Curriculum Vitae - Erik Meijer

Erik Meijer

University of Southern California
Center for Economic and Social Research
635 Downey Way
Los Angeles, CA 90089-3332
(213) 821-2788
erik.meijer@usc.edu
http://dornsife.usc.edu/erik-meijer/

Education

Ph.D., Social Sciences, Leiden University, Netherlands, 1998
MA, Econometrics, University of Groningen, Netherlands (cum laude), 1990

Positions

2013–Present: Senior Economist, University of Southern California, Center for Economic and Social Research
2013–Present: Adjunct Economist, RAND Corporation
2014–2015, 2022: Lecturer, University of Southern California, Department of Economics
2006–2013: Economist, RAND.
2010–2012: Professor of Economics, Frederick S. Pardee RAND Graduate School
2003–2007: Assistant professor, University of Groningen, Faculty of Economics
Spring 2003: Assistant professor (teaching), Leiden University, Department of Psychology
2000–2003: Postdoctoral researcher, University of Groningen, Faculty of Economics
1998–2000: Assistant professor, University of Groningen, Faculty of Economics
1997–1998: Junior project manager, MuConsult BV (transportation research consultants), Amersfoort, Netherlands
1995–1997: Researcher, MuConsult BV
1991–1995: Research associate, Leiden University, Department of Psychometrics and Research Methodology

Visits and consultancy

Department special adviser, Bank of Canada, Department of Monetary and Financial Analysis, September 2004, November 2006
Freelance researcher, MuConsult BV, 1999–2004
Guest researcher, University of California, Los Angeles (UCLA), Department of Psychology, September–October 1993
Short visits: University of Groningen (Dept. of Economics, 2010, 2012, 2015–2019, 2023); HEC Montr´eal (Dept. of Applied Economics, 2017); VU University Amsterdam (Dept. of Economics, 2012); University of Venice Ca’ Foscari (Dept. of Economics, 2010); McGill University (Faculty of Management, 2006, 2008); Tilburg University (Dept. of Economics, 2008); University of Amsterdam (Dept. of Economics, 2008)

Contracts and grants

NIA (U01); Health and Retirement Study (Weir, Langa, PIs); Role: Investigator; 2018–2029
NIA (U24); Gateway Exposome Coordinating Center (GECC) For AD/ADRD Research (Lee, Adar. Knapp, PIs); Role: Investigator; 2024–2029
NIA (R01); Multilingualism as a factor of resilience to Alzheimer’s disease and related dementias in India (Lee, Aksman, Arce, PIs); Role: Investigator; 2022–2027
NIA (R01); Integrating Information about Aging Surveys: Novel Integration of Contextual Data to Study Late-Life Cognition and Alzheimer’s Disease and Related Dementia and Dementia Care (Lee, PI); Role: Investigator; 2022–2027
NIA (R01); Harmonized Diagnostic Assessment of Dementia (DAD) for the Longitudinal Aging Study in India (LASI) (Lee, PI); Role: Investigator; 2018–2026
Bright Focus (Special Opportunity Award); Expanding and enhancing LASI-DAD for better understanding of AD and Dementia (Lee, PI); Role: Investigator; 2022–2025
NIA (R01); Testing early markers of cognitive decline and dementia derived from survey response behaviors (Schneider, PI); Role: Investigator; 2020–2025
NIA (R01); Integrating Information about Aging Surveys (Lee, PI); Role: Investigator; 2013–2022
NIA (U01); Toward Next Generation Data on Health and Life Changes at Older Ages (Kapteyn, PI); Role: Investigator; 2017–2022
NIA (R01); Longitudinal Aging Study in India (Bloom/Lee, PIs); Role: Investigator; 2013–2021
SSA through MRDRC; A Framework for Cost-Beneft Analysis of Totalization Agreements (Prados, PI); Role: Investigator; 2019–2020
NIA (R01); Early Life Conditions, Work, Subjective Well-Being, and Cognition (Kapteyn, PI); Role: Investigator; 2019–2020
SSA through MRDRC; The Efects of Totalization Agreements on Economic Activity (Prados, PI); Role: Investigator; 2019
Sloan Foundation through NBER; Sorting Into Jobs and Labor Supply and Demand at Older Ages (Kapteyn, PI); Role: Investigator; 2019
NIA (R01); Harmonizing Dementia Assessment in Korea: Study on Dynamics of the KLoSA MCI, Dementia & Labor Loss (Lee, PI); Role: Investigator; 2018–2019
SSA through MRRC; Social Security Coverage Around the World (P´erez-Arce, PI); Role: Investigator; 2018
MRRC; Work-Life Balance and Labor Force Attachment at Older Ages (Angrisani, PI); Role: Investigator; 2016–2017
University of Michigan; Health and Retirement Study (Crimmins, PI); Role: Investigator; 2015–2017
Lucas Education Foundation; Efcacy Study of the Knowledge in Action Advanced Placement U.S. Government and Environmental Science Projects (Saavedra, PI); Role: Investigator; 2016
MRRC; Non-Monetary Job Characteristics and Employment Transitions at Older Ages (Angrisani, PI); Role: Investigator; 2014–2015
NIA (R01); From Understanding to Reducing Health Disparities: A Model-Based Evaluation (Galama, PI); Role: Investigator; 2010–2015
NIA (P30 subproject); Investigating the Relation between DRM and Experienced Yesterday Measures (Kapteyn, PI); Role: Investigator; 2014
University of Michigan; Health and Retirement Study (Hurd, PI); Role: Investigator; 2012–2014
NICHD (R01); Health Risk Behaviors Among Palestinian Youth (Glick, PI); Role: Investigator; 2011–2014
MRRC; Working at Older Ages: The Roles of Work Environment and Psychological Factors (Angrisani, PI); Role: Investigator; 2013
AUSAID; Indonesia Family Life Survey – East (Sikoki, Strauss, PIs); Role: Investigator; 2012–2013
NIA (R01); Robust Integration Modeling of Health, Wealth, and Disability (Kapteyn, PI); Role: Investigator; 2007–2013
MRRC; Investment Decisions in Retirement: The Role of Expectations, Health Risk, and Pension Benefts; Role: PI; 2011–2012
MRRC; An Analysis of the Representativeness of the Low-Income Population in the HRS; Role: PI; 2010–2012
World Bank/Russia Financial Literacy and Education Trust Fund; Toolkit for Evaluation of Financial Capability Interventions (Kapteyn, Yoong, PIs); Role: Investigator; 2010–2012
World Bank; Indonesia: Analysis of Urban Poverty and Program Review (Glick, Yoong, PIs); Role: Investigator; 2011
SSA; Polisim Model Integration (Rendall, PI); Role: Investigator; 2008–2010
DOL; Financial Decision Making in Retirement Accounts (Kapteyn, PI); Role: Investigator; 2008–2009
SSA; Estimation of Potential Eligibility for Low-Income Subsidies under Medicare Part D (Karoly, PI); Role: Investigator; 2007–2009
Postdoctoral research grant from the University of Groningen; 2000–2003
Various contracts to MuConsult from the Dutch Ministry of Transport, Dutch National Railways, and other government agencies and companies; Role: Investigator; 1995–1998

