73237 Concept Note Global Monitoring Report 2013 Rural-Urban Dynamics and the Millennium Development Goals I. Introduction and thematic focus The 2013 Global Monitoring Report (GMR) will mark the tenth edition of the GMR since the inception of the report in 2004. The GMR continues to provide an annual assessment of progress towards the Millennium Development Goals (MDGs). This assessment allows the Development Committee of the World Bank and the IMF together with the larger international development community to reinforce accountabilities among developing and developed countries and institutional partners.1 As a monitoring report, the GMR draws on recent and ongoing research work at the IMF, the World Bank, and the UN, and other outside sources. Each year‘s report also has a thematic focus, an aspect of the development agenda on which the GMR provides a more in-depth assessment.2 The theme of this year‘s report, rural urban dynamics, is highly relevant for assessing progress within the current MDG framework, but it also has the potential to inform discussions about the post-2015 development framework. Urbanization matters for the MDGs in several ways. Two main channels by which the MDGs are impacted are through the benefits of agglomeration, as cities have the potential to generate higher living standards for all their residents (not just migrants), and through the benefits of scale economies, as basic public services can be provided at lower unit cost in urban areas. Overall, urbanization can play a positive role in reaching both the income and service delivery MDGs if these potential benefits of agglomeration and scale economies are realized. In addition, the official list of MDG indicators calls for disaggregating the MDGs by urban/rural as far as possible. This disaggregation has not been addressed in earlier GMRs. The year 2015, the deadline by which the MDGs should be achieved, is fast approaching and the remaining challenges are becoming clearer. Three MDGs are estimated to have been achieved as of 2012: MDG1a (halve, between 1990 and 2015, the proportion of people whose income is less than $1.25 a day), one half of MDG7c, halve, by 2015 (the proportion of people without sustainable access to safe drinking water), and MDG7d (by 2020, to have achieved a significant improvement in the lives of at least 100 million slum dwellers). Overall progress on the remaining MDGs has been less than stellar, however, particularly those related to education and health, and an acceleration of progress is needed in order to achieve all of the goals by 2015.3 1 See Development Committee Communiqué, Washington, DC, April 13, 2003. 2 See annex 1 for a complete overview of the thematic focus of the GMRs since 2004. 3 Note that regional progress is quite diverse, with various regions making strong progress towards achieving the education and health related MDGs, while other regions, particularly Sub-Saharan Africa and South Asia as well as fragile states, progression towards these goals has been less and therefore making global achievement of these MDGs unlikely without an acceleration (See various GMRs). With 2015 just around the corner, 2012 has marked the start of discussions on a post-2015 development framework and a second generation of MDGs (UN 2012a). The findings of the GMR 2013 can assist with the discussion of the post-2015 development agenda not least because they will point to how countries can reap the benefits of economies of scale in service delivery and agglomeration of economic activity and make progress on important development outcomes as currently measured by the MDGs. There are also regional considerations to be taken into account. Sub-Saharan Africa and South Asia, which face the most challenging development trajectories, are less urbanized than other regions are, and therefore the lessons articulated in the GMR 2013 can help inform policy choices beyond 2015. But the findings of the report certainly will not be limited to certain regions. Urbanization is a long-term worldwide trend that is still very much underway, and the implications of a more urbanized global population will be felt for decades into the future. How countries manage urbanization will be a key part of making progress in the international development agenda. The 2013 GMR will be prepared jointly by the World Bank and the IMF, with consultations and collaborations with regional development banks, other multilateral and bilateral partners, and potentially think tanks and NGOs. The report will serve as one of the key documents for discussion at the 2013 Spring Development Committee Meeting. The GMR will be disseminated widely, using not only well- established forms of dissemination along with social media as a means to reach a large audience. II. Report Structure and Content The 2013 GMR will include an overview that integrates and summarizes the report‘s findings and presents the main messages and issues for discussion and two substantive parts. Part I will discuss major elements of the agenda for achievement of the MDGs i.e. economic growth and macroeconomic developments, development financing and the role of donors, and IFIs. Part II will investigate how rural– urban migration and the transformation of rural settlements into towns and cities affect the ability of developing countries to make progress towards various MDGs. This will include policy options that are available to developing countries to spur the positive and mitigate the