THE GROWTH OF URBAN POPULATION AND ITS DETERMINANTS IN TURKEY1
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THE GROWTH OF URBAN POPULATION AND ITS DETERMINANTS IN
TURKEY1
Ertugrul DELIKTAS2, A.Özlem ÖNDER3, Metin KARADAĞ4
ABSTRACT
The aim of this study is twofold. Firstly, this study examines the size distribution of urban population by using
Zipf’s Law. The second objective of this study is to investigate the effects of determinants of urban growth in
Turkey by using the data for the 1980-2007 time period.
The main findings of the study show that there is some evidence that Zipf’s law hold in Turkey.
Moreover, according to the rank-minus -half rule the results suggest stronger support fo r the Zip Law in urban
population growth. Moreover, the regression results indicate that fertility rate, location of the city, migration,
health indicator, agglomeration, specialization, competition indices have positive impact, whereas schooling rate
has a negative effect on growth of the urban population.
Key words: Urban population growth, Zipf’s law, Pareto exponent
JEL Classification: C23, O18, R10
1
This paper is based on the project supported by TUBITAT. The project number is 106K 283. The previous
version of this study was also presented at IASK international conference 13-15 October 2008, Porto, Portugal
2
Ege University Dep. of Economics, 3500 Izmir-Turkey. E-mail: ertugrul.deliktas@ege.edu.tr
3
Ege University Dep. of Economics, 3500 Izmir-Turkey.E-mail: ozlem.onder@ege.edu.tr
4
Ege Universit y Dep. of Economics, 3500 Izmir-Turkey.E-mail: metin.karadag@ege.edu.tr
11. Introduction
The world’s population increased three times between the 1800 and 2000 and it is predicted
that the world’s population, which is around 6.8 billion in 2009, will become around 8 billion
in 2025 and 9.5 billion in 2050. Also the largest part of the population growth is in the
developing countries and it is predicted that 84 % of the world population will live in the
developing countries in 2020 (Bongaarts, 2001).
In line with the population growth, another important issue is the rapid urbanisation
process. In industrialised countries 75 % of the population lives in the city centres, while only
around 33 % of the population lives in the city centres in the developing countries. However,
it is predicted that 58 % of the world population will live in the cities in the world before 2020
(Bongaarts, 2001). Therefore, city administrations should be aware of this development and
take necessary decisions regardingly.
Regarding Turkey, the population growth has been relatively high since its foundation
in 1923. For example, Turkey’s total population increased from 13.6 million in 1927 to 20.9
million in 1950, to 56.4 million in 1990, to 67.8 million in 2000 and finally to around 71
million in 2007. On the other hand, the population of the metropolitan cities has increased
more rapidly. For example, the population of Istanbul increased from 4.7 million in 1987 to
11.1 in 2007, the population of Ankara increased from 2.8 million to 4.1 million, while the
population of Izmir increased from 1.9 million to around 3 million for the same time period.
On the other hand, the total population in the Turkish cities increased from 3.3 million in
1927 to 5.2 million in 1950, to 30.5 million in 1990, to 44 million in 2000, and finally to 49.7
million in 2007. Thus, the share of the population of the cities in total population is 24%,
25%, 54%, 65% and 70% in order. These figures indicate that Turkey has experienced a very
rapid urbanization process.
Hence, investigating the urban-size distribution using Zipf’s Law, and the reasons why
there are large differences in the growth of the cities gains importance as far as Turkey is
concerned. There are a few studies related urbanization process in Turkey (see, for example,
Filiztekin 2006; Turk and Dokmeci, 2001; Gedik, 2003). However, to our best knowledge,
there appears no study related to the investigation of determinants of the urban growth in
Turkey.
2The main aim of this study is to investigate the size distribution of urban population and
to find out the determinants of urban growth in Turkey by using the data for the time period
1980-2007. In this respect, firstly, we examine the size distribution of the urban by using
Zipf’s Law and try to find out whether the urban growth has been even or uneven for the time
period under consideration. Then secondly, we estimate the effects of factors that may have
impact on the urban growth in Turkey over the period 1980-2007.
The remainder of this paper is organised as follows. Section two gives brief descriptive
information about city growth in Turkey. Section three presents a theoretical explanation of
Zipf’s Law. Section four includes the data set used in the study. Section four also provides the
results of empirical analysis based on Zipf’s Law and the regression analysis including the
estimation of effects of determinants of urban population growth. Finally, section five
concludes.
