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Miroslav Beblavy - Centre for European Policy Studies

Comparing the educational requirements in vacancies and the educational levels of jobholders for 131 occupations in tHE CZECH REPUBLIC. Miroslav Beblavy - Centre for European Policy Studies Anna-Elisabeth Thum - Centre for European Policy Studies

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Miroslav Beblavy - Centre for European Policy Studies

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  1. 2 Comparing the educational requirements in vacancies and the educational levels of jobholders for 131 occupations in tHECZECH REPUBLIC Miroslav Beblavy - Centre for European Policy Studies Anna-Elisabeth Thum - Centre for European Policy Studies Kea Tijdens - University of Amsterdam, Amsterdam Institute for Advanced Labour Studies (AIAS) Ingrid project is financed by the the European Commission 7th Framework Program

  2. Introduction – skills mismatch • Large body of literature, main institutions: PIAAC/OECD and CEDEFOP • “In its most general interpretation, mismatch refers to a mis-alignment between the composition of labour demand and labour supply.” • >> the supply and demand side of the labour marketshouldbecompared (aggregate/individual level) • Commonfeatures of current studies: • most studies address supply side and use micro-level data, lacking data from demand side • sometimes implicit understanding of mismatch = driven by supply/demandheterogeneity in the labour market • differences in assumptions = differences in mismatch definitions • many questions stillleftunanswered • the extent to which actual skills and skill requirements vary within these broad categories • the extent to which individuals who are “mismatched” in terms of their education are sorted according to this heterogeneity on both the supply and the demand side of the labour market • Skeptics question the extent to which educational mismatches are a real problem =˃the focus of the debate has shifted increasingly from educational mismatches to skill mismatches (undervs. overeducated workers example) 2

  3. Methodology - Data • our study aims at comparing vacancy and jobholder data from distinct data sources at a detailed level of occupations • we merged data of the EURES vacancies database and the WageIndicator web survey of jobholders • Country selection: Czech Republic • We selected a country which • provide a large number of their vacancies to EURES & • has large number of observations in WageIndicator database • Both data sources use ILO’s occupational classification ISCO • vacancy data coded at ISCO-88 4-digit level • jobholders data codedISCO-08 4-digit level • we recoded the ISCO-88 vacancies into ISCO-08, using ILO’s correspondence tables 3

  4. Methodology - Jobholder Data • WageIndicatorweb-survey • self-administered, volunteer, multi-country WageIndicator web-survey posted continuously at all national WageIndicator websites (started in the Netherlands 2001, today in more than 70 countries @ 5 continents) • WageIndicator websites • provide job-related content =˃ labor law, minimum wage information, free Salary Check, high web traffic, almost 20 million visitors (2012) • consulted by employees, self-employed persons, students, job seekers • visitors invited to complete web survey with lottery prize =˃ 1- 5% do so • Data selection • CZ data merged from JANUARY 2009 to OCTOBER 2013 (to get sufficient observations) • restricted to jobholders with valid value for education and occupation and at most 10 years of service 4

  5. Methodology – Vacancy Data • EURES website: JOB VACANCY DATA • platform with harmonized job vacancy data with the aim to improve labour mobility in Europe • website is linked to national public employment services, which provide a total or partial selection of job vacancies to the EURES platform • codes of the job vacancies (e.g. ISCO codes) automatically mapped to EURES code tables =˃ data harmonized to a certain extent • holds approx. 1.5 million vacancy adds in 31 EU and EEA countries (Kurekova et al. 2012) • covers 30-40 % of overall job vacancies in the concerned countrieson avg. (Ackers 2012) • monthly visits:3.6 million and increasing(Ackers 2012; GHK consulting and EPEC 2009) • sample selection issues: online data that entail some concerns about selection • Country selection: Czech Republic • Czech public employment service ‘Správaslužebzaměstnanosti’ (Employment Services Administration) selects vacancies on the regional level across 15 regions. • ratioof vacancies not uploaded on the EURES database is negligible 5

  6. Methodology – final database • The units of analysis • both databases are aggregated into a database of occupational units • EURES vacancy database =>> 174 occupational units, number of observations per unit ranges from 1 vacancy to 925 vacancies • WageIndicatorjobholders database =>> 165 occupational units, number of observations per unit ranges from 0 to 1987 jobholders • Notes • occupational units with less than 5 jobholder or vacancy observations were excluded • occupational units for the armed forces were excluded • The merged and final database • 131 overlapping occupational units • 12,744 vacancies • 8,304 jobholders with 0-10 years experience 6

  7. Methodology – educational level • Educational coding >> ISCED 1997 classification • VACANCY DATABASE: we are not able to trace how individual employment agencies coded the required educational levels of the vacancies • JOBHOLDERS DATABASE: respondents are asked to tick their highest educational category from a list of national educational categories • Notes • Both datasources have category 0 ‘None Specified’ in Eures and 0 ‘No education’ in WageIndicator • Merged with category 1 because very few cases in this category (Eures 0.5% of the vacancies, WageIndicator 0.2% of the jobholders) 7

  8. Five research objectives • Does the occupational distribution of vacancies reflects that of the jobholders? • Are educational levels of vacancies and jobholders similar across occupations? • Does a high demand occurs in occupations with low skill levels? • Is the educational level of vacancies more condensed for occupations at higher skill levels? • Which occupations have the “widest” and “narrowest” educational requirements in vacancies? 8

