Journal Issue
Share
Article

Internal Migration, Center-State Grants, and Economic Growth in the States of India

Author(s):
International Monetary Fund. Research Dept.
Published Date:
January 1996
Share
  • ShareShare
Show Summary Details

ASTRIKING FEATURE of India’s economic development since it became independent in 1947 is its low rate of per capita income growth, particularly in comparison with most other Asian countries. This is all the more noticeable given the favorable preconditions in the late 1940s of a well-diversified resource base, the world’s fourth largest pool of skilled (scientific and technical) manpower, a sizable group of entrepreneurs, a long experience with public administration, and a relatively stable political system.

India’s growth process since the 1950s has largely been influenced by the economic policies of the Congress Party and the country’s first Prime Minister, Jawaharlal Nehru. The Congress Party embraced the “socialist pattern of development” whereby central planning guided public and private sector activities, with emphasis placed on import substitution policies and heavy industry. Industrial licensing was an integral part of government intervention in the economy, as it directed investment flows both across sectors and across states. In addition, the “commanding height” industries (defense, heavy industry, mining, air and rail transport, communications, and power) were exclusively the purview of the public sector. India’s planners also sought to influence interregional investment flows so as to engender balanced regional development, which is particularly important in the context of this paper. The process of public sector expansion and nationalization continued through the 1970s with several new public financial institutions being set up or nationalized during the period. Thus, until the mid-1980s, India essentially operated as a closed economy, with the public sector dominating economic activity. Macroeconomic crises were typically resolved through the imposition of quantitative and price controls that contributed, in part, to India’s relatively low inflation rates, in comparison with other developing countries.

In common with many of the developed world’s federal countries (Germany, Switzerland, Australia, Canada, and the United States), there have been concerns within India since independence about regional disparities in national economic development. In addition, the efficiency costs of using income-equalizing center-state grants to promote objectives such as national economic equity have been an important related theme in the economics of fiscal federalism.1

Early work by Myrdal (1957), Hirschman (1958), and Kaldor (1970) gave rise to the hypothesis of an inverted U-shaped curve relationship between the extent of regional income disparity and a nation’s level of income. Some of the first empirical tests of the validity of rising, and then falling, cross-regional disparities in income were conducted by Kuznets (1958) and Williamson (1965) for regions of developed countries. This body of work sparked much interest in India, the developing world’s most populous federal country (see Section IV).

While there are many studies of the international processes of growth and convergence across countries (see Baumol (1986), DeLong (1988), Dowrick and Nguyen (1989), and Barro (1991), among others), there are relatively few that examine regional growth patterns within any given country. Exceptions have been Easterlin (1960a and 1960b), Williamson (1965), and Barro and Sala-i-Martin (1992a) for the states of the United States of America; Williamson (1965) and Barro and Sala-i-Martin (1991) for the regions of Europe; Coulombe and Lee (1993) for the provinces of Canada; Cashin (1995) for the regions of Australasia; De Gregorio (1992) for several South American nations; and Barro and Sala-i-Martin (1992b) and Shioji (1993) for the prefectures of Japan. This paper represents one of the first formal econometric analyses of the neoclassical growth model’s predictions for the process of income growth and convergence across the regions of a developing country.

By undertaking an analysis of the 20 Indian states, we hope to minimize the problems that would arise if the various economies exhibited different steady-state real per capita incomes.2 Assuming that all regions within a given country possess similar levels of technology and similar preferences, and that there are no institutional barriers to the flow of either capital or labor across regional borders, then the neoclassical growth model would predict all regions to have similar levels of real per capita income in the steady state. Accordingly, absolute convergence should then be closely approximated by conditional convergence.3

It is important to note that, given the neoclassical growth model’s assumption of closed economies, such a model of the convergence process obviously cannot be applied literally to the Indian states because, for given technologies, convergence in both per capita income and capital stocks will occur faster in open than in closed economies. However, as shown by Barro, Mankiw, and Sala-i-Martin (1995), in the presence of imperfect capital markets that constrain only a fraction of physical capital to serve as collateral for investment by governments and/or individuals, aggregate income exhibits very similar behavior to that which would be predicted by a closed economy model. That is, partial capital mobility in an open economy version of the neoclassical growth model can explain the gradual incidence of cross-state income convergence. Undoubtedly, these constraints on the role of capital for collateral were present in India over the period 1961–91.

Across regions of a given country, which share a common steady state, convergence of per capita incomes in the neoclassical growth model is driven by diminishing returns to capital. Each addition to the capital stock of a region generates large (small) increases in output when the regional stock of capital is small (large). Accordingly, if the only difference between regional economies lies in the level of their initial stock of capital, the neoclassical growth model predicts that poor regions will grow faster than rich ones; regions with lower starting values of the capital-labor ratio will have higher per capita income growth rates. Two other channels by which convergence can occur are the redistribution of incomes from relatively rich regions to relatively poor regions of a federal country by its central government, and flows of labor from poor to rich regions. We examine these last two channels in this paper. These issues are relevant from a policy perspective in that this study evaluates whether, in the Indian context, fiscal federalism and the flow of labor across states have contributed to the equalization of state per capita incomes.

Accordingly, answers to the following questions will be explored in this paper. First, did the initially poor states of India subsequently grow faster than the initially rich states? Second, has the cross-sectional dispersion of per capita incomes across the states widened or narrowed over the period of analysis? Third, did cross-state migration from poor to rich states respond to differentials in per capita incomes across the states? Evidence is found that supports the conjectures of the neoclassical growth model of Solow (1956) and Swan (1956): the relatively poor states did indeed grow faster, with 1.5 percent of the gap between per capita incomes in initially poor and initially rich states being closed every year. There has also been a widening in the cross-sectional dispersion of real per capita state incomes over the period 1961–91. However, center-state grants ensured that the dispersion of real per capita state disposable incomes remained relatively constant over the period. Finally, net migration across states does not appear to be greatly influenced by differentials in state per capita incomes, which indicates that there are sizable barriers to labor flows across the states of India. There is also little evidence that cross-state migration is an important cause of the convergence of real state per capita incomes in India.

I. Overview of the Indian State Economies

Before rule by the British crown was officially validated in the Indian subcontinent in 1858, it is perhaps fair to say, the region as a whole had been united only twice in recorded history. The first unification was by the Mauryan emperor, Ashoka, during the third century B.C., and the second by Akbar during the Mughal empire during the 16th century A.D. (Wolpert (1993)).4 Even at the time of India’s independence from Britain in 1947, the Indian states remained fairly diverse in their ethnic and linguistic background.

Center-State Relations

The need to maintain a strong sense of national unity was clearly recognized during 1947–50, when the Indian constitution was drafted. Consequently, within the confines of a federal system, the constitution gave strong political and economic powers to the center, in particular, the power to allocate financial resources between itself and the states. One of the notable exceptions is in the area of agriculture, where the states have primary control; this includes the taxation of land and agricultural income and the implementation of land reforms (Joshi and Little (1994)).5 The constitution also provides for the establishment every five years of a Finance Commission, to review the distribution of tax revenues both between the central and state governments and across state governments.

The relative financial strength of the center vis-à-vis the states can be ascertained from the following facts: most taxes are levied and accrue to the center (the vast majority accounted for by income, excise, and custom taxes);6 the relatively elastic sources of tax revenues have also been the purview of the center; and the center can borrow from domestic and international markets while the states cannot borrow abroad and need the center’s permission, de facto, to borrow domestically.7 In case of a conflict on overlapping governmental responsibilities, the center’s decision (through legislation) dominates. Broadly speaking, taxes on income and production (with some exceptions) are levied by the center and those on sales and purchases are levied by the states. It is interesting to note that while the states could potentially have tapped their own resources by levying taxes on agriculture and land, they have shown little inclination to do so. Indeed, the share of such taxes in total state tax revenue has been barely 1 percent (Sury (1992)).

Given the vertical imbalance between the resource raising powers and expenditure needs of the center and the states, the constitution has provided for a complex mechanism of transfers from the center to the states.8 Essentially, there are three direct channels: statutory transfers (comprising tax sharing and “grants-in-aid”) through the Finance Commission mechanism;9 plan grants through the Planning Commission mechanism; and “discretionary” grants through central ministries, primarily for centrally sponsored schemes. There also exist indirect channels such as loans from the central government and the allocation of credit by financial institutions controlled by the central government.10

The purpose of examining transfers in this paper is to determine whether they served their intended purpose of reducing regional income disparities, by the central government allocating relatively greater grants to low-income states. Since not all transfers defined in the Indian context are intended to reduce such disparities, for estimation purposes we use data published in state budgets that can best be singled out as outright intended grants. Specifically, these include statutory “grants-in-aid,” as specified by the Finance Commission; grants for plan purposes as assessed by the Planning Commission; and grants for centrally sponsored schemes.11 Thus we exclude from the typical Indian definition of transfers those designed for center-state tax sharing, and indirect transfers through loans. The justification for excluding the tax-sharing transfers is primarily the lack of transparency in determining the magnitude of the income equalizing component.12 Loans are clearly distinct from grants in that they have to be repaid. Accordingly, to the extent that we exclude those center-state tax-sharing transfers designed explicitly to reduce regional disparities, and exclude loans (including external assistance) that may have been subsequently forgiven,13 our estimate of what we will henceforth call “grants” understates the role played by the center in reducing regional disparities.

Economic Features of Indian States and Regional Disparities

Although the Indian states have long shared common political institutions and national economic policies, there is wide diversity in geographic, demographic, and economic features (Tables 1 and 2). While the states in central India—Madhya Pradesh, Rajasthan, and Maharashtra—are the largest in land area, the eastern states—Uttar Pradesh and Bihar—have the highest population levels. The highest population density (persons per square kilometer) is observed at extreme geographical ends, the highest in Delhi in the north, followed by Kerala in the south and West Bengal in the east. The states that lag far behind the others in literacy rates (total as well as female) and in reducing death rates are Uttar Pradesh, Bihar, Madhya Pradesh, and Rajasthan; these four regions also have the highest birthrates.

Of the six initially poor states (Manipur, Bihar, Orissa, Tripura, Uttar Pradesh, and Madhya Pradesh) in 1961, five remained among the six poorest (in per capita income) in 1991.14 The exception was Madhya Pradesh, which had moved up three notches by 1991, and was replaced by Jammu and Kashmir (Table 2). Delhi, the richest region in 1961 as well as in 1991, is clearly an outlier in that its per capita income in both years was more than double the average of the remaining states. Apart from Delhi, six other states (Maharashtra, West Bengal, Punjab, Gujarat, Tamil Nadu, and Haryana) had above average per capita income in 1961 and all, with the exception of West Bengal, remained above average in 1991.

