WP. No.: SAUFE-WP-2020-005

Does City Size Matter for Migration and Poverty: A study of Million-plus cities in India

Arup Mitra Professor

Faculty of Economics, South Asian University Akbar Bhawan, Chanakyapuri New Delhi 110021, INDIA Email: arupmitra@sau.int

Sabyasachi Tripathi Postdoctoral Research Fellow

National Research University Higher School of Economics Moscow, RUSSIA

Email: sabya.tripathi@gmail.com

Working Paper Number: SAUFE-WP-2020-005

http://www.sau.int/fe-wp/wp005.pdf

FACULTY OF ECONOMICS SOUTH ASIAN UNIVERSITY

NEW DELHI

March, 2020

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Does City Size Matter for Migration and Poverty:

A study of Million-plus cities in India

Arup Mitra1

Sabyasachi Tripathi2

Abstract

City size plays an important role in the context of growing urbanization in developing countries

such as India. Are economic activities concentrated in a few large cities? What are the important

economic factors that determine the size of large cities? Is it possible to trace different groups

among the large cities? Is the relationship pertaining to city size, migration, and poverty

quantifiable? In this paper, we answer these questions by considering 47 million-plus cities in

India. The estimated Pareto distribution suggests that within the group of large million-plus cities

production is not so diversified though the extreme form of concentration like primacy or rank

size rule does not hold. In fact, three broad groups are evident as brought out by the results of the

cluster analysis. Both OLS and Tobit regression results indicate that the wage rate for which the

is the average monthly per capita expenditure (MPCE), amount of municipal solid waste

generation (proxy for the quantum of economic activities) and the number of salaried employed

persons generate a positive effect on city population while the number of registered motor

vehicles, a proxy for congestion, has a negative effect on it. However, the infrastructure index

calculated through factor analysis does not show any effect on the city size. Based on the results

of factor analysis increased migration and reduced poverty are seen to be associated with city

size. Finally, we suggest that to optimize the benefits of future urbanization, the promotion of

non-million plus cities and towns is essential with higher infrastructural investment. Small cities

and towns will be the next destination for increasing the rural to urban migration and reduction

of rural poverty in India.

Keywords: city size, agglomeration economies, migration, poverty, congestion

1Professor of Economics, South Asian University, New Delhi, India, email: arup@iegindia.org 2Postdoctoral Research Fellow, National Research University Higher School of Economics,

Moscow, Russia, email: sabya.tripathi@gmail.com

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1. Introduction

City size captures a central position in the urban economics literature. Every city can be said to

be in equilibrium while at the same time city size differs significantly within a given country.

Even within a specific category, say million plus cities, population size may differ substantially

in a large country like India where it is difficult to have the rule of primate city. Even if primacy

were to hold, different regions would warrant its existence, thus making impossible for one city

to dominate the whole nation. Thus, it may be pertinent to revisit the set of million plus cities and

examine the variations in their sizes explainable in terms of the nature of activities or the

structure of the cities, wages and so on. The wages are likely to differ significantly as the scale

economies and productivity vary across space. Though the large cities with their higher scale

economies also encounter higher diseconomies and costs, the benefits do not get completely

swept away as the cost reduction mechanisms are plenty, implying that real wages may show a

positive association with city size. Though the data availability compels us in some sense to

restrict the analysis to the million plus cities, there is sufficient reason to consider them on

analytical grounds as well. Large cities are a good proxy for a city district as they cover a large

portion of a district than smaller cities and further, Indian urbanization is mainly concentrated in

and around the large cities: 47 million-plus cities in India accommodated about 40 percent of the

total urban population in 2011.

The paper begins by examining the size variability within the million plus category by adhering

to the Pareto Distribution framework. If the coefficient of city size is seen to be greater than 1

then primacy may hold even within the category of the million plus cities. On the other hand, if

the coefficient is unity it implies rank size rule and the lower magnitude of the coefficient is

indicative of dispersion rather than concentration of economic activities. Secondly it delves into

the issue of differences in the structure and wages across cities which are considered to be the

major determinants of city size. A cluster analysis confirms that within the category of million

plus cities different groups are traceable. Extending the analysis further it is argued that rural to

urban migration is a significant constituent of population increase in cities. If so, what are the

drivers of population movement and whether city poverty is related to the population shift from

the rural areas? Migration to large cities may help poor escape their vulnerability; hence

provision for space selective migration is beneficial for a country’s poverty reduction objective.

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Even when economic growth may not be spectacular cities are expected to be the better

performers, which would result in higher mean effect in poverty reduction in the poverty

decomposition framework [Mazumdar and Son, 2002 and Bhanumurthy and Mitra, 2004].

The paper is organized as follows. In the next section, we review the literature in brief.

Distribution and determinants of city sizes are explained in sections 3 and 4, respectively. The

relationships among city, migration, and poverty are explained in section 5, followed by major

conclusions and policy implications in section 6.

2. Review of literature

One of the major sources of agglomeration economies is related to the infrastructure endowment.

A classic paper by Tiebout (1956) argued that the availability and quality of public facilities and

services, such as schools, municipal golf courses, beaches, parks, police protection, roads, and

parking facilities, affect the decision-making process for choosing a municipality. Harris and

Todaro (1970) explained that rural–urban migration mainly occurs as a result of expected income

differentials between rural and urban areas: this implies that the urban areas with much better

conditions would attract migrants on a large scale.

