Directory UMM :Data Elmu:jurnal:S:Socio-Economic Planning Sciences:Vol34.Issue4.2000:

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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.72000 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

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* Corresponding author. Tel.: +972-7-659-6875; fax: +972-7-659-6881.

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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).

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Portnov,

Y.

Etzion

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Socio-Economic

Planning

Sciences

34

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239±269

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Table 1

The components and goals of the PPD in Israel and its expected eects on interregional population change

Policy goals

Geo-political Economic and environmental Ideological Social

Establishing a national presence and sovereignty over the territory of the Negev, which constitutes nearly two-thirds of the land area of Israel [12]

Redistributing population from diverse new towns in remote areas [13].

Policy measures

Direct involvement of the state in construction and development

Financial incentives to private investors

Allocation of public land for new development

Population size Rate of growth Migration attractiveness Population make-up

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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 large-scale 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

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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

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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].

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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].

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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}

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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

Table 2

Suggested grouping of districts in Israel (as of 1996)a

District Population (1000) Land area (km2) Density of population per km2

A. Core districts

Tel Aviv 1139.7 170.0 6704.4

Central 1257.5 1242.0 1012.5

Jerusalem 677.2 627.0 1080.1

B. Peripheral districts

Haifa 758.2 854.0 887.8

Northern 977.9 4501.0 217.3

Southern 798.7 14,107.0 56.6

a_{Source: Compiled from [39].}

4_{Since the analysis covered the 30-year period of 1968±1997, it was considered whether the actual rates of public}

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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.

Table 3

Distribution of population and residential construction by administrative district of Israel in 1985 and 1995 (%)a

1985 1995

Jerusalem 12.0 28.1 8.6 11.8 22.1 5.6

Northern 16.6 7.4 30.0 16.9 3.1 20.7

Haifa 13.7 4.3 11.1 13.2 9.9 9.3

Central 21.0 17.9 28.2 21.6 22.9 37.6

Tel Aviv 23.5 4.5 15.9 20.3 2.7 13.1

Southern 12.0 6.7 5.9 13.7 31.9 10.7

Judea, Samaria and Gaza Areab _1.2 _31.1 _0.3 _2.5 _7.4 _3.1

Total: 100.0 100.0 100.0 100.0 100.0 100.0

a

Source: Compiled from [39].

b

Jewish localities.

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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

Table 4

Per capita rates of public housing and road construction in core and peripheral districts of Israel in 1968±1997a

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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:

GROWTH_i b0b1F1...b5F5e,

where: b0, b1,...,,b5 are regression coecients, F1,

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economy (F1), unemployment change (F2), immigration (F3), infrastructure (F4) and public construction (F5); eis 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

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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, one-year 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, R2=0.670, linear ®t vs R2=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 corre-late or follow a ®rst-order autoregressive process [42].

7

AnR2comparison 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,R2between the antilog of the observed and predicted values was calculated.

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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=

Table 5

Tests of functional forms of the regression modelsa

Core districts Peripheral districts

a_All_F_{-ratios are signi®cant at a 0.01 con®dence level.} b

Districts aggregated in territorial entities Ð either core or periphery, respectively.

c

Districts introduced independently.

9_{For more discussion on the factors governing the distribution of both public and private construction in Israel,}

see [41].

10

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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 bi-directional. 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.

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 tsigni®cance Tolerance VIFb

A. Periphery

Economy 0.044 0.785 0.442 0.588 1.759

Immigration 0.391 5.456 0.000 0.469 2.134

Unemployment change 0.003 0.062 0.951 0.532 1.879

Public construction 0.106 2.199 0.040 0.608 1.646

Infrastructure 0.095 0.935 0.361 0.627 1.595

Constant ÿ1.723 ÿ3.014 0.007

No of observations 30

Economy 0.049 0.597 0.558 0.894 1.118

Immigration 0.275 3.829 0.001 0.736 1.358

Infrastructure ÿ0.008 ÿ0.070 0.945 0.544 1.839

Constant ÿ2.568 ÿ2.101 0.051

No. of observations 30

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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

