Banque de France

For over three decades, Dynare has been a cornerstone of dynamic stochastic modeling in economics, relying primarily on perturbation-based local solution methods. However, these techniques often falter in high-dimensional, non-linear models that demand more comprehensive approaches. This paper demonstrates that global solutions of economic models with substantial heterogeneity and frictions can be computed accurately and swiftly by augmenting Dynare with adaptive sparse grids (SGs) and high-dimensional model representation (HDMR). SGs mitigate the curse of dimensionality, as the number of grid points grows significantly slower than in traditional tensor-product Cartesian grids. Additionally, adaptivity focuses grid refinement on regions with steep gradients or non-differentiabilities, enhancing computational efficiency. Complementing SGs, HDMR tackles large state spaces by approximating policy functions with a hierarchical expansion of low-dimensional terms. Using a time iteration algorithm, we benchmark our approach on an international real business cycle model. Our results show that both SGs and HDMR alleviate the curse of dimensionality, enabling accurate solutions for at least 100-dimensional models on standard hardware in relatively short times. This advancement extends Dynare's capabilities beyond perturbation approaches, establishing a versatile platform for sophisticated non-linear models and paving the way for integrating the most recent global solution methods, such as those from machine learning.

In standard models, economic activity fluctuates symmetrically around a “natural rate” and stabilization policies can dampen these fluctuations but do not affect the average level of activity. An alternative view—labeled the “plucking model” by Milton Friedman—is that economic fluctuations are drops below the economy’s full potential ceiling. If this view is correct, stabilization policy, by dampening these fluctuations, can raise the average level of activity. We show that the dynamics of the unemployment rate in the US display a striking asymmetry that strongly favors the plucking model: increases in unemployment are followed by decreases of similar amplitude, while the amplitude of the increase is not related to the amplitude of the previous decrease. We develop a microfounded plucking model of the business cycle. The source of asymmetry in our model is downward nominal wage rigidity, which we embed in an explicit search model of the labor market. Our search framework implies that downward nominal wage rigidity is consistent with optimizing behavior and equilibrium. In our plucking model, stabilization policy lowers average unemployment and thereby yields sizable welfare gains.

This paper investigates the impact of introducing junior unsecured loans (i.e., FinTech loans) into the small business lending market. Using French administrative data, we find that firms experience a 13% increase in bank credit after receiving a FinTech loan. We use propensity score matching procedures and a shift-share instrument to account for credit demand. The credit increase only occurs when FinTech borrowers invest in new assets, and Fintech borrowers are subsequently more likely to pledge collateral to banks. This suggests that firms use FinTech loans to acquire assets that they then pledge to banks, thereby increasing total borrowing capacity.

During heatwaves in cities, urban heat islands (UHI) can occur that unequally affect different neighborhoods due to variations in their structures, the quality of their buildings, vegetation, and human activity. Some populations are particularly vulnerable, such as older adults, young children, and low-income households, all of whom have fewer options when exposed to an UHI. For the first time, in our paper, we present the first analysis of climate inequality with respect to UHI in France. We build and match finely localized data in nine of the largest French cities on the air temperature and vegetation in the neighborhoods as well as the density, heights, and periods of construction of its dwellings, and finally on the socioeconomic characteristics of the households. We find that the relationship between UHI exposure and income depends on their pre-existing spatial sorting. In cities like Paris where both affluent and low-income households reside close to the city center, the relationship between UHI exposure and income follows a U-shaped curve. In contrast, in cities like Lyon where affluent households live in rich suburbs, the exposure to UHI decreases with income. We also find that vulnerable households, defined by both age and income criteria, are slightly more exposed but far less able to renovate their dwellings or leave cities during heatwaves.

Using French firm-level data on AI adoption from 2017-2020, we find that, first, firms adopting AI are larger and more productive and skill intensive. Second, difference-in-difference estimates reveal an increase in firm-level employment and sales after AI adoption, suggesting that the induced productivity gains allow firms to grow and outweigh potential displacement effects. Third, occupations classified in recent work as substitutable with AI expand. Fourth, AI usage is a relevant dimension of heterogeneity in the labor demand response: We find positive employment growth for certain uses (e.g., information and communications technology security) and negative for others (e.g., administrative processes).