Chapter

4 Outlook for the Millennium Development Goals

Author(s):
International Monetary Fund
Published Date:
April 2010
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How will the global economic crisis alter precrisis trends in the Millennium Development Goals (MDGs)? With only five years left until the target date of 2015, it is obvious that several of the MDGs will not be attained, globally or by a majority of countries. Many of the goals are too high for low-income countries, given their low starting points. Many countries, including low-income ones, have seen substantial gains in recent years, however, and entered the current crisis in a stronger position than in past crises (chapter 1 and chapter 2). Important questions are whether the gains will be preserved, and what happens if the fragile recovery slips into a prolonged stagnation.

The crisis is likely to have a lasting impact on human development indicators that will not overcome even a robust economic recovery. Although growth in emerging and developing countries is currently accelerating, should growth slow or deteriorate, progress toward the MDGs will suffer even more. A decline in growth would have a significant impact on poverty and undernourishment. The impact of a growth slowdown on some of the other MDG indicators analyzed is more muted, although the cost in absolute numbers—additional children dying or uneducated, additional people left without clean water—could be large because of the size of the population underlying each rate. Countries can achieve better development outcomes through improved policies, most notably shifts in expenditures, increases in domestic revenue, and better service delivery. Stronger policies are unlikely to compensate fully for the deterioration in human development indicators that result from slower growth, however. In the current context, better development outcomes will thus depend on the speed at which the global economic recovery supports increases in developing countries’ export revenues and external finance.

This chapter looks at these issues in two ways. It first presents alternative scenarios for progress on some key human development–related MDGs based solely on different forecasts of GDP growth, with the results aggregated by regions. This relatively limited approach provides a general sense of the impact of the crisis and the potential envelope for the MDGs looking ahead over the next five to ten years. The second part of the chapter then takes into account a broader set of determinants of progress in the MDGs, including fiscal policy (public expenditures and their composition plus revenue efforts), export revenues, terms of trade, aid flows, remittances, and foreign borrowing. This richer analysis allows a much more robust view of how the external economic environment and developing-country policies will affect progress toward the MDGs. The scope of the analysis, however, and the variables involved, make it extremely difficult to provide comprehensive forecasts of human development indicators for developing countries. Instead, this section illuminates the channels that influence MDG outcomes through the lens of two types of low-income developing-country structure based on natural endowments—those that are resource poor and those that are resource rich.1

Forward analysis of the MDGs

The original analytical framework underpinning the assessment of policies as developed by the World Bank and the International Monetary Fund (IMF) in the first Global Monitoring Report in 2004 remains very valid today for organizing this policy assessment (figure 4.1). The two key pillars for achieving the development outcomes are economic growth and delivery of services to the poor—the very two factors likely to be most affected by global economic crisis. That is why the lessons of history regarding the effects of growth decelerations on various human development indicators are examined in chapter 2. Although not the only driver, growth will likewise be a key factor in projecting the postcrisis trends for the MDGs. The other key factor, effective service delivery, is difficult to assess even in the best of circumstances.2

FIGURE 4.1Framework linking policies and actions with development outcomes

Source: World Bank 2004a.

The current crisis has resulted in a deterioration in human development indicators that will have important future effects even with a robust economic recovery. If growth were to stagnate or slow, the impact on human welfare in developing countries would be severe. Projecting the aggregate outlook for the MDGs is fraught with difficulties (box 4.1).3 Nevertheless, it is essential to assess where things stand in the aftermath of the crisis, as developing countries enter a new and less favorable external environment.

The alternative scenarios of progress toward the MDGs presented here are based on a simplified reduced-form analysis linking economic growth—the key variable of the crisis and the recovery scenarios—to the MDG indicators.4 The simulations are based on GDP growth because it is a major determinant of progress toward the MDGs, and it is the only determinant that is projected for a large group of countries and that is anchored by the short-, medium-, and long-term economic outlook in the International Monetary Fund’s (IMF) World Economic Outlook and the long-term growth projections that underpin the World Bank’s Global Economic Prospects. Because of the many uncertainties described in box 4.1, these projections relating progress in the MDGs to alternative scenarios for GDP growth are necessarily subject to large margins of error and should be taken as illustrative.

BOX 4.1Uncertainty and risk in projecting attainment of the MDGs

There are many uncertainties and risks in projecting development outcomes. One is the strength and timing of the economic recovery. Another is the complexity of the relationships between the MDGs and their determinants, which are still poorly understood. Among the MDGs the impact of economic performance on poverty is better established, although the elasticity of poverty to growth can vary with country circumstances and initial conditions. Furthermore, human development outcomes are influenced by a wide range of factors, including the evolution of household incomes and public resources, as well as the consequences of supply and demand for policies, institutional actions, and microlevel services. Given the complexity and differing assumptions about the recovery, assessments of human development outcomes can be wide ranging.

