Welcome

 
Raffaele Saggio

 PhD Candidate

​ UC Berkeley Department of Economics

 I am currently on the job market and will be available for interviews at the ASSA Annual Meeting in Philadelphia, PA (January 5th-7th, 2018).

 Email: 
 raffaele.saggio@berkeley.edu

 Primary Fields: 
 Labor Economics
 Applied Econometrics

 CV


Working Papers

(joint with Sabrina Di Addario and Diego Daruich)

We combine matched employer-employee data with firm financial records to study a 2001 Italian reform that lifted constraints on the employment of temporary contract workers while maintaining rigid employment protection regulations for employees hired under permanent employment contracts.  Exploiting the staggered implementation of the law across different collective bargaining agreements, we find that the reform led to an increase in the incidence of temporary contracts but failed to raise employment significantly and lowered the earnings of new entrants. By contrast,  the reform was successful in decreasing firms' labor costs, leading to higher profitability, some gains in managerial pay, and a rise in within-firm earnings inequality. Our findings suggest that the main beneficiaries of this ``partial reform" to employment protection were firms, managers, and older incumbent workers. Rent-sharing estimates show that workers on a temporary contract receive only 66% of the rents shared by firms with workers hired under a permanent contract.



Leave-out estimation of variance components (DRAFT WILL BE AVAILABLE SOON)
(joint with Patrick Kline and Mikkel Sølvsten

We propose a general framework for unbiased estimation of quadratic forms of regression coefficients in linear models with unrestricted heteroscedasticity. Economic applications include variance component estimation in multi-way fixed effects and random coefficient models. The large sample distribution of our estimator is studied in an asymptotic framework where the number of regressors may grow in proportion to the number of observations. Consistency is established in a variety of settings where standard ``automatic'' bias correction procedures fail. We show that the limiting distribution of our estimator is non-standard and can be represented by a linear combination of independent normal and non-central Chi Square random variables. Consistent variance estimators are proposed along with a uniform inference procedure. In an application to Italian worker-firm data, we demonstrate that ignoring heteroscedasticity can substantially bias conclusions about the relative contribution of workers, firms, and worker-firm sorting to wage inequality.


(joint with Davide Malacrino)

This paper investigates how time to college completion affects subsequent labor market outcomes. We study a recent reform in which the Italian government consolidated the teaching offer in all universities in an attempt to decrease time to graduation. The reform was successful in reducing the proportion of students graduating late from a second level degree, but worsened a variety of post-graduation outcomes including earnings. Using this policy change as an instrumental variable, we find that late graduation is associated with better after graduation labor market outcomes. To disentangle the human capital effect of late graduation from working experience accumulated while still in college, we use a student choice model combined with revealed preferences restrictions. We believe that our findings have implications for the current policy debate as national governments are increasingly investing in public programs explicitly aimed at reducing time to graduation. 



By restricting the support of the unobserved heterogeneity and allowing cross-sectional units to be classified into a finite number of classes, we provide a non-linear panel data estimator that leaves the relationship between unobservables and observables unrestricted and it is able to use the whole sample in estimating the effects of interest. The latter represents an important improvement over typical maximum likelihood fixed effects models where the parameters are identified only through the sample of movers. Using results from Hahn and Moon (2010), we show that this non-linear group fixed effects estimator is consistent as both N and T goes to infinity under correct specification.  It is also higher-order unbiased compared to standard non-linear panel data estimators. We apply this new estimator to different empirical applications. The results suggest that the non-linear group fixed effects estimator can be considered as a reliable solution to deal with the problem of unobserved heterogeneity in a flexible but yet parsimonious way.