The India Infrastructure Report 2011 (Infrastructure Development Finance Company 2011)

noted that inadequate access, poor quality, and poor reliability are the major problems with urban

water supply. Nair (2012) estimated that approximately 21% of the urban population lives in

squatter settlements with poor access to basic services: about 30–50% of the households do not

have sewerage connections, and less than 30% of the total wastewater is treated. Toutain and

Gopiprasad (2006) found inadequate urban service provisioning mainly in terms of drinking

water, sanitation, energy, transport, solid waste management, environmental degradation, and

pollution. Very importantly, findings from the report on the Urban Infrastructure in India

(Federation of Indian Chambers of Commerce & Industry 2011) confirm that present urban

infrastructure is grossly inadequate to meet the demand of the existing urban population. Pradhan

(2007) investigated the impact of infrastructure on urbanization, using a composite infrastructure

development index and confirmed that it has a significant positive impact. However, Tripathi

(2018) suggested that improvement in infrastructure does not increase population agglomeration

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(measured by size, density, and growth rate of city population) in large cities, though it

substantially improves the potential contribution of the cities to national economic growth.

Whether growth benefits spill-over in terms of wellbeing improvements is an important question.

As employment is the basic link between the two, we may turn to this aspect first. Several

studies (e.g., Mehrotra et al. 2014; Maiti, 2015; Institute for Human Development, 2014; Bhalla

and Kaur, 2011; Papola and Sahu, 2012) have tried to understand the trends and patterns of

employment and unemployment in India though few of them reflect specifically on urban

employment. Chen and Raveendran (2012) explore the trends in urban employment, with a

special focus on urban informal employment. Ramaswamy and Agrawal (2012) found that

manufacturing employment in urban India grew at a rate (2.8 per cent) faster than the all-India

average (1.8 per cent) over the period 1999–2000 through 2009–10. Indian Institute for Human

Settlements (2012) using data from Economic Census, noted that workforce participation rates

are highest in the “major metros” (population with 4 million plus), and employment in “high-

tech” sector (ICT, high technology manufacturing, and fast growing exports) is also highly

concentrated in the large cities. Mitra and Singh (2019) estimated that with increased

urbanization the unemployment rate rises as the possibility of disguised unemployment in the

urban labour force is low compared to the rural areas. Besides, frictional unemployment and

technological unemployment are also part of the urban labour force in the relatively skilled jobs.

The India: Urban Poverty Report 2009 by the Government of India (2009) noted that across the

Indian states, poverty is negatively correlated with the level of urbanization, and large and

medium cities have a lower incidence of poverty than small cities in India. A World Bank study

(World Bank, 2010) observed that poverty is more widespread in very small towns than in large

cities. An important study by Gangopadhyay, et al. (2010) applied the small area estimation

methodology in three states of India in 2004-05, and confirmed that in the states of West Bengal,

Orissa and Andhra Pradesh, the poverty level in large cities is much lower than that in small

towns. Bhanumurthy and Mitra (2004) decomposed changes in poverty into growth effect,

inequality effect, and migration effect for two periods: from 1983 to1993/94 and 1993/94 to

1999/2000. As per their findings, rural-to urban migration contributed to poverty reduction in at

least seven of the fifteen major states and at the all-India level too, in both the periods. In other

words, the overall incidence of poverty in these states fell, though rural-urban migration might

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have raised the incidence of poverty in the urban areas. However, Tripathi (2013b) argued that

higher levels of city economic growth and large city population agglomerations are associated

with a reduction in the poverty in cities.

3. Distribution of City Size

The framework most commonly employed to study city/town sizes is based on Pareto

distribution: G(x) = A x-a

where, G(x) is the number of urban areas with at least x people and A and a are the parameters to

be estimated from the data1.

Pareto distribution can be estimated by computing the least-squares regression:

ln G(x) = ln A – a ln x

The million plus cities were grouped into 16 different size classes and the log of the number of

cities in each size class has been regressed on the log of the average population size. Table 1

shows that while the adjusted R2 is high, the coefficient of the log of population size is negative

as expected but the magnitude is far below unity2, indicating that the rank size rule does not

apply to the million plus cities in India. In other words, population seems to be more equitably

Table 1: Impact of city population size on number of cities in each size class

Variables Dependent variable :

Log of number of cities

Log of population size of cities -0.642***

(0.114)

Constant 10.51***

(1.73)

F statistics 31.76***

R2 0.694

Adjusted R2 0.672

No. of observations 16

Standard errors in parentheses

*** p<0.01

1 If a is estimated to be 1, G(x)=Ax-1

which is known as rank size rule:

putting G(x) = 1, x=A (A is the population of the largest urban area). If A is estimated to be 1, xG(x) = A that is the

product of an urban area’s rank and population is a constant which is equal to the population of the largest urban

area. So the rank size rule implies that the second largest city is half of the largest city size and so on because rank of

the city multiplied by the population size is said to be equal to the population of the largest city. 2 We notice from the results that the sign of ‘a’ is negative and the magnitude is 0.64 (below unity) and the

corresponding t is statistically significant.

