the time of loan approval. To address this difficulty, I rely on predicted rather actual disbursements. Predicted disbursements are based on the combination of actual loan commitments with typical disbursement profiles such as those shown in Figure 1, but for more finely-disaggregated groups of loans. Specifically, I begin by assigning loans to a set of creditor/decade/region-specific bins. The creditor bins are based on the major creditors listed in Table 1, as well as the residual categories of other multilateral and bilateral creditors. I then separate loans issued by each of these creditor groups into decades by approval year, and further divide them into six geographical regions in which the borrower is located.<sup>12</sup> This procedure results in 443 creditor/decade/region bins, with a median of 65 loans in each bin. For each loan, I compute the average disbursement profile across all other loans within the same creditor/decade/region bin, i.e. excluding the loan in question. I then apply this average disbursement profile to the original commitment to obtain a series of predicted loan-level disbursements. Finally, I aggregate predicted disbursements across all loans to the country-year level, but excluding loans committed in the same year. By construction, the only borrower-specific information in this measure of aggregate predicted disbursements consists of the original loan commitment decisions made in previous years, which I assume are uncorrelated with contemporaneous macroeconomic shocks.

Figure 2 helps to visualize the steps in the construction of my instrument based on predicted disbursements on previously-approved loans, using data for Kenya. Kenya is a fairly typical country in my sample, in that financing from official creditors accounts for 3% of GDP and 15% of government spending on average over the period 1970-2010. The overall height of the bars in the graph display disbursements on loans from official creditors as a fraction of GDP. These vary considerably from year to year, ranging from lows around 1% of GDP to highs around 6% of GDP. The dark-shaded lower portion of each bar corresponds to the portion of disbursements in each year that is associated with loans approved in previous years, but not the current year. In most years, these disbursements on previously-approved loans account for a sizeable majority of total disbursements. However, there are a few years during which there are substantial disbursements on loans approved in the same year. Finally, the solid line shows predicted disbursements on previously-approved loans, which reflects the

> 12 The geographical regions are Sub-Saharan Africa, Middle East and North Africa, South Asia, East Asia and the Pacific, Europe and Central Asia, and Latin America and the Caribbean.  For the regional development banks listed in Table 1, I omit the geographical disaggregation.  Also, four major creditors (Saudi Arabia, United Kingdom, the USSR and Russia) have only a fairly small number of loans in each region/decade bin.  To avoid over-fitting my predicted disbursements measure for these creditors, I also omit the geographical disaggregation and compute typical disbursement profiles only by decade for these creditors.

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