With respect to operationalizing and testing the large literature on measuring rent-extraction and economic voting provide clear guidance regarding which variables need to be utilized for this exercise to be valid. Specifically, most of the variables of interest are available from Persson and Tabellini’s (2003) canonical dataset, which includes a dummy variable for high-income democracies<sup>13</sup> . In order to construct the EDD/NEDD ratio the World Bank’s (2011) measure of regulatory quality (regulatory density<sup>14</sup> ) is added to the dataset (1998-2000).

# **The Sample**

The data used to test the model is the same dataset used by Persson and Tabellini (2003) in their seminal analysis of the economic effects of constitutions. The dataset has been augmented to include a measure of regulatory quality (one of the independent variables of interest) as well as additional control variables identified in the literature as being potentially critical determinants of rent extraction. Furthermore, two separate analyses of the dataset are undertaken. One utilizes only high-income democracies (n=19-21)<sup>15</sup> , with the aim of establishing whether the theoretically anticipated relationship between the EDD/NEDD ratio holds, given a relatively homogenous set of countries. The other utilizes all countries available (n=52-58)<sup>16</sup> , which introduces heterogeneity in the sample (hence a need to control for more variables), but also enables more tests of the robustness of the initial results. In both cases, the substantive results should be the same. Specifically, it should be observed that an increase in the EDD/NEDD ratio is associated with a reduced level of rent extraction.

# **The Dependent Variable**

There is a lively debate regarding how to measure corruption and rent extraction (see for example Tresiman, 2007, Olken 2009, and Kauffman et al 2007). However, despite continued controversies over (1) the validity of subjective measures of rent extraction (such as Transparency International’s (2011) Corruption Perceptions Index or the World Bank’s (2011) Good Governance Indicators) and (2) the external validity of more narrow objective measures of rent extraction (such as the UN’s (2011) data on bribery) there is now a growing consensus that, as long as standard errors are taken into account most of these major measures of rent extraction are valid and reliable and measuring the same underling variable (Kauffman et al 2007). Given that Transparency International’s Corruption Perception Index (CPI) is most substantively focused on politically sensitive corruption vis-à-vis other indicators (Hamilton, 2012; Persson and Tabellini, 2003) this measure of rent extraction (see Appendix A for details) is used as the dependent variable of interest (it is in fact inverted so that lower scores denote less corruption). However, substituting this depended variable for other measures of rent extraction (see next Section and Hamilton 2012) does not alter the robustness of the findings.

Because the CPI scores is constrained (cannot exceed or be less than a given value; 0-10) the CPI is not, strictly speaking, drawn from normal distributions (which must, by definition, vary from negative to positive infinity). While many researchers simply use OLS to run regressions on such variables, this is potentially problematic because the nature of these variables means that the residuals of such models may only approximate that of a normal distribution. If the residuals are not normally distributed, then the results of such analyses will be biased either over- or under-estimating the influence of the independent variable of interest on the dependent variable.

> 13 Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Japan, Luxembourg, the Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, USA and the UK.

> 14 It is important to note that the measure of regulatory quality measures regulatory density rather than corruption/rentextraction, which is captured by one of the other good governance variables (Control of Corruption).

> 15 See Footnote 13.

> 16 This includes a variety of new democracies and non-democracies. See Persson and Tabelinni (2003) for details.

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