Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-11T04:01:22.310Z Has data issue: false hasContentIssue false
  • English
  • Français

Impact of South Carolina’s TANF Program on Earnings of New Entrants Before and During the Great Economic Recession

Published online by Cambridge University Press:  10 December 2020

MARILYN EDELHOCH ,
CYNTHIA FLYNN  and
QIDUAN LIU Open the ORCID record for QIDUAN LIU [Opens in a new window]
MARILYN EDELHOCH
Affiliation:
Office of Research and Evaluation (retired), South Carolina Department of Social Services, Columbia, SC29201, USA email: Marilyn.edelhoch@gmail.com
CYNTHIA FLYNN
Affiliation:
Center for Child and Family Studies, College of Social Work, University of South Carolina, Columbia, SC29208, USA email: CYNTHIAF@mailbox.sc.edu
QIDUAN LIU*
Affiliation:
Center for Child and Family Studies (retired), College of Social Work, University of South Carolina, Columbia, SC29208, USA email: qiduan101@yahoo.com
*
*Corresponding author. Qiduan Liu, qiduan101@yahoo.com
Get access Rights & Permissions [Opens in a new window]

Abstract

This study assesses the impact of South Carolina’s Temporary Assistance for Needy Families (TANF) program, Family Independence (FI), on the longitudinal earnings of three cohorts of new entrants who entered the study before, at the beginning of, and at the height of the 2007-2009 recession. Applicants who began the application process but did not enroll in TANF were propensity-score matched to entrants by background characteristics including pre-intervention earnings history, and served as the comparison group. We constructed a latent growth curve model to test whether earnings histories were similar for the program and comparison groups up until FI intake, to estimate program impact by comparing post-intake earnings of program participants to those of the comparison group, and to determine the statistical significance of cohort differences in program impact. The findings showed FI had a positive impact on the earnings of participants before the recession. The effect became weaker during the state’s period of rising unemployment, and disappeared during the worst economic recession in decades. This study demonstrates the usefulness of longitudinal administrative data, propensity score matching, and latent growth modeling techniques for evaluating the impact of program interventions.