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distributed across these cities than being concentrated in a few of the million plus cities. From

this it may be inferred that economic activities are undertaken in a number of large cities than

being pursued in a concentrated manner in a few large cities. Thus, each has its own importance

though from another perspective it may be pointed out that some of the large cities could have

been even larger (Mathur, 2019). In this sense there is a population deficit in some of the very

large cities which could have been augmented had there been greater concentration of activities.

In other words, the full benefits of the agglomeration economies have not been realized in some

of the very large cities as they still do not seem to have the double the population size of the next

tier cities.

4. Determining City Size

In the context of the determinants of city size, Mera (1973), Calem and Carlino (1991), Moomaw

(1983), and Seitz (1993) considered both agglomeration and public infrastructure simultaneously

in the analysis of productivity. For instance, Mera (1973) highlighted the importance of public

infrastructure in achieving agglomeration economies and included the level of employment as a

measure of localization economies. O'Clery and Lora (2016) observed that formal employment

depends on city size. They showed that a level of agglomeration equivalent to between 45 and 75

minutes of commuting time, corresponding to cities between 62 and 43, maximizes the impact

that the availability of skills has on the ability of agglomerations to generate formal employment

in Colombia. The studies on the relationship between city size and urban wages claimed that

productivity and wages are higher in larger cities. Echeverri-Carroll and Ayala (2009) estimated

that a doubling of the human capital density in a metropolitan area results in approximately a 2

percent increase in average individual hourly wages in the United States. A simple model by

Haynes (1973) however, explained that large cities have higher crime rates per person than small

cities. Krugman (1991) pointed out that spatial economic structure is dispersed due to centrifugal

forces which include pure external diseconomies.

In the context of India, Pandey (1977) examined the role of socio-economic variables in

determining the rate of urbanization at the state level. The author noted a positive effect of

industrialization, negative effect of cropping intensity, and no effect of average worker income

on the urbanization rate. Mathur (2005) urged that post-liberalization urban growth was driven

by the substantial growth of the urban population and changes in the share of employment in the

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manufacturing and service sectors. Sridhar (2010) estimated the determinants of city growth and

output both at the district and city levels and showed that factors such as proximity to a large city

and the process of moving from agriculture to manufacturing, determines the size of a city.

Tripathi (2013a) investigated the economic determinants of the population size of the large cities

(750,000 or more inhabitants) and the external diseconomies such as city vehicle density were

seen to have a negative effect on the size of the city population. However, geographical,

environmental, second nature geography, infrastructure, and government policies also matter for

the population size.

Based on the literature review, infrastructure, city structure, employment type, wage rate and

diseconomies such as crime rate appear to be some of the major determinants of city size. City

structure can be envisaged in terms of solid waste generation and registered motor vehicles. An

industrial city vis-à-vis a services led city would generate different types of solid waste.

Similarly certain activities may involve motor vehicles to a larger extent in comparison to the

other activities. The nature of occupation can be taken in terms of employment status and the

wage rate, which in our analysis is captured through consumption expenditure. Since wage rate

may have an endogeneity problem consumption expenditure is possibly a good instrument.

Crime rate is a broad proxy for the diseconomies, and infrastructure involves a number of

components as mentioned below. Since there are three types of employment (self-employed,

regular salaried and casual wage) two dummies have been introduced, taking casual wage as the

reference category.

We have tried to construct an index of infrastructure, using factor analysis, for each of the

million plus cities. Generally, infrastructure is defined as a relatively permanent and foundational

capital investment of a country that facilitates smooth economic activity. It includes

administrative, telecommunications, transportation, utilities, education, health care, research and

development, and training facilities (Tripathi, 2018). To create infrastructure index, given the

availability of data, we consider city-wise different infrastructure variables, such as total road

length, total number of latrines, total water supply, total number of electricity connections, total

number of hospitals, and total number of schools, colleges, and universities, which are then

combined through factor analysis to construct the infrastructure index.

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Before performing factor analysis, we evaluate and ensure the validity of the data. Validity refers

to the closeness of the measured values. We measure the validity using the Kaiser-Meyer-Olkin

(KMO) index and the Bartlett’s test of Sphericity. We use STATA version 13 (Stata Corp,

College Station, TX, USA) to perform the KMO test. The KMO value is 0.595, indicating that

factor analysis is relatively suitable. The significance probability of Bartlett’s test of Sphericity is

0.000 < 0.01, which rejects the original hypothesis and indicating that the parameters are related,

and thus are suitable for factor analysis. The test results are shown in Table 2.

Table 2: KMO and Bartlett’s test

KMO measure of sampling adequacy 0.595

Bartlett’s test of sphericity Approximate chi-square 72.447

Df 15

Sig. 0.000 KMO, Kaiser-Meyer-Olkin.

Table 3 presents the initial eigenvalues. An eigenvalue is the variance of the factor. The first

factor accounts for the most variances, the second account the next highest amount of variance,

and so on. Some of the eigenvalues are negative which indicates that the matrix is not full rank.

Therefore, we can consider at most three factors for the analysis. However, we retained only one

factor as the KMO criterion suggests that factors with Eigenvalues ≥ 1 are to be considered

(Appendix Fig. 1). Table 3 shows that factor1 accounts for about 83% of the variance in the

solution and, thus, for one factor is relevant for the analysis.

