Higher Education in Israel in 2022: Peripheral Areas and the Impact of COVID-19

Abstract

This paper explores the continued growth in higher education in Israel since COVID-19 and specifically in its peripheral areas, using quantitative and qualitative measures. Access to higher education increased during the pandemic in 2020–2022, with more students from all parts of Israel, including its social and geographic periphery, beginning their studies during those years. Nevertheless, the gap between students from higher and lower-income localities persisted. The quantitative gap between the groups was also maintained when analyzing the number of people who pursued higher education within eight years of high school graduation. Qualitative gaps reflected by the prestige of the fields in higher education that students chose to study were maintained, and sometimes even grew. Students from higher-income localities took better advantage of opportunities for education than students from lower-income localities, thus preserving the gaps in earning power and limiting social mobility. Choosing professions that earn lower salaries is more common in the peripheral areas, due to the smaller selection of jobs there. Overeducation is also more common in the peripheral areas, where people often settle for jobs that do not suit their education to avoid moving away. This results in lower salaries, dissatisfaction with their jobs, and instability in workplaces.

1. Introduction

The revolution in higher education in Israel and worldwide in the past decades has resulted in a significant rise in the number of academic students and professionals in Western countries [1]. The traditional roles of higher education have changed, and are no longer limited to training researchers and creating the elitist leaders of the future. Instead, they now provide advanced professional training for anyone seeking high-paying careers and social mobility [2]. Many scholars have identified higher education as having a crucial impact on many aspects of life, including enhancing innovation and employment opportunities, improving productivity rates in the workforce, contributing to economic grow [3, 4] and social mobility, bridging social gaps, and reducing inequality in employment and in salaries [5]. Inequality in professional achievements creates social and economic challenges that can have a severe impact on social cohesion, result in a waste of economic resources, inhibit democratic processes, and compromise values of dignity and mutual equality [6]. One aspect of this inequality is reflected in gaps between the salaries of members of the different groups that comprise a society, as a result of many years of lack of access to higher education.

Over two decades have passed since the reform in higher education was introduced in Israel, but researchers in this field are still exploring its impact on reducing social gaps and unequal earning power. According to data for 2022 published by the Central Bureau of Statistics (CBS), higher education reduces social gaps in multiple areas:

1. Higher education effects rates of participation in the workforce, monthly salaries, and salary increases after graduation [4].

2. Higher education has been linked to reducing employment gaps between men and women [7].

3. The average monthly salary of all BA graduates was 29% higher in 2019 than the average salary of that year [7].

4. People with degrees in computer science, engineering, architecture, and medicine have higher average salaries that are as much as 3–4 times higher than people with degrees in education, teaching, languages, and arts [7].

5. Having parents with or without academic education impacts the number of people who pursue higher education, the subjects they chose to study, and dropout rates [8].

However, research has also identified a new phenomenon caused by the reform, known as “excess higher education” or “overeducation” [9]. This refers to a situation in which graduates struggle to find employment that suits their education and must suffice with work that generally requires less education than they acquired [10, 11]. As a result, academic graduates earn lower salaries because they are employed in roles that do not match their education, and often in less prestigious and lower-paying jobs. Another aspect of this phenomenon is that people settle for jobs that are unaligned with their education in order to remain in a certain geographic area in which suitable employment is unavailable.

In addition, although access to higher education has increased the number of academic students all over Israel, fields related to science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) result in greater earning power and more social mobility than fields such as education, teaching, humanities, etc. [12], thus creating qualitative gaps between different types of academic degrees. Substantial data published by CBS between 2019 and 2022 indicate a significant difference between the percentages of people who study STEM and ICT-related fields from higher and lower-income localities.

This data indicates the perpetuation of inequality of earnings, despite the increased accessibility. As a result, the social mobility of students from lower-income localities is limited. Furthermore, there is a significant correlation between the location of the localities that were ranked lower than others and the social and geographic periphery in Israel. In many cases, the peripheral localities are populated by new immigrants, Arabs, and the ultra-orthodox.

Although the COVID-19 pandemic that began in March 2020 increased the number of students in all institutions of higher education in Israel, this increase was not equally divided throughout in Israel, and lower growth rates were shown in lower-income localities that are ranked between 1 and 5 on the socio-economic scale.

1.1 Research questions

This study addressed the following questions:

1. Has increased access to higher education in Israel in the past 25 years helped reduce the gaps in higher education between young adults from low and high-income localities?

2. Has the phenomenon known as “overeducation” become more prevalent in peripheral areas and within weaker groups since access to higher education improved, thus preserving inequality between the earnings of people from lower and higher-income localities?

3. Did the increase in the number of students in all academic institutions since the COVID-19 pandemic change patterns in the selection of academic fields all over Israel, or has this only expanded the quality gap between students from lower and higher-income localities?

1.2 Hypotheses

1. The increased access to higher education in Israel in the past 25 years has enabled new populations such as Arabs and the ultra-orthodox to receive higher education and has increased the percentage of students from the peripheral areas. However, quantitative gaps remain in place, as the percentage of these populations in the academia is still lower than their percentage in the general population. In addition, the percentage of students from lower-income localities in the academia did not increase relative to the number of high school graduates in these areas who graduated during the eight years in which they were tracked, until 2019 [13].

2. The COVID-19 crisis, which increased the number of students in all institutions of higher education after a six-year recession, did not increase participation rates equally in all sectors of society. Students from lower-income localities struggled to take advantage of the pandemic to advance their studies, often due to technological challenges. This was especially applicable for students from the Arab sector, ultra-orthodox sector, and Israel’s geographic periphery.

3. Higher education is considered an essential factor for earning power and for reducing inequality in the workforce [3, 5, 14]. However, the percentage of students studying prestigious fields that can lead to higher earnings in the workforce is lower in low-income localities than in high-income localities, which preserves inequality in earnings.

4. The phenomenon called “overeducation” results in BA graduates from lower-income localities, new immigrants, and Arabs, to compromise on their careers and accept jobs for which they are overqualified. As a result, their earnings are lower than graduates with the same education who are employed in jobs that match their education level [10].

These hypotheses are also based on the credential inflation theory, caused by the unmonitored rise in the number of academic institutions and students [15, 16]. The labor market has been flooded with graduates with academic degrees that are unsuited for the needs of the economy, especially in the geographical periphery. This has caused people to compromise on jobs and workplaces, and to accept lower salaries than graduates employed in fields that match their education [10]. The result is a phenomenon known as overeducation, which is especially evident in the Arab sector, among new immigrants, and people of Eastern origin, and is less prevalent within the veteran Israeli sector and among people of Ashkenazi origin [11].

2. Literature review

During the first decades following the reform in higher education, the literature focused primarily on two main aspects – the changes that access to higher education caused among populations that had been underrepresented in academic institutions beforehand, and how the reform contributed to reducing social gaps in Israel and inequality in earnings, and to improving social mobility.

Since the spread of higher education in Israel and worldwide, the literature has presented two contradictory stances regarding its impact on society. One is the diversity approach, and the other is the stratification approach, each offering a different explanation for the social impacts of the reform.