Publications

Journal Articles

Snoke, J., Meijer, E., Phillips, D., Wilkens, J., & Lee, J. (2024). Synthesizing surveys with multiple units of observation: An application to the Longitudinal Aging Study in India. Journal of Survey Statistics and Methodology. (Accepted for publication)
Petrosyan, S., et al. (2024). The association of multilingualism with diverse language families and cognition among adults with and without education in India. Neuropsychology. (Accepted for publication)
Gao, H., et al. (2024). Early identifcation of cognitive impairment in community environments through modeling subtle inconsistencies in questionnaire responses: Machine learning model development and validation. JMIR Formative Research. (Accepted for publication) https://doi.org/10.2196/54335
Schneider, S., et al. (2024). Can you tell people’s cognitive ability level from their response patterns in questionnaires? Behavior Research Methods, 56, 6741–6758. https://doi.org/10.3758/s13428-024-02388-2
Angrisani, M., et al. (2024). Modifable risk factors for dementia in India: Revisiting estimates and reassessing prevention potential. BMJ Public Health, 2, e001362. https://doi.org/10.1136/bmjph-2024-001362
Lee, J., & Meijer, E. (2024). Diferent reasonable methodological choices can lead to vastly diferent estimates of the economic burden of diseases [Invited comment]. The Lancet Healthy Longevity, 5, e504–e505. https://doi.org/10.1016/S2666-7568(24)00130-2
Nichols, E., et al. (2024). Considerations for the use of the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) in cross-country comparisons of cognitive aging and dementia. Alzheimer’s & Dementia, 20, 4635–4648. https://doi.org/10.1002/alz.13895
Geldsetzer, P., Chang, A. Y., Meijer, E., Sudharsanan, N., Charu, V., Kramlinger, P., & Haarburger, R.
(2024). Interviewer biases in medical survey data: The example of blood pressure measurements. PNAS Nexus, 3(3), pgae109. https://doi.org/10.1093/pnasnexus/pgae109
Angrisani, M., Casanova, M., Lee, J., & Meijer, E. (2024). The economic burden of dementia in India. AEA Papers and Proceedings, 114, 418–422. https://doi.org/10.1257/pandp.20241061
Schneider, S., et al. (2024). Cognitive functioning and the quality of survey responses: An individual participant data meta-analysis of 10 epidemiological studies of aging. Journals of Gerontology: Psychological Sciences, 79(5), gbae030. https://doi.org/10.1093/geronb/gbae030
Kobayashi, L. C., et al. (2024). Cross-national comparisons of later-life cognitive function using data from the Harmonized Cognitive Assessment Protocol (HCAP): Considerations and recommended best practices. Alzheimer’s & Dementia, 20, 2273–2281. https://doi.org/10.1002/alz.13694
Hernandez, R., et al. (2024). Attrition from longitudinal ageing studies and performance across domains of cognitive functioning: An individual participant data meta-analysis. BMJ Open, 14, e079241. https://doi.org/10.1136/bmjopen-2023-079241
Gross, A. L., et al. (2024). Prevalence of DSM-5 mild and major neurocognitive disorder in India: Results from the LASI-DAD. PLoS One, 19, e0297220. https://doi.org/10.1371/journal.pone.0297220
Khobragade, P., et al. (2024). Performance of the Informant Questionnaire on Cognitive Decline for the Elderly (IQCODE) in a nationally representative study in India: The LASI-DAD study. International Psychogeriatrics, 36, 177–187. https://doi.org/10.1017/S1041610222000606
Schneider, S., et al. (2023). Using item response times in online questionnaires to detect mild cognitive impairment. Journals of Gerontology: Psychological Sciences, 78, 1278–1283. https://doi.org/10.1093/geronb/gbad043
Lee, J., et al. (2023a). Prevalence of dementia in India: National and state estimates from a nationwide study. Alzheimer’s & Dementia, 19, 2898–2912. https://doi.org/10.1002/alz.12928
Lee, J., et al. (2023b). Deep phenotyping and genomic data from a nationally representative study on dementia in India. Scientifc Data, 10, 45. https://doi.org/10.1038/s41597-023-01941-6
Schneider, S., Jin, H., Orriens, B., Junghaenel, D. U., Kapteyn, A., Meijer, E., & Stone, A. A. (2023). Using attributes of survey items to predict response times may beneft survey research. Field Methods, 35, 87–99. https://doi.org/10.1177/1525822X221100904
Junghaenel, D. U., et al. (2023). Inferring cognitive abilities from response times to web-administered survey items in a population-representative sample. Journal of Intelligence, 11(1), 3. https://doi.org/10.3390/jintelligence11010003
