Robust Inferences from Random Clustered Samples: Applications Using Data from the Panel Survey of Income Dynamics

Many large data sets are created using clustered, rather than random sampling schemes. Clustered data arise when multiple observations exist on the same respondent, as in panel data, and when respondents share a common factor, such as a neighborhood or family. In the presence of clustered data, methods that rely on random sampling to measure the precision of an estimator may be incorrect. Many researchers, however, continue to treat respondents from the same sampling cluster as independent observations and thus implicitly ignore the potential intracluster correlation. In this paper, I use a robust method for drawing inferences and data from the Panel Survey of Income Dynamics, to examine the implications of clustered samples on inference. Consistent with the previous survey sampling literature, important differences are revealed in comparisons between the estimated asymptotic variances derived assuming random and clustered sampling, even when there are only a few observations per cluster. The estimates derived under random sampling are generally biased downward.Clustered Samples; Design Effects; PSID

To Train or Not To Train: Optimal Treatment Assignment Rules Using Welfare-to-Work Experiments

Planners often face the especially difficult and important task of assigning programs or treatments to optimize outcomes. Using the recent welfare-to-work reforms as an illustration, this paper considers the normative problem of how administrators might use data from randomized experiments to assign treatments. Under the new welfare system, state governments must design welfare programs to optimize employment. With experimental results in-hand, planners observe the average effect of training on employment but may not observe the individual effect of training. If the effect of a treatment varies across individuals, the planner faces a decision problem under ambiguity (Manski, 1998). In this setting, I find a straightforward proposition formalizes conditions under which a planner should reject particular decision rules as being inferior. An optimal decision rule, however, may not be revealed. In the absence of strong assumptions about the degree of heterogeneity in the population or the information known by the planner, the data are inconclusive about the efficacy of most assignment rules.ambiguity, randomized experiments, treatment choice, welfare-to-work programs

Empirical Search Models

This chapter discusses various methods that have been used to estimate structural models of search and survival models. Most of the focus is on using available data, usually on unemployment spell lengths and accepted wage o¤ers, to estimate the parameters of speci…c search models. In particular, we focus on estimating the parameters of the wage o¤er distribution, the reservation wage or reservation wage function, the cost of search, o¤er arrival rate, and discount rate. There is an added section on survival models because survival models have been used so extensively to look at unemployment spell data.empirical search

Feasibility of Using Technology to Disseminate Evidence to Rural Nurses and Improve Patient Outcomes

Background: Rural African American women receive less frequent mammography screening and die of breast cancer at a higher rate than is seen in the general population. To overcome this disparity, it is necessary to assist rural providers in their efforts to influence women to obtain screening. Method: This study examined the feasibility of using distance education to disseminate knowledge about timely and appropriate mammography screening to rural nurses, using patient outcome data to evaluate the effectiveness of this intervention. Results: Overall, there was a decline in referrals and mammography screening, but the intervention group centers showed a smaller decline after the educational intervention than did the control group. Conclusion: The findings show the effect of dissemination of information and the feasibility of using patient outcome data for educational evaluation. Neighboring academic health centers and nursing schools should include in their mission the provision of educational programs for relatively isolated rural nurses.health technology, rural health

Identification of Expected Outcomes in a Data Error Mixing Model with Multiplicative Mean Independence

We consider the problem of identifying a mean outcome in corrupt sampling where the observed outcome is a mixture of the distribution of interest and some other distribution. We make two contributions to this literature. First, the statistical independence assumption maintained under contaminated sampling is relaxed to the weaker assumption that the outcome is mean independent of the mixing process. We then generalize this restriction to allow the two conditional means to differ by a known or bounded factor of proportionality. Second, in the special case of a binary outcome, we consider the possibility that draws from the alternative distribution are known to be erroneous, as might be the case in a mixture model of response error. We illustrate how these assumptions can be used to inform researchers about the population's use of illicit drugs in the presence of nonrandom reporting errors. In this application, we find that a response error model with multiplicative mean independence is easy to motivate and can have substantial identifying power.

Disability and Employment: Reevaluating the Evidence in Light of Reporting Errors

Measurement error in health and disability status has been widely accepted as a central problem for social science research. Long-standing debates about the prevalence of disability, the role of health in labor market outcomes, and the influence of federal disability policy on declining employment rates have all emphasized issues regarding the reliability of self-reported disability. In addition to random error, inaccuracy in survey datasets may be produced by a host of economic, social, and psychological factors that can lead respondents to misreport work capacity. We develop a nonparametric foundation for assessing how assumptions on the reporting error process affect inferences on the employment gap between the disabled and nondisabled. Rather than imposing the strong assumptions required to obtain point identification, we derive sets of bounds that formalize the identifying power of primitive nonparametric assumptions that appear to share broad consensus in the literature. Within this framework, we introduce a finite-sample correction for the analog estimator of the monotone instrumental variable (MIV) bound. Our empirical results suggest that conclusions derived from conventional latent variable reporting error models may be driven largely by ad hoc distributional and functional form restrictions. Under relatively weak nonparametric assumptions, nonworkers appear to systematically overreport disability.

Inferring Disability Status from Corrupt Data

In light of widespread concerns about the reliability of self-reported disability, we investigate what can be learned about the prevalence of work disability under various assumptions on the reporting error process. Developing a nonparametric bounding framework, we provide tight inferences under our strongest assumptions but then find that identification deteriorates rapidly as the assumptions are relaxed. For example, we find that inferences are highly sensitive to how one models potential inconsistencies between subjective self-assessments of work limitation and more objective measures of functional limitation. These two indicators appear to measure markedly different aspects of health status.

Monotone Instrumental Variables: With an Application to the Returns to Schooling

Econometric analyses of treatment response commonly use instrumental variable (IV) assumptions to identify treatment effects. Yet the credibility of IV assumptions is often a matter of considerable disagreement. There is therefore good reason to consider weaker but more credible assumptions. To this end, we introduce monotone instrumental variable (MIV) assumptions and the important special case of monotone treatment selection (MTS). We study the identifying power of MIV assumptions alone and combined with the assumption of monotone treatment response (MTR). We present an empirical application using the MTS and MTR assumptions to place upper bounds on the returns to schooling.

Essays on the Intergenerational Transmission of Welfare Receipt and Inferences in Clustered Samples: Dissertation Summary

My thesis includes three chapters that collectively extend the literatures on welfare program participation and survey sampling. Chapters 2 and 3 reassess the effect that growing up in an AFDC household has on future welfare participation using data from the Panel Study of Income Dynamics (PSID), while Chapter 4 empirically examines a method for drawing inferences that account for the fact that the PSID includes multiple respondents from the same household

Ohio State University Commencement

Commencement address given by John E. Pepper, Chairman of the Board of Directors of Procter & Gamble, to the Summer 2000 graduating class of The Ohio State University, St. John Arena, Columbus, Ohio, August 31, 2000