INSTITUTIONS AND INTERREGIONAL INEQUALITIES IN INDIA: FINDING A LINK USING HAYAMI'S THESIS AND CONVERGENCE HYPOTHESIS Kaliappa Kalirajan (Professor,

INSTITUTIONS AND INTERREGIONAL INEQUALITIES IN INDIA: FINDING A LINK USING HAYAMI'S THESIS AND CONVERGENCE HYPOTHESIS Kaliappa Kalirajan (Professor, Foundation for Advanced Studies on International Development, Tokyo) and Akita Takahiro (Director, International Development Program, International University of Japan) Introduction A major concern in Development Economics is to find a satisfactory answer to a basic question why do different countries or different states within a country grow differently leading to different degrees of income inequalities and poverty. Several researchers have identified different factors as responsible for suppressing the growth rate of countries. Thus, there is no single answer to the main question. A recent book by Hayami (1997) discusses intensively the question of how is the other so rich. With cross-country comparisons and historical data, he concludes that those country-specific factors such as governance, institutions and culture play a dominant role in determining the growth path of a country. Even countries with similar resource endowments have experienced sharply different economic growth because of country-specific governance and organizations. Examples are Kenya versus Tanzania, North Korea versus South Korea, and India versus Pakistan. The inference is that unless poor countries are able to adapt their country-specific institutions for suitable application of the models of success, improvements in their economic performance cannot be guaranteed. A distinct example of success in this approach in recent times is that of China. Researchers have argued that the salient feature of the East Asian success is the better utilization of their comparative advantage at each stage of their development (Lin, Cai and Li, 1996). But as Hayami has argued, what is more important is to create and nurture appropriate institutions and organizations to reap the benefits from the comparative advantage. In the Chinese case, the growth of 'township and village enterprises' (TVEs) is the institutional framework that has facilitated China to be on the international arena of trade and investment. Hayami's arguments about creating right institutions to achieve technological progress and overall growth, therefore, are convincing. Knack and Keefer (1995) provide empirical evidence to show specifically that a country s economic performance is positively related to the quality of that country s institutions. Recently, Chong and Calderon (2000) have proved that a country s institutional framework is an important determinant of not only its economic performance but also the way income are distributed among its members. They used information about the quality of institutions mainly from the International Country Risk Guide (ICRG) and the Business Environment Risk Intelligence (BERI). In the absence of such data and particularly when researchers want to study the dynamic link between institutions and interregional inequalities within a federal economy, what is an alternative methodology? The objective of this paper is to suggest an indirect method of examining the dynamic link between the quality of institutions and interregional income inequalities in per capita income. The indirect method is construed using both Hayami s thesis and the convergence hypothesis of income. The following section describes an alternative (indirect) method to examine the link between institutions and income inequalities. The next section discusses the relationship between institutions and economic performance in India. Analytical framework used in this paper is discussed in the next section. Interregional income inequalities and the contribution of different sectors to inequalities are quantified in the following section. The link between institutions and income inequalities is examined in the next section. A final section brings out the overall conclusions of this paper.

48 THE INDIAN ECONOMIC JOURNAL Institutions and Income Inequalities: An Alternative Method A direct method of examining the link between the quality of institutions and income inequalities is to run a regression with a measure of income inequality as the dependent variable and variables explaining the quality of institutions taken mainly from the data from ICRG and BERI as independent variables. A simple correlation analysis can also be used, if one is not interested in finding the relative importance of the determining variables on income inequality. ICRG and BERI data are available for cross-country analysis only for certain years. However, such kinds of data are not available easily for cross-regional analysis within a federal economy either in a static sense or in a dynamic framework. In this context, is there any valid alternative method? Several answers to the question of how is the other so rich have been put forward by both quantitative and qualitative researchers. Of these, a notable one is the convergence hypothesis of income. When does the convergence hypothesis hold? Unless the output levels of different economies are significantly different at some time in the past, the question of convergence does not arise. What is convergence? It is argued that convergence of per capita income between countries will take place in the long run regardless of their initial economic conditions even in the absence of international trade, provided that different countries share the same technology with constant returns to scale, and investment is a constant fraction of output. This type of convergence is called the absolute convergence. If per capita income of countries converges after controlling for their initial conditions that would characterize the economies such as the patterns of consumption and savings, and the rate of population growth, this type of convergence is called the conditional convergence. Does the study of convergence contradict Hayami s thesis of the importance of country-specific institutions and organizations in determining the growth process? No. Both theories insist that different countries should have the same technology and thus highlight the