2. Brief Descriptive Information About Urban Growth in Turkey
As mentioned before, Turkey has experienced a rapid urbanisation process since 1950.
The urbanisation rate has increased more rapidly especially since 1980s. For example, the rate
was around 44 % in 1980, while it was around 65 % in 2000 (see Evcil et.al 2006). It is
estimated that urbanisation rate will become 74% in 2010 and 80% in 2030. However, the
urbanization rate increased more rapidly in the metropolitan cities. For example, the
urbanization rate increased from 85% to 88% in Istanbul, from 75% to 85% in Izmir, and
from 81% to 92% in Ankara between 1980 and 2007. However, the urbanisation rate
increased from 49% to 70% on average for Turkey for the same time period (see
TURKSTATdifferent issues). These figures show us that both population growth and
urbanisation rate are above Turkey’s average in the metropolitan cities. The main factor for
the growth of urban population in Turkey is the migration of villagers to the urban areas
continuously (see Evcil et. al 2006). One of the main reasons for his migration can be given by the
structural change in the Turkish economy especially after 1980s. Since 1980s the share of
agricultural sector decreased rapidly, and thus, the percentage of the labour force in the
agricultural sector is also decreased rapidly 5.
5
The share of agriculural sector in GDP was around 40 % in 1968 and it was around 9 % in 2008 and the
sectoral breakdown of labour force in the agricultural sector was around 55 % in 1980 and it aws around 33 %
in 2003. (See TURKSTAT, Turkey in Statistics ,for different years).
3The important differences in the size distribution of urban population might well be
explained by the various factors including economic, social, cultural, political, demographical
variables and, migration and geographical situation. Depending on these factors, some cities
may grow either faster or slower. In this process, the population of the urban has increased on
the one hand; the number of the provinces has increased on the other. Currently, the number
of provinces has reached to 81. However, there are great differences in the size distribution of
the provinces. For example, being the biggest city in Turkey, the population of Istanbul as a
province is 12.5 million, while the population of the urban is 11.1 million according to the last
population census in Turkey. However, being the last regarding the population size, the
population of Bayburt is 76.6 thousand in the province, while it is 35 thousand in the urban.
Also, the following table shows the data related to distribution of urban sizes in
Turkey. The urban size is measured as the population of the city excluding the population of
the villages.
< Insert Table 1 here>
As can be seen from the table, 64% of the cities are medium size cities the population of
which lies between 150,000 and 500,000, while 25% of them are small size cities (the
population is less than 100.000) 6. While Istanbul is the biggest city, Bayburt is the smallest
one. In 1990, the proportion of medium size cities is 62, and it is 17 for the small size cities.
Two cities, namely, Ankara and Izmir, have a population between 2-4 million. In the 2000s,
the percentage of medium size cities is 63, whereas it is 11 % for the small cities. In 2000 the
cities that have a population between 1-4 million are, namely, Ankara, Izmir, Adana, Bursa,
Gaziantep and Konya. In 2007, Mersin is added to those cities. On the other hand, the cities
that have a population over 4 million are Istanbul (11,174,257) and Ankara (4,140, 890). In
this year, the smallest population is in Ardahan. In general, one can say that over 60 % of
Turkish cities are the medium size one for the all years. Also, as can be seen from Table 1,
recently, there have been nine metropolitan cities which have over 1 million population, and
Istanbul is the biggest of them.
6
The population of cities indicates urban population.
43. Zipf’s Law
Zipf’s law (Zipf 1949) for cities is one of the empirical facts in the area of economics, or in
the social sciences in general. The significance of this law is that, given very strong empirical
support, it makes up a minimum criterion of acceptability for any model of local growth (see
also Gabaix, 1999). Therefore, Zipf’s law is also one of the well-known approaches to explain
the size distribution of cities for many countries (see Gabaix, 1999 and Soo, 2005 and 2007
for details). Zipf’s Law, also known as "Rank-Size Rule", is based on the Pareto distribution
which was firstly suggested by Auerbach (1913). He introduced Pareto distribution as
follows:
Ri AS i (1)
where R is the rank of the cities ordered as from the largest to the smallest, S is population of
city i, A and are constants. is also called as Pareto exponent. Zipf (1949) indicated that
the size distribution of cities not only has Pareto distribution but also (Pareto exponent)
should equal to 1 and A is the expected population of the largest city (Soo, 2005). If the Pareto
exponent equal to 1, Zipf’s law holds that means the cities show a parallel growth. (Eaton and
Eckstein, 1997). On the other hand, if the value of Pareto exponent is larger than 1, the
population of cities in the urban system is more even and if the value of Pareto exponent is
smaller than 1 the distribution of city size is more uneven (Soo 2005).
