Mismatch Between Education and Occupation in Pakistan

Dr. Shujaat FarooqResearch Analytics International Inc. Pakistan Institute of Development Economics (PIDE)Islamabad, Pakistan

Introduction

• The wave of supply of fresh graduates in the U.S triggered first research on education-job mismatch by Freeman (1976) in his research ‘Overeducated Americans’.

• Job mismatch has three dimensions;- Education-job mismatch- Qualification-job mismatch/ skill Job mismatch- Field of study and job mismatch

• Since late 80s, the literature on education-job mismatch mushroomed in a number of developed countries.

• Both economists and sociologists have consigned the job mismatch phenomenon as a serious efficiency concern with its pertinent socio-economic costs at individual, at firm and at national level.

Potential of Job Mismatch in Pakistan

• The phenomenon of job mismatch can be perceived from recent official statistics and studies conducted over socio-demographic factors, educational system and labour market issues.

• The recent official reports have highlighted this phenomenon by connecting it with poor level of skills, limited labour market opportunities and rising share of youth in labour force.

• A variety of socio-demographic characteristics, customs and barriers are causing the job mismatch especially for women.

• Despite recent socio-economic development, the educational system is not coping with the right demands of labour market and following a variety of tiers.

Cont…

Number of Universities & Degree Awarding Institutions (DAIs) in Pakistan

• The recent rapid expansion at higher education has also raised the heterogeneity of skills across regions and institutes. The return to education has a declining trend in Pakistan.

• Over last few decades, the economy and labour market of Pakistan has undergone incredible structural changes. During 2001-09 period, both labour force participation and employment rates rose with 4% per annum on average while unemployment rates also remained high (6–10%).

• The quality of jobs and the access to modest earning opportunities still remained an issue as the labour force grew faster than the employment rate.

• With rising employment participation, the labour market imperfections and imbalances have also rose; rising job search periods, rising share of informal economy, rising risks of vulnerability and educated unemployment especially for female and youth. It suggests that a number of educated workers are going down in ‘less productive’ jobs (GoP, 2008b).

Educational attainment of the unemployed (age 15+) (%)

Education level FY00 FY02 FY04 FY06 Change(% point)

Below prim. 47.7 45.7 42.3 44.7 -3.0

Prim. & middle 28.6 27.4 25.8 26.1 -2.5

Matric and IntermediateOverall 19.3 21.2 24.8 22.4 +3.1Male 23.2 23.7 28.8 26.1 +2.9Female 12.2 15.7 15.7 14.3 +2.1Degree level educationOverall 4.3 5.8 7.2 6.8 +2.5Male 5 6.4 7.1 6.9 +1.9Female 2.9 4.4 7.3 6.6 +3.7Source: GoP (2007a)

Theoretical background of job mismatch

• Human capital theory (HCT) (Schultz, 1962; Becker, 1964)• Job competition model (Thurow, 1975)• Assignment model (Sattinger, 1993)• Occupational mobility (Sicherman and Galor, 1990)• Job screening model (Spence, 1973)• Matching theory (Rosen, 1972)• Theory of differential over-qualification explains the higher

probability of being over-education among women (Frank,1978)

• Robest (1995) noted that those who attend the lowestquality schools may be over-educated throughout theircareer.

• Dolton and Silles (2001) found a positive influence ofregional mobility on the quality of match.

• Buchel and Ham (2003) found that ethnic minorities arelikely to be more affected.

Measurement of job mismatch

• For education-job mismatch, four methods are implicit inliterature;

- Job analyst method (JA)- Worker’s self assessment method (WSA)- Realized match method (RM)- Occupation-satisfaction approach (Chevalier, 2003)

• For qualification mismatch, two measurement approaches have been used in literature;

- Overall qualification mismatch (subjective approach) - Specific qualification mismatch approach

• For field of study and job mismatch, the existing three studies have adopted the subjective approach.

Job Analyst Method (JA)

• Professional job analysts grade the jobs and recommend the minimum educational requirements for a certain job/occupation Preparation

• This approach is based on the GED and SVP scores available from the DOT (U.S Department of Labour) or SOC in UK. In Netherlands, a similar method has been used by Huijgen (1989).– Please describe your highest level of education (in years?– What level of formal education and experience is

demanded by your employer/organization to get the job like yours? --------------------Education (in years)

Worker’s self assessment method (WSA)

• Sicherman (1991) used response to the following question; “how much formal education is required to get a job like yours?”.

