combination of country-specific loan approval decisions from previous years with "typical" disbursement rates. I will use changes in this predicted disbursement series as an instrument for changes in government total spending.

The second major data requirement for this paper is data on government spending itself. My primary source for this is the total government expenditure series reported in the IMF's World Economic Outlook (WEO) database. While this is by far the most comprehensive single data source for government spending, its country-year coverage is nevertheless limited, particularly for low-income countries. Data on official creditor lending from DRS is available for 2804 country-years over the period 1970-2010 in my full regression sample. However, WEO data on government spending cover only 1732, or 62%, of these observations. To fill this gap, I substantially expand coverage of the government spending data by piecing together additional information from a variety of other published sources, including current and previous editions of the IMF's Government Finance Statistics, the African Development Indicators of the World Bank, and data published by the Fiscal Affairs Department of the International Monetary Fund.<sup>13</sup> The success of my identification strategy requires a strong correlation between fluctuations in government spending and fluctuations in predicted disbursements on loans from official creditors. This is unlikely to be the case in countries that do not rely significantly on official creditors as a source of financing for public spending. Accordingly, I restrict attention to those countries where disbursements on loans from official creditors are on average equal to at least one percent of GDP, averaging over the entire period 1970-2010. In addition, in order to have meaningful within-country time series variation for each country, I further restrict the sample to those countries that have at least 15 years of data on government spending.<sup>14</sup> This results in a core regression sample of 2804 country-year observations covering 102 countries listed in Table 2, and averaging 28 annual observations per country. Averaging

> 13 Specifically, I draw on the total government spending series reported in the dataset accompanying Clements, Gupta, and Nozaki (2011).   While the resulting merged dataset on government spending based on these various sources remains highly imperfect, it is important to keep in mind that the inevitable measurement error in government spending will not bias my estimates of the spending multiplier as long as it (plausibly) is uncorrelated with fluctuations in my instrument based on past loan approval decisions.  If this is the case, the only consequence of measurement error in government spending is to reduce the strength of my first-stage relationship between changes in spending and changes in predicted disbursements, and accordingly also the precision of my 2SLS estimates of the multiplier.

> 14 In addition, to prevent a relatively small number of extreme changes in output, government spending, and the predicted disbursement instrument from unduly influencing my estimates, I trim the sample at the first and 99th percentiles of the distributions of these three variables.

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