A Squire Index Update

Abstract

The measure of legislative professionalization I developed was first published a quarter century ago. Since then, updates have appeared periodically. In this note, I briefly document the measure's usefulness in academic research and then calculate it for 2015. For reasons I detail, the updated measure is corrected for a misestimate of days in session for some states.

State Taxes and Legislative Turnover in the United States

“…We separate professionalized and nonprofessionalized legislatures using the Squire Index for 2015 (Squire ). Following other studies using the index, we categorized a legislature as professionalized if the index is greater than 0.25, from a scale between 0.048 and 0.629.…”

“…Since legislatures with term limits are also less likely to be professionalized, these differences are more comparable if we condition them on the degree of professionalization. We separate professionalized and nonprofessionalized legislatures using the Squire Index for 2015 (Squire 2017). Following other studies using the index, we categorized a legislature as professionalized if the index is greater than 0.25, from a scale between 0.048 and 0.629.…”

Network Structure and Governance Performance: What Makes a Difference?

“…The results reported in table 4 are for models with default standard errors, as no noticeable differences were found between the models estimated with default and robust standard errors. The adjusted R 2 for the models were Squire ' s index Control Squire ( 2017 ) around 0.4 for the green jobs indicator and between 0.7 and 0.8 for renewable energy capacity.…”

“…The size of the public utility regulatory commission (PURC) was also controlled for, as it has been shown to be a significant force driving renewable energy development (Yi and Feiock ). Legislative professionalism was also included in the models; it measures the level of professionalism of legislative officials (Squire ). Higher levels of professionalism in state legislatures could result in better designs of renewable energy policies that facilitate effective policy implementation.…”

Candidate Extremism and Electoral Design in U.S. State Legislative Elections

“…Comparative studies, both country case studies and large-N multicountry analyses, have identified a number of variables, including public opinion (Ward, Ezrow, and Dorussen, 2011;Adams et al, 2004), parties' organizational attributes (Schumacher, De Vries, and Vis, 2013), rival parties' policy positions (Adams and Somer-Topcu, 2009;Han, 2015;Spoon, 2011;Williams, 2015), the preferences of the affluent (Gilens and Page, 2014), the preferences of the party's core supporters , and past election results (Laver, 2005;Somer-Topcu, 2009). Investigations of party and candidate polarization in U.S. states yield still more predictive variables, including legislative professionalism (Squire, 2017), population size, divided government, unemployment, population size, term limits (Olson and Rogowski, 2018), economic inequality, and the proportion of the population that is foreign born (McCarty, Poole, and Rosenthal, 2006). While the subnational method does not totally eliminate variation in these other sources of polarization, it significantly mitigates them, which makes identifying the relationship between institutional variation and candidate behavior more feasible.…”