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 However, a wide range of historical documents of exceptional quality remain underutilised: their use has been hampered by their original format or by the massive amount of information to be recovered.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and when the flexibility and predictive power of machine learning can help researchers exploit the potential of these historical documents.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.

This article examines the role played by assisted loans in the access to homeownership and in the residential segregation of low-income households in France. During the 1996-2006 period, no-interest loans affected 1.4 million households and were the main policy tool favoring homeownership. We rely on French housing surveys (INSEE) and administrative records on no-interest loans (SFGFAS) to compare the position of social groups in the housing market before and after the introduction of no-interest loans. We show that, in a context of increasing housing prices, no-interest loans have limited the exclusion of lower- and middle-class households from the new-build housing market, especially outside the Paris region. Nevertheless, households with no-interest loans tend to relocate to peripheral areas characterized not only by a lower proportion of professionals and managers relative to central areas, but also by lower access to public transportation, the childcare system, high schools, and job opportunities. Moreover, in-depth interviews at the individual level suggest that low-income households had no clear perception of the social and physical disconnections they would experience when purchasing their new homes.

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.

We develop a new dartboard methodology to delineate urban areas using detailed information about building location, which we implement using a map of all buildings in France. For each pixel, our approach compares actual building density after smoothing to counterfactual smoothed building density computed after randomly redistributing buildings. We define as urban any area with statistically significant excess building density. Within urban areas, extensions to our approach allow us to distinguish ‘core’ urban pixels and detect centres and subcentres. Finally, we develop novel one- and two-sided tests that provide a statistical basis to compare maps with different delineations, which we use to assess the robustness of our approach and to document large differences between our preferred delineation and the corresponding official one.

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.

Spatial mismatch relates the unemployment and poverty of vulnerable population groups to their remoteness from job opportunities. Although the intuition initially applied to African Americans in US inner cities, spatial mismatch has a broader validity beyond the sole US context. In light of a detailed presentation of the mechanisms at work, we present the main results from various empirical tests of the spatial mismatch theory. Since key aspects of that theory remain to be tested, we also discuss methodological approaches and provide guidance for further research. We derive lessons for policy implications and comment on the appropriateness of related urban policies.