Spatial Inequality Between and Within Urban Areas: The Case of Israeli Cities

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Spatial Inequality Between and WithinUrban Areas: The Case of Israeli CitiesDaniel Shefer a & Malka Antonio aa Center for Urban and Regional Studies, Technion—Israel Instituteof Technology, Haifa, Israel

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Spatial Inequality Between and WithinUrban Areas: The Case of Israeli Cities

DANIEL SHEFER & MALKA ANTONIO

Center for Urban and Regional Studies, Technion—Israel Institute of Technology, Haifa, Israel

(Received April 2012; accepted August 2012)

ABSTRACT Central areas enjoy greater efficiency in the production of goods and services than dooutlying areas. Because of the inherent advantages of central areas over outlying areas, disparitiesamong regions do not vanish over time. On the contrary, centripetal forces increase inequalitiesacross space. The phenomena of increased globalization, trade liberalization and treaties amongcountries not only enable the flow of labour, products (export) and foreign direct investment butalso help reduce spatial inequality between countries. These phenomena also induce greaterspatial economic concentration within a country. Thus, although disparities among countriesdecrease, a widening gap is observed between regions within countries and within large urbanareas. In the empirical part, we analyse the general patterns of spatial inequality found among55 localities in Israel with population size over 20,000. Looking at the spatial inequalityrelationship, both within and between cities in Israel, we show how all economic indicatorsmeasured, including inequality, decrease with distance from the core. Localities in the peripherythat experience greater equality also experience lower average income, lower education, lessself-employment and more unemployment.

1. Introduction

Research in urban economics has long investigated types of externalities that promote the

growth of cities (Glaeser et al., 1992). While firm size and economies of scale play a major

factor in economic success, there is a growing consensus that diverse industrial organiz-

ation and scale economies, that are external to the firm, are critical to the continued

growth of large cities and major metropolitan areas. The new economic geography

(Krugman, 1991a, 1991b) shows that the concentration of diverse economic activities

drives growth, while it also segments the economic landscape into leading and lagging

regions. The concentration of economic growth in leading areas deepens inter- and

intra-regions inequality, making it harder for lagging areas to catch up to economic pros-

perity. Innovation and specialization have increased demand for high-skilled labour and

Correspondence Address: Daniel Shefer, Center for Urban and Regional Studies, Technion—Israel Institute of

Technology, Haifa, Israel. Email: [email protected]

European Planning Studies, 2012, 1–15, iFirst article

ISSN 0965-4313 Print/ISSN 1469-5944 Online/12/000001–15 # 2012 Taylor & Francishttp://dx.doi.org/10.1080/09654313.2012.718198

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have widened wage inequality between people. The spatial concentration of economic

activities increases inequality at the local scale, between skilled and unskilled workers,

and at the regional scale between the core and periphery.

This article analyses the sources for spatial inequalities between and within urban areas

in the central and peripheral regions of Israel. Section 2 reviews the literature on spatial

inequality in urban areas. In this section, we evaluate the relationship between agglomera-

tion, knowledge spillovers, innovation and spatial inequality. The impact of agglomeration

economies, diversity and specialization on urban growth are also discussed. The role of

human capital, social capital and labour skills on innovation and inequality is reviewed

as well. Sections 3 and 4 present data sources and empirical results for the case of

Israel. Finally, in Section 5, we present some concluding remarks for the case of Israel

and for urban areas in advanced economies in general.

2. Spatial Inequality in Urban Areas—Literature Review

In a classic article published in l955, Simon Kuznets hypothesized that poor economies

tend to grow faster than rich economies, thus decreasing disparities among regions.

Kuznets suggests that the relationship between economic growth and inequality follows

an inverted U-shaped curve. In the early development stage, regional income differentials

increase, subsequently stabilize and then, when the economy matures, personal income

inequality among regions diminishes. Indeed, empirical studies in general support this

hypothesis (Barro & Sala-i-Martin, l995, Chap. 11). Convergence is further reinforced

by the phenomena of increased globalization, trade liberalization and treaties among

countries like the EU, GATT and NEFTA that enable the flow of production factors—

labour mobility, products (export) and foreign direct investment. These phenomena also

induce greater spatial economic concentration facilitated by specialization and increasing

returns to scale. Although disparities among countries decrease, a widening gap was

observed between regions within countries. This divergence phenomenon originates

from a greater concentration of economic activity in a few central areas, enabling the cre-

ation of agglomeration economies fuelled by innovation, technological progress and

pecuniary externalities.