Keywords

TANF program evaluationrecessionlongitudinal administrative datapropensity-score matchinglatent growth curve modeling
Type
Article
Information
Journal of Social Policy , Volume 50 , Issue 4 , October 2021 , pp. 871 - 890
Copyright
© The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Acs, G., Coe, N., Watson, K. and Lerman, R. (1998), Does work pay? An analysis of the work incentives under TANF, Washington, DC: The Urban Institute. Google Scholar
Acs, G., Loprest, P. and Roberts, T. (2001), Final synthesis report of findings from ASPE’s ‘leavers’ grants, Washington, DC: The Urban Institute.Google Scholar
Bell, S., Orr, L., Blomquist, J. and Cain, G. (1995), ‘Methods used to evaluate employment and training programs in the past’, in Program applicants as a comparison group in evaluating training programs: Theory and a test, Kalamazoo, MI: W.E. Upjohn Institute for Employment Research, 1–19.CrossRefGoogle Scholar
Berg, N. and Gabel, T. (2015), ‘Did Canadian welfare reform work? The effects of new reform strategies on social assistance participation’, Canadian Journal of Economics, 48, 2, 494–528.CrossRefGoogle Scholar
Berg, N. and Gabel, T. (2017), ‘Who was affected by new welfare reform strategies? Microdata estimates from Canada’, Applied Economics, 49, 14, 1395–1413.CrossRefGoogle Scholar
Blank, R. (2002), ‘Evaluating welfare reform in the United States’, Journal of Economic Literature, 40, 4, 1105–1166.CrossRefGoogle Scholar
Blank, R. (2006), ‘What did the 1990s welfare reform accomplish?’, in Card, D. and Quigley, J. (eds.), Poverty, the distribution of income and public policy, New York, NY: Russell Sage Foundation, 33–79.Google Scholar
Blank, R. (2009), ‘What we know, what we don’t know, and what we need to know about welfare reform’, in Zilliak, J. P. (ed.), Welfare reform and its long-term consequences for America’s poor, Cambridge, UK: Cambridge University Press, 22–58.CrossRefGoogle Scholar
Card, N. A. and Little, T. D. (2007), ‘Longitudinal modeling of developmental processes’, International Journal of Behavioral Development, 31, 4, 297–302.CrossRefGoogle Scholar
Connelly, R., Playford, C. J., Gayle, V. and Dibben, C. (2016), ‘The role of administrative data in the big data revolution in social science research’, Social Science Research, 59, 1–12.CrossRefGoogle ScholarPubMed
Dehejia, R. H. and Wahba, S. (1999), ‘Causal effects in nonexperimental studies: reevaluating the evaluation of training programs’, Journal of the American Statistical Association, 94, 448, 1053–1062.CrossRefGoogle Scholar
Dengler, K. (2019), ‘Effectiveness of active labour market programmes on the job quality of welfare recipients in Germany’, Journal of Social Policy, 48, 4, 807–838.CrossRefGoogle Scholar
Edelhoch, M. (1999), ‘South Carolina’s welfare reform: roughly right social policy’, Social Policy Magazine, Spring 1999.Google Scholar
Edelhoch, M., Martin, L. and Liu, Q. (2000), ‘The post-welfare progress of sanctioned clients in South Carolina’, The Journal of Applied Social Sciences, 24, 2, 55–78.Google Scholar
Gueron, J. and Rolston, H. (2013), Fighting for reliable evidence, New York, NY: Russell Sage Foundation.Google Scholar
Hamilton, G. and Scrivener, S. (2012), Increasing employment stability and earnings for low-wage workers - Lessons from the employment retention and advancement (ERA) project, Washington, DC: US DHHS OPRE.CrossRefGoogle Scholar
Hancock, G., Harring, J. and Lawrence, F. (2013), ‘Using latent growth modeling to evaluate longitudinal change’, in Hancock, G. and Mueller, R. (eds.), Structural equation modeling: A second course, Charlotte, NC: Information Age Publishing, 309–341.Google Scholar
Harris, A. D., McGregor, J. C., Perencevich, E. N., Furuno, J. P., Zhu, J., Peterson, D. E. and Finkelstein, J. (2006), ‘The use and interpretation of quasi-experimental studies in medical informatics’, Journal of the American Medical Informatics Association, 13, 1, 16–23.CrossRefGoogle ScholarPubMed
Moffitt, R. (2008), Welfare reform: The US experience (Working Paper Series 2008:13), IFAU - Institute for Evaluation of Labour Market and Education Policy: Uppsala.Google Scholar
Pavetti, L. and Schott, L. (2011), TANF’s inadequate response to recession highlights weakness of block-grant structure, Washington, DC: Center on Budget and Policy Priorities.Google Scholar
Pavetti, L., Trisi, D. and Schott, L. (2011), TANF responded unevenly to increase in need during downturn, Washington, DC: Center on Budget and Policy Priorities.Google Scholar
Shadish, W., Cook, T. and Campbell, D. (2002), Experimental and Quasi-experimental designs for generalized causal inference, Boston and New York, NY: Houghton Mifflin Company.Google Scholar
Shadish, W. R., Clark, M. H. and Steiner, P. M. (2008), ‘Can nonrandomized experiments yield accurate answers? A randomized experiment comparing random and nonrandom assignments’, Journal of the American Statistical Association, 103, 484, 1334–1344.CrossRefGoogle Scholar
US DHHS and US DOE. (2001), National evaluation of welfare-to-work strategies: How effective are different welfare-to-work strategies? Five year adult and child impact for eleven programs, Washington, DC: Department of Health and Human Services and Department of Education.Google Scholar
Willett, J. B. (1989), ‘Some results on reliability for the longitudinal measurement of change: implications for the design of studies of individual growth’, Educational and Psychological Measurement, 49, 3, 587–602.CrossRefGoogle Scholar
Wood, R., Moore, Q. and Rangarajan, A. (2008), ‘Two steps forward, one step back: the uneven economic progress of TANF recipients’, Social Service Review, 82, 1, 3–28.CrossRefGoogle Scholar
Ziliak, J. (2016), ‘Temporary assistance for needy families’, in Moffitt, R. (ed.), Economics of Means-Tested Transfer Programs, Chicago, IL: University of Chicago Press, 303–393.Google Scholar