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Abstract

In recent years, education–occupation mismatch has become an important area of social research. However, little is known about its impact on the intergenerational transmission of educational attainment. This study investigates the possible negative consequences of a specific aspect of parental education–occupation mismatch, also known as overeducation, for high school students. Drawing from a sample of high school students in an Israeli city with a high incidence of overeducation, our analysis suggests that parental education–occupation mismatch does not affect student expectations for progressing to higher education. The results did reveal, however, that maternal education–occupation mismatch is related to school truancy among boys and girls, and that paternal education–occupation mismatch contributes to lower odds of enrollment in advanced science courses, especially among boys.

Keywords

education–occupation mismatch overeducation high school aspirations truancy course-taking Israel

Introduction

Education–occupation mismatch arises when one possesses a higher—or lower—level of formal education than that required for one’s job. This study focuses on a specific aspect of education–occupation mismatch, also known as overeducation. In most cases, overeducation occurs when individuals with higher education degrees are employed in jobs that do not require this level of education. Research on overeducation has mainly focused on the negative consequences for employees, such as lower wages and higher job dissatisfaction (e.g., F. Green & Zhu, 2010; McGuinness, 2006). However, there is little research regarding the intergenerational consequences of parental overeducation. The present study is a first attempt to examine the possible consequences of such parental education–occupation mismatch. More specifically, we ask whether sons and daughters of parents in this position differ from the sons and daughters of parents with academic degrees who are employed in professions commensurate to their educational credentials. The study focuses on educational attitudes and behaviors, such as expectations for higher education, school truancy, and enrollment levels for the most challenging high school academic programs.

In recent years, education–occupation mismatch has become an important area of economic and sociological research. This interest can be attributed to the increased manifestation of two distinct social phenomena. The first is mass immigration, which contributes to the phenomenon of migrants with (relatively) high levels of education employed in low-status jobs in their host countries. The second is the rapid expansion of the higher education that may contribute to overeducation in the general population, particularly during periods of economic recession.

Research on the incorporation of migrants into host country labor markets has repeatedly demonstrated that immigrants often experience difficulties in finding jobs that commensurate with their levels of education (Chiswick, 1998; Chiswick & Miller, 2009a; Gans, 2009; Gorodzeisky & Semyonov, 2011). Language proficiency difficulties, the lack of social ties and connections in a new country, together with limited knowledge of the host society’s functional mechanisms and limited access to information all contribute to weakening the potential for migrants to compete effectively in the labor market against natives with comparable levels of education. Moreover, a significant proportion of migrants possess pre-immigration professional skills and education characterized by a low level of international transferability. Others must complete retraining programs and pass professional exams, to acquire the certification required for the level of professional employment aligned to the educational credentials and occupational qualifications acquired in their countries of origin (Chiswick & Miller, 2009b; Friedberg, 2000).

The second explanation for growing interest in the topic of education–occupation mismatch is the rapid expansion of the higher education, and the concern that this expansion is causing overeducation (e.g., Barone & Ortiz, 2011; Hartog, 2000). From an economic perspective, overeducation occurs when “the growth in the supply of higher-educated workers outpaces the growth in demand . . . Workers are overeducated if the skills they bring to their jobs exceed the skills required for that job” (Groot & Maassen van den Brink, 2000, p. 149). Sociological research has demonstrated that the structure of the education system, together with differentiation within higher education, is associated with the risk of overeducation (e.g., Barone & Ortiz, 2011). The existing evidence suggests that the last economic recession and rise in unemployment rates contributed to the increased likelihood of education–occupation mismatch, especially among young people (International Labour Organization, 2013).

Despite the current prevalence of education–occupation mismatch, little is known about the impact of parental education–occupation mismatch on adolescents. As a step toward addressing this lacuna in the literature, the present study examines four possible negative consequences of parental education–occupation mismatch for high school students. The first is students’ lower expectations of progressing to higher education; the second is weaker perceived parental encouragement for progressing to higher education; the third is school truancy; and the fourth is a lower tendency to enroll on the most challenging academic programs in high school. The first two outcomes represent student attitudes toward the value of schooling. As we explain in the next section of this article, parental education–occupation mismatch may affect how young people perceive societal and labor market rewards for investment in education. In other words, sons and daughters of parents with higher education degrees who are employed in low-status jobs—that is, parents experiencing education–occupation mismatch—may be discouraged by their parents’ occupational circumstances; such circumstances may negatively affect adolescents’ own educational expectations. The third and fourth outcomes measure student academic behaviors with a potential impact on both present and future academic attainment. Research in the sociology of education suggests that gaps between student attitudes to education and student behavior in school are not uncommon. Thus, in addition to the issue of expectations for higher education, we also collected data for two additional factors: school behavior problems with the potential for reducing the likelihood of attaining a higher education degree, and enrollment in high school academic programs which increase the odds of subsequent enrollment in higher education.