Table 1.Comparative Demographic and Geographic Indicators a
Population (millions)Literacy ratesb per 1,000 personsFemale literacy rates b per 1,000 personsVital rates avg. 1980–88 (per 1,000 persons) cUrban share of total population (percent)
States19611991Annualized 1961–91 population growth rate (percent)1991 land area (1,000 sq. km.)1991 density (persons per sq. km.)1961199119611991CBRCDR19611991
Andhra Pradesh (AP)35.9866.312.0427524124645114033731.011.417.426.8
Assam (A)10.8422.292.407828433053419643734.712.67.211.1
Bihar (B)46.4586.342.071744972183858223138.314.58.413.2
Delhi (D)2.669.374.202631862076150968028.67.388.890.0
Gujarat (G)20.6341.172.3019621036260922848534.012.125.834.4
Haryana (H)7.5916.322.554436924155311340937.110.517.324.8
Himachal Pradesh (HP)2.815.111.99569224963511252532.010.16.48.7
Jammu & Kashmir (JK)3.567.722.58222351305134.39516.623.8
Kamataka (KA)23.5944.812.1419223429856016744329.810.122.330.9
Kerala (KE)16.9029.031.803974755190645686923.76.415.126.5
Madhya Pradesh (MP)32.3766.142.384431492054358128438.915.814.323.2
Maharashtra (A1)39.5578.752.3030825635163119850529.910.128.238.7
Manipur(MN)0.781.832.84228236061018948628.16.99.027.7
Orissa (O)17.5531.511.9515620225248610134432.713.86.313.4
Punjab (P)11.1420.191.985040131557120749729.96.523.129.7
Rajasthan (R)20.1643.882.593421281813887020838.714.116.322.9
Tamil Nadu (TN)33.6955.641.6713042836463721]52326.911.926.734.2
Tripura (T)1.142.742.921026224360412450027.19.08.815.3
Uttar Pradesh (UP)73.76139.032.112944722074178326039.516.612.919.9
West Bengal (WB)34.9367.982.228976634557720347234.611.024.527.4
All Indiad439.24843.932.183.28725728352115339433.412.918.025.7
Sources: Registrar General and Census Commissioner for India (1991); Government of India (1983); Government of India (1991a and 1991b) and earlier issues.

The 1991 census has not yet been conducted in Jammu and Kashmir; its 1991 population figure is an official projection.

The literacy rates for 1961 exclude that part of each state’s population aged 0–4 years; the rates for 1991 are for the number of literates per 1,000 persons aged 7 years and above.

CBR denotes the crude birthrate per 1,000 persons in the rural areas of each state; CDR denotes the crude death rate per 1,000 persons in the rural areas of each state. The all-India figures are weighted averages, with the state/union territory population as weights.

Includes data from states/union territories other than our sample of 20 regions.

Sources: Registrar General and Census Commissioner for India (1991); Government of India (1983); Government of India (1991a and 1991b) and earlier issues.

The 1991 census has not yet been conducted in Jammu and Kashmir; its 1991 population figure is an official projection.

The literacy rates for 1961 exclude that part of each state’s population aged 0–4 years; the rates for 1991 are for the number of literates per 1,000 persons aged 7 years and above.

CBR denotes the crude birthrate per 1,000 persons in the rural areas of each state; CDR denotes the crude death rate per 1,000 persons in the rural areas of each state. The all-India figures are weighted averages, with the state/union territory population as weights.

Includes data from states/union territories other than our sample of 20 regions.

Table 2.Comparative Economic Indicators
Real per capita NDP (1990 rupees)Center-state grants as percent of state NDPa (subperiod average)Nominal NDP (current prices, million rupees)Share of agriculture in state NDP (percent)Share of manufacturing in state NDP (percent)
19611991Annualized per capita real NDP growth rate 1961–91 (percent)1961–651971–751981–85196119911961198119611981
Andhra Pradesh (AP)2.5674.7282.041.451.803.929,832311.65058.1845.597.7911.22
Assam (A)2.9414.0141.044.535.404.703,36089.05055.2754.0117.146.94
Bihar (B)2,0072,6550.931.181.592.849,930227,41053.5854.149.676.40
Delhi (D)6,23610,1771.631,74694,2807.014.5523.3023.31
Gujarat (G)3,3795.6871.741.041.411.427,382233,16041.5938.4820.8221.92
Haryana (H)3,0537,5023.001.161.412,450122,29062.7154.2311.2414.14
Himachal Pradesh (HP)2,4654.7902.21...9.1319.0874224,29060.5950.075.554.38
Jammu & Kashmir (JK)2,5113,8721.446.9013.7713.0294029,51067.5550.675.785.01
Karnataka (KA)2.7634,6961.771.631.381.566,916209,90060.4142.788.9618.18
Kerala (KE)2,4184,2071.852.112.351.874,322121,95055.6339.5312.4514.05
Madhya Pradesh (MP)2,3534,1491.891.591.582.538,073271,71062.1149.356.9211.96
Maharashtra (MH)3,8187,3162.170.691.331.1115,974571,78041.5827.7921.5927.41
Manipur (MN)1,4383.3226.6637.161196,94055.6948.858.344.71
Orissa (O)2,0263,0771.393.393.935.433,74196,64061.3154.947.287.41
Punjab (P)3,4178,3732.992.391.440.964.038167,29054.0048.8910.1211.97
Rajasthan (R)2,6514,1131.461.692.873.095.594179,40056.2150.3410.1611.08
Tamil Nadu (TN)3,1185,0471.611.081.261.8711.118280,31051.8825.4315.0327.42
Tripura (T)2,3253.4201.2919.6328.672849,31062.7257.155.714.4S
Uttar Pradesh (UP)2,3533,5161341.121.402.8618.431484,77060.0151.687.8310.66
West Bengal (WB)3,6414,7530.890.882.051.4113,394320,62040.5131.8820.2624.68
All Indiab2,8574,9341.82142,42c4139,43c48.56d41.23d17.20d22.98d
Sources: Authors’ calculations, derived from Reserve Bank of India (1993) and earlier issues; Government of India (1986); Government of India (1995) and earlier issues.

Subperiod average of center-state grants, as a percentage of the subperiod average, state NDP. Data on transfers from the central government to Delhi are unavailable.

Includes data from states/union territories other than our sample of 20 regions.

The all-India product figures are net national product (at factor cost).

The all-India sectoral composition data relate to net domestic product at factor cost (current prices), for agriculture, forestry and fishing, and manufacturing, respectively. The 1981 all-India figures include mining in the share of agriculture, and construction in the share of manufacturing.

Sources: Authors’ calculations, derived from Reserve Bank of India (1993) and earlier issues; Government of India (1986); Government of India (1995) and earlier issues.

Subperiod average of center-state grants, as a percentage of the subperiod average, state NDP. Data on transfers from the central government to Delhi are unavailable.

Includes data from states/union territories other than our sample of 20 regions.

The all-India product figures are net national product (at factor cost).

The all-India sectoral composition data relate to net domestic product at factor cost (current prices), for agriculture, forestry and fishing, and manufacturing, respectively. The 1981 all-India figures include mining in the share of agriculture, and construction in the share of manufacturing.

While, in general, the richer states in 1991 were more industrialized than others (for example, Tamil Nadu, Maharashtra, Delhi, and Gujarat). Punjab and Haryana, primarily agricultural states, had the second and third highest per capita income in 1991 (Table 2). The success story of Punjab and Haryana is mainly accounted for by the “green revolution” during the 1960s, when the productivity of agricultural output (mainly wheat) rose sharply; their agricultural productivity has remained high compared with that of other agrarian states. Also, Punjab has invested heavily in irrigation and flood control measures, both of which have helped to reduce its susceptibility to weather-induced output shocks. West Bengal stands out as a highly industrialized state that was among the richest in 1961, but fell below average in 1991. Supply shocks in the form of power shortages and labor unrest have frequently beset industry in West Bengal; it also saw a rapid decline in one of its significant export industries—jute—as artificial fibers flooded international markets. In some states (Assam, Bihar, and Manipur) the share of manufacturing in state NDP actually declined substantially between 1961 and 1981.

Figures 1–3 illustrate the variations in per capita output for the 20 states in our sample over the period 1961–91. Among the six initially poor states in 1961 (Figure 1), Manipur has recorded the highest per capita annual growth rate between 1961 and 1991 at 3.3 percent, despite having the third highest population growth rate in the whole sample. Manipur and Tripura (with the second highest population growth rate) also clearly benefited the most from center-state grants in the post-1970 period (Table 2). Several northeastern states, including Tripura, have faced a large influx of refugees from Bangladesh, following that country’s creation in 1971. At the other extreme, Bihar has grown at a very slow pace, recording the second lowest growth in per capita NDP during 1961–91. This dismal performance, despite net out-migration over the period of analysis, appears to be closely related to low agricultural productivity in a largely agrarian state, poor infrastructure, and disincentives to invest because of political uncertainty, industrial unrest, and the steady erosion of law and order.

Among the nine initially middle-income states (Figure 2), Haryana, Himachal Pradesh, Andhra Pradesh, and Kerala grew at a faster pace in annual per capita terms during 1961–91 than the country-wide average of 1.82 percent a year (Table 2). Moreover, both Himachal Pradesh and Jammu and Kashmir benefited greatly from center-state grants (Table 2). Andhra Pradesh and Kerala were among the main exporters of labor to the Middle East during the 1970s and the early 1980s; remittances to these states through the late 1980s may also have been a significant factor contributing to their relatively high rates of income growth during 1961–91.

Figure 1.Real Per Capita NDP (1990 rupees) Six Initially Poor States, 1961–91

Figure 2.Real Per Capita NDP (1990 rupees) Nine Initially Middle-Income States, 1961–91

Figure 3.Real Per Capita NDP (1990 rupees) Five Initially Rich States. 1961–91

Of the five initially rich states (Delhi, Punjab, Maharashtra, Gujarat, and West Bengal). Punjab and Maharashtra have grown at the highest rates (Table 2 and Figure 3) during 1961–91. Punjab’s success has already been accounted for above, while Maharashtra appears to have made inroads into expanding industrial production (compare the declining share of agriculture and the rising share of manufacturing in state NDP during 1961–81 in Table 2) and exports.

A common feature shared by virtually all the 20 states is large intertemporal variations in output. The main factors accounting for these variations include terms of trade shocks (particularly movements in the price of oil and other commodities), border conflicts and civil disturbances, variations in weather conditions, and other supply-side constraints. Not only is agricultural output in many states dependent on the timeliness and extent of rainfall (the monsoons), but weather conditions also affect agriculture-based industries (such as food and textiles) and infrastructure (water supply and hydro-based power plants). Finally, rigidities in state-based product and factor markets have also precluded more rapid adjustment by states to unforeseen macroeconomic shocks.

