(Working Papers SES ; 514)
Causal mediation analysis aims at disentangling a treatment effect into an indirect mechanism operating through an intermediate outcome or mediator, as well as the direct effect of the treatment on the outcome of interest. However, the evaluation of direct and indirect effects is frequently complicated by non-ignorable selection into the treatment and/or mediator, even after controlling for...
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(Working Papers SES ; 513)
We propose a new method for flagging bid rigging, which is particularly useful for detecting incomplete bid-rigging cartels. Our approach combines screens, i.e. statistics derived from the distribution of bids in a tender, with machine learning to predict the probability of collusion. As a methodological innovation, we calculate such screens for all possible subgroups of three or four bids...
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In: Journal of Business & Economic Statistics, 2016, vol. 34, no. 1, p. 139-160
Using a comprehensive simulation study based on empirical data, this article investigates the finite sample properties of different classes of parametric and semiparametric stimators of (natural) direct and indirect causal effects used in mediation analysis under sequential conditional independence assumptions. The estimators are based on regression, inverse probability weighting, and...
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(Working Papers SES ; 508)
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...
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(Working Papers SES ; 506)
The apprenticeship market is the earliest possible entry into the workforce in developed economies. Since early labor market shocks are likely magnified throughout professional life, avoiding mismatches between talent and occupations e.g. due to gender- or status-based discrimination appears crucial. This experimental study investigates the effects of applicant gender and its interaction with...
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(Working Papers SES ; 504)
This chapter covers different approaches to policy evaluation for assessing the causal effect of a treatment or intervention on an outcome of interest. As an introduction to causal inference, the discussion starts with the experimental evaluation of a randomized treatment. It then reviews evaluation methods based on selection on observables (assuming a quasi-random treatment given observed...
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In: Empirical Economics, 2014, vol. 47, no. 1, p. 75-92
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In: Educational studies. Moscow, 2016, no. 1, p. 61-83
The authors investigate the effect of anti-corruption educational materials — an informational folder with materials designed by Transparency International — on the willingness of students to participate in an anti-corruption campaign and their general judgment about corruption in two cities in Russia and Ukraine by conducting experiments. During a survey of 350 students in Khabarovsk,...
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In: Discussion papers on business and economics, 2017, vol. 2, p. 1-69
In heterogeneous treatment effect models with endogeneity, identification of the LATE typically relies on the availability of an exogenous instrument monotonically related to treatment participation. We demonstrate that a strictly weaker local monotonicity condition identifies the LATEs on compliers and on defiers. We propose simple estimators that are potentially more efficient than 2SLS,...
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In: Journal of applied statistics, 2011, vol. 38, no. 12, p. 2881-2899
Consistencyofpropensityscorematchingestimatorshingesonthepropensityscore’sabilityt obalancethe distributions of covariates in the pools of treated and non-treated units. Conventional balance tests merely check for differences in covariates’means, but cannot account for differences in higher moments. For this ...
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