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Socio-Economic Planning Sciences 34 (2000) 239±269
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Investigating the eects of public policy on the
interregional patterns of population growth: the case of
Israel
B.A. Portnov*, Y. Etzion
Center for Desert Architecture and Urban Planning, J. Blaustein Institute for Desert Research, Ben-Gurion University
of the Negev, Sede-Boker Campus, 84990, Israel
Abstract
The eect of a regional policy can be determined by comparing the actual disparity in population
between core and periphery regions to the disparity that would have been achieved in the absence of
policy. To test this thesis, the policy of population dispersal (PPD) in Israel is considered. The analysis
indicates that although this policy, aimed at achieving a more even distribution of the country's
population, generally failed to reduce the population imbalance between the core and periphery, it
appears to have prevented the population gap from becoming even wider. Based on this conclusion, a
counter-balancing approach to improving the future performance of this and similar regional policies is
proposed. This approach assumes that location disadvantages of peripheral areas (a lack of urban
development, inferior infrastructures, etc.) should be counter-balanced rather than compensated. Such
counter-balancing development strategies may include the formation of dense urban clusters, in which
individual urban settlements share essential socio-economic functions, and the redirecting of
development priorities on a step-by-step basis. 7 2000 Elsevier Science Ltd. All rights reserved.
1. Introduction
The policy of population dispersal (PPD) in Israel is an example of a broad variety of
regional policies aimed at redirecting population growth and economic development from
overpopulated core regions to underdeveloped peripheral areas. Similar development policies
* Corresponding author. Tel.: +972-7-659-6875; fax: +972-7-659-6881.
E-mail address: [email protected] (B.A. Portnov).
0038-0121/00/$ - see front matter 7 2000 Elsevier Science Ltd. All rights reserved.
PII: S 0 0 3 8 - 0 1 2 1 ( 0 0 ) 0 0 0 0 2 - 1
240
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
are found in Europe (Sweden, Norway, United Kingdom and Greece), Asia (Japan and South
Korea) and other countries elsewhere in the world [e.g., 1±8].
The main objective of this policy Ð settling the underpopulated areas of Israel through
population dispersal Ð was announced for the ®rst time in 1949 in response to the
predominant concentration of the country's population in two metropolitan areas Ð Tel Aviv
and Haifa [9]. To achieve this goal, population growth of the country's periphery in the 1950s±
1960s was sustained primarily by directing new immigrants to so called ``priority development
zones'' (Fig. 1). Since the early 1970s, this approach was gradually replaced by various
economic incentives. These incentives are of four basic types (Table 1):
. planning and development (public housing construction, infrastructure provision);
. ®nancial incentives to private investors (investment grants, tax exemption and loan
guarantees);
. allocation of public land (long-term land leases and price reduction for publicly-owned
land);
. housing and location aid (low-interest housing loans, housing subsidies, etc.).
Although 50 years have passed since the policy in question was announced for the ®rst time, its
actual eect on the interregional distribution of the country's population has not yet been
suciently studied and understood. Whilst numerous studies were carried out to trace the
changes in the population balance between Israel's core and peripheral districts [6,9,15±18], the
question of whether these changes can be attributed to the policy itself, rather than to other
exogenous and endogenous factors, remains unanswered.
The present paper attempts to investigate the inter-link between interregional population
change in Israel and the eect of the PPD. Three main questions are posited for discussion: (1)
What changes have occurred in the population balance between the core and peripheral regions
of the country over the past decades? (2) To what extent can these changes be attributed to the
government PPD? (3) Which policy measures and strategies can improve the future
performance of PPD and similar development policies?
2. Regional policy evaluation: general trends
Three groups of approaches to regional policy evaluation can be singled out [4,19,20]:
. Indirect methodologies that use simple statistical techniques to identify relationships between
the intensity of regional policy and movements in particular indicators [1,21±23].
. Partial methodologies that attempt to isolate the speci®c eects of policies on changes in
selected evaluation indicators [18,24±26].
. Cost-bene®t methodologies that measure and compare the whole range of bene®ts and costs
of policy intervention on welfare or national eciency grounds [7].
Alternatively, methods of policy evaluation can be grouped according to the applied techniques
involved [27]:
1. Statistical techniques (evaluation research).
2. Optimization techniques (cost-bene®t analysis, linear and nonlinear programming).
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
Fig. 1. Maps of priority development zones (PDZ) in Israel: (a) 1968; (b) 1993; (c) 1997. Compiled from [10,11].
241
242
Table 1
The components and goals of the PPD in Israel and its expected eects on interregional population change
Geo-political
Economic and environmental
Ideological
Social
Establishing a national presence and
sovereignty over the territory of the
Negev, which constitutes nearly twothirds of the land area of Israel [12]
Redistributing population from Re-establishing the nation's ties
congested central regions to
with its historic land [9]
sparsely populated areas [2]
Accelerating the process of new
immigrant's integration into the host
society in multi-cultural and socially
diverse new towns in remote areas
[13].
Direct involvement of the state in
construction and development
Financial incentives to private
investors
Allocation of public land for new
development
Housing and location aid
Residential construction; provision of
services and facilities; infrastructure
works and industrial development;
planning
Investment grants; tax holiday
option; loan guaranties;
training grants; reduced
rentals, etc.
Long-term land leases; price
reduction for publicly-owned land;
planning and development of
infrastructure
Low interest housing loans; housing
subsidies; provision of low-rent
public housing; income tax
exemptions; local housing grants
[10,11]
Population size
Rate of growth
Migration attractiveness
Population make-up
Increase in the population size and
density in peripheral districts Ð
reducing population concentration in
the core
Increasing the overall rate of
population growth in the
periphery and reducing that in
the core
Enhancing the attractiveness of
peripheral regions to new
immigrants and in-country
immigrants
Directing the ``weak strata'' of
immigrant population (1950s 1960s)
towards peripheral areas in order to
control them politically [14]
Policy measures
Population targets
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
Policy goals
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
243
3. Decision-making analysis under risk and uncertainty Ð decision theory, games and queues.
4. Simulation and modeling techniques (dierence equations, Markov processes and diusion
processes).
Apart from general evaluation techniques, approaches ®tted for speci®c evaluation tasks can
also be found in the literature.
The quasi-experimental control group method [25,28] is designed for the evaluation of largescale development projects. According to this approach, selected development indicators
between a given geographic area and its controls (a set of places whose economic development
enables measurement of what would have happened in the place under study without policy
intervention) are compared. The authors acknowledge, however, that use of this technique may
be impeded by a lack of suitable controls, speci®cally, if the number of comparable geographic
units is limited.
The potential of meta-analysis for environmental policy evaluation was examined in [29].
This approach is designed to generate additional information from existing empirical studies
using elaborated statistical techniques. While meta-analysis is, undoubtedly, an eective tool
for policy evaluation, its applicability to regional studies is restricted by a need for a large
body of comparable case studies suitable for statistical treatment using meta-regression.
It is to be noted, however, that while the bulk of policy evaluation techniques were primarily
designed for studying changes in various employment and welfare indicators (employment
change, interregional income disparities, etc.), the assessment of the eects of regional policies
on inter-area population change (overall population growth, inter-area migration) has received
relatively little attention. The evaluation methodology discussed in the following section is
focused speci®cally on the latter evaluation task and thus attempts to identify and incorporate
controls in the general evaluation framework.
3. Proposed evaluation approach
The model suggested for evaluation of the eect of a regional policy on interregional
population change is outlined in Fig. 2. This model includes four major components: policy
targets and measures (A), objects of intended impact (B), controls (C) and policy eects (D).
Each of these components is discussed below.
3.1. Policy targets and measures
This category is formed by two aggregated groups of data: (a) indicators of policy targets;
(b) factors related to policy measures. In the case of a regional policy aecting population
change, the former group of data (policy targets) may include the following population indices:
migration balance, population share and the annual rates of population growth in various
geographic areas. At the same time, the latter group of data (policy measures) may include the
following quantitative indices: funds allocated for training grants, public investments in
infrastructure development, the annual rates of public construction, etc.
244
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
3.2. Objects of intended impact
Spatial targets of regional policies are not always clearly delimited. These may include
speci®c settlements or lagging geographic areas. In the case of the PPD in Israel, the spatial
targets are somewhat less clearly de®ned. Since this policy explicitly refers to overpopulated
and underpopulated areas, its spatial targets can be expressed in terms of a ``core-periphery''
paradigm [5,30]. To demarcate these areas, the population density criterion can be used [31,32].
3.3. Controls
A simpli®ed model is suggested to describe the major exogenous factors aecting population
growth in various geographic areas (Fig. 3). According to this model, the factors in question
fall into two functional groups Ð population and economy.
Variations in the population composition across various geographic areas have a substantial
eect on the patterns of interregional population growth. Dierences in fertility and mortality
rates between regions (natural growth) may substantially in¯uence the overall patterns of areas'
population increase. These disparities are often due to dierences in the ethnic makeup of the
local populations [23,33]. In a number of regional studies [21,34,35], it is also argued that a
mass in¯ux of immigrants to a country may trigger a chain of long-term population exchanges
between the areas.
The economic performance of the country as a whole and, speci®cally that of individual
regions (as expressed by employment, unemployment and housing), is perceived as a key
regulator of interregional population growth due to the following considerations:
. Economic performance of the country as a whole has a substantial eect on interregional
migration. During the years of macro-economic instability (high unemployment, overall
economic slowdown, etc.), in-country migrants tend to leave peripheral areas opting for
Fig. 2. A model for regional policy evaluation.
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
245
more prosperous core regions [23].
. Availability of employment is commonly considered one of the strongest motives for inward
migration to a certain settlement or region [34,36,37]. High unemployment rates, on the
other hand, tend to discourage migrants from settling in a given city or region, while
encouraging local residents to migrate to other locations.
. Availability of housing (speci®cally, aordable public housing) is also traditionally
considered a key factor aecting the rates of inward and outward migrations and of
population growth in general [21,35].
3.4. Policy eects
As suggested, the eect of a regional policy can be expressed as the cumulative dierence
between the actual values of selected measurable indicators (policy targets) and the expected
values of these indicators in the absence of policy [8]. While changes in the actual values of
policy indicators can be traced using available time±series data, the expected values of the
indicators in question can be obtained by representing the rates of population growth in a
particular geographic area as a function of the area's socio-economic and physical
characteristics. In addition to policy measures, such characteristics may include groups of
controls as diagrammed in Fig. 3: macro-economic performance, population, immigration and
local economy. The relative importance of each of these groups of factors may be identi®ed
and measured using regression modeling, as is commonly done in regional studies [e.g.,
4,26,35].