Table 3: Explanation of total variance Factor Eigenvalue Difference Proportion Cumulative

Factor1 1.98719 1.44147 0.8277 0.8277

Factor2 0.54572 0.15173 0.2273 1.055

Factor3 0.39399 0.41488 0.1641 1.2191

Factor4 -0.02089 0.1947 -0.0087 1.2104

Factor5 -0.21559 0.07404 -0.0898 1.1206

Factor6 -0.28963 . -0.1206 1

Table 4 presents the factor loadings (pattern matrix) as per uniqueness. Uniqueness is the

variance that is ‘unique’ to the variable and not shared with other variables. The higher values of

uniqueness for some of the variables imply that these variables are not well explained by the

factors. For example, 81.8% of the variance in ‘total water supply’ is not shared with other

variables in the overall factor model. On the contrary ‘total number of electricity connections’

has low variance not accounted by other variables (43.34%). The values of the factor loadings

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for all the variables are greater than 0.3. Therefore, we can infer that factor1 is defined by all the

six variables which are then considered to create the infrastructure index3. It is also important to

note that as we are using one factor only, factor rotation which helps to see the underlying

dimensions (scales) more clearly is not suitable as there’s nothing to rotate.

Table 4: Factor loadings (pattern matrix) and unique variances for one factor model

Variable Factor1 Uniqueness

Total road length 0.6481 0.5799

Total number of latrines 0.5841 0.6589

total water supply 0.4266 0.818

Total number of electricity connections 0.7527 0.4334

Total number of hospitals 0.4627 0.7859

Total number of schools, colleges, and universities 0.5131 0.7367

Regression analysis has been carried out to investigate the determinants of city size. Ordinary

Least Square (OLS) as base run and Tobit estimate both have been carried out. Since million-

plus cities form only a small part of the distribution pertaining to all types of cities and towns, a

Tobit function deems appropriate.

Table 5 presents the results of the multiple regression analysis. Models 1 and 2 consider the

logarithm of the city population as the dependent variable. While model 1 gives the OLS

regression results, model 2 presents the Tobit estimates. In model 1, the statistically significant

value of F-statistics indicates that there is a significant relationship between the dependent and

the independent variables. The regression explains 58% of the total variation in the dependent

variable. On the other hand, the likelihood ratio (chi-square of 40.26 with 7 degrees of freedom

and significant at 1 % level indicates that our model as a whole performs well statistically in

comparison to an empty model (i.e., a model with no predictors). Table 5 shows that the OLS

and Tobit regression results are consistent and provide similar results. The results are indicative

that higher average monthly per capita consumption expenditure, total municipal solid waste

generation, and salaried employment have a positive and statistically significant effect on the log

of the city population. Regression model 1 shows that a 1 % increase in average monthly per

capita expenditure increases the city population by 0.22 %. Regression model 2 shows that for

one unit increase in total municipal solid waste generation, there is a 1.23 point increase in the

predicted size of the city population. A one-unit increase in salaried employment is associated

3 Quite importantly, factor1 is mostly related to the city-wise number of electricity connections.

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with a 0.03 unit increase in the predicted value of the size of the city population. On the

contrary, total registered motor vehicles have a negative effect on the size of the city population.

Table 5: Determinants of million plus city populations

Dependent variable:

Log of city population

VARIABLES OLS Tobit

Model 1 Model 2

Average monthly per capita consumption

expenditure

0.220* 0.220*

(0.130) (0.118)

Total municipal solid waste generation 1.233* 1.233*

(0.683) (0.622)

Infrastructure index -0.103 -0.103

(0.112) (0.102)

Salaried employed persons 0.0295*** 0.0295***

(0.00978) (0.00891)

Self-employed persons 0.0132 0.0132

(0.00984) (0.00897)

Total registered motor vehicles -0.1053** -0.1053***

(0.042) (0.0382)

Incidence of total cognizable crimes -0.0473 -0.0473

(0.0360) (0.0328)

Constant 12.42*** 12.42***

(0.733) (0.668)

F statistics 7.55***

R2 / Pseudo R2 0.575 0.3705

Adj R-squared 0.4992

Log likelihood -34.2032

Likelihood ratio chi2(7) 40.26***

/sigma 0.50096

Observations 47 47

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The coefficient of -0.105 in regression model 1 indicates that a 10% increase in total registered

motor vehicle decreases a million-plus city population by 1 %. This indicates that congestion of

cities which is measured by total registered motor vehicles deter the size of million-plus cities.

This signifies that million-plus cities are overcrowded in India. However, the city-wise crime

rate and index of infrastructure do not turn out to be statistically significant. This specifies that

million-plus cities have several negative externalities which may not show large variations

across. Similarly infrastructure provision might have been already very high and thus, may not

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contribute to further population growth as it may be approaching the saturation point. To

generate a new equilibrium with a higher population level massive investment may be required.

Moreover, after the saturation point is reached it may not be possible to expand the city further.

Rather out-migration would unfold a negative relation between infrastructure investment and

population size.

