Synthetic Control Estimation Beyond Case Studies: Does the Minimum Wage Reduce Employment?

Powell, David

doi:10.7249/wr1142

Cited by 34 publications

(41 citation statements)

References 42 publications

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“…For the first goal, a natural approach is to follow the SC literature on evaluating the accuracy of the SC estimator or combining multiple SC estimators by using the inverse of the MSPE values for each SC estimator . For cluster i in period j , the MSPE of the SC fit is given by Equation .…”

Section: Methodsmentioning

confidence: 99%

Novel methods for the analysis of stepped wedge cluster randomized trials

Kennedy‐Shaffer

Gruttola

Lipsitch

2019

Statistics in Medicine

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Stepped wedge cluster randomized trials (SW‐CRTs) have become increasingly popular and are used for a variety of interventions and outcomes, often chosen for their feasibility advantages. SW‐CRTs must account for time trends in the outcome because of the staggered rollout of the intervention. Robust inference procedures and nonparametric analysis methods have recently been proposed to handle such trends without requiring strong parametric modeling assumptions, but these are less powerful than model‐based approaches. We propose several novel analysis methods that reduce reliance on modeling assumptions while preserving some of the increased power provided by the use of mixed effects models. In one method, we use the synthetic control approach to find the best matching clusters for a given intervention cluster. Another method makes use of within‐cluster crossover information to construct an overall estimator. We also consider methods that combine these approaches to further improve power. We test these methods on simulated SW‐CRTs, describing scenarios in which these methods have increased power compared with existing nonparametric methods while preserving nominal validity when mixed effects models are misspecified. We also demonstrate theoretical properties of these estimators with less restrictive assumptions than mixed effects models. Finally, we propose avenues for future research on the use of these methods; motivation for such research arises from their flexibility, which allows the identification of specific causal contrasts of interest, their robustness, and the potential for incorporating covariates to further increase power. Investigators conducting SW‐CRTs might well consider such methods when common modeling assumptions may not hold.

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Section: Methodsmentioning

confidence: 99%

Novel methods for the analysis of stepped wedge cluster randomized trials

Kennedy‐Shaffer

Gruttola

Lipsitch

2019

Statistics in Medicine

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“…This research needs to better illuminate the determination of minimum wage variation, as well as how the determination of this variation influences the different estimators. Of course, if we have a means of implementing a synthetic control estimator across all minimum wage increases, as in Powell (2016), then maybe this point is now moot with respect to the U.S. evidence, because we do not need to rely on a priori assumptions about which controls are valid. Still, it would be better to uncover -if we can -an empirically-grounded, behavioral basis for specifying control areas.…”

Section: Iii3 Should We Be Convinced By the Absence Of Disemploymenmentioning

confidence: 99%

“…Most recently, in what appears to be the most satisfactory and flexible approach, Powell (2016) develops and uses a method that can be applied to multiple treatments with continuous variation, and that simultaneously estimates the treatment effect and the weights on the control states. His method avoids the problem of selecting minimum wage increases with clean controls, and hence can use all the data.…”

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confidence: 99%

“…In my view, this table establishes the rather clear result I alluded to earlier: Studies using close controls generally find small disemployment effects that are indistinguishable from zero. But other identification strategies -differencing estimators that control for state-specific shocks, IV estimates that purge the minimum wage variable of correlation with these shocks, as well as the most advanced synthetic control estimator (Powell, 2016) -tend to find larger disemployment effects.…”

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The Econometrics and Economics of the Employment Effects of Minimum Wages: Getting from Known Unknowns to Known Knowns

Neumark

2019

German Economic Review

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I discuss the econometrics and the economics of past research on the effects of minimum wages on employment in the United States. My intent is to try to identify key questions raised in the recent literature, and some from the earlier literature, which I think hold the most promise for understanding the conflicting evidence and arriving at a more definitive answer about the employment effects of minimum wages. My secondary goal is to discuss how we can narrow the range of uncertainty about the likely effects of the large minimum wage increases becoming more prevalent in the United States. I discuss some insights from both theory and past evidence that may be informative about the effects of high minimum wages, and try to emphasize what research can be done now and in the near future to provide useful evidence to policymakers on the results of the coming high minimum wage experiment, whether in the United States or in other countries.

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“… The controls selected in the LASSO model include age, education, race, gender, marital status, number of people in family/household, number of children in family/household, state regular minimum wage, state per‐capita GDP, state prime age wage rate, state refundable EITC credit, the average age of the state population, and state‐specific linear time trends and Census division‐specific year effects for most states and census divisions. As an alternative approach, some minimum‐wage scholars have used synthetic control design (Powell ; Neumark, Salas, and Wascher , ; Sabia, Burkhauser, and Hansen , ), though these approaches, along with the LASSO approach, have been subject to critiques (Allegretto et al. ; Neumark and Wascher ). …”

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Minimum Cash Wages, Tipped Restaurant Workers, and Poverty

Sabia¹,

Burkhauser²,

Mackay³

2018

Industrial Relations

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This is the first study to examine the effect of increases in the tipped minimum cash wage—the wage employers must pay to tipped employees—on poverty. Using March Current Population Survey data (1988–2014), we find that tipped minimum cash wage increases are associated with declines in the risk of a tipped restaurant worker living in a poor family (elasticities around –0.2). However, we find little evidence of poverty‐alleviating effects when using the household rather than the family as the sharing unit. This result is consistent with evidence that a substantial share of tipped workers who live in a poor family live in a nonpoor household with persons unrelated by blood, marriage, or adoption who contribute to the household's income. Furthermore, we find that tipped minimum cash wage hikes are associated with increases in the risk of a younger, less‐educated individual living in a poor family or household. Adverse labor demand effects that redistribute income among low‐skilled individuals drive these results. We conclude that raising the tipped minimum cash wage is a poorly targeted policy to deliver income to poor restaurant workers.

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Synthetic Control Estimation Beyond Case Studies: Does the Minimum Wage Reduce Employment?

Cited by 34 publications

References 42 publications

Novel methods for the analysis of stepped wedge cluster randomized trials

Novel methods for the analysis of stepped wedge cluster randomized trials

The Econometrics and Economics of the Employment Effects of Minimum Wages: Getting from Known Unknowns to Known Knowns

Minimum Cash Wages, Tipped Restaurant Workers, and Poverty

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