Table 7

Factors aecting the annual rate of population growth (%) in the peripheral and core districts of Israel (double-log form; districts introduced independently)

A. Peripheral districts

Economy 0.120 2.602 0.011 0.700 1.428

Immigration 0.431 5.928 0.000 0.677 1.476

Infrastructure 0.078 1.717 0.090 0.832 1.202

Constant ÿ2.188 ÿ5.990 0.000

Economy 0.072 1.256 0.213 0.811 1.233

Immigration 0.211 2.959 0.004 0.905 1.105

Infrastructure 0.093 1.462 0.147 0.816 1.226

Constant ÿ1.469 ÿ2.423 0.018

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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 time-series 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

Dependent variables/explanatory factors B t tsigni®cance Tolerance VIFb

A. Overall growth

Overall growth (tÿ1) 0.512 9.250 0.000 0.692 1.445

Infrastructure (tÿ1) 0.006 2.950 0.004 0.818 1.222

Public construction (tÿ1) 0.001 2.599 0.010 0.734 1.363

Constant 0.676 5.475 0.000

R2 0.575

F 70.785

B. Infrastructure

Constant 5.220 1.312 0.191

R2 _0.448

F 22.744

C. Public construction

Constant ÿ9.991 ÿ0.552 0.582

(21)

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 andt= 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)

Economy 0.079 2.073 0.051 0.629 1.589

Immigration 0.347 6.265 0.000 0.497 2.011

Unemployment change ÿ0.067 ÿ1.241 0.229 0.608 1.644

Infrastructure 0.000 0.008 0.994 0.731 1.367

Constant ÿ0.463 ÿ1.411 0.174

(22)

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?

Assuming the population size of the periphery in 1968 as a conditional baseline, the trend in question can be estimated using the regression model computed for the periphery (see Table 7) and the respective annual rates of housing and road construction in the core (Table 4).

Two counterfactual scenarios are considered with corresponding data presented in Table 13.

Scenario 1: The rates of both housing and road construction in the periphery are reduced to those in the core (as mentioned, actual per capita rates of construction in the core and peripheral areas of the country can be found in Table 4).

Table 10

Factors aecting the rate of population growth in the Northern district of Israel (double-log form)

Economy 0.012 0.278 0.784 0.639 1.565

Immigration 0.146 2.575 0.018 0.647 1.545

Infrastructure 0.123 1.507 0.147 0.742 1.348

Constant ÿ0.231 ÿ0.572 0.574

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Scenario 2: Not only the rates of housing and road construction in the periphery are reduced to the level of those in the core, but also the rate of foreign immigration to the country reaches only 70% of its actual level in a given year. (We may recall that foreign immigration is a major source of population growth of the country's peripheral areas. Evaluating the eect of counterfactual reduction of immigration rates on the overall population growth of the country's periphery was assumed, therefore, to be an interesting task to perform.)

Although, under these two scenarios, reduction of the overall population growth in the periphery could be due to the positive sign of the respective variables in the regression model (see Table 7: peripheral districts), the above tests make it possible to quantify this hypothetical eect. As Table 13 shows, the expected decrease of the cumulative per cent of population growth over the entire time-span in question would reach 31.3% according to Scenario 1 (100.4±69.1=31.3), and 58.3% according to Scenario 2. As a result of the latter's relative decline (58.3%), the gap in population size between the core and periphery would reach some 1,350,000 residents by 1997, while, in reality, it was equal to about 590,000 residents (see Fig. 5). This result is thus in line with the above conclusion that public involvement in construction and infrastructure development has indeed stimulated population growth in the periphery, although it has failed to reduce the population imbalance between the country's core and peripheral areas.

10. Conclusion and policy implications

There are many policy approaches aimed at encouraging socio-economic development and population growth in underpopulated peripheral areas. The impact of these policies is,

Table 11

Factors aecting the rate of population growth in the Haifa district of Israel (double-log form)

Economy 0.044 0.916 0.369 0.688 1.453

Immigration 0.490 7.763 0.000 0.845 1.183

Infrastructure 0.085 1.438 0.164 0.876 1.141

Constant ÿ1.553 ÿ3.317 0.003

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however, not always clear. Beyond mere descriptions of the ocial objectives of such policies, there is little knowledge of the long-term and indirect eects, the side eects and the unintended and eventually unwanted consequences.

