measure progress towards the Millennium Development Goals (MDGs) that we use as our dependent variables.

- _4.2. Project Rating Significance: Do project outcome ratings provide information on the relationship between aid flows and sector outcomes?_

The results for project ratings, reported in detail in Appendix A and summarized in Table 2, are also clear. Only for fiscal management outcomes do the weighted average ratings convey information about outcomes. In the other sectors, the ratings are not significant: the coefficients are near zero and their inclusion makes no difference to the coefficients on aid volume. These results are robust to using the alternate measures of ratings and to the restriction of aid flows to particular donors or the World Bank Group alone. The one exception is in a specification for sanitation, but with a small sample size, a small coefficient and a negative sign.

The more sensitive “debiased/double machine learning” techniques confirm the absence of effects seen in the traditional regressions. Table 2 shows, for each sector, the _r_<sup>2</sup> of the partialing out step, the _r_<sup>2</sup> of the treatment test, the coefficient on the treatment (weighted average rating) in the treatment test and the _p_ -value of the treatment test.

It might be argued that ratings’ absence of information in four out of five sectors is a result of projects focusing on other outcomes than those we are testing against. But this cannot explain why aid volume enters significantly against the sector outcomes, and, when examined in detail, requires implausible assumptions to account for the results. Assume that some proportion _X_ of the aid in a sector targeted the MDG sector outcome, and the rest targeted entirely unrelated outcomes. Then the “true” coefficient on aid volume would be 1 _/X_ times the coefficient detected in our regression. If overall project ratings did convey information, then they should convey information on the _X_ proportion targeting the outcome, and hence should modify the coefficient on volume, even if attenuated. The ratings on the 1 _− X_ share of aid explicitly targeted to non-MDG outcomes would diminish the rating effect. But the coefficient could only be reduced to insignificance if the ratings on the 1 _− X_ proportion were negatively correlated to the _X_ share and neutralized them precisely.

We also note that, during this period, projects were predominantly MDG related and that dividing the period into two, one peak MDG period and one after, does not alter the estimate. Further, we do detect a relationship for fiscal management, even though not all fiscal projects explicitly aim to increase the tax share of GDP. Finally, as noted in section 3.5, the use of lags makes it even more reasonable to expect effects on the dominant MDG outcome from projects in the sector. In all, it seems far more plausible that ratings are not providing information than that ratings on a small share of non-primary-outcome projects precisely cancel out information in the primary-outcome projects, even though the results are unchanged in periods where that share was trivial and even accounting for lags in within-sector spillovers, and not least because this neutering effect would have to mysteriously vanish in one out of five sectors.

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