negative implications of the ongoing rural-urban dynamics. The report will include an assessment of progress toward the MDGs in the form of an insert. This assessment will include a detailed analysis of regional progress and a discussion about the acceleration needed to achieve the MDGs by 2015. The thematic focus of rural- urban dynamics will run through all of the chapters, to varying degrees. Part I: Monitoring the Global Development Agenda Growth and macroeconomic performance in emerging and developing countries Chapter 1will review macroeconomic performance in low- and middle-income countries in light of recent developments in the world economy. In order to make progress on achieving the Millennium Development Goals (MDGs), emerging and developing countries need to continue to show rapid economic growth in the run-up to the 2015 deadline. The recovery of the world economy from the Great Recession has been uneven, protracted and risks weigh on the global outlook. Low-income-countries -2- (LICs)—the countries that are most challenged in meeting the MDGs—remain very vulnerable. The questions to be explored are: • What has been emerging and developing countries‘ macroeconomic performance in recent years? Which groups of countries are growing relatively fast or slow? Has growth been sustainable or has it taken place in the context of rising imbalances? To what extent has growth been shaped by economic policies or the external environment? • What is the macroeconomic outlook for emerging and developing countries through 2015? Can these countries continue to grow while maintaining price stability? Is the external outlook—e.g., demand in advanced countries, financial flows, and commodity prices—conducive for continued growth? • Depleted policy buffers have yet to be fully restored, and many low-income countries have more limited fiscal space and larger external current account deficits than before the crisis. Against this backdrop, how vulnerable are low-income countries to a possible growth slowdown? How would low- income countries cope if global growth were to be significantly lower over a protracted period? • Using a World Bank country classification system based on an agglomeration index (see World Bank 2009), the chapter will explore whether there are any discernable differences in macroeconomic performance among emerging and developing countries related to the extent to which countries are relatively more urban than rural. Aid and international financial institutions Chapter 2 will assess progress on the MDG agenda as it relates to volume and composition of aid to developing countries, and discuss performance by international financial institutions (IFIs). The analysis of aid volumes will build primarily on recent trends in Development Assistance Committee (DAC) members‘ aid flows and composition. This will be complemented, pending availability of data, with trends in aid flows from non-DAC members and private donors. In addition to analyzing aid volume and composition, chapter 2 will analyze issues of aid quality and effectiveness such as its predictability, alignment, harmonization, and coherence with the MDG agenda. Special attention will be given to examining aid flows to fragile states, and to countries/regions that have been least successful in making progress towards the MDGs. The role of Official Development Assistance (ODA) in the field of rural-urban development, including support for urbanization, will also be addressed. This chapter will also assess elements of performance by IFIs. It will present and update lending trends, progress with the aid effectiveness agenda, and an assessment of IFI support in the field of rural-urban development and discuss IFI assistance for urbanization. The team will draw on various ongoing initiatives to monitor international support in this area.4 4 A good example is DeLoG an organization that aims at contributing to the implementation of the Paris-Accra-Busan process on aid effectiveness in the field of decentralization and local governance. Currently 27 bilateral and multilateral development partners are members of DeLog. -3- Part II: Rural-Urban Dynamics and the MDGs An estimated 3.6 billion people—more than 50 percent of the world‘s population—now live in urban areas (UN 2012b). Developing countries have urbanized rapidly over the past two decades, with the number of people living in urban settlements rising from about 1.5 billion in 1990 (36 percent of the population) to 2.7 billion in 2011 (46.5 percent). It is likely that 1 billion people live in urban slums in developing countries (World Bank 2009). Managing the process of urbanization, such that it continues to play a positive role regarding growth, poverty reduction and other development outcomes addressed by the MDGs has become more pressing since the adoption of the Millennium Declaration in 2001(see UN 2012a). Despite higher fertility rates in rural areas than in urban areas, projections indicate that global population increases will be largely an urban phenomenon in the decades ahead and will be concentrated in the developing world (Satterthwaite 2007), in large part because of an ongoing rural-urban migration trend. For developing countries, well over 45 percent of the increase in urban population over the last decade is estimated to have resulted from rural-urban migration.5 Given that the degree of urbanization is significantly higher in the developed world, at 77.1 percent compared to 46.5 