1. The impact of overeducation on wages of recent economic sciences graduates An application to the case of the Universidad Nacional de Córdoba (#)   Mariana De Santis(*)(**) María Cecilia Gáname(*)(**) Pedro Esteban Moncarz(*)(**)(***)   (*) Facultad de Ciencias Económicas, Universidad Nacional de Córdoba (**) Centro de Investigaciones en Ciencias Económicas, CIECS (UNC-CONICET) (***) Consejo Nacional de Investigaciones Científicas y Técnicas Instituto de Economía y Finanzas (FCE-UNC), September 11, 2018
(#) This research has benefited from the financial support of the Fondo para la Investigación Científica y Tecnológica (FONCyT) under grant PICT All opinions, as any remaining errors, are the sole responsibility of the authors.

2. Overeducation is the reflection of a (vertical) mismatch in the labor market: an overeducated individual can be defined as an employee with more formal schooling than required by the worker’s occupation (Rubb, 2006). Another mismatch (horizontal) is concerned with the difference between the field of study (knowledge/skills acquired) of the worker and the one required by the job.

3. Why to study overeducation
Why to study overeducation? - Human capital theory, in its crudest form, implies the return to education is not contingent on how the workers' skills are utilized in the labor market (Sloane, 2002) However, after controlling for other differences, the empirical evidence has shown that workers with the same education can be paid differently. - Young people are more likely to experience a mismatch between their formal education and the one required by the job. - There is not a consensus about the reasons for the mismatch, there is one about the consequences in terms of wages: overeducation means a penalty in terms of income.

4. Theoretical explanations of the causes of overeducation - Imperfect information * Overeducation is a temporary situation. * Experience and age inversely correlated. * Job mobility. - Human capital (supply side) * Incomplete measure of the worker’s human capital. * Experience, skills and training compensations. - Assignment theory (demand-side) * Workers are ranked top-down by skills and schooling. * Jobs are ranked top-down by complexity. * Returns to schooling depend on the job (difference in wages is not constant). * Overeducation (undereducation) would increase (reduce) wages as compensating effort levels.

5. How overeducation is measured
How overeducation is measured? - Self declared: the worker declares her/his status comparing her/his level of formal education with the one she/he believes the job requires. - Characteristics of each job (“objective measure”): each job after being systematic characterized is assigned a required level of education, and then that requirement is compared with the level of education of the worker. - So-called “empirical method”: a person is compared to a group of her/his peers using the mean or modal values of formal education, usually measured in years, as point of reference.

6. Empirical approaches to the study of overeducation on income A) ORU model (Duncan and Hoffman, 1981) In a standard Mincerian equation, the education of the person is divided among: over-required (O), required (R) and under-required (U):

7. Empirical approaches to the study of overeducation on income A) ORU model (Duncan and Hoffman, 1981) - 2 and 3 are interpreted relative to workers in the same occupation (requiring the same years of schooling) which are correctly matched, - 1 > 2 > 0: the return to each year of schooling beyond those required is positive but lower than for the required ones, - 3 < 0 and 1 > -3: the return to each year of underschooling is negative, but the absolute value of the penalty is lower than the return to each year of required schooling.

8. Empirical approaches to the study of overeducation on income B) Dummy variable approach (Verdugo and Verdugo, 1989) In a standard Mincerian equation, in addition to the person’s education (Eit), two dummy variables are included:

9. Empirical approaches to the study of overeducation on income B) Dummy variable approach (Verdugo and Verdugo, 1989) - Now the comparison is between workers with the same level of education (equal Eit) but with one of them being overeducated (OVit=1) or undereducated (UNit=1), - 2 < 0: overeducated workers earn less than others with the same level of education that are correctly matched, - 3 > 0: undereducated workers earn more than others with the same level of education that are correctly matched.

10. Empirical approaches to the study of overeducation on income If wages were paid according to the human capital theory, we should have: 1 = 2 = -3 for the ORU model, and 2 = 3 = 0 for the Verdugo-Verdugo model.

11. The related literature
Types O V E R D U C A T I N
Badillo-Amador and Vila (2013)
Allen and Van der Velden (2001)
Mavromaras et al. (2013)
Sellami et al. (2017)
Zhu (2014)
Rub (2006)
Robst (1995) y (2007)
Dekker et al. (2002)
Sicherman and Galor (1990)
Sicherman (1991)
Carroll and Tani (2013)
Hartog (2000)
Deželan and Hafner (2014)
Wages
Vulnerable groups
Different countries
Job satisfaction
Time
Reasons of acceptance
Job mobility
Explanations

12. The related literature - Educational and skill mismatches are weakly related (Badillo-Amador and Villa 2013). - Skill and/or education mismatches have negative impacts on wages. - The negative effect of being overeducated and overskilled is larger than when the person experiences just one of those states. - The negative effect of overeducation on wages is stronger than the premium of being undereducated (Allen and van der Velden, 2001). - The experience of overeducated workers is rewarded at lower rates than the experience of undereducated workers. - The percentage of overeducated individuals decreases as the range of age increases, Dekker et al. (2002).

