DETERMINANTS OF CITY GROWTH AND OUTPUT IN INDIA

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1 RURDS Vol. 22, No. 1, March 2010 doi: /j X x DETERMINANTS OF CITY GROWTH AND OUTPUT IN INDIA Kala Seetharam Sridhar Public Affairs Centre, Bommasandra-Jigani Link Road, Anekal Taluk, Bangalore, India To investigate what determines urban population and economic growth, the determinants of urban population growth and economic output in India are examined empirically. City growth and economic output regressions are estimated using several approaches. Since only growing areas are usually designated as towns, the urban population of districts that are larger geographical areas than cities are estimated. It is found that, at the district level, manufacturing has a positive impact on city size, and proximity to large cities causes nearby cities to be larger, reflecting agglomeration effects. 1. Introduction The nature of urban transition in the second half of the 20 th century differed from the first half of the century (see Mohan & Dasgupta 2005; Cohen 2004). Cohen (2004) points out that there are several factors that characterize this transition. First is that the scale of change has been unprecedented. Second, urbanization has occured at a rapid (though not unprecedented) pace. Most importantly, Cohen points out that urbanization has now been occurring more rapidly in countries that have relatively lower levels of per capita income, under different sets of demographic regimes. He attributes this to urban change being more closely related to changes in the global economy than ever before. As in other countries, and as Sridhar (2007b) points out, cities in India are growing in size and number. At the beginning of the twentieth century, there was only one city with population over a million people, namely Calcutta, with a population of 1.5 million. 1 Bombay joined this league in In 1991, four metropolitan areas (Mumbai, Chennai, Kolkata and Delhi) were the only mega cities (cities with five million-plus population), but by 2001, the number of mega cities had increased to six (along with Bangalore and Hyderabad). This trend continued steadily. In 1991, there were 23 cities with a population of over one million (which accounted for 33 percent of the urban population), 300 cities with a population ranging between 100,000 and a 1 These, being called as million-plus cities, are at the apex of the urban hierarchy in India. Below the million-plus cities, the Census of India s definition for various class sizes of cities is as follows: Class I: Population >100,000 Class II: Population of 50,000-99,999 Class III: Population of 20,000-49,999 Class IV: Population of 10,000-19,999 Class V: Population of 5,000-9,999 Class VI: Population <5,000.. Published by Blackwell Publishing Asia Pty Ltd.

2 Sridhar, Determinants of City Growth and Output in India 23 million, and over 4,000 towns. In 2001, the number of cities with a million-plus population grew to 35 (housing 38 percent of the total urban population), with 14 of these 35 cities growing at higher than average rate during (Lahiri-Dutt & Samanta 2001). In 2001, the number of cities in the population size category of 100,000 to one million grew to 388 from only 300 in It should be noted that cities in India are not growing just in size but also are gaining in political influence (see Yeung 2002). The 74 th Constitutional Amendment Act of 1992 in India formally recognized urban local governments as the third tier of government. In addition to increasing the accountability of urban governments to their residents, the Amendment also expanded the public service responsibilities of local governments (Rao & Singh 2005). The Constitution now lists 37 different public services for which urban local governments are responsible, in some cases in conjunction with state government. Although some municipal governments were already providing most of the newly listed services, for others the Amendment has resulted in a substantial increase in public service responsibilities. Given the fact that India is rapidly growing and urbanizing, and the fact that it has over 5,000 towns that are going to soon become politically influential which contribute to nearly 65 percent of GDP, it is crucial to understand what factors determine city growth in India. Further, a city may grow in terms of its population, but may or may not grow economically. Examining this question is the focus of this paper. India is experiencing extensive urban growth. That is, an increasing number of cities rather than intensive growth that is, higher growth within existing cities. If UAs grow at a faster rate than the cities, then it might well be the case that India is experiencing intensive rather than extensive growth. It was found that the number of towns in the country grew at 12 percent on average over , whereas growth in the number of UAs was only 2.4 percent on average. 2 Hence there is some support to believe that India is indeed experiencing more extensive rather than intensive urban growth. Because of the existence of limits to growth in large cities imposed by regulation such as rent control in India s cities, Sridhar (2004) points out that it is likely that a large number of rural areas are converted to urban areas, rather than more growth of existing urban areas. Cohen (2004) also argues that most urban growth over the next 25 years will not take place in mega-cities at all as the UN publication World Urbanization Prospects (2002) predicts, but will occur in far smaller cities and towns. 3 In fact, Cohen (2004) argues that India s definition of an urban area is so restrictive that it misses population growth occurring in urban areas just outside of the official city boundary. 4 He suggests that if India were to adopt a different definition of an urban area, it would be transformed from being largely rural to being predominantly urban. 2 The number of towns in the country grew from only 4,615 in 1991 to 5,179 in 2001, and the number of UAs in the country increased from 375 in 1991 to 384 in 2001, recording growth rates of percent and 2.4 percent respectively. 3 Cohen s (2004) analysis uses the 2001 revisions of the World Urbanization Prospects. The latest revision is the 2007 revision of this publication. Given Cohen s (2004) results are robust, the implications from the 2001 revisions may be assumed to carry over to the 2007 revisions. This is based on personal communications with Barney Cohen. 4 The Census of India defines settlements having the following characteristics as urban areas: a population of five thousand or more; a minimum density of 1,000 people per square mile; and at least seventy five percent of work force outside agriculture.

