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Introduction

General

1. GeneralThe publication contains data on employment characteristics among recipients of Bachelor's degrees between 2000 and 2004. These findings are based on linkage of records from three main sources: the Population Register of the Israel Ministry of Interior, administrative files of Bachelors' degree recipients from accredited academic institutions in Israel, and data from the Israel Tax Authority. Special data processing on employment and earnings was conducted, where each graduate was tracked over a period of five years after attaining a Bachelor's degree. The results of this follow-up were tabulated with respect to employment rates, self-employment propensities, job mobility trends, distributions by industry, and earnings from employment among Bachelor’s degree recipients. The tables also distinguish between graduates by field and subject of study, type of institution attended, and demographic characteristics (sex, age, and population group).

The publication is divided into three sections: an Introduction, Tables (appearing separately on the CBS website), and an Appendix. The Introduction contains detailed definitions relating to the data, the study population, and methods. The section also contains a descriptive analysis (with accompanying diagrams), which highlights and explains the main findings. Finally, the Introduction presents a multivariate analysis, which elaborates on specific factors associated with total annual earnings of Bachelor's degree recipients.

The Tables section includes high-resolution information on employment, self-employment, number of jobs (mobility), and earnings from employment among Bachelor's degree recipients. The tables contain nested data on as many relevant variables as possible (including those not covered in Introduction section), given space constraints. For the reader's convenience, the earnings tables are presented in both arithmetic means and medians.

Finally, the Appendix section contains supplementary tables that provide interested readers with additional relevant information for understanding the tables. Specifically, the Appendix contains tables relating to variable class aggregations, data on rates of continuation for Master's degree studies, Gini indices for annual earnings by type of institution, and additional information relating to the above-mentioned multivariate analysis.

Interested readers can also refer to other CBS publications on the topic: Recipients of First Degrees from Institutions of Higher Education, Special Publications No. 1296 (for 1999/2000-2001/02), and No. 1349 (for 2003/04); Recipients of Degrees from the Universities and Other Institutions of Higher Education, Current Statistics No. 22/2002 (for 1999/2000-2000/01), 12/2003 (for 2001/02), 18/2004 (for 2002/03), and 18/2005 (for 2003/04), as well as CBS Working Paper No. 42, which presents a thorough econometric analysis of employment and earnings among Bachelor’s degree recipients.

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Employment and Earned Income among Bachelor's Degree Recipients 2000-2004

2. Data and MethodologyStatistical compilation was undertaken by linking the three administrative data files1 via identification numbers, which are provided to all permanent residents of Israel by the Ministry of Interior. The data sources include:

1. Administrative files of Bachelor's Degree recipients during the period from 2000 to 2004. The files also include the subset of Bachelor's degree recipients who continued studying towards a Master's Degree (in the given follow-up year), enabling a distinction to be made between graduates who concurrently study and those who do not.

2. Individual earnings data from the Israel Tax Authority file, covering all individuals whose income from salaried jobs or from self-employment is reported.

3. Demographic data as listed within the national Population Register, administered by the Israel Ministry of Interior.

After the data files were linked through the specific ID number, the data were reduced to the individual record level, so that employment data for persons who had worked for more than one employer2 during a calendar year were aggregated and calculated as one total amount.

After linkage and aggregation at the individual level, the employment data were further processed, so that the respondents’ earned income in a given calendar year was evaluated in relation to their year of graduation. In other words, the individual’s earned income in a given calendar year was indexed to a fixed time interval since the year they received a Bachelor’s degree (e.g., Degree Year+1, Degree Year+2, Degree Year+3, etc.). Such data configuration enables comparison of different cohorts, as long as the time intervals since Bachelor’s degree attainment are held constant (see Main Findings section for elaboration).

1 Each data file underwent rigorous cleaning and standardization prior to linkage.2 Each employer/firm has a unique tax identifier number in the Israel Tax Authority.

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Definitions and Limitations of the Data

3. Definitions and Limitations of the Data The study population included all persons who attained a Bachelor's Degree between

2000 and 2004 (academic years 1999/2000-2003/04), and whose ID number was linked to the national Population Register3. It should be noted that Bachelor’s degree recipients who were listed in the national Population Register but have since left the country are included in the study population. Consequently, employment rates may be biased downward to a slight degree, because graduates leaving the country are not generally active in the Israeli labour market (see definition of employment rate for more details)4. Bachelor's degree recipients are restricted to persons who were enrolled in the following institutions, and do not include graduates of foreign college extensions (see Appendix C for a detailed list of institutions):

Israeli universities

The Open University

Academic colleges accredited by the Israel Council for Higher Education

Colleges of education granting a Bachelor’s degree in education (B. Ed.) under the supervision of the Ministry of Education.

The unit of analysis presented in the tabulations is graduates of a particular type of higher education institution, who attained a Bachelor's Degree between 2000 and 2004 (inclusive). Persons who received a Bachelor’s degree from two different types of institutions (e.g., a university and an academic college) are counted twice (once for each type of institution). Similarly, individuals may be counted twice within tables containing information on field of study and subject of study if they completed their Bachelor’s degree in more than one field or subject.

Degree Year (DY) represents the year of graduation ceremonies. Because most institutions of higher education hold one graduation ceremony per calendar year (in mid-summer), there is usually a time lag ranging from half a year to a year between the time an individual receives official qualifications for a degree and the date the ceremony takes place. In other words, the persons listed in this report as attaining a Bachelor’s degree in the middle of 2001 (by the time of the graduation ceremony) may have actually attained their degree a year earlier, i.e., just after the previous ceremony in the middle of 2000. However, in most cases the time lag is half a year.

Bachelor’s degree only represents the continuing education status of individuals after receiving their Bachelor’s degree, and refers to persons who are not engaged in continuing education at the Master's Degree level during the relevant calendar year or period since attaining their Bachelor’s degree. This distinction is relevant, because Bachelor’s degree recipients continuing toward a Master’s degree tend to devote more time to their studies and tend to work in part-time jobs or in the secondary labour

3 A few Bachelor’s degree recipients were foreign students who did not have Israeli ID numbers. Hence, they were not included in the survey (i.e., they were not linked).

4 Limited examinations revealed that the magnitude of this immigration effect was negligible.

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market. Thus, they earn significantly less while they are studying than do their counterparts who work without studying simultaneously. Unless otherwise specified, the analysis and description of the main findings pertain only to Bachelor’s degree holders who were not concurrently studying towards a Master’s degree (in the relevant time period). In the Tables section (appearing on the CBS website), earnings and employment rates are presented separately, by status of concurrent Master’s degree studies for each follow-up time frame. In order to calculate the absolute number of Bachelor’s degree recipients by continuing education status for each follow-up time frame, the reader is advised to deduct the number of persons who were studying toward a Master’s degree during the relevant year from the corresponding cohort total for that same year using the information provided in Appendix A. It should also be noted that a small percentage (5.8%) of the Bachelor’s degree recipients from the 2000-2002 cohorts who were listed as not concurrently studying toward a Master’s degree in the follow-up period had in fact completed their Master’s degree by DY+3 and DY+4. Further examination has revealed that the impact of this small group on earnings and employment data was minimal.

Field of study represents the field of specialization pursued by the Bachelor's Degree recipient, and conforms generally – but not entirely – to the subject of study aggregation5

found in the annual CBS publication on higher education enrollment (for further information on data collection, see Central Bureau of Statistics, Students at Universities and Other Institutions of Higher Education 2005/06, Publication 1293, Jerusalem 2007). Because field of study aggregations can differ to some degree in different institutional settings, caution should be exercised in interpreting institutional differences in fields of study that are especially diverse (e.g., Social Sciences). Finally, in order to maximize comparability between institutions, fields of study within the colleges of education were re-coded generically to Education.

Type of institution represents the setting or type of higher education institution, and is generally understood as distinguishing between institutions with a research orientation versus those with a practical orientation. In addition, particular types of institutions often specialize in a specific field of study (e.g., Colleges of Law and Engineering), and often carry different levels of prestige. Hence, when making global comparisons across institution type, the reader is advised to take into account the considerable heterogeneity in field of study and socio-demographic distributions of the student body found within particular institutional settings. Similarly, academic institutions vary in terms of the level of continuing education offered at the Master’s degree level. This is particularly true of universities, which focus on training for research at the Master’s and doctoral levels6.

5 See Appendix B for a detailed list of fields of study, which includes an aggregation of each field of study.

6 6 The statistics in Appendix A show that among Bachelor’s degree recipients (at the field of study level), 25% of those who graduated from universities were studying toward a Master’s degree one year after the year of Bachelor’s degree attainment, compared to 7% of the Bachelor’s degree recipients from the Open University, 8% of the Bachelor’s degree recipients from academic colleges, and 6% of the Bachelor’s degree recipients from colleges of education.

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Years since degree attainment. This variable represents the calendar year in which the earnings of Bachelor’s degree recipients were reported to the tax authority, in relation to the year of degree attainment. Based on this method of calculation, it is possible to compare different time periods, as long as the time frame since degree attainment is held constant (i.e., a cohort account). It should be noted that the follow-up period is not equal for all five graduate cohorts (2000-2004). Rather, it tapers off by one year for each cohort (starting in 2001), given the fact that tax data on earnings were not yet available. For example, DY+4 is only available for those who received Bachelor’s degrees in 2000; DY+3 is available for those who received degrees in 2000 and 2001; and DY+2 is available of those who received degrees in 2000, 2001, and 2002. In addition, because the cohort effects were found to be substantial with regard to earnings levels and self-employment rates, these topics are covered in the analysis section only for cohorts from the same potential follow-up period (i.e., up to DY+3 only for Bachelor’s degree recipients in 2000 and 2001). In contrast, the other sections (on employment rates and number of jobs) provide global time period comparisons (i.e., aggregate comparisons without cohort year), because the cohort effects were generally weak for those variables.