Fig. 3. Factors aecting interegional population growth (controls).
246
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
4. Policy of population dispersal in Israel: objectives and evaluation attempts
4.1. PPD: objectives and geographic framework
Over-concentration of population and economic activity in the core districts of Israel, and a
lack of development in outlying regions are traditionally perceived as signi®cant problems for
national security as well as for maintaining future capacity for immigrant-absorption [38].1
Realizing the extent of these problems, government and planning ocials in Israel have, at
various times, favored implementation of a policy promoting dispersal of the population to
peripheral regions of the country (Fig. 1). The major goals of this policy include (see Table 1):
(1) establishing a national presence and sovereignty over the territory of the Negev, which
constitutes nearly two-thirds of the land area of Israel; (2) redistributing population from
congested central regions to sparsely populated peripheral areas, stemmed from security and
ecological considerations; (3) accelerating the process of new immigrants' integration into the
host society in multi-cultural and socially diverse new towns in remote areas.
Sheer [14] points out another factor, which might also stimulate initial urban development
in the country's periphery. The new immigrants of the 1950s, which constituted a predominant
source of the initial growth of the new settlements, largely came to the country from the
Middle East and North Africa. They did not import capital, were less educated than previous
waves of immigrants, and thus were more dependent on the state for their absorption. The
location of these immigrants in isolated peripheral settlements might thus be perceived as an
attempt to control them politically.
Given the above strategic objectives, urban development in the periphery did not initially
follow an existing transportation network and thus led to the establishment of new settlements
widely scattered across the area at varying distances from each other [23]. Since the early
1970s, this allocation policy was gradually replaced by various governmental incentives
designed to indirectly encourage the growth of the so-called ``priority development zones''
(PDZ).2 Although the spatial frontiers of PDZ zones were subject to numerous changes, they
always included two peripheral areas of the country Ð the Northern and Southern districts
(see Fig. 1).
4.2. Evaluation attempts
Since PPD was introduced for the ®rst time in 1949, a number of attempts have been made
to evaluate its eects on various aspects of the country's regional development.
Drabkin-Darin [9] carried out one of the earliest assessments of this policy. He analyzed
changes in the geographic distribution of the country's Jewish population between 1948 and
1955 using selected statistical data such as the absolute number of residents, population share
1
The majority of urban settlements in Israel is concentrated along its Mediterranean coast and in the Tel Aviv±
Jerusalem ``corridor''. The overall population of these areas (the Tel Aviv, Central, Jerusalem and Haifa districts)
amounts to over 3.5 million residents, or nearly 70% of the country's population [39].
2
In the mid-1980s, the policy of ``direct absorption'' was introduced. According to this policy, new immigrants to
Israel receive a certain mount of money that lasts for a limited period of time (``the basket of absorption'') and can
decide for themselves where they want to settle [21].
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
247
and annual population increase in districts and subdistricts of Israel. Based on this analysis, he
concluded that PPD caused a considerable shift in the country's population distribution,
particularly in the south where the population increased in 1948±55 by some 1130%.
Shefer [17], however, called in question the success of PPD in achieving a more even
distribution of the country's population. In particular, he argued that despite ``the heroic
eects that were unvested to eect the spatial distribution of population in Israel, no major
changes in percentage distribution by district has taken place''. To support this claim, he
argued that although the percentage of those residing in the core Tel Aviv district fell sharply
from 1948 to 1983, the combined percentage of those in Tel Aviv and in the adjacent Central
district decreased only marginally, from 49% in 1948 to 45.4% in 1982.
The results of Gradus and Krakover's [6] analysis of the eect of PPD on the dispersal of
manufacturing and employment across districts and sub-districts of Israel appear to be in
disagreement with Shefer's ®ndings. In particular, they found that PPD caused a considerable
increase in the number of employees in most peripheral areas of the country, which, as they
argued, represented ``a great achievement of governmental policy''.
These contradictory assessments may have two possible explanations. First, as Lipshitz
[16,30] justly points out, analytical studies of spatial concentration and deconcentration of
population in Israel did not appear to dierentiate between two geographic levels: metropolitan
and national. The importance of this distinction, according to Lipshitz, is that population shift
in Israel, similarly to that in other developed countries, does not occur simultaneously, in the
same direction, in all areas. Second, the above evaluation studies made no attempt to separate
the eects of PPD on inter-area population change from that of other interfering factors such
as interregional dierences in the level of natural population growth, annual ¯uctuation of
immigration rates and macro-economic performance of the country as a whole.
5. Research approach
Following the evaluation approach suggested in the section on policy evaluation, our
analysis of PPD was carried out in four phases: de®nition of the samples, analytical
structuring, selection of controls and policy evaluation.
5.1. De®nition of the samples
Six geographic regions of the country (the Jerusalem, Northern, Haifa, Central, Tel Aviv
and Southern administrative districts) were divided into two contrast groups: the ``core'' and
``periphery''.3 The core regions were de®ned as those with the greatest density of population,
while the remainder of the country was termed the periphery (Table 2).
3
The ``core-periphery'' dichotomy is an important concept in social science, which is closely associated with Friedmann [40]. Although similar ideas of interregional divides can be found in early works of G. Myrdal, A LoÈsch, G.
Zipf and others, Firedmann's contribution to this idea is primarily associated with a switch from natural geographic
regions to city-dominated functional areas [5]. Among various criteria identifying spatial limits of core areas, population density is one of the most commonly used (see inter alia [30,32]).
248
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
Table 2
Suggested grouping of districts in Israel (as of 1996)a
District
A. Core districts
Tel Aviv
Central
Jerusalem
B. Peripheral districts
Haifa
Northern
Southern
a
Population (1000)
Land area (km2)
Density of population per km2
1139.7
1257.5
677.2
170.0
1242.0
627.0
6704.4
1012.5
1080.1
758.2
977.9
798.7
854.0
4501.0
14,107.0
887.8
217.3
56.6
Source: Compiled from [39].
5.2. Analytical structuring
The annual rates of population growth in various geographic areas (%) were included in the
analysis as the dependent variable. To control for dierences between fertility and mortality
rates in various regions, the actual rates of population growth in an area were adjusted using
the rate of natural growth for the country as a whole in a given year as the conditional
baseline.
To measure the intensity of the policy under consideration, two quantitative policy measures
were used:
. PUBLIC CONSTRUCTION: the annual rate of public housing construction in the area
initiated by the government, local authorities and companies entirely controlled by these
institutions (thousands of m2);4
. INFRASTRUCTURE: the average annual rate of road construction in a district (km).
As mentioned in the Introduction, public housing construction and infrastructure development
are essential components of PPD. As Tables 3 and 4 show, some, speci®cally peripheral regions
of Israel were always ``positively discriminated'' by public construction as a mean of attracting
potential migrants. At the same time, private construction tended primarily to major
population centers of the country in which the immediate demand was greater and more pro®t
could be expected (for more details, see [41]).
5.3. Selection of controls
To control for other factors presumably in¯uencing the annual rates of population growth in
4
Since the analysis covered the 30-year period of 1968±1997, it was considered whether the actual rates of public
housing construction in more recent years should be de¯ated or, as an alternative, the number of housing units be
used instead. Although the average size of a standard housing unit during the time span in question grew by some
35%, housing standards in the country also increased. It was decided, therefore, that a de¯ation of housing rates
might bring an undesirable bias to the models.
249
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
Table 3
Distribution of population and residential construction by administrative district of Israel in 1985 and 1995 (%)a
1985
District
Population Public
Private
Population Public
Private
construction construction
construction construction
Jerusalem
12.0
Northern
16.6
Haifa
13.7
Central
21.0
Tel Aviv
23.5
Southern
12.0
Judea, Samaria and Gaza Areab
1.2
Total:
100.0
a
b
1995
28.1
7.4
4.3
17.9
4.5
6.7
31.1
100.0
8.6
30.0
11.1
28.2
15.9
5.9
0.3
100.0
11.8
16.9
13.2
21.6
20.3
13.7
2.5
100.0
22.1
3.1
9.9
22.9
2.7
31.9
7.4
100.0
5.6
20.7
9.3
37.6
13.1
10.7
3.1
100.0
Source: Compiled from [39].
Jewish localities.
the core and periphery of the country, our analysis included the following indicators whose
in¯uence was hypothesized in the section on policy controls:
. ECONOMY: annual change in the gross domestic product (per cent) considered as a proxy
for macro-economic performance of the country as a whole;
. UNEMPLOYMENT CHANGE: annual change in the number of unemployed in a district,
per cent;5
. IMMIGRATION: annual number of foreign immigrants to the country (thousands of new
immigrants).
5.4. Policy evaluation
Biannual data for 1948±1996 were used to analyze the general trend of population change
between the core and periphery of the country (see the section on Preliminary results and
discussion). The respective time±series data were drawn from the annual publications of the
Israeli Central Bureau of Statistics [39]. For estimation of PPD's eects on interregional
population growth (see the section of this paper on In¯uencing factors), annual data for 1968±
1997 were employed. This reduction of the time-span covered by the research sample was
determined by restrictions on data availability and comparability.
5
In view of the unavailability of other comparable employment-related statistics, this indicator was considered as
a proxy for changes in the availability of employment in the area. It is to be noted that in Israel, unemployment is
de®ned as the percentage of adult work-seekers in the civilian labor force that are registered in Labor Exchanges
and the Employment Center for Professionals [39].