Advocates of the diversity approach claim that the reform in higher education increased accessibility and made the academia more diverse by enabling different types of institutions to emerge, which can better address the changing needs of the contemporary market and economy [17]. The opportunities for education expanded, and new populations began to join the academia and acquire education in prestigious fields that would enable them to join the contemporary workforce in prestigious fields that would facilitate social equality and mutual respect, while the number of researchers and scientists grew as well. Statistical reports published by the Central Bureau of Statistics (CBS) and the OECD demonstrated the advantages of higher education [18, 19]. Educated employees earn 20% more than people with high school educations only, and climb the professional ranks faster than their peers without academic degrees. The statistics also show that the more educated the employees, the more likely they are to climb to higher socio-economic percentiles. Higher education and experience complement one another, and therefore the educated employee who works and gains experience will earn a higher salary than someone with equal or more experience but without an academic education [5].

Advocates of the stratification approach view unmonitored and uncontrolled access to the academia as potentially creating a stratified system [20]. The inconsistent and unmonitored expansion of higher education has created unequal competition between the new and veteran institutions. Valimaa [21] argued that stratification emerges as a result of conflict between the state, which strives for diversity to facilitate equal opportunities, and the public entities that oversee the system, which are controlled by the veteran institutions or at least influenced by their interests. Although this conflict can produce a diverse system, the more this diversity is applied, the more stratified it becomes.

Researchers who adopted the stratification approach claim that from the social perspective, although the reforms aspired to facilitate access to higher education and to include members of populations that had not been included beforehand, inequality in higher education persists. A study by the CBS tracked high school graduates who began their higher education up to eight years after graduating high school, and found that the percentage of academic students remained the same as it had been over a decade earlier [13].

The researchers who adopted the stratification approach identified problems that prove that the reform did not reduce social gaps, including the following:

1. The groups with higher socio-economic rankings took better advantage of the opportunities created by the expansion of the academic system than groups with lower socio-economic rankings, resulting in what is referred to as maximally maintained inequality (MMI).

2. The education system in lower-income localities is not always well-suited for meeting the acceptance criteria for institutions of higher education. Therefore, young adults from these localities are more likely to attend the newer institutions and study less prestigious fields that do not facilitate social mobility [13].

3. When access to higher education was first expanded, it created a situation that Collins [15] and Dore [16] called “credential inflation”. People are asked to present academic certification even when applying for jobs that do not require certification of this kind, and therefore academic education no longer contributes to social mobility as it did in the past. The assumption seems to be that anything that is available to the masses must be of inferior quality [22].

4. The phenomenon known as overeducation, in which education and employment are mismatched, remains evident. This is caused by the following:

   1. The academic fields selected: Overeducation is more common among graduates of the humanities and in certain fields in social sciences that among graduates of computer science departments [10].

   2. Lack of proficiency in the local language can force people with BAs to work in fields that do not require an academic education [23].

   3. Age and experience: The older the employees and the longer they remain in a certain job, the less likely they will switch to a job that is more aligned with their education.

   4. Gaps between ethnic groups: Overeducation is more common among Arabs, immigrants, and people of Eastern origin than among veteran Israelis of Ashkenazi origin [10, 11]. These scholars found that people of Eastern origin earn higher salaries on average than Arab employees, but Ashkenazi employees earn higher average salaries than all other ethnic groups analyzed, which are approximately 47% higher than salaries among the Arab population. Their study showed that ethnic groups such as immigrants from the former Soviet Union, people of Eastern origin, and Arabs do not have equal opportunities for their education to match their employment, compared to their veteran Israeli, Ashkenazi peers.

   5. Overeducated people employed in jobs that do not match their academic achievements tend to be unsatisfied with their jobs and to switch jobs more frequently. The researchers’ far-reaching conclusion was that higher rates of overeducation have as negative an impact on employees as lack of higher education [11].

   6. This phenomenon is also related to the employee’s geographic location and spatial flexibility. Employees who are more mobile and have more flexibility are less likely to experience overeducation than less-mobile employees who are more likely to remain in a specific area even if the job opportunities there are mismatched with their education and earning power.

5. The gaps between the earnings of people with academic degrees in lower and higher-income localities persist, as a result of the higher percentage of students from higher-income localities who study prestigious, high-paying fields. Graduates from lower-income localities tend to choose less prestigious fields and therefore earn lower salaries than those offered in professions that are in high demand, such as hi-tech and engineering [12].

Studies published by the OECD indicate that BA diplomas in STEM and ICT fields are those that facilitate social mobility, while the humanities and social sciences contribute less to social mobility and higher earnings [1]. The data demonstrates a process in which the gaps between different parts of society grow, as lower-income populations are less likely to pursue degrees in STEM and ICT fields due to the inadequate high school educations offered in peripheral areas. In addition, lower-income populations take less advantage of the opportunities created by easier access to higher education and new market trends, compared to higher-income groups. This is the theoretical basis for the maximally maintained inequality (MMI) theory, which states that higher-income groups will take better advantage of opportunities created by the emergence of academic institutions with high tuition fees, compared to lower-income groups [24]. The prestigious institutions of higher education will be dominated by people from higher-income sectors, and remain inaccessible to lower-income ones, thus preserving the gaps despite the reform in higher education [25, 26].

3. Methodology

The methodology used for this study was based on quantitative analysis tools and critical assessment of relevant literature. The research was also based on data collected from documents published by the CBS, Council of Higher Education (CHE), and OECD between 2017 and 2022.

The statistical correlations were tested using Spearman’s correlation in order to analyze two indices – one fixed and the other varying. The socio-economic index was the fixed index in this case, and the number of students in each category varied. The gaps between localities were analyzed based on the socio-economic index used by CBS to assess the social level of the residents of a local authority based on demographic composition, education, standard of living, employment, and retirement. Two groups of localities were compared – localities ranked low on the socio-economic scale, and localities with high socio-economic rankings.

This paper is divided into several sections. The first describes the various approaches in the literature towards the impact of accessible higher education on Israeli society; the second presents the variables and methodology used in this study; the third presents the findings of this study and new understandings that emerged regarding higher education and its impact on social and economic needs in Israel; and the fourth analyzes and discusses the findings. The paper ends with a summary section and the main conclusions of this study.

4. Results

4.1 COVID-19 and higher education

In the academic year of 2022, 336,553 students studied in institutions of higher education in Israel, with no significant growth compared to the previous year (2021). Beforehand, registration slowed between 2015 and 2018 in a process that lasted six years, with a decrease of 0.5% in registration in 2018 compared to the previous year (2017). However, in 2020–2021 there was a significant increase in the number of academic students due to the COVID-19 pandemic. In 2020, the number of BA students grew by 1.5% compared to the previous year, and by 7.5% in 2021. This trend stopped in the 2022 academic year and growth slowed for all tracks, leaving the numbers similar to those recorded for 2021, as shown in the Table 1.

Year	2020	2021	2022	2020	2021	2022	2020	2021	2022	2020	2021	2022
Universities1	76,278	81,630	88,641	38,603	39,590	39,582	11,641	11,829	11,855	126,522	133,049	140,078
Open University	42,581	46,171	44,839	2174	2496	2500	—	—	—	44,755	48,667	47,339
Budgeted academic colleges	59,147	63,585	63,919	4931	5693	5864	—	—	—	64,078	69,278	69,783
Extrabudgetary academic colleges	35,595	40,696	37,040	9584	12,018	11,297	5	28	—	45,184	52,742	48,337
Academic colleges of education	23,253	22,579	21,714	7926	9044	9302	—	—	—	31,179	31,623	31,016
Total	236,854	254,661	256,153	63,218	68,841	68,545	11,646	11,857	11,855	311,718	335,359	336,553

Table 1.