Because earlier studies on education–occupation mismatch have not explored these possible negative consequences of parental education–occupation mismatch, we turned to the sociology of education literature to formulate our research questions and hypotheses. Consequently, utilizing data collected in the largest city in the southern part of Israel, the study empirically examines the association between parental education–occupation mismatch and the negative outcomes stated above.

Research Questions, Theoretical Considerations, and Hypotheses

In the following research questions, we define education–occupation mismatch as a situation in which parents have an academic degree but are employed in a profession that does not require such a degree.

Research Question 1: Do students whose parents are experiencing education–occupation mismatch have lower expectations regarding their progress to higher education, and report weaker parental encouragement for such expectations, than students whose parents have an academic degree and are employed in professions commensurate with their education?

Since the publication of the Wisconsin model of status attainment (Sewell, Haller, & Portes, 1969), educational expectations and aspirations have been seen as a central mediation mechanism between social background and educational and occupational attainment. One of the most important theoretical contributions of the Wisconsin model is the observation that expectations and aspirations are formed through interactions with significant others, mainly parents and peers. The extensive literature on aspirations and expectations presents a rich exploration of the differences between these two concepts. Aspirations are usually seen as the more abstract, even idealistic, attitudes that reflect not just personal hopes but also the dominant ideology of meritocracy. Expectations, on the contrary, reflect more concrete attitudes, influenced by the actual experience of the student and his or her family (see, for example, Bohon, Johnson, & Gorman, 2006; Marjoribanks, 1997; Mickelson, 1990; Morgan, 2005).

Both concepts are widely used in the research literature, and both have been found to have explanatory power in relation to achievement (see Khattab, 2015). For the purpose of this study, we decided to focus on expectations, because this concept is more closely connected to the actual experience of the family, and to the social structure that contextualizes the student’s experiences. Studies show that, in line with the Wisconsin model, parental social class is related to their offspring’s expectations (e.g., Duncan, Featherman, & Duncan, 1972; Kao & Tienda, 1998; Teachman & Paasch, 1998). Following Mickelson’s (1990) distinction between abstract and concrete attitudes toward the future, we predict that students who live in families that are experiencing education–occupation mismatch will be less optimistic about the opportunities that the labor market will present them in due course; consequently, they will have lower expectations regarding their progress to higher education than counterparts who live with parents who were successful in converting their academic degrees into employment commensurate to their education (i.e., not contending with the phenomenon of overeducation). The perceived encouragement from parents to their offspring regarding their progress to higher education is also expected to be lower among parents who did not succeed in converting their academic qualifications into commensurate job positions.

Research Question 2: Are students whose parents are experiencing education–occupation mismatch more prone to school behavior problems, as expressed through truancy, than students whose parents have an academic degree and are employed in a profession commensurate with their education?

A significant body of research suggests that academic commitment and school engagement are critical for student achievement (for a review, see Fredricks, Blumenfeld, & Paris, 2004). In this study, we chose to focus on student truancy as a proxy for school behavior, given the connection between truancy and self-motivation, academic commitment, and school engagement (J. Green et al., 2012). There is ample evidence that truancy is related to social background, and that students from less affluent families—especially students living in poverty—are at higher risk of truancy (e.g., Balfanz & Byrnes, 2012; Ready, 2010).

Parental involvement, both in the form of cultural capital and social capital, is a key factor influencing behavior and success at school; it is also an important mechanism mediating between social class and school engagement (Coleman, 1988; Lareau, 1987). McNeal (1999) identified four main dimensions of parental involvement that modify student school behavior: parent–child discussions, parent involvement in organizational activities that bring together teachers and parents, parental monitoring of children’s behavior and academic progress, and direct parental support for the schooling process. The second, third, and the fourth dimensions are especially relevant for the comparison undertaken in this study. The academic literature suggests that parents who are experiencing education–occupation mismatch may have fewer opportunities for monitoring their children and assisting them in their schooling process, for a number of reasons. First, employment in low-status jobs with minimal workplace flexibility can negatively impact parent–teacher communication and relationships (Lareau, 1987; Weiss et al., 2003). Earlier research has shown that communication between families and school is crucial for reducing truancy, and that lower levels of such communication is related to the higher likelihood of truancy (e.g., Epstein & Sheldon, 2002; Sheldon & Epstein, 2004). Second, the inability of parents to secure employment commensurate with their higher levels of education may reduce the motivation of students to accept educational support from their parents, and their willingness to be academically monitored by their parents. In her study on immigrant families in Israel, Remennick (2012) found that parents with an academic degree who had experienced downward occupational mobility due to immigration worked longer hours, to compensate for their low per-hour income. As a result, communication between them and their teenage children deteriorated, weakening their capacity to act as role models and supervisors. Our hypothesis, thus, is that parental education–occupation mismatch will be related to a greater tendency to school truancy.

