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Archives of Business Review – Vol. 8, No.7
Publication Date: July 25, 2020
DOI: 10.14738/abr.87.8688.
Jayadevan, C. M. (2020). Does Urbanization Promote Development? Archives of Business Research, 8(7). 249-257.
Does Urbanization Promote Development?
C. M. Jayadevan
Swinburne University of Technology. Australia
ABSTRACT
Urbanization can influence the economic growth and development.
Urbanization experienced in the last three decades really promoted
economic growth and development. Using a sample of 107 countries
over the period 1991-2018, this study verifies and finds the support for
the hypothesis that urbanization promotes development with the help
of structural equation modeling.
Keywords: Development, Urbanization, Structural, equation, modeling.
INTRODUCTION
Urbanization is a complex socio-economic process that transforms the built environment,
converting formerly rural into urban settlements, while also shifting the spatial distribution of a
population from rural to urban areas. It includes changes in dominant occupations, lifestyle, culture
and behaviour, and thus alters the demographic and social structure of both urban and rural areas.
A major consequence of urbanization is a rise in the number, land area and population size of urban
settlements and in the number and share of urban residents compared to rural dwellers. The
degree or level of urbanization is typically expressed as the percentage of population residing in
urban areas (UN, World Urbanization Prospects, 2018). Throughout history, urbanization has been
a key element in the process of development (Bairoch, 1988). Arguably, these two processes are
inextricably linked- development does not occur without urbanization- although the casual link
between these processes is not clear-cut (Jacobs, 1969). Between 1950 and 2018 the world’s
population was urbanizing rapidly, with the proportion urban rising from 30 per cent in 1950 to
55 per cent in 2018 (UN, World Urbanization Prospects, 2018).
Does concentration in space promote economic growth ? There are few econometric studies
involving the impact of agglomeration on economic growth. Past studies show that spatial
proximity is good for economic growth. There is a complementarity between economic growth and
urbanization. Martin and Ottaviano (1999) shows that growth and geographic agglomeration as
“mutually self-reinforcing processes.”. According to Fujita and Thisse (2002 ) “growth and
agglomeration go hand-in-hand”. In the review paper by Baldwin and Martin (2004) stresses that
“spatial agglomeration is conducive to growth” given localized spillovers.
Bulhart and Federica (2009) explore the causal link running from agglomeration to growth,
mediated by stage of development and openness using cross-section OLS and dynamic panel GMM
estimation methods using dataset containing up to 105 countries over the period 1960-2000. The
study finds evidence that supports the “Williamson hypothesis”: agglomeration boosts GDP growth
only upto a certain level of economic development. Another study by Crozet and Koenig (2007)
using the data for EU regions over the period 1980-2000 explore the effect of spatial concentration
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Jayadevan, C. M. (2020). Does Urbanization Promote Development? Archives of Business Research, 8(7). 249-257.
URL: http://dx.doi.org/10.14738/abr.86.8688 250
of economic activity within regions on the growth performance of these regions. The study shows
that agglomeration is growth-promoting. Regions with a more uneven internal spatial distribution
of production appear to grow faster. Henderson’s study(2003) draws on panel data covering upto
70 countries over the period 1960-1990 using dynamic panel estimation methods finds that
urbanization per se has no significant growth-promoting effect, but that urban primacy (the share
of country’s largest city) is advantageous to growth in low income countries. Their results support
the Williamson’s hypothesis-interaction terms with initial per capita income are negative for both
urbanization and urban primacy. The study of Ades and Glaeser (1995) involving a cross-section
of 85 countries indeed find a negative partial correlation between openness and urbanization.
According to Krugman and Elizonto (1996) agglomeration matter more to closed economies than
to open economies because domestic trading can be conducted cheaply over shorter distances than
the international trade. Benefits of urbanization arise due to the presence of Marshallian
externalities (Henderson, 1974).
The primary aim of this research is to test a hypothesis that urbanization promotes development.
This paper uses a novel methodology to address the hypothesis that development depends on the
urbanization. This study provides new empirical evidence concerning the role of urbanization in
development for the modern economies of the world over the period from 1991 to 2018 using
structural equation modeling. This study will help to formulate policies to increase the
urbanization to achieve further development. Further research on the role of urbanization on the
development is warranted for the following reasons: 1) No studies were found on this topic since
2009, 2) There were no studies in the literature examining the role of urbanization on
development using structural equation modeling. In this paper we address both of these
limitations, considering 107 countries for which comprehensive data were available. This study
examines the role of urbanization on development using the structural equation modeling
involving maximum likelihood methods for estimation instead of traditional econometric methods.
We develop the remaining discussions as follows: Section II describes the descriptive statistics. The
basic model is summarized in section III. Section IV reports the results of the study. Section V
summarizes the findings and outlines directions for further research.
DATA DESCRIPTION
The data used in this study to measure urbanization and development has been obtained from
World Bank indicators statistics(2020). A brief description of variables are provided in Table 1.
Foreign direct investment was also considered but this variable did not have a significant
correlation so this has been excluded from the analysis.
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Table 1: Variables of Urbanization and Development
Variables Description of Variables
Urbanization
UPP Urban population (% of total population)
PUA Population in urban agglomerations of more than 1
million (% of total population)
Development
GPC GDP per capita, PPP (constant 2011 international $)
LIFEX Life expectancy at birth, total (years)
FRT Fertility rate, total (births per woman)
EMP
Employment in industry and services (% of total
employment), represents total non-agricultural
employment.
TER School enrollment, tertiary (% gross)
SEC School enrollment, secondary (% gross)
Source: Statistical Indicators Provided by World Bank
The average urban population is 58.50%. The lowest urban population is 5.49% which is almost
eleven times lower than the average urban population. The range for urban population is between
5.49% and 100%. There is a gap of 94.51% in urban population between minimum and maximum.
The average urban agglomeration is 23.67%. The lowest urban agglomeration is 2.17%. The range
for urban agglomeration is between 2.17% and 100%. The gap between minimum and maximum
urban agglomeration is 97.83%. The coefficient of variation was highest for GDP per capita
followed by tertiary enrolment and lowest for life expectancy (Table 2).
Table 2:Descriptive Statistics
Variable N Mean Std Dev Minimum Maximum Coeff Variation
UPP 2968 58.50 22.90 5.49 100.00 39.15
PUA 2968 23.67 16.93 2.17 100.00 71.52
GPC 2968 15998.21 16789.52 438.64 96850.37 104.94
LIFEX 2968 68.89 10.12 26.17 84.93 14.69
FRT 2968 3.11 1.67 1.00 7.76 53.83
EMP 2968 69.98 24.42 10.19 99.94 34.90
TER 2968 31.69 26.04 0.51 136.60 82.17
SEC 2968 73.09 34.04 5.28 163.93 46.57
Source: Computed from Statistical Indicators Provided by World Bank
MODEL
The Structural equation models include variables that are proportions, rates and ratios. Modelling
of such “compound” quantities is less straightforward than absolute quantities. The use of factor
analysis assumes that each of the observed variables being analyzed is measured on an interval or
ratio scale (Rourke et.al., 2013). So all the variables are rescaled using the min-max normalization
formula Yi=(xi- minimum(xi) /maximum(xi) - minimum(xi). This type of manipulation of data
results in the interval [0,1] for all the variables. Labels of rescaled variables are identified by adding
a prefix “V_” to the original variables.