3 24 Sridhar, Determinants of City Growth and Output in India 2. Objectives and approach Given the economic importance of cities and the lack of adequate research in an Indian context, this paper poses the following question: what determines city population growth and city economic growth (output) in India? Population and economic growth are important to distinguish because a city population can grow, but the city may or may not grow economically. This is based on general equilibrium models of optimum city size which show that cities, if their size grows beyond certain limits, will experience congestion and decline in their economic output. To investigate the question of what determines city population and economic growth in India s cities, the determinants of city growth and output in India using the Census of India s 2001 town directories, and using newly published data by the various state directorates of Economics and Statistics regarding district domestic products, are examined. City growth and output regressions are estimated using several approaches. Since only growing areas are usually designated as towns, to get rid of this selection bias, the urban population of districts that are larger geographical areas than cities, but which are designated as districts irrespective of whether they are growing or declining, are estimated. It is found that, at the district level, proximity to large cities and large manufacturing bases cause cities to be bigger. The literacy rate has a statistically significant impact on city-level non-primary output per capita. The contribution of this paper is to distinguish between population growth and economic growth of cities, to understand what determines city growth in India, and to examine whether individual cities can grow forever. There is some evidence on optimum city size in China (see Au & Henderson 2005). However this study aims to throw light on this issue in an Indian context. City growth, especially in an Indian context, has not been extensively studied in the literature. Sridhar (2007a) makes an attempt to conceptualize the factors that drive city growth in India. In addition to economic reforms that have been the driving force of city growth in countries like China and India, the city s economic base, as reflected in the share of manufacturing vis-à-vis service employment, is important to a city s growth. This is because the economic output of a city might be impacted by the extent of manufacturing versus service employment. Paul and Sridhar (2009) find that the service sector led the surge in economic growth in the southern Indian states in the post-1991 period. Furthermore, consistent with the Tiebout hypothesis, competition in providing public services such as education, and good roads, attract more residents and firms and accelerates city growth. Next, the instance of fast-growing cities in India such as Bangalore, Pune and Hyderabad suggests that the accumulation of human capital could cause city growth by attracting firms to their pool of skills. The Census of India town directories are a rich source of information regarding these aspects. In the Becker-Mills-Williamson model, a computable general equilibrium model of the Indian economy, limits to city growth occur due to the inflation of urban site rents. Rents are aggravated in India s cities because of the existence of strong land use controls such as rent control and ULCRA. The Urban Land Ceiling and Regulation Act (ULCRA) of 1976 was enacted to exercise social control over urban land so it is equitably distributed. This act specifies that no individual or enterprise can hold beyond a certain limit of vacant land above which they need to declare to the government and sell for a predetermined price. First, there was

4 Sridhar, Determinants of City Growth and Output in India 25 no incentive for individual landowners to declare his land. While many social groups demand better implementation of this law, most of the states repealed it due to its adverse impacts on restricting the supply of urban land by reserving too much for public purposes. ULCRA has also become a charter for corruption since it enables large scale land holding by the government to direct it to whatever purpose they considered fit. There is also no evidence that the land so acquired was used for the urban poor, hence the law has long outlived its utility. In any case, this law enabled the government to possess large amounts of land at its disposal, and artificially created scarcity of precious urban land. However a few Indian states continue to have ULCRA in place. Kundu (2006) pointing out that the ULCRA is considered one of the key hindrances in the development of cities, highlighting that it would be important to analyze its impact on the functioning of land, labor, and capital markets in urban areas and how that has affected the pace and pattern of urban growth in the country. City growth and output in an Indian context are modeled in this paper, controlling for these factors. 3. Past literature There is a vast body of literature that studies the determinants of city growth in various countries. Glaeser et al. (1995) examine economic growth in a cross-section of American cities, studying the relationship between urban characteristics in 1960 and urban growth between 1960 and They find that income and population growth move together and that both income and population growth are positively related to initial schooling, negatively related to initial unemployment, and also negatively related to the initial share of employment in manufacturing. Furthermore, they find that government expenditures (except for sanitation) are uncorrelated with growth. But government debt is positively correlated with later growth. Mills and Becker (1986) analyze and estimate city growth in India, first using a national sample of large Indian cities and then using a sample of cities in a large Indian state, Madhya Pradesh. They find that rapid growth of a city s manufacturing employment and of the national population induces fast growth in a city s population. They further find that large initial population stimulates growth at low initial population levels but the effects disappear at higher initial population levels. Their results imply that a large initial population discourages further city growth starting at initial populations somewhat below 1 million, and that cities grow faster in higher income than they do in lower income states. Finally, they find that the farther cities are from the nearest class I city, the faster they grow. Since selection bias is likely to arise in the choice of cities as unit of analyses, Beeson et al. (2001) take counties in the United States as the unit of analysis. This paper finds natural characteristics (e.g., access to water transportation) heavily influenced where populations located in 1840, and produced characteristics in existence in 1840 (e.g., educational infrastructure) had a significant influence on subsequent growth. They found evidence of population convergence only when the most-heavily-populated counties in 1840 were excluded from the sample. Moreover, when counties located on the western frontier were excluded from the full sample, on the assumption that they were relatively far from their steady-state populations, evidence was found of population divergence. Da Mata et al. (2005) estimate the determinants of city growth in Brazil. Their main findings are that increases in rural population supply, improvements in inter-regional transport