Research Question 3: Do students whose parents are experiencing education–occupation mismatch have a lower likelihood of enrolling for advanced science high school programs than students whose parents have an academic degree and are employed in a profession commensurate with their education?

A differentiated curriculum with formal or informal hierarchies is a major characteristic of many high school systems. Over the last four decades, a range of sociological studies have demonstrated that curriculum differentiation plays a crucial role in creating and maintaining inequalities in learning opportunities. These studies show that students from lower social class and from some (but not all) ethnic minority groups are more prone to placement in low-status classes. Hence, curriculum differentiation restricts opportunities for upward mobility (e.g., Gamoran, 2010; Van de Werfhorst & Mijs, 2010). Because the empirical tests of the hypotheses were carried out in an Israeli context, it is important at this point to stress the role played by advanced science courses in Israeli high schools.

Israeli secondary education is extensively tracked. Students can study in academic or vocational schools, or in different tracks within a comprehensive high school. Even though high school subjects are not formally stratified, there is informal stratification, between the sciences (physics, chemistry, biology, computer science) on one hand, and the humanities and social sciences on the other. The sciences are highly regarded by students, parents, and teachers alike; students who take classes in the advanced sciences are considered as the school elite. Schools tend to play an active role in the assignment of students to advanced science classes and programs. In contrast, schools control to a lesser degree the assignment of students to advanced humanities and social science classes, which are sometimes considered the default option for students unable to take advanced sciences classes. Consequently, students who take advanced courses in different fields of study differ in their educational and social profiles. More importantly, Israeli students who take advanced science classes have the greatest likelihood of enrolling in higher education; research also shows that students from socioeconomic advantaged backgrounds are overrepresented in these courses (Ayalon, 2006; Feniger, Mcdossi, & Ayalon, 2015; Mizrachi, Goodman, & Feniger, 2009).

Students with parents experiencing education–occupation mismatch may be at a disadvantage regarding enrollment on advanced science programs for several reasons. First, as mentioned earlier, they may be less motivated to pursue higher education and therefore less willing to invest the effort required for the most demanding courses. Second, these students may be subject to lower levels of social capital, in the form of parental involvement, support, and information. Parents who have acquired higher education usually tend to have high expectations of their children. However, to turn these expectations into real educational opportunities—such as, for example, enrollment on selective advanced courses—parents need to be active in their support of their children’s learning processes and decision making (Crosnoe & Schneider, 2010).

Third, the familial financial circumstances may curtail or remove the possibility of private tutoring. Private tutoring is a commonplace phenomenon with Israeli students, especially at the high school level. According to a recent Ministry of Education survey, about 70% of high school students receive some private tutoring, with math the most popular subject (Davidovich-Weisberg, 2013). Data from Israel’s Central Bureau of Statistics show a clear link between private tutoring and parental education and income (Vurgan, 2011). Our hypothesis, thus, is that parental education–occupation mismatch will be related to the lower likelihood of enrollment on advanced science programs.

Data and Method

Data Collection

Research on social inequality usually gives priority to national representative samples. However, in some cases these samples do not provide enough information for the examination of new research questions. Because existing Israeli representative samples do not include the specific items needed to test our hypotheses, we collected data in Beersheba, the largest city in the southern part of Israel. Beersheba has a population of about 200,000 inhabitants. About 98% of the city’s population identify as Israeli Jewish; 27% of the population are new immigrants (i.e., arrived in Israel since 1989), most from the former Soviet Union (FSU). The total share of post-1989 new immigrants in the Israeli Jewish population is about 15%, indicating an overrepresentation of new immigrants in population of Beersheba. Among Beersheba’s young population (ages 0-17), new immigrants comprise about 35% of the total number. From a socioeconomic perspective, Beersheba is a socially diverse community, and is ranked by the Israeli Central Bureau of Statistics in the fifth (out of 10) cluster of the socioeconomic index of localities in Israel (Central Bureau of Statistics, 2013, 2014).

