where _yit_ is the average of CPIA cluster A and B for country _i_ in year _t_ . _Xit_ represents the cumulative number of market reform loans (or conditions) for country _i_ in year _t_ . For both variables, we estimate a linear effect, but also specified a model with diminishing returns as well as a quadratic relation.<sup>12</sup> _Zit_ is a vector of control variables. Aid from other donors could have direct or indirect effects on policy reform, so we include total aid over GDP as a control variable. Following Besley and Persson (2011) among other studies, we include a measure of democracy, specifically the Freedom House index of political freedoms. We include a time trend, to control for any secular improvements in economic policy independent of any impact of World Bank loans, and for any potential tendency for inflation over time in CPIA ratings. To correct for the possibility that policy quality may be inferred in part from performance, we control for the logarithm of GDP per capita. _δi_ are country fixed effects. Descriptive statistics for these variables are presented in table A.1. We estimate the coefficients of this model by employing OLS on a comprehensive country-year panel of aid recipient countries that runs from 1995 to 2008. Standard errors are adjusted for country clustering of observations.

Because number of loans and conditions are continuous variables, we correct for sample selection using instrumental variables techniques as in Easterly (2005) rather than Heckman selection models. We use two alternative methods. First, we estimate equation 1 with system GMM (Arellano and Bover, 1995; Blundell and Bond, 1998) and instrument our variables of interest with their lagged differenced values.<sup>13</sup> The Arellano and Bond (1991) tests indicate the presence of substantial autocorrelation: though we can reject serial correlation in differences at the five percent level from _AR_ (5) onwards, the p-values for _AR_ (7) and _AR_ (9) are respectively 0.059 and 0.089 with the number of cumulative loans as the key independent variable. For the number of conditions variable, the p-value drops below the five percent level for _AR_ (7) to 0.036. Hence, we lag our variables of interest to the highest extent possible, i.e., 15 periods. Furthermore, as the number of time periods grows large, the instrument count increases exponentially, making results about estimators and related specification tests invalid (Roodman, 2009). One solution to this problem is to use only certain lags. Thus, we limit the number of lags per time period to one. In order to minimize correlation across countries in the idiosyncratic errors, we also include time dummies instead of a time trend.<sup>14</sup> As a second correction for possible selection bias we employ 2SLS using a cross-sectional version of the dataset. With the panel dataset, we are limited to using mechanical instru-

> 12In order to retain the zero observations when making the log transformation, we added 1 to the number of cumulative EP loans and to the number of cumulative prior actions. Results are not sensitive to the specific values added for the log transformations.

> 13System GMM is mainly used to estimate a dynamic panel model with a lagged dependent variable on the right-hand side. However, it can also be used – as here – to lag endogenous regressors (Roodman, 2009).

> 14Alternative specifications – e.g., collapsing the instrument matrix, increasing the number of lags per time period, including different lags – generate equally significant coefficient estimates for both loans and conditions, with acceptable test statistics for overidentification. See the appendix for a regression with a collapsed instrument matrix, using lags five to ten for loans and lags ten to fifteen for conditions.

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