Abstract

In the past, aid recipients often experienced sharp swings in aid flows. Net aid flows to Pakistan, for example, increased by a factor of 2.5 between 1997 and 2004, and nearly tripled to Ethiopia during the same period.51 Analyzing country experiences around such aid spurts can be useful, both for understanding the transmission mechanism of scaled-up aid to various fiscal variables and for drawing lessons regarding appropriate institutional arrangements for facilitating aid management and absorption.

APPENDIX 1. Country Experiences with Scaled-Up Aid

In the past, aid recipients often experienced sharp swings in aid flows. Net aid flows to Pakistan, for example, increased by a factor of 2.5 between 1997 and 2004, and nearly tripled to Ethiopia during the same period.51 Analyzing country experiences around such aid spurts can be useful, both for understanding the transmission mechanism of scaled-up aid to various fiscal variables and for drawing lessons regarding appropriate institutional arrangements for facilitating aid management and absorption.

Some Statistical Properties of Aid Flows

Many low-income countries already receive more funds in the form of aid than they collect in the form of own revenues (Appendix Table A1.1).52 This is particularly true for African countries, which account for almost 60 percent of the sample. African countries received, on average, 16 percent of GDP in aid flows, substantially more than the Latin American or Asian countries in the sample. In contrast, the average revenue-to-GDP ratio in African countries was less than 10 percent. Breaking down the sample into five-year intervals shows that aid levels, expressed as a share of GDP, have declined in many countries.

Table A1.1.

Aid and Revenue, 1990–2004

(Means and medians are in percent of GDP)

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Sources: OECD, Development Assistance Committee (DAC) database; and IMF, World Economic Outlook database and staff estimates.* and ** denote significance at 5 and 10 percent levels, respectively.

Ratio of variances between the aid and revenue variables, as in Bulíř and Hamann (2006).

Also, at least for Africa, aid flows have remained substantially more volatile than revenues (Table A1.1). Although the absolute volatility of both aid and revenues has declined, aid flows remain more volatile than revenues, a finding that is similar to the findings of other researchers.53 Volatility of aid is higher in African countries than for the sample as a whole, reflecting the quantitative importance of aid (both grants and loans). Conversely, relative aid volatility, which is measured as a ratio of the variances of aid and revenues, has worsened in recent years. Volatility of aid has contributed to additional fiscal uncertainties in aid recipient countries.

Among the main components of aid, grants are much more volatile than loans (Table A1.2). The fairly large standard deviation around the mean for grants underscores that spending financed by external grants faces larger uncertainty than spending financed by loans. Statistically, this simply reflects the fact that grants are usually substantially larger than loans, but for actual fiscal management, absolute volatility is more relevant than relative volatility (that is, a normalized measure of volatility such as the coefficient of variation).

Table A1.2.

Total Aid, Loans, and Grants

(Means are in percent of GDP)

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Sources: OECD, DAC database; and IMF staff estimates.

Past aid surges have been relatively short-lived. Achieving the Millennium Development Goals (MDGs) would require countries to manage and execute ambitious social and infrastructure projects that often have long gestation periods. Aid inflows for financing such projects would have to be much smoother and more sustained in the coming years than what appears to have been the norm in the past.

Aid Flows, Government Spending, and Fiscal Institutions

In general, aid flows have remained difficult to predict while past aid surges have been short-lived. A set of panel regressions of aid shows that only revenues and lagged values of aid consistently explain aid flows, and even then with relatively weak explanatory power (Table A1.3).54 The negative relationship between aid and revenues conforms to the findings of other researchers (for example, Gupta and others, 2004). There is also some indication that aid flows rise with growth and behave countercyclically with respect to the output gap and revenues (that is, as the output gap widens and revenues increase, aid flows decline). For the most part, however, and despite trying out a wide range of explanatory variables, regression residuals remained large. The significantly smaller-than-unity coefficient of the lagged dependent variable suggests that aid is mean reverting, meaning a large aid spurt seldom persists. Various event studies carried out to probe deeper into the issue of aid flow volatility confirm that large increases in aid have consistently been followed by a tapering off of aid (Figure A1.1).55

Figure A1.1.
Figure A1.1.

Event Study: Aid Flows After an Aid Spurt

(In percent of GDP)

Sources: OECD, Development Assistance Committee (DAC) database; and IMF staff estimates.Note: t denotes years, and the dotted lines denote 1 standard deviation error bands.
Table A1.3.

Selected Regression Results

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Source: IMF staff estimates.Note: Standard errors in brackets, * significant at 5 percent; ** significant at 1 percent. Generalized least squares regression results are reported. Most results were robust under ordinary least squares regression specifications as well.

