WP 2018-384

Social Security offers two types of benefits for spouses: spousal and survivor benefits. Regardless of his or her own work history, a married individual can claim spousal Social Security benefits, which are equal to half of his or her spouse’s Social Security benefits. Furthermore, a widow or widower can claim survivor benefits and receive or his or her deceased spouse’s full benefit if it is larger than his or her own benefit. Ideally, married individuals think about the impact of their Social Security choices on their spouse. However, if people do not fully understand the rules for the spousal and survivor benefits, they may make suboptimal choices, not only about Social Security claiming, but perhaps also about labor and marriage decisions. In this paper we make use of new data from the Understanding America Study to assess households’ understanding of these benefits. Overall, our results suggest that knowledge of spousal and survivors benefits is low. Furthermore, our results suggest that people’s perceptions of their knowledge is misaligned with their actual knowledge, with many perceiving that they know more about Social Security than they actually do. The results in this paper suggest particular areas where policymakers might be able to increase knowledge of spousal and survivors benefits. However, future research is needed to better understand how to increase knowledge in this area.Social Security Administration, Award RRC08098401-10, R-UM18-05https://deepblue.lib.umich.edu/bitstream/2027.42/147437/1/wp384.pdfDescription of wp384.pdf : Working pape

Knowledge as a Predictor of Insurance Coverage Under the Affordable Care Act

Background: The Affordable Care Act established policy mechanisms to increase health insurance coverage in the United States. While insurance coverage has increased, 10%-15% of the US population remains uninsured. Objectives: To assess whether health insurance literacy and financial literacy predict being uninsured, covered by Medicaid, or covered by Marketplace insurance, holding demographic characteristics, attitudes toward risk, and political affiliation constant. Research Design: Analysis of longitudinal data from fall 2013 and spring 2015 including financial and health insurance literacy and key covariates collected in 2013. Subjects: A total of 2742 US residents ages 18-64, 525 uninsured in fall 2013, participating in the RAND American Life Panel, a nationally representative internet panel. Measures: Self-reported health insurance status and type as of spring 2015. Results: Among the uninsured in 2013, higher financial and health insurance literacy were associated with greater probability of being insured in 2015. For a typical uninsured individual in 2013, the probability of being insured in 2015 was 8.3 percentage points higher with high compared with low financial literacy, and 9.2 percentage points higher with high compared with low health insurance literacy. For the general population, those with high financial and health insurance literacy were more likely to obtain insurance through Medicaid or the Marketplaces compared with being uninsured. The magnitude of coefficients for these predictors was similar to that of commonly used demographic covariates. Conclusions: A lack of understanding about health insurance concepts and financial illiteracy predict who remains uninsured. Outreach and consumer-education programs should consider these characteristics

Ancient Antimicrobial Peptides Kill Antibiotic-Resistant Pathogens: Australian Mammals Provide New Options

Background: To overcome the increasing resistance of pathogens to existing antibiotics the 10× 20 Initiative declared the urgent need for a global commitment to develop 10 new antimicrobial drugs by the year 2020. Naturally occurring animal antibiotics are an obvious place to start. The recently sequenced genomes of mammals that are divergent from human and mouse, including the tammar wallaby and the platypus, provide an opportunity to discover novel antimicrobials. Marsupials and monotremes are ideal potential sources of new antimicrobials because they give birth to underdeveloped immunologically naïve young that develop outside the sterile confines of a uterus in harsh pathogen-laden environments. While their adaptive immune system develops innate immune factors produced either by the mother or by the young must play a key role in protecting the immune-compromised young. In this study we focus on the cathelicidins, a key family of antimicrobial peptide genes. Principal Finding: We identified 14 cathelicidin genes in the tammar wallaby genome and 8 in the platypus genome. The tammar genes were expressed in the mammary gland during early lactation before the adaptive immune system of the young develops, as well as in the skin of the pouch young. Both platypus and tammar peptides were effective in killing a broad range of bacterial pathogens. One potent peptide, expressed in the early stages of tammar lactation, effectively killed multidrug-resistant clinical isolates of Pseudomonas aeruginosa, Klebsiella pneumoniae and Acinetobacter baumannii. Conclusions and Significance: Marsupial and monotreme young are protected by antimicrobial peptides that are potent, broad spectrum and salt resistant. The genomes of our distant relatives may hold the key for the development of novel drugs to combat multidrug-resistant pathogens

You can be too thin (but not too tall): Social desirability bias in self-reports of weight and height

Previous studies of survey data for the United States and other countries find that on average women tend to understate their body weight, while on average both men and women overstate their height. Social norms have been posited as a potential explanation for misreporting of weight and height, but researchers disagree on the validity of that explanation. This paper is the first to present a theoretical model of self-reporting behavior for weight and height that explicitly incorporates social desirability bias. The model generates testable implications that can be contrasted with predictions based on alternative explanations for self-reporting errors. Using data from the National Health and Nutrition Examination Survey (NHANES) from 1990-2010, we find that self-reporting patterns for both weight and body mass index (BMI) offer robust evidence of social desirability bias, such that reports are biased (from both sides) towards social norms. The BMI norm inferred for women lies squarely within the range considered "healthy" by public health officials, while the BMI norm inferred for men lies just above this healthy range. Lack of awareness of one's current body weight may explain the presence of large (negative) self-reporting errors among those with very high values of examined weight, but the evidence of social desirability bias is robust to this alternative explanation over most of the weight distribution. Social desirability bias in self-reporting of height is observed primarily among those of below-average height and no clear height norms are discernible. The framework also helps to explain previous findings that the degree of self-reporting bias in weight depends on the survey mode

Measuring risk perceptions: What does the excessive use of 50% mean?

Objectives. Risk perceptions are central to good health decisions. People can judge valid probabilities but use 50% disproportionately. The authors hypothesized that 50% is more likely than other responses to reflect not knowing the probability, especially among individuals with low education and numeracy, and evaluated the usefulness of eliciting “don’t know” explanations. Methods. Respondents (n = 1020) judged probabilities for living or dying in the next 10 years, indicating whether they gave a good estimate or did not know the chances. They completed demographics, medical history, and numeracy questions. Results. Overall, 50% was more likely than other probabilities to be explained as “don’t know” (v. “a good estimate”). Correlations of using 50% with low education and numeracy were mediated by expressing “don’t know.” Judged probabilities for survival and mortality explained as “don’t know” had lower correlations with age, diseases, and specialist visits. Conclusions. When judging risks, 50% may reflect not knowing the probability, especially among individuals with low numeracy and education. Probabilities expressed as “don’t know” are less valid. Eliciting uncertainty could benefit theoretical models and educational efforts