As a large city with overrepresentation of highly educated FSU immigrant population on one hand, and limited employment opportunities for highly skilled workers (Central Bureau of Statistics, 2014)¹ on the other, Beersheba provides a unique setting for examining the consequences of parental education–occupation mismatch for the intergenerational transmission of educational attainment. By sampling students in Beersheba’s high schools, we were able to construct a database with information on a large number of students with parents experiencing education–occupation mismatch, without the need to rely on complex sampling strategies such as quotas. In our sample, about 58% of migrant families from the FSU had at least one parent with an academic degree but employed in a profession defined by a lower level of education (i.e., manual or secretarial jobs). With the rest of the sample, about 32% of the families were experiencing this phenomenon. However, preliminary analyses showed that while students from FSU migrant families reported much higher rates of parental education–occupation mismatch, the effect of this phenomenon on students’ attitudes and behavior among children of FSU immigrant families was similar to its effect on the children from nonmigrant families. (Our preliminary analysis found no effect for an interaction term between migration status and occupation–education mismatch on students’ attitudes and behavior). For this reason, we present analysis of the sample that includes both students from migrant families and students from nonmigrant families, and not separate analyses for each one of the groups.

Data were collected using a questionnaire administered to 11th-grade students in the seven high schools that participated in the study. All the 11th-grade students in these schools were approached in their classes during school hours, and were asked if they were willing to fill out the questionnaire.² Almost 600 students agreed to participate in survey—a total response rate of about 65%. About 30 questionnaires were partially completed, and were consequently deleted from the data file.

Dependent Variables

Expectations for higher education

Students were asked, “In your opinion, will you attend a university or a college?” A Likert-type scale of five response categories ranged from “I am confident that I will not [attend a university or a college]” to “I am confident that I will [attend a university or a college].”

Parents’ encouragement for higher education

This was measured as the extent of agreement with the following statement: “It is very important for your parents that you will attend a university or a college.” A Likert-type scale of 10 response categories ranged from “Do not agree at all” to “Very much agree.” It should be emphasized that this variable measures how the respondents perceived their parents’ encouragement for higher education, and not actual parental encouragement. Although there is the possibility of bias in this measure, the salient issue here is whether students with parents experiencing education–occupation mismatch perceive parental encouragement for higher education differently from students whose parent are not in this situation.

Truancy

This was defined as the number of times in the last month that a student was absent from school without legitimate cause, such as through illness. The response categories were as follows: (1) zero, (2) 1 to 3 times, and (3) 4 times or more. After preliminary analyses, we constructed a dummy variable distinguishing between students absent from school without legitimate cause (coded 1) and those who were not absent from school without legitimate cause (coded 0). Truancy is a difficult phenomenon to measure, given that students may (understandably) be reluctant to self-report negative behavior. Consequently, the measure itself may be downwardly biased. However, as will be seen below, respondents reported surprisingly high levels of truancy, thus eliminating the concern of a potential downward bias. It should be noted that according to the international PISA (Program for International Student Assessment) 2012 study, Israeli high school students report higher levels of truancy than students in most other developed countries (Organisation for Economic Co-Operation and Development, 2013).

Advanced science courses

Students were asked about enrollment on advanced science courses. The variable distinguishes between students who took advanced science courses (physics, biology, chemistry, and computer science) and those who specialized in other subjects—the social sciences, humanities, or vocational subjects.

Independent Variables

Female

A dummy variable coded 1 for girls and 0 for boys.

Immigration background

Students were asked about their, their parents,’ and their grandparents’ country of birth. In addition, they were asked about the main language spoken at home. According to their responses, we defined students as immigrants from the FSU, “other immigrants,” and non-immigrants. Immigrants from the FSU are students whose families migrated to Israel from the FSU; they were either born in an FSU republic or were born in Israel to parents from the FSU; and the main language spoken at their home is Russian or another language native to the republics of the FSU (e.g., Georgian, Azerbaijani). “Other immigrants” are either those who were born outside of Israel but not in FSU (e.g., Ethiopia, France, Argentina) or those born in Israel whose parents migrated to Israel from countries other than the FSU, and the main language spoken at their home is not Hebrew. This is a small and very heterogeneous group, and we include it in the multivariate analysis mostly for control purposes. All other students were defined as non-immigrants (or students without an immigrant background).

Parental education

Students were asked if their mother and father completed an academic degree (bachelors’ degree or higher).

Parental occupation

Students were asked to describe in detail the occupation of their mother and father (i.e., what they do in their job, their occupation or job title, type of workplace). Categorization of occupations was conducted according to the class scheme proposed by Goldthorpe (2000) and its modifications for the Israeli context (Yaish, 2004). In the analysis, we distinguished between professional (e.g., engineer, lawyer), managerial (e.g., bank manager, head of a department in a hospital), and intermediate white-collar jobs (e.g. high school teacher, nurse in a hospital) (Goldthorpe’s categories I-IIIa, henceforth “white collar jobs”) and all other categories (i.e., “non-white collar” jobs, such as driver or factory worker). Unemployment rate was relatively low, in line with the general low unemployment rate in Israel in recent years. In our sample, about 6.1% of the fathers and about 14.5% of the mothers were not employed. Among fathers who completed an academic degree, only 2.3% were not employed; among mothers with the same level of education, 6.3% were not employed. In light of these numbers, we decided to include those who were not employed in the non-white-collar category.