Own revenues are correlated positively with loans and negatively with grants.56 The contemporaneous correlation findings do not necessarily imply that grants induce reduced tax effort; rather, the finding could well be associated with the fact that donors give more grants to less-developed, fiscally constrained countries. This argument is also advanced in a recent paper by Morrissey (2006). Adding an indicator of political risk as an explanatory variable yields positive and statistically significant coefficients, indicating that countries with better political institutions and lower risk tend to be associated with higher revenue collection.

The impact of aid on spending was analyzed with four regressions that use different spending aggregates as the dependent variable (Table A1.3). The main results follow:

  • Capital spending rises with total aid, although the result is more robust with increases in grants as opposed to loans. However, capital spending does not increase proportionately with more aid, as shown by a negative but small coefficient in the squared aid-to-GDP term.

  • Current spending also increases with grants and, overall, tends to rise with aid flows by more than capital spending does.

  • Social spending (that is, health and education) is fairly unaffected by aid flows. Health spending is positively correlated with grants (but not with loans), although the parameter is very small. There is no statistically significant effect of different aid aggregates on education spending. In general, countries with better political risk ratings are also associated with higher levels of health and education spending. The lack of responsiveness of health and education spending to aid flows may reflect government attempts to maintain such spending even when funding is volatile and uncertain. Indeed, countries use various mechanisms to protect certain spending items in these sectors from allocation shortfalls.

Although data on the quality of fiscal institutions are scarce, countries with better fiscal institutions would also seem to experience less aid volatility (Figure A1.2). Scatter plots of the standard deviations of aid flows with total HIPC-AAP (Heavily Indebted Poor Countries Assessments and Action Plans) scores—or any of the components (that is, budget formulation, execution, and reporting)—suggest that a higher institutional quality score goes hand in hand with lower aid volatility. Similar results hold when the HIPC-AAP scores are replaced by the fiscal portion of the World Bank Country Policy and Institutional Assessment (CPIA) ratings for a larger group of countries.

Figure A1.2.
Figure A1.2.

Aid Volatility and Fiscal Institutional Quality

Sources: IMF country documents and staff estimates.Note: AAP = Assessments and Action Plans.

Countries that improved their ratings for budget execution also tended to reduce current spending while increasing capital spending (Figures A1.3 and A1.4). The two HIPC-AAP surveys, done a few years apart, allow analyzing the impact of improvements in fiscal institutions on budgetary activities. The data suggest that five out of seven countries with a deterioration in budget execution ratings during 2001–04 increased current spending relative to GDP; similar results were found for other components of the HIPC-AAP scores. Conversely, countries that improved their budget execution ratings during 2001–04 also increased their capital spending, on average, although only slightly.

Figure A1.3.
Figure A1.3.

Changes in Current Spending and Institutional Quality

Sources: IMF country documents, PEFA secretariat, and IMF staff estimates.
Figure A1.4.
Figure A1.4.

Changes in Capital Spending and Institutional Quality

Sources: IMF country documents, PEFA secretariat, and IMF staff estimates.

APPENDIX 2. Expenditure Efficiency—An Empirical Assessment

It will be essential for low-income countries to make efficient use of scaled-up aid to ensure sustained progress toward the Millennium Development Goals (MDGs). In particular, efficient spending in priority areas—for example, health care, education, public investment—will be critical. This appendix describes how a sample of Poverty Reduction and Growth Facility (PRGF)-eligible countries have fared in transforming inputs into outcomes in health and education; this may help to shed light on current absorptive capacities and the likely efficiency of using scaled-up aid (Table A2.1). In addition, this appendix attempts to identify factors that may help explain differences in expenditure efficiency across countries.

Table A2.1.

Countries Included in the Efficiency Analysis1

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This list includes countries that were PRGF-eligible in September 2006, excluding some small island economies and transition countries. The list also excludes PRGF-eligible countries without available data on health and education spending. Countries with missing information on outcome measures were dropped from the analysis of that outcome measure.

Expenditure Efficiency in Health and Education in Low-Income Countries—The Scoreboard

Expenditure efficiency is assessed here by measuring how effective countries are in producing health and education outcomes. An implicit assumption is that spending affects outcomes and that a relatively more efficient country achieves the same outcome with lower spending. The analysis is done in two stages. In the first stage, the spending efficiency for each country in the sample is measured using Data Envelopment Analysis (DEA).57 This methodology estimates the overall spending efficiency of the use of inputs (for example, health expenditure) to “produce” outputs (for example, health outcomes). The countries that provide the best combination (that is, the maximum outputs for a given level of inputs or, alternatively, the minimum inputs for the level of outputs) define the best-practice frontier. The countries that are not on the frontier are then ranked according to the distance from the frontier, which is a measure of relative efficiency expressed as the efficiency score. The second stage attempts to identify key factors that account for differences in the relative efficiency scores, using correlation coefficients and multivariate truncated regressions that relate relative efficiency scores to various control variables. The inputs used in the analysis are per capita health and education spending58 in purchasing power parity dollars, and the outcomes are indicators that are used to monitor progress toward the MDGs. Table A2.2 shows the different indicators used, their definitions, the corresponding MDGs, and the availability of data. Because of a relative paucity of data that directly measure outcomes, the analysis also uses intermediate indicators of outcomes, such as enrollment rates and the numbers of births attended by skilled staff.

