Publications
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.
When can exogenous changes in beliefs generate endogenous fluctuations in rational expectation models? We analyze this question in the canonical one-sector and two-sector models of the business cycle with increasing returns to scale. A key feature of our analysis is that we express the uniqueness/multiplicity condition of equilibirum paths in terms of restrictions on five critical and economically interpretable parameters: the Frisch elasticities of the labor supply curve with respect to the real wage and to the marginal utility of wealth, the intertemporal elasticity of substitution in consumption, the elasticity of substitution between capital and labor, and the degree of increasing returns to scale. We obtain two clear-cut conclusions: belief-driven fluctuations cannot exist in the one-sector version of the model for empirically consistent values for these five parameters. By contrast, belief-driven fluctuations are a robust property of the two-sector version of the model—with differentiated consumption and investment goods—, as they now emerge for a wide range of parameter values consistent with available empirical estimates. The key ingredients explaining these different outcomes are factor reallocation between sectors and the implied variations in the relative price of investment, affecting the expected return on capital accumulation.
We propose an alternative dynamic theory of coalition breakdown. Motivated by recent coalition-splitting events of unilateral country withdrawals, we assume that: (i) the payoff-sharing rule within coalitions is not necessarily set according to any optimality and/or stability criterion and (ii) players initially behave as if the coalition will last forever. If the sharing rule is non-negotiable or if renegotiation is very costly, compliance with these rules may become unbearable for a given member because the rule, being too rigid, would make exit preferable as time passes. We examine this endogenous exit problem in the case of time-invariant sharing rules. Assuming a Nash non-cooperative game after a (potential) split where players play Markovian strategies, we characterize the solutions of the endogenous exit problem in a linear-quadratic frame with endogenous splitting time. We find that splitting countries are precisely those that used to benefit the most from the coalition. Suitable sharing rules should be used to prevent coalition splitting. When initial pollution is high, all shares should be low enough and none of the players should receive a payoff share larger than 1/2. If initial pollution is low, we provide an explicit interval for sharing-rule values to prevent the collapse of the coalition. Finally, we demonstrate that the latter properties are qualitatively consistent with the optimal behaviors and equilibrium outcomes resulting from players anticipating the end of the coalition and acting accordingly.
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 propose Fieller-type methods for inference on generalized entropy inequality indices in the context of the two-sample problem which covers testing the statistical significance of the difference in indices, and the construction of a confidence set for this difference. In addition to irregularities arising from thick distributional tails, standard inference procedures are prone to identification problems because of the ratio transformation that defines the considered indices. Simulation results show that our proposed method outperforms existing counterparts including simulation-based permutation methods and results are robust to different assumptions about the shape of the null distributions. Improvements are most notable for indices that put more weight on the right tail of the distribution and for sample sizes that match macroeconomic type inequality analysis. While irregularities arising from the right tail have long been documented, we find that left tail irregularities are equally important in explaining the failure of standard inference methods. We apply our proposed method to analyze income per-capita inequality across U.S. states and non-OECD countries. Empirical results illustrate how Fieller-based confidence sets can: (i) differ consequentially from available ones leading to conflicts in test decisions, and (ii) reveal prohibitive estimation uncertainty in the form of unbounded outcomes which serve as proper warning against flawed interpretations of statistical tests.
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.
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.
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.
