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36
What Mean Impacts Miss: Distributional Effects of Welfare Reform Experiments
 American Economic Review
, 2006
"... Labor supply theory predicts systematic heterogeneity in the impact of recent welfare reforms on earnings, transfers, and income. Yet most welfare reform research focuses on mean impacts. We investigate the importance of heterogeneity using randomassignment data from Connecticut’s Jobs First waiver ..."
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Cited by 70 (8 self)
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Labor supply theory predicts systematic heterogeneity in the impact of recent welfare reforms on earnings, transfers, and income. Yet most welfare reform research focuses on mean impacts. We investigate the importance of heterogeneity using randomassignment data from Connecticut’s Jobs First waiver, which features key elements of post1996 welfare programs. Estimated quantile treatment effects exhibit the substantial heterogeneity predicted by labor supply theory. Thus mean impacts miss a great deal. Looking separately at samples of dropouts and other women does not improve the performance of mean impacts. We conclude that welfare reform’s effects are likely both more varied and more extensive than has been recognized. (JEL D31, I38, J31) Nearly a decade has now passed since the elimination of Aid to Families with Dependent Children (AFDC), the principal U.S. cash assistance program for six decades. In 1996, enactment of the Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) required all
What Are We Weighting For?
, 2013
"... The purpose of this paper is to help empirical economists think through when and how to weight the data used in estimation. We start by distinguishing two purposes of estimation: to estimate population descriptive statistics and to estimate causal effects. In the former type of research, weighting i ..."
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Cited by 14 (0 self)
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The purpose of this paper is to help empirical economists think through when and how to weight the data used in estimation. We start by distinguishing two purposes of estimation: to estimate population descriptive statistics and to estimate causal effects. In the former type of research, weighting is called for when it is needed to make the analysis sample representative of the target population. In the latter type, the weighting issue is more nuanced. We discuss three distinct potential motives for weighting when estimating causal effects: (1) to achieve precise estimates by correcting for heteroskedasticity, (2) to achieve consistent estimates by correcting for endogenous sampling, and (3) to identify average partial effects in the presence of unmodeled heterogeneity of effects. In each case, we find that the motive sometimes does not apply in situations where practitioners often assume it does. We recommend diagnostics for assessing the advisability of weighting, and we suggest methods for appropriate inference.
2008), ‘Distributional Impacts of the SelfSufficiency Project
 in 20 Countries
, 1996
"... A large literature has been concerned with the impacts of recent welfare reforms on income, earnings, transfers, and laborforce attachment. While one strand of this literature relies on observational studies conducted with large surveysample data sets, a second makes use of data generated by exper ..."
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Cited by 10 (1 self)
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A large literature has been concerned with the impacts of recent welfare reforms on income, earnings, transfers, and laborforce attachment. While one strand of this literature relies on observational studies conducted with large surveysample data sets, a second makes use of data generated by experimental evaluations of changes to meanstested programs. Much of the overall literature has focused on mean impacts. In this paper, we use randomassignment experimental data from Canada’s SelfSufficiency Project (SSP) to look at impacts of this unique reform on the distributions of income, earnings, and transfers. SSP offered members of the treatment group a generous subsidy for working full time. Quantile treatment effect (QTE) estimates show there was considerable heterogeneity in the impacts of SSP on the distributions of earnings, transfers, and total income; this heterogeneity would be missed by looking only at average treatment effects. Moreover, these heterogeneous impacts are consistent with the predictions of labor supply theory. During the period when the subsidy is available, the SSP impact on the earnings distribution is zero for the bottom half of the distribution. The SSP earnings distribution is higher for much of the upper third of the distribution except at the very top, where the earnings
How to Control for Many Covariates? Reliable Estimators Based on the Propensity Score
, 2010
"... ..."
Efficient Estimation of the Dose Response Function under Ignorability using Subclassification on the
, 2011
"... This chapter studies the large sample properties of a subclassificationbased estimator of the Dose Response Function under Ignorability. Under regularity conditions, it is shown that the estimator is rootn consistent, asymptotically linear and semiparametric efficient. A consistent estimator of th ..."
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Cited by 7 (4 self)
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This chapter studies the large sample properties of a subclassificationbased estimator of the Dose Response Function under Ignorability. Under regularity conditions, it is shown that the estimator is rootn consistent, asymptotically linear and semiparametric efficient. A consistent estimator of the standard errors is also developed under the same assumptions. We investigate the finite sample performance of this simple and intuitive estimator in a Monte Carlo experiment, and compare it to other commonly employed estimators.
Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations
, 2013
"... This paper concerns robust inference on average treatment effects following model selection. In the selection on observables framework, we show how to construct confidence intervals based on a doublyrobust estimator that are robust to model selection errors and prove that they are valid uniformly o ..."