13. The related literature - Individuals may accept jobs with low returns to education if the odds of being upgraded is high (Sicherman and Galor, 1990) . Overeducated individuals have greater probability to obtain promotion than those of are well matched (Sicherman, 1991). - A transitional phenomenon for young graduates. In terms of wages, overeducation penalizes more older overeducated graduates than young ones (Carroll and Tani, 2013). - Overeducation is more frequent in women that men (Groot et al., 2000). Men are more likely to be mismatched than women; there are gender differences across the reasons for accepting to be mismatched (Robst, 2007). On average, mismatched males earn 5.25% more than mismatched females (Zhu, 2014). - Only well-matched graduate men who change status to a job where they are both overeducated and overskilled suffer an approximate 5.9% wage penalty (Mavromaras et al., 2013).

14. Data and empirical specification - The dataset was specially collected for this project. - The population under study are the graduates of the FCE-UNC. - Every year, the FCE celebrates four graduation ceremonies, in which approximately 700 students graduate. Our sample covers about half of that population for the year 2016 (third and fourth graduations) and a quarter of 2017 (third graduation). - Each graduate was interviewed at the time of registering for the graduation, and then four additional times, one every three months, on aspects related to their job performance, as well on some piece of information about personal characteristics. - The collection of a fourth sample (fourth graduation of 2017) has just finished. A period of 28 months collecting data!!!!! (May 2016-August 2018).

15. Data and empirical specification

16. Data and empirical specification

17. Data and empirical specification Definition of overeducation: a person is defined as overeducated if she/he declares that her/his job requires a tertiary non-university degree or less. Additionally, overeducated graduates are subdivided into: - Moderately overeducated: whose job requires a tertiary non-university degree. - Severely overeducated: whose job does not require a tertiary non-university nor a university degree.

18. Data and empirical specification Horizontal match
Data and empirical specification Horizontal match. We make use of two questions: - The first question asks the graduates to rank, in a range from 1 (the worst match) to 10 (the best match), the degree of correspondence between the knowledge learnt during their undergraduate studies at the FCE-UNC and the ones required by the job. - The second question asks the person to rank to what extent she/he uses in the job the skills acquired during their undergraduate studies at the FCE-UNC. The first case we refer to it as knowledge matching, while the second one we refer to it as skills matching.

19. Data and empirical specification

20. Data and empirical specification

21. Data and empirical specification

22. Data and empirical specification w: monthly graduate’s income in main occupation. OV=1 if person i is overeducated (0 otherwise). HSM: level of skill matching (ranges from 1 to 10). HSK: level of knowledge matching (ranges from 1 to 10). X: set of other explanatory variables. We work only with those observations the graduates declared to be salaried employees. This leaves us with 1282 observations corresponding to 403 graduates.

23. Data and empirical specification Measurement of income: a) Defined in an ordinal manner in the range of 1 to 24, with each value corresponding to a given income range (1=less than $3000, 24=more than $25000, in between we use a $1000 interval). b) For each individual, we assign him/her an income equal to the middle point of the interval he/she declared. c) Instead of allocating a particular income, the interval declared by the respondent is used. For b) and c), and because of the increase in prices that occurred during the collection of the data, nominal values were deflated using the consumer price index, which as a side result means an increase in the possible values taken by our dependent variable, rendering it almost continuous.

24. Data and empirical specification Estimators: - Pool OLS for a) and b), and Pool IR for c). - Random effects for a) and b), and Random IR for c). - Pool OLS, Random and Fixed effects for b). Advantage of panel data estimators: controlling for unobserved heterogeneity might be important if individuals with lower innate ability need more education to attain a job for which they are formally overeducated  the coefficient for the overeducation status should be lower in absolute value (since the unobserved ability and the probability of being overeducated are negatively correlated).

25. Data and empirical specification Other control variables: - Gender: female; male. - Civil status: single; non-single. - Working experience while student: no; yes. - Working experience in econ. sciences while student: no; yes. - Formal labor: no; yes. - Firm size: 1 to 5; 6 to 20; 21 to 50; more than Tenure: in years. - Working hours: 1 to 10; 11 to 20; 21 to 30; 31 to 40; more than Training activities: no; yes. - Surveys: Base, Follow-up 1; Follow-up 2; Follow-up 3; Follow-up 4.

26. Results

27. Results

28. Summary and conclusions - By the human capital theory, wages are determined by the worker productivity  not contingent on how the workers skills are utilized in the labor market. - However, jobs are quite different in many characteristics that impact on labor productivity, and so in pay. - Empirical studies have found that a substantial proportion of young people experience a mismatch between their educational background and the requirements of the job. - While the empirical analysis has yet not so far reached a consensus over which of the different explanations is more likely to be behind the phenomenon, there is instead a clear message on the consequences of overeducation in terms of wages: overeducation means a penalty in terms of income.

29. Summary and conclusions For the FCE-UNC: - Overeducated graduates suffer a wage penalty when compared to those working in a job requiring a university degree. - The result is robust to different specifications and to the use of alternative estimators. - While the difference is not statistically significant, the penalty for those severely overeducated is larger than for those with a mild level of overeducation. - Having working experience while at the university helps to reduce the cost of overeducation. - The overall impact found for the whole sample appears to be driven by the impact of overeducation on female graduates.

30. Summary and conclusions To-do list: - Measurement error in OV (Sellami et al., 2017).

31. Summary and conclusions To-do list: - Estimate a distribution of the impact of overeducation instead of an average effect (Zhu, 2014).