arXiv subjects

Anthony Strittmatter

Publications and source records attributed to Anthony Strittmatter.

14 recordsLinked to original sources

arXiv2026

A Machine-Learning-Compatible Omnibus Test for Treatment Effect Heterogeneity

This study proposes a formal, computationally efficient nonparametric omnibus test for treatment-effect heterogeneity that is compatible with a broad class of estimators, including modern machine-learning methods. The test is designed for settings in which identification can rely on high-dimensional controls while heterogeneity is assessed with respect to a low-dimensional subset of covariates. We derive the test statistic's asymptotic null distribution and develop a bootstrap procedure that is efficient because it avoids re-estimating nuisance parameters in each iteration. The testing approach applies to multiple empirical designs, including randomized experiments, selection-on-observables, difference-in-differences, and instrumental-variables settings. Monte Carlo simulations show that the test attains near-nominal size under the null and exhibits good power against heterogeneous alternatives. We further illustrate the procedure using two empirical applications on retirement savings and trade liberalization.

econ.EM↗

arXiv2022

Speed, Quality, and the Optimal Timing of Complex Decisions: Field Evidence

This paper presents an empirical investigation of the relation between decision speed and decision quality for a real-world setting of cognitively-demanding decisions in which the timing of decisions is endogenous: professional chess. Move-by-move data provide exceptionally detailed and precise information about decision times and decision quality, based on a comparison of actual decisions to a computational benchmark of best moves constructed using the artificial intelligence of a chess engine. The results reveal that faster decisions are associated with better performance. The findings are consistent with the predictions of procedural decision models like drift-diffusion-models in which decision makers sequentially acquire information about decision alternatives with uncertain valuations.

econ.GN↗

arXiv2021

Optimal Targeting in Fundraising: A Causal Machine-Learning Approach

Ineffective fundraising lowers the resources charities can use to provide goods. We combine a field experiment and a causal machine-learning approach to increase a charity's fundraising effectiveness. The approach optimally targets a fundraising instrument to individuals whose expected donations exceed solicitation costs. Our results demonstrate that machine-learning-based optimal targeting allows the charity to substantially increase donations net of fundraising costs relative to uniform benchmarks in which either everybody or no one receives the gift. To that end, it (a) should direct its fundraising efforts to a subset of past donors and (b) never address individuals who were previously asked but never donated. Further, we show that the benefits of machine-learning-based optimal targeting even materialize when the charity only exploits publicly available geospatial information or applies the estimated optimal targeting rule to later fundraising campaigns conducted in similar samples. We conclude that charities not engaging in optimal targeting waste significant resources.

econ.EM↗

arXiv2021

The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?

The vast majority of existing studies that estimate the average unexplained gender pay gap use unnecessarily restrictive linear versions of the Blinder-Oaxaca decomposition. Using a notably rich and large data set of 1.7 million employees in Switzerland, we investigate how the methodological improvements made possible by such big data affect estimates of the unexplained gender pay gap. We study the sensitivity of the estimates with regard to i) the availability of observationally comparable men and women, ii) model flexibility when controlling for wage determinants, and iii) the choice of different parametric and semi-parametric estimators, including variants that make use of machine learning methods. We find that these three factors matter greatly. Blinder-Oaxaca estimates of the unexplained gender pay gap decline by up to 39% when we enforce comparability between men and women and use a more flexible specification of the wage equation. Semi-parametric matching yields estimates that when compared with the Blinder-Oaxaca estimates, are up to 50% smaller and also less sensitive to the way wage determinants are included.

econ.GN↗

arXiv2020

Identifying causal channels of policy reforms with multiple treatments and different types of selection

We study the identification of channels of policy reforms with multiple treatments and different types of selection for each treatment. We disentangle reform effects into policy effects, selection effects, and time effects under the assumption of conditional independence, common trends, and an additional exclusion restriction on the non-treated. Furthermore, we show the identification of direct- and indirect policy effects after imposing additional sequential conditional independence assumptions on mediating variables. We illustrate the approach using the German reform of the allocation system of vocational training for unemployed persons. The reform changed the allocation of training from a mandatory system to a voluntary voucher system. Simultaneously, the selection criteria for participants changed, and the reform altered the composition of course types. We consider the course composition as a mediator of the policy reform. We show that the empirical evidence from previous studies reverses when considering the course composition. This has important implications for policy conclusions.

econ.EM↗

arXiv2020

Decisions and Performance Under Bounded Rationality: A Computational Benchmarking Approach

This paper presents a novel approach to analyze human decision-making that involves comparing the behavior of professional chess players relative to a computational benchmark of cognitively bounded rationality. This benchmark is constructed using algorithms of modern chess engines and allows investigating behavior at the level of individual move-by-move observations, thus representing a natural benchmark for computationally bounded optimization. The analysis delivers novel insights by isolating deviations from this benchmark of bounded rationality as well as their causes and consequences for performance. The findings document the existence of several distinct dimensions of behavioral deviations, which are related to asymmetric positional evaluation in terms of losses and gains, time pressure, fatigue, and complexity. The results also document that deviations from the benchmark do not necessarily entail worse performance. Faster decisions are associated with more frequent deviations from the benchmark, yet they are also associated with better performance. The findings are consistent with an important influence of intuition and experience, thereby shedding new light on the recent debate about computational rationality in cognitive processes.

