Hostname: page-component-7479d7b7d-rvbq7 Total loading time: 0 Render date: 2024-07-10T13:34:46.808Z Has data issue: false hasContentIssue false
  • English
  • Français

A cost-benefit analysis of Tulsa’s IDA program

Published online by Cambridge University Press:  19 January 2015

David H. Greenberg
David H. Greenberg*
Affiliation:
University of Maryland - Baltimore County – Economics1000 Hilltop Circle, Baltimore, MD 21250, USA; and 5531 High Tor Hill, Columbia, MD 21045, USA
*
David H. Greenberg, University of Maryland – Baltimore County – Economics 1000 Hilltop Circle, Baltimore, MD 21250, USA; and 5531 High Tor Hill, Columbia, MD 21045, USA, Tel.: +410-884-9620, e-mail: dhgreenb@umbc.edu
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

This article presents findings from a cost-benefit analysis of the Tulsa Individual Development Account (IDA) program, a demonstration program that was initiated in the late 1990s and is being evaluated through random assignment. The program put particular emphasis on using savings subsidies to help participants accumulate housing assets. The key follow-up data used in the evaluation was collected around 10 years after random assignment, about 6 years after the program ended. The results imply that, during this 10-year observation period, program participants gained from the program and that the program resulted in net costs to the government and private donors, and that society as a whole was probably worse off as a consequence of the program. The article examines in some detail whether these findings are robust to a number of different considerations, including the assumptions upon which the results depend, uncertainly reflected by the standard errors of the impact estimates used to derive the benefits and costs, and omitted benefits and costs, and concludes that they are essentially robust. For example, a Monte Carlo analysis suggests that the probability that the societal net benefits of the Tulsa program were negative during the observation period is over 90% and that the probability that the loss to society exceeded $1000 is 80%. Further analysis considered benefits and costs that might occur beyond the observation period. Based on this analysis, it appeared plausible, although far from certain, that the societal net benefits of the Tulsa program could eventually become positive. This would occur if the program’s apparent positive net impact on educational attainment generates substantial positive effects on the earnings of program participants after the observation period ended. However, there was no evidence that the educational impacts had yet begun to produce positive effects on earnings by the end of the observation period.

Keywords

benefit-cost analysisIndividual Development Account (IDA)longterm follow-upMonte Carlo analysisrandom assignment field experiment
Type
Article
Information
Journal of Benefit-Cost Analysis , Volume 4 , Issue 3 , 01 December 2013 , pp. 263 - 300
Copyright
Copyright © Society for Benefit-Cost Analysis 2013

