We use an indicator variable for global activities that is based on the World Bank’s own classification of trust fund activities (World Bank 2014b).

Finally, when the activities of the fund are perceived as high-risk, cooperation among multiple donors allows the risks to be shared. Even if the expected impact of the program overall is positive, some specific projects may fail. When many donors contribute, the project portfolio can be larger and more diversified, hence reducing the overall risk of failure. Moreover, it may be politically useful for individual donors to share the responsibility if individual projects turn out to be problematic. Risk sharing is particularly relevant in the context of fragile states, e.g., post-conflict states such as Afghanistan, or countries having experienced major natural disasters. In this context, cooperation of many donors should therefore increase the utility of each. Assistance to fragile and post-conflict states is also likely to be an area where donors have strong common interests, as instability, conflict, and insecurity are problems that tend to spill across borders.

**Hypothesis 6** A contribution to activities in support of fragile states will be most attractive in the form of large multi-donor trust funds.

A binary indicator variable for whether a trust fund supports fragile states can be drawn directly from the World Bank’s (2014b) trust fund database.

# **5 Empirical analysis**

We test these hypotheses using a dataset of all World Bank trust funds (either IBRD/IDA or IFC trust funds) that received at least one contribution within fiscal years 2002-2013 (World Bank 2014b). This is the longest time span for which donor participation information is systematically available. The donors considered are the 24 DAC member countries for which information on trust fund membership and a range of key predictors are available. The dependent variable is a binary indicator reflecting the participation of each individual donor _i_ in fund _f_ (see equation 3).<sup>10</sup> Trust funds were created in different years, and the corresponding year effects can be controlled for, but note that we do not have panel data since each fund is observed only once. We run the estimations separately for SDTFs, small MDTFs, and large MDTFs, using robust linear probability models with standard errors clustered at the level of donor countries.<sup>11</sup> However, we consider that the decisions are not taken independently of each other. Possible correlations of errors across equations are taken into account using seemingly unrelated estimation with unbalanced equations, a

> 10 We also considered using contribution amounts for a robustness check, but the necessary data are not available.

> 11 As already noted above, we cannot estimate a full-fledged choice model in the spirit of the decision tree in Figure 1 because the data have different units of analysis. This also precludes a Heckman model for potentially correlated sequences of decisions. However, our data allow us to include control variables for factors that indicate the use of pure bi- or pure multilateral aid as an alternative to multi-bi aid. Assuming that all relevant factors are observable, this should solve the selection problem.

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