The Effects of Antitrust Laws on Horizontal Mergers: International Evidence
Chune Young Chung, Iftekhar Hasan, JiHoon Hwang, Incheol Kim
Journal of Financial and Quantitative Analysis,
Nr. 7,
2024
Abstract
This study examines how antitrust law adoptions affect horizontal merger and acquisition (M&A) outcomes. Using the staggered introduction of competition laws in 20 countries, we find antitrust regulation decreases acquirers’ five-day cumulative abnormal returns surrounding horizontal merger announcements. A decrease in deal value, target book assets, and industry peers' announcement returns are consistent with the market power hypothesis. Exploiting antitrust law adoptions addresses a downward bias to an estimated effect of antitrust enforcement (Baker (2003)). The potential bias from heterogeneous treatment effects does not nullify our results. Overall, antitrust policies seem to deter post-merger monopolistic gains, potentially improving customer welfare.
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Employment Effects of Investment Grants and Firm Heterogeneity – Evidence from a Staggered Adoption Approach
Eva Dettmann, Mirko Titze, Antje Weyh
IWH Discussion Papers,
Nr. 6,
2023
Abstract
This study estimates the firm-level employment effects of investment grants in Germany. In addition to the average treatment effect on the treated, we examine discrimination in the funding rules as potential source of effect heterogeneity. We combine a staggered difference-in-differences approach that explicitly models variations in treatment timing with a matching procedure at the cohort level. The findings reveal a positive effect of investment grants on employment development in the full sample. The subsample analysis yields strong evidence for heterogeneous effects based on firm characteristics and the economic environment. This can help to improve the future design of the program.
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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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