Table A2.2.

Spending and Outcome Indicators for the Efficiency Analysis

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Spending variables have been corrected for differences in purchasing power. PPP = purchasing power parity.

MDG 2 is “Achieve universal primary education,” MDG 3 is “Promote gender equality and empower women,” MDG 4 is “Reduce child mo rtality,” and MDG 5 is “Improve maternal health.”

Data on private spending on education are not available.

Years with available data.

Available for broadly the same number of countries and the same years.

World Bank model estimate.

First-stage results

First-stage results point to large variances in spending efficiency, implying that higher levels of spending do not always translate into better outcomes. More specifically—

  • Countries with the lowest per capita incomes tend to have the lowest efficiency scores for health (Table A2.3). This general conclusion holds broadly, irrespective of the outcome indicator used or whether total health spending or only public health spending is considered.59 Three outcome indicators—infant mortality, child mortality, and maternal mortality—were used in the analysis. Overall, only about 40 percent of the countries in the poorest half of the sample ranked in the top half with respect to their outcome efficiency scores.60

  • Relative efficiency analysis for education spending yields comparable results (Table A2.4). The two indicators used for this exercise are the primary enrollment rate and youth literacy rate. Only one-third of the poorest countries in the sample are ranked in the top half of efficient countries on the basis of the primary enrollment rate. The results are more favorable for youth literacy rates, with as many as 64 percent of the poorest countries in the top half based on efficiency scores.

Table A2.3.

Percent of Countries in Top Half of the Efficiency Distributions for Health by Income Level1

article image
Sources: World Bank, World Development Indicators database; and IMF staff estimates.

Countries in the first two quartiles of the efficiency distribution have better efficiency scores than the median country.

In PPP U.S. dollars. The median per capita income in the sample is $1,457 in PPP terms.

Table A2.4.

Percent of Countries in Top Half of the Efficiency Distribution for Education by Income Level1

article image
Sources: World Bank, World Development Indicators database; and IMF staff estimates.

Countries in the first two quartiles of the efficiency distribution have better efficiency scores than the median country.

In PPP U.S. dollars. The median per capita income in the sample is $1,457 in PPP terms.

Second-stage results

The second-stage analysis is limited, because of data constraints, to efficiency of health spending. The control variables used in this stage are listed in Table A2.5. Second-stage results point to several factors that may help to explain differences in spending efficiency in the sample:

  • Governance and the quality of fiscal institutions have a strong positive correlation with efficiency in health. Several indicators of governance and institutions were used in the analysis, such as the International Country Performance Rating (ICPR), the average Country Policy and Institutional Assessment (CPIA) score, and some of the latter’s components for the quality of fiscal institutions. Table A2.6 presents those correlation coefficients between the relative efficiency scores and the control variables that are robust.61 Thus, on average, countries with better governance and fiscal institutions achieve higher health outcomes at lower levels of spending.62

  • The level of aid and aid volatility are not correlated with health efficiency scores.63 This is because aid volatility does not translate into similar changes in health spending (see Appendix 1) or health outcomes. In other words, short-term changes in aid do not affect the relationship between spending and outcomes in the health sector, and therefore do not affect spending efficiency.

  • The efficiency of health sector spending is correlated positively with outcomes in the education sector and with infrastructure, and negatively with the prevalence of HIV/AIDS. Higher adult literacy rates and improved access to sanitation are associated with higher efficiency of health spending (Table A1.6). These results reflect the importance of adequate sanitation infrastructure to health outcomes and the well-known fact that better education and health outcomes reinforce each other: better education leads to better decisions on health-related matters, and improved child health promotes investment in education (Miguel and Kremer, 2004). The prevalence of HIV/AIDS in a country lowers the relative expenditure efficiency in the health sector.

Table A2.5.

Control Variables

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Note: CPIA = Country Policy and Institutional Assessment; ODA = official development assistance; ICPR = International Development Agency Country Performance Rating; PPP = purchasing power parity.

The level of aid received is measured as aid received as a percent of GDP for each type of aid. Volatility of aid received is measured in three ways: standard deviation of aid, coefficient of variation of aid, and the relative variance of aid to revenue.

Table A2.6.

Correlation Matrix of Relative Efficiency Scores and Control Variables1

article image
Sources: World Bank; and IMF staff estimates.

A negative sign means that more of the control variable is negatively correlated with the efficiency score and hence positively correlated with level of efficiency. * and ** denote significance at the 10 and 5 percent levels, respectively.

