metadata: authors: 0: author: Kraay,Aart C.
count: World
docna: 0: docna: Government spending multipliers in
            developing countries : evidence from lending by official creditors
docty: Policy Research Working Paper
theme: Public expenditure, financial management and procurement
majtheme: Public sector governance
subtopic: Conflict and Fragile States,Macro-Fiscal Policy,Consumption,Fiscal & Monetary Policy,Economic Adjustment and Lending,Public Sector Economics,Macroeconomic Management,Public Financial Management,Public Finance Decentralization and
            Poverty Reduction
teratopic: Public Sector Development,Urban Development,Finance and Financial Sector Development
abstracts: cdata!: This paper uses a novel loan-level
            dataset covering lending by official creditors to developing
            country governments to construct an instrument for public
            spending that can be used to estimate government spending
            multipliers. Loans from official creditors (primarily
            multilateral development banks and bilateral aid agencies)
            are a major source of financing for government spending in
            developing countries. These loans typically finance public
            spending projects that take several years to implement, with
            multiple disbursements linked to the stages of project
            implementation. The long disbursement periods for these
            loans imply that the bulk of government spending financed by
            official creditors in a given year reflects loan approval
            decisions made in many previous years, before current-year
            macroeconomic shocks are known. Loan-level commitment and
            disbursement transactions from the World Bank's Debtor
            Reporting System database are used to isolate a
            predetermined component of government spending associated
            with past loan approvals. This can be used as an instrument
            to estimate spending multipliers for a large sample of 102
            developing countries. The one-year government spending
            multiplier is reasonably-precisely estimated to be around
            0.4, and there is some suggestive evidence that multipliers
            are larger in recessions, in countries less exposed to
            international trade, and in countries with flexible exchange
            rate regimes.
repnme: repnme: Government spending multipliers in
            developing countries : evidence from lending by official creditors
display_title: Government spending multipliers in
            developing countries : evidence from lending by official creditors
listing_relative_url: /research/2012/06/16398361/government-spending-multipliers-developing-countries-evidence-lending-official-creditors
topicv3: Aid Dependence m1326279 1111,Public Finance Management m1326316 716,Government Expenditure m1326649 1112,Macroeconomic and Structural Policies
            m1327380 439,Military m1505651 3036
publishtoextweb_dt: 2012-06-01T00:00:00Z
historic_topic: Public Sector Development,Urban Development,Finance and Financial Sector Development
no_of_pages: 45
author_keys: 0: author_key: 123715
model_extractions: WPS6099 Policy Research Working Paper 6099 Government Spending Multipliers in Developing Countries Evidence from Lending by Official Creditors _Aart Kraay_ The World Bank Development Research Group Macroeconomics and Growth Team June 2012
Policy Research Working Paper 6099

# **Abstract**

his paper uses a novel loan-level dataset covering lending by official creditors to developing country governments to construct an instrument for public spending that can be used to estimate government spending multipliers. Loans from official creditors (primarily multilateral development banks and bilateral aid agencies) are a major source of financing for government spending in developing countries. These loans typically finance public spending projects that take several years to implement, with multiple disbursements linked to the stages of project implementation. The long disbursement periods for these loans imply that the bulk of government spending financed by official creditors in a given year reflects loan approval decisions made in many previous years, before current-year macroeconomic shocks are known. Loan-level commitment and disbursement transactions from the World Bank's Debtor Reporting System database are used to isolate a predetermined component of government spending associated with past loan approvals. This can be used as an instrument to estimate spending multipliers for a large sample of 102 developing countries. The one-year government spending multiplier is reasonably-precisely estimated to be around 0.4, and there is some suggestive evidence that multipliers are larger in recessions, in countries less exposed to international trade, and in countries with flexible exchange rate regimes.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at akraa@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# GOVERNMENT SPENDING MULTIPLIERS IN DEVELOPING COUNTRIES: EVIDENCE FROM LENDING BY OFFICIAL CREDITORS

Aart Kraay The World Bank

## **JEL Classification Codes:** E62, O23

**Keywords:** Government spending multipliers, fiscal policy

_____________________________________________________________________________________

> 1818 H Street NW, Washington, DC 20433, USA, akraay@worldbank.org.  I am grateful to Alexandra Jarotschkin for outstanding research assistance, to Luis Serven and seminar participants at the International Monetary Fund for helpful comments, and to Ibrahim Levent, Nanasamudd Chhim, Evis Rucaj, and Shelley Lai Fu for their guidance with the Debtor Reporting System database.  Financial support from the Knowledge for Change Program of the World Bank is gratefully acknowledged.  The views expressed here are the author's, and do not reflect those of the World Bank, its Executive Directors, or the countries they represent.
# **1.  Introduction**

Empirically estimating government spending multipliers requires the isolation of a source of variation in government spending that is likely to be uncorrelated with contemporaneous macroeconomic shocks. In this paper, I construct an instrument for fluctuations in government spending, drawing on a near-comprehensive dataset of about 60,000 individual loans from official creditors (primarily multilateral development banks and bilateral aid agencies) to developing country governments over the period 1970-2010, as recorded in the Debtor Reporting System database of the World Bank. My identification strategy exploits two key features of this data. First, for many developing country governments, loans from official creditors are a major source of financing for public spending. In my largest sample of 102 countries, disbursements on these loans account for 11 percent of government spending for the median country-year observation, while the 75th and 90th percentiles correspond to 19 and 28 percent, respectively. Second, rather than simply financing the difference between government expenditure and revenue in a given period, these loans typically are tied to specific multi-year spending projects, and accordingly disburse over a period of several years following the original commitment of the loan, with disbursements linked to stages of project implementation.

My core identifying assumption is that the decision to approve a loan in a given year, and to embark on the associated spending plans, is uncorrelated with shocks to growth occurring in subsequent years when the spending plans are implemented and the actual loan disbursements take place. If this identifying assumption holds, and if loan disbursements follow a schedule specified at the time of loan approval, then disbursements occurring in the years following loan approval will also be uncorrelated with contemporaneous macroeconomic shocks. Moreover, the long disbursement profiles observed on these loans imply that disbursements occurring in the years following loan approval are substantial: for the average loan in my dataset, only 22 percent of the original commitment is disbursed in the year that the loan is initially approved, and only a further 18 and 13 percent are disbursed in the first and second years following the approval year. The remaining nearly 50 percent of the loan is disbursed three or more years after loan approval. This in turn implies that the bulk of disbursements on loans from official creditors in a given country-year reflects loan approval decisions made in many previous years, and -- crucially -- before contemporaneous macroeconomic shocks are known.

An immediate concern with strategy is that, even though loan approvals are by definition made prior to the realization of macroeconomic shocks that occur during the subsequent disbursement 2
period, the size and timing of loan disbursements may be tailored in response to current events. For example, lenders might choose to accelerate disbursements on previously-approved loans to a country experiencing a natural disaster, as a way of rapidly delivering resources to the affected country. Or alternatively, lenders might suspend disbursements on existing loans in response to an adverse political event, such as the outbreak of civil conflict, that disrupts the implementation of the associated spending plans. In either case, this would undermine my identification strategy by creating a correlation between contemporaneous macroeconomic shocks and actual disbursements on previously-approved loans. To circumvent this problem, I construct an artificial predicted disbursements series for each loan, based on the observed average disbursement rates for other loans from the same creditor approved in the same decade, and extended to countries in the same geographical region. Conditional on my identifying assumption that loan approvals are independent of future macroeconomic shocks, these artificial loanlevel predicted disbursements in the years following loan approval, and their aggregation to the countryyear level, are by construction independent of contemporaneous country-specific macroeconomic shocks.

I am interested in estimating overall government spending multipliers, and not simply the shortrun effects of spending projects financed by official creditors. This distinction is important given potential concerns about the fungibility of the latter. Specifically, it is possible that increases in government spending associated with official creditor-financed projects might very well lead to changes in the level and composition of other forms government spending. To address this concern, I use fluctuations in predicted disbursements on previously-approved loans as an instrument for changes in total government spending. As a result, any responses of other forms of government spending to official creditor-finance spending will be subsumed into the first-stage relationship between my instrument and changes in total government spending.

I apply this methodology in a sample of 102 developing countries where loans from official creditors are an important source of financing for government spending. I find baseline estimates of the one-year government spending multiplier of around 0.4, i.e. a dollar of additional government spending raises GDP in the same year by about 40 cents. I subject these basic results to a battery of robustness checks designed to address potential concerns with data quality, as well as possible objections to the identifying assumption. While the estimates of the one-year multiplier vary somewhat across these checks, they typically remain in a range from around 0.3 to 0.5. The large cross-sectional dimension of my dataset also makes it feasible to investigate the empirical relevance of a number of potential sources 3
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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spending from predictions based on past approvals are likely to be endogenous responses to macroeconomic shocks in the borrowing country. For example, as discussed above, an official creditor might suspend disbursements on previously-approved loans in response to negative shocks in the borrowing country that disrupt the implementation of the projects being financed by the loans. In this case, unexpectedly-low disbursements and associated government spending (relative to initial plans) would be an endogenous response to the contemporaneous negative shocks. Instead, I rely on predicted disbursements on previously-approved loans as a strategy for excluding this potentiallyendogenous component of fluctuations in actual disbursements. This approach of course does not permit the identification of output responses to unanticipated spending shocks, an issue to which I return in Section 5 of the paper.

This paper contributes to a rapidly-expanding empirical literature on identifying the short-run output effects of government spending, nearly all of which is focused on developed countries, most notably the United States. One strand of this literature has followed the seminal contribution of Barro (1981), who observed that fluctuations in defense spending are an important source of fluctuations in total government spending in the United States, and are driven primarily by geopolitical factors rather than domestic macroeconomic shocks. As a result, they can be viewed as a plausibly exogenous source of variation in government spending that can be used to estimate spending multipliers.<sup>2</sup> Another strand of this literature has followed Blanchard and Perotti (2002) in assuming that discretionary fiscal policy changes take sufficiently long to implement that they cannot respond to macroeconomic shocks during the same quarter.<sup>3</sup> This assumption permits the identification of VAR-based estimates of 2 Other papers extending this basic insight include Ramey and Shapiro (1998), Hall (2009), Fisher and Peters (2010), Ramey (2011b), and Barro and Redlick (2011). A shared drawback of these military spending-based studies is that it is difficult to control for the macroeconomic effects of other key features of wartime economies, such as price controls or mandatory military service. Moreover, this approach to identification is applicable only to the United States, where the conflicts associated with the spending increases occurred outside the United States, so that there were no direct effects of wartime destruction on the US economy. Nakamura and Steinsson (2011) also focus on military spending in the United States, but exploit cross-state variation in the intensity of defense spending. This approach is shared with Giavazzi and McMahon (2012) who investigate heterogeneity across households in the response to these defense spending shocks. These papers are based on a weaker identifying assumption that military spending buildups are unrelated to _differences_ in macroeconomic conditions across US states.

3 Notable recent contributions along these lines include Auerbach and Gorodnichenko (2012a, 2012b) and Ilzetzki, Mendoza and Vegh (2010). These studies also examine heterogeneity in multipliers, with the former emphasizing the state of the business cycle, and the latter a range of factors such as the exchange rate regime and trade openness, as I do in this paper as well.

