2019
DOI: 10.1111/psj.12307
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Measuring Complex State Policies: Pitfalls and Considerations, with an Application to Race and Welfare Policy

Abstract: Welfare policy is multidimensional because of the political compromises, competing goals, and federalist structure underpinning it. This complexity has hindered measurement and, therefore, the comparability of research on race and welfare policy. This paper describes a measurement strategy that is transparent, replicable, and attuned to matching the assumptions of statistical models to the policy process. We demonstrate that this strategy leads to more nuanced conclusions regarding the relationship between min… Show more

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Cited by 7 publications
(12 citation statements)
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References 47 publications
(88 reference statements)
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“…Are the results sensitive to analyst choices or are they relatively robust across a range of decisions? A substantial literature has worked to make sense of state-level policy in this area, and scholars differ in how they measure state immigrant policy activity (Boushey & Luedtke, 2011;Filindra, 2013;Hero & Preuhs, 2007;Marquez & Schraufnagel, 2013;Monogan, 2013a;Nicholson-Crotty & Nicholson-Crotty, 2011;Pham & Van, 2014;Reich & Barth, 2012;Rivera, 2014;Schildkraut, 2001;Ybarra, Sanchez, & Sanchez, 2016). Some studies aggregate over time to analyze cross-sectional variance only, others split welcoming and hostile laws to be considered separately, some use panel data to consider the overtime patterns, and finally some subdivide laws by policy subarea.…”
Section: Measurement Theory and Scaling Immigrant Policymentioning
confidence: 99%
“…Are the results sensitive to analyst choices or are they relatively robust across a range of decisions? A substantial literature has worked to make sense of state-level policy in this area, and scholars differ in how they measure state immigrant policy activity (Boushey & Luedtke, 2011;Filindra, 2013;Hero & Preuhs, 2007;Marquez & Schraufnagel, 2013;Monogan, 2013a;Nicholson-Crotty & Nicholson-Crotty, 2011;Pham & Van, 2014;Reich & Barth, 2012;Rivera, 2014;Schildkraut, 2001;Ybarra, Sanchez, & Sanchez, 2016). Some studies aggregate over time to analyze cross-sectional variance only, others split welcoming and hostile laws to be considered separately, some use panel data to consider the overtime patterns, and finally some subdivide laws by policy subarea.…”
Section: Measurement Theory and Scaling Immigrant Policymentioning
confidence: 99%
“…Best practices for weighting immigration policy modeling suggest that various laws and rules do not have the same substantive effect on targeted populations and thus should not be judged as equal (Bjerre et al, 2015). This point is echoed herein in the analysis by Plutzer et al (2019). At a minimum, researchers should take into account the material (and even psychological) impact that a law may have on the lives of noncitizens as well as the reach a law may have, that is, the likely number of people who may be affected by its provisions (Bjerre et al, 2015).…”
Section: Measurement and Aggregationmentioning
confidence: 84%
“…Aggregation decisions can influence the relative ranking of units within the index, whether those are countries, or U.S. states (Bjerre et al, ). Moreover, as Monogan (), Reich () and Plutzer et al () all demonstrate, aggregation choices can influence the correlations with various predictors and thus either overestimate or hide significant relationships (also see D'Urso, ). Cerna and Chou () add another real‐world perspective on how measurement counts, that is, affects policy decisions.…”
Section: Measurement and Aggregationmentioning
confidence: 99%
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