percent in the developing world, rural-urban migration can be expected to remain a large part of urban growth in the coming decades. For various measures of human development that can be disaggregated between urban and rural areas, people living in urban areas typically fare better than those in rural areas. For example, Ravallion, Chen, and Sangraula (2007) find that the global rural poverty headcount index stood at 29.3 in 2002 while the same global measure for urban areas stood at 12.8 for the same year. Similar observations can be made in the data related to other MDGs. Access to water and sanitation facilities, for example, is typically much lower in rural areas than in urban areas. In 2010, 96 percent of urban population had access to safe drinking water, compared to 81 percent of the rural population. These disparities are more pronounced in access to basic sanitation: 80 percent in urban areas compared to 50 percent in rural areas (see World Bank 2012a and UN 2012c). Urban environments not only have the advantage of providing economies of scale for the delivery of services such as water, sanitation, and education, but their density of economic activities allows for faster economic development relative to rural areas (World Bank 2009; Montgomery 2009). As rural dwellers migrate to cities in search of a better livelihood, then, the shift in population distribution can be expected to spur progress towards the achievement of MDGs related to the delivery of services. But urban areas do not provide guarantees. Migrants can find themselves in urban slums where expected improvements in overall service delivery necessary to achieve the MDGs do not ensue. The important point here is that rural-urban dynamics and the possibility of urban areas to improve living conditions have implications for progress toward the MDGs. Chapter 3 will report on the status of various MDGs (disaggregated into urban and rural areas), discuss the challenges that come along with slums, and differentiate between cities of different sizes given that 5 Authors‘ calculations. Montgomery (2009) gives a figure of 40 percent. -4- more than 50 percent of the world‘s urban population lives in relatively smaller size cities. Chapter 4 will discuss the dynamics of urbanization and rural-urban migration and note circumstances under which both can jointly improve or worsen the ability of countries to achieve the MDGs. The chapter will delineate the challenge of designing public policies that can enhance factors that grow the benefits of urbanization and offset those factors that potentially worsen the implications of urbanization and rural-urban migration on a country‘s ability to achieve an overall reduction in poverty and make progress towards the MDGs. Clearly, intergovernmental relations, political, administrative and/or fiscal, plays an important role in the effectiveness of service delivery and consequently progress towards the MDGs. This chapter will describe how integovermental realstions can assist the harnassing of urbanization and provide incentives for service delivery in rural and urban areas. To analyze the various channels through which the MDGs are affected by urbanization, the GMR will synthesize quantitative and qualitative research to analyze policy responses and implications of rural- urban dynamics; this will be accomplished by drawing on surveys and discussions of urban and/or slum residents and by undertaking modeling exercises simulating the implications of various policies that would deliver services to urban and rural populations and consequently affect rural-urban inequalities and their implications on progress towards the MDGs. The recently completed World Development Report on jobs (World Bank 2012b) will also be drawn upon, and integrating throughout the GMR the report‘s lessons on making progress toward the MDGs. Specific topics and analysis that will be covered in the two chapters of this part of the report, are as follows: Urban-rural differences, city size, and the MDGs Chapter 3 will start by highlighting critical disparities in poverty and service delivery between rural and urban areas and within urban areas, the role of migration, and the implications of urbanization, for progress towards the MDGs. Empirical evidence points to stark and large differences in access to basic services such as education, health, and water and sanitation between rural and urban areas, with obvious implications for the MDGs due to expire in 2015. Even though Ravallion, Chen, and Sangraula, 2007, find that, the urbanization of poverty occurred more rapidly than urbanization of population, urbanization played an important positive role in overall poverty reduction. The combination of these two trends means that the world ‗s urban poverty headcount between 1993 and 2002 changed relatively little dropping 13.5 percent to 12.8 percent for 2002, while for the same period the rural poverty headcount declined much more from 36.6 percent to 29.3 percent. The 2007 study also finds that both urban and rural poverty rates tend to be lower at higher degrees of urbanization, and that there tends to be a convergence of rural and urban poverty rates at higher levels of urbanization.6 Undoubtedly, marked regional differences exist: Latin America has the most urbanized poverty problem, East Asia has the least; and there has been a ―ruralization‖ of poverty in Eastern Europe and Central Asia. In marked contrast to other regions, Africa‘s urbanization