Publications
The impact of US allocation of family planning aid on other donors is studied in order to gain new insights into donor interactions. Within this context, the dominant player in the sector is the United States, whose policies on family planning undergo changes influenced by domestic debates surrounding abortion. By utilizing the Mexico City Policy and considering exposure to this particular policy as an instrumental factor, it has been observed that other donors do not immediately react to policy changes made by the United States, either contemporaneously or within one year. However, a noticeable shift occurs after a two-year period, indicating that these donors eventually align their allocation strategies with those of the United States. Further analysis of this phenomenon reveals varying patterns among different types of donors. While smaller donors exhibit a clear intention to compensate for US policy changes, larger donors display a mix of competitive tendencies and herding behavior, thereby reinforcing the impact of the Mexico City Policy after the two-year time frame.
We present an inexact proximal point algorithm using quasi distances to solve a minimization problem in the Euclidean space. This algorithm is motivated by the proximal methods introduced by Attouch et al., section 4, (Math Program Ser A, 137: 91-129, 2013) and Solodov and Svaiter (Set Valued Anal 7:323-345, 1999). In contrast, in this paper we consider quasi distances, arbitrary (non necessary smooth) objective functions, scalar errors in each objective regularized approximation and vectorial errors on the residual of the regularized critical point, that is, we have an error on the optimality condition of the proximal subproblem at the new point. We obtain, under a coercivity assumption of the objective function, that all accumulation points of the sequence generated by the algorithm are critical points (minimizer points in the convex case) of the minimization problem. As an application we consider a human location problem: How to travel around the world and prepare the trip of a lifetime.
In this paper, we provide a better understanding of what drives sovereign wealth funds (SWFs) to improve their governance. Using the most recent SWF governance scoreboard from Maire et al. (2021), we estimate a fractional response model to determine whether SWF governance disclosure norms are driven by the search for internal or external legitimacy. Overall, we find that SWFs have better governance when they originate from democratic countries with high-quality, national governance. Our results also show that SWFs tend to have better governance quality when they need to acquire external legitimacy vis-à-vis the target company and its government. In particular, we find that SWFs have an incentive to improve their governance when they are sufficiently internationalized, when the amount of foreign assets invested abroad is sufficiently large or when the amount of shares acquired in developed countries is significant. These findings demonstrate how SWFs may proactively build legitimacy in host countries when they need to adapt their foreign entry strategies. Our results have important implications for understanding the determinants of SWF governance in general.
We study whether fiscal policies, especially public debt, can help to curb the macroeconomic and health consequences of epidemics. Our approach is based on three main features: we introduce the dynamics of epidemics in an overlapping generations model to take into account that old people are more vulnerable; people are more easily infected when pollution is high; public spending in health care and public debt can be used to tackle the effects of epidemics. We show that fiscal policies can promote convergence to a stable disease-free steady state. When public policies are not able to permanently eradicate the epidemic, public debt, and income transfers could reduce the number of infected people and increase capital and GDP per capita. As a prerequisite, pollution intensity should not be too high. Finally, we define a household subsidy policy that eliminates income and welfare inequalities between healthy and infected individuals.
In this paper, we develop an overlapping generations model with endogenous fertility and calibrate it to the Swedish historical data in order to estimate the economic cost of the 1918-19 influenza pandemic. The model identifies survivors from younger cohorts as main benefactors of the windfall bequests following the influenza mortality shock. We also show that the general equilibrium effects of the pandemic reveal themselves over the wage channel rather than the interest rate, fertility or labor supply channels. Finally, we demonstrate that the influenza mortality shock becomes persistent, driving the aggregate variables to lower steady states which costs the economy 1.819% of the output loss over the next century.