5 26 Sridhar, Determinants of City Growth and Output in India connectivity and education attainment of the labor force have strong impacts on city growth. They also find that local crime and violence, measured by homicide rates impinge on city growth. In contrast, a higher share of private sector industrial capital in the local economy stimulates growth. Haurin (1980) models the impact of a favorable climate (which is a site-specific factor) on city population (along with the price of housing, and price of factors) and finds that a relative increase in the climatic differential would induce migration toward the improved area. Using his model, Haurin also finds that in equilibrium, average wage rates will vary among regions and compensate for differences in endowments of location-specific attributes. He shows that compensation can also occur through differences in the price of housing, and this result is dependent on the parameters of the model and the manner in which the site-specific factor (climate) affects production and consumption. Hence there is every reason for us to believe that urban areas better endowed with location-specific attributes attract more population and grow more when compared with areas with less favorable climate. Economic models of cities postulate an inverted-u shape of real income per worker against city employment, where the inverted-u shifts with industrial composition across the urban hierarchy of cities. Au and Henderson (2005) model and estimate net urban agglomeration economies for Chinese cities. They find that urban agglomeration benefits are high real incomes per worker rise sharply with increases in city size from a low level. They find that real incomes (per worker) level out nearer the peak, and then decline very slowly past the peak. They find that a large fraction of cities in China are undersized, due to nationally imposed, strong migration restrictions, resulting in large income losses. Kundu (2006) points out that the ULCRA has been considered one of the key hindrances in city development, highlighting that it would be important to analyze its impact on the functioning of land, labor, and capital markets in urban areas and to study how the law has affected the pace and pattern of urban growth in the country. Despite this body of empirical work, there are several reasons why this paper contributes to city growth literature. There are several testable hypotheses that emerge out of recent occurrences as part of India s economic reform. Firstly, cities in India are not only growing in size, but also gaining in political influence, as a result of the 74 th Constitutional Amendment Act of With this, cities are expected to contribute much more actively to macroeconomic growth targets. Secondly, the economic reforms have positively affected the cities and the way in which they function. As discussed earlier, a large number of urban reform programs (such as the Jawaharlal Nehru National Urban Renewal Mission and Urban Infrastructure Development Scheme for Small and Medium Towns) now dot the landscape of urbanization in the country. Several examples may be cited of the effect of reform on cities, as Sridhar (2007a) points out. For instance, the ULCRA which thwarted private holdings of vacant land above certain limits in urban areas, was repealed in With freedom from this control (in all except a handful of states), urban land should be in a position to claim its market price. This will make housing more affordable, and encourage city growth. Thirdly, the instances during the 1990s of fast-growing cities such as Bangalore, Pune and Hyderabad suggest that accumulation of human capital causes city growth. This is especially the case if jobs were to follow people, as Mills and Price (1984) and Sridhar (2007b) find respectively with respect to the United States and India. Sridhar (2007b) finds that for a variety of employment sub-sectors in India, such as manufacturing, transport, communications, trade and commerce, specific skills are important in determining suburbanization of those jobs. So,

