FEATURE STORYIN THIS ISSUE

AFRICA RESEARCHN E W S L E T T E R

OCTOBER 2015

BI-MONTHLY NEWSLETTER FEATURING WORLD BANK RESEARCH AND ANALYSIS ON DEVELOPMENT ISSUES IN SUB-SAHARAN AFRICA

REGIONAL STUDIES PROGRAMFEATURE STORYHighways to Success or Byways to Waste? Estimating the bene�ts of roads in Africa

SPOTLIGHTNatural Resource Booms: A Mixed Blessing for Local Communities

SUMMARY OF AFRICA REGIONAL STUDIESA glance at the portfolio of Regional Studies under implementation in the Africa Region

FROM AFRICA REGION CHIEF ECONOMIST AGRICULTURE IN AFRICA: TELLING FACTS FROM MYTHSWomen provide most of the labor in African Agriculture

POLICY AND SOCIAL IMPACT ANALYSISChallenges and Opportunities of Mobile Phone-Based Data Collection: Evidence from South Sudan

CONVERSATIONSBridge the Gap between Research and Policy, One Panel Discussion (and 145 Studies) at a Time

ADVISORY SERVICES & ANALYTCSESW DELIVERED BY GLOBAL PRACTICEA listing of all the reports on African countries that have been delivered in Fiscal Year 15 and Fiscal Year 16

ESW DELIVERED BY COUNTRYA listing of all the reports on African countries that have been delivered in Fiscal Year 15 and Fiscal Year 16

01

06

08

10

13

15

18

26

HIGHWAYS TO SUCCESS OR BYWAYS TO WASTE?This comprehensive study of the local effects of improving a country’s local or national road network develops new techniques to better answer some of the more elusive questions in infrastructure economics. This report models and empirically estimates the effects of road construction and improvement on a variety of variables important to policy makers and planners. It considers the positive benefits that can result from road improvements, but also looks at potential downside risks associated with roads in conflict-prone and environ-mentally sensitive regions.

1

WHY IS THIS TOPICIMPORTANT?Clearly, one key to reducing poverty in Africa is to transform agriculture, and to do this will require improved connectivity. Agriculture is fundamental to growth and poverty reduction, since more than 70 percent of the continent’s poorest populations live in rural areas, with agriculture as their most important economic activity. It is also critical for managing the urban transition that Africa will undergo. This process needs to be driven more by improving economic opportunities in the cities that gradually pull rural residents in, rather than by declining conditions and periodic disasters in rural areas that push the residents out; the latter creates conflict and waves of migration that typically just lead to expanded slums in the cities.

There are many reasons to believe that African agriculture is ripe for transfor-mation (World Bank, 2013), including the prospect of having a trillion dollar regional market for food products by 2030 (Byerlee, et al., 2013)(Figure 1). But to take full advantage of this opportunity, the huge agricultural productivity gap

between Africa and other regions needs to be reduced, and existing barriers to market access and trade within and outside the region overcome. Currently, these barriers fragment natural ‘market-sheds,’ preventing the connection between food surplus and food deficit regions, reducing the welfare of both, and making it more difficult to trade within Africa than with the rest of the world (World Bank, 2013; World Bank, 2012; Haggblade, 2013).

However, integration is complicated by the region’s economic geography. The spatial distribution of economic activity in Africa is highly skewed, with five coastal nations accounting for over 70 percent of the continent’s GDP (Figure 2). Integrating the leading regions with those that lag is crucial to ensure that growth spills over and creates opportunities and shared prosperity across the continent.

Figure 1. The trillion-dollar opportunity: projected value of food markets, Sub-Saharan Africa

Source: World Bank (2013)

UrbanRural

2010 2030

12001000

800600400200

0

US$ B

illion

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

REGIONAL STUDIES PROGRAM

2

alternative approach in the literature, and the one adopted in this study, uses actual data from existing roads to estimate the roads’ benefits, and then applies these estimates to forecast benefits from proposed construction or upgrading. This approach also faces challenges, including the difficulty of obtaining data that accurately reflect the conditions of the roads and the cost of traveling along them. This is always a concern when dealing with road infra-structure, the quality of which is constantly in flux, but it is especially a challenge in Africa where infrastructure assessments are infrequent and rural roads are often unaccounted for. The second econometric challenge, as discussed above, is overcoming several potential sources of endogeneity, which, if not accounted for, will bias estimates of benefits.