250
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
Table 4
Per capita rates of public housing and road construction in core and peripheral districts of Israel in 1968±1997a
Core
Periphery
Year
Public housing construction
(m2)
Road construction
(m)
Public housing construction
(m2)
Road construction
(m)
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
0.15
0.16
0.26
0.38
0.47
0.41
0.47
0.49
0.50
0.31
0.23
0.18
0.20
0.23
0.35
0.24
0.13
0.16
0.11
0.08
0.06
0.08
0.06
0.15
0.34
0.21
0.21
0.13
0.30
0.38
0.07
0.05
0.05
0.08
0.07
0.10
0.06
0.07
0.08
0.03
0.02
0.02
0.05
0.02
0.00
0.01
0.02
0.03
0.01
0.02
0.02
0.03
0.02
0.01
0.02
0.04
0.06
0.04
0.04
0.05
0.38
0.33
0.38
0.54
0.72
0.73
0.69
0.77
0.76
0.50
0.61
0.24
0.39
0.42
0.44
0.25
0.14
0.06
0.05
0.06
0.05
0.05
0.07
0.75
1.33
0.55
0.20
0.14
0.30
0.40
0.14
0.25
0.17
0.12
0.09
0.06
0.09
0.09
0.15
0.07
0.06
0.05
0.07
0.06
0.03
0.12
0.09
0.04
0.05
0.04
0.04
0.05
0.08
0.06
0.06
0.08
0.13
0.08
0.07
0.09
a
Source: Compiled from [39].
6. Preliminary results and discussion
The average rates of population growth in various geographic areas of the country
(subdistricts) are represented in Fig. 4, while changes in the population size of the core and
periphery over the past ®ve decades (1948±95) are illustrated in Fig. 5.
As Fig. 4 shows, since the foundation of the state in 1948, the highest rates of population
growth have predominantly occurred in the country's periphery Ð the Be'er Sheva and
Ashqelon subdistricts. In the wake of the 1990±91 mass immigration from the former Soviet
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
251
Union, the population of the Ashqelon subdistrict [see Fig. 4(c)] grew each year by some 8±
10%, i.e. three times as fast as that of the country as a whole. High rates of population
increase were also observed in other peripheral subdistricts ± Zfat, Golan and Yizre'el (see
Fig. 4). At the same time, annual growth in most of the country's core areas was somewhat
less substantial during this period. In the Tel Aviv district, for instance, the population in
1970±75 grew by less than 2%, and less than 1% in 1990±95. We thus ask: does this indicate
that the spatial distribution of the country's population is becoming gradually more even?
As Fig. 5 shows, the answer to this question is rather negative. Indeed, until 1990±95, the
gap in population size between core and peripheral areas of the country tended to increase.
This gap was equal to 170,000 residents in 1948, then increased to 400,000 in 1960; to 500,000
in 1980 and to 700,000 in 1990. Between 1990 and 1995, this gap, however, decreased to some
600,000 residents. This decrease was primarily attributed to the recent patterns of outward
migration from the country's overpopulated core to its periphery where housing is more
available and aordable [21,35].
In general, between 1948 and 1995, the population of the core grew by some 2.5 million
residents, while that of the periphery increased by only 2.0 million people (see Fig. 5). This
trend clearly contradicts the main objectives of PPD, which were intended, from its outset, to
restrict overpopulation of the core by redirecting future growth to the periphery (see Table 1).
Should the fact that the population gap increased between regions lead to the conclusion
that the policy of population dispersal was generally ineective? The answer to this question is
not straightforward. The growing gap in absolute population size between the core and
periphery was accompanied by a steady increase in the proportional share of the periphery in
the overall balance of the national total. In 1948, population of the periphery amounted to
only 40% of the country's population. By 1970, the share of the periphery increased to 42%,
and at the beginning of 1996 it accounted for 45%. However, the proportional population
share of the periphery cannot grow inde®nitely while the gap in absolute population size
between the respective regions also tends to increase. While it is clear, then, that the absolute
population gap has increased while the proportional share has been only slightly moderated, at
this point of the analysis we cannot con®dently say whether the policy in question prevented
the gap in population size between the central core and periphery from becoming even wider.
A regression analysis may help to answer this important question.
7. Modeling procedure
In the section on Research approach, quantitative policy measures (public construction and
infrastructure) and their controls (immigration, economy and unemployment change) were
introduced. In the following analysis, these indicators are considered as explanatory variables.
Regression models were separately computed for the core and peripheral districts of the
country (see Table 2) using the rates of population growth in the core and periphery
(GROWTH1 and GROWTH2, respectively) as the dependent variables. The regression model
used for the analysis is thus as follows:
GROWTHi b0 b1 F1 . . . b5 F5 e,
where: b0, b1, . . . ,,b5 are regression coecients, F1,. . . , F5 are explanatory variables; namely:
252
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
Fig. 4. Aveage annual rates of population growth by subdistrict of Israel in 1948±1995.
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
253
Fig. 5. Changes in the population size of core and peripheral areas of Israel during the period 1948±1995.
economy (F1), unemployment change (F2), immigration (F3), infrastructure (F4) and public
construction (F5); e is a random error term.
As mentioned in the previous discussion, data for the analysis were drawn from the annual
publications of Israeli Central Bureau of Statistics, Statistical Abstract of Israel, and covered
the 30-year period 1968±1997 [39]. This provided us with 30 observations for each of the six
administrative districts of the country (see Fig. 1). (Selected statistical parameters of the
research data are given in Tables A.1 and A.2 in the Appendix)
Two dierent modeling approaches were used:
1. The districts were aggregated into two territorial groups Ð the core and periphery (see
Table 2) Ð for which actual growth rates were calculated. According to this approach, only
time±series data (30 yearly observations) were considered (see Tables 5 and 6).
2. The districts forming the core and periphery were introduced in the regression models
independently. This provided us with 90 observations for each of the aforementioned areas
(3 districts 30 observations, see Tables 5 and 7). This (disaggregated) representation of
core and peripheral areas was essential, since it was assumed that the aggregation of socially
254
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
and economically heterogeneous districts might interfere, at least theoretically, with the
results of the analysis.
Various statistical tests of the data quality Ð multicollinearity, autocollinearity, homogeneity
of variance and the Durbin±Watson test of the independence of regression residuals Ð were
performed (for more details on these tests, see [42]).6
The results of these tests are reported in Tables 6 and 7 and in Tables A.1±A.4 in
theAppendix. In addition, Geary's spatial autocollinearity test was performed where districts
were introduced in the models separately (for more details on this test, see [43]). All tests
provided satisfactory results concerning the quality of data.
It was also assumed that the actual eect of some of the above discussed explanatory
variables is time-lagged, since it is the perception of reality, rather than the actual conditions,
that aects the decision-making process of individuals [34,36]. The values of some of these
variables (public construction, immigration and unemployment change) were, therefore, oneyear lagged in order to re¯ect this process.
8. In¯uencing factors
Three functional forms of regression equation were tested Ð the linear form, semi-log form
(only the explanatory variables were logarithmically transformed) and double-log form (both
the left-hand and right-hand variables were transformed). As Table 5 shows, the linear model
appears to provide the best ®t in most cases (for instance, R 2=0.670, linear ®t vs R 2=0.582,
double-log ®t for core areas).7 Reluctance to use this function form (linear ®t) in the
subsequent analysis was due to a heterogeneity-of-variance consideration. On the other hand,
the logarithmic transformation helps to insure that variances are more homogenous (Tables
A.1 and A.2 in theAppendix).
As Tables 6 and 7 show, nearly all the estimated coecients fall within expected signs and a
number of factors are signi®cant at 0.05 and 0.01 levels. In the following discussion, we shall
refer primarily to the factors that are signi®cant at these levels of probability.
8.1. Peripheral areas
As Table 6 shows, immigration and public construction are the main factors that increase
the rates of population growth in the peripheral districts of the country. The statistical
signi®cance of public construction (t = 2.199; p < 0.05)8 is especially notable. This positive
eect implies that involvement of the state in development of the country's periphery indeed
stimulated population growth of the peripheral areas and thus prevented the gap in population
size between the central core and periphery of the country from becoming even wider.
6
These test if the residuals from regression are independent, against the alternative that the residuals wither correlate or follow a ®rst-order autoregressive process [42].
7
An R 2 comparison is meaningful only if the dependent variable is the same for all models [42]. Therefore, for the
double-log model, the antilog of the predicted values was obtained. Then, R 2 between the antilog of the observed
and predicted values was calculated.
8
t-statistic and the probability of its occurrence by chance, respectively.
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B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
Table 5
Tests of functional forms of the regression modelsa
Core districts
Functional form
Test 1b
Linear
Semi-log
Double-log
Test 2c
Linear
Semi-log
Double-log
R2
Peripheral districts
F
R2
F
0.670
0.571
0.582
7.919
4.518
4.732
0.808
0.849
0.851
16.841
22.448
22.896
0.427
0.550
0.425
12.532
7.104
5.411
0.653
0.598
0.579
28.203
22.295
20.611
a
All F-ratios are signi®cant at a 0.01 con®dence level.
Districts aggregated in territorial entities Ð either core or periphery, respectively.
c
Districts introduced independently.
b
This straight conclusion may, however, raise a number of legitimate questions. It might be
argued, for instance, that public construction simply ``follows'' population growth rather than
causes such growth in the periphery. This assumption does not, however, seem to be justi®ed
due to the following considerations. First, as mentioned in the previous section, nearly all the
explanatory variables, including public construction, were time-lagged in order to reduce the
eect of endogeneity. Second, as mentioned in the section on the research approach, the
distribution of public construction in Israel is heavily aected by political considerations,
rather than by actual demand. Data for 1985 and 1995, represented in Table 3, help to
illustrate this spatial bias. While private construction was concentrated in the Central, Tel Aviv
and Northern districts,9 which are, in fact, the most populated areas of the country, public
construction was skewed toward the Jerusalem and Southern districts, Judea, Samaria and the
Gaza Area. This trend is clearly due to the governmental policy of settling the country's
periphery and other ``strategically sensitive'' areas. In view of these considerations, the
conclusion concerning the causal eect of public construction on population growth in speci®c
geographic areas of Israel seems to be fully justi®ed.
Despite these general considerations, more formal testing of the causality of relationship
between public construction (housing and infrastructure) and the dependent variable
(population growth) is required. To investigate whether public investments in housing and road
construction preceded population growth or simply followed it, the Granger causality test was
performed (see Table 8).10 As Table 8 shows, the eects of both infrastructure and public
housing construction lags on the overall population growth appear to be highly signi®cant (t =
9
For more discussion on the factors governing the distribution of both public and private construction in Israel,
see [41].
10
According to this test, the values of each variable are compared simultaneously with its own lags, and with the
lagged values of all other variables included in the analysis. The analysis of regression estimates then helps to identify the actual direction of the factors' interrelationship (for more details on this test, see [24]).