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spending multipliers in those countries where high-frequency macroeconomic and fiscal data are available.<sup>4</sup> A third strand of the literature has proposed a variety of creative instruments to isolate a plausibly exogenous component of changes in government spending, primarily focusing on subnational government spending in the United States. Papers such as Cohen, Covall and Malloy (2010) and Fishback and Kachanovskaya (2010) have exploited political determinants of federal transfers to states, while Chodorow-Reich, Feiveson, Liscow and Woolston (2011), Serrato and Wingender (2011), and Wilson (2011) emphasize particular institutional features driving federal-state transfers that are likely to be orthogonal to state-level economic activity. Clemens and Miran (2010) and Shoag (2011) study fluctuations in state-level spending driven by variations in the stringency of balanced-budget rules, and pension fund windfalls, respectively.

Finally, two important caveats about these results are worth noting at the outset. The first is that the empirical spending multipliers I estimate are by no means deep structural parameters. As is well-known from a large body of theoretical work, the short-run effects of government spending on output depend on a host of factors including technology, preferences, the nature of spending, the associated burden of current and future taxes, the stance of monetary policy, and a range of other country- and episode-specific characteristics. In light of this, the multipliers that I estimate are best understood as a description of the short-run average empirical relationship between changes in a plausibly predetermined component of government spending and changes in output. This caveat is of course shared with the bulk of the existing empirical literature on the short-run effects of government spending. The second caveat is that, while my identification strategy relies on lending by official creditors, which frequently is a vehicle for foreign aid delivery, the resulting evidence should not be interpreted as contributing to the long-standing empirical debate on the growth effects of aid. In contrast with the aid-growth literature, which has primarily been concerned with the medium- to longrun effects of aid on growth, my interest in this paper is in the short-run cyclical effects of increased

> 4 A key practical difficulty with implementing this approach in developing countries is the relative scarcity of highfrequency data in these countries.  In a notable effort to fill this gap, Ilzetzki, Mendoza and Vegh (2010) assemble quarterly data for a sample of 20 developed and 24 emerging markets, and use this to implement BlanchardPerotti (2002)-style estimates of spending multipliers.  There are however only 11 countries in common between their emerging-market sample and the sample of 102 low- and middle-income countries used in this paper. The countries included in their 24 country sample but not mine are primarily richer emerging-market economies that rely little on borrowing from official creditors.  Conversely, my paper covers 91 developing countries not covered in Ilzetzki, Mendoza and Vegh (2010), where this source of financing of government spending is important.

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government spending on output. These short-run responses of output to government spending are potentially consistent with a variety of longer-run responses of output to aid.

The rest of this paper proceeds as follows. Section 2 sets out the empirical methodology and identification strategy, and Section 3 describes the construction of the instrument based on loan-level data from the DRS database. Section 4 contains my core results, and Section 5 subjects them to a variety of robustness checks designed to explore the plausibility of the identifying assumption. Section 6 investigates a number of potential sources of heterogeneity in multipliers, and Section 7 concludes.

# **2.  Empirical Strategy**

I estimate variants on this simple empirical specification to assess the short-run effects of government spending on output: (1) Here, and denote GDP and total government spending in country and year , both measured in constant local currency units; and the composite error term denotes all other sources of GDP fluctuations, such as other fiscal or monetary policy changes, terms of trade shocks, changes in productivity, natural disasters, and many other shocks. I sweep out the country-specific and yearspecific components of the error term, and , by including a full set of country and year effects in all specifications. The key parameter of interest is , which captures the short-run government spending multiplier, i.e. the contemporaneous change in output due to a change in government spending. As noted in the introduction, cannot be interpreted as a deep structural parameter. Rather, it should simply be thought of as a reduced-form empirical summary of the contemporaneous relationship between annual fluctuations in government spending and output.

The standard difficulty in statistically identifying is that changes in government spending are likely to be correlated with other contemporaneous shocks to output captured in the error term, so that OLS estimation of Equation (1) will be inconsistent. For example, if government spending increases endogenously in response to an economic downturn, perhaps due to the role of automatic stabilizers, then OLS estimates of the multiplier would be biased downwards. On the other hand, if government 7
spending is procyclical and falls with the realization of negative macroeconomic shocks, perhaps due to an inability of governments to borrow during bad times, then OLS estimates of the multiplier would be biased upwards. A further possibility is that, in aid-dependent countries such as many of those studied here, any procyclical tendencies in domestically-financed government spending are offset by countercyclical tendencies in aid-financed government spending, so that total spending could be either procyclical or countercyclical.

I address this endogeneity problem by constructing an instrument based on the lags between commitment and eventual disbursements on loans by official creditors to developing country governments. Some institutional background is helpful in order to better understand this identification strategy. My dataset, described in more detail in Section 3, covers loans from multilateral and bilateral official creditors to developing country sovereign borrowers. Table 1 provides some summary statistics on the lending activities of official creditors included in my dataset. I first report total disbursements, disaggregated by major multilateral and bilateral creditors, for the decades of the 1970s, 1980s, 1990s, and 2000s. Disbursements on these loans are substantial, totaling nearly $1.8 trillion in constant 2005 prices over the past 40 years. Over time, the importance of multilateral creditors relative to bilateral creditors has increased substantially, with the share of the former increasing from about one-third in the 1970s to nearly three-quarters in the 2000s. This reflects a steady shift on the part of most bilateral creditors over the past 40 years to providing aid in the form of grants (which are not reflected in the DRS database), rather than loans (which are).

These loans are a traditional vehicle for aid donors to provide financial assistance to developing country governments. Consistent with this objective, the loans in this dataset are highly concessional on average, typically with long grace and repayment periods, as well as below-market interest rates. The bottom panel of Table 1 shows that, on a loan-weighted average basis, these loans have a maturity between 20 and 25 years, and an initial grace period (during which no payment is required) of approximately 6 years. The interest rates on these loans are also highly concessional, with nominal spreads over 20-year US Treasury Bill rates between approximately -2% and -5%. These simple spreads probably understate the concessional value of the loans to many recipient countries, given that the market rates they would otherwise face on international borrowing from private creditors are likely to be much higher, if not prohibitive.

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A key feature of these loans is that they typically are tied to specific public spending projects identified by the donor and the recipient government.<sup>5</sup> These projects might consist of infrastructure construction, health and education initiatives, public sector reform efforts, or any other of a wide variety of development projects supported by aid donors. Crucially for my purposes, such projects often take several years to implement, and the loan disbursements typically are tied to various stages in the implementation of the project that they are intended to finance. As a result, disbursements on the original loan commitment usually are spread out over multiple years following loan approval, rather than the loan disbursing in full at the time of loan approval. These long disbursement profiles in turn imply that, in any given country-year, aggregate disbursements on loans from official creditors consist primarily of disbursements on loans approved in many previous years, rather than the current year.

To construct my instrument, for each country-year I first isolate disbursements on loans approved in previous years, but not the current year. In order for this to be a valid instrument for fluctuations in total government spending, it must be the case that (a) loan approval decisions do not anticipate future macroeconomic shocks, and (b) disbursements on previously-approved loans also do not respond to contemporaneous macroeconomic shocks. While (a) is plausible given the timing of events, with project and loan approvals occurring before the realization of future macroeconomic shocks, (b) is much less plausible because the decision to disburse a portion of a loan is made in real time, and may very well respond to contemporaneous shocks. For example, creditors may suspend disbursements on previously-committed loans to a country falling into a civil conflict that disrupts the implementation of the associated projects. Conversely, creditors might choose to accelerate disbursements on previously-committed loans as a way of quickly delivering additional resources to a country experiencing an adverse shock. Either of these possibilities would lead to a correlation between actual disbursements on previously-approved loans and contemporaneous macroeconomic shocks.

In order to circumvent this problem, I replace actual disbursements on previously-approved loans with predicted disbursements, based on typical disbursement profiles for similar loans. In particular, I construct loan-level predicted disbursement series by applying to each initial loan commitment the average disbursement profile across all other loans issued by the same creditor in the 5 For example, World Bank loan agreements typically contain several pages of text describing the specific project the loan is intended to finance, conditions for monitoring the implementation of the project, and guidelines for procurement and disbursement. These loan agreements also contain a standard clause specifically committing the borrower to the project, along the lines of _"The Borrower declares its commitment to the objectives of the Project and the Program. To this end, the Borrower shall carry out the Project .... in accordance with the provisions of Article V of the General Conditions."._ This language is found in Article III of standard World Bank loan agreements.

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same decade to all countries in the same geographical region as the actual borrower. I then construct my instrument by aggregating these predicted loan-level disbursements on previously-approved loans to the country-year level. By construction, aggregate predicted disbursements reflect only the combination of country-specific loan approval decisions from previous years with typical disbursement profiles, based on averages taken across many loans to many countries. My identifying assumption is that these loan approval decisions do not anticipate future shocks to growth, and under this assumption, changes in aggregate predicted disbursements will be uncorrelated with the error term in Equation (1). I can therefore use changes in predicted disbursements as an instrument for changes in total government spending when estimating the government spending multiplier based on Equation (1).

# **3.  Data**

I work with loan-level data drawn from the Debtor Reporting System (DRS) database maintained by the World Bank. The DRS database contains information on loan commitments, terms, disbursements, and repayments, for all external loans contracted or guaranteed by the government in the borrowing country, beginning in 1970. The DRS data are, in principle, comprehensive in their coverage of all individual external public and publicly-guaranteed debt obligations, from all creditors, and for all countries that borrow from the World Bank. This is because annual reporting to DRS is mandatory for World Bank clients: a country must be in good standing with respect to these reporting requirements in order for new projects for that country to be considered by the Board of Directors of the World Bank.<sup>6</sup> Countries are required to report basic information on the amount, terms and purpose of new commitments, drawings and repayments on existing loans, and details of loan restructurings when applicable.<sup>7</sup> Loan-level transactions reported in DRS are confidential. However, the aggregation of this loan-level data to the country-year level provides the basis for country-level debt data published by the World Bank in its annual Global Development Finance publication.<sup>8</sup>

> 6 See the World Bank's Operational Manual, BP14.10, available at: http://web.worldbank.org/WBSITE/EXTERNAL/PROJECTS/EXTPOLICIES/EXTOPMANUAL/0,,contentMDK:20064540~ menuPK:4564187~pagePK:64709096~piPK:64709108~theSitePK:502184,00.html

> 7  Details on reporting requirements can be found in the World Bank Debtor Reporting System Manual, available at <u>http://siteresources.worldbank.org/DATASTATISTICS/Resources/drs_manual.doc.  These loan-level transactions</u> are typically provided as paper records or in spreadsheet format, and staff in the Development Data Group of the World Bank enter it manually into DRS.

> 8 To my knowledge, the loan-level data in DRS has been used only in a handful of previous scholarly papers, all of which are focused in one way or another on alternative characterizations of the financial value of concessional loans (Chang, Fernandez-Arias, and Serven (2002), Dikhanov (2004), and Dias, Richmond, and Wright (2011)).