Central areas enjoy greater efficiency in the production of goods and services than do

outlying areas. Consequently, economies of agglomeration are the principal force that

exacerbates inequalities among regions in a given country (Duranton, 1999; Kanbur &

Venables, 2005; Venables, 2005; Groot et al., 2011). In China, for example, although

the economy was growing at an astonishing rate in the last decades, a significant differen-

tial annual rate of growth was observed between the booming coastal regions and the

interior, and these gaps are rapidly increasing (Fujita & Hu, 2001; Li & Xu, 2008). Simi-

larly, in countries of the European Union, disparities in per-capita income levels between

countries have narrowed, while regional disparities within countries have widened (Rey &

Janikas, 2005; Geppert & Stephan, 2008; Fan et al., 2009).

In 1991, Krugman (1991a) published his seminal paper, “Increasing Returns and Econ-

omic Geography”, which presented a synthesis of the core–periphery model and the neo-

classical endogenous growth model. In order to reduce the cost of transporting goods and

to benefit from increasing returns to scale, firms and workers are pulled together towards

selected places where agglomeration economies exist.

2 D. Shefer & M. Antonio

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Krugman (1991b) showed how in equilibrium, inequality in per-capita income exists

between regions. He alluded to centripetal and centrifugal forces that shape the economic

landscape. The former, centripetal forces, pull economic activities together to form the

spatial concentration of economic activities in a few selected points in space and in

locations where agglomeration economies are in existence. Agglomeration economies

are spurred by knowledge spillovers and the clustering of economic activities in some

unique and selected points in space (Porter, 1998). Clustering is primarily fed by the

desire to save on transport cost, especially between activities that are linked through

inputs or outputs. The latter, centrifugal forces, such as congestion, land rents and environ-

mental pollution push them apart.

Fujita et al. (1999) showed how trade theory, based on natural endowments, compara-

tive advantages, trade liberalization and globalization, followed by free-trade agreements

induce greater economic concentration. Central areas are locations where most inno-

vations are generated because of the existence of entrepreneurs and the access to relatively

inexpensive venture capital. Because of the inherent advantages of central areas over out-

lying areas, disparities among regions, like inequalities in per-capita income, do not vanish

over time. On the contrary, the centripetal forces exacerbate inequalities across space as is

so vividly demonstrated by empirical studies in China (Fan et al., 2009), Japan (Lopez-

Rodrigues & Nakamura, 2011), Mexico (Rodriguez-Pose & Sanchez-Reaza, 2005), as

well as in Europe (Geppert & Stephan, 2008).

In a seminal article, Glaeser et al. (1992) showed how diversity was found to be the pro-

minent source of growth in cities (see also Quigley, 1998; Duranton & Puga, 2001). On the

other hand, Romer (1987) showed how specialization economies induce economic growth.

Theoretical and empirical studies support the effect of agglomeration economies and clus-

tering of industries on production efficiency (Shefer, 1973; Fujita & Thisse, 2002).

Agglomeration economies play a significant part in the increase in the rate of firm’s pro-

ductivity and innovation potential (Fujita & Thisse, 2002; Glaeser, 2008, 2010). There-

fore, regions characterized by a high level of technological change and innovation will

show a greater acceleration of economic growth.

In a celebrated article: “Contrasts in Agglomeration: New York and Pittsburgh”, Chinitz

(1961) identified two types of agglomeration economies—one which is based on special-

ization like the then steel-based industry in Pittsburgh and the other is based on diversity

like the one found in New York City. The agglomeration economies based on specializ-

ation of Pittsburgh was not sustained over time and Pittsburgh has become a much

more diversified economy. The debate over the merit of these two types of agglomeration

economies and their effect on urban growth and development is an ongoing one in the

urban and regional economic literature (Duranton & Puga, 2000, 2001).

Marshall’s (1890) localization economies refer primarily to external economies (within

an industry) that facilitate labour pooling and labour specialization, while Jacobs’ (1969)

diversity (urbanization economies) are shared by all industries and economic activities

within a concentration (Feldman & Audretsch, 1999). Specialization infers productive

efficiency and economies of scale, monopoly power that maximizes the firm’s ability to

appropriate economic value from innovation (Marshall, 1890; Feldman & Audretsch,

1999). Diversity, on the other hand, refers to the presence of a variety of industries provid-

ing production inputs and consumption goods (Jacobs, 1969; Quigley, 1998).

Jacobs (1969) argues that cities are essentially natural generators of diversity,

that homogeneous use patterns strip the city of the diversity. Diversity creates ample

Spatial Inequality Between and Within Urban Areas 3

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opportunities for social interactions, facilitates exchange and instigates innovation; diver-

sity is the vitality of city life. Florida (2002) more recently showed that diverse environ-

ments are necessary conditions for growth in today’s knowledge-based economy.

Duranton and Puga (2000) point to the fact that diversity and specialization are not

exact opposites. A city with a main industry and a broad base of other industries can be

both diversified and specialized. The coexistence of diversified and specialized cities

appears to result from economic interaction taking place within and between sectors.

