Laurent Gobillon

We study the effects of the SRU law introduced in France in December 2000 to support scattered development of public housing in cities and favor social mixity. This law imposes 20% of public dwellings to all medium and large municipalities of large-enough cities, with fees for those not abiding by the law. Using exhaustive fiscal data, we evaluate the effects of the law over the 1996-2008 period using a difference-in-differences approach at the municipality and neighborhood levels. We find that the law stimulated public housing construction in treated municipalities, but only slightly increased the presence of low-income households. Indeed, new public dwellings enter categories to which medium-income are eligible and most additional occupants are not poor. Within municipalities, the policy decreased public housing segregation but it barely decreased low-income segregation. This comes from local authorities increasing over time the presence of public dwellings in neighborhoods away from existing public housing but in places concentrating low-income households.

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.

A recent literature has used a historical perspective to better understand fundamental questions of urban economics. However, a wide range of historical documents of exceptional quality remain underutilised: their use has been hampered by their original format or by the massive amount of information to be recovered. In this paper, we describe how and when the flexibility and predictive power of machine learning can help researchers exploit the potential of these historical documents. We first discuss how important questions of urban economics rely on the analysis of historical data sources and the challenges associated with transcription and harmonisation of such data. We then explain how machine learning approaches may address some of these challenges and we discuss possible applications.

We propose an assignment model in which positions along a hierarchy are attributed to individuals depending on their characteristics. Our theoretical framework can be used to study differences in assignment and pay-offs across groups and we show how it can motivate decomposition and counterfactual exercises. In an application, we study gender disparities in the public and private sectors with a French exhaustive administrative dataset. The gender wage gap in the public sector is 13.3% and it increases by only 0.7 percentage points when workers are assigned to job positions according to the rules of the private sector.