This chapter presents quantitative evidence on spatial development patterns across Israel using internationally comparable indicators and methodologies. The analysis reveals concerning trends: rapid low-density development in urban peripheries, growing socioeconomic disparities between localities, and environmental pressures. The analysis also shows significant disparities in access to public services and environmental amenities, with high-income areas exhibiting much better access to hospitals and schools, and substantially more high-density vegetation. These findings highlight the need for reformed spatial planning and development approaches that better address land scarcity, sustainability, and socioeconomic inequalities.
Redefining Spatial Planning and Development in Israel
Abstract
Israel is a small and dense country with limited natural resources and land reserves. Rapid population growth, together with strong economic growth and intensive spatial development has increased demand for land, housing, public services and infrastructure, resulting in constant spatial pressures. Furthermore, despite being a small country, socioeconomic disparities across Israel are one of the widest among OECD countries, largely due to the high concentration of economic activity in the country’s centre in Tel Aviv and the spatial clustering of ethnic or religious groups such as the Haredi-Jews and Arab-Israelis (OECD, 2020[1]; OECD, 2023[2]).
Increasing demand for development and the need to accommodate additional populations puts pressure on spatial planning and development policies to provide ample housing and public amenities to the population while simultaneously utilising land resources in an efficient manner. Adding to these challenges is the need to direct spatial development to better distribute amenities, public services and economic opportunities across space. Underlying all these issues is the urgent need to address climate change. Israel has significantly raised its climate ambitions in recent years by, for example, declaring carbon neutrality by 2050 and launching a multi-sectoral government process “Israel 2050 - a thriving economy in a sustainable environment” in 2019 (OECD, 2023[3]). Furthermore, Israel's unique geopolitical situation and ongoing security concerns significantly impact spatial planning decisions, necessitating careful consideration of strategic locations and protective infrastructure.
This chapter examines patterns of spatial development across regions and localities in Israel using data and internationally comparable indicators. It starts by presenting a unifying definition of regions and metropolitan areas using the degree-of-urbanisation framework (Eurostat, 2021[4]). It provides the basis for presenting harmonised statistics across subnational regions in Israel and other OECD countries. The chapter analyses demographic trends, land-use and settlement patterns, environmental challenges, and spatial socioeconomic disparities within a spatial context. These analyses feed the policy recommendations that call attention to the need for spatial development efforts to better address Israel’s current and future needs, particularly in dealing with scarce land and natural resources, promoting sustainability, and addressing socioeconomic inequalities.
For meaningful international comparisons of statistical indicators, there is a need for a definition that is both nationally relevant and internationally comparable.
The degree of urbanisation classification
The degree of urbanisation classification was developed jointly by the European Commission, the Food and Agriculture Organization of the United Nations (FAO), the International Labour Organization (ILO), the Organisation for Economic Co-operation and Development (OECD), the United Nations Human Settlements Programme (UN-Habitat), and The World Bank (European Commission, ILO, FAO, OECD, UN-Habitat, World Bank, 2020[5]). It was designed to create a simple and neutral method to define cities, towns and rural areas that could be applied in every country in the world. The degree of urbanisation was endorsed by the United Nations (UN) Statistical Commission in March 2020 as ‘the methodology for delineation of cities and urban and rural areas for international and regional statistical comparison purposes'.
The typology relies primarily on population size and density thresholds applied to a population grid with cells of one square kilometre. In the case of Israel, population grids were obtained by disaggregating population data from administrative units into a uniform grid structure of one square kilometre cells. The population of administrative units is distributed proportionally across the grid cells using auxiliary satellite imagery data on built-up surfaces and land use densities obtained using the Global Human Settlement Layer (European Commission, 2023[6]), to ensure greater spatial precision. For example, if an administrative unit includes both built-up and undeveloped areas, the method allocates population only to the grid cells where the built-up areas are located. This uniform grid-based approach allows for a more precise analysis of urban and rural patterns and provides a reliable basis for international comparisons, as it eliminates the bias introduced by administrative units of varying shapes and sizes.
Urban centres are defined as clusters of contiguous one square kilometre grid-cells with at least 1 500 inhabitants per square kilometre and at least 50 000 inhabitants overall. Urban clusters are defined as contiguous grid-cells (including diagonals) with a density of at least 300 inhabitants per square kilometre and where the number of inhabitants is at least 5 000. Rural areas are grid-cells that are not within urban clusters. For the purposes of this study, we define cities to be urban centres, towns and semi-dense areas to be urban clusters not in urban centres, and rural areas to be the grid cells not included in urban clusters. As an illustration, Figure 3.1 depicts the degree of urbanisation classifications for grid cells in the vicinity of Be’er Sheva.
Note: Locality boundaries shown in black. Degree of urbanisation classifications are based on estimations using satellite imagery and classification algorithms and thus can produce errors. Satellite imagery from five primary observation epochs ranging from 1975 to 2018 are used for the underlying classifications.
Source: Israel Central Bureau of Statistics (2023[7]), MMG products - Geographical Information System (GIS), https://www.cbs.gov.il/he/publications/Pages/2022/%D7%A7%D7%98%D7%9C%D7%95%D7%92.aspx (accessed on 4 July 2023); European Commission (2023[6]), GHSL Data Package 2023, https://doi.org/doi:10.2760/098587, JRC133256 (accessed on 3 July 2023).
Utilising population grids, local authorities in Israel are further classified into 1) cities, 2) towns and semi-dense areas and 3) rural areas. Like for grids, local authorities are classified based on the share of population within each classification. For example, in Figure 3.1, Be’er Sheva is classified as a city while other local authorities such as Omer and Tel Sheva are classified as towns and semi-dense areas. Regional councils that contain multiple settlements that are not necessarily contiguous are classified into the Degree of Urbanisation category where the largest share of the population resides. Roughly 60% of the population in Israel live in cities, while 33% live in towns and semi-dense areas. Towns and semi-dense areas constitute the majority of local authorities both for majority Arab-Israeli and majority Jewish local authorities.