II. Concepts of Convergence

This section lays out the concepts of convergence that are used in this study. Barro and Sala-i-Martin (1992a) derive an equation in discrete time for the average growth rate of per capita output, y, over the interval between t – T and t:

where i indexes the economy; T is the length of the observation interval; t is time; i,t-T is real per capita net domestic product (NDP) for each economy at time t – T, the beginning of the subperiod;15yit is real per capita NDP at time t; β is the convergence coefficient;16e is the exponential constant; ϵji is an independent error term; and C is the constant term, which is common across states. In the neoclassical growth model of Solow (1956) and Swan (1956), convergence is conditional, because β is driven by the level of per capita income for each economy relative to its own steady-state per capita income and steady-state growth rate, which need not be homogeneous across economies. The probability of such homogeneity is, however, greater for regions of a given country, which are more likely to share common levels of technology, common preferences, and common political institutions. Here we follow Barro and Sala-i-Martin (1992a) and assume that all 20 state economies have the same steady-state levels of real per capita NDP and steady-state growth rates, and so equation (1) implies absolute convergence if β > 0.17

Two measures of convergence follow from equation (1). The first, known as β-convergence, asks whether initially poor economies tend to grow faster than initially rich ones (that is, whether there is mean reversion in the level of real per capita NDP across economies). Another concept is σ-convergence, which considers the decline of the cross-sectional dispersion of real per capita NDP over time. That is, it asks whether the standard deviation of the logarithm of per capita NDP (the coefficient of variation) is shrinking across economies over time. Barro and Sala-i-Martin (1992a) note that β-convergence is a necessary but not a sufficient condition for σ-convergence, as a positive β will tend to reduce σt (the dispersion of In yit in equation (1)) for a given distribution of ϵji, but new exogenous shocks to ϵji will tend to raise σt.

An aggregate shock such as a large relative fall in the price of agricultural commodities would reduce the value of real output (akin to an income effect) in agriculture-based states. Conversely, it would raise the value of real output for those states that did not have a relatively large agricultural sector. Such disturbances alter the distribution of the error term, ϵji so that ϵji is no longer distributed independently of ϵji for states i and j, thus tending to raise σt temporarily above its steady-state value, σ. However, given that the steady-state distribution of ϵji does not change, for any given temporary shock, σt approaches σ over time.

Omitted variable bias can result if we do not control for these shocks. For example, such an aggregate shock to agricultural prices would differentially affect the more rural-based Indian states. If such states were initially poor, then an adverse price shock would induce underestimation of the subsequent speed of convergence, as the omitted (shock) variable would be positively correlated with initial income, yi,t-T.18 Moreover, the main sectoral shift of employment in the Indian states over this period was from agriculture to other sectors, principally manufacturing and services. As economies develop, workers generally shift out of agriculture, and if these other sectors have higher labor productivity than agriculture, then this shift alone in the pattern of the workforce would generate growth in those states with initially high shares of their economy in agriculture (Kuznets (1966)).19 Hence the share of each state’s NDP derived from agriculture in the initial year of each subperiod (AGRi,t-T), and the share derived from manufacturing in the initial year of each subperiod (MANi,t-T) are added as explanatory variables in the estimation of equation (1), to control for the sectoral composition of state production.

III. Data

In this paper we consider the period 1961–91, using data on 20 states of India.20 The output data used are per capita state net domestic product (PCNDP) in constant (1990 rupees) prices, derived from current price data on state and union territory NDP and per capita NDP at factor cost (Government of India (1986 and 1995)), deflated by the national GDP deflator (DEF), base year 1990 (International Monetary Fund (1994)). The state-based measures of NDP are analogs of national net domestic product—they measure income originating from factors of production physically located within the boundaries of each state, and represent the value of goods and services produced within a state. The NDP and per capita NDP series are prepared on the basis of a uniform methodology as prescribed by the Central Statistical Organization (CSO), which is discussed in detail by Dholakia (1985), Government of India (1986), and Choudhury (1993).21

Two additional points should be made regarding the output data. First, at the regional level there could be important differences between the income originating within the boundaries of any given state and the income accruing to the residents of that state, due to flows of factor incomes across state borders. However, data on income accruing to residents by state in India do not exist. Second, the relative standard of living of the residents of any given state may not be accurately reflected in per capita income, to the extent that state-based per capita consumption expenditure diverges from per capita income. Choudhury (1993) found that between 1967 and 1987 the divergence was small for most states, except for Rajasthan and Uttar Pradesh where consumption exceeded income (as both states were large net exporters of goods and services to other states), and for Tamil Nadu and Karnataka where income exceeded consumption.

The state population estimates (POP) are derived from census figures for 1961, 1971, 1981, and 1991, and midyear population estimates are used for all other years (Government of India (1991a and 1991b); Registrar General and Census Commissioner (1991)). Data on the share of manufacturing (MAN) and agriculture, forestry and fishing (AGR) in state NDP at factor cost are taken from Government of India (1986).

As a measure of internal population mobility, census data on migration during the 1960s and 1970s are used to calculate the intercensal annual average net migration into each state as a share of that state’s population at the beginning of each intercensal period (MIG). Net migration figures for the subperiod 1981–91 are derived from state-based vital statistics (population growth and crude birth- and death rates) due to the unavailability of 1991 census data (Government of India (1991a and 1991b): Registrar General and Census Commissioner for India (1977 and 1988)).22

Estimates of state per capita disposable income (SDI) are derived by adding the grant component of transfers (TR) from the central government to state governments to NDP, then dividing by POP and applying the appropriate DEF. As noted in Section I. TR comprises statutory grants-in-aid, grants from state and central plan schemes, and grants from centrally sponsored schemes. State disposable income (SDI) is the state-based analog of national disposable income, in that it represents the total income available to residents of a given state for consumption and saving. As mentioned above, in the Indian context this concept will not be a perfect state-based analog of the national accounts definition of national disposable income, as our measure of SDI excludes net factor incomes flowing across state borders to residents of a state.

To examine whether there are regional differences in the steady-state level of per capita income to which the states of India are converging, each of the 20 states is allocated into one of four geographic regions; these dummy variables were east (six states), north (six states), south (four states), and west (four states). Further details on the definition, derivation, and sources of all the variables used in this study can be found in the Appendix. Tables 1, 2, and A1 present summary statistics of the above data for each of the 20 state economies.

IV. Did the Initially Poor States Grow Faster than the Initially Rich Ones?

The analysis of disparities in per capita incomes and growth rates across the states of India has been a popular theme for research on the Indian union, with key contributions by Chaudhry (1966), Mukherjee (1969), Nair (1971), Majumdar (1976), Majumdar and Kapoor (1980), Choudhury (1980), Dholakia (1985), Nair (1985), Rao (1985), Singh (1985), Sastry and Nag (1990), Singh (1992), Choudhury (1993), and Ghuman and Kaur (1993). However, apart from the important work of Dholakia (1985), most of these papers did not move beyond analyses of trend movements in NDP and per capita NDP, or the ranking of states by per capita income, or they focused more narrowly on determining the causes of sectoral-based shocks to income or consumption in particular states. The task of this empirical section is to analyze formally whether the initially poor states grew faster than the initially rich ones between 1961 and 1991, using equation (1) and real per capita NDP as the measure of income.

Column (1) of Table 3 reports the regression estimates of the convergence coefficient (β) in equation (1), where the only explanatory variables are a constant term (not reported) and the logarithm of initial subperiod income (In yi,t-T). Note that a positive coefficient on initial income can be translated as initially poor states growing faster than initially rich ones. The first row of column (1) in Table 3 reports the results for a single regression on the period 1961–91, and it is found that β^ = 0.0027 (s.e.= 0.0057) is the result, with a coefficient of determination of 0.654 and standard error of the regression of 0.207. However, while this estimate of β is not statistically different from zero, the simple correlation between In y1961 and the 1961–91 growth rate of –0.116 reflects β-convergence (Figure 4). As expected, both Manipur (MN) and Himachal Pradesh (HP) had below-average per capita incomes in 1961, and relatively high rates of growth of per capita incomes in the 30 years thereafter. While Delhi (D) clearly had the highest per capita income in 1961, its 1961–91 growth rate was close to that which would be predicted given its initial level of per capita NDP.

Rows two to four of column (1) in Table 3 divide the 1961–91 period into three intercensal subperiods: 1961–71, 1971–81, and 1981–91. Nonlinear least squares estimates of equation (1) find that the estimated convergence coefficients for 1961–71, 1971–81, and 1981–91 have the appropriate (positive) sign, indicating β-convergence, but are not statistically significant.

Figures 5–7 depict the negative correlation between initial income and the subsequent growth rate for these subperiods, which is clearly strongest for the subperiod 1961–71. The relatively strong growth performance in the 1961–71 subperiod of initially poor Manipur (MN), Kerala (KE), and Himachal Pradesh (HP), and the relatively poor performance of initially rich Delhi (D), is clear from Figure 5. Accordingly, there is quite rapid β-convergence in the 1960s as the initially poor states grew faster than their initially rich counterparts, which barely grew at all in per capita terms—the simple correlation of In yl961 with the growth rate for 1961–71 is -0.237.

The relatively good growth performance of initially rich Delhi (D), Punjab (P), Haryana (H), Maharashtra (MH), and Gujarat (G) in the 1971–81 and 1981–91 subperiods stands out in Figures 6 and 7. The correlation of In y1971 with the 1971–81 growth rate is much lower at –0.065; and the correlation of ln y1981 with the 1981–91 growth rate is also low at 0.064. Accordingly, there is only slight β-convergence in these intercensal periods.23 A multivariate regression on the three-equation system yields a restricted estimate of β = –0.0012, which is not statistically significant, and a Wald test of the hypothesis of the same β-coefficient in all three subperiods indicates that this hypothesis is rejected.

Table 3.Cross-State Regressions for Indian NDP, 1961–91a
(1)(2)(3)
Basic equationEquation controlling for agricultural shocksEquation controlling for agricultural and manufacturing shocks
β^R2β^θ^R2β^θ^τ^R2
Period(se)[ŝ](se)(se)[ŝ](se)(se)(se)[ŝ]
1961–910.00270.654
(0.0057)[0.207]
1961–710.01250.7690.00100.09020.779–0.00770.0730–0.09840.790
(0.0129)[0.149](0.0172)(0.1061)[0.150](0.0183)(0.1083)(0.1079)[0.151]
1971–810.00340.7810.0220–0.17360.8300.0223–0.15670.02750.832
(0.0124)[0.158](0.0165)(0.0791)[0.344](0.0170)(0.0886)(?.0589)[0.147]
1981–910.00220.8900.0029–0.00600.8900.00750.05770.12830.927
(0.0083)[0.116](0.0114)(0.0653)[0.120](0.0102)(0.0595)(0.0455)[0.101]
β restrictedb–0.00120.00520.0153
(0.0040)(0.0059)(0.0069)
Wald testc11.5816.3143.827
p-value0.00410.04260.1476
Notes: The regressions use nonlinear least squares to estimate equations of the form: In yit = α + (ln yi,t-T) (e-βT) + other variables, where yi,t-T is the real per capita NDP (in constant 1990 rupees) in state i at time t - T; yit is the real per capita NDP (in constant 1990 rupees) in state i at time t: τ is the length of each subperiod; “other variables” are the share of agriculture in each state’s NDP at time t — T, AGRi,t-T (reported as θ^), and the share of manufacturing in each state’s NDP at time t — T, MANi,t-T (reported as τ^).