256
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
Table 6
Factors aecting the annual rate of population growth (%) in the peripheral and core districts of Israel (double-log
form; districts aggregated in territorial entities)
Collinearity statistics
Factor (see text)
B
t
t signi®cance
Tolerance
VIFb
A. Periphery
Economy
Immigration
Unemployment change
Public construction
Infrastructure
Constant
No of observations
R2
F
Durbin±Watson d testa
dL/dV critical ( p = 0.05)
0.044
0.391
0.003
0.106
0.095
ÿ1.723
30
0.851
22.896
2.028
2.93/2.17
0.785
5.456
0.062
2.199
0.935
ÿ3.014
0.442
0.000
0.951
0.040
0.361
0.007
0.588
0.469
0.532
0.608
0.627
1.759
2.134
1.879
1.646
1.595
B. Core
Economy
Immigration
Unemployment change
Public construction
Infrastructure
Constant
No. of observations
R2
iF
Durbin±Watson d test
dL/dV critical ( p = 0.05)
0.049
0.275
0.524
0.032
ÿ0.008
ÿ2.568
30
0.582
4.732
1.254
1.07/1.89
0.597
3.829
2.489
0.277
ÿ0.070
ÿ2.101
0.558
0.001
0.024
0.785
0.945
0.051
0.894
0.736
1.299
0.611
0.544
1.118
1.358
2.489
1.638
1.839
a
Durbin±Watson autocollinearity test.
Variance in¯ation factor; B = unstandardized regression coecient; t=t-statistic; t signi®cance=actual signi®cance level of t-statistic; F=F-ratio; dL/dV=critical intervals of Durbin±Watson (d ) statistic.
b
2.950 and 2.599, respectively; p < 0.01). At the same time, the eect of population growth lags
is highly signi®cant in the case of public construction (t = 4.525; p < 0.01) and statistically
insigni®cant in the case of infrastructure (t = 0.945; p > 0.3). This implies that public
investments in infrastructure appeared to cause population growth across various regions,
while the relationship between population growth and housing construction is rather bidirectional. Population growth both followed the patterns of public housing construction and
caused more public construction in various regions (with the latter link somewhat more
signi®cant).
8.2. Core areas
Two additional observations concerning results of the regression analysis deserve comment.
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B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
Table 7
Factors aecting the annual rate of population growth (%) in the peripheral and core districts of Israel (double-log
form; districts introduced independently)
Collinearity statistics
Factor (see text)
B
t
t signi®cance
Tolerance
VIFb
A. Peripheral districts
Economy
Immigration
Unemployment change
Public construction
Infrastructure
Constant
No. of observations
R2
F
Durbin±Watson d testa
dL/dV critical ( p = 0.05)
0.120
0.431
0.115
0.118
0.078
ÿ2.188
90
0.579
20.611
1.891
1.542/1.776
2.602
5.928
2.181
2.423
1.717
ÿ5.990
0.011
0.000
0.032
0.018
0.090
0.000
0.700
0.677
0.718
0.717
0.832
1.428
1.476
1.393
1.396
1.202
B. Core districts:
Economy
Immigration
Unemployment change
Public construction
Infrastructure
Constant
No. of observations
R2
F
Durbin±Watson d testa
dL/dV critical ( p = 0.05)
0.072
0.211
0.044
0.201
0.093
ÿ1.469
90
0.425
5.411
1.628
1.542/1.776
1.256
2.959
0.490
2.788
1.462
ÿ2.423
0.213
0.004
0.626
0.007
0.147
0.018
0.811
0.905
0.803
0.850
0.816
1.233
1.105
1.245
1.177
1.226
a
Durbin±Watson autocollinearity test.
Variance in¯ation factor; B = unstandardized regression coecient; t=t-statistic; t signi®cance=actual signi®cance level of t-statistic; F=F-ratio; dL/dV=critical intervals of Durbin±Watson (d ) statistic.
b
As Table 6 shows, in the case of the core areas, both immigration and unemployment change
have statistically signi®cant eects on the rate of population growth (t = 3.829 and t = 2.489,
respectively; p < 0.05). The positive sign of the latter variable (unemployment change) is, at
®rst glance, surprising. This result might, however, have two possible explanations. First, it
may indicate that in years of rising unemployment, the population of the country moves to
more prosperous core regions, away from the periphery. This phenomenon tends to be more
aected by negative changes in the national economy as a whole [23], and is in line with the
fact that, in the peripheral Southern district of Israel (see Table 9), unemployment change
appears to have a negative, although not highly statistically signi®cant (t = 1.241; p > 0.2),
eect on the rates of population growth. Alternatively, the above phenomenon can be related
to the relationship between employment growth and unemployment change. These two
variables are often positively correlated because growing places often have higher
258
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
unemployment rates. The negative sign of the unemployment variable may thus be a response
to the omission of the employment change variable that was not included in the analysis due to
the unavailability of comparable data.
8.3. Heterogeneity of core and peripheral areas
Although the ``core-periphery'' paradigm is a dichotomous concept, neither core nor
peripheral regions of Israel can be considered a homogenous entity. This conclusion is
validated by two separate tests: (a) the simultaneous introduction of cross-section and timeseries data (see Table 7), (b) separate examination of the factors aecting population growth in
peripheral districts (Tables 9, 10 and 11).
Although the models obtained by dierent computation procedures Ð aggregation of
districts (Table 6) vs their separate introduction into the models (Table 7) Ð exhibit some
Table 8
Granger's causality testa
Collinearity statistics
Dependent variables/explanatory factors
A. Overall growth
Overall growth (t ÿ 1)
Infrastructure (t ÿ 1)
Public construction (t ÿ 1)
Constant
No. of observations
R2
F
B. Infrastructure
Overall growth (t ÿ 1)
Infrastructure (t ÿ 1)
Public construction (t ÿ 1)
Constant
No. of observations
R2
F
C. Public construction
Overall growth (t ÿ 1)
Infrastructure (t ÿ 1)
Public construction (t ÿ 1)
Constant
No. of observations
R2
F
a
B
t
t signi®cance
Tolerance
VIFb
0.512
0.006
0.001
0.676
180
0.575
70.785
9.250
2.950
2.599
5.475
0.000
0.004
0.010
0.000
0.692
0.818
0.734
1.445
1.222
1.363
1.695
0.542
0.038
5.220
180
0.448
22.744
0.949
7.885
3.345
1.312
0.344
0.000
0.001
0.191
0.692
0.818
0.734
1.445
1.222
1.363
36.750
0.596
0.458
ÿ9.991
180
0.583
73.208
4.525
1.906
8.839
ÿ0.552
0.000
0.058
0.000
0.582
0.692
0.818
0.734
1.445
1.222
1.363
Durbin±Watson autocollinearity test.
Variance in¯ation factor; B = unstandardized regression coecient; t=t-statistic; t signi®cance=actual signi®cance level of t-statistic; F=F-ratio; (t ÿ 1)=one-year lag of the respective variable.
b
259
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
similarities (for instance, the signi®cance of such key factors as public construction and
immigration remained virtually unchanged), substantial changes in more ``weak'' factors are
clearly noticeable. Thus, after disaggregation of the periphery, economy, unemployment change
and infrastructure emerged as statistically signi®cant factors (see Table 7, peripheral districts: t
= 2.602, p < 0.05; t = 2.181, p < 0.05 and t = 1.717, p < 0.1, respectively). The emergence of
the latter factor (infrastructure) is especially important for the following analysis since it is
considered as one of the two major policy-related tools (see the section of this paper on the
research approach). The absence of this factor as statistically signi®cant in the aggregated
models may thus be explained by its interdependency with another policy variable Ð housing
construction. Unsurprisingly, when these variables are integrated into a composite construction
variable (Table 12), the statistical signi®cance of the integrated index exceeds that of both
infrastructure and housing variables introduced separately (Table 7).
Separate regression models computed for the Southern, Northern and Haifa districts (Tables
9, 10 and 11, respectively) also help to illustrate the dierences between the country's
peripheral areas. In the case of the Southern district (see Table 9), immigration and public
construction are highly signi®cant (t = 6.265 and t = 4.236, respectively; p < 0.01). Only
immigration has such a signi®cance level in the Haifa district (Table 11), while none of these
factors is highly signi®cant in the Northern district (Table 10). On the other hand, the eect of
infrastructure on the rate of population growth, which is extremely weak in the Southern
district, is more signi®cant in the case of both the Northern and Haifa districts. This allows us
to clarify our previous conclusion concerning the eect of public construction on the
development of the country's periphery. Although involvement of the state in housing
construction and infrastructure development indeed appeared to have stimulated population
growth of the country's periphery in general, this involvement had dierent eects on the
Table 9
Factors aecting the rate of population growth in the Southern district of Israel (double-log form)
Collinearity statistics
Factor (see text)
B
t
t signi®cance
Tolerance
VIFb
Economy
Immigration
Unemployment change
Public construction
Infrastructure
Constant
No. of observations
R2
F
Durbin±Watson d testa
dL/dV critical ( p = 0.05)
0.079
0.347
ÿ0.067
0.147
0.000
ÿ0.463
30
0.909
40.121
2.101
2.93/2.17
2.073
6.265
ÿ1.241
4.236
0.008
ÿ1.411
0.051
0.000
0.229
0.000
0.994
0.174
0.629
0.497
0.608
0.548
0.731
1.589
2.011
1.644
1.826
1.367
a
Durbin±Watson autocollinearity test.
Variance in¯ation factor; B = unstandardized regression coecient; t=t-statistic; t signi®cance=actual signi®cance level of t-statistic; F=F-ratio; dL/dV=critical intervals of Durbin±Watson (d ) statistic.
b
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B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
patterns of population growth across various peripheral areas. In particular, the eect of public
housing construction appeared to be somewhat more substantial in the earliest least populated
and underdeveloped Southern region, while the eect of infrastructure investment was more
pronounced in the more developed and populated Haifa and Northern districts.
9. Alternative scenarios
The analyses of the previous sections leads naturally to the question of what would have
happened to the population gap between the core and periphery if the latter's population
growth were not arti®cially stimulated? To answer this question, the approach suggested in the
section on the evaluation approach (see Fig. 2) can be used.
Since the average per capita rates of public housing and road construction in peripheral
districts of the country have exceeded those in the core (see Table 4), the above general
question can be reformulated in a more speci®c form. What would have happened to the
population size of the periphery if the per capita rate of public construction in this area had
been reduced to the level of that in the core?
