chapter 1

CHAPTER 1
INTRODUCTION
1.1 Motivation for the study
In general, economic indicators such as population, employment and wealth are
largely found concentrated in a limited number of urban centres and clusters.
Historically, ―First Nature Geography‖ (i.e., exogenously given characteristic of
different sites, such as, types of climate, presence of raw materials and proximity to
natural ways of communication) had been extensively used to explain the locationspecific nature of industries. The major determinants of economic activities in the
early location theories included the trade-off between various forms of increasing
returns and different types of mobility costs, price competition, high transport costs
and land use patterns which foster the dispersion of production and consumption,
differentiated products and low transport costs, availability of a wide array of final
goods and specialized labor markets and cumulative process of supply and demand
side factors. Nevertheless, location theories fall short of providing a reasonable
explanation to many other clusters of activities which are less dependent on natural
resources and advantages, as also how, a priori, identical locations develop in
divergent ways. On the other hand, in terms of stressing the importance of the spatial
interactions between regions, countries or cities (also known as Second Nature
Geography), the ‗New Economic Geography‘ (henceforth, NEG) literature pioneered
by Krugman (1991) explains why human activity is unevenly distributed across places
and resulted in formation of a large variety of economic agglomerations. The NEG
models explain the relationship between urban agglomeration and urban economic
growth through productivity differentials, which lead to a shift of resources out of
agriculture or a hinterland region into an urban sector or core region. As compared to
earlier location theories, NEG is an improvement, and consists of a general
equilibrium framework with imperfect competition.
Urban India is characterized by growing population size, increase in number of urban
centres and expansion of original geographical boundaries and urban centres.
Growing population is attributable to natural growth of population, rural to urban
migration and reclassification of rural areas as urban. For instance, at the beginning
1
of the twentieth century, there was only one city with population over a million (1.5
million), namely Calcutta (now Kolkata). Bombay (now Mumbai) joined this league
in 1911. 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).
There were 23 cities with population of over one million (accounting for 33 percent of
the urban population), 300 cities with population ranging between 100,000 and a
million, and over 4,000 towns in 1991. By 2001, the number of cities with a millionplus population increased to 35 (accounting for 38 percent of the total urban
population), with 14 among these 35 cities registering higher than average rate of
growth during 1991-2001, and the number of cities in the population size category of
100,000 to one million grew to 388.
The urban economy of India has been growing and making a sizeable contribution to
the country‘s national income. For instance, the share of urban economy in the total
net domestic product (NDP) increased from 37.65 per cent in 1970-71 to 52.02 per
cent in 2004-05 and accounted for about 6.2 per cent growth rate of urban NDP from
1970-71 to 2004-05 at constant prices (1999-00). Within urban NDP, the share of the
industrial and service sectors was about 27 per cent and 72 per cent respectively in
2004-05 at constant (1999-00) prices.
In the process of urban economic growth rural and urban disparities in per capita
consumption have widened in India. For instance, Vaidyanathan (2001) finds that the
per capita total consumption (or food consumption) in urban areas is 63 (or 41) per cent
higher than in rural areas. Within urban India, there are wide intra urban inequalities. It
is interesting to note that the ratio of urban poverty in some of the larger states is
higher than that of rural poverty, leading to the phenomenon of ‗Urbanization of
Poverty‘ (India-Urban Poverty Report, GOI, 2009a). Thus, reduction in consumption
inequality and poverty between rural and urban India as well as within urban India is
a high priority objective in the context the inclusive growth strategy planned for in the
XI Five Year Plan.
Thus, urban India presents an interesting and complex interaction of growing
agglomeration of people and related economic activities, increasing economic growth
and persisting spatial consumption inequalities and poverty coexisting. This situation
2
calls for a systematic analysis and explanation of interrelationships between urban
agglomeration, economic growth and spatial inequity. A key motivation of this
research is to offer plausible economic explanations for these interrelationships in the
Indian context. This analysis raises the following theoretical and empirical research
questions/issues to be answered / resolved:
First, how to identify and explain the
sources of urban agglomeration, and predict its growth trajectory. Second, how to
arrive at a plausible framework to estimate the impact of urban agglomeration on
urban economic growth. Third, how to establish and explain the economic
relationships between urban agglomeration, growth and spatial income distribution
with special reference to inclusive growth?
1.2 Review of literature
The review of literature (both of theoretical and empirical studies) is focused on the
aforementioned research questions. Empirical studies include both Indian and
international studies. The ultimate objective of review of literature is to identify
research gaps and researchable issues for this study.
1.2.1 Review of theoretical studies
Theoretical studies focus on modeling the sources, determinants and welfare effects of
urban agglomeration economies on urban growth, and provide a useful background
for empirical analysis.
Ottaviano and Thisse (2005) provide an excellent review of early contributors to
economic agglomeration, such as Adam Smith, Von Thunen, Alfred Weber, August
Losch and Alfred Marshall. According to Marshall, agglomeration economies (and
externalities) arise due to mass production, labour-market interactions, linkages
between intermediate and final-goods, knowledge spillovers,
formation of a highly
specialized labor force and the production of new ideas based on the accumulation of
human capital and face-to-face communications and existence of modern
infrastructures.
Agglomeration
economies
are
distinguished
by localization
economies, which are external to firms but internal to an industry, while urbanization
economies are external to industries and depend on the overall scale as well as scope
of the economic activity in one location. Duraton and Puga (2004) explain three types
of micro-foundation of urban agglomeration economies:
sharing, matching and
3
learning mechanisms. For instance, sharing refers to sharing individual goods and
facilities, and learning mechanisms deal with the generation of knowledge. Hawley
and Fogarty (1981) find that labor will unambiguously migrate from large cities with
an increase in agglomeration economies only if large city goods are income-elastic
and large city economies are relatively capital intensive. Agglomeration economies
operate on the demand side as well. For instance, Haig (1926) argues that people
value the large assortment of consumption goods and services offered by cities such
that migration from a small town to a large city may be explained purely by
consumption motivate.
Lindsey et al. (1995) consider the desires of agents to be close to one another and
find spatial (Nash) equilibrium, first on a line, and then for higher dimensional spaces.
Under reasonable conditions of disutility functions, individuals will have a unique
optimal choice in reaction to the choices of others. Moreover, the equilibrium will be
unique, though not Pareto optimal. Surprisingly, some Pareto-superior outcomes are
actually less dense than the equilibrium. Desmet and Rossi-Hansberg (2010) have
found that, after eliminating efficiency differences across cities, lower equilibrium
utility levels declined by a mere 2.5%, while eliminating amenity differences reduced
welfare by just 2.3%. They also find that welfare effects are an order of magnitude
higher than in the U.S. For example, after eliminating efficiency differences across
Chinese cities, welfare was found to increase by 47% as compared to 2.5% in the U.S.
According to them agents obtain utility out of the amenities in the city and, hence,
consumption of goods is only one of the elements that determine an agent‘s utility.
Efficiency is modeled as TFP differences and amenities as preference shocks.
Wasmer and Zenou (2002) show that, despite higher inefficiency of the search
process in a segregated city, the resulting welfare loss is small as compared to the
other equilibria, because of higher commuting costs paid by the employed in the
integrated city. In the context of their matching model, the welfare is taken as the sum
of the utilities of the employed and the unemployed.