• Alba (1993) used the question; “what kind of education does a person need in order to perform your job?”

• Farooq (2010) used “In your opinion, how much formal education and experience is required to perform your current job well? ”

Realized match method (RM)

• Verdugo and Verdugo (1989) measured the degree of education-job mismatch by two variables; years of schooling and occupational group of a job holder.

Occupation-satisfaction approach (Chevalier, 2003; Farooq 2010)

• This method further split the over-educated workers into two groups; those who are over-educated but are satisfied with their occupational mismatch are called apparently over-educated and those over-educated but dissatisfied with the current job are called genuinely over-educated worker.– Considering your education and skills, how much you are

satisfied with your current job? – [1] Very dissatisfied [2] Dissatisfied[3] Neither satisfied

nor dissatisfied [4] Satisfied [5] Very satisfied

Qualification mismatchGreen and McIntosh, 2002; Lourdes et al. (2005)

Overall qualification mismatch (subjective approach) based on two questionsi. “do you have more/less skills/capabilities than

required by your current job?”ii. “do you think your knowledge/skills/capabilities

would enable you to hold on to a more qualified job than your current job?”

Specific qualification mismatch approach

• In this approach, the qualification mismatch has been measured by measuring the various specific attained skills possessed by the workers and the required skills in their current job (Lourdes et al., 2005; Chevalier and Lindley, 2006; Farooq, 2010).

Job mismatchJob mismatch

Education-job mismatchEducation-job mismatch Qualification mismatchQualification mismatch Field of study mismatchField of study mismatch

Over-educationOver-education

Under-educationUnder-education

Adequate Adequate

Genuine overeducatedGenuine overeducated

Apparent overeducatedApparent overeducated

AdequateAdequate

Under-qualificationUnder-qualification

Over-qualificationOver-qualification

IrrelevantIrrelevant

Slightly relevantSlightly relevant

Moderate relevantModerate relevant

Complete relevantComplete relevant

Gender-wise sample distribution of LFS and SEG dataset

Datasets Female(% of total)

Male (% of total) N

LFS, 2006-07 16.1 83.9 2,839

LFS, 2008-09 14.8 85.2 3,896

SEG, 2010 15.8 84.2 514

The determinants of job mismatch

MIS saki = α0 + α1 Iki + α2 Edki + α3 Wkki + µ1i (1)

MIS ja ki = α0 + α1 Iki + α2 Edki + α3 Wkki + µ2i (2)

MIS qki = α0 + α1 Iki + α2 Edki + α3 Wkki + µ3i (3)

MIS hki = α0 + α1 Iki + α2 Edki + α3 Wkki + µ4i (4)

Impact of job mismatch on graduate’s earnings

Standard Mincer earning equation generally written as;Ln yi = δ 0+ δ 1 year_school i + δ’ X ki + µ I (5) Ln yi = β0+ β1 year_school i + β2 D oi + β3 D u

i + β’ Xi + εi (6)Ln y i= β0+ β1 year_school i+ β2D og

i +β3E oai +β 4D u

i+β’X ki +µ I (7)Lny i = β0+ β1 year_school i + β2 oqi + β 3 uqi + α’ X ki + µ i (8)Lnyi = β0+ β1 year_school i + β2 sri + β 3 mri + β 4 cri +β’ Xi + εi (9)

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Non-pecuniary consequences of job mismatch

Job satisfaction Sti = α 0 + α 1 E i + α’ Xi + εi (10) Sti = β 0 + β 1 D oj

i + β 2 D uji + β’ Xi + εi (11)

Sti = ρ0 + ρ1 D osai + ρ2 D usa

i + ρ’ Xi + εi (12) Sti = γ0+ γ1 E i + γ2 oqi + γ3 uq i + γ’ Xi + εi (13) Sti = δ0+ δ 1 E i + δ 2 wri + δ 3 mr + δ 4 cri + δ’ X ki + ε i (14)

Turnover intentionSbi = α 0 + α 1 E i + α’ Xi + εi (15) Sbi = β 0 + β 1 E i + β 2 D oj

i + β 3 D uji + β’ Xi + εi (16)

Sbi = ρ0 + ρ1 E i + ρ2D osai + ρ3 D usa

i + ρ’ Xi + εi (17) Sbi = γ0+ γ1 E i + γ2 oqi + γ3 uq i + γ’ Xi + εi (18) Sbi = δ0+ δ 1 E i + δ 2 wri + δ 3 mri + δ 4 cri + δ’ X ki + ε i (19)