econ.GN↗

arXiv2019

Direct and Indirect Effects based on Changes-in-Changes

We propose a novel approach for causal mediation analysis based on changes-in-changes assumptions restricting unobserved heterogeneity over time. This allows disentangling the causal effect of a binary treatment on a continuous outcome into an indirect effect operating through a binary intermediate variable (called mediator) and a direct effect running via other causal mechanisms. We identify average and quantile direct and indirect effects for various subgroups under the condition that the outcome is monotonic in the unobserved heterogeneity and that the distribution of the latter does not change over time conditional on the treatment and the mediator. We also provide a simulation study and an empirical application to the Jobs II programme.

econ.EM↗

arXiv2019

Sorting on the Used-Car Market After the Volkswagen Emission Scandal

The disclosure of the VW emission manipulation scandal caused a quasi-experimental market shock to the observable environmental quality of VW diesel vehicles. To investigate the market reaction to this shock, we collect data from a used-car online advertisement platform. We find that the supply of used VW diesel vehicles increases after the VW emission scandal. The positive supply side effects increase with the probability of manipulation. Furthermore, we find negative impacts on the asking prices of used cars subject to a high probability of manipulation. We rationalize these findings with a model for sorting by the environmental quality of used cars.

econ.GN↗

arXiv2019

Heterogeneous Earnings Effects of the Job Corps by Gender Earnings: A Translated Quantile Approach

Several studies of the Job Corps tend to nd more positive earnings effects for males than for females. This effect heterogeneity favouring males contrasts with the results of the majority of other training programmes' evaluations. Applying the translated quantile approach of Bitler, Hoynes, and Domina (2014), I investigate a potential mechanism behind the surprising findings for the Job Corps. My results provide suggestive evidence that the effect of heterogeneity by gender operates through existing gender earnings inequality rather than Job Corps trainability differences.

econ.EM↗

arXiv2018

What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?

Recent studies have proposed causal machine learning (CML) methods to estimate conditional average treatment effects (CATEs). In this study, I investigate whether CML methods add value compared to conventional CATE estimators by re-evaluating Connecticut's Jobs First welfare experiment. This experiment entails a mix of positive and negative work incentives. Previous studies show that it is hard to tackle the effect heterogeneity of Jobs First by means of CATEs. I report evidence that CML methods can provide support for the theoretical labor supply predictions. Furthermore, I document reasons why some conventional CATE estimators fail and discuss the limitations of CML methods.

econ.EM↗

arXiv2018

Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence

We investigate the finite sample performance of causal machine learning estimators for heterogeneous causal effects at different aggregation levels. We employ an Empirical Monte Carlo Study that relies on arguably realistic data generation processes (DGPs) based on actual data. We consider 24 different DGPs, eleven different causal machine learning estimators, and three aggregation levels of the estimated effects. In the main DGPs, we allow for selection into treatment based on a rich set of observable covariates. We provide evidence that the estimators can be categorized into three groups. The first group performs consistently well across all DGPs and aggregation levels. These estimators have multiple steps to account for the selection into the treatment and the outcome process. The second group shows competitive performance only for particular DGPs. The third group is clearly outperformed by the other estimators.

econ.EM↗

arXiv2018

Religion and Terrorism: Evidence from Ramadan Fasting

We study the effect of religion and intense religious experiences on terrorism by focusing on one of the five pillars of Islam: Ramadan fasting. For identification, we exploit two facts: First, daily fasting from dawn to sunset during Ramadan is considered mandatory for most Muslims. Second, the Islamic calendar is not synchronized with the solar cycle. We find a robust negative effect of more intense Ramadan fasting on terrorist events within districts and country-years in predominantly Muslim countries. This effect seems to operate partly through decreases in public support for terrorism and the operational capabilities of terrorist groups.

econ.GN↗

arXiv2018

Expropriations, Property Confiscations and New Offshore Entities: Evidence from the Panama Papers

Using the Panama Papers, we show that the beginning of media reporting on expropriations and property confiscations in a country increases the probability that offshore entities are incorporated by agents from the same country in the same month. This result is robust to the use of country-year fixed effects and the exclusion of tax havens. Further analysis shows that the effect is driven by countries with non-corrupt and effective governments, which supports the notion that offshore entities are incorporated when reasonably well-intended and well-functioning governments become more serious about fighting organized crime by confiscating proceeds of crime.

econ.GN↗

arXiv2017

Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach

We systematically investigate the effect heterogeneity of job search programmes for unemployed workers. To investigate possibly heterogeneous employment effects, we combine non-experimental causal empirical models with Lasso-type estimators. The empirical analyses are based on rich administrative data from Swiss social security records. We find considerable heterogeneities only during the first six months after the start of training. Consistent with previous results of the literature, unemployed persons with fewer employment opportunities profit more from participating in these programmes. Furthermore, we also document heterogeneous employment effects by residence status. Finally, we show the potential of easy-to-implement programme participation rules for improving average employment effects of these active labour market programmes.

econ.EM↗