10
I rely on commitment and disbursement transactions on loans extended by official creditors to developing countries, as recorded in the DRS database. Official creditors include a range of multilateral lenders such as the World Bank, the African Development Bank, the Asian Development Bank, and the European Investment Bank. My dataset also includes loans issued by bilateral official creditors. The majority of these are major OECD aid donors such as Japan, Germany and the United States, but the dataset also includes a number of non-OECD creditors such as Kuwait, Saudi Arabia, Russia, and the former Soviet Union (in the earlier half of my sample).<sup>9</sup> The dataset, retrieved from DRS in January 2012, contains 60,192 loans issued by 188 distinct creditor countries and organizations. A large number of creditors represented in DRS account for only a handful of loans each, and usually for very small amounts. I discard a total of 768 loans issued by 113 creditors who have fewer than 50 loans each in the DRS data (and on average fewer than seven loans each), leaving a total of 59,424 loans issued by 75 distinct major official creditors. Loan commitments are reported in the currency of origination of the loan, and subsequent disbursements are recorded in DRS in current US dollars. I discard a further 36 loans for which data on the exchange rate used to convert the disbursements denominated in the currency of origination into US dollar could not be retrieved from DRS. This reduces the sample further to 59,388 loans.

The first step in the development of my instrument is to construct a disbursement profile for each loan, i.e. the fraction of the original loan commitment that is disbursed in the commitment year and each subsequent year. I calculate these by converting the US dollar disbursements on each loan back to the currency of denomination of the original commitment, using the corresponding disbursement-year exchange rates, and then express this as a fraction of the original commitment. Roughly 10 percent of loans have accumulated disbursements greater than initial commitments. This typically reflects increases in the loan amount that occur at some point during the disbursement period, but that are not recorded in the original commitment. Because these revisions in loan size are potentially endogenous responses to contemporaneous shocks, I use only the original commitment and

> 9 Lending by official creditors to governments is formally defined follows _" Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans).  Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies."_ (data.worldbank.org). From this total, I exclude IMF credits, as these typically take the form of budget support as opposed to financing specific projects, and typically also are approved for strongly cyclical reasons, i.e. in response to macroeconomic crises in the borrowing countries.

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not the subsequent increases. For a small number of loans, reported total disbursements exceed initial commitments by several multiples. To avoid data entry errors that may be responsible for such implausibly high disbursements relative to original commitments, I drop a further 194 loans for which accumulated disbursements are more than five times the initial commitment amount.<sup>10</sup> As a final step, I discard 10 loans that are implausibly large relative to recipient country GDP but have very low disbursement rates, again because these possibly reflect data entry errors. This results in a sample of 59,184 loans on which my results are based.<sup>11</sup> As noted earlier, a key feature of these loans from official creditors is that disbursements are typically spread out over several years following the loan commitment. This is apparent from Figure 1, which reports typical disbursement profiles, i.e. the fraction of the initial loan commitment that is disbursed in year zero (i.e. the year the loan was approved) and the ten subsequent years. The top panel reports the simple average disbursement profile, averaging across all loans in my dataset, while the bottom panel reports disbursement profiles separately for loans issued by bilateral and multilateral lenders. Taking all loans together, on average only 22% of the initial loan commitment is disbursed in the year the loan is approved, and only another 18% in the next year, with the remaining 60% spread out over subsequent years. Disbursement profiles are even more strongly backloaded for multilateral creditors than for bilaterals. The average multilateral loan disburses only 13% of the original commitment in the approval year, while for the average bilateral loan the figure is 29%. These long disbursement profiles in turn imply that actual aggregate disbursements on loans from official creditors in a given country-year are largely associated with past loan commitments, and not loans approved in the current year. In the median country-year observation in my full sample of 102 countries over the period 1970-2010, 89% of disbursements are associated with loans approved in previous years. For the 25th and 75th percentiles, the corresponding figures are 72% and 99%.

Nevertheless, as discussed in the previous section, even these disbursements on previouslyapproved loans might still be endogenous responses to contemporaneous shocks, to the extent that the disbursement decision in the current year reflects current shocks rather than being predetermined at

> 10 Anecdotally, another potential explanation for this pattern is that occasionally loans take the form of revolving credit lines that can be drawn upon and paid down multiple times.  In this case the maximum loan amount is recorded as the loan commitment, and subsequent multiple drawings are recorded as disbursements and can easily exceed the recorded commitment amount.  Unfortunately, however, the DRS database does not systematically identify such credit lines.

> 11 This strategy is conservative in the sense that if I exclude valid loans using these criteria, this will only weaken my first-stage relationship between changes in predicted disbursements and changes in government spending.

12
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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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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across countries, disbursements on loans from official creditors account for 13.4 percent of government spending, and range from a low of 3.1 percent in Latvia to a high of 37.6 percent in The Gambia.

In the empirical work that follows, I will also consider two subsamples, corresponding to (a) countries that are more reliant on official creditor financing, and (b) countries that are poorer. I define the former as the set of 70 countries for which disbursements on loans from official creditors exceed 10% of government spending (as opposed to 3% for the full sample), and the latter as a set of 60 countries that are currently eligible for concessional lending from the World Bank-administered International Development Association (IDA, indicated in bold in Table 2).<sup>15</sup> In these two subsamples, disbursements on loans from official creditors are substantially higher than in the full sample, averaging 16.7 and 16.2 percent of government spending, respectively. Not surprisingly, the first-stage relationship between fluctuations in predicted disbursements and government spending will be stronger in these sub-samples.

Table 3 reports summary statistics on fluctuations in real GDP, government spending, and actual and predicted disbursements, in the three samples of countries. All variables are expressed as constant price annual changes, scaled by lagged GDP (as defined in Equation (1)). In addition, I remove countryand year-specific means before calculating summary statistics, in order to be consistent with the empirical specifications that follow, all of which will also include a full set of country and year dummies. Real GDP growth and changes in government spending are quite volatile, with standard deviations of 4.0 and 3.3 percent, respectively, in the full sample, and of similar magnitudes in the two subsamples. Actual disbursements on loans from official creditors are also quite volatile, with standard deviations around 2 percent in the three samples. Naturally, my instrument based on predicted disbursements is less volatile than actual disbursements, with standard deviations of around 0.6 to 0.7 percent of GDP, but it nevertheless exhibits substantial variation. Fluctuations in predicted disbursements are correlated with fluctuations in government spending, and much more strongly so in the two subsamples of countries. The strength of this first-stage relationship will of course be crucial to the success of my identification strategy.

> 15 IDA eligibility depends on a country's GDP per capita falling below a given threshold, equal to $1,175 US at market exchange rates as of 2012.  A further eight countries with higher per capita GDP are nevertheless IDAeligible under the "small island economies exception" (Kiribati, Cape Verde, Tonga, Vanuatu, Dominica, Grenada, Saint Lucia, and Saint Vincent).  I exclude these countries from the IDA sub-sample used in this paper.

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# **4.  Benchmark Estimates of the Government Spending Multiplier**

Table 4 reports benchmark estimates of the government spending multiplier based on Equation (1). The three panels of the table report the ordinary least squares (OLS), two-stage least squares (2SLS), and first-stage regressions, while the three columns refer to the three country samples discussed in the previous section. In addition, Figure 3 displays the scatterplots corresponding to the first-stage and second-stage regressions, partialling out the country and year fixed effects. The OLS estimates of the multiplier are quite similar across samples, ranging from 0.26 to 0.31, and are very precisely estimated, with standard errors ranging from 0.04 to 0.05. As discussed above, however, these OLS estimates are likely to be biased to the extent that fluctuations in government spending are correlated with other shocks to GDP growth that are reflected in the error term. The 2SLS estimates in Panel B, which are designed to correct for such biases, are somewhat larger than the OLS estimates, ranging from 0.38 to 0.42. They are also fairly precisely estimated, with standard errors between 0.20 and 0.25.<sup>16,17,18</sup> While the estimated multipliers are significantly greater than zero in the IDA and highdisbursement samples, in all three specifications I can reject the null hypothesis that the multiplier is equal to one at the 5 percent level. A further noteworthy feature of these benchmark results is that in all cases the 2SLS estimates of the multiplier are larger than the OLS estimates, suggesting that the latter are biased downwards. This may reflect a combination of (a) attenuation bias in the OLS estimates due 16 These estimated standard errors for spending multipliers are respectable when compared with other papers in the literature. For example, Barro and Redlick (2011) use US data over the past century to estimate defense spending multipliers, and obtain standard errors ranging from 0.06 to 0.27 (their Table 2, first row). Similarly, the confidence bands around VAR-based impulse responses reported in Figure 5 of Blanchard and Perotti (2002) imply a standard error for the impact multiplier of 0.35.

17 The predicted disbursements measure is a generated instrument (consisting of actual loan commitments multiplied by estimated average disbursement rates). However, this does not matter for the asymptotic distribution of the 2SLS estimator as long as actual loan approvals in year t are not correlated with macroeconomic shocks in year t+1 and higher, as per my core identifying assumption. See Wooldridge (2002) Chapter 6.1.2. 18 One notable assumption underlying these estimates and standard errors is that cross-sectional dependence in the error term is adequately captured by year fixed effects. This embodies the simple but unappealing assumption that common shocks have the same effect on all countries, and can be swept out using year dummies, as I do in the default specification. As a robustness check, I relax this assumption in two ways: 1) I implement the estimator based on the cross-sectional averages of moment conditions which is asymptotically valid as under very general cross-sectional dependence, as proposed by Driscoll and Kraay (1998), and 2) I implement the correlated common effects estimator of Pesaran (2006) and extended to the 2SLS setting by Harding and Lamarche (2011), which is asymptotically valid as under the assumption that the cross-sectional dependence can be captured by a very general unobserved factor structure. The first option delivers slightly smaller estimates of the multiplier ranging from 0.22 to 0.29, with standard errors ranging from 0.17 to 0.29, while the second option delivers slightly larger multipliers around 0.63 with estimated standard errors ranging from 0.18 to 0.23.

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to measurement error in the government spending, as well as (b) a countercyclical response of overall government spending to macroeconomic shocks.<sup>19</sup> In Panel C of Table 4, I report the corresponding first-stage regressions for the three country samples. The first-stage relationship between changes in government spending and changes in predicted disbursements is quite precisely estimated, with first-stage F-statistics greater than the Staiger and Stock (1997) rule of thumb of 10 in all three samples. Not surprisingly, the first-stage relationship is also much stronger in the second and third columns, in which the first-stage F-statistics are 28.1 and 22.2, respectively. This reflects the fact that lending from official creditors is a relatively more important source of financing for government spending in these more aid-dependent countries, and so the fluctuations in the predetermined component of this spending captured by my instrument have greater explanatory power for fluctuations in overall government spending. As would be expected given the strength of the instrument, the weak-instrument consistent 95% confidence intervals reported in Panel B are quite similar to ones based on the usual asymptotic normal approximation.

