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Abstract
This paper analyzes the impact of university quality, family background and mismatch on the wages of young Italian graduates. An empirical analysis is undertaken using a representative sample of graduates merged with a dataset containing information on the characteristics of universities. By utilizing quantile regression techniques, some evidence of the impact of factors that may explain the differentials of earnings among individuals with a similar educational attainment is reported. Significant differences in the impact of explanatory variables across the quantiles of wage distribution are found and the role played by educational quality, family background and educational mismatch is stressed. These findings are consistent with a polarized view of the labor market where some educated workers earn a wage premium deriving from their specific educational choices, while others are confined to occupations at the bottom end of the wage distribution.
Acknowledgements
We thank two anonymous referees for their valuable suggestions and comments. All errors remain the responsibility of the authors. The usual disclaimers apply.
Notes
1. For a survey and some interesting insights, see Autor, Katz, and Kearney (Citation2008).
2. Early works investigating educational mismatch do not discriminate between vertical mismatch (overeducation and undereducation) and horizontal mismatch (overskilling). Instead, more recent papers tend to separate the two phenomena. However, this discrimination seems to play a minor role (Mavromaras et al. Citation2010). As we discuss in Section 4, in this paper, we consider as ‘mismatched’ those graduates who are in job positions that do not require their degree qualification. Hence, we do not distinguish between horizontal mismatch and vertical mismatch and – since we consider only graduates – mismatch is defined in the sense of overeducation only.
3. See Brewer and Ehrenberg (Citation1996) for a detailed discussion.
4. Among others, see Bratti, Checchi, and de Blasio (Citation2008) and Naticchioni, Ricci, and Rustichelli (Citation2010). These works relate wage inequality to skill-bias changes finding mixed results.
5. These series are computed using individual data from several waves of the SHIW of the Bank of Italy.
6. An insightful description of quantile regression techniques is given in Koenker and Hallock (Citation2001).
7. In particular, Budria and Moro-Egido (Citation2008) argue that overall the impact of the correction for sample selection is not of major importance in Spanish studies. On the contrary, Ordine and Rose (Citation2009) using Italian data find important bias when estimating the determinants of overeducation without addressing sample selection issues.
8. For a complete description, see Poti and Reale (Citation2005).
9. A review of this literature is presented in McGuinness (Citation2006, 396–399).
10. State and regional transfers per student to high schools are almost identical across Italian regions. A description of the Italian school funding system is presented in Granello (Citation2010).
11. We point out that the presented results remain unchanged if we use multi-level dummy variables for parents' education instead of a single dummy indicating the university degree qualification for at least one parent. The presented specification simplifies the illustration of the results.
12. Since the pattern of coefficients is almost identical across the two specifications reported in , in , only estimates obtained by considering all universities are plotted.
13. In order to obtain a readable picture, in , we do not report means for all observable characteristics. However, we replicate the analysis for almost all variables in our sample. For variables that have not been reported, the means of missing values for ‘Age’ are almost identical across different groups. The same applies to .