6 Sridhar, Determinants of City Growth and Output in India 27 if cities were to accumulate human capital, or skilled workforce, appropriately measured, firms follow them and city growth is expected to occur. Finally, Mills and Becker (1986) analyzed determinants of city growth for large cities. However, Indian urban development over the next 25 years is likely to take place outside of the major cities, in small cities and towns, as noted by Cohen (2004). So it is important to understand what drives the growth of these small cities and towns. In this paper, the determinants of city population and economic growth are estimated, using data from all cities and towns in the country using the Indian town directories from the 2001 Census. 4. Model, methodology, data and variable definitions In this paper, city growth and city output regressions are estimated using several approaches. In one approach, a simple regression of the levels of population of the towns in various years as a function of the characteristics indicated above is estimated. In another specification, the growth rates of populations in cities are explained. Since only growing areas are usually designated as towns, in a different specification, the urban population of districts that are larger geographical areas than cities, is estimated. These are equivalent to counties in the USA. Furthermore, districts are designated whether they are growing, declining or stagnating. This is similar to the approach Beeson et al. (2001) take. Another possible approach to explain city growth is to use city output. This is because a city might grow, stagnate or decline even with increased population growth. In fact, Cohen (2004) points out that in sub-saharan Africa as well as in east Asia, the change in the urbanization level was very similar. However the difference was that in sub-saharan Africa it was unaccompanied by a corresponding increase in GDP per capita which stagnated, whereas in east Asia GDP per capita shot up. Taking the preceding discussion into account, city growth is estimated using measures of city-level output. The basic model which is estimated here may be stated as follows: L i = f (MS i, PS i, A i, D i, H i ) (1) L i is urban population, or growth rate of urban population of district i or net district domestic product (DDP) of city or district i. MS i refers to the ratio of manufacturing to service employment in city i, PS i refers to public services such as roads, and coverage of schools in the city, A i refers to amenities such as temperature differences. Furthermore, the distance to cities with 100,000 or above population is important, as shown by earlier work, in determining city growth. This distance to larger cities is represented by D i. H i refers to human capital in the city. This is measured by the literacy rate. The economic base of the city (measured in terms of the proportion of manufacturing versus service employment) has some impact on city growth. Specifically the hypothesis is that due to India s booming services sector, which contributes nearly 53 percent to the country s GDP 5, higher service employment in a city s economic base might cause an increase in city growth or output. 5 See also Paul and Sridhar (2009) who find that the service sector led the rapid surge in per capita incomes experienced by economically better performing states such as Tamil Nadu.

7 28 Sridhar, Determinants of City Growth and Output in India The quality of public services, through its impact via a Tiebout means of voting with the feet, is expected to impact city growth positively. The chosen measures of public services are the average road (both surfaced and unsurfaced) length per 1,000 population and the population coverage per school. These are representative of the quality of public services as they are used by all. Better quality of public services such as road length and population coverage with schools will attract residents and firms. Amenities such as temperature differences affect city growth as may be expected with a favorable climate to encourage in-migration of population. Therefore lower temperature differences are expected to attract population. Distance to large cities can be expected to impact city growth because large cities thwart the growth of cities in their vicinity by becoming primary magnets of economic activity. Finally, human capital can be expected to impact city growth because the accumulation of human capital creates a pool of skilled labor force and attracts firms and residents, leading to city growth. Equation (1) was estimated at the district-level, in three different specifications firstly, by using a basic model. In this basic model, the district s 2001 urban population as being dependent on various exogenous factors indicated in Equation (1) was estimated. The other specifications at the district level examined growth in urban population (during ) of the district as the dependent variable. This is another simple specification which assumes that if there is an increase in the urban population of a district during the decade, then city growth has occurred. At the district level, in a final specification, city output was estimated to understand city economic growth. In order to do this, the new district domestic product (DDP) estimates published by the various state directorates of Economics and Statistics were obtained by calling the individual states, with the result that information was obtained regarding 240 districts in eight states. 6 Using the district instead of the town as the unit of observation has a distinct advantage which is that only growing places are designated as towns, whereas even areas that are declining in terms of population are designated as districts, with the result that the selection bias that could arise with the choice of towns as the unit of observation, does not arise with respect to districts. The non-primary portion of the net DDP per capita at constant ( prices) was used as the measure of city-level output. This is consistent with the definition of cities in India s context (see footnote 4). While gross and net DDP estimates at current and constant prices were available for (240) districts in the eight states from to , the net DDP estimates for were chosen since, for a few states such as Assam, that was the most recent year for which the data were available. The net estimates were chosen to exclude any depreciation. The net DDP was computed in per capita terms to account for any scale effects in the case of large or highly urban or metropolitan districts such as Mumbai. It must be clear that in the simpler specifications dependent on population it was possible to get around the problem of DDP estimates being available only for a limited number of districts. The exogenous variables summarized in Equation (1) were constructed with utmost care. The literacy rate for 2001 for the districts was obtained from the Census of India. The remaining variables were constructed from the Census of India town directories. The town directories 6 The states that had prepared and published DDP estimates were Andhra Pradesh, Assam, Karnataka, Kerala, Maharashtra, Tamil Nadu, Uttar Pradesh and West Bengal. The remaining states were still in the process of preparing the estimates at the time work for this paper was completed.