Employment rate. The percentage of graduates in a cohort whose earnings are reported to the tax authority in a calendar year, given particular characteristics and weighted by the number of months worked during the same calendar year. For example, a person who worked in any job for six months during a calendar year would have an employment rate of 0.5. For any particular cell, the calculation is the average of all employed persons (given the accompanying characteristics presented in the cell) weighted by the number of months that each employed person worked in each calendar year. Because the tax authority file represents a universal data source of formal employment in Israel, persons who are not listed in that file are interpreted within this publication as being out of the labour force during the relevant calendar year. Nevertheless, this definition of employment assumes that all Bachelor's degree recipients are active in the Israeli labour force throughout the year, which is not necessarily the case. Hence, a distinction should be made between the above definition of employment and traditional measures of labour force participation examined in the CBS Labour Force Survey7.

Self-employment rate as presented in Tables 8A-8C (appearing on the CBS website) was calculated as the percentage of persons with income from self-employment in relation to the total population of employed persons (i.e., persons with annual earnings) in a given tax year.

Number of jobs as presented in Tables 9A-9C (appearing on the CBS website) represents the number of different tax files listed for the same individual in a given tax year, where each file is for a specific employer. The calculation of the average number of jobs per Bachelor’s degree recipient relates only to those graduates who were employed. This figure may be interpreted as crudely indicating the degree of an individual’s labour market mobility, which usually tends to decrease as a function of the

7 For Labour Force definitions, see Central Bureau of Statistics, Labour Force Survey: Changes in the Methodology, Definitions and Questionnaire 1954-2003 (Technical Publication No. 78).

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time that elapsed since Bachelor’s degree attainment. Notably, caution should be exercised in interpreting this variable, because it was not possible to define the nature of the job changes that reflect mobility, either with respect to timing (e.g., concurrent or sequential employment in multiple jobs) or with respect to quality (e.g., multiple jobs in the secondary labour market, transition into professional employment, mobility between professional jobs, etc.).

Earned income is defined as the total gross employment earnings in a calendar year (i.e., before compulsory deductions) per person. Hence, income earned in multiple jobs, including income from self-employment, is summed up. Individual income from other sources such as pension, rent, capital gains, and other support are not included. In addition, income from sources that are not disclosed to the tax authority are not included (e.g., Israelis working abroad who are not required to deduct tax, non-reported cash gratuities, etc.). The income figures reflect cost-of-living adjustments and are in the New Israeli Shekel denomination (NIS), at 2004 fixed prices. Because the employment rates are weighted (see above), estimates of monthly earnings (for the average cohort) from salaried employment8 can be obtained by multiplying the annual earnings by the parallel employment rate cell (where available), and dividing that outcome by 12 (months). It should also be noted that unless indicated otherwise, the tabulated income includes earnings from all jobs, irrespective of occupation or economic industry. This is noteworthy because many recent Bachelor’s degree recipients are still employed at relatively unskilled jobs in the secondary labour market (e.g., waiters or security guards) while they are searching for professional positions in their field of study. Hence, the process of transition into skilled employment is likely to impact on job mobility and earnings, particularly for those Bachelor’s degree recipients who lack the levels of skills required to work in the professional labour market. Similarly, because the distribution of income is generally not a normal one, it is advisable to also refer to the median measures when they are available. Finally, in order to ensure privacy, data on individual earnings for cases above the 99th percentile (of all earnings) were substituted by the average earnings in that top 1%. The influence of this top-coding on income inequality, as measured by the Gini index, is presented in Appendix D.

Demographic variables such as age, sex, and population group were derived from the Population Register of the Israeli Ministry of Interior. In this publication, the population group "Jews" also includes non-Arab Christians and those not classified by religion at the Ministry of Interior. Age was categorized into two groups: age 17-27, and age 28 and over. Both groups were similar in size (i.e., 53% and 47% of the study population, respectively), and the median age of the Bachelor’s degree recipients was 27 years.

Limitations of the Data

Period effects such as the impact of macroeconomic factors during the follow-up period as well as the deep recession between 2001 and 2003, which influenced the situation of employment, were not directly observed in the data set. Hence, caution is

8 Because the data on number of months of work among self-employed graduates are not reliable, imputations were conducted for a full (12-month) work period in order to calculate the employment rates for those graduates.

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warranted in interpreting cohort differences in earnings and self-employment as the single product of macroeconomic context, even though both cohort and macroeconomic effects might appear to overlap during the period of this study.

Data on work hours per week, which enable calculation of hourly wages, were not available in the administrative files used for this publication. This variable is likely to have a significant impact on earning levels and employment rates, particularly among populations that tend to engage in part-time employment (e.g., women with children, fields of study that are not in high demand, distant geographic areas, students concurrently pursuing a Master’s degree, etc.). Hence, caution is warranted in interpreting comparative figures for these groups.

Employment in the field of study. Because the data used in preparing this publication did not include information on occupations, it was not possible to distinguish between employment in skilled jobs that generally require a Bachelor’s degree, and lower level jobs in the secondary labour market. Hence, caution should be exercised in interpreting the data – especially data on employment rates, which do not distinguish qualitative differences in employment in occupations related to the field of study on the one hand, versus employment in unskilled occupations or in skilled occupations that are not related to the field of study on the other.

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4. Main Findings

4.1 Employment Rates

The employment rate among recipients of Bachelor’s degrees between 2000 and 2004 ranged from 83% to 90%, depending on the year of degree of attainment and number of years that elapsed since attaining the degree. As can be expected, employment rates increased as a function of the time that elapsed since degree attainment (see Diagram 1 below). At DY+0, the average employment rate was 84%, and rose progressively to 87.5%, 88.6%, 89.3% and 89.6%, for each year from DY+1 to DY+4, respectively.

The diagram below also shows that employment rates were influenced to a certain extent by a cohort effect, which may be related to the economic recession that occurred during that period. For example, at DY+0, the employment rates by calendar year of Bachelor’s degree attainment were 85.2%, 84.5%, 83.9%, and 83.0% among graduates in 2001, 2002, 2003, and 2004, respectively.

4.1.1 Employment Rates by Field of Study

Recipients of Bachelor’s degrees in 2000-2004 differed with regard to employment rates by field of study. The highest employment rates were found among those who received a Bachelor’s degree in Education, which suggests that there was a high general demand for that qualification within the teaching workforce (91.3% multiyear average), followed by Business Administration (89.4%), Engineering and Architecture (89.1%), Mathematics and

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Main Findings

Computer Sciences (88.8%), Social Sciences (87.5%), Humanities (84.4%), Natural Sciences (82.6%), and Law (82.3%) (see Diagram 2 below).

These multiyear employment rate averages, however, do not reflect variations in employment growth rates among different fields of study. Hence, graduates in the field of Law were found to have especially low employment rates in the initial years following Bachelor’s degree attainment, perhaps owing to preparations for the Bar Association Exam. However, there was a steep increase in employment rates among Law graduates between DY+0 and DY+4 (from 68.0% to 89.4%, respectively). Similarly, graduates in Natural Sciences were found to have an increased employment rate of 9 percentage points between DY+0 and DY+4, in comparison to a range of 2 to 6 percentage points in the remaining fields of study.

4.1.2 Employment Rates by Population Group

Comparison of employment rates (see Diagram 3 below) among Bachelor’s degree recipients by population group reveals that Israeli Arabs had slightly higher employment rates than did Jews at DY+1 and beyond. Thus, both population groups showed increased employment rates, even if the Israeli Arab degree recipients were characterized by higher rates of employment growth. For example, between DY+0 and DY+4, employment among Israeli Arabs increased by 11.2 percentage points, compared with 5.2 percentage points among Jews. Similarly, observation of employment rates between DY+0 and DY+2 shows that the average growth in employment among Arabs was 7.8 percentage points, compared with 4.2 percentage points among the Jews. By DY+4, the employment rate among Arabs

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was 92.5%, compared to 89.3% among Jews. No significant differences in employment rates were found between both population groups by field of study. The relatively high employment rates among Arabs can be attributed to the fact that recipients of academic degrees are a much more selective group among the Arab population than Jewish graduates among the Jewish population. Therefore, academic degree recipients have a stronger relative advantage over non-academics in the Arab labour market, such as in the Arab Education sector9. Another factor that may explain the higher employment rates among the Arab Bachelor’s degree recipients is their relatively high predisposition to pursue market-oriented fields of study which, in turn, increase the rate of self-employment (see below).

4.1.3 Employment Rates, by Sex

Men with Bachelor’s degrees were found to have slightly higher employment rates than their female counterparts, among all cohorts and irrespective of years since degree attainment (overall average of 89.4% versus 87.2% among men and women, respectively). In addition, trends over time show an increasing employment rate gap between the sexes. For example, the difference in employment rates between men and women at DY+4 was 3.6 percentage points, compared to only 0.5 percentage points at DY+0 (see Diagram 4 below).