Defining metropolitan areas using the degree of urbanisation
The degree of urbanisation classification is utilised to define Functional Urban Areas (FUAs), which are widely considered to represent functional boundaries for metropolitan areas (Dijkstra, Poelman and Veneri, 2019[8]).
Given the degree of urbanisation classifications for grid cells, FUAs are defined in three steps. First, urban centres are identified as one or more small spatial units (in most cases statistical areas for Israel) that have at least 50% of their population in grid cells also identified as a city (urban centre). Next, a commuting zone is defined as a set of contiguous small spatial units (localities for Israel) that have at least 15% of their employed residents working in the urban centre. Finally, a FUA is defined as the urban centre plus its commuting zone. Table 3.1 presents the 14 FUAs identified for Israel. The name of the FUA corresponds to the locality with the largest population within the FUA. FUAs are particularly useful to support policymaking in several domains, including spatial planning and development, economic development, transportation planning, and public services provision (Eurostat, 2021[4]).
Note: The TL2 region corresponds to the district in which the largest locality within the FUA is located. OECD large (TL2) regions represent the first administrative tier of subnational government.
Source: Israel Planning Administration (IPA).
Israel is a highly urbanised country. Utilising the degree of urbanisation classification, cities make up 4.4% of Israel’s land. Israel ranks sixth among OECD countries and well above the OECD average of 0.58% (OECD, 2022[9]). Including towns and semi-dense areas, this percentage increases to 13.2%, nearly 10 times the OECD average of 1.5%. Population wise, only 8.7% live in rural areas, which is the lowest among OECD countries, while 58.8% live in cities.
Along with urbanisation, Israel has experienced rapid population growth. Between 2000 and 2022, Israel’s population increased by 52%, more than three times the OECD average of 15% (OECD, 2023[10]).Population growth across space has been uneven. Growth has been concentrated in the urban periphery, particularly in areas between 10 and 30 kilometres from the urban centre (Figure 3.2). Population growth in areas between 15-20 kilometres from the urban centre of the nearest FUA was 16 percentage points higher than overall population growth. On the contrary, areas between 0 and 5 kilometres from the urban centre of the nearest FUA experienced population growth 9 percentage points below the national rate. Among FUAs, Netanya experienced an increase 18 percentage points above the national rate, followed by Tel Aviv- Yafo with 9 percentage points (Figure 3.3). Meanwhile, FUAs relatively further away from Israel’s centre such as Haifa and Nazareth experienced population growth significantly lower than the national rate. This suggests that Israel is continuing to concentrate in Tel Aviv and its surrounding areas, but that its population is spreading out from urban centres, resulting in already large cities further expanding in territorial terms.
Percentage point difference between population growth in distance band and the national rate
Note: Author’s calculations based on the European Commission GHSL (GHS-POP, R2023). Population change is calculated for 1 square kilometre grids based on the European Commission GHSL (GHS-POP, R2023) and aggregated to distance bands.
Source: European Commission (2023[6]), GHSL Data Package 2023, https://doi.org/doi:10.2760/098587, JRC133256 (accessed on 10 Aug 2023).
Note: Author’s calculations based on the European Commission GHSL (GHS-POP, R2023). Population change is calculated for 1 square kilometre grids within each FUA based on the European Commission GHSL (GHS-POP, R2023).
Source: European Commission (2023[6]), GHSL Data Package 2023, https://doi.org/doi:10.2760/098587, JRC133256 (accessed on 10 Aug 2023).
The composition of population change is strongly dependent on ethnicity and religion as well as on the degree of urbanisation (Figure 3.4). From 2016-2020, 99% of the population increase in Arab-Israeli local authorities was due to natural growth, while in Jewish local authorities, the percentage was 71%, the rest being attributable to migration. Across cities, towns and semi-dense areas, and rural areas, the natural growth was similar in Arab-Israeli local authorities, while for Jewish local authorities, the natural increase was lowest in cities. Considering that Haredi local authorities make up the majority of Jewish local authorities with high natural growth, the difference in natural growth between Arab-Israeli and non-Haredi Jewish local authorities is greater than shown.
These trends translate to divergent population age compositions across local authorities (Figure 3.5). Arab-Israeli local authorities in cities tend to be the youngest, with the median share of population aged 19 and under at 36% and between 20 and 34 at 26%. Jewish local authorities tend to be older overall, and oldest in cities. Two noteable exceptions are Tel Aviv-Yafo and Jerusalem, where the share of population between 20 and 34 was among the highest of majority Jewish cities. Nonetheless, the median share of the population 65 and over was 16% in Jewish cities, which is significant considering the relatively young population composition of Israel. This percentage is also similar to the OECD average of 18%, which includes many countries particularly in Europe and East Asia that are experiencing ageing (OECD, 2023[2]). The divergent demographics of local authorities depending on degree of urbanisation and ethnicity will necessitate differentiation across regions in the level and type of public services, amenities, and employment opportunities that are provided or incentivised through spatial planning and development efforts.
Note: The Israel Central Bureau of Statistics identifies majority Arab and Jewish local authorities based on population shares. Due to extreme values, the figure excludes Harish, a rural, Jewish local authority with a natural increase of 98% and net migration of 1242%. Size of bubbles correspond to the population size of the local authority. Boxes represent the interquartile range (IQR) corresponding to values between the 25th and 75th percentiles. Lines inside boxes indicate the median value. Vertical lines outside boxes (whiskers) extend to the furthest data point in each wing that is within 1.5 times the IQR. P-values correspond to the t-tests for difference of means between groups.
Source: Israel Central Bureau of Statistics (2023[11]), Regional Statistics, https://www.cbs.gov.il/en/settlements/Pages/default.aspx?mode=MoazaEzorit (accessed on 2 August 2023); European Commission (2023[6]), GHSL Data Package 2023, https://doi.org/doi:10.2760/098587, JRC133256 (accessed on 3 July 2023).
