Documents de travail
We present an overview of selected contributions of the Journal of Mathematical Economics' authors in the last half century. We start with the classical optimal growth theory within a benchmark multisector model and outline the successive developments in the analysis of this model, including the turnpike theory. Different refinements of the benchmark are considered along the way. We after survey the abundant literature on endogenous fluctuations in two-sector models. We conclude with two strong trends in the recent growth literature: green growth and infinite-dimensional growth models.
This paper derives closed-form solutions for a strategic, simultaneous harvesting in a predator-prey system. Using a parametric constraint, it establishes the existence and uniqueness of a linear feedback-Nash equilibrium involving two specialized fleets and allow for continuous time results for a class of payoffs that have constant elasticity of the marginal utility. Theses results contribute to the scarce literature on analytically tractable predator-prey models with endogenous harvesting. A discussion based on industry size effects is provided to highlight the role played by biological versus strategic interactions in the multi-species context.
The assessment of binary classifier performance traditionally centers on discriminative ability using metrics, such as accuracy. However, these metrics often disregard the model’s inherent uncertainty, especially when dealing with sensitive decision-making domains, such as finance or healthcare. Given that model-predicted scores are commonly seen as event probabilities, calibration is crucial for accurate interpretation. In our study, we analyze the sensitivity of various calibration measures to score distortions and introduce a refined metric, the Local Calibration Score. Comparing recalibration methods, we advocate for local regressions, emphasizing their dual role as effective recalibration tools and facilitators of smoother visualizations. We apply these findings in a real-world scenario using Random Forest classifier and regressor to predict credit default while simultaneously measuring calibration during performance optimization.
Can forced sterilization programs targeting men lead to male-perpetrated violence? This paper investigates the impact of a government-mandated male sterilization program introduced in India on the rise of violence. Launched in April 1976, the program predominantly targeted men and saw heterogeneous implementation across India over 10 months. Using various household surveys and newly digitized historical data sources, we study whether the program triggered unintended effects on violence, measured by crime rates. Using a difference-indifferences strategy by exploiting geographical variation in coercion intensity, we find that an increase in exposure to the program led to an increase in violent crime rates of 7% for the average district, which persisted over time. Violent crimes against women primarily drive the increase in crime rates, as rapes are increasing by 22% for the average district. We find that the program was ineffective in reducing fertility, so we hypothesize that a forced sterilization program targeting men may increase violence against women through two main channels: the program inducing trauma and impacting perceptions of masculinity. In line with those channels, we see that districts with high coercion intensity correlate with more harmful gender norms: higher levels and acceptance of Intimate Partner Violence, lower bargaining power of women and lower contraception adoption.
This paper investigates the dynamic effects of weather shocks on monthly agricultural production in Peru, using a Local Projection framework. An adverse weather shock, measured by an excess of heat or rain, always generates a delayed negative downturn in agricultural production, but its magnitude and duration depend on several factors, such as the type of crop concerned or the timing at which it occurs. On average, a weather shock –a temperature shock– can cause a monthly decline of 5% in agricultural production for up to four consecutive months. The response is time-dependent: shocks occurring during the growing season exhibit a much larger response. At the macroeconomic level, weather shocks are recessionary and entail a decline in inflation, agricultural production, exports, exchange rate and GDP.
This paper shows how to recover behavioral biases from revealed preference ranking implied by choices. The approach formalizes and unifies well-known behavioral models, including salience thinking, inattention, and logarithmic perception, thereby accounting for many well-documented choice puzzles. I show that this approach provides a way to filter out choice data from behavioral biases explaining rationality breaches before fitting parametric utility models. The approach is applied to workhorse data sets of the literature on choice under risk and scanner consumer choices.