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An Age–Period–Cohort Approach to the Incidence and Evolution of Overeducation and Skills Mismatch

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Abstract

This paper provides new evidence on the changes in the level and persistence of occupational mismatch across countries by investigating whether differences among generations (cohorts) are at the core of these changes. Using data from the 1994–1998 International Adult Literacy Survey, the 2003–2008 Adult Literacy and Life Skills Survey, and the 2011–2012 OECD Survey of Adult Skills, we estimate an age–period–cohort model in three European countries to examine the extent to which younger cohorts face a greater (smaller) risk of being occupationally mismatched in their jobs than their older counterparts. Two definitions of occupational mismatch are used, focusing on both educational attainment and literacy skills. Results indicate that countries present different patterns in the evolution of occupational mismatch from older to younger generations according to the definition employed (overeducation or skills mismatch). Different macro-economic and educational contexts may be at the core of these results, suggesting that tailored policy responses are desirable for effectively addressing the occupational mismatch problem.

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Fig. 1

Source: Own calculations pooling IALS, ALL, and PIAAC data

Fig. 2

Source: own calculation using PIAAC, ALL, and IALS data

Fig. 3

Source: own calculation using PIAAC, ALL, and IALS data

Fig. 4

Source: own calculation using PIAAC, ALL, and IALS data

Fig. 5

Source: own calculation using PIAAC, ALL, and IALS data

Fig. 6

Source: own calculation using PIAAC, ALL, and IALS data

Fig. 7

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Notes

  1. For the European Union, see https://ec.europa.eu/education/policies/european-policy-cooperation/et2020-framework_en.

    For Norway, see https://www.regjeringen.no/en/topics/european-policy/areas-cooperation/Education/id686144/.

  2. An overeducated worker is an individual whose educational attainment exceeds the educational requirements of their workplace, whereas an overskilled worker is an individual whose skills and competences exceed those required to perform their job.

  3. Further information about these Surveys can be found at:

    https://nces.ed.gov/statprog/handbook/pdf/ials.pdf (for IALS Survey).

    https://nces.ed.gov/surveys/all/ (for ALL Survey).

    https://www.oecd.org/skills/piaac/ (for PIAAC Survey).

  4. The USA and Canada also participated in all of the surveys, but we exclude them as the variable age is not reported as continuous in the original dataset, which does not allow us to build the right cohorts.

  5. The Netherlands represents a peculiar situation as in some features, it resembles the Scandinavian regime.

  6. ISCO 1-digit occupations have been used for the estimation of occupational mismatch variables. The ISCO 1-digit occupation divides jobs into ten major groups. Although a 2-digit or larger ISCO classification would have been preferred to reduce the heterogeneity in the entry requirements to the occupations included, this is not possible as ISCO 2-digit is only available in the PIAAC sample. Nonetheless, we conduct two checks using only PIAAC data, where the two classifications are available. First, we build two measures of overeducation using both the ISCO 1-digit and ISCO 2-digit classifications, and we estimate the correlation between these two, which is as high as 0.98. Second, we check how many individuals would have been classified as overeducated using one measure and not the other. In the PIAAC sample, only 6% of the working individuals do not overlap when using the two different measures. We believe that these two checks indicate that using the ISCO 1-digit or ISCO 2-digit categories to build the measure of overeducation is equivalent in this paper. Moreover, Mavromaras et al. (2010), McGuinness et al. (2018b) or Bar-Haim et al. (2019) also rely on ISCO 1-digit to calculate their overeducation variable providing results in line with other empirical evidence in this field, which reassures our approach. Nevertheless, it should be kept in mind that this does not refute the argument that there might still be a problem of heterogeneous requirements among the ten occupations used, being this issue probably more relevant in some countries than others. More information about using ISCO classifications and national features can be found at https://www.ilo.org/public/english/bureau/stat/isco/docs/publication08.pdf (last accessed September 2020).

  7. Note that to guarantee sufficient variability to compute the indicators, the identification of matched and mismatched individuals is performed for each measure only when the number of sample observations on which the indicator is based is at least 20. This minimum threshold is a standard procedure in European surveys such as the European Union Statistics on Income and Living Conditions (EUSILC) and the Labour Force Survey (LFS).

  8. Further details about the construction of this variable are reported in the Appendix (Tables 3 and 4).

  9. The working sample is composed of currently working individuals. This may generate sample selection since being mismatched or unemployed may not be independent decisions. However, many of the papers studying overeducation simply focus on the working individuals and do not consider these sample selection issues (some examples are Bar-Haim et al. 2019; McGuinness et al. 2018b; McGuinness 2006; Mavromaras et al. 2010). We also follow this approach.