Diversity is based on local non-transferable amenities, such as cultural attractions and

education, while it is also comprised of the mobile business environment that creates

employment opportunities and generates growth. To maintain both non-transferable ame-

nities and an innovative business environment, a mix of occupations and skills, that ulti-

mately imply a mix of industries is required, suggesting a balanced diversity needed to

sustain high-growth environments (Shefer & Frenkel, 1998).

Evidence suggests that residential location in urban centres is in response to land-

based amenities which play a significant role (Glaeser, 2011). Shapiro (2006) separated

the share of employment growth that is due to human capital and that which is due to

quality of life. He finds that while human capital has a positive effect on growth, a

significant portion of growth depends on consumer amenities. Florida (2002) contends

that urban diversity, amenities that require low-skilled labour along with high-skilled,

high-productivity labour is necessary to increase productivity and hence wages across

the spectrum.

Spatial disparities are traditionally explained in terms of human capital theory (Becker,

1964). However, increased inequality and segmentation that reduces the flow of infor-

mation and threatens growth can also be explained in terms of social capital theory, par-

ticularly apparent in organizational change that segments high-skilled and low-skilled

workers. Social capital theory identifies community links as predominant factors of

growth, and so while social capital resides in relationships, human capital resides in indi-

viduals (Putnam, 2000). Human capital may not be a strong predictor of growth as it

measures potential productivity rather than actual productivity. Instead, some writers

identify the presence of people engaged in creative occupations, “the creative class” as

a more appropriate indicator of productivity and growth (Marlet & Van Woerkens,

2004; Mellander & Florida, 2011). The creative class, unlike other forms of social

class, is not based on economic status but rather on occupational engagement in creative

processes, such as the arts, knowledge expansion, technological development and inno-

vation; a form of diversity that relies on both individual knowledge as well as relation-

ships. Studies show, for example, that production relocates over the product life-cycle

from diversified to specialized cities and that human capital and knowledge spillovers

are of central importance to endogenous growth theories of regional change (Rauch,

1993; Duranton & Puga, 2001; Simon & Nardinelli, 2002).

The economy’s heavy dependence on this diverse and increasingly mobile “class” has

redefined the way in which regions compete for innovative talent. This class has the power

to make quality of life demands and can easily move to places that offer urban amenities

and desirable environments. Not only are the creative class able to choose location, Florida

(2002) finds that quality of place is of high priority among this (not yet cohesive) popu-

lation group. This is consistent with Shapiro’s (2006) findings indicating that quality of

life demands, or urban amenities, are stronger motivators of residential choice than

higher incomes. Disparities between cities/regions can be partly attributed to localities’


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inability to accommodate shifting economic trends, while increasing income inequality is

due to widening wage differentials.

Widening wage differentials can harm growth by creating social, political and economic

instability, low investment in education, reduced consumption and inefficient barriers to

credit, as well as through more subtle social interactions, such as higher crime rates and

some claim higher rates of self-reported unhappiness. Inequality may, on the other

hand, increase growth by instigating greater savings and investment, promoting incentives

for risk taking, entrepreneurial behaviours that promise the possibility of “jackpot prizes”

(Aghion et al., 1999). Some researchers maintain that the positive effects of inequality

override the negative effects in rich countries and increase growth as a result, while in

poor countries, inequality decreases growth.

Inequality experienced as the result of growth differs by scale—international, regional

and local. Trade in specialized intermediate goods decreases inequality between econom-

ies and increases growth, as competition from the international market improves pro-

ductivity. In a national context, economic concentration, knowledge spillovers,

improved productivity and wages increase inequality between cities and regions (Fujita

& Hu, 2001), while at the local scale, agglomeration economies expand opportunities

for both high-skilled and low-skilled individuals, also increasing local inequality.

Spatial inequality, most frequently is measured by disparities in wage/income between

cities/regions or within cities/regions. In recent years, a number of such studies were pub-

lished in the urban, regional and geographical economics literature. Most of these studies

attribute the level of wage/income to the local or regional level of human capital/skills,

which was found to be a major factor that drives economic growth.

Wages in large urban areas were found to be high because of the productivity of labour

enhanced by human capital/skills, knowledge spillovers and agglomeration economies

(Jaffe et al., 1993). Combes et al. (2008) have concluded that a major portion of the

spatial wage disparity is connected to the local/regional skill composition of the labour

pool, which tends to agglomerate in larger and denser labour markets (Puga, 1999;

Overman & Puga, 2010). Localization and urbanization economies enable specialization

of labour, which enhances the skill level of workers and promotes knowledge spillovers,

all of which increases the productivity of labour in larger urban areas (Audretsch &

Feldman, 1996; Glaeser, 2008, p. 317; Echeverri-Caroll & Ayala, 2011; Florida et al.,

2012).

