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Published in: Quality & Quantity 2/2016

14-02-2015

Heterogeneous economic returns to higher education: evidence from Italy

Authors: P. G. Lovaglio, S. Verzillo

Published in: Quality & Quantity | Issue 2/2016

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Abstract

This paper uses official Italian micro data and different methods to estimate, in the framework of potential outcomes, the marginal return to college education allowing for heterogeneous returns and for self-selection into higher education. Specifically, the paper is focused on the estimation of heterogeneity of average treatment effect (ATE) on a cohort of college and high school graduates using the 2008 survey on household, income and wealth of the Bank of Italy. Methodologically, this study was carried out by using both propensity-score-based (PS-based) methods and a new approach based on marginal treatment effects (MTE), recently proposed by Heckman and his associates as a useful strategy when the ignorability assumption may be violated. In the PS-based approach, heterogeneous treatment effects are estimated in three different manners: the traditional stratification approach (propensity score strata), the regression adjustment within propensity score strata and, finally, a non-parametric smoothing approach. In the MTE approach, the treatment effect heterogeneity across individuals is estimated in a parametric as well as a semi-parametric strategy. Our empirical analysis shows that the estimated heterogeneity is substantial: following MTE based results (quite representative of other methods) the return to college graduation for a randomly selected individual varies from as high as 20 % (for persons who would add one fifth of wage from graduating college) to as low as −22 % (for persons who would lose from college graduation), suggesting that returns are higher for individuals more likely to attend college. Furthermore, the results of different methods show very low (point) estimates of ATE: average college returns vary from 3.5 % by the PS-smoothing method to 1.8 % by the parametric MTE method, which also leads a greater treatment effect on treated (5.5 %), a moderate, but significant sorting gain and a negligible selection bias.

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Appendix
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Footnotes
1
A Mincerian equation consists in estimating a semi-logarithmic regression, in which one regresses the logarithm of income over the years of study, age or experience in the labour market. The estimated coefficient associated with the years of study represents the marginal effect of one additional year of education on labour income or, in other words, the rate of return to an additional year of education. An advanced specification of the Mincer function consists in estimating the rate of return to education by distinguishing between years of education completed, or the last level of education attained, by graduates, based on a series of dichotomous variables. Once the earnings function is estimated, the rate of private return associated with the various levels of education can be derived by comparing the adjacent coefficients of the dichotomous variables.
 
2
Selection bias can be distinguished in two parts: “ability or pre-treatment bias” (when subjects with higher innate ability are more likely to select into treatment and tend to have best outcomes) and “sorting gain” (when subjects who benefit the most from treatment are most likely to select into treatment and thus have better outcomes).
 
3
The estimated marginal rate of return to schooling of an additional year of schooling results considerably vary across studies: it is around 6 % in Ciccone (2004) and Cingano and Cipollone (2009), whereas Brunello, Comi and Lucifora (2000) find evidence of a greater return to schooling for women (7.7 % vs. 6.2 % for males), as confirmed by Mendolicchio (2006) obtaining 6.5 % for females versus 5.3 % for males.
 
4
The sample used in the most recent surveys comprises about 8000 households (24,000 individuals), distributed over about 300 Italian municipalities (Italian regions and classes of population size).
 
5
Authors suggest to use Wald tests to test for the joint significance of all of the nonlinear terms in P, whereas standard errors are obtained bootstrapping and re-estimating the propensity scores P, as well as the outcome equation E(Y | P(Z) = p) in each bootstrap sample.
 
6
In both stratification approaches, we used the STATA™ implementation of Becker and Ichino (2002), which enabled to obtain the optimal number of blocks to guarantee the balancing of all specified pre-treatment variables (given the PS) between two groups. This approach splits the sample into k equally spaced blocks of the propensity score, where k is determined by the condition that, in all intervals, the average PS (such as the means of each pre-treatment variable) of treated and control units does not differ. We restricted this algorithm to the common support, implying that the number of k blocks and the balancing procedure is performed only in the intersection of the supports of the PS of treated and untreated.
 
7
For these analyses we used SAS™ 9.3Proc GLM and Proc GAM.
 
8
For the MTE analyses, we used the package “stats”, “sampleSelection”, “Hmisc”, “splines” and “KernSmooth” of the R software (version 2.14.1).
 
9
The non parametric functions for estimating \( {\text{E}}(Y^{ 1} /S = { 1},p) \) and \( {\text{E}}(Y^{0} /S = \, 0,p) \) are cubic smoothing splines with, based on the minimization of the generalized cross validation (GCV) criterion, 5 knots (or equivalently a span of 0.22), choice that is robust to alternative values for smoothing parameters.
 
10
Concerning the results of the semi-parametric MTE, the smoothing parameter (bandwidth) of the Local Linear Regression was found to be 0.04, whereas the bandwidth of the local polynomial regression (where data were binned over an equally-spaced grid of 100 points) was found to be 0.225, which is robust to the choice of bandwidths between 0.15 and 0.35.
 
11
Another possible choice would be to use the region of residence in 2008 as a proxy of the region of residence at 18. However, the geographical mobility between region of residence at the moment of last graduation and region of work, typically higher for graduates than high school graduates, is not marginal in the Italian context, especially in Southern Italy: AlmaLaurea (2013) analyzing college graduates in 2011, reports that, between the time of high school graduation and time of work, Southern Italy has lost 40 % of resident high school graduates.
 
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Metadata
Title
Heterogeneous economic returns to higher education: evidence from Italy
Authors
P. G. Lovaglio
S. Verzillo
Publication date
14-02-2015
Publisher
Springer Netherlands
Published in
Quality & Quantity / Issue 2/2016
Print ISSN: 0033-5177
Electronic ISSN: 1573-7845
DOI
https://doi.org/10.1007/s11135-015-0176-2

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