The major explanation of urban agglomeration as well as its effect on economic
growth has been provided by the NEG theory since the pioneering work of Krugman
(1991). The NEG models involve a tension between the ―centripetal‖ forces (pure
external economics, variety of market scale effects and knowledge spillovers) that
4
tend to pull population and the production process towards agglomerations and the
―centrifugal‖ forces (congestion and pollution, urban land rents, higher transportation
costs and competition) that tend to break up such agglomerations (Overman and
Ioannides, 2001; Tabuchi, 1998). While formalizing the interplay of agglomeration
and dispersion forces, the CP model explains the formation of dynamic urban system,
and finds a ―
‖-shaped curve between the distance of a regional center and a local
market potential in a single-core urban system (Partridge et al., 2009; Fujita et al.,
1999). This curve shows that as the relative distance to a central city increases, the
market potential first declines, rises subsequently and then declines again. But CP
models mostly remain difficult to manipulate analytically (or making the model
consistent with data) as most of the results derived in the literature are based on
numerical simulation (Fujita and Mori, 1997; Fujita et al., 1999a) and the nonlinear
nature of geographical phenomena [Fujita and Krugman, 2004].
New Economic Geography (NEG) is an important and recent approach to explain
urban agglomeration or dispersion, especially in the core-periphery model (Lafourcade
and Thisse, 2008). It aims at explaining how agglomerations arise, and their linkages
with urban growth in terms of endogenous growth model. For instance, the coreperiphery model assumes that one region becomes slightly bigger than the other. First,
this increase in market size leads to a higher demand for the manufactured goods.
When one region is larger in terms of population and purchasing power, the push and
pull system reaches equilibrium when this region attracts a more than proportional
share of firms by way of home market effect (Helpman and Krugman 1985, Combes
et al, 2008). This increase in market size generates a more than proportionate increase
in the share of firms and push nominal wages up. Second, the presence of more firms
means a greater variety of local products and, therefore, a lower local price index – a
cost-of-living effect. Accordingly, real wages should rise, and this region should
attract a new flow of workers. The combination of these two effects should mutually
reinforce each of its components and lead to the eventual agglomeration of all firms
and workers in a single region – the core of the economy, while the other regions
form the periphery. Even though this process seems to generate a ―snowball‖ effect; it
is not obvious that it will always develop according to that prediction. The
combination of other factors (competition in the labour market, lower wage,
commuting costs, urban land rent, congestion and pollution) may lead to a ―snowball
5
meltdown‖, which results in the spatial dispersion of firms and workers. That is why,
it is the interplay of, and the relative changes in, these dispersion and agglomeration
forces that determine whether people, and firms alike, tend to favour living in larger
agglomeration, or instead prefer to live in a less crowded environment (see
Lafourcade and Thisse, 2008 for more details).
World Development Report 2009 (World Bank, 2009) provides insights into three
dimensions of development, i.e., density, distance and division, which are interrelated
with urbanization and are explained in the framework of NEG. If migration to the
cities is essentially demand driven, the flow of human capital towards high-skill jobs
in the industrialized cities is likely to result in higher individual income and welfare
(Bloom et al., 2010). Further, urban agglomeration predicted by NEG models may be
socially efficient when transport costs are sufficiently low because firms are then able
to take advantage of the large market created by their concentration to exploit scale
economies while ensuring the inhabitants of the periphery unstinted access to their
products.
Krugman‘s (1991) simple static general equilibrium model shows how a country can
endogenously become differentiated into an industrialized ―core‖ and an agricultural
―periphery‖. In order to realize scale economies while minimizing transport costs,
manufacturing firms tend to locate in the region with larger demand, but the location
of demand in turn depends on the distribution of manufacturing. Emergence of a coreperiphery pattern depends on transportation costs, economies of scale and the share of
manufacturing in national income. Krugman and Elizondo (1996) explain that there is
a perceivable influence of trade policy on internal economic geography. This paper
suggests that third world urbanization history is an important model and establishes a
linkage between trade policy and urban development particularly in a NEG
perspective.
Many studies find transportation cost as one of the main factors for urban
agglomeration. For instance, Mai et al. (2008) show that when the transport cost is
low, tariff competition with firm migration leads to a core-periphery economy, where
one of the two countries does not impose tariff in Nash equilibrium. Picard and
Tabuchi‘s (2010) static general equilibrium model finds that constant-access seamless
equilibrium distributions are unstable for a large class of ‗acceptable‘ transport cost
6
functions, and that agglomeration of workers and firms in a few cities is likely to be
stable. The main result of the model by Tabuchi (1998) is that dispersion necessarily
takes place when transportation cost is sufficiently low.
The other aspects and factors of NEG models are highlighted by many researchers.
The home market effect which leads to emergence of Core-Periphery model does not
hold well for the multi-country case, because production patterns are crucially
affected by the third country effects (Behrens et al., 2009). Ago et al. (2006) show
that hub region ―first nature‖ advantage is not necessarily true due to the existence of
the ―second nature‖ of market interaction among firms and consumers. Tabuchi and
Thisse (2002) model investigates the impact of heterogeneity of the labor force on the
spatial distribution of activities and show that taste heterogeneity acts as a strong
dispersion force. Introducing land of production with general new economic
geography equilibrium model, Pfluger and Tabuchi (2010) suggest that the marketsize based agglomeration forces may not be strong enough to overcome the very
strong congestion force associated with competition for land, unless the consumers‘
desire for variety is very strong. Tabuchi and Thisse (2006) in their model highlight
that economic integration tends to foster the emergence of large cities supplying
(almost) all goods that can coexist with small and specialized cities that produce only
handful of goods.
Selected papers focus on welfare consequences of urban agglomeration. The Static
general equilibrium model (Behrens et al., 2007) shows that changes in non- transport
frictions do not allow for any clear prediction on changes in industry location and
welfare, whereas changes in transport frictions may allow for such predictions.
Pfluger and Sudekum (2008) show that the market equilibrium is characterized by
over-agglomeration for high trade costs and under-agglomeration for low trade costs,
and they further establish analytically that a net pecuniary externality is the
underlying cause of this market failure. They also find that more agglomeration is
socially desirable when trade integration has developed far enough. Unfortunately,
welfare analyses do not deliver a simple and unambiguous message about the
equilibrium spatial pattern of economic activity in the core-periphery model. Neither
of the two possible equilibria - agglomeration or dispersion - Pareto dominates the
other, because farmers living in the periphery always prefer dispersion, whereas
7
farmers and workers living in the core always prefer agglomeration. In order to
compare these two market outcomes, Charlot et al. (2006) use compensation
mechanisms to evaluate the social desirability of a move, using market prices and
equilibrium wages to compute the compensations to be paid either by those who gain
from the move (Kaldor criterion), or by those who would be hurt by the move (Hicks
criterion). They show that, once transport costs are sufficiently low, agglomeration is
preferred to dispersion, in that, farmers and workers in the core can compensate
farmers staying in the periphery. However, the latter are unable to compensate
farmers and workers who would choose to form what becomes the core. This implies
that none of the two configurations is preferred to the other with respect to the two
criteria.
Economic historians document a strong positive correlation between growth and
geographic agglomeration of economic activity (Hohenberg and lees, 1985; Bairoch,
1993), in particular, in relation to the industrial revolution in Europe during the
nineteenth century. In this case, as the growth rate in Europe as a whole had sharply
increased, agglomeration resulted not only in an increase in the urbanization rate but
also in the formation of industrial clusters in the core of Europe, and the thrust of this
movement is felt even today.
The role of cities in economic growth and technological progress has been
emphasized by urban economists [Henderson (1988), Fujita and Thisse (2002)],
development economists [Williamson (1988] as well as by economists of growth
[Lucas (1988]. Economic growth literature mainly follows five stylized facts on
economic growth (Jones and Romar, 2009). These are: (i) GDP per capita increases
over time, (ii) differences in growth are persistent, (iii) scale matters (within –
between country), (iv) growth process is lumpy (stagnation/ growth spurts), and
changes in GDP ranking - ―leapfrogging‖. Standard growth models focus on points
(i), (ii) and (iii); NEG emphasizes (iii), (iv), and (v).