Multivariate truncated regressions confirm these findings.64 Efficiency scores for infant mortality were regressed on the prevalence of HIV, adult literacy rate, access to sanitation services, and the average CPIA scores for fiscal institutions. The coefficients for all variables are significant and of the expected sign (Table A2.7). It is worth noting that the coefficient for the CPIA indicator is significant in each of the three alternative specifications.

Table A2.7.

Truncated Regressions of Expenditure Efficiency Scores1

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Sources: World Bank; and IMF staff estimates.

A negative sign means that more of the control variable is negatively correlated with the efficiency score and hence positively correlated with level of efficiency. * and ** denote significance at the 10 and 5 percent levels, respectively.

Sigma is the standard error of the regression.

This analysis of the efficiency of education and health spending should be interpreted with some caution. Health and education outcomes are influenced by a host of factors beyond spending that can only be partially captured by the use of controls in the second stage of the analysis. In addition, the methodology focuses on quantifiable inputs and outcomes, and only partially takes into account harder-to-measure factors such as quality. Finally, efficiency is measured in relative terms, implying that if a country is on the frontier, it is relatively more efficient than other countries in the sample. In relatively small samples, such as for this analysis, this may result in some bias in the result. Nevertheless, the thrust of the findings presented here is consistent with those reported in the literature on expenditure efficiency.65

Fiscal Policy Implications for Scaled-Up Aid

The above analysis points to two important implications for expenditure policy in the context of scaled-up aid: (1) improving efficiency of spending in low-income countries is critical to achieving the MDGs; and (2) in most low-income countries, effective utilization of scaled-up aid will require a further strengthening of fiscal institutions. These reforms will contribute to enhancing the efficiency of spending in low-income countries.

References

  • Aiyar, S., A. Berg, and M. Hussain, 2005, “The Macroeconomic Challenge of More Aid,” Finance and Development, Vol. 42 (September).

  • Allen, R., and D. Tommasi, eds., 2001, Managing Public Expenditure: A Reference Book for Transition Countries (Paris: Organization for Economic Cooperation and Development).

    • Search Google Scholar
    • Export Citation
  • Azariadis, C., and J. Stachurski, 2005, “Poverty Traps,” in Handbook of Economic Growth, Vol. 1A, ed. by P. Aghion and S. Durlauf (Amsterdam: North-Holland).

    • Search Google Scholar
    • Export Citation
  • Baldacci, E., B. Clements, S. Gupta, and Q. Cui, forthcoming, “Social Spending, Human Capital, and Growth in Developing Countries,” World Development (forthcoming).

    • Search Google Scholar
    • Export Citation
  • Barro, R., and X. Sala-i-Martin, 1995, Economic Growth (New York: McGraw-Hill).

  • Brooke, P., 2003, “Study of Measures Used to Address Weaknesses in Public Financial Management Systems in the Context of Policy Based Support,” final report for the Public Expenditure & Financial Accountability (PEFA) Secretariat (Washington: PEFA Secretariat).

    • Search Google Scholar
    • Export Citation
  • Bulíř, A., and J. Hamann, 2007, “Volatility of Development Aid: An Update,” IMF Staff Papers, Vol. 54, No. 4, pp. 72734.

  • Celasun, O., J. Walliser, 2006, “Predictability of Budget Aid: Recent Experiences,” in Budget Support as More Effective Aid? ed. by S. Koeberle, Z. Stavreski, and J. Walliser (Washington: World Bank).

    • Search Google Scholar
    • Export Citation
  • Clemens, M., S. Radelet, R. Bhavnani, 2004, “Counting Chickens When They Hatch: The Short-Term Effect of Aid on Growth,” CGD Working Paper No. 44 (Washington: Center for Global Development).

    • Search Google Scholar
    • Export Citation
  • Diamond, J., 2006, Budget Systems Reform in Emerging Economies: The Challenges and the Reform Agenda, IMF Occasional Paper No. 245 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Dorotinsky, W., and R. Floyd, 2004, “Public Expenditure Accountability in Africa: Progress, Lessons, and Challenges,” in Building State Capacity in Africa, ed. by B. Levy and S. Kpundeh (Washington: World Bank).

    • Search Google Scholar
    • Export Citation
  • Eifert, B., and A. Gelb, 2005, “Improving the Dynamics of Aid: Towards More Predictable Budget Support,” Policy Research Working Paper No. 3732 (Washington: World Bank).

    • Search Google Scholar
    • Export Citation
  • Estache, A., M. Gonzalez, and L. Trujillo, 2007, “Government Expenditures on Education, Health, and Infrastructure: A Naive Look at Levels, Outcomes, and Efficiency,” Policy Research Working Paper No. 4219 (Washington: World Bank).

    • Search Google Scholar
    • Export Citation
  • Fedelino, A., G. Schwartz, and M. Verhoeven, 2006, “Aid Scaling Up: Do Wage Bill Ceilings Stand in the Way?IMF Working Paper 06/106 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Feyzioglu, T., V. Swaroop, and M. Zhu, 1998, “A Panel Data Analysis of the Fungibility of Foreign Aid,” World Bank Economic Review, Vol. 12, No. 1, pp. 2958.