4.1 Cluster analysis

Cluster analysis has been conducted to find out if within the category of million-plus cities

different groups are traceable. Cluster analysis helps us to group similar observations into a

number of clusters based on the observed values of several variables for each individual. In other

words, it is done to identify the set of objects with similar characteristics

We use a hierarchical cluster which is the most common method in cluster analysis. It creates a

series of models with cluster solutions from 1 (all cases in one cluster) to n (each case is an

individual cluster). It is also very useful when the number of observations is less than 100. We

follow the agglomerative clustering in which most hierarchical methods fall into. For this

process, we need to specify a linkage algorithm to define the distance from a newly formed

cluster to other clusters in the solution. For this purpose, we use most commonly used Ward's

minimum variance method. The method combines those objects whose merger increases the

overall cluster variance (i.e., the homogeneity of clusters) to the smallest possible degree. The

approach is usually used in combination with (squared) Euclidean distances. The squared

Euclidean distance increases the importance of a large distance while fading the importance of

small distances.

It is very much important to select clustering variables. To represent a million-plus city structure

we choose several important economic factors. We choose rural to urban migration, wage

structure which is defined by average monthly per capita consumption expenditure, poverty rate,

solid waste generation, number of road accidents, total cognizable crime, infrastructure

availability, employment status, and size of city population to define a million-plus city structure.

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Table 6: Correlation matrix of clustering variables

migr Mpce phr swm rc tcc self_m self_f casual_m casual_f infras pop

migr 1.00

mpce -0.07 1.00

phr 0.06 -0.74 1.00

swm -0.39 0.06 -0.16 1.00

rc 0.10 0.08 0.04 -0.13 1.00

tcc 0.10 0.23 0.05 -0.22 0.49 1.00

self_m -0.11 -0.48 0.45 0.04 -0.11 -0.08 1.00

self_f -0.13 -0.23 0.20 -0.17 -0.19 -0.15 0.60 0.01

casual_m 0.34 -0.21 0.22 -0.41 0.01 0.11 -0.25 -0.11 1.00

casual_f 0.23 -0.33 0.28 -0.24 0.05 0.14 -0.10 -0.31 0.73 1.00

infras 0.02 -0.04 0.06 0.42 -0.07 0.08 0.03 -0.13 -0.19 -0.09 1.00

pop -0.37 0.48 -0.44 0.37 -0.14 -0.18 -0.27 -0.17 -0.38 -0.48 -0.08 1.00

Note: See table 9 for variable definitions

Table 6 presents the pair-wise correlation coefficients of the clustering variables. The results

show that collinearity is not at a critical level. The variables casual female employment and

casual male employment show the highest correlation of 0.73, which is clearly lower than 0.90

thresholds. This indicates that we can proceed to the analysis using all twelve clustering

variables.

Table 7: The Variance ratio criterion (VRC) and Duda-Hart indices

Duda/Hart Je(2)/J2(1) index VRC

No. of

clusters

Je(2)/Je(1) pseudo T-squared

Calinski/Harabasz

pseudo-F

1 0.8029 11.05

2 0.7367 12.15 11.05

3 0.7054 9.19 13.08

4 0.6511 5.36 11.91

5 0.6132 8.2 11.82

6 0.6511 4.82 11.87

7 0.5565 3.98 11.77

8 0.5039 2.95 11.42

9 0.5235 6.37 11.33

10 0 . 11.32

Now to decide the number of the cluster we depend on graphical and statistical measures. Table

7 suggests that the largest Duda–Hart Je(2)/Je(1) stopping-rule value is 0.8029, corresponding to

the first group. The smallest pseudo-T-squared value is 2.95 for the eight-group solution, but the

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pseudo-T-squared value for the three-group solution is also low, with a value of 9.19. The three-

group solution with a Calinski–Harabasz pseudo - F value of 13.08 is largest, indicating that the

three-group solution is most distinct compared to any other group solution. The results are also

confirmed by the dendrogram presented in Appendix figure 2.

Table 8: Number of clusters

cluster Frequency Percent Cumulative

1 24 51.06 51.06

2 12 25.53 76.6

3 11 23.4 100

Total 47 100

The output in Table 8 shows that the cluster analysis assigned to all 47 million-plus population

size cities unravels the three segments. There is no city having missing values. The first

cluster comprises 24 cities (51 %), the second cluster 12 cities (26%), and the third

cluster 11 cities (23 %).

Table 9: Comparison of means

cluster Migr mpce phr swm rc tcc self_m self_f casual_m casual_f infras pop

1 24.56 3.24 12.56 0.28 0.73 3.45 33.98 36.65 14.09 13.89 0.02 14.79

2 27.88 2.24 38.50 0.23 0.89 3.72 37.26 36.99 25.82 26.77 0.07 14.17

3 14.81 2.43 27.40 0.29 0.68 3.34 54.10 56.80 8.78 10.51 -0.12 14.58

Total 23.13 2.80 22.65 0.27 0.76 3.49 39.53 41.45 15.84 16.39 0.00 14.58

Note: See table 9 for variable definitions

The mean values for the three clusters are given in Table 9. Comparing the variable means across

the three clusters, we find that among the different variables the first cluster stresses on rural to

urban migration, self-employment male and self-employment female, while the other variables

are less important. The main variables in the second cluster are rural to urban migration, poverty

head count ratio, self-employment male, self-employment female, casual employment male, and

casual employment female. The third cluster considers poverty headcount ratio, self-employment

male, self-employment female, city population size as significant variables. This indicates that

rural to urban migration, poverty rate, employment status, and city population size play the most

important role in clustering the cities into three segments. Finally, Table 10 presents the name of

the million-plus cities that belong to three different clusters.