All regressions are for 19 states and the Union Territory of Delhi. Underneath the estimates of β, ?, and τ are reported the heteroscedastic-consistent standard errors (in parentheses). R2 is the coefficient of determination: underneath it is the standard error of the regression [ŝ]. All regressions are run with a constant term, α (not reported).

Restricted refers to a combined regression that constrains the value of β to be the same across the equations of a given system, and the restricted βs are estimated using iterative, weighted seemingly unrelated regression, which allows for the correlation of error terms across subperiods.

The Wald test and associated p-value (a X2 with n — 1 degrees of freedom in an n-equation system) refers to the test for equality of the coefficient on the logarithm of initial income (β) across the subperiods. The 0.05 X2 value with two degrees of freedom is 5.9915.

Notes: The regressions use nonlinear least squares to estimate equations of the form: In yit = α + (ln yi,t-T) (e-βT) + other variables, where yi,t-T is the real per capita NDP (in constant 1990 rupees) in state i at time t - T; yit is the real per capita NDP (in constant 1990 rupees) in state i at time t: τ is the length of each subperiod; “other variables” are the share of agriculture in each state’s NDP at time t — T, AGRi,t-T (reported as θ^), and the share of manufacturing in each state’s NDP at time t — T, MANi,t-T (reported as τ^).

All regressions are for 19 states and the Union Territory of Delhi. Underneath the estimates of β, ?, and τ are reported the heteroscedastic-consistent standard errors (in parentheses). R2 is the coefficient of determination: underneath it is the standard error of the regression [ŝ]. All regressions are run with a constant term, α (not reported).

Restricted refers to a combined regression that constrains the value of β to be the same across the equations of a given system, and the restricted βs are estimated using iterative, weighted seemingly unrelated regression, which allows for the correlation of error terms across subperiods.

The Wald test and associated p-value (a X2 with n — 1 degrees of freedom in an n-equation system) refers to the test for equality of the coefficient on the logarithm of initial income (β) across the subperiods. The 0.05 X2 value with two degrees of freedom is 5.9915.

Figure 4.Convergence of Real Per Capita NDP Across 20 Indian States: 1961 NDP and 1961–91 NDP Growth

Figure 5.Convergence of Real Per Capita NDP Across 20 Indian States: 1961 NDP and 1961–71 NDP Growth

Figure 6.Convergence of Real Per Capita NDP Across 20 Indian States: 1971 NDP and 1971–81 NDP Growth

Figure 7.Convergence of Real Per Capita NDP Across 20 Indian States: 1981 NDP and 1981–91 NDP Growth

The apparent instability of the convergence coefficients in the three sub-periods could reflect aggregate disturbances that differentially affected state NDP (as mentioned in Section II above).24 Accordingly, in column (2) of Table 3 the share of NDP derived from the agricultural sector of each state (AGRi,t–T) is added to the basic regression to control for aggregate shocks. As a result, the estimated coefficient for the subperiod 1971–81 is raised considerably (from β^ = 0.0034 to β^ = 0.0220), and that for the subperiod 1961–71 is lowered considerably (from β^ = 0.0125 to β^ = 0.0010). The restricted coefficient in the multivariate regression (row five, column (2)) now has a value of β^ = 0.0052, which is not statistically significant, and a Wald test of the hypothesis of equality of the estimated β-coefficients across the three subperiods indicates that this hypothesis is again rejected. It appears that AGRi,t–T is unable to fully capture the influence of aggregate shocks on the growth process, although it does provide information on the sectoral pattern of state growth across the three subperiods.

Accordingly, in column (3) of Table 3 the share of NDP derived from the manufacturing sector of each state (MANi,t–T) is added to the basic regression to further control for aggregate shocks. This variable is likely to be particularly important in the Indian context, given the industrialization strategy pursued in India from the early 1960s until the mid-1980s. Its absence from the growth regression would thus be expected to result in omitted variable bias. The result is that the estimated coefficient for the subperiod 1971–81 remains much the same as in column (2) (from β^ = 0.0220 to β^ = 0.0223); that for the subperiod 1981–91 is raised (from β^ = 0.0029 to β^ = –0.0075); and that for the subperiod 1961–71 is lowered (from β^ = 0.0010 to β^ = –0.0077). The restricted coefficient in the multivariate regression (row five, column (3)) now has a statistically significant value of β^ = 0.0153, and a Wald test of the hypothesis of equality of the estimated β-coefficients across the three subperiods indicates that this hypothesis is not rejected. Such a value for β implies a half-life of the logarithm of per capita income (the time it takes to close half of the gap between any state’s initial level of per capita income and the common steady-state level of per capita income) of 45 years.25

The agricultural variable (reported as θ^ in column (2) of Table 3) is negative for the 1971–81 and 1981–91 subperiods and positive for the 1961–71 subperiod. This indicates that, for example, those states where the agricultural sector was a large contributor to NDP had relatively lower levels of final per capita income in the 1971–81 subperiod. That is, they enjoyed relatively lower rates of growth of per capita income over that subperiod (θ^ = -0.1736). Note that in row three of column (2) it is the period 1971–81 that exhibits the largest convergence coefficient (β^ = 0.0220). The relative decline in agricultural commodity prices over the decade hurt those economies specializing in such products. In 1971 Bihar, Orissa, Tripura, and Uttar Pradesh had below-average per capita incomes, yet each had a relatively large share of its 1971 NDP derived from agriculture: the correlation of ln y1971 with AGR1971 is -0.534. Consequently, because of the positive correlation between the aggregate shock and initial income, β^ is underestimated in row three of column (1): it reflects the tendency of the poorer states to be agricultural and hence to experience relatively slow growth during this subperiod (Table 3). For the 1961–71 subperiod, again agriculture-based states tended to be relatively poor (the correlation of In y1961 with AGR1961 is –0.766). yet the positive shock to agriculture means that in row two of column (1), β^ is overestimated: controlling for the aggregate shock lowered β^ in row two of column (2), because of the negative correlation between the agricultural shock and initial income (Table 3).

Similarly, the manufacturing variable (reported as τ^ in column (3) of Table 3) is positive for the 1971–81 and 1981–91 subperiods and negative for the 1961–71 subperiod. This indicates, for example, that those states where the manufacturing sector was a large contributor to NDP had relatively higher levels of final per capita income in the 1981–91 subperiod. That is, they enjoyed relatively higher rates of growth of per capita income over that subperiod (τ^ = 0.1283). Note that in row two of column (3) it is the period 1961–71 that exhibits the greatest shift in its convergence coefficient (from β^ = 0.0010 to β^ = –0.0077). The relative decline in manufacturing prices over that decade hurt those economies specializing in such products. In 1961 Delhi, Maharashtra, West Bengal, Gujarat, Tamil Nadu, and Assam had above-average per capita incomes, yet for each a relatively large share of its 1961 NDP was derived from manufacturing: the correlation of In y1961 with MAN1961 is 0.718. Consequently, because of the negative correlation between the aggregate shock and initial income, β^ was overestimated in row two of column (2): it reflected the tendency of richer states to be manufacturing-based and hence to experience relatively slow growth during this subperiod (Table 3). For the 1981–91 subperiod, again manufacturing-based states tended to be relatively rich (the correlation of ln y1981 with MAN1981 was 0.504). yet the positive shock to manufacturing meant that β^ in row four of column (2) was underestimated: controlling for the aggregate shock raised β^ in row four of column (3) because of the positive correlation between the manufacturing shock and initial income (Table 3).

The estimated speed of convergence for the Indian states between 1961 and 1991 (β^ = 0.0153) is slower than that found in most earlier studies of regional economies of developed countries: the states of the United States (β = 0.0249) between 1880 and 1988 by Barro and Sala-i-Martin (1992a); the regions of European OECD countries (β = 0.0178) between 1950 and 1985 by Barro and Sala-i-Martin (1991); the provinces of Canada (β = 0.024) between 1961 and 1991 by Coulombe and Lee (1993); the regional economies of Australasia (β = 0.0121) between 1861 and 1991 by Cashin (1995); 98 (OECD and non-OECD) countries (β = 0.0111) between 1960 and 1985 by Barro (1991); the prefectures of Japan (β = 0.034) between 1930 and 1987 by Barro and Sala-i-Martin (1992b); the prefectures of Japan (β = 0.033) between 1960 and 1988 by Shioji (1993); and the developed and developing island economies of the South Pacific (β = 0.0432) between 1971 and 1993 by Cashin and Loayza (1995).26Barro and Sala-i-Martin (1991) hypothesized that the more heterogeneous the steady states to which a group of economies are converging, the slower the speed of convergence, even after controlling for the disparate steady states. That is, regions of a given country (such as the United States, Canada, Japan, India, and Australasia) should exhibit the fastest convergence, followed by similar national economies (such as the OECD), followed by all national economies. While for some subperiods the present findings fit into this hierarchy of convergence speeds, over the full sample period this does not appear to be the case for the Indian states. However, the fact that β-convergence is observed in India without controlling for differences in steady-state growth rates or steady-state levels of per capita incomes is indicative of homogeneous steady states across the states of India but heterogeneous initial levels of per capita state incomes.27 Hence, absolute and conditional convergence in the Indian states do appear to be almost synonymous.28

V. Did the Cross-State Dispersion of Per Capita Incomes Widen or Narrow?

To determine the extent of the dispersion of per capita incomes across the 20 Indian states, the unweighted cross-sectional standard deviation of ln yit, σNDPt, was calculated for the period 1961–91.29Figure 8 shows that over this period there has been an increase in the dispersion of real per capita incomes (σNDPt) across the Indian states, except for the subperiods 1962–68, 1972–75, 1977–78, and 1980–84. The dispersion fell from 0.292 in 1961 and 0.328 in 1962 to 0.268 in 1975, then increased to reach 0.339 in 1980, fell to 0.297 in 1984, and then rose to 0.333 by 1991.30

Figure 8.Dispersion of Real Per Capita Incomes: 20 Indian States, 1961–91

The dispersion of real per capita NDP across the states narrowed between 1961 and 1971 because of robust growth rates in initially poor states (Manipur, Kerala, and Himachal Pradesh) and slow growth rates in initially rich states (Delhi, West Bengal, and Maharashtra). However, in the 1971–81 and 1981–91 subperiods the initially poor states (Manipur, Bihar, and Orissa in 1971; Bihar, Assam, and Orissa in 1981) and the initially rich states (Delhi, Punjab, and Haryana in 1971; Delhi, Punjab, and Maharashtra in 1981) had similar rates of economic growth (Figures 6 and 7).31

This process of a widening in the cross-sectional dispersion of real per capita NDP for the Indian states contrasts with the pattern exhibited by developed countries (the states of Australia, the prefectures of Japan, and the states of the United States), where the minimum value of σ, was found to be 0.12, 0.12, and 0.14, respectively (Cashin (1995) and Barro and Sala-i-Martin (1992b)). One explanation for the observed pattern of σNDPt for India is that the steady-state value for σ is about 0.32, and that σNDPt should remain close to this level until there is an aggregate shock that differentially affects the states. Interestingly, India’s steady-state value of σ is over twice the level of those for the regional economies of Australia, Japan, and the United States, and most likely reflects higher barriers to the free flow of capital and labor across the Indian states than those existing in these developed economies.

