Tax Reform and Automatic Stabilization

A fundamental property of a progressive income tax is that it provides implicit collective insurance against idiosyncratic shocks to income by dampening the variability of disposable income and consumption. The Economic Recovery Tax Act of 1981 (ERTA) and the Tax Reform Act of 1986 (TRA86) greatly reduced the number of marginal tax brackets and the maximum marginal rate, which limits the ability of households to stabilize consumption in the face of transitory fluctuations in taxable income. We examine the effect of the federal income tax reforms of the 1980s on the associated degree of automatic stabilization of consumption. The empirical framework derives from the consumption insurance literature where the ideal outcome is spatially equal changes in households' marginal utilities of consumption. Because evidence for U.S. households rejects complete consumption insurance we begin with a model of partial consumption insurance, which we use to identify how the degree of partial insurance has changed since ERTA and TRA86. Our data come from interview years 1980-1991 in the Panel Study of Income Dynamics. Although in some cases the tax reforms of the 1980s actually increased the automatic stabilization inherent in a progressive income tax (especially when the Social Security payroll tax and the Earned Income Tax Credit are included), our overall outcome is that ERTA and TRA86 reduced consumption stability by about 50 percent. More recent tax reforms, most notably increased EITC generosity, have restored or enhanced consumption insurance.

Causal Modeling Under Complex Dependency in Clustered and Longitudinal Observations

In assessing the efficacy of a time-varying treatment Marginal Structural Models (MSMs) and Structural Nested Mean Models (SNMMs) are useful in dealing with confounding by variables affected by earlier treatments. MSMs model the joint effect of treatments on the marginal mean of the potential outcome, whereas SNMMs model the joint effect of treatments on the mean of the potential outcome conditional on the treatment and covariate history. These models often consider independent subjects with noninformative time of observation. The first two chapters extend the two classes of models to clustered observations with time-varying treatments in the presence of time-varying confounding. We formulate models with both cluster- and unit-level treatments and derive semiparametric estimators of parameters in such models. For unit-level treatments, we consider both the presence and absence of interference, namely the effect of treatment on outcomes in other units of the same cluster. For MSMs, we show that the use of unit-specific inverse probability weights and certain working correlation structures can improve the efficiency of estimators under specified conditions. The properties of the estimators are evaluated through simulations and compared with the conventional GEE regression method for clustered outcomes. To illustrate our methods, we use data from the treatment arm of a glaucoma clinical trial to compare the effectiveness of two commonly used ocular hypertension medications. The third chapter extends SNMMs to situations with intermittent missing observations. In observational longitudinal studies, subjects often miss prescheduled visits intermittently. Previous literature has mainly focused on dealing with monotone censoring due to early dropout. Here we focus on intermittent missingness that can depend on the subjects\u27 covariate and treatment history. We show that under certain assumptions the standard SNMMs can be used for situations where non-outcome covariates are missing intermittently. In situations where outcomes are also missing intermittently, we use a method that does not require artificially censoring the data, but requires a strict missing at random assumption. The estimators are shown to be consistent and achieve reasonable efficiency. We illustrate the method by estimating the effect of non-steroidal anti-inflammatory drugs (NSAIDs) on genitourinary pain using data from a study of chronic pelvic pain

A cautionary note concerning the use of stabilized weights in marginal structural models

Marginal structural models (MSMs) are commonly used to estimate the causal effect of a time-varying treatment in presence of time-dependent confounding. When fitting a MSM to data, the analyst must specify both the structural model for the outcome and the treatment models for the inverse-probability-of-treatment weights. The use of stabilized weights is recommended since they are generally less variable than the standard weights. In this paper, we are concerned with the use of the common stabilized weights when the structural model is specified to only consider partial treatment history, such as the current or most recent treatments. We present various examples of settings where these stabilized weights yield biased inferences while the standard weights do not. These issues are first investigated on the basis of simulated data and subsequently exemplified using data from the Honolulu Heart Program. Unlike common stabilized weights, we find that basic stabilized weights offer some protection against bias in structural models designed to estimate current or most recent treatment effects. Copyright © 2010 John Wiley & Sons, Ltd

Functional Structure and Approximation in Econometrics (book front matter)

This is the front matter from the book, William A. Barnett and Jane Binner (eds.), Functional Structure and Approximation in Econometrics, published in 2004 by Elsevier in its Contributions to Economic Analysis monograph series. The front matter includes the Table of Contents, Volume Introduction, and Section Introductions by Barnett and Binner and the Preface by W. Erwin Diewert. The volume contains a unified collection and discussion of W. A. Barnett's most important published papers on applied and theoretical econometric modelling.consumer demand, production, flexible functional form, functional structure, asymptotics, nonlinearity, systemwide models

CRTgeeDR: An R Package for Doubly Robust Generalized Estimating Equations Estimations in Cluster Randomized Trials with Missing Data

International audienceSemi-parametric approaches based on generalized estimating equation (GEE) are widelyused to analyse correlated outcomes. Most available softwares had been developed forlongitudinal settings. In this paper, we present a R package CRTgeeDR for estimatingparameters in marginal regression in cluster randomized trials (CRTs). Theory for adjustingfor missing at random outcomes by inverse-probability weighting methods (IPW)based on the use of a propensity score had been largely studied and implemented. Weexhibit that in CRTs most of the available softwares use an implementation of weightsthat lead to a bias in estimation if a non-independence working correlation structure ischosen. In CRTgeeDR, we solve this problem by using a different implementation whilekeeping the consistency properties of the IPW. Moreover, in CRTs using an augmentedGEE (AUG) allow to improve efficiency by adjusting for treatment-covariate interactionsand imbalance in baseline covariates between treatment groups using an outcome model.In CRTgeeDR, we extend the abilities of existing packages such as geepack and geeMto allow such data augmentation. Finally, one may want to combine IPW and AUG ina Doubly Robust (DR) estimator, which lead to consistent estimation when either thepropensity score or the outcome model corresponds to the true data generation process(Prague, Wang, Stephens, Tchetgen Tchetgen, and De gruttola 2015). The DR approachis implemented in CRTgeeDR. Simulations studies demonstrate the consistency of IPWimplemented in CRTgeeDR and the gains associated with the use of the DR for analyzinga binary outcome using a logit regression. Finally, we reanalyzed data from a sanitationCRT in developing countries (Guiteras, Levinsohn, and Mobarak 2015a) with the DRapproach compared to classical GEE and demonstrated a signiffcant intervention effect