The core-periphery model of Krugman (1991) highlighted the endogenous growth
model of Romar (1990). This model postulates that location and growth could be
jointly determined, and shows how the introduction of endogenous growth can affect
the stability of symmetric equilibrium. Martin and Ottaviano (1996) incorporated
Romar-type endogenous growth model into an economic geography model based on
8
the Venables (1996), and Krugman and Venables (1995) models of agglomeration
with vertically linked industry and focus on illustrating how growth affects location
and location effects growth. Baldwin and Forslid (2000) present a model in which
long-run growth and industrial location are jointly endogenous; by introducing
Romerian product innovation growth into Krugman‘s core-periphery model, it shows
that growth is a powerful centripetal force, but that acknowledges that spillovers are
powerful centrifugal force. They also argue that agglomeration is favorable for growth
and that the link between growth and agglomeration depends crucially on capital
mobility. Baldwin et al. (2003) model introduced capital into a geography model with
the ‗footloose capital‘ model (FC model). The FC model, however, takes the capital
stock as given.
1.2.2 Review of empirical studies
Empirical studies identify and estimate the economic determinants of urban
agglomerations and impact of urban agglomeration on urban economic growth and
equity. These studies are reviewed by international and Indian studies to distinguish
their approaches, methods and results as they are related to the research questions of
the study.
1.2.2.1 International studies
International empirical literature on agglomeration estimates the elasticity of average
productivity by measure sectors, total factor productivity (TFP), with respect to
measures of local scale, such as employment density or total population. Combes et
al. (2010) explain the relative importance of agglomeration and firm selection using
French establishment-level data and find that better workers are located in more
productive areas. They also find that spatial productivity differences in France are
mostly explained by agglomeration effect. Combes et al. (2011) argue that significant
progress has been achieved in the estimation of agglomeration economies over the last
ten years and find that standard estimates for the density elasticity of wages now
typically range from 0.02 to 0.05. Moomaw (1988) shows that the pattern of
agglomeration economies by industry is consistent with observed patterns of
industrial location for the American cities. Nakamura (1985) estimated the
urbanization and localization economies separately and found that light industries
9
received more productive advantages from urbanization economies than from
localization economies, but heavy industries experienced these economies more from
localization economies than from urbanization economies in case of Japanese cities.
Hansen (1990) examined the industrial location trade-offs between the productivity
advantages of the metropolitan center and the lower land and the labor costs in
outlying regions within the state of Sao Paulo, Brazil. The results indicate that while
plant productivity is enhanced by the considerable agglomeration economies as
existing within metropolitan Sao Paulo, firms pay for these benefits in high wages and
land costs. Offsetting labor cost savings can be achieved by locating firms in outlying
regions. This evidence suggests that market forces may lead eventually to a
spontaneous decentralization of industry. Henderson (1986) for Brazil and Wheaton
and Shishido (1981) have shown using a small sample of 38 developed and
developing countries that both concentration of government expenditures and nonfederalist governments can lead to urban concentration.
The World Development Report 2009 (World Bank, 2009) provides a global analysis
on emergence of urban agglomeration from the perspective of NEG models with the
evidence of several countries around the world. For instance, the experience of
Dongguan in China shows its rapid growth in the 1990s was due to low cost of
labour, economies of scale, whether in the production of intermediate goods or
differentiated products, and agglomeration effects within and across industries.
The NEG theory emphasizes the interplay of agglomeration and dispersion forces in
determining urban system. Krugman and Elizondo (1996) were the first to view the
Mexican experience through the new economic geography in order to explain
population and concentration of manufacturing in Mexico City in terms of the
powerful backward and forward linkages of agglomeration and economic growth.
Many empirical studies have attempted to focus on these two factors and to measure
their effect on urban agglomeration. Using data from 85 countries, Ades and Glaeser
(1995) find that, as predicted by Krugman and Elizondo (1996), countries with high
shares of trade in GDP or low tariff barriers (even holding trade levels constant) rarely
have their population concentrated in a single city, and urban concentration is
negatively related to international trade and urban centralization also governed by the
development of transportation networks in a country. Kanemoto et al. (1996) measure
10
the agglomeration economies and test the optimal city sizes in Japan. Their estimates
show that the agglomeration economies are statistically significant and positive for all
size groups although they are small in Integrated Metropolitan Areas (IMA) with
population less than 200,000.
Urban economics literature (for example, Crozet and Koeing, 2007; Glaeser and
Gottlieb, 2009; Henderson, 2010; Vogel, 2012; Leitão, 2012) finds a strong positive
relationship between agglomeration and growth. However, a number of authors had
earlier found a pattern of initially increasing and subsequently decreasing urban
concentration across countries corresponding to rise and fall of incomes (Wheaton
and Shishido, 1981; Junius, 1999). Henderson‘s (2003) later work to measure the nonlinear effect of agglomeration on growth supports the Williamson hypothesis.
Brülhart and Sbergami (2009) have extended Henderson‘s (2003) study and
revalidated Williamson hypothesis.
Black and Henderson‘s (1999) establish that that population growth is faster in cities
that are closer to a coast and cities with bigger initial populations, though this effect
weakens as neighboring population masses become larger. Dobkins and Ioannides
(2000), Ioannides and Overman (2004), using the U.S. metropolitan data for 19001990, provide evidence that the distance from the nearest higher-tier city is not always
a significant determinant of size and growth, and that there is no evidence of
persistent non-linear effects of either size or distance on urban growth. Chen et al.
(2011) estimate the impact of spatial interactions in China‘s urban system on urban
economic growth over the period 1990-2006. Their results offer evidence for the nonlineraity of the CP Model of urban system and find presence of agglomeration shadow
in Chinese urban economies.
A good number of studies have attempted to explain industrialization-lead- urban
agglomeration. Duraton and Overman (2005) estimate that 52% of four-digit
industries exhibit localization at a 5% confidence level and 24% of them show
dispersion at the same confidence level. Localization in four-digit industries takes
place mostly between 0 and 50 km. In some industrial branches, localization at the
industry level is driven by larger establishments, whereas in others it is smaller
establishments which have a tendency to cluster. Combes and Lafourcade (2005)
show that when intermediate inputs are included, transport costs have a significant
11
impact on specialisation patterns in nearly all sectors. Kim (1995) does not find an
important role for external economies in industrial location. Rather scale economies
explain the pattern of industry location over time, and factor endowments explain
patterns of localisation across industries in U.S.
Paillacar (2008) provides the empirical justification of NEG theories-based prediction
that international spatial disparities in wages can be explained by the market or
supplier access of countries and finds that a good performance of the price effect is
accompanied by a bad performance of a quantity effect, especially in the international
setting, where migration is expected to play a limited role in equalizing factor
rewards. Redding and Venables (2004) find that demand and cost linkages explain up
to 70% of variation in per capita income between countries, and up to 50% of
variation in wages in manufacturing.
Glaeser et al. (1995) studied the relationship between urban characteristics in 1960
and urban growth between 1960 and 1990 in a cross-section American city and
showed that income and population growth move together and are positively related.
Au and Henderson (2006) estimated the net urban agglomeration economies for
Chinese cities and found a high benefit of urban agglomeration with increases in city
size from low level to high level, as well as sharp rise of real income per worker.
They have also found that due to nationally imposed, strong migration restrictions, a
large fraction of cities in China are undersized and resulted in large income loss.
In case of Brazil, Da Mata et al. (2005) find that increases in rural population supply,
improvements in inter-regional transport connectivity and education attainment of the
labor force have strong impacts on city growth. Using data from 33 Asian countries
and using 20 indicators, Henderson (2010) finds that the level of urbanization and
income per capita are highly correlated.
Kundu and Kundu (2010) find that
correlation coefficient was higher, positive and significant between indicators of both
urban population growth during 1990 and 2030 (as projected by UNPD) and average
annual growth of value added by industry during 1990-95.They also find a negative
correlation coefficient (i.e.,-0.134) between export of goods and services as
percentage of GDP, and growth rate of urban population from 2000-2005.