Number of students in all academic degrees by type of institution, 2020–2022.

¹

The students registered at the IDC were added to the lists of university students in 2022, and beforehand were listed as students in extrabudgetary colleges.

Source: CBS, Table A – Students in institutions of higher education based on type of institution and degree; absolute numbers, for the academic years of 2019–2020 and 2020–2021 [27].

Excluding Open University, the number of academic students in 2022 was 302,700, while in 2020 (before the pandemic), there were 266,963 registered students. The largest increases in BA and MA students in 2020–2021 were recorded in the budgeted and extrabudgetary academic colleges, with 9462 more students than in the previous year, comprising 2.8% of all BA students in the entire system. However, registration for BA degrees in colleges of education decreased that year, as part of a trend that persisted for four years, including during the COVID-19 pandemic, while percentages rose in all other types of institutions. Registration for MA programs in colleges of education increased in 2021 following a decrease in 2020, which indicates internal growth within these institutions as opposed to new students joining the system.

As noted, the rise in enrollment rates stopped in the 2022 academic year in all institutions, and the number of BA students in Open University decreased significantly. The only type of institution in which the numbers rose was universities, but this was the result of the change in the status of the IDC, which became a university.

However, when analyzing the number of new students who joined the system in 2022, it becomes apparent that the number of new BA students declined by 9.5% compared to the previous year, as shown in the Table 2:

Year/Type of institution	Universities	Budgeted colleges	Extrabudgetary colleges	Colleges of education	Total
2016–2017	19,326	15,678	9858	8352	53,214
2017–2018	18,883	15,407	9752	7940	51,982
2018–2019	19,354	15,724	11,093	7583	53,754
2019–2020	20,819	16,538	11,403	7469	56,229
2020–2021	24,313	19,045	13,709	7802	64,869
2021–2022	22,921	16,970	11,271	7519	58,681

Table 2.

The number of new BA students in 2017–2022.

Source: CBS, Figure 1 – New BA students by type of institution, 2017–2022 [28].

As shown, the numbers decreased in all types of institutions, practically returning to the number of students at these institutions during the 2019–2020 academic year, before the pandemic.

An important figure that can testify to the change in the academic population is the percentage of BA students who pursue an MA degree within five years of completing their undergraduate studies. The rates have been between 29% and 32% for over twenty years, although the number of institutions that grant MA degrees has grown from 11 to 57 in that time. This finding is the result of a change in the identities of the students who have sought higher education in the past 40 years and more. The majority are motivated by a desire to acquire a high-paying profession and are less interested in research and science [29]. This argument is supported by the percentage of students who complete research MA tracks compared to those who complete non-research MA tracks. For the past eight years and more, the percentage of MA graduates from research tracks has been declining, and in the past few years has been only 25% of all MA graduates in Israel. This is a global trend, as indicated by OECD publications and CBS data from Israel.

4.2 The rise in the number of students during the COVID-19 pandemic

1. The number of new BA students in 2021, an academic year that began during the pandemic, was significantly higher than in the previous year. Experts assume that much of this growth can be attributed to the fact that young adults remained in Israel at this time, and to the high accessibility of higher education during the pandemic [30]. This was reflected in distance learning, easier terms of acceptance, and the temporary cancelation of the requirement for psychometric exam scores by many departments due to the difficulty in holding these exams during the frequent lockdowns and the prohibitions against convening in closed spaces.

2. The greatest increase in the number of students was recorded in the academic colleges, i.e., a 17% increase in the number of BA students and 25% increase for MA students, and the increase in the number of students at extrabudgetary colleges was even higher (20% for BA students and 29% for MA students). On the other hand, the number of students in colleges of education dropped by 3%.

3. More than one-third of the increase in the number of new BA students in 2021 at budgeted institutions was in the engineering and computer science departments.

4. The most significant increase in the number of new students was in the 21–24 age group, in which there was a 25% increase compared to the previous year. This reinforces the hypothesis that most of the increase during the pandemic can be attributed to the fact that young people were staying in Israel instead of traveling abroad after their military service, as they had done before the pandemic.

4.3 Higher education in peripheral areas during COVID-19

Higher education became more accessible in peripheral areas of Israel as well, and 25 years after the reform in this field, the percentage of academic students from southern Israel increased from 8.7% in 1990 to 14.3% in 2021, though in 2010 participation rates in this group reached 15.3%. This means that during the pandemic, when the number of students grew throughout Israel, the percentage of students from southern Israel dropped by 1%. In northern Israel, the percentage of academic students rose from 5.5% in 2000 to 9.1% in 2021.

Despite the significant increase in the past two years on a national level, the growth rates in localities ranked 1–5 remain lower than in the localities ranked 7–10. The gaps between the percentage of academic students in these two groups are both quantitative and qualitative.

4.4 The quantitative measure

As noted above, the CBS tracked the percentage of students who began higher education within eight years of graduating high school and found gaps between 2020 and 2021. The national percentage of high school graduates who pursue an academic education within eight years of their high school graduation was higher than that of high school graduates from the peripheral areas. The national percentage of students who graduated high school in 2011 and began academic studies by 2020 was 44.3%. The COVID-19 crisis increased the national average to 44.4% in 2022. However, in localities ranked 1–4, the percentage was 32.6% in the Jewish education system and 34% in the Arab education system, which was very similar to 2020.

The overall average percentage of students who graduated high school in 2012 and did not begin academic studies by 2020 was 55.6%, compared to 67.4% (in the Jewish education system) and 66% (in the Arab education system) in localities ranked 1–4. The report on students who had graduated high school in 2011 and were tracked until 2019 did not find any significant differences either, as shown in the Table 3.

Year of high school graduation	National percentage of academic students	Percentages of graduates of the Jewish and Arab education systems, tracked from 2012 to 2020 and from 2011 to 2019
		University	Open University	Academic colleges	Colleges of education	No academic studies
2010	44.3	16.5	5.8	17.1	5.9	54.7
2011	44.3	15.9	5.7	17.0	5.8	55.7
2012	44.4	15.2	5.8	17.5	5.9	55.6
		Jewish education system
2011	47.2	16.9	5.5	19.0	5.7	52.9
2012	47.2	16.4	5.6	19.3	5.9	52.8
Socio-economic cluster (2012)
1–4	32.6	9.2	3.8	12.3	7.2	67.4
5–7	48.1	5.6	5.9	20.6	6.1	51.9
8–10	63.0	26.5	7.3	25.1	4.1	37.0
		Arab education system
2011	33.7	11.9	6.2	9.6	6.1	66.3
2012	34.0	11.0	6.5	10.8	5.7	65.0
Cluster 1–2	30.2	9.4	6.1	8.9	5.8	69.8
Cluster 3–4	37.8	12.4	7.2	12.3	6.0	62.2
Cluster 5–8	47.8	18.7	7.5	18.9	2.8	52.2

Table 3.

Students tracked until 2020 and 2021, eight years after graduating high school.