Another important uncertainty in forecasting progress toward the MDGs is fiscal adjustment—public expenditures and their composition are key determinants of human development indicators in low-income countries. A deterioration in the macroeconomic environment may reduce government income, thus endangering public expenditures essential for progress toward the MDGs. However, aid, external borrowing, and international reserves may provide the fiscal space needed to protect social spending, while remittances may help to support private expenditures. Hence, fiscal adjustment and thus the implications of slower growth for the MDGs will vary from country to country depending on circumstances and conditions entering the crisis.

Several studies point to other problems in accounting for all of the influences on human development indicators. An increase in public expenditures does not necessarily improve education and health outcomes; nor does economic growth alone. Links between public expenditures and social sector outcomes are weak. Supply-side factors associated with effective service delivery are preconditions for improving basic service provision—school facilities, books, health clinics, vaccination programs, qualified teachers and health staff, and the like. Client demand for services and various other factors at the local level—household incomes, distance and opportunity costs, voice and participation of clients, educational attainment of mothers, corruption, and cultural and religious norms—also matter and may vary by community. The empirical regularity of these potential determinants can become difficult to establish at the country, regional, and global levels.

Source: Dinh, Adugna, and Myers 2002; Adams and Bevan 2000; Filmer, Hammer, and Pritchett 2000, 2002; Devarajan and Reinikka 2004; World Bank 2004.

The estimated relationship between poverty and growth is based on household surveys in more than 100 countries and assumes that the underlying income or expenditure distribution is relatively stable during changes in economic growth.5 The poverty analysis brings 31 new household surveys to the 2010 Global Monitoring Report and new projections of per capita income growth in the aftermath of the crisis. The analysis also considers four other MDGs—primary education completion, infant mortality, gender equality in education, and access to clean water—for which aggregate quantitative analysis is currently feasible (future reports will expand the analysis to other MDGs). The relationship between GDP growth and each indicator is estimated for each country.

MAP 4.1In 2007, 72 million children worldwide were denied access to education

Source: World Development Indicators.

The results show that growth generally was significantly related to progress in the human development indicators. However, confirming all the caveats mentioned above, the estimations using growth alone accounted for only 30-40 percent of past variations of the MDG indicators across countries and time. These coefficients were then used to forecast each MDG indicator for each country, based on alternative scenarios for GDP growth. Although it is certainly possible to include other determinants of the MDG indicators in the estimation, it is not practical to forecast these other indicators on a country-by-country basis (box 4.2).

Three global scenarios for progress on human development-related MDGs

Three global scenarios for GDP growth address the risks of the current global economic crisis: a postcrisis trend; a high-growth or precrisis trend; and a low-growth scenario.

The postcrisis trend assumes a relatively rapid economic recovery in 2010, with strong growth continuing into the future, as described in chapter 2.6 This is essentially the base case forecast for growth in developing countries after the crisis.

The precrisis trend gives the forecast path for the MDGs if developing countries had continued their impressive growth performance during 2000-07, the period just before the global economic crisis. The impact of the crisis on the MDGs can thus be measured by comparing the postcrisis trend with this one.

The low-growth scenario assumes that the recovery projected for the postcrisis trend will not take place in the medium run. The scenario assumes little or no growth for about five years, when it begins to slowly recover. This scenario follows the pattern of past responses to severe external shocks in developing countries.

The impact on the very poor

Recent economic shocks have taken a toll on the poor. The crisis left an estimated 50 million more people in extreme poverty in 2009, and some 64 million more will fall into that category by the end of 2010 relative to a pre-crisis trend.7 New estimates suggest that the large global spike in food prices in 2008 may have led the incidence of undernourishment to rise by around 63 million people, while the crisis itself may have led to an additional 41.3 million undernourished people, or 4.4 percent more undernourished people in 2009 than would have been the case without the economic crisis.8

BOX 4.2Estimating the impact of growth on human development indicators

The relationship between GDP growth and the MDGs was estimated taking into account a policy index reflecting the country’s level of policy and institutions plus a set of initial conditions (for example, adult female literacy rate, urbanization, ethnic fractionalization, level of income, and location by geographical region).a Several policy indexes had a significant relationship with the MDG indicators. Among these, the World Bank’s Country Policy and Institutional Assessment (CPIA) rating was selected because it is a broader measure than one based solely on governance indexes. It covers economic management, structural policies, policies for social inclusion and equity, and public sector management and institutions. The explanatory power of the equations jumps from 30-40 percent using growth as the sole explanatory variable to 80 percent when the index of policy and initial conditions are taken into account. These estimations help to refine the understanding of the relationship between growth and the human development indicators. However, it did not prove possible to use the policy index or the initial conditions in the alternative scenarios for future progress in the MDG indicators, given the difficulties involved in forecasting these variables in many countries.

a. The use of the policy index is similar to the empirical works of Wagstaff and Claeson (2004), Rajkumar and Swaroop (2002), and Filmer and Pritchett (1999). The CPIA is available in the World Development Indicators.

