High-Tech as a Path to Intergenerational Mobility
Study on the integration and mobility of individuals from disadvantaged economic backgrounds in the high-tech industry
The article is an abstract of a full study published in Hebrew. Link to the full report
Abstract
This study is part of a broader research effort aimed at identifying pathways for economic mobility in Israel. In this study, we focus on the link between employment in high-tech and upward economic mobility: To what extent is employment in high-tech unique as a pathway to economic mobility, as compared with other such avenues? How accessible is this path to individuals from disadvantaged economic backgrounds, and how much does their ethnic identity or gender make a difference? What factors enable workers from disadvantaged economic backgrounds to integrate into the high-tech industry?
The study addresses these questions using a unique database containing a wide variety of data about more than 400,000 individuals, including information about their parents’ income. This makes it possible to examine the relationship between an individual’s starting point and his or her integration into the labor market, and in particular- into the high-tech industry.
1. Employment in high-tech is associated with a high degree of economic mobility. Among individuals who grew up in households in the lowest income quartile, the likelihood of moving up to the highest income quartile is four times higher for those employed in high-tech than for others from a similar background. Around half of the individuals from a disadvantaged economic background who enter high-tech reach the highest income quartile. Similarly, among individuals from such a background, the income percentile of those employed in high-tech is around 17 percentage points higher than those employed in other industries, when controlling for level of education and area of residence.
2. The majority of high-tech employees come from strong economic backgrounds; only a small minority come from disadvantaged backgrounds. Around 70% of those employed in the sector were raised in households in the top two income quartiles, and just 10%- in households in the lowest quartile. Only 5% of all individuals from households in the lowest income quartile have gained entry to the high-tech industry, and among them- the representation of women, Haredim, and Arabs is particularly low.
3. The education tracks characterizing the majority of employees in high-tech are rare among individuals from disadvantaged economic backgrounds. Most of those employed in high-tech industries (around 55%) have attained higher education in STEM subjects or in other subjects for which entrance requires a high psychometric score. Only 7.5% of those raised in households in the bottom income quartile have an education that meets these criteria.
4. Education in STEM subjects greatly increases the chances of entering the high-tech industry. Individuals who were raised in households in the lowest income quartile but completed STEM degrees at university or college are 5.3 times and 6.5 times more likely (respectively) to be employed in high-tech than those with only a matriculation certificate. By contrast, there is no significant difference in the likelihood of entering high-tech between individuals who completed degrees not in STEM subjects, and those with just a matriculation certificate.
The technological vocational training provided by the Ministry of Labor’s Government Institute for Technology and Science Training (MAHAT) is also associated with a greater likelihood of an individual from a disadvantaged economic background finding his or her place in high-tech. MAHAT graduates have a 2.6-times higher chance of employment in high-tech than individuals with only a matriculation certificate, and when compared to academic education, MAHAT training programs are second only to STEM degrees, with regard to their positive impact on the likelihood of entering the industry. However, the correlation between MAHAT programs and progression to the highest income quartile is lower than that of almost every academic degree program.
Finally, the number of matriculation units in English and in mathematics has a moderate impact on the chances of hi-tech employment. The greater the number of matriculation units in English and math, the greater the likelihood of hi-tech employment. This association is not linear, as the increase in likelihood between four and five units is larger than that between three and four units.
5. Education explains a significant proportion of the discrepancy in employment in high-tech between workers from different economic backgrounds, but does not explain the gender gap or the gap between Jews and Arabs. Demographic characteristics explain about one-third of the discrepancy in employment in high-tech between those raised in households from the lowest income quartile and those raised in households in the top two quartiles. Educational characteristics explain approximately 80% of the remaining gap. Comparisons between individuals from a disadvantaged economic background with a similar level of education reveal that ethnic identity has no impact on the chances of integration into high-tech.
However, the gaps between men and women and between Jews and Arabs remain large even among individuals with equivalent achievements on psychometric tests for entry into higher education and with similar family backgrounds. Specifically, the chances of a Jew from a disadvantaged economic background being employed in high-tech are 8.3 times higher than those of an Arab from a similar economic background; and even after controlling for demographic and education factors, Jews are 5 times more likely than Arabs to enter high-tech. Moreover, the analysis reveals that the individual’s education track has almost no impact on the size of the gap between Arabs and Jews, while controlling for psychometric scores reduces the gap only slightly.