Source: CBS, Table 4.62, Integration of high school graduates in higher education within eight years of graduating high school, based on selected characteristics (tracked until 2020) [31].

The data on people who pursue higher education based on age groups, as opposed to considering high school graduates only, paints an even more problematic picture. The percentage of academic students among people from the 17-year age group after eight years was 31.1%, which did not change in the four years preceding 2020 [13].

The percentage of BA students per socio-economic cluster in 2021–2022 further supports the trend identified, as shown below (Table 4):

Year/Cluster	1–2	3–4	5–6	7–8	9–10
2021	7.9	22.0	16.3	29.7	20.7
2022	9.2	23.5	17.5	25.5	21.7

Table 4.

BA students per socio-economic cluster, 2021–2022.

Source: Council for Higher Education.

When analyzing the accumulated average of new BA students in the 18–30 age group, it is apparent that the percentage of new students in the 1–4 socio-economic cluster is much lower than the 7–10 cluster, as shown in the Table 5.

Socio-economic cluster	Percentage
1–2	16.5
3–4	33.0
5–6	43.4
7–8	55.0
9–10	69.3

Table 5.

Accumulated national percentage of new BA students in the 18–30 age group, per socio-economic cluster, 2019–2020.

Source: Knesset, Research and Information Center, Higher Education System in Israel; data and budgeting for 2022.

The Figure 1 demonstrates how the gap between the percentage of students from higher and lower socio-economic clusters has remained consistent over the past 25 years, from 1994 to 2022, despite the reform in the higher education system.

Figure 1.

New BA students in STEM fields based on the socio-economic cluster of the locality in which the student resides. Source: CBS, figure 2: New BA students in STEM fields based on the socio-economic cluster of the locality in which the student resides. Press release, January 5, 2022.

Also note that the highest increase in the number of new students was identified in academic colleges in general, and budgeted academic colleges in particular, as shown in Table 2.

4.5 Academic fields, high earnings, and their impact on reducing social gaps

The Bank of Israel measured the contribution of human capital to economic growth based on the number of years of education that people in the working age group (25–64) have acquired. According to the Bank of Israel, the average number of years of education rose from 9.5 in the late 1960s, to 13.5 in 2011. When combining these calculations with qualitative estimates of market yields, it appears that the increase in education levels has increased economic growth by an average of 0.6–0.8 percentage points, which constitutes 33%–45% of the average annual increase in GDP per capita. According to the bank’s analysts, the expansion of higher education has reached its maximum effect and since then, its contribution has dropped to 0.4–0.5 percentage points. The bank’s forecast for the next 50 years is that the effect of higher education will gradually decline over the years and reach 0.1 percentage points.

In order to maintain the effect of higher education, the analysts at the Bank of Israel recommend the following:

1. Increase integration of the ultra-orthodox sector in higher education.

2. Increase the percentage of high school graduates who pursue higher education.

3. Improve the quality of elementary school and high school education, especially since Israel has started falling behind in mathematics and science in international indices.

4. Increase the number of university students, who demonstrate higher earning power than college graduates [32], and take action to improve the level of education at academic colleges [14].

Studies in Israel since the 1970s have identified higher education as a means for reducing social gaps and increasing social mobility, in addition to contributing to economic growth. The main conclusion of many related studies is that factors related to human capital correlate significantly and consistently with employment and earnings. The more value attributed to academic degrees, the more opportunities the Israeli employee have, and earnings increase accordingly [33]. Recent publications by the CBS about the integration of academic graduates in the workforce and their higher earnings compared to employees without higher education, reinforce this claim:

1. The participation rates of people with higher education in the workforce in 2019, among BA graduates who completed their studies between 2009 and 2019, was 83% of all graduates, excluding graduates employed by security entities, comprising another 1.7%, and excluding graduates residing abroad for three years or more and are employed abroad, comprising another 2% according to the OECD. This percentage is 19% higher than the participation rates of the general population in the workforce, which is 63.5%, including those employed by security entities.¹

   A study from 2019 found that the higher the level of education, the higher the employment rates. The percentages are similar for both men and women. The percentage of employed women with higher education was 86%, compared to 65.5% of women without higher education and 41.1% of women without high school education. The trend was similar for men, showing that 88.9% of men with higher education participated in the workforce, compared to 74.2% of men with high school educations and 69.1% of men without high school educations.

   These numbers are slightly lower than the OECD average, though the patterns are similar. Women with higher education were much more likely to participate in the work force (91.1%) than women with high school education (68.9%) and women without high school education (45.2%).

2. The average monthly salary of BA graduates during the first year after receiving their degree was NIS 9600, and the median salary was NIS 7200. Salaries rose as a function of the employees’ work experience. Their average salaries after nine years of employment reached NIS 17,700 and the median salary at that stage was NIS 12,100. The average salary of a BA graduate in the workforce in 2019 was 29% higher than the overall average monthly salary for that year in Israel (NIS 13,900 compared to NIS 10,800).

   The average salaries of BA graduates increased after nine years and was 1.8 times higher than the average salary of employees with high school educations only. Similar ratios were found when comparing median salaries during the year after graduation and nine years later (NIS 12,100 and NIS 7200, respectively).

3. On January 5, 2022, the CBS published a paper on BA students in STEM fields per social cluster and found significant gaps in the quality of higher education, which can perpetuate social gaps and inequality in earnings that can lead to social inequality as well. Higher average monthly salaries were recorded for BA graduates between their first and ninth years of employment in fields related to mathematics, statistics, and computer science, ranging from NIS 17,000 to NIS 34,000. The gap was doubled for employees who had studied engineering and architecture.

   Lower earnings were recorded for BA graduates in fields such as education, art, languages, and regional studies, during the first nine years after receiving their degrees. Initial salaries in these fields were NIS 6800–7300, and nine years after graduation, average salaries reached NIS 11,800–12,800.

4. The percentage of the increase in average salaries between the first and ninth years after graduation and of integration in the workforce differs between fields. In education and teaching, the recorded increase was 4.9% for men and 5% for women.

5. As explained above, the reform in higher education in Israel in general, and particularly in its peripheral areas, has created overeducation. A study published by the OECD in 2016 that analyzed the percentage of employees whose jobs require lower levels of education than they have, found high rates of overeducation in Israel. Approximately 30% of employees in Israel in 2016 worked at jobs that did not require higher education, or that required less education than they had. The largest gap in OECD countries was found in New Zealand (35%), followed by Israel.²

4.6 Inequality in earnings – The gap from the qualitative perspective

The data on academic students from Israel’s social and geographical periphery, based on the socio-economic clusters defined by the CBS, demonstrates differences between students from higher-income localities ranked 8–10, and students from localities ranked 1–3. The differences can be divided into three categories:

1. Academic fields selected.

2. Percentage of students who pursue graduate degrees and research studies.

3. Types of academic institutions.

Studies presented here have described the influence of these parameters towards reducing qualitative gaps in higher education and increasing earning power and social mobility.

4.6.1 Academic fields

Many studies have shown that STEM and ICT degrees offer more potential for high salaries and social mobility than degrees in the humanities, education, and teaching. Data published by the CHE indicates a clear difference between the percentage of students who study each type of field in localities ranked 1–3 and those ranked 8–10. This gap has not changed in over a decade, as shown in the Table 6.

