What We Don\u27t Know About Class Actions but Hope to Know Soon

Legislation that would alter class action practice in the federal courts has been pending in Congress. Nearly a decade’s worth of U.S. Supreme Court cases have restricted the scope and ease of use of the class action device. Class action critics argue that class litigation is a “racket” that fails to compensate plaintiffs and instead enriches plaintiffs’ lawyers at the expense of legitimate business practices. On the other hand, defenders of class actions decry the legislative and judicial forces aligned against them, warning that trends in class action law will eviscerate the practical rights held by consumers and workers. In short, there is considerable controversy over whether class actions are an economic menace or a boon to the little guys. We have two purposes in this brief Article. First, we wish to focus continuing attention on the need for more empirical information about the actual functioning of the federal class action system. Second, we wish to share our current efforts to use a one-of-a-kind collection of docket reports, originally harvested from Public Access to Court Electronic Records (PACER), to fill the empirical gap. Presentation of empirical findings resulting from this effort awaits a future article. However, this Article includes suggestions as to how the federal judiciary and Administrative Office of the United States Courts (“AO”) could improve data management and data reporting so as to make information about federal class actions more accessible to scholars and others interested in how the class action device operates in practice and what reforms, if any, would be advisable

Enabling high confidence detections of gravitational-wave bursts

With the advanced LIGO and Virgo detectors taking observations the detection of gravitational waves is expected within the next few years. Extracting astrophysical information from gravitational wave detections is a well-posed problem and thoroughly studied when detailed models for the waveforms are available. However, one motivation for the field of gravitational wave astronomy is the potential for new discoveries. Recognizing and characterizing unanticipated signals requires data analysis techniques which do not depend on theoretical predictions for the gravitational waveform. Past searches for short-duration un-modeled gravitational wave signals have been hampered by transient noise artifacts, or "glitches," in the detectors. In some cases, even high signal-to-noise simulated astrophysical signals have proven difficult to distinguish from glitches, so that essentially any plausible signal could be detected with at most 2-3 $\sigma$ level confidence. We have put forth the BayesWave algorithm to differentiate between generic gravitational wave transients and glitches, and to provide robust waveform reconstruction and characterization of the astrophysical signals. Here we study BayesWave's capabilities for rejecting glitches while assigning high confidence to detection candidates through analytic approximations to the Bayesian evidence. Analytic results are tested with numerical experiments by adding simulated gravitational wave transient signals to LIGO data collected between 2009 and 2010 and found to be in good agreement.Comment: 15 pages, 6 figures, submitted to PR

Bootstrap-Based Improvements for Inference with Clustered Errors

Researchers have increasingly realized the need to account for within-group dependence in estimating standard errors of regression parameter estimates. The usual solution is to calculate cluster-robust standard errors that permit heteroskedasticity and within-cluster error correlation, but presume that the number of clusters is large. Standard asymptotic tests can over-reject, however, with few (5-30) clusters. We investigate inference using cluster bootstrap-t procedures that provide asymptotic refinement. These procedures are evaluated using Monte Carlos, including the example of Bertrand, Duflo and Mullainathan (2004). Rejection rates of ten percent using standard methods can be reduced to the nominal size of five percent using our methods.

Robust Inference with Multi-way Clustering

In this paper we propose a new variance estimator for OLS as well as for nonlinear estimators such as logit, probit and GMM, that provcides cluster-robust inference when there is two-way or multi-way clustering that is non-nested. The variance estimator extends the standard cluster-robust variance estimator or sandwich estimator for one-way clustering (e.g. Liang and Zeger (1986), Arellano (1987)) and relies on similar relatively weak distributional assumptions. Our method is easily implemented in statistical packages, such as Stata and SAS, that already offer cluster-robust standard errors when there is one-way clustering. The method is demonstrated by a Monte Carlo analysis for a two-way random effects model; a Monte Carlo analysis of a placebo law that extends the state-year effects example of Bertrand et al. (2004) to two dimensions; and by application to two studies in the empirical public/labor literature where two-way clustering is present.

