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786 results on '"Laibson, David"'

1. Influencing Retirement Savings Decisions with Automatic Enrollment and Related Tools

Author

Beshears, John, Choi, James, Laibson, David, and Madrian, Brigitte C.

Subjects
Retirement benefits, Personal finance, Personal income, Retirees, Social security, Defined contribution plans, Defined benefit plans, Individual retirement accounts, Retirement income, Savings, Salary reduction savings plans, Business, general, Business, Economics
Abstract

Historically, retirees in the US relied on the 'three-legged stool' of Social Security, defined benefit (DB) pension plans, and personal savings to provide retirement income. (1) Beginning in the late [...]

Published
2024

5. Public views on polygenic screening of embryos

Author

Meyer, Michelle N, Tan, Tammy, Benjamin, Daniel J, Laibson, David, and Turley, Patrick

Subjects
Policy and Administration, Human Society, Multifactorial Inheritance, Genetic Testing, Risk, Embryo, Mammalian, Humans, Gene Editing, Policy Making, United States, General Science & Technology
Abstract

Understanding moral acceptability and willingness to use is crucial for informing policy.

Published
2023

6. Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

Author

Okbay, Aysu, Wu, Yeda, Wang, Nancy, Jayashankar, Hariharan, Bennett, Michael, Nehzati, Seyed Moeen, Sidorenko, Julia, Kweon, Hyeokmoon, Goldman, Grant, Gjorgjieva, Tamara, Jiang, Yunxuan, Hicks, Barry, Tian, Chao, Hinds, David A, Ahlskog, Rafael, Magnusson, Patrik KE, Oskarsson, Sven, Hayward, Caroline, Campbell, Archie, Porteous, David J, Freese, Jeremy, Herd, Pamela, Watson, Chelsea, Jala, Jonathan, Conley, Dalton, Koellinger, Philipp D, Johannesson, Magnus, Laibson, David, Meyer, Michelle N, Lee, James J, Kong, Augustine, Yengo, Loic, Cesarini, David, Turley, Patrick, Visscher, Peter M, Beauchamp, Jonathan P, Benjamin, Daniel J, and Young, Alexander I

Subjects
Human Genome, Genetics, Pediatric, Aetiology, 2.1 Biological and endogenous factors, Genome-Wide Association Study, Humans, Multifactorial Inheritance, Polymorphism, Single Nucleotide, 23andMe Research Team, Social Science Genetic Association Consortium, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57.

Published
2022

7. Resource profile and user guide of the Polygenic Index Repository

Author

Becker, Joel, Burik, Casper AP, Goldman, Grant, Wang, Nancy, Jayashankar, Hariharan, Bennett, Michael, Belsky, Daniel W, Karlsson Linnér, Richard, Ahlskog, Rafael, Kleinman, Aaron, Hinds, David A, Caspi, Avshalom, Corcoran, David L, Moffitt, Terrie E, Poulton, Richie, Sugden, Karen, Williams, Benjamin S, Harris, Kathleen Mullan, Steptoe, Andrew, Ajnakina, Olesya, Milani, Lili, Esko, Tõnu, Iacono, William G, McGue, Matt, Magnusson, Patrik KE, Mallard, Travis T, Harden, K Paige, Tucker-Drob, Elliot M, Herd, Pamela, Freese, Jeremy, Young, Alexander, Beauchamp, Jonathan P, Koellinger, Philipp D, Oskarsson, Sven, Johannesson, Magnus, Visscher, Peter M, Meyer, Michelle N, Laibson, David, Cesarini, David, Benjamin, Daniel J, Turley, Patrick, and Okbay, Aysu

Subjects
Epidemiology, Health Sciences, 2.6 Resources and infrastructure (aetiology), Aetiology, Data Analysis, Databases, Genetic, Genome-Wide Association Study, Humans, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Polygenic score, Polygenic index, Repository, Measurement error, 23andMe Research Group, Biomedical and clinical sciences, Health sciences, Psychology
Abstract

Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs' prediction accuracies, we constructed them using genome-wide association studies-some not previously published-from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the 'additive SNP factor'. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.

Published
2021

10. Problems with Using Polygenic Scores to Select Embryos

Author

Turley, Patrick, Meyer, Michelle N, Wang, Nancy, Cesarini, David, Hammonds, Evelynn, Martin, Alicia R, Neale, Benjamin M, Rehm, Heidi L, Wilkins-Haug, Louise, Benjamin, Daniel J, Hyman, Steven, Laibson, David, and Visscher, Peter M

Subjects
Reproductive Medicine, Biomedical and Clinical Sciences, Aging, Educational Status, Embryo, Mammalian, Fertilization in Vitro, Gene-Environment Interaction, Genetic Testing, Genetic Variation, Genome-Wide Association Study, Humans, Multifactorial Inheritance, Phenotype, Predictive Value of Tests, Preimplantation Diagnosis, Medical and Health Sciences, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract

Companies have recently begun to sell a new service to patients considering in vitro fertilization: embryo selection based on polygenic scores (ESPS). These scores represent individualized predictions of health and other outcomes derived from genomewide association studies in adults to partially predict these outcomes. This article includes a discussion of many factors that lower the predictive power of polygenic scores in the context of embryo selection and quantifies these effects for a variety of clinical and nonclinical traits. Also discussed are potential unintended consequences of ESPS (including selecting for adverse traits, altering population demographics, exacerbating inequalities in society, and devaluing certain traits). Recommendations for the responsible communication about ESPS by practitioners are provided, and a call for a society-wide conversation about this technology is made. (Funded by the National Institute on Aging and others.).