importance of borrowing technology from developed nations by developing countries. But, Hayami adds a caveat that developing countries should have proper institutions and organizations not only to borrow technology, but also to adapt the technology to suit their country-specific comparative advantages to sustain technological progress and growth. The principal force driving convergence in the neoclassical growth model is diminishing returns to reproducible capital. Thus, economies with lower initial values of capital-labour ratios will have high marginal products of capital and therefore, tend to grow at higher rates (Evans and Karras, 1996). But, inefficient and poor quality institutions and organizations could lead to violation of the critical assumption of diminishing returns to reproducible capital. In an economy with large unutilised resources and a poor state of social and physical infrastructure, there will be increasing returns to reproducible capital. In terms of the Kuznets (1955) paradigm, this situation will accentuate inequality in the rising part of the inverted U. This means divergence of income for a considerable period of time in the development process. Thus, it is logical to argue that the convergence hypothesis will hold only when country-specific institutions and organizations do not intervene in the process negatively to delay or constrain the convergence process. Higher the quality of institutions, lower will be the inequality, and therefore, quicker will be the convergence. On the other hand, if there is divergence in per capita income across regions, this means that institutions contribute to widening income inequalities. Thus, drawing on Hayami s findings, testing the convergence hypothesis of income provides an alternative method of examining the link between institutions and inequalities. Institutions and Economic Performance in India There are numerous studies on income inequality and poverty in India and it will be difficult to summarize all these studies. Some of the important studies that are relevant to this paper include, Mathur (1983), Nair (1983), Sen (1992), Datta Roy Choudhury (1993), Bhalla, (1994), Ravallion and Datt (1996), Das and Barua (1996), Rao, Shand and Kalirajan (1999) among

Volume 49, No.4 49 others. However, empirical studies directly linking income inequality and institutions in India are rare. The aim of this study is to provide a simple empirical method that explores this link. To place in context the link between interregional income inequality and institutions in India, it becomes necessary to study first the link between institutions and economic performance in India. Here, it is useful to understand the growth path chosen by India over a number of years since independence. Harrod-Domar model of growth, which identifies the source of instability in the growth, provided a framework for economic planning that was promoted by Mahalanobis who was the primary economic adviser to the First Prime Minister of India (Srinivasan, 1990). The basic characteristic of the model is that economic growth is determined by investment in tangible capital. When combined with population theory, this model produces a vicious circle between low per capita income and low savings in low-income countries. This concept is popularly known as the low-equilibrium trap in the literature. Low income growth coupled with relatively high population growth will contribute to widening of income inequalities. But, it is possible for a country to remain above the low-equilibrium at the threshold level where the population growth rate and the income growth rate coincide by applying the production techniques efficiently and controlling population growth effectively. This latter point may be named as pseudo-equilibrium'. But, lack of proper institutions, organizations and community involvement can constrain the economy from using production techniques efficiently and population control effectively. This in turn, would force the country to remain at the lowequilibrium trap with low per capita income for a long time, which was the case in India until recently (Dreze and Sen, 1997). This argument raises several interesting questions about India's productive efficiency, technological progress and overall growth process during the pre-reform period of 1991. First, it is clear that the Government economic policy towards industrialization did not yield the anticipated results of increasing employment and reducing interregional income inequality, though industrial output increased as discussed by Rosen (1992). For example, in the late 1980s, employment in manufacturing was only 11% while employment in agriculture was over 60% of the total employed work force (Papola, 1992). What type of institutions and organizations was responsible for such a slow growth in employment in the industrial sector? The organizational set up that India followed in its industrialization process has been heavy reliance on public sector enterprises with an elaborate network of controls on private sector to limit entry of new firms and also to stop expansion of existing firms in the production of low priority areas. Capital goods and basic goods such as Cement and Steel were given to public sector enterprises, while consumer goods and other low priority production were given to private sector. Public sector industries instead of making profits have in fact accumulated substantial losses over the years. As a consequence, instead of being a source of reinvestible surplus, they have become a source of liability to the economy. Lack of profitability has been partly the result of a low rate of capacity utilization. For example, in 1970, the capacity utilization rates in the public sector steel plants in Bhilai, Rourkela and Durgapur were 65%, 48% and 30% respectively. The rate of profit in the public sector industries has been highly sensitive to the rate of capacity utilization, given the high ratio of fixed to variable costs. It is the Government s inability to maintain a high rate of public investment and its tendency to shift its pattern of expenditure away from development-oriented projects that has had adverse effects on the profitability of these industries. Economic institutions and organizations, in this respect, had been destabilising. Also, this type of industrial structure and organization that protected the internal market from competition between public and private sectors discouraged technological change in both public and private sectors (Bhagwati and Srinivasan, 1975). Lack of technological progress resulted in low growth rates in industrial production (Table 1) and shortage in consumer goods availability. The rapid population growth coupled with the slow growth of industrial production did not allow industry to absorb the unemployed labour force (Inoue, 1992). Further, the then financial sector policies did not leave much leeway for