In order to better understand the source of identification underlying my benchmark results, Table 5 reports a series of 2SLS estimates of the multiplier for several alternative versions of the instrument. The three columns refer to the three country samples, which are fixed across these alternatives, and so the corresponding OLS regressions are the same as those reported in the top panel of Table 4, and are not repeated here. The first variant corresponds to constructing the instrument by aggregating predicted disbursements on loans extended by multilateral creditors only, while the second variant corresponds to a version of the instrument based on loans extended by bilateral creditors only. The difference between the two sets of results is stark. The strength of identification, as measured by the first-stage F-statistics, is much higher in the results based on multilateral predicted disbursements than for bilateral predicted disbursements. The first-stage F-statistics in the first panel are similar to those in Table 4 and range from 11.7 to 23.6. In contrast, the first-stage F-statistics are below 10 in all three samples when the instrument is based on predicted disbursements on loans from bilateral creditors.

> 19 Absent direct information on the signal-to-noise ratio in fluctuations in government spending, it is not possible to distinguish between these two possible explanations.  Since the OLS estimates are roughly 75 percent of the IV estimates, standard textbook calculations suggest that the signal-to-noise ratio in government spending would have to be about three in order to account for the gap between the two estimates.   If measurement error is less (more) extreme than this benchmark, then government spending would also have to be countercyclical (procyclical) in order to explain the differences between the OLS and IV estimates.

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The reasons for these differences are straightforward. First, as shown in Table 1, loans from multilateral creditors account for the majority of total disbursements in my sample, averaging 59 percent over the whole sample, and substantially more in recent years. Second, as is apparent from Figure 1, disbursements on loans from multilateral creditors are on average substantially more backloaded than disbursement on loans from bilateral creditors. The first observation implies that fluctuations in disbursements on loans from official creditors are a relatively more important source of variation in government spending in my sample of developing countries. The second observation implies that predicted disbursements on loans approved in previous years are larger in the case of loans from official creditors. Together, these two factors contribute to a much stronger first-stage relationship between changes in government spending and changes in predicted disbursements. The weak identification based on loans from bilateral creditors is reflected in much more imprecise estimates of multipliers when only this source of exogenous variation is used. In contrast, the estimates of the multiplier identified from fluctuations in predicted disbursements from multilateral creditors are much more precisely estimated, and moreover are somewhat larger than those reported in Table 4, ranging from 0.43 to 0.61.<sup>20</sup> Overall, this shows that much of the identification of my benchmark estimates comes from the strong first-stage relationship between government spending and predicted disbursements on loans from multilateral creditors.

The third set of results in Table 5 addresses the possibility of over-fitting the loan-level predicted disbursements series. Recall that loan-level predicted disbursements are based on average disbursement rates calculated within 443 creditor/decade/region bins. A potential concern is that the more disaggregated are these bins, the closer predicted disbursements will be to actual disbursements. To take an extreme case, if there were just one loan per bin, then predicted and actual disbursements would coincide. Less extreme versions of this argument would suggest that, the more disaggregated are the bins on which predicted disbursements are based, the less effective are predicted disbursements in purging the endogenous component of actual disbursements. To address this concern, I construct an alternative extreme version of the instrument, based on combining all loans from all creditors into a single bin, i.e. applying the overall average disbursement profile shown in the top panel of Figure 1 to all

> 20 While these differences are small relative to the estimated standard errors and should not be over-interpreted, one possible explanation for this difference in magnitude is that loan approvals by bilateral donors are more likely to anticipate future negative shocks to growth than loan approvals by multilaterals.  If this potential violation of the exclusion restriction were important for the component of the instrument based on bilateral lenders, it would imply a downward bias in the 2SLS estimates of the multiplier, that is corrected when bilateral lending is removed from the instrument.

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of the loans in my dataset. Naturally, doing so leads to a somewhat weaker first-stage relationship between changes in government spending and changes in predicted disbursements, with first-stage F- statistics ranging from 9.8 to 19.5 (as opposed to 12.6 to 28.1 using the default instrument). However, the first-stage fit remains respectable, and the point estimates of the multiplier change only slightly to around 0.5 as compared with around 0.4 in the default specification. This robustness check suggests that over-fitting of the predicted disbursement instrument is not a major concern in my benchmark results.

Another possible objection to the predicted disbursements instrument is it indirectly includes some information on future country-specific shocks, as it is based on typical disbursement rates averaging across all loans within the creditor/decade/region bins, including future loans to the country in question. This potential violation of the exclusion restriction is unlikely to be very important given the large number of loans within each bin (recall that the median bin includes 65 loans). However, as a further robustness check, I reconstruct the instrument, but now excluding all other loans to the country in question when calculating average disbursement rates. This eliminates any potential country-specific information in the predicted disbursement instrument that comes through the inclusion of the country in question in the calculation of average disbursement rates. The fourth set of results in Table 5 show that this robustness check has only minimal effects on my benchmark estimates. The first-stage F- statistics are actually slightly higher than in the benchmark results in Table 4, and the estimates of the multiplier are slightly smaller (ranging from 0.33 to 0.37).

In summary, the benchmark results in this section suggest that the one-year government spending multiplier is in the vicinity of 0.4, and moreover is reasonably precisely estimated. Specifically, I find that the multiplier is in most cases significantly different from zero and also significantly less than one. The statistical identification of these multipliers comes primarily from a strong first-stage relationship between fluctuations in the predetermined component of disbursements on loans from multilateral, as opposed to bilateral, creditors. These findings are robust to variants on the instrument designed to address possible concerns about over-fitting of predicted disbursements, and the possible incorporation of future information country-specific information in the calculation of typical disbursement profiles.

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# **5.  Further Robustness Checks**

I next address a variety of potential concerns about the robustness of the benchmark estimates of the multiplier presented in Table 4. Given the noisy and highly-imperfect data on government spending and output in many of the developing countries that comprise my sample, a first concern is that the results in Table 4 might be driven by a small number of influential observations. To investigate this possibility more systematically, I use a procedure suggested by Hadi (1992) to identify influential observations in the reduced-form and first-stage regressions (the ratio of the corresponding two slope coefficients being the 2SLS estimate of the multiplier). I then re-estimate the OLS, first-stage, and 2SLS regressions, excluding these influential observations. The results of this first robustness check are reported in the first three columns of Table 6. The OLS estimates of the multiplier change very little relative to the benchmark results. The 2SLS estimates of the multiplier are virtually unchanged once influential observations are removed, ranging from 0.37 to 0.39, and moreover they are slightly more precisely estimated than before. This is in part due to an even stronger first-stage relationship after removing influential observations in the IDA and high-disbursement samples, in which the first-stage F- statistics jump to 41.2 and 30.3, respectively.

A second potential concern is that I am estimating multipliers using data on total government _spending_ , whereas much of the theoretical and empirical literature on multipliers has focused on government _purchases_ , i.e. total spending less interest payments and net transfers. Unfortunately, data on the disaggregation of total government spending into purchases, interest payments, and net transfers is not available for the large set of developing country-year observations included in this paper. As a first partial step towards addressing this concern, I use readily-available data on interest payments on external public and publicly-guaranteed debt to net out this portion of interest expenditures from total government spending.<sup>21</sup> I then re-estimate the spending multipliers using this proxy for government non-interest expenditures. The results are presented in the second set of three columns in Table 6. Doing so weakens identification somewhat, particularly in the full sample, in which the firststage F-statistic now falls below the Staiger and Stock (1997) threshold of 10. However, in the remaining two samples, the first-stage F-statistics remain respectable at 19.9 and 16.0, respectively, and the point

> 21 Specifically, I use data on interest payments on public and publicly-guaranteed external debt as reported in the World Bank's Global Development Finance publication.  Aggregate interest payments from this source are based on loan-level transactions reported in the DRS database that I also use to construct my instrument.  This is only an imperfect adjustment, since subtracting interest payments on external debt from total government will not correct for interest payments on domestic debt, or net transfers.

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estimates of the multiplier increase only slightly relative to the benchmark estimates, ranging from 0.46 to 0.50.

A second way of partially addressing this concern is instead to rely on national accounts data on government consumption. This alternative spending measure excludes interest payments and transfers, and has the additional virtue of being much more readily-available than the data on government spending on which my benchmark results are based. However, the drawback of this measure is of course that it reflects government consumption expenditures only, and excludes government investment expenditures. I report results using this alternative measure of government spending in Columns (7)-(9) of Table 6. Unfortunately, using this measure of government spending results in much weaker identification, with first-stage F-statistics ranging from 6.5 to 11.0. This is only natural, to the extent that disbursements on loans from official creditors are disproportionately likely to finance public investment expenditures rather than consumption expenditures. Moreover, to the extent that government investment has a positive effect on output, the 2SLS estimates of the government consumption multiplier will be biased upwards, as the predicted disbursements instrument will be positively correlated with excluded public investment expenditures. Consistent with this observation, the resulting estimates of the multiplier are considerably higher than in the benchmark specification of Table 4, and range from 0.74 to 0.81. Naturally, they are also much more imprecisely estimated, with standard errors ranging from 0.40 to 0.54 (as compared with 0.20 to 0.25 in the benchmark specification).

A third potential concern with my results has to do with anticipation effects. I identify the government spending multiplier using fluctuations in predicted disbursements on loans from official creditors, which I have argued are plausibly uncorrelated with future macroeconomic shocks. At the same time, however, the spending plans set in motion at the time of loan commitment, as well as the associated burden of future taxes required to eventually repay the loan, are in principle both known at the time of loan approval. As stressed by Ramey (2011a), it is likely that private agents will respond to these anticipated future events at the time that the future spending plans are announced, rather than when the spending actually occurs. In particular, one should expect that the standard positive neoclassical labour supply response to the negative wealth effect of an increase in government spending should occur at the time that the spending plans are announced, and not when they are implemented.

In my context, one way to interpret this concern is as a potential violation of the exclusion restriction due to an omitted variable that is correlated with the instrument. If loan approvals are 21
serially correlated within countries over time, then predicted disbursements on previously-approved loans in a given year may be correlated with contemporaneous loan approvals in the same year, which themselves may have direct output effects. In principle, a straightforward way of addressing this concern is to control for the commitment of new loans. Doing so, however, is complicated by the same basic problem that motivates my identification strategy -- loan commitment decisions are potentially endogenous responses to contemporaneous macroeconomic events. As a result, I cannot simply include contemporaneous loan approvals as an additional regressor in Equation (1). Instead, it is necessary to somehow distinguish between loans that are committed for cyclical reasons and those that are not. In Kraay (2012), I developed a coding of World Bank projects according to their cyclical motivation based on a reading of project documentation. Not surprisingly, I found that projects approved for cyclical reasons also typically disbursed much more quickly than projects approved for other reasons. Applying the same reasoning in this context, it is plausible that loans that ultimately take many years to disburse are less likely to have been approved for cyclical reasons, whereas it is more likely that fast-disbursing loans are cyclically motivated.

Accordingly, I construct a variable containing the total value of new loan commitments as a fraction of GDP in a given country-year, restricting attention to loans that ultimately take four or more years to fully disburse.<sup>22</sup> Based on the discussion above, I assume that these loans are unlikely to have been approved for cyclical reasons, and so I can include this variable as an additional exogenous control variable in Equation (1). The results are shown in the last three columns of Table 6. This proxy for anticipation effects enters positively in all three samples, and significantly so in the full sample and IDA sample. Importantly, however, doing so does not appreciably weaken identification, and the 2SLS estimates of the multiplier fall only slightly relative to the benchmark estimates. Taken together these results suggest some evidence in favour of the hypothesis that output responds to loan approvals, but that this channel has little effect on the estimates of the contemporaneous effects of government spending when the spending eventually occurs.