Gatz, M., Schneider, S., Meijer, E., Darling, J. E., Orriens, B., Liu, Y., & Kapteyn, A. (2023). Identifying cognitive impairment among older participants in a nationally representative internet panel. Journals of Gerontology: Psychological Sciences, 78, 201–209. https://doi.org/10.1093/geronb/gbac172
Meijer, E., Casanova, M., Kim, H., Llena-Nozal, A., & Lee, J. (2022). Economic costs of dementia in 11 countries in Europe: Estimates from nationally representative cohorts of a panel study. The Lancet Regional Health – Europe, 20, 100445. https://doi.org/10.1016/j.lanepe.2022.100445
Schneider, S., Junghaenel, D. U., Meijer, E., Zelinski, E. M., Jin, H., Lee, P.-J., & Stone, A. A. (2022). Quality of survey responses at older ages predicts cognitive decline and mortality risk. Innovation in Aging, 6(3), 1–11. https://doi.org/10.1093/geroni/igac027
Liu, Y., Schneider, S., Orriens, B., Meijer, E., Darling, J. E., Gutsche, T., & Gatz, M. (2022). Self-administered web-based tests of executive functioning and perceptual speed: Measurement development study with a large probability-based survey panel. Journal of Medical Internet Research, 24(5), e34347. https://doi.org/10.2196/34347
Meijer, E., Spierdijk, L., & Wansbeek, T. (2022). Moment conditions for the quadratic regression
model with measurement error. Econometric Reviews, 41, 749–774. https://doi.org/10.1080/07474938.2022.2052666
Lee, J., Wilkens, J., Meijer, E., Sekher, T. V., Bloom, D. E., & Hu, P. (2022). Hypertension awareness, treatment, and control and their association with healthcare access in the middle-aged and older Indian population: A nationwide cohort study. PLoS Medicine, 19(1), e1003855. https://doi.org/10.1371/journal.pmed.1003855
Schneider, S., Junghaenel, D. U., Zelinski, E. M., Meijer, E., Stone, A. A., Langa, K. M., & Kapteyn, A. (2021). Subtle mistakes in self-report surveys predict future transition to dementia. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 13(1), e12252. https://doi.org/10.1002/dad2.12252
Jung, D., Lee, J., & Meijer, E. (2021). Revisiting the efect of retirement on cognition: Heterogeneity and endowment. Journal of the Economics of Ageing, 21, 100361. https://doi.org/10.1016/j.jeoa.2021.100361
Jin, H., Chien, S., Meijer, E., Khobragade, P., & Lee, J. (2021). Learning from clinical consensus diagnosis in India to facilitate automatic classifcation of dementia: Machine learning study. JMIR Mental Health, 8(5), e27113. https://doi.org/10.2196/27113
Lee, J., Meijer, E., Phillips, D., & Hu, P. (2021). Disability incidence rates for men and women in 23 countries: Evidence on health efects of gender inequality. Journals of Gerontology: Medical Sciences, 76, 328–338. https://doi.org/10.1093/gerona/glaa288
Meijer, E., Oczkowski, E., & Wansbeek, T. (2021). How measurement error afects inference in linear regression. Empirical Economics, 60, 131–155. https://doi.org/10.1007/s00181-020-01942-z
Angrisani, M., Lee, J., & Meijer, E. (2020). The gender gap in education and late-life cognition: Evidence from multiple countries and birth cohorts. Journal of the Economics of Ageing, 16, 100232. https://doi.org/10.1016/j.jeoa.2019.100232
Gross, A. L., Khobragade, P. Y., Meijer, E., & Saxton, J. A. (2020). Measurement and structure of cognition in the Longitudinal Aging Study in India -Diagnostic Assessment of Dementia (LASI-DAD). Journal of the American Geriatrics Society, 68, S11–S19. https://doi.org/10.1111/jgs.16738
Angrisani, M., Casanova, M., & Meijer, E. (2020). Work-life balance and labor force attachment at older ages. Journal of Labor Research, 41, 34–68. https://doi.org/10.1007/s12122-020-09301-8
Kapteyn, A., et al. (2020). Tracking the efect of the COVID-19 pandemic on American households. Survey Research Methods, 14(2), 179–186. https://doi.org/10.18148/srm/2020.v14i2.7737
Lee, J., Lau, S., Meijer, E., & Hu, P. (2020). Living longer, with or without disability? A global and longitudinal perspective. Journals of Gerontology: Medical Sciences, 75(1), 162–167. https://doi.org/10.1093/gerona/glz007
Hurd, M. D., Meijer, E., Moldof, M., & Rohwedder, S. (2019). Reducing cross-wave variability in survey measures of household wealth. Journal of Economic and Social Measurement, 44, 117–139. https://doi.org/10.3233/JEM-190465