  10. The corresponding nonlinear age and period effects are also simultaneously estimated.

  11. Estimates are done in STATA using the APCD command.

  12. Notice that the APCD method identifies deviations from the linear trends of age, period, and cohort: it can identify specific cohorts defined by higher/lower overeducation rates, but it cannot identify the actual linear trend.

  13. OECD (2013), Education at a Glance 2013: OECD Indicators, OECD Publishing, Paris, https://dx.doi.org/10.1787/eag2013-en.

  14. After having estimated the APCD, they implement post-estimation regression of the cohort APCD coefficients found in the model and regress them on variables that may explain those effects. They can do this because their analysis is based on 9 countries, for 12 cohorts, a much larger sample than the one available in our case which consisted of 3 countries, for 8 cohorts, making this analysis unfeasible.

  15. New waves of PIAAC data with detailed information of occupations at ISCO 2-digit level will also allow having a more precise measure of occupational mismatch overcoming the current limitation of ISCO 1-digit being the only classification available with the existing data.

  16. https://ec.europa.eu/assets/eac/education/policy/strategic-framework/.

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Authors and Affiliations

  1. Melbourne Institute: Applied Economic and Social Research, Faculty of Business and Economic, The University of Melbourne, Melbourne, Australia

    Esperanza Vera-Toscano

  2. Modelling, Indicators and Impact Evaluation Competence Centre On Microeconomic Evaluation (CC-ME), DG Joint Research Centre, European Commission, Ispra, Italy

    Elena C. Meroni

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  1. Esperanza Vera-Toscano
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  2. Elena C. Meroni
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Appendices

Appendix

See Tables 3 ,

Table 3 List of questions asked in the IALS, ALL, and PIAAC regarding skills used at work
Full size table
Table 4 Possible responses regarding the frequency of skills used at work as recorded in the IALS, ALL, and PIAAC and the proposed reclassification for the skills mismatch indicator built
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4 and

Table 5 Descriptive statistics of the working sample
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5

Details on how to build the skills mismatch indicator based on skill use

The skills mismatch measure is built following Krahn and Lowe (1998). In their paper, they build “workplace requirement indices” using the questions on how often respondents write or read documents of different types. Then, they build a measure of Literacy fit and mismatch in the workplace, combining the level of literacy skills and the literacy requirement in the workplace. They define a worker as having a literacy surplus if he has high literacy skills and low literacy requirements. We apply the same methodology to our data. However, since the surveys did not ask the exact same questions for workplace literacy requirements, we had to adjust the methodology to make it comparable across the three surveys.

First, we considered only common questions on workplace literacy requirements s to build the workplace requirement indices (see Table 3 below):

Second, we needed to adjust the scales as the list of possible answers regarding the frequency of the different tasks (workplace literacy requirements) was not the same in the three surveys. For example, as shown in Table 4 for low-frequency use in the PIAAC, respondents could choose between Never or Less than once per month, while in the IALS there was a single choice: Rarely or never. Therefore, we grouped some of the items as shown in Table 4.

In our indicator of skills mismatch, there are 3 possible frequencies of skills used at work for each of the 9 questions: 1. Rarely or never, 2. Less than once per week, 3. At least once per week/daily. Higher values indicate more frequent reading/writing requirements.

The average of these three-category “workplace literacy requirements” across the questions is taken for each individual (ranging from 1 to 3) and is compared to the 3 literacy levels, built as explained in the next paragraph.

The literacy levels are built based on the distribution of skills in the working population. The population is divided into three groups according to where their level of skill falls compared to the 33rd and 66th percentile of the skill distribution in the population (Level 1: individuals whose skills level, measured by the first plausible value—variable PVLIT1—is below the 33rd percentile of the distribution of skills in the working population in the country; Level 2: between the 33rd and 66th percentile; Level 3: above the 66th percentile). A comparison of workplace literacy requirement to the skill level tells us whether an individual is overskilled or not: individuals whose literacy level is higher than the “workplace literacy requirement” category are considered to be overskilled.

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Vera-Toscano, E., Meroni, E.C. An Age–Period–Cohort Approach to the Incidence and Evolution of Overeducation and Skills Mismatch. Soc Indic Res 153, 711–740 (2021). https://doi.org/10.1007/s11205-020-02514-5

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