Prioritization also requires evaluation of the potential costs of building roads over and above the cost of physical construction. The most significant of these is probably the impact on forests. Deforestation in the Amazon is clearly associated with road construction. Recent World Bank assessments suggest that the major threats to the Congo are likely to occur not from forest harvesting but rather from induced (and often unintended) land-use changes, deriving from activities such as agriculture and mining, facilitated by transport investments (Megevand, 2013). Given its economic significance, there is a special need to carefully assess and evaluate impacts and trade-offs to prevent excessive damage to this global asset. This analysis applies its tools to this question as well.

ROAD INFRASTRUCTURE AND WELFAREThe study first looks at how transport cost affects welfare in rural areas, with an empirical focus on Nigeria. Recognizing that there is no perfect measure of economic wellbeing, a variety of outcome metrics are used, including

Removing non-physical trade barriers will be important to promote integration. However, as the African Infrastructure Country Diagnostic (Foster and Briceno-Garmendia, 2011) has shown, integra-tion cannot occur without far better physical infrastructure, especially roads. Africa’s road infrastructure is deficient by almost any measure, even when compared with countries with similar income levels in other regions. The road network is significantly smaller than that of any other region, with only 204 km of road per 1,000 square kilometers, nearly one fifth the world average and less than 30 percent of the next worst region, South Asia.

PRIORITIZATION IS CRITICALImproving connectivity will require making well-informed choices. Governments and donors in Sub-Saharan Africa devote considerable resources to the construction and rehabilitation of roads. The World Bank commits a larger share of resources to transport infrastructure than education, health and social services combined (World Bank 2007). In fiscal year 2013, total transport commitments amounted to USD 5.9 billion; rural and inter-urban roads remained the largest sub-sector with USD 3.2 billion, that is, 60 percent of lending (World Bank 2014). Proposals for ambitious future transport linkages in Africa abound. The African Infra-structure Country Diagnostic identified

amounting to over USD 18 billion per year. The New Partnership for Africa’s Development (NEPAD) has constructed a large proposal for road construction and improvement of nine ‘Trans-African Highways’ (TAHs) at an estimated cost of USD 4.2 billion (African Development Bank, 2003). The policy implication is that prioritization is crucial because the gap between requirements and available funds is vast. A recent review by Dercon and Lee (2012) states the problem concisely in its conclusion that there is still “…. limited understanding of…what needs to be done to avoid investing massive resources in large infrastruc-ture which does not result in growth.”

Analytical ChallengesPrioritization will require reliable estimates of the benefits and costs of specific proposed investments. Estimating construction costs is not necessarily simple in all cases, but it is primarily an engineering issue. Evaluating benefits, on the other hand, is analytically challenging. One approach is simply to forecast the increase in traffic or the time-savings that would come from building or upgrading a road. But, apart from the difficulty of accurately forecasting future developments, this fails to capture the potential indirect benefits from increasing connectivity of a region, including economic multiplier effects and better access to social infra-structure for the affected populations. An

Figure 2. Spatial Distribution of GDP

Calculated from Ghosh et al (2010)

REGIONAL STUDIES PROGRAM

3

thereby create heterogeneity of responses. Constraints on technology adoption can impede entry into markets and lock farmers into traditional, low-input modes of production, while variations in transport costs generate differences in returns. This model generates three testable hypotheses: (1) adoption of new technologies will be more pervasive where transport costs are lower; (2) reductions in transport costs will have a larger impact on the marketed output of farmers utilizing modern farm-ing techniques; and (3) farmers with more modern technologies are likely to be better integrated into markets.