A rapid economic recovery (the postcrisis trend) would improve the situation for many of the extremely poor and lead to substantial reductions in the poverty rate, to 15 percent in 2015, well below the MDG target of 20.4 percent (table 4.1). Nevertheless, the crisis has imposed a lasting cost on poverty reduction. Had the crisis not interrupted the rapid economic progress made by developing countries through 2007 (the precrisis trend), the poverty rate at $1.25 a day would have fallen to about 14 percent by 2015, implying that an additional 53 million people would have been lifted out of extreme poverty. Things could be worse than the postcrisis trend, however. If the economic outlook deteriorates to the low-growth scenario, the poverty rate could fall only to 18.5 percent, with an additional 214 million people living in absolute poverty by 2015 (relative to the postcrisis trend).

TABLE 4.1Poverty in developing countries, alternative scenarios 1990-2020
Region and scenario1990200520152020
Global level
Percentage of the population living on less than $1.25 a day
Postcrisis41.725.215.012.8
Precrisis41.725.214.111.7
Low-growth41.725.218.516.3
Number of people living on less than $1.25 a day (millions)
Postcrisis1,8171,371918826
Precrisis1,8171,371865755
Low-growth1,8171,37111321053
Sub-Saharan Africa
Percentage of the population living on less than $1.25 a day
Postcrisis57.650.938.032.8
Precrisis57.650.935.929.9
Low-growth57.650.943.839.9
Number of people living on less than $1.25 a day (millions)
Postcrisis296387366352
Precrisis296387346321
Low-growth296387421428
Source: World Bank staff calculations using the PovcalNet database.
Source: World Bank staff calculations using the PovcalNet database.

On current or postcrisis growth trends, poverty in Sub-Saharan Africa is projected to drop to 38 percent by 2015—more than 9 percentage points short of its target. Before the crisis the region had been on a path to reach a poverty rate of 35.9 percent, which would have lifted another 20 million people out of poverty by 2015. If growth stagnates into the low-growth scenario, the trend gap could more than double, implying an additional 55 million people remaining in extreme poverty by 2015.

The long-term nature of the cumulative effects becomes clearer when global projections are extended 10 years forward. The postcrisis trend suggests that by 2020, 826 million people (12.8 percent) in developing countries will be living on less than $1.25 a day, implying that 71 million more people will be living in absolute poverty in 2020 as a result of the crisis. The low-growth scenario would result in a rise of 227 million living in absolute poverty compared with the postcrisis trend. The corresponding increases in poverty for Sub-Saharan Africa in 2020 are 31 million more people in poverty for the postcrisis trend and 76 million more for the low-growth scenario. The five additional years would leave Sub-Saharan Africa still short of halving poverty, the MDG target for 2015.

Poverty rates vary considerably among the other regions (annex table 4A.1 and table 4A.2). Even in the low-growth scenario, the East Asia and Pacific region more than meets its poverty target, in large part because of China’s success in reducing poverty. South Asia, on the strength of India’s achievement, meets the poverty target in the postcrisis trend but not in the low-growth scenario. Middle-income countries in Europe and Central Asia are projected to miss the poverty reduction MDG at poverty lines of both $1.25 and $2 a day. However, the poverty rates in these countries are very low to start with (about 4 percent at $1.25 a day and about 9 percent at $2 a day in 2005), so a higher poverty line of $4 to $5 a day is more meaningful for this group of countries.

Overall, the projection for the $2 a day poverty threshold is less promising. In the postcrisis trend, 2 billion people, or one-third of the population of developing countries—more than half of the 1990s level—remain in poverty at $2 a day.

Impact on selected human development indicators

The crisis will have serious and lasting costs and gaps for other human development indicators as well (figure 4.2 and table 4.2). According to the projections for 2015, as a result of the crisis:

  • The number of infants dying would increase by 55,000. Without the crisis, 260,000 additional children under the age of five could have been prevented from dying in 2015. The cumulative total from 2009 to 2015 could reach 265,000 and 1.2 million, respectively. The consequences for infant mortality in Africa are grave, with some 30,000-50,000 additional infant deaths in 2009, virtually all of them girls.9 The tragedy is not just these added deaths—more than 3 million infants die in Africa every year, a number that could be reduced through better policies and interventions.
  • Some 350,000 more students will fail to complete primary school.
  • Some 100 million more people will lose access to safe drinking water.

FIGURE 4.2The long-run effect of slower growth on selected MDGs is worrisome

Source: World Bank staff calculations.

