National Academy of Sciences | 150 Year Anniversary

Questions? Call 800-624-6242

| Items in cart [0]

The National Academies Press

PAPERBACK
price:$50.00
add to cart

Rights & Permissions

topleft topright

Biosocial Surveys (2007)
Committee on Population (CPOP)

Citation Manager

. "15 Genoeconomics--Daniel J. Benjamin, Christopher F. Chabris, Edward L. Glaeser, Vilmundur Gudnason, Tamara B. Harris, David I. Laibson, Lenore J. Launer, and Shaun Purcell." Biosocial Surveys. Washington, DC: The National Academies Press, 2007.

Please select a format:

BibTeX EndNote RefMan


Page
306
bottomleft bottomright

The following HTML text is provided to enhance online readability. Many aspects of typography translate only awkwardly to HTML. Please use the page image as the authoritative form to ensure accuracy.


Biosocial Surveys

Finally, ongoing research will eventually enable researchers to employ new genetic control variables, thereby improving the power of statistical procedures.

Much of the promise of genoeconomics is based in part on economists’ long tradition of policy analysis. The economic approach is one in which governments are not seen as infallible custodians of the public good, but rather as separate actors that often have their own objectives (Stigler, 1971). Information economics may also play an important role in the analysis of policy questions. Economists have identified competitive forces that cause individuals to reveal information that is privately beneficial but potentially socially harmful. Economists understand how the public release of certain genetic information can theoretically undermine insurance institutions and thereby inefficiently increase social inequality. Genoeconomics will also identify specific gene-environment interactions with policy implications. For example, imagine that particular genes turn out to be associated with risk factors for poor educational outcomes, poor performance in the labor market, and consequently low levels of income. Imagine too that particular educational interventions are found that mitigate these disadvantages. Then gene-based policies could target disadvantaged at-risk groups with focused interventions. Such interventions will remain purely speculative until the necessary precursor research is implemented and ethical questions are resolved, but focused interventions nevertheless hold considerable long-run potential.

Despite the promise of genoeconomics, there are clearly enormous pitfalls. Even under the best of circumstances—in which a particular genetic pathway has been clearly established—there are concerns about informing individuals of their own risks, especially when there are few interventions to alleviate those risks or when the risks are very small. Providing information to parents about the genome of a fetus or a child creates a different set of dilemmas, including the risk of selective abortion. This has been well discussed with reference to a genetic endowment as straightforward as gender; in many societies economic investment in a daughter is seen as less beneficial than economic investment in a son (e.g., Garg and Morduch, 1998). If the same issues arose in relation to more complex economic traits, a host of ethical and policy questions would arise. Documenting the power of the genome to society at large also creates risks as identifiable social and ethnic groups may face discrimination (or become beneficiaries of positive discrimination) on the basis of their presumed genetic endowments.

These problems are multiplied when genetic research is done carelessly. Historically, there have been many cases of false positives in which early genetic claims have evaporated under subsequent attempts at replication. These false positives can create tremendous mischief. A failure to

Page
306
Front Matter (R1-R14)
Introduction--James W. Vaupel, Kenneth W. Wachter, and Maxine Weinstein (1-12)
Part I: What We've Learned So Far (13-14)
1 Biological Indicators and Genetic Information in Danish Twin and Oldest-Old Surveys--Kaare Christensen, Lise Bathum, and Lene Christiansen (15-41)
2 Whitehall II and ELSA: Integrating Epidemiological and Psychobiological Approaches to the Assessment of Biological Indicators--Michael Marmot and Andrew Steptoe (42-59)
3 The Taiwan Biomarker Project--Ming-Cheng Chang, Dana A. Glei, Noreen Goldman, and Maxine Weinstein (60-77)
4 Elastic Powers: The Integration of Biomarkers into the Health and Retirement Study--David Weir (78-95)
5 An Overview of Biomarker Research from Community and Population-Based Studies on Aging--Jennifer R. Harris, Tara L. Gruenewald, and Teresa Seeman (96-135)
6 The Women's Health Initiative: Lessons for the Population Study of Biomarkers--Robert B. Wallace (136-148)
7 Comments on Collecting and Utilizing Biological Indicators in Social Science Surveys--Duncan Thomas and Elizabeth Frankenberg (149-155)
8 Biomarkers in Social Science Research on Health and Aging: A Review of Theory and Practice--Douglas C. Ewbank (156-172)
Part II: The Potential and Pitfalls of Genetic Information (173-174)
9 Are Genes Good Markers of Biological Traits?--Mary Jane West-Eberhard (175-193)
10 Genetic Markers in Social Science Research: Opportunities and Pitfalls--George P. Vogler and Gerald E. McClearn (194-207)
11 Comments on the Utility of Social Science Surveys for the Discovery and Validation of Genes Influencing Complex Traits--Harald H.H. Göring (208-230)
12 Overview Thoughts on Genetics: Walking the Line Between Denial and Dreamland, or Genes Are Involved in Everything, But Not Everything Is "Genetic"--Kenneth M. Weiss (231-248)
Part III: New Ways of Collecting, Applying, and Thinking About Data (249-250)
13 Minimally Invasive and Innovative Methods for Biomeasure Collection in Population-Based Research--Stacy Tessler Lindau and Thomas W. McDade (251-277)
14 Nutrigenomics--John Milner, Elaine B. Trujillo, Christine M. Kaefer, and Sharon Ross (278-303)
15 Genoeconomics--Daniel J. Benjamin, Christopher F. Chabris, Edward L. Glaeser, Vilmundur Gudnason, Tamara B. Harris, David I. Laibson, Lenore J. Launer, and Shaun Purcell (304-335)
16 Mendelian Randomization: Genetic Variants as Instruments for Strengthening Causal Inference in Observational Studies--George Davey Smith and Shah Ebrahim (336-366)
17 Multilevel Investigations: Conceptual Mappings and Perspectives--John T. Cacioppo, Gary G. Berntson, and Ronald A. Thisted (367-380)
18 Genomics and Beyond: Improving Understanding and Analysis of Human (Social, Economic, and Demographic) Behavior--John Hobcraft (381-400)
Appendix: Biographical Sketches of Contributors (401-414)