The empirical results of the study are consistent with these hypotheses. It found that reducing transport costs increases production of crops grown using modern farming techniques, but has either a negative or negligible impact on production of traditionally farmed crops. This finding suggests that reduced transport cost leads to a switch from traditional to modern farming techniques. This conclusion is reinforced by analysis using direct, survey-based observations of whether households report using a more modern farming technique, specifically mechanization, with the finding that reducing transport costs increases the probability of use. Furthermore, while both non-mechanized and mechanized farmers see higher crop revenues from lower transport costs, the benefit to mechanized farmers is significantly higher.

crop revenue, livestock revenue, non-agricultural income, probability of non-agricultural employment, probability of being multi-dimensionally poor, and local GDP. By considering revenue and income, the analysis shows how roads affect short-term flow variables. The results indicate that reducing transport cost to market by 10 percent increases crop revenue by 3.8 percent, livestock sales by 3.0 percent, and nonagricultural income by 3.9 percent. Other indicators also suggest significant benefits from improved access. The spatial distribution of these benefits is shown in Figure 3. Using these elasticities, it is possible to forecast the economic impact of the construction of future roads, or the improvement of any portion of the current network. This, along with cost information, can help decision-makers prioritize construction of those roads that would have the biggest impact on spurring economic growth and reducing poverty in the region. This technique is illustrated in the study by evaluating some of the regional projects in NEPAD’s proposal.

AGRICULTURAL TECHNOLOGY CHOICE AND TRANSPORT One of the most vexing questions facing efforts to trigger transformational change in African agriculture is how to boost productivity by encouraging greater uptake by farmers of modern technologies such as fertilizers (Figure 4). There have been many diagnoses of the underlying cause of the failure of farmers to adopt these technologies, and this study posits that transport costs are a significant contributory factor. This report outlines a minimalist model of technology adoption with transport costs. It is assumed that there are fixed costs, or minimum threshold costs, to adopting more modern agricultural inputs (for instance, a minimum rental or purchase price of tractors, harvesters, and planters; learning costs for new production techniques, etc.). The fixed costs (which can be interpreted broadly) create a hurdle that households must overcome in order to adopt the more productive technology. Transport costs influence the returns to technology adoption and

Figure 3. Increase in local GDP (US$ million per cell of 10 sq. km.)

Source: Authors’ calculations

Figure 4. Fertilizer use lags badly in Africa

Source: Calculated from FAOSTAT (http://faostat.fao.org/site/339/default.aspx)

1970 1975 1980 1985 1990 1995 2000

East Asia

South Asia

Latin AmericaSoutheast Asia

4540353025201510

50Fe

rtiliz

er co

nsum

ption

(mill

tons

)

Sub-Saharan Africa

REGIONAL STUDIES PROGRAM

4

variables: ethnic fractionalization, a measure of the diversity of tribal populations within a given area, as well as distance to the eastern border, which is where most of the conflict originates. And for the second, it developed a novel technique to measure conflict by calculating a kernel that takes into account the intensity of conflict at a given location, as well as that in nearby areas, with weights decreasing with distance (Figure 5).

The results show that in the case of high levels of conflict, reducing transport costs leads to a fall in the asset wealth of households as well as in local GDP, and multidimensional poverty is seen to increase. These findings are consistent with the idea that better roads facilitate the movement of rebels. This study further finds a differential impact of transport costs, depending on the location of conflict. Conflict near the household is found to be more detrimental to welfare, probably because the house-hold cannot escape the conflict by retreating to subsistence farming. In contrast, when conflict is relatively low, a reduction in transport costs leads to increased wealth and local GDP, and reduced probability of multidimensional poverty. The most policy-relevant discovery is that the climate of conflict matters a great deal when deciding whether and when to invest in improving the roads. The results suggest that it would be prudent to wait for at least a reduction in the degree of fighting before launching a road improvement project.