TABLE 4.2Trends for other MDG human development indicators by region and alternative economic scenarios
2015
MDG and regionTarget19912007PostcrisisPrecrisisLow-growth
MDG 2: Primary completion rate (%)
East Asia and Pacific1001019810010099.3
Europe and Central Asia100939899.910099.9
Latin America and the Caribbean1008410097.910097.7
Middle East and North Africa100789094.995.693.6
South Asia100628082.491.781.9
Sub-Saharan Africa100516067.367.666.7
All developing countries100788591.591.890.4
MDG 3: Ratio of girls to boys in primary and secondary education (%)Target19912007
East Asia and Pacific1008999100100100
Europe and Central Asia10010010299.410097.8
Latin America and the Caribbean10098103100100100
Middle East and North Africa100789695.698.294.7
South Asia100708992.794.492.1
Sub-Saharan Africa100798689.789.989.1
All developing countries100839596.096.595.6
MDG 4: Child mortality under five (per 1,000)Target19902007
East Asia and Pacific19562724.618.624.9
Europe and Central Asia17502318.815.421.7
Latin America and the Caribbean18552623.719.725.4
Middle East and North Africa26783836.729.237.3
South Asia421257876.062.776.6
Sub-Saharan Africa61183146139.5138.7141.0
All developing countries341017468.668.169.5
MDG 7.c: Access to improved water source (% population w/access)Target19902006
East Asia and Pacific1632133.30.64.1
Europe and Central Asia5105001.8
Latin America and the Caribbean81695.44.57.1
Middle East and North Africa611128.37.410.0
South Asia1327139.35.110.2
Sub-Saharan Africa26514239.138.839.8
All developing countries12241410.19.611
Source: World Bank staff estimates.
Source: World Bank staff estimates.

The impact on gender equality in education and on access to safe water is muted in these scenarios (although even small changes in these indicators can translate into large numbers of people affected) because these indicators are influenced by forces that change only slowly. For example, the participation by girls in school reflects in part the educational level of the mother, and access to safe water is affected by the degree of urbanization. The impact of slower growth on the MDGs increases, however, as the time horizon is extended further into the future (for example, fewer girls being educated now means that eventually women of childbearing age will have less education).

In general, the impact of the low-growth scenario on development outcomes will be cumulative and long term (figure 4.3).

FIGURE 4.3The long-run effect of slower growth is especially worrisome in Sub-Saharan Africa

Source: World Bank staff estimates.

Note: The precrisis period is 2000-07.

  • If the baseline scenario (the postcrisis trend) holds up, human development indicators will continue to improve albeit less rapidly owing to the extended impact of the crisis. By 2015 the differences between the gains projected in the postcrisis trend and those for the precrisis trend will become discernible, especially for human development outcomes such as primary school completion and infant mortality.
  • Like the compounding effects of interest rates, these gaps will intensify from 2015 to 2020. A look at the long-term impact reveals that the projected slide in human development outcomes will become damaging and irreversible unless action is taken now.
  • The world needs to avoid economic stagnation. If the growth trend in developing countries becomes sluggish for a long time, as in the low-growth scenario, development outcomes will deteriorate or stall, as happened in many low-income countries in Sub-Saharan Africa during the 1970s and 1980s.

Spending strategies under less favorable circumstances

What can developing countries do if the external economic environment remains unfavorable, and what impacts might their policy and spending strategies have on development outcomes? The three global growth scenarios provided a broad picture of the likely impact of the crisis on poverty. But these scenarios cannot be used to explore the scope for mitigating the effects of external shocks on poverty through appropriate policy adjustments. For this purpose, the broad country coverage achieved in the scenarios given above is set aside in favor of a richer analysis of the impact of policies.

To begin, low-income countries are divided into two groups—those that are resource rich and those that are resource poor.10 A representative economy of each type is then constructed based on the average indicators for all of the low-income countries in that group (tables 4A.1 and tables 4A.2 in the annex summarize the social and economic indicators that characterize each country archetype; for the most part they correspond to the latest median statistics from the World Bank’s World Development Indicators database. The assumptions and data used in constructing each of the two representative economies are given in box 4.3.

These simulations use the World Bank’s Maquette for MDG Simulations (MAMS), a model that analyzes the implications of strategic choices for economic outcomes, including changes in human development indicators (see box 4A.1 in the annex for more discussion).11 MAMS’ main contribution is its integration of government services and their impact on the economy, including on the MDGs and the labor market, within a standard recursive dynamic computable general equilibrium framework. Several MAMS features are useful for assessing the impact of alternative scenarios on MDGs. The model incorporates a formal representation of the production of government services (education, health, and infrastructure) that takes into account demand, supply, and efficiency. It allows for complementarity or synergy effects across the MDGs—for example, better access to clean water may improve health, which may boost school attendance, labor productivity, and economic growth. It shows the economywide repercussions of scaling up (or down) human development services, including the impact on economic growth, relative prices, the exchange rate, and the allocation of resources between government and nongovernment sectors. And it makes possible the consideration of sequencing and time-related trade-offs through a recursive treatment of dynamics that tracks indicators over time.

The low-income, resource-poor country

The analysis for the low-income, resource-poor archetype (LIRP) considers four cases (the reference year for the analysis is 2009, and the simulation period is 2010-20):