    • Search Google Scholar
    • Export Citation
  • Fölscher, A., and N. Cole, 2007, “South Africa: Transition to Democracy Offers Opportunity for Whole System Reform,” OECD Journal on Budgeting, Vol. 6 (January), pp. 5390.

    • Search Google Scholar
    • Export Citation
  • Foster, M., and T. Killick, 2006, “What Would Doubling Aid Do for Macroeconomic Management in Africa?ODI Working Paper No. 06/264 (London: Overseas Development Institute).

    • Search Google Scholar
    • Export Citation
  • Goldsbrough, D., 2006, “The Nature of the Debate Between the IMF and Its Critics,” background paper for the Working Group on IMF Programs and Health Expenditures (Washington: Center for Global Development).

    • Search Google Scholar
    • Export Citation
  • Group of Eight (G-8), 2005, The Gleneagles Communiqué on Climate Change, Energy and Sustainable Development (Gleneagles, Scotland).

  • Gupta, S., B. Clements, A. Pivovarsky, and E. Tiongson, 2004, “Foreign Aid and Revenue Response: Does the Composition of Aid Matter?” in Helping Countries Develop: The Role of Fiscal Policy, ed. by S. Gupta, B. Clements, and G. Inchauste (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Gupta, S., C. Pattillo, and S. Wagh, 2007, “Impact of Remittances on Poverty and Financial Development in Sub-Saharan Africa,” IMF Working Paper 07/38 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Gupta, S., R. Powell, and Y. Yang, 2006, Macroeconomic Challenges of Scaling Up Aid to Africa: A Checklist for Practitioners (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Gupta, S., S. Tareq, B. Clements, A. Segura-Ubiergo R. Bhattacharya, and T. Mattina, 2005, Rebuilding Fiscal Institutions in Postconflict Countries, IMF Occasional Paper No. 247 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Gupta, S., and M. Verhoeven, 2001, “The Efficiency of Government Expenditure: Experiences from Africa,” Journal of Policy Modeling, Vol. 23 (May), pp. 43367.

    • Search Google Scholar
    • Export Citation
  • Hausmann, R., D. Rodrik, and A. Velasco, 2006, “Getting the Diagnosis Right: A New Approach to Economic Reform,” Finance and Development, Vol. 43 (March).

    • Search Google Scholar
    • Export Citation
  • Heller, P., M. Katz, X. Debrun, T. Thomas, T. Koranchelian, and I. Adenauer, 2006, “Making Fiscal Space Happen: Managing Fiscal Policy in a World of Scaled-Up Aid,” IMF Working Paper 06/270 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Herrera, S., and G. Pang, 2005, “Efficiency of Public Spending in Developing Countries: An Efficiency Frontier Approach,” Policy Research Working Paper No. 3645 (Washington: World Bank).

    • Search Google Scholar
    • Export Citation
  • Independent Evaluation Office (IEO), 2007, An Evaluation of the IMF and Aid to Sub-Saharan Africa (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF), 2004, “Technical Assistance Evaluation Program—Findings of Evaluations and Updated Program” (Washington). Available via the Internet: http://www.imf.org/external/np/ta/2004/eng/030104.pdf

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF), 2005a, “The Macroeconomics of Managing Increased Aid Inflows: Experiences of Low-Income Countries and Policy Implications” (Washington). Available via the Internet: http://www.imf.org/external/np/pp/eng/2005/080805a.pdf

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF), 2005b, “The Managing Director’s Report on the Fund’s Medium-Term Strategy” (Washington). Available via the Internet: http://www.imf.org/external/np/omd/2005/eng/091505.pdf

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF), 2005c, “Public Investment and Fiscal Policy—Lessons from the Pilot Country Studies” (Washington). Available via the Internet: https://www.internationalmonetaryfund.com/external/np/pp/eng/2005/040105a.pdf

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF), 2005d, Technical Assistance Evaluation—Public Expenditure Management Reform in Anglophone African Countries (Washington).

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF), 2006a, Regional Economic Outlook: Sub-Saharan Africa, May 2006 (Washington). Available via the Internet: http://www.imf.org/external/pubs/ft/afr/reo/2006/eng/01/ssareo.pdf

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF), 2006b, Selected African Countries: IMF Technical Assistance Evaluation—Public Expenditure Management Reform, IMF Country Report No. 06/67 (Washington).

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF), 2007a, “Aid Inflows—The Role of the Fund and Operational Issues for Program Design” (Washington). Available via the Internet: http://www.imf.org/external/np/pp/2007/eng/061407.pdf

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF), 2007b, “Fiscal Policy Response to Scaled-Up Aid: Macro-Fiscal and Expenditure Policy Challenges” (Washington). Available via the Internet: http://www.imf.org/external/np/pp/2007/eng/060507a.pdf

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF), 2007c, “Fiscal Policy Response to Scaled-Up Aid: Strengthening Public Financial Management” (Washington). Available via the Internet: http://www.imf.org/external/np/pp/2007/eng/060507b.pdf

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF), and World Bank, 2004, “Aid Effectiveness and Financing Modalities” (Washington).