ROAD IMPROVEMENT AND DEFORESTATION IN THE CONGO BASIN COUNTRIES Saving the forests in the Congo Basin from the same fate as the Amazon rainforest is an issue of importance for both the region itself and for the entire world. Locally, the forests provide hugely important eco-services and are critical in hydrological regulation,

ROLE OF TRANSPORT INFRA-STRUCTURE IN POST-CONFLICT AND FRAGILE ENVIRONMENTS The rehabilitation of damaged road infrastructure is an overarching invest-ment priority amongst donors and governments in post-conflict and fragile states. All of NEPAD’s proposals for new highways mentioned above (African Development Bank, 2003) pass through fragile states and, in most cases, the portions of the roads that need the most rehabilitation lie within these countries. There is, however, little empirical evidence on the direct causal impact of access to markets on well-being in fragile situations when the risks of reversion to conflict are high. There is even less evidence on the combined impact of transportation costs and conflict. These questions are addressed using a two-stage model of conflict in which agents choose to be either farmers or rebels. Their choice is influenced by their economic ‘distance’ from the market, which is a function of transport costs. Rebels must choose where to focus their

Figure 5. Kernel density estimate of con�ict intensity

Source: Authors’ calculations

attacks – on the market area or on farm households - with implications for behavior and welfare of farmers.

Using data from the Democratic Republic of Congo, the study tested empirically the predictions from this model that (1) an increase in transport costs diverts attacks from goods sold at the market to subsistence goods (at the farmer’s location); and (2) in certain situations, lower transport costs would induce a switch from farming to rebellion and vice versa. This econometric testing is challenged not only by the data and endogeneity issues described already but also by two additional problems: (1) conflict itself is endogenous (is welfare low because there is conflict nearby or is conflict generated by poverty?); and (2) data on conflict is usually in the form of point estimates, whereas the economic influence of conflict is undoubtedly more far-reaching and also depends on the intensity of the conflict. To resolve the first issue, the estimation technique used two instrumental

REGIONAL STUDIES PROGRAM

5

source of much of the rainfall in the Sahel and East Africa (Megevand, 2013). Importantly too, they host an array of charismatic and threatened species, such as, the lowland gorilla, chimpanzee and the African forest elephant. Globally, the planet also benefits from the carbon sink provided by the Congo basin, which contains 30 to 40 gigatons of carbon, 8 percent of the world’s forest carbon, the equivalent of 3-5 years of world emissions of CO2 equivalent. Deforestation is clearly linked with opening roads through the forest. Given the importance of roads for raising the welfare of the populations of the region, which are some of the poorest in the world, it is not desirable to avoid road-building altogether. But it is clearly in everyone’s best interest to place roads so that they reap the maximum benefits while causing the minimum collateral damage to the ecosystem. The study addresses critical issues for decision-makers: (1) What are the factors and conditions that influence the degree to which building a road will result in deforestation in the Congo region?; and (2) How can the likely damage from a given road be quantified in a way that will allow it to be compared to other options?

The lesson that emerges from the theoretical model developed to study this issue is relevant for how decisions are made when siting roads in ecologically sensitive areas. In most current road planning, decision-making is sequential: decisions on road improvement projects in an area are made first, followed by an Environmental Impact Assessment (EIA) that seeks to mitigate forest clearing by strengthening environmental manage-ment rather than affecting the selection of projects. The modeling exercise showed why this kind of sequential decision regime may actually increase deforestation and the associated ecological impacts. Instead, potential ecological costs should be considered

The Report: Rubaba Ali, Alvaro Federico Barra, Claudia N. Berg, Richard Damania, John Nash, and Jason Russ. March 2015. “Highways to Success or Byways to Waste? Estimating the Economic Benefits of Roads.” World Bank, Washington D.C.

early in the process of planning road improvements, rather than after the road location has been determined. The study then empirically investigated the impact of road building on deforestation and biodiversity in eight Congo Basin countries, using a recently published, first of its kind, dataset of high-resolution, consistently-derived estimates of global forest clearing. It also constructed an index of ecosystem risks and overlaid the Basin-wide road network on this map to provide a first-order guide to risk assessment for proposed road corridor improvements (Figure 6). Lastly, it combined the ecological risk indicator with pixel-level predictions of forest clearing produced by road upgrading.

Figure 6: Biodiversity and the a�ected area

Source: Authors’ calculations

The result is a high-resolution map of expected risks for road upgrading in road segments, corridors and regional networks.

This comprehensive study of the local effects of improving a country’s local or national road network, develops new techniques to better answer some of the more elusive questions in infrastructure economics. The results in this report are meant to be useful for policy-makers

within their respective countries, and the methodologies developed and applied here can inform future policy decisions.