Zipf’law in city distribution system indicates that the second largest city is half the
size of the largest, the third largest city a third the size of the largest, and the n th largest city is
the n th of the largest one. Then, the number of cities of population greater than S is
proportional to 1/S P( Size N ) AS and =1 (Gabaix and Ionnides, 2004, Nitsch, 2005).
There is a considerable amount of literature about Zipf’s law. These studies have
attempted to derive the Zipf's law for city-size distributions. (see, for example, Rosen and
Resnick 1980, Glaeser et al. 1995, Eaton and Eckstein 1997, Gabaix 1999, Overman and
Ioannides 2000, Eechout 2004, and Soo 2005 and 2007, Gabaix and Ibragimov 2008, Giesen
and Suedekum 2009, Ioannides and Skouras 2009).
5Glaeser et al. (1995) carried out their studies for the 135 cities in USA, Eaton and
Eckstein (1997) conducted a study for the 39 cities for the period 1876 and 1990 for France
and 40 cities for Japan for the time period 1925 and 1985 regarding city size distribution.
Both studies indicate that the distribution of the cities is stable and Zipf’s law holds. Also,
Gabaix (1999) theoretically proves that city-size distribution converges to a power law if all
cities follow some proportional growth process. Soo (2007) carried out a research to test
Zipf’s law for the Malaysian cities. The author concludes that there is some evidence that
Zipf’s law holds for Malaysia. Giesen and Suedekum (2009) found that Pareto exponent
ranges from 0.87 to 1.22 in the national and regional level in Germany. They indicated that
this range doesn’t show deviation from the Zipf’s Law because the exact Zipf’s Law is only
achieved in limit. Ioannides and Skouras (2009) showed that the estimated exponent for US
urban places is not exactly 1, but it is not far and Zipf’s law provides a good fit. However, like
Krugman (1996) they still believe that the power law of city sizes and its exponent is still
discussable.
As mentioned before, there are only a few studies related to urbanistion growth
regarding Turkey. Turk and Dokmeci (2001), studied distribution of city population in terms
of geographical regions and Turkey as a whole regarding Zipf’s law. They found parallel
growth of city centres for the time period 1980-1997. Gedik (2003) studied the growth of the
cities which have a population of 125.000 and over with regard to urbanisation and
polarisation for the time period 1955-2000. She concludes that small and big cities have
different growth rates. Filiztekin (2006) investigated the city-size distribution and its
evolution in Turkey for the time period 1950-2000 by applying Zipf’s law and Markov
Process. He concludes that the distribution of city-sizes is right skewed and quite stable,
especially after 1960. He also states that the coastal cities tend to grow faster. Marın (2007)
studied Zipf’s Law in the transformation process of Turkish urban system after 1985. He
concluded that Turkish urban system experienced uneven development after 1985 and
deviated from Zipf’s Law because of wrong liberalisation policies.
In order to test the validity of the Zipf’s law, equation 1 can be estimated in the double-
logarithmic form as the following:
ln Ri ln S i u i (2)
where, represents lnA, and u shows the error term.
Zipf regression in equation 2 can be estimated by the ordinary least squares (OLS)
method.
6It has been argued that the OLS estimation of Zipf’s law may cause biased coefficients
in small samples. There are several approaches to correct the bias of Zipf regression such as
Hill (1975) estimator and the rank-minus-half rule. However, Hill estimator is also criticized
since it is also found to be biased in small samples (see Gabaix and Ioannides 2004 for the
details).
The rank-minus-half rule that is proposed by Gabaix and Ibragimov (2008) is as
follows:
ln(Ri 1 / 2) ln(S i ) u i (3)
They also proposed unbiased standard errors (2 / n ) ˆ of Pareto exponent, where n is
the corresponding sample size7.