9 The Israeli education system is divided into different sectors, including the Arab education system where teaching is conducted in Arabic. Between 2000 and 2004, the workforce of teachers in this system (Kindergarten to grade 12) grew dramatically by 29.4%, in keeping with the large demographic growth of the Arab population in Israel.

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Main Findings

As expected, examination of employment rates by sex and field of study reveals variations in employment rates between the sexes. Diagram 5 below displays average employment rates among all cohorts (2000-2004), by sex and field of study. The fields of study showing a gap of at least 4 percentage points in absolute employment rates include: Humanities, Law, and Engineering and Architecture. Fields of study with a difference of 2 percentage points or less in absolute employment rates include: Natural Sciences and Business Administration.

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4.1.4 Employment Rates by Age

Among bachelor’s degree recipients in 2000-2004, those up to age 27 were found to have lower employment rates than those aged 28 years and over: 86.2% and 89.7%, respectively. This summary however, does not take into account the trend toward convergence over time between the two age groups. Hence, at DY+0, the absolute difference between employment rates among Bachelor’s degree recipients in the two age groups was 4.8 percentage points, whereas the difference at DY+4 was 3.1 percentage points (see Diagram 6).

Employment rates by age group reveal sharper differences when field of study is taken into account as well. Diagram 7 indicates that the fields of study with large differences in employment rates by age (over 4 percentage points) were Education, Natural Sciences, Law, Mathematics and Computer Sciences, and Engineering and Architecture. In contrast, the absolute difference between younger and older Bachelor’s degree recipients in the field of Business Administration was 2 percentage points.

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Main Findings

4.1.5 Employment Rates by Type of Institution

Examination of employment rates among Bachelor’s degree recipients in 2000-2004 by type of institution reveals that the highest rates were among graduates of colleges of education (an average of 92.1% across all cohorts, and irrespective of the time that elapsed since attainment of a Bachelor’s degree, see Diagram 8 below). This high rate reflects the strong occupational links between these colleges and the teaching profession. By comparison, the employment rate among graduates of the Open University was 90.7%, followed by graduates of public academic colleges (87.7%), graduates of universities (86.9%), and graduates of private academic colleges (85.4%). The high rates of employment found among graduates of the Open University can be attributed to the older student population within that institution, as well as to its flexible learning framework, which enables students to combine employment with studies.

Examination of the extent of growth in employment rates between DY+0 and DY+1 indicates that the timing of entry into a career differed among graduates of different types of institutions. Graduates of private academic colleges showed the highest rate of growth in employment during that period (14%), followed by university graduates (6.6%), and graduates of public colleges (5.8%). In contrast, graduates of colleges of education, who usually begin teaching directly after they complete their studies, showed a modest rate of growth in employment (2.7%). The relatively low rates of growth in employment among graduates of private colleges at DY+0 and DY+1 may be explained by the higher proportion of students in the fields of Law and Accounting in those institutions. Hence, it can be expected that those degree recipients will show lower rates of employment growth, because

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they have to prepare for exams to receive licenses from their respective professional associations.

Because employment rates among graduates of different types of institutions are often a reflection of the different fields of study in those institutions, Diagram 9 displays employment rates by both of those variables (field of study and type of institution) among the 2000 and 2001 cohorts at DY+1 and DY+3. For example, average employment rates (for both DY+1 and DY+3) among degree recipients in the Humanities were 92.5%, 86.1%, and 75.5% for graduates of the Open University, universities, and pubic colleges, respectively. Employment rates among degree recipients in Mathematics and Computer Sciences were 92.5%, 90%, 88%, and 87.6% for graduates of the Open University, public colleges, universities, and private colleges, respectively. Average employment rates for recipients of degrees in Law were 86% and 83% among graduates of universities and private colleges, respectively.

Examination of the differences in levels of employment over time (DY+1 to DY+3) by field of study among recipients of Bachelor's degrees in 2000 and 2001 also revealed differences (albeit minimal) by type of institution. The greatest difference was observed among graduates in Law, where the increase in employment amounted to nearly 11 and 4.5 percentage points among recipients of degrees from private colleges and universities, respectively. In Mathematics and Computer Sciences, the differences (in percentage points) by type of institution were as follows: universities (2.1), public colleges (1.4), The Open University (0.7) and private colleges (-0.3). Similarly, the remaining fields of study all showed differences of up to 2 percentage points in employment rates over time by type of institution.

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Main Findings

4.2 Self-Employment

The findings reveal a steady increase in self-employment rates as a function of years since Bachelor’s degree attainment, with only a small cohort effect. Hence, self-employment rates

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at DY + 0 among the earlier cohorts (2000-2001) were slightly higher than among the cohorts of 2003-2004 (5% versus 4.5%). However, global self-employment rates increased progressively as a function of time since degree attainment for all cohorts: 4.7% at DY+0, 5.8% at DY+1, 6.9% at DY+2, 7.7% at DY+3 and 8.8% at DY+4. Finally, self-employment rates also varied significantly by field of study, sex, population group, and age. These trends are summarized briefly below, and can be viewed in detail in Tables 8A, 8B, and 8C (appearing on the CBS website).

4.2.1 Self-Employment by Field of Study

Examination of self-employment rates among Bachelor’s degree recipients by field of study reveals sharp contrasts. As expected, the highest self-employment rates were found in fields of study that are occupationally oriented (e.g., Law, Architecture, and Business Administration). Hence, among the 2000 cohort, the self employment rates reported at DY+4 by field of study were as follows: Law (29.7%), Business Administration (11.1%), Engineering and Architecture (9%), Humanities (7.2%), Natural Sciences (7.1%), Social Sciences (6.5%), Mathematics and Computer Sciences (5.1%), and Education (3.7%) (see Diagram 10 below).

Diagram 10 also highlights differences in self-employment growth trends over time by field of study. Self-employment rates for the 2000 cohort grew substantially between DY+0 and DY+4, especially among Bachelor’s degree recipients in the fields of Engineering and Architecture (4% versus 9%), followed by Natural Sciences (4% versus 7%), Law (16% versus 30%), and Business Administration (7% versus 11%).

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Main Findings

4.2.2 Self-Employment by Sex

The rates of self-employed male graduates were significantly higher than the rates of self-employed female graduates. Among the 2000 cohort, the observed self-employment rates were 16.4% and 4.6% among men and women, respectively, at DY+4.

Examination of self-employment rates by sex and field of study reveals relatively high concentrations of self-employment among men. At DY+4, the self-employment rate among male Bachelor’s degree recipients in Law was 43.7%, compared to 13.7% among their female counterparts. Among recipients of Bachelor’s degrees in Business Administration, the respective self-employment rates for men and women at DY+4 were 17.1% and 3.5%. Similarly, among graduates in the Humanities and Mathematics and Computer Sciences, the self-employment rates for men and women were 15% versus 5% and 7% versus 2%, respectively. These numbers are consistent with the share of women in the overall population of Israeli self-employed persons (about 15%).

4.2.3 Self-Employment by Population Group

On the average, Arab graduates in 2000-2004 showed a greater propensity toward self-employment than did their Jewish counterparts. The disparity between the two population groups increased as a function of the time that elapsed since attainment of a Bachelor’s degree, so that at DY+4 the Arab graduates showed a self-employment rate of 12.3%, whereas the self-employment among the Jews was 8.5% (see Diagram 12 below). Nevertheless, these global rates do not reflect the disproportionate representation of Arabs

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versus Jews in the field of education and teaching, which is generally characterized by low levels of self-employment. Hence, self-employment rates were higher among Arab graduates in fields of study that are not generally relevant to teaching (e.g., Law and Business). For example, the self-employment rate for Arab graduates in Business Administration was 30%, compared to 10.5% for the Jewish graduates in that field (at DY+4). Similarly, among graduates in Law, the self-employment rates for Arabs and Jews were 49.1% and 28%, respectively; and in Engineering and Architecture, the rates for Arab and Jewish graduates were 27.8% and 7.9%, respectively.

4.2.4 Self-Employment by Age

Age generally reflects both work experience and time required to accumulate start-up capital. Hence, it is not surprising that the proportion of self-employed persons aged 28 and over was higher than among their younger counterparts aged 27 and under. Among the 2000 cohort, there was a substantial increase in self-employment rates over time – from 3.2% and 7.2% among younger and older graduates, respectively, at DY+0, to 6.3%, 11.6%, respectively, at DY+4 (see Diagram 13 below). In other words, the differential self-employment rates between age groups remained constant, even as they increased for both groups as a function of years since Bachelor’s degree attainment.

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Main Findings

4.3 Number of Jobs Per Year

Number of jobs per year reflects overall employment mobility per Bachelor's degree recipient in a given calendar year. Such mobility should be understood generally and cautiously, because this study did not distinguish patterns of mobility over time or the qualitative nature of mobility (e.g., transition into professional jobs, multiple jobs in the secondary labour market, etc.). The data did, however, reveal a significant negative correlation between average annual earnings and number of jobs. This suggests, as may be expected, that lower levels of mobility correlate with better paying jobs that yield higher annual earnings or, in other words, better paying jobs can be characterized by higher levels of stability. Nevertheless, the sources of this stability are varied and complex (e.g., factors related to changes in the levels of supply and demand for Bachelor's degree recipients in the labour market). Hence, the sources of job stability among Bachelor’s degree recipients were not examined directly in this study.