Galesic, M., Bruine de Bruin, W., Dumas, M., Kapteyn, A., Darling, J. E., & Meijer, E. (2018). Asking about social circles improves election predictions and illuminates voting behaviour. Nature Human Behaviour, 2, 187–193. https://doi.org/10.1038/s41562-018-0302-y
Meijer, E., Spierdijk, L., & Wansbeek, T. (2017). Consistent estimation of linear panel data models with measurement error. Journal of Econometrics, 200, 169–180. https://doi.org/10.1016/j.jeconom.2017.06.003
Meijer, E., & Karoly, L. A. (2017). Representativeness of the low-income population in the Health and Retirement Study. Journal of the Economics of Ageing, 9, 90–99. https://doi.org/10.1016/j.jeoa.2016.08.004
Angrisani, M., Hurd, M. D., Meijer, E., Parker, A. M., & Rohwedder, S. (2017). Personality and employment transitions at older ages: Direct and indirect efects through non-monetary job characteristics. Labour, 31, 127–152. https://doi.org/10.1111/labr.12090
Gutsche, T. L., Kapteyn, A., Meijer, E., & Weerman, B. (2014). The RAND Continuous 2012 Presidential Election Poll. Public Opinion Quarterly, 78, 233–254. https://doi.org/10.1093/poq/nfu009
Galama, T. J., Hullegie, P., Meijer, E., & Outcault, S. (2012). Is there empirical evidence for decreasing returns to scale in a health capital model? Health Economics, 21, 1080–1100. https://doi.org/10.1002/hec.2843
Meijer, E., Rohwedder, S., & Wansbeek, T. (2012). Measurement error in earnings data: Using a mixture model approach to combine survey and register data. Journal of Business & Economic Statistics, 30, 191–201. https://doi.org/10.1198/jbes.2011.08166
Meijer, E., Kapteyn, A., & Andreyeva, T. (2011). Internationally comparable health indices. Health Economics, 20, 600–619. https://doi.org/10.1002/hec.1620
Meijer, E., Karoly, L. A., & Michaud, P.-C. (2010). Using matched survey and administrative data to estimate eligibility for the Medicare Part D low-income subsidy program. Social Security Bulletin, 70(2), 63–82. http://www.socialsecurity.gov/policy/docs/ssb/v70n2/
Meijer, E., & Ypma, J. Y. (2008). A simple identifcation proof for a mixture of two univariate normal distributions. Journal of Classifcation, 25, 113–123. https://doi.org/10.1007/s00357-008-9008-6
Wansbeek, T., & Meijer, E. (2007). Comments on: Panel data analysis—advantages and challenges. TEST, 16, 33–36. https://doi.org/10.1007/s11749-007-0050-1
Meijer, E., & Wansbeek, T. (2007). The sample selection model from a method of moments perspective. Econometric Reviews, 26, 25–51. https://doi.org/10.1080/07474930600972194
Meijer, E. (2007). Citations, reference list, and author index with apacite. Eutypon, 16–19, 1–31. http://www.eutypon.gr/eutypon/e-cont-16-19.html
Meijer, E., & Rouwendal, J. (2006). Measuring welfare efects in models with random coefcients. Journal of Applied Econometrics, 21, 227–244. https://doi.org/10.1002/jae.841
Meijer, E. (2005). Matrix algebra for higher order moments. Linear Algebra and its Applications, 410, 112–134. https://doi.org/10.1016/j.laa.2005.02.040
De Haan, J., Leertouwer, E., Meijer, E., & Wansbeek, T. (2003). Measuring central bank independence: A latent variables approach. Scottish Journal of Political Economy, 50, 326–340. https://doi.org/10.1111/1467-9485.5003005
Rouwendal, J., & Meijer, E. (2001). Preferences for housing, jobs, and commuting: A mixed logit analysis. Journal of Regional Science, 41, 475–505. https://doi.org/10.1111/0022-4146.00227
Wansbeek, T., Wedel, M., & Meijer, E. (2001). Comment on Microeconometrics by J.A. Hausman. Journal of Econometrics, 100, 89–91. https://doi.org/10.1016/S0304-4076(00)00065-8
Meijer, E., & Wansbeek, T. (2000). Measurement error in a single regressor. Economics Letters, 69, 277–284. https://doi.org/10.1016/S0165-1765(00)00328-1
Meijer, E., & Wansbeek, T. (1999). Quadratic prediction of factor scores. Psychometrika, 64, 495–507.
https://doi.org/10.1007/BF02294569
Busing, F. M. T. A., Meijer, E., & Van der Leeden, R. (1999). Delete-m jackknife for unequal m. Statistics and Computing, 9, 3–8. https://doi.org/10.1023/A:1008800423698
Meijer, E., & Mooijaart, A. (1996). Factor analysis with heteroscedastic errors. British Journal of Mathematical and Statistical Psychology, 49, 189–202. https://doi.org/10.1111j.2044-8317.1996.tb01082.x
Meijer, E., & Mooijaart, A. (1994). The use of third-order moments in structural models. Q¨uestii´o, 18, 75–84. http://www.idescat.cat/sort/questiio/sumaris/sum181.html