  • International Monetary Fund (IMF), 2005, “Update on the Assessments and Implementation of Action Plans to Strengthen Capacity of HIPCs to Track Poverty-Reducing Public Spending” (Washington).

    • Search Google Scholar
    • Export Citation
  • International Monetary Fund (IMF), 2006, “Applying the Debt Sustainability Framework for Low-Income Countries Post Debt Relief” (Washington). Available via the Internet: http://www.internationalmonetaryfund.org/external/np/pp/eng/2006/110606.pdf

    • Search Google Scholar
    • Export Citation
  • Isard, P., L. Lipschitz, A. Mourmouras, and B. Yontcheva, eds., 2006, The Macroeconomic Management of Foreign Aid: Opportunities and Pitfalls (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Keen, M., and A. Simone, 2004, “Tax Policy in Developing Countries: Some Lessons from the 1990s and Some Challenges Ahead,” in Helping Countries Develop: The Role of Fiscal Policy, ed. by S. Gupta, B. Clements, and G. Inchauste (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Kuteesa, F., I. Magona, M. Wanyera, and J. Wokadala, 2007, “Uganda: A Decade of Budget Reform and Poverty Reduction,” OECD Journal on Budgeting, Vol. 6 (January), pp. 91116.

    • Search Google Scholar
    • Export Citation
  • Mattina, T., 2006, “Money Isn’t Everything: The Challenge of Scaling-Up Aid to Achieve the Millennium Development Goals in Ethiopia,” IMF Working Paper 06/192 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Miguel, E., and M. Kremer, 2004, “Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities,” Econometrica, Vol. 72 (January), pp. 159217.

    • Search Google Scholar
    • Export Citation
  • Morrissey, O., 2006, “Aid Loans Versus Aid Grants: What Are the Impacts?paper presented at the Wilton Park Conference, “Scaling Up and Absorbing Resources: Challenges for Poverty Eradication,” Sussex, United Kingdom, October 25–28.

    • Search Google Scholar
    • Export Citation
  • Organization for Economic Cooperation and Development (OECD), 2005, “Making Poverty Reduction Work: OECD’s Role in Development Partnership” (Paris). Available via the Internet: http://www.oecd.org/dataoecd/31/5/34839878.pdf.

    • Search Google Scholar
    • Export Citation
  • Organization for Economic Cooperation and Development (OECD), 2006a, “2006 Survey on Monitoring the Paris Declaration” (Paris). Available via the Internet: http://www.oecd.org/document/7/0,2340,en_2649_15577209_36162932_1_1_1_1,00.html.

    • Search Google Scholar
    • Export Citation
  • Organization for Economic Cooperation and Development (OECD), 2006b, DAC Guidelines and Reference Series: Harmonizing Donor Practices for Effective Aid Delivery, Vol. 2: Budget Support, Sector Wide Approaches and Capacity Development in Public Financial Management (Paris).

    • Search Google Scholar
    • Export Citation
  • Paris High Level Forum, 2005, The Paris Declaration on Aid Effectiveness (Paris).

  • Public Expenditure and Financial Accountability (PEFA) Secretariat, 2005, Public Financial Management Performance Measurement Framework (Washington).

    • Search Google Scholar
    • Export Citation
  • Reinikka, R., and J. Svensson, 2006, “Using Micro-Surveys to Measure and Explain Corruption,” World Development, Vol. 34 (February), pp. 35970.

    • Search Google Scholar
    • Export Citation
  • Schiavo-Campo, S., 2007, “Budgeting in Postconflict Countries,” in Budgeting and Budget Institutions, ed. by A. Shah (Washington: World Bank).

    • Search Google Scholar
    • Export Citation
  • Schick, A., 1998, A Contemporary Approach to Public Expenditure Management (Washington: World Bank).

  • Selassie, A., B. Clements, S. Tareq, J. Martijn, and G. Di Bella, 2006, Designing Monetary and Fiscal Policy in Low-Income Countries, IMF Occasional Paper No. 250 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Serieux, John, and Terry McKinley 2007, “Why Not ‘Front-Load’ ODA for HIV/AIDS,” UNDP International Poverty Centre One Pager No. 29 (New York: United Nations Development Programme). Available via the Internet: http://www.undp-povertycentre.org/pub/IPCOnePager29.pdf

    • Search Google Scholar
    • Export Citation
  • Simwaka, C., 2007, “Malawi: Lessons Learnt from First Reforms Lead to New Approach,” OECD Journal on Budgeting, Vol. 6 (January), pp. i167.