Diagram 14 shows that among the cohorts of the period from 2001 to 2004, there was a slight rise in the average number of jobs per year. For example, at DY+1, the number of jobs per year for the cohorts of 2001, 2002, and 2003 were 1.498, 1.513, and 1.548, respectively. Even if cohort effects are still present, the amount of time that elapsed since degree attainment showed a stronger effect, and lowered the overall number of jobs across the board. For example, the average number of jobs from DY+0 to DY+4 in sequential order was 1.67, 1.51, 1.45, 1.41, and 1.39.

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4.3.1 Number of Jobs Per Year by Field of Study

The data show large variations in the number of jobs per calendar year by field of study, as presented in Diagram 15 below. The field of study with the lowest average number of jobs per year was in Engineering and Architecture (1.32), followed (in ascending order) by Mathematics and Computer Sciences (1.35), Law (1.36), Business Administration (1.37), Social Sciences (1.47), Natural Sciences (1.55), Humanities (1.59), and Education (1.63).

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Main Findings

4.3.2 Number of Jobs Per Year by Sex

The number of jobs per calendar year among recipients of Bachelor’s degrees during the period between 2000 and 2004 was lower for men than for women (1.43 versus 1.52, respectively). However, Diagram 16 below shows that although the differences in rates were high at DY+0 (1.74 and 1.57 among women and men, respectively – a ratio of 1.11), they tended to converge over time to 1.41 and 1.35 among women and men, respectively, at DY+4 (a ratio of 1.04).

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4.3.3 Number of Jobs Per Year by Population Group

The data show consistent differences between population groups in the number of job positions per calendar year. Overall, across all five years between DY+0 and DY+4, the average number of jobs per calendar year for Arab Bachelor’s degree recipients was 1.57, compared to 1.48 for Jews. Diagram 17 below reveals that whereas there was a general trend toward convergence over time, this did not occur by population group. In fact, the ratio between the number of jobs of Arabs to that of Jews at DY+1 and DY+4 increased, from 1.066 to 1.087. It should be noted, however, that on the average the Arab Bachelor’s degree recipients were younger than the Jews. Hence, this effect may be partially attributed to age differences (see below).

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Main Findings

4.3.4 Number of Jobs Per Year by Age Group

The data reveal that the number of job positions per calendar year for younger Bachelor’s degree recipients (up to age 27) was greater than the number for their older counterparts (aged 28 and over): the global averages for all cohort and follow-up periods were 1.42 versus 1.36 for the younger and older groups, respectively. Diagram 18 below also highlights the patterns over time, and shows a general trend toward convergence. The ratios between both age groups (i.e., up to age 27 and 28+) at DY+0 and DY+4 were 1.18 and 1.04, respectively.

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4.4 Earnings

Data on average annual earnings from employment among recipients of Bachelor’s degrees between 2000 and 2004 who were not concurrently studying toward a Master’s degree are displayed in Diagram 19 below. The data reveal very strong cohort effects (i.e., the year of degree attainment) on average annual earnings, particularly between the 2000 and 2001 cohorts and those who completed their Bachelor’s degrees afterwards. For example, average annual earnings during the year of degree attainment (i.e., DY+0) dropped from approximately NIS 90,000 among the 2000 cohort to approximately NIS 68,000 among the 2004 cohort – a difference of nearly 32%. These substantial differences may reflect the impact of the macroeconomic recession that began in late 2000 (and ended by 2004). As expected, that the recession had an especially strong effect on new Bachelor’s degree recipients, who were entering the professional labour market for the first time. As was also expected, that the expanding supply of Bachelor’s degree recipients, which rose by about 19% between 2000 and 2004, caused a further drop in earnings among new graduates during that period due to an ongoing process of gradual saturation of the labour market with academic degree holders. Under those conditions, it is likely that occupational downgrading and sharp wage reductions affected graduates who entered the professional job market after 2001 (i.e., after the beginning of the recession). In contrast, the data show that Bachelor’s degree recipients who secured professional employment before the recession generally maintained their average levels of earnings during that period.

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Main Findings

4.4.1 Earnings by Sex

The data show a large earnings gap between men and women who received Bachelor’s degrees between 2000 and 2004. For example, one year after receipt of a Bachelor’s degree (i.e., DY+1), women earned an average of NIS 73,152, whereas men earned NIS 130,061 – a ratio of nearly 56% among the 2000-2003 cohorts (see Diagram 20 below). Notably, some of the differences in earnings by sex can be attributed to the younger median age of women at the time of Bachelor’s degree attainment (26 years for women, versus 28 years for men).

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One explanation for the large earnings gap between the sexes is the different distribution of field of study between the sexes. Tables 3B and 3C (appearing on the CBS website) presents a detailed breakdown of earnings by sex and field of study, and Diagram 21 below displays the ratio of average female to male earnings by field of study and time that elapsed since Bachelor’s degree attainment. It is noteworthy that data on the number of work hours per month, which define part-time employment, were not available in the data set used to prepare this report (including the tables in the text – see definitions). Hence, calculation of hourly wage rates would narrow the earnings gap further, because women tend to work fewer hours per month than men on the average.

Diagram 21 below highlights average earnings by sex and field of study among the 2000 and 2001 cohorts. The highest earnings ratios between the sexes were found among graduates in Business Administration (about 75%), followed by Mathematics and Computer Sciences (72%), and Natural Sciences (69%). By comparison, the following earnings ratios were found in the remaining fields of study10 (in descending order): Law (67%), Education (62%), Engineering and Architecture and Social Sciences (60% in both), and Humanities (55%).

Diagram 21 also provides information on the effects of the time that elapsed since Bachelor’s degree attainment on gaps in earnings between women and men. The data reveal a decline in the earnings gap (i.e., increased earnings ratios between DY+1 and DY+3) among graduates in Law (64% versus 71%) and Humanities (52% versus 59%), and an increase in the earnings gap (i.e., a decline in earnings ratios) among graduates in Business

10 General studies were not included because of the high level of heterogeneity in those fields. Nor was medicine included, because nearly all Bachelor’s degree recipients in at field continue to the Master’s degree level (see Appendix B for field of study aggregations). Paramedical Studies are covered, however, in Section 4.4.5.6.

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Main Findings

Administration (75% versus 72%), Mathematics and Computer Sciences (74% versus 71%), and Natural Sciences (70% versus 68%).

4.4.2 Earnings by Population Group

Among recipients of Bachelor’s degrees during the period from 2000 to 2004, the earnings of Arabs amounted to only 66% of the earnings of Jews. Nevertheless, this figure does not take into account the substantial differences in earnings between the respective cohorts, which increased during the period in question. Thus, comparison of the earnings ratios of Arabs to Jews at DY+1 among the cohorts of 2000 and 2003 reveals ratios of 69% and 61%, respectively. Similarly, at DY+2, the earnings ratios for the cohorts 2000, 2001, 2002 were 71%, 65%, and 61%, respectively. It should be noted that on the average, Arab Bachelor’s degree recipients were younger than their Jewish counterparts. Hence, the earnings gap can be attributed in part to age effects11. These trends are highlighted in Diagram 22 below.

11 Median age at degree at Degree Year was 27 among Jews versus 25 among Arabs.

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Diagram 23 displays earnings ratios between population groups for graduates in 2000 and 2001, by field of study and time that elapsed since Bachelor’s degree attainment. At DY+1, the data reveal the highest disparities among graduates in Mathematics and Computer Sciences (40%), followed by (in ascending order) Business Administration (44%), Engineering and Architecture (48%), Natural Sciences (54%), Law (65%), Social Sciences (69%), Humanities (81%), and Education (91%).

At DY+3, the earnings gap narrowed in some fields of study and widened in others. Increased earnings gaps were reported among graduates in the Humanities (a decline of 8 percentage points in the earnings ratio) and Law (a decline of 3 percentage points in the earnings ratio). In contrast, the earnings gap narrowed between DY+1 and DY+3 among graduates in Natural Sciences (an increased earnings ratio of 27 percentage points), followed by Business Administration (an increase of 8 percentage points), Engineering and Architecture (an increase of 4 percentage points), and Social Sciences and Education (both with an increase of 3 percentage points).

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Main Findings

4.4.3 Earnings by Age

Another demographic factor that correlated positively with earnings is age at the year Bachelor’s degree attainment. As a result of accumulating job experience, older Bachelor’s degree recipients earn considerably more than those who are younger. Overall, Bachelor’s degree recipients up to age 27 earned only 70% of the amount earned by graduates aged 28 and over among the cohorts of 2000-2004. Nevertheless, the time that elapsed since degree attainment narrows the earnings gap among Bachelor’s degree recipients in the different age groups. For example, among graduates in 2000, the earnings gap between those up to age 27 and those aged 28 and over declined steadily by the time that elapsed since the year of degree attainment: 60%, 68%, 68%, 73%, and 76% (for DY+0, DY + 1, DY + 2, DY + 3, and DY+4, respectively). It should be noted that in contrast to sex and population group, which were covered previously, no significant cohort effect was found for age group.

Diagram 24 displays the earnings ratio by age group, field of study, and time elapsed since the year of Bachelor's degree attainment among the cohorts of 2000 and 2001. At DY+1, the effect of age, as indicated by the earnings ratio between the two above-mentioned age groups, is highlighted in the following fields of study: Humanities (58%), Social Sciences (62%), Law (64%), Education (65%), Business Administration (70%), Natural Sciences (74%), Mathematics and Computer Sciences (77%), and Engineering and Architecture (79%).