process was not associated with falling overall poverty rates between 1993 and 2002. Following the approach of Ravallion, Chen, and Sangraula, 2007, the GMR 2013 team will update the "decomposition" of trends in poverty by rural and urban areas across regions to gauge progress towards the poverty MDG. 6 An important issue in comparing rural-urban disparities will be to take into account properly, differences in prices of goods and services in rural and urban areas (see Glaeser and Gottlieb 2009). -5- In addition to inter-regional differences, there are significant spatial differences within countries which have in recent years drawn attention to the complex challenges in meeting the service delivery MDGs. Along the spectrum from ―rural‖ to ―urban‖ lie many types of locations that vary from small towns to small cities and peri-urban areas to the well-known metropolises. Traditionally, these classifications were based on population size and density, but recent research has shown that there are important differences in poverty and service delivery between these locations. Research for India, for example, indicates that while poverty is primarily a rural phenomenon at the aggregate level, urban poverty is becoming a larger problem. The poverty rate for rural areas in India was 28 percent in 2004-5, compared to 26 percent in urban areas. Among urban areas in India, poverty rates are highest in small towns (population less than 50,000), at 30 percent, relative to 15 percent in large cities (population of 1 million or more) (Lanjouw et al. 2011). One key challenge resulting from the rural-urban migration trend underway in many countries is the ability of cities to absorb the inflow of new residents. In many cities in the developing world, migrants— particularly poor migrants—find themselves confined to inner-city or peri-urban informal settlements and slums (UN-Habitat 2012), where service delivery is likely to be worse than in other areas of the city. An estimated one-third of the urban population of developing countries lives in slums, although the figure has been declining in recent years. Sub-Saharan Africa is the unfortunate negative outlier in this regard with well over 60 percent of its urban population living in slums. However, the situation differs very much by city size as most of the new jobs are being created in small towns (Ghani et al 2011) and intermediate size cities (McKinsey Global Institute 2011) where urbanization has been more rapid like in China and India. The job growth in small and intermediate size towns will assist countries to achieve poverty reduction and the MDGs. At the aggregate level, MGD 7D, i.e., to have achieved a significant improvement in the lives of at least 100 million slum dwellers through better service delivery, was met in 2012, well ahead of the 2020 target UN 2012c). However, at a regional and country level, progress indicators show that there is significance diversity in MDG 7D for example in Sub-Saharan Africa where the prevalence of slums remains a major challenge. Evidence also shows that slum dwellers are often worse off than rural populations with respect to access to services. Child mortality rates in the slums in Nairobi, for example, are much higher than in rural areas of Kenya (African Population and Health Research Center 2002). The GMR 2013 will discuss lessons learned over the years regarding slum improvements (e.g., Martin and Mathena 2010). The report will also examine country cases to illustrate specific characteristics that need emphasis, such as comprehensive versus narrowly focused slum improvements, relying on community participation and multi-stake holder alliances to identify priorities for actions, and employing conditional cash transfer programs (Annez and Linn 2010). There is considerable spatially heterogeneity among urban areas and one important dimension of that heterogeneity is across city sizes. In Brazil, for instance, though most anecdotal discussion of urban poverty focuses on the sprawling slums of Rio de Janeiro or São Paulo, more than 50 percent of the country‘s urban poor live in cities with fewer than 50,000 inhabitants. Only around 10 percent live in cities with populations greater than a million. In Kazakhstan, the incidence of poverty in smaller towns is six times larger than in Almaty (Ferre, Ferreira, and Lanjouw 2010). -6- Access to local public goods and services, also differs greatly across city sizes and between leading and lagging regions. Average access to sewerage services in Morocco, for example, is more than 80 percent in cities with a population greater than 1 million, but less than 50 percent in the smallest towns. In China, more than 80 percent of the urban poor live in prefectural or lower-level cities (World Bank 2012c). The four largest provincial megacities—Beijing, Shanghai, Chongqing, and Tianjin—have the lowest urban disadvantaged rate of around 1 percent. This chapter will include analysis that follows the approach of Ferre, Ferreira, and Lanjouw (2010) and Lanjouw and Marra (2012) to improve the understanding of how welfare varies across different types of cities, both in terms of income or consumption expenditure and in terms of access to public services. The report will show various examples including from Brazil and China, to illustrate how policy makers have successfully closed the services divide. These findings, together with the various country examples, should help inform global and national