While the actual changes in regional development over time can easily be estimated using available statistical data, the main question is, however, whether these changes can be attributed to a policy itself, rather than to other exogenous and endogenous factors. To answer this question, an evaluation model is suggested. This model is based on the approach that the eect of a regional policy can be evaluated as the dierence between the actual values of selected development indicators and the values that would have been achieved in the absence of the policy.

This methodology was used to evaluate the eects of the PPD in Israel. Although the involvement of the government in housing construction and infrastructure development appeared to prevent a further increase of the population gap between the core and periphery of

Table 12

Repeated analysis of the factors aecting the annual rate of population growth in the peripheral and core districts of Israel (integrated construction index; districts introduced independently)

A. Peripheral districts

Economy 0.071 1.851 0.068 0.704 1.421

Immigration 0.377 6.391 0.000 0.695 1.438

Integrated construction index 0.250 1.423 0.000 0.711 1.407

Constant ÿ1.843 ÿ6.126 0.000

Economy 0.067 1.174 0.244 0.818 1.223

Immigration 0.193 2.809 0.006 0.967 1.034

Integrated construction index 0.328 3.752 0.000 0.983 1.018

Constant ÿ1.251 ÿ2.226 0.029

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the country, this policy failed, in general, to achieve its main goal of preventing further concentration of population in the country's overpopulated core areas.

In an attempt to explain possible reasons for this failure, a counter-balanced model of regional development can be proposed (Fig. 6). This model assumes that a major reason for failure of regional policy to achieve a desirable interregional population shift may stem from a compensatory approach to selecting development incentives. For instance, PPD is Israel attempts to compensate relative disadvantages of the country's peripheral regions (a lack of urban development, limited employment opportunities, less advanced infrastructure and communication networks) with aordable public housing, tax bene®ts, etc. As the present analysis shows, these measures, however, appear to have only limited eect on the interregional population change.

Table 13

Population growth of the periphery (regression estimates)a

Year Expected growth (%)a Scenario 1 Scenario 2

1968 1.69 1.18 0.80

Total growth (cumulative %) 100.37 69.12 42.07

a

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In contrast, the suggested approach assumes that relative disadvantages of peripheral regions should be counter-balanced by a set of ``pull'' factors rather than compensated (Fig. 6). For instance, a lack of urban development in a peripheral region can be reduced by creating dense urban clusters in which small urban settlements share some essential urban functions Ð employment, educational, cultural and recreational services and facilities Ð where each of the small localities cannot individually sustain themselves (for more details on this strategy, see [23]). In order to diversify the employment base of peripheral areas, another strategy Ð the strategy of redirecting priorities [35,41] Ð can be employed. Such a strategy assumes that development resources should be primarily concentrated on a limited number of selected urban communities in frontier areas. This should be done until they become considerably attractive to both migrants and private developers. Such support can then be redirected on a stage-by-stage basis to adjacent frontier settlements.

Combined with traditional compensatory policy measures (provision of public housing, tax incentives, etc.), the aforementioned development strategies may substantially improve the

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future performance of PPD and similar regional development policies by attracting both migrants and private developers to peripheral development areas.

Appendix A

Table A.1

Statistical parameters of the research variables before and after logarithmic transformation (core districts)

Variable Mean Variance Minimum Maximum

A. Before transformation

Economy 1.51 2.40 ÿ0.60 4.51

Employment change 10.73 432.38 ÿ17.08 60.50

Immigration 47.30 2542.91 9.50 199.50

Infrastructure 71.59 1855.38 13.20 154.20

Overall growth 2.29 0.83 1.31 5.16

Public construction 462.65 69,808.06 135.00 926.00

B. After transformation

Economy 0.79 0.50 ÿ0.51 1.74

Employment change 4.19 0.09 3.71 4.77

Immigration 3.45 0.79 2.25 5.30

Infrastructure 4.07 0.45 2.58 5.04

Overall growth 0.77 0.12 0.27 1.64

Public construction 5.97 0.38 4.91 6.83

Table A.2

Statistical parameters of the research variables before and after logarithmic transformation (peripheral districts)