References

Andersen, E. M., Malmgren, J. A., Carter, W. B., & Patrick, D. L. (1994). Screening for depression in well older adults: Evaluation of a short form of the CES-D. American Journal of Preventive Medicine, 10, 77–84.Google Scholar
Ashraf, N., Karlan, D., & Yin, W. (2006). Tying Odysseus to the Mast: Evidence from a commitment savings product in the Philippines. Quarterly Journal of Economics, 121(2), 635–672.Google Scholar
Boardman, A., Greenberg, D., Vining, A., & Weimer, D. (2011). Cost-benefit analysis: Concepts and practice (4th ed.). Upper Saddle River, NJ: Prentice Hall. (ISBN-13: 978-0-13-700269-6)Google Scholar
Carroll, R. J., & Yinger, J. (1994). Is the property tax a benefit tax? The case of rental housing. National Tax Journal, 47(2), 295–316.Google Scholar
Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24, 385–396.Google Scholar
Corporation for Economic Development (CFED) (2011). Frequently Asked Questions about Individual Development Accounts. Retrieved from http://cfed.org/programs/ida_faq_article/.Google Scholar
DiPasquale, D., & Glaeser, E. (1999). Incentives and social capital: Are homeowners better citizens? Journal of Urban Economics, 45(2), 354–384.Google Scholar
Duflo, E., & Saez, E. (2003). The role of information and social interactions in retirement plans decisions: Evidence from a randomized experiment. Quarterly Journal of Economics, 118(3), 815–842.Google Scholar
Duflo, E., Gale, W., Liebman, J., Orzag, P., & Saez, E. (2006). Saving incentives for low- and middle-income families: Evidence from a field experiment with H&R Block. Quarterly Journal of Economics, 121(4), 1311–1346.Google Scholar
Emrath, P. (2009). How long buyers remain in their homes. HousingEconomics.Com. Retrieved from http://www.nahb.org/fileUpload_details.aspx?contentTypeID=3&contentID=110770&subContentID=194118&channelID=311.Google Scholar
Engelhardt, G. V., Eriksen, M. D., Gale, W. G., & Mills, G. B. (2010). What are the social benefits of homeownership? Experimental evidence for low-income households. Journal of Urban Economics, 67(3), 349–358.Google Scholar
Engelhardt, G. V., Dubnicki, A. L., Marks, E. L., & Rhodes, B. B. (2011). Educational saving incentives for low-income families: Experimental evidence from the Michigan SEED Program. Unpublished paper.Google Scholar
Fujiwara, D. (2010). The Department for Work and Pensions Social Cost-Benefit Analysis Framework: Methodologies for Estimating and Incorporating the Wider Social and Economic Impacts of Work in Cost-Benefit Analysis of Employment Programmes. Sheffield, UK: Department for Work and Pensions, Working Paper No 86.Google Scholar
Government of the District of Columbia (2009). Tax Rates and Tax Burdens in the District of Columbia – A Nationwide Comparison. Retrieved July 2012 from http://cfo.dc.gov/cfo/frames.asp?doc=/cfo/lib/cfo/09STUDY.pdf Google Scholar
Gramlich, E. M. (1990). A Guide to cost-benefit analysis (2nd ed.). Englewood Cliffs, NJ: Prentice Hall (ISBN-0-13-074543-X)Google Scholar
Grinstein-Weiss, M., Lee, J. S., Greeson, J. K. P., Han, C. K., Yeo, Y. H., & Irish, K. (2008). Fostering low-income homeownership through individual development accounts: A longitudinal randomized experiment. Housing Policy Debate, 19(4), 711–739.Google Scholar
Grinstein-Weiss, M., Sherraden, M. W., Rohe, W., Gale, W. G., Schreiner, M., & Key, C. (2012a). Long-term follow-up of individual development accounts: Evidence from the add experiment. Chapel Hill: The University of North Carolina.Google Scholar
Grinstein-Weiss, M., Sherraden, M. W., Gale, W. G., Rohe, W., Schreiner, M., & Key, C. (2012b). Long-term effects of individual development accounts on post-secondary education: Evidence from a randomized experiment. Chapel Hill: The University of North Carolina.Google Scholar
Hendra, R., Riccio, J. A., Dorsett, R., Greenberg, D., Knight, G., Phillips, J., … Walter, J. (2011). Breaking the low-pay, no-pay cycle: Final evidence from the UK Employment Retention and Advancement (ERA) Demonstration. Sheffield, UK: Department for Work and Pensions, Research Report 765.Google Scholar
U.K. Treasury (2003). Green book, appraisal and evaluation in central government treasury guidance. London: HM Treasury.Google Scholar
Jerome, L. (2012). A test of the robustness of benefit-cost evaluations of welfare-to-work programs. Unpublished paper, Baltimore County: University of Maryland.Google Scholar
Marginal Tax Rate Calculator-Smart Money.com (2011). What’s Your Marginal Tax Rate? Retrieved in July 2012 from http://www.smartmoney.com/personal-finance/taxes/whats-your-marginal-tax-rate-9546/ Google Scholar
Mills, G., Gale, W. G., Patterson, R., Englehardt, G. V., Eriksen, M. D., & Apostolov, E. (2008). Effects of individual development accounts on asset purchases and saving behaviour: Evidence from a controlled experiment. Journal of Public Economics, 92, 1509–1530.Google Scholar
Moore, M., Boardman, A., Vining, A., Weimer, D., & Greenberg, D. (2004). Just give me a number!’ Practical values for the social discount rate. Journal of Policy Analysis and Management, 23(4), 789–812.Google Scholar
National Association of Realtors (2012). Newsletter: Tulsa Area Local Market Report. Retrieved from http://www.housingtrendsenewsletter.com/show-local-market-data.cfm?pid=120&loadid=59&id=3249.Google Scholar
National Bureau of Economic Research (2012). Internet TAXSIM Model Version 9.0. Retrieved from http://www.nber.org/~taxsim/taxsim-calc9/index.htm.Google Scholar
Nichol, K. L. (2001). Cost-benefit analysis of a strategy to vaccinate healthy working adults against influenza. Archives of Internal Medicine, 161, 749–759.Google Scholar
Orr, L. L., & Patrabansh, S. (2010). Lifelong learning: What are the returns to education for workers who return to school? Unpublished paper.Google Scholar
Prante, G. (2007). Fiscal Fact No. 95. The Tax Foundation.Google Scholar
Saez, E. (2009). Details matter: The impact of presentation and information on the take-up of financial incentives for retirement. American Economic Journal: Economic Policy, 1(1), 204–228.Google Scholar
Schreiner, M. (2004). Program costs for individual development accounts: Final figures from CAPTC in Tulsa. Washington University in Saint Louis, Center for Social Development.Google Scholar
Schreiner, M. (2005). What does an IDA cost? some measures from ADD. Washington University in Saint Louis, Center for Social Development Research Report 05-38.Google Scholar
The Tax Foundation (2011). Marginal tax rates calculator. Retrieved from http://interactive.taxfoundation.org/taxgraph/#GraphAncho.Google Scholar
Tulsa Housing Authority (2012). Options for homeownership, 2008. Retrieved from http://www.tulsahousing.org/HousingOptions/Homeownership/tabid/60/Default.aspx.Google Scholar
Weimer, D. L., & Sager, M. A. (2009). Early identification and treatment of Alzheimer’s disease: Social and fiscal outcomes. Alzheimer’s & Dementia, 5, 215–226.Google Scholar
Whittington, D., Lauria, D. T., Prabhu, V., & Cook, J. (2004). An economic reappraisal of the Melamchi water supply project – Kathmandu, Nepal. Portuguese Economic Journal, 3, 157–178.Google Scholar
Zimbardo, P., & Boyd, J. (2003). Time orientation. In Fernandez-Ballesteros, R. (Ed.) Encyclopedia of Psychological Assessment (pp. 1031–1035). Thousand Oaks, CA: Sage Publications.Google Scholar