest two periods, to and to In the first period, to , the ATGRs of per capita income display large inter-state variations ranging from 0.9 per cent (Assam) to 4.7 per cent (West Bengal). The median growth rate in this period is 2.8 per cent (Rajasthan). In terms of per capita income, four states, viz. Bihar, Odisha, Uttar Pradesh and Assam, had the lowest ranks in this period and even in terms of growth rates they occupied the bottom positions. However, the four top ranking states in terms of per capita income (Punjab, Maharashtra, Haryana and Gujarat) were not the fastest growing in this period. It is the middle income states like West Bengal, Karnataka, Andhra Pradesh and Kerala that grew the fastest at 4 per cent-plus. In the second period, to , inter-state variations continued to be large, ranging from 4.5 per cent (Odisha) to 8.4 per cent (Tamil Nadu). However, in this period, the overall growth rate of the low income group states was four times that of the previous period. Between these periods, the median growth rate more than doubled to 6.5 per cent (Madhya Pradesh). All the 15 states, across the different income levels, show higher growth rates in the second period compared with the first. Interestingly, the growth rate of 10 out of the 15 states under review doubled or more than doubled during the second period (Annexure 2). The evidence of the catch-up phenomenon Chart 1 PCI Trend Growth Rates of 15 Major States i c r a b u l l e t i n In the second period, to , inter-state variations continued to be large, ranging from 4.5% (Odisha) to 8.4% (Tamil Nadu). However, in this period, the overall growth rate of the low income group states was four times that of the previous period. 47

6 48 It is difficult to explain why low income states grew faster in the period to Several factors appear to have been responsible. Improved law and order situation and greater commitment to efficient administration seem to have attracted more investment to these states. for the lower income states is quite striking in the period to If instead of using the per capita figures we look at GSDP for the period to , the performance of the low income group states looks even better. Bihar, Madhya Pradesh and Rajasthan grew above, while Odisha was very close to, the all-india average of 7.8 per cent. Uttar Pradesh, a poor performer in the earlier periods, turned in a growth rate of almost 7 per cent, reflecting the buoyancy and resurgence in this period. Even Assam, which ranked last in this period, had a growth rate of 6.14 per cent a significant improvement over its previous achievement. It is difficult to explain why low income states grew faster in the period to Several factors appear to have been responsible. Improved law and order situation and greater commitment to efficient administration seem to have attracted more investment to these states. These states have also laid greater emphasis on education and infrastructure investment. However, more in-depth analysis of all the states is required to understand causality and to assess the role each of these factors has played in promoting growth. IV. Measuring Inequality There are a number of measures that can be used to analyse regional inequality. We use three measures in this paper, viz. max-min ratio, coefficient of variation and Gini coefficient. These measures ignore the inequality within a state and assume that all individuals have an income equal to the per capita income. The simplest of these, the max-min ratio, has the major advantage of ease of calculation. But on the flip side, it takes only the extreme values and disregards the remaining ones. This disadvantage can be somewhat neutralized if we use a population weighted coefficient of variation. This takes into account all the observations in the distribution and is the second measure adopted for our analysis. The third, the Gini coefficient, is a popular measure of inequality that gives an estimate of inequality between the states. In the period to , the max-min ratio of the per capita GSDP of the 15 states under review shot up from 2.4 to 3.1, increasing at an annual rate of 2 per cent, indicating an increasing divergence during the period. In the period to , the ratio increased from 3.1 to 3.6, indicating a further increase in divergence. In this period, the CAGR of the max-min ratio was lower at 1.4 per cent. In the third period, to , the max-min ratio displayed an increase in the first three years and then showed a declining trend, 4 finally reaching 3.6 in Except for the year