Socio-economic cluster/STEM education	2000	2010	2021
1–3	24%	19%	24%
8–10	35%	27%	35%

Table 6.

BA students in STEM fields in the years 2000, 2010, and 2021.

Source: CHE.

The findings also showed that the higher the locality was ranked, the more likely the students living there would be to study STEM fields that offer high earning opportunities, than to study education, teaching, and humanities. The lower the ranking of the locality, the less likely that students will study STEM fields and the more likely they will study education, teaching, and humanities.

The Figure 2 presents data that demonstrates significant gaps between the two groups, beginning with high school education and the subjects in which the students choose to major. The gap in the percentage of high school graduates in the two groups grows when going on to analyze the number of graduates who pursue academic degrees and specifically in STEM fields.

Figure 2.

Continued studies based on the socio-economic cluster of the locality in which the students reside. Source: CBS, figure 1: Continued studies based on the socio-economic cluster of the locality in which the students reside. Press release, January 5, 2022.

The information in Table 7 below shows a distinction between the groups. In localities ranked 8–10, the percentage of humanities students is gradually decreasing compared to the lower-ranking localities. In cluster 8 localities, the percentage of humanities students is 21.5%, while in clusters 1 and above, the percentages range from 30% to 55.3%. Degrees in natural sciences, mathematics, and agriculture, which include STEM and ICT studies, are pursued by 16.3%–18.6% of students from localities ranked 8–10, as opposed to 5.9%–11.3% in localities ranked 1–3. Similar percentages were found for engineering and architecture as well.

Subjects Socio-economic cluster	1	2	3	4	5	6	7	8	9	10
Percentage	100	100	100	100	100	100	100	100	100	100
Humanities	55.3%	30.2%	31.4%	29.4%	23.9%	24.7%	22.1%	21.5%	19.6%	18.3%
Social sciences	22.6%	30.7%	29.4%	31.6%	31.2%	30.8%	29.9%	30.6%	29.7%	30.5%
Medicine and paramedicine	7.3%	11.0%	9.8%	8.2%	8.0%	6.9%	7.4%	7.3%	8.4%	7.0%
Natural sciences, mathematics, agriculture	5.9%	11.4%	11.3%	9.9%	11.1%	14.2%	16.4%	18.3%	18.3%	18.4%
Engineering and architecture	5.9%	11.1%	11.9%	15.1%	19.0%	17.0%	18.1%	16.3%	18.1%	18.6%

Table 7.

Academic students in the 2021 academic year based on the socio-economic cluster of their place of residence at age 18 and by field of study.

Source: CBS, Table 2.34.3 Students in institutions of higher education, including Open University, per socio-economic cluster of the place of residence at age 18; field of study, gender, and degree [34].

In order to explore this phenomenon on a practical level, I chose several localities that represent all possible clusters, including those with Arab, immigrant, and ultra-orthodox populations, and analyzed the changes in the fields of study selected. The information in the tables in Appendix 1 indicates that the higher the socio-economic cluster of the locality in which the students reside, the lower the percentage of students studying teaching and education and the higher the percentage of students in technological fields. Accordingly, the lower the socio-economic ranking of the student’s residence, the higher the percentage of students studying education and the lower the percentage of students studying technology. The table in the Appendix shows a consistent difference between the localities over the course of three years – 2019, 2020, and 2021 – the former two before the COVID-19 pandemic, and the latter affected by the pandemic. The data shows that the pandemic and the significant increase in the number of students as a result did not change existing patterns, and that students from higher-ranking localities took better advantage of the crisis than students from lower-ranking localities.

4.6.2 Percentage of students who pursue graduate degrees and research studies

The second parameter is the difference in the percentages of undergraduate students who pursue graduate studies from localities ranked 1–6 compared to students from localities ranked 7–10. The study found that the lower the socio-economic ranking of the locality, the less likely the students are to pursue more advanced degrees (Table 8).

Socio-economic cluster	Percentage of people in the 18–30 age group in Israel per socio-economic clusters	BA	MA	PhD
1–2	22.8%	9.9%	7.9%	4.3%
3–4	28.5%	24.7%	26.6%	20.6%
5–6	16.7%	18.7%	19.3%	17.2%
7–8	19.1%	26.2%	28.3%	34.7%
9–10	12.9%	20.6%	17.9%	23.2%
Total	100%	100%	100%	100%

Table 8.

Distribution of the population in the 18–30 age group in Israel by socio-economic clusters, and the percentage of students in institutions of higher education in 2022, including Open University, in all degrees.

Source: Knesset, Research and Information Center. Data on students in institutions of higher education in Israel based on socio-economic background, May 30, 2023.

The data in this table demonstrates the growing gaps between students from higher and lower-income localities, so that the more advanced the degree, the greater the gap between the groups. When looking at the percentage for each category in the age groups that are most relevant for academic studies, the gaps are significant, except for in clusters 5–6 in which there is no underrepresentation. However, clusters 1–4 are underrepresented, and 7–10 are overrepresented for the relevant age group.

4.6.3 Types of academic institutions

When considering the percentage of students in universities and the various types of colleges, it becomes apparent that the groups also differ in the types of academic institutions in which they study. Overall, the percentage of university students from the high socio-economic clusters is higher than that of students from the lower clusters. The Table 9 shows data for 2021 and 2022 that indicates that this distinction was maintained during the pandemic in 2021, compared to post-COVID in 2022. The pattern remained the same, despite the increase in the number of students.

Type of institution	Socio-economic cluster	1–2		3–4		5–6		7–8		9–10
	Year	2021	2022	2021	2022	2021	2022	2021	2022	2021	2022
Total	100	7.9	9.2	22.0	23.5	16.3	17.5	24.7	25.5	20.7	21.7
Universities	100	5.6	5.5	19.6	19.2	15.0	15.2	26.0	26.8	26.2	30.1
Budgeted colleges	100	9.5	11.3	25.2	26.6	18.8	20.2	23.5	24.3	14.9	15.5
Extra-budgetary colleges	100	9.6	13.9	21.5	27.7	14.6	17.9	24.0	24.8	19.5	14.0

Table 9.

BA students in 2021 and 2022, per socio-economic cluster and type of institution (not including Open University).

Overall, it seems that the population associated with socio-economic clusters 1 and 2 are significantly underrepresented in all institutions, and the population associated with clusters 7–10 is significantly overrepresented in many institutions, especially in universities.

In-depth analysis of the data regarding more advanced degrees shows that the gaps continue to grow. Data published by the Research and Information Center at the Knesset shows that people from localities in clusters 1 and 2 are significantly underrepresented in advanced degrees compared to their percentage of the population and compared to other clusters [35, 36]. The data for 2022 shows that only 7.9% of MA students come from socio-economic clusters 1 and 2, compared to 46.2% from clusters 7–10. MA students in universities from clusters 1 and 2 in 2022 comprised only 5.4% of all students, compared to 53.9% in clusters 7–10. For PhD studies, the percentage of students from clusters 1 and 2 was only 4.3% in 2022, compared to 57.9% from clusters 7–10.³

A study by the Ministry of Finance published in December 2022 also described significant variance in the prime of higher education between graduates of extrabudgetary colleges and public colleges, and between graduates of both types of colleges and university graduates.

