Using expectations data to study subjective income expectations

We have collected data on the one-year-ahead income expectations of members of American households in our Survey of Economic Expectations (SEE), a module of a national continuous telephone survey conducted at the University of Wisconsin. The income-expectations questions take this form: "What do you think is the percent chance (or what are the chances out of 100) that your total household income, before taxes, will be less than Y over the next 12 months?" We use the responses to a sequence of such questions posed for different income thresholds Y to estimate each respondent's subjective probability distribution for next year's household income. We use the estimates to study the cross- sectional variation in income expectations one year into the future.

Learning about social programs from experiments with random assignment of treatments

The importance of social programs to a diverse population creates a legitimate concern that the findings of evaluations be widely credible. The weaker are the assumptions imposed, the more widely credible are the findings. The classical argument for random assignment of treatments is viewed by many as enabling evaluation under weak assumptions, and has generated much interest in the conduct of experiments. But the classical argument does impose assumptions, and there often is good reason to doubt their realism. Some researchers, finding the classical assumptions implausible, impose other assumptions strong enough to identify treatment effects of interest. In contrast, the recent literature examined in this article explores the inferences that may be drawn from experimental data under assumptions weak enough to yield widely credible findings. This literature has two branches. One seeks out notions of treatment effect that are identified when the experimental data are combined with weak assumptions. The canonical finding is that the average treatment effect within some context-specific subpopulation is identified. The other branch specifies a population of a priori interest and seeks to learn about treatment effects in this population. Here the canonical finding is a bound on average treatment effects. The various approaches to the analysis of experiments are complementary from a mathematical perspective, but in tension as guides to evaluation practice. The reader of an evaluation reporting that some social program "works" or has "positive impact" should be careful to ascertain what treatment effect has been estimated and under what assumptions.

Legislative behaviour absent re‐election incentives: findings from a natural experiment in the Arkansas Senate

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141545/1/rssa12293.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141545/2/rssa12293-sup-0001-SupInfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141545/3/rssa12293_am.pd

Reform of Unemployment Compensation in Germany : A Nonparametric Bounds Analysis Using Register Data

Economic theory suggests that an extension of the maximum length of entitlement for unemployment benefits increases the duration of unemployment. Empirical results for the reform of the unemployment compensation system in Germany during the 1980s are less clear. The analysis in this paper is motivated by the controversial empirical findings and by recent developments in econometrics for partial identification. We use extensive administrative data with the drawback that registered unemployment is not directly observed. For this reason we bound the reform effect on unemployment duration over different definitions of unemployment. By exploiting the richness of the data we use a nonparametric approach without imposing critical parametric model assumptions. We identify a systematic increase in unemployment duration in response to the reform in samples that amount to less than 15% of the unemployment spells for the treatment group

Identification and Inference in a Simultaneous Equation Under Alternative Information Sets and Sampling Schemes

In simple static linear simultaneous equation models the empirical distributions ofIV and OLS are examined under alternative sampling schemes and compared with their first-order asymptotic approximations. It is demonstrated why in this context the limiting distribution of a consistent estimator is not a¤ected by conditioning on exogenous regressors, whereas that of an inconsistent estimator is. The asymptotic variance and the simulated actual variance of the inconsistent OLS estimator are shown to diminish by extending the set of exogenous variables kept fixed in sampling, whereas such an extension disrupts the distribution of consistent IV estimation and deteriorates the accuracy of its standard asymptotic approximation, not only when instruments are weak. Against this background the consequences for the identification of the parameters of interest are examined for a setting in which (in practice often incredible) assumptions regarding the zero correlation between instruments and disturbances are replaced by (generally more credible) interval assumptions on the correlation between endogenous regressors and disturbances. This leads to a feasible procedure for constructing purely OLS-based robust confidence intervals, which yield conservative coverage probabilities in finite samples, and often outperform IV-based intervals regarding their length