Another set of concerns has to do with further potential objections to my core identifying assumption that loan commitment decisions are uncorrelated with future macroeconomic shocks. A first and basic possibility is that, while loan commitments are made before subsequent shocks are realized, these shocks may be persistent, or otherwise predictable in some way. If, in addition, loan

> 22 This is the same threshold used in Kraay (2012) for World Bank-financed projects.  I obtain similar results considering loans that require at least three or at least five years to fully disburse.

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commitments are correlated with contemporaneous shocks, then they will also be correlated with future shocks, in violation of my exclusion restriction. The most straightforward way to address this possibility is to control for lagged growth in my core specification. I do this in the first set of three columns in Table 7. While lagged growth enters statistically significantly in all three samples, indicating some persistence in annual growth rates, the estimated multipliers change only minimally relative to the benchmark specification.<sup>23</sup> This suggests that, although growth rates are persistent over time in these samples of countries, loan approval decisions are not sufficiently correlated with contemporaneous shocks to growth so as to seriously undermine my identification strategy. While clearly not a test of my identification strategy, this observation does provide some support for the plausibility of my identifying assumption, as it suggests that loan approvals do not respond importantly even to contemporaneous growth shocks, let alone future ones.<sup>24</sup> A second potential concern is that the short-run stimulative effects of government spending take more than one year to become apparent in the data. If this is the case, then the failure to control for lagged government spending would invalidate my identifying assumption, since lagged government spending may in part be financed by the same loan commitments from previous years that are used to construct my instrument. This in turn would imply an upward bias in my benchmark 2SLS estimates of the one-year impact multiplier. The most straightforward solution to this potential problem is simply to control for lagged changes in government spending. I do this in the second set of three columns in Table 7, instrumenting for lagged government spending with the lagged change in predicted disbursements. Unfortunately, in this case the dynamic response of output to current and lagged changes in government spending is rather imprecisely estimated in the 2SLS specifications in Panel B. The StockYogo test indicates that I cannot reject the null of weak instrument biases sufficiently strong as to lead to size distortions of 20 percent or more across the three specifications. This suggests that the alreadylarge standard errors reported in the table need to be interpreted with considerable caution. Interestingly, the two-year cumulative effect, i.e. the sum of the estimated coefficients on current and lagged spending, ranges from 0.30 to 0.44, which is in the same range as the estimated one-year multiplier in my benchmark results from Table 4. Overall, however, my identification strategy is not

> 23 While there is some evidence that second (but not third) lags of growth are also significant in this specification, including them has little effect on the magnitude or significance of the estimated spending multipliers. 24 Another strategy for addressing this concern is to base the predicted disbursement instrument only on loans that plausibly are not cyclically-motivated.  Specifically, I construct a version of the predicted disbursement instrument, using only slow-disbursing loans that take four or more years to disburse.  Re-estimating the benchmark specification using this alternative version of the instrument delivers nearly identical results to those reported in Table 4.

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sufficiently powerful to be able to identify the dynamic profile of the response of output to government spending.

A third potential objection to the identifying assumption has to do with possibility that commitments and/or disbursements on loans trigger changes in policy performance that themselves have direct effects on economic growth. If, for example, the approval of a loan from an official creditor such as the World Bank is conditional on specific policy reforms that themselves might affect growth over the next several years, this would violate my exclusion restriction that loan commitments are uncorrelated with future shocks to growth. Another possibility might be that loan approvals are a response to policy reforms which themselves are persistent over time.<sup>25</sup> To the extent that the associated reforms are growth-enhancing, this would contribute to a positive correlation between my instrument and the error term in Equation (1), which would in turn imply an upward bias in my 2SLS estimates of the multiplier.

I investigate this possibility in columns (7)-(9) of Table 7, by controlling for contemporaneous changes in policy performance, as measured by the World Bank's Country Policy and Institutional Assessment (CPIA) ratings. These ratings have been produced annually since 1978 by World Bank country economists for all World Bank borrowers. The ratings are on a six-point scale, based on an underlying checklist of various policy areas, with higher values corresponding to better policy performance. Annual changes in policy performance are significantly positively correlated with growth in the full sample and high disbursements sample in the 2SLS specification. However, controlling for policy does not significantly change the 2SLS estimates multipliers relative to the benchmark specification, with estimates that fall between 0.29 and 0.44. In summary, while short-run changes in policy performance do appear to matter for short-run fluctuations in growth, these changes in policy performance are not very strongly correlated with past loan commitment decisions, and so do not significantly impact my estimates of the multiplier.<sup>26</sup> To sum up, in this section I have considered a range of alternative specifications designed to check the robustness of my results to a variety of potential objections to the validity of the exclusion

> 25 Note however that, since I rely on predicted rather than actual disbursements, I do not need to be concerned with the possibility that _disbursements_ on loans are triggered by policy reforms.  This is because any countryspecific variation in disbursement rates has been eliminated from the predicted disbursements instrument. 26 There is however some weak evidence that loan approvals might be correlated with lagged improvements in policy.  Adding the first lag of changes in the CPIA reduces the estimates of the multiplier somewhat relative to the benchmark, with 2SLS estimates ranging from 0.11 to 0.31 across the three samples (as compared with 0.38 to 0.42 in the benchmark specification).

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restriction. For the most part, these changes have only small effects on estimated multipliers, relative to the benchmark results of the previous section. Overall, they suggest that the one-year impact of an additional dollar of government spending is to raise GDP in the same year by somewhere between 0.3 and 0.5 dollars in most specifications.

# **6.  Heterogeneity in Estimated Multipliers**

As noted in the introduction, an important benefit of my data and identification strategy is that it is applicable to a very large set of developing countries that rely on loans from official creditors as an important source of financing for government spending. The large cross-sectional dimension of my dataset makes it feasible to empirically examine a variety of possible hypotheses regarding differences in spending multipliers across countries, and over time, as I do in this section.

A first potential source of heterogeneity in estimated multipliers is the state of the business cycle. A variety of theoretical mechanisms for spending multipliers imply larger short-run effects of government spending during economic downturns when there is greater slack in the economy. Consistent with this view, Auerbach and Gorodnichenko (2012a, 2012b) provide extensive evidence for the US and for OECD countries that multipliers are indeed smaller during booms and larger during recessions. To investigate this possibility in my sample of developing countries, I classify each countryyear observation in my sample as being in a boom or recession based on whether real GDP growth is above or below the corresponding country-decade average. I then estimate multipliers separately for booms and recessions. The results are shown in the first panel of Table 8.

Consistent with the theory, I find that estimated multipliers are much larger during recessions than during booms in all six specifications (OLS and 2SLS, for the three country samples). Particularly in the case of the 2SLS estimates, the differences are quite dramatic. During recessions, the estimated multipliers range from 0.61 to 0.81, while in booms the multipliers are between 0.01 and 0.15. Although these differences in point estimates are sufficiently large as to be economically meaningful, they are small relative to estimated standard errors. In none of the six cases do I find a statistically significant difference in the multipliers in booms versus recessions.<sup>27</sup> This in part reflects the rather weaker identification in several subsamples -- first-stage F-statistics are above 10 in only the last two columns, corresponding to booms in the IDA and high disbursement subsamples. Qualitatively, at least, this

> 27 I assess the statistical significance of these estimated differences in multipliers by estimating an equivalent specification in the full sample that includes a dummy variable indicating the two groups, and its interaction with the change government spending.  I then test the significance of this interaction term.

25
evidence is broadly consistent with the view that there is a greater scope for spending increases to stimulate economic activity during recessions rather than during booms.

A second potential source of heterogeneity in estimated multipliers has to do with the extent of trade openness of the country. For example, simple open-economy Keynesian models imply that when the marginal propensity to import is high, a fraction of the increase in income due to an increase in government spending "leaks" into imports, reducing the multiplier. To consider this possibility, I calculate the average trade share of GDP for each country-decade, and then divide my sample in half at the median country-decade-average trade ratio. The estimated multipliers in the two subsamples are reported Panel B of Table 8. Qualitatively, the results here are again unambiguous -- in all six specifications the estimated multiplier is larger in the more closed half of the sample. For the 2SLS estimates, the differences in estimated multipliers are also fairly large: in the more closed subsample, the multipliers range from 0.57 to 0.71 and are statistically significantly different from zero, while in the more open subsamples the multipliers vary between 0.12 and 0.18 and are insignificantly different from zero. While identification is respectable in several of the subsamples, these differences in estimated multipliers are however small relative to estimated standard errors, and are again not statistically significant.

Yet another potential source of heterogeneity has to do with the exchange rate regime. A textbook implication of the basic Mundell-Fleming model is that increases in government spending are more expansionary under a flexible-exchange rate regime, as the resulting depreciation of the exchange rate has an additional expansionary effect (at least when capital mobility is limited, which seems a plausible benchmark for this set of developing countries). In Panel C of Table 8, I use the Ilzetzki, Reinhart and Rogoff (2008) classification of exchange rate regimes to identify country-year observations corresponding to flexible and fixed exchange rates.<sup>28</sup> Qualitatively, the findings are again consistent with this basic theory, with estimated multipliers that are larger in the flexible exchange rate regime in five out of six specifications. In this case, however, the magnitude of the differences is less stark: in the flexible exchange rate regime the 2SLS estimates of the multiplier vary from 0.32 to 0.48 across country samples, while in the fixed exchange rate group the multipliers range from 0.19 to 0.45.<sup>29</sup>

> 28 I classify the exchange rate regime as fixed where the IRR "coarse" measure is equal to 1 or 2, corresponding to fixed or slowly-crawling pegs, and flexible otherwise. .

> 29 Interestingly, this finding is the opposite of Ilzetzki, Mendoza and Vegh (2011) who work with a set of developed and emerging market economies where financial openness is likely to be greater than in my developing country

26
Finally, in Panel D of Table 8 I revisit a potential source of heterogeneity in multipliers discussed in Kraay (2012), based on cross-country differences in the extent to which government spending is aidfinanced. An important component of the neoclassical mechanism for a positive government spending multiplier operates through wealth effects: when spending increases, private agents are poorer by the present value of the current and future tax obligations required to finance the increase in spending. According to this mechanism, private agents react by consuming less, investing more, and providing more labour input, so that output increases. The key point here is that the magnitude of this wealth effect depends on the size of the burden of current and future tax obligations associated with the increase in government spending. This burden is smaller in countries where a greater proportion of government spending is financed by aid flows from abroad that do not need to be repaid, and as a result, the neoclassical mechanism predicts a smaller spending multiplier in more aid-dependent countries.

To investigate this possibility in this large set of developing countries, I divide each sample in half at the median level of official development assistance (ODA) as a fraction of GDP, and then estimate spending multipliers separately in the two subsamples. The OLS estimates of the multiplier display the pattern suggested by the theory, and are consistently larger in the low-aid sample. However, the differences are not large. Turning to the 2SLS estimates, in the IDA sample of countries I do again find the same pattern, with a larger multiplier in the less aid-dependent subsample of these IDA countries (0.59 versus 0.43 in the more aid-dependent subsample). Unfortunately, however, in the full and high disbursement samples of countries, identification is extremely weak in the less aid-dependent groups. As a result, the multipliers are very imprecisely estimated, to the point of being meaningless, and it is not possible to draw any conclusion about differences in multipliers by the degree of aid dependence.

Summing up, the novel DRS loan-level data on which the instrument developed in this paper is based covers a very large set of developing countries. This makes it feasible to investigate a range of plausible hypotheses regarding various potential sources of heterogeneity in government spending multipliers in developing countries. This section has provided some suggestive (although not statistically significant) evidence that the short-run effects of government spending do vary with the state of the business cycle, the degree of trade openness, the exchange rate regime, and (to a limited extent) with sample. Consistent with the predictions of the Mundell-Fleming model with capital mobility, they find that multipliers are higher under fixed exchange rate regimes in their sample of countries. They also find higher multipliers in countries that are more open to trade, as I do here.

27
the degree of aid-dependence of the economy, in ways that are consistent with the implications of some basic theory.

# **7.  Conclusions**

In this paper, I have used a novel dataset of loan-level transactions covering lending by official creditors to developing country governments to estimate government spending multipliers. My identification strategy exploits the substantial lags that occur between loan commitment and the eventual full disbursement of the loan, a process which typically takes several years. The key identifying assumption is that loan approvals, and the decision to embark on the associated spending plans, do not anticipate future shocks to growth. Given this assumption, fluctuations in disbursements that are attributable to fluctuations in past loan approval decisions are plausibly exogenous to contemporaneous shocks, and can be used as an instrument for fluctuations in government spending. Deploying this strategy in a large sample of developing countries, I find reasonably precise estimates of the government spending multiplier that are in the vicinity of 0.4. These results survive a range of robustness checks designed to address concerns about data quality and potential violations of the exclusion restriction. I find some evidence of heterogeneity in estimated multipliers that is consistent with the implications of basic theories. However, these differences typically are not statistically significant.

To put these findings in context, it is useful to compare them with estimates of the government spending multiplier in the empirical literature, which has overwhelmingly been based on evidence from developed countries, most notably the United States. For the United States, Hall (2009) suggests that estimates of the federal government spending multiplier are between 0.5 and 1, while Ramey (2011b) suggests a somewhat higher range from 0.8 to 1.5. Moreover, many of the studies of subnational spending multipliers cited in the introduction are even higher, ranging from 1 to 2. Policy discussions in many countries are also often premised on the assumption that the spending multiplier is substantially above one. The smaller, but nevertheless reasonably-precisely estimated, multipliers estimated in this paper for developing countries stand in sharp contrast to this evidence, and if anything, are more consistent with the modest short-run effects of government spending in emerging markets uncovered by Ilzetzki, Mendoza and Vegh (2010) using VAR-based techniques.

The small multipliers estimated in this paper suggest a rather limited output effect of countercyclical responses of government spending in response to economic downturns in developing 28
countries. This finding should, however, be interpreted in light of several qualifications. First, my empirical results can only uncover evidence regarding the average short-run effects of government spending over the large set of countries and years included in my dataset, while the actual effects in particular situations might very well be different. Indeed, the limited evidence on heterogeneity in multipliers in the last part of the paper suggests that such differences may be important in reality, even if they are difficult to isolate empirically. Second, as noted in the introduction, my empirical estimates of aggregate multipliers are not "deep" structural parameters, knowledge of which would be crucial for understanding likely future effects of any given fiscal policy response to an economic downturn. As such, my estimates are better interpreted as contributing a stylized fact on the correlation between changes in output and a plausibly predetermined component of changes in government spending that can serve as an empirical reference point for further theoretical work on this issue, particularly as it applies to developing countries. And finally, it is worth emphasizing that the absence of evidence in support of a large spending multiplier does not imply there is no role for a fiscal response to economic downturns. For example, in many developing countries, there is a strong rationale and scope for expanding social protections to the most vulnerable during economic crises, independent of any macroeconomic stimulative effects of such policies. In such a case, a countercyclical fiscal response to crises would be warranted even it had limited short-run effects on aggregate economic growth.

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31
**Figure 1:  Average Disbursement Profiles**

<!-- Start of picture text --> All Creditors<br>0.25<br>0.2<br>All Creditors<br>0.15<br>0.1<br>0.05<br>0<br>0 1 2 3 4 5 6 7 8 9 10<br>Years Since Loan Commitment<br>Bilateral Versus Multilateral Creditors<br>0.35<br>0.3<br>Multilaterals<br>0.25<br>Bilaterals<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>0 1 2 3 4 5 6 7 8 9 10<br>Years Since Loan Commitment<br>Note: This figure reports average disbursement profiles on loans from official creditors to developing country<br>government, defined as the fraction of the initial loan commitment disbursed in the year of loan approval (year<br>zero) and subsequent years (years one through 10), averaging across loans. The top panel reports averages across<br>all 59,184 loans included in my loan-level dataset. The bottom panel reports average disbursement profiles<br>separately for loans from multilateral and bilateral creditors.<br>Fraction of Original<br>Commitment Disbursed<br>Fraction of Original<br>Commitment Disbursed<br><!-- End of picture text --> 32
**Figure 2:  Disbursements on Loans from Official Creditors -- Kenya**

<!-- Start of picture text --> 1970 1980 1990 2000 2010<br>year<br>Current Year Approvals Previous Years' Approvals<br>Predicted<br>.06<br>.04<br>.02<br>0<br><!-- End of picture text --> Note: This graph reports annual disbursements on loans from official creditors to Kenya. The overall height of the bars shows total disbursements, and the light (dark) shaded portions separate this into disbursements on loans approved in the current year (past years). The solid line reports predicted disbursements on loans approved in <u>previous years, as described in Section 3.</u> 33
**Figure 3:  Scatterplots of Benchmark Results**

<!-- Start of picture text --> First Stage<br>-.02 -.01 0 .01 .02 .03<br>Change in Predicted Disbursements<br>OLS (thin) and IV (thick) Estimates of Multiplier<br>-.2 -.1 0 .1 .2<br>Change in Government Spending<br>Note: This graph shows the first-stage relationship between government spending and predicted disbursements<br>(top panel) and the second-stage relationship between GDP and government spending (bottom panel). All three<br>variables are scaled by lagged GDP and expressed as deviations from country- and year-specific means.<br>.2<br>.1<br>0<br>-.1<br>-.2<br>.2<br>.1<br>0<br>-.1<br>-.2<br><!-- End of picture text --> 34
**Table 1:  Summary Statistics on Lending by Official Creditors**

||**1970-79**|**1980-89**|**1990-99**|**2000-2010**|
|---|---|---|---|---|
|**_Disbursements by Multilateral Creditors(Billions Constant_**|**_2005$US)_**||||
|African Development Bank|0.9|4.9|12.6|8.9|
|African Development Fund|0.3|3.5|6.4|7.4|
|Asian Development Bank|2.4|8.7|26.0|37.0|
|Asian Development Fund|1.2|5.4|11.9|13.5|
|European Investment Bank|1.5|4.2|11.6|21.7|
|International Bank for Reconstruction and Development|37.9|112.0|135.0|131.0|
|International Development Association|19.2|41.2|59.3|68.0|
|Interamerican Development Bank|6.8|20.6|41.9|62.7|
|Interamerican Development Fund|2.9|4.7|3.9|3.8|
|Other Multilaterals|5.5|25.9|49.6|65.1|
|**_Total Multilateral Disbursements_**|**_78.4_**|**_231.1_**|**_358.2_**|**_419.1_**|
|**_Disbursements by Bilateral Creditors(Billions Constant 200_**|**_5$US)_**||||
|Canada|10.2|11.0|6.2|1.9|
|China|4.8|2.8|3.3|26.6|
|France|7.7|14.5|15.5|8.2|
|Germany|18.5|23.0|32.5|9.6|
|Italy|1.7|4.8|5.6|1.6|
|Japan|20.5|53.1|105.0|72.7|
|Kuwait|5.8|6.8|4.1|4.5|
|Russia|0.0|0.2|2.5|5.6|
|Saudi Arabia|7.2|9.8|1.6|1.8|
|United Kingdom|6.2|2.7|1.3|1.3|
|United States|45.9|48.6|23.9|4.0|
|USSR|8.0|18.8|1.1|0.0|
|Other DAC Bilaterals|8.9|17.0|16.8|10.0|
|Other Non-DAC Bilaterals|13.8|16.8|6.5|9.6|
|**_Total Bilateral Disbursements_**|**_159.2_**|**_230.0_**|**_225.9_**|**_157.3_**|
|**_Total Disbursements All Creditors_**|**_237.6_**|**_461.0_**|**_584.2_**|**_576.4_**|
|**_Loan-Weighted Average Terms of New Commitments_**|||||
|Interest Spread Over 20 Yr US TreasuryBill Rate(%)|-2.6|-4.7|-2.0|-2.1|
|Grace Period(Years)|6.9|5.9|6.0|6.4|
|Maturity (Years)|25.2|22.9|21.4|22.6|

Note: This table provides summary statistics on disbursements on loans from official creditors to developing countries captured in the Debtor Reporting Statistics (DRS) database of the World Bank. Disbursements are measured in billions constant 2005 $US. "Other Multilaterals" includes all other multilateral creditors with loans recorded in DRS. "Other DAC Bilaterals" and "Other Non-DAC Bilaterals" include all other bilateral creditors with loans recorded in DRS. DAC refers to membership in the OECD's Development Assistance Committee as of 2012. Data on terms in the last three lines are calculated on a loan-weighted basis, weighting by initial loan commitment. The interest spread refers to the nominal interest rate on the loan less the 20-Year US T-Bill rate, except for the <u>period 1987-1992 when no 20-Year T-Bills were issued: during this period the 30-Year rate is used instead.</u> 35
# **Table 2:  Disbursements from Official Creditors as Percentage of Total Government Spending**

