Modeling Qualitative Outcomes by Supplementing Participant Data with General Population Data: A New and More Versatile Approach

Although one often has detailed information about participants in a program, the lack of comparable information on non-participants precludes standard qualitative choice estimation. This challenge can be overcome by incorporating a supplementary sample of covariate values from the general population. New estimators are introduced that exploit the parameter restrictions implied by the relationship between the marginal and conditional response probabilities in the supplementary sample. An important advantage of these estimators over the existing alternatives is that they can be applied to exogenously stratified samples even when the underlying stratification criteria are unknown. The ability of these new estimators to readily incorporate sample weights make them applicable to a much wider range of data sources. The new estimators are also easily generalized to address polychotomous outcomes

Modeling Qualitative Outcomes by Supplementing Participant Data with General Population Data: A Calibrated Qualitative Response Estimation Approach

Often providers of a program or service have detailed information about their clients, but only limited information about potential clients. Likewise, ecologists frequently have extensive knowledge regarding habitats where a given animal or plant species is known to be present, but they lack comparable information on habitats where they are certain not to be present. In epidemiology, comprehensive information is routinely collected about patients who have been diagnosed with a given disease; however, commensurate information may not be available for individuals who are known to be free of the disease. While it may be highly beneficial to learn about the determinants of participation (in a program or service) or presence (in a habitat or of a disease), the lack of a comparable sample of observations on subjects that are not participants (or that are non-present) precludes the application of standard qualitative response models, such as logit or probit. In this paper, we examine how one can overcome this challenge by combining a participant-only sample with a supplementary sample of covariate values from the general population. We derive some new estimators of conditional response probabilities based on this sampling scheme by exploiting the parameter restrictions implied by the relationship between the marginal and conditional response probabilities in the supplementary sample. When the prevalence rate in the population is known, we demonstrate that the choice of estimator is especially important when this rate is relatively high. Our simulation results indicate that some of our new estimators for this case rival the small sample performance of the best existing estimators. Our estimators for the case where the prevalence rate is unknown also perform comparably to the best existing estimator for this situation in our simulations. In contrast to most existing estimators, our new estimators are straightforward to apply to exogenously stratified samples (such as when the supplementary sample is drawn from a Census survey), even when the underlying stratification criteria are not available. Our new estimators also readily generalize to accommodate situations with polychotomous outcomes

Ghosts in the Income Tax Machinery

Much of the tax compliance literature focuses on taxpayers who choose to underreport their income when they file their tax returns. In this paper, we instead concentrate on those individuals who take the ultimate compliance shortcut of not filing a return at all – a group commonly referred to as “ghosts” by academics, tax administrators, and policy-makers. To learn more about this relatively understudied population, we undertake a detailed analysis of administrative data and Census survey data spanning the period from 2001 through 2013. Our results indicate that 10-12 percent of taxpayers with a US federal filing requirement fail to submit a timely income tax return in any given year, and 6.5-8 percent never file at all. The federal tax gap associated with these ghosts is substantial, amounting to an estimated $37 billion per year. We employ a novel pooled time-series cross-sectional econometric methodology to examine the drivers of late filing and nonfiling behavior. The results establish that filing compliance is influenced by income visibility as well as financial incentives, such as refundable credits, tax rebates, and the monetized filing burden. In addition, we find strong evidence of socio-economic and demographic influences. Our results also reveal substantial persistence in filing behavior. The estimated likelihood of filing a timely return for the current tax year is estimated to be 45 percentage points higher if the taxpayer filed a return for the preceding year. At the same time, we find that one-time financial incentives have only a temporary impact on filing compliance, overturning the prevailing view that, once an individual is brought into the tax system, that individual will continue to file in subsequent years

The Relationship between State and Federal Tax Audits

In this paper we present an econometric analysis of state and federal tax audits. We first present results from a survey of state tax administrators. The survey results indicate that most state tax audit programs are small and rely extensively on information provided by the IRS, although some programs are large and sophisticated. We then present results from a detailed econometric analysis of Oregon state and federal tax returns and tax audits for tax year 1987. Our analysis generates three main conclusions. First, Oregon state and IRS selection criteria are similar, but not identical, suggesting that both tax agencies might benefit from greater sharing of information, especially in some audit classes. Second, Oregon state and IRS audit assessments are strongly positively correlated, as expected. Third, we estimate the shadow values associated with providing additional audit resources to the Oregon Department of Revenue and the IRS in various audit classes, and find that for the IRS the shadow values range from two to five dollars, while for Oregon the values range from one to three dollars.

Mapping the Compliance Continuum: From Pathologically Honest to Flagrantly Defiant

There are by now vast academic literatures on both tax compliance and the underground economy. These literatures provide estimates of the overall degrees of non-compliance and shadow (or hidden) activity as well as numerous insights into their causes and consequences. However, we believe it is fair to say that they provide an incomplete perspective on the characteristics of the individuals or groups who engage in such behaviors. Methods for evaluating shadow activity are frequently based on indirect measures, such as discrepancies in national account or labor force statistics, trends in the demand for currency or in monetary transactions, or variations over time in national electricity consumption.Working Paper Number 03-19