Showing papers by "David S. Lee published in 2012"
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TL;DR: In this article, the effect of new unionization on firms' equity value over the 1961-1999 period using a newly assembled sample of National Labor Relations Board (NLRB) representation elections matched to stock market data.
Abstract: We estimate the effect of new unionization on firms’ equity value over the 1961-1999 period using a newly assembled sample of National Labor Relations Board (NLRB) representation elections matched to stock market data. Event-study estimates show an average union effect on the equity value of the firm equivalent to a cost of at least $40,500 per unionized worker. At the same time, point estimates from a regression-discontinuity design – comparing the stock market impact of close union election wins to close losses – are considerably maller and close to zero. We find a negative relationship between the cumulative abnormal returns and the vote share in support of the union, allowing us to reconcile these seemingly contradictory findings. Using the magnitudes from the analysis, we calibrate a structural “median voter” model of endogenous union determination in order to conduct counterfactual policy simulations of policies that would marginally increase the ease of unionization.
269 citations
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TL;DR: In this article, the authors provide a theoretical analysis of optimal minimum wage policy in a perfectly competitive labor market and obtain two key results: 1) a binding minimum wage while leading to unemployment is nevertheless desirable if the government values redistribution toward low-wage workers and if unemployment induced by the minimum wage hits the lowest surplus workers first.
133 citations
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TL;DR: In this article, a nonparametric identification and estimation in a nonseparable model where a continuous regression of interest is a known, deterministic, but kinked function of an observed assignment variable is considered.
Abstract: We consider nonparametric identification and estimation in a nonseparable model where a continuous
regressor of interest is a known, deterministic, but kinked function of an observed assignment variable.
This design arises in many institutional settings where a policy variable (such as weekly unemployment
benefits) is determined by an observed but potentially endogenous assignment variable (like previous
earnings). We provide new results on identification and estimation for these settings, and apply our
results to obtain estimates of the elasticity of joblessness with respect to UI benefit rates. We characterize
a broad class of models in which a “Regression Kink Design” (RKD, or RK Design) provides valid
inferences for the treatment-on-the-treated parameter (Florens et al. (2008)) that would be identified
in an ideal randomized experiment. We show that the smooth density condition that is sufficient for
identification rules out extreme sorting around the kink, but is compatible with less severe forms of
endogeneity. It also places testable restrictions on the distribution of predetermined covariates around
the kink point. We introduce a generalization of the RKD – the “fuzzy regression kink design” – that
allows for omitted variables in the assignment rule, as well as certain types of measurement errors
in the observed values of the assignment variable and the policy variable. We also show how standard
local polynomial regression techniques can be adapted to obtain nonparametric estimates for the sharp
and fuzzy RKD. We then use a fuzzy RKD approach to study the effect of unemployment insurance
benefits on the duration of joblessness in Austria, where the benefit schedule has kinks at the minimum
and maximum benefit level. Our estimates suggest that the elasticity of joblessness with respect to
the benefit rate is on the order of 1.5
114 citations
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TL;DR: In this article, a nonparametric identification and estimation in a nonseparable model where a continuous regressor of interest is a known, deterministic, but kinked function of an observed assignment variable is considered.
Abstract: We consider nonparametric identification and estimation in a nonseparable model where a continuous regressor of interest is a known, deterministic, but kinked function of an observed assignment variable. This design arises in many institutional settings where a policy variable (such as weekly unemployment benefits) is determined by an observed but potentially endogenous assignment variable (like previous earnings). We provide new results on identification and estimation for these settings, and apply our results to obtain estimates of the elasticity of joblessness with respect to UI benefit rates. We characterize a broad class of models in which a "Regression Kink Design" (RKD, or RK Design) provides valid inferences for the treatment-on-the-treated parameter (Florens et al. (2008)) that would be identified in an ideal randomized experiment. We show that the smooth density condition that is sufficient for identification rules out extreme sorting around the kink, but is compatible with less severe forms of endogeneity. It also places testable restrictions on the distribution of predetermined covariates around the kink point. We introduce a generalization of the RKD - the "fuzzy regression kink design" - that allows for omitted variables in the assignment rule, as well as certain types of measurement errors in the observed values of the assignment variable and the policy variable. We also show how standard local polynomial regression techniques can be adapted to obtain nonparametric estimates for the sharp and fuzzy RKD. We then use a fuzzy RKD approach to study the effect of unemployment insurance benefits on the duration of joblessness in Austria, where the benefit schedule has kinks at the minimum and maximum benefit level. Our estimates suggest that the elasticity of joblessness with respect to the benefit rate is on the order of 1.5.
18 citations