Number of books at home

Number of books at home is a well-established measure of cultural capital, and has been demonstrated to be a strong predictor of educational achievement in Israel and in many countries (De Graaf, De Graaf, & Kraaykamp, 2000; Evans, Kelley, Sikora, & Treiman, 2010; Feniger, Shavit, & Ayalon, 2014). Students were asked about the number of books in their home. The response categories were (1) 0 to 10, (2) 11 to 25, (3) 26 to 100, (4) 101 to 250, and (5) more than 250. For the purpose of the analyses, we assumed that this is an interval scale variable.

Intact family

A dummy variable coded 1 for intact families and 0 for students who lived in a single-parent household.

Private tutor

Students were asked if they had taken private lessons in the preceding 2 years. Based on this information, we constructed a dummy variable coded 1 if the student had attended private tutoring, and 0 if not.

Treatment of Missing Values

In most of the variables, the percentage of missing values was relatively low (3%-5%). Only the measurement of mother’s and father’s occupation yielded somewhat higher rates of missing values (9% and 14% respectively). In the continuous variables, we replaced missing values with the variable’s mean; in the nominal variables, they were omitted. To ensure that the omission of missing values did not affect our results, we also repeated the analyses with dummy variables for father’s occupation missing and mother’s occupation missing. The results were similar, so we chose to omit the missing values, thus avoiding the inclusion of interaction terms with those dummy variables in the statistical models.

Findings

Table 1 presents the four dependent variables, according to parental education, parental occupation, and immigration status. As can be expected, parental education and occupation were associated with expectations for higher education and enrollment on advanced science course. Students with either a mother or father with an academic degree also reported higher levels of encouragement for higher education than students with parents who did not possess a degree.

Table 1.

Expectations for Higher Education, Parental Encouragement, Truancy, and Enrollment in Advanced Science Courses, by Parental Education, Parental Occupation, and Immigration Status (n = 449).

Variable	Mean expectations for higher education (SD)(Range:1-10)	Mean parents’ encouragement for higher education (SD)(Range:1-10)	Percent absent from school	Percent enrolled on advanced science courses
Mother’s education
Nonacademic (51.7%)	3.7 (1.1)	8.6 (2.0)	66.7	27.6
Academic (48.3%)	4.2 (0.9)*	9.4 (1.2)*	59.3	50.6*
Father’s education
Nonacademic (54.6%)	3.6 (1.1)	8.6 (2.0)	63.9	27.5
Academic (45.4%)	4.3 (0.8)*	9.5 (1.0)*	62.1	52.9*
Mother’s occupation
Non-white-collar (69.7%)	3.8 (1.1)	8.9 (1.8)	65.6	35.7
White-collar (30.3%)	4.1 (1.0)*	9.2 (1.5)	57.4	49.3*
Father’s occupation
Non-white-collar (77.5%)	3.9 (1.1)	9.0 (1.7)	63.4	34.1
White-collar (22.5%)	4.1 (1.0)*	9.1 (1.6)	62.6	58.7*
Immigration status
Nonimmigrant family (reference category—54.8%)	3.8 (1.1)	8.7 (1.9)	66.0	27.8
FSU immigrant (39.0%)	4.2 (1.0)*	9.4 (1.3)*	61.7	56.9*
Other immigrant (6.2%)	3.9 (1.1)	8.8 (2.0)	46.4	26.8

Note. For difference in means, a t test was used; for difference in proportions, the chi-square test was used. FSU = former Soviet Union.

The difference is statistically significant at the .05 level.

The findings also indicate that compared with non-immigrant students, students whose families immigrated from the FSU reported higher expectations of progressing to higher education, higher levels of parental encouragement for higher education, and much higher likelihood of enrollment on advanced science courses. These findings are consistent with previous Israeli research (Chachashvili-Bolotin, 2010; Eisikovits, 1995; Feniger, 2017). It should be noted that immigrants’ optimism with regard to securing opportunities for social mobility through the education system is a well-documented phenomenon in many countries (e.g., Kao & Tienda, 1995; Salikutluk, 2016). Regarding truancy, we did not find a statistically significant difference between FSU immigrants and non-immigrant students.