discussions of appropriate poverty reduction and national development strategies. Policy implications of rural-urban disparities in service delivery and the MDGs The objective of chapter 4 is to examine the policy implications in the context of disparities in service delivery and incomes between rural and urban areas, rural-urban migration and the opportunities through urbanization to reduce poverty and potentially accelerate progress toward achieving the MDGs. The discussion will shed light on the challenges policy makers face in fostering urbanization to reduce overall poverty and achieve the MDGs, while containing potential adverse impacts coming from rural-urban migration that leads to urban congestion. A large number of poor individuals seek to improve their well-being by migrating from a location with poor service delivery and/or income opportunities to an area where they expect to be better off. As industries emerge in urban agglomerations and the manufacturing and services sectors create productive employment opportunities in cities, rural residents migrate to urban areas in order to improve their income earning potential (Lucas 2004). Typically, economic development has been accompanied by an increase in the share of the population living in urban areas and by growth in the urban economy (Kuznets 1955). All developed countries are marked by a larger share of economic production in urban than rural areas, and more importantly, a larger share of their labor force is gainfully employed in the urban economy When rural residents move to urban areas because of the attraction to improve their income earning potential and find indeed a job with a higher income, there is usually an unmistakable improvement in their individual wellbeing. In this case, there is also an overall reduction in poverty as the economy-wide number of poor decreases. However, if residents leave rural areas in desperation due to lack of basic amenities, and either fail to find a better paying job or find one that does not lift them out of poverty, migration is unlikely to dampen poverty (World Bank 2009, p. 147). In particular, when migrants do not benefit from relocating to urban areas, urban congestion is likely to increase, slums can emerge, and urban poverty can rise. In such circumstances, migrants usually end up in slums. -7- Successful urbanization has two important characteristics:  First, the process nurtures human capital accumulation and productivity, and enables rural migrants to contribute to agglomeration economies in urban areas. This usually occurs when rural areas offer the basic education and health services that migrants need for a better paying job in urban areas. For a resident who does not have the prerequisite skills, the decision to migrate can be a poverty worsening one.  Second, the process involves efficient and sufficient provision of public services such as water, sanitation, and other urban infrastructure, as well as health and education. Efficiency in the provision of such services is easier in urban than in rural areas given that scale economies are more likely to be achieved in urban areas. Simply living in an urban area does not guarantee efficient services delivery, however, nor does is guarantee that a necessary level of services will be provided. The pressures of a growing urban population and continued rural-urban migration can lead to urban congestion and a situation in which the supply of services falls short of demand, in turn reducing the well-being of migrants and often causing firms and urban residents to relocate to peri-urban areas and/or slums. In addition to improved income prospects, the desire to access better education and health services that can enrich their families‘ human capital and future income provides strong additional motivation for people to move to urban areas. At the same time, firms like to locate where workers would like to live. Lall, Timmins, and Yu (2009) combine a rich data set of public services at the municipality level with individual records from four decades of Brazilian census data to evaluate the relative importance of wage differences and public services in migrant‘s decisions to move. The findings show a clear distinction in preferences according to income level: for the relative well off people, basic public services were not important in the decision to move; but for the poor, differences in access to basic public services did matter. In fact, poor migrants in Brazil are willing to pay for access to basic services. A Brazilian minimum wage worker earning R$7 per hour (about US$2.30 in February 2008), for example, was willing to pay R$420 a year to have access to better health services, R$87 for better water supply, and R$42 for electricity. Though a comprehensive tabulation of domestic migration flows is not possible, evidence from some household surveys is compelling in demonstrating the scale of relocation that has occurred over the past several decades. For example, in Korea, during 1970-1995, the urban share of the population quadrupled to 82 percent with migration accounting for more than 50 percent in the 1960 and 1970s (Lall et al 2012). Clearly, the empirical evidence on poverty reduction presented in the preceding discussion points to the central role that urbanization can play in enabling developing countries to achieve the poverty reduction and service delivery MDGs. However, the extent to which the relevant MDGs can be achieved by 2015 hinges critically on at least two factors:  Fostering urbanization: The efficiency and expediency of concurrently implementing policies that foster agglomeration economies on one hand and delivering public services in pace with demand in urban areas, including smaller towns and cities, on the other, will factor heavily into a -8- successful urbanization process in all countries (World Bank 2009). If a balance is maintained, the positive externalities of urbanization will offset the urbanization of poverty (at least in the aggregate) and lead to an overall reduction in poverty (Ravallion, Chen, and Sangraula 2007).  