4. Empirical Results
4.1. Data
The census data for this study obtained from Turkish Statistical Institute (TURKSTAT) for
different years. We used the population data belonging to the 81 cities for the time period
1980-2007. As mentioned before, urban size is measured as the population of the city
excluding the population of the villages in this study. In Turkey, a usual definition of a city is
the administrative center of a province. In this respect, there are 81 provincial centers
including the population of city centers and town centers in Turkey and in this study we use
those centers as the city. In other words, city is an administrative unit regardless of
population size (see also Gedik, 1996).In the regression analysis, to estimate the determinants
of city growth the following variables are used: average growth rate of city size, net migration
rate, the number of hospital bed per 100.000 population (as a health indicator), dummy
variable for location of the city (D=1 if coast, D=0 otherwise), secondary schooling rate,
agglomeration, specialization, competition, fertility, and geographical situation by using panel
data estimation for the year 1980–2007.
7
Nota and Song (2008) used both OLS and the rank-minus-half rule methods to estimate Pareto exponent
for the same sample and they found that the estimated coefficients of cities by using the rank-minus-half rule are
bigger than the estimated coefficients of cities by using the OLS method.
74.2. Zipf’s Law
Our empirical results are based on the OLS estimation of Zipf’s Law and the rank-minus-half
rule introduced by Gabaix and Ibragimov (2008).
We rank the cities according to amount of population from the biggest one for the time
period 1980-2007 and give numbers beginning from 1 for the biggest city (Istanbul=1,
Ankara=2, Izmir=3,………….Bayburt=81 etc.) . The change in the urban size distribution is
followed with the cross-section data for the years 1980, 1985, 1990, 2000 and 2007. Then we
regress log-rank on log-population to estimate equation 2 and given as OLS 1 in the results .
Table 2 presents the parameter estimates related to the cross section regression
analysis.
< Insert Table 2 here>
As can be followed from the table, the value of Pareto exponent changes through the
years. Although it is less than 1, the is not found to be significantly different than 1
indicating that we could not reject Zipf law. We find some evidence that Zipf’s law hold in
Turkey. After 1990 the coefficient has declined, which indicates that the size distribution of
cities becoming less equal over time.
Table 2 further presents the results of some diagnostic tests. According to the diagnostic
test results, the evidence of heteroscedasticity and autocorrelation is found in the residuals.
Therefore we have used Newey-West standard errors in OLS 1 regressions8. In addition, in
order to address the potential endogeneity problem we used instrumental variable (IV)
estimator by taking lagged population of cities as instrument for further analyses. The results
are quite similar to the OLS estimator results and therefore are not presented here.
8
A test for nonlinearity is to include quadratic term to equation (2) as in Black and Henderson (2003). We have
also estimated the equation with quadratic term and fi nd some evidence on the significance of this term.
However, Gabaix and Ioannides (2004) show that the estimated coefficient of this quadratic term will turn out to
be statistically significant in situations where Zipf’s law perfectly holds. Therefore, the results are not presented
here.
8In Table 2 we also present the Gabaix-Ibragimov (2008) estimates using equation (3) or
the rank-minus-half rule and their associated standard errors for all regressions (given as OLS
2)9 .
In OLS 2 the Pareto exponent is closer to 1 compared to OLS 1 estimates. This
indicates that Gabaix-Ibragimov estimates and standard errors reduce the biased estimator and
standard error problem. The coefficients still are not significantly different than 1. Hence we
find stronger support for the Zipf law. The decline of the coefficient after 1990 can also be
followed through corrected coefficients in OLS 2. Hence we find strong evidence on the
validity of Zipf’s law for Turkish cities, however, the cities becoming less equal after 1990.
The ecrease in the value of Pareto exponent after 1990s can well be explained by the
structural change in the Turkish economy as explained in section 2 (see also Marin 2007). .
4.3. Determinants of urban growth
In recent years, there has been some interest to investigate the determinants of urban growth
and population distribution for many countries. For example, Rosen and Resnick (1980)
expressed that Pareto exponent which measures population distribution is positively related
to per capita GNP, total population and railroad density, but negatively related to land area.
Alperovich (1993) showed that Pareto exponent is positively related to per capita GDP,
population density and land area, but negatively related to the government share of GDP, and
share of manufacturing value added in GDP. Soo (2005) tried to explain variations in Pareto
exponents using the economic geography and political variables in regression analysis. He
found that the political variables are more important than the economic geography variables
in determining the size distribution of cities. Anderson and Ge (2004) investigated the
determinants of urban growth in China. The results of their study show that economic
reforms played an important role in accelerating urban growth in China. Also, the results
indicate that the city’s openness to foreign direct investment, industrial structure and human
capital accumulation have a positive impact on urban growth.