Book Chapters

Meijer, E., Spierdijk, L., & Wansbeek, T. (2015). Measurement error in panel data. In B. H. Baltagi (Ed.), The Oxford handbook of panel data (pp. 325–362). Oxford, UK: Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199940042.013.0011
Kapteyn, A., & Meijer, E. (2014). A comparison of diferent measures of health and their relation to labor force transitions at older ages. In D. A. Wise (Ed.), Discoveries in the economics of aging (pp. 115–156). Chicago, IL: University of Chicago Press. (with a comment by S. F. Venti) https://doi.org/10.7208/chicago/9780226146126.003.0004
Meijer, E., Spierdijk, L., & Wansbeek, T. (2013). Measurement error in the linear dynamic panel data model. In B. C. Sutradhar (Ed.), ISS-2012 proceedings volume on longitudinal data analysis subject to measurement errors, missing values, and/or outliers (pp. 77–92). New York, NY: Springer. https://doi.org/10.1007/978-1-4614-6871-4 4]
Fernandes, M., Meijer, E., & Zamarro, G. (2008). Comparison between SHARE, ELSA, and HRS. In A. B¨orsch-Supan et al. (Eds.), Health, ageing and retirement in Europe (2004–2007): Starting the longitudinal dimension (pp. 23–63). Mannheim, Germany: Mannheim Research Institute for the Economics of Aging (MEA). http://www.share-project.org/share-publications/frst-results-books-methodology-volumes.html
De Leeuw, J., & Meijer, E. (2008). Introduction to multilevel analysis. In J. de Leeuw & E. Meijer (Eds.), Handbook of multilevel analysis (pp. 1–75). New York: Springer. https://doi.org/10.1007/978-0-387-73186-5 1
Van der Leeden, R., Meijer, E., & Busing, F. M. T. A. (2008). Resampling multilevel models. In J. de Leeuw & E. Meijer (Eds.), Handbook of multilevel analysis (pp. 401–433). New York: Springer. https://doi.org/10.1007/978-0-387-73186-5 1https://doi.org/10.1007/978-0-387-73186-5 1
Wansbeek, T., & Meijer, E. (2001). Measurement error and latent variables. In B. H. Baltagi (Ed.), A companion to theoretical econometrics (pp. 162–179). Malden, MA: Blackwell. https://doi.org/10.1002/9780470996249.ch9
Meijer, E., Busing, F. M. T. A., & Van der Leeden, R. (1998). Estimating bootstrap confdence intervals for two-level models. In J. J. Hox & E. D. De Leeuw (Eds.), Assumptions, robustness, and estimation methods in multivariate modeling (pp. 35–47). Amsterdam: TT Publicaties. http://dornsife.usc.edu/erik-meijer/papers/
Busing, F. M. T. A., Meijer, E., & Van der Leeden, R. (1995). The MLA program for two-level analysis with resampling options. In T. A. B. Snijders, B. Engel, J. C. Van Houwelingen, A. Keen,
G. J. Stemerdink, & M. Verbeek (Eds.), SSS’95: Toeval zit overal (pp. 37–58). Groningen: iec ProGAMMA. http://dornsife.usc.edu/erik-meijer/papers/

Books

De Leeuw, J., & Meijer, E. (Eds.). (2008). Handbook of multilevel analysis. New York: Springer. https://doi.org/10.1007/978-0-387-73186-5
Wansbeek, T., & Meijer, E. (2000). Measurement error and latent variables in econometrics. Amsterdam: North-Holland.
Meijer, E. (1998). Structural equation models for nonnormal data. Leiden: DSWO Press.