12
Díaz-Bautista (2005) studied the regional economic growth in Mexico based on the
new economic geography, where distance played an important role in explaining
urban regional economic growth. The results show that distance to the northern border
of Mexico and labor migration between states of Mexico, after the passage of NAFTA
are important factors that explain the regional state growth and agglomerations in
Mexico between 1994 and 2000. The results also indicate that job growth and FDI are
not significant for the period of study.
Particularly in Latin America, the main problem is the highly unequal distribution of
urban income, resulting in a ―dual urban economy and a highly segregated urban
social structure: luxury apartments and elegant high-rise office and hotel towers in the
city centres, huge shanty-towns in the outskirt‖ (Hall and Pfeiffer, 2000). At the end
of the 1990s, different authors studied the impact of globalisation in the cities and
emergence of growing inequalities (Castells, 1996; Borja and Castells, 1997; Castells,
1999; Harvey, 2000). Castells studied the ―informational age‖ and globalisation
thoroughly and clearly explained the concept of ―switched-off‖ areas and the unequal
access to technology, and how it affected cities around the world, independent of their
level of development (Castells, 1996).
Glaeser et al. (2009) find that Manhattan in New York is the physical embodiment of
big-city inequality and has a Gini coefficient of 0.6. In contrast, the Gini coefficient
of Kendall County in Illinois is only about one half as much. They also find that sixty
percent of the heterogeneity in skills across larger metropolitan areas can be explained
by the proportion of high school dropouts and the share of Hispanic population in
them. Inequality across metropolitan areas reflects the distribution of human capital,
the returns to human capital and governmental redistribution. Fainstein (2001) finds
that the massive increase in income inequality that occurred in London during the
period 1979–93 resulted almost wholly from increases in earnings at the top rather
than loss at the bottom of the income distribution. Within London, the share of the top
ten percent income increased from 26 percent to 33 percent, as compared to Great
Britain as a whole, where the shift was from 25 percent to 31 percent.
Distribution implications of growth are discussed in current literature in terms of
inclusive growth. Ali and Zhung (2007) define inclusive growth by considering the
growth that not only creates new economic opportunities but also as one that ensures
13
equal access to all segments including the disadvantaged and marginalized to the
opportunities created. Growth is inclusive when it allows all members of a society to
participate in, and contribute to the growth process on an equal footing regardless of
their individual circumstances. The inclusive growth strategy rests on three anchors,
i.e., expanding opportunity, broadening access to opportunity, and social protection
that acts as a safety net and a springboard. Ali and Son (2007) measure inclusive
growth by using opportunity curve approach and equity index of opportunity (EIO).
They define inclusive growth with the help of increase of social opportunity function,
which depends on two factors, viz., average opportunity available to the population,
and as to how opportunity is distributed across the population.
Many studies emphasize on different government policies and strategies for inclusive
growth. For instance, in the Chinese case, Tang (2008)
proposes seven policy
measures for inclusive growth in China: (i) modifying the poverty threshold to
redefine poverty targeting, (ii) enhancing financial institutions‘ participation in
poverty reduction programs, (iii) providing more equitable access to education, (iv)
improving health care for the rural poor, (v) improving the minimum living standard
support system in both rural and urban areas, (vi) putting in place a sound public
finance mechanism and improving governance in antipoverty efforts and (vii)
enhancing the role of nongovernment organizations in poverty reduction programs.
Zhuang (2008) argues that an inclusive growth strategy for China should include
continued reforms to keep growth high and sustainable, carefully designed
redistributive policy to promote equal access to opportunities, and good governance
and strong institutions to ensure economic and social justice and an even playing
field. Lin (2004) finds that a transition to a policy regime that facilitates industrial
development along the countries‘ comparative advantages is necessary for developing
countries to improve their growth performance and to allow the poor to benefit from
growth. As many distortions in developing countries are endogenous to the viability
issues of firms in the priority sectors in previous development strategies, governments
have to find a way to resolve the firms‘ viability problem to achieve dynamic growth
in their transition processes.
It is important to mention that the above reviewed empirical international studies
differ by their objectives, estimation model, technique of estimation, and data sources.
14
These differences are summarized in Table 1.1 to draw the researchable issues and
draw methodological lessons for this study.
Table 1.1: International empirical studies
Objective
Estimation
Techniques
Model
of
Estimation
Moomaw To estimate the
Kelley‘s (1977) OLS
(1988)
localization and
labour demand
regression of
urbanization
equation based on log linear
economies for
a CES production model
American cities.
function
Author (s)
Data source (s)
State and
Metropolitan Area
Data Book, 1979.
Industry data
are from the
Census of
Manufactures,
1972 and
1977.
Combes To estimate the local Instrumental
Instrumental large- scale French
et al.
scale effects by using variable approach variable
wage and TFP
(2010)
French data.
and worker fixed regression
data.
effect approach
BRN (Bénéfi ces
Réels Normaux)
and the RSI
(Régime Simplifi
é d‘Imposition).
European Soil
Database.
SIREN data
(Système
d‘Identifi cation
du Répertoire des
ENtreprises),
Kanemoto To estimate the
Cobb–Douglas
OLS
1985 Population
et al.
aggregate production Production
regression
Census,
(1996)
functions for
function
Annual Report on
metropolitan areas in and Henry
Prefecture
Japan, to derive the George Theorem
Accounts,
magnitudes of
Private capital
agglomeration
stock from
economies and to test
Ohkawara et al.
if Japanese cities, in
(1985) (Estimated
particular, Tokyo, are
by the CRIEPI),
too large.
Social overhead
capital from
Ohkawara et al.
(1985) (Estimated
by the
CRIEPI).
15
Table 1.1 (Continued): International empirical studies
Wheaton To study the
Structural non Matching
and
relationship
linear equation linear
Shishido between economic model
regression
(1981)
development and
urban concentration
for 38 countries
from the world.
Black and To study the U.S.
Log-linear
OLS
Henderson relative size
production
estimation
(1999)
distribution of
function
cities.
National Accounts
Yearbook,
Rand McNally‘s
Commercial and
Marketing Atlas.
Census of
Manufactures
for 1972, 1977, 1982,
1987, and 1992,
Population
Census data for 19001990.
Dobkins To address the city Parametric and OLS
Metropolitan
and
size distributions in non-parametric Regression, Statistical Areas from
Ioannides the United States in distributional
GLS
1900 to 1990.
(2000)
tweenth century and approaches
regression
to find the
including the
with fixed
determinants of
Pareto law and effects and
those distributions. convergence
random
aspects of those effects.
dynamics.
Transition
metrics.
Paillacar To empirically
Static General Gravity
CEPII,
(2008)
justify the NEG
equilibrium
equation
COMTRADE,UNIDO
theories prediction model of new estimation. and OECD-STAN
that international
economic
Simple
spatial disparities in geography
linear
wages can be
developed by
regression
explained by the
Fujita et al.,
model
market or supplier (1999).
access of countries.
Chen et To test the
Economic
OLS
Chinese Cities
al. (2011) nonlinear
growth model estimation Statistical Yearbook
relationship
in Barro (2000).
(National Bureau of
between distance to
Statistics, 1991–
core and market
2007).
potential with
practical application
of the CP model of
urban systems of
China‘s urban
system.
16
Table 1.1 (Continued): International empirical studies
Ioannides To analyse the spatial Parametric
Static and
and
evolution of the US approach and
dynamic
Overman urban system over the non-parametric panel data
(2004)
period 1900 to 1990. approach
approach
Combes To measure the
Generalized
Geographic
and
transport cost for
Transport Cost
Information
LafourcadeFrance by
(GTC) in the
System (GIS)
(2005)
encompassing the
sense that it
analysis
characteristics of the encompasses both
infrastructure, vehicle distance and time
and energy used, as costs related to
well as labor,
shipments.
insurance, tax and
general charges borne
by transport carriers.