Notes: See notes for Figure 3.4.
Source: Israel Central Bureau of Statistics (2023[11]), Regional Statistics, https://www.cbs.gov.il/en/settlements/Pages/default.aspx?mode=MoazaEzorit (accessed on 2 August 2023); European Commission (2023[6]), GHSL Data Package 2023, https://doi.org/doi:10.2760/098587, JRC133256 (accessed on 3 July 2023).
Israel is densely populated, with 5% of its land mass classified as built-up in 2014, five times the OECD average of 1% (OECD, 2023[10]). 0.5 percentage points of Israel’s land mass was developed between 2000 and 2014, also higher than the OECD average of 0.15 percentage points (OECD, 2023[10]). This can partially be attributed to Israel’s rapid population growth. However, the density of new development in some regions in Israel also tends to be lower than in other OECD regions, especially in Europe, as the amount of new built-up area per additional person reveals (Figure 3.6). The density of new developments was particularly low in the Northern District, where an additional 242 square metres of land was newly built-up for every additional person, almost three times the amount of 85 square metres for a representative region with similar increases in population on the trendline. This gap widens when excluding North American regions especially in the United States and Canada, where land tends to be abundant and population density low. The density of new developments in the Jerusalem and Tel Aviv Districts were higher than in other regions in Israel, likely due to the limited amount of additional land that is available for development in Israel’s centre.
Density of new development versus population change, logarithmic scale
Note: Development density is calculated as the amount of newly built-up area divided by the change in population. Excludes TL3 regions where population declined during the period. The blue line corresponds to the trendline. More information on territorial grids classifying TL1, TL2, and TL3 regions can be found in OECD (2022[12]).
Source: OECD (2025[13]), OECD Regions, cities and local statistics database https://www.oecd.org/en/topics/regions-cities-and-local-statistics.html (accessed on 2023 Aug 1).
This pattern of development is also evident when examining changes in built-up area per capita across FUAs (Figure 3.7). Built-up area per capita decreased sharply between 2000 and 2020 in the FUAs near the centre of Israel, including in Netanya, Jerusalem, Ashdod, and Tel Aviv-Yafo, while increasing in FUAs further away from the centre, such as in Umm al-Fahm and Qiryat Gat. Within FUAs, when comparing between cities and commuting zones, a clear pattern emerges where cities in general are either densifying or at least maintaining their current densities, while commuting zones are becoming increasingly less dense, despite strong population growth in semi-dense areas. The sharp increases in built-up area per capita in the communiting zones of FUAs, particularly in Ashdod, Ashqelon, Haifa and Qiryat Gat, suggest that new developments in commuting zones largely fail to attract populations that continue to concentrate in urban centres.
Note: Author’s calculations based on the European Commission GHSL (GHS-BUILT-S, R2023). Values are calculated for 1 square kilometre grids and aggregated to Functional Urban Areas.
Source: European Commission (2023[6]), GHSL Data Package 2023, https://doi.org/doi:10.2760/098587, JRC133256 (accessed on 10 Aug 2023).
This phenomenon can be observed more clearly when comparing the change in built-up surface area versus volume by distance to urban centres (Figure 3.8). Areas in the urban periphery, between 5 and 20 kilometres from the main urban centre in the FUA, experienced relatively large increases in built-up surface. However, the change in built-up volume in these areas did not increase in similar proportions. For example, the built-up surface in areas between 5 and 10 kilometres from urban centres increased by 30% between 2000 and 2020, while built-up volume increased only by 20%. As a result, the overall built-up height in towns and semi-dense areas decreased by about 6%, compared to 2% for cities. Overall, the density of built-up areas in Israel is decreasing across all regions, especially for towns and semi-dense areas in the urban outskirts. An exception is the areas very close to urban centres, within 2.5 kilometres, where built-up surface and volume increased in similar proportions, although at overall lower rates.
Note: Horizontal lines correspond to country-level averages. Author’s calculations based on the European Commission GHSL (GHS-BUILT-S, GHS-BUILT-V, R2023). Values are calculated for 1 square kilometre grids and aggregated to distance bands.
Source: European Commission (2023[6]), GHSL Data Package 2023, https://doi.org/doi:10.2760/098587, JRC133256 (accessed on 10 Aug 2023).
The planning of new housing tends to be misaligned with demand across space (Figure 3.9). Housing is most expensive in areas within 10 kilometres of urban centres, which reflects greater housing demand in these areas. However, new residential building permits are being issued at a greater proportion in areas further away from large urban centres. The number of residential building permits was smaller than the number of housing starts in authorities within 10 kilometres from the nearest FUA core, suggesting that, in these areas, the supply of new housing is restricted by land-use regulations. On the contrary, more building permits were issued than housing starts in local authorities further away from cities, suggesting an over-supply of new building permits in the urban peripheries. These trends reinforce sprawling development patterns while hindering densification in urban centres where housing is needed most.
By distance of local authorities to the closest FUA core
Note: Number of building permits are averaged across 2014 to 2020. House prices are the average across local authorities weighted by the number of residential units. Data exclude regional councils and other local authorities without relevant data.
Source: Israel Central Bureau of Statistics (2023[11]), Regional Statistics, https://www.cbs.gov.il/en/settlements/Pages/default.aspx?mode=MoazaEzorit (accessed on 2 August 2023); European Commission (2023[6]), GHSL Data Package 2023, https://doi.org/doi:10.2760/098587, JRC133256 (accessed on 3 July 2023).
