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Institutional Selectivity, Curricular Policy, and Field of Study Stratification in Expanded Higher Education Systems: The Case of Israel

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Abstract

The paper focuses on the contradictory results on the effect of social background on choice of field of study (field stratification) in expanded higher education systems. We predicted that the contradictory results stem from variations in institutional selectivity and curricular policy. Based on two surveys conducted in 1999 (4146 students) and 2014 (7384 students) in the Israeli expanded higher education system, this paper analyzes changes in the ratio of continuing-generation college students in fields of study offered by institutions with varying degrees of selectivity. The results show a decrease in the selectivity of the second-tier institutions in the second analyzed period, accompanied by an increase in field stratification. We suggest that this increase stems from the differential curricular policies of second-tier higher education institutions. In the second period, the second-tier institutions initiated labor market-oriented programs for the less popular fields, thus opening them to first-generation students. In popular and lucrative fields, some of them regulated by professional associations, the second-tier institutions kept to the traditional orientation of the programs, and attracted less qualified continuing-generation students. We discuss the implications of the findings on social stratification.

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Fig. 1
Fig. 2

(aStudents with psychometric test and/or in the top 10% of the matriculation score)

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Notes

  1. Based on information from the Hebrew-language website https://www.universities-colleges.org.il.

  2. We considered using student’s parental income as an additional indicator of social background. However, high parental income does not necessarily correlate with the “traditional” clientele of higher education (Ayalon and Mcdossi 2019). Thus, the study focuses on generation status, which better represents our theoretical arguments.

    • There are variations in parental education, matriculation score, and field of study within the Jewish group. Nevertheless, an additional analysis using detailed Jewish subgroups did not change the results.

  4. Multinomial regression is an extended version of the binary logit model, in which the outcome includes multiple alternatives. In our analysis, it estimates the odds of continuing- versus first-generation students belonging to each category of the dependent variable, compared to the reference category. The algebraic specification of the model takes the form of: \({\text{Pr}}(y_{i} = m|x_{i} ) = \frac{{exp\left( {x_{i} \beta_{m} } \right)}}{{\mathop \sum \nolimits_{j = 1}^{J} exp\left( {x_{i} \beta_{j} } \right)}}{ }\) where \(\Pr \left( {y_{i} = m{|}x_{i} } \right)\) is a function of the linear combination \(x_{i} \beta_{m}\). The vector \(\beta_{m}\) includes the intercept \(\beta_{0m}\) and coefficients \(\beta_{km}\) for the effect of \(x_{k}\) on outcome m (Long, 1997:152). It is estimated by the Newton–Raphson maximum likelihood using Stata mlogit command.

  5. In a supplementary analysis (not shown here), we tested a model that included the interactions between year and all the explanatory and control variables. However, many of the interactions did not reach statistical significance, and we decided not to include them for the sake of a more parsimonious and clearer model.

  6. We computed the average marginal effects (AMEs) using Stata’s margins command. The AMEs of a dummy variable—generation status, in our case—are the difference between the average probabilities of CG and FG students selecting the alternative j. While the multinomial regression includes j-1 sets of coefficients, there are j sets of marginal effects. The AMEs represent the effect of generation status across institution and field combinations. Since the model includes interaction between two nominal (binary) independent variables (survey year and generation status), the AMEs were calculated separately for the two survey years. All control variables were set on their observed values (Mize, 2019; Williams, 2012).

  7. Specifically, we used Stata’s multiple imputation with chained equations command to impute five datasets.

  8. In 1995, 14.3% of the population aged 15 and over had more than 16 years of schooling, while the parallel value in 2014 was 25.9% (ICBS 1998, 2018).

  9. In our sample, 87% of the students took the psychometric test in 1999, compared to 70% in 2014 (see Table 4 in the “Appendix”).

  10. We sought to test the possibility that the effect of generation in higher education on the probability of studying the various combinations of field and institution type may differ for students with different abilities. To do so, we computed the marginal effects for different ability groups according to psychometric test and matriculation scores. The results did not yield significant differences between the various ability groups.

  11. The Israeli Bar Association (law), and the Association of Engineers, Architects and Graduates in Technological Science (engineering).

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Authors and Affiliations

  1. Department of Sociology, Tel Aviv University, Tel Aviv, Israel

    Hanna Ayalon

  2. Department of Sociology, Ohio State University, Columbus, OH, USA

    Oded Mcdossi

  3. School of Education, Tel Aviv University, Tel Aviv, Israel

    Abraham Yogev

Authors
  1. Hanna Ayalon
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  2. Oded Mcdossi
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  3. Abraham Yogev
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Correspondence to Hanna Ayalon.

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Appendix

Appendix

See Tables 3, 4, and 5.

Table 3 Distribution of the combinations of field of study and institution type, by year
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Table 4 Descriptive statistics of the independent variables by year
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Table 5 Multinomial logistic regression for predicting the combination of field and institution type (logarithmic coefficients)
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Ayalon, H., Mcdossi, O. & Yogev, A. Institutional Selectivity, Curricular Policy, and Field of Study Stratification in Expanded Higher Education Systems: The Case of Israel. High Educ Policy 36, 93–115 (2023). https://doi.org/10.1057/s41307-021-00248-8

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