Reports

Wilkens, J., et al. (2024). Harmonized LASI-DAD documentation, version A.4. Los Angeles, CA: Gateway to Global Aging Data. (Also earlier versions [2020–2022]) https://doi.org/10.25553/h5wx-ay45
Young, C., et al. (2024). Harmonized HRS-HCAP documentation, version A. Los Angeles, CA: Gateway to Global Aging Data.https://doi.org/10.25553/sfaq-br29
Bugliari, D., et al. (2024a). RAND HRS detailed imputations fle 2020 (v2) documentation. Santa Monica, CA: RAND Center for the Study of Aging. http://hrsonline.isr.umich.edu/modules/meta/rand/index.html (Also earlier versions under slightly diferent names [2013–2023])
Bugliari, D., et al. (2024b). RAND HRS longitudinal fle 2020 (v2) documentation. Santa Monica, CA: RAND Center for the Study of Aging. (Also earlier versions under slightly diferent names [2013–2023]) https://doi.org/10.7249/TLA2097-1-v3
Wilkens, J., et al. (2024). Harmonized ELSA-HCAP documentation, version A.2. Los Angeles, CA: Gateway to Global Aging Data. https://doi.org/10.34729/gmcv-6g43
Chien, S., et al. (2023a). Harmonized Mex-Cog documentation, version A.2. Los Angeles, CA: Gateway to Global Aging Data. https://doi.org/10.34729/3JTX-WE73
Chien, S., et al. (2023b). Harmonized LASI documentation, version A.3 (2017–2021). Los Angeles, CA: University of Southern California, Center for Economic and Social Research. https://g2aging.org/ (Also earlier versions under slightly diferent names [2020–2021])
LASI-DAD Study Collaborators. (2022). Harmonized diagnostic assessment of dementia for the Longitudinal Aging Study in India (LASI-DAD) wave 1 report. Los Angeles, CA: University of Southern California, Center for Economic and Social Research. https://doi.org/10.34729/HAKB-5045
LASI Investigators. (2021). User guide for 2017–2019 Longitudinal Aging Study in India (LASI) wave 1. Los Angeles, CA: University of Southern California, Center for Economic and Social Research. https://g2aging.org/
Angrisani, M., Kapteyn, A., Meijer, E., & Saw, H.-W. (2019). Sampling and weighting the Understanding America Study (Working Paper No. 2019-004). Los Angeles, CA: University of Southern California, Center for Economic and Social Research. https://doi.org/10.2139/ssrn.3502405
Hurd, M. D., Meijer, E., Pantoja, P., & Rohwedder, S. (2018). Addition to the RAND HRS longitudinal fles: IRA withdrawals in the HRS, 2000 to 2014 (Working Paper No. WP 2018-388). Ann Arbor, MI: Michigan Retirement Research Center. https://doi.org/10.2139/ssrn.3337842
Pantoja, P., et al. (2018). RAND HRS tax calculations 2014 (v2) documentation. Santa Monica, CA: RAND Corporation, Center for the Study of Aging.http://hrsonline.isr.umich.edu/modules/meta/rand/index.html (Also an earlier version under a slightly diferent name [2017])
Beaumaster, S., et al. (2018). Harmonized SHARE documentation, version D.4. Los Angeles, CA: University of Southern California, Center for Economic and Social Research. https://g2aging.org/ (Also versions A–D.3 [2011–2017])
Sikoki, B., Witoelar, F., Strauss, J., Meijer, E., & Suriastini, W. (2013). IFLS East user’s guide and feld report. Yogyakarta, Indonesia: SurveyMETER. http://surveymeter.org/research/3/ifseast
Chien, S., Feeney, K., Liu, J., Meijer, E., & Lee, J. (2013). Harmonized LASI pilot data documentation, version: A (Working Paper No. WR-1018). Santa Monica, CA: RAND Corporation. https://doi.org/10.2139/ssrn.2354660
Kapteyn, A., Meijer, E., & Weerman, B. (2012). Methodology of the RAND Continuous 2012 Presidential Election Poll (Working Paper No. WR-961). Santa Monica, CA: RAND Corporation. https://doi.org/10.2139/ssrn.2146149
Foster, K., Meijer, E., Schuh, S., & Zabek, M. A. (2011). The 2009 Survey of Consumer Payment Choice (Public Policy Discussion Paper No. 11-01). Boston, MA: Federal Reserve Bank of Boston. https://doi.org/10.2139/ssrn.1864854
Lee, J., Kapteyn, A., Meijer, E., & Yang, J.-S. (2010). Pre-and post-retirement asset portfolios (Publication No. 213). Madison, WI: Filene Research Institute. http://staging.flene.org/research/report/pre-and-post-retirement-asset-portfolios
Foster, K., Meijer, E., Schuh, S., & Zabek, M. A. (2009). The 2008 Survey of Consumer Payment Choice (Public Policy Discussion Paper No. 09-10). Boston, MA: Federal Reserve Bank of Boston. https://doi.org/10.2139/ssrn.1559959
Meijer, E., Karoly, L. A., & Michaud, P.-C. (2009). Estimates of potential eligibility for low-income subsidies under Medicare Part D (Tech. Rep. No. TR-686). Santa Monica, CA: RAND Corporation. http://www.rand.org/pubs/technical reports/TR686.html
Meijer, E. (2004). Computation of characteristics of value-of-time distributions and their standard errors (Research Report No. 04F09). Groningen, Netherlands: University of Groningen, SOM Research School. https://doi.org/10.2139/ssrn.2052669
MuConsult. (1995–2004). (Numerous non-public reports in Dutch written for Dutch national and local governments and private frms). Amersfoort, Netherlands: Author.
Busing, F. M. T. A., Meijer, E., & Van der Leeden, R. (1994). MLA: Software for multilevel analysis of data with two levels. User’s guide for version 1.0b (Tech. Rep. No. PRM 94-01). Leiden, Netherlands: Leiden University, Department of Psychology. http://dornsife.usc.edu/erik-meijer/papers/ (Version 4.1, 2005)