As
www.elsevier.com//locate/dsw
Investigating the eects of public policy on the
interregional patterns of population growth: the case of
Israel
B.A. Portnov*, Y. Etzion
Center for Desert Architecture and Urban Planning, J. Blaustein Institute for Desert Research, Ben-Gurion University
of the Negev, Sede-Boker Campus, 84990, Israel
Abstract
The eect of a regional policy can be determined by comparing the actual disparity in population
between core and periphery regions to the disparity that would have been achieved in the absence of
policy. To test this thesis, the policy of population dispersal (PPD) in Israel is considered. The analysis
indicates that although this policy, aimed at achieving a more even distribution of the country's
population, generally failed to reduce the population imbalance between the core and periphery, it
appears to have prevented the population gap from becoming even wider. Based on this conclusion, a
counter-balancing approach to improving the future performance of this and similar regional policies is
proposed. This approach assumes that location disadvantages of peripheral areas (a lack of urban
development, inferior infrastructures, etc.) should be counter-balanced rather than compensated. Such
counter-balancing development strategies may include the formation of dense urban clusters, in which
individual urban settlements share essential socio-economic functions, and the redirecting of
development priorities on a step-by-step basis. 7 2000 Elsevier Science Ltd. All rights reserved.
1. Introduction
The policy of population dispersal (PPD) in Israel is an example of a broad variety of
regional policies aimed at redirecting population growth and economic development from
overpopulated core regions to underdeveloped peripheral areas. Similar development policies
* Corresponding author. Tel.: +972-7-659-6875; fax: +972-7-659-6881.
E-mail address: [email protected] (B.A. Portnov).
0038-0121/00/$ - see front matter 7 2000 Elsevier Science Ltd. All rights reserved.
PII: S 0 0 3 8 - 0 1 2 1 ( 0 0 ) 0 0 0 0 2 - 1
240
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
are found in Europe (Sweden, Norway, United Kingdom and Greece), Asia (Japan and South
Korea) and other countries elsewhere in the world [e.g., 1±8].
The main objective of this policy Ð settling the underpopulated areas of Israel through
population dispersal Ð was announced for the ®rst time in 1949 in response to the
predominant concentration of the country's population in two metropolitan areas Ð Tel Aviv
and Haifa [9]. To achieve this goal, population growth of the country's periphery in the 1950s±
1960s was sustained primarily by directing new immigrants to so called ``priority development
zones'' (Fig. 1). Since the early 1970s, this approach was gradually replaced by various
economic incentives. These incentives are of four basic types (Table 1):
. planning and development (public housing construction, infrastructure provision);
. ®nancial incentives to private investors (investment grants, tax exemption and loan
guarantees);
. allocation of public land (long-term land leases and price reduction for publicly-owned
land);
. housing and location aid (low-interest housing loans, housing subsidies, etc.).
Although 50 years have passed since the policy in question was announced for the ®rst time, its
actual eect on the interregional distribution of the country's population has not yet been
suciently studied and understood. Whilst numerous studies were carried out to trace the
changes in the population balance between Israel's core and peripheral districts [6,9,15±18], the
question of whether these changes can be attributed to the policy itself, rather than to other
exogenous and endogenous factors, remains unanswered.
The present paper attempts to investigate the inter-link between interregional population
change in Israel and the eect of the PPD. Three main questions are posited for discussion: (1)
What changes have occurred in the population balance between the core and peripheral regions
of the country over the past decades? (2) To what extent can these changes be attributed to the
government PPD? (3) Which policy measures and strategies can improve the future
performance of PPD and similar development policies?
2. Regional policy evaluation: general trends
Three groups of approaches to regional policy evaluation can be singled out [4,19,20]:
. Indirect methodologies that use simple statistical techniques to identify relationships between
the intensity of regional policy and movements in particular indicators [1,21±23].
. Partial methodologies that attempt to isolate the speci®c eects of policies on changes in
selected evaluation indicators [18,24±26].
. Cost-bene®t methodologies that measure and compare the whole range of bene®ts and costs
of policy intervention on welfare or national eciency grounds [7].
Alternatively, methods of policy evaluation can be grouped according to the applied techniques
involved [27]:
1. Statistical techniques (evaluation research).
2. Optimization techniques (cost-bene®t analysis, linear and nonlinear programming).
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
Fig. 1. Maps of priority development zones (PDZ) in Israel: (a) 1968; (b) 1993; (c) 1997. Compiled from [10,11].
241
242
Table 1
The components and goals of the PPD in Israel and its expected eects on interregional population change
Geo-political
Economic and environmental
Ideological
Social
Establishing a national presence and
sovereignty over the territory of the
Negev, which constitutes nearly twothirds of the land area of Israel [12]
Redistributing population from Re-establishing the nation's ties
congested central regions to
with its historic land [9]
sparsely populated areas [2]
Accelerating the process of new
immigrant's integration into the host
society in multi-cultural and socially
diverse new towns in remote areas
[13].
Direct involvement of the state in
construction and development
Financial incentives to private
investors
Allocation of public land for new
development
Housing and location aid
Residential construction; provision of
services and facilities; infrastructure
works and industrial development;
planning
Investment grants; tax holiday
option; loan guaranties;
training grants; reduced
rentals, etc.
Long-term land leases; price
reduction for publicly-owned land;
planning and development of
infrastructure
Low interest housing loans; housing
subsidies; provision of low-rent
public housing; income tax
exemptions; local housing grants
[10,11]
Population size
Rate of growth
Migration attractiveness
Population make-up
Increase in the population size and
density in peripheral districts Ð
reducing population concentration in
the core
Increasing the overall rate of
population growth in the
periphery and reducing that in
the core
Enhancing the attractiveness of
peripheral regions to new
immigrants and in-country
immigrants
Directing the ``weak strata'' of
immigrant population (1950s 1960s)
towards peripheral areas in order to
control them politically [14]
Policy measures
Population targets
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
Policy goals
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
243
3. Decision-making analysis under risk and uncertainty Ð decision theory, games and queues.
4. Simulation and modeling techniques (dierence equations, Markov processes and diusion
processes).
Apart from general evaluation techniques, approaches ®tted for speci®c evaluation tasks can
also be found in the literature.
The quasi-experimental control group method [25,28] is designed for the evaluation of largescale development projects. According to this approach, selected development indicators
between a given geographic area and its controls (a set of places whose economic development
enables measurement of what would have happened in the place under study without policy
intervention) are compared. The authors acknowledge, however, that use of this technique may
be impeded by a lack of suitable controls, speci®cally, if the number of comparable geographic
units is limited.
The potential of meta-analysis for environmental policy evaluation was examined in [29].
This approach is designed to generate additional information from existing empirical studies
using elaborated statistical techniques. While meta-analysis is, undoubtedly, an eective tool
for policy evaluation, its applicability to regional studies is restricted by a need for a large
body of comparable case studies suitable for statistical treatment using meta-regression.
It is to be noted, however, that while the bulk of policy evaluation techniques were primarily
designed for studying changes in various employment and welfare indicators (employment
change, interregional income disparities, etc.), the assessment of the eects of regional policies
on inter-area population change (overall population growth, inter-area migration) has received
relatively little attention. The evaluation methodology discussed in the following section is
focused speci®cally on the latter evaluation task and thus attempts to identify and incorporate
controls in the general evaluation framework.
3. Proposed evaluation approach
The model suggested for evaluation of the eect of a regional policy on interregional
population change is outlined in Fig. 2. This model includes four major components: policy
targets and measures (A), objects of intended impact (B), controls (C) and policy eects (D).
Each of these components is discussed below.
3.1. Policy targets and measures
This category is formed by two aggregated groups of data: (a) indicators of policy targets;
(b) factors related to policy measures. In the case of a regional policy aecting population
change, the former group of data (policy targets) may include the following population indices:
migration balance, population share and the annual rates of population growth in various
geographic areas. At the same time, the latter group of data (policy measures) may include the
following quantitative indices: funds allocated for training grants, public investments in
infrastructure development, the annual rates of public construction, etc.
244
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3.2. Objects of intended impact
Spatial targets of regional policies are not always clearly delimited. These may include
speci®c settlements or lagging geographic areas. In the case of the PPD in Israel, the spatial
targets are somewhat less clearly de®ned. Since this policy explicitly refers to overpopulated
and underpopulated areas, its spatial targets can be expressed in terms of a ``core-periphery''
paradigm [5,30]. To demarcate these areas, the population density criterion can be used [31,32].
3.3. Controls
A simpli®ed model is suggested to describe the major exogenous factors aecting population
growth in various geographic areas (Fig. 3). According to this model, the factors in question
fall into two functional groups Ð population and economy.
Variations in the population composition across various geographic areas have a substantial
eect on the patterns of interregional population growth. Dierences in fertility and mortality
rates between regions (natural growth) may substantially in¯uence the overall patterns of areas'
population increase. These disparities are often due to dierences in the ethnic makeup of the
local populations [23,33]. In a number of regional studies [21,34,35], it is also argued that a
mass in¯ux of immigrants to a country may trigger a chain of long-term population exchanges
between the areas.
The economic performance of the country as a whole and, speci®cally that of individual
regions (as expressed by employment, unemployment and housing), is perceived as a key
regulator of interregional population growth due to the following considerations:
. Economic performance of the country as a whole has a substantial eect on interregional
migration. During the years of macro-economic instability (high unemployment, overall
economic slowdown, etc.), in-country migrants tend to leave peripheral areas opting for
Fig. 2. A model for regional policy evaluation.
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
245
more prosperous core regions [23].
. Availability of employment is commonly considered one of the strongest motives for inward
migration to a certain settlement or region [34,36,37]. High unemployment rates, on the
other hand, tend to discourage migrants from settling in a given city or region, while
encouraging local residents to migrate to other locations.
. Availability of housing (speci®cally, aordable public housing) is also traditionally
considered a key factor aecting the rates of inward and outward migrations and of
population growth in general [21,35].
3.4. Policy eects
As suggested, the eect of a regional policy can be expressed as the cumulative dierence
between the actual values of selected measurable indicators (policy targets) and the expected
values of these indicators in the absence of policy [8]. While changes in the actual values of
policy indicators can be traced using available time±series data, the expected values of the
indicators in question can be obtained by representing the rates of population growth in a
particular geographic area as a function of the area's socio-economic and physical
characteristics. In addition to policy measures, such characteristics may include groups of
controls as diagrammed in Fig. 3: macro-economic performance, population, immigration and
local economy. The relative importance of each of these groups of factors may be identi®ed
and measured using regression modeling, as is commonly done in regional studies [e.g.,
4,26,35].