Does more for the poor mean less for the poor? The politics of tagging

Proposals aimed at improving the welfare of the poor often include indicator targeting, in which non-income characteristics (such as race, gender, or land ownership) that are correlated with income are used to target limited funds to groups likely to include a cincentration of the poor. Previous work shows that efficient use of a fixed budget for poverty reduction requires such targeting, either because agents'income cannot be observed or to reduce distortionary incentives arising from redistributive interventions. Inspite of this, the authors question the political viability of targeting. After constructing a model that is basically an extension of Akerlof's 1978 model of"tagging", they derive three main results: 1) Akerlof's result continues to hold: that, ignoring political considerations, not only will targeting be desirable but recipients of the targeted transfer will receive a greater total transfer than they would if targeting were not possible. 2) A classical social-choice analysis-in which agents vote simultaneously about the level of taxation and the degree of targeting-shows that positive levels of targeted transfers will not exist in equilibrium (an unsurprising finding, given Plott's 1968 theorem). It also shows that a voting equilibrium often will exist with no targeting but with non-zero taxation and redistribution. 3) In a game in which the policymaker chooses the degree of targeting while voters choose the level of taxation, the redistributive efficiency gains from tagging may well fail to outweigh the resulting reduction in funds available for redistribution. These results may be extended readily to account for altruistic agents. The authors stress that even when these results hold, the alternative to targeted transfers - a universally received lump-sum grant financed through a proportional tax - will nonetheless be supported politically and will be quite progressive relative to the pretransfer income distribution.Economic Theory&Research,Services&Transfers to Poor,Poverty Impact Evaluation,Environmental Economics&Policies,Poverty Monitoring&Analysis,Services&Transfers to Poor,Rural Poverty Reduction,Environmental Economics&Policies,Poverty Impact Evaluation,Safety Nets and Transfers

Bootstrap-Based Improvements for Inference with Clustered Errors

Microeconometrics researchers have increasingly realized the essential need to account for any within-group dependence in estimating standard errors of regression parameter estimates. The typical preferred solution is to calculate cluster-robust or sandwich standard errors that permit quite general heteroskedasticity and within-cluster error correlation, but presume that the number of clusters is large. In applications with few (5-30) clusters, standard asymptotic tests can over-reject considerably. We investigate more accurate inference using cluster bootstrap-t procedures that provide asymptotic refinement. These procedures are evaluated using Monte Carlos, including the much-cited differences-in-differences example of Bertrand, Mullainathan and Duflo (2004). In situations where standard methods lead to rejection rates in excess of ten percent (or more) for tests of nominal size 0.05, our methods can reduce this to five percent. In principle a pairs cluster bootstrap should work well, but in practice a Wild cluster bootstrap performs better.clustered errors; random effects; cluster robust; sandwich; bootstrap; bootstrap-t; clustered bootstrap; pairs bootstrap; wild bootstrap.

Distributional Impacts of the Self-Sufficiency Project

A large literature has been concerned with the impacts of recent welfare reforms on income, earnings, transfers, and labor-force attachment. While one strand of this literature relies on observational studies conducted with large survey-sample data sets, a second makes use of data generated by experimental evaluations of changes to means-tested programs. Much of the overall literature has focused on mean impacts. In this paper, we use random-assignment experimental data from Canada’s Self-Sufficiency 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; heterogeneity that 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 distribution is the same under either program or possibly lower under SSP. Further, during the period when SSP receipt was possible, the impacts on the distributions of transfer payments (IA plus the subsidy) and total income (earnings plus transfers) are also different at different points of the distribution. In particular, positive impacts on the transfer distribution are concentrated at the lower end of the transfer distribution while positive impacts on the income distribution are concentrated in the upper end of the income distribution. Impacts of SSP on these distributions were essentially zero after the subsidy was no longer availablewefare reforms, labor force

What mean impacts miss: distributional effects of welfare reform experiments

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 random-assignment data from Connecticut's Jobs First waiver, which features key elements of post-1996 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

The impact of welfare reform on marriage and divorce

The goal of the 1996 Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) was to end the dependency of needy parents on government benefits, in part by promoting marriage; the pre-reform welfare system was widely believed to discourage marriage because it primarily provided benefits to single mothers. However, welfare reform may have actually decreased the incentives to be married by giving women greater financial independence via the program's new emphasis on work. This paper uses Vital Statistics data on marriages and divorces during 1989-2000 to examine the role of welfare reform and other state-level variables on marriage and divorce rates. The results indicate that implementation of TANF is negatively associated with marriage and divorce rates, as are pre-TANF waivers from the AFDC program in some specifications

Distributional impacts of the self-sufficiency project

A large literature has been concerned with the impacts of recent welfare reforms on income, earnings, transfers, and labor-force attachment. While one strand of this literature relies on observational studies conducted with large survey-sample data sets, a second makes use of data generated by experimental evaluations of changes to means-tested programs. Much of the overall literature has focused on mean impacts. In this paper, we use random-assignment experimental data from Canada's Self-Sufficiency 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; heterogeneity that 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 distribution is the same under either program or possibly lower under SSP. Further, during the period when SSP receipt was possible, the impacts on the distributions of transfer payments (IA plus the subsidy) and total income (earnings plus transfers) are also different at different points of the distribution. In particular, positive impacts on the transfer distribution are concentrated at the lower end of the transfer distribution while positive impacts on the income distribution are concentrated in the upper end of the income distribution. Impacts of SSP on these distributions were essentially zero after the subsidy was no longer availabl