Mismatch Between Education and Occupation in Pakistan

Transcription

1 Mismatch Between Education and Occupation in Pakistan Dr. Shujaat Farooq Research Analytics International Inc. Pakistan Institute of Development Economics (PIDE) Islamabad, Pakistan

2 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.

3 Potential of Job Mismatch in Pakistan The phenomenon of job mismatch can be perceived from recent official statistics and studies conducted over sociodemographic 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

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

5 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 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).

6 Educational attainment of the unemployed (age 15+) (%) Education level FY00 FY02 FY04 FY06 Change (% point) Below prim Prim. & middle Matric and Intermediate Overall Male Female Degree level education Overall Male Female Source: GoP (2007a)

7 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 lowest quality schools may be over-educated throughout their career. Dolton and Silles (2001) found a positive influence of regional mobility on the quality of match. Buchel and Ham (2003) found that ethnic minorities are likely to be more affected.

8 Measurement of job mismatch For education-job mismatch, four methods are implicit in literature; - 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.

9 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)

10 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?

11 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.

12 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 overeducated 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

13 Qualification mismatch Green and McIntosh, 2002; Lourdes et al. (2005) Overall qualification mismatch (subjective approach) based on two questions i. 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?

14 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).

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17 Job mismatch Education-job mismatch Qualification mismatch Field of study mismatch Adequate Under-education Over-education Adequate Under-qualification Over-qualification Irrelevant Slightly relevant Moderate relevant Complete relevant Genuine overeducated Apparent overeducated

18 Gender-wise sample distribution of LFS and SEG dataset Datasets Female (% of total) Male (% of total) N LFS, ,839 LFS, ,896 SEG,

19 The determinants of job mismatch MIS sa ki = α 0 + α 1 I ki + α 2 Ed ki + α 3 Wk ki + µ 1i (1) MIS ja ki = α 0 + α 1 I ki + α 2 Ed ki + α 3 Wk ki + µ 2i (2) MIS q ki = α 0 + α 1 I ki + α 2 Ed ki + α 3 Wk ki + µ 3i (3) MIS h ki = α 0 + α 1 I ki + α 2 Ed ki + α 3 Wk ki + µ 4i (4)

20 Impact of job mismatch on graduate s earnings w w N s s s 1 w i s 1 i 1 ( 1 r s ) c s r s W s W S W 1 s 1 Standard Mincer earning equation generally written as; Ln y i = δ 0 + δ 1 year_school i + δ X ki + µ I (5) Ln y i = β 0 + β 1 year_school i + β 2 D o i + β 3 D u i + β X i + ε i (6) Ln y i = β 0 + β 1 year_school i + β 2 D og i +β 3 E oa i +β 4 D u i+β X ki +µ I (7) Lny i = β 0 + β 1 year_school i + β 2 oq i + β 3 uq i + α X ki + µ i (8) Lnyi = β 0 + β 1 year_school i + β 2 sr i + β 3 mr i + β 4 cr i +β X i + ε i (9)

21 Non-pecuniary consequences of job mismatch Job satisfaction St i = α 0 + α 1 E i + α X i + ε i (10) St i = β 0 + β 1 D oj i + β 2 D uj i + β X i + ε i (11) St i = ρ 0 + ρ 1 D osa i + ρ 2 D usa i + ρ X i + ε i (12) St i = γ 0 + γ 1 E i + γ 2 oq i + γ 3 uq i + γ X i + ε i (13) St i = δ 0 + δ 1 E i + δ 2 wr i + δ 3 mr + δ 4 cr i + δ X ki + ε i (14) Turnover intention Sb i = α 0 + α 1 E i + α X i + ε i (15) Sb i = β 0 + β 1 E i + β 2 D oj i + β 3 D uj i + β X i + ε i (16) Sb i = ρ 0 + ρ 1 E i + ρ 2 D osa i + ρ 3 D usa i + ρ X i + ε i (17) Sb i = γ 0 + γ 1 E i + γ 2 oq i + γ 3 uq i + γ X i + ε i (18) Sb i = δ 0 + δ 1 E i + δ 2 wr i + δ 3 mr i + δ 4 cr i + δ X ki + ε i (19)

22 Distribution of the sampled graduates by occupation (%) Manager LFS ( ) Professional Ass. professional Clerical support Elementary occupations Total Female Male Total LFS ( ) Female Male Total SEG (2010) Female Male Total

23 Distribution of sampled graduates by monthly Income in categories Monthly Earning (Rs) SEG, 2010 LFS, Female Male Total Female Male Total up to min. wage* Min. wage and above Total *minimum wage is 6,000 for LFS, For SEG, 2010 is 7,000

24 Objective 1 Estimated results over the three types of job mismatch

25 The level of education-job mismatch by various approaches (%) Datasets Matched Under-educated Over-educated RM method (LFS ) RM method (LFS ) Female Male Total Female Male Total WSA SEG, 2010 JA RM

26 The level of genuine and apparent over-education (%)-SEG, 2010 Education-Job Mismatch WSA JA RM Over-educated (total) Apparent over-educated Genuine over-educated

27 Distribution of respondents by the level of qualification mismatch (%) Matched Under-qualified Over-qualified Female Male Total *based on the weights estimated by PCA approach