In Figure 8 is also plotted a measure of the dispersion of state per capita disposable incomes, σSDIt, where SDI is defined as state NDP plus center-state grants.32 Given the presence of center-state grants, which are allocated more to relatively poor states than to relatively rich ones, it would be expected a priori that the dispersion of per capita income would be greater for σNDPt than σSDIt. This is indeed the case, as σNDPt > σSDIt for all t (Figure 8). Accordingly, center-state grants have been operating to equalize per capita incomes across the 20 states—the poor states are the relative beneficiaries of this aspect of Indian fiscal federalism, at the expense of their relatively rich counterparts. For state disposable income there is only slight σ-divergence over the 1961–91 period: σSDIt rose from 0.290 in 1961 and a period high of 0.326 in 1962 to reach 0.324 in 1980, fell to 0.284 in 1984, and then rose to 0.306 by 1991.

The gap between σSDIt and σNDIt widened considerably after the mid-1960s, which reveals the much greater role played by center-state grants after this date (Figure 8). That is, while the dispersion of per capita NDP has widened, there has also been an increase in grants to relatively poor states over the 1961–91 period. This has resulted in relatively little change in the dispersion of per capita SDI across the states of India during this period, as grants have compensated for the widening dispersion of the per capita NDP component of per capita SDI. In particular, after 1975 the value of σSDIt has fluctuated around 0.30, while that of σNDIt has fluctuated around 0.32; between 1966 and 1975 the σt values fluctuated around 0.26 and 0.27, respectively.

A useful disaggregation of the data is to examine whether the initially rich economies in 1961 (Delhi, Maharashtra, West Bengal, Gujarat, and Punjab) experienced σ-convergence as a subgroup, and whether the initially poor economies (Manipur, Bihar, Orissa, Tripura, Madhya Pradesh, and Uttar Pradesh) and initially middle-income economies (Andhra Pradesh, Assam, Haryana, Himachal Pradesh, Jammu and Kashmir, Karnataka, Kerala, Rajasthan, and Tamil Nadu) did likewise. The results are depicted in Figures 911.

The gap between σNDPt and σSDIt is small for the five initially rich states, indicating that grants have had little effect on the dispersion of per capita incomes across these states (Figure 9). However, even among these rich states σNDPt is greater than σSDIt for all t, indicating that the poor members of this subgroup benefited from center-state grants relatively more than their richer counterparts. Overall, there is σ-divergence for both measures of income; σNDPt rises from 0.229 in 1961 to 0.271 in 1991, and σSDIt rises from 0.226 in 1961 to 0.263 in 1991. This result can be largely attributed to the relatively rapid growth of rich Delhi, and the relatively slow growth of poor West Bengal.

Figure 9.Dispersion of Real Per Capita Incomes: Five Initially Rich Indian Slates, 1961–91

Figure 10.Dispersion of Real Per Capita Incomes: Nine Initially Middle-Income Indian States, 1961–91

Figure 11.Dispersion of Real Per Capita Incomes: Six Initially Poor Indian States. 1961–91

Interestingly, while σNDPt is greater than σSDIt from 1961 to 1975 for the nine initially middle-income states, σNDIt is less than σSDIt between 1976 and 1988 (Figure 10). While the poor members of this subgroup were relative beneficiaries of center-state grants in the former subperiod, the reverse occurred in the latter subperiod. From 1990 onward, σNDIt is again greater than σSDIt. Overall, there is clear σ-divergence for both measures of income; σNDIt rises from 0.089 in 1961 to 0.188 in 1991, and σSDIt rises from 0.087 in 1961 to 0.171 in 1991. This result can be largely attributed to the relatively rapid growth of rich Haryana, and the relatively weak growth performance of poor Jammu and Kashmir.

For the six initially poor states there is little difference between σNDIt and σSDIt until 1970—center-state grants played a minor role in influencing the dispersion of per capita income across the initially poor states in these early years (Figure 11). However, beginning in 1970 σSDIt exhibits erratic behavior—σNDIt is less than σSDI1970, then the dispersion of per capita NDP jumps so that σNDI1971 is greater than σSDI1971, and then the dispersion of per capita disposable income jumps so that σNDI1972 is less than σSDI1972. This erratic behavior can be largely attributed to the beginning of payments of grants to Manipur (in 1971) and Tripura (in 1972). However, between 1972 and 1991 σNDIt is clearly smaller than σSDIt, indicating that the rich members of this subgroup were relative beneficiaries of center-state grants. That is, the grants acted to exacerbate inequalities in per capita incomes across the six poor states, especially after 1974. The high level of per capita grants received by both Manipur and Tripura, combined with the relatively low level of per capita grants received by Bihar and Uttar Pradesh, resulted in σ-divergence for σSDIt between 1974 and 1985, and slight σ-convergence for σSDIt after this period.33 The value of σSDIt for the six initially poor states rises from 0.176 in 1961 to 0.214 in 1991, after reaching a period high of 0.253 in 1985 and a period low of 0.075 in 1971. Indeed, there is σ-convergence for the six initially poor states with respect to σNDIt, which declined from 0.173 in 1961 to 0.147 in 1991; per capita incomes in the poorest Indian states became more similar over this period. This was largely due to the relatively rapid growth of initially poor Manipur, and the relatively weak growth performance of initially rich Tripura and Uttar Pradesh.

VI. How Strongly Does Net Migration Respond to Cross-State Differentials in Per Capita Incomes?

One important mechanism by which differences in cross-regional per capita incomes can be equalized within national economies is by population movements from relatively poor to relatively rich regions. Interstate migration in India is of particular interest, because of the strong heterogeneity across states in their levels of per capita income and demographic characteristics (see Tables 1 and 2). In this section we examine the strength of the interrelationship between net in-migration and initial per capita incomes for the 20 states of India.

In terms of total volume, rural-to-rural migration dominates over other streams of migration (such as rural-to-urban) in India. Moreover, there is a clear preponderance of women in rural-to-rural migration, due to the system of patrilocal migration after marriage. For example, intercensal migration across the states between 1971 and 1981 resulted in 70 male rural-to-rural migrants per 100 females; for rural-to-urban migrants the ratio was 142 males per 100 females (Skeldon (1986)). However, this marriage-based migration is mainly across district boundaries separating neighboring settlements of a given state; most of this type of population movement is eliminated from census data on cross-state migration (Datta (1985)). Urban-to-urban and rural-to-urban migration are the dominant components of interstate migration in India; each comprised about 32 percent of all intercensal cross-state migrants between 1971 and 1981 (Skeldon (1986)). As with most other developing countries, long-distance migration in India is male dominated and overwhelmingly urban oriented.

Table 4 sets out the volume of interstate migration between 1961 and 1991, on an intercensal basis, taken from official migration data from the Registrar General and Census Commissioner for India (1977 and 1988) for cross-state migration in the 1960s and 1970s, and implied net migration (derived from vital statistics) for cross-state migration in the 1980s.34 Our intercensal cross-state migration calculations closely approximate those of Datta (1985) for the 1971 census and Skeldon (1986) for the 1981 census. Gross intercensal migration across states between 1961 and 1971 was 2.07 percent of the all-India population in 1961; gross interstate migration between 1971 and 1981 was 1.96 percent of the 1971 all-India population; and gross interstate migration between 1981 and 1991 was 1.97 percent of the 1981 all-India population (Table 4).

The strong (and increasing) attraction of Delhi for the rest of India stands out in the data, with the aggregate of net migration during the decade as a share of its initial census year population being 0.215 for the 1960s, 0.234 for the 1970s, and 0.293 for the 1980s. Other relatively large net immigration states over the 1961–91 period were Manipur, Maharashtra, Madhya Pradesh, and Tripura, while Punjab, Himachal Pradesh, Kerala, and Bihar were net emigration states over the 1961–91 sample period.35 In general, the states of northern India (particularly Punjab and Himachal Pradesh) and Bihar in the east can be characterized as net out-migration regions; the western states (particularly Maharashtra) as net in-migration regions; and the southern states exhibit close to zero net migration. Moreover, net in-migration across the states of India is highly persistent—the simple correlation between MIG1961 and MIG1971 is 0.974; that between MIG1971 and MIG1981 is 0.817; and that between MIG1961 and MIG1981 is 0.825.

Table 4.Volume of Interstate Migration, Intercensal Basis, 1961–91a
Average annual net migration as share of 1961 state populationAverage annual net migration as share of 1971 state population1982–91 vital statisticscAverage annual net migration as share of 1981 state population
1971 census migrationb1981 census migrationbNet migration ratefNet migration (million)
IncOutcNetIncOutdNet
Andhra Pradesh (AP)400,955529,405–128,450–0.0004426,399554,634–128,235–0.00034.262.2800.0043
Assam (A)271,415152,878118,5370.0011195,7550.00511.470.2650.0015
Bihar (B)443,725963,433–519,708–0.0011432,0081,030,990–598,982–0.0011–0.34–0.239–0.0003
Delhi (D)813,459241,711571,7480.02151,229,744277,686952,0580.023429.301.8220.0293
Gujarat (G)407,375338,40268,9730.0003527,791383,207144,5840.0005–1.15–0.392–0.0011
Haryana (H)483,205417,79765,4080.0009595,343524,96170,3820.0007–0.40–0.051–0.0004
Himachal Pradesh (HP)123,3010.0010113,743135,720–21,977–0.0006–2.49–0.107–0.0025
Jammu & Kashmir (JK)49,31464,418–15,104–0.000460,63861,662–1,024–0.00004.130.2470.0041
Karnataka (KA)592,335509,33882,9970.0004704,612666,93937,6730.00010.940.3500.0009
Kerala (KE)169,550467,697–298,147–0.0018220,833498,062–277,229–0.0013–3.17–0.808–0.0032
Madhya Pradesh (MP)808,895500,859308,0360.0010854,856738,108116,7480.00033.631.8920.0036
Maharashtra (MH)1,423,880699,062724,8180.00181,886,291742,1111,344,1800.00235.683.5680.0057
Manipur (MN)11,3688,4652,9030.000410,89512,891–1,996–0.00027.360.1050.0074
Orissa (O)282,145218,62963.5160.0004301,134239,53561,5990.00030.600.1580.0006
Punjab (P)371,805601,871–230,066–0.0021494,956519,993–25,037–0.0002–3.17–0.532–0.0032
Rajasthan (R)431,200621,593–190,393–0.0009580,773703,401–122,628–0.00053.461.1860.0035
Tamil Nadu (TN)886,385533,513352,8720.0010420,714670,635–249,921–0.0006–0.05–0.026–0.0001
Tripura (T)19,90319,982–79–0.000022,28912,6449,6450.000615.540.3190.0155
Uttar Pradesh (UP)658,5811,509,040–850,459–0.0012685,8242,245,809–1,559,985–0.00182.502.7690.0025
West Bengal (WB)820,165516,407303,7580.0009725,817469,502256,3150.00060.900.4910.0009
Sources: Registrar General and Census Commissioner (1977 and 1988); Government of India (1991a and 1991b) and earlier issues; and authors’ calculations.