50 Table 1: Annual Growth Rate of Net Domestic Product, Industrial Production and Agriculture and Allied Sectors (at 1980-81 prices). Year NDP Indus. Productio n States THE INDIAN ECONOMIC JOURNAL Table 2: Decomposition of Output Growth in Indian Agriculture, Input growth Ag. & allied Prod. 1970-71 5.2 4.9 6.6 1971-72 0.6 4.4-1.7 1972-73 -0.8 6.0-4.7 1973-74 4.9 0.5 7.0 1974-75 1.3 1.8-1.3 1975-76 9.5 5.4 12.9 1976-77 0.9 12.1-5.5 1977-78 7.7 3.4 9.8 1978-79 5.6 6.9 2.3 1979-80 -6.0 1.1-12.3 1980-81 7.5 0.8 12.9 1981-82 5.8 9.3 6.2 1982-83 2.2 3.2-0.7 1983-84 8.1 6.7 10.4 1984-85 3.4 8.6 0.0 1985-86 3.9 8.7 0.5 1986-87 3.8 9.1-1.0 1987-88 3.8 7.3 0.5 1988-89 10.7 8.7 16.3 1989-90 7.0 8.6 2.0 1990-91 5.4 7.2 3.8 1991-92 0.8-1.3-2.3 Source: Compiled from the Economic Survey 1996-97, Ministry of Finance, Government of India, New Delhi. Output Growth (%) due to Technology change Technical efficiency change Andhra Pradesh 50.21 13.60 36.19 Bihar 145.26-2.88-42.38 Gujarat 65.16 13.01 21.83 Haryana 57.35 14.56 28.09 Karnataka 58.44 13.24 28.32 Kerala 73.22 6.25 20.53 Madhya Pradesh 126.72-2.21-24.51 Maharashtra 72.06 12.42 15.52 Orissa 128.33-2.11-26.22 Punjab 52.72 18.85 28.43 Rajasthan 50.03 12.54 37.43 Tamil Nadu 55.28 12.85 31.87 Uttar Pradesh 52.32 12.24 35.44 West Bengal 54.09 13.20 32.71 Source: Kalirajan and Shand (1997), p. 703. business enterprises to choose their capital structure. As Inoue (1992) has argued, the inference is that entrepreneurship in India did not develop significantly relative to the size of its population. Importantly, small private enterprises lack efficiency in manufacturing. Also, it may be argued that the linkage between small businesses, medium enterprises and large scale enterprises has not been strong. Integration from the production of raw materials up to the assembly of final products in the form of sub-contracting that one can see in the Japanese growth process has been missing in the Indian growth process. Another unfavourable aspect of the industrial structure has been the imbalance that exists in the industrial development of different regions. Government s attempts to correct this have not succeeded in many States such as Bihar, Orissa and Madhya Pradesh. As argued by Elizondo and Krugman (1992), this type of industrial development, leads to concentration of production and trading activities in States which have traditionally developed infrastructural facilities for large-scale production, manpower training and financial transaction. This type of industrial development has largely aggravated interregional income inequalities in India. It is logical to argue that institutions, which lead to such industrial structure and performance, may have the potential to contribute considerably to per capita income inequalities across States. Second, the dynamism that was generated by the Green Revolution had worked its way fully into production in the 1980s, and there was no alternative source of strong productivity growth (Bhalla, 1995). Lack of infrastructure and various policy constraints affecting agriculture productivity and trade have been major constraints on technological breakthrough in agriculture as discussed by Vaidyanathan (1995). Though the Green Revolution increased food production dramatically from 95 million tons in 1967-68 to 130 million tons in 1980-81, the per capita availability of foodgrains in India, which from 1956 to 1960 stood at about 161 kilograms per year, was unchanged from 1976 to 1980 due to population growth and inefficient organization in distribution (Rao, 1996). Though agricultural sector did receive input subsidies, other constraints such as low infrastructure development affected its growth tremendously. In this context, an important question is whether farmers have been able to achieve the best practice potential of the chosen technology without wasting resources. Drawing on Kalirajan and Shand (1997), the decomposition of total output growth in agriculture into technical efficiency change, technological progress and input growth helps to explain these changes (Table 2).

Volume 49, No.4 51 For example, their analysis shows that output growth came increasingly from input growth during 1985-1990. Input growth contributed more than 50 per cent in seven states in 1985-90. The share of fertilizer and electricity in the consumption of core inputs, which enjoy heavy subsidies in Indian agriculture, increased from 16.8 per cent in the seventies to 29.2 per cent in the eighties (Misra and Hazell, 1996). An average of only around 18 per cent could be attributed to technological change in the pre-reform period but more to gains in technical efficiency. Importantly, the contribution of increasing technical efficiency to output growth remained more or less at the same levels in most states in the pre-reform periods. Thus since the introduction of the HYVP, Indian agriculture experienced low rates of technological progress together with negligible improvements in technical efficiency, and output growth in the sector became increasingly dependent on input growth. There are at least two explanations for the slow technical progress in agriculture in India. First, throughout 1985-90, government intervention in the market and production intensified which resulted in deterioration in the terms of trade (ratio of prices received to prices paid by the agricultural sector), touching their lowest point at 83.4 in 1986-87. Lower real procurement prices, had a negative effect on technological innovations in the pre-1991 period. Second, the deterioration of land