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Original Articles

Evaluating evolutionary changes in state TANF policies

, &
Pages 1753-1758 | Published online: 16 Mar 2012
 

Abstract

Over the past decade narrowly focused studies have evaluated the effectiveness of state-level welfare policies. In general, they evaluate reforms within a particular state, focus on a small number of outcome variables (usually caseload levels) and/or use a very narrowly defined time period. This narrow and partial analysis is perplexing, from an institutional perspective, as Temporary Assistance for Needy Families (TANF) forces states into a zero-sum funding game, where shares depend on differential relative success in achieving policy objectives metrics. This institutional structure incentivizes states to mimic and improve upon more successful counterparts to recapture a larger share of TANF block grants. Given this dynamic institutional structure, an evolutionary evaluation of state TANF programmes is warranted. This article uses cluster analysis to explore evolutionary changes in state TANF policies (as characterized by a comprehensive set of outcome variables) immediately following the imposition of TANF (1997–2005). We identify or benchmark clusters of ‘successful’ and ‘less successful’ TANF programmes. The results allow us to track which states in which year fall into the ‘successful’ and ‘less successful’ clusters over the 9-year period. The results support the notion that initially unsuccessful states mimic other successful state programmes over time.

JEL Classification:

Notes

1 See Social Security Act, sections 401(a) and 403(a)(2).

2 See Grogger et al. (Citation2004) for a comprehensive review of mainstream studies. See Hisnanick (Citation2004), Axelsen et al. (Citation2009) and Underwood et al. (Citation2010) for a review of nonmainstream studies.

3 Results were similar using other linkages.

4 Variables are standardized using z-scores.

5 The complete set of results is available upon request.

6 We did not use ‘running’ means because it can potentially bias results.

7 Results using the distance between the middle of each cluster are similar.

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