8 Sridhar, Determinants of City Growth and Output in India 29 contain data on maximum and minimum temperatures for all the 5,000 towns in the country. The difference was calculated between the maximum and minimum temperatures for every town; a relatively lower difference between the maximum and minimum temperatures means a more salubrious climate. Since this is indicative of a natural amenity, this was used as an exogenous factor influencing city growth, following the literature (for instance Haurin 1980, examines the influence of climate on population and migration). The difference between the maximum and minimum temperature for towns was aggregated (averaged) across districts containing them. In this way, district-specific estimates of these temperature differences were obtained for all districts. The ratio of employment in manufacturing to that in services indicates whether city growth is caused by one or the other sector. The ratio of employment in manufacturing to services was computed for every town from the 1991 town directories and was used as an instrument, given the 2001 town directories did not contain the manufacturing and service sector employment by town. The manufacturing to service ratio from the 1991 directory was averaged across towns in every district, providing a district-specific estimate of the indicator. The distance to large cities can deter a city s growth, as found by Mills and Becker (1986). The distance to a city is also used as a measure of market potential or gravity as done by Au and Henderson (2005). The distance from every town to a city with 100,000 population or greater was obtained from the 2001 Census of India town directories, and was averaged across towns within a district. This way, district-specific estimates were obtained of the average distance to large cities for all cities within districts. Finally, measures were chosen of public services to examine their impact on city growth. These are the average road (both surfaced and unsurfaced) length per 1,000 population and the population coverage per school. Both these indicators were computed from the 2001 town directories and then averaged at the district level. 5. Description of data Tables 1 2 describe the data at the district and town levels respectively. Data on certain variables such as literacy rate, population and population growth are available for all or most of the 558 districts of the country. As discussed earlier, the net district domestic product (NDDP) data are available only for roughly half of the districts (located in eight states). The district with the largest population in 2001 was Medinipur in West Bengal, while the largest in 1991 were Mumbai and Mumbai (suburban) districts. The district with the largest urban population for both 1991 and 2001 is Mumbai. While the absolute NDDP for (in constant terms) is the highest for Thane district (Maharashtra), the highest per capita non-primary NDDP is for Mumbai (see also Figure 1). This is consistent with the fact that in terms of actual GDP size, the country s financial capital Mumbai is the biggest ( Mumbai-largest/266636/ retrieved March 18, 2010). The lowest absolute (non-primary) NDDP is for North Cachar Hills, Assam, while the lowest per capita (non-primary) NDDP is for Shravasti, Uttar Pradesh. The largest temperature differences are in the desert district of Bikaner, Rajasthan, which is to be expected, and the lowest is in Karnataka s Bidar district. Haora in West Bengal has the maximum ratio of the proportion employed in manufacturing to that in services. Mizoram s Saiha is located the farthest from a large city, being located

9 30 Sridhar, Determinants of City Growth and Output in India Table 1. Description of data for districts Std. Number of Variable Mean Minimum Maximum Deviation observations Literacy rate, Temperature differences (in degrees centigrade) Ratio of employment in manufacturing to that in services, 1991 Distance to a large city (100,000 or greater population) (in kilometers) Road length per 1,000 population (in kilometers) Population coverage per primary school Population, ,816, , ,638, ,341, Urban population, ,343 27,478 11,914, ,145, Population, ,051, , ,925, ,344, Population growth, Urban population growth, Net district domestic product (NDDP), 3,28, , ,871, , (in constant prices) (Rs.) NDDP per capita (in Rs.) 11, , , Sources: Census of India, Census Town Directories 2001 and 1991, Directorates of Economics and Statistics, Various States, Author s Computations and Analyses. Table 2. Description of data for towns Std. Variable Mean Minimum Maximum Deviation N Temperature differences (in degrees centigrade) Distance to a large city (100,000 or greater population) (in kilometers) Road length per 1,000 population (in kilometers) Population coverage per primary school Dummy for existence of ULCRA (1 = Yes; 0 = No) Economic base (1 = Agricultural; 2 = Manufacturing;3 = Services) Growth rate of population, Population, Sources: Census of India, Census Town Directories 2001 and 1991, Author s Computations and Analyses. remotely about 79 kilometres from a city of 100,000 population (which is Aizawl). Tamil Nadu s Sivaganga has the best road length coverage at kilometres per 1,000 population (see Paul & Sridhar 2009, which makes a comparison of public services (among many other things) between Tamil Nadu and Uttar Pradesh). Jammu and Kashmir s Doda has the best coverage of schools at 288 persons for every school (minimum population coverage per school, Table 1), whereas Uttar Pradesh s Firozabad is the worst covered in terms of schools at 5,128 persons for every school. These findings are consistent with those found by Paul and Sridhar (2009) in the case of TN and UP.