At the next stage of the analysis, to examine the consequences of living in a family in which parents possess academic degrees but are employed in a non-white-collar occupation, we focused only on students with a mother or father who possessed an academic degree. Table 2 presents comparisons between students with a father employed in a white-collar job and students with a father experiencing education–occupation mismatch (i.e., with an academic education, but employed in a non-white-collar occupation). The findings in this table show that students whose father is experiencing education–occupation mismatch have lower odds of enrollment on advanced science courses than students with a father employed in a white-collar job. Regarding the other three dependent variables, we did not find any statistically significant differences between the two groups.

Table 2.

Expectations for Higher Education, Parental Encouragement, Truancy, and Enrollment in Advanced Science Courses, by Father’s Occupation Among Students Whose Father Completed an Academic Degree (n = 204).

Father’s occupation	Mean expectations for higher education (SD)	Mean parents’ encouragement for higher education (SD)	Percent absent from school	Percent enrolled on advanced science courses
Non-white-collar (60.3%)	4.3 (0.8)	9.4 (1.1)	63.1	45.1
White-collar (39.7%)	4.2 (0.9)	9.4 (1.1)	60.5	65.1*

The difference is statistically significant at the .05 level.

Table 3 shows that maternal education–occupation mismatch is not significantly related to enrollment on advanced science courses. It does show, however, that students with a mother experiencing education–occupation mismatch are more prone to report school truancy than students with a mother employed in a white-collar job. Regarding expectations and parental encouragement for higher education, we did not find any statistically significant difference between the two groups. Thus, we did not find evidence supporting the hypothesis that paternal or maternal education–occupation mismatch is related to lower levels of expectations for higher education, or perceived parental encouragement for higher education.

Table 3.

Expectations for Higher Education, Parental Encouragement, Truancy, and Enrollment in Advanced Science Courses, by Mother’s Occupation Among Students Whose Mother Completed an Academic Degree (n = 217).

Mother’s occupation	Mean expectations for higher education (SD)	Mean parents’ encouragement for higher education (SD)	Percent absent from school	Percent enrolled on advanced science courses
Non-white-collar (49.8%)	4.2 (0.9)	9.4 (1.3)	67.3	50.0
White-collar (50.2%)	4.3 (0.9)	9.5 (1.1)	51.4*	57.8

The difference is statistically significant at the .05 level.

To further investigate the effects of paternal education–occupation mismatch on science course-taking, and maternal education–occupation mismatch on truancy, we used logistic regression models. In Table 4 we present findings from the analyses of students with a father possessing an academic degree. Model 1 in Table 4 examined whether paternal education–occupation mismatch affects the odds of enrollment on advanced science courses while controlling for immigration background, maternal education and occupation, gender, family structure, and cultural capital (as measured by the number of books at home). As the model was estimated only among students with a father possessing an academic degree, the coefficient of Father White-Collar variable should be interpreted as the net difference in the odds of enrollment on advanced science courses between students whose father does not experience education–occupation mismatch (has academic education and white-collar job) and students with a father who does experience education–occupation mismatch (has academic education but non-white-collar job). The main finding of this model is that the odds to enroll on advanced science courses for students with a father employed in a white-collar job are 2 times higher than the odds for students with a father experiencing education–occupation mismatch (when all other variables in the model are held constant). The findings also show that immigration from the FSU and living in an intact family are associated with higher odds of enrollment on advanced science courses. The finding regarding FSU immigrants is in line with previous Israeli studies, which found that students in this population group report higher motivation than non-immigrant Jews for progressing to science-related careers after high school (Eisikovits, 1995; Feniger, 2017); and that this group has a greater tendency than non-immigrant Jews to specialize in science, in both high school and higher education (Chachashvili-Bolotin, 2010; Feniger et al., 2015).

Table 4.

Odds Ratios from Logistic Regression Analyses of Enrollment in Advanced Science Courses among Students Whose Father Completed an Academic Degree (n = 204).

Variable	Model 1	Model 2	Model 3
Immigration status (reference: non-immigrant)
FSU immigrant	2.2*	2.3*	2.4*
Other immigrant	0.4	0.5	0.8
Father white-collar	2.1*	5.7**	5.9**
Mother white-collar	1.4	1.5	1.5
Academic mother	1.6	1.2	1.1
Girl	0.7	1.5	1.5
Intact family	3.0**	3.0**	2.9*
Number of books at home	1.1	1.1	1.0
Father White-Collar × Girl		0.2*	0.2*
Private tutor			2.6*
Intercept	0.3	0.2	0.1*
Nagelkerke pseudo R²	.404	.429	.449

Note. The models included school fixed effects, to account for unobserved heterogeneity across schools. Results with and without this control were fairly similar. FSU = former Soviet Union.

p < .05. **p < .01.

