I propose a quantile-based nonadditive fixed effects panel model to study heterogeneous causal effects. Similar to standard fixed effects (FE) model, my model allows arbitrary dependence between regressors and unobserved heterogeneity, but it generalizes the additive separability of standard FE to allow the unobserved heterogeneity to enter nonseparably. Similar to structural quantile models, my model's random coefficient vector depends on an unobserved, scalar ''rank'' variable, in which outcomes (excluding an additive noise term) are monotonic at a particular value of the regressor vector, which is much weaker than the conventional monotonicity assumption that must hold at all possible values. This rank is assumed to be stable over time, which is often more economically plausible than the panel quantile studies that assume individual rank is iid over time. It uncovers the heterogeneous causal effects as functions of the rank variable. I provide identification and estimation results, establishing uniform consistency and uniform asymptotic normality of the heterogeneous causal effect function estimator. Simulations show reasonable finite-sample performance and show my model complements fixed effects quantile regression. Finally, I illustrate the proposed methods by examining the causal effect of a country's oil wealth on its military defense spending.

Many experimental studies report that economics students tend to act more selfishly than students of other disciplines, a finding that received widespread public and professional attention. Two main explanations that the existing literature offers for the differences found in the behavior between economists and noneconomists are the selection effect, and the indoctrination effect. We offer an alternative, novel explanation. We argue that these differences can be explained by differences in the interpretation of the context. We test this hypothesis by conducting two social dilemma experiments in the US and Israel with participants from both economics and non-economics majors. In the experiments, participants face a tradeoff between profit maximization, that is the market norm and workers welfare, that is the social norm. We use priming to manipulate the cues that the participants receive before they make their decision. We find that when participants receive cues signaling that the decision has an economic context, both economics and non-economics students tend to maximize profits. When the participants receive cues emphasizing social norms, on the other hand, both economics and non-economics students are less likely to maximize profits. We conclude that some of the differences found between the decisions of economics and non-economics students can be explained by contextual cues.

The past two decades have witnessed a surge of new research in the analysis of randomized experiments. The emergence of this literature may seem surprising given the widespread use and long history of experiments as the "gold standard" in program evaluation, but this body of work has revealed many subtle aspects of randomized experiments that may have been previously unappreciated. This article provides an overview of some of these topics, primarily focused on stratification, regression adjustment, and cluster randomization.

With the official end of the COVID-19 pandemic, debates about the return to office have taken center stage among companies and employees. Despite their ubiquity, the economic implications of return to office policies are not fully understood. Using 260 million resumes matched to company data, we analyze the causal effects of such policies on employees' tenure and seniority levels at three of the largest US tech companies: Microsoft, SpaceX, and Apple. Our estimation procedure is nonparametric and captures the full heterogeneity of tenure and seniority of employees in a distributional synthetic controls framework. We estimate a reduction in counterfactual tenure that increases for employees with longer tenure. Similarly, we document a leftward shift in the seniority distribution towards positions below the senior level. These shifts appear to be driven by employees leaving to larger firms that are direct competitors. Our results suggest that return to office policies can lead to an outflow of senior employees, posing a potential threat to the productivity, innovation, and competitiveness of the wider firm.

Recent advances in the literature of decomposition methods in economics have allowed for the identification and estimation of detailed wage gap decompositions. In this context, building reliable counterfactuals requires using tighter controls to ensure that similar workers are correctly identified by making sure that important unobserved variables such as skills are controlled for, as well as comparing only workers with similar observable characteristics. This paper contributes to the wage decomposition literature in two main ways: (i) developing an economic principled network based approach to control for unobserved worker skills heterogeneity in the presence of potential discrimination; and (ii) extending existing generic decomposition tools to accommodate for potential lack of overlapping supports in covariates between groups being compared, which is likely to be the norm in more detailed decompositions. We illustrate the methodology by decomposing the gender wage gap in Brazil.

We consider treatment-effect estimation under a parallel trends assumption, in heterogeneous adoption designs where no unit is treated at period one, and units receive a weakly positive dose at period two. First, we develop a test of the assumption that the treatment effect is mean independent of the treatment, under which the commonly-used two-way-fixed-effects estimator is consistent. When this test is rejected, we propose alternative, robust estimators. If there are stayers with a period-two treatment equal to 0, the robust estimator is a difference-in-differences (DID) estimator using stayers as the control group. If there are quasi-stayers with a period-two treatment arbitrarily close to zero, the robust estimator is a DID using units with a period-two treatment below a bandwidth as controls. Finally, without stayers or quasi-stayers, we propose non-parametric bounds, and an estimator relying on a parametric specification of treatment-effect heterogeneity. We use our results to revisit Pierce and Schott (2016) and Enikolopov et al. (2011).

We study a dynamic contracting problem with multiple agents and a lack of commitment. A principal who can only commit to one-period contracts wants to screen efficient agents over time. Once an agent reveals his type, the principal becomes tempted to revise contract terms, causing a "ratchet effect." Alterations of contracts are observable and, hence, whenever past promises are not honored future information revelation stops. We provide a necessary and sufficient condition under which the principal is able to foster information revelation. When players are sufficiently patient, the agents' private information is either never revealed or fully revealed in a sequential manner. Optimal contracts entail high-powered incentives after an agent's type is initially disclosed, and rewards for information revelation disappear in the long run.

Forecasting, to estimate future events, is crucial for business and decision-making. This paper proposes QxEAI, a methodology that produces a probabilistic forecast that utilizes a quantum-like evolutionary algorithm based on training a quantum-like logic decision tree and a classical value tree on a small number of related time series. By using different cycles of the Dow Jones Index (yearly, monthly, weekly, daily), we demonstrate how our methodology produces accurate forecasts while requiring little to none manual work.