  9. H1: Do vacancies mirror jobs? • We assume that the occupational distribution of the vacancies reflects that of the jobholders • Yes => for 99 of the 131 occupational units the shares of vacancies and jobholders deviate less than 1 percentage point of each other >> indicating that no major mismatch in the labour market can be noticed • No => the rank correlation (using average ranking) for the two occupational distributions is low (0.17) >> indicating major mismatches • Hence, for a minority of occupational units a major supply and demand mismatch exists (see table next slide) 9

  10. H2: Are educational levels similar? • We assume that educational requirements of vacancies and educational attainment of jobholders are similar across all occupational units • No => mean educational level of occupational units is higher for jobholders than for vacancies for all but 8 of 131 occupations • No => SD of the educational level of occupations is larger for vacancies than for jobholders in 79 of 131 occupations • Yes => maximum educational level is similar for vacancies and jobholders in 77 occupations & no occupation has higher maximum requirements for vacancies than for jobholders • No => maximum educational level is higher for jobholders than for vacancies for 55 of 131 occupations, of which • in 13 occupations 1 step above the level of vacancies • in 34 occupations 2 levels higher • in 8 occupations even 3levels higher • Overeducation is frequent & the incidence of overeducation is higher for occupations with a lower mean educational requirements (r=.71) 10

  11. H3 Which occuations with high demand? • Does a high demand occurs in occupations with low skill levels? • No => Skilled and high-skilled occupations have higher demand in CZ 11

  12. H4 Condensed educational requirements • We assume that the educational levels of vacancies are more condensed for occupations at higher skill levels (ISCO skilled and highly skilled), checking for ISCO 1-digit level – shown in Table 3 • No => the upper ISCO codes are not more compressed, rather the opposite • No => the highest dispersal is for the three top ISCO codes, while the medium-skilled occupations show a high level of diversity • Note: there is an upper and lower boundary for the educational leevels (1 and 5), which could artificially compress occupations with most and least average education requirements • However, if that were the key factor, then the mid-level occupations would have the largest dispersal, with upper and lower having the lowest. That is not the case though. 12

  13. H4 cont’d: SD for educational levels of vacancies and jobholders at ISCO 1-digit 13

  14. H4 cont’d: Vacancies with condensed edu. levels • Table 4: Distribution of ISCO 4-digit occupations on the list of most dispersed and condensed compared to the general sample • Both high-skilled and medium-skilled are represented in the most condensed list in a rough proportion to the general sample • The high-skilled are highly overrepresented on the list of occupations with highest dispersal • There are no low-skilled occupations in either group, but they also have very low representation in our general sample 14

  15. H5 “Widest” and “narrowest” education levels • Which occupations have the “widest” and “narrowest” educational requirements in vacancies? • >> We compile a list with the most and least condensed education requirements • Most condensed occupations: • For the most condensed occupation, we select 1/3 of the sample in both EURES and WI0-10 (40 occupations in each) with the lowest standard deviation • Then select only those occupation which overlap in both groups • The two datasets were compiled from absolutely different sources and for different purposes, and such an occupation has low dispersal of educational requirements both in a vacancy database and in a database of actual jobholders • This makes the list robust to classification mistakes and biases and other problems • There are 20 such occupations. • We do the same for the occupations with most dispersed education requirements, which yields 15 occupations (TABLE 4). • To see whether certain types of occupations are overrepresented, we also compare the distribution of the 4-digit ISCO least/most condensed occupations to the general sample. 15

  16. H5 cont’d: Occupations with condensed/dispersed educational requirements (Table 4) 16

  17. H5 cont’d: Conclusions - 1 • 4 out of 15 most dispersed occupations are from the “not elsewhere classified” category (miscellaneous subcategory used in all categories for occupations that do not fit elsewhere + all service occupations in this case) • 2 law enforcement occupations are present in the list of most dispersed, but NOT on the list of most condensed. These are “environmental and occupational health inspectors and associates” and “police inspectors and detectives”. • The level of skill does not have an obvious influence. • Manufacturing vs. services does not appear as an obvious distinguishing factor • The clear differences are between professionals (ISCO2) and managers (ISCO1). • 4 managerial occupations are on the most dispersed list, but none on the least dispersed list. • The opposite applies to professionals, which have 6 representatives in the most condensed category, but only 1 among the highly dispersed • The third highly skilled category ISCO 3 (Technicians and associate professionals) has 6 representatives in the category of occupations with most dispersed educational requirements, but only 1 in the least dispersed. 17

  18. H5 cont’d: Conclusions -- 2 • Skilled and semi-skilled blue collar workers from categories ISCO 7 and 8 (skilled manual workers, plant and machine operators, and assemblers) have 12 occupations on the least dispersed, but none on the most dispersed list. • For skilled and semi-skilled white-collar ISCO 5 workers (Service and sales workers), the picture is much complicated since it has 3 occupations on the most dispersed list, but also 2 on the most condensed one • Whether analyzed at ISCO 1-digit or 4-digit level, level of skill does not appear to be important in determining how condensed or dispersed are the educational requirements for a given occupation • If anything, high-skilled occupations dominate occupations with high dispersal of educational requirements 18

  19. H5 final: Which occupation has the “widest” and “narrowest” educational requirements? • Patterns in the data: • Do not appear to matter much: • Skill level • blue collar vs. white collar • sector of the economy • distinction between public administration and the rest • Clear distinctions in: • professionals and blue-collar skilled and semi-skilled workers on one hand and managers on the other. • The former live in the world where both vacancies and actual jobholders indicate clear expectation about the required level of education • Whereas for managers, it matters much less. • Service workers are much more evenly split between the two 19

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