Reducing rural poverty: Fostering urbanization alone will not be enough to attain the service delivery and poverty MDGs. Given its magnitude, rural poverty is unlikely to be transformed into urban prosperity anytime soon—certainly not before 2015. What are the implications of the existence of this large number of absolute poor for the service delivery MDGs?7 While there are many complex policy issues related with the ―how to‖ of urbanization, in the narrower and directly pertinent context of the poverty and service delivery MDGs, key challenges for governments are: to enable the rural and urban poor with the human capital (i.e., education and health) to participate in the industrialization process associated with agglomeration, and supply basic public services in line with demand. Turning to the challenge of service delivery to achieve the MDGs and its cost effectiveness, raises the issue of how services should be delivered when there are spatial disparities in service delivery and better paying job opportunities. Since the cost of service delivery is subject to scale economies which are most likely found in densely populated urban areas, and since the urban areas are also home to agglomeration economies, it makes sense to equip the poor to migrate from rural (or lagging) areas to urban areas and deliver services to urban areas. This is part of a well-known debate on whether poverty reducing policies should be targeted at ―poor people‖ as opposed to ―poor places.‖ In principle, if individuals can freely migrate from poor to rich places, geographic targeting of poverty reduction and service delivery is inefficient (Atkinson and Stiglitz (1980), Lipton and Ravallion 1995, Lall, Timmins and Yu (2009). The obvious implication of this theoretical approach is to focus service delivery on urban not rural areas (poor places with poor people). From a practical perspective, especially for the GMR 2013 that monitors reality on the ground, the implications of this debate are tricky as the majority of the poor today are in the rural areas. They do not necessarily have portable human capital that enables them to migrate at once or even rapidly to urban areas where they can access better services and higher paying jobs. In fact, as the history of the developed countries and middle-income countries shows, the transformation of a rural to an urban economy in these circumstances is a longer-term phenomenon. Under certain circumstances, researchers have found good reasons for targeting local public goods in ―poor places‖ 8 instead of poor people. Of course, case-specific conditions matter, not least of which are 7 The modern approach to rural-urban migration fosters agglomeration economies, innovation activity, rapid economic growth, catch-up and better paying jobs, i.e., all around economic progress. These directly pertain to the MDGs. 8 The case for targeting poor areas is not obvious in a setting in which there are no evident barriers to migration. Suppose that households can freely choose their location; we can term that state of affairs ―free migration.‖ If the economy is in equilib rium, such that nobody wants to move, then standards of living must be completely determined by mobile non-geographic household characteristics. For if geographic location were to have an effect on consumption after controlling for those characteristics, then households would move to the areas with positive geographic effects. But, as long as it was possible to target according to non- geographic characteristics, there would be no point to geographic targeting. Attempts to redistribute between rich areas and poor areas would generate migration until a new equilibrium is restored, consistent with the new distribution of non-geographic attributes. There would be no point using residential location as an indicator for targeting anti-poverty schemes (Lipton and Ravallion 1995, section 6). -9- the pace of migration and/or the implicit or explicit costs of migrating, which can be significant for the poor. One source notes that ―the same, observationally equivalent household is poor in one place but not another. Moreover, these geographic effects appear to be stable over time… Our results reinforce the case for anti-poverty programs targeted to poor areas even in an economy with few obvious impediments to mobility‖ (for Bangladesh, Ravallion, and Wodon (1997), Ravallion (1993), Lipton and Ravallion (1995)). Atkinson and Stiglitz (1980) add that ―there may be constraints on the ability of policy makers to target household characteristics when attempting to reduce poverty. There may still be an efficiency case for poor-area programs when regional governments supply public services. Without appropriate inter-regional transfers, free migration between the regional jurisdictions does not, in general, imply efficient local provisioning.