Income Source Confusion Using the SILC.

We use a unique panel of household survey data-the Austrian version of the European Union Statistics on Income and Living Conditions (SILC) for 2008-2011-which have been linked to individual administrative records on both state unemployment benefits and earnings. We assess the extent and structure of misreporting across similar benefits and between benefits and earnings. We document that many respondents fail to report participation in one or more of the unemployment programs. Moreover, they inflate earnings for periods when they are unemployed but receiving unemployment compensation. To demonstrate the impact of income source confusion on estimators, we estimate standard Mincer wage equations. Since unemployment is associated with lower education, the reports of unemployment benefits as earnings bias downward the returns to education. Failure to report unemployment benefits also leads to substantial sample bias when selecting on these benefits, as one might in estimating the returns to job training.

Balancing data privacy and usability in the federal statistical system.

The federal statistical system is experiencing competing pressures for change. On the one hand, for confidentiality reasons, much socially valuable data currently held by federal agencies is either not made available to researchers at all or only made available under onerous conditions. On the other hand, agencies which release public databases face new challenges in protecting the privacy of the subjects in those databases, which leads them to consider releasing fewer data or masking the data in ways that will reduce their accuracy. In this essay, we argue that the discussion has not given proper consideration to the reduced social benefits of data availability and their usability relative to the value of increased levels of privacy protection. A more balanced benefit-cost framework should be used to assess these trade-offs. We express concerns both with synthetic data methods for disclosure limitation, which will reduce the types of research that can be reliably conducted in unknown ways, and with differential privacy criteria that use what we argue is an inappropriate measure of disclosure risk. We recommend that the measure of disclosure risk used to assess all disclosure protection methods focus on what we believe is the risk that individuals should care about, that more study of the impact of differential privacy criteria and synthetic data methods on data usability for research be conducted before either is put into widespread use, and that more research be conducted on alternative methods of disclosure risk reduction that better balance benefits and costs.

Estimating the cumulative rate of SARS-CoV-2 infection.

Accurate estimates of the cumulative incidence of SARS-CoV-2 infection remain elusive. Among the reasons for this are that tests for the virus are not randomly administered, and that the most commonly used tests can yield a substantial fraction of false negatives. In this article, we propose a simple and easy-to-use Bayesian model to estimate the infection rate, which is only partially identified. The model is based on the mapping from the fraction of positive test results to the cumulative infection rate, which depends on two unknown quantities: the probability of a false negative test result and a measure of testing bias towards the infected population. Accumulating evidence about SARS-CoV-2 can be incorporated into the model, which will lead to more precise inference about the infection rate.

Capacity Assessment of Prosthetic Performance for the Upper Limb (CAPPFUL): Characterization of Normative Kinematics and Performance.

BACKGROUND: Evaluation of maladaptive compensatory movement is important to objectively identify the impact of prosthetic rehabilitative intervention on body mechanics. The Capacity Assessment of Prosthetic Performance for the Upper Limb (CAPPFUL) scores this type of compensation by comparing movements of the prosthesis user to movements of individuals with intact, sound upper limbs (ULs). However, expected movements of individuals with sound, intact ULs have not been studied for the set of tasks performed in the CAPPFUL. OBJECTIVE: To enhance the scoring approach for the maladaptive compensatory movement domain of the CAPPFUL by defining normative kinematic movement and characterizing variability and repeatability. DESIGN: Clinical measurement. SETTING: Laboratories at the U.S. Food and Drug Administration (FDA) and University of Texas-Arlington. PARTICIPANTS: Convenience sample of 20 participants with no upper limb (UL) disability or impairment. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASUREMENTS: Kinematic trajectories, range of motion, maximum angle, and completion time were calculated. Repeatability and intersubject variability were assessed by calculating Pearson's correlation coefficient (R), adjusted coefficient of multiple correlation (CMCadj), and max SD (SDmax) for nine joint angles at the elbow, shoulder, neck, and torso. RESULTS: For most joints evaluated, repeatability was lower (R < 0.8) for CAPPFUL 3-Zip vest, CAPPFUL 7-Cut w/ knife, and CAPPFUL 8-Squeeze water, implying inconsistent approaches within a subject from trial to trial for a given task. For most tasks, the joint angle SDmax across all participants was <20°. The approach for completing CAPPFUL 1 - Weights in crate and CAPPFUL 4 - Pick up dice was generally similar across participants (CMCadj >0.4). For other tasks, however, different approaches across participants at the torso and shoulder joint can be seen. CONCLUSION(S): This work established the expected movements of individuals with sound, intact ULs for tasks performed in the CAPPFUL that can be used to inform consistent, standardized scoring of the maladaptive compensatory movement domain.