Estimates of the level of inequality of opportunity have traditionally been interpreted as lower bounds due to the downward bias resulting from the partial observability of circumstances that affect individual outcome. We show that such estimates may also suffer from upward bias as a consequence...

We show that, when measuring inequality of opportunity with survey data, scholars incur two types of biases. A well-known downward-bias, due to partial observability of circumstances that affect individual outcome, and an upward bias, which depends on the econometric method used and the quality...

A large strand of the literature on panel data models has focused on explicitly modelling the cross-section dependence between panel units. Factor augmented approaches have been proposed to deal with this issue. Under a mild restriction on the correlation of the factor loadings, we show that...

In this paper we show that highly skilled undocumented migrants are more likely to return home than migrants with low or no skills when illegality causes quot;skill waste,quot; i.e., when illegality reduces the rate of return of individual capabilities (i.e., skills and human capital) in both...

Given the growing availability of large datasets and following recent research trends on multi-dimensional modelling, we develop three dimensional (3D) panel data models with hierarchical error components that allow for strong cross-sectional dependence through unobserved heterogeneous global...