infrastructure, particularly the existing water conservation systems, exerted a constraint on research that is generally irrigation-oriented. Gross fixed capital formation in agriculture at 1980 constant prices sharply declined to an average annual growth rate of 1 per cent during 1980-90 from a corresponding growth rate of 5 per cent in 1970-80. Also, constitutionally agriculture is under the control of States and with increasing industrial lobbying for protection and farmers lobbying for subsidies at State levels pose additional problem for agricultural growth in India. Thus, the inference is that the agricultural institutions across States would not have significantly contributed to widening regional income inequality over and above the existing inequality due to initial differences in factor endowments across States. Third, while the spectacular growth of rural industries in China has attracted significant physical and human capital from agriculture, why it did not happen in India? Though India has institutional framework such as small-scale sector and village industries engaged in the production of consumer goods, the performance of this sector has not been impressive due to various restrictive (regulations and restrictions) and supportive (protecting them from competition) government policies on their operations (Inoue, 1992). As Hayami has argued, capital accumulation on large scale requires institutional innovations in various areas such as taxation, financial system, education and research organization. Unfortunately, during the prereform period, India s potential strength in these areas could not be realized due to various bottlenecks. For example, as discussed by Rao and Vaillancourt (1994), interstate exportation of taxes from the consuming to the producing States on account of the Central Sales Tax worked against the poorer States. Thus, the then existing institutions could not function effectively. An interesting question is why the policy makers in India continued with such inefficient institutional and organizational framework for a long time. A simple answer to the above question may be the lack of effective community involvement in questioning and changing the institutional and organizational structures adopted by India for economic growth. Generally, in democratic countries such as India, community involvement in governance is through voting. Decisions on institutional and organizational structures taken in Parliament or State assemblies are based on the majority of the votes cast. Drawing on the 'median voter theorem', it may be concluded that no matter how many voters there are, majority voting tends to produce an outcome in line with the preferences of the median voter. There is no guarantee that the preference of the median voter would be optimal and the inference is that normal voting procedures usually do not allow adequate expression for intensities of preferences. Further, the existing political competition is a major source for accounting for the increase in government expenditures, particularly subsidies, since the mid sixties. So far as ruling parties

52 THE INDIAN ECONOMIC JOURNAL use state resources to gain support for itself, as political parties often do in competitive electoral democracies, it reduces resources available for development-related expenditures (Chhibber, 1995). As a consequence, the squeeze on capital and maintenance expenditure has been severe in poorer States and this has considerably contributed to interstate growth disparities (Rao and Sen, 1995). After examining the link between economic performance and institutions in India, it becomes necessary to examine how seriously these ineffective institutions, and governance have affected interregional inequalities in per capita income in the pre-reform period in the seventies and eighties. Analytical Framework The analysis in this paper is pitched at State level data, which is important and necessary for the following reasons. First, it has been shown that the poor are concentrated in backward and slow growing regions and therefore, analysis of inter-state disparities is relevant to evolving an effective strategy to combat income inequality and poverty. This is particularly true in an economy where the mobility of labour is less than perfect due to various institutional rigidities. Second, inter-regional disparities have ramifications for the stability of the federal polity. Third, economic liberalization enhances inter-state competition and potential gains from competitive federalism depend upon the competitive strength of competing jurisdictions. Growth performance of the States under a regime of liberalized economic policy is, thus, affected by competitive federalism (Rao, Shand and Kalirajan, 1999). Hypotheses tested in this paper are as follows: (1) Interregional per capita income inequalities increased over time due to the poor quality of economic institutions and organizations in the pre-reform period of 1991. (2) Economic institutions and organizations have exerted significant influence on per capita income across States and there was divergence in the pre-reform period. First, interregional income inequalities and the contribution of the primary, secondary and tertiary sectors to inequalities are examined using the well known Theil's T and L indices and Williamson s weighted coefficient of variation and its decomposition. Secondly, convergence of per capita income across States is examined using the Barro and Sala-i-Martin (1991) approach. Our analysis concerns 15 major States in the Indian Union. These 15 major States account for 95 per cent of population and 92.5 per cent of net domestic product in the country and are therefore representative. It should also be noted that the concept of NSDP only indicates the income originating in different States and does not represent total income accruing to them. Unfortunately, there are no estimates of net factor income accruing to a State from outside its boundaries, and therefore it is not possible to take these into account. Hypothesis 1: Interregional income inequalities and economic institutions in India Since the work of Williamson (1965), the weighted coefficient of variation (CV w ) has been n 1 2 Pi CVw = ( Yi Y *) Y * i= 1 P widely used as a measure of interregional income inequality. Where P i = population of the ith State, P = population of the country, Y i = per capita income of the ith State, Y* = per capital national income = 1/P ΣY i P i, and n = number of States. As pointed out by Metawally and Jensen (1973), the weighted coefficient of variation based on regional per capital income fails to explain either the dispersion of incomes nationally or the dispersion of incomes within regions. It is quite possible for the coefficient to decrease over