28 Marginal and joint distribution of education and qualification match (%) Matched Under-qualified Over-qualified Job Analyst Method (JA) Matched Under-educated Over-educated Worker Self Assessment Method (WSA) Matched Under-educated Over-educated

29 The % Distribution of the Respondents by Reported Field of Study and Job Mismatch Level of Mismatch Female Male Total Irrelevant Slightly relevant Moderately relevant Completely relevant

30 Objective 2 Results over the determinants of job mismatch

31 The Determinants of Education-Job Mismatch Multinomial Logit Model (Relative Risk Ratios) Regressors Worker Self Assessment (WSA) Under/Match Over/Match sex (male=1) ** age (years) ** relative in govt. (yes=1) 0.309** 0.392* political family (yes=1) ** education 0.236* 2.494* full time degree (yes=1) ** annual System (yes=1) * field of study (traditional subjects as ref.) computer admin, marketing, finance law, journalism stat, math, eco * health natural science, engineering ** 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

32 Determinants of Qualification Mismatch-Multinomial Logit Model (Relative Risk Ratios) Regressors Under/Match Over/Match age (years) 1.382* 0.784** political family (yes=1) 2.315* education ** field of study (traditional subjects as ref.) computer admin, marketing, finance 0.274* 0.79 law, journalism 0.168* stat, math, eco 0.29* 0.141* health 0.259** natural science, engineering 0.165* 1.532** full time student (yes=1) 0.50** 1.11 annual System (yes=1) ** occupation (elementary occupation as ref.) manager * professional * associate professional * clerk

33 Determinants of the Field of Study and Job Mismatch- Marginal effects Regressors dy/dx Std. Error sex (male=1) 0.057** education field of study (traditional subjects as ref.) computer 0.138* admin, marketing, finance 0.135* law, journalism statistics,mathematics, Economics 0.122* health 0.133* natural science, Engineering 0.171* full time degree (yes=1) 0.12* occupation (elementary as ref.) manager 0.166* professional 0.274* associate professional 0.205* clerical support workers

34 Objective 3 Results over the impact of job mismatch on graduate s earnings

35 Impact of Education-job Mismatch on Graduate s Earnings SEG, 2010 Regressor Attained Edu. (model 1) WSA-I (model 2) JA-I (model 3) WSA-II (model 4) JA-II (model 5) over-education * * - - under-education Genuine over-educated * * Apparent over-educated * * 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

36 Regressor Attain 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 distance learning * * * * * organization (govt.=1) ** ** ** ** ** occupation (manager as ref.) professional * * * * * assoc. professional * * * * * clerk * * * ** * elementary ** ** ** ** **

37 Impact of Qualification Mismatch on Graduates Earnings SEG, 2010 Regressors Coeff. St. Err. over-qualification * under-qualification 0.155* education 0.102* experience 0.026* 0.01 sex (male=1) 0.102** marital status (married=1) 0.103** foreign diploma (yes=1) 0.194* occupation (manager as ref.) professional * assoc. professional * clerk * elementary * 0.15 tenure (up to 1 year as ref.) 1 to 2 year to 4 year 0.197* more than 4 year 0.292* 0.092

38 Impact of Field of Study and Job Mismatch on Graduates Earnings SEG, 2010 Regressors Coeff. St. Err. weak relevant/irrelevant moderate relevant/irrelevant 0.228* complete relevant/irrelevant 0.203* 0.09 education 0.102* experience 0.029* 0.01 sex (male=1) 0.099** marital status (married=1) 0.118** foreign diploma (yes=1) 0.218* 0.09 occupation (manager as ref.) professional * assoc. professional * clerk * elementary * tenure (up to 1 year as ref.) 1 to 2 year to 4 year 0.216* more than 4 year 0.298* constant 7.735* 0.409

39 Objective 4 Results over non-pecuniary consequences of job mismatch

40 Impact of Job Mismatch on Job Satisfaction Odd ratios by Ordered Logit Model Regressors Attain Field of WSA Qualification Education Study over-education * - - under-education * - - over-qualification * - under-qualification weak relevance/irrel moderate relevance/irrel * complete relevance/irrel * education 0.766* * 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 associate professional clerk 0.126* 0.378* 0.128* 0.204* elementary 0.132* * 0.237* stress due to boss behavior 0.708** 0.608* 0.717** colleague cooperate colleague motivate 1.498* 1.593* 1.464* 1.540* colleague not criticize 1.483* 1.449* 1.483* 1.476*

41 The Effect of Job Mismatch on Turnover Intention Odd ratio by Ordered Logit Model Regressors Education WSA Qualification Field of Study over-education * - - under-education * - - over-qualification under-qualification weak relevance/irrelevant moderate relevance/irrelevant * complete relevance/irrelevant * education 1.509* 1.176** 1.515* 1.800* age 0.966* 0.969** 0.966* 0.946* log(wage) 0.631* * 0.720* occupation (manager as ref.) professional associate professional clerk 7.582* 2.264* 7.687* 3.964* elementary 8.636* 2.575** 9.005* 3.659* stress due to boss (yes=1) colleague cooperate (yes=1) 0.656* 0.689* 0.646* 0.630* colleague motivate (yes=1) colleague not criticize (yes=1)

42 Conclusions One-third of graduates are mismatched either in overeducation 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/overqualification. 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

43 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.

44 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.

45 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.

46 Thank You