  • The base case is relatively optimistic. It assumes that GDP growth recovers by 2011 to the growth rate in 2008. The annual growth rate in 2012-20 is slightly higher than in 2011 (see figure 4.4 for GDP growth under different LIRP cases). Growth in foreign aid is slower after 2010 than in the previous decade, reflecting a decline in GDP growth in donor countries.12 Remittance growth and foreign direct investment (FDI) fall relative to the previous decade, also reflecting a decline in GDP growth in the countries from which the payments flow. By 2015 world prices have recovered to 2008 precrisis levels.
  • The low-aid case represents an extreme, negative case with a weak recovery in GDP growth (to just 40 percent of real GDP growth in the base case), driven by a deteriorating external environment and a decline in productivity growth. World prices, FDI, and foreign aid all grow at slower rates than in the base case (25 percent of base case rates). The growth slowdown for foreign aid and other government receipts leads to reduced development spending (defined as spending on education, health, water and sanitation, and infrastructure), as the government fails to reduce spending in other areas. Remittances are assumed to grow at the same annual rate as in the base case because these payments are based on personal connections, and there is little reason to expect them to respond negatively to slower growth in the developing countries.
  • In the low-aid internal 1 case the government makes internal adjustments to offset the effects on the MDGs of a weak recovery in GDP and reduced growth in foreign inflows. The government reduces growth in nondevelopment spending (to 90 percent of such spending in the base case), increases receipts from domestic taxes (by half a percentage point of GDP over the base case), and uses the resulting fiscal space to expand development spending.
  • In the low-aid internal 2 case, the government further improves policies and service delivery relative to the low-aid internal 1 case, resulting in a moderately higher GDP growth (55 percent of the base-case rate).13

FIGURE 4.4Annual GDP growth for LIRP under four cases

Source: Go and others, forthcoming.

BOX 4.3Assumptions for the archetype countries

The low-income, resource-rich (LIRR) archetype has a natural resource that it exports. The government receives 70 percent of the income, and foreign investors get the rest. In 2009 government income from the natural resource was 8.4 percent of GDP. All output of the natural resource commodity is exported and accounts for 56 percent of the value of total exports. Government borrowing is 2.6 percent of GDP, and foreign debt is 49 percent of GDP. The country receives no debt relief during the simulation period.

The low-income, resource-poor (LIRP) archetype is more dependent than the LIRR on foreign aid, which equals about 6.5 percent of GDP, and its foreign debt is higher, at 65 percent of GDP. Like the LIRR, it receives no debt relief during the analysis period.

The poverty headcount rate at $1.25 a day (the indicator for MDG 1) is 49.6 percent for the LIRP archetype and 61.8 percent for the LIRR—the median values for the countries in each group. The poorest statistics for the LIRR result partly from the “natural resource curse” associated with past conflicts and corruption; see, for example, Collier and Goderis (2007). Median GDP per capita is $598 for the LIRP and $482 for the LIRR. Similarly, both the LIRP and the LIRR are assumed to have the median value of their group for share of the population with access to clean water (MDG 7); the under-five mortality rate (MDG 4); and selected education indicators, including the gross completion rate for primary school (MDG 2) and gross enrollment rates at all three levels (primary, secondary, and tertiary). The analysis looks especially at the evolution of MDGs 1, 2, 4, and 7.

Government and nongovernment payments and foreign debt of archetype countries, 2009percent of GDP
PaymentLow-income, resource-poor countriesLow-income, resource-rich countries
Income from natural resourcen.a.8.4
Foreign aid6.51.2
Taxes20.216.0
Private borrowing0.50.4
Foreign borrowing4.02.6
Foreign debt65.048.8
Foreign direct investment1.91.7
Remittances1.31.2
Source: Go and others, forthcoming.Note: n.a. = not applicable.
Source: Go and others, forthcoming.Note: n.a. = not applicable.

Slow growth in the low-income, resource-poor country results in a severe deterioration in human development indicators. All four of the MDGs covered by the analysis (poverty, primary school gross completion rate, under-five mortality rate, and share of population with access to safe water) decline in the low-aid simulation relative to the base case (figure 4.5). By 2020 the poverty rate is more than 20 percentage points higher, the under-five mortality rate 15 points higher, and the share of the population with access to safe water 4 percentage points lower in the low-aid case than in the base case. The gross primary school completion rate improves in all scenarios, as students enrolled in lower grades (reflecting recent strong expansion in primary enrollment) proceed through the primary level. Because of a natural decline in the intake of out-of-cohort students, progress tends to level off.14

FIGURE 4.5Simulated MDG outcomes for the LIRP archetype under alternative cases

Source: World Bank staff calculations using the Maquette for MDG Simulations (MAMS). See Go and others, forthcoming.

With better expenditure management and internal effort in the low-aid internal 1 case, including a government shift in expenditures to protect development spending and increased domestic tax collection, all the MDGs (except poverty reduction) do better than under the low-aid case. The poverty rate in the low-aid internal 1 case is marginally higher than in the low-aid case, because the increase in taxes (predominantly indirect taxes in low-income countries) reduces expenditures or incomes of the poor.

Under the low-aid internal 2 case, a more substantial improvement in MDG indicators can be accomplished by combining improved fiscal policies with policies that improve overall productivity. Progress toward the MDGs improves relative to the low-aid internal 1 and the low-aid cases, although not enough to catch up with the base case.

Thus policy adjustments to support development spending and improve overall economic productivity are critical to limiting the impact on human development indicators of an externally induced decline in GDP growth (for example, the current crisis). However, to the extent that policies cannot maintain trend growth in the face of an external shock, then a deterioration in human development indicators is inevitable. This fact highlights the importance of a global response to the crisis that focuses on ensuring strong flows of aid, limiting the deterioration in developing countries’ access to external finance, and maintaining open export markets to permit trade expansion at more attractive world prices.