Volume 49, No.4 53 time (i.e., a convergence in regional mean incomes), while the dispersion of actual incomes could show an opposite trend. Despite this technical problem, however, we use the Williamson s coefficient, since reliable time-series of individual income data are not yet available for each State. This study uses NSDP as a substitute for income at State level. Since NSDP is equal to the sum of sectoral NSDPs, the squared weighted coefficient of variation can be decomposed as m j= 1 j k ( j k) 2 2 2 CV = z CV + z z COV, w j wj follows: Where z j = the share of the jth sector in NSDP, (1) CV wj = weighted coefficient of variation of the jth sector, and CV w (j,k) = weighted coefficient of variation between sector j and sector k. Now, CV wj and CV w (j,k) are calculated as follows: CV wj COV 1 = Y w * j n * ( Y ji Y j ) i= 1 n 1 1 * * Pi ( j, k ) = ( Y ji Y j )( Yki Yk ) * * P Y j Y k i= 1 2 Where, Y* j and Y* k are the national NSDP per capita of sector j and k respectively, Table 3: Theil Indices and Weighted Coefficient of Variation and Covariation in NSDP Per Capita at Constant (1980-81) Prices Year Theil Indices Coefficient of Variation and Covariation Theil T Theil L CV CV1 CV2 CV3 COV12 COV13 COV23 1970 0.027 0.027 0.23 0.26 0.54 0.35-0.027-0.004 0.160 1971 0.030 0.030 0.25 0.25 0.54 0.35-0.011 0.003 0.165 1972 0.025 0.025 0.23 0.26 0.53 0.35-0.046-0.013 0.161 1973 0.031 0.031 0.25 0.24 0.52 0.36-0.006 0.012 0.155 1974 0.033 0.032 0.26 0.26 0.53 0.36-0.004 0.017 0.164 1975 0.032 0.032 0.26 0.24 0.53 0.36 0.006 0.019 0.164 1976 0.039 0.038 0.29 0.27 0.53 0.37 0.016 0.031 0.171 1977 0.038 0.037 0.28 0.25 0.53 0.37 0.018 0.028 0.175 1978 0.043 0.042 0.30 0.27 0.54 0.39 0.020 0.036 0.188 1979 0.056 0.054 0.35 0.32 0.56 0.38 0.059 0.066 0.195 1980 0.041 0.039 0.30 0.27 0.55 0.36 0.018 0.032 0.179 1981 0.041 0.039 0.30 0.28 0.51 0.36 0.024 0.042 0.164 1982 0.041 0.040 0.30 0.32 0.49 0.37 0.007 0.036 0.168 1983 0.040 0.038 0.29 0.28 0.51 0.38 0.015 0.028 0.177 1984 0.041 0.039 0.30 0.28 0.50 0.37 0.026 0.035 0.174 1985 0.044 0.041 0.31 0.32 0.53 0.39 0.019 0.024 0.193 1986 0.043 0.040 0.31 0.32 0.55 0.38 0.011 0.016 0.193 1987 0.044 0.042 0.31 0.34 0.49 0.37 0.024 0.035 0.164 1988 0.045 0.043 0.31 0.32 0.50 0.37 0.031 0.038 0.170 1989 0.055 0.053 0.35 0.34 0.53 0.40 0.058 0.056 0.192 1990 0.052 0.049 0.34 0.34 0.52 0.40 0.042 0.041 0.192 1991 0.052 0.050 0.33 0.37 0.52 0.39 0.021 0.034 0.191 1992 0.063 0.062 0.37 0.37 0.54 0.41 0.073 0.065 0.205 Source: Authors' calculations. j P i P k w Y ji, Y ki are the per capita NSDP of sector j and k in the ith State respectively, and m = the number of sectors which is three in this study. Thus, equation (1) allows us to examine the extent to which each sector contributes to the overall weighted coefficient of variation of per capita NSDP. Since it includes covariation terms, it can also account for the magnitude and direction of covariations between sectors in the overall weighted coefficient of variation. Table 3 shows the weighted coefficient of variation in NSDP per capita at constant prices (1980-81 prices). The results show that the weighted coefficient of variation has increased over time (Figure 1). Theil's indices also confirm that regional inequalities have been raising over time. The increase has been slow but steady. Sectoral NSDP per capita is then used to estimate the weighted coefficient of variation for each sector and the weighted coefficient of variation between sectors.

54 THE INDIAN ECONOMIC JOURNAL 0.6 We igh ted Co effi cie nt of Var iati on 0.5 0.4 0.3 0.2 CV1 CV2 CV3 CV 0.1 0.0 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 Year Figure 1 These results are given in Table 3. From the weighted coefficients of variation for the primary sector it is difficult to observe any pattern. On the whole, CV 1 has incresed from 0.258 in 1970 to 0.371 in 1992. The weighted coefficient of variation for the secondary sector has been more or less stable at 0.5, indicating that this sector has developed fairly uniformly with population size during this period. Nevertheless, the coefficient of variation for the secondary sector has been higher than that for the primary sector from 1970 to 1992 without any exception. This suggests that the impact of green revolution, which occurred during the mid 1970s, on regional disparity in agricultural growth has been less than the impact of pattern of industrial development on regional disparity on industrial growth. On the other hand, the weighted coefficient of variation for the tertiary sector has been above that for the primary sector but below that for the secondary sector throughout the period of analysis. In general, the increase in the coefficients of variation for the primary sector and the tertiary sector reflect uneven development across States relative to population distribution. The estimates of the weighted coefficient of variation between sectors provide some interesting results. Low positive values for COV w between secondary and tertiary sectors signify that States with higher SDP per capita in the secondary sector tend also to have slightly higher SDP per capita in the tertiary sector. These sectors thus appear to be complementary in their development, though not very significant. On examining the COV w between primary and secondary sectors, and between primary and tertiary sectors, no clear patterns emerge. The negative values indicate a shift in value added from the primary to the secondary and tertiary sectors. But, the shift does not seem to be uniform. The interesting result is that both secondary and tertiary sectors appear to be growing in a complementary manner leading to the strengthening of the disparity over time.