The wage spatial envelope resembles the concentration of population and economic

activities in space. Recently, Groot et al. (2011) have shown how in the Netherlands,

the Randstad Urbanized Area—consisting of the Amsterdam–Hague–Rotterdam and

Utrecht Metropolitan Areas—represents the higher level of wage in the country (p. 6).

In a similar study from Japan, Lopez-Rodrigues and Nakamura (2011) have shown how

per-capita income in Japan is downward sloping as one move away from Tokyo (p. 4).

All of these studies corroborate the hypothesis that spatial wage inequality exists and

there are explanations for these disparities. Generally, there is a negative association

between area inequality and average income. However, the declining strength of that

relationship reflects the enormous gains in wealth at the top end of the income distribution

(Glaeser et al., 2008).

Spatial inequality has been identified in cross-national studies showing that increased

inequality in developed countries is due to trade liberalization that extends the market for

intermediate goods (Aghion et al., 1999). This shifts the economy towards skill-intensive


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technologies, resulting in the skill secession of low-skilled labour (Duranton, 1999).

Research in the US shows that cities characterized by high growth also have higher inequal-

ity measures (Florida, 2002).

We hypothesize that inequality increases both within-city (vertical) and between-city

(horizontal). The self-reinforcing cycle of agglomeration economies effectively segments

the landscape into high- and low-growth areas, evident especially in core–periphery div-

isions (Fujita & Hu, 2001; Shefer & Frenkel, forthcoming).

We further hypothesize that the diversity of cities will decrease with distance from the

core and that inequality between the core and periphery would have increased over time.

Two major factors led to the spatial income inequality: distance from the centre of econ-

omic activity and technological change and innovation. Both factors generate imperfect

capital markets that result in differential growth patterns and contribute to persistent

and growing inequality. By examining the role of socio-economic diversity in stimulating

urban growth, we show that geographic concentration, that is, agglomeration economies

and the skill-biased technological change increase spatial inequality both at the local

city scale as well as between areas. However, increased local inequality is beneficial to

growth, due to the positive externalities of diversity, while inequality between regions

is detrimental to growth.

The policy challenge in Israel is to bring lagging regions closer (in economic terms) to

economic density and agglomeration economies, thereby lowering inequality between

cities and exploiting the periphery’s comparative advantage.

In the next section, we present the empirical analysis describing general trends

of spatial inequality found among 55 localities in Israel with population size over

20,000. In Israel, localities with over 20,000 residents are generally categorized as

cities, and therefore provide an appropriate analytical base. Looking at the spatial

inequality relationship, both within and between cities in Israel, we assess inequality

using average income and selected economic indicators and the Gini coefficient as a

measure of inequality (0 ¼ complete equality and 1¼ complete inequality).

3. Data Sources

The evaluation of spatial inequality is based primarily on data published by Israel’s

Central Bureau of Statistics (CBS) in its annual publication “Local Authorities in

Israel” (in Hebrew). The analysis was based on the year 2006, (CBS, 2006; publication

No. 1315), prepared by the Department of Construction and Local Authorities at CBS,

with assistance from the Department of Municipal Research at the Ministry of Interior.

Income and employment data are reported to CBS by the National Insurance Institute

of Israel (Bituach Leumi) that collects income data reported to the Tax Authority by all

employers and self-employed. The National Insurance Institute publishes data on

income, wages, employment, self-employment, number of employees and inequality,

among other variables, in its annual publication “Average Wage, Income, and Other Econ-

omic Variables by Local Authorities” (in Hebrew). Data for 1995 and 1999 were gathered

directly from the National Insurance Institute of Israel. Data on population were gathered

for each year from Statistical Abstracts of Israel.

Distance data from the core, were calculated by commuting distance to the centre of Tel

Aviv, using the mapping website, www.ymap.co.il. This measure captures real driving

time and as such takes into account road and traffic conditions. Tel Aviv is considered


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www.ymap.co.il

as the business and cultural capital of Israel. The city attracts employees from the entire

metropolitan area, including the outskirt and beyond, who commute to Tel Aviv on a

daily basis. The metropolitan area consists of over 3.3 million inhabitants or about 43%

of Israel’s total population. Geographically, the core region consists of cities that fall

within 50 km of Tel Aviv (based on commuting patterns), and peripheral cities are

located between 50 and 184 km from the core (Beenstock & Felsenstein, 2008). As resi-

dences within the core and periphery generally imply commuting patterns within those

boundaries, such a division captures (primarily) a single producing/consuming market.

4. Results

Using CBS data on 55 localities for 2006, we show that there is a positive correlation

between average monthly income of employees (yearly income divided by 12 months)

and inequality using the Gini coefficient. That is, cities with higher average income also

have higher inequality. Therefore, it can be concluded that higher income localities tend

to be less equal (representing a wide gap between income groups).