Distribution of the sampled graduates by occupation (%)

Manager ProfessionalAss.

professionalClericalsupport

Elementary occupations

Total

LFS (2006-07)Female 10.1 21.0 64.6 1.1 3.3 100Male 27.3 19.0 27.5 10.4 15.8 100Total 24.6 19.3 33.5 8.9 13.8 100

LFS (2008-09)

Female 8.5 22.7 64.6 1.4 2.8 100Male 25.6 18.7 31.7 10.3 13.7 100Total 23.1 19.3 36.6 9.0 12.1 100SEG (2010)Female 12.4 40.7 27.2 18.5 1.2 100Male 20.6 29.0 33.7 12.9 3.7 100Total 17.7 32.6 32.7 13.8 3.3 100

Distribution of sampled graduates by monthly Income in categories

Monthly Earning (Rs)

SEG, 2010 LFS, 2008-09

Female Male Total Female Male Total

up to min. wage* 11.1 3.7 4.9 21.4 9.4 11.3

Min. wage-12000 24.7 13.4 15.2 33.2 28.6 29.4

12001-15000 19.8 10.2 11.7 12.7 15.4 14.9

15001-20000 14.8 18.2 17.7 14.9 17.4 17.0

20001-30000 12.4 24.5 22.6 11.8 15.2 14.6

30001-50000 16.1 20.3 19.7 4.7 11.9 10.8

50001 and above 1.2 9.7 8.4 1.3 2.1 2.0

Total 100 100 100 100 100 100

*minimum wage is 6,000 for LFS, 2008-09 For SEG, 2010 is 7,000

Objective 1

Estimated results over the three types of job mismatch

The level of education-job mismatch by various approaches (%)

Datasets Matched Under-educated Over-educated

RM method(LFS 2006-07)

Female 65.7 4.4 30.0

Male 69.4 9.7 20.9

Total 68.8 8.9 22.3

RM method(LFS 2008-09)

Female 60.5 4.2 35.4

Male 71.2 2.3 26.6

Total 69.6 2.5 27.9

SEG, 2010

WSA 65.4 9.9 24.7

JA 69.5 4.5 26.1

RM 63.4 21.6 15.0

The level of genuine and apparent over-education (%)-SEG, 2010

Education-Job Mismatch WSA JA RM

Over-educated (total) 24.7 26.1 15.0

Apparent over-educated 14.0 16.3 10.3

Genuine over-educated 10.7 9.7 4.7

Distribution of respondents by the level of qualification mismatch (%)

Matched Under-qualified Over-qualified

Female 66.7 11.1 22.2

Male 72.8 13.9 13.4

Total 71.8 13.4 14.8

*based on the weights estimated by PCA approach

Marginal and joint distribution of education and qualification match (%)

Matched Under-qualified Over-qualified

Job Analyst Method (JA)

Matched 52.0 10.3 7.2

Under-educated 3.5 0.4 0.6

Over-educated 16.3 2.7 7.0

Worker Self Assessment Method (WSA)

Matched 48.8 9.0 7.6

Under-educated 6.8 2.1 1.0

Over-educated 16.2 2.3 6.2

The % Distribution of the Respondents by Reported Field of Study and Job Mismatch

Level of Mismatch Female Male Total

Irrelevant 14.8 10.6 11.3

Slightly relevant 18.5 12.9 13.8

Moderately relevant 33.3 39.3 38.3

Completely relevant 33.3 37.2 36.6

Objective 2

Results over the determinants of job mismatch

The Determinants of Education-Job Mismatch—Multinomial Logit Model (Relative Risk Ratios)

RegressorsWorker Self Assessment (WSA)

Under/Match Over/Matchsex (male=1) 0.651 0.334**age (years) 1.251 0.797**relative in govt. (yes=1) 0.309** 0.392*

political family (yes=1) 1.307 0.513**

education 0.236* 2.494*full time degree (yes=1) 1.051 0.562**annual System (yes=1) 1.875 2.229*field of study (traditional subjects as ref.)computer 2.106 0.778admin, marketing, finance 0.895 0.696law, journalism 0.282 0.520stat, math, eco 1.121 0.334*health 0.844 0.335natural science, engineering 0.601 0.384**occupation (elementary as ref.)manager 1.770e+08* 0.017*professional 1.730e+08* 0.032*associate professional 7.130e+07* 0.090*clerk 5.044e+06* 1.356

