Refine
Year of publication
- 2020 (4) (remove)
Document Type
- Article (1)
- Doctoral Thesis (1)
- Report (1)
- Working Paper (1)
Is part of the Bibliography
- yes (4)
Keywords
Interest in evaluating the effects of continuous treatments has been on the rise recently. To facilitate the estimation of causal effects in this setting, the present paper introduces entropy balancing for continuous treatments (EBCT) by extending the original entropy balancing methodology of Hainmüller (2012). In order to estimate balancing weights, the proposed approach solves a globally convex constrained optimization problem, allowing for much more computationally efficient implementation compared to other available methods. EBCT weights reliably eradicate Pearson correlations between covariates and the continuous treatment variable. This is the case even when other methods based on the generalized propensity score tend to yield insufficient balance due to strong selection into different treatment intensities. Moreover, the optimization procedure is more successful in avoiding extreme weights attached to a single unit. Extensive Monte-Carlo simulations show that treatment effect estimates using EBCT display similar or lower bias and uniformly lower root mean squared error. These properties make EBCT an attractive method for the evaluation of continuous treatments. Software implementation is available for Stata and R.
This thesis offers new insights on the effects of Start-Up Subsidies (SUS) for unemployed individuals as a special kind of active labor market program (ALMP) that aims to re-integrate individuals into the labor market via the route of self-employment. Moreover, this thesis contributes to the literature on methods for causal inference when the treatment variable is continuous rather than binary. For example, this is the case when individuals differ in their degree of exposure to a common treatment.
The analysis of the effects of SUS focuses on the main current German program called “Gründungszuschuss” (New Start-Up Subsidy, NSUS) after its reform in 2011. Average Effects on participants' labor market outcomes - as measured by employment and earnings - as well as subjective well-being are estimated mainly based on propensity score matching (PSM) techniques. PSM aims to achieve balance in terms of observed characteristics by matching participants with at least one comparable non-participant in terms of their probability to receive the treatment. This estimation strategy is valid as long as all relevant characteristics that explain selection patterns into treatment are observed and included in the estimation of the propensity score. To make our analysis as credible as possible, we control for a large vector of characteristics as observed through the combination of rich administrative data from the Federal Employment Agency as well as through survey data.
Chapters two to four of this thesis puts special emphasis on aspects regarding (the evaluation of) SUS programs that have received no or only limited attention thus far. The first aspect relates to the interplay of institutional details of the program and its effectiveness. So far, relatively little is known about the importance of SUS program features such as the duration of support. Second, there is no experimental benchmark evaluation of SUS available and thus, the reliability of non-experimental estimation techniques such as PSM is of crucial importance as estimates are biased when relevant confounders are omitted from the analysis. Third, there may be potentially detrimental effects of transitioning into (relatively risky) self-employment on subjective well-being among subsidized founders out of unemployment. These were to remain undetected if the analysis would focus exclusively on labor market outcomes of participants. The results indicate positive long-term effects of SUS participation on employment and earnings among participants. These effects are substantially larger than what estimated before the reform, indicating room for improvement in program design via changes in institutional details. Moreover, non-experimental estimates of treatment effects are remarkably robust to hidden confounding. Regarding subjective well-being, this thesis finds a positive long-run impact on job satisfaction and a detrimental effect on satisfaction with social security. The latter appears to be driven by adverse effects on social insurance contributions.
In chapter five, a novel automated covariate balancing technique for the estimation of causal effects in the context of continuous treatments is derived and assessed regarding its performance compared to other (automated) balancing techniques. Although binary research designs that only differentiate between participants and non-participants of some treatment remain the most-common case in empirical practice, many applications can be adapted to include continuous treatments as well. Often, this will allow for more meaningful estimates of causal effects in order to further improve the design of programs. In the context of SUS, one may further investigate the effects of the size of monetary support or its duration on participants' labor market outcomes. Both Monte-Carlo investigations and analysis of two well-known datasets suggests superior performance of the proposed Entropy Balancing for continuous treatments (EBCT) compared to other existing estimation strategies.
Background:
The literature on start-up subsidies (SUS) for the unemployed finds positive effects on objective outcome measures such as employment or income. However, little is known about effects on subjective well-being of participants. Knowledge about this is especially important because subsidizing the transition into self-employment may have unintended adverse effects on participants’ well-being due to its risky nature and lower social security protection, especially in the long run.
Objective:
We study the long-term effects of SUS on subjective outcome indicators of well-being, as measured by the participants’ satisfaction in different domains. This extends previous analyses of the current German SUS program (“Gründungszuschuss”) that focused on objective outcomes—such as employment and income—and allows us to make a more complete judgment about the overall effects of SUS at the individual level.
Research design:
Having access to linked administrative-survey data providing us with rich information on pretreatment characteristics, we base our analysis on the conditional independence assumption and use propensity score matching to estimate causal effects within the potential outcomes framework. We perform several sensitivity analyses to inspect the robustness of our findings.
Results:
We find long-term positive effects on job satisfaction but negative effects on individuals’ satisfaction with their social security situation. Supplementary findings suggest that the negative effect on satisfaction with social security may be driven by negative effects on unemployment and retirement insurance coverage. Our heterogeneity analysis reveals substantial variation in effects across gender, age groups, and skill levels. Estimates are highly robust.