Diagram 24 also displays differences in earnings ratios over time (i.e., DY+1 and DY+3), by field of study. More specifically, the earnings gaps narrowed most greatly (in percentage points) among Humanities graduates (11%) followed by Law (9%), Social Sciences (7%),

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Engineering and Architecture (7%), Education (6%), Business Administration (5%), and Mathematics and Computer Sciences (2%). By contrast, the earnings gap in the Natural Sciences increased by 0.5%. One explanation for the decrease in earnings gaps is the apparently faster pace of wage increases at the early stages of professional careers in these fields.

4.4.4 Earnings by Type of Academic Institution

Type of academic institution describes the classification of academic institutions that have been accredited to grant academic degrees, as follows: universities, the Open University, academic colleges (public and privately funded), and colleges of education. This classification represents a host of underlying attributes, including: fields of study, pedagogic resources, student body characteristics, and research or teaching orientations, all of which together and apart help shape the demand for Bachelor’s degree recipients in the labour market and the level of their earnings.

Examination of average annual earnings among recipients of Bachelor’s degrees in the 2000-2004 cohorts by type of institution reveals the following ranking (in descending order): The Open University (NIS 122,000), private academic colleges (NIS 102,000), universities (NIS 89,000), public academic colleges (NIS 88,000), and colleges of education (NIS 66,000). The high earnings among graduates of the Open University can be explained in part by the relatively high concentration of older students with greater work experience and higher levels of self-discipline in that institution. Similarly, the relatively high earnings among graduates of academic colleges (both private and publicly funded) and graduates of the

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Main Findings

Open University might be attributed to the disproportionate concentration of those specializing in higher paying professional fields of study such as Computer Sciences, Business Administration, and Law. Because all of these factors evidently contribute to the effect of the type of academic institution on the earnings of graduates, multivariate analysis was conducted to account for their simultaneous impact (see Section 4.6 below).

Diagram 25 below presents findings on average annual earnings among Bachelor’s degree recipients in 2000-2003 at DY+1, by type of institution. The declining slope of earnings for graduates of all types of institutions reflects the cohort effects discussed earlier. However, these slopes differ by type of institution, with the steepest decline among graduates of private colleges between 2000 and 2003 (approximately 30%) followed by public colleges (25%), colleges of education (25%), universities (23%), and the Open University (18%).

4.4.5 Earnings by Field and Subject of Study

Among other characteristics, field of study can reflect specializations that increase productivity in jobs requiring specific skills. Regarding Bachelor’s degree recipients in the period from 2000 to 2004, the data show variations in earnings by field of study. Global average annual earnings for those cohorts by field of study were as follows (in descending order): Mathematics and Computer Sciences (NIS156,000), Engineering and Architecture (NIS 144,000), Business Administration (NIS 141,000), Law (NIS 102,000), Social Sciences (NIS 91,000), Natural Sciences (NIS 84,000), Humanities (NIS 77,000), and Education (NIS 72,000).

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Examination of trends in earnings over the time that elapsed since degree attainment reveals differences by field of study, as can be seen in Diagram 26. More specifically, the diagram displays the average annual earnings among Bachelor’s degree recipients in 2000 and 2001 by field of study and time that elapsed since Bachelor’s degree attainment (i.e., DY+1 and DY+3). The highest increase in earnings between those two periods was found in Law (14%), followed by Business Administration (13.7%), and Engineering and Architecture (4.5%). In the remaining fields of study, there was either no change or a decline in earnings between DY+1 and DY+3, as follows: Social Sciences (0.7%), Mathematics and Computer Sciences (0.6%), Education (0.4%), Natural Sciences (-1%), and Humanities (-7%).

The following section presents earnings by subject of study, as they are grouped within the respective fields of study. Subjects of study with especially small numbers of graduates were aggregated within larger subject groupings (see Appendix B). Similarly, a detailed breakdown of earnings by subject of study can be found in Tables 5B and 5C (appearing on the CBS website). Finally, it is noteworthy that subject of study is often characterized by large variations due to the differences in sex composition of graduates which, as shown in Section 4.4.1, affect differences in earnings.

4.4.5.1 Earnings among Bachelor’s Degree Recipients in Education

Bachelor’s degrees in Education (B.Ed.) are granted by universities, colleges of education, and, in some cases, also by academic colleges (e.g., a B.Ed. degree in physical education is granted by the Wingate Institute). Earnings in different subjects of specialization within the field of education are relatively uniform because wages are standardized in the state education sector in Israel, where most graduates find employment. Nonetheless, there are

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Main Findings

some differences in the levels of supply and demand for specific subjects that require specialized skills. Among B.Ed. degree recipients in 2000 and 2001, the subject of specialization with the highest annual earnings in the field of Education was Educational Administration, where average annual earnings amounted to approximately NIS 87,000 between DY+1 and DY+3. By comparison, the proportional earnings of B.Ed. degree recipients in other subjects of study in relation to the earnings of those who specialized in Educational Administration were as follows (in descending order): Physical Education (91%), General Education, Science-Technology Education, and Special Education (82%-83%), and Education Research (75%).

Similarly, the data show variations in growth of earnings between DY+1 and DY+3 by subject of study. For example, among graduates who specialized in teaching Science-technology, earnings increased by 12% during this period. By comparison, the rates of change in earnings in other subjects of specialization were as follows: Special Education (2%), Education Research and General Education (both 1%), Physical Education (-2%), and Education Administration (-4%) (see Diagram 27 below).

4.4.5.2 Earnings among Bachelor’s Degree Recipients in the Humanities

Bachelor’s degree recipients in the Humanities earned an average annual income of approximately NIS 78,000 during the period between DY+1 and DY+3 (among the 2000 and 2001 cohorts). Average annual earnings for the same period by subject of study within the Humanities were as follows: History and Philosophy (NIS 97,000), General Humanities (NIS 96,000), Jewish Studies (NIS 87,000) Regional Studies and Foreign Languages (NIS

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64,000), Performing and Fine Arts (NIS 63,000), and Hebrew Literature and Linguistics (NIS 62,000).

Examination of the change in average annual earnings between DY+1 and DY+3 reveals increases for Arts, Regional Studies and Foreign Languages (all 4%), and Hebrew Literature and Linguistics (3%). In contrast, average annual earnings declined by about 11% among graduates specializing in General Humanities, History, and Philosophy, and by about 2% among graduates specializing in Jewish Studies (see Diagram 28 below).

4.4.5.3 Earnings among Bachelor’s Degree Recipients in the Social Sciences

Diagram 29 below presents data on earnings among Bachelor’s degree recipients in the Social Sciences, by subject of study and years since Bachelor’s degree attainment. As the diagram shows, Bachelor’s degree recipients in the Social Sciences from the 2000 and 2001 cohorts earned an average annual income of NIS 90,500 between DY+1 and DY+3. Within that time frame, the highest overall earnings in the Social Sciences were found in Economics (NIS 112,000). By comparison, average annual earnings in other subjects in the Social Sciences were as follows (in descending order): Political Science and International Relations (NIS 110,000), General Social Sciences12 (NIS108,000), Communications (NIS 87,000), Sociology and Anthropology (NIS 81,000), Geography (NIS 80,000), Psychology (NIS 78,000), and Social Work (NIS 68,000) (see Diagram 29 below).

12 Including Behavioural Sciences.

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Main Findings

Regarding changes in earnings between DY+1 and DY+3 among the 2000 and 2001 cohorts, the highest increase was found for graduates in Economics (12%), followed by those in Sociology and Anthropology (5.4%), Social Work (2.6%), Communications (2%), and Geography (1.4%). In contrast, there was a decline in earnings among graduates in Political Science and International Relations, General Social Sciences and Psychology (-0.7%, -4%, and -10%, respectively).

4.4.5.4 Earnings among Bachelor’s Degree Recipients in the Natural Sciences

Among graduates in the Natural Sciences in the 2000-2001 cohorts, average annual earnings amounted to NIS 87,000 between DY+1 and DY+3. Examination of average annual earnings by subject of study reveals that the highest earnings during that period were among graduates in Physics (NIS 119,000), followed by Agriculture and Geology (about NIS 86,000), General Science (NIS 78,000), Chemistry (NIS 77,000), and Biology (NIS 71,000).

Between DY+1 and DY+3, the highest increase in earnings was recorded for graduates in Chemistry (14%), followed by Agriculture (11%), Physics (3%), and General Sciences (3%). A decline in earnings was found among graduates in Biology (1%), and Geology (8%) (see Diagram 30 below).

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It is also noteworthy that the percentage of Bachelor’s degree recipients in the Natural Sciences who continued studying toward a Master’s degree, was significantly greater than in other fields of study (see Appendix A). As a result, Bachelor’s degree recipients in the Natural Sciences who do not continue toward a Master’s degree are more likely to be affected by selectivity effects. In other words, because the proportion of Bachelor's degree recipients in Natural Sciences who continue studying toward a Master' degree is higher than in other fields of study, those who do not continue are likely to differ with respect to characteristics such as scholastic achievement. Hence, the earnings of those who do not continue toward a Master’s Degree in that field might also be relatively low.

4.4.5.5 Earnings among Bachelor’s Degree Recipients in Law and Business

Administration

Bachelor’s degree recipients in Law and Business Administration in the 2000 and 2001 cohorts earned an average annual income of NIS 110,000 between DY+1 and DY+3. Examination of earnings by subject of study reveals that the highest average annual earnings within that time frame were among graduates who specialized in Insurance (NIS 120,000), followed by General Business Administration (NIS 117,000), Law (NIS 108,000), Management (NIS 106,000), and Accounting (NIS 95,000) (see Diagram 31 below).