Table 4: Share of Each Component in Weighted Coefficient of Variation (Based on Equation (1)) (in%) Year CV1 CV2 CV3 COV12 COV13 COV23 Total 1970 29.2 25.3 19.7-10.5-1.9 38.3 100 1971 23.9 22.9 19.0-3.7 1.7 36.3 100 1972 25.9 30.1 24.0-18.8-7.2 46.1 100 1973 19.5 22.1 19.8-1.9 5.4 35.1 100 1974 20.4 21.5 18.5-1.3 6.9 33.9 100 1975 18.6 20.2 17.9 1.9 8.1 33.4 100 1976 16.5 20.1 16.7 4.2 10.6 31.9 100 1977 15.3 19.8 17.2 4.8 10.1 32.8 100 1978 15.9 18.9 17.0 4.8 11.2 32.2 100 1979 13.6 17.6 14.3 10.4 15.1 29.1 100 1980 15.6 20.6 16.2 4.1 10.3 33.2 100 1981 16.9 16.9 16.5 5.7 13.8 30.1 100 1982 19.3 16.6 18.7 1.5 11.5 32.4 100 1983 16.0 18.2 19.0 3.7 9.2 34.0 100 1984 15.0 16.5 18.8 5.8 11.0 32.9 100 1985 15.8 18.3 19.6 3.9 6.9 35.5 100 1986 14.7 20.3 20.2 2.1 4.6 38.1 100 1987 15.6 17.1 19.5 4.7 9.9 33.3 100 1988 15.1 17.4 18.3 6.2 10.5 32.6 100 1989 12.8 15.8 18.6 9.1 12.5 31.2 100 1990 12.7 17.4 19.3 7.1 9.7 34.0 100 1991 14.5 18.0 20.5 3.5 8.0 35.6 100 1992 12.6 14.8 18.4 10.0 13.2 31.1 100 Source: Authors' calculations. Volume 49, No.4 55 Table 4 presents the results of a sectoral decomposition analysis discussed earlier. It can be seen that the tertiary sector contributed more relative to the primary and secondary sectors to the overall level of interregional inequality measured by CV w in per capita NSDP. However, relative to the primary sector, the secondary sector contributed more to widening regional income inequalities. The share of covariation terms between the tertiary and the secondary sectors has been positive and larger in the 1970s and 1980s indicating complementary and mutually strengthening development of the secondary and tertiary sectors leading to the increase in disparity. On the other hand, the share of covariation terms between the primary and secondary sectors has been very low and negative mostly in the early 1970s indicting that these two sectors have not been strengthening mutual development. The share of covariation terms between primary and tertiary sectors has been higher than that between primary and secondary sectors. This indicates relatively a larger shift in NSDP from the primary sector to the tertiary sector rather than to the secondary sector. This corroborates the arguments of Rosen (1992) and Papola (1992) that the industrial sector was not able to absorb the labour force from agriculture in the seventies and eighties. Hypothesis 2: Convergence of per capita income and economic institutions Using the neoclassical growth model, Barro and Sala-i-Martin (1991) showed clear evidence of absolute convergence for the 48 contiguous U.S. States for the period 1840-1988. By assuming consumers maximize their utility and firms maximize their profits, a general equilibrium for the growth rates of income, capital, and consumption of the economy can be derived from which steady state levels of income, capital and consumption can be calculated. Then the questions are as to whether the economy is converging to the steady state and what is the speed of convergence. Drawing on Barro and Sala-i-Martin (1995), the following regression model is given in which the current level of depends on the initial level of income: 1/T.ln [y it /y i,t-t ]= α - [ln (y i,t-t ) (1 - e -βt ) (1/T) + δs it-t + u i (2) where y it refers to per capita NSDP in the ith State at constant (1980-81) prices, y i,t-t denotes per capita NSDP in the ith State in the beginning of the period, T is the length of time period and S it is the vector of other variables to control for variations in the steady-state values * of x i and y * i, across the States. Given the correlation between sectoral growth and poverty, the share of the primary sector in total NSDP (S it-t ) in the initial period is included to minimize inter- * State differences in the steady state values x i and y * i. Inclusion of S it-t also facilitates examining whether there is any conditional convergence. Testing for absolute convergence is done dropping the variable S it-t from Equation (2).