Published
2021

11. Predicting mid-life capital formation with pre-school delay of gratification and life-course measures of self-regulation

Author

Benjamin, Daniel J, Laibson, David, Mischel, Walter, Peake, Philip K, Shoda, Yuichi, Wellsjo, Alexandra Steiny, and Wilson, Nicole L

Subjects
Economics, Applied Economics, Behavioral and Social Science, Basic Behavioral and Social Science, Self-regulation, Delay of gratification, Mid-life capital formation, D140, D910, I120, I210, I310, Economic Theory, Econometrics, Banking, finance and investment, Applied economics
Abstract

How well do pre-school delay of gratification and life-course measures of self-regulation predict mid-life capital formation? We surveyed 113 participants of the 1967-1973 Bing pre-school studies on delay of gratification when they were in their late 40's. They reported 11 mid-life capital formation outcomes, including net worth, permanent income, absence of high-interest debt, forward-looking behaviors, and educational attainment. To address multiple hypothesis testing and our small sample, we pre-registered an analysis plan of well-powered tests. As predicted, a newly constructed and pre-registered measure derived from preschool delay of gratification does not predict the 11 capital formation variables (i.e., the sign-adjusted average correlation was 0.02). A pre-registered composite self-regulation index, combining preschool delay of gratification with survey measures of self-regulation collected at ages 17, 27, and 37, does predict 10 of the 11 capital formation variables in the expected direction, with an average correlation of 0.19. The inclusion of the preschool delay of gratification measure in this composite index does not affect the index's predictive power. We tested several hypothesized reasons that preschool delay of gratification does not have predictive power for our mid-life capital formation variables.

Published
2020

13. A megastudy of text-based nudges encouraging patients to get vaccinated at an upcoming doctor’s appointment

Author

Milkman, Katherine L., Patel, Mitesh S., Gandhi, Linnea, Graci, Heather N., Gromet, Dena M., Ho, Hung, Kay, Joseph S., Lee, Timothy W., Akinola, Modupe, Beshears, John, Bogard, Jonathan E., Buttenheim, Alison, Chabris, Christopher F., Chapman, Gretchen B., Choi, James J., Dai, Hengchen, Fox, Craig R., Goren, Amir, Hilchey, Matthew D., Hmurovic, Jillian, John, Leslie K., Karlan, Dean, Kim, Melanie, Laibson, David, Lamberton, Cait, Madrian, Brigitte C., Meyer, Michelle N., Modanu, Maria, Nam, Jimin, Rogers, Todd, Rondina, Renante, Saccardo, Silvia, Shermohammed, Maheen, Soman, Dilip, Sparks, Jehan, Warren, Caleb, Weber, Megan, Berman, Ron, Evans, Chalanda N., Snider, Christopher K., Tsukayama, Eli, Van den Bulte, Christophe, Volpp, Kevin G., and Duckworth, Angela L.

Published
2021

14. Multi-trait analysis of genome-wide association summary statistics using MTAG

Author

Turley, Patrick, Walters, Raymond K, Maghzian, Omeed, Okbay, Aysu, Lee, James J, Fontana, Mark Alan, Nguyen-Viet, Tuan Anh, Wedow, Robbee, Zacher, Meghan, Furlotte, Nicholas A, 23andMe Research Team, Social Science Genetic Association Consortium, Magnusson, Patrik, Oskarsson, Sven, Johannesson, Magnus, Visscher, Peter M, Laibson, David, Cesarini, David, Neale, Benjamin M, and Benjamin, Daniel J

Subjects
Biological Sciences, Genetics, Human Genome, Algorithms, Data Interpretation, Statistical, Datasets as Topic, Depression, Diagnostic Self Evaluation, Genetic Association Studies, Genome-Wide Association Study, Health, Humans, Meta-Analysis as Topic, Multifactorial Inheritance, Neuroticism, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, 23andMe Research Team, Social Science Genetic Association Consortium, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N eff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations.

