Taxpayer Search for Information: Implications for Rational Attention

We examine novel data on searches for capital-gains-tax-related information to determine when and how taxpayers acquire information. We find strong seasonal increases in information search around tax filing deadlines, suggesting that taxpayers seek information to comply with tax law. Positive correlations between stock market activity and information search and year-end spikes in information search on capital losses suggest that taxpayers seek information for tax planning purposes. Policy changes and news events cause noteworthy information search. Overall, these data suggest that taxpayers are not always fully informed, but that rational attention and exogenous shocks to tax salience drive taxpayer information search.http://deepblue.lib.umich.edu/bitstream/2027.42/99756/1/1198_Slemrod.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/99756/4/1198_Slemrod_sept13.pd

Who Sold During the Crash of 2008-9? Evidence from Tax-Return Data on Daily Sales of Stock

We examine individual stock sales from 2008 to 2009 using population tax return data. The share of sales by the top 0.1 percent of income recipients and other top income groups rose sharply following the Lehman Brothers bankruptcy and remained elevated throughout the financial crisis. Sales by top income and older age groups were relatively more responsive to increased stock market volatility. Volatility-driven sales were not concentrated in any one sector, but mutual fund sales responded more strongly to increased volatility than stock sales. Additional analysis suggests that gross sales in tax return data are informative about unobserved net sales.http://deepblue.lib.umich.edu/bitstream/2027.42/118067/1/1316_Nagel.pd

Analysis of alcohol dependence phenotype in the COGA families using covariates to detect linkage

Linkage analysis methods that incorporate etiological heterogeneity of complex diseases are likely to demonstrate greater power than traditional linkage analysis methods. Several such methods use covariates to discriminate between linked and unlinked pedigrees with respect to a certain disease locus. Here we apply several such methods including two mixture models, ordered subset analysis, and a conditional logistic model to genome scan data on the DSM-IV alcohol dependence phenotype on the Collaborative Studies on Genetics of Alcoholism families, and compare the results to traditional nonparametric linkage analysis. In general, there was little agreement among the various covariate-based linkage statistics. Linkage signals with empirical p-values less than 0.001 were detected on chromosomes 3, 4, 7, 10, and 12, with the highest peak occurring at the GABRB1 gene using the ecb21 covariate

The analysis of survey data with framing effects

A well-known difficulty in survey research is that respondents’ answers to questions can depend on arbitrary features of a survey’s design, such as the wording of questions or the ordering of answer choices. In this paper, we describe a novel set of tools for analyzing survey data characterized by such framing effects. We show that the conventional approach to analyzing data with framing effects—randomizing survey-takers across frames and pooling the responses—generally does not identify a useful parameter. In its place, we propose an alternative approach and provide conditions under which it identifies the responses that are unaffected by framing. We also present several results for shedding light on the population distribution of the individual characteristic the survey is designed to measure