For 19 states and the Union Territory of Delhi.

Aggregate of migration for duration of residence of less than 1 year, 1–4 years, and 5–9 years.

In-migration to the particular state from states of India beyond the state of enumeration. In-migration data on Himachal Pradesh from the 1971 census and Assam from the 1981 census are unavailable. Accordingly, net migration for those states and years has been estimated from vital statistics.

Out-migration from the particular state to states of India beyond the state of enumeration.

Migration estimates for the 1980s are based on vital statistics, as no census data are available.

Net migration rate is popgri – (cbr – cdr)i, where popgri is the rate of population growth of state i between 1981 and 1991 (in percentage terms); cbri is the rural crude birthrate per 1,000 persons for state i; and cdri is the rural crude death rate per 1,000 persons for state i.

Sources: Registrar General and Census Commissioner (1977 and 1988); Government of India (1991a and 1991b) and earlier issues; and authors’ calculations.

For 19 states and the Union Territory of Delhi.

Aggregate of migration for duration of residence of less than 1 year, 1–4 years, and 5–9 years.

In-migration to the particular state from states of India beyond the state of enumeration. In-migration data on Himachal Pradesh from the 1971 census and Assam from the 1981 census are unavailable. Accordingly, net migration for those states and years has been estimated from vital statistics.

Out-migration from the particular state to states of India beyond the state of enumeration.

Migration estimates for the 1980s are based on vital statistics, as no census data are available.

Net migration rate is popgri – (cbr – cdr)i, where popgri is the rate of population growth of state i between 1981 and 1991 (in percentage terms); cbri is the rural crude birthrate per 1,000 persons for state i; and cdri is the rural crude death rate per 1,000 persons for state i.

In explaining migration we follow Braun (1993) and use a reduced form expression for MIGit, the annual rate of in-migration to state i as a share of the population of state i in the initial year of each intercensal period:

where yi,t–T is real (constant 1990 rupees) per capita NDP of state i at the beginning of the intercensal period; πi,t–T is the population density (persons per square kilometer) of state i at the beginning of the intercensal period; and ϵji is an independent error term. The equation also includes the square of the population density, which captures nonlinearities in the relation between migration and density. We expect initial income, population density, and the square of population density to have, respectively, positive, negative, and positive effects on net in-migration to state i.36 The empirical relationship is tested using iterative, weighted (by initial state populations) least squares.

Figure 12 reveals the relationship between the annual average migration rate between 1961 and 1991 and the logarithm of real per capita income in 1961.37 The relationship is clearly positive (with simple correlation 0.574), which supports the proposition that net in-migration is positively affected by cross-state differentials in per capita incomes.

The extremely strong attraction of Delhi (Figure 12) to the rest of India is indicated by much higher net migration rates than would be predicted by its initial level of per capita NDP. While the slope of the regression line would still be positive in the absence of Delhi, the relationship of migration to initial income would have been much weaker. Delhi has successfully attracted migrants for several reasons. First, the differential in per capita incomes between Delhi and all other states has been substantial. This is likely to induce large-scale in-migration, even if the prospects for employment in Delhi are limited (Harris and Todaro (1970)). Second, the private sector (industry and services) has expanded rapidly between 1961 and 1991. In India’s highly regulated economic environment during 1961–91, physical proximity to a strong central government was a key to success in lobbying efforts. Finally, the central government itself, along with other public sector companies, has expanded and absorbed a growing labor force.

Figures 1315 present the net migration and income relationship for the three subperiods (1961–71, 1971–81, and 1981–91). The results are similar to those depicted in Figure 12—the positive outlier is again Delhi, and apart from Assam in the 1970s and Tripura and Manipur in the 1980s, most states are bunched close to the zero net migration line.

Figure 12.Migration and Initial State Income — 20 Indian States: 1961–91

Figure 13.Migration and Initial State Income – 20 Indian States: 1961–71

Figure 14.Migration and Initial State Income — 20 Indian States: 1971–81

Figure 15.Migration and Initial State Income — 20 Indian States: 1981–91

Table 5.Regressions for Net Migration into Indian States, 1961–91a
PeriodPersonal incomePopulation densitySquare of population densityR2[ŝ]
1961–910.0030–0.15E-040.16E-070.857
(0.0029)(0.31E-05)(0.21E-08)[0.0017]
1961–710.0016–0.83E-050.11E-070.839
(0.0017)(0.24E-05)(0.19E-08)[0.0010]
1971–810.0014–0.88E-050.58E-080.854
(0.0006)(0.27E-05)(0.11E-08)[0.0011]
1981–91–0.0010–0.10E-040.39E-080.660
(0.0028)(0.34E-05)(0.88E-09)10.0025]
Restrictedb0.0012
(0.0009)
Sources: Authors’ calculations, derived from Government of India (1977 and 1988): Government of India (1995) and earlier issues; Government of India (1991a) and earlier issues.

All regressions are for 19 states of India, and the Union Territory of Delhi. The regressions use iterative, weighted (by initial state populations) least squares to estimate equations of the form: MIGit = μ + vln(yi,t–T) + ξπi,t–T + ω(πi,t–T)2 + other variables, where MIGit is the average annual net migration into state i between years t – T and t, expressed as a share of the state’s population in year t – T; yi,t–T is real per capita NDP at the beginning of the subperiod t – T, as described in Table 3; πi,t–T is the population density (thousands of people per square kilometer) of state i at the beginning of the subperiod t – T; T is the length of each subperiod; and “other variables” (unreported) are the share of agriculture in each state’s NDP at time t – T, AGRi,t–T, and the share of manufacturing in each state’s NDP at time t – T, MANi,t–T. R2 is the coefficient of determination. All regressions contain a constant term (unreported). Heteroscedastic-consistent standard errors are in parentheses. The standard errors of the regression, ŝ, are in brackets.

The restricted regression requires the values of v to be the same across all three subperiods, and the restricted v are estimated using iterative, weighted seemingly unrelated regression, which allows for the correlation of error terms across sub-periods. The Wald test statistic for equal values for v is 3.231 and the p-value is 0.199. The 0.05 X2 value with two degrees of freedom is 5.9915.

Sources: Authors’ calculations, derived from Government of India (1977 and 1988): Government of India (1995) and earlier issues; Government of India (1991a) and earlier issues.

All regressions are for 19 states of India, and the Union Territory of Delhi. The regressions use iterative, weighted (by initial state populations) least squares to estimate equations of the form: MIGit = μ + vln(yi,t–T) + ξπi,t–T + ω(πi,t–T)2 + other variables, where MIGit is the average annual net migration into state i between years t – T and t, expressed as a share of the state’s population in year t – T; yi,t–T is real per capita NDP at the beginning of the subperiod t – T, as described in Table 3; πi,t–T is the population density (thousands of people per square kilometer) of state i at the beginning of the subperiod t – T; T is the length of each subperiod; and “other variables” (unreported) are the share of agriculture in each state’s NDP at time t – T, AGRi,t–T, and the share of manufacturing in each state’s NDP at time t – T, MANi,t–T. R2 is the coefficient of determination. All regressions contain a constant term (unreported). Heteroscedastic-consistent standard errors are in parentheses. The standard errors of the regression, ŝ, are in brackets.

The restricted regression requires the values of v to be the same across all three subperiods, and the restricted v are estimated using iterative, weighted seemingly unrelated regression, which allows for the correlation of error terms across sub-periods. The Wald test statistic for equal values for v is 3.231 and the p-value is 0.199. The 0.05 X2 value with two degrees of freedom is 5.9915.

Table 5 presents the results of the regressions on equation (2). The regression on the migration rate for the full period 1961–91 results in a positive coefficient on initial income, yet it is not statistically significant, while the coefficients on density and the square of density are significantly negative and positive, respectively. The next three regressions break up this period into the three intercensal subperiods analyzed in Section IV (1961–71, 1971–81, and 1981–91). The values for v^ are positive for two of the three subperiods, and statistically significant only for the 1971–81 subperiod. Moreover, the values for ξ and ξ^ and ω^ all statistically significant and have the appropriate signs, in each of the three regressions. A multivariate regression on the three-equation system yields in row five a restricted estimate of v^ = 0.0012, which is not statistically significant. However, a Wald test of the hypothesis of the same v-coefficient in all three subperiods indicates that this hypothesis cannot be rejected. Everything else held constant, a 10 percent differential in initial per capita income would raise net in-migration to the richer state by a very small 0.012 percentage points per year.

This result can be contrasted with those for the states of the United States between 1900 and 1987 and the prefectures of Japan between 1955 and 1985 of Barro and Sala-i-Martin (1992b), who find that, everything else held constant, a 10 percent differential in initial per capita income would raise net in-migration to the richer region by a relatively large 0.26 and 0.27 percentage points per year, respectively. However, Braun (1993) finds that migration across 80 regions of the five largest European countries (Germany, the United Kingdom, Italy, France, and Spain) between 1950 and 1990 responds only weakly to initial income—everything else held constant, a 10 percent differential in initial per capita income would raise net in-migration to the richer region by only 0.064 percentage points per year. Accordingly, it appears that while the migration rate for the states of India is positively related to initial per capita income, it is not statistically different from zero. In that sense, the income elasticity of migration across the states of India more closely resembles the relatively weak responsiveness of population movements to income differentials in the regions of Europe than the relatively stronger responsiveness to differentials in the states of the United States or the prefectures of Japan. Implicitly, the costs of cross-regional labor mobility are high in India and Europe—they are relatively low in Japan and the United States. This anemic Indian response of cross-state migration to income differentials is most likely due to a combination of several barriers to the mobility of labor: strong local workers’ unions that act to keep out competing potential employees; rigidities in nominal wages (Joshi and Little (1994)); lack of housing in fast-growing urban areas; and most important, social, cultural, and linguistic barriers to the cross-regional substitutability of labor.

