of a small change in GOVERNANCE on the quartile _τ_ . The intuition behind UQR/RIF-OLS is worth repeating: a negative co-efficient on _β_<sup>_RIF_</sup> implies that as GOVERNANCE increases, there is a downward shift in the distribution of Political Influence at a given moment ( _τ_ ). The thing that will be of most interest to many—previewing our results—is the relative size of these coefficients. For example, a larger magnitude of coefficients at higher moments of the distribution (e.g., _|βτ_<sup>_RIF_</sup> =75<sup>_| > |β_</sup> _τ_<sup>_RIF_</sup> =25<sup>_|_),asillustratedinFigure5),</sup> implies that changes in governance have a more dramatic relationship with the long rightward tail shown in our Political Influence Index, compared to movements at the leftward parts of the distribution.

We use several well-known measures of governance quality to estimate the movement of Political Influence with the underlying political environment. These include Transparency International’s Corruption Perceptions Index (CPI), which we reverse to have 0 indicate the lowest level of corruption and 100 the highest, as we find it more intuitive to have higher levels indicate greater levels of corruption. The second measure is Henisz (2002)’s political constraints score (POLCON), with higher values indicating more veto points in a political system, and thus more constraints on unitary action. Then, we take the six sub-indices from the World Governance Indicators (WGI), which include Voice & Accountability, Political Stability, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. For comparison with our index, we re-scale each of these sub-indices from 0 to 100, using the global minimums and maximums, where 100 indicates a higher level of governance quality.<sup>31</sup> Lastly, we include a dummy measure we denote as ‘Power Shift’ if there was a shift in executive power (excluding transitions where the same party retained power) in the last 5 years.<sup>32</sup> Figure 6 graphically shows the results for a number of estimations following Eq. 10 and Eq. 11. All estimations use survey weights and use robust standard errors, adjusted for clustering at the level of stratum, to reflect the representative survey design (Abadie et al., 2017). The horizontal dashed lines (in grey) show the point estimate and 95% confidence intervals from the OLS estimates ( _β_ 1<sup>_OLS_</sup> ). The darker (blue) shaded area in each panel shows the point estimates and 95% confidence intervals from the RIF-OLS estimates, at the 10th, 25th, 50th, 75th, and 90th percentiles ( _β_ 1<sup>_RIF_</sup> _,τ_ =10 _,_ 25 _,_ 50 _,_ 75 _,_ 90<sup>).</sup>

> 31Note this re-scaling is done for interpretation purposes. Kaufmann et al. (2011) note on the WGI website that the indices are in units of standard deviation, and range from -2.5 to 2.5. No economy in our data reports values outside of this range, and so we use the transformation: 100 _∗_<sup>_−_</sup><sup><u>2</u></sup> _−_<sup>_<u>.</u>_</sup><sup><u>5</u></sup> 2<sup>_−_</sup> _._ 5<sup>_<u>subindex</u>_</sup> _−_ (2 _._ 5) . The original score and the re-scaled one have a perfect correlation, and we run results on both the re-scaled score and the original sub-index(not shown).

> 32More details are provided in the Data Appendix.

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