4.1 General Results

In Israel, the growing spatial inequality is commonly attributed to global changes in tech-

nology, globalization and trade liberalization, mass migration from the former Soviet

Union, increased number of foreign workers and increased weight of the poor population.

It is unclear, however, which of these factors is causing sustained and growing inequality

over time. While inequality has increased in developed economies since the 1970s, Israel’s

Gini coefficient (2006) of 0.4771 is among the highest in developed countries.

Assessing the relationship between several economic indicators using simple linear

regression analysis, we show that average income per worker is strongly and positively

correlated with both the rate of education (measured by the rate of students earning

high-standard high-school degrees which allow them to enter university) and the rate of

self-employment (a proxy of entrepreneurships in each locality). The positive relationship

between education and income highlights the dependence of income on skills; localities

that achieve higher levels of education, also experience higher average incomes per

worker. Much empirical work has confirmed the positive relationship between income

and human capital, which is closely related to the increased labour productivity

(Aghion et al., 1999). More surprising, however, is the positive relationship between

income and entrepreneurship (measured by self-employment) or increasing returns to

entrepreneurship. Such a relationship appears to indicate higher accessibility, lower bar-

riers to entry and favourable conditions for entrepreneurship where incomes are higher.

In fact, all the selected economic indicators (excluding unemployment compensation)

decrease as distance from the core increases, including average income, rate of self-

employment, level of education and net population growth. The rate of unemployment

compensation increases with distance from the core.

4.2 Analysis of Selected Economic Variables

Close evaluation of the relationship between selected economic variables is important in

order to isolate factors that influence the spatial inequality among cities in Israel. Primary


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economic indicators in cities include average income (returns to productivity), human

capital (education) and entrepreneurship (self-employment), as an important factor of

innovation. Human capital theory claims that income disparities can be explained in

terms of educational attainment. The correlation between average income and education

is positive and highly significant (Figure 1)—local average income increases as the

level of human capital increases. Skills are in fact an important determinant of income.

The correlation between average income and the rate of self-employment (entrepreneur-

ship) is also positive and highly correlated (Figure 2), indicating that average income is

higher where a larger share of the population is self-employed.

4.3 Selected Economic Variables and Inequality

In order to examine the relationship between some selected economic indicators and

inequality, we regress the Gini coefficient value calculated for each locality on several

selected economic variables. The correlation between average income and inequality is

positive and highly significant (Figure 3), as is the correlation between education and

inequality (Figure 4); as the rate of education and income increase, local inequality also

increases. Finally, the rate of self-employment and inequality are also highly significant

Figure 1. Average income vs. education.

Figure 2. Average income vs. self-employment.


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and positively correlated (Figure 5), indicating that inequality is higher where entrepre-

neurship is greater.

Our empirical analysis shows that cities with higher average income, education and self-

employment are also less equal; that is, a wide gap exists between income groups within

Figure 3. Average income vs. inequality.

Figure 4. Education vs. inequality.

Figure 5. Rate of self-employment vs. inequality.


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localities. This income gap is possibly due to the presence of a greater variety of economic

activities (diversity) which may, in turn, create more opportunities as well as income

inequality. Income inequality can reflect a diversity of economic activities, while it may

also be interpreted as economic disparity.

Growth processes differ based on the city size and location due to a locality’s charac-

teristics and distinct functional objectives—large major cities versus smaller specialized

cities. In Israel, we find that the best distinction can be made between cities located in

the core versus those located in the periphery. Limiting the analysis to cities with popu-

lation size over 20,000 controls for the size effect as it captures only localities that are con-

sidered major cities (relative to country size). In addition, arbitrary municipal divisions in

cities in the centre especially means that any size division is irrelevant as distances are

small and cities in the centre function as one unit in terms of consumption and production,

as reflected also in commuting distance that defines the metropolitan area.

4.4 Spatial Inequality

The distribution of spatial inequality is evaluated both in terms of distance of individual

cites from the core as well as the difference between the groups of cities located in the

core versus those located in the periphery. All distance regressions show a division at

50 km from Tel Aviv, the farthest commuting distance of cities included in the metropo-

litan area, as defined by the 1995 census. Cities within 50 km are “core” and cities that are

farther than 50 km and up to 184 km are “periphery”. Looking at the distribution of

average income over space, we find that average income is negatively correlated with dis-

tance from the core; average income tends to fall as cities are located farther away from the

core (Figure 6). We do not adjust for cost of living because the purpose is to measure

earning potential across space—returns to productivity. Differences in real wages may

be due to amenities that are part of the benefits (increasing social returns) of agglomeration

economies. The regression analysis further shows that localities with average income

greater than 6000 New Israeli Shekel (NIS; just above the national average in 2006)

tend to be clustered around metropolitan Tel Aviv. Chi-square test of income below

and above 6000 NIS in relation to distance from the core shows a significant difference

in income between the groups of cities at the core versus those located in the periphery

Figure 6. Average income vs. distance from Tel Aviv.


