@article {5801, title = {Occupational Learning, Financial Knowledge, and the Accumulation of Retirement Wealth}, number = {WP 2010-237}, year = {2010}, institution = {Michigan Retirement Research Center, University of Michigan}, address = {Ann Arbor, MI}, abstract = {This study explores the relationship between general human capital investment, financial knowledge, occupational spillovers, and the accumulation of wealth in a primarily descriptive manner. Drawing upon human capital theory and following previous related work by Delavande, Rohwedder and Willis (2008), we hypothesized that individuals with daily exposure to financial knowledge through their occupation would benefit by having greater financial knowledge that would translate into greater wealth accumulation than individuals who do not enjoy such spillovers from their occupation. Using data from the Cognitive Economics Study and the Health and Retirement Study, we find strong evidence that individuals in financial occupations tend to have greater financial knowledge and moderate evidence that they also have greater wealth accumulation.}, keywords = {Health Conditions and Status, Net Worth and Assets}, url = {http://hdl.handle.net/2027.42/78354}, author = {Brooke Helppie and Kandice Kapinos and Robert J. Willis and Michigan Retirement Research Center} } @article {5780, title = {Cross-Wave Prospective Social Security Wealth Measures of Pre-Retirees, Public Release: Data Description and Usage}, year = {2009}, institution = {Institute for Social Research, University of Michigan}, address = {Ann Arbor, Michigan}, abstract = {The Prospective Social Security Wealth Measures of Pre-Retirees data set consists of respondent-level, cross-sectional files constructed from the employment sections of the HRS 1992 (wave 1), HRS 1998 (wave 4), HRS 2004 (wave 7) and the restricted SSA summary and detailed earnings and benefits files. In this public use file, we calculate wealth only for individuals who have not yet retired (as evidenced by claiming SS benefits) (see Section III.C). Each individual is uniquely identified by the concatenation of the household ID and the person number, HHID and PN. We organize the data to match the organization of the RAND HRS data files.}, keywords = {Net Worth and Assets, Social Security}, author = {Kandice Kapinos and Charles Brown and Michael A. Nolte and Helena Stolyarova and David R Weir} }