VII. Is Cross-State Migration a Likely Cause of the Convergence of State Per Capita Incomes in India?

As argued above, migration from poor states to rich states should accelerate the speed of convergence of per capita incomes across the 20 states of India. If so, then the estimated convergence coefficients of Table 3 also embody the contribution of migration to the convergence process. Accordingly, the expectation is that in-migration should have a negative effect on the rate of growth of per capita incomes, and that the introduction of migration as an explanatory variable in the growth regressions should lead to a reduction in the estimated β.

The inclusion of migration (MIGit) in the growth regression of column (3) of Table 3 results in a statistically significant restricted estimate of β for the three subperiods of β^ = 0.0244. For two of the three subperiods, the coefficient on MIGit is negative, yet only one is statistically significant. This restricted estimate of β (with migration) is larger than that calculated in the absence of migration (β^ = 0.0153), and most likely reflects the endogeneity of migration and the growth of state per capita incomes—fast-growing states are more likely to attract migrants.

Accordingly, the growth regression was estimated by generalized instrumental variables, using fitted values from reduced form estimation of MIG as instruments for actual MIG in the structural growth regression (White (1982)).38 The restricted coefficient on initial income is now β^ = 0.0168 and is statistically significant, yet the coefficients on MIGit for two of the three subperiods are positive, which is the opposite of what would be expected if migration is the cause of cross-state income convergence. If we then restrict the coefficients on MIGit to be the same for all three subperiods, the estimated coefficient on MIGit is positive and statistically insignificant, while the restricted estimate of β is statistically significant at 0.0157.39 This speed of convergence is very close to that calculated in the absence of migration (β^ = 0.0153). These results suggest that the process of migration has little effect on the convergence of per capita incomes in the states of India. Holding net migration rates constant, the speed of convergence of per capita incomes in poor states to those in rich states is very close to that estimated in the absence of controls for cross-state migration.

VIII. Conclusions

Have the initially poor economies of India grown faster than their initially rich counterparts? A key conclusion of this paper is that there has indeed been convergence in real per capita incomes across the states of India during the period 1961–91. The convergence found is absolute because it occurs when no explanatory variables other than the initial level of per capita income are held constant. That is, the 20 states of India displayed homogeneity across states with respect to the steady-state level of per capita income, yet exhibited heterogeneous initial levels of per capita income. However, while convergence has occurred, the speed at which the initially poor states have caught up to the initially rich states, with 1.5 percent of the gap between them being closed each year, is slower than that obtained in analyses of regional convergence in developed countries, which generally center on 2 percent per year. Accordingly, while a typical Indian state would take about 45 years to close one half of the gap between its initial per capita income and the steady-state per capita income, the typical region of a developed country would take only about 35 years to complete the same task.

There has also been a widening in the dispersion of real state per capita incomes in India during the period 1961–91. However, grants from the central government to the states did ensure that the dispersion of state real per capita disposable incomes was narrower than the dispersion of state real per capita incomes, as relatively more grants were transferred to poor states than to their rich counterparts.

The extent to which population movements occurred in response to differential state incomes was rather weak, indicating that significant economic, social, and cultural barriers to the free migration of labor across the states of India continue to exist. In that sense the labor markets of Indian states resemble more closely the relatively closed regional labor markets of Europe than the relatively open regional labor markets of the United States and Japan. Finally, as for the above developed countries, there is little evidence that population movements are an important factor in the convergence of state real per capita incomes in India.

Registrar General and Census Commissioner for India, Census of India, for census years 1961, 1971, 1981, and 1991 (COI);

Government of India, Central Statistical Organization. Estimates of State Domestic Product 1960–61 to 1983–84 (ESDP);

Reserve Bank of India, Reserve Bank of India Bulletin, various issues (RBI);

Government of India, Basic Statistics Relating to the Indian Economy, various issues (STAT); and

Government of India, Statistical Pocket Book: India, various issues (BOOK).

Table A1 contains a detailed listing of the mean and standard deviation of the key variables used in the cross-sectional growth regressions. The derivation and description of the data used in the paper are as follows:

AGR—The logarithm of the share of agriculture, forestry, logging, and fishing in net state domestic product at factor cost at current prices; taken from ESDP. The figure for Assam in 1961 is for its present boundaries (excludes Meghalaya, Nagaland, and Mizoram). The 1961 figure for Himachal Pradesh is the 1968 share, because in 1961 it was part of Punjab State. The figures for Punjab and Haryana for 1961 are both for their present boundaries. In the growth regressions, AGR enters in logarithmic form.

AREA—Geographic area (in thousands of square kilometers) of each state in each census year; taken from the same sources as POP. For 1961: Himachal Pradesh has the area it had as a Union Territory in 1961; and Haryana is assumed to have the area it had upon the granting of its statehood in 1966.

CBR—Crude birthrate per 1,000 persons in the rural areas of each state; taken from STAT.

CDR—Crude death rate per 1,000 persons in the rural areas of each state; taken from STAT.

DEF—NDP deflator for India; taken from IMF line 99b, base 1990 = 100.

DEN—The density of each state’s population, defined as the number of persons per square kilometer; derived as (AREA/POP)* 1,000, and taken from the same sources as POP.

DENSQ—The square of DEN; taken from the same sources as POP.

DUM—Regional dummies for the four regions of India; the 19 states and the Union Territory of Delhi have been allocated as follows: East (Assam, Bihar, Manipur, Orissa, Tripura, West Bengal); North (Haryana, Himachal Pradesh, Jammu and Kashmir, Punjab, Uttar Pradesh, Delhi); South (Andhra Pradesh, Karnataka, Kerala, Tamil Nadu); West (Gujarat, Madhya Pradesh, Maharashtra, Rajasthan).

FLIT—State-specific literacy rates, indicating the number of literate females per 1,000 females at each census year; taken from Government of India (1983). Data for Assam for 1981 are not available, as the 1981 census was not conducted in that state because of civil disturbances. The 1961 data exclude that part of each state’s female population aged between 0 and 4 years.

LIT—State-specific literacy rates, indicating the number of literates per 1,000 persons at each census year; taken from Government of India (1983). Data for Assam for 1981 are not available, as the 1981 census was not conducted in that state because of civil disturbances. The 1961 data exclude that part of each state’s population aged between 0 and 4 years.

Table A1.Data for Indian States, 1961–91
VariableYear(s)MeanStandard deviation
Logarithm of NDPa19617.9180.292
19718.0660.294
19818.1870.322
19918.4580.333
Growth of NDPb1961–910.01800.0066
1961–710.01480.0145
1971–810.01210.0151
1981–910.02700.0111
Share of agriculture in slate NDPc19610.5340.128
19710.5250.141
19810.4400.127
Share of manufacturing in state NDPd19610.1180.057
19710.1130.056
19810.1340.076
Regional dummies
East0.3000.458
North0.3000.458
South0.2000.400
West0.2000.400
Net migration ratee1961–910.00320.0093
1961–710.00110.0048
1971–810.00140.0052
1981–910.00340.0072
Population density1961236.483369.031
1971323.538565.251
1981447.543864.243
Square of population density1961192.107E+03678.712E+03
1971424.185E+031590.375E+03
1981947.211E+033729.197E+03
Sources: Authors’ calculations; see Appendix text for sources and definitions.

The logarithm of income is the logarithm of real (constant 1990 rupees) per capita NDP in state i at time t, In yit.

The growth of income is the annual average growth rate of real (constant 1990 rupees) per capita NDP in state i between years t – T and t: (1/T)ln(yit/yi,t-T).

The share of agriculture is the share of NDP derived from the agriculture, forestry, and fishing sectors of state i at time t, AGRit.

The share of manufacturing is the share of NDP derived from the manufacturing sector of state i at time t, MANit.

The net migration rate is the annual average rate of net in-migration (on an intercensal basis) as a share of the population of state i at the initial year of each intercensal period, MIGit. Indian census years were 1961, 1971, 1981, and 1991.

Sources: Authors’ calculations; see Appendix text for sources and definitions.

The logarithm of income is the logarithm of real (constant 1990 rupees) per capita NDP in state i at time t, In yit.

The growth of income is the annual average growth rate of real (constant 1990 rupees) per capita NDP in state i between years t – T and t: (1/T)ln(yit/yi,t-T).

The share of agriculture is the share of NDP derived from the agriculture, forestry, and fishing sectors of state i at time t, AGRit.

The share of manufacturing is the share of NDP derived from the manufacturing sector of state i at time t, MANit.

The net migration rate is the annual average rate of net in-migration (on an intercensal basis) as a share of the population of state i at the initial year of each intercensal period, MIGit. Indian census years were 1961, 1971, 1981, and 1991.

MAN—The logarithm of the share of manufacturing in net state domestic product at factor cost at current prices; taken from ESDP. Additional details are as for AGR, In the growth regressions, MAN enters in logarithmic form.

MIG—Intercensal annual net migration as a share of the state’s population in the initial year of the intercensal period; the net migration data are taken from the COI Migration Tables for 1971 (Series 1, Part II-D(i)) and 1981 (Series 1, Part V, A and B). The migration data for the 1980s are an implied net immigration rate and are derived as the difference between the annual rate of population growth and the rate of natural increase (crude birthrates less crude death rates) and are taken from STAT for all slates. Where census data were unavailable (Himachal Pradesh for 1971 and Assam for 1981) the implied net immigration rate was calculated, based on data taken from STAT.

NDP—State net domestic product at factor cost, in current Rs. million; taken from the same sources as PCNDP. Additional details are as for PCNDP.

PCNDP—Per capita state net domestic product at factor cost, in current rupees; taken from ESDP for 1961 to 1980, and ES for 1981 to 1991. The figures for Delhi for 1982–84 are taken from BOOK. The figure for Himachal Pradesh for 1961 (based on its present boundary) is taken from Lal (1985). The figure for Assam in 1961 is for its present boundaries (excludes Meghalaya, Nagaland, and Mizoram). The figures for Punjab and Haryana for 1961 are both for their present boundaries. Figures for the following states (based on their present boundaries) and years are not available: Assam (1962–65, 1967–68). Haryana (1962–65), Himachal Pradesh (1962–67), and Punjab (1962–65).

POP—State population (in millions) at census dates; taken from STAT for 1961, 1971, and 1981, and from COI for 1991. As no census was carried out in Assam in 1981, the official statistics interpolate its population using the 1971 and 1991 census results. Similarly, the 1991 census has yet to be conducted in Jammu and Kashmir; the figure in COI is an official projection.

TR—The grant component of transfers from the central government to state governments, in current Rs. Million; taken from RBI. This measure comprises statutory grants-in-aid, grants on account of state and central plan schemes, and grants on account of centrally sponsored schemes.