The low-income, resource-rich country

The pattern of results for the low-income, resource-rich archetype (LIRR) is similar to that of the LIRP, including GDP growth rates in figure 4.4. Under the optimistic base case, which, unlike the other scenarios, includes a strong recovery in the world price of the natural resource export, all MDG indicators continue to improve. Internal adjustment (that is, the government reduces growth in nondevelopment spending, increases domestic taxes, and uses the resulting fiscal space to expand spending on education, health, water and sanitation, and infrastructure) in the context of stagnant export prices for the natural resource improves progress toward the MDGs but is not sufficient to bring the country up to the path of the base case (figure 4.6). A resource-rich country has the ability to draw down reserves accumulated from its resource exports or to increase government foreign borrowing, in both cases creating a capital inflow, captured by the government. This option, incorporated into the low-aid internal 2 simulation, can move progress on the MDGs closer to the base path.15 But at the level reported, the LIRR country cannot make up for the impact of the financial crisis on MDGs through internal adjustment alone.

FIGURE 4.6Simulated MDG outcomes for the LIRR archetype under alternative cases

Source: World Bank staff calculations using the Maquette for MDG Simulations (MAMS). See Go and others, forthcoming.

MAP 4.2Tuberculosis kills around 1.3 million people a year, or 3,500 a day

Source: World Development Indicators.

Summary and conclusions

This chapter presented forecasts of MDG outcomes at the global level and for Sub-Saharan Africa based solely on alternative assumptions for growth in developing countries. It also explored the scope for policy improvements to mitigate the impact of slower growth on progress toward the MDGs through simulations using two archetypical low-income countries, one representing those that are resource rich and the other those that are resource poor. While understanding the prospects for progress toward the MDGs is of crucial importance as the world looks forward to 2015 and beyond, it should be recognized that such analysis inevitably is fraught with difficulties given data gaps and still-limited knowledge about the processes that determine these outcomes.

The projections given here indicate that the economic crisis will lead to a deterioration across all MDGs, extending beyond 2015. In all the growth scenarios, the world will meet the MDG of halving its headcount poverty rate using a poverty line of $1.25 a day. However, the poverty rate in 2015 is considerably higher in the low-growth scenario (18.5 percent) than in the postcrisis trend (15 percent), which assumes a rapid recovery from the crisis. The rough magnitude of the projected effects on hunger is similar. Underlying these figures are considerable regional variations. Sub-Saharan Africa poses the greatest challenge—it has the highest poverty rates and will have the most difficulty achieving its regional poverty reduction targets.

The projected impact of alternative scenarios for growth on the other MDGs analyzed here—completion of primary school, under-five mortality rate, gender equality in education, and access to safe water—is more limited, although small changes in these percentages may involve large numbers of people. This muted effect reflects the presence of significant lags, perhaps most obviously in education. The negative effects of slower growth will make themselves more strongly felt in the long run, however.

Country-level simulations for the two low-income archetypes indicate that, if the global economic environment and domestic GDP growth recover rapidly, continued progress will take place across the MDGs that are covered here (poverty, primary completion, under-five mortality, and access to safe water). If the global recovery is weak, internal efforts (including spending switches toward development and tax increases) lead to some improvement in the MDGs compared with a scenario with no improvement in policies. However, the improvement from internal efforts alone falls far short of that required to achieve the base-case levels of the MDG indicators. Thus, while policy matters, better development outcomes hinge critically on a rapid global recovery that improves export conditions, terms of trade, and capital flows for low-income countries. Chapter 5 turns to this subject.

Annex: Forecast, Tools, and Data

BOX 4A.1MAMS: A tool for country-level analysis of development strategies

MAMS (Maquette for MDG Simulations) is an economywide simulation model developed at the World Bank to analyze development strategies. The model integrates a dynamic recursive computable general equilibrium model with an additional module that links specific MDG or poverty-related interventions to progress on poverty and other MDGs. This link is made possible by a disaggregation of government activities into functions related to MDG services (education, health, and water and sanitation) and infrastructure as well as a residual for other government activity. The government finances its activities from domestic taxes, domestic borrowing, and foreign aid (borrowing and grants). The private sector disaggregation varies between applications; where private provision of MDG services is important, such services are included, complementing the contribution of government services to MDG progress. The factors of production in the model typically include three types of labor, each of which is linked to an education cycle: those with incomplete secondary education (unskilled), those with completed secondary education but incomplete tertiary (semi-skilled), and those with completed tertiary (skilled). The labor force variable depends on the functioning of the education system in the model. The other factors of production include public capital stocks by government activity and a private capital stock. Growth in the stock of government infrastructure capital contributes to overall growth by adding to the productivity of other production activities.