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Table 10: Cities divided by different clusters

Cluster City name Average population

size (in millions)

1

Bangalore, Chandigarh, Chennai, Coimbatore, Delhi, Ghaziabad, Hyderabad,

Jodhpur, Kochi, Kollam, Kozhikode, Madurai, Malappuram, Mumbai,

Nagpur, Nashik, Pune, Rajkot, Surat, Thiruvananthapuram, Thrissur,

Tiruchirappalli, Vadodara, Vijayawada

4

2 Agra, Allahabad, Asansol, Aurangabad, Bhopal, Dhanbad, Durg and Bhilai

Nagar, Jabalpur, Jamshedpur, Kannur, Lucknow, Raipur 1.5

3 Ahmedabad, Amritsar, Gwalior, Indore, Kanpur, Kolkata, Meerut, Patna,

Ranchi, Srinagar, Varanasi 3

5. City, Migration and Poverty

Various economic theories suggest that structural change which is an inevitable component of

economic growth not only involves shift in the value added and work force composition but also

the transfer of population from the rural to the urban areas. In the Lewis (1954) model, shifting

of labor from agriculture sector to manufacturing triggers growth by generating an investible

surplus; and in the process of structural change it results in both internal and external

agglomeration economies. The Todaro (1969) model explained rural-urban migration as a

function of income differential adjusted for the probability of finding a job. The Harris-Todaro

(1970) model attempted to explain the phenomenon of accelerating rural-urban labor migration

despite the existence of positive marginal products in agriculture and significant levels of urban

unemployment.

The intrastate migration rate is much higher in magnitude than the interstate migration rate in

India (Mitra and Murayama, 2009) Male and female migration rates are closely interconnected,

irrespective of whether they migrate from the rural areas within the states or other states though

the social and cultural diversity in India stands as a major hindrance to population mobility. The

migration rate in the urban areas and the urbanization level are positively associated, very

moderately though. But with an increase in the city size, the migration rate rises mainly because

employment prospects are better in large cities due to agglomeration effects (Mitra, 2019).

Despite being a source of employment, MGNREGA has not been able to check the out-migration

from the developed region because of higher market wage rates in urban destination (Ahuja et al.

2011). Besides, an increasing level of education of the migrants acts as the main pull factor

(Sridhar et al., 2013). The overall rural to urban migration rate is low due to the lowering of the

15

gap between urban–rural health, infrastructure, employment opportunities, and economic

conditions (Tripathi, forthcoming). Also, as Chauvin et al. (2017) argued, India’s rural-urban

migration is limited by strong place-based preferences such as those related to caste-based social

networks. Hence, if the contact person is located in a large city the potential migrant will have a

higher probability of moving to the same city.

Is this migration process beneficial? India: Urban Poverty Report 2009 (Government of India,

2009) indicated that the relationship between poverty and migration is not established clearly and

it is evidenced that middle and higher-income groups have a higher propensity to move. Based

on better education and skills, the most effective group of migrants is the urban to urban stream

directed to large and metropolitan cities. Cali and Menon (2013) noted the positive spillovers of

urbanization on the rural economy rather than the movement of the rural poor to the urban areas.

This rural poverty-reducing effect of urbanization is primarily explained by the increased

demand for local agricultural products.

While large cities may offer better employment opportunities compared to the small towns even

within the informal sector, excess supplies of labour and the segmentation of the labour market

due to information asymmetry may restrict the pace of upward mobility. Thus, large cities may

still comprise poverty. It would be therefore worthwhile to pursue further analysis in this regard.

In order to study the relationship among migration, poverty and city size factor analysis has been

carried out. We evaluate and ensure the validity of the data by using the Kaiser-Meyer-Olkin

(KMO) index and the Bartlett’s test of Sphericity. The KMO value is 0.569, indicating that factor

analysis is relatively suitable. The significance probability of Bartlett’s test of Sphericity is 0.000

< 0.01 which indicates that the parameters are not related, and thus, are suitable for factor

analysis. Table 11 presents the test results.

Table 11: KMO and Bartlett’s test

KMO measure of sampling adequacy 0.569

Bartlett’s test of sphericity Approximate chi-square 217.756

df 66

Sig. 0.000

KMO, Kaiser-Meyer-Olkin.

16

Table 12 presents the eigenvalues, the percentage of variance, and the cumulative percentage of

variance associated with each other. It reveals that the first three factors explain

approximately 88.53% of the total variance, and hence, consideration of three factors is

appropriate for the analysis. Table 7 and appendix figure 3 also suggest that we should retain

three factors as the Eigenvalues are equal or greater than 1.

