The Utilisation of Education and Skills: Incidence and Determinants among Pakistani Graduates

Abstract

This study estimates the incidence of job mismatch and its determinants in Pakistan, based on three categories: (i) qualification mismatch, (i) skill mismatch, and (iii) field-of-study mismatch. It uses both primary and secondary datasets that target graduates employed by the formal sector. The study measures the qualification mismatch using three approaches and finds that about one third of the graduates sampled face a qualification mismatch. Similarly, more than one fourth are mismatched in terms of skill, about half are over-skilled, and half are under-skilled. The analysis also shows that 11.3 percent hold jobs that are irrelevant to their discipline and 13.8 percent have jobs that are slightly relevant to their discipline. Women are more likely than men to be over-qualified, and age has a negative association with over-qualification. Graduates who belong to political families have a better qualification match but a lower field-of-study match. While a higher level of schooling prevents graduates from being under-qualified, it also raises the likelihood of being over-qualified and over-skilled. Occupation-specific disciplines offer more protection against the possibility of job mismatch. Both full-time education and semester-system education reduce job mismatch, while distance learning raises job mismatch. The phenomena of being over-qualified and over-skilled is more prevalent in lower occupations, as is field-of-study mismatch. JEL classification: I23, I24, J21, J24 Keywords: Education and Inequality, Higher Education, Human Capital, Labour Market

Horizontal Mismatch Between Employment and Field of Education: Evidence From a Systematic Literature Review

“…However, as the findings apply to PhD graduates in a science, mathematics or engineering field, these results cannot be generalized. Other studies provide evidence that females are more likely to be mismatched as opposed to their male counterparts (Hensen et al ., ; Farooq, ). For men, mismatch is more likely to be the result of career‐oriented reasons like pay and promotion opportunities, or changing career interests, whilst women are more likely to report mismatch due to family‐related reasons, the job location or working conditions (Robst, ; Bender and Heywood, ).…”

“…In addition to study programme characteristics, horizontal mismatch is determined by whether students were enrolled in a part‐time or full‐time programme. Several studies found that graduates who studied full‐time are more likely to obtain a job that matches their field compared to graduates who studied part‐time (Farooq, ; Boudarbat and Chernoff, ).…”

“…Whilst self‐employed men are more likely to be mismatched predominantly due to working conditions, self‐employed women tend to accept horizontal mismatch mainly for family‐related reasons. Employees in specialized occupations, managers, professionals, associate professionals and women in white‐collar jobs are less likely to be horizontally mismatched than employees in elementary occupations (Witte and Kalleberg, ; Farooq, ). This can be explained by the fact that the duties and requirements for white‐collar occupations, also in terms of the education acquired, are less specifically defined.…”

The Utilisation of Education and Skills: Non-Pecuniary Consequences Among Graduates

“…For the WSA approach in SEG dataset, graduates were asked: "In your opinion, how much formal education/qualification (years) and experience (years) is required to perform your current job well?" By comparing the attained education/qualification with required education/qualification, the graduates have been classified into three categories: over-qualified, under-qualified and matched graduates [see Farooq (2011) for detail]. For the third, RM measure, the methodology of Verdugo and Verdugo (1989), and NG (2001) has been followed to measure the required 4 Grade drift is dropped in quality of education.…”

“…Islamabad and Rawalpindi to study the job mismatch phenomenon in depth. At a broader level, the targeted universe in the SEG dataset has been divided into three major groups: graduates in the federal government, graduates in the autonomous/semi-autonomous bodies under the federal government and finally graduates in the private sector [see Farooq (2011) for detailed sampling and data collection]. Figure 2 shows the distribution of complete sample of 514 graduates across three major groups according to their relative employment share.…”