‖ Empirical studies for several countries suggest that context-specific interventions are best. In the case of Cambodia, Ecuador, and Madagascar, it appears that the impact on poverty of transferring an exogenously given budget is less than the gains that would be possible if the policy makers also had access to information on household level income or consumption. A useful way forward might be to combine fine geographic targeting (at the level of districts or villages) using a poverty map with within- community targeting mechanisms, where the within-community characteristics are based on geographically defined subgroups of the population according to their relative poverty status (Elbers, Fujii, Lanjouw, Özler, and Yin 2004). In a study that supports targeting poor people rather than poor places, Shilpi (2008) finds that in Bangladesh, rural-urban differences arise partly due to rivers dividing regions of the country acting as physical constraints on migration. The study shows that there are substantial differences in returns to human capital, and that households composed of people with relatively high levels of education and ability are concentrated in urban areas, where returns are higher. Because migration is restricted by natural factors, causing differences in returns between the regions to persist, there is need for improving connectivity between the regions, and for investing in the portable assets of the poor, such as human capital (Shilpi 2008). The GMR team proposes to balance the theoretical debate on the pros and cons of urbanization with the practical challenges of achieving the MDG targets for 2015. The empirical evidence indicates that the prudence of targeting poverty is country-specific. In general, it is unlikely that there will be a simple and single formula for achieving the MDGs for all low-income countries. There is a high probability that as the GMR team evaluates the status of the poverty and service delivery MDGs on a country-specific basis, it will find that in countries where the rural-urban differences are stark and the number of rural poor large, there is a case for targeting poor places (or rural areas) rather than poor people. Whether scaling up or otherwise improving service delivery to achieve the MDGs should target poor rural people or poor rural places is very country-specific and needs to be taken seriously. The question will be analyzed in two ways. First, chapter 2 will examine the implications of identifiable factors of rural-urban migration on the non-poverty MDGs by using various country case studies and simulations from a CGE model with an explicit MDG module (MAMS). The framework permits investigating how geographic - 10 - differences in the level of service delivery, in combination with differences in real wage levels, affect development outcomes. The databases and the model will be developed to capture the impact of service delivery standards in areas such as health, education, water, and sanitation, on household consumption and welfare, including the impact on various MDGs and the links to government policies and budgets (see box). Second, where possible, the team will use BOOST to relate service delivery outputs and/or outcomes with government spending disaggregated by type of service and location of service (rural and urban areas) within a country. Although BOOST does not provide a rigorous estimate of the unit cost of service delivery in rural and urban areas, in the absence of any cost statistics, it will be useful in illustrating rural- urban differences in efficiencies across population centers and providing insights to policy makers about which services and/or regions perform well or poorly with available resources.9 Box: A dynamic general equilibrium perspective on rural-urban MDG gaps As a complement to micro and sector analyses, analysis considering progress on MDGs and rural-urban inequalities in their broader country economic context is valuable: urbanization is linked to changes throughout the economy (including the evolution of wages gaps and job demands in rural and urban areas) and influences the scope for providing more equal access to human development services for everyone, considering fiscal constraints and rural- urban unit cost differences. Furthermore, urbanization may boost growth in production and incomes as more workers become employed in higher productivity jobs. In addition, agglomeration effects associated with urbanization may also lead to more rapid productivity growth, especially for sectors that are predominantly urban. In order to explore these issues in a low-income country context, MAMS (Maquette for MDG Simulations), a computable general equilibrium (CGE) model developed for country-level development strategy analysis, will be augmented to capture interactions between urbanization and MDG progress, including rural-urban gaps.1 More specifically, drawing on cross-country econometric analysis and available research findings, three new relationships will be endogenized in MAMS. First, urbanization (the population share living in urban areas) will be expressed as a function of endogenous variables in MAMS (including sectoral employment shares, relative rural-urban access to human development services, and infrastructure development). Second, in addition to their current arguments, which include infrastructure stocks and economic openness, the functions defining sectoral total factor productivity (TFP, a measure of efficiency) will be augmented to