This study will consider how best to measure the incentives from pensions in models of retirement and saving. In the course of analysis, we will make use of measures of pension value and pension change based on several different ways of collecting the data and several waves of data, embedding them in various dynamic, stochastic models of retirement saving. Data are from the Health and Retirement Study (HRS), covering 1992 through 2006.
One aim of the study is to clean the data from each source and remove errors. Accordingly, employer provided pension plan descriptions will be carefully examined as to dates, criteria used in matching plan descriptions to respondents, matching at the level of the plant or the firm and for other sources of errors. Respondent data will be similarly examined for a variety of measurement errors. Models explaining imperfect pension measures will be developed. When possible, we will correct the measures for errors that we uncover. Where necessary, we will search for a hybrid measure that best represents the incentives that pensions create. Second, we will estimate which pension measure or combination of measures derived from self reported and firm reported data best explains retirement and saving outcomes. Dynamic structural models of retirement and saving will be fit using different measures of pensions. We then will model and estimate the value of pension knowledge. A third issue is how best to incorporate pension changes into the analysis. Virtually all models of retirement and saving fit to date assume the pension plans covering workers in a particular year will remain unchanged over time. Yet a number of the pension plans do change over time. In those cases, one cannot rely on accrual profiles derived from cross section data to determine the rewards from pensions to continued work. We will determine the importance of pension changes over time, how to adapt models of retirement and saving to pension changes, and how best to estimate these models in the face of pension changes. We also will analyze how errors in estimating pension levels affect measures of pension changes, and how best to correct for these errors. Throughout the analysis, we will work with staff from the Health and Retirement Study to improve the pension data that HRS provides to users.
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| Lei, Xiaoyan; Shen, Yan; Smith, James P et al. (2017) SIBLING GENDER COMPOSITION'S EFFECT ON EDUCATION: EVIDENCE FROM CHINA. J Popul Econ 30:569-590 |
| Vega, Alma; Aguila, Emma (2017) Foreign retirement income among new older immigrants in the United States. Int Migr 55:38-56 |
| Aguila, Emma; Kapteyn, Arie; Perez-Arce, Francisco (2017) Consumption Smoothing and Frequency of Benefit Payments of Cash Transfer Programs. Am Econ Rev 107:430-435 |
| Blanco, Luisa R; Aguila, Emma; Gongora, Arturo et al. (2017) Retirement Planning Among Hispanics: In God's Hands? J Aging Soc Policy 29:311-331 |
| Aguila, Emma; Kapteyn, Arie; Tassot, Caroline (2017) Designing Cash Transfer Programs for an Older Population: The Mexican Case. J Econ Ageing 9:111-121 |
| Kim, Jibum; Lee, Yun-Suk; Lee, Jinkook (2016) Living arrangements and suicidal ideation among the Korean older adults. Aging Ment Health 20:1305-1313 |
| Aguila, Emma; Guerrero, Erick G; Vega, William A (2016) Sociodemographic characteristics associated with alcohol use among low-income Mexican older adults. Subst Abuse Treat Prev Policy 11:16 |
| Aguila, Emma; Weidmer, Beverly A; Illingworth, Alfonso Rivera et al. (2016) Culturally Competent Informed-Consent Process to Evaluate a Social Policy for Older Persons With Low Literacy: The Mexican Case. Sage Open 6: |
| Aguila, Emma; Mejia, Nelly; Perez-Arce, Francisco et al. (2016) Costs of Extending the Noncontributory Pension Program for Elderly: The Mexican Case. J Aging Soc Policy 28:325-43 |
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