10 Sridhar, Determinants of City Growth and Output in India 31 Figure 1. Trend in non-primary NDDP per capita, selected districts of the country Per Capita NDDP (in Constant Prices) Year Hyderabad Bangalore Thiruvananthapuram Mumbai Pune Kanpur Nagar Lucknow Kolkata Sources: Directorates of Economics and Statistics, Various States, Author s Computations and Analyses. Figure 1 shows the trend in the per capita non-primary net DDP (in constant prices) for selected districts in the country. As may be readily clear, with the exception of a few districts such as Mumbai, Kolkata and Pune, the per capita NDDP for other districts has not grown much since While Bangalore s NDDP per capita is quite high, the NDDP data were available in prices only for and The NDDP data for for all districts was used for purposes of running estimations at the district level since that was the most recent year for which data for all districts was available. The data for towns concur with that for districts (see Table 2). The reader should note in Table 2 that the highest temperature difference occurs in a Rajasthan town (Bhadra), to be expected, given the existence of the Thar desert and associated climatic conditions. A town in Mizoram (Saiha) is located the farthest from a large city (at a distance of 79 kilometres from Aizawl). The growth rate of population is highest for a town in the Bangalore UA, and Mumbai is the largest city. In the town-level regressions, an indicator is included for strong land use controls present in towns, in the form of a dummy for ULCRA. Based on Table 2, 32 percent of towns continue to have ULCRA in place. These data on the existence or otherwise of ULCRA in the states are obtained from the Ministry of Urban Development, Government of India. A majority (nearly 80 percent) of towns have an agricultural base (with possibly less than 25% agricultural employment, consistent with the definition of urban areas by the Census, see footnote 4), with about 12 percent having some manufacturing. Estimations were performed to study the impact of each of these exogenous variables on urban growth in the districts. The first couple of regressions use the district urban population and urban population growth during as measures of city growth. The defense for using DDP estimates as a measure of growth is that districts or cities may grow in population, but they may or may not grow economically. The objective in using net per capita non-primary DDP estimates is to capture economic growth of the city, i.e., growth in city-level output. 7 The data for the remaining years for Bangalore (and all of Karnataka s districts) were in prices (except for which were in current prices). There was no method considered suitable to scale the data for the remaining years from to prices, as the conditions of the economy were considered to be quite different during these years.

11 32 Sridhar, Determinants of City Growth and Output in India Table 3. Determinants of City Size Dependent Variable: Urban Population of District, 2001 Unstandardized coefficients Variable B Std. Error t value Constant 1,480, ,045, Literacy rate, ,952, ,266, Temperature differences 7, , Distance to a large city (100,000 or greater population) 47, , Road length per 1,000 population 9, , Population coverage per primary school Ratio of employment in manufacturing to that in services, , , Number of observations 113 R Adjusted R Dependent variable mean 590, Sources: Census of India, Census Town Directories 2001 and 1991, Author s Computations and Analyses. Statistically significant at 1 percent level of significance. Statistically significant at 5 percent level of significance. Statistically significant at 10 percent level of significance. 6. Results from estimations Table 3 presents estimates of the most basic regression in which the urban population of district in 2001 is regressed on human capital characteristics (the literacy rate), natural amenities (temperature differences), economic base (ratio of employment in manufacturing to services, 1991), distance to a large city, and public services such as road length and population coverage by schools. By choosing the district as the unit of observation, the problem of selection bias which could arise by choosing towns, has been avoided. This study has some interesting and some expected findings. Firstly, larger manufacturing employment bases relative to that in services cause cities to be large. This is to be partly expected because cities are by definition non-agricultural. However, manufacturing, not service employment has a positive impact on city size. Furthermore, proximity to large cities (cities with 100,000 or greater population) causes cities to be larger. This implies that a large city encourages other cities to grow within a certain vicinity. Specifically, the magnitude of the estimate on this factor implies that for every 1 kilometre that the city is closer to a city with 100,000 or greater population, its own population increases by nearly 50,000. Conversely, the reverse is also true in the sense that the farther a city is from a large city, it will remain small, likely due to the absence of agglomeration effects. While public services such as road length (per 1,000 population) do not have the expected impact on city size, factors such as coverage of population by primary schools (large number of persons per school) cause cities to be larger. This implies that poorer level of public services such as schools (implied by larger number of persons per school) cause cities to be larger, not to be expected. One would have expected higher levels of public services such as schools to cause cities to be larger if a Tiebout like voting with the feet by the public were to be assumed (in other words, a negative impact). The correlation matrix of all independent variables was examined and none of them are big enough to suggest collinearity. Table 4 summarizes the estimation of the growth rate of urban population during (of the districts) as a function of several characteristics. With only temperature differences and

