Jeff Dominitz

Estimation of income expectations models using expectations and realization data

This paper considers the use of expectations and realizations data to estimate income expectations conditional on observed attributes. When the researcher has no expectations data, inferring expectations from realizations requires knowledge of the information individuals possess and how they process this information to form expectations. While income expectations data may be used to avoid this requirement, the data may also be used to supplement or replace realizations data in estimation of these expectations formation models. The strength of the maintained assumptions and the potential usefulness of expectations data for identification and estimation are discussed in the context of models adopted in the consumption and saving literature. Estimates of these models based on Survey of Economic Expectations data and Panel Study of Income Dynamics realizations data yield strikingly similar findings, highlighting the potential complementary of the two forms of data.

CRIME MINIMISATION AND RACIAL BIAS: WHAT CAN WE LEARN FROM POLICE SEARCH DATA?*

Are variations in the success rate of searches by race informative about racial bias if police are motivated by crime minimization rather than success-rate maximization? We show that the basic idea of extracting information from hit rates may still be valid, provided one can verify some simple restrictions on the joint distribution of criminality by race. We also extend these results to the case where the police minimize the rate of unpunished crime.

Some Convergence Theory For Iterative Estimation Procedures With An Application To Semiparametric Estimation

We develop general conditions for rates of convergence and convergence in distribution of iterative procedures for estimating finite-dimensional parameters. An asymptotic contraction mapping condition is the centerpiece of the theory. We illustrate some of the results by deriving the limiting distribution of a two-stage iterative estimator of regression parameters in a semiparametric binary response model. Simulation results illustrating the computational benefits of the first-stage iterative estimator are also reported.We thank a co-editor and two referees for comments and criticisms that led to significant improvements in this paper. We also thank Roger Klein for providing us with Gauss code to compute his estimator.

Sharp Bounds Under Contaminated or Corrupted Sampling With Verification, With an Application to Environmental Pollutant Data

Let F denote a distribution of interest and G a possibly spurious distribution. This article derives and nonparametrically estimates sharp bounds on characteristics of F when the data are a mixture of F and G, and a fraction of the data is verified to be from F. Contaminated and corrupted mixtures, with and without monotonicity restrictions, are analyzed. The methods are particularly useful in analyzing environmental pollutant measurements obtained using gas chromatography-mass spectroscopy. Results are applied to measurements of organic pollutant concentrations from the Love Canal. We argue that a corruption with monotonic verification model may be the most appropriate model for this type of data.

Survey Data, Analysis of

When economists analyse survey data, they must confront characteristics of the data-generating process that may distinguish these data from other types, such as administrative records.

How do Mutual Fund Fees Affect Investor Choices?: Evidence from Survey Experiments

Over the past few decades, risks associated with providing for financial security in retirement have increasingly shifted from employers to employees as employer-provided pensions have shifted from defined-benefit to defined-contribution (DC) plans. Recent work in behavioral finance suggests that investors do not make optimal investment decisions in their DC plans. The authors designed and administered a pair of mutual fund choice experiments to over 1000 survey respondents who participate in the RAND American Life Panel. Their analysis sheds light on the question of how mutual fund investors respond to variation in fees in a hypothetical scenario in which fees should be obvious to the investor. The results show that some aspects of individual behavior are consistent with rational wealth-maximization and the majority of the respondents are able to provide estimates of fees that lie within a benchmark range. However, they find that respondents tend not to minimize expected fees and are more averse to backend load fees than to front-end loads. The trade-off between expense ratios and loads is found to be somewhat sensitive to the expected holding period in a manner consistent with expected-wealth maximization, but investors may tend to be too averse to loads. Differences in measured financial literacy predict differences in behavior, with lower rates of literacy among women accounting for differences in choice behavior by gender. They also find that financial literacy mediates individual responses to the presentation of information intended to enhance decision making.

Retirement Savings Portfolio Management

We assess the welfare implications of alternative retirement plan investment options given that households may not invest according to optimal portfolio choice theory but may instead use simple decision rules. We simulate the performance of lifestyle, lifecycle, and other simple strategies for allocating retirement savings. We find that if investors use simple rules of thumb to choose investments, then the impact of these strategies on welfare depend to a large extent on the choice set they are offered. If larger choice sets cause them to undertake more risk, then risk tolerant individuals may tend to be made better off. If larger choice sets cause them to reduce suboptimally low levels of portfolio risk, then the increased choice set may make them substantially worse off. The welfare effects of plan designs that induce lifecycle investing, which tends to be conservative over the lifetime, therefore depend crucially on the counterfactual portfolio composition, as well as preferences and non-retirement wealth.