To apply the technique, we impose a set of balance constraints, which imply that the covariate distributions of the treatment and control group in the preprocessed data match exactly on all pre-specified moments, taking care to only use pre-treatment variables in the balancing equation. The entropy balancing algorithm then searches for the set of weights that satisfies the balance constraints but remains as close as possible to a set of uniform base weights to retain information. This recalibration technique assures maximum balance between treatment and control groups (Hainmueller 2012). After re-weighting, mayoral and council-manager systems are now directly matched in terms of average value, variation, and skew (See Table 1). 1 about Here> Taking advantage of this statistically generated region of common support, we estimate the following sets of analyses. First, we examine the probability of a city offering an incentive, and second the size of incentives in Models 1 and 2. These are presented in the top panel of Table 1. The results are very similar to the naïve specifications. Taking survey effects into account, the results show that the mayors offer 30.7% more in incentives per project. Although the results are compelling, our theory also offers an additional observable implication that mayor-council systems would offer less rigorous oversight of their incentives. In this section, we test the second hypothesis by focusing on three questions in these surveys to address the political use of incentives directly. First, do these incentive programs have written performance criteria, requiring specific elements (such as job creation) to be eligible for the incentive program? Second, do municipalities perform a cost-benefit analysis in the offering of location incentives? Third, if performance criteria are used, how many different items is the respective project assessed against in order to measure effectiveness. The ICMA survey presents respondents with a list of six items, asking them to check off all that they currently use. The list includes: jobs created, capital invested in construction and labor, capital invested in land, existing company sales, number of new
To apply the technique, we impose a set of balance constraints, which imply that the covariate distributions of the treatment and control group in the preprocessed data match exactly on all pre-specified moments, taking care to only use pre-treatment variables in the balancing equation. The entropy balancing algorithm then searches for the set of weights that satisfies the balance constraints but remains as close as possible to a set of uniform base weights to retain information. This recalibration technique assures maximum balance between treatment and control groups (Hainmueller 2012). After re-weighting, mayoral and council-manager systems are now directly matched in terms of average value, variation, and skew (See Table 1). 1 about Here> Taking advantage of this statistically generated region of common support, we estimate the following sets of analyses. First, we examine the probability of a city offering an incentive, and second the size of incentives in Models 1 and 2. These are presented in the top panel of Table 1. The results are very similar to the naïve specifications. Taking survey effects into account, the results show that the mayors offer 30.7% more in incentives per project. Although the results are compelling, our theory also offers an additional observable implication that mayor-council systems would offer less rigorous oversight of their incentives. In this section, we test the second hypothesis by focusing on three questions in these surveys to address the political use of incentives directly. First, do these incentive programs have written performance criteria, requiring specific elements (such as job creation) to be eligible for the incentive program? Second, do municipalities perform a cost-benefit analysis in the offering of location incentives? Third, if performance criteria are used, how many different items is the respective project assessed against in order to measure effectiveness. The ICMA survey presents respondents with a list of six items, asking them to check off all that they currently use. The list includes: jobs created, capital invested in construction and labor, capital invested in land, existing company sales, number of new