In this paper, we investigate the use of interactive effect or linear factor models in regional policy evaluation. We contrast treatment effect estimates obtained using Bai (2009) with those obtained using difference in differences and synthetic controls (Abadie and coauthors). We show that difference in differences are generically biased, and we derive support conditions for synthetic controls. We construct Monte Carlo experiments to compare these estimation methods in small samples. As an empirical illustration, we provide an evaluation of the impact on local unemployment of an enterprise zone policy implemented in France in the 1990s.

We derive wage equations with individual-specific coefficients from a structural model of human capital investment over the life-cycle. This model allows for interruptions in labour market participation and deals with missing data and attrition problems. We propose a new framework that deals with missingness at random and is based on factor decompositions that allow for flexible control of selection. Our approach leads to an interactive effect wage specification, which we estimate using long administrative panel data on male wages in the private sector in France. A structural function approach shows that interruptions negatively affect average wages. Interestingly, they also negatively affect the inter-decile range of wages after twenty years. This is only partly due to the fact that interruptions are endogenous.

This paper proposes a new measure of gender differences in access to jobs based on a job assignment model. This measure is the probability ratio of getting a job for a female and a male at each rank of the wage ladder. We derive a nonparametric estimator of this access measure and estimate it for French full-time executives aged 40–45 in the private sector. Our results show that the gender difference in the probability of getting a job increases along the wage ladder from 9% to 50%. Females thus have a significantly lower access to high-paid jobs than to low-paid jobs.

We develop a new methodology to estimate the elasticity of urban costs with respect to city population using French house and land price data. After handling a number of estimation concerns, we find that the elasticity of urban costs increases with city population with an estimate of about 0.03 for an urban area with 100,000 inhabitants to 0.08 for an urban area of the size of Paris. Our approach also yields a number of intermediate outputs of independent interest such as the share of housing in expenditure, the elasticity of unit house and land prices with respect to city population, and within-city distance gradients for house and land prices.

We propose a new nonparametric approach to estimate the production function for housing. Our estimation treats output as a latent variable and relies on a first-order condition for profit maximization combined with a zero-profit condition. More desirable locations command higher land prices and, in turn, more capital to build houses. For parcels of a given size, we compute housing production by summing across the marginal products of capital. For newly built single-family homes in France, the production function for housing is close to constant returns and is well, though not perfectly, approximated by a Cobb-Douglas function with a capital elasticity of 0.65.

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