In addition, differences in earnings growth rates between DY+1 and DY+3 were found by subject of study. The largest increase in earnings by far (33%) was reported among Accounting graduates. This finding can be attributed to the relatively low average wages received by those graduates during the period of internship, immediately after they attain their degree and before they obtain professional licenses. By comparison, graduates in

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Main Findings

Insurance and Law increased their earnings by 14% between DY+1 and DY+3, whereas graduates in General Business Administration and Management increased their earnings by 12% and 3%, respectively, during that period.

4.4.5.6 Earnings among Graduates in Paramedical Studies13

Recipients of Bachelor’s degrees in paramedical studies in the 2000 and 2001 cohorts earned an average annual income of NIS 87,500 between DY+1 and DY+3. However, as in other fields, large variations in earnings by subject of study were revealed. Graduates in Pharmacology showed the highest average annual earnings within this time frame (NIS 117,000) compared to (in descending order): Laboratory Medical Sciences (NIS 110,000), Nursing (NIS 98,000), Optometry (NIS 89,000), Occupational and Physical Therapy (NIS 72,000), Communication Disorders (NIS 69,000), and Nutrition (NIS 59,000) (see Diagram 32 below).

Annual earnings between DY+1 and DY+3 increased in the following subjects of study: Nutrition (9%), Communication Disorders (6.4%), Optometry (5.7%), Pharmacology (5.5%), and Laboratory Medical Sciences (1%). By contrast, annual earnings declined during that period in Nursing (-1%), and Occupational and Physical Therapy (-1%).

13 Graduates in Medicine (including Dentistry and Veterinary medicine) are not included in this category, because only a small minority of students completed their studies with only a Bachelor’s degree.

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4.4.5.7 Earnings among Bachelor’s Degree Recipients in Mathematics, Statistics and

Computer Sciences

Due to the great demand for Bachelor’s degree recipients in Mathematics, Statistics and Computer Sciences in the high-tech sector, which typically pays high salaries, average annual earnings for the 2000 and 2001 cohorts amounted to NIS140,000 between DY+1 and DY+3. Examination of annual earnings for that time frame by cohort and subject of study reveals the following average earnings: Computer Sciences (NIS 182,000), Mathematics and Computer Sciences (dual major) (NIS 145,000), Mathematics (NIS 127,000), and Statistics )NIS 104,000).

In contrast to the extensive changes in earnings over time in other fields of study, all subjects in the fields of Mathematics, Statistics and Computer Sciences showed almost no change (between 1.7% and -0.4%) during the period considered here (DY+1 to DY+3) (see Diagram 33 below).

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Main Findings

4.4.5.8 Earnings among Bachelor’s Degree Recipients in Engineering and

Architecture

Bachelor’s degree recipients in Engineering and Architecture in the 2000 and 2001 cohorts earned an average annual income of NIS 127,000 (NIS 146,000, excluding Architecture and Industrial Design) during the period from DY+1 to DY+3. Within that broad area, the highest average annual earnings (during the same time frame) were revealed among graduates in Electrical Engineering (NIS 226,000), followed by: Industrial Engineering (NIS 153,000), Mechanical Engineering (NIS149,000), Chemical Engineering (NIS 124,000), Other Engineering14 (NIS 118,000), Civil Engineering (NIS 104,000), Industrial Design (NIS 73,000), and Architecture (NIS 65,000).

Examination of the change in earnings during the period between DY+1 and DY+3 by field of study reveals the following rates of change in earnings: Other Engineering (13%), Civil and Chemical Engineering (11% both), Electrical Engineering and Architecture (5% both), Mechanical and Industrial Engineering (4% both), and Industrial Design (-3%).

14 Other engineering includes: Aeronautical Engineering, Material Engineering, Nuclear Engineering, Agricultural Engineering, Biomedical Engineering, and Mechanical/Material/Civil Engineering (synthetic program).

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4.5 Earnings and Employment by Industry15

Tables 7A-7C (appearing on the CBS website) provide detailed findings on employment rates and earned income by industry, while table 1 presented below highlights findings about earned income for the 2000 and 2003 cohorts at the year of degree attainment.

Table 1 reveals that between 2000 and 2003 there were also sharp declines in earnings by industry among Bachelor's degree recipients at their year of degree attainment. This finding may indicate flexibility in wages offered to recent Bachelor’s degree recipients, who are generally entering the professional labour market for the first time. Nevertheless, as mentioned, this study could not determine hourly wage rate levels because data on work hours were not available. Hence, it is possible that declines in earnings in specific industries were the result of other factors, including reduced demand for labour, as measured in total average months of employment per annum per Bachelor's degree recipient (see definitions), shorter working hours, or the larger instance of part-time work. Finally, changes in earnings among cohorts in specific industries should be considered in light of the considerable overall variation already found between industries (see the CBS Labour Force Surveys for those years).

Average annual earnings by industry for the 2000 and 2003 cohorts (together) at the respective years of degree attainment were as follows (in descending order): Manufacturing (NIS 122,400), Electricity and Water Supply (NIS 109,000), Banking, Insurance and other Financial Institutions (NIS 97,700), Public Administration (NIS 90,000), Transport, Storage

15 In cases where graduates worked in more than one job during the course of a year, the classification by industry refers to the job with the greatest total income per tax year.

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Main Findings

and Communications (NIS 89,900), Real Estate, Renting and Business Activities (NIS 85,500), Construction (NIS 79,300), Wholesale and Retail Trade and Repairs (NIS 78,700), Extra-territorial Organizations and Bodies (NIS 69,800), Health Services and Welfare and Social Services (NIS 65,500), Education (NIS 60,900), Agriculture (NIS 57,600), Community, Social and Personal and Other Services (NIS 55,700), and Accommodation Services and Restaurants (NIS 44,800).

Examination of earnings growth rates by industry between the 2000 and 2003 cohorts at their respective degree attainment year reveals contraction in all industries. The largest declines in earned income were found in the following divisions: Public Administration (-32%), Retail and Wholesale Trade (-29%), Construction (-23%), Accommodation Services and Restaurants (-23%), and Banking and Finance (-22%). The other divisions showed the following declines in earnings: Transport, Storage and Communications (-21%), Education (-21%), Agriculture (-21%), Real Estate and Business Activities (-19%), Education (-21%), Manufacturing (-19%), Water and Electricity (-13%), Health Services and Welfare and Social Services (-12%).

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Table 1

Average Annual Earnings among Bachelor's Degree Recipients at Degree Attainment Year,

by Industry, 2000 and 2003 Cohorts

Category Industry

Average annual earnings at degree year

Percentage

of change in

earnings2 between 2000 &

2003

2000 cohort

(1)

2003 cohort

(2)

NIS 1

Total 91,064 71,005 -22A Agriculture 64,389 50,853 -21B Manufacturing 131,502 113,245 -14C Electricity and Water Supply 116,695 101,408 -13D Construction 89,662 68,914 -23E Wholesale and Retail Trade and Repairs 92,147 65,214 -29F Accommodation Services and Restaurants 50,586 39,011 -23G Transport, Storage and Communications 100,633 79,235 -21H Banking, Insurance and Other Financial

Institutions109,840 85,485 -22

I Real Estate, Renting and Business Activities 94,413 76,487 -19J Public Administration 107,274 72,729 -32K Education 68,125 53,752 -21L Health Services and Welfare and Social Work 69,782 61,294 -12M+O Community, Social & Personal Services,

and Extra-territorial Organizations and Bodies64,123 49,799 -22

1. New Israeli Shekels at 2004 fixed prices.2. Percentage of change between column (1) to column (2): column (2) minus column (1), divided by column (1).

4.6 Multivariate Analysis on Earnings

4.6.1 Background and Methods

In order to measure the marginal effect of degree attainment and personal background characteristics on earnings while controlling for correlations between the explanatory variables, Ordinary Least-Squares (OLS) estimation was conducted. Altogether, five models were estimated – one for each year since attainment of a Bachelor’s degree – among all recipients of Bachelor’s degrees in the 2000-2004 cohorts who showed earnings in the particular "target" year (e.g., DY+1). Although it is possible that the restriction of the estimates to Bachelor’s degree recipients whose earnings were reported to the tax authorities caused some selection bias, it can be expected that the bias will be negligible in

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Main Findings

light of the broad coverage of earnings data and the high employment rates measured among this population.

The dependent variable is total annual earnings (i.e., self-employment and salaried employment combined) within a tax year since Bachelor’s degree attainment, by years since degree attainment16 (i.e., DY+0 to DY+4), transformed to its natural logarithm (i.e., semi-log function). In an attempt to minimize the estimation biases that stem from model misspecification, the variables inserted into the analysis included those that were covered in the descriptive part of this publication, as well as additional relevant variables (available in our data sets)17. The OLS model assumptions were also tested in order to ensure that the parameter estimates were sufficiently robust. The tests included checks for outlier sensitivity, heteroscedasticity, serial correlation, and multi-collinearity of the predictor variables. The results of those tests showed that the models tend to be robust with regard to parameter estimates and standard errors. Nevertheless, the findings of the multivariate model should be interpreted indicatively, and any form of causality should certainly not be inferred from the results. In order to estimate average predicted values given the parameter estimates, the reader can refer to the descriptive statistics (means) presented in Appendix G.