12 Sridhar, Determinants of City Growth and Output in India 33 Table 4. Determinants of city growth, using district-level data: Dependent Variable: District s urban population growth rate during Unstandardized coefficients Variable B Std. Error t value Constant Literacy rate, Temperature differences Distance to a large city (100,000 or greater population) Road length per 1,000 population Population coverage per primary school Ratio of employment in manufacturing to that in services, Number of observations 108 R Adjusted R Dependent variable mean 0.25 (24.71%) Sources: Census of India, Census Town Directories 2001 and 1991, Author s Computations and Analyses. Statistically significant at 10 percent level of significance. the distance to a large city exerting a statistically significant impact on urban population growth during , the model here is a much poorer explanation of changes occurring in urban population growth than of urban population. Temperature differences have a positive impact on urban population growth, which comes as a surprise. The expectation was that lower temperature differences, controlling for other factors, would cause city growth to be higher (hence the expected impact was negative). The actual finding here suggests that even poorer climatic conditions such as extreme weather can indeed encourage city growth, when controlled for other factors. Alternatively it could be the case that temperature differences are not a good indicator of amenities, however the availability of city-level data are quite limited in India. Table 4 also shows that the distance to a large city with a 100,000 or more population, has a positive impact on population growth. This is in contrast with the earlier result (in Table 3), where proximity to a large city increases the size of a city. The result in Table 4 implies that distance to a large city positively impacts the rate at which a city grows, if not its current size (implied by Table 3). Table 5 summarizes estimates of city-level output, using the district-level per capita net district domestic product as the dependent variable. As discussed earlier, a city might grow in population, but its output might increase or decrease. Only if its output increases that city economic growth may be said to have occurred. The findings from this regression are as expected. The literacy rate has a positive impact on the economic growth of a city, which means that human capital increases city-level non-primary output. Specifically for a one percentage point increase in the literacy rate, there is an increase in the non-primary per capita net district domestic product by roughly Rs.31,000. The ratio of employment in manufacturing to that in services has a positive impact on city output implying that manufacturing adds more to the city-level non-primary domestic product than services, consistent with the findings in Table 3, a contrast to Paul and Sridhar s (2009) who found that the service sector led the growth in southern Indian states. Finally, the lower the coverage of population by schools, the higher the city-level non-primary output. That is, for every 1 person extra that is covered by a primary school, there

13 34 Sridhar, Determinants of City Growth and Output in India Table 5. Determinants of city output dependent variable: Non-primary NDDP per capita Unstandardized coefficients Variable B Std. Error t value Constant Literacy rate, Temperature differences Ratio of employment in manufacturing to that in services, Distance to a large city (100,000 or greater population) Road length per 1,000 population Population coverage per primary school Number of observations 113 R Adjusted R Dependent variable mean 12, Sources: Census of India, Census Town Directories 2001 and 1991, Directorates of Economics and Statistics, Various states, Author s Computations and Analyses. Statistically significant at 1 percent level of significance. Statistically significant at 5 percent level of significance. is an increase in the city output to the extent of Rs.5 per capita. This implies that lower school coverage increases city output, either by diverting more resources for higher education or by reducing the inefficiencies of primary education. Paul and Sridhar (2009) find that Tamil Nadu, a southern Indian state, for instance, was much more efficient in its spending on roads relative to related outcomes (such as road length), in contrast with Uttar Pradesh, a northern Indian state, which spent nearly three times as much for an additional kilometer of road length. 6.1 Town growth regressions Next, growth regressions at the town-level were estimated, understanding that there could be a selection bias since usually only growing areas are frequently designated as towns. In the most basic regression (Table 6), town population in 2001 was estimated as a function of characteristics such as temperature differences, distance to a large city, road length, population coverage with primary school, the existence of the ULCRA and the economic base of the town. The ULCRA is meant to control for the extraordinary role played by land use regulations in India s cities (see Sridhar (2010)). Since the existence of the ULCRA or otherwise is not policy relevant in the context of the district (unit of observation in the previous regressions) it was not included in the previous set of regressions. This also enables econometric identification of the equations. The correlation matrix of all independent variables was examined and none of them were found to be significant enough or suggestive of collinearity. Table 6 summarizes the most basic town-level regression estimated. Temperature differences have a negative impact on town population. This implies that temperature extremes cause cities to be smaller. Proximity to larger cities causes cities to be larger as well, implying the existence of markets and scale economies, in contrast to what Mills and Becker (1986) find. The population coverage per primary school has a positive impact, suggesting that the greater the number of persons per school (implying poorer level of the service), the larger the city. As with the districtlevel regression, this negates the impact of primary education on city size. Finally, the economic

