109 records from EconBiz based on author Name

1. Identification of Causal Effects Using Instrumental Variables

Angrist, Joshua D.; Imbens, Guido; Rubin, Donald B.;
2021
Availability: Link

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2. Estimating the Effect of Unearned Income on Labor Supply, Earnings, Savings, and Consumption : Evidence from a Survey of Lottery Players

abstract

Knowledge of the effect of unearned income on economic behavior of individuals in general, and on labor supply in particular, is of great importance to policy makers. Estimation of income effects, however, is a difficult problem because income is not randomly assigned and exogenous changes in income are difficult to identify. Here we exploit the randomized assignment of large amounts of money over long periods of time through lotteries. We carried out a survey of people who played the lottery in the mid-eighties and estimate the effect of lottery winnings on their subsequent earnings, labor supply, consumption, and savings. We find that winning a modest prize ($15,000 per year for twenty years) does not affect labor supply or earnings substantially. Winning such a prize does not considerably reduce savings. Winning a much larger prize ($80,000 rather than $15,000 per year) reduces labor supply as measured by hours, as well as participation and social security earnings; elasticities for hours and earnings are around -0.20 and for participation around -0.14. Winning a large versus modest amount also leads to increased expenditures on cars and larger home values, although mortgages values appear to increase by approximately the same amount. Winning $80,000 increases overall savings, although savings in retirement accounts are not significantly affected. The results do not vary much by gender, age, or prior employment status. There is some evidence that for those with zero earnings prior to winning the lottery there is a positive effect of winning a small prize on subsequent labor market participation

Imbens, Guido; Rubin, Donald B.; Sacerdote, Bruce;
2021
Availability: Link

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3. Combining Panel Data Sets with Attrition and Refreshment Samples

abstract

In many fields researchers wish to consider statistical models that allow for more complex relationships than can be inferred using only cross-sectional data. Panel or longitudinal data where the same units are observed repeatedly at different points in time can often provide the richer data needed for such models. Although such data allows researchers to identify more complex models than cross-sectional data, missing data problems can be more severe in panels. In particular, even units who respond in initial waves of the panel may drop out in subsequent waves, so that the subsample with complete data for all waves of the panel can be less representative of the population than the original sample. Sometimes, in the hope of mitigating the effects of attrition without losing the advantages of panel data over cross-sections, panel data sets are augmented by replacing units who have dropped out with new units randomly sampled from the original population. Following Ridder (1992), who used these replacement units to test some models for attrition, we call such additional samples refreshment samples. We explore the benefits of these samples for estimating models of attrition. We describe the manner in which the presence of refreshment samples allows the researcher to test various models for attrition in panel data, including models based on the assumption that missing data are missing at random (MAR, Rubin, 1976; Little and Rubin, 1987). The main result in the paper makes precise the extent to which refreshment samples are informative about the attrition process; a class of non-ignorable missing data models can be identified without making strong distributional or functional form assumptions if refreshment samples are available

Hirano, Keisuke; Imbens, Guido; Ridder, Geert; Rubin, Donald B.;
2021
Availability: Link

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4. On optimal rerandomization designs

Johansson, Per; Rubin, Donald B.; Schultzberg, Mårten;
2021
Availability: Link
Citations: 7 (based on OpenCitations) Cited By

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5. C. R. Rao's century

Efron, Bradley; Amari, Shun‐ichi; Rubin, Donald B.; Rao, Arni S. R. Srinivasa; Cox, David R.;
2020
Availability: Link
Citations: 2 (based on OpenCitations) Cited By

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6. Causal inference for statistics, social and biomedical sciences : an introduction

Imbens, Guido; Rubin, Donald B.;
2015
Type: Handbuch; Handbook;

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7. Missing data and imputation methods

Mattei, Alessandra; Mealli, Fabrizia; Rubin, Donald B.;
2012
Type: Aufsatz im Buch; Book section;

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8. Statistical inference for causal effects

Mealli, Fabrizia; Pacini, Barbara; Rubin, Donald B.;
2012
Type: Aufsatz im Buch; Book section;

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9. Causal effects of perceived immutable characteristics

Greiner, D. James; Rubin, Donald B.;
2011
Type: Aufsatz in Zeitschrift; Article in journal;

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10. Reducing Bias in Observational Studies Using Subclassification on the Propensity Score

Rosenbaum, Paul R.; Rubin, Donald B.;
2010
Availability: Link

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