56 THE INDIAN ECONOMIC JOURNAL Table 5: Non-linear Least Squares Estimates of β (Convergence) Coefficients. (per capita NSDP) Period Absolute convergence Conditional convergence Pre-reform (1970-75) -0.0287 (-2.5562) -0.0298 (-2.6755) Pre-reform (1976-80) -0.0301 (-2.6752) -0.0306 (-2.3345) Pre-reform (1981-85) -0.0276 (-2.9113) -0.0281 (-2.8922) Pre-reform (1986-90) -0.0272 (-2.1456) -0.0278 (-2.3118) Notes: Years represent fiscal years, i.e., 1985 refers to 1985-86. Figures in parentheses are t-ratioes. All the coefficients are significant at the 5 per cent level. Source: Authors' calculations. Equation (2) implies that absolute convergence exists when β, the speed of convergence, is positive and significant. If β is negative, it means divergence. The nonlinear least squares estimates of Equation (2) with and without the variable S it-t (Table 5) for periods of 1970-1975, 1976-1980, 1981-1985, and 1986-1990. The results reveal a number of interesting features of the inter-state growth process in India in the pre-reform periods in the seventies and eighties. The estimates of β are negative and significant in the pre-reform periods showing a divergent trend in incomes over the years. Thus, there are no evidences of either absolute or conditional convergence of per capita income across States in the pre-reform periods. This implies that growth of per capita NSDP in the States in India is positively related to their initial levels. In other words, States with initially high per capita NSDP tended to grow faster than those with lower per capita NSDP. These findings are contrary to the predictions of the neoclassical growth models and the empirical findings for cross sections of countries as well as different States within the U.S.A. But, these results confirm the proposition of Elizondo and Krugman (1992) that interregional income inequalities, given the degree of government intervention, would increase as an economy moves away from the liberalized regime to a restricted regime with several controls on economic activities with inefficient institutions. Thus, the strong influence of country-specific institutions and organizations and their economic policies pursued by India on income inequalities becomes clearly evident in the light of our arguments presented in the above section. The coefficient of the initial share of income from the primary sector is positive and significant, which means that States with an initially high share of income from the primary sector tended to grow faster than those with a lower share. In the absence of technological progress, this characteristic of growth does not have the potential to contribute to the increase in interregional inequality further. Nevertheless, the finding of divergence, which is contrary to the prediction of neoclassical growth theory, casts doubts on the validity of the critical assumption of diminishing returns to reproducible capital. The positive association of growth rates with the initial level of incomes probably shows that, in an economy with large unutilised resources and a poor state of social and physical infrastructure due to the poor quality of institutions, there will be increasing returns to reproducible capital in the initial stage of development. Thus, combining the inequality measures and the speed of divergence during this period, we may postulate that there is an inverted U-shaped relationship between the quality of institutions and income inequalities as argued by Chong and Calderon (2000), though their cross section data did not allow them to establish this dynamic relationship empirically. Thus, lack of both absolute and conditional convergence in the pre-reform periods indicates the need for changes in domestic institutions and policies in the form of more reforms to boost economic growth (for further discussion on this see, Rao, Shand and Kalirajan, 1998). The post-reform average annual growth rate of 6% clearly indicates that with appropriate institutional and organizational changes it is possible to achieve sustained economic growth. Conclusions Combining Hayami s findings of the importance of country-specific institutions for promoting sustained economic growth with the convergence of income hypothesis, an indirect method to examine the link between the quality of institutions and interregional income inequalities in India is worked out. The principal force driving convergence in the neoclassical growth model is

Volume 49, No.4 57 diminishing returns to reproducible capital. Thus, economies with lower initial values of capitallabour ratios will have high marginal products of capital and therefore, tend to grow at higher rates. But, inefficient and poor quality institutions and organizations could lead to violation of the critical assumption of diminishing returns to reproducible capital. This means divergence of income for a considerable period of time in the development process. Thus, it is logical to argue that the convergence hypothesis will hold only when country-specific institutions and organizations do not intervene in the process negatively to delay or constrain the convergence process. Thus, drawing on Hayami s findings, testing the convergence hypothesis of income provides an alternative method of examining the link between institutions and inequalities. First, using Theil's T and L indices and the Williamson s weighted coefficient of variation and covariation across sectors, the degree of interregional income inequalities is examined from 1970 to 1992. The results indicate that interregional income inequality increased over time, which indicates the inefficient functioning of the institutions in India during the period. The growth of tertiary sector has contributed more relative to the growth of the primary and secondary sectors to interregional inequality. Per capita incomes across States over the prereform period have shown divergence indicating the accentuation of inter-state disparities in the pre-reform periods. This result is contrary to the predictions of the neoclassical growth models and the empirical findings for different States within the U.S.A. However, this result confirms our earlier argument based on Hayami's thesis that country-specific institutions and their economic policies would influence the convergence process and that with prolonged inappropriate policies there would be divergence. This result also supports the arguments of Elizondo and Krugman (1992). Further, the results indicate an inverted U-shaped relationship between the quality of institutions and inequality. The policy implications of the foregoing analysis are as follows: The results are consistent with the recent view that greater equality can be positively associated with growth (Birdsall et al., 1995). The link is provided by the quality of institutions. Thus, primary importance in the governance should be given to improving and sustaining the quality of country-specific institutions. The accelerated acceptance of better technologies and best techniques depend on sustained investment in agricultural infrastructure including agricultural credit. Central and State government expenditures on subsidising inputs such as power and fertilizer would be better spent on infrastructure. Relaxing government regulations and promoting competition from enterprises within and outside India would improve the performance of the secondary sector, particularly the manufacturing. Accountability and not paternalism should be the driving force for the public sector enterprises. The recent economic reform appears to be working in these directions to improve the overall performance of the Indian economy. NOTES 1. Institutions are rules that influence the behaviour of economic decision making units and their performances. 2. Inoue (1992) has presented a comprehensive review of industrial development policy of India. 3. However, in States like Punjab and Haryana the number of rural industries supplying agricultural implements and related products has been very high compared to other States mainly because these two States did not have major heavy industries and historically they were considered by policy makers as areas suitable for foodgrain production (Zarkovic, 1987). 4. The disintegration of the Soviet Union (exogeneous), and the mounting fiscal deficit pressure (endogeneous) in India finally paved way for institutional innovations through the introduction of the 1991 economic reform in India. 5. Breton (1996) argues that competitive equality and cost-benefit appropriability of jurisdictions are important contributory factors for the stability of horizontal intergovernmental competition. 6. For example, Green (1969), Gilbert and Goodman (1976), Mathur (1983), Akita (1988), and Akita and Lukman (1995).