9
We have also used Hill approach to estimate the Pareto exponent but we fi nd Hill estimator always smaller
than OLS estimator as in Soo (2007), indicating that Hill estimator is unable to correct for the downward bias
od the OLS estimate. The results are available upon request.
9Da Mata et al. (2007) examined the determinants of Brazilian city growth and found
out that decreases in rural income opportunities, increases in market potential for goods and
labour force quality, and reduction of intercity costs have strong impacts on city growth.
Filiztekin (2006) investigated the evaluation of urban population and concluded that the main
determinants of city growth were being a costal town and having a large market potential.
In order to investigate determinants of urban growth in Turkey, we used in this study
the urban population growth rate as explained variable and, fertility rate, location, net
migration rate, health indicator, schooling rate, agglomeration index, specialisation index, and
competition index as the explanatory variables of urban growth in Turkey10.
In determining the choice variables, we considered the previous theoretical and
applied studies in the literature and availability of the data. In this context, Beeson et al.
(2001) indicated that the location and weather have promoted population growth in U. S.
Henderson and Au (2004) found that the benefit of urban agglomeration, which is real income
per worker, is higher in the largest cities. Also the cities that industrial and commercial
activities are intensive may have a high population growth because of the job opportunities,
mass production, knowledge spillover or positive externalities (Glaeser et al. 1992). These
effects are tried to catch up considering agglomeration, specialisation and competition
structure of the cities in Turkey.
Hence, in order to study the determinants of urban growth in Turkey, we estimate the
following equation:
NAHi 1FERTi 2 LOC i 3 MIGi 4 HEALTH i 5 SCHOOLi
(5)
6 AGGLOMi 7 SPECi 8 COMPi u i
10
The indices are calculated by aıuthors in the study.
10In this model NAH represents the average population growth, FERT is the average
fertility rate, LOC is the dummy variable for location of the city, MIG is the average net
migration rate, HEALTH is the average number of hospital bed per 100.000 population,
SCHOOL denotes average secondary schooling rate in ith city for the for the time period
between 1980-2007. As mentioned before, we also use some indexes, namely, agglomeration
index, specialisation index, and competition index as the explanatory variables in the study. In
line with Glaeser et al. (1992) and Porter (1990) approach, we derive the indexes from the
following equations:
Firtly, we derive agglomeration index (AGGLOM) which shows an increase in
employment in the industry, and thus an increase in the population of city.
CIEcurrent
AGGLOM = ln ( ) (6)
CIEbase
where CIEcurrent shows the city’s industrial employment in current year, while CIEbase shows
city’s industrial employment in the initial year.
Secondly we derive specialisation index (SPEC) which measures how specialized a city is in
an industry relative to industry across the country. As Glaeser et al. (1992) states high
specialization of an industry in a city should speed up growth of that industry and population
in that city.
CIE / CTE
SPEC= ln( ) (7)
TIE / TTE
where CIE shows employment in the city’s industrial sector, CTE represents total
employment in the city, TIE indicates total employment in the Turkish industrial sector, and
TTE is total employment in Turkey.
Thirdly, we competition index (COMP) which whows how competitive cites attract
new firms and thus opportunity of more employment in the city. Regarding theis, if the
competition index has a value greater than one, that means the city is more competitive than
other cities regarding manufacturing industry (see Glaeser et al. 1992).
CMF / CME
COMP =ln( ) (8)
TMF / TME
11where CMF shows the number of firms which employ 10 or more workers in the city’s
manufacturing industry and CME shows the employment in the city’s manufacturing industry
while TMF represents the number of firms in the Turkish Manufacturing industry, and TME
indicates the employment in the Turkish manufacturing industry.
The following table gives the estimation results for the main determinants of urban
growth regarding Turkey.
< Insert Table 3 here>
As can be seen from the table, fertility rate, location, and net migration rate, have a
positive and significant effect as expected, while schooling rate has a negative and significant
effect on average urban growth. The negative effect related to schooling rate may be
indirectly explained with the relationship between education and fertility. As education level
increases the fertility rate decreases (see Miah 1993 and Deliktaş 2001) and as a result urban
growth rate decreases. On the other hand, health indicator has a positive but statistically
insignificant effect on average growth of the city. Moreover, the coefficients of agglomeration
index and competition index are both positive and significant. However, the coefficient of
specialisation index is positive and insignificant. Therefore one may think that agglomeration
and competition have positive effects on growth of cities.
