@article {angrisani_samek_kapteyn_2021, title = {Introduction to the Special Issue on New Longitudinal Data for Retirement Analysis and Policy}, journal = {Journal of Pension Economics and Finance}, year = {2021}, pages = {1{\textendash}5}, abstract = {The number of data sources available for academic research on retirement economics and policy has increased rapidly in the past two decades. Data quality and comparability across studies have also improved considerably, with survey questionnaires progressively converging towards common ways of eliciting the same measurable concepts. Probability-based Internet panels have become a more accepted and recognized tool to obtain research data, allowing for fast, flexible, and cost-effective data collection compared to more traditional modes such as in-person and phone interviews. In an era of big data, academic research has also increasingly been able to access administrative records (e.g., Kost{\o}l and Mogstad, 2014; Cesarini et al., 2016), private-sector financial records (e.g., Gelman et al., 2014), and administrative data married with surveys (Ameriks et al., 2020), to answer questions that could not be successfully tackled otherwise.}, keywords = {Longitudinal data, Methodology}, doi = {10.1017/S1474747221000044}, author = {Marco Angrisani and Samek, Anya and Arie Kapteyn} } @conference {11003, title = {Cognitive Ability, Cognitive Aging, and Debt Accumulation}, booktitle = {Retirement and Disability Research Consortium 22nd Annual Meeting }, year = {2020}, publisher = {Retirement and Disability Research Consortium}, organization = {Retirement and Disability Research Consortium}, address = {Virtual}, abstract = {While a large literature has examined savings behavior and accumulation among older adults, relatively little research has explored older adults{\textquoteright} debt behaviors and outcomes. Recent work by Lusardi, Mitchell, and Oggero (2020) shows that older adults from recent generations tend to hold more debt than their predecessors, particularly mortgage debt, and correspondingly face greater financial insecurity near retirement age. While documenting such trends is an important first step, developing policy interventions to counteract them requires identifying the underlying drivers of the observed surge in debt burdens among recent older adults. One potential candidate is the increasing complexity of financial products targeted to consumers in the past few decades (C{\'e}l{\'e}rier and Vallee, 2017), particularly among mortgage products (Amromin et al., 2018). Figure 1 documents that originations of complex mortgages with zero or negative amortization surged in the early 2000s and subsequently reduced sharply after the financial crisis. Consumers from later cohorts may have difficulty appropriately selecting among and using these increasingly complicated instruments (Brown et al., 2017; Hastings and Mitchell, 2018). This may be particularly true for individuals with low cognitive ability and older individuals experiencing cognitive decline. As the financial landscape has become progressively more complex, the rise in debt burdens may be concentrated on those who are less cognitively able, raising concerns about the economic security of individuals who may not be adequately equipped to navigate the system.}, keywords = {Cognition, Debt, Mortgages}, url = {https://crr.bc.edu/wp-content/uploads/2020/01/2020-RDRC-Meeting-Booklet.pdf$\#$page=96}, author = {Marco Angrisani and Burke, Jeremy and Arie Kapteyn} } @inbook {10360, title = {Can Internet Match High-quality Traditional Surveys? Comparing the Health and Retirement Study and its Online Version}, booktitle = {The Econometrics of Complex Survey Data}, series = {Advances in Econometrics}, volume = {39}, year = {2019}, pages = {3 - 33}, publisher = {Emerald Publishing Limited}, organization = {Emerald Publishing Limited}, abstract = {Abstract We examine sample characteristics and elicited survey measures of two studies, the Health and Retirement Study (HRS), where interviews are done either in person or by phone, and the Understanding America Study (UAS), where surveys are completed online and a replica of the HRS core questionnaire is administered. By considering variables in various domains, our investigation provides a comprehensive assessment of how Internet data collection compares to more traditional interview modes. We document clear demographic differences between the UAS and HRS samples in terms of age and education. Yet, sample weights correct for these discrepancies and allow one to satisfactorily match population benchmarks as far as key socio- demographic variables are concerned. Comparison of a variety of survey outcomes with population targets shows a strikingly good fit for both the HRS and the UAS. Outcome distributions in the HRS are only marginally closer to population targets than outcome distributions in the UAS. These patterns arise regardless of which variables are used to construct post-stratification weights in the UAS, confirming the robustness of these results. We find little evidence of mode effects when comparing the subjective measures of self-reported health and life satisfaction across interview modes. Specifically, we do not observe very clear primacy or recency effects for either health or life satisfaction. We do observe a significant social desirability effect, driven by the presence of an interviewer, as far as life satisfaction is concerned. By and large, our results suggest that Internet surveys can match high-quality traditional surveys.}, keywords = {internet survey comparison}, isbn = {978-1-78756-726-9, 978-1-78756-725-2/0731-9053}, url = {https://doi.org/10.1108/S0731-905320190000039001}, author = {Marco Angrisani}, editor = {Finley Brian} } @article {5857, title = {Nonmonetary Job Characteristics and Employment Transitions at Older Ages}, year = {2015}, note = {WP 2015-326}, institution = {Ann Arbor, MI, University of Michigan}, abstract = {This paper studies to what extent job characteristics such as physical and cognitive demands, use of technologies, responsibility, difficulty, stress, peer pressure, and relations with co-workers are related to full or partial retirement. We study employment transitions and retirement expectations of older workers by exploiting the wealth of information about individuals older than age 50 in the Health and Retirement Study (HRS), and characteristics of different occupations provided by the Occupation Information Network (O NET) database. Controlling for basic demographics, wages, benefits, health, cognitive ability, personality, and other personal characteristics, we find strong and statistically significant relationships between labor force transitions and job characteristics. These relationships are typically more pronounced and more precisely estimated when we use objective job attributes taken from the O NET than when we use self-reported job characteristics taken from the HRS, but self-reported characteristics are more strongly related to moves from full-time to part-time employment. Using expected retirement age or subjective probabilities of working full-time at older ages gives similar results to using actual labor force transitions as the dependent variable. The estimated effects of job characteristics are again stronger and more robust to alternative specifications when measures of job attributes are taken from the O NET than from the HRS. Our findings suggest that nonmonetary job characteristics are important determinants of labor supply decisions at older ages, but our analysis is still preliminary in its attempt to uncover causal relationships: Unobservable individual characteristics responsible for sorting into specific occupations may also shape retirement decisions.}, keywords = {Employment and Labor Force, Health Conditions and Status, Retirement Planning and Satisfaction}, author = {Marco Angrisani and Arie Kapteyn and Erik Meijer} }