FROM THE OFFICE OF THE CHIEF ECONOMIST FOR AFRICA

6

NATURAL RESOURCE BOOMS ARE A MIXED BLESSING FOR LOCAL COMMUNITIES.The focus of the Working Paper “The Local Economic Impacts of Resource Abundance: What Have We Learned?” is on exploring whether extractive activities improve or harm welfare in adjoining regions, and how the benefits or costs are transmitted to the local population. The answers to these questions can inform policy, leading to better outcomes, and may shed light on the sources of regional and social tensions associated with extractive industries. The simple analytical framework presented in charts below describes at least three broad channels through which resource booms could benefit or harm local communities in developing countries.

SPOTLIGHT

1. LOCAL DEMAND SHOCK. A resource boom can represent an increase in demand for locally supplied inputs, such as labor or intermediate materials. This should raise wages and other incomes and increase employment opportunities in the non-extractive sector and generally improve local welfare and reduce poverty. There could, how-ever, also be some negative spillovers from this. Often, the start of an extractive industry, such as the opening of a mine, will attract workers from other districts. This could temper the rise in wages, put a strain on local services such as health and education, and raise the price of non-tradable goods and services such as housing and, therefore, actually reduce the real incomes of some local residents.

Natural resourcesabundance

Increase in locallabor demand

Increase in nominal wages

Increase in real wages

Crowding outtradable sectors

Agglomerationeconomies

Increase inproductivity

No signi�cant e�ecton real wages

Decrease in nominalwages, increase in housing costs

Congestion in public services(i.e. education)

Attracting workers from other cities, increase in population

Increase in non-tradablessectors output, ambiguous e�ect

on tradable sectors output

If strong enoughbackwards linkages

If workers arerelatively immobile

If workers aremobile

2. LOCAL GOVERNMENTREVENUE WINDFALLNatural resources can be considered a fiscal revenue windfall which eases the hard budget constraint of local governments, and supports higher public spending. As such, it has the potential to improve health and education outcomes and increase local income and growth by improving the quantity or quality of local public goods and services, such as roads, hospital, schools, and housing. However, a lack of responsiveness of local politicians to demands from the broad popu-lation or lack of technical capacities of local bureaucrats, may undermine the positive effect of revenue windfalls on public good provision and local living conditions. In the presence of weak local institutions, an increase in fiscal revenue can lead to more rent seeking, more corruption, and more conflict. But the risks of conflicts can be mitigated if there are strong enough backward linkages with the natural resource.

3. ENVIRONMENTAL POLLUTIONExtractive industries, such as mining and oil extraction, have the potential to pollute the environ-ment creating several negative externalities especially in terms of human health, learning and cognitive outcomes. A potentially important pollution externality when extractive industries are near rural areas where agriculture is the main source of livelihood is the loss of agricultural productivity (Aragón and Rud 2014), which can have a negative impact on agricultural output, thereby affecting the income of farmers and the rural population.

This paper is part of the Regional Study: Fernando M. Aragón, Punam Chuhan-Pole, Bryan Christopher Land 2015. “Socio-economic Impact of Mining on Local Communities.” World Bank, Washington DC

FROM THE OFFICE OF THE CHIEF ECONOMIST FOR AFRICA

Natural resourceabundance

Increase in�scal revenue

Increase in public

spending

Improve public services &

infrastructure

If good local

institutions

Increase in rent seeking opportunities

Increase in violent con�ict

Improving livingstandards

Increase in corruption

If weak localinstitutions

Natural resourceabundance

Loss of humancapital

Environmentalpolution

Decrease in agriculture productivity

If the extractiveindustry is located

in a rural area

Decrease in laborsupply and productivity

Negative e�ect on school& cognitive outcomes,

increase in schooladsenteeism

Decrease inrural income

Decrease in icome& living conditions

Negative e�ecton human health

7

SUMMARY OF AFRICA REGIONAL STUDIES

DUE IN FY16Turning Natural Resource Wealth into Human Capital (P148368)This report will assist countries in trans-forming natural resource windfalls into long-term sustainable development through strategic investment in human capital, which is essential for the eradi-cation of extreme poverty and the promotion of shared prosperity. Team Leader: Deon FilmerDECRG: Human Development Email: Dfilmer@worldbank.org

