variable of interest. Table 2 also reports a significant negative time trend over the 1995 to 2008 period. Higher per capita income and higher aid/GDP are associated with better economic policies. Political freedoms are not significant, perhaps in part due to limited variation in the data over time for many countries, coupled with the inclusion of country Table 3 reports findings for the number of cumulative conditions. The first equation presents the results from estimating the quadratic specification using the full sample. A highly significant concave relation appears, with a predicted turning point at 149 cumulative conditions - equal to three times the average number in our sample, and two standard – deviations above the mean. However, figure 2 the partial residual plot for the number of – cumulative conditions suggests that Argentina is a highly influential case in estimating this relationship. Without Argentina in the sample (table 3, equation 3), the coefficient on number of conditions squared declines and is no longer significant at conventional levels. The estimated turning point drops from 149 to 20 cumulative conditions. Because Argentina is an extreme outlying and influential (in the quadratic specification) observation, we drop it from the sample in subsequent tests.

Equations 2 and 4 of table 3 show that the coefficient for the number of cumulative conditions is positive and significant in both the linear and logarithmic specifications. According to equation 2, one additional market reform condition increases the CPIA score for the typical country with 0.04 points. The logarithmic model predicts that the first market reform condition increases the CPIA score with 0.11 points on average. Table 3 also shows that control variables behave in similar fashion as in table 2: income and aid are positively associated with policy quality, and controlling for other variables there is a significant negative time trend. Concerning model fit, neither the reported goodnessof-fit measures, nor the J or Cox-Pesaran test, nor the semiparametric estimation (see figure 6) provide robust indications which specification has the best fit. For the number of cumulative conditions, we thus report all three specifications for most tests.

Next, we also check whether the implementation of market reform programs has “crowded out” policy improvements in other areas. We do so by substituting the CPIA social policy (CPIA C) and public sector governance (CPIA D) cluster averages for clusters A and B as dependent variables. A priori, there are reasons to expect negative spillovers on other policy areas. For instance, improving policy in one sector might divert rent-seeking activities to other sectors. Also, focusing on one policy area could attract human capital and other resources from other sectors, reducing the ability to design and implement adequate policies in those sectors. On the other hand, new rules and norms of behavior in one part of the public sector might transplant to other departments or agencies (see, e.g., Banerjee, 1992; Mullainathan, 2006). Thus we might also expect some “crowding in” of reforms, i.e., positive spillovers. Table 4 however shows that neither loans nor conditions designed to improve policies related to clusters A and B have any significant net impact on CPIA cluster C or cluster D. Coefficient signs in the CPIA C regressions are consistent with positive spillovers, but p-values are above conventional significance levels. Coefficient signs are mixed in the CPIA D regressions, and none come close to significance. One possible explanation for the lack of (positive) spillovers is the length of the governance results chain.

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