Israel has raised its climate ambitions in recent years, by, for example, setting an 85% greenhouse gas (GHG) reduction target for 2050 and declaring an overall ambition of carbon neutrality by the same year (OECD, 2023[3]). In May 2022, the government also approved a draft Climate Law that enshrines 2030 overall climate change mitigation targets while formulating long-term frameworks for action to reach carbon neutrality by 2050. However, Israel is not on track to reaching these targets with existing measures. Israel recorded emissions of 8.8 tonnes of CO2 equivalent per capita in 2021. While still below the average of 10.5 for OECD countries, growth has nonetheless been steady since 1990, as opposed to many other countries that have reduced per capita emissions in recent years (OECD, 2023[3]). Pressure for more ambitious action is likely to rise, as net zero GHG emission targets by 2050 have been adopted by many OECD countries (OECD, 2021[14]) and the risks of catastrophic consequences of global warming beyond 1.5 degrees call for very steep upfront emission reductions (OECD, 2022[15]).
GHG emissions grew by 95% between 1990 and 2018 in Israel, fourth among OECD countries and 8 times the OECD average of 12% (OECD, 2022[9]). Estimated growth in GHG emissions has outpaced most OECD regions even after accounting for population change (Figure 3.10). In particular, emissions grew 144% and 140% respectively in the Jerusalem and Southern districts, nearly twice the amount of other regions that experienced similar population growth. Only the Central District, where land use also tends to be most dense, observed emissions growth below that which is expected given population growth figures.
Note: Regions within the 38 OECD countries are classified on 2 territorial levels reflecting the administrative organisation of countries. The 433 OECD large (TL2) regions represent the first administrative tier of subnational government. Estimated emissions growth for production-based greenhouse gas emissions on the basis of infrastructure proxies. Production-based greenhouse gas emissions are equal to the sum of the amount of emissions of the six greenhouse gases targeted in the Kyoto Protocol (CO2, CH4, N20, CFK, HFK and SF6, in CO2 equivalents).
Source: OECD (2022[9]), OECD Regions and Cities at a Glance 2022, OECD Publishing, Paris, https://doi.org/10.1787/14108660-en.
Taken together, the rapid growth in built-up area, lower built-up density, and growth in GHG emissions present challenges for Israel. Electricity generation and transport are the largest contributors to emissions, contributing 49% and 24% to estimated total emissions respectively in 2019 (OECD, 2023[3]). Car use accounts for the bulk of transport emissions. Greater built-up area at lower densities, in particular in the form of low-density detached housing away from urban centres, with little access to public facilities and jobs through active mobility or public transport, significantly increases energy use by reducing energy efficiency, while also increasing reliance on private vehicles, as well as energy-intensive and costly infrastructure construction needs. To meet climate targets, Israel will need to incorporate sustainability considerations into future land use and spatial development efforts. The electrification of energy use combined with the needed shift in electricity generation from fossil fuels to renewables requires integrating large-scale expansion of renewable electricity generation in spatial plans as well integrating major energy efficiency efforts to keep the expansion manageable.
Increasing built-up area also affects biodiversity and the degradation of land, pressures natural landscapes and adds to land scarcity and habitat fragmentation. More mammal species are threatened in Israel than in any other OECD country. Rising artificial surface is a driver of biodiversity loss, durably prevents land to provide ecosystem services and may accelerate water run-off. It is expected that climate change will further pressure biodiversity (OECD, 2023[3]). The number of days of exposure to heat stress has also increased by 26 days in Israel when comparing 1981-2010 with 2017-2021 figures (Figure 3.11), which is the largest increase among OECD countries (OECD, 2022[9]). In particular, the Haifa and Tel Aviv districts experienced increases of greater than 35 days per year. This will add to the pressure to increase emission-free electricity generation, notably from renewables, and the electricity grid as indoor cooling needs increase, yet electricity generation is very carbon intensive in Israel at 516 grams of CO2 equivalent per kWh, third among OECD countries. The share of renewable energy in electricity generation is one of the lowest in the OECD, despite having vast potential for low-cost solar power generation, with major potential economic and environmental benefits (OECD, 2023[2]).
Note: Universal Thermal Climate Index (UTCI) values are divided in 5 classes of heat stress: no thermal stress (9°C-26°C), moderate heat stress (26°C-32°C), strong heat stress (32°C-38°C), very strong heat stress (38C°-46C°), and extreme heat stress (>46°C). Number of days of exposure to heat stress is measured as the number of days with a UTCI greater than 32 degrees Celsius. The minimum value for Colombia (COL) is -13.4 in Putumayo. The maximum value for Australia (AUS) is 61.5 in Northern Territory.
Source: Di Napoli, C. et al. (2020[16]), Thermal comfort indices derived from ERA5 reanalysis, https://doi.org/10.24381/cds.553b7518; OECD (2022[9]), OECD Regions and Cities at a Glance 2022, OECD Publishing, Paris, https://doi.org/10.1787/14108660-en.
Given these trends, maintenance and densification of vegetation and green spaces, also within residential and built-up areas, is a key priority. Urban green spaces also benefit the overall built environment and resident well-being. However, low-density vegetation makes up 63% of all vegetation in cities, and 53% of vegetation in towns and semi-dense areas (Figure 3.12, upper panel). The lack of high-density vegetation is more pronounced in low-income areas. 74% of vegetation is low-density in low-income areas, compared to 44% in high-income areas. This disparity applies across all region types yet is most pronounced in towns and semi-dense regions. High-density vegetation comprises 31% of total land mass and 70% of total vegetated area in high-income areas in towns and semi-dense regions, contrasting with low-income areas in these regions where the percentages are just 8% and 21% respectively. Disparities in residential characteristics are also significant, with most low-density housing located in high-income areas (Figure 3.12, lower panel). These differences are again most pronounced in towns and semi-dense areas.
To summarise, high-income areas in towns and semi-dense areas between cities and rural areas tend to be dominated by low-density, sprawling developments. These towns and semi-dense areas are home to detached housing and better amenities such as ample green spaces, which in turn aggravates spatial segregation by further attracting wealthy residents. The example of high-income city areas suggests that there is room to both densify settlements and vegetation.