Gender differences are also large: The likelihood of a woman from a disadvantaged economic background entering high-tech is 42% lower than that of a man from a similar background. A comparison of men and women with similar education tracks and similar demographic characteristics reveals an even larger gap, averaging 50%. The difference is slightly smaller when comparing women and men without young children, for whom it stands at 32%.
The findings show that high-tech can serve as a pathway to upward economic mobility for individuals from disadvantaged economic backgrounds only if they gain a suitable education, including the right kind of matriculation certificate and of higher education. However, their chances of receiving such an education are not high. This is due to the lack of sufficient high-quality education in Israel’s geographic and social periphery, and the need for a more differential investment of resources in the education system, which would provide more equal opportunities for population groups with a lower starting point.
At the same time, among the Arab population, we may note that even gaining a suitable education barely increases the likelihood of integrating into high-tech. Presumably, part of the explanation for the limited entry of Arabs into high-tech compared to Jews, even among those with a similar education, may be attributed to military service and the skills it provides, (At this stage we have no data on military service).
The large gap in integration into high-tech between men and women with a similar background and education, especially among parents to young children, indicates that adapting working conditions to the needs of younger parents may enhance the chances of mothers to integrate into high-tech, and offer them a pathway to economic mobility.
This study is part of a broader research effort aimed at identifying pathways through which individuals from disadvantaged backgrounds can improve their economic situation in Israel’s labor market. In the study, we sought to examine one such pathway for economic mobility—employment in the high-tech industry. Not surprisingly, the findings reveal that employment in high-tech does indeed offer a significant route to upward economic mobility. However, they also indicate that only a small minority of high-tech employees grew up in a household in the lowest income quartile. Thus, our analysis offers a number of insights regarding policy actions that may increase the percentage of individuals from disadvantaged economic backgrounds who make their way into high-tech industry.
The study's findings show that education is one of the main factors that account for the differences in the employment rates in high-tech between individuals from different economic backgrounds. The analysis points to three educational components that particularly influence the chances of individuals from disadvantaged backgrounds for employment in high-tech, beginning with their high-school education. We found that the number of matriculation units in English and math correlates strongly with the likelihood of an individual’s employment in high-tech, consistent with findings in other studies (for example, Hashai, Sumkin & Nir, 2022). This correlation holds beyond the effect of having an academic education, and can be discerned at mid-career. The finding suggests that individuals’ high-school education, and particularly the level they attain in core curriculum studies, has significant implications on their careers years after they graduate.
Furthermore, the likelihood that individuals from disadvantaged economic backgrounds will be employed in high-tech increases sharply if they enter specific higher education tracks, chief among them-programs towards a degree in STEM subjects. Similarly, we find that participation in training programs at the Government Institute for Technology and Science Training (MAHAT) is another route that affords individuals a better chance integrating into high-tech, albeit with lower salaries than their peers. This finding is significant, given the fact that these training programs are much more accessible than academic degree programs in terms of the percentage of individuals from disadvantaged backgrounds who participate in them. In stark contrast, we find that investing in an academic education in non-STEM subjects has almost no positive impact on the chances of employment in high-tech.
Another influential factor is the demographic composition of the population. The lowest income quartile is marked by an over-representation of Arab Israelis relative to their share in the general population, and the rate of employment in high-tech among Arabs is extremely low. Moreover, even when controlling for individuals’ demographic characteristics (including parents’ level of education and relative income), the gap in the rate of employment in high-tech between Jews, and Arabs from disadvantaged backgrounds remains very large.
There are several possible explanations for these gaps:
• Differences in social capita: It is possible that the gap can be explained by the fact that Arabs’ have a relatively weak social network in the tech industry that can help guide individuals toward employment in the industry and provide help in getting hired.
• Differences in human capital: The gap between Jews and Arabs may also be attributed to differences in skills that we are unable to measure, such as those acquired in the course of military service.