Variable Mean Variance Minimum Maximum

A. Before transformation

Economy 3.06 10.67 ÿ0.60 11.62

Employment change 25.23 16,964.61 ÿ54.91 648.49

Immigration 37.51 577.16 9.50 79.80

Infrastructure 132.48 3218.06 58.90 282.60

Overall growth 2.35 0.68 1.08 3.69

Public construction 649.00 305,894.96 84.00 2693.00

B. After transformation

Economy 1.17 0.64 ÿ0.51 2.55

Employment change 3.93 1.05 1.13 6.56

Immigration 3.41 0.46 2.25 4.38

Infrastructure 4.80 0.17 4.08 5.64

Overall growth 0.79 0.15 0.08 1.31

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Table A.3

Autocorrelation test (Bartlett's approximations). Core districtsa

Overall growth Employment change Economy

Lag Auto-corr. Stand. error Box-ljung Auto-corr. Stand. error Box-ljung Auto-corr. Stand. error Box-ljung

1 0.441 0.209 5.085 0.177 0.209 0.823 ÿ0.162 0.209 0.685

2 0.032 0.246 5.114 ÿ0.108 0.215 1.140 0.019 0.214 0.695

3 ÿ0.054 0.246 5.198 0.090 0.217 1.373 ÿ0.405 0.214 5.416

4 ÿ0.017 0.246 5.207 0.095 0.219 1.647 ÿ0.164 0.245 6.230

5 ÿ0.202 0.247 6.505 ÿ0.441 0.221 7.850 0.236 0.250 8.013

6 ÿ0.100 0.254 6.843 ÿ0.508 0.256 16.575 ÿ0.020 0.259 8.027

7 ÿ0.203 0.255 8.328 0.059 0.297 16.700 0.379 0.259 13.182

8 ÿ0.359 0.262 13.257 0.029 0.297 16.733 ÿ0.194 0.282 14.619

9 ÿ0.394 0.283 19.642 ÿ0.286 0.297 20.098 ÿ0.010 0.288 14.623

10 ÿ0.039 0.306 19.710 0.080 0.309 20.384 ÿ0.296 0.288 18.502

Public construction Infrastructure Immigration

Lag Auto-corr. Stand. error Box-ljung Auto-corr. Stand. error Box-ljung Auto-corr. Stand. error Box-ljung

1 0.645 0.209 10.864 0.605 0.209 9.556 0.743 0.209 14.445

2 0.394 0.282 15.110 0.333 0.274 12.584 0.442 0.303 19.798

3 0.348 0.305 18.596 0.308 0.291 15.306 0.291 0.329 22.237

4 0.155 0.322 19.319 0.265 0.305 17.437 0.153 0.340 22.946

5 ÿ0.074 0.325 19.492 ÿ0.041 0.315 17.491 0.013 0.343 22.951

6 ÿ0.075 0.326 19.684 ÿ0.229 0.315 19.266 ÿ0.093 0.343 23.243

7 ÿ0.157 0.327 20.566 ÿ0.249 0.322 21.495 ÿ0.244 0.345 25.380

8 ÿ0.186 0.330 21.891 ÿ0.322 0.331 25.465 ÿ0.317 0.352 29.241

9 ÿ0.199 0.334 23.525 ÿ0.374 0.344 31.201 ÿ0.321 0.364 33.461

10 ÿ0.197 0.340 25.245 ÿ0.394 0.361 38.082 ÿ0.213 0.376 35.461

a

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a

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Directory UMM :Data Elmu:jurnal:S:Socio-Economic Planning Sciences:Vol34.Issue4.2000:

Investigating the eects of public policy on the

interregional patterns of population growth: the case of

Israel

B.A. Portnov*, Y. Etzion

Investigating the eects of public policy on the