Fig. 3. Factors aecting interegional population growth (controls).
246
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4. Policy of population dispersal in Israel: objectives and evaluation attempts
4.1. PPD: objectives and geographic framework
Over-concentration of population and economic activity in the core districts of Israel, and a
lack of development in outlying regions are traditionally perceived as signi®cant problems for
national security as well as for maintaining future capacity for immigrant-absorption [38].1
Realizing the extent of these problems, government and planning ocials in Israel have, at
various times, favored implementation of a policy promoting dispersal of the population to
peripheral regions of the country (Fig. 1). The major goals of this policy include (see Table 1):
(1) establishing a national presence and sovereignty over the territory of the Negev, which
constitutes nearly two-thirds of the land area of Israel; (2) redistributing population from
congested central regions to sparsely populated peripheral areas, stemmed from security and
ecological considerations; (3) accelerating the process of new immigrants' integration into the
host society in multi-cultural and socially diverse new towns in remote areas.
Sheer [14] points out another factor, which might also stimulate initial urban development
in the country's periphery. The new immigrants of the 1950s, which constituted a predominant
source of the initial growth of the new settlements, largely came to the country from the
Middle East and North Africa. They did not import capital, were less educated than previous
waves of immigrants, and thus were more dependent on the state for their absorption. The
location of these immigrants in isolated peripheral settlements might thus be perceived as an
attempt to control them politically.
Given the above strategic objectives, urban development in the periphery did not initially
follow an existing transportation network and thus led to the establishment of new settlements
widely scattered across the area at varying distances from each other [23]. Since the early
1970s, this allocation policy was gradually replaced by various governmental incentives
designed to indirectly encourage the growth of the so-called ``priority development zones''
(PDZ).2 Although the spatial frontiers of PDZ zones were subject to numerous changes, they
always included two peripheral areas of the country Ð the Northern and Southern districts
(see Fig. 1).
4.2. Evaluation attempts
Since PPD was introduced for the ®rst time in 1949, a number of attempts have been made
to evaluate its eects on various aspects of the country's regional development.
Drabkin-Darin [9] carried out one of the earliest assessments of this policy. He analyzed
changes in the geographic distribution of the country's Jewish population between 1948 and
1955 using selected statistical data such as the absolute number of residents, population share
1
The majority of urban settlements in Israel is concentrated along its Mediterranean coast and in the Tel Aviv±
Jerusalem ``corridor''. The overall population of these areas (the Tel Aviv, Central, Jerusalem and Haifa districts)
amounts to over 3.5 million residents, or nearly 70% of the country's population [39].
2
In the mid-1980s, the policy of ``direct absorption'' was introduced. According to this policy, new immigrants to
Israel receive a certain mount of money that lasts for a limited period of time (``the basket of absorption'') and can
decide for themselves where they want to settle [21].
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
247
and annual population increase in districts and subdistricts of Israel. Based on this analysis, he
concluded that PPD caused a considerable shift in the country's population distribution,
particularly in the south where the population increased in 1948±55 by some 1130%.
Shefer [17], however, called in question the success of PPD in achieving a more even
distribution of the country's population. In particular, he argued that despite ``the heroic
eects that were unvested to eect the spatial distribution of population in Israel, no major
changes in percentage distribution by district has taken place''. To support this claim, he
argued that although the percentage of those residing in the core Tel Aviv district fell sharply
from 1948 to 1983, the combined percentage of those in Tel Aviv and in the adjacent Central
district decreased only marginally, from 49% in 1948 to 45.4% in 1982.
The results of Gradus and Krakover's [6] analysis of the eect of PPD on the dispersal of
manufacturing and employment across districts and sub-districts of Israel appear to be in
disagreement with Shefer's ®ndings. In particular, they found that PPD caused a considerable
increase in the number of employees in most peripheral areas of the country, which, as they
argued, represented ``a great achievement of governmental policy''.
These contradictory assessments may have two possible explanations. First, as Lipshitz
[16,30] justly points out, analytical studies of spatial concentration and deconcentration of
population in Israel did not appear to dierentiate between two geographic levels: metropolitan
and national. The importance of this distinction, according to Lipshitz, is that population shift
in Israel, similarly to that in other developed countries, does not occur simultaneously, in the
same direction, in all areas. Second, the above evaluation studies made no attempt to separate
the eects of PPD on inter-area population change from that of other interfering factors such
as interregional dierences in the level of natural population growth, annual ¯uctuation of
immigration rates and macro-economic performance of the country as a whole.
5. Research approach
Following the evaluation approach suggested in the section on policy evaluation, our
analysis of PPD was carried out in four phases: de®nition of the samples, analytical
structuring, selection of controls and policy evaluation.
5.1. De®nition of the samples
Six geographic regions of the country (the Jerusalem, Northern, Haifa, Central, Tel Aviv
and Southern administrative districts) were divided into two contrast groups: the ``core'' and
``periphery''.3 The core regions were de®ned as those with the greatest density of population,
while the remainder of the country was termed the periphery (Table 2).
3
The ``core-periphery'' dichotomy is an important concept in social science, which is closely associated with Friedmann [40]. Although similar ideas of interregional divides can be found in early works of G. Myrdal, A LoÈsch, G.
Zipf and others, Firedmann's contribution to this idea is primarily associated with a switch from natural geographic
regions to city-dominated functional areas [5]. Among various criteria identifying spatial limits of core areas, population density is one of the most commonly used (see inter alia [30,32]).
248
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
Table 2
Suggested grouping of districts in Israel (as of 1996)a
District
A. Core districts
Tel Aviv
Central
Jerusalem
B. Peripheral districts
Haifa
Northern
Southern
a
Population (1000)
Land area (km2)
Density of population per km2
1139.7
1257.5
677.2
170.0
1242.0
627.0
6704.4
1012.5
1080.1
758.2
977.9
798.7
854.0
4501.0
14,107.0
887.8
217.3
56.6
Source: Compiled from [39].
5.2. Analytical structuring
The annual rates of population growth in various geographic areas (%) were included in the
analysis as the dependent variable. To control for dierences between fertility and mortality
rates in various regions, the actual rates of population growth in an area were adjusted using
the rate of natural growth for the country as a whole in a given year as the conditional
baseline.
To measure the intensity of the policy under consideration, two quantitative policy measures
were used:
. PUBLIC CONSTRUCTION: the annual rate of public housing construction in the area
initiated by the government, local authorities and companies entirely controlled by these
institutions (thousands of m2);4
. INFRASTRUCTURE: the average annual rate of road construction in a district (km).
As mentioned in the Introduction, public housing construction and infrastructure development
are essential components of PPD. As Tables 3 and 4 show, some, speci®cally peripheral regions
of Israel were always ``positively discriminated'' by public construction as a mean of attracting
potential migrants. At the same time, private construction tended primarily to major
population centers of the country in which the immediate demand was greater and more pro®t
could be expected (for more details, see [41]).
5.3. Selection of controls
To control for other factors presumably in¯uencing the annual rates of population growth in
4
Since the analysis covered the 30-year period of 1968±1997, it was considered whether the actual rates of public
housing construction in more recent years should be de¯ated or, as an alternative, the number of housing units be
used instead. Although the average size of a standard housing unit during the time span in question grew by some
35%, housing standards in the country also increased. It was decided, therefore, that a de¯ation of housing rates
might bring an undesirable bias to the models.
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B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
Table 3
Distribution of population and residential construction by administrative district of Israel in 1985 and 1995 (%)a
1985
District
Population Public
Private
Population Public
Private
construction construction
construction construction
Jerusalem
12.0
Northern
16.6
Haifa
13.7
Central
21.0
Tel Aviv
23.5
Southern
12.0
Judea, Samaria and Gaza Areab
1.2
Total:
100.0
a
b
1995
28.1
7.4
4.3
17.9
4.5
6.7
31.1
100.0
8.6
30.0
11.1
28.2
15.9
5.9
0.3
100.0
11.8
16.9
13.2
21.6
20.3
13.7
2.5
100.0
22.1
3.1
9.9
22.9
2.7
31.9
7.4
100.0
5.6
20.7
9.3
37.6
13.1
10.7
3.1
100.0
Source: Compiled from [39].
Jewish localities.
the core and periphery of the country, our analysis included the following indicators whose
in¯uence was hypothesized in the section on policy controls:
. ECONOMY: annual change in the gross domestic product (per cent) considered as a proxy
for macro-economic performance of the country as a whole;
. UNEMPLOYMENT CHANGE: annual change in the number of unemployed in a district,
per cent;5
. IMMIGRATION: annual number of foreign immigrants to the country (thousands of new
immigrants).
5.4. Policy evaluation
Biannual data for 1948±1996 were used to analyze the general trend of population change
between the core and periphery of the country (see the section on Preliminary results and
discussion). The respective time±series data were drawn from the annual publications of the
Israeli Central Bureau of Statistics [39]. For estimation of PPD's eects on interregional
population growth (see the section of this paper on In¯uencing factors), annual data for 1968±
1997 were employed. This reduction of the time-span covered by the research sample was
determined by restrictions on data availability and comparability.
5
In view of the unavailability of other comparable employment-related statistics, this indicator was considered as
a proxy for changes in the availability of employment in the area. It is to be noted that in Israel, unemployment is
de®ned as the percentage of adult work-seekers in the civilian labor force that are registered in Labor Exchanges
and the Employment Center for Professionals [39].
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B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
Table 4
Per capita rates of public housing and road construction in core and peripheral districts of Israel in 1968±1997a
Core
Periphery
Year
Public housing construction
(m2)
Road construction
(m)
Public housing construction
(m2)
Road construction
(m)
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
0.15
0.16
0.26
0.38
0.47
0.41
0.47
0.49
0.50
0.31
0.23
0.18
0.20
0.23
0.35
0.24
0.13
0.16
0.11
0.08
0.06
0.08
0.06
0.15
0.34
0.21
0.21
0.13
0.30
0.38
0.07
0.05
0.05
0.08
0.07
0.10
0.06
0.07
0.08
0.03
0.02
0.02
0.05
0.02
0.00
0.01
0.02
0.03
0.01
0.02
0.02
0.03
0.02
0.01
0.02
0.04
0.06
0.04
0.04
0.05
0.38
0.33
0.38
0.54
0.72
0.73
0.69
0.77
0.76
0.50
0.61
0.24
0.39
0.42
0.44
0.25
0.14
0.06
0.05
0.06
0.05
0.05
0.07
0.75
1.33
0.55
0.20
0.14
0.30
0.40
0.14
0.25
0.17
0.12
0.09
0.06
0.09
0.09
0.15
0.07
0.06
0.05
0.07
0.06
0.03
0.12
0.09
0.04
0.05
0.04
0.04
0.05
0.08
0.06
0.06
0.08
0.13
0.08
0.07
0.09
a
Source: Compiled from [39].