Ades and To investigate the
Theoretical
OLS
Glaeser factors behind the
model of political regression
(1995)
concentration of a
factors to urban
nation‘s urban
concentration
population in a single
city for 85 countries
from world.
Junius
To test the hypothesis Variants of the
OLS
(1999)
that economic
general equation estimates
development
first increases and
subsequently
decreases primacy
and the alternative
hypothesis that cities
grow
in a parallel way,
such that primacy
ratios depend on
geography or history
rather than
economics.
To test the
Modern
Static and
Leitao
relationships between economic growth dynamic
(2012)
urban agglomeration theory (Romer
panel data
and
1990)
approach
economic growth.
Census reports
Bureau of
Technical Studies
on Roads and
Highways
(SETRA)
Prospects of
World
Urbanization,
World Bank CDROM (1996)
World Bank,
OECD
17
Table 1.1 (Continued): International empirical studies
Crozet
To empirically
A function of
Spatial error
and
investigate the
regional growth model
Koenig
cohesion versus
that refers both to
(2007)
growth tradeoff on
conventional
European regions at a convergence
fine geographical
literature
disaggregation level. and new
economic
geography
models.
Vogel
To examine the
Mankiw, Romer Panel data
(2012)
empirical relationship and Weil (1992) model
between
growth model
agglomeration and
economic
growth for a panel of
48 Central and
Eastern European
regions from
1995 to 2006.
Henderson To estimate the
Williamson
OLS, 2SLS,
(2003)
effects of
hypothesis
3SLS, GMM
urbanization and
urban concentration
on productivity
growth.
Brülhart To investigate the
Williamson
Cross-section
and
impact of withinhypothesis
OLS and
Sbergami country spatial
dynamic
(2009)
concentration of
panel GMM
economic activity on
estimation
country level
Growth.
Henderson To explain the strong Population
OLS, 3SLS
(1986)
positive correlation composition,
between city size and couched
educational
in conventional
attainment of the
demand and
adult population for supply model
Brazil
Glaeser et To find the causes
Gini coefficient OLS
al. (2009) and consequences of
Regression
urban inequality in
America
Eurostat
Cambridge
Econometrics
(CE) European
Regional Database
Penn World Table
and other various
sources
World
Urbanization
Prospects and
other various
sources
Census of Brazil
The 5 percent
integrated public
use
micro-samples
(IPUMS) for the
1980 and 2000.
18
Table 1.1 (Continued): International empirical studies
DíazTo study the regional NEG theoretical TSLS with
Bautista economic growth in models
instrumental
(2005)
Mexico is based on
variables
the new economic
regression
geography, where
model
distance plays an
important role in
explaining urban
regional economic
growth.
Mata et al. To examine the
A model of a city, GMM-IV,
(2005)
determinants of
which consists of and OLS
Brazilian city growth a demand side —
between 1970 and
what utility levels
2000.
a city can pay out
—and a supply
side — what
utilities people
demand to live in
a city.
Redding To estimate a
Standard new
Tobit Model
and
structural model of trade theory
Venables economic geography model, extended
(2004)
using cross-country to have transport
data on per
frictions in trade
capita income,
and intermediate
bilateral trade, and the goods in
relative price of
production
manufacturing goods
Au and To model and
Simple model of IV estimation
Henderson estimate the net urban productivity and structural
(2006)
agglomeration
industrial
model
economies for
composition in a Ordinary
Chinese cities.
city.
non-linear
least squares
structural
model
Secretaría de
Economía.
Dirección General
de Inversión
Extranjera
Brazilian Bureau
of Statistics
(IBGE)
Population and
Housing Censuses
of 1970, 1980,
1991 and 2000
World Bank‘s
COMTRADE
database
Urban Statistical
Yearbook of
China.
China CountyLevel Data on
Population
(Census)
19
Table 1.1 (Continued): International empirical studies
Kim
To examine the long- Krugman‘s index, Panel data
(1995)
run trends in U. S.
Hoover‘s
model
regional specialization coefficient of
and localization
localization
Redding
and
Venables
(2004)
To estimate the
empirically the
structural model of
economic geography
A standard new
trade theory
model follows
Fujita et al.
(1999). The
general
equilibrium
model consists of
an agriculture and
manufacturing
sector.
Manufacturing
can be composite
of manufacturing
and service
activities, this
model has
extended to have
transport frictions
in trade and
intermediate
goods in
production.
Censuses of
Agriculture and
Manufactures
1840 to 1987.
Annual Survey of
Manufac-tures,
1947 to 1988; the
Agricultural
Statistics, 1937 to
1987; the annual
Carload Waybill
Statistics, 1954 to
1987; the Census
of Transportation,
1963 to 1983; and
the County
Business Patterns,
1947 to 1987.
OLS, TOBIT World Bank
model, 2SLS COMTRADE data
Method.
base. World Bank.
UNIDO Industrial
Wage
Statistics
equation
Database.
estimated
using
predicted
values of
supplier
access and
market access
as right-hand
side variables,
and crosscountry data
on factor
incomes as
the depend
variables.
20
Table 1.1 (Continued): International empirical studies
Duration To study the detailed New economic
Distanceand
location patterns of geography
based tests of
Overman industries, and
models
localization.
(2005)
particularly the
Index of
tendency for industries
localization.
to cluster relative to
Index of
overall manufacturing.
Dispersion.
Ali and
Son
(2007)
To measure the
inclusive growth for
Philippines
Annual Census of
Production from
Office for
National Statistics
(ONS). Standard
Industrial
Classification of
Industries.
CODE-POINT
data set from the
Ordance
Survey(OS).
This paper
Opportunity Annual poverty
introduces a new curve
Indicator survey.
approach to
approach and Which collects
measuring
equity index data from 78
inclusive growth of
provinces on more
by introducing
opportunity than 38,000
social opportunity (EIO)
households and
function. Growth
190,000
is defined as
individuals across
inclusive if it
the Philippines in
increases the
year of 1998 and
social opportunity
2004.
function, which
depends on two
factors: (i)
average
opportunities
available to the
population and
(ii) how
opportunities are
distributed in the
population.
Source: Author
1.2.2.2 Indian studies
Studies on India focus mainly on determinants of industrialization – lead urban
agglomeration and urban economic development. For instance, Chakravorty et al.
(2005) use the disaggregated industry location and size data from Mumbai, Kolkata,
and Chennai, to analyze eight industrial sectors. Their results suggest that general
urbanization economies are more important than localization economies for location
decisions. The study by Lall et al. (2004) suggests that the access to market through
21
improvements in inter-regional infrastructure is an important determinant of firm
level productivity. Lall and Rodrigo (2001) suggest that benefits of locating in dense
urban areas do not appear to offset associated costs.
The study by Lall and Mengistae (2005a) find that both the local business
environment and agglomeration economies significantly influence business location
choices across Indian cities. Lall and Mengistae (2005b) study at plant level from
India‘s major industrial centers shows large productivity gaps across cities due to
differences in agglomeration economies, degree of labor regulation, severity of power
shortages, and market access. They also argue that government can mitigate narrow
regional disparities in industrial growth by fostering the ―right business environment‖
in locations where industry might otherwise be held back by powerful forces of
economic geography. Lall et al. (2003) use the Ellison-Glaeser (1997) index of
concentration and find evidence of high spatial concentration in the leather and metals
sectors, and moderate concentration in food products, textiles, mechanical machinery
and computing and electronics, with considerable benefits accruing from being
located in a diverse economic region. Chakravorty‘s (2003) findings provide evidence
both of inter-regional divergence and intra-regional convergence, and suggest that
‗concentrated decentralization‘ is the appropriate framework for understanding
industrial location in post-reform India. Lall and Chakravorty (2005) examine the
contribution of economic geography factors to the cost structure of firms in eight
industry sectors, and show that local industrial diversity is an important factor with
significant and substantial cost-reducing effects. Mukherjee (2008) finds evidence to
support the hypothesis that the trade liberalization of 1991 has resulted in increasingreturn based agglomeration in India and identifies four industries, namely Iron and
Steel, Chemical, Textile and Non-electrical, where some locational shifts have taken
place after trade liberalization.