|**Country**|**Disb/Gov**|**Country**|**Disb/Gov**|**Country**|**Disb/Gov**|
|---|---|---|---|---|---|
|GAMBIA,THE*|37.6%|BELIZE|15.6%|PERU|9.8%|
|MAURITANIA*|29.4%|SWAZILAND|15.4%|DOMINICA|9.5%|
|GUINEA-BISSAU*|28.1%|KENYA*|15.3%|CONGO,REP.*|9.2%|
|MADAGASCAR*|26.7%|HAITI*|15.0%|MAURITIUS|9.0%|
|MALI*|24.0%|COSTA RICA|14.7%|MALDIVES*|8.9%|
|GHANA*|23.6%|CAPE VERDE|14.1%|COLOMBIA|8.8%|
|GUYANA*|22.9%|EL SALVADOR|14.1%|ST. LUCIA|8.7%|
|MOZAMBIQUE*|22.8%|JAMAICA|13.9%|EGYPT|7.9%|
|LAO P.D.R.*|22.5%|JORDAN|13.8%|ALBANIA|7.2%|
|GUINEA*|22.4%|PARAGUAY|13.7%|MOLDOVA*|7.1%|
|BURKINA FASO*|22.3%|TOGO*|13.7%|PAPUA NEW GUINEA|7.1%|
|MALAWI*|21.7%|TUNISIA|13.7%|MACEDONIA|6.8%|
|HONDURAS*|21.6%|PHILIPPINES|13.5%|YEMEN*|6.7%|
|NICARAGUA*|21.6%|LESOTHO*|13.4%|PANAMA|6.2%|
|NEPAL*|20.5%|ST. VINCENT & GREN.|12.9%|THAILAND|6.1%|
|NIGER*|20.1%|SRI LANKA*|12.8%|BOTSWANA|5.8%|
|CHAD*|19.8%|LIBERIA*|12.8%|SUDAN*|5.8%|
|SENEGAL*|19.7%|CAMEROON*|12.7%|VANUATU|5.4%|
|TAJIKISTAN*|19.3%|INDONESIA|12.0%|FIJI|5.3%|
|TANZANIA*|19.0%|MOROCCO|12.0%|UZBEKISTAN*|5.2%|
|UGANDA*|18.9%|COMOROS*|11.9%|ALGERIA|5.1%|
|KYRGYZ REPUBLIC*|18.8%|MONGOLIA*|11.8%|SEYCHELLES|5.1%|
|SIERRA LEONE*|18.8%|GRENADA|11.3%|URUGUAY|5.0%|
|BURUNDI*|18.7%|GEORGIA*|11.3%|GABON|4.7%|
|BANGLADESH*|18.6%|GUATEMALA|11.2%|ROMANIA|4.3%|
|PAKISTAN*|18.4%|SAO TOME & PRINCIPE|11.2%|BULGARIA|4.1%|
|BENIN*|17.6%|ST. KITTS & NEVIS|11.2%|SYRIA|3.9%|
|ARMENIA*|17.5%|DJIBOUTI*|11.1%|TURKEY|3.5%|
|ETHIOPIA*|17.4%|ECUADOR|11.1%|SOLOMON ISLANDS*|3.4%|
|ZAMBIA*|17.2%|VIETNAM*|10.9%|ANGOLA*|3.3%|
|BOLIVIA*|17.1%|ERITREA*|10.7%|MALAYSIA|3.3%|
|BHUTAN*|16.3%|COTE D`IVOIRE*|10.4%|LATVIA|3.1%|
|RWANDA*|16.1%|CENTRAL AFR. REP.*|10.3%|||
|DOMINICAN REPUBLIC|15.8%|CONGO,DEM. REP.*|10.2%|||
|CAMBODIA*|15.6%|TONGA|10.0%|||
|||||**_Averages_**||
|||||Full Sample|13.4%|
|||||IDA Sample|16.2%|
|||||High Disb Sample|16.7%|

Note: This table lists the 102 countries that make up the full sample, together with the over-time average of disbursements on loans from official creditors as a fraction of total government spending, for each country. These countries satisfy the criteria that (a) disbursements on loans from official creditors average at least one percent of GDP over the period 1970-2010, and (b) at least 15 annual observations on government spending are available. IDA-eligible countries that make up the IDA subsample are indicated with asterisks. The high disbursements subsample consists of countries in the first two columns, in which disbursements on loans from official creditors average more than 10 percent of government spending.

36
**Table 3:  Summary Statistics**

||N|StdDev|Total<br>Disbursements|Predicted<br>Disbursements|GDP|Government<br>Spending|
|---|---|---|---|---|---|---|
|**Full Sample**|||||||
|Total Disbursements|2804|2.0%|1.00||||
|Predicted Disbursements|2804|0.6%|0.15|1.00|||
|GDP|2804|4.0%|0.00|0.03|1.00||
|Government Spending|2804|3.3%|0.12|0.11|0.25|1.00|
|**IDA Sample**|||||||
|Total Disbursements|1508|2.3%|1.00||||
|Predicted Disbursements|1508|0.7%|0.19|1.00|||
|GDP|1508|4.0%|0.00|0.06|1.00||
|Government Spending|1508|3.5%|0.11|0.17|0.23|1.00|
|**High Disbursments Sample**|||||||
|Total Disbursements|1950|2.1%|1.00||||
|Predicted Disbursements|1950|0.7%|0.19|1.00|||
|GDP|1950|3.8%|0.00|0.05|1.00||
|Government Spending|1950|3.1%|0.11|0.16|0.23|1.00|

Note: This table reports summary statistics on the indicated variables. All variables are expressed as constant local-currency price changes scaled by lagged GDP, as in Equation (1). In addition, all variables are in terms of deviations from country- and year-averages, consistent with the inclusion of country and year fixed effects in Equation (1).

37
# **Table 4:  Benchmark Estimates of the Government Spending Multiplier**

||(1)|(2)|(3)|
|---|---|---|---|
|Sample of Countries|**Full**|**IDA**|**Disb/G>10%**|
|**_Panel A: OLS Estimates_**||||
|_(Dependent variable is Change in Real GDP)_||||
|Change in Total Government Spending|0.306***|0.259***|0.277***|
||(0.0377)|(0.0501)|(0.0431)|
|**_Panel B: 2SLS Estimates_**||||
|_(Dependent variable is Change in Real GDP)_||||
|Change in Total Government Spending|0.375|0.408**|0.417**|
||(0.248)|(0.197)|(0.204)|
|Weak Instrument Consistent 95% Confidence Interval|[-0.058,0.827]|[0.071,0.774]|[0.082,0.776]|
|**_Panel C: First-Stage Regressions_**||||
|_(Dependent variable is Change in Total Government Spending)_||||
|Change in Predicted Disbursements|0.531***|0.796***|0.699***|
||(0.150)|(0.150)|(0.149)|
|First-Stage F-Statistic on Excluded Instrument|12.62|28.08|22.18|
|_Number of Observations_|2804|1508|1950|
|_Number of Countries_|102|60|70|
|Note: *** (**) (*) denotes significance at the 1 (5) (10) percent<br>errors are clustered at the country level. All regressions are es<br>a full set of country and year fixed effects. Changes in GDP, go<br>are all scaled by lagged GDP. Weak instrument consistent confi<br>conditional likelihood ratio statistic. Panels A and B report OLS<br>OLS estimates of the corresponding first-stage regression. The<br>countries, the sample of IDA-eligible countries, and the sampl<br>official creditors average at least 10 percent of government spe|level. Heteroske<br>timated using po<br>vernment spendi<br>dence intervals c<br>and 2SLS estimat<br>three columns co<br>e of countries wh<br>nding over the s|dasticity-consiste<br>oled country-year<br>ng, and predicted<br>omputed using th<br>es of Equation (1)<br>rrespond to the f<br>ere disbursement<br>ample period.|nt standard<br>data and include<br>disbursements<br>e Moreira (2003)<br>. Panel C reports<br>ull sample of<br>s on loans from|

38
# **Table 5:  Estimates of the Government Spending Multiplier --  Variants on the Predicted Disbursements Instrument**

|(1)|(2)|(3)|
|---|---|---|
|Sample of Countries<br>**Full**|**IDA**|**Disb/G>10%**|
|_(Dependent variable is Change in Real GDP)_|||
|**_(1) Multilateral Creditors Only_**|||
|Change in Total Government Spending<br>0.433*|0.612***|0.526***|
|(0.238)|(0.187)|(0.171)|
|_First-Stage F-Statistic on Excluded Instrument_<br>11.67|23.62|21.46|
|**_(2) Bilateral Creditors Only_**|||
|Change in Total Government Spending<br>0.293|0.0814|0.247|
|(0.406)|(0.313)|(0.346)|
|_First-Stage F-Statistic on Excluded Instrument_<br>4.11|5.73|6.25|
|**_(3) Typical Disbursement Rates Based on Pooling All Loans_**|||
|Change in Total Government Spending<br>0.532*|0.532*|0.511*|
|(0.317)|(0.280)|(0.265)|
|_First-Stage F-Statistic on Excluded Instrument_<br>9.83|19.52|15.95|
|**_(4) Typical Disbursement Rates Calculated Excluding Country InQuestion_**|||
|Change in Total Government Spending<br>0.332|0.339*|0.366*|
|(0.238)|(0.189)|(0.197)|
|_First-Stage F-Statistic on Excluded Instrument_<br>14.68|28.11|24.17|
|_Number of Observations_<br>2804|1508|1950|
|_Number of Countries_<br>102<br>Note: *** (**) (*) denotes significance at the 1 (5) (10) percent level. Hetero<br>are clustered at the country level. All regressions are estimated using pooled<br>of country and year fixed effects. Changes in GDP, government spending, and<br>GDP. Table reports 2SLS estimates of Equation (1) using the following varian<br>instrument: (1) using loans from official creditors only; (2) using loans from b<br>disbursement rates calculated pooling across all loans, rather than within cre<br>excluding country in question when calculating typical disbursement rates. T<br>to the full sample of countries, the sample of IDA-eligible countries, and the<br>disbursements on loans from official creditors average at least 10 percent of g<br>period.|60<br>skedasticity-cons<br>country-year da<br>disbursements<br>ts on the predict<br>ilateral creditors<br>ditor/region/dec<br>he three country<br>sample of countri<br>overnment spen|70<br>istent standard errors<br>ta and include a full set<br>are all scaled by lagged<br>ed disbursements<br>only; (3) using typical<br>ade bins; and (4)<br>samples correspond<br>es where<br>ding over the sample|