Determinants of Qualification Mismatch-Multinomial Logit Model (Relative Risk Ratios)

Regressors Under/Match Over/Matchage (years) 1.382* 0.784**

political family (yes=1) 2.315* 0.756

education 1.252 1.302**field of study (traditional subjects as ref.)computer 0.471 1.269admin, marketing, finance 0.274* 0.79law, journalism 0.168* 1.407stat, math, eco 0.29* 0.141*health 0.259** 0.619

natural science, engineering 0.165* 1.532**

full time student (yes=1) 0.50** 1.11annual System (yes=1) 1.73 0.467**occupation (elementary occupation as ref.)manager 0.755 0.065*professional 0.53 0.188*associate professional 0.691 0.228*clerk 0.889 0.453

Determinants of the Field of Study and Job Mismatch- Marginal effects Regressors dy/dx Std. Errorsex (male=1) 0.057** 0.035education 0.019 0.0198field of study (traditional subjects as ref.)computer 0.138* 0.029admin, marketing, finance 0.135* 0.032law, journalism 0.033 0.052statistics ,mathematics, Economics 0.122* 0.034health 0.133* 0.041natural science, Engineering 0.171* 0.028full time degree (yes=1) 0.12* 0.061occupation (elementary as ref.)manager 0.166* 0.040professional 0.274* 0.082associate professional 0.205* 0.070clerical support workers 0.050 0.074

Objective 3

Results over the impact of job mismatch on graduate’s earnings

Impact of Education-job Mismatch on Graduate’s Earnings—SEG, 2010

RegressorAttained Edu.(model 1)

WSA-I(model 2)

JA-I(model 3)

WSA-II(model 4)

JA-II(model 5)

over-education - -0.367* -0.295* - -

under-education - -0.051 -0.051 -0.044 -0.044

Genuine over-educated - - - -0.532* -0.487*

Apparent over-educated - - - -0.265* -0.203*

education 0.101* 0.136* 0.138* 0.139* 0.142*

Experience (years) 0.029* 0.025* 0.027* 0.024* 0.025*

sex (male=1) 0.107** 0.113** 0.118** 0.114** 0.121**

marital status (married=1) 0.123* 0.118* 0.117** 0.118* 0.120*

Cont…

RegressorAttain edu. (model 1)

WSA-I(model 2)

JA-I(model 3)

WSA-II(model 4)

JA-II(model 5)

foreign edu. (yes=1) 0.202* 0.226* 0.209* 0.207* 0.203*

type of institution (university as ref.)

college -0.072 -0.05 -0.07 -0.055 -0.067

distance learning -0.286* -0.282* -0.279* -0.292* -0.287*

organization (govt.=1) -0.056** -0.049** -0.050** -0.045** -0.048**

occupation (manager as ref.)

professional -0.202* -0.197* -0.191* -0.201* -0.197*

assoc. professional -0.220* -0.168* -0.187* -0.161* -0.179*

clerk -0.462* -0.432* -0.412* -0.425** -0.416*

elementary -0.379** -0.365** -0.357** -0.367** -0.358**

Impact of Qualification Mismatch on Graduates’ Earnings—SEG, 2010 Regressors Coeff. St. Err.over-qualification -0.195* 0.066under-qualification 0.155* 0.069education 0.102* 0.023experience 0.026* 0.01sex (male=1) 0.102** 0.063marital status (married=1) 0.103** 0.062foreign diploma (yes=1) 0.194* 0.089occupation (manager as ref.)professional -0.183* 0.076assoc. professional -0.198* 0.076clerk -0.429* 0.095elementary -0.311* 0.15tenure (up to 1 year as ref.)1 to 2 year -0.018 0.0842 to 4 year 0.197* 0.079more than 4 year 0.292* 0.092

Impact of Field of Study and Job Mismatch on Graduates’ Earnings—SEG, 2010 Regressors Coeff. St. Err.weak relevant/irrelevant 0.115 0.09moderate relevant/irrelevant 0.228* 0.083complete relevant/irrelevant 0.203* 0.09education 0.102* 0.024experience 0.029* 0.01sex (male=1) 0.099** 0.062marital status (married=1) 0.118** 0.062foreign diploma (yes=1) 0.218* 0.09occupation (manager as ref.)professional -0.198* 0.077assoc. professional -0.210* 0.077clerk -0.412* 0.098elementary -0.307* 0.152tenure (up to 1 year as ref.)1 to 2 year 0.000 0.0842 to 4 year 0.216* 0.079more than 4 year 0.298* 0.093constant 7.735* 0.409