Published
2018

15. Redefine statistical significance.

Author

Benjamin, Daniel J, Berger, James O, Johannesson, Magnus, Nosek, Brian A, Wagenmakers, E-J, Berk, Richard, Bollen, Kenneth A, Brembs, Björn, Brown, Lawrence, Camerer, Colin, Cesarini, David, Chambers, Christopher D, Clyde, Merlise, Cook, Thomas D, De Boeck, Paul, Dienes, Zoltan, Dreber, Anna, Easwaran, Kenny, Efferson, Charles, Fehr, Ernst, Fidler, Fiona, Field, Andy P, Forster, Malcolm, George, Edward I, Gonzalez, Richard, Goodman, Steven, Green, Edwin, Green, Donald P, Greenwald, Anthony G, Hadfield, Jarrod D, Hedges, Larry V, Held, Leonhard, Hua Ho, Teck, Hoijtink, Herbert, Hruschka, Daniel J, Imai, Kosuke, Imbens, Guido, Ioannidis, John PA, Jeon, Minjeong, Jones, James Holland, Kirchler, Michael, Laibson, David, List, John, Little, Roderick, Lupia, Arthur, Machery, Edouard, Maxwell, Scott E, McCarthy, Michael, Moore, Don A, Morgan, Stephen L, Munafó, Marcus, Nakagawa, Shinichi, Nyhan, Brendan, Parker, Timothy H, Pericchi, Luis, Perugini, Marco, Rouder, Jeff, Rousseau, Judith, Savalei, Victoria, Schönbrodt, Felix D, Sellke, Thomas, Sinclair, Betsy, Tingley, Dustin, Van Zandt, Trisha, Vazire, Simine, Watts, Duncan J, Winship, Christopher, Wolpert, Robert L, Xie, Yu, Young, Cristobal, Zinman, Jonathan, and Johnson, Valen E

Subjects
Data Interpretation, Statistical, Models, Statistical, Reproducibility of Results, Scholarly Communication
Abstract

We propose to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005.

Published
2018

18. Measuring Time Preferences

Author

Cohen, Jonathan, Ericson, Keith Marzilli, Laibson, David, and White, John Myles

Published
2020

27. A Debt Puzzle

Author

LAIBSON, DAVID, primary, REPETTO, ANDREA, additional, and TOBACMAN, JEREMY, additional

Published
2021
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31. The Promises and Pitfalls of Genoeconomics

Author

Benjamin, Daniel J., Cesarini, David, Chabris, Christopher F., Glaeser, Edward L., Laibson, David I., Guðnason, Vilmundur, Harris, Tamara B., Launer, Lenore J., Purcell, Shaun, Smith, Albert Vernon, Johannesson, Magnus, Magnusson, Patrik K.E., Beauchamp, Jonathan P., Christakis, Nicholas A., Atwood, Craig S., Hebert, Benjamin, Freese, Jeremy, Hauser, Robert M., Hauser, Taissa S., Grankvist, Alexander, Hultman, Christina M., and Lichtenstein, Paul

Published
2012
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33. Do physician incentives increase patient medication adherence?

Author

Kong, Edward, Beshears, John, Laibson, David, Madrian, Brigitte, Volpp, Kevin, Loewenstein, George, Kolstad, Jonathan, and Choi, James J.

Subjects
Physicians -- Influence, Patient compliance -- Analysis, Prescription drug plans -- Analysis, Medical care quality -- Analysis, Physician-patient relations -- Analysis, Business, Health care industry
Abstract

Objective: To test the effectiveness of physician incentives for increasing patient medication adherence in three drug classes: diabetes medication, antihypertensives, and statins. Data Sources: Pharmacy and medical claims from a large Medicare Advantage Prescription Drug Plan from January 2011 to December 2012. Study Design: We conducted a randomized experiment (911 primary care practices and 8,935 nonadherent patients) to test the effect of paying physicians for increasing patient medication adherence in three drug classes: diabetes medication, antihypertensives, and statins. We measured patients' medication adherence for 18 (6) months before (after) the intervention. Data collection/extraction methods: We obtained data directly from the health insurer. Principal Findings: We found no evidence that physician incentives increased adherence in any drug class. Our results rule out increases in the proportion of days covered by medication larger than 4.2 percentage points. Conclusions: Physician incentives of $50 per patient per drug class are not effective for increasing patient medication adherence among the drug classes and primary care practices studied. Such incentives may be more likely to improve measures under physicians' direct control rather than those that predominantly reflect patient behaviors. Additional research is warranted to disentangle whether physician effort is not responsive to these types of incentives, or medication adherence is not responsive to physician effort. Our results suggest that significant changes in the incentive amount or program design may be necessary to produce responses from physicians or patients. KEYWORDS health economics, medication adherence, physician payment incentives, primary care, quality improvement, 1 I INTRODUCTION Nonadherence to medication is a significant health problem that increases mortality, morbidity, and costs. 33-69 percent of medication-related hospital admissions in the United States are due to [...]

Published
2020
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38. Optimal Defaults

Author

Choi, James J., Laibson, David, Madrian, Brigitte C., and Metrick, Andrew

Published
2003

46. Preface

Author

Bernheim, B. Douglas, primary, DellaVigna, Stefano, additional, and Laibson, David, additional

Published
2019
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47. Measuring Trust

Author

Glaeser, Edward L., Laibson, David I., Scheinkman, Jose A., and Soutter, Christine L.

Published
2000

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