39
**Table 6:  Robustness Checks, 1**

||**Removing**|**Influential**|**Observations**|**Using No**|**n-Interest**<br>**Spendin**|**Government**<br>**g**|**Using**<br>**on Gov**|**National A**<br>**ernment C**|**ccounts Data**<br>**onsumption**|**Controlli**<br>|**ng for Appr**<br>**Disbursing **|**ovals of Slow-**<br>**Loans**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Sample of Countries|**Full**|**IDA**|**Disb/G>10%**|**Full**|**IDA**|**Disb/G>10%**|**Full**|**IDA**|**Disb/G>10%**|**Full**|**IDA**|**Disb/G>10%**|
|_(Dependent variable is Change in Real G_|_DP)_||||||||||||
|**_Panel A: OLS Estimates_**|||||||||||||
|Change in Government Spending|0.313***|0.266***|0.275***|0.292***|0.239***|0.262***|0.343***|0.269***|0.289***|0.305***|0.257***|0.276***|
||(0.0380)|(0.0505)|(0.0437)|(0.0399)|(0.0535)|(0.0414)|(0.0639)|(0.0596)|(0.0581)|(0.0375)|(0.0498)|(0.0429)|
|Approvals of Slow-DisbursingProjects||||||||||0.0591**|0.0700**|0.0535*|
|||||||||||(0.0272)|(0.0318)|(0.0278)|
|**_Panel B: 2SLS Estimates_**|||||||||||||
|Change in Government Spending|0.371|0.387*|0.389**|0.462|0.445**|0.496**|0.741|0.740*|0.808|0.335|0.364*|0.389*|
||(0.268)|(0.194)|(0.191)|(0.297)|(0.213)|(0.230)|(0.536)|(0.402)|(0.495)|(0.249)|(0.193)|(0.202)|
|Approvals of Slow-DisbursingProjects||||||||||0.0586**|0.0676**|0.0520*|
|||||||||||(0.0267)|(0.0311)|(0.0272)|
|_First-Stage F-Statistic_|11.48|41.20|30.34|7.87|19.94|15.97|6.51|11.02|8.69|12.20|26.65|22.98|
|_Number of Observations_|2786|1503|1939|2744|1481|1909|2672|1407|1865|2787|1494|1935|
|_Number of Countries_<br>*** (**) (*) denotes significance at the<br>year data and include a full set of count<br>Equation (1). Panel B reports 2SLS estim<br>reduced-form regressions for the bench<br>Columns (7)-(9) use national accounts d<br>correspond to the full sample of countri<br>government spendingover the sample|102<br>1 (5) (10) pe<br>ry and year<br>ates of Equ<br>mark speci<br>ata on gove<br>es, the sam<br>period.|60<br>rcent level.<br>fixed effect<br>ation (1). C<br>fications in<br>rnment con<br>ple of IDA-|70<br>Heteroskedasti<br>s. Changes in GD<br>olumns (1)-(3) re<br>Table 4. Column<br>sumption spend<br>eligible countries|102<br>city-consiste<br>P, governm<br>port results<br>s (4)-(6) subt<br>ing , Column<br>, and the sa|60<br>nt standard<br>ent spendin<br>after using<br>ract interes<br>s (10)-(12)<br>mple of cou|70<br>errors are cluste<br>g, and disbursem<br>the Hadi (1992) p<br>t payments on p<br>control for conte<br>ntries where dis|102<br>red at the c<br>ents are all<br>rocedure to<br>ublic and pu<br>mporaneou<br>bursements|60<br>ountry lev<br>scaled by<br>remove i<br>blicly-gua<br>s approval<br>on loans f|70<br>el. All regressio<br>lagged GDP. Pa<br>nfluential obser<br>ranteed debt fro<br>s of slow-disbur<br>rom official cred|102<br>ns are estima<br>nel A reports<br>vations from<br>m total gove<br>sing loans. T<br>itors averag|60<br>ted using p<br>OLS estima<br>the first-st<br>rnment sp<br>he three co<br>e at least 10|70<br>ooled country-<br>tes of<br>age and<br>ending.<br>untry samples<br>percent of|
# **Table 7:  Robustness Checks, 2**

||**Control**<br>|**ling for Lagg **<br>|**ed Growth**<br>|**Controlli**<br>|**ng for Lagge **<br>|**d Spending**<br>|**Con**<br>|**trolling for**<br>|**Policy**<br>|
|---|---|---|---|---|---|---|---|---|---|
|_(Dependent variable is Change in Real GDP)_|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Sample of Countries|**Full**|**IDA**|**Disb/G>10%**|**Full**|**IDA**|**Disb/G>10%**|**Full**|**IDA**|**Disb/G>10%**|
|**_Panel A: OLS Estimates_**||||||||||
|Change in Total Government Spending|0.290***|0.249***|0.267***|0.311***|0.259***|0.284***|0.305***|0.253***|0.267***|
||(0.0381)|(0.0512)|(0.0437)|(0.0363)|(0.0458)|(0.0413)|(0.0390)|(0.0508)|(0.0433)|
|Lagged Change in GDP|0.130***|0.0987***|0.105***|||||||
||(0.0310)|(0.0353)|(0.0352)|||||||
|Lagged Change in Total Government Spending||||0.103***|0.0778***|0.118***||||
|||||(0.0246)|(0.0286)|(0.0304)||||
|Change in CPIA PolicyIndicator|||||||0.0109***|0.00809**|0.00999***|
||||||||(0.00284)|(0.00371)|(0.00336)|
|**_Panel B: 2SLS Estimates_**||||||||||
|Change in Total Government Spending|0.337|0.391**|0.404**|0.053|0.116|0.243|0.286|0.373*|0.441*|
||(0.249)|(0.191)|(0.202)|(0.267)|(0.205)|(0.202)|(0.301)|(0.218)|(0.232)|
|Lagged Change in GDP|0.127***|0.0914**|0.0971**|||||||
||(0.0356)|(0.0361)|(0.0372)|||||||
|Lagged Change in Total Government Spending||||0.247|0.259|0.204||||
|||||(0.169)|(0.158)|(0.166)||||
|Change in CPIA PolicyIndicator|||||||0.0111***|0.00706|0.00856**|
||||||||(0.00362)|(0.00428)|(0.00396)|
|_First-Stage F-Statistic_|11.81|26.66|21.39||||8.7|21.03|16.66|
|_Stock-Yogo Weak Identification Test Statistic_||||2.90|2.93|4.06||||
|_Number of Observations_|2782|1493|1937|2702|1452|1883|2516|1386|1755|
|_Number of Countries_<br>Note: *** (**) (*) denotes significance at the 1 (<br>estimated using pooled country-year data and in<br>by lagged GDP. Panel A reports OLS estimates of<br>lagged growth, (b) controlling for lagged changes<br>(20%) maximal IV size is 3.63 (3.95). The three co<br>countries where disbursements on loans from of|102<br>5) (10) perce<br>clude a full s<br>Equation (1<br>in governm<br>untry sampl<br>ficial credito|60<br>nt level. Het<br>et of country<br>). Panel B re<br>ent spending<br>es correspon<br>rs average at|70<br>eroskedasticity-c<br>and year fixed e<br>ports 2SLS estima<br>, and (c) controll<br>d to the full samp<br>least 10 percent|102<br>onsistent sta<br>ffects. Chan<br>tes of Equati<br>ing for policy<br>le of countri<br>of governme|60<br>ndard errors<br>ges in GDP, g<br>on (1). The t<br>. The Stock-Y<br>es, the samp<br>nt spending|70<br>are clustered at<br>overnment spen<br>hree sets of colu<br>ogo weak identi<br>le of IDA-eligible<br>over the sample|102<br>the country l<br>ding, and dis<br>mns corresp<br>fication test<br>countries, a<br>period.|60<br>evel. All re<br>bursement<br>ond to (a) co<br>critical valu<br>nd the sam|70<br>gressions are<br>s are all scaled<br>ntrolling for<br>es for 25%<br>ple of|

41
|**Ta**<br>Sample of Countries|**ble 8: Het**<br>(1)<br>**Full**|**erogeneity i**<br>(2)<br>**IDA**|**n Estimated**<br>(3)<br>**Disb/G>10%**|**Multipliers**<br>(4)<br>**Full**|<br>(5)<br>**IDA**|(6)<br>**Disb/G>10%**|
|---|---|---|---|---|---|---|
|**_Panel A: State of Business Cycle_**<br>_OLS Estimate_||**_Recession_**|||**_Boom_**||
|Change in Government Spending|0.195***|0.186***|0.204***|0.101***|0.0611|0.0796**|
||(0.0365)|(0.0457)|(0.0456)|(0.0326)|(0.0432)|(0.0384)|
|_2SLS Estimate_|||||||
|Change in Government Spending|0.660*|0.614*|0.807**|0.146|0.0398|0.00873|
||(0.353)|(0.328)|(0.383)|(0.265)|(0.171)|(0.215)|
|_First-Stage F-Statistic_|7.40|7.99|8.01|8.02|18.64|14.76|
|_Number of Observations_|1312|701|919|1492|807|1031|
|**_Panel B: Trade Openness_**||**_Closed_**|||**_Open_**||
|_OLS Estimate_|||||||
|Change in Government Spending|0.337***|0.274***|0.319***|0.281***|0.236***|0.243***|
||(0.0617)|(0.0723)|(0.0745)|(0.0465)|(0.0645)|(0.0526)|
|_2SLS Estimate_|||||||
|Change in Government Spending|0.634**|0.571*|0.712**|0.116|0.180|0.150|
||(0.295)|(0.284)|(0.353)|(0.491)|(0.328)|(0.320)|
|_First-Stage F-Statistic_|10.23|13.42|8.71|4.42|13.75|10.95|
|_Number of Observations_|1398|750|966|1406|758|984|
|**_Panel C: Exchange Rate Regime_**<br>_OLS Estimate_||**_Flexible_**|||**_Fixed_**||
|Change in Government Spending<br>_2SLS Estimate_|0.320***<br>(0.0513)|0.301***<br>(0.0649)|0.308***<br>(0.0632)|0.269***<br>(0.0487)|0.209***<br>(0.0656)|0.244***<br>(0.0498)|
|Change in Government Spending|0.387|0.482**|0.320|0.306|0.188|0.450|
||(0.304)|(0.199)|(0.208)|(0.371)|(0.280)|(0.342)|
|_First-Stage F-Statistic_|9.55|25.54|21.88|6.03|11.46|7.25|
|_Number of Observations_|1009|504|592|1795|1004|1358|
|**_Panel D: Aid Dependence_**||**_Low_**|||**_High_**||
|_OLS Estimate_|||||||
|Change in Government Spending<br>_2SLS Estimate_|0.349***<br>(0.0603)|0.321***<br>(0.0818)|0.393***<br>(0.0716)|0.265***<br>(0.0388)|0.209***<br>(0.0538)|0.204***<br>(0.0433)|
|<br>Change in Government Spending|-0.146|0.587*|0.275|0.547**|0.430*|0.438**|
||(0.951)|(0.343)|(0.750)|(0.224)|(0.255)|(0.173)|
|_First-Stage F-Statistic_|0.82|8.21|1.26|13.22|16.22|22.92|
|_Number of Observations_|1373|747|970|1431|761|980|
|*** (**) (*) denotes significance at t<br>the country level. All regressions ar<br>effects. Changes in GDP, governme<br>2SLS estimates of Equation (1) for va<br>below/above the country-decade av<br>average trade/GDP being below/ab<br>fixed exchange rates, defined as bel<br>less and more aid dependent countr<br>sample median. The three country<br>the sample of countries where disb<br>spending over the sample period.|he 1 (5) (10) p<br>e estimated u<br>nt spending, a<br>rious sample<br>erage. Panel<br>ove the corres<br>ow/above 2 i<br>ies, defined a<br>samples corre<br>ursements on|ercent level. H<br>sing pooled cou<br>nd disburseme<br>splits. Panel A<br>B distinguishes<br>ponding sampl<br>n the Ilzetzki, R<br>s the decade-a<br>spond to the fu<br>loans from offi|eteroskedasticity<br>ntry-year data a<br>nts are all scaled<br>distinguishes rec<br>countries less an<br>e median. Panel<br>einhart and Rogo<br>verage ODA to GD<br>ll sample of coun<br>cial creditors ave|-consistent st<br>nd include a fu<br>by lagged GDP<br>essions from b<br>d more open<br>C distinguishe<br>ff (2008) classi<br>P ratio below<br>tries, the sam<br>rage at least 10|andard errors a<br>ll set of countr<br>. All four pane<br>ooms, defined<br>to trade, define<br>s countries wit<br>fication. Panel<br>/above the corr<br>ple of IDA-eligi<br>percent of gov|re clustered at<br>y and year fixed<br>ls report OLS and<br>as growth<br>d as the decade-<br>h flexible and<br>D distinguishes<br>esponding<br>ble countries, and<br>ernment|
43