7 Chart 2 Trend in Gini Coefficient i c r a b u l l e t i n In the latest period, to , the Gini coefficient remained The population weighted coefficient of variation (CV) also shows an increasing inequality in the per capita GSDPs of the 15 states during the periods to and to However, in the recent past, it has remained relatively constant in the range of per cent. In the first period, to , the CV increased from 23.2 per cent to 31.4 per cent, and in the next period it further increased to 38.6 per cent. This result mirrors the trends shown by the max-min ratio. In the latest period, to , the CV rose from 40.0 per cent to 45.0 per cent. There was a clear tendency towards divergence in these three periods. The only mitigating factor was that in the latter half of the third period the CV appeared to have stabilized somewhat. To understand the impact of growth on regional inequality we construct a Gini coefficient 5 using the cumulative relative proportions of GSDP and population for the 15 states for the three periods under study (Chart 2). In the first period, to , the Gini coefficient rose from 0.13 to 0.17 and in the second it further increased to 0.22, indicating a rise in regional inequalities. Overall, in these two periods, the Gini coefficient shows a steadily increasing trend. In the latest period, to , the Gini coefficient remained at a slightly elevated level of 0.25 as compared with the earlier two periods. Just like in the case of CV, a positive development in this period is that the at a slightly elevated level of 0.25 as compared with the earlier two periods. 5 The Gini coefficients were calculated as G = 1 n k = 1 ( X k X k 1) ( Yk + Y where X 0 = Y 0 = O and X n = Y n = 100 Y k is the cumulative relative frequency of income of the k th state. X k is the cumulative relative frequency of population of the k th state. k 1 ) 49

8 50 Theoretically, if the states were converging, the states with a low initial income would experience faster growth than the ones starting with a high initial income. In other words, convergence would translate into a negative relationship between growth rate and initial income and a positive relationship would imply divergence. coefficient seems relatively static for a fairly long period. Additionally, a slight declining trend is discernible in the recent past (Chart 2 and Annexure 1). V. Test for Convergence/Divergence Before going into the test for convergence in per capita income, we may refer to the analysis in the 12 th Five-Year Plan document which looks at the average growth of the bottom five per capita income states in relation to that of the remaining states, the top five per capita income states and all-india. The ratios, average growth rate of the bottom five PCI states to the average growth rate of the remaining states, the top five PCI states and all-india, show an upward trend in the latest period, to (Chart 3). The ratio of the average growth rate of the bottom five PCI states to that of the top five PCI states rose from 0.59 (in to ) to 0.82 (in to ), showing a positive trend towards convergence. In order to study the convergence in growth, we looked at the relationship between the average growth rates in PCI of a state in a particular period with that of the previous period. Accordingly, we estimate two cross-sectional regression lines. First, the state-wise average growth rate of PCI in the period is regressed on the average growth rate of PCI in the period We get a statistically significant positive relationship (0.74) between the growth rates; that is, the states that had a high growth rate in the period also had high growth rate in the period and vice versa. A similar exercise relating the average growth rate of the period to the average growth rate of the period yields a positive relationship; the coefficient is however smaller at 0.37 and it is not statistically significant (Chart 4). Theoretically, if the states were converging, the states with a low initial income would experience faster growth than the ones starting with a high initial income. In other words, convergence would translate into a negative relationship between growth rate and initial income and a positive relationship would imply divergence. To test the absolute convergence in per capita income between the 15 states under review for the three periods to , to and to , we regressed the state-wise average growth rate in per capita income of a period with the natural log of the initial per capita income of that period. As expected, the period to displays a statistically significant coefficient of divergence (coefficient of the log of initial per capita income in the regressed equation) of 2.0. States with a lower initial income like Bihar, Odisha, Madhya Pradesh and Assam show low growth rates, ranging between 1 and 2 per cent, while the high initial income states like Maharashtra, Haryana and Punjab show relatively higher growth rates of between 3 and 4 per cent. In this period, there are some states like Rajasthan, Andhra Pradesh, Tamil Nadu and Karnataka which show a contrary

