kets deepen, returns fall. This effect is largest at the 90th percentile, indicating that in particular financial openness and development remove “home runs” from the economy. This pattern is similar in Table 8, Panel B, for fund investments.<sup>21</sup> The quantitative magnitude of these results is substantial: using the specification in Column (6), a one standard deviation higher financial openness is associated with a reduction in the PME by 0.60 at the 90th percentile, or, for an 8-year investment, a (1 _._ 60<sup>(</sup><sup><u>1</u></sup> 8<sup>)</sup> _−_ 1) x 100 = 6 _._ 05 percentage point lower return than the S&P 500 _each year_ . As a benchmark, in the data between 20002001, as Poland prepared to enter the European Union, its value on the Chinn-Ito openness index increased by approximately one standard deviation. A one standard deviation increase in banking sector development reduces the PME by 0.73 at the 90th percentile, or, for an 8-year investment, a (1 _._ 73<sup>(</sup><sup><u>1</u></sup> 8<sup>)</sup> _−_ 1) x 100 = 7 _._ 09 percentage point lower return than the S&P 500 _each year._ A one standard deviation increase in private sector credit to GDP is 39 percentage points, approximately equal to the growth experienced by Brazil from 1990 to 2020, or double the growth experienced by Kenya during the same 30-year period.

Our interpretation of these results is that capital controls and limited banking system depth have prevented capital from flowing to viable projects in less financially open and developed economies. Were this not the case, we would not observe a change in returns as economies open and develop.<sup>22</sup> Overall, these results are consistent with the idea that misallocation creates profit opportunities for private equity investors. As financial systems open and become more competitive, “home run” investments are harder to find. Table A11 tests whether the effect of financial openness can be explained by changes in perceived corruption, which may also have changed during this reform. The direction of the result in Column (6) of Table 8 is robust to this control.

It is worth relating these findings to what is known about flows of private equity investments across countries. Lerner et al. (2009) use data from Capital IQ to construct a database of 76,398 private equity investments made in 1984-2008 across 123 countries. Although they do not measure returns, they find associations between country characteristics and investment volume as a share of GDP. The authors report that private equity investment flows to countries with higher GDP per capita and less corruption, although not to markets with less private sector credit to GDP. Although their paper examines a different sample of investors with preferences that potentially differ from the IFC’s, one way to reconcile their results with ours is that the greatest volume of investment need not necessarily go to markets with the highest returns; indeed, returns could be highest in markets with lower private sector credit to GDP precisely because there is less investment in those markets.

> 21Table A8 reports results using the IRR as measure of performance. Table A9 reports results when restricting the sample to only realized investments. Results are qualitatively similar.

> 22Table A10 reports results without country fixed effects for comparison. The fact that associations become stronger when we include country fixed effects indicates that the largest differences in returns across economies occur _within_ economies that transition from closed to open. Such differences are obscured somewhat when only the cross-section is considered.

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