MAMS covers MDGs in the areas of poverty, education, health, and water and sanitation. For poverty, a log-normal distribution is assumed; other applications have used microsimulations. For other MDGs, a set of functions links the level of each indicator to a set of determinants. The determinants include the delivery of relevant services and other indicators, also allowing for the recognition that achievements in one MDG can have an impact on other MDGs. Other than education, service delivery for other MDGs is expressed relative to the size of the population. In education, students successfully complete their grade, repeat it, or drop out of their cycle. Student performance depends on educational quality (quantity of services per student), household welfare, public infrastructure, wage incentives, and health status.

A MAMS country database is a synthesis of information from a variety of sources, structured to meet the requirements of the model. The model parameters are defined using this data. The main components of the database are a social accounting matrix and other data that reflect the functioning of the economy, with some emphasis on human development and infrastructure. More specifically, the information is primarily related to stock data (for labor and other production factors, students, and population) and elasticities (related to substitutability in production, consumption, and trade as well as to responses in MDG indicators to various determinants). For the simulations, it is also necessary to provide assumptions about the evolution of policies and other factors that are exogenous to the model.

The government policies that may be considered include spending—its level and allocation across different areas, including education, health, and infrastructure—and financing—policies for taxation, domestic and foreign borrowing, and foreign aid. Economic performance is measured by the evolution of:

  • poverty and other MDG targets
  • macro-indicators, including GDP (split into private and government consumption and investment, exports, and imports); the composition of the government budget, the balance of payments, and the savings-investment balance; total factor productivity; and domestic and foreign debt stocks
  • sectoral structure of production, employment, incomes, and trade
  • the labor market, including unemployment and the educational composition of the labor force
Note: For more information about MAMS, see www.worldbank.org/mams.
TABLE 4A.1Alternate scenarios for poverty reduction, based on a poverty line of $1.25 a day, by region
ScenarioRegion or country19902005201520201990200520152020
PostcrisisPercentage of the population living on less than $1.25 a dayNumber of people living on less than $1.25 a day (millions)
East Asia and Pacific54.716.85.94.087331712083
China60.215.95.14.06832087056
Europe and Central Asia2.03.71.71.291675
Latin America and the Caribbean11.38.25.04.350453027
Middle East and North Africa4.33.61.81.5101166
South Asia51.740.322.819.4579595388352
India51.341.623.620.3435456295268
Sub-Saharan Africa57.650.938.032.8296387366352
Total41.725.215.012.81,8171,371918826
PrecrisisPercentage of the population living on less than $1.25 (2005 PPP) a dayNumber of people living on less than $1.25 (2005 PPP) a day (millions)
East Asia and Pacific54.716.85.53.587331711173
China60.215.95.03.96832086955
Europe and Central Asia2.03.71.51.191675
Latin America and the Caribbean11.38.24.63.950452825
Middle East and North Africa4.33.61.71.4101166
South Asia51.740.321.517.9579595367326
India51.341.622.719.6435456283259
Sub-Saharan Africa57.650.935.929.9296387346321
Total41.725.214.111.71,8171,371865755
Low-growthPercentage of the population living on less than $1.25 (2005 PPP) a dayNumber of people living on less than $1.25 (2005 PPP) a day (millions)
East Asia and Pacific54.716.87.85.8873317159122
China60.215.96.04.76832088267
Europe and Central Asia2.03.72.52.29161110
Latin America and the Caribbean11.38.26.55.750453936
Middle East and North Africa4.33.63.32.710111211
South Asia51.740.328.624.6579595489447
India51.341.629.425.2435456367333
Sub-Saharan Africa57.650.943.839.9296387421428
Total41.725.218.516.31,8171,3711,1321,053
Source: World Bank staff calculations, using PovcalNet.
Source: World Bank staff calculations, using PovcalNet.
TABLE 4A.2Alternate scenarios for poverty reduction, based on a poverty line of $2.00 a day, by region
ScenarioRegion or country19902005201520201990200520152020
PostcrisisPercentage of the population living on less than $2.00 a dayNumber of people living on less than $2.00 a day (millions)
East Asia and Pacific79.838.719.414.31,274730394299
China84.636.316.012.0961473220168
Europe and Central Asia6.98.95.04.132392218
Latin America and the Caribbean19.716.611.19.786916762
Middle East and North Africa19.716.98.36.644523026
South Asia82.773.957.051.09261,091973926
India82.675.658.351.9702828728686
Sub-Saharan Africa76.273.059.655.4391555574595
Total63.247.033.729.82,7542,5572,0601,926
PrecrisisPercentage of the population living on less than $2.00 (2005 PPP) a dayNumber of people living on less than $2.00 (2005 PPP) a day (millions)
East Asia and Pacific79.838.718.613.41,274730379280
China84.636.315.711.8961473216166
Europe and Central Asia6.98.94.53.732392016
Latin America and the Caribbean19.716.610.38.886916256
Middle East and North Africa19.716.98.06.144522924
South Asia82.773.955.549.09261,091946890
India82.675.657.250.9702828715674
Sub-Saharan Africa76.273.057.652.4391555555563
Total63.247.032.628.42,7542,5571,9911,830
Low-growthPercentage of the population living on less than $2.00 (2005 PPP) a dayNumber of people living on less than $2.00 (2005 PPP) a day (millions)
East Asia and Pacific79.838.722.218.11,274730451379
China84.636.316.913.6961473233191
Europe and Central Asia6.98.97.16.232393127
Latin America and the Caribbean19.716.614.512.986918882
Middle East and North Africa19.716.914.111.444525245
South Asia82.773.963.957.89261,0911,0891,049
India82.675.664.657.9702828808766
Sub-Saharan Africa76.273.065.162.5391555627671
Total63.247.038.234.92,7542,5572,3382,254
Source: World Bank staff calculations, using PovcalNet.
Source: World Bank staff calculations, using PovcalNet.
TABLE 4A.3Detailed data for archetypesMedian values by archetype, selected variables
VariableLow-income, resource-poor