Note: Local authorities are classified as high or low income based on the average salary per month of employees relative to the national median. High-density vegetation areas correspond to areas with a Normalized Difference Vegetation Index (NDVI) of greater than 0.3. Low-density residential areas correspond to areas where the average residential building height in residential built-up areas is less than 6m. Author’s calculations based on the European Commission GHSL (GHS-BUILT-C, R2023).
Source: Israel Central Bureau of Statistics (2023[11]), Regional Statistics, https://www.cbs.gov.il/en/settlements/Pages/default.aspx?mode=MoazaEzorit (accessed on 2 August 2023); European Commission (2023[6]), GHSL Data Package 2023, https://doi.org/doi:10.2760/098587, JRC133256 (accessed on 3 July 2023).
These development practices also result in disparities in environmental footprints. The number of private vehicles per 1 000 inhabitants varies across cities and towns and semi-dense areas based on levels of income (Figure 3.13). Unsurprisingly, private vehicle ownership is highest in high-income towns and semi-dense areas, where sprawling development practices necessitate greater private vehicle usage, especially for commuting. Even for low-income areas, the reliance on private vehicles is higher in towns and semi-dense areas and rural areas compared to cities. This also exacerbates socioeconomic inequalities as road infrastructure takes precedence over public transport provision, depriving low-income households who cannot afford private vehicles of alternative transport options, potentially affecting their access to jobs and socioeconomic opportunities. Other vulnerable individuals who cannot drive cars, such as the elderly or youth, may also be adversely affected. Private vehicle use contributes to small particle pollution exposure especially in urban areas. Small particle pollution has major public health impacts, notably on children and on their cognitive development (OECD, 2021[14]).
Source: Israel Central Bureau of Statistics (2023[11]), Regional Statistics, https://www.cbs.gov.il/en/settlements/Pages/default.aspx?mode=MoazaEzorit (accessed on 2 August 2023).
Strong relationships between income, ethnicity and environmental footprints can also be observed for other indicators, such as wastewater generation (Figure 3.14). A positive relationship exists between wastewater generation per capita and average incomes at the municipal level. Local authorities with high wastewater generation mostly host high-income Jewish populations in towns and semi-dense areas. The average amount of wastewater generated per capita was 47 square metres for low-income (mostly Arab-Israeli) local authorities, compared to 85 square metres for high-income (mostly Jewish) local authorities. Similar patterns can be observed for the amount of municipal waste generated per capita (Israel Central Bureau of Statistics, 2023[11]). Overall, the observed spatial disparities in residential environments and in the quality of built-up areas suggest that Israel’s current spatial development practices exacerbate inequalities across multiple domains. In addition, current spatial development practices hinder the efficient use of scarce resources, such as land and water.
Source: Israel Central Bureau of Statistics (2023[11]), Regional Statistics, https://www.cbs.gov.il/en/settlements/Pages/default.aspx?mode=MoazaEzorit (accessed on 2 August 2023).
Economic disparities between local authorities in Israel are large. The average monthly salary of employees in the richest local authority was more than three times that of the poorest local authority in 2019 although Israel is one of the smallest OECD countries (Israel Central Bureau of Statistics, 2023[11]). These income disparities continue to widen, with growth in disposable income being largely confined to wealthy local authorities. Between 2006 and 2016, the income disparity between the top 10% and the bottom 10% of regions grew by an annual average of more than 5%, the fastest pace among all OECD countries with comparable data (Figure 3.15). Experience across OECD countries suggests that territorial disparities in income and income growth, access to jobs, services and amenities can generate a “geography of discontent”, where rising dissatisfaction in left-behind regions can manifest itself in opposition to national policies. Poverty rates also vary significantly across regions. Only a tenth of Tel Aviv’s residents live below the poverty line, while in Jerusalem almost half of residents do so (OECD, 2020[1]).
Difference in growth rate of per capita income between the top 10% and the bottom 10% of large regions (2006-16)
Note: The figure shows the change between 2006 and 2016 in the ratio of average disposable income per capita of the richest 10% and poorest 10% TL2 regions. Richest and poorest regions are the aggregation of regions with the highest and lowest income per capita and representing 10% of national population, respectively.
Source: OECD (2020[1]), OECD Economic Surveys: Israel 2020, OECD Publishing, Paris, https://doi.org/10.1787/d6a7d907-en.
Income levels across cities, towns and semi-dense areas and rural areas also vary widely (Figure 3.16). This corresponds to the spatial segregation of ethnic and religious groups with weak labour market outcomes. For example, in cities, the median monthly income of employees in mainly Jewish local authorities was around 11 200 Israeli New Shekels (NIS), compared to less than 7 000 NIS for Arab-Israeli local authorities (Israel Central Bureau of Statistics, 2023[11]). Inequalities across ethnicities decrease in rural areas, mainly due to median incomes being lower in Jewish local authorities in rural areas. Nonetheless, even in rural areas, the difference in income levels between Jewish and Arab-Israeli local authorities is greater than 3 000 NIS and is highly statistically significant. The difference in income levels across the different Degree of Urbanisation categories is more significant for Jewish local authorities, with the difference between Jewish cities and Jewish rural areas being greater than 1 500 NIS and highly statistically significant (p-value of 0.0008). On the other hand, the difference in incomes across Degree of Urbanisation categories for Arab-Israeli local authorities is not significant. As cities are typically more productive and award higher wages, this suggests that Arab-Israeli local authorities in cities are mostly inhabited by workers working in low-skill occupations. From a spatial perspective, tackling income inequalities will require addressing disparities especially across cities and towns and semi-dense areas, in particular addressing disparities between Arab-Israeli and Jewish local authorities.
Average monthly salaries of all employees by degree of urbanisation and ethnicity of local authority
Note: See notes for Figure 3.4.