Working papers

Kapteyn, A., et al. (2024). COVID-19 infections and cognitive function (CESR-Schaefer Working Paper No. 2024-003). Los Angeles, CA: University of Southern California. https://doi.org/10.2139/ssrn.4884504
Perez-Arce, F., Prados, M., Meijer, E., & Lee, J. (2021). Social security coverage around the world: The case of China, India and Mexico (Working Paper No. WP 2021-439). Ann Arbor, MI: Michigan Retirement and Disability Research Center. https://mrdrc.isr.umich.edu/publication types/working-papers/
Meijer, E., P´erez-Arce, F., & Prados, M. (2020). A framework for cost-beneft analysis of totalization agreements (Working Paper No. WP 2020-410). Ann Arbor, MI: Michigan Retirement and Disability Research Center. https://doi.org/10.2139/ssrn.3885782
Angrisani, M., Kapteyn, A., & Meijer, E. (2019). Sorting into jobs and labor supply and demand at older ages (Working Paper). National Bureau of Economic Research. https://doi.org/10.2139/ssrn.3493892
Prados, M. J., Meijer, E., & P´erez-Arce, F. (2019). Macroeconomic efects of social security totalization agreements (Working Paper No. WP 2019-407). Ann Arbor, MI: Michigan Retirement and Disability Research Center. https://doi.org/10.2139/ssrn.3885751
Angrisani, M., Kapteyn, A., & Meijer, E. (2015). Nonmonetary job characteristics and employment transitions at older ages (Working Paper No. WP 2015-326). Ann Arbor, MI: Michigan Retirement Research Center. https://doi.org/10.2139/ssrn.2689805
Lee, J., Meijer, E., & Phillips, D. (2015). The efect of using diferent imputation methods for economic variables in aging surveys (Working Paper No. 2015-019). Los Angeles, CA: University of Southern California, Center for Economic and Social Research. https://doi.org/10.2139/ssrn.2650214
Angrisani, M., Hurd, M. D., & Meijer, E. (2012). Investment decisions in retirement: The role of subjective expectations (Working Paper No. WP 2012-274). Ann Arbor, MI: Michigan Retirement Research Center.https://doi.org/10.2139/ssrn.2188403
Hung, A. A., Meijer, E., Mihaly, K., & Yoong, J. (2009). Building up, spending down: Financial literacy, retirement savings management, and decumulation (Working Paper No. WR-712). Santa Monica, CA: RAND Corporation. https://doi.org/10.2139/ssrn.1520203
Gilbert, P. D., & Meijer, E. (2006). Money and credit factors (Working Paper No. 2006-3). Ottawa: Bank of Canada.http://www.bankofcanada.ca/2006/03/working-paper-2006-3/
Gilbert, P. D., & Meijer, E. (2005). Time series factor analysis with an application to measuring money (Research Report No. 05F10). Groningen, Netherlands: University of Groningen, SOM Research School. http://irs.ub.rug.nl/ppn/289322812
Cools, K., Le Grand, H., Meijer, E., & Wansbeek, T. (2004). Solving the value metrics puzzle (Working Paper). Groningen, Netherlands: University of Groningen, Department of Economics. https://doi.org/10.2139/ssrn.568423
Meijer, E. (2000). An asymmetric distribution with zero skewness (Working Paper). Groningen, Netherlands: University of Groningen, Department of Economics. https://doi.org/10.2139/ssrn.2531847

Other publications

Meijer, E., & Wansbeek, T. (2001, 2005). Microeconometrie (Lecture Notes). Groningen, Netherlands: University of Groningen, Faculty of Economics.
Meijer, E. (1999). [Book review of D. A. Harville (1997), Matrix algebra from a statistician’s perspective]. Kwantitatieve Methoden, 20(60), 129–131. (in Dutch)
Rosenberg, F. A., Meurs, H., & Meijer, E. (1997a). Grote prijsveranderingen: Een empirische budgetrestrictie-benadering [Large changes in prices: An empirical controlled budget approach]. In B. Egeter & N. Kalfs (Eds.), Colloquium Vervoersplanologisch Speurwerk – 1997 – Sprong in het duister? Lange termijn ontwikkelingen in het vervoersplanologisch onderzoek (pp. 1463–1482). Delft, Netherlands: C.V.S. http://www.cvs-congres.nl/cvspdfdocs/CVS1997deel3C.pdf (in Dutch)
Rosenberg, F. A., Meurs, H., & Meijer, E. (1997b). Large changes in prices: An empirical controlled budget approach. In Policy, planning and sustainability: Proceedings of Seminars C and D held at the European Transport Forum Annual Meeting, Brunel University, England, 1–5 September 1997 (pp. 367–378). London: PTRC. http://abstracts.aetransport.org/paper/index/id/548/confd/3
Meurs, H., Meijer, E., & Pommer, J. (1997a). No parking, no business? Verkeerskunde, 48(9), 30–34. (in Dutch)
Meurs, H., Meijer, E., & Pommer, J. (1997b). Parkeerkwaliteit langs de meetlat [A yardstick for parking quality]. Verkeerskunde, 48(7/8), 30–34. (in Dutch)
Busing, F. M. T. A., Van der Leeden, R., & Meijer, E. (1995). MLA: Software for two-level analysis with resampling options. Multilevel Modelling Newsletter, 7(3), 11–13. http://www.bristol.ac.uk/cmm/learning/support/new7-3.pdf
Meijer, E., Van der Leeden, R., & Busing, F. M. T. A. (1995). Implementing the bootstrap for multilevel models. Multilevel Modelling Newsletter, 7(2), 7–11. http://www.bristol.ac.uk/cmm/learning/support/new7-2.pdf

