Research

This paper examines empirically the economic relationship between local labour market size and firm returns to training. Anchoring in the literature of micro-foundation of agglomeration economies, we suspect that this relation is driven by two mechanisms: (i) labour pooling which should positively influence the returns to training through matching and learning effects and (ii) the risk of labour poaching, which tends to reduce the returns to training in larger labour markets. Our estimates, based on a large sample of French industrial firms, reveal that returns to training are increasing with the labour market size, suggesting that labour pooling dominates labour poaching effects. On average, returns to training lie between 6.7 and 7.7%, more in line with the microeconomic literature on education than previous studies focusing on training.

The synthetic difference-in-differences method provides an efficient method to estimate a causal effect with a latent factor model. However, it relies on the use of panel data. This paper presents an adaptation of the synthetic difference-in-differences method for repeated cross-sectional data. The treatment is considered to be at the group level. Thus, it is possible to aggregate data by group to compute the two types of synthetic difference-in-differences weights on these aggregated data. Then, I compute a third type of weight that account for the different number of observations for each cross-section. I also provide simulation results showing the performance of the synthetic difference-in-differences estimator is improved when using the third type of weights on repeated cross-sectional data.

We evaluate the impact of the rent control regulation implemented by the city of Paris in July 2019 on the Parisian rental market. We take advantage of the large amount of data available in real-time on the SeLoger platform containing the ads published by professional realtors. Using a database of 559,300 observations from January 2018 to June 2023, we apply a difference-in-differences model, where control units are located in eight major French cities in which the rental market is particularly tense but not regulated during the analysis period. We show that the rent control policy decreased rents by 3.7% to 4.2% in Paris on average. Yet, the policy is heterogeneous depending on dwelling characteristics with a stronger effect on small apartments. We also esti- mate the higher bound of the effectiveness of the policy and show that if every dwelling respected the rent caps, rents would have decreased by 8.2% to 8.7%.

Mining deposits are not randomly distributed through space. Yet, most previous studies on the effects of mining on conflicts used a control group that include all regions of Africa. In this paper, we suggest using areas with discovered mines that were never active as a control group for areas with active mines. Using georeferenced data in cells of 0.5° X 0.5°, we first show that areas with discovered mines have more similar characteristics with areas having active mines than with areas that have neither. Then, we find no evidence of discovered mines having an effect on conflicts (otherwise using them as a control group would bias our results). Our findings demonstrate that using areas with discovered mines as a control group drastically change the estimated result of mining activities on conflicts. The effect of mining activities on conflict is close to zero and insignificant when using our control group while it is significant and positive if we use all areas as the control group. We also estimate the effects of mineral price variations on conflicts. Using our control group reduces the estimated effect of price variations on conflict from 20% to 40% depending on the definition of mine activity used.

CO2 is one of the gases responsible for global warming and the quantities emitted differ from one country to another depending on their economic situation and activities. In this paper, we analyse the effects of economic development on CO2 emissions in the Sahel, and measure its effects for the period from 1961 to 2018. We estimate an econometric model that includes both the environmental curve of Kuznets and the CO2 convergence between the countries of the Sahel. Our results show that CO2 emissions converge between these countries. However, there is no evidence that the level and growth of GDP reduce CO2 emissions as Sahelian countries develop.

This paper studies the role of agglomeration economies on firms training offer. Two mechanisms can explain why firms train less in larger areas : (i) because of the poaching of trained workers, firms may train less in denser local labour markets, (ii) matching and learning effects lessen the firm training needs of workers to acquire new skills. To discriminate between those two explanations, we construct two proxies for poaching effects: firm turnover and turnover in the local labour market. When controlling for poaching with those two measures, we still find employment density to have a negative and significant effect on firm provided training. This implies matching and learning effects are more likely to be responsible for the negative effect of employment density on training than poaching effects.

This paper studies the allocation of training among workers. We focus on inequalities in access to training depending on years of schooling and on workers’ location. Because firms train less in denser areas, we study how this decrease in the training offer is allocated among workers depending on their skills. We consider firm training choices to be a two-step process. First, they decide which worker to train, then,they choose the training duration. Our results confirm that firms train fewer workers in denser areas. However, they train those workers longer. Those two effects counterbalance in a way that, on average, firms provide as much training in all areas. Thus, agglomeration externalities affect the allocation of training among workers, but do not change the total amount of firm-provided training. In denser areas, workers with more years of schooling are more trained. However, they also pursue shorter training than those with a lower education. While our results show that agglomeration externalities do not affect training inequalities depending on years of schooling, they also highlight the effect of education on the training allocation.