• Cultural differences: Cultural differences between Jews and Arabs, such as in the perceptions of what accounts for a “desirable occupation,” can theoretically lead to a choice of different professions. However, this explanation seems unlikely given the much higher incomes earned by high-tech employees relative to those in other industries.
• Discrimination: Finally, the employment gap may also stem from employers’ discrimination and their preference for Jewish candidates. This explanation seems plausible given the size of the gap and the fact that it cannot be significantly reduced by education.
It should be noted that the difference in rate of employment in high-tech between Jews and Arabs is not limited to individuals from disadvantaged households: Arab representation in the industry is low across the board. Thus, additional research into the issue is warranted, to examine differences in individual preferences, social capital, and skills. If the gap is due to a lack of social or human capital or to discrimination, tailored policies can be implemented to address the issue. However, the findings suggest that even increasing the number of Arabs with a suitable education will not in and of itself significantly narrow the huge gap between Jews and Arabs in high-tech employment.
Women are another population group relatively excluded from high-tech. We find that even after controlling for demographic characteristics and level of education, the chances of a man from a disadvantaged economic background being employed in high-tech is double that of a woman with a similar profile. The data show that the gap between men and women persists across all education levels. This gender gap may stem from differences in skills that we are unable to measure using the data at our disposal (such as military training), from differences between men and women regarding preferred types of work, from decisions made within households on the allocation of tasks between partners, from employer discrimination, or from cultural norms. If preferences relating to work-life balance are a factor, then adapting working conditions to the needs of young parents may help enhance the chances of mothers of integrating into high-tech.
Two limitations of this study highlight several potentially fruitful directions for future research. First, the study's findings focus on the intermediate generation of workers, aged 31–36. Among other reasons, this narrow focus is due to data limitations. Expanding the database of the Central Bureau of Statistics to additional ages will permit future tracking of the employment experiences of individuals and their integration into high-tech in later stages of life. Similarly, the data in this study relate to pre-COVID trends, making it necessary to assess the extent to which the study findings are applicable to high-tech workers in the period after the pandemic.
It is hard to overstate the importance of the high-tech industry to Israel’s economy—both as a factor in attracting high-quality human capital to remain in Israel, and as an engine for economic growth. At the same time, the high-tech industry is quite a limited – perhaps-too limited – source of economic mobility for Israelis, first and foremost because individuals from disadvantaged economic backgrounds struggle to gain employment in the industry. Much of this problem can be attributed to inadequate education: Given an appropriate education in relevant science and math (STEM) subjects, the likelihood of individuals from disadvantaged backgrounds gaining employment in the industry increases more than fivefold. Participation in the training programs provided by the Government Institute for Technology and Science Training (MAHAT) also had sizable effects. Yet, even with the right education and training, the chances of entering high-tech for women and Arabs remain alarmingly low.
Given that the local high-tech industry has for some time been suffering from a shortage of workers in specific fields (such as software development), and in light of the findings presented above, several policy steps may promote the integration into high-tech of those from disadvantaged economic backgrounds. First, a significant increase in investment is needed in teaching high-level math and English in high schools serving students from disadvantaged economic backgrounds. The study's findings indicate that English is a highly significant factor in the likelihood of integration into the industry, and thus it would be a mistake to focus efforts exclusively on mathematics.
In addition, intensive efforts are also required to help students from disadvantaged economic backgrounds with academic potential to continue on to post-secondary STEM education in frameworks, other than colleges and universities, or to enter technological training programs such as those offered by MAHAT. These could include subsidies for pre-degree preparatory programs in scientific subjects; marketing campaigns to increase students’ awareness of the employment potential gained by choosing such a track; or making MAHAT courses more accessible to the geographical periphery.
Finally, action towards integrating women and Arabs into the industry must be taken. The findings indicate that there is a larger barrier to entry into high-tech for both women and Arabs, even in cases in which their educational background is similar to that of Jewish men. Future studies may shed light on the reasons underlying this phenomenon, but clearly- new policy steps and new programs are required in order to reduce these discrepancies.
Taken together, such steps can broaden the circle of those benefitting from Israeli high-tech, and enhance economic mobility in Israel. In turn, this will have important benefits both for economic growth and for greater economic equality in Israeli society. One would be hard-pressed to think of areas of government activity that are more essential or more promising.