6. Preliminary results and discussion
The average rates of population growth in various geographic areas of the country
(subdistricts) are represented in Fig. 4, while changes in the population size of the core and
periphery over the past ®ve decades (1948±95) are illustrated in Fig. 5.
As Fig. 4 shows, since the foundation of the state in 1948, the highest rates of population
growth have predominantly occurred in the country's periphery Ð the Be'er Sheva and
Ashqelon subdistricts. In the wake of the 1990±91 mass immigration from the former Soviet
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
251
Union, the population of the Ashqelon subdistrict [see Fig. 4(c)] grew each year by some 8±
10%, i.e. three times as fast as that of the country as a whole. High rates of population
increase were also observed in other peripheral subdistricts ± Zfat, Golan and Yizre'el (see
Fig. 4). At the same time, annual growth in most of the country's core areas was somewhat
less substantial during this period. In the Tel Aviv district, for instance, the population in
1970±75 grew by less than 2%, and less than 1% in 1990±95. We thus ask: does this indicate
that the spatial distribution of the country's population is becoming gradually more even?
As Fig. 5 shows, the answer to this question is rather negative. Indeed, until 1990±95, the
gap in population size between core and peripheral areas of the country tended to increase.
This gap was equal to 170,000 residents in 1948, then increased to 400,000 in 1960; to 500,000
in 1980 and to 700,000 in 1990. Between 1990 and 1995, this gap, however, decreased to some
600,000 residents. This decrease was primarily attributed to the recent patterns of outward
migration from the country's overpopulated core to its periphery where housing is more
available and aordable [21,35].
In general, between 1948 and 1995, the population of the core grew by some 2.5 million
residents, while that of the periphery increased by only 2.0 million people (see Fig. 5). This
trend clearly contradicts the main objectives of PPD, which were intended, from its outset, to
restrict overpopulation of the core by redirecting future growth to the periphery (see Table 1).
Should the fact that the population gap increased between regions lead to the conclusion
that the policy of population dispersal was generally ineective? The answer to this question is
not straightforward. The growing gap in absolute population size between the core and
periphery was accompanied by a steady increase in the proportional share of the periphery in
the overall balance of the national total. In 1948, population of the periphery amounted to
only 40% of the country's population. By 1970, the share of the periphery increased to 42%,
and at the beginning of 1996 it accounted for 45%. However, the proportional population
share of the periphery cannot grow inde®nitely while the gap in absolute population size
between the respective regions also tends to increase. While it is clear, then, that the absolute
population gap has increased while the proportional share has been only slightly moderated, at
this point of the analysis we cannot con®dently say whether the policy in question prevented
the gap in population size between the central core and periphery from becoming even wider.
A regression analysis may help to answer this important question.
7. Modeling procedure
In the section on Research approach, quantitative policy measures (public construction and
infrastructure) and their controls (immigration, economy and unemployment change) were
introduced. In the following analysis, these indicators are considered as explanatory variables.
Regression models were separately computed for the core and peripheral districts of the
country (see Table 2) using the rates of population growth in the core and periphery
(GROWTH1 and GROWTH2, respectively) as the dependent variables. The regression model
used for the analysis is thus as follows:
GROWTHi b0 b1 F1 . . . b5 F5 e,
where: b0, b1, . . . ,,b5 are regression coecients, F1,. . . , F5 are explanatory variables; namely:
252
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
Fig. 4. Aveage annual rates of population growth by subdistrict of Israel in 1948±1995.
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
253
Fig. 5. Changes in the population size of core and peripheral areas of Israel during the period 1948±1995.
economy (F1), unemployment change (F2), immigration (F3), infrastructure (F4) and public
construction (F5); e is a random error term.
As mentioned in the previous discussion, data for the analysis were drawn from the annual
publications of Israeli Central Bureau of Statistics, Statistical Abstract of Israel, and covered
the 30-year period 1968±1997 [39]. This provided us with 30 observations for each of the six
administrative districts of the country (see Fig. 1). (Selected statistical parameters of the
research data are given in Tables A.1 and A.2 in the Appendix)
Two dierent modeling approaches were used:
1. The districts were aggregated into two territorial groups Ð the core and periphery (see
Table 2) Ð for which actual growth rates were calculated. According to this approach, only
time±series data (30 yearly observations) were considered (see Tables 5 and 6).
2. The districts forming the core and periphery were introduced in the regression models
independently. This provided us with 90 observations for each of the aforementioned areas
(3 districts 30 observations, see Tables 5 and 7). This (disaggregated) representation of
core and peripheral areas was essential, since it was assumed that the aggregation of socially
254
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
and economically heterogeneous districts might interfere, at least theoretically, with the
results of the analysis.
Various statistical tests of the data quality Ð multicollinearity, autocollinearity, homogeneity
of variance and the Durbin±Watson test of the independence of regression residuals Ð were
performed (for more details on these tests, see [42]).6
The results of these tests are reported in Tables 6 and 7 and in Tables A.1±A.4 in
theAppendix. In addition, Geary's spatial autocollinearity test was performed where districts
were introduced in the models separately (for more details on this test, see [43]). All tests
provided satisfactory results concerning the quality of data.
It was also assumed that the actual eect of some of the above discussed explanatory
variables is time-lagged, since it is the perception of reality, rather than the actual conditions,
that aects the decision-making process of individuals [34,36]. The values of some of these
variables (public construction, immigration and unemployment change) were, therefore, oneyear lagged in order to re¯ect this process.
8. In¯uencing factors
Three functional forms of regression equation were tested Ð the linear form, semi-log form
(only the explanatory variables were logarithmically transformed) and double-log form (both
the left-hand and right-hand variables were transformed). As Table 5 shows, the linear model
appears to provide the best ®t in most cases (for instance, R 2=0.670, linear ®t vs R 2=0.582,
double-log ®t for core areas).7 Reluctance to use this function form (linear ®t) in the
subsequent analysis was due to a heterogeneity-of-variance consideration. On the other hand,
the logarithmic transformation helps to insure that variances are more homogenous (Tables
A.1 and A.2 in theAppendix).
As Tables 6 and 7 show, nearly all the estimated coecients fall within expected signs and a
number of factors are signi®cant at 0.05 and 0.01 levels. In the following discussion, we shall
refer primarily to the factors that are signi®cant at these levels of probability.
8.1. Peripheral areas
As Table 6 shows, immigration and public construction are the main factors that increase
the rates of population growth in the peripheral districts of the country. The statistical
signi®cance of public construction (t = 2.199; p < 0.05)8 is especially notable. This positive
eect implies that involvement of the state in development of the country's periphery indeed
stimulated population growth of the peripheral areas and thus prevented the gap in population
size between the central core and periphery of the country from becoming even wider.
6
These test if the residuals from regression are independent, against the alternative that the residuals wither correlate or follow a ®rst-order autoregressive process [42].
7
An R 2 comparison is meaningful only if the dependent variable is the same for all models [42]. Therefore, for the
double-log model, the antilog of the predicted values was obtained. Then, R 2 between the antilog of the observed
and predicted values was calculated.
8
t-statistic and the probability of its occurrence by chance, respectively.
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B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
Table 5
Tests of functional forms of the regression modelsa
Core districts
Functional form
Test 1b
Linear
Semi-log
Double-log
Test 2c
Linear
Semi-log
Double-log
R2
Peripheral districts
F
R2
F
0.670
0.571
0.582
7.919
4.518
4.732
0.808
0.849
0.851
16.841
22.448
22.896
0.427
0.550
0.425
12.532
7.104
5.411
0.653
0.598
0.579
28.203
22.295
20.611
a
All F-ratios are signi®cant at a 0.01 con®dence level.
Districts aggregated in territorial entities Ð either core or periphery, respectively.
c
Districts introduced independently.
b
This straight conclusion may, however, raise a number of legitimate questions. It might be
argued, for instance, that public construction simply ``follows'' population growth rather than
causes such growth in the periphery. This assumption does not, however, seem to be justi®ed
due to the following considerations. First, as mentioned in the previous section, nearly all the
explanatory variables, including public construction, were time-lagged in order to reduce the
eect of endogeneity. Second, as mentioned in the section on the research approach, the
distribution of public construction in Israel is heavily aected by political considerations,
rather than by actual demand. Data for 1985 and 1995, represented in Table 3, help to
illustrate this spatial bias. While private construction was concentrated in the Central, Tel Aviv
and Northern districts,9 which are, in fact, the most populated areas of the country, public
construction was skewed toward the Jerusalem and Southern districts, Judea, Samaria and the
Gaza Area. This trend is clearly due to the governmental policy of settling the country's
periphery and other ``strategically sensitive'' areas. In view of these considerations, the
conclusion concerning the causal eect of public construction on population growth in speci®c
geographic areas of Israel seems to be fully justi®ed.
Despite these general considerations, more formal testing of the causality of relationship
between public construction (housing and infrastructure) and the dependent variable
(population growth) is required. To investigate whether public investments in housing and road
construction preceded population growth or simply followed it, the Granger causality test was
performed (see Table 8).10 As Table 8 shows, the eects of both infrastructure and public
housing construction lags on the overall population growth appear to be highly signi®cant (t =
9
For more discussion on the factors governing the distribution of both public and private construction in Israel,
see [41].
10
According to this test, the values of each variable are compared simultaneously with its own lags, and with the
lagged values of all other variables included in the analysis. The analysis of regression estimates then helps to identify the actual direction of the factors' interrelationship (for more details on this test, see [24]).
