Finally, Nebraska: A Synthetic Control Analysis of Legislative Structure

Abstract

I estimate the impact of Nebraska’s 1937 switch from a bicameral to a unicameral legislature on state-level government expenditures. Using the synthetic control method I create a counterfactual Nebraska from a weighted average of other potential control states and compare spending in this “synthetic Nebraska” to spending in the real Nebraska. Relative to the synthetic control, Nebraska experiences a sharp decrease in expenditures per capita immediately following the switch to a unicameral legislature; however, the difference appears to diminish over time. Placebo tests show that if the change in Nebraska’s legislative structure were randomly assigned among the sample of states, and legislative structure had no real impact on spending, the likelihood of obtaining a treatment-effect estimate as large as Nebraska’s would be 0.0213. While the initial drop in expenditures per capita lends support to the theory that bicameralism, by requiring more veto players, is associated with higher levels of government spending, the fact that the difference between Nebraska and synthetic Nebraska diminishes suggests that legislators are able to circumvent this constraint.

Legislature Size and Interest Mobilization: The Effects of Institutional Change

“…First, organization totals from synthetic Illinois generally mirror those from actual Illinois prior to the Amendment's implementation. The fit is substantially better than the fit Hankins (2020) in the context of the American states, compares the gap between actual and synthetic observations in the treated state with gaps between control-state observations and their synthetic versions. In other words, every state is modeled as if its legislature was similarly reformed in 1983.…”

“…Nearly all of these measurements are provided by the Correlates of State Policy Project (Jordan & Grossmann, 2020), a compendium of data on state politics and policy. To improve the predictive utility of my analyses, and following Abadie et al (2015) and Hankins (2020), among others, I also include as a predictor the average number of registered lobby organizations (the outcome variable) within each state from before its reform or treatment. I now turn to each of the three reformed states.…”

“…To determine if treatment effects are truly discernible from trends seen in other states, Abadie et al (2010) prescribe placebo tests. The first test, also implemented by Hankins (2020) in the context of the American states, compares the gap between actual and synthetic observations in the treated state with gaps between control-state observations and their synthetic versions. In other words, every state is modeled as if its legislature was similarly reformed in 1983 .…”

“…Yet, while legislatures vary widely in size, social scientists know little about the effects of size on interest mobilization. Most research on legislature size examines effects on government spending (Chen & Malhotra, 2007; Hankins, 2020) or legislator collaboration (Kirkland, 2014). Building on the work of Powell (2012) and McCormick and Tollison (1981), I propose that changes in legislature size affect numbers of interest groups that lobby.…”

Impact of VAT Reform on Swedish Restaurants: A Synthetic Control Group Approach*

“…Various selection criteria are available, but the most common method is to select the control group that minimizes the mean square prediction error (i.e., the pre-treatment deviance between the synthetic control group and the treated sector). Most studies use a donor pool from other countries (Castañeda and Vargas, 2012;Billmeier and Nannicini, 2013;Abadie et al, 2015;Karlsson and Pichler, 2015;Smith, 2015), other regions within the same country (Abadie and Gardezabal, 2003;Abadie et al, 2010;Leight, 2010;Bohn et al, 2011Bohn et al, , 2015Coffman and Noy, 2011;Bassok et al, 2012;Dorsett, 2013;Hankins, 2014;Ando, 2015;Dietrichtson and Ellegard, 2015;Munasib and Rickman, 2015), or other districts within a region (Bauhoff, 2014). One study uses a donor pool consisting of other persons (Chan et al, 2015).…”