Other studies on India‘s urban economic development are as follows: Sridhar (2010)
estimates determinants of city growth and output at district level as well as city level
in India. The study finds that proximity to a large city and movement from agriculture
towards manufacturing encourages a city to be larger. An important deterrent to urban
growth includes urban land ceiling act. Mathur (2005) in his assessment of the
implication of India‘s post-1991 liberalization and globalization on the national urban
22
system finds that population growth rate of million plus cities has declined, and
credits it to the noticeable changes in India‘s macro-economic policies in post-1991
period. He also finds that post-globalization urban growth has been driven by growth
in urban population and changes in share of employment in manufacturing and
service sectors. Narayana (2011) highlights the role of information and
communication technology sector in globalizing and urbanizing Bangalore City, and
its impact on urban economic growth. The author finds that the contribution of ICT
sector to economic growth of Bangalore increased from about 15 percent in 2000-01
to 17 percent in 2002-03 and to 24 percent in 2004-05. Mills and Becker (1986) find
that a large initial population generally discourages further city growth. With initial
populations somewhere below 1 million, cities grow faster with higher income than
they do in lower income states. Using Pareto distribution, Narayana (2009) estimated
the size distribution of metropolitan areas in India from 1981 to 2025 and offered
evidence for dispersion of urban population in metropolitan areas. Kundu (2006a)
finds that India‘s urban agglomeration grew due to higher per capita urban income
derived through private sector demand for land in cities as well as the huge subsidized
funds under government schemes. Khasnobis and Mitra (2008) find that the role of
rural-to-urban migration (due to availability of more productive industries,
employment opportunity, large quantum of investment, availability of basic
opportunity amenities and infrastructure) in explaining urban population growth is not
as significant as the natural growth of urban population. Sridhar (2005) argues that
infrastructure, power, telecom, roads and banking are important determinants of firm
location in the growth centres of India.
Other studies emphasize the various factors that determine firm location choice.
These factors include availability of abundant power (Rajaraman et al., 1999);
reliable infrastructure and factors of production (Mani et al., 1996); sales tax
incentive (Tulasidhar and Rao, 1986) and labour regulation (Besley and Burgess,
2004 and Lall and Mengistae, 2005b). Sridhar and Wan (2010), using the World
Bank‘s Investment Climate Survey (ICS) data for India, find that more labourintensive firms tend to refrain from locating in medium-sized cities relative to smaller
cities in India, and that Indian firms find capital cities attractive. This reinforces that
public investments are biased in favour of capital cities where policy makers live
23
(Henderson et al., 2001). In addition, they find that firm efficiency has a significant
positive impact on the log odds of a firm locating in the large cities of India.
There are few international studies on urban agglomeration that include Indian
experience as well. Investment Climate and Manufacturing Industry report (World
Bank, 2004) by World Bank shows that the two main factors affect the individual
firm‘s location decision: First,
―business environment‖ includes access to inputs
(quality and cost of labor and capital), access to markets, provision of basic
infrastructure, institutional environment and industry-specific subsidies or tax breaks.
Second, ―agglomeration economies‖ increase returns to scale. World Development
Report (World Bank, 2009) finds that since the 1990s, expatriate Indians have been
pulling distant places like Bangalore and Hyderabad closer to world markets, just as
the overseas Chinese did for Shanghai and Gauanghou more than a decade earlier.
Falling costs of transport and communication have helped greatly for labor mobility
to these cities for making higher urban agglomerations.
There is a vast corpus of literature that measures poverty and inequality separately for
rural and urban sectors in India and for the country‘s states and union territories,
especially since 1990s. In general, these studies reveal increasing inequality between
urban and rural sectors (Deaton 2005; Deaton and Dreze 2002; Sen and Himanshu
2004; Sundaram and Tendulkar 2003). Kundu (2006a) finds that there are wide
regional variations and rural and urban disparities in per capita consumption. The per
capita total consumption in urban areas is 63 percent greater than that of rural areas,
while the per capita food consumption expenditure in urban areas exceeds that of
rural areas by 41 percent.
Deaton and Dreze (2002) find three differing patterns of inequality between 1993–
1994 and 1999 -2000. Using per capita consumption expenditure as a measure of
welfare, they found that inter-state inequality increased during this period, and that
urban– rural inequality increased not only throughout India but also within states. Jha
(2002) finds higher inequality in both urban and rural sectors during the post-reform
period compared to the early 1990s. Using real monthly per capita household
consumption expenditure as the measure of welfare, Chamarbagwala (2010)
estimated quantile regressions to analyze the urban–rural welfare gap across the entire
welfare distribution spectrum and found that while the urban–rural welfare gap was
24
fairly convex across the welfare distribution in 1993–1994, it became more concave
in 2004, with the gap narrowing for the lowest and highest quintiles and widening for
the middle three quintiles. The urban–rural gap in returns to all levels of education
widened substantially for the bottom four quintiles but became increasingly negative
for the top quintile. Kundu (2006a) finds that economic base is unequal across the
million plus cities (one million or more population), medium towns (50,000 to one
million population) and small towns (less than 50,000 population) in terms of
employment, consumption, and poverty. The million plus cities, on an average, have
about 30 percent of their workers in non-household (NH) manufacturing while class
IV (10,000 to 19,999) and V (5,000 to 9,999) towns have less than 7 percent. The
difference in consumption expenditure across various size classes of urban centers
sharply highlights intra urban inequality. The per capita monthly consumption
expenditure in million plus cities works out to be Rs 1070 which is about 53 per cent
higher than that reported from towns with lesser population and small towns as of
1999-2000.
In the context of inclusive growth, Unni and Raveendran (2007) find that employment
growth slowed down slightly in 1993-2004, as compared to 1983-1993, the slowdown
being quite noticeable in rural India. They have also found that employment within
urban areas over the past decade was mainly self-employment, and that there has been
a decline in the real wage rates of regular salaried workers and urban casual workers.
Tilak‘s (2007) paper critically looks at the development of education as outlined in
the Approach to the Eleventh Five-Year Plan, and highlights its weaknesses and
points out the persistence of a big policy vacuum. Most importantly, Suryanarayana
(2008) attempts to define the concept and aims to quantify the inclusive growth in
India. Based the broad-based growth process in terms of mean-based averages of
income and absolute-norm based measures of deprivation, the tentative estimates
indicate that the growth process between 1993-94 and 2004-05 has bypassed the
majority, and was not inclusive. Thorat and Dubey (2012) examine the changes in
poverty incidence and monthly per capita expenditure in India using the National
Sample Survey‘s unit record data of three rounds, 1993-94, 2004-05 and 2009-10.
They found that some groups benefited more than the others from poverty reduction
strategies. In addition, inequality has also begun to adversely affect poverty reduction,
particularly in the urban sector. In the context of urban inclusive growth, Kundu and
25
Samanta (2011) analyse the present urban development policies (for instance,
Jawaharlal Nehru National Urban Renewal Mission was launched) with a focus on
inclusive development of urban centres. Jayaraj and Subramanian (2012) suggest that
a little evidence of inclusiveness in India‘s consumption growth experience over the
last four decades or so.
Like international studies, the Indian studies reviewed above differ by their
objectives, estimation model, technique of estimation, and data sources. These
differences are summarized in Table 1.2 to draw researchable issues and draw
methodological lessons for this study.
26
Table 1.2: Indian empirical studies
Author (s)
Objective
Estimation
Techniques
Model
of
Estimation
Jha (2002) To examine the
Gini
Convergence
empirical
Coefficient,
test
relationship among Head count
inequality, poverty index, Poverty
and
Gap Ratio and
economic growth in Squared Poverty
India from 1957 to Gap ratio
1997
Mani et al. To analyze the
Profit
Conditional
(1996)
impact of
maximization
Logit
environmental
principle
regulations on
locational choice in
Indian firms during
1994.