Source: Israel Central Bureau of Statistics (2023[11]), Regional Statistics, https://www.cbs.gov.il/en/settlements/Pages/default.aspx?mode=MoazaEzorit (accessed on 2 August 2023); European Commission (2023[6]), GHSL Data Package 2023, https://doi.org/doi:10.2760/098587, JRC133256 (accessed on 3 July 2023).
Spatial clustering can also be observed when considering overall socioeconomic levels. The correlation coefficient between the Socioeconomic index1 of a local authority and those of its neighbours (the Moran’s I index) is 0.23 and the positive relationship is highly statistically significant. Indeed, Figure 3.17 suggests a marked pattern of spatial segregation in the socioeconomic capacities of municipalities across Israel. Local authorities such as Savyon are located in the upper-right quadrant of the scatterplot, meaning that the socioeconomic index of the local authority and its neighbours are both high (high-high cluster). Local authorities located in the lower-left quadrant such as Tel Sheva are part of low-low clusters. Utilising such methods, clusters of local authorities can be identified for targeted interventions to avoid them falling behind further (Box 3.1). Such policies could aim at improving access to better job or education opportunities, for example.
The Moran’s I scatterplot for local authorities
Note: Neighbours are defined using a distance-based spatial weights matrix of 15-nearest-neighbours. Different spatial weights matrices and different nearest-neighbour thresholds will produce different clusters. Local authorities in red are part of significant spatial clusters. The local authority is used as the level of analysis due to its significance as a formal administrative body with powers over local taxes, regulations and policies. Author’s calculations based on the Socioeconomic index, 2019 (Israel Central Bureau of Statistics, 2023[11]).
Source: Israel Central Bureau of Statistics (2023[11]), Regional Statistics, https://www.cbs.gov.il/en/settlements/Pages/default.aspx?mode=MoazaEzorit (accessed on 2 August 2023).
Global indicators of spatial autocorrelation, such as the Moran’s I, Geary's C, and Getis-Ord Gi*, provide an overview of spatial patterns in a dataset by considering the overall spatial arrangement of a given variable. These indicators aim to answer the fundamental question of whether there is a significant spatial pattern (i.e. clustering) in the variable of interest, or whether it is distributed randomly. In other words, they evaluate whether nearby locations tend to have similar or dissimilar values for the variable of interest. Moran's I, one of the most commonly used indicators, measures the correlation between a location's attribute value and the values of neighbouring locations. Neighbours are determined using a number of methods, including among others, 1) distance-based metrics, where neighbours closer in geographical distance are awarded higher weights, 2) contiguity-based metrics, where only areas sharing a common border are considered neighbours, and 3) K-nearest neighbour metrics, which consider as neighbours the closest k number of areas.
Local Indicators of Spatial Autocorrelation (LISA) extend the concept by examining individual locations in detail. In the case of the Moran's I for example, values for each location reflect the extent to which a specific point and its neighbours exhibit similar or dissimilar values. For example, each individual point in Figure 3.17 is a LISA value for one local authority, showing its socioeconomic status as well as the socioeconomic status of its neighbours. The areas that contribute particularly strongly to the overall trend can be considered to be significant clusters.
LISA can be mapped, yielding insights into spatial clusters (Figure 3.18). In essence, LISA provide a granular analysis of spatial patterns, offering the ability to pinpoint areas of interest where clustering is particularly pronounced. By visualizing values on a map, areas with high-high or low-low clustering can be identified. This information can be used to inform policy and develop targeted interventions. LISA indices have been used in many applications, such as to identify clusters of disease hotspots for COVID-19 (Jesri et al., 2021[17]), patches of areas with poor accessibility to services (Iraegui, Augusto and Cabral, 2020[18]), and areas eligible for economic development programs (Carroll, Reid and Smith, 2007[19]).
Note: Clusters are determined by calculating the local Moran’s I for local authorities using a distance-based spatial weights matrix of 15-nearest-neighbours. Different spatial weights matrices and different nearest-neighbour thresholds will produce different clusters. The local authority is used as the level of analysis due to its significance as a formal administrative body with powers over local taxes, regulations and policies. Author’s calculations based on the Socioeconomic index, 2019.
Source: Israel Central Bureau of Statistics (2023[11]), Regional Statistics, https://www.cbs.gov.il/en/settlements/Pages/default.aspx?mode=MoazaEzorit (accessed on 2 August 2023).
Source: Anselin, L. (2010[20]), “Local Indicators of Spatial Association-LISA”, Geographical Analysis, Vol. 27/2, pp. 93-115, https://doi.org/10.1111/j.1538-4632.1995.tb00338.x.
Poorer local authorities often lack resources to finance adequate public services, further widening socioeconomic gaps between local governments (OECD, 2020[1]; OECD, 2021[21]). As a result, access to key services varies significantly across regions and the difference is even greater between low- and high-income local authorities, when measuring access to facilities as the count of facilities reachable within a 15-minute drive per 1 000 people (Figure 3.19, see Annex 3.A for details on accessibility calculations). When considering access to hospitals using geolocated data (Israel Ministry of Health, 2023[22]) measured on a scale of 0 to 100 (with 100 being the maximum), the median for high-income local authorities in cities was 2.5 times that of low-income local city authorities. The differences were larger in towns and semi-dense areas, where accessibility in high-income areas was 3 times that of low-income areas. Similar disparities can be identified when considering access to schools, using data from the Israel Ministry of Education (2023[23]). Overall, the difference in accessibility to public services is mostly driven by differences between income levels across local authorities, rather than across Degrees of Urbanisation, which is partially due to the fact that accessibility is calculated on a per capita basis. While the location of hospitals and schools is not necessarily determined by local authorities and their financial resources, these gaps highlight that even accounting for population densities, high income local authorities enjoy better access to public services on a per capita basis. This suggests that, in particular, central government efforts to provide for equitable public service provision should be focused more in lower-income local authorities.