    • Search Google Scholar
    • Export Citation
  • Takizawa, H., E. Gardner, and K. Ueda, 2004, “Are Developing Countries Better Off Spending Their Oil Wealth Upfront?IMF Working Paper 04/141 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • U.K. Department for International Development (DfID), 2007, “Understanding the Politics of the Budget: What Drives Change in the Budget Process?DfID Practice Paper (London).

    • Search Google Scholar
    • Export Citation
  • United Nations, 2005, Human Development Report: International Cooperation at a Crossroads: Aid, Trade and Security in an Unequal World (New York).

    • Search Google Scholar
    • Export Citation
  • United Nations Millennium Project, 2005, Investing in Development: A Practical Plan to Achieve the Millennium Development Goals (New York: United Nations Development Programme). Available via the Internet: http://www.unmillenniumproject.org/reports/fullreport.htm

    • Search Google Scholar
    • Export Citation
  • World Bank, 2004, Global Monitoring Report 2004: Policies and Actions for Achieving the Millennium Development Goals and Related Outcomes (Washington).

    • Search Google Scholar
    • Export Citation
  • World Bank, 2005, Improving the World Bank’s Development Effectiveness—What Does Evaluation Show? (Washington).

  • World Bank, 2006, Global Monitoring Report 2006: Strengthening Mutual Accountability—Aid, Trade & Governance (Washington).

  • World Bank, 2007a, “Aid Architecture: An Overview of the Main Trends in Official Development Assistance Flows,” background paper for the Joint Ministerial Committee of the Boards of Governors of the Bank and the Fund on the Transfer of Real Resources to Developing Countries, Washington, April 15.

    • Search Google Scholar
    • Export Citation
  • World Bank, 2007b, “Fiscal Policy for Growth and Development—Further Analysis and Lessons from Country Case Studies” (Washington).

    • Search Google Scholar
    • Export Citation
  • World Bank, 2007c, Global Monitoring Report 2007: Confronting the Challenges of Gender Equality and Fragile States (Washington).

  • World Health Organization (WHO), 2007, Towards Universal Access: Scaling Up Priority HIV/AIDS Interventions in the Health Sector (Geneva). Available via the Internet: http://www.who.int/hiv/mediacentre/univeral_access_progress_report_en.pdf

    • Search Google Scholar
    • Export Citation
  • Zhu, J., 2003, Quantitative Models for Performance Evaluation and Benchmarking (Boston: Kluwer Academics).

51

See Mattina (2006) for a detailed discussion.

52

The analysis presented here is based on panel data from 51 PRGF-eligible countries during 1990–2004. Data on aid flows are taken from the OECD’s Development Assistance Committee (DAC) database, which captures the majority of (but not all) aid flows to the sample countries. The rest of the information is obtained from the IMF’s World Economic Outlook (WEO) and Monitoring of Fund Arrangements (MONA) databases.

53

Bulíř and Hamann (2006) find that the average volatility of a country’s aid share in GDP is about 40 times higher than that of its revenue share in GDP.

54

The regressions use a fairly large number of explanatory variables, including economic growth, outcome gaps, commodity prices, political risk, revenues, and past values of aid. To test for robustness, the regressions were run in various permutations, using levels and changes of the variables, and with different estimation techniques. Some of the selected regression results are presented in Table A1.3.

55

Aid spurts were defined as periods when a country’s aid flows were notably higher than its average aid flows (by 0.75, 1, or 1.5 standard deviation), and then the average aid flows before and after these events were plotted.

56

Only selected regression results are reported. The initial set of regressions was run with data from the 51-country sample for the period 1990–2004. Adding the political risk variable in the specification significantly reduces the number of observations. The core results discussed in this section, however, hold across both the larger and smaller samples. Overall, the results are strong and survive a battery of controls and robustness tests.

57

The DEA methodology derives from the literature on the estimation of production functions (for a detailed exposition of DEA and other methods of assessing efficiency, see Zhu, 2003). DEA has the advantage of being sparse in its assumptions about the characteristics of the production technology. This is particularly important for assessing spending efficiency, because little is known about the nature of the relationship between spending and outcomes.

58

Health spending includes both public and private spending. However, education spending data relate to public spending only, because private spending data are not available.

59

Taking into account both private and public health spending is important for assessing health outcomes. Some countries with low public health spending have relatively better health outcomes. It would be reasonable to assume that this could reflect higher private spending; however, no direct relationship was found between the share of private spending in health and relative efficiency scores.

60

The first-stage efficiency scores computed with the three health outcome indicators are strongly correlated, indicating that the results are robust.

61

A control variable is considered correlated with the health efficiency scores when the correlation coefficient of that variable is statistically significant at the 10 percent level or higher and with the expected sign. To be considered robustly correlated, the relationship has to hold for at least three out of five efficiency score indicators.

62

The sample size does not allow computing correlation coefficients for education with sufficient confidence.