The table below presents OLS regression parameter estimates and their respective t-values. Unless otherwise specified, all estimates are statistically significant at or below the p<.01 level. Estimates highlighted in dark shades (pink) are not significant at the p< .05 level. Estimates highlighted in light shades (yellow) are significant at the p< .05 level. The table also lists the adjusted R2 values as well as the number of observations.

16 A separate model was estimated for each year that elapsed since the year of Bachelor’s degree attainment.

17 A full list of the variable descriptors in the model is provided in Appendix F. Results provided for the dummy variables are in their original output units, as are the interpreted findings. However, the reader may want to transform the coefficients to the inverse logarithmic function in order to interpret the results more precisely.

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Table 2OLS Regression Estimates of Determinants of Log-Earnings,

by Year Since Degree Attainment and Selected Characteristics, Bachelor's Degree Recipients, 2000-2004 Cohorts

Variable

Degree Year+0 Degree Year+1 Degree Year+2 Degree Year+3 Degree Year+4Paramete

rEstimate

tValue

Parameter

Estimate

tValue

Parameter

Estimate

tValue

Parameter

Estimate

tValue

Parameter

Estimate

tValue

Intercept 6.16953 156.41 6.65552 148.77 6.98344 131.93 7.14224 103.29 7.31684 75.2Months Worked (in a year) 0.25311 431.88 0.24867 353.48 0.24484 283.77 0.24535 213.59 0.23832 146.98Sex )men vs. women) 0.21579 53.47 0.23771 51.52 0.2618 47.38 0.27796 38.39 0.31185 31.14Age 0.10157 43.52 0.08437 31.91 0.06666 21.25 0.05846 14.33 0.06053 10.52Age Squared -0.00101 -31.98 -0.00082 -22.98 -0.00062 -14.62 -0.00055 -9.99 -0.00063 -8Israeli born (vs. born abroad) 0.06168 12.52 0.06169 11 0.05317 7.91 0.03739 4.28 0.03234 2.67Ever-married (vs. never-married) 0.07315 18.47 0.07444 16.56 0.07083 13.21 0.06365 9.26 0.04615 4.8Self-employed (vs. employees) 0.12439 14.86 -0.00222 -0.25 -0.07345 -7.52 -0.12437 -10.33 -0.14997 -9.51Number of jobs in a year -0.0724 -41.67 -0.07573 -34.84 -0.08203 -30.21 -0.07745 -20.47 -0.08189 -15.03Continued toward Master's degreeat follow-up year (vs. not continued) -0.27937 -27.85 -0.17601 -18.07 -0.10132 -8.92 -0.08482 -5.62 -0.12077 -5.51Population group (Jews vs. Arabs) 0.18762 25.19 0.21218 24.67 0.24812 23.79 0.25267 18.59 0.22854 11.7Type of Institution Granting Bachelor’s Degree (vs. Graduates of Universities)The Open University 0.08185 9.62 0.06609 6.89 0.04138 3.6 0.04143 2.75 -0.01753 -0.79Pubic college -0.12213 -20.65 -0.10526 -15.37 -0.12926 -15.19 -0.12108 -10.65 -0.0736 -4.42Private college -0.10993 -15.94 -0.04627 -5.76 -0.07166 -7.29 -0.06489 -5.08 -0.09384 -5.36College of education -0.05099 -4.56 -0.03227 -2.54 -0.06454 -4.45 -0.03109 -1.78 -0.05165 -2.3Cohort Year (vs. 2000 Cohort) 2001 0.03093 5.63 -0.11314 -20.66 -0.09676 -17.05 -0.02162 -3.57 - -2002 -0.09613 -17.6 -0.22141 -40.1 -0.12283 -21.63 - - - -2003 -0.19577 -35.88 -0.24553 -44.94 - - - - - -2004 -0.2017 -38.16 - - - - - - - -Bachelor's Degree - Field of Study (vs. Graduates in Social Sciences)Humanities -0.13769 -22.27 -0.13824 -19.05 -0.15298 -17.82 -0.18354 -16.78 -0.20956 -14.56Education -0.19004 -17.12 -0.21286 -16.62 -0.18936 -12.99 -0.22647 -13.01 -0.2097 -9.66Business Administration 0.09642 12.3 0.13282 14.16 0.19971 17.21 0.25124 16.39 0.28766 13.94Natural Sciences -0.01414 -1.18 0.01726 1.12 0.03089 1.71 -0.01449 -0.64 0.00237 0.07Paramedical 0.1493 15.36 0.14537 12.76 0.13674 9.8 0.09121 5.08 0.06928 2.76Mathematics, Statistics and Computer Sciences 0.47467 62.41 0.50027 55.39 0.50854 46.53 0.51853 35.72 0.45393 22.45Law 0.27421 33.01 0.16205 16.63 0.13453 11.4 0.11972 7.81 0.19326 9.39Engineering and Architecture 0.34871 49.77 0.36977 44.74 0.38191 37.33 0.37269 27.46 0.3357 17.4Continuation toward Master's Degree, by Field of Study (vs. Graduates in Social Sciences)Humanities -0.01925 -1.25 -0.0742 -4.77 -0.07721 -4.17 -0.05786 -2.33 -0.05483 -1.55Education 0.16578 7.72 0.14091 6.89 0.1015 4.28 0.07952 2.52 0.1454 3.26Business Administration 0.19219 7.02 0.14938 6.1 0.09937 3.52 0.10376 2.84 0.17724 3.26Natural Sciences -0.48908 -25.45 -0.59081 -27.28 -0.56299 -21.53 -0.66626 -18.98 -0.67559 -13Paramedical Studies -0.05633 -1.83 -0.03576 -1.3 -0.127 -3.98 -0.1303 -3.12 -0.15987 -2.62Mathematics, Statistics and Computer Sciences -0.14368 -7.11 -0.20896 -10.48 -0.22614 -9.71 -0.2572 -8.01 -0.25982 -5.52Law 0.26852 10.01 0.22703 9.24 0.20259 7.34 0.1869 5.19 0.14313 2.84Engineering and Architecture -0.15656 -7.92 -0.17377 -9.21 -0.12436 -5.73 -0.09723 -3.19 -0.12031 -2.59District of Residence at the Follow-up Period (vs. Tel Aviv District)Central District -0.05575 -11.5 -0.05079 -9.28 -0.04426 -6.76 -0.03954 -4.66 -0.02556 -2.18Jerusalem District -0.11178 -16.22 -0.10815 -13.7 -0.10807 -11.53 -0.1194 -9.77 -0.13137 -7.91Haifa District -0.13928 -23.22 -0.13033 -18.91 -0.12575 -15.21 -0.13374 -12.41 -0.12466 -8.34Southern District -0.08825 -14.19 -0.0728 -10.25 -0.06739 -7.89 -0.06558 -5.82 -0.04673 -2.87Northern District -0.13337 -20.7 -0.11817 -15.97 -0.09383 -10.56 -0.07408 -6.39 -0.11447 -6.97Judea, Samaria and Gaza areas -0.14815 -15.38 -0.1426 -13.14 -0.13463 -10.45 -0.15801 -9.61 -0.12107 -5.27

R-Squared 0.6561 0.6279 0.5915 0.5579 0.545Number of Observations 144,832 109,819 79,895 51,507 26,222

All parameter estimates are statistically significant at the p<.01 level unless highlighted.Parmaters hightlighted in yellow are statistically signifiant at the p<.05 level.Parameter estimates highlighted in pink are not statistically significant.

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Main Findings

4.6.2 Labour Force Variables

The summary table of parameter estimates above reveals that nearly all of the effects were observed in the expected direction, and were generally consistent with the human capital theory. The strongest predictor of annual earnings was the number of months per year. The estimates show that every additional month of work increased annual earnings by about 25%. The strong marginal impact of work months can be understood in the context of the relatively high job mobility among recent graduates18. That is, greater job stability was not only associated with a proportional increase in annual earnings, but might also have indicated a better match between the employer and employee, as reflected in higher hourly wage rates, given that number of jobs was already controlled for (see explanation below).

Other labour force characteristics that influenced average earnings were number of jobs and self employment status. Number of jobs in a tax year correlated negatively with earnings, and the marginal effect was approximately -7% to -8%. Data on earnings from self employment revealed a significant positive effect at DY+0 (average 12.5%), and negative effects thereafter (-0.2%, -7%, -12%, and -15% for DY+1 to DY+4) compared to earnings from salaried work.

Cohort year, which also refers to the year that the Bachelor’s degree recipient first entered the labour market as a graduate (among other characteristics), was found to have a significant impact on earnings – although that effect diminished somewhat over time (after an initial rise). For example, comparison of average earnings of Bachelor’s degree recipients in the 2000 cohort with those in the cohorts of 2001, 2002, 2003, and 2004 reveals differences of 3%, -10%, -19.6%, and -20%, respectively at DY+0. At DY+1, the earnings gap between the Bachelor’s degree recipients in the 2000 cohort and those in the 2001, 2002, and 2003 cohorts increased slightly to approximately -11%, -22%, and -25%, respectively. At DY+2 and DY+3, the earnings gap declined significantly but remained negative (among the 2001 and 2002 cohorts) in comparison to the reference year 2000 (-9% and -12% at DY+2, and -2% at DY+3). In addition to other possible explanations, these earnings differences by cohort correspond well with the timing of the macroeconomic recession between 2001 and 2003. In sum, Bachelor’s degree recipients who entered the labour market earlier (before the recession in 2000-2001) had a significant earnings advantage in their initial years of work compared to their counterparts who entered the market just a few years later. (See Diagram 35).

