of heterogeneity in multipliers. I find some suggestive evidence that multipliers are larger in recessions, in countries that are relatively less exposed to international trade, and in countries with flexible exchange rate regimes.

This paper builds on my previous work in Kraay (2012), which exploited lags between the approval of and subsequent disbursements on individual World Bank projects to isolate a predetermined component of World Bank-financed public spending that could be used as an instrument to estimate government spending multipliers. Out of necessity, that paper focused on a small set of 29 mostly very poor countries where World Bank-financed spending is large relative to the size of the recipient economy, and over the period 1985-2009. In contrast, the DRS data used in this paper covers lending by virtually all multilateral and bilateral official creditors to all developing countries, and extends back to 1970. The combined disbursements on loans from all these creditors account for a much larger fraction of public spending than World Bank financing alone. This substantially strengthens identification compared to the previous paper, and moreover permits extending the analysis to a much larger set of 102 developing countries where lending from official creditors is an important source of financing for public spending.<sup>1</sup> This greater sample size in turn expands the relevance of the findings to a broader set of countries, and in addition makes it feasible to assess a variety of possible sources of heterogeneity in estimated multipliers, as is done in this paper.

My strategy of exploiting delays between loan approval decisions and the ultimate spending that they finance is also related to Leduc and Wilson (2012), who study the dynamic effects of federal highway spending in the United States. The nature of the projects in question, and the institutional environment in which they are financed, also give rise to long delays between the authorization of federally-financed highway spending, and the actual state-level spending itself. Their use of this lag structure is different from mine, however, in that they estimate responses to "surprises" in spending, measured as deviations in spending from what would have been predicted from past financing approvals. This strategy is appropriate in their context, given their emphasis on isolating responses to unanticipated spending shocks. In contrast, in my context I am concerned that such deviations of

> 1 In fact, restricting attention to the same set of 29 countries covered in Kraay (2012), the F-statistic from the firststage regression of changes in government spending on changes in predicted disbursements increases from 15 in the previous paper, to 28 in this paper.  Nevertheless, the point estimates of the multiplier I find  using this much larger dataset are remarkably similar to those based on World Bank-financed spending alone.  Using only World Bank project-level data to construct the instrument, I found a multiplier of 0.48 in the benchmark specification, while I obtain a multiplier much-more-precisely estimated multiplier of 0.61 using the DRS loan-level data in this paper.

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