Count of facilities reachable within 15 minutes by car per capita, 0 (minimum) to 100 (maximum)
Note: See notes for Figure 3.4. Hospitals include health institutions and clinics that do not classify as laboratories, nurse services, or pharmacies. Schools include education facilities that are classified as schools and excludes other facilities including kindergartens and colleges. Accessibility is calculated by dividing the number of facilities reachable within 15 minutes driving time by the population count. Driving time data obtained from map data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org.
Source: Israel Central Bureau of Statistics (2023[11]), Regional Statistics, https://www.cbs.gov.il/en/settlements/Pages/default.aspx?mode=MoazaEzorit (accessed on 2 August 2023); Israel Ministry of Education (2023[23]), Data Gov: Coordinates of the educational institutions, https://data.gov.il/dataset/coordinates (accessed on 6 September 2023); Israel Ministry of Health (2023[22]), GIS-health-opendata, https://gis-health-opendata-imoh.hub.arcgis.com/ (accessed on 6 September 2023); European Commission (2023[6]), GHSL Data Package 2023, https://doi.org/doi:10.2760/098587, JRC133256 (accessed on 3 July 2023).
Densification of new developments mixed-uses and preparing decarbonisation should be the top priority.
Israel suffers from rapid degradation of open spaces and agricultural land (OECD, 2023[3]). Rapid increases in built-up area and lower development densities cause more pressure on open natural landscapes and add to land scarcity and habitat fragmentation in a country already severely limited in land resources. Sprawling development also affects biodiversity, with ecosystems under significant stress outside protected areas (OECD, 2023[3]). Sprawling development also increases dependence on private vehicles and increases overall GHG emissions.
Israel should intensify efforts to minimise urban sprawl and protect terrestrial ecosystems. Such efforts are also crucial for Israel to meet its ambitious climate goals, such as the 85% reduction in GHG emissions by 2050. New developments, especially for residential areas, should focus on the densification of urban cores, and the utilisation of brownfields and other areas already built-up within cities. New developments in urban peripheries, if needed, should be located as close as possible to current urban centres, and be built at higher densities.
Spatial planning should integrate the needed massive expansion of renewable electricity generation, including through the use of grey field sites, available rooftops (including for water reservoirs), and the needed boost to energy efficiency. It should also anticipate expected extreme climate events, including the incidence of draught and extreme heat.
Prioritize transit-oriented development to advance multiple spatial planning objectives
Israel should systematically implement transit-oriented development (TOD) as a cornerstone of its spatial planning and development framework, Current development patterns reinforce sprawling development, car dependency, and socioeconomic segregation. New spatial development should be strategically concentrated around existing and planned public transit nodes, with minimum density requirements and mixed-use zoning that combines residential, commercial, and public service functions. This approach would be particularly beneficial in towns and semi-dense areas between 5-20 kilometres from urban centres, where the analysis shows that built-up density has systematically decreased, indicating inefficient, sprawling development patterns.
The implementation of TOD would simultaneously address multiple challenges identified in the analysis. TOD can help reduce GHG emissions by reducing car dependency, improve accessibility to jobs and services for low-income households, encourage residential density in areas well-served by transit, and promote social integration by creating mixed-income communities around transit nodes. To maximize these benefits, TOD zones should require a minimum share of affordable housing units and include adequate public services and facilities. This would help counter current trends while also supporting Israel's climate goals and social cohesion objectives.
Housing should be provided where it is needed most.
The current spatial planning and development framework incentivises the development of residential areas in urban peripheries and rural areas that are not well connected to employment centres in the urban cores. For example, the plan for Complex E in Rosh Ha’ayin includes just two road connections to the central city for roughly 4 500 residential units contained within an area of 812 dunams (Figure 3.20). Such development practices exclude low-income households that cannot afford private vehicles from benefiting from new housing provision. It also creates a mismatch of housing supply and demand that significantly raises house prices especially in large urban cores.
Complex E plan, developed gradually since 2005
Source: Savay, Y. (2023[24]), Complex E plan for Rosh Ha’avin.
To increase affordable housing supply where it is most needed and reduce the prevalence of planned-but-not-built neighbourhoods (Feitelson et al., 2021[25]), housing development needs to target urban cores in cities where housing demand is high, instead of following current development patterns that prioritise large-scale residential developments in urban peripheries. In addition, residential development should not incentivise the use of private vehicles, and should follow transit-oriented development (TOD) practices. Publicly owned land should be managed strategically and used for the provision of affordable housing where possible. This can be done, for example, through joint development agreements with private developers or not-for-profit housing providers.
Quality green spaces and vegetation should be better provided in built-up areas, especially in low-income neighbourhoods.
Population exposure to heat stress in Israel is severe and increasing at the fastest pace among OECD countries. This will necessitate the smart utilisation of nature-based solutions such as increased surface greenery, vegetation, green roofs, and vegetated vertical surfaces. Such solutions are especially important considering that electricity production is highly carbon-intensive in Israel, despite vast solar generation potential (OECD, 2023[2]).
To address the effects of climate change, the provision of dense vegetation and green spaces, together with other nature-based solutions, should be prioritised especially in low-income areas where households are less able to afford indoor cooling. This would also have benefits for biodiversity protection and to halt the degradation of land. Given that the share of low-density vegetation is much higher in low-income neighbourhoods while the overall amount of vegetated areas are similar, this can be achieved by increasing vegetation densities without significantly altering land uses. In addition, residential areas in high-income areas, especially in the urban outskirts, need to be densified.
Addressing socioeconomic inequalities across space is crucial for well-being and productivity growth.
Socioeconomic gaps in Israel are among the widest in the OECD. The country remains a two-speed economy, with its highly productive high-tech sector coexisting with low productivity traditional sectors (OECD, 2023[2]). This duality extends to labour market outcomes, especially between the Haredim (ultra-Orthodox Jews) and Arab-Israeli on the one hand, and non-Orthodox Jews on the other and leaves economic development opportunities for the country as a whole unexploited.