URB—Urban share of state populations in each census year; taken from the same sources as POP,

REFERENCES

Paul Cashin was an Economist in the IMF’s Research Department when this paper was written, and holds a Ph.D. in economics from Yale University. He is now Lecturer in economics at the University of Melbourne and Principal Economist at the Victorian Department of Agriculture, Energy and Minerals, Australia. Ratna Sahay, who has a doctorate from New York University, is an Economist in the IMF’s Research Department. The authors are grateful to Ajai Chopra. Charles Collyns, Nadeem Haque, Mohsin Khan, Neeraj Prasad, Arvinder Singh Sachdeva, Michael Sarel, and Sunil Sharma for their invaluable input. Brooks Dana Calvo provided excellent research assistance.

A key early discussion of the efficiency-equity aspects occurred between Buchanan (1950 and 1952) and Scott (1950). The efficiency case against center-state grants is that they result in a misallocation of national resources, because the consequent expansion of slate and local public services acts to slow the movement of labor out of regions where it has a low marginal product to regions where its marginal product is high. Balogh (1962) and Gupta (1973) make similar points in discussing India’s programs designed to reduce disparities in regional incomes.

In this paper the term “state” will be used to describe the 20 regional economies of India, although for the period of analysis Delhi was a union territory and not a state of India. The key difference between a state and a union territory is that the taxing and spending powers of the latter are severely circumscribed: their budget is essentially derived from the central government. The acronyms used for the 20 Indian regions we study are: Andhra Pradesh (AP), Assam (A), Bihar (B), Delhi (D), Gujarat (G), Haryana (H), Himachal Pradesh (HP), Jammu and Kashmir (JK), Karnataka (KA), Kerala (KE), Madhya Pradesh (MP), Maharashtra (MH), Manipur (MN), Orissa (O), Punjab (P), Rajasthan (R), Tamil Nadu (TN), Tripura (T), Uttar Pradesh (UP), and West Bengal (WB).

In 1991 India comprised 25 states and 7 union territories: in 1961 there were 15 states and 12 union territories. The 20 regions studied in this paper accounted for 93.1 percent of India’s net national product (at factor cost) and 99.0 percent of India’s population in 1991; the corresponding figures for 1961 were 90.1 and 99.3 percent, respectively (Government of India (1991a and 1995)).

If the states vary in their savings rates and technologies, then the neoclassical growth model predicts conditional convergence—state per capita incomes still converge, but this convergence is conditional on each economy’s own steady state.

An interesting observation is that the capital city of Pataliputra (now Patna) under Ashoka is today the capital of the poorest state. Bihar, while Delhi, the capital city under Akbar and of India today, is the richest (measured in per capita income terms; see Table 2).

Along with agriculture, the responsibilities of state governments extend to power generation, education, health, sanitation, small industries, and road transport.

During 1951–85, on average, the center accounted for more than 70 percent of the total resources raised by the center and the states, of which only 31.4 percent was transferred to the states (Sarkaria Commission (1988)).

The center’s consent is needed either if a loan from the center to the states remains outstanding or if the center has guaranteed an outstanding loan to the states. Since all states have typically been indebted to the center from the very beginning, the center’s permission has de facto been sought for raising all fresh loans.

See Toye (1973) for an early analysis of the structure of both states’ receipts (revenues plus transfers) and states’ expenditures.

While the constitution provides for financial transfers from the center, it does not specify the criteria for dividing the divisible pool of taxes between the center and the states and among the states. Finance Commissions, which determine these shares, have often recommended grants to fill gaps between projected current revenues and expenditures of states. This may have discouraged states from increasing public savings, because of the resultant loss in grants. Although the Finance Commission’s recommendations are not binding on the Government of India, the center has generally accepted its major recommendations (Sury (1992)).

Of total gross transfers from the center to the states in 1961, some 24 percent comprised the sharing of taxes; 30 percent, grants: and 46 percent, gross loans. The equivalent shares for 1971 were 32, 26, and 42 percent; for 1981 the shares were 39, 29. and 32 percent: and for 1991 the shares were 34, 32, and 34 percent, respectively (Reserve Bank of India (1993) and Government of India (1994)).

See Bhat (1993) for an analysis of the determinants of the level of grants to Indian states, and Sastry and Nag (1990) for a discussion of the influence of such center-state transfers on states’ economic growth.

The formula that determines the states’ shares in the center’s revenue has, over time, depended to varying degrees on collections by states, state population, and several indicators of per capita income.

The Seventh (1979–84) and Eighth (1984–89) Finance Commissions provided limited amounts of debt relief in the form of debt rescheduling and/or write-offs.

Unless otherwise denoted, state “income” refers to the value of state net domestic product at factor cost (NDP)—see Section III and the Appendix for details.

The concept of state net domestic product is discussed in detail in Section III.

For a Cobb-Douglas production function in intensive form, and assuming a constant saving rate (as do Solow (1956) and Swan (1956)). there is a closed-form solution for the convergence coefficient: β = (1 – α)(g + n + δ), where α is the share of capital in output, n is the rate of population growth, g is the exogenous rate of labor-augmenting technical progress, and δ is the depreciation rate.

This assumption is tested (and could not be rejected) empirically in Section IV below.

It is assumed here that yit represents real per capita income from the production of goods and services in economy i, and so changes in relative prices appear as changes in yit. That is. assuming no quantities change, a fall in agricultural (or manufacturing) prices generates a lower growth rate of yit in economies that are large agricultural (manufacturing) producers.

See Mitra (1988) for an analysis of Kuznets’ hypothesis in the context of the states of India.

The data are taken from official Government of India sources, to ensure consistency in definition and compilation and to aid the comparability of data across states and through time. All income, price, and fiscal data are for years ending March. See the Appendix for further details.

The CSO-consolidated series for slate NDP uses the same methodology and source material as those for the national estimates of NDP. However, to the extent that minor revisions called for by the availability of new source material arc not retrospectively incorporated for earlier years, the state NDP estimates are not strictly comparable over time. Similarly, differences in the source material used, data availability, and the extent of statistical development mean that the quality of income measures may vary across states at any given point in time (Government of India (1986)).

Unless otherwise denoted, net migration in this paper is synonymous with net immigration.

States in which real per capita NDP declined were Assam, Delhi, and Tamil Nadu in the 1960s; Assam, West Bengal, and Rajasthan in the 1970s; and Jammu and Kashmir in the 1980s. However, it is important to recognize that, while decennial rates of growth of real NDP were positive in all these cases, they did not keep pace with intercensal population growth rates.

A further cause of potential bias is our use of a national deflator to adjust nominal state NDP figures for the change in prices. That is, where PINDIA (the level of India’s national GDP deflator) is used rather than Pi (state-based deflators) to derive real NDP for each state from nominal state NDP, if prices differ across states at points in lime, the correlation between PINDIA and the error term will induce bias in the estimated coefficients. However, the use of a common (national) deflator for each state at each point in time in a cross-sectional analysis will affect only the constant term in each regression. Moreover, work by Dholakia (1985) confirms that for the 1960s, the series of real per capita state NDP at local and at national prices were statistically identical. Bhattacharyay (1982) also finds that there was little cross-state variation in the purchasing power of a rupee between 1964 and 1978.

The formula for the “half-life” (HL) in years is HL = log(2)/β.

As noted in Section II, using the Cobb-Douglas-based closed-form solution for the speed of convergence from the Solow-Swan (1956) model yields β = (1 – α) (n + g + δ). Assuming that (g + δ) = 0.04 (reflecting the slow rate of exogenous technical change in developing countries); letting n = 0.03 (replicating India’s rapid rate of population growth), then β^ = 0.015 can only be approximated with a value for α of about 0.75. As argued by Barro and Sala-i-Martin (1995), such a capital share is too high for a narrow concept of physical capital, but would be consistent with a broad concept of capital that also includes human capital.

A speed of convergence of about 1.5 percent a year is also close to that obtained for a sample of 95 developing countries (β = 0.014) between 1970 and 1990 by Khan and Kumar (1993).

Moreover, a formal test of the null hypothesis that the coefficients on the regional dummy variables are all equal to zero found that the hypothesis could not be rejected. A test of this restriction, run by adding the dummies to the three inter-censal regressions of Table 3, yielded a Likelihood Ratio test statistic of 8.112; the corresponding X2 value with 9 degrees of freedom at the 0.05 percent level is 16.919.

In future work the authors will examine the robustness of this result, using alternative models of the growth process.

The σNDPt calculations exclude certain states for certain years, due to the unavailability of data on state per capita NDP. These are Assam, Haryana, Himachal Pradesh, and Punjab for 1962–65: Himachal Pradesh for 1966; Assam and Himachal Pradesh for 1967; and Assam for 1968.

A least squares regression of σNDPt on a time trend and a constant term revealed that for the 1961–91 subperiod the coefficient on the time trend (

) was small yet significantly positive (
= 0.0015 [s.e.= 0.0004]). This indicates that σNDPt increased at the small trend rate of growth of 0.15 percent a year over the period 1961–91.

As noted in Section I and by both Barro and Sala-i-Martin (1992a) and Quah (1993), even if absolute β-convergence holds (as it does for the states of India), the dispersion of per capita incomes across economies need not decline.

Evidence of increasing regional disparities in the 1980s was also found in the work of Majumdar and Kapoor (1980), Nair (1985), Singh (1985), and Rao (1985) on the cross-state dispersion of per capita incomes.

Data on grants from the central government to the Union Territory of Delhi over the period 1961–91 are unavailable. Accordingly, our measure of σSDIt does not include center-state grants to Delhi.

An examination of the dispersion of per capita state disposable incomes for four of the six initially poor states, excluding Manipur and Tripura, yields σNDIt approximately equal to σSDIt between 1961 and 1988. while σNDIt was clearly greater than σSDIt after 1989. Accordingly, it appears that in comparison with Manipur and Tripura, the states of Bihar, Orissa, Uttar Pradesh, and Madhya Pradesh benefited much less from center-state grants prior to the late 1980s.

Mukerji (1982) also uses crude birth- and death rates in estimating net migration to the eastern states of India in the 1970s.

Care needs to be taken in interpreting the figure for net migration to the eastern states of India, in particular to Tripura. This is because the derivation of the large figure for migration in the 1980s is based on vital statistics that, unlike those taken directly from the census data for the 1960s and 1970s, also include international migrants.

The marginal effect of yi,t–T on MIGit is positive if v > 0; the marginal effect of πi,t–T on MIGit is negative if ξ + 2ω < 0.

The variable on the vertical axis of Figure 12 is the annual average of in-migration to each state during 1961–91 (the numerator), expressed as a share of the population of each slate in 1961 (the denominator).

The lilted values of MIGit were obtained using the following set of independent variables (the exogenous variables from the structural and reduced form regressions): In yi,t–T. πi,t–T,

In AGRi,t–T, In MANi,t–T. The R2 statistic on the reduced form regressions for MIG1961, MIG1971, and MIG1981 are 0.839. 0.854. and 0.660. respectively (Table 5).

A Wald test did not reject the hypothesis that the coefficient on MIGit is the same for each of the three subperiods.

Other Resources Citing This Publication