Table 12: Explanation of total variance Factor Eigenvalue Difference Proportion Cumulative

Factor1 2.76788 0.72829 0.4244 0.4244

Factor2 2.03959 1.07374 0.3128 0.7372

Factor3 0.96585 0.09586 0.1481 0.8853

Factor4 0.86999 0.49338 0.1334 1.0187

Factor5 0.37661 0.14052 0.0578 1.0765

Factor6 0.23609 0.15491 0.0362 1.1127

Factor7 0.08118 0.1146 0.0124 1.1251

Factor8 -0.03343 0.05349 -0.0051 1.12

Factor9 -0.08691 0.09845 -0.0133 1.1067

Factor10 -0.18536 0.0397 -0.0284 1.0782

Factor11 -0.22506 0.06017 -0.0345 1.0437

Factor12 -0.28522 . -0.0437 1

Table 13 presents the estimated values of factor score coefficients and the rotated orthogonal

varimax, respectively. A varimax rotation attempts to maximize the squared loadings of the

columns. The higher values of uniqueness for infrastructure index, the total number of road

accidents, and crime rate indicate that these variables are not well explained by the factors. On

the other hand, lower values of average monthly per capita expenditure, casual female

employment, self-employment male, and self-employment female show that these variables are

well explained by the factors under consideration.

Table 13 suggests that migration and city population size are moderately associated. However,

there is a negative association between the two, indicating that migration tends to decline with a

rise in city population size. Large cities also reveal a lower incidence of poverty and migration

and city poverty are positively associated, very mildly though. Large cities also show a lower

level of self-employment for males, self-employment for females, casual employment for males,

and casual employment for females in factors 1 and 2, respectively. The relationships are

moderately associated.

17

Table 13: Loading for varimax rotated factor matrix of three-factor model

Variable Factor1 Factor2 Factor3 Uniqueness

Total migration from rural India to million plus cities

(migr) -0.0133 0.3751 0.295 0.7721

Average monthly per capita consumption expenditure

(mpce) -0.7948 -0.3097 0.1224 0.2574

City-wise poverty headcount ratio (phr) 0.7059 0.2955 0.0369 0.413

Total municipal solid waste generation (swm) -0.0743 -0.2551 -0.7016 0.4372

Number of road accident (rc) -0.1696 0.1297 0.3008 0.8639

Incidence of total cognizable crimes (tcc) -0.2013 0.1766 0.3669 0.7937

Self-employed male (self_m) 0.7431 -0.2933 0.0542 0.3588

Self-employed female (self_f) 0.5602 -0.4497 0.3015 0.393

Casual employed male (casual_m) 0.0377 0.7423 0.2688 0.3753

Casual employed female (casual_f) 0.1183 0.8398 0.052 0.278

Infrastructure index (infras) 0.0422 0.0177 -0.4133 0.8271

Log of city population (pop) -0.4706 -0.4716 -0.3145 0.4572

Finally, Table 14 presents the factor rotation matrix. The conversion matrix estimates the rotated

factor loadings (RFL): RFL = Factor loadings* Factor rotation. This matrix produces rotated

factor matrix. The result indicates larger rotations as off-diagonal elements are larger.

Table 14: Factor rotation matrix

Factor1 Factor2 Factor3

Factor1 0.6118 0.7026 0.3634

Factor2 -0.7896 0.57 0.2272

Factor3 0.0475 0.4259 -0.9035

6. Conclusion and policy implications

City sizes play a pivotal role in the context of urbanization, as India’s urban population is mainly

concentrated in and around large cities. In this paper on million plus cities, we estimate the city

size distribution by applying Pareto distribution framework. Though the rank-size rule does not

apply to the million-plus cities, there is evidence to suggest somewhat concentration of economic

activities rather than a highly diversified system. In fact, the cluster analysis also divides cities

into only three categories rather than suggesting that each one is different from the other. Cities

18

can be grouped into three clusters with the average population sizes of 4, 3 and 1.5 million

respectively.

We consider infrastructure, city structure, employment type, wage rate and diseconomies such as

crime rate as the major determinants of city size. City structure envisaged in terms of waste

generation and employment status, wage rate captured in terms of consumption expenditure, and

capacity measured in terms of registered motor vehicles turn out to be the important

determinants. Crime and index of infrastructure are not statistically significant, though.

As regards the impact of million-plus cities on migration and poverty, we noted that migration

tends to decline with a rise in city size. However, large cities reveal a lower incidence of poverty,

which is suggestive of the beneficial effects of agglomeration economies. Though migration

tends to raise the incidence of poverty at the place of destination, positive externalities associated

with city size contribute to livelihood creation and poverty reduction. These findings have

important policy implications. Facilitating the rural migrants in cities in terms of basic amenities

and land tenure can contribute to poverty reduction in a more cost effective manner in

comparison to the anti-poverty measures implemented at the place of rural origin.

Appendix

Measurement of variables, data sources, and description of data

City population: Million-plus city population data is collected from Census of India, 2011.

Website: https://www.census2011.co.in/urbanagglomeration.php

Employment: National Sample Survey does not provide city level employment data. Urban

samples of a city district (i.e. the district to which the sample city is located) are considered to

measure the distribution of employment. The percentage distribution of all usual status (principal

status+ subsidiary status) of age 15 years and above in different employment categories are

estimated. Source: Unit level data of NSS 68th Round on Employment and Unemployment in

2011-12.

