Models of giving have often been based on altruism. Examples include charity and intergenerational transfers. The literatures on both subjects have centered around neutrality hypotheses: charity is subject to complete crowding out, while intergenerational transfers are subject to Ricardian equivalence. This paper formally develops a model of giving in which altruism is not "pure." In particular, people are assumed to get a "warm glow" from giving. Contrary to the previous literature, this model generates identifiable comparative statics results that show that crowding out of charity is incomplete and that government debt will have Keynesian effects.

Giving with Impure Altruism: Applications to Charity and Ricardian Equivalence

The goal of multiple imputation is to provide valid inferences for statistical estimates from incomplete data. To achieve that goal, imputed values should preserve the structure in the data, as well as the uncertainty about this structure, and include any knowledge about the process that generated the missing data. Two approaches for imputing multivariate data exist: joint modeling (JM) and fully conditional specification (FCS). JM is based on parametric statistical theory, and leads to imputation procedures whose statistical properties are known. JM is theoretically sound, but the joint model may lack flexibility needed to represent typical data features, potentially leading to bias. FCS is a semi-parametric and flexible alternative that specifies the multivariate model by a series of conditional models, one for each incomplete variable. FCS provides tremendous flexibility and is easy to apply, but its statistical properties are difficult to establish. Simulation work shows that FCS behaves very well in ...

Multiple imputation of discrete and continuous data by fully conditional specification

Missing data in clinical trials can have a major effect on the validity of the inferences that can be drawn from the trial. This article reviews methods for preventing missing data and, failing that, dealing with data that are missing.

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The Prevention and Treatment of Missing Data in Clinical Trials

Subjects often drop out of longitudinal studies prematurely, yielding unbalanced data with unequal numbers of measures for each subject. Modern software programs for handling unbalanced longitudinal data improve on methods that discard the incomplete cases by including all the data, but also yield biased inferences under plausible models for the drop-out process. This article discusses methods that simultaneously model the data and the drop-out process within a unified model-based framework. Models are classified into two broad classes—random-coefficient selection models and random-coefficient pattern-mixture models—depending on how the joint distribution of the data and drop-out mechanism is factored. Inference is likelihood-based, via maximum likelihood or Bayesian methods. A number of examples in the literature are placed in this framework, and possible extensions outlined. Data collection on the nature of the drop-out process is advocated to guide the choice of model. In cases where the drop-...

Modeling the Drop-Out Mechanism in Repeated-Measures Studies

A model is proposed for continuous longitudinal data with non-ignorable or informative drop-out (ID). The model combines a multivariate linear model for the underlying response with a logistic regression model for the drop-out process. The latter incorporates dependence of the probability of drop-out on unobserved, or missing, observations. Parameters in the model are estimated by using maximum likelihood (ML) and inferences drawn through conventional likelihood procedures. In particular, likelihood ratio tests can be used to assess the informativeness of the drop-out process through comparison of the full model with reduced models corresponding to random drop-out (RD) and completely random processes

https://rss.onlinelibrary.wiley.com/doi/pdf/10.2307/2986113

Informative Drop‐Out in Longitudinal Data Analysis

A method is developed for imputing missing values when the probability of response depends upon the variable being imputed. The missing data problem is viewed as one of parameter estimation in a regression model with stochastic censoring of the dependent variable. The prediction approach to imputation is used to solve this estimation problem. Wages and salaries are imputed to non-respondents in the Current Population Survey and the results are compared to the nonrespondents' IRS wage and salary data. The stochastic censoring approach gives improved results relative to a prediction approach that ignores the response mechanism.

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Imputation of Missing Values When the Probability of Response Depends on the Variable Being Imputed

When charitable contributions are tax deductible, the marginal price of charitable giving in other consumption foregone per dollar of contributions is generally less than unity. Further, if the income tax schedule is a progressive step function, the marginal price of contributions is generally a rising step function of the level of contributions. The problem of estimating a contributions demand function for individuals is therefore complicated by the spurious correlation between the level of contributions and the observed marginal price. We take this econometric problem into account in estimating a contributions demand function using data from the 1972-73 Consumer Expenditure Survey. After comparing our results with those of estimation techniques used by other authors, we provide evidence on the impacts of alternative tax policies on charitable giving using our estimates of the model parameters.

Consistent Estimation of the Impact of Tax Deductibility On the Level of Charitable Contributions

Evidence on taxation and charitable giving from the 1983 U.S. treasury tax model file

Output price indices for regulated monopoly

This paper evaluates the rhetoric of the US bishops' pastoral letter on the US economy from two perspectives Is the letter convincing? Does it conform to the “conversational norms of civilization?” The paper argues that the bishops' letter fails by both standards because it ignores serious research on the US economy, it misstates important facts about the economy, and it sneers at professional economists The paper concludes that the bishops' letter will not be convincing to well informed readers

William S. Reece

Papers

Imputation of Missing Values When the Probability of Response Depends on the Variable Being Imputed

Consistent Estimation of the Impact of Tax Deductibility On the Level of Charitable Contributions

Evidence on taxation and charitable giving from the 1983 U.S. treasury tax model file

Output price indices for regulated monopoly

Why is the bishops' letter on the U.S. economy so unconvincing?