Salience theory relies on the assumption that not only the marginal distribution of lotteries, but also the correlation of payoffs across states impacts choices. Recent experimental studies on salience theory seem to provide evidence in favor of such correlation effects. However, these studies fail to control for event-splitting effects (ESE). In this paper, we seek to disentangle the role of correlation and event-splitting in two settings: (1) the common consequence Allais paradox as studied by Bordalo et al. (Q J Econ 127:1243–1285, 2012), Frydman and Mormann (The role of salience in choice under risk: An experimental investigation. Working Paper, 2018), and Bruhin et al. (J Risk Uncertain 65:139–184, 2022); (2) choices between Mao pairs as studied by Dertwinkel-Kalt and Köster (J Eur Econ Assoc 18:2057–2107, 2020). In both settings, we find evidence suggesting that recent findings supporting correlation effects are largely driven by ESE. Once controlling for ESE, we find no consistent evidence for correlation effects. Our results thus shed doubt on the validity of salience theory in describing risky behavior.
We study the impact of socioeconomic factors on two key parameters of epidemic dynamics. Specifically, we investigate a parameter capturing the rate of deceleration at the very start of an epidemic, and a parameter that reflects the pre-peak and post-peak dynamics at the turning point of an epidemic like coronavirus disease 2019 (COVID-19). We find two important results. The policies to fight COVID-19 (such as social distancing and containment) have been effective in reducing the overall number of new infections, because they influence not only the epidemic peaks, but also the speed of spread of the disease in its early stages. The second important result of our research concerns the role of healthcare infrastructure. They are just as effective as anti-COVID policies, not only in preventing an epidemic from spreading too quickly at the outset, but also in creating the desired dynamic around peaks: slow spreading, then rapid disappearance.
We show that least squares cross-validation methods share a common structure which has an explicit asymptotic solution, when the chosen kernel is asymptotically separable in bandwidth and data. For density estimation with a multivariate Student t(ν) kernel, the cross-validation criterion becomes asymptotically equivalent to a polynomial of only three terms. Our bandwidth formulae are simple and noniterative thus leading to very fast computations, their integrated squared-error dominates traditional cross-validation implementations, they alleviate the notorious sample variability of cross-validation, and overcome its breakdown in the case of repeated observations. We illustrate our method with univariate and bivariate applications, of density estimation and nonparametric regressions, to a large dataset of Michigan State University academic wages and experience.
Some complex models are frequently employed to describe physical and mechanical phenomena. In this setting, we have an input X\ X \ in a general space, and an output Y=f(X)\ Y=f(X) \ where f\ f \ is a very complicated function, whose computational cost for every new input is very high, and may be also very expensive. We are given two sets of observations of X\ X \, S1\ S_1 \ and S2\ S_2 \ of different sizes such that only fS1\ f\left(S_1\right) \ is available. We tackle the problem of selecting a subset S3⊂S2\ S_3\subset S_2 \ of smaller size on which to run the complex model f\ f \, and such that the empirical distribution of fS3\ f\left(S_3\right) \ is close to that of fS1\ f\left(S_1\right) \. We suggest three algorithms to solve this problem and show their efficiency using simulated datasets and the Airfoil self-noise data set.
Earnings inequality in Germany has increased dramatically. Measuring inequality locally at the level of cities annually since 1985, we find that behind this development is the rapidly worsening inequality in the largest cities, driven by increasing earnings polarisation. In the cross-section, local earnings inequality rises substantially in city size, and this city-size inequality penalty has increased steadily since 1985, reaching an elasticity of .2 in 2010. Inequality decompositions reveal that overall earnings inequality is almost fully explained by the within-locations component, which in turn is driven by the largest cities. The worsening inequality in the largest cities is amplified by their greater population weight. Examining the local earnings distributions directly reveals that this is due to increasing earnings polarisation that is strongest in the largest places. Both upper and lower distributional tails become heavier over time, and are the heaviest in the largest cities. We establish these results using a large and spatially representative administrative data set, and address the top-coding problem in these data using a parametric distribution approach that outperforms standard imputations.