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(Table 1). Clearly, there is income inequality between the core and periphery. This differ-

ence is possibly due to agglomeration economies in the centre that foster specialization in

production, input sharing and better employment matching that increases economic

activity and yields higher returns (Quigley, 1998).

Human capital, measured by the percent of students earning high-standard high school

degrees is negatively and significantly correlated with distance from the centre (as is

income, see Figure 7), and a chi-square test for statistical significance of the difference

between distance and education shows that differences are significant at 0.001 between

the core and periphery. The rate of education is in fact higher in the core. This

measure, however, points to the quality of education and not to the agglomeration of edu-

cated people in urban centres. Using data from the labour force survey of 2008, we regress

the number of years of education, using 16+ years of schooling on distance from the

centre and find that the regression is negative and significant at 0.01. The concentration

of educated people decreases with distance from the centre.

Regression analysis shows that the rate of self-employment is negatively and signifi-

cantly correlated with distance from the core (Figure 8), indicating higher levels of entre-

preneurship in the centre. The clear separation in the data at 50 km from the centre (from

which point the relationship appears to be insignificant) indicates that proximity to the

centre or activity within the metropolitan area plays a significant role in the number of

entrepreneurs.

Correlation between the rate of unemployment compensation and distance is positive

and significant (Figure 9), indicating increased economic stagnation in cities that are

located farther from the centre.

Table 1. Core-periphery by monthly income—chi-square

Income . 6000 NISa Income , 6000 NISa Total x2

Core 0–50 km 17 (11.57) 8 (13.43) 25Periphery 51–184 km 8 (13.43) 21 (15.57) 29Total 25 29 54 0.01

aNIS 3.5 ¼ $1.0.

Figure 7. Education vs. distance from Tel Aviv.


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Looking at the distribution of inequality across space, we find that as localities are

located away from the core, inequality decreases (Figure 10). Overall, localities in the per-

iphery experience less inequality but also lower average income, lower education, a lower

rate of self-employment and higher rate of unemployment. In the core, the trend is the

opposite; education, income, entrepreneurship and inequality all increase.

Figure 8. Self-employment vs. distance from Tel Aviv.

Figure 9. Unemployment rate vs. distance from Tel Aviv.

Figure 10. Inequality vs. distance from Tel Aviv.


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In addition to average income, a measure of standard of living for each locality is cap-

tured by the socio-economic rank that is composed of a number of factors, primarily econ-

omic, demographic and educational. According to Israel’s CBS, economic monetary

considerations are central but are not the only factors that contribute to the rank

measure; other variables that are only partly associated with economic conditions

include factors that represent future economic potential, such as education. Central com-

ponents that make up the economic rank of localities include: source of income (work,

welfare benefits, other), level of motorization, education, character of employment/unem-

ployment, various types of socio-economic distress and demographics. These variables are

combined into a single quantitative scale by the use of factor analysis, which results in a

common index. Cluster analysis is then used to classify the geographic units into clusters

as homogeneous as possible with respect to the socio-economic index (CBS, 2003).

Socio-economic rank and average income of a locality are highly correlated, but not

perfectly correlated. This result is expected considering that the ranking measure is

made up of a significant number of economic factors. The average income of localities

at the core is significantly higher than that of localities at the periphery. Similarly, the

percent of “strong” socio-economic localities (6–10) at the core is significantly higher

than that of localities at the periphery (Table 2).

5. Conclusions

The empirical analysis shows that 79% of the variation in local average income can be

explained by the rate of education obtained in each locality, by the percent of workers

who are self-employed and by specialization. Education, income, self-employment and

inequality are all negatively correlated with distance from the core, and localities in the

core grew at a faster rate than localities in the periphery in terms of average income.

These findings confirm our hypothesis that growth increases inequality both at the regional

and local scales. However, since the data pertain only to cities over 20,000, the conclusions

are relevant for this group of cities only.

Self-employment, used as a proxy for entrepreneurship, highlights the importance of inno-

vation to growth. Considering factors that explain self-employment, we find that education

and location in the core are positively correlated with self-employment. Together, these three

factors explain 64% of the variation in self-employment. Population growth is also lower in

the periphery, with many locations experiencing negative population growth. Negative

population growth is due specifically to out-migration, often of highly skilled individuals.

Agglomeration economies are strong in the core region and activities that benefit less

from agglomeration economies, such as manufacturing, choose to locate in the periphery.

