Empirical Economics

Nguyen, Phuong Anh; Wolf, Michael

doi: 10.1007/s00181-026-02905-6pmid: N/A

Return event studies typically involve a large number of event instances. In some applications, however, this number may be very small–sometimes as few as two event instances. In such cases, standard approaches to testing average abnormal returns (AAR) or cumulative average abnormal returns (CAAR) are less effective, or may not apply at all, as they rely on central limit theorems that require large sample sizes. We propose a nonparametric permutation test that remains valid for arbitrarily small numbers of event instances. Its performance is evaluated via Monte Carlo studies, and the method is further illustrated using two empirical applications.

Hu, Min; Daley, Angela; Watson, Barry

doi: 10.1007/s00181-026-02903-8pmid: N/A

The impact of the 2016 Canada Child Benefit (CCB) on parental health has not yet been evaluated. We assess this using the Canadian Community Health Survey (2011–2021) and a difference-in-differences approach. Our findings indicate a small yet statistically significant positive effect on self-reported general and mental health, possibly driven by increased physical activity. This suggests that the CCB helped buffer against the average decline in parental health during our study period. The improvement in maternal general health was concentrated among those with young children, and the positive effect on mental health was observed for both mothers and fathers, regardless of the child’s age. An event study further reveals that improvements in mental health were fairly immediate and sustained over time. However, these benefits did not extend to economically vulnerable parents, lone parents or parents with four or more children, and overall the CCB did little to reduce the socioeconomic gradient in parental health. Our heterogeneity analysis shows that improvements in parental health were concentrated among higher-income parents and two-parent families, while the CCB had a detrimental effect on the mental health of lone parents, despite being income-tested. We argue that policy adjustments, such as larger or better-targeted child benefits, are necessary to address these health disparities.

Clark, Joshua; Delgado, Michael S.

doi: 10.1007/s00181-026-02894-6pmid: N/A

Medical marijuana was legalized in Oklahoma as recently as 2018, and since then Oklahoma has rapidly grown to have the largest number of medical marijuana dispensaries of any states in the USA. What have been the impacts of this rapid proliferation of legalized medical marijuana? We use a hedonic pricing model to assess how legalized marijuana dispensary activity (both distance to the nearest dispensary and the number of nearby dispensaries) has impacted residential property values. The dataset spans six different counties from across the State of Oklahoma, and we explore a variety of different regression specifications to rigorously explore the effects of dispensary activity on local residents. While we find evidence of some heterogeneity in the dispensary effects, one broad finding is that residents prefer not to live in immediate proximity to a dispensary but prefer access to dispensaries moderately distanced from their home. Understanding these broad trends and the localized heterogeneity in effects are important for residents and policymakers alike in Oklahoma and in other states that may be considering similar legislation.

Ayad, Fayssal

doi: 10.1007/s00181-026-02906-5pmid: N/A

Despite extensive research on innovation subsidies, the critical question of how to optimally assign these public subsidies to firms remains largely unexplored. This paper introduces an optimal policy learning (OPL) approach to map firm characteristics to subsidy assignment under welfare maximization of firm-innovation output, using (i) threshold-based, (ii) linear-combination, and (iii) fixed-depth decision tree policies over a large sample of Spanish firms. Exploiting comprehensive data from the Spanish Technological Innovation Survey, I construct an aggregate measure of innovation output using a generalized least squares weighting procedure, relying on a standardized inverse-covariance weighted average of innovation outcomes. Harnessing a mix of subsidies managed at national, regional, and European levels, critical firm characteristics are selected from a large pool of variables on the basis of a post-double-selection Lasso method. Leveraging regression adjustment and causal forests to estimate the heterogeneous treatment effects of subsidies on innovation, OPL findings indicate that innovation expenditures and firm size consistently emerge as key determinants for subsidy allocation, with decision trees providing maximal expected constrained welfare. The paper offers actionable insights for policymakers, emphasizing tailored subsidy frameworks over generic, one-size-fits-all approaches. The proposed approach not only optimizes innovation output but also provides a scalable and replicable framework for subsidy allocation, within institutional contexts similar to Spain’s multi-level innovation subsidy system.