14 Sridhar, Determinants of City Growth and Output in India 35 Table 6. Determinants of city size, using town-level data: Dependent Variable: Town population, 2001 Unstandardized coefficients Variable B Std. Error t value (Constant) Temperature differences Distance to a large city (100,000 or greater population) Road length per 1,000 population Population coverage per primary school ULCRA Economic base (1 = Agricultural; 2 = Manufacturing) Number of observations 4,080 R Adjusted R Dependent variable mean 55, Sources: Census of India, Census Town Directories 2001 and 1991, Author s Computations and Analyses. Statistically significant at 1 percent level of significance. Statistically significant at 5 percent level of significance. base of the town has a profound impact on its size. Specifically, towns with a manufacturing base are larger. This could be an artifact of the data since the Census of India defines urban areas as having a minimum of 75 percent non-agricultural employment. However, the model is a poor explanation of variations in city populations, as may be seen in the low R 2. However, the reader should note that the R 2 is only a descriptive statistic. In cross-sectional data, a lower R 2 might occur even if the model is a satisfactory one, because of the large variation across individual units of observation. This suggests that the R 2 alone may not be a suitable measure of the explanatory power of a model. Table 7 summarizes the estimates of population growth at the city level as dependent on various characteristics. The table shows some interesting findings. While temperature differences, distance to a large city and public services such as road length do not have significant impacts on city growth at the town level, population coverage of primary schools has an impact. This estimate shows that the greater the extent of population coverage with schools, the greater the city growth, a finding that does not concur with our expectations. Interestingly, as with city size, a shift in the economic base towards manufacturing increases city growth, consistent with expectations and with Census defined urban areas. The most striking finding in Table 7 is the negative impact of land use controls such as the ULCRA on city growth. This means that wherever the ULCRA exists, it stifles the growth of those areas by regulating land markets unnecessarily, consistent with what Kundu (2006) argues. Specifically towns in states which have not yet repealed the ULCRA grow nearly 4 percentage points slower than cities of states which have revoked it, holding other factors constant. There are of course a number of data caveats which should qualify these findings. Firstly, the R 2 is low in the case of most models. However, the reader should note that the R 2 is only a descriptive statistic (see discussion above). Secondly, the district level NDDP are available for only half the districts in the country, with the remaining districts not yet having prepared them. It would be better to use measures of net value added output per capita, as Au and Henderson (2005) do, but these data are not published by the states for the districts. At the district and town levels, better measures of human capital

15 36 Sridhar, Determinants of City Growth and Output in India Table 7. Determinants of city growth, using town-level data: Dependent Variable: Growth rate of town population during Unstandardized coefficients Variable B Std. Error t value (Constant) Temperature differences Distance to a large city (100,000 or greater population) Road length per 1,000 population Population coverage per primary school ULCRA Economic base (1 = Agricultural; 2 = Manufacturing) Number of observations 3,735 R Adjusted R Dependent variable mean 27.68% Sources: Census of India, Census Town Directories 2001 and 1991, Directorates of Economics and Statistics, Various states, Author s Computations and Analyses. Statistically significant at 5 percent level. Statistically significant at 1 percent level. such as the proportion with high school or higher degrees are not available, with the result that potentially less accurate measures such as population coverage with schools and literacy rate are used. 7. Summary, policy implications and concluding remarks This paper attempted to study city growth at different levels of aggregation at the city level and at the district level. Summarizing the findings, at the district level, a higher proportion of manufacturing to service employment, proximity to large cities, and public services such as primary school coverage per population cause cities to be larger. Furthermore, higher temperature differences (or poorer/unfavorable climatic conditions) do not discourage city growth. While human capital, measured by the literacy rate, has no impact on urban population size or its growth, it has a positive and statistically significant impact on city-level non-primary output per capita. This shows that city growth as measured by output that may or may not be captured by population growth is impacted by the literacy rate or human capital. A higher ratio of employment in manufacturing to that in services causes increases in the city s non-primary output. Finally, good public services such as population coverage with primary schools also increase non-primary output per capita. The finding at the city level is that, proximity to a large city causes a city to be larger. A city s size becomes larger and it also grows faster as it moves away from agriculture towards manufacturing. An important finding of policy interest at the city level is that strong land use controls such as the existence of the urban land ceiling act deters city growth by artificially creating a scarcity of urban land. Given the data caveats, the findings have several policy implications. Given the selection bias with choice of towns, this paper advocates more confidence in estimates at the district level. Human capital as measured by the literacy rate has a significant impact on net output per