Table 2. Core-periphery by average income and socio-economic ranking

Average monthlyincome (in NISa)

Number oflocalities

Number of localitiesranking 6–9

Percentranking

Core 0–50 km 7092 25 19 76Periphery 51–184 km 5282 30 8 27

aNIS 3.5 ¼ $1.0.


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All measured economic indicators decrease with distance from the core and inequality is

also negatively correlated with distance from the core. Localities in the periphery that

experience greater equality also experience lower average income, lower education, less

self-employment and more unemployment.

In the core, inequality is high and so are income, education and self-employment. The

negative relationship between income and distance from the core became stronger with

time, as did the positive relationships between income and education, income and self-

employment, and income and inequality. This shows that income is more likely to increase

with education, more likely to increase with self-employment or in localities where con-

ditions are conducive to entrepreneurship, and that inequality is likely to be higher where

incomes are higher. Strengthening of these relationships with time points to a shift in pro-

ductivity that is due to skills and technology where localities in the core have benefited

much more than the periphery.

References

Aghion, P., Caroli, E. & Garcia-Penalosa, C. (1999) Inequality and economic growth: The perspective of the new

growth theories, Journal of Economic Literature, 37(4), pp. 1615–1660.

Audretsch, D. & Feldman, M. P. (1996) R&D spillovers and the geography of innovation and production,

American Economic Review, 86(3), pp. 630–640.

Barro, R. J. & Sala-I-Matin, X. (1995) Economic Growth (New York: McGraw Hill).

Becker, G. (1964) Human Capital (New York: NBER).

Beenstock, M. & Felsenstein, D. (2008) Regional heterogeneity, conditional convergence and regional inequality,

Regional Studies, 42(4), pp. 475–488.

Central Bureau of Statistics (CBS) (2006). Available at www.cbs.gov.il (accessed 2010).

Central Bureau of Statistics (CBS). (2003) Characterization and classification of local authorities by the socio-

economic level of the population – 2003. Jerusalem. Available at www.cbs.gov.il (accessed 2010).

Chinitz, B. (1961) Contrasts in agglomeration: New York and Pittsburgh, American Economic Review, 51(2),

pp. 279–289.

Combes, P.-P., Duranton, G. & Gobillon, L. (2008) Spatial wage disparities: Sorting matters? Journal of Urban

Economics, 63(2), pp. 723–742.

Duranton, G. (1999) Trade, wage inequalities and disparities between countries: The technology connection,

Growth and Change, 30(4), pp. 455–478.

Duranton, G. & Puga, D. (2000) Diversity and specialisation in cities: Why, where and when does it matter?

Urban Studies, 37(3), pp. 533–555.

Duranton, G. & Puga, D. (2001) Nursery cities: Urban diversity, process innovation, and the life cycle of pro-

ducts, American Economic Review, 91(5), pp. 1454–1477.

Echeverri-Caroll, E. & Ayala, S. (2011) Urban wages: Does city size matter? Urban Studies, 48(2), pp. 253–271.

Fan, S., Kanbur, R. & Zhang, X. (2009) Regional Inequality in China: Trends, Explanations, and Policy

Responses (London: Routledge).

Feldman, M. & Audretsch, D. (1999) Innovation in cities: Science-based diversity, specialization and localized

competition, European Economic Review, 43(2), pp. 409–429.

Florida, R. (2002) The Rise of the Creative Class (New York: Basic Books).

Florida, R., Mellander, C., Stolarick, K. & Ross, A. (2012) Cities, skills and wages, Journal of Economic

Geography, 12(2), pp. 355–377.

Fujita, M. & Hu, D. (2001) Regional disparity in China 1985–1994: The effects of globalization and economic

liberalization, Annals of Regional Science, 35(1), pp. 3–37.

Fujita, M. & Thisse, J.-F. (2002) Economics of Agglomeration, Cities, Industrial Location, and Regional Growth

(Cambridge: Cambridge University Press).

Fujita, M., Krugman, P. & Venables, A. (1999) The Spatial Economy: Cities, Regions, and International Trade,

(Cambridge, MA: MIT Press).

Geppert, K. & Stephan, A. (2008) Regional disparities in the European Union: Convergence and agglomeration,

Papers in Regional Science, 87(2), pp. 93–217.


Dow

nloa

ded

by [

Tec

hnio

n -

Isra

el I

nst o

f T

ech]

at 1

8:36

21

Sept

embe

r 20

12

www.cbs.gov.il

www.cbs.gov.il

Glaeser, E. (2008) Cities, Agglomeration, and Spatial Equilibrium (Oxford: Oxford University Press).

Glaeser, E. (2010) Agglomeration Economics (Chicago, IL: University of Chicago Press).

