Publications
Experts argue that the adoption of healthy sanitation practices, such as hand washing and latrine use, requires focusing on the entire community rather than individual behaviors. According to this view, one limiting factor in ending open defecation lies in the capacity of the community to collectively act toward this goal. Each member of a community bears the private cost of contributing by washing hands and using latrines, but the benefits through better health outcomes depend on whether other community members also opt out of open defecation. We rely on a community-based intervention carried out in Mali as an illustrative example (Community-Led Total Sanitation or CLTS). Using a series of experiments conducted in 121 villages and designed to measure the willingness of community members to contribute to a local public good, we investigate the process of participation in a collective action problem setting. Our focus is on two types of activities: (1) gathering of community members to encourage public discussion of the collective action problem, and (2) facilitation by a community champion of the adoption of individual actions to attain the socially preferred outcome. In games, communication helps raise public good provision, and both open discussion and facilitated ones have the same impact. When a community member facilitates a discussion after an open discussion session, public good contributions increase, but there are no gains from opening up the discussion after a facilitated session. Community members who choose to contribute in the no-communication treatment are not better facilitators than those who choose not to contribute.
In this article, we propose a theory that explains how Free/Libre Open Software (FLOSS) projects work and how companies rely on these FLOSS projects to develop their commercial offers, what we refer to as their “open-source” business model(s). This article builds on and refines the studies of the FLOSS organization by connecting two interrelated aspects: (1) how this organization evolves over time, in order to (2) better understand the value that users create and capture at each moment of a FLOSS project, with a particular focus on open-source companies, which are specific users who do business based on the software created by the FLOSS project. We describe these models and show that the open-source business models of companies are based on contributing to FLOSS projects in order to be able to provide “3A” services (assurance, adaptation, and assistance or support for use) that are complementary to the access to the software. Providing these services requires participation in the FLOSS project, which provides the project with the resources to operate. This work can help the software engineering community by showing how FLOSS evaluation tools can be improved by taking into account the maturity of the solution, the strategic need of the target user, and the complementary open-source offers that exist.
This paper asks whether macroeconomic policy can affect fertility and education by documenting a slow-down of long-term improvements in these two outcomes in the wake of a major protectionist shock that shielded low-skilled individuals from the adverse consequences of the first wave of globalisation. We build a novel dataset for 19th-century France where, following decades of rising grain imports at low prices, high tariffs on cereal were introduced in 1892, shifting relative prices in favour of agriculture and away from industry. We exploit regional data that allow us to measure differences in the intensity of the protectionist shock and find that the tariff halted the long-term increase in schooling and slowed-down the decline in fertility that were already well underway.
Earnings are often top-coded (right-censored) in administrative registers. The censoring threshold in the case of Germany is the limit value for social security contributions, leading to a substantial fraction of censoring: For example, about 12 % of male workers in West Germany are affected, rising to above 30 % for highly educated prime-aged workers. This missing right tail of the earnings distribution constitutes a major problem for researchers studying earnings inequality and top incomes. We overcome this challenge by taking a distributional approach and semi-parametrically modelling the right tail as being Pareto-like. Non-censored earnings survey data matched to administrative records, derived from the SOEP-RV project, let us operate in a laboratory-like setting in which the targets are known. Our approach outperforms alternative imputation methods based on Tobit regressions.
This paper exploits daily infrared images taken from satellites to track economic activity in advanced and emerging countries. We first develop a framework to read, clean, and exploit satellite images. Our algorithm uses the laws of physics (Planck's law) and machine learning to detect the heat produced by cement plants in activity. This allows us to monitor in real-time whether a cement plant is working. Using this on around 1,000 plants, we construct a satellite-based index. We show that using this satellite index outperforms benchmark models and alternative indicators for nowcasting the production of the cement industry as well as the activity in the construction sector. Comparing across methods, neural networks appear to yield more accurate predictions as they allow to exploit the granularity of our dataset. Overall, combining satellite images and machine learning can help policymakers to take informed and swift economic policy decisions by nowcasting accurately and in real-time economic activity.
This paper introduces a novel mechanism driving endogenous business cycle fluctuations within a frictionless three-sector intertemporal equilibrium model. We emphasize the critical role of consumer preferences as a primary driver of cyclical dynamics by considering a consumption bundle composed of a pure consumption good and a mixed consumption-investment good that simultaneously serves as both a final consumption good and a capital-accumulating investment good. Endogenous fluctuations naturally arise from sectoral capital intensity differences, an intertemporal consumption trade-off between the two goods, or the interaction of both mechanisms. We offer a detailed characterization of the economy's dynamics, identifying the Hopf bifurcation conditions that trigger persistent cyclical behavior. Additionally, we explore the periodicity of the resulting limit cycles, providing insights into how shifts in preferences and sectoral complementarities can generate self-sustained macroeconomic fluctuations.
Using the text from matrimonial ads, we assemble a novel data set to describe the evolution of partner preferences over time and space. Analyzing ads published in Canada, France and India between 1950 and 1995, we show that stated preferences for economic criteria have fallen sharply in favor of personality traits in the two Western countries while they remain the most prevalent in India. Using ads covering various regions from the US and Canada in 1995, we show that personality traits are consistently more demanded than economic criteria. We provide evidence that these results are unlikely to be driven by the composition effects over time, role of parents or changing social norms. We show that the changes over time are particularly strong for women and accompany narrowing gender gaps in labor force participation in Western countries. We discuss the implications for understanding the evolution of assortative mating over time.
Higher trade policy uncertainty has recessionary effects on U.S. states. To demonstrate this, we first build a novel empirical measure of regional trade policy uncertainty based on the volatility of national import tariffs at the sectoral level and on the sectoral composition of imports in U.S. states. We find that a state that is more exposed to an unanticipated increase in tariff volatility suffers from a larger drop in real GDP and employment than the average U.S. state. We then build a two-region open-economy model and find that the precautionary saving behavior is the main driver of the recession, although this effect is reinforced by high exposure to import tariffs. The feedback effect resulting from trade connections with the Foreign country primarily influences the persistence of these dynamics.
This paper uses a novel dataset on ethnic warfare to shed light on how conflict affects social identification and cohesion. A large body of anecdotal studies suggests that ethnic identities become more salient at times of conflict. Using data from thirty-six African countries, I provide econometric evidence to this notion. The relationship between ethnic conflict and various measures of social cohesion is also examined, revealing a positive link between the two. The finding is understood as a result of the ethnocentric dynamics generated by conflict: as warfare strengthens ethnic identification, prosocial behaviour increases, albeit primarily towards co-ethnics. This parochial interpretation is strengthened by the use of remote violence and the conditionality of conflict-induced prosocial behaviour on low levels of ethnic fractionalisation.
In this paper, we link two existing approaches to derive counterfactuals: adaptations based on a causal graph, and optimal transport. We extend "Knothe's rearrangement" and "triangular transport" to probabilistic graphical models, and use this counterfactual approach, referred to as sequential transport, to discuss fairness at the individual level. After establishing the theoretical foundations of the proposed method, we demonstrate its application through numerical experiments on both synthetic and real datasets.
