In: Empirical Economics, 2015, vol. 49, no. 1, p. 1-31
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In: Journal of Educational and Behavioral Statistics, 2012, vol. 37, no. 3, p. 443-474
As any empirical method used for causal analysis, social experiments are prone to attrition which may flaw the validity of the results. This paper considers the problem of partially missing outcomes in experiments. Firstly, it systematically reveals under which forms of attrition - in terms of its relation to observable and/or unobservable factors - experiments do (not) yield causal...
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In: Journal of Business and Economic Statistics, 2019, p. 1-60
We propose a novel approach for causal mediation analysis based on changesin- 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...
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In: Journal of Econometric Methods, 2019, vol. 8, no. 1, p. 1-20
Using a sequential conditional independence assumption, this paper discusses fully nonparametric estimation of natural direct and indirect causal effects in causal mediation analysis based on inverse probability weighting. We propose estimators of the average indirect effect of a binary treatment, which operates through intermediate variables (or mediators) on the causal path between the...
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In: Agricultural and Resource Economics Review, 2019, vol. 48, no. 1, p. 117-141
This study investigates the effects of a local information campaign on farmers’ interest in a rural development programme (RDP) in the former Yugoslav Republic of Macedonia. The results suggest that while our intervention succeeded in informing farmers, it had a negative, albeit only marginally significant, effect on the reported possibility of using future RDP support. This puzzling result...
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In: International Journal of Industrial Organization, 2019, vol. 65, p. 277-301
We combine machine learning techniques with statistical screens computed from the distribution of bids in tenders within the Swiss construction sector to predict collusion through bid-rigging cartels. We assess the out of sample performance of this approach and find it to correctly classify more than 84% of the total of bidding processes as collusive or non-collusive. We also discuss...
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In: HMD Praxis der Wirtschaftsinformatik, 2019, vol. 57, p. 106-116
Die datenbasierte Kausalanalyse versucht, den kausalen Effekt einer Intervention auf ein interessierendes Ergebnis zu messen, häufig unter Kontrolle beobachtbarer Charakteristiken, die ebenfalls das Ergebnis beeinflussen. Beispiele für kausale Fragestellungen sind: Was ist der Effekt einer Marketingkampagne (Intervention) auf die Verkaufszahlen (Ergebnis) unter ansonsten identischen...
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In: Eurasian Geography and Economics, 2019, p. 304-332
This paper presents the outcomes of an anti-corruption educational intervention among Ukrainian students based on an online experiment. More than 3,000 survey participants were randomly assigned to one of three different videos on corruption and its consequences (treatment groups) or a video on higher education (control group). The data suggest a high level of academic dishonesty and...
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In: Journal of Business and Economic Statistics, 2019, vol. 37, no. 4, p. 710-720
We propose a difference-in-differences approach for disentangling a total treatment effect within specific subpopulations into a direct effect and an indirect effect operating through a binary mediating variable. Random treatment assignment along with specific common trend and effect homogeneity assumptions identify the direct effects on the always and never takers, whose mediator is not...
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In: Journal of Business and Economic Statistics, 2019, vol. 37, no. 4, p. 736-748
This article proposes a fully nonparametric kernel method to account for observed covariates in regression discontinuity designs (RDD), which may increase precision of treatment effect estimation. It is shown that conditioning on covariates reduces the asymptotic variance and allows estimating the treatment effect at the rate of one- dimensional nonparametric regression, irrespective of the...
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