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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Taking the First Step - What Determines German Laser Source Manufacturers' Entry into Innovation Networks?
Jutta Günther, Muhamed Kudic, Andreas Pyka
International Journal of Innovation Management,
Nr. 5,
2015
Abstract
Early access to technological knowledge embodied in the industry’s innovation network can provide an important competitive advantage to firms. While the literature provides much evidence on the positive effects of innovation networks on firms’ performance, not much is known about the determinants of firms’ initial entry into such networks. We analyze firms’ timing and propensity to enter the industry’s innovation network. More precisely, we seek to shed some light on the factors affecting the duration between firm founding and its first cooperation event. In doing so, we apply a unique longitudinal event history dataset based on the full population of German laser source manufacturers. Innovation network data stem from official databases providing detailed information on the organizations involved, subject of joint research and development (R&D) efforts as well as start and end times for all publically funded R&D projects between 1990 and 2010. Estimation results from a non-parametric event history model indicate that micro firms enter the network later than small-sized or large firms. An in-depth analysis of the size effects for medium-sized firms provides some unexpected findings. The choice of cooperation type makes no significant difference for the firms’ timing to enter the network. Finally, the analysis of geographical determinants shows that cluster membership can, but do not necessarily, affect a firm’s timing to cooperate.
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Network Formation: R&D Cooperation Propensity and Timing Among German Laser Source Manufacturers
Muhamed Kudic, Andreas Pyka, Marco Sunder
IWH Discussion Papers,
Nr. 9,
2013
Abstract
Empirical evidence on the evolution of innovation networks within high-tech industries is still scant. We investigate network formation processes by analyzing the timing of firms to enter R&D cooperations, using data on laser source manufacturers in Germany, 1990-2010. Network measures are constructed from a unique industry database that allows us to track both the formation and the termination of ties. Regression results reveal that a firm's knowledge endowment (and cooperation experience) shortens the duration to first (and consecutive) cooperation events. The previous occupation of strategic network positions is closely related to the establishment of further R&D cooperations at a swift pace. Geographic co-location produces mixed results in our analysis.
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Evaluation of Further Training Programmes with an Optimal Matching Algorithm
Eva Reinowski, Birgit Schultz, Jürgen Wiemers
IWH Discussion Papers,
Nr. 188,
2004
Abstract
This study evaluates the effects of further training on the individual unemployment duration of different groups of persons representing individual characteristics and some aspects of the economic environment. The Micro Census Saxony enables us to include additional information about a person's employment history to eliminate the bias resulting from unobservable characteristics and to avoid Ashenfelter's Dip. In order to solve the sample selection problem we employ an optimal full matching assignment, the Hungarian algorithm. The impact of participation in further training is evaluated by comparing the unemployment duration between participants and non-participants using the Kaplan-Meier-estimator. Overall, we find empirical evidence that participation in further training programmes results in even longer unemployment duration.
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