5. Discussion and conclusions

CBS research and data from the past few years demonstrate the significant influence of higher education on reducing social gaps. Higher education increases workforce participation and monthly salaries, and helps reduce employment gaps between men and women. This impact is especially evident in technological academic fields. Graduates of STEM and ICT studies reach high earning levels that are at least double those of graduates who study education, languages, and arts.

The argument made as the academic world was opened to the public some three decades ago about its ability to reduce social gaps proved true, but only partially and insufficiently. Although higher education has become more accessible in the past decades, new, previously underrepresented populations have joined the academia, and workforce participation rates have grown, including in peripheral areas, there are still qualitative and quantitative gaps between students from high-income localities and those from low-income ones. The data presented for the years up to 2022 present quantitative gaps, especially when considering the percentage of students relative to the distribution of the population in the 18–30 age group by socio-economic clusters. The data in Table 7 above shows distinct gaps between different parts of society, which have not changed in many years.

This data confirms the first hypothesis that the number of students in higher education from the peripheral areas has increased, but the gap between the percentage of students from socio-economic clusters 7–10 and from clusters 1–6 has not been closed.

An analysis of the quality gaps presents an even more distressing picture. The data in Table A1 in the Appendix regarding the percentage of students by cities selected from both socio-economic groups shows a clear pattern that corresponds with the Spearman’s correlation shown in the Table 10.

Independent variable	Dependent variable	Rs	Explanation
Socio-economic ranking	Average studying education (2019–2021)	−0.84	Strong negative correlation: The lower the socio-economic ranking, the higher the average percentage of students in this field.
Socio-economic ranking	Average studying medicine (2019–2021)	0.59	Moderate positive correlation: The higher the socio-economic ranking, the higher the average percentage of students in this field.
Socio-economic ranking	Average studying mathematics and computer science (2019–2021)	0.57	Moderate positive correlation: The higher the socio-economic ranking, the higher the average percentage of students in this field.
Socio-economic ranking	Average studying physics (2019–2021)	0.58	Moderate positive correlation: The higher the socio-economic ranking, the higher the average percentage of students in this field.
Socio-economic ranking	Average studying architecture (2019–2021)	0.53	Moderate positive correlation: The higher the socio-economic ranking, the higher the average percentage of students in this field.
Socio-economic ranking	Average STEM (2019–2021)	0.77	Strong positive correlation: The higher the socio-economic ranking, the higher the average percentage of students in this field.
Socio-economic ranking	Average STEM (2020 only)	0.79	Strong positive correlation: The higher the socio-economic ranking, the higher the average percentage of students in this field.

Table 10.

The strength of the connection between the independent variable and the dependent variables.

A strong negative correlation was found that increased based on how low or high the socio-economic ranking of the locality was. The lower the ranking of the locality, the higher the average percentage of students studying education and the lower the average percentage of students studying STEM and ICT. The higher the ranking of the locality from which the students originated, the lower the average percentage of students studying education and the higher the average percentage studying STEM, ICT, and medicine. This pattern was not influenced by the COVID-19 pandemic or by increased accessibility to all academic fields. It seems that students from higher-income localities took better advantage of the crisis posed by the pandemic than students from lower-income localities, as the percentage of students in fields that can yield higher earnings was higher in the former group than in the latter.

This pattern becomes even more evident in the data regarding MA students. In this case, the percentage of students studying education in budgeted and extrabudgetary colleges is higher among students from localities in socio-economic clusters 1 and 2.

The findings also show that in clusters 7–10, the percentage of MA students in universities is higher than their relative percentage in their age group in Israel in general, and compared to students from clusters 1–4 in particular. The gaps increase even further for PhD students. The percentage of PhD students from clusters 1 and 2 was only 4.3%, compared to 23.2% from clusters 9 and 10.

Although access to higher education has expanded in the past 30 years and increased the number of participants from all socio-economic clusters, this has created a new challenge known as overeducation. This phenomenon is especially prevalent in peripheral areas and among ethnic groups such as Arabs, Jews of Eastern origin, and immigrants from the former Soviet Union, often because of language barriers, as well as in the geographic periphery due to mobility challenges that cause graduates to continue working at jobs that are not aligned with their education due to the proximity of their workplace to their homes.

The studies presented above confirm the fourth hypothesis as they show that overeducation is more common in socio-economic clusters 1–6 than in clusters 7–10. Therefore, access to higher education in the peripheral areas has less of an impact on closing social gaps than in the higher-ranking clusters.

5.1 Earning power and reducing social gaps

A report published by the Knesset Research and Information Center in October 2020 identified gaps in workforce participation rates in Israel’s northern region and in the greater Beersheba region compared to the national average and to the average in central Israel. The average salary in peripheral regions is lower than the national average, and a high percentage of employees earn less than minimum wage. The percentage of people from the peripheral regions who are employed in R&D and startup companies is significantly lower than in central Israel.

The study also analyzed employment levels and salaries in central Israel compared to the north and the Beersheba region, areas with 2.2 million residents who comprised 24.5% of Israel’s population in 2019. Participation in the workforce in the Beersheba region was 55% in 2019, which is lower than in all other districts in Israel other than the Jerusalem district. Workforce participation in the northern district was 59% compared to the national average of 63.5%. The average monthly salary in Beersheba was NIS 8502, and in the north, NIS 8026, compared to the national average salary of NIS 9634 and the average in central Israel of NIS 11,114. The largest gap in job opportunities was found in the finance, electronics, and computers sector, in which there were 74,800 jobs in total in 2019, of which 14,600 were in the north and 3500 in the Beersheba district.⁴

This data confirms the third hypothesis that students from the lower-income localities tend to choose less prestigious fields in higher education that offer less earning power, and therefore the inequality in earnings between graduates from the higher-income and lower-income localities is maintained, despite the fact that access to higher education improved during the COVID-19 pandemic.

5.2 The COVID-19 crisis and the peripheral regions

The number of new BA students in 2021, which began during the pandemic, increased significantly due to the fact that young adults remained in Israel and had easier access to higher education [30] thanks to distance learning and easier acceptance criteria, especially when psychometric scores were waived by many departments due to the difficulty in taking these exams during lockdowns and with social distancing requirements. The significant increase in the number of new students was manifest mostly in academic colleges, and particularly budgeted colleges (with the exception of colleges of education, in which enrollment decreased). There was a substantial increase in registration for engineering and computer science studies in all types of academic institutions.

During the 25 years that have passed since the reform in higher education in Israel, the percentage of students from southern Israel grew from 8.7% in 1990 to 14.3% in 2021. However, in 2010, 15.3% of students were from southern Israel, meaning that during the pandemic, when the number of students rose all over Israel, the percentage of students from the south decreased by 1%. In the northern district, the percentage of students grew from 5.5% in 2000 to 9.1% in 2021.

Despite the impressive increase in the number of students on a national level in the past two years, the growth rate in localities ranked 1–5 remains lower than in localities ranked 7–10. The gaps between the percentages of students from these two groups are both qualitative and quantitative. When tracking high school graduates who began higher education within eight years of graduation, gaps were found between 2020 and 2021. The percentage of high school graduates who pursue academic degrees on a national level remains higher than among high school graduates from peripheral areas. On the national level, the percentage of people who graduated high school in 2011 and began higher education by 2020 was 44.3%. The pandemic increased average enrollment in 2022 to 44.4%. However, in the 1–4 cluster, the percentages were 32.6% in the Jewish education system and 34% in the Arab system, figures that are almost identical to the ones recorded in 2020.