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Cited by 7 (1 self)
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This paper concerns robust inference on average treatment effects following model selection. In the selection on observables framework, we show how to construct confidence intervals based on a doublyrobust estimator that are robust to model selection errors and prove that they are valid uniformly over a large class of treatment effect models. The class allows for multivalued treatments with heterogeneous effects (in observables), general heteroskedasticity, and selection amongst (possibly) more covariates than observations. Our estimator attains the semiparametric efficiency bound under appropriate conditions. Precise conditions are given for any model selector to yield these results, and we show how to combine datadriven selection with economic theory. For implementation, we give a specific proposal for selection based on the group lasso and derive new technical results for highdimensional, sparse multinomial logistic regression. A simulation study shows our estimator performs very well in finite samples over a wide range of models. Revisiting the National Supported Work demonstration data, our method yields accurate estimates and tight confidence intervals.
Improving semiparametric estimation using surrogate data
"... This paper considers estimating a parameter that denes an estimating function U(y; x; ) for an outcome variable y and its covariate x when the outcome is missing in some of the observations. We assume that, in addition to the outcome and the covariate, a surrogate outcome is available in every obse ..."
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Cited by 5 (1 self)
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This paper considers estimating a parameter that denes an estimating function U(y; x; ) for an outcome variable y and its covariate x when the outcome is missing in some of the observations. We assume that, in addition to the outcome and the covariate, a surrogate outcome is available in every observation. The eciency of existing estimators for depend critically on correctly specifying the conditional expectation of U given the surrogate and the covariate. When the conditional expectation is not correctly specied, which is the most likely scenario in practice, the estimation eciency can be severely compromised even if the propensity function (of missingness) is correctly specied. We propose an estimator that is robust against the choice of the conditional expectation via an empirical likelihood. We demonstrate that the proposed estimator achieves eciency gain whether the conditional score is correctly specied or not. When the conditional score is correctly specied, the estimator reaches the semiparametric variance bound within the class of estimating functions generated by U. The practical performance of the estimator is evaluated using simulation and a dataset based on the 1996 U.S. presidential election.
Is the Stock Market Just a Side Show? Evidence from a Structural Reform*
, 2012
"... The 2005 splitshare reform in China mandated the conversion of previously nontradable stocks into tradable status. The reform was swift and changed investors’ability to trade corporate equities in a US$400 billion market. This paper examines the e¤ects of stock markets on …rms ’ real and …nancial ..."
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Cited by 3 (0 self)
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The 2005 splitshare reform in China mandated the conversion of previously nontradable stocks into tradable status. The reform was swift and changed investors’ability to trade corporate equities in a US$400 billion market. This paper examines the e¤ects of stock markets on …rms ’ real and …nancial outcomes. It does so exploiting multiple institutional features of the Chinese equity conversion program. We …rst examine a pilot trial conducted at the beginning of the reform, which we are able to replicate using the same data and selection criteria that was used by policymakers. We also take advantage of the staggered nature of the conversion schedule used in the second phase of the reform, whereby over one thousand …rms converted their shares at di¤erent times within a governmentdictated window. These various wrinkles produce counterfactuals against which to gauge the economic importance of secondary equity trading. Using a timevarying treatment estimation approach, we identify increases in corporate pro…tability, investment, value, and productivity as shares start to trade freely in organized exchanges. We also identify changes in …rms’propensity to issue new shares and engage in merger deals, as well as changes in their dividend and capital structure policies. Our …ndings provide new insights on the role of stock markets in shaping corporate activity
Isolating the Roles of Individual Covariates in Reweighting Estimation
, 2011
"... A host of recent research has used reweighting methods to analyze the extent to which observable characteristics predict betweengroup differences in the distribution of an outcome. Much less attention has been paid to using reweighting methods to isolate the roles of individual covariates. We analy ..."
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Cited by 1 (1 self)
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A host of recent research has used reweighting methods to analyze the extent to which observable characteristics predict betweengroup differences in the distribution of an outcome. Much less attention has been paid to using reweighting methods to isolate the roles of individual covariates. We analyze two approaches that have been used in previous studies, and we propose an approach that can be viewed as a generalization of regressionbased methods. We illustrate the differences between the methods with Monte Carlo evidence and an empirical analysis of blackwhite wage differentials among males.
AUTHORS
, 2013
"... national and international strategies and policies for meeting the food needs of the developing world on a sustainable basis, with particular emphasis on lowincome countries and on the poorer groups in those countries. IFPRI is a member of the CGIAR Consortium. ..."
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national and international strategies and policies for meeting the food needs of the developing world on a sustainable basis, with particular emphasis on lowincome countries and on the poorer groups in those countries. IFPRI is a member of the CGIAR Consortium.