In Model 2, we examined whether the Father White-Collar effect is similar for girls and boys. This was done by adding to Model 1 an interaction between paternal white-collar job and gender (girl = 1). As can be seen in Table 4, we found that the interaction term is statistically significant. Among boys, the odds to enroll on science courses of those with a father employed in a white-collar job were 5.7 times higher than the odds of boys with a father experiencing education–occupation mismatch. Among girls, this effect is much smaller—only 20%, Exp (B) = .2, of the effect of Father White-Collar among boys. Thus, for girls, a father employed in a white-collar job is associated with only an increase of 14% (5.7 × 0.2 = 1.14) in the odds of enrolling on advanced science courses, compared to girls with a father possessing an academic degree but employed in a blue-collar job. Separate analyses for boys and girls (not shown here) confirmed that among boys there was a noticeable difference between those with a father employed in a white-collar occupation and those with a father experiencing education–occupation mismatch. Among girls, this difference was not statistically significant.

In Model 3, we tested the explanation that the positive effect of Father White-Collar is due to the better economic resources that would enable investment in private tutoring. This was done by adding the Private Tutor variable to the variables in Model 2. The findings from this analysis suggest that private tutoring cannot, by itself, account for the advantage of students with a father not experiencing education–occupation mismatch. Although the Private Tutor effect is strong (odds ratio of 2.6) and statistically significant, the Father White-Collar coefficient remains positive and statistically significant.

Table 3 shows that school truancy is more prevalent among students with mother experiencing education–occupation mismatch. In Table 5, we further examined this finding by estimating logistic regression models only for students with a mother who possesses an academic qualification. Model 1 shows that after taking into account immigration background, paternal education and occupation, gender, cultural capital, and family structure, the odds of reporting school truancy are 2 times higher for students with a mother experiencing education–occupation mismatch as compared to students with a mother in a white-collar job. In Model 2, we examined whether this finding differs between boys and girls. The results from this analysis do not show a statistically significant difference; thus, the negative effect of maternal education–occupation mismatch is similar for both genders.

Table 5.

Odds Ratios from Logistic Regression Analyses Predicting Truancy among Students With a Mother Who Completed an Academic Degree (n = 217).

Variable	Model 1	Model 2
Immigration status (reference: non-immigrant)
FSU immigrant	0.7	0.7
Other immigrant	0.6	0.6
Mother white-collar	0.5*	0.4^† (p = .058)
Academic father	1.4	1.4
Father white-collar	0.9	0.9
Intact family	0.8	0.9
Number of books at home	0.8	0.8
Girl	0.6	0.5
Mother White-Collar × Girl		1.6
Intercept	7.0**	8.4**
Nagelkerke pseudo R²	.077	.080

Note. The models included school fixed effects, to account for unobserved heterogeneity across schools. Results with and without this control were fairly similar. FSU = former Soviet Union.

†

p < .1. *p < .05. **p < .01.

Tables 4 and 5, then, support our hypotheses that parental education–occupation mismatch is related to school behavior problems and to advanced course-taking, but in a more nuanced manner than expected. Regarding enrollment on advanced science courses, our findings indicate that male students are responsive to the type of job held by their fathers. Among boys with a father who completed an academic degree, those with a father employed in a white-collar job have a much higher odds of enrolling on advanced science courses, compared to those with a father experiencing education–occupation mismatch. Girls, on the contrary, are much less responsive to their father’s type of employment. Regarding school truancy, it was found that the type of employment of the mother had a greater influence: Both boys and girls with a mother experiencing education–occupation mismatch had a greater risk of truancy than students with a mother employed in a white-collar job.

Conclusion

This study is the first to examine the consequences of education–occupation mismatch (i.e., having an academic degree, but being employed in a non-white-collar occupation) for the intergenerational transmission of educational attainment. The findings suggest that, contrary to our hypothesis, parental education–occupation mismatch did not exert an influence over either students’ expectations or perceived parental encouragement for higher education. We did find evidence, however, of a relationship between maternal education–occupation mismatch and behavior problems among both girls and boys; and of a relationship between paternal education–occupation mismatch and a lower likelihood of enrollment on advanced science courses, especially for boys. Why then are student attitudes unaffected by parental education–occupation mismatch, when actual student behavior does seem to be influenced by it? The students’ reported expectations regarding higher education may reflect a social norm, rather than personal attitude. There is ample evidence in the sociology of education indicating that student attitudes toward schooling are not necessarily connected to actual behavior and achievements (e.g., Khattab, 2015; Mickelson, 1990; Schneider & Stevenson, 1999). Thus, this finding—that students have high expectations of progressing to higher education, even though their school experience reduces the likelihood of realizing these expectations—is in line with many previous studies.