The overall average percentage of high school graduates in 2012 who did not begin academic studies by 2020 was 55.6%. However, in the 1–4 cluster, the percentages were 67.4% in the Jewish sector and 66% in the Arab sector. These findings were similar to those found for high school students who graduated in 2011 and did not pursue academic degrees by 2019. The findings per age group (as opposed to high school graduates) present an even grimmer picture. The percentage of those who pursued academic degrees within 8 years of age 17 was 31.1%. This figure remained consistent for the four years researched, up to 2020 [13].

In order to analyze the qualitative differences between students from high and low-income localities, we considered gaps in three areas:

1. Academic fields

2. Percentage of students who pursue graduate degrees or research studies.

3. The types of institutions in which these groups study.

These parameters present a gap in the quality of higher education that has implications for future social mobility. According to CBS, the Ministry of Finance, and the OECS, STEM and ICT fields are those with the highest earning power, compared to education, teaching, and art. The data shows gaps both on the national level and the local level between the percentage of students who choose the more prestigious fields from high and low-income localities. This gap inhibits social mobility, for which earning power is a critical factor. We found that in the 1–4 clusters, the percentage of students studying to become teachers and educators is higher than in the 7–10 clusters. A reverse pattern was found for technological fields that facilitate higher salaries, in which localities in the 1–4 clusters had lower percentages of students in these fields than the 7–10 clusters.

The second parameter explored the percentage of students who pursue graduate studies and found that students from the 1–4 clusters were more likely to complete undergraduate studies and not pursue more advanced degrees. On the other hand, students from the 7–10 clusters were more likely to pursue advanced degrees. The gap between these groups was even larger for PhD studies, which are research-oriented. The gaps can perpetuate social gaps, as earning power rises as employees gain more advanced degrees, and particularly in academic fields that are crucial for both hi-tech and low-tech development.

The third parameter is the percentage of students who attend academic colleges in the two groups, as this can predict earning power in the workforce and social mobility after graduation. Studies by the Bank of Israel and the Ministry of Finance show gaps between the earning power of college graduates and university graduates. The data published by CBS on the percentage of students who pursue advanced degrees also demonstrates a gap between university and college graduates. When combining the gaps identified for the three parameters, it is evident that the gaps in higher education are both qualitative and quantitative, and that the differences in earning power and social mobility may be perpetuated over time, despite the increased access to higher education in Israel.

This data shows an increase in the number of people from peripheral areas who pursue higher education. However, the quantitative ratio between the number of academic students from higher-income and lower-income localities has not changed. COVID increased the number of students in both groups, but the quantitative gaps remain and the qualitative gaps have grown, as suggested in the second hypothesis presented in the methodology section above.

6. Summary

After analyzing the data and validating the hypotheses presented above, we can conclude the following:

1. Access to higher education in Israel has increased significantly in the past 25 years, thanks to the academic colleges that have opened during this time.

2. The rise in the number of students beginning higher education slowed in 2016–2020, but resumed as a result of the COVID-19 pandemic. The pandemic caused many young adults to remain in Israel and not depart for long trips around the world, as is common among this population in Israel. This, combined with the lack of job opportunities during the pandemic, resulted in an increase in the number of academic students.

3. Although higher education has become much more accessible, the ratio between students from higher-income and lower-income localities in Israel has remained practically the same.

4. Many studies, as well as reports published by the Ministry of Finance, distinguish between professional fields with high earning potential (STEM and ICT) and those with relatively low earning potential (humanities and education).

   The studies reviewed in this paper, including OECD analyses, show that jobs that guarantee high salaries help reduce social gaps and increase social mobility. However, these studies identified discrepancies between the percentage of graduates of STEM and ICT fields from higher-income localities compared to the percentage from lower-income localities.

   Research shows that the lower the locality is ranked on the socio-economic scale, the higher the percentage of students in the humanities and education, and the lower the percentage of students in STEM and ICT fields. Accordingly, the higher the ranking of the locality, the higher the percentage of students in STEM and ICT fields and the lower the percentage of students in humanities and education.

5. The percentage of graduates with jobs that are unrelated to their education is higher in the peripheral areas than in the high-income areas, making overeducation a more common phenomenon in the periphery.

7. Limitations

1. This study is based on data collected up until 2022, when data on the major impact of the COVID-19 pandemic was still incomplete.

2. It is still too soon to determine the effect of distance learning during the pandemic on the quality of the graduates from both the higher and lower-income localities, and whether the education they acquired is well-suited for the fields in which they are employed.

8. Recommendations for future research

1. Analyze the quality of education using technological platforms in 2020–2023, and explore the influence of this type of education on the quality of the graduates’ education, particularly in peripheral areas.

2. Institutions of higher education have lowered the admission requirements for nearly all faculties over the years and now accept students with relatively low grades. Future research should explore the extent to which graduates are able to meet the professional requirements in all sectors of the workforce.

A. Appendix 1

Locality	Index 2019	Total			Education			Average	Ranking
	Independent variable	2019	2020	2021	2019	2020	2021	2019–2021
Beitar Ilit	1	520	462	509	42.5	41.3	52.7	45.5	24
Rahat	1	844	1034	1147	41.6	40	39.1	40.2	23
Beit Shemesh	2	1764	1858	2006	26.4	23.3	27.9	25.9	17
Bnei Brak	2	1300	1313	1382	26.4	24.4	44.2	31.7	20
Jerusalem	2	15,557	16,198	17,459	24.9	22.8	22	23.2	13
Taibe	3	1103	1174	1278	38.7	36	33.8	36.2	22
Netivot	3	687	720	746	36.4	36.3	31.1	34.6	21
Yeruham	4	211	211	259	24.2	26.1	24.7	25.0	16
Kuseife	4	289	347	387	64	61.4	61.5	62.3	25
Kiryat Qat	4	1021	1058	1103	18.7	17.8	17.4	18.0	9
Kiryat Malachi	4	434	456	462	28.8	31.1	28.1	29.3	19
Ashdod	5	4252	4161	4355	12.4	12.1	14.3	12.9	8
Ashkelon	5	2875	2868	3052	13	12	10.5	11.8	7
Beit Shean	5	426	444	473	32.6	31.3	25.5	28.4	18
Dimona	5	634	632	732	20.8	17.9	18.6	19.1	10
Tira	5	663	701	750	20.7	21.5	20.7	21.0	11
Migdal Haemek	5	480	504	550	20.2	22.4	24	22.2	12
Sderot	5	906	930	916	28.6	23.3	22.6	24.8	15
Shlomi	6	133	129	148	24.1	24	22.9	23.7	14
Givat Shmuel	8	1032	1125	1153	11.9	11.7	10.2	11.3	6
Herzliya	8	2403	2451	2584	7.7	7.8	7.9	7.8	1
Tel Aviv – Jaffa	8	12,760	13,202	14,063	8.9	8.3	8	8.4	3
Givatayim	9	1443	1464	1582	11.3	10	7.6	9.6	5
Hod Hasharon	9	1426	1469	1707	9.3	9	7.5	8.6	4
Ramat Hasharon	9	1001	998	1117	8.3	8.8	6.8	8.0	2
									−0.84

