flexpaneldid: A Stata Toolbox for Causal Analysis with Varying Treatment Time and Duration
Eva Dettmann, Alexander Giebler, Antje Weyh
IWH Discussion Papers,
Nr. 3,
2020
Abstract
The paper presents a modification of the matching and difference-in-differences approach of Heckman et al. (1998) for the staggered treatment adoption design and a Stata tool that implements the approach. This flexible conditional difference-in-differences approach is particularly useful for causal analysis of treatments with varying start dates and varying treatment durations. Introducing more flexibility enables the user to consider individual treatment periods for the treated observations and thus circumventing problems arising in canonical difference-in-differences approaches. The open-source flexpaneldid toolbox for Stata implements the developed approach and allows comprehensive robustness checks and quality tests. The core of the paper gives comprehensive examples to explain the use of the commands and its options on the basis of a publicly accessible data set.
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flexpaneldid: A Stata Command for Causal Analysis with Varying Treatment Time and Duration
Eva Dettmann, Alexander Giebler, Antje Weyh
Abstract
>>A completely revised version of this paper has been published as: Dettmann, Eva; Giebler, Alexander; Weyh, Antje: flexpaneldid. A Stata Toolbox for Causal Analysis with Varying Treatment Time and Duration. IWH Discussion Paper 3/2020. Halle (Saale) 2020.<<
The paper presents a modification of the matching and difference-in-differences approach of Heckman et al. (1998) and its Stata implementation, the command flexpaneldid. The approach is particularly useful for causal analysis of treatments with varying start dates and varying treatment durations (like investment grants or other subsidy schemes). Introducing more flexibility enables the user to consider individual treatment and outcome periods for the treated observations. The flexpaneldid command for panel data implements the developed flexible difference-in-differences approach and commonly used alternatives like CEM Matching and difference-in-differences models. The novelty of this tool is an extensive data preprocessing to include time information into the matching approach and the treatment effect estimation. The core of the paper gives two comprehensive examples to explain the use of flexpaneldid and its options on the basis of a publicly accessible data set.
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Vertical Grants and Local Public Efficiency
Ivo Bischoff, Peter Bönisch, Peter Haug, Annette Illy
Abstract
This paper analyses the impact of vertical grants on local public sector efficiency. First, we develop a theoretical model in which the bureaucrat sets the tax price while voters choose the quantity of public services. In this model, grants reduce efficiency if voters do not misinterpret the amount of vertical grants the local bureaucrats receive. If voters suffer from fiscal illusion, i.e. overestimate the amount of grants, our model yields an ambiguous effect of grants on efficiency. Second, we use the model to launch a note of caution concerning the inference that can be drawn from the existing cross-sectional studies in this field: Taking into account vertical financial equalization systems that reduce differences in fiscal capacity, empirical studies based on cross-sectional data may yield a positive relationship between grants and efficiency even when the underlying causal effect is negative. Third, we perform an empirical analysis for the German state of Saxony-Anhalt, which has implemented such a fiscal equalization system. We find a positive relationship between grants and efficiency. Our analysis shows that a careful reassessment of existing empirical evidence with regard to this issue seems necessary.
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