The gendered findings of this study may be understood in light of research on family relations. In a review of studies on sons, daughters, and family processes, Raley and Bianchi (2006) concluded that mothers spend much more time with their offspring than fathers, and that their investment is gender-neutral. Remennick (2012), who studied intergenerational mobility in families who migrated to Israel from the FSU, found that parental downward occupational mobility due to immigration was associated with lower achievement of offspring, a higher likelihood of dropping out of high school, and skepticism about the value of education as a vehicle for occupational mobility. In Remennick’s (2012) study, one mother recounted her boy saying “[y]our college diplomas did not help you or Dad in securing good positions in Israel, you are still toiling in unskilled jobs despite all your knowledge and ambition. Why push me in the same direction?” (p. 1540). This situation may not be necessarily unique to immigrant families, and may also be evident in other families experiencing education–occupation mismatch. We can speculate, then, that reduced maternal authority may be an important explanation for the higher level of truancy among the sons and daughters of mothers experiencing education–occupation mismatch. This issue warrants deeper exploration in future research.

Our findings indicated that the father effect with regard to a son’s enrollment on advanced science courses cannot be accounted for by either financial investment in private tutoring or differences in expectations for higher education. Thus, it is probably an outcome of father–son interactions. Previous research suggests that fathers tend to spend more time interacting with their sons than with their daughters and that interactions with fathers are related to self-worth (Lam, McHale, & Crouter, 2012; Mammen, 2011; Raley & Bianchi, 2006). Paternal education–occupation mismatch may interfere with father–son socialization processes, and reduce the ability of fathers to transfer to their offspring the importance of investing time and effort in the most demanding high school programs. Here, again, a quote from Remennick’s (2012) study illustrates the effect of education–occupation mismatch, one father explaining:

[m]y two sons basically grew up all by themselves, without my being there for them . . . I hardly knew what they were learning at school . . . I simply was never at home, taking shift after shift in my two jobs. (p. 1453)

Thus, while highly educated fathers employed in blue-collar jobs may transfer their high expectations for higher education, or even pay for private lessons for their sons, it may be more difficult for them to be directly involved in educational decision-making processes. We hope that future research will enable a better understanding of such familial processes.

From a policy perspective, it is important to remember that education–occupation mismatch, in the form of overeducation, is more prevalent among immigrants. Our analysis shows that compared with non-immigrant students, students from families who immigrated to Israel from the FSU reported higher expectations for progressing to higher education, more parental encouragement for higher education, and had higher rates of enrollment on advanced science courses. However, among FSU immigrants, about two thirds of fathers and about 55% of mothers who completed an academic degree find themselves in a situation of education–occupation mismatch. This implies that the academic potential of children of this population is constrained by the structure of the local labor market. We believe that this information should be brought to the attention of school personnel and policy makers, to find ways of counterbalancing the negative consequences of education–occupation mismatch.

The study has two main limitations. First, despite a satisfactory response rate, the sample is relatively small. In addition, as it was limited to schools in one city, it should not be considered a representative sample of Israeli high school students. The small sample size has some methodological implications for this study. It is possible, for example, that with a larger sample size the effect of maternal education–occupation mismatch on enrollment on advanced science courses would have been statistically significant. The relatively small sample size also restricted our ability to apply the broader categorization of occupations generally used in the literature on overeducation. Second, our analysis is cross-sectional, and does not include information on previous achievement and other individual and educational processes that occurred prior to data collection. The findings of this study, therefore, cannot be interpreted as causal effects. In spite of these limitations, we hope that the present study will inspire other scholars to collect new data or use existing suitable databases, to broaden our understanding of the consequences of parental education–occupation mismatch for girls and boys.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

ORCID iD

Yariv Feniger

Author Biographies

Yariv Feniger is a senior lecturer in the Department of Education at Ben-Gurion University of the Negev.

Anastasia Gorodzeisky is a senior lecturer in the Department of Sociology and Anthropology at Tel-Aviv University.

Michal Krumer-Nevo is professor in the Spitzer Department of Social Work at Ben-Gurion University of the Negev.

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Who Pays the Price for Parental Education–Occupation Mismatch? Evidence From an Israeli City

Abstract

Keywords

Introduction

Research Questions, Theoretical Considerations, and Hypotheses

Data and Method

Data Collection

Dependent Variables

Expectations for higher education

Parents’ encouragement for higher education

Truancy

Advanced science courses

Independent Variables

Female

Immigration background

Parental education

Parental occupation

Number of books at home

Intact family

Private tutor

Treatment of Missing Values

Findings

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

Notes

ORCID iD

Author Biographies

References