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  1. Conventional advice discourages controlling for postoutcome variables in regression analysis. By contrast, we show that controlling for commonly available postoutcome (i.e., future) values of the treatment variable can help detect, reduce, and even remove omitted variable bias (unobserved confounding). The premise is that the same unobserved confounder that affects treatment also affects the future value of the treatment. Future treatments thus proxy for the unmeasured confounder, and researchers can exploit these proxy measures productively. We establish several new results: Regarding a commonly assumed data-generating process involving future treatments, we (1) introduce a simple new approach and show that it strictly reduces bias, (2) elaborate on existing approaches and show that they can increase bias, (3) assess the relative merits of alternative approaches, and (4) analyze true state dependence and selection as key challenges. (5) Importantly, we also introduce a new nonparametric test that uses future treatments to detect hidden bias even when future-treatment estimation fails to reduce bias. We illustrate these results empirically with an analysis of the effect of parental income on children’s educational attainment.
    Free, publicly-accessible full text available August 1, 2023
  2. Free, publicly-accessible full text available August 1, 2023
  3. This study examines the relationship between health and adolescent employment. Using data from the Panel Study of Income Dynamics’ Child Development Supplement and Transition into Adulthood Supplement, we examine a cohort of 2,925 youth who were followed from childhood into adolescence. We focus on two outcomes measured when sample members were ages 16, 17, and 18: employment status and average weekly hours worked. With these data, we test the hypothesis that youth with health conditions will be less likely to work and if they do work, they work fewer hours a week. We find mixed support for this hypothesis. Youth with sensory limitations, developmental disabilities, and externalizing problem behaviors are less likely to work than their peers without these conditions. However, conditional on being employed, youth with externalizing problem behaviors and ADHD work more hours a week than their peers without those conditions.
    Free, publicly-accessible full text available April 1, 2023
  4. Free, publicly-accessible full text available March 4, 2023
  5. Abstract We document changes in U.S. children's family household composition from 1968 to 2017 with regard to the number and types of kin that children lived with and the frequency of family members' household entrances and departures. Data are from the U.S. Panel Study of Income Dynamics (N = 30,412). Children experienced three decades of increasing instability and diversification in household membership, arriving at a state of “stable complexity” in the most recent decade. Stable complexity is distinguished by a decline in the number of coresident parents; a higher number of stepparents, grandparents, and other relatives in children's households; and less turnover in household membership compared with prior decades, including fewer sibling departures. College-educated households with children were consistently the most stable and least diverse. On several dimensions, household composition has become increasingly similar for non-Hispanic Black and White children. Children in Hispanic households are distinct in having larger family sizes and more expected household entrances and departures by coresident kin.
    Free, publicly-accessible full text available March 2, 2023
  6. Abstract Adaptive survey designs are increasingly used by survey practitioners to counteract ongoing declines in household survey response rates and manage rising fieldwork costs. This paper reports findings from an evaluation of an early-bird incentive (EBI) experiment targeting high-effort respondents who participate in the 2019 wave of the US Panel Study of Income Dynamics. We identified a subgroup of high-effort respondents at risk of nonresponse based on their prior wave fieldwork effort and randomized them to a treatment offering an extra time-delimited monetary incentive for completing their interview within the first month of data collection (treatment group; N = 800) or the standard study incentive (control group; N = 400). In recent waves, we have found that the costs of the protracted fieldwork needed to complete interviews with high-effort cases in the form of interviewer contact attempts plus an increased incentive near the close of data collection are extremely high. By incentivizing early participation and reducing the number of interviewer contact attempts and fieldwork days to complete the interview, our goal was to manage both nonresponse and survey costs. We found that the EBI treatment increased response rates and reduced fieldwork effort and costs compared to a control group. Wemore »review several key findings and limitations, discuss their implications, and identify the next steps for future research.« less
    Free, publicly-accessible full text available February 1, 2023
  7. Free, publicly-accessible full text available January 1, 2023
  8. We conducted an experiment to evaluate the effects on fieldwork outcomes and interview mode of switching to a web-first mixed-mode data collection design (self-administered web interview and interviewer-administered telephone interview) from a telephone-only design. We examine whether the mixed-mode option leads to better survey outcomes, based on response rates, fieldwork outcomes, interview quality and costs. We also examine respondent characteristics associated with completing a web interview rather than a telephone interview. Our mode experiment study was conducted in the 2019 wave of the Transition into Adulthood Supplement (TAS) to the US Panel Study of Income Dynamics (PSID). TAS collects information biennially from approximately 3,000 young adults in PSID families. The shift to a mixed-mode design for TAS was aimed at reducing costs and increasing respondent cooperation. We found that for mixed-mode cases compared to telephone only cases, response rates were higher, interviews were completed faster and with lower effort, the quality of the interview data appeared better, and fieldwork costs were lower. A clear set of respondent characteristics reflecting demographic and socioeconomic characteristics, technology availability and use, time use, and psychological health were associated with completing a web interview rather than a telephone interview.
    Free, publicly-accessible full text available January 1, 2023
  9. Abstract Multiple imputation (MI) is a popular and well-established method for handling missing data in multivariate data sets, but its practicality for use in massive and complex data sets has been questioned. One such data set is the Panel Study of Income Dynamics (PSID), a longstanding and extensive survey of household income and wealth in the United States. Missing data for this survey are currently handled using traditional hot deck methods because of the simple implementation; however, the univariate hot deck results in large random wealth fluctuations. MI is effective but faced with operational challenges. We use a sequential regression/chained-equation approach, using the software IVEware, to multiply impute cross-sectional wealth data in the 2013 PSID, and compare analyses of the resulting imputed data with those from the current hot deck approach. Practical difficulties, such as non-normally distributed variables, skip patterns, categorical variables with many levels, and multicollinearity, are described together with our approaches to overcoming them. We evaluate the imputation quality and validity with internal diagnostics and external benchmarking data. MI produces improvements over the existing hot deck approach by helping preserve correlation structures, such as the associations between PSID wealth components and the relationships between the household net worthmore »and sociodemographic factors, and facilitates completed data analyses with general purposes. MI incorporates highly predictive covariates into imputation models and increases efficiency. We recommend the practical implementation of MI and expect greater gains when the fraction of missing information is large.« less