Besley and To investigate
Framework
OLS, 2SLS,
Burgess
whether the
depends on a
Static Panel
(2004)
industrial relations relative price
model
climate in
effect and an
Indian states has
expropriation
affected the pattern effect.
of manufacturing
growth
in the period 195892.
Jayaraj and To measure the
Inter-temporal Annual
Subramanian ‗Inclusiveness‘ of
product of
compound
(2012)
India‘s
growth model
growth.
consumption
Quintile
expenditure growth
estimates
SuryanarTo define the
Order-based
Mean based
ayana (2008) concept and aims to averages for
estimates
develop measures
verifying the
of inclusion for
presence of
India.
broad-based
growth and
extent of
inclusion of the
poor in terms of
the consumer
expenditure
distribution
Data source (s)
Reserve Bank of
India (1998),
Central Statistical
Organization
Center for
Monitoring the
Indian
Economy (CMIE)
Indian Labor
Yearbook,
Central Statistical
Office (Industrial
Statistics Wing),
Annual Survey of
Industries,
Ozler et al.
(1996).
NSSO
NSS unit record
data
27
Table 1.2 (Continued): Indian empirical studies
Sridhar To investigate the
Empirical model
and Wan determinants of the on locational
(2010)
locational choice of choice of firms
firms among cities in
China, India and
Brazil.
Narayana To estimate the size Pareto
(2009)
distribution of
Distribution
metropolitan
areas in India from
1981 through 2025.
Sridhar To investigate the
Basic empirical
(2010)
determinants
model on
of urban population determinants of
growth and economic city growth
output in India
Lall and To understand the
Hansen‘s
Chakra- process of spatial
statistics,
vorty
industrial variation: Herfindahl
(2005)
identifying the spatial measures,
factors that have cost Tradition cost
implications for
function
firms, and the factors
that influence the
location
decisions of new
industrial units in
India.
Chakra- To investigate the
Spatial
vorty
factors behind Indian autocorrelation
(2003)
industrial location
Theoretical
and whether follow model of firm
the model of
location decision
‗divergence followed
by convergence‘.
Lall et al. To analyze the
Fujita and Thisse
(2003)
influence of economic (1996) and Fujita
geography on the cost (1989) model
structure of
manufacturing firms
by firm size for eight
industry sectors in
India.
Multinomial Investment
regression
Climate Surveys
model
by World Bank
OLS
regression
OLS
regression
Census of India,
various years.
data from United
Nations (2007).
NSSO (2007).
Census of India,
Census Town
Directories 2001
and 1991.
OLS
Annual Survey of
Industries (ASI)
Moran‘s I,
OLS/
Hecknman
selection
model
CMIE, ASI,
Geographic
information
system
Herfindahl
index.
classical‘
accessibility
indicator
(Hansen,
1959),
Annual Survey of
Industries
28
Table 1.2 (Continued): Indian empirical studies
Lall and To analyze the
The underlying
Mengistae location decisions of location decision
(2005a) individual Indian
model for each
firms.
firm by
considering profits
as a function of
observable
attributes of the
business
environment and
agglomeration
economies, and a
set of unobserved
local attributes of
the city.
Lall and To analyze the
Estimation of TFP
Mengistae large productivity gaps
(2005b) for India‘s major
industrial centers
across cities.
Lall and
Rodrigo
(2001)
To examine the
technical efficiency
variation across four
industrial sectors in
India
Chakra- To examine the
vorty
importance of
et al.
localization
(2005)
economies for cluster
formation of
industries
Lall et al. To examine the
(2004)
extent to which
agglomeration
economies contribute
to economic
productivity
Neoclassical
production
function
Global clustering
and local
clustering
Fijita and Thisse
(1996) and Fujita
(1989) framework
in the form of
Translog
logarithmic
production
function
Narayana Economic analysis of Jalava and
(2011)
globalization, ICT
Pohjola(2002)
sector and urban
framework to
growth of Bangalore. accounting for
ICT‘s contribution
to aggregate
economic growth.
Conditional Investment
logit, IV
Climate Survey
estimates
(ICS) for India
OLS
estimates
LevinsohnPetrin
Estimates,
GLS
Estimates
Stochastic
production
frontier
technique
World Bank‘s
Investment
Climate Survey
Annual Survey of
Industries
Moran's I , Central Statistical
Correlation Organization
coefficients (CSO)
Allen
Central Statistical
elasticities Organization
of
(CSO)
substitution,
OLS
regression
technique.
Descriptive
statistics.
Annual
growth rate.
National Accounts
Statistics
2001-02, 2004-05.
ASI by CSSO:
NIC-1998, Gross
value added at 3digit level.
29
Table 1.2 (Continued): Indian empirical studies
GuhaTo examine the
No specific
Factor
Khasnobis urbanization pattern model has been analysis.
and
on India.
used in this paper. Wellbeing
Mitra
Index.
(2008)
Mukherjee To test the hypothesis An empirical test Location
(2008) that the trade
for certain aspects quotients
liberalization in 1991 of increasinghas resulted in
return based
increasing return basedmechanism of
agglomeration in
new economic
India.
geography model
by Fujita et al.
(1999).
Population Census
data.
Press Release on
Rural-Urban
Distribution of
Population – India
and States/ Union
Territories: 2001
and Estimates of
State Domestic
Product,
Central Statistical
Organisation,
Government of
India.
WITS, UNCTAD
Handbook of
statistics.ASI data.
1.3 Research gaps and researchable issues
The review of theoretical literature shows the diversified sources of urban
agglomeration and relates them to general urban economic growth. International
empirical literature offers useful frameworks and lessons for estimation of impact of
urban agglomeration on urban economic growth. Review of Indian studies clearly
reveals the lack of specific studies on estimation of urban agglomeration and its
impact on urban economic growth and equity. Most importantly, the non-application
of NEG as a framework for analysis of urban agglomeration and economic growth is
a particular research gap in Indian studies. Therefore, an empirical estimation and
explanation of economic relationship between urban agglomeration, growth and
spatial income distribution with special reference to the framework of NEG is the key
research gap taken up for analysis in this empirical study. Measurement of urban
agglomeration, explanation of economic determinants of urban agglomeration,
estimation of impact of these determinants on urban economic growth, and the
linkages of urban agglomeration with urban spatial equity through urban economic
growth are the key researchable issues in this empirical research. These identified
30
research gaps and researchable issues constitute the background for formulation of
the major objectives of the study, which are listed below:
1.4 Objectives
Major objectives of this study are as follows.
1. Analyze the significance of urban agglomeration for urban economic growth
and urban equity in India.
2. Critically review the public policies and programmes for promotion of urban
agglomeration, urban economic growth and urban equity in India.
3. Examine the measurement issues and provide a plausible empirical
measurement of agglomeration economies for urban India with special
reference to
manufacturing industries.
4. Provide a theoretical background with special reference to NEG as also an
empirical framework to estimate the economic impact of urban agglomeration
on urban economic growth in India, with special reference to manufacturing
industries.
5. Develop a simple framework to examine the impact of urban agglomeration
on
urban equity, with special reference to inclusive urban growth.
6. Elucidate implications for higher urban economic growth and reduction in
urban inequities through promotion of urban agglomeration economies.
1.5 Methodology
The methodology of this research is focused on (a) definition and measurement of
urban agglomeration economies, urban economic growth and urban equity and (b)
estimation of their relationships based on published data and available in public
domain.
