| | USA | dataset from the US National Science Foundation (NSF 2013) | Probit regression | Larger earnings penalties for mismatch among the self-employed and no diminution in job satisfaction. |
| | 14 countries | REFLEX data set, which includes information on early career outcomes of school leavers graduating ISCED 5 in 1999/2000. The survey was carried out in 2005 among higher graduates | Nonparametric kernel methods | A negative effect of the education-job mismatch on wages in most of the countries. |
| | Spain | The 2006 Spanish Wage Structure Survey | Mincerian wage specification | The R on human capital and the real hourly wage may be quantitatively influenced by educational mismatch. |
| | 27 countries | European Social Survey Round 5 data | Mixed effect logit | 1) industries form two clusters: low OvEdu–low share of below-tertiary occupations, and high OvEdu–high share of below-tertiary occupations; 2) industries form a continuous cloud along a positively-sloped line; and 3) industries appear along a horizontal line, suggesting no relationship between the two variables. |
| | Egypt and Tunisia | Survey of the European Training Foundation (ETF) between 2006 and 2007 in Albania, Egypt, Moldavia and Tunisia on a sample of approximately 1000 non-migrants and 1000 returnees in each country | R migrant’s job level upon R and compute the mean and median | Tunisian R migrants are more prone to be OvEdu than Egyptian R. |
| | 17 European countries | PIAAC survey (2013) | Conceptualisation and measurement of occupational mismatch | 1) a small percentage of mismatched individuals are mismatched with respect to both education and skill, whereas the majority are mismatched with respect to either education or skill only; 2) negative correlation between the incidence of education and skill mismatch. |