58 THE INDIAN ECONOMIC JOURNAL 7. It should be noted at this point that the coefficient could take on different values depending on how a country is divided into regions (Parr, 1976). Whether the nation s metropolitan region is treated as a separate region or not affects the coefficient greatly (Gilbert and Goodman, 1976). Thus, comparisons with other countries are not very meaningful. 8. The conditional convergence hypothesis assumes a single steady-state equilibrium. But, as discussed by Galor (1996), an economic system may be characterised by multiple steady-state equilibria and may thus lead to club convergence even in neoclassical growth models that exhibit diminishing marginal productivity of capital and constant returns to scale. A testing of a club convergence hypothesis with these data will be attempted in a subsequent study. REFERENCES 1. Akita, T. (1988), Regional development and income disparities in Indonesia, Asian Economic Journal, Vol. 2, pp. 165-191. 2. Akita, T. and Lukman, R.J. (1995), Interregional inequalities in Indonesia: a sectoral decomposition analysis for 1975-92, Bulletin of Indonesian Economic Studies, Vol. 31, No. 2, pp. 61-81. 3. Barro, Robert and Sala-I-Martin, Xavier (1991), Convergence Across States and Regions, Brookings Papers on Economic Activity, I, pp. 107-181. 4. Barro, Robert and Sala-I-Martin, Xavier (1995), Economic Growth, New York: McGraw-Hill. 5. Bhagwati, Jagdish N. and T.N. Srinivasan (1975), Foreign Trade Regimes and Economic Development: India, New York: Columbia University Press. 6. Bhalla, G.S. (1995) Globalisation and Agricultural Policy in India, Indian Journal of Agricultural Economics, Vol. 50, pp. 8-26. 7. Birdsall Nancy, David Ross, and Richard Sabot (1995), Inequality and Growth Reconsidered: Lessons from East Asia, World Bank Economic Review, Vol. 9, pp.477-508. 8. Breton, Albert (1996), Competitive Governments, Cambridge: Cambridge University Press. 9. Chhibber, Pradeep (1995), Political Parties, Electoral Competition, Government Expenditures and Economic Reform in India, Journal of Development Studies, Vol.32, pp.74-96. 10. Chong Alberto and Cesar Calderon (2000), Institutional Quality and Income Distribution, Economic Development and Cultural Change, Vol. 48, pp.761-786. 11. Das, S.K. and A. Barua (1996), Regional Inequalities, Economic Growth and Liberalization: A Study of the Indian Economy, Journal of Development Studies, Vol. 32, No.3, pp. 364-390. 12. Datta Roy Choudhury, U. (1993), Inter-State and Intra-State Variations in Economic development and standard of Living, New Delhi: National Institute of Public Finance and Policy. 13. Dreze, J. and A. K. Sen (1997), Indian Development: Selected Regional Perspectives, Delhi: Oxford University Press. 14. Elizondo, R.L. and Paul Krugman (1992), Trade Policy and Third World Metropolis, NBER Working Paper No. 4238. 15. Evans, P. and G. Karras (1996), Do Economies Converge? Evidence from a Panel of U.S. States, Review of Economics and Statistics, Vol. 78 (August), pp. 384-388. 16. Galor, O (1996), Convergence? Inferences from Theoretical Models, The Economic Journal, Vol. 106, pp. 1056-1069. 17. Gilbert, A.G. and D.E. Goodman (1976), Regional Income Disparities and Economic Development: a Critique in A.G. Gilbert (ed.) Development Planning and Spatial Structure, New York: John Wiley & Sons, pp.113-141. 18. Green, A.G. (1969), Regional Inequality, Structural Change, and Economic Growth in Canada: 1890-1957, Economic Development and Cultural Change, Vol. 17, No. 4, pp. 567-583. 19. Hayami, Y. (1997), Development Economics, Oxford: Clarendon Press. 20. Inoue, K. (1992), Industrial Development Policy in India, Tokyo: Institute of Developing Economies. 21. Kalirajan, K.P. and R.T. Shand (1997), "Sources of Agricultural Growth in India", Indian Journal of Agricultural Economics, Vol. 52, pp.693-706. 22. Knack Stephen and Philip Keefer (1995), Institutions and Ecoomic Peformance: Cross-Country Tests Using Alternative Institutional Measures, Economics and Politics, Vol.7 pp.207-228. 23. Kuznets, Simon (1955), Economic Growth and Income Inequality, American Economic Review, Vo.45, pp. 1-28. 24. Lin, Justin Yifu, Cai Fang and Li Zhou (1996), The China Miracle: Development Strategy and Economic Reform, Hong Kong: Chinese University Press. 25. Mathur, A. (1983), Regional development and income disparities in India: a sectoral analysis, Economic Development and Cultural Change, Vol. 31, No.3, pp. 475-505. 26. Metawally, M.M. and R.C. Jensen (1973), A note on the measurement of regional income dispersion, Economic Development and Cultural Change, Vol. 22, No. 1, 27. pp. 135-136.

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