Glaeser, E. (2011) Cities, productivity, and quality of life, Science, 333(6042), pp. 592–594.

Glaeser, E., Hedi, D., Kallal, H. D., Scheinkman, J. A. & Shleifer, A. (1992) Growth in cities, Journal of Political

Economy, 100(6), pp. 1126–1152.

Glaeser, E., Resseger, M. & Tobio, K. (2008) Urban inequality. Harvard and NBER working paper no 14419,

Cambridge, MA.

Groot, S., Groot, H. & Smit, M., (2011) Regional wage differences in the Netherlands: Micro-evidence on

agglomeration externalities. Discussion paper TI 2011-050/3, Tinbergen Institute, Amsterdam.

Jacobs, J. (1969) The Economy of Cities (New York: Random House).

Jaffe, A. B., Trajtenberg, M. & Henderson, R. (1993) Geographic localization of knowledge spillovers as

evidenced by patent citation, Quarterly Journal of Economics, 108(3), pp. 577–598.

Kanbur, R. & Venables, A. (2005) Spatial Inequality and Development (Oxford: Oxford University Press).

Krugman, P. (1991a) Increasing returns and economic geography, Journal of Political Economy, 91(3),

pp. 483–499.

Krugman, P. (1991b) Geography and Trade (Cambridge, MA: MIT Press).

Kuznets, S. (1955) Economic growth and income inequality, American Economic Review, 45(1), pp. 1–28.

Li, S., and Xu, Z. (2008) The trend of income disparity in the People’s Republic of China. Discussion paper

no. 85, ADB Institute, Tokyo, Japan.

Lopez-Rodrigues, J. & Nakamura, D. (2011) Mind and remoteness! Income across Japanese prefectures. Paper

presented at the RSA Tripartite Workshop, the Resource Centre London, May 2–4.

Marlet, G. & Van Woerkens, C. (2004) Skills and creativity in a cross-section of Dutch cities. Discussion paper

series 04–29, Koopmans Research Institute, Utrecht.

Marshall, A. (1890) Principles of Economics (London: MacMillan).

Mellander, C. & Florida, R. (2011) Creativity, talent, and regional wages in Sweden, Annals of Regional Science,

46(3), pp. 637–660.

Overman, H. G. & Puga, D. (2010) Labour pooling as a source of agglomeration: An empirical investigation, in:

E. Glaeser (Ed.) Agglomeration Economies, pp. 133–150 (Chicago, IL: University of Chicago Press).

Porter, M. (1998) Clusters and the new economics of competition, Harvard Business Review, 76(6), pp. 77–90.

Puga, D. (1999) The rise and fall of regional inequalities, European Economic Review, 43(2), pp. 303–334.

Putnam, D. R. (2000) Bowling Alone (New York: Simon and Schuster).

Quigley, J. M. (1998) Urban diversity and economic growth, Journal of Economic Perspectives, 12(2),

pp. 127–138.

Rauch, J. E. (1993) Productivity gains from geographic concentration of human capital: Evidence from the cities,

Journal of Urban Economics, 34(3), pp. 380–400.

Rey, S. & Janikas, M. (2005) Regional convergence, inequality, and space, Journal of Economic Geography,

5(2), pp. 155–176.

Rodriguez-Pose, A. & Sanchez-Reaza, J. (2005) Economic polarization through trade: Trade liberalization and

regional growth in Mexico, in: R. Kanbur & A. J. Venables (Eds), Spatial Inequality and Development,

pp. 237–259 (Oxford: Oxford University Press).

Romer, P. M. (1987) Growth based on increasing returns due to specialization, American Economic Review,

77(2), pp. 56–62.

Shapiro, J. (2006) Smart cities: Quality of life, productivity, and the growth effects of human capital, The Review

of Economics and Statistics, 88(2), pp. 324–335.

Shefer, D. (1973) Localization economies in SMSA’s: A production function analysis, Journal of Regional

Science, 13(1), pp. 55–64.

Shefer, D. & Frenkel, A. (1998) Local milieu and innovations: Some empirical results, The Annals of Regional

Science, 32(1), pp. 185–200.

Shefer, D. & Frenkel, A. (Forthcoming) The center-periphery dilemma and the issue of equity in regional devel-

opment. A chapter in a forthcoming reader: Planning as if people mattered, in: N. Carmon & S. Feinstein

(Eds), Chap. 9 (University of Pennsylvania Press).

Simon, C. J. & Nardinelli, C. (2012) Human capital and the rise of American cities, Regional Science and Urban

Economics, 32(1), pp. 59–96.

Venables, A. (2005) Spatial disparities in developing countries: Cities, regions, and international trade, Journal of

Economic Geography, 5(1), pp. 3–22.


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Spatial Inequality Between and Within Urban Areas: The Case of Israeli Cities

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