9 Chart 3 Ratio of Average Growth Rates of States with Lowest PCI to Average Growth Rates of Different Categories of States i c r a b u l l e t i n In the period to , one can clearly observe the formation of converging clusters Chart 4 Relationship between Average Growth Rates in PCI of a State in Two Consecutive Periods of states at two different income levels, one at the lower income level and the other at the middle and higher income levels. trend with high growth rates exceeding 3 per cent despite a lower initial income. The overall trend however is of absolute divergence. In the period to , the picture is still of divergence (Chart 6). Some states like Bihar, Assam, Uttar Pradesh and Odisha continue to lag behind. However, in this period we find that the coefficient of the log of initial per capita income in the regressed equation decreases to 1.4 even though it is not statistically significant, 51

10 Chart 5 PCI in and Income Growth during to In the period to , this phenomenon of clustering seems to have vanished. The low income states of the earlier period Chart 6 PCI in and Income Growth during to show an increase in growth rates to 4 5%, but this is inadequate for them to catch up with others, which grew at 7 8% despite starting out with high initial incomes. 52 indicating that the divergence is somewhat attenuated. More interestingly, in this period, one can clearly observe the formation of converging clusters of states at two different income levels, one at the lower income level (A) and the other at the middle and higher income levels (B). In the period to , this phenomenon of clustering seems to have vanished (Chart 7). The low income states of the earlier period like Uttar Pradesh and Assam show an increase in

11 Chart 7 PCI in and Income Growth during to i c r a b u l l e t i n Even though the per capita incomes of these 15 states do not show convergence, over growth rates to 4 5 per cent, but this is inadequate for them to catch up with others like Maharashtra, Gujarat, Kerala and Haryana, which grew at 7 8 per cent despite starting out with high initial incomes. The major transformation happens in Bihar, Rajasthan and Madhya Pradesh, which manage to catch up by growing at 6 8 per cent in this period. The growth rates of these states more than tripled as compared with the first period. However, even in this period, the relationship between the log of initial per capita income (in ) and the growth rate in per capita income (in the period to ) continues to be positive. But the value of the coefficient of the log of PCI goes down to 1.1, though it is not statistically significant. A comparison of the results for the three periods detailed above yields an interesting result. Even though the per capita incomes of these 15 states do not show convergence, over time the tendency to diverge appears to be coming down, suggesting a move towards convergence. There is clear evidence of strong growth rates of the erstwhile lower income states in the recent period. VI. Conclusion A question posed in this analysis was: What, if any, is the evidence on convergence between states? Strictly speaking, the three periods to , to and to do not show convergence. Absolute convergence in the sense of the per capita incomes of the different states converging to a single number has not happened. However, in the recent period, there is strong evidence of catching up by the lower income states. The median growth rate more than doubled in the recent period, increasing from 2.8 per cent during to to 6.3 per cent during time the tendency to diverge appears to be coming down, suggesting a move towards convergence. 53

12 54 The declining trend in the positive values of the coefficients of divergence points towards increased catching up and greater convergence to All the 15 states in the sample show higher growth rates in the recent period. States with lower per capita income ( ) like Assam, Madhya Pradesh, Uttar Pradesh and Bihar have made significant gains in growth rates in the recent period to Notwithstanding these gains, convergence did not happen in the recent period mainly because some states, especially Maharashtra, Gujarat, Kerala, Tamil Nadu and Haryana, which had high initial per capita incomes, also posted high growth rates (in the range of 7 per cent and above). The declining trend in the positive values of the coefficients of divergence points towards increased catching up and greater convergence. Annexure 1 Trend in Inter-State Inequality to Year Gini CV Max-Min Ratio Source: Calculated using State-wise PCI data from CSO, MoSPI.

13 Annexure 2 PCI Trend Growth Rates of 15 Major States State PCNSDP Annual trend PCNSDP Annual trend PCNSDP Annual trend growth rate 1993 growth rate growth rate ( ) ( ) ( ) ANDHRA PRADESH ASSAM BIHAR GUJARAT HARYANA KARNATAKA KERALA MADHYA PRADESH MAHARASHTRA ODISHA PUNJAB RAJASTHAN TAMIL NADU UTTAR PRADESH WEST BENGAL Source: Calculated using State-wise PCI data from CSO, MoSPI. 55

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