1.5.1 Definitions and measurements
Urban agglomeration is defined by concentration of urban population and related
economic activities. This implies that urban agglomeration includes but not equal to
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urbanization. The two methodological issues involved here are: choice of urban units
of study and method to combine different indicators of population and related
economic activities. Therefore, urban agglomeration is defined in two ways in this
study. First is population agglomeration where size of the city population is measured
as unit of analysis for urban agglomeration. Second is firm level agglomeration where
number of firms operating in a particular city is considered for the measurement of
urban agglomeration. In the context of population agglomeration the study considers
the cities with 750,000 or more inhabitants in 2005 are defined as large urban
agglomerations. The reasons behind the selection of these large agglomerations as
unit of analysis are the following: First, World Urbanization Prospects provides
updated data for the cities with 750,000 or more inhabitants from 1950 to 2025 with
five years interval, whereas Indian census data only provides data up to 2001 census
(as latest census 2011 data are yet to be published) with 10 years interval. Second, due
to unavailability of city specific data for large number of variables (e.g., city income
data) used in this study, city district (where the sample city in located) is used as
proxy of a city. Large cities are good proxy for a city district as it covers bigger
portion of a district than smaller cities. Therefore, the entire study is mainly focused
on 59 largest cities in India.
Urban economic growth is defined by growth rate of urban domestic income. At
present, domestic income is well defined and measured at the state level by Gross
State Domestic Product, although it is not separated by rural and urban areas. Only
national level rural-urban break up of income data are available for the period of
1970-71, 1980-81, 1993-94, 1999-2000 and latest 2004-05. Thus, growth rate of
urban GDP is measured the national level urban economic growth. On the other hand,
available district level estimates of domestic income/product are used to approximate
the economic growth of an urban centre (as city income data are not available)
located in the district.
Urban equity is defined by levels of inter-urban equality in income distribution. The
study considers three poverty measures: The headcount index (H) is the percentage of
the population living in households with income per person below the poverty line.
The poverty gap index (PG) gives the mean distance below the poverty line as a
proportion of that line (the mean is taken over the whole population, counting the
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non-poor as having zero gap). For the squared poverty gap index (SPG) the individual
poverty gaps are weighted by the gaps themselves, so as to reflect inequality amongst
the poor (Foster et al., 1984). On the other hand inequality is measured by the familiar
Gini coefficient which measures the inequality in per capita consumption levels by
using the individual level consumption data from the NSSO Consumer Expenditure
Surveys. As city wise poverty lines are not available, the State (where the sample city
is located) level poverty line is considered as a proxy of city poverty line.
Finally to define inclusive growth we construct a composite inclusive index (CII) by
considering changing trends between 2004-05 and 2009-10 in the key economic
variables based on ‗Borda ranking‘ to measure overall inclusiveness of a city. ‗Borda
Rank‘ follows the methodology of ‗Borda Rule‘ This rule provides a method of rankorder scoring, the procedure being to give each alternative a point equal to its rank in
each criterion of ranking adding each alternative‘s scores to obtain its aggregate score,
and then ranking alternatives on the basis of their aggregate scores. We rank the cities
accordingly to their position by seven major variables (i.e., economic growth, poverty,
inequality, employment, unemployment, education, and standard of living index)
comprising different sub-variables. Finally, we add the ranks of the cities to measure
the city specific inclusive growth. Ranking of the cities are done such a way that
higher the value of CII indicates lower level of inclusive growth and lower value of
CII indicates higher level of inclusive growth of a city.
1.5.2 Estimations
Methodology for estimation of relationship between urban agglomeration, economic
growth and equity is as follows. Theoretical basis for estimation of agglomeration
effects on growth is drawn from the NEG literature as contributed by Krugman
(1991), Krugman and Venables (1995), Fujita et al. (1999) and Krugman and Livas
Elizondo (1996). These studies dwell on the interaction between economies of scale,
market size and transportation costs for generation of agglomeration economies and
relate them to urban growth in models of general equilibrium. The explanations and
predictions of these models are important to (a) justify the inclusion of variable for
empirical estimations and (b) offer welfare justifications for growth of large and
bigger cities and metropolitan areas in India. Thus, using the theoretical background
of urban agglomeration economics in general and NEG in particular, testable
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hypotheses between urban agglomeration and urban economic growth are formulated.
Using these hypotheses, nature and magnitude of impact of urban agglomeration on
urban economic growth are estimated by standard econometric techniques of OLS
regression and dynamic panel data models (especially for state level analysis). Next,
the pattern of consumption distribution is correlated with the agglomeration-induced
economic growth.
This analysis is intended to capture the indirect relationship
between agglomeration economies and urban equity through economic growth.
1.5.3 Period of the study
The study period largely based from 1999-00 to 2009-10. In addition, the study also
explains the trends and patterns of urbanization and economic growth from 1971 to
latest available years. The year 2009-10 is specifically chosen for the availability of
latest 66th Round of National Sample Survey Organisation‘s (NSSO) Household
Consumer Expenditure Survey in India for that year.
1.6 Databases
This study is based on multiple data sources at aggregate and unit levels. The major
databases include Annual Survey of Industries by CSO, State Income and District
Income estimates by the Directorate of Economics and Statistics, Household
Consumer Expenditure Surveys and Employment and Unemployment Situation in
Cities and Towns in India by NSSO (61st Round to 66th to Round), Town Directory
by population census, projected urban population by cities by the United Nations in
the reports on World Urbanization Prospects and Statistical Abstracts by the CSO.
1.7 Relevance of the study
This study adds insights into the urban agglomeration as well as urban economic
growth and its overall impact on urban equity in the context of India. Most
importantly, application of NEG models in explaining and linking India‘s urban
agglomeration with urban economic growth and empirical validation of the
theoretical NEG models especially is new and form the key contribution of this study.
In addition, it also tries to measure urban inequality, poverty, and inclusive growth
are estimated by using the recently developed relevant mathematical analysis and unit
level NSS data bases. The estimated results provide insights and empirical bases for
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designing and implementing policies and programmes for promotion and
development of urban growth at national and sub national level, with special
reference to agglomeration economies and inclusive growth.
1.8 Organization of the thesis
The thesis is organized into eight following chapters.
Chapter 1 is introductory. It includes the motivation for the study, research questions,
review of literature, research gaps, main objectives, and methodology of the study.
Chapter 2 provides an overview of urbanization, urban economic growth, and urban
equity by describing the trends and patterns of India‘s urbanization, urban economic
growth, and urban equity. In addition, it critically reviews the public policies and
programmes taken in different Plan Periods for promotion of urban agglomeration,
urban economic growth and urban equity in India.
Chapter 3 measures the agglomeration economies for urban India with reference to
registered manufacturing industries.
Chapter 4 provides theoretical background and empirical framework by emphasizing
on NEG models and estimates the economic impact of urban agglomeration on per
capita city economic growth in India, by taking into account of manufacturing and
service sector‘s output in cross section a study.
Chapter 5 measures the impact of urban agglomeration on urban economic growth in
long run and test the ―Williamson hypothesis‖ that agglomeration increases economic
growth only up to certain level of economic development.
Chapter 6 measures the distributive effect of urban agglomeration and urban
economic growth. The select estimation of urban inequality and poverty indices,
policy linkages between inequality and poverty indices, and the estimation of the
economic determinants of urban poverty and inequality are addressed in chapter 6.
Chapter 7 tries to measure an important dimension of urban economic growth by
measuring distributive effect of urban inclusive growth.
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Finally, chapter 8 summarizes the main findings of this study and provides the
relevant policy implications for promotion of urban agglomeration and urban
economic growth by reducing extent of urban inequalities and level of urban poverty.
The scopes of future research are also addressed in this chapter.
Further, Tables (or Figures) are sequentially presented and numbered by the chapters,
e.g., Table 2.1 (or Figure 2.1) refers to Table (or Figure) number 1 in chapter 2 and so
on. Similarly, the equations used for estimation purposes are sequentially presented
and numbered for respective chapters. Notes are presented by footnotes on the bottom
of the page of the respective chapters. Finally, all the references are given at the end
of the thesis.
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