5. Conclusion
The main aim of this paper was to explore the size distribution of cities and the determinants
of urban growth in Turkey. For this purpose, firstly, we used Zipf’s Law and estimated Pareto
exponents using the OLS and the rank-minus half rule for different years for 81 cities in
Turkey. Secondly, we estimated the effects of determinants of urban growth in Turkey. For
the analysis, we constructed the data set belonging to the 81 cities for the time period 1980-
2007.
The results of the study showed some evidence that Zipf’s law hold in Turkey.
However, after 1990 the coefficient has declined, indicating that the size distribution of cities
becoming less equal over time. According to the results based on the rank-minus half rule
12there is stronger evidence on the validity of Zipf law for Turkish cities. However, the size of
cities became less equal after 1990.
According to the regression results fertility rate, location of the city, migration, health
indicator, agglomeration, specialization, competition indices have positive impact, while
schooling has a negative effect on average growth rate of the cities.
As the results show, metropolitan cities in Turkey grow faster relative to the other cities. The
most important reason why some cities grow faster than the others is because these cities offer
more economic advantages compared to the others.
Hence, we can say that the government can play a key role to solve the problems
created by uneven city size distribution. For instance, the government can develop better and
coordinated planning and administration to solve the main problems of the cities. Also, the
government can devote more investment especially in the form of infrastructure services to
the relatively less developed cities.
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Urban Settlements”, 41st Congress of the European Regional Science Association,
Zagreb, Croatia.
www. tuik.gov.tr
Zipf, G. K. (1949) Human Behavior and the Principle of Least Effort, Reading
MA:Addison-Wesley
16Table 1: City size distribution in Turkey City population size 1980* 1985* 1990** 2000 2007 Population > 4 million 1a 1a 1a 1a 2b 2 million - 4 million 1c 1c 2d 2d 1e 1- 2 million 1e 2f 2g 4h 6i 500-1 million 4j 4k 12l 14m 13n 250.000-500.000 16 16 16 23 23 100.000-250.000 27 31 34 28 28
Table 2: The Estimation Results for the Zipf Regression
ln (Rank)
1980 1985 1990 2000 2007
OLS 1 OLS 2 OLS 1 OLS 2 OLS 1 OLS 2 OLS 1 OLS 2 OLS 1 OLS 2
Constant 7.781 7.923 8.415 8.518 8.459
(0.430)*** (0.433)*** (0.417)*** (0.496)*** (0.556)***
ln(Pop) -0.875 -0.930 -0.869 -0.925 -0.917 -0.977 -0.896 -0.954 -0.865 -0.921
(0.084)*** (0.146)*** (0.081)*** 0.145*** (0.075)*** (0.153)*** (0.085)*** (0.150)*** (0.093)*** (0.145)***
AdjR2 0.91 0.90 0.92 0.91 0.93 0.93 0.92 0.91 0.93 0.89
Heteros. 0 0 0 0 0
Autocorr 0 0 0 0 0
RESET 0 0 0 0 0
W 0.14 0.47 0.11 0.52 0.27 0.15 0.23 0.31 0.15 0.54
Notes: * , ** and *** denotes 10%, 5% and 1% significance level. Standard errors are in parenthesis. For OLS 1 regressions Newey-West Standard errors are
reported. W has a Wald statistic under the null hypothesis of =1. Autocorrelation is a Lagrange multiplier test for first order serial correlation. White
heteroscedasticity test has F statistic under the null of homoscedastic error. RESET F is a regression specification test. It tests the null correct specification of
the original model
18Table 3: The Estimation Results for the determinants of urban growth in Turkey
Method: Least Squares (Cross Section)
Dependent Variable: (NAHi), n=81
Explanatory Variables Coefficient t-statistic Probability R2 F-statistic
Constant
0.01562 2.2642 0.0266
FERT
0.60320 5.2679** 0.0000
LOC
0.00516 2.5644** 0.0124
MIG
0.00969 3.4406** 0.0010
HEALTH
0.00005 1.5444 0.1268
SCHOOL
-0.02221 -2.0357** 0.0455
AGGLOM
0.01710 1.7925* 0.0772
SPEC 0.00614 1.4849 0.1419
COMP 0.587 12.787
0.006081 1.7879* 0.0780
*, ** and *** denotes 10%, 5% and 1% significance level respectively.
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