(LIRP)
Low-income, resource-rich

(LIRR)
Poverty headcount ratio at $1.25 a day (% of population)49.661.8
Poverty headcount ratio at $2.00 a day (% of population)76.780.5
Elasticity of poverty to income-1.01-1.01
Poverty headcount ratio at national poverty line (% of population)44.245.4
Primary school completion rate, total (% gross)55.859.9
Primary school enrollment (% gross)95.995.2
Secondary school enrollment (% gross)31.635.5
Tertiary school enrollment (% gross)3.24.7
Under-five mortality rate (per 1,000)115.2141.6
Maternal mortality ratio, modeled estimate (per 100,000 live births)720.0825.0
Maternal mortality ratio, national estimate (per 100,000 live births)478.0613.0
Improved water source (% of population with access)65.060.0
Improved sanitation facilities (% of population with access)30.031.5
Foreign direct investment, net inflows (% of GDP)2.76.5
Foreign direct investment, net outflows (% of GDP)0.00.0
Foreign direct investment inflow outflows (% of GDP)2.76.5
Net current transfers, remittances (% of GDP)8.94.5
Official current transfers, receipts, foreign aid (% of GDP)2.51.7
External debt stocks (% GNI)29.949.5
External debt stocks private (% GNI)0.00.0
External debt stocks public (% GNI)29.949.5
External debt stocks public, median (% GDP)28.048.6
Gross fixed capital formation (% of GDP)20.818.3
Gross fixed capital formation, private (% of GDP)10.711.3
Labor force participation rate (% of total population ages 15-64)74.371.4
Resource exports (% of GDP)0.419.0
Resource exports (% of merchandise exports)3.467.9
Mining value added (% of GDP)0.73.3
Interest payment on private external debt (% of GDP)0.00.0
Interest payment on public external debt (% of GDP)0.30.5
Source: World Bank 2009b.
Source: World Bank 2009b.
1

Resource intensity is an important factor in the performance of low-income countries and has been used to classify developing countries in several studies; see Collier and O’Connell (2006); IMF (2006); Ndulu and others (2007); and Arbache, Go, and Page (2008).

2

World Bank 2004b.

3

Even short-term assessments are necessarily projections because of the infrequency of the underlying data. Household surveys of incomes and expenditures are generally undertaken only every five or more years in many developing countries.

4

The estimation uses a logistic function, similar to Clemens, Kenny, and Moss (2007) but with per capita income as a key determinant instead of a time trend. Income rather than social spending is used as the independent variable because of data and other difficulties with fiscal adjustment and public expenditures. The logistic curve was used for the projections because it has a smoother transition across income levels, although the elasticity form (double-log regressions) by income level or region yielded similar results.

5

World Bank PovcalNet database.

7

World Bank 2003, p. 41. These calculations update estimates found in Ravallion (2009) and World Bank (2009a).

8

Tiwari and Zaman 2010; World Bank 2010a.

10

The low-income countries are disaggregated into resource rich and resource poor using data on exports of fuel ore and minerals as a share of merchandise exports. See table 4A.3 in the annex for more details.

12

The growth rate is set at 3 percent, the assumed annual GDP growth in developed countries from 2010 onward.

13

This is 15 percent higher than the annual GDP growth in the pessimistic scenario with internal adjustment (low-aid internal 1 case).

14

The primary gross completion rate (MDG 2) is defined as the total number of primary school graduates (regardless of age) as a share of the total population of the theoretical graduation age. If MDG 2 were measured by the net completion rate (the number of graduates of the theoretical right age as a share of the total population of the same age), the tendency for the indicator to level off would be weaker, especially for the base case.

15

In the model this is done by increasing foreign borrowing, which reduces the net asset position of the country relative to the rest of the world and is equivalent to drawing down foreign exchange reserves or liquidating foreign investment financed by the natural resource in the past. Here, the annual growth rates in foreign borrowing are assumed to be twice the annual growth rates in the base. As a result, the foreign debt stock in foreign currency is 30 percent higher in 2020 for the low-aid internal 2 case than for the other scenarios. Relative to GDP, the foreign debt stock is around 10 percentage points higher in 2020 for the low-aid internal 2 case than for the low-aid internal 1 case (which has a slightly slower rate of GDP growth and similar evolution for the exchange rate). References

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