This duality has a clear spatial dimension, as the tendency for religious communities or ethnic groups to cluster together is strong. The socioeconomic status of residents in majority Arab-Israeli local authorities is on average significantly lower than those in Jewish localities across all available metrics. Furthermore, the gap between Jewish and Arab-Israeli local authorities is much wider in cities than in rural areas. Addressing these inequalities will be a major priority for Israel, especially as the population share of Arab- and Haredi- Israelis is projected to increase from 30% currently to 50% by 2060 (OECD, 2023[2]).
Demographics wise, the share of youth is highest in Arab-Israeli and Haredi localities within and near cities. Spatial planning and development should prioritise providing affordable housing, good public transportation access, and better access to public services and infrastructure to these localities. Such measures would not only improve inclusiveness, but enhance labour market participation, improve skills, and foster labour mobility and upward social mobility for the disadvantaged. On the contrary, the share of elderly is higher in non-Orthodox Jewish localities, especially in cities. Measures to improve access to health care and other welfare services should be prioritised in such areas, together with inclusive mobility and transport solutions.
Government interventions in spatial development should target and identify spatially clustered disadvantaged areas through multi-criteria analysis
A multi-criteria spatial clustering analysis that combines the Local Indicators of Spatial Association (LISA) methodology outlined in Box 3.1 with key socioeconomic and accessibility indicators can be utilised to effectively identify lagging regions for targeted spatial interventions. Targeting should focus on identifying clusters of local authorities that exhibit both low socioeconomic index values and significant spatial correlation with similarly disadvantaged neighbours. This clustering methodology can be enhanced by incorporating additional criteria including accessibility to public services, income levels, and environmental quality indicators. Such a targeting framework would help pinpoint not just individual struggling localities, but geographic clusters of disadvantage that could benefit from coordinated intervention.
Targeted interventions should be designed as integrated packages that address multiple dimensions of spatial inequality. These packages should include: (1) targeted infrastructure investments to improve connectivity to employment centres, particularly through public transit to reduce car dependency; (2) strategic placement of public services to address the identified gaps in accessibility between high and low-income areas; (3) environmental interventions, including to increase high-density vegetation coverage in low-income neighbourhoods and reduce car dependency; and (4) incentives for mixed-use, dense development to counter current sprawling patterns while improving access to jobs and services. Given that socioeconomic disparities are particularly pronounced in cities, with Arab-Israeli localities showing significantly lower incomes than Jewish localities, special attention should be paid to urban clusters of disadvantage.
Accessibility measures allow comparing levels of physical accessibility to facilities for a certain region (Boisjoly, Moreno-Monroy and El-Geneidy, 2017[26]). A commonly used measure of accessibility using areas as point of reference is the cumulative opportunities index that counts the number of opportunities (e.g. number of hospitals or schools) that are reachable to users living in a given area (Moreno-Monroy, Lovelace and Ramos, 2018[27]). The cumulative opportunity index can be defined as:
where is the travel time between area i and facility j, and is a weight function that takes the value of 1 if the travel time is equal or less than a predefined threshold t, and zero otherwise.
While fairly easy to compute, the cumulative opportunities index fails to account for the fact that areas closer to more facilities are usually home to a greater number of potential users for the facilities. To overcome this limitation, the competitive accessibility index extends the measure to account for the level of competition for access to the same facility, by discounting the number of facilities accessible from each area by the potential users for those facilities (Shen, 1998[28]). The competitive accessibility index can be defined as:
with defined as before, and being a measure of the size area k (e.g. population).
This study utilises the competitive accessibility index with a time threshold of 15 minutes via private automobile to calculate accessibility values for each local authority in Israel with respect to hospitals and schools. For hospitals, is defined as the total population of area k, while for schools, is defined as the population between 5 and 18 years of age.
A methodological challenge of using area-based accessibility indicators is defining a reference point, that is, a single set of coordinates (corresponding to area i) from which travel times can be calculated. For the purposes of the study, the reference points are defined as the centroids of 1 square kilometre population grid cells, and the accessibility value for a local authority is the population-weighted average of the accessibility values for the grid cells that are within its boundaries.
Operationally, the accessibility index is calculated by first computing the areas that are reachable within 15 minutes from a population grid centroid. The result is an isochrone (from the Greek ʌɪsə/iso – same, krɒn/chronos: times), a set of lines connecting all points representing the maximum distance that can be travelled in every direction from the centroid within the time threshold (Annex Figure 3.A.1.). The isochrones are calculated using the Open Source Routing Machine (OSRM) provided through OpenStreetMap, using the osrm package in R. A 1-kilometre buffer is applied to the isochrone to account for the dimensions of the population grid. Afterwards, the count of facilities within this isochrone is calculated, which corresponds to the numerator in the equation above. The denominator is calculated by computing the isochrones with the facilities as the origin, and subsequently summing the population values for all population centroids that are located within the computed isochrone.
Notes: Isochrone calculated with a time threshold of 15 minutes for private automobile transportation. Driving time data obtained from map data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org.
Source: Author’s calculations based on European Commission (2023[6]), GHSL Data Package 2023, https://doi.org/doi:10.2760/098587, JRC133256 (accessed on 3 July 2023); Israel Central Bureau of Statistics (2023[7]), MMG products - Geographical Information System (GIS), https://www.cbs.gov.il/he/publications/Pages/2022/%D7%A7%D7%98%D7%9C%D7%95%D7%92.aspx (accessed on 4 July 2023).
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← 1. The Socioeconomic Index is a composite index used by the Israel Central Statistical Bureau to characterise the socioeconomic condition of the population of local governments based on 14 demographic, economic and social variables. The index is used by the central government in the implementation of a number of policies related to local governments. The local authority is used as the level of analysis due to its significance as a formal administrative body with powers over local taxes, regulations and policies.