Locality	Index 2019	Medicine			Average	Ranking	Mathematics/Computer Science			Average	Ranking
	Independent variable	2019	2020	2021	2019–2021		2019	2020	2021	2019–2021
Beitar Ilit	1	—	0.2	—	0.2	4.5	9.8	8.7	9.6	9.4	18
Rahat	1	—	0.2	0.1	0.2	3	—	1.9	1.8	1.9	2
Beit Shemesh	2	—	0.6	1.6	1.1	18	8.7	8.6	7.6	8.3	15
Bnei Brak	2	—	0.4	0.4	0.4	9.5	11.2	11	8.3	10.2	19
Jerusalem	2	1.4	1.3	1.3	1.3	19	8.1	8.6	8.4	8.4	16
Taibe	3	—	0.6	0.5	0.6	12.5	4.4	5.2	4.9	4.8	6
Netivot	3	—	0.3	0.3	0.3	6.5	—	3.3	3.4	3.4	3
Yeruham	4	—	0.5	0.4	0.5	11	—	4.3	5.4	4.9	7.5
Kuseife	4	—	0.3	0.3	0.3	6.5	—	0.9	0.5	0.7	1
Kiryat Qat	4	—	0.5	0.6	0.6	12.5	4.8	4	3.9	4.2	5
Kiryat Malachi	4	—	0.2	—	0.2	4.5	—	7	7.1	7.1	13
Ashdod	5	—	0.4	0.4	0.4	9.5	7.4	7.9	5.6	7.0	12
Ashkelon	5	—	0.8	0.8	0.8	15	6.9	7.4	7.9	7.4	14
Beit Shean	5	—	—	—	—		7	7	6.4	6.8	11
Dimona	5	—	0.5	0.7	0.6	14	—	3.3	3.8	3.6	4
Tira	5	—	0.1	0.1	0.1	1.5	—	4.4	5.3	4.9	7.5
Migdal Haemek	5	—	0.2	0.4	0.3	8	—	9.5	8.5	9.0	17
Sderot	5	—	—	0.1	0.1	1.5	4.6	6	4.5	5.0	9
Shlomi	6	—	1.6	1.4	1.5	21		5.4	5.4	5.4	10
Givat Shmuel	8	—	1.3	1.8	1.6	22	11.7	13.2	14.5	13.1	24
Herzliya	8	—	1	1	1.0	17	12.1	13.6	13.5	13.1	23
Tel Aviv – Jaffa	8	2	2	2	2.0	24	9.1	10.8	11.1	10.3	20
Givatayim	9	—	1.3	0.6	1.0	16	10.9	12.4	10	11.1	21
Hod Hasharon	9	—	1.8	2	1.9	23	11.2	12.8	12.3	12.1	22
Ramat Hasharon	9	—	1.5	1.3	1.4	20	—	14.2	15.4	14.8	25
						0.59	Rs =				Rs = 0.57

Locality	Index 2019	Science–Physics			Average	Ranking	Engineering and architecture			Average	Ranking
	Independent variable		2020	2021	2019–2021		2019	2020	2021	2019–2021
Beitar Ilit	1	—	—	0.5	0.5	3.5	7.5	8.9	4.4	6.9	4
Rahat	1	—	0.6	0.8	0.7	8	6.6	5.9	6.1	6.2	3
Beit Shemesh	2	—	0.9	1.2	1.1	12	12.6	12.9	11.2	12.2	7
Bnei Brak	2	—	0.7	0.4	0.6	5.5	8.4	6.5	2.7	5.9	2
Jerusalem	2	1.4	1.6	1.6	1.5	17	12.7	12.6	12.5	12.6	8
Taibe	3	—	0.7	1.3	1.0	11	10.4	11.7	13.2	11.8	6
Netivot	3	—	0.7	0.3	0.5	3.5	14.3	14.3	15.8	14.8	10
Yeruham	4	—	2.8	2.7	2.8	23	20.4	19	17.4	18.9	17
Kuseife	4	—	0.9	0.3	0.6	7	—	4.9	4.4	4.7	1
Kiryat Qat	4	—	0.8	0.9	0.9	9	19.9	19.7	20.4	20.0	19
Kiryat Malachi	4	—	0.7	0.2	0.5	1	15.9	16.9	15.6	16.1	11
Ashdod	5	1.3	1.5	0.9	1.2	14	24.8	24.6	23	24.1	24
Ashkelon	5	1.2	0.5	0.9	0.9	10	18.8	20.4	19.8	19.7	18
Beit Shean	5	25.4	—	—	25.4	25	—	23.4	24.1	23.8	23
Dimona	5	—	1.3	1	1.2	13	26.7	25.8	26.8	26.4	25
Tira	5	—	2.3	2.7	2.5	22	12.1	10.8	12	11.6	5
Migdal Haemek	5	—	0.4	0.5	0.5	2	24	20.4	19.8	21.4	22
Sderot	5	—	0.6	0.5	0.6	5.5	13.9	12.7	13.3	13.3	9
Shlomi	6	—	1.6	1.4	1.5	16		18.6	14.9	16.8	14
Givat Shmuel	8	—	2.4	1.5	2.0	18	20.6	21	19	20.2	21
Herzliya	8	1.5	1.3	1.2	1.3	15	17.4	17.2	16.9	17.2	15
Tel Aviv – Jaffa	8	2.2	2	1.8	2.0	20	16.6	16.9	16.3	16.6	13
Givatayim	9	—	1.8	2.1	2.0	19	19	19.1	16.6	18.2	16
Hod Hasharon	9	3.1	2.4	3.3	2.9	24	20.3	22.1	17.6	20.0	20
Ramat Hasharon	9	—	2.6	2.3	2.5	21	15.4	17.4	16	16.3	12
		Rs=				0.58	Rs=				Rs = 0.53

Locality	Average STEM	Average STEM
	2019–2021		2020 only
Beitar Ilit	4.3	4	5.9	8
Rahat	2.2	2	2.2	2
Beit Shemesh	5.7	9	5.8	7
Bnei Brak	4.2	3	4.7	5
Jerusalem	6.0	10.5	6.0	9
Taibe	4.5	5	4.6	4
Netivot	4.7	6	4.7	6
Yeruham	6.7	14	6.7	13
Kuseife	1.6	1	1.8	1
Kiryat Qat	6.4	13	6.3	11
Kiryat Malachi	6.0	10.5	6.2	10
Ashdod	8.2	21	8.6	20
Ashkelon	7.2	15	7.3	15
Beit Shean	18.7	25	15.2	25
Dimona	7.9	18	7.7	17
Tira	4.8	8	4.4	3
Migdal Haemek	7.8	17	7.6	16
Sderot	4.7	7	6.4	12
Shlomi	6.3	12	6.8	14
Givat Shmuel	9.2	23	9.5	23
Herzliya	8.1	20	8.3	19
Tel Aviv – Jaffa	7.7	16	7.9	18
Givatayim	8.1	19	8.7	21
Hod Hasharon	9.2	24	9.8	24
Ramat Hasharon	8.7	22	8.9	22
		0.77	Rs=	0.79

Table A1.

Number of students in all academic degrees by type of institution, 2020–2022.