include urbanization, with the strength of the link for any given sector reflecting the extent to which the sector tends to benefit from urban agglomeration effects. Third, the relationships for selected MDG indicators (potentially in the areas of poverty, health, education, water, and sanitation) will be disaggregated into rural and urban areas, linking the indicators to a set of determinants that are expected to include per-capita incomes and government services. The costs of the latter enter the government budget; drawing on available data, the service unit costs will be higher in rural areas. The parameters underlying these relationships are part of the broader model database, which will be representative of an archetype low-income country. Key data sources will include existing MAMS applications to low-income countries, selected data in World Development Indicators and EdStats, the harmonized household surveys of I2D2, and demographic and health surveys. The model and its database will be used to explore links between economic policies, external shocks, and MDG indicators in rural and urban areas, drawing on dynamic simulations covering the period 2013-2030. The following types of questions will be analyzed: What are the impacts of providing more equal and/or expanded access to human development services on growth, MDGs, and other welfare indicators? How are these impacts conditioned by alternative scenarios for efficiency in service provision and relative unit cost differences between rural and urban areas? How do the effects differ depending on whether required financing comes from domestic taxation or foreign grant aid? 1/ For information about the standard version of MAMS, visit www.worldbank.org/mams. 9 The BOOST tool allows analysis of spending and performance data by sector and geographical location. See Europe and Central Asia knowledge brief, May 2010, for a detailed description. - 11 - III. Process and Timeline The GMR is prepared in partnership with the IMF and relies on close collaboration and contributions from many networks and regional departments in the Bank and the IMF. Consultations and contributions from other international and bilateral institutions are central, including other Multilateral Development Banks, UN, the OECD-DAC, and various NGOs. 1. Preliminary structure of the report Overview Insert on progress towards the MDGs Part I: Monitoring the global development agenda Chapter 1: Growth and macroeconomic performance in emerging and developing countries Chapter 2: Aid and international financial institutions Part II: Rural-urban dynamics and the MDGs Chapter 3: Urban-rural differences, city size and the MDGs Chapter 4: Policy Implications of rural-urban disparities in service delivery and the MDGs 2. Timeline Informal Board meeting on GMR concept note October 2012 Informal Board meeting on key GMR messages Early February 2013 CoW Meeting on draft report/IMF Board review Late March 2013 Development Committee meeting April 21, 2013 Dissemination April–June 2013 3. Organization Team. A primary team of World Bank and IMF staff will work in close partnership to prepare the GMR relying on contributions from many networks and regional departments in the two institutions. Consultations with and contributions from other international and bilateral institutions—including other multilateral development banks, the UN, the OECD Development Assistance Committee, and various NGOs—also feature prominently in the development of the report. Jos Verbeek will be the task manager and lead author. Lynge Nielsen will lead the team from the IMF. Contributors and consultants may include: Brad McDonald (IMF), Shaohua Chen Eugenia - 12 - Suarez Moran, Maryla Maliszewska, Peter Lanjouw, Somik Lall, Hans Lofgren, Israel Osorio- Rodarte, Martin Ravallion, Ejaz Ghani, Prem Sangraula, Dana Vorisek, Amy Gautam, and Vandana Chandra (all World Bank). Contributors from other international institutions will be sought: Netherlands Environmental Assessment Agency (PBL) – Paul Lukas and Henk Hilderink The African Development Bank (AfDB) – Patricia Laverley Asian Development Bank (ADB) – Indu Bhushan, Vand Gina Marie Umali European Bank for Reconstruction and Development (EBRD) – Murat Jadraliyev, Anita Taci, and James Earwicker Inter-American Development Bank (IDB) Supervision. Martin Ravallion, Acting Senior Vice President and Chief Economist (until September 30, 2012), Kaushik Basu, Senior Vice President and Chief Economist (starting October 1, 2012) and Hans Timmer, Director, DEC, the World Bank. Advisers. Hugh Bredenkamp (IMF), Asli Demirguc-Kunt, Marianne Fay, Shantayanan Devarajan, Ariel Fiszbein, Delfin Go, and Mohammad Zia Qureshi (World Bank). - 13 - Annex 1: Special topics in previous GMRs 2004 Policies and Actions for Achieving the Millennium Development Goals and Related Outcomes 2005 Millennium Development Goals: From Consensus to Momentum 2006 Millennium Development Goals: Strengthening Mutual Accountability, Aid, Trade, and Governance 2007 Millennium Development Goals: Confronting the Challenges of Gender Equality and Fragile States 2008 MDGs and the Environment: Agenda for Inclusive and Sustainable Development 2010 A Development Emergency and Supporting the Private Sector 2010 MDGs after the Crisis 2011 Improving the Odds of Achieving the MDGs 2012 Food Prices, Nutrition and the Millennium Development Goals - 14 - References Annez, P. C., and L. F. 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