Objective 4

Results over non-pecuniary consequences of job mismatch

Impact of Job Mismatch on Job Satisfaction –Odd ratios by Ordered Logit Model

Regressors Attain Education WSA Qualification Field of

Study over-education - 0.110* - -under-education - 2.205* - -over-qualification - - 0.565* -under-qualification - - 0.934 -weak relevance/irrel. - - - 1.169moderate relevance/irrel. - - - 4.491*complete relevance/irrel. - - 5.344*education 0.766* - 0.781* 0.696*log(wage) 2.608* 1.881* 2.502* 2.393*tenure 1.034** 1.040* 1.038* 1.031**age 1.036* 1.039* 1.039* 1.029**occupation (manager as ref.)professional 1.072 0.999 1.095 1.039associate professional 0.771 0.920 0.776 0.832clerk 0.126* 0.378* 0.128* 0.204*elementary 0.132* 0.461 0.141* 0.237*stress due to boss behavior 0.708** 0.608* 0.717** 0.718colleague cooperate 1.030 0.902 0.998 1.053colleague motivate 1.498* 1.593* 1.464* 1.540*colleague not criticize 1.483* 1.449* 1.483* 1.476*

The Effect of Job Mismatch on Turnover Intention –Odd ratio by Ordered Logit Model

Regressors Education WSA Qualification Field of Study

over-education - 8.762* - -under-education - 0.551* - -over-qualification - - 0.866 -under-qualification - - 0.879 -weak relevance/Irrelevant - - - 1.030moderate relevance/Irrelevant - - - 0.174*complete relevance/Irrelevant - - - 0.115*education 1.509* 1.176** 1.515* 1.800*age 0.966* 0.969** 0.966* 0.946*log(wage) 0.631* 0.880 0.628* 0.720*occupation (manager as ref.)professional 0.805 0.807 0.805 0.828associate professional 1.226 0.973 1.234 1.116clerk 7.582* 2.264* 7.687* 3.964*elementary 8.636* 2.575** 9.005* 3.659*stress due to boss (yes=1) 1.040 1.143 1.049 0.978colleague cooperate (yes=1) 0.656* 0.689* 0.646* 0.630*colleague motivate (yes=1) 0.959 0.987 0.959 1.012colleague not criticize (yes=1) 0.797 0.813 0.800 0.792

Conclusions• One-third of graduates are mismatched either in over-

education or in under-education. More than one-fourth of graduates are mismatched in qualification; half of them are over-qualified.

• More than one-tenth of graduates consider that their current jobs are totally irrelevant, while 14% reported that their jobs are slightly relevant to their studied field of discipline.

• Women are more likely than men to be over-educated. Age has a negative association with over-education/over-qualification.

• Education prevent graduates to be under-educated but increase the likelihood of over-education/ over-qualification. The probability of over-education/over-qualification is less among those graduates who completed their education as a full-time student or from semester system.

Cont…

• The graduates who have studied the occupation-specific subjects have a better job match.

• The graduates employed in better occupations have relatively good match than those who are in elementary occupations.

• Overeducated/overqualified graduates face wage penalty while under-qualified get wage premium. A good field of study and job match also improves the wages of graduates.

• The over-educated and over-qualified graduates are less satisfied and have high turnover intention rate. Similarly, a good match between field of study and job improve the job satisfaction and reduce the likelihood of turnover intention.

Policy implications and recommendations

• Coordination among various stakeholders is prerequisite. • Ensure equality across the regions and institutes. • More focus on occupational-specific knowledge base

education.• Tracer type studies should be conducted to understand the

employment patterns and skills demanded by economy. • Launch policies and programs to raise the women

participation.• Create more jobs and knowledge base activities for youth to

ensure decent work. • Establish dynamic education policy. • Improvement in labour market information system.

Way forward• Panel type tracer studies would be helpful to investigate the

relevance of skills and to understand the timing and depth of job mismatch.

• There is a need to estimate the impact of job mismatch on productivity losses and training costs associated with mismatched workers.

• There is a need to estimate direct and indirect hiring and firing costs to both employees and employers.

• Additional research is required to explore the areas where new job opportunities can be made especially for youth and females.

Thank You