256
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
Table 6
Factors aecting the annual rate of population growth (%) in the peripheral and core districts of Israel (double-log
form; districts aggregated in territorial entities)
Collinearity statistics
Factor (see text)
B
t
t signi®cance
Tolerance
VIFb
A. Periphery
Economy
Immigration
Unemployment change
Public construction
Infrastructure
Constant
No of observations
R2
F
Durbin±Watson d testa
dL/dV critical ( p = 0.05)
0.044
0.391
0.003
0.106
0.095
ÿ1.723
30
0.851
22.896
2.028
2.93/2.17
0.785
5.456
0.062
2.199
0.935
ÿ3.014
0.442
0.000
0.951
0.040
0.361
0.007
0.588
0.469
0.532
0.608
0.627
1.759
2.134
1.879
1.646
1.595
B. Core
Economy
Immigration
Unemployment change
Public construction
Infrastructure
Constant
No. of observations
R2
iF
Durbin±Watson d test
dL/dV critical ( p = 0.05)
0.049
0.275
0.524
0.032
ÿ0.008
ÿ2.568
30
0.582
4.732
1.254
1.07/1.89
0.597
3.829
2.489
0.277
ÿ0.070
ÿ2.101
0.558
0.001
0.024
0.785
0.945
0.051
0.894
0.736
1.299
0.611
0.544
1.118
1.358
2.489
1.638
1.839
a
Durbin±Watson autocollinearity test.
Variance in¯ation factor; B = unstandardized regression coecient; t=t-statistic; t signi®cance=actual signi®cance level of t-statistic; F=F-ratio; dL/dV=critical intervals of Durbin±Watson (d ) statistic.
b
2.950 and 2.599, respectively; p < 0.01). At the same time, the eect of population growth lags
is highly signi®cant in the case of public construction (t = 4.525; p < 0.01) and statistically
insigni®cant in the case of infrastructure (t = 0.945; p > 0.3). This implies that public
investments in infrastructure appeared to cause population growth across various regions,
while the relationship between population growth and housing construction is rather bidirectional. Population growth both followed the patterns of public housing construction and
caused more public construction in various regions (with the latter link somewhat more
signi®cant).
8.2. Core areas
Two additional observations concerning results of the regression analysis deserve comment.
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B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
Table 7
Factors aecting the annual rate of population growth (%) in the peripheral and core districts of Israel (double-log
form; districts introduced independently)
Collinearity statistics
Factor (see text)
B
t
t signi®cance
Tolerance
VIFb
A. Peripheral districts
Economy
Immigration
Unemployment change
Public construction
Infrastructure
Constant
No. of observations
R2
F
Durbin±Watson d testa
dL/dV critical ( p = 0.05)
0.120
0.431
0.115
0.118
0.078
ÿ2.188
90
0.579
20.611
1.891
1.542/1.776
2.602
5.928
2.181
2.423
1.717
ÿ5.990
0.011
0.000
0.032
0.018
0.090
0.000
0.700
0.677
0.718
0.717
0.832
1.428
1.476
1.393
1.396
1.202
B. Core districts:
Economy
Immigration
Unemployment change
Public construction
Infrastructure
Constant
No. of observations
R2
F
Durbin±Watson d testa
dL/dV critical ( p = 0.05)
0.072
0.211
0.044
0.201
0.093
ÿ1.469
90
0.425
5.411
1.628
1.542/1.776
1.256
2.959
0.490
2.788
1.462
ÿ2.423
0.213
0.004
0.626
0.007
0.147
0.018
0.811
0.905
0.803
0.850
0.816
1.233
1.105
1.245
1.177
1.226
a
Durbin±Watson autocollinearity test.
Variance in¯ation factor; B = unstandardized regression coecient; t=t-statistic; t signi®cance=actual signi®cance level of t-statistic; F=F-ratio; dL/dV=critical intervals of Durbin±Watson (d ) statistic.
b
As Table 6 shows, in the case of the core areas, both immigration and unemployment change
have statistically signi®cant eects on the rate of population growth (t = 3.829 and t = 2.489,
respectively; p < 0.05). The positive sign of the latter variable (unemployment change) is, at
®rst glance, surprising. This result might, however, have two possible explanations. First, it
may indicate that in years of rising unemployment, the population of the country moves to
more prosperous core regions, away from the periphery. This phenomenon tends to be more
aected by negative changes in the national economy as a whole [23], and is in line with the
fact that, in the peripheral Southern district of Israel (see Table 9), unemployment change
appears to have a negative, although not highly statistically signi®cant (t = 1.241; p > 0.2),
eect on the rates of population growth. Alternatively, the above phenomenon can be related
to the relationship between employment growth and unemployment change. These two
variables are often positively correlated because growing places often have higher
258
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
unemployment rates. The negative sign of the unemployment variable may thus be a response
to the omission of the employment change variable that was not included in the analysis due to
the unavailability of comparable data.
8.3. Heterogeneity of core and peripheral areas
Although the ``core-periphery'' paradigm is a dichotomous concept, neither core nor
peripheral regions of Israel can be considered a homogenous entity. This conclusion is
validated by two separate tests: (a) the simultaneous introduction of cross-section and timeseries data (see Table 7), (b) separate examination of the factors aecting population growth in
peripheral districts (Tables 9, 10 and 11).
Although the models obtained by dierent computation procedures Ð aggregation of
districts (Table 6) vs their separate introduction into the models (Table 7) Ð exhibit some
Table 8
Granger's causality testa
Collinearity statistics
Dependent variables/explanatory factors
A. Overall growth
Overall growth (t ÿ 1)
Infrastructure (t ÿ 1)
Public construction (t ÿ 1)
Constant
No. of observations
R2
F
B. Infrastructure
Overall growth (t ÿ 1)
Infrastructure (t ÿ 1)
Public construction (t ÿ 1)
Constant
No. of observations
R2
F
C. Public construction
Overall growth (t ÿ 1)
Infrastructure (t ÿ 1)
Public construction (t ÿ 1)
Constant
No. of observations
R2
F
a
B
t
t signi®cance
Tolerance
VIFb
0.512
0.006
0.001
0.676
180
0.575
70.785
9.250
2.950
2.599
5.475
0.000
0.004
0.010
0.000
0.692
0.818
0.734
1.445
1.222
1.363
1.695
0.542
0.038
5.220
180
0.448
22.744
0.949
7.885
3.345
1.312
0.344
0.000
0.001
0.191
0.692
0.818
0.734
1.445
1.222
1.363
36.750
0.596
0.458
ÿ9.991
180
0.583
73.208
4.525
1.906
8.839
ÿ0.552
0.000
0.058
0.000
0.582
0.692
0.818
0.734
1.445
1.222
1.363
Durbin±Watson autocollinearity test.
Variance in¯ation factor; B = unstandardized regression coecient; t=t-statistic; t signi®cance=actual signi®cance level of t-statistic; F=F-ratio; (t ÿ 1)=one-year lag of the respective variable.
b
259
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
similarities (for instance, the signi®cance of such key factors as public construction and
immigration remained virtually unchanged), substantial changes in more ``weak'' factors are
clearly noticeable. Thus, after disaggregation of the periphery, economy, unemployment change
and infrastructure emerged as statistically signi®cant factors (see Table 7, peripheral districts: t
= 2.602, p < 0.05; t = 2.181, p < 0.05 and t = 1.717, p < 0.1, respectively). The emergence of
the latter factor (infrastructure) is especially important for the following analysis since it is
considered as one of the two major policy-related tools (see the section of this paper on the
research approach). The absence of this factor as statistically signi®cant in the aggregated
models may thus be explained by its interdependency with another policy variable Ð housing
construction. Unsurprisingly, when these variables are integrated into a composite construction
variable (Table 12), the statistical signi®cance of the integrated index exceeds that of both
infrastructure and housing variables introduced separately (Table 7).
Separate regression models computed for the Southern, Northern and Haifa districts (Tables
9, 10 and 11, respectively) also help to illustrate the dierences between the country's
peripheral areas. In the case of the Southern district (see Table 9), immigration and public
construction are highly signi®cant (t = 6.265 and t = 4.236, respectively; p < 0.01). Only
immigration has such a signi®cance level in the Haifa district (Table 11), while none of these
factors is highly signi®cant in the Northern district (Table 10). On the other hand, the eect of
infrastructure on the rate of population growth, which is extremely weak in the Southern
district, is more signi®cant in the case of both the Northern and Haifa districts. This allows us
to clarify our previous conclusion concerning the eect of public construction on the
development of the country's periphery. Although involvement of the state in housing
construction and infrastructure development indeed appeared to have stimulated population
growth of the country's periphery in general, this involvement had dierent eects on the
Table 9
Factors aecting the rate of population growth in the Southern district of Israel (double-log form)
Collinearity statistics
Factor (see text)
B
t
t signi®cance
Tolerance
VIFb
Economy
Immigration
Unemployment change
Public construction
Infrastructure
Constant
No. of observations
R2
F
Durbin±Watson d testa
dL/dV critical ( p = 0.05)
0.079
0.347
ÿ0.067
0.147
0.000
ÿ0.463
30
0.909
40.121
2.101
2.93/2.17
2.073
6.265
ÿ1.241
4.236
0.008
ÿ1.411
0.051
0.000
0.229
0.000
0.994
0.174
0.629
0.497
0.608
0.548
0.731
1.589
2.011
1.644
1.826
1.367
a
Durbin±Watson autocollinearity test.
Variance in¯ation factor; B = unstandardized regression coecient; t=t-statistic; t signi®cance=actual signi®cance level of t-statistic; F=F-ratio; dL/dV=critical intervals of Durbin±Watson (d ) statistic.
b
260
B.A. Portnov, Y. Etzion / Socio-Economic Planning Sciences 34 (2000) 239±269
patterns of population growth across various peripheral areas. In particular, the eect of public
housing construction appeared to be somewhat more substantial in the earliest least populated
and underdeveloped Southern region, while the eect of infrastructure investment was more
pronounced in the more developed and populated Haifa and Northern districts.
9. Alternative scenarios
The analyses of the previous sections leads naturally to the question of what would have
happened to the population gap between the core and periphery if the latter's population
growth were not arti®cially stimulated? To answer this question, the approach suggested in the
section on the evaluation approach (see Fig. 2) can be used.
Since the average per capita rates of public housing and road construction in peripheral
districts of the country have exceeded those in the core (see Table 4), the above general
question can be reformulated in a more speci®c form. What would have happened to the
population size of the periphery if the per capita rate of public construction in this area had
been reduced to the level of that in the core?
As