ments in GMM, because substantive instruments that significantly predict DPLs exhibit little or no time series variation. Moving to cross-section data allows us to avoid that problem, as well as complications associated with serial correlation in the dependent variable. We estimate the following cross-sectional equation: The dependent variable here is the change in policy quality, measured over the period 1996-2008.<sup>15</sup> Key independent variables are the logarithm of the number of cumulative market reform loans (or conditions) from 1996 through 2008. In the first stage we instrument for number of loans or conditions with the logarithm of population in 1996 (Boone, 1996) and the average fraction of key votes in the UN General Assembly (UNGA) aligned with the G-7 over the period 1995-2008 (Barro and Lee, 2005; Kilby, 2011). As controls we include the initial level of policy quality, average annual aid as a share of GDP, and average annual growth in GDP per capita over the period 1996-2008, the logarithm of initial income per capita, a measure for ethnic fractionalization (Alesina et al., 1999; Collier, 2000), initial political freedom and the change in political freedom over the period 1996-2008. See table A.2 for descriptive statistics. The coefficients of equation 2 are estimated using 126 observations, one for each country for which CPIA data are available from both 1996 and 2008. In the next section we discuss our empirical findings.

# **4. Empirical Findings**

# _4.1. Baseline results and spillovers_

Table 2 presents the results for the number of market reform loans. Number of loans is – significantly related to policy quality in each of the three specifications linear, quadratic and logarithmic. In the linear specification (table 2, equation 1), each additional market reform loan is estimated to increase the CPIA score by .07 on average. Results for the quadratic model imply that the maximum improvement in CPIA (relative to the case of no DPLs) is about 0.90, corresponding to the case of 13 loans. For the logarithmic specification, a first loan increases the CPIA score by 0.40 points on average, and a second loan by 0.21 points. However, the reported goodness-of-fit measures suggest that the logarithmic specification is most appropriate. Furthermore, both the J-test and Cox-Pesaran test for non-nested models indicate that the model with positive but diminishing returns to more DPLs better fits the data than the linear and quadratic models.<sup>16</sup> The graphical – – output of a semiparametric estimation see figure 5 further confirms the choice of the logarithmic model. For space considerations, we will therefore report only the findings of the logarithmic model in subsequent regressions when the number of loans is the main

> 15In order to maximize the number of observations, we took 1996 instead of 1995 as the base year.

> 16For instance, the J-test rejects the quadratic specification as the correct model, with a J-statistic of 2.01 with corresponding p-value of 0.046. It does not reject the logarithmic model (J-statistic = -0.61 with p-value 0.54). Similarly, the linear model is rejected in favor of the logarithmic (J-statistic = 2.18, p-value = 0.031), without rejecting the logarithmic model (J-statistic = - 0.10 with p-value = 0.981). 10