Software

Gilbert, P. D., & Meijer, E. (2006–2021). tsfa [Computer software and manual]. Available from
10 https://cran.r-project.org/src/contrib/Archive/tsfa/. (Package for the statistical software system R that computes time series factor analysis estimators, factor scores, and ft statistics.)
Meijer, E. (1994–2013). apacite [Computer software and manual]. Available from the CTAN sites (e.g.,http://www.ctan.org) in the directory tex-archive/biblio/bibtex/contrib/apacite. (LATEX/BibTEX package that automatically generates citations, reference lists, and author indexes according to the rules of the Publication Manual of the American Psychological Association, with extensive manual and test fles.)
Busing, F. M. T. A., Meijer, E., & Van der Leeden, R. (1994–2005). MLA [Computer software]. Available from http://dornsife.usc.edu/erik-meijer/software/. (Computer program for multilevel analysis with resampling options.)

Data sets

Harmonized LASI-DAD, version A.4 [Data fle]. (2024). Los Angeles, CA: Gateway to Global Aging Data.
https://doi.org/10.25553/h5wx-ay45 (Requires registration)
Harmonized HRS-HCAP, version A [Data fle]. (2024). Los Angeles, CA: Gateway to Global Aging Data. https://doi.org/10.25553/sfaq-br29 (Requires registration)
RAND HRS detailed imputations fle 2020 (v2) [Data fle]. (2024). Santa Monica, CA: RAND Center for the Study of Aging. http://hrsonline.isr.umich.edu/modules/meta/rand/index.html (Requires registration. Also earlier versions under slightly diferent names [2013–2023])
RAND HRS longitudinal fle 2020 (v2) [Data fle]. (2024). Santa Monica, CA: RAND Center for the Study of Aging. http://hrsonline.isr.umich.edu/modules/meta/rand/index.html (Requires registration. Also earlier versions under slightly diferent names [2013–2023])
Harmonized ELSA-HCAP, version A.2 [Data fle]. (2024). Los Angeles, CA: Gateway to Global Aging Data. https://doi.org/10.34729/gmcv-6g43 (Requires registration)
Harmonized Mex-Cog documentation, version A.2 [Data fle]. (2023). Los Angeles, CA: Gateway to Global Aging Data. https://doi.org/10.34729/3JTX-WE73 (Requires registration)
Harmonized LASI documentation, version A.3 (2017–2021) [Data fle]. (2023). Los Angeles, CA: University of Southern California, Center for Economic and Social Research. https://g2aging.org/ (Requires registration. Also earlier versions under slightly diferent names [2020–2021])
Harmonized ELSA-HCAP, version A [Data fle]. (2021). Los Angeles, CA: University of Southern California, Center for Economic and Social Research. https://g2aging.org/ (Requires registration)
Longitudinal Aging Study in India (LASI) wave 1 [Data fle]. (2021). Los Angeles, CA: University of Southern California, Center for Economic and Social Research. https://g2aging.org/ (Requires registration)
Understanding America Study [Data fles]. (2014–2020). Los Angeles, CA: University of Southern California, Center for Economic and Social Research. https://uasdata.usc.edu/ (Requires registration)
RAND HRS tax calculations 2014 (v2) [Data fles]. (2018). Santa Monica, CA: RAND Corporation, Center for the Study of Aging. http://hrsonline.isr.umich.edu/index.php?p=data (Requires registration)
Harmonized SHARE, version D.4 [Data fle]. (2018). Los Angeles, CA: University of Southern California, Center for Global Aging Research. https://g2aging.org/ (Requires registration. This is distributed as a Stata script that creates the data set from the raw data fles. Also earlier versions [2011–2017])
The RAND 2012 Continuous Presidential Election Poll [Data fle]. (2014). Santa Monica, CA: RAND Corporation. https://alpdata.rand.org/index.php?page=election2012
Indonesia Family Life Survey East [Data fle]. (2013). Yogyakarta, Indonesia: SurveyMETER. http://surveymeter.org/research/3/ifseast (Requires registration)
Harmonized LASI pilot data, version A [Data fle]. (2013). Los Angeles, CA: University of Southern California, Center for Global Aging Research. https://g2aging.org/
The 2009 Survey of Consumer Payment Choice [Data fle]. (2011). Boston, MA: Federal Reserve Bank of Boston. http://www.bostonfed.org/economic/cprc/SCPC/index.htm
The 2008 Survey of Consumer Payment Choice [Data fle]. (2009). Boston, MA: Federal Reserve Bank of Boston. http://www.bostonfed.org/economic/cprc/SCPC/index.htm