# _Matching propensity scores for multilateral support_

Weights for projects without MLS were calculated in proportion to their proximity to projects with MLS.<sup>9</sup> These weights make projects without MLS suitable for comparison with projects receiving multilateral funding. When applied, the weighted average of the probability of receiving MLS for projects without multilateral funding is statistically identical to the average probability of getting MLS for projects that actually receive it. The bias in the propensity to receive MLS between groups is reduced by 99% (see Annex 2).

# _Cancellation rates and the role of multilateral funding_

The comparison of cancellation rates between the matched sets suggests that multilateral development organizations have a positive effect on the survival of long-term PPP infrastructure contracts _._ Table 3 shows that, without matching, the cancellation rates for PPP projects with and without MLS are statistically the same (6.1% versus 5.8%). However, as shown above, the sets of projects with and without MLS do not have the same probability of receiving multilateral support (28.5% versus 10.7%, respectively), suggesting that the groups are systematically different.<sup>10</sup> As such, a simple direct comparison of the two groups is misleading when trying to understand the real effect of MLS on cancellation.

When projects with MLS are instead matched to a set of PPP projects with the _same probability_ of having MLS but _no actual_ multilateral funding, a different result is revealed: the mean cancellation rate for the counterfactual set of non-MLS projects is 8.6%, as compared to a much lower 5.8% cancellation rate for projects with multilateral support. In other words, it is estimated that the cancellation rate of projects with MLS would have been 2.8 percentage points (about 48%) higher without funding from multilaterals.<sup>11</sup> Ta <mark>ble 3.</mark> Propensity score matching summary, mean rates of cancellation<sup>12</sup>

|Averag|e Treatment Effect on Cancellation Ra|tes of PPPs|with MLS. M|atching metho|d: Kerne|l|
|---|---|---|---|---|---|---|
|Variable|Sample|Treated|Controls|Difference|S.E.|T-stat|
|PPP Status:|Unmatched|0.061|0.058|0.00|0.01|0.22|
|Canceled|Matched using propensity scores|0.058|0.086|-0.03|0.02|-1.83|
||||P(T|< |t|) = 0.074|9 after m|atching|

Source: Authors’ calculations To put this result in perspective, the 24 canceled PPP contracts with MLS identified between 1990 and 2010 (Table 1) represent total committed inve <mark>stments of US$19.3 billion (Annex 5). Without MLS, that number could have risen to 36</mark>

> 9. An Epanechnikov Kernel density function was applied to smooth the calculated distances. Distances are equal to the difference in the propensities between projects with and without MLS. 10. The balance tests in Annex 2 show that projects with and without MLS are different, not just in terms of their probability of receiving MLS, but also in terms of several other project-, country–, and regional– level characteristics.

> 11. For only 11 PPP projects with estimated probabilities of receiving MLS above 67%, it was not possible to find a match. These projects represent 2.8% of the total number of PPP with MLS. See Annex 2 for more details.

> 12. This comparison includes canceled, operational and concluded PPP projects.

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