63

However, Herrera and Pang (2005) find that countries with high ratios of aid to fiscal revenues tend to score lower on efficiency.

64

The number of control variables that could be included was limited by the number of observations available.

65

For example, Baldacci and others (forthcoming) find that increased public spending has a lower effect on outcomes when the quality of spending and the governance and institutional arrangements are weak. A paper by the IMF’s Policy Development and Review Department (IMF and World Bank, 2004) states that improved country policies, institutions, and public expenditure management in low-income countries are important for aid to be more effective. Estache, Gonzalez, and Trujillo (2007) find that low-income countries have lower expenditure efficiency in achieving health and education outcomes than do lower-middle-income, upper-middle-income, and high-income countries. Finally, Gupta and Verhoeven (2001) find that efficiency of education spending is lower in African countries than in Asian and Western Hemisphere countries.

  • Aiyar, S., A. Berg, and M. Hussain, 2005, “The Macroeconomic Challenge of More Aid,” Finance and Development, Vol. 42 (September).

  • Allen, R., and D. Tommasi, eds., 2001, Managing Public Expenditure: A Reference Book for Transition Countries (Paris: Organization for Economic Cooperation and Development).

    • Search Google Scholar
    • Export Citation
  • Azariadis, C., and J. Stachurski, 2005, “Poverty Traps,” in Handbook of Economic Growth, Vol. 1A, ed. by P. Aghion and S. Durlauf (Amsterdam: North-Holland).

    • Search Google Scholar
    • Export Citation
  • Baldacci, E., B. Clements, S. Gupta, and Q. Cui, forthcoming, “Social Spending, Human Capital, and Growth in Developing Countries,” World Development (forthcoming).

    • Search Google Scholar
    • Export Citation
  • Barro, R., and X. Sala-i-Martin, 1995, Economic Growth (New York: McGraw-Hill).

  • Brooke, P., 2003, “Study of Measures Used to Address Weaknesses in Public Financial Management Systems in the Context of Policy Based Support,” final report for the Public Expenditure & Financial Accountability (PEFA) Secretariat (Washington: PEFA Secretariat).

    • Search Google Scholar
    • Export Citation
  • Bulíř, A., and J. Hamann, 2007, “Volatility of Development Aid: An Update,” IMF Staff Papers, Vol. 54, No. 4, pp. 72734.

  • Celasun, O., J. Walliser, 2006, “Predictability of Budget Aid: Recent Experiences,” in Budget Support as More Effective Aid? ed. by S. Koeberle, Z. Stavreski, and J. Walliser (Washington: World Bank).

    • Search Google Scholar
    • Export Citation
  • Clemens, M., S. Radelet, R. Bhavnani, 2004, “Counting Chickens When They Hatch: The Short-Term Effect of Aid on Growth,” CGD Working Paper No. 44 (Washington: Center for Global Development).

    • Search Google Scholar
    • Export Citation
  • Diamond, J., 2006, Budget Systems Reform in Emerging Economies: The Challenges and the Reform Agenda, IMF Occasional Paper No. 245 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Dorotinsky, W., and R. Floyd, 2004, “Public Expenditure Accountability in Africa: Progress, Lessons, and Challenges,” in Building State Capacity in Africa, ed. by B. Levy and S. Kpundeh (Washington: World Bank).

    • Search Google Scholar
    • Export Citation
  • Eifert, B., and A. Gelb, 2005, “Improving the Dynamics of Aid: Towards More Predictable Budget Support,” Policy Research Working Paper No. 3732 (Washington: World Bank).

    • Search Google Scholar
    • Export Citation
  • Estache, A., M. Gonzalez, and L. Trujillo, 2007, “Government Expenditures on Education, Health, and Infrastructure: A Naive Look at Levels, Outcomes, and Efficiency,” Policy Research Working Paper No. 4219 (Washington: World Bank).

    • Search Google Scholar
    • Export Citation
  • Fedelino, A., G. Schwartz, and M. Verhoeven, 2006, “Aid Scaling Up: Do Wage Bill Ceilings Stand in the Way?IMF Working Paper 06/106 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation
  • Feyzioglu, T., V. Swaroop, and M. Zhu, 1998, “A Panel Data Analysis of the Fungibility of Foreign Aid,” World Bank Economic Review, Vol. 12, No. 1, pp. 2958.

    • Search Google Scholar
    • Export Citation
  • Fölscher, A., and N. Cole, 2007, “South Africa: Transition to Democracy Offers Opportunity for Whole System Reform,” OECD Journal on Budgeting, Vol. 6 (January), pp. 5390.

    • Search Google Scholar
    • Export Citation
  • Foster, M., and T. Killick, 2006, “What Would Doubling Aid Do for Macroeconomic Management in Africa?ODI Working Paper No. 06/264 (London: Overseas Development Institute).

    • Search Google Scholar
    • Export Citation