Participation in Regional and Global Value Chains as a Driver of Structural Change in Africa (P153793)The objective of this study will be to invigorate and deepen the discussion about structural change in Africa and the appropriate trade and industrial policies that will allow countries in Africa to drive employment growth in higher value-added activities. Team Leader: Nora Carina DihelTrade & Competitiveness – GPmail: ndihel@worldbank.org

Promoting Green Urban Development in Africa (P148662)This study will improve the understanding and enhance the ability of national and local governments to make well-informed strategic, planning, land-use, budgetary and investment decisions that impact urban ecosystems and environment.Team Leader: Roland White Urban, Rural & Social Development — GPEmail: rwhite@worldbank.org

Building African Cities that Work (P148736)This study examines the spatial evolution of ten large cities across Africa, in order to assess the key drivers of city development and the implications of spatial evolution for productivity, living standards, and risk from natural hazards.Team Leader: Somik Lall Urban, Rural & Social Development — GP Email: Slall1@worldbank.org

State Ownership in West Africa - A Regional Study on SOE GovernanceThis study aims to do a comparative analysis of SOE governance in the West African region, with a focus on companies with primarily commercial objectives. It seeks to determine how the state as an owner takes a more proactive role in improving SOE performance, and to identify the most important focus areas.Team Leader: : Kjetil Hansen Governance — GPEmail: khansen1@worldbank.org

Accelerating Poverty Reduction in Africa, Report 2 (P149419)This second report reviews the poverty-inequality-growth nexus in the Region. It looks at the basic building blocks and occupational and spatial development patterns that can enhance income opportunities for the poor and reduce household vulnerability to risk. Team Leader: Kathleen Beegle, Office of the Chief EconomistEmail: kbeegle@worldbank.org

Luc Christiaensen, Office of the Chief Economist, Africa Email: lchristiaensen@worldbank.org

Reforming Agriculture Input Subsidies in Africa (P153531)This study intends to support better decision-making by policy-makers interested in promoting input use as a way of stimulating economically efficient, environmentally sustainable, pro-poor growth in agriculture. The study will summarize policy implications and key lessons learned from efforts to promote input use in Africa through innovative technology. Team Leader: Aparajita Goyal Agriculture – GPEmail: agoyal3@worldbank.org

GP= Global PracticeCCSA = Cross Cutting Solutions Area

Making Transformation Work for Africa: Leveraging the Earnings Potential of the Bottom 40 percent This study aims to increase Africa’s capacity to reduce poverty by putting a ‘jobs lens’ to the Africa structural transformation agenda. It will identify the key elements, building blocks and cornerstones of a ‘transformational’ jobs agenda that can deliver sustainable ‘shared prosperity ’ to the African continent.Team Leader: Pierella Paci Poverty — GPEmail: ppaci@worldbank.org

REGIONAL STUDIES PROGRAM

8

9

REGIONAL STUDIES PROGRAM

DUE IN FY17 AND BEYOND

The State of Social Protection in Africa This study aims to take stock of the state of social protection in Africa; document social protection’s contribution to reducing poverty and inequality, as well as boosting productivity and growth; and develop the evidence-base for further investments in the delivery and effectiveness of social protection systems.Team Leader: Aline CoudouelSocial Protection & Labor –GPEmail: acoudouel@worldbank.org

Skills Development Agenda for Africa Skills upgrading has a role in the accel-eration of economic transformation and productivity growth, both in the informal and formal sectors. The objective of this report is to provide policy guidance to African governments on the following overall question: How can AFR most effectively invest in skills to enhance inclusive growth and reduce poverty over the next 10 years and beyond?Team Leader: Omar AriasSocial Protection & Labor –GPEmail: oarias@worldbank.org

David EvansOffice of the Chief Economist, AfricaEmail: devans2@worldbank.org

Forced Displacement in AFR and MNA This study aims to provide ‘hard’ evidence to inform the debate on forced displacements among country-level policy-makers and international stakeholders; identify specific interