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Are Educational Mismatches Responsible for the ‘Inequality Increasing Effect’ of Education?

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Abstract

This paper asks whether educational mismatches can account for the positive association between education and wage inequality found in the data. We use two different data sources, the European Community Household Panel and the Portuguese Labour Force Survey, and consider several types of mismatch, including overqualification, underqualification and skills mismatch. We test our hypothesis using two different measurement methods, the ‘statistical’ and the ‘subjective’ approach. The results are robust to the different choices and unambiguously show that the positive effect of education on wage inequality is not due to the prevalence of educational mismatches in the labour market.

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Notes

  1. For evidence on the social and economic benefits of education see, for example, Ashenfelter and Rouse (2000), Wolfe and Haveman (2001) and Dolton et al. (2009).

  2. Through the paper we abuse language somewhat and will refer to these workers as workers who ‘lack necessary skills’. We are aware, however, that there might be individuals who have not had formal education and training for unskilled jobs but who have acquired the necessary background through other sources, including peer observation, learning by doing and general work experience. Although these channels are typically less relevant, they might be important for a small fraction of uneducated individuals working in low level jobs. As most other measures of mismatch, a limitation of our definition is that it focuses on formal education and training and disregards other sources of skills acquisition.

  3. In Portugal, only 27.6% of the adult population (25–64 years old) has completed upper secondary education, while in Europe as a whole (EU-25) this proportion rises to 69.7%. Similarly, training participation in Portugal is 3.8%, against 10.1% in EU-25 (Eurostat 2007).

  4. Using the mean rather than the modal value produced only small changes in the estimates. The results are available upon request.

  5. This exclusion restriction affected 3 occupations and 0.1% of the workers in the initial sample.

  6. The estimates for the full set of controls are available upon request.

  7. Through the paper we refer to the coefficients reported in the tables as ‘wage effects’ or ‘wage differentials’. To be precise, however, the percentage wage difference is given by eβ−1, rather than by β itself, especially when the estimates are large. We do not perform this transformation to facilitate the correspondence between the tables and the text. .

  8. Following a human capital interpretation of the mismatch phenomenon, some authors have suggested that workers may accept mismatched work in exchange of training, to compensate for low tenure and experience, or to access higher level occupations (Sicherman 1991; Groot 1996; Sloane et al. 1999). The full set of controls used in our earnings equations is aimed to remove the impact of these and other variables from the mismatch effect. It may be argued, however, that most of these covariates are endogenous and that a more parsimonious specification would capture the ‘true’ penalty of mismatch more appropriately. Thus, for example, we may not include controls for training. As the acceptance of mismatched work may allow some individuals to participate in training activities that later on are rewarded in the labour market, we could interpret these wage gains as a return to mismatch rather than a return to training. A similar argument applies to other variables, such as occupation and tenure. Following this reasoning, the estimates reported in “Appendix 2” are obtained dropping from vector X i all the controls except experience.

  9. The more differentiated view provided in Panel 3 comes at the cost of reduced cell size in some groups. Thus, for example, the number of overqualified workers drops from 1,047 in the total sample to 231 when we consider only the group of workers with university education, and a more reduced group results when we consider skills mismatches and strong mismatch among university graduates. Even though the interaction coefficients reported in Panel 3 exhibit moderate standard errors and are not erratic across quantiles, the reduced cell size of specific groups in a quantile regression framework recommend us to interpret the results with some caution.

  10. Across studies, the return to surplus, required and deficit schooling range from 3 to 5%, from 5 to 11%, and from −2 to −6%, respectively.

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Acknowledgments

I thank an anonimous referee for a large set of remarks and interesting suggestions. I also thank Ana Moro-Egido, Vítor Sousa and Carlospaki Korre for their comments in the earlier stages of the paper. The financial support from the Madeira Electriciy Company, the Spanish Ministry of Education through grant SEJ2006-11067 and the Junta de Andalucía through grant P07-SEJ-03261 is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Economics, University of Madeira, Rua Penteada 9000-390, Funchal, Portugal

    Santiago Budría

  2. CEEAplA, Ponta Delgada, Portugal

    Santiago Budría

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Correspondence to Santiago Budría.

Appendices

Appendix 1

1.1 Definition of Variables

Net hourly wage: ECHP and PLFS: monthly net salary in the main job (in euros) divided by four times the weekly hours worked in the main job.

Education: Maximum level of completed schooling. ECHP: three categories based on the ISCED-97 classification: less than upper secondary, upper secondary, and tertiary education. PLFS: ten categories, each of them paired with the corresponding years of schooling.

Training: Dummy variable. ECHP: activated if the employer provided training to the worker during the previous year. PLFS: activated if the worker has ever participated in a training activity.

Experience: ECHP and PLFS: age minus age of first job.

Tenure: ECHP and PLFS: difference between the year of the survey and the year of the start of the current job. Three categories were constructed: from 1 to 4 years, from 5 to 9 years, and 10 years or more.

Permanent contract: PLFS: dummy variable. Takes the value 1 if the individual has a permanent contract, zero otherwise.

Single: ECHP and PLFS: dummy that takes the value 1 if the individual is single (including widow and divorced), zero otherwise (married or living in a couple).

Immigrant: ECHP and PLFS: dummy activated if the individual was born in a foreign country.

Services: ECHP: dummy that takes the value 1 if the individual works in the services sector, zero if he works in the industry sector.

Firm size: ECHP and PLFS: decomposed into four categories, from 1 to 19 employees, from 20 to 99 employees, from 100 to 499 employees, and 500 employees or more.

Bad health: ECHP: individuals report their health status using a scale that ranges from 1 (very good) to 5 (very bad). The dummy ‘bad health’ takes value one if the answer is 4 or 5.

Past unemployment: ECHP: dummy variable, activated in case of unemployment experience before current job.

Occupation: ISCO-88 classification disaggregated at the 2-digit level up to 25 occupations (ECHP) and National Classification of Occupations disaggregated at the 2-digit level up to 29 occupations (PLFS). In Tables 1 and 3 this variable has been aggregated into 9 broader categories.

Appendix 2

2.1 Estimates with a Restricted Set of Controls

See Tables 6 and 7 below.

Table 6 Returns to schooling and mismatch effects—ECHP, resctricted set of controls
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Table 7 Returns to schooling and mismatch effects—PLFS, restricted set of controls
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Budría, S. Are Educational Mismatches Responsible for the ‘Inequality Increasing Effect’ of Education?. Soc Indic Res 102, 409–437 (2011). https://doi.org/10.1007/s11205-010-9675-7

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