18 Means and standard deviations of total months worked at DY+0 by field of study ranged from a low of 8.2/3.5 and 9.0/3.4 (Law and Natural Sciences) to a high of 10.7/2.5 in Education and Paramedical Studies. By DY+4, the means and standard deviations ranged from 10.2/2.3, respectively, in Natural Sciences, to 10.9/2.4, respectively, in Education.

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4.6.3 Higher Education Variables

As expected, tertiary education characteristics such as type of institution, field of study, and continuing education status significantly affected the average earnings of Bachelor’s degree recipients.

Type of institution explained some of the variance in earnings among Bachelor’s degree recipients after controlling for the other factors in the model. In other words, comparison of different types of institutions in this model was restricted to cases in which the graduates received degrees in the same fields of study. In particular, recipients of degrees from the Open University had an advantage in earnings over university graduates, which amounted to approximately 8% in the Degree Year. However, this advantage diminished with time since the year of degree attainment (7%, 4%, 4% at DY+1, DY+2, and DY+3, respectively, and was not statistically significant at DY+4). Earnings differences were also found for recipients of degrees from colleges of education, who earned approximately 3% to 6% less than university graduates, followed by degree recipients from private and public colleges. Regarding degree recipients from private colleges, the earnings gap compared with university graduates was -11%, -5%, -7%, -6.5%, and -9% for each year from DY+0 to DY+4, respectively. Regarding degree recipients from public colleges, the earnings gap compared with university graduates was -12%, -10.5%, -13%, -11.4%, and -7%, respectively, for each year since Bachelor’s degree attainment.

The estimates support the findings presented in the tables as well as those presented in the descriptive summary, which indicate that field of study is strongly associated with differences in earnings among Bachelor’s degree recipients. Bachelor’s degree recipients in Computer

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Main Findings

Sciences, Mathematics and Engineering, Law, Business Administration, and Paramedical Studies who were not concurrently studying toward a Master’s degrees all had higher average earnings than did their counterparts who received Bachelor’s degrees in Social Sciences (and were also not studying toward a Master’s degree). More specifically, the average earnings of degree recipients in Computer Sciences and Mathematics who were not studying concurrently toward a Master’s degree were 47%, 50%, 50%, 51%, and 45% higher than the earnings of their counterparts in the Social Sciences for each year between DY+0 and DY+4, respectively, when other factors were held constant. Among Bachelor’s degree recipients in Engineering who were not studying concurrently toward a Master’s degree, the estimated average earnings were 34% and 38% greater than their counterparts in the Social Sciences in the corresponding follow-up time period. Although the average earnings of Bachelor’s degree recipients in Law were higher than the earnings of Bachelor’s degree recipients in the Social Sciences during the period in question, the difference diminished between DY+0 and DY+3 but rose at DY+4 (27%, 16%, 13%, 12%, and 19%, respectively). In contrast, the earnings of Bachelor’s degree recipients in Business Administration rose progressively between DY+0 and DY+4 (10%, 13%, 20%, 25%, 29% for each year since degree attainment, respectively). In addition, there was a progressive decline in the earnings advantage of Bachelor’s degree recipients in Paramedical Studies over those who received degrees in the Social Sciences (not studying toward a Master’s degree: 15%, 14.5%, 13.6%, 9%, and 7% for each year between DY+0 and DY+4, respectively). Bachelor’s degree recipients in the Humanities who were not studying concurrently toward a Master’s degree reported lower earnings than did Bachelor’s degree recipients in the Social Sciences, and the gap increased as a function of years since Bachelor’s degree attainment (-14%, -14%, -15%, -18%, and -21% for each year between DY+0 and DY+4, respectively). Bachelor’s degree recipients in Education who were not studying concurrently toward a Master’s degree earned between 17% and 20% less than did Bachelor’s degree recipients in the Social Sciences between DY+0 and DY+4. Finally, the differences in earnings between Bachelor’s degree recipients in the Natural Sciences not studying concurrently toward a Master’s degree and their counterparts who received Bachelor’s degrees in the Social Sciences were not statistically significant throughout the 5-year period.

Studying toward a Master’s degree can have both a positive and negative impact on earnings levels. Although it was not observed directly in this study, this factor may be interpreted as the extent to which continuing education can be combined with full-time employment. Moreover, insofar as Bachelor’s degree recipients who continue studying toward a Master’s degree are a select group with higher human capital, they have an additional earnings advantage which is expressed in improved access to higher-skilled employment that can accommodate concurrent studies toward a Master’s degree. The inconsistency in the interaction between field of study and concurrent studies toward a Master’s degree is corroborated by the data, which revealed heterogeneity in the magnitude and direction of the coefficients for earnings levels by field of study and continuation of studies toward a Master’s degree. Notably, part of the population of Bachelor’s degree recipients who continued studying toward a Master’s degree was not formally employed, but received monetary assistance in the form of stipends, which are not included in the Tax Authority data.

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The fields of study in which continuation of studies was found to have a negative impact on earnings of Bachelor’s degree recipients included Natural Sciences, Humanities, Social Sciences, Paramedical Studies, and Education. Bachelor’s degree recipients in the Natural Sciences who continued studying towards a Master’s degree were found to have the highest declines in earnings compared to their counterparts who did not continue their studies (the drop in earnings was between 64% and 79% during the follow-up period DY+0 to DY+4). Bachelor’s degree recipients in the Humanities who studied concurrently toward a Master’s degree also earned less than their counterparts who did not continue studying (44%, 39%, 33%, 33%, and 38%, respectively for each year between DY+0 and DY+4, respectively). Bachelor’s degree recipients in the Social Sciences who continued studying toward a Master’s degree reported a disadvantage in earnings – although the disadvantage diminished over the time that elapsed since Bachelor’s degree attainment (28%, 18%, 10%, 8%, and 12% for each year between DY+0 and DY+4, respectively). Bachelor’s degree recipients in Paramedical Studies and Education who continued studying toward a Master’s degree showed a decline in earnings compared with those who did not continue their studies, amounting to approximately 18%, 7%, 9%, 12%, and 21% among graduates in Paramedical Studies, and 30%, 24%, 19%, 23%, 18% among graduates in Education for each year between DY+0 and DY+4, respectively.

In contrast to the above-mentioned fields of study, Bachelor’s degree recipients in Business, Law, Mathematics and Computer Sciences, and Engineering who continued studying toward a Master’s degree all had an earnings advantage over their counterparts in the same fields who did not continue studying. The earnings advantage of Bachelor’s degree recipients in Business who continued studying toward a Master’s degree also increased progressively over time. Specifically, the difference between those who continued studies in that field and those who did not increased for each year between DY+0 and DY +4, as follows: 1%, 11%, 20%, 27%, 35%, respectively. Among Bachelor’s degree recipients in Law who studied concurrently toward a Master’s degree, the earnings advantage over those who did not continue studying was 27%, 21%, 24%, 22%, and 22% for each year between DY+0 to DY+4, respectively. In Engineering and Architecture, those who continued toward a Master’s degree reported an earnings advantage over those who did not continue, except in the Degree Year: -9%, 2%, 16%, 19%, and 9% for each year between DY+0 and DY+4, respectively. Finally, in Mathematics and Computer Sciences, those who continued studing toward a Master’s degree showed an earnings advantage over those who did not: 5%, 12%, 18%, 18%, 7% for each year between DY+0 and DY+4, respectively.

4.6.4 Demographic Variables

The estimation also revealed significant differences in annual earnings by demographic characteristics. When all other factors in the model were held constant, male graduates earned significantly more than did their female counterparts, which also rise over time by an average amount of: 22%, 24%, 26%, 28% and 32% for each year between DY+0 and DY+4, respectively.

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Main Findings

Similarly, Jewish Bachelor’s degree recipients earned significantly more than did their Arab counterparts. These advantages also increased slightly as a function of the time that elapsed since Bachelor’s degree attainment: 19%, 21%, 25%, 25%, and 23% for each year between DY+1 and DY+4, respectively. The model estimates also indicate that age significantly explained the variance in log-earnings. Among Bachelor’s degree recipients below age 50, earnings increased for each year of age. In contrast, among those over age 50, earnings declined for each year of age.

The model also shows that Israel-born Bachelor’s degree recipients earned between 3% and 6% more than their foreign-born counterparts in the follow-up period, even though there was a consistent decrease in the gap as a function of years since degree attainment. Similarly, ever-married Bachelor’s degree recipients showed higher earnings compared to their never-married counterparts, which ranged from 5% to 8% (also declining as a function of year since degree attainment).

Finally, as expected, in light of the higher concentration of skilled job opportunities in the Tel Aviv District, Bachelor’s degree recipients from that district (i.e., the reference group) earned more than did their counterparts in the rest of the country. Consistent with that geographic effect, the earnings gap was smallest among graduates residing in the Central District, who had an earnings disadvantage over their counterparts in the Tel Aviv District, ranging from 2% to 5% between DY+0 and DY+3. By DY+4, the difference in earnings was not statistically significant.

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Documents

בית - old.cbs.gov.il€¦ · Web view1. General The publication contains data on employment characteristics among recipients of Bachelor's degrees between 2000 and 2004. These