Total road length: Both Kachcha road length and Pucca road length are considered for the

measurement of total road length of a city. Source: Town amenities, District Census Hand Book,

Census of India 2011. Website: http://censusindia.gov.in/2011census/dchb/DCHB.html

19

Electricity connection: Total number of electricity connections in domestic, industrial,

commercial, road lighting, electricity, and other connections. Source: Town amenities, District

Census Hand Book, Census of India 2011.

Number of Latrines: Total number of pit, flush/pour, services, and other latrines. Source: Town

amenities, District Census Hand Book, Census of India 2011.

Total water supply: Total protected water supply in city. Source: Town amenities, District

Census Hand Book, Census of India 2011.

Total hospitals: It includes allopathic hospitals, alternative medicine hospitals, dispensary/health

Centers, family welfare centers, maternity and child welfare centers, maternity homes, TB

hospitals/ clinic, and nursing homes Source: Town amenities, District Census Hand Book,

Census of India 2011.

Total number of schools, colleges, and universities: It includes all the private and governments’

school, colleges and universities of a city. Source: Town amenities, District Census Hand Book,

Census of India 2011.

Average monthly per capita consumption expenditure (MPCE): The MPCE is measured by the

modified mixed reference period (MMRP). MPCE is estimated using urban samples of a city

district. Source: Author’s calculation using unit level data of the NSS 68th Round on

consumption expenditure of 2011-12.

Poverty headcount ratio: Head count ratio (HCR) is the proportion of a population that exists, or

lives, below the poverty line. For measuring city poverty rate, we use the Rangarajan committee

–recommended poverty line in 2011-12 by considering monthly per capita consumption

expenditure based on MMRP. However, as India’s official estimates do not provide the city-level

poverty line, state-specific urban poverty lines have been used for measuring district-level

(which is used as proxy of a city) urban poverty for the districts located in the corresponding

states. Source: Author’s calculation using unit level data of the NSS 68th Round on consumption

expenditure of 2011-12.

Migration: City-specific percentage of migration is defined by the total number of migrants from

India’s rural areas to a particular city district with duration of residency from less than one year

to more than 10 years divided by the total population of that city. Migration Table, Census of

India 2011, The Government of India.

Registered motor vehicles: Data on city wise registered motor vehicles in 2012 is collected

from Statistical Year Book India 2017, Ministry of Statistics and Programme Implementations,

The Government of India. Website: http://mospi.nic.in/statistical-year-book-india/2017/189.

Cognizable crimes: Data on city wise incidence of total cognizable crimes in 2011 is collected

from National Crime Records Bureau, Ministry of Home Affairs, The Government of India.

20

Solid Waste Generation: Biodegradable and Non-biodegradable waste are considered. Total

municipal solid waste generation per day in 2011 is collected from Central Pollution Control

Board (CPCB), the Ministry of Environment, Forest and Climate Change., The Government of

India.

Road Accidents: Data is collected from the Ministry of Road Transport and Highways, The

Government of India.

Appendix Table 1: Description of data

Variable Mean

Standard

Deviation Minimum Maximum

Coefficient of

variation

Log of city population 14.58 0.78 13.84 16.73 5.33

Total road length per 1000 population

(kilometers) 0.85 0.58 0.03 2.03 68.18

Total number of latrines per 1000 population 121.86 81.76 0.89 414.87 67.09

Total water supply per 1000 population (in

kiloliters) 99.47 122.86 0.46 636.23 123.51

Total number of electricity connections per

1000 population 187.92 75.13 16.05 328.46 39.98

Total number of hospitals per 1000 population 0.17 0.25 0.00 1.36 145.63

Total number of schools, colleges, and

universities per 1000 population 0.56 0.40 0.01 2.48 71.83

Average monthly per capita consumption

expenditure (in thousands rupees) 2.80 0.82 1.44 4.89 29.29

Total municipal solid waste generation per

1000 population (tons per day) 0.27 0.15 0.01 0.59 53.98

Salaried employed persons (%) 43.38 13.71 12.61 67.85 31.60

Total registered motor vehicles per 1000

population 418.21 217.39 35.34 1139.60 51.98

Incidence of total cognizable crimes per 1000

population 3.49 2.48 1.22 16.35 70.91

Migration from rural India to a million plus

cities (%) 23.13 13.96 2.32 62.51 60.36

Poverty headcount ratio (%) 22.65 14.22 1.20 72.54 62.78

Number of road accidents per 1000 population 0.76 0.57 0.05 2.30 74.62

Self-employed male (%) 39.53 11.55 21.44 80.95 29.23

Self-employed female (%) 41.45 13.53 20.02 85.90 32.63

Casual employed male (%) 15.84 11.76 1.14 49.22 74.27

Casual employed female (%) 16.39 11.33 1.02 51.99 69.15

Note: Calculation is based on 47 observations

21

Appendix Figure 1. Scree plot of Eigen values after factor solution

Appendix Figure 2: Dendrogram for wards linkage cluster analysis

Appendix Figure 3: Scree plot of Eigen values after factor solution

-.5

0.5

11.

52

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lues

1 2 3 4 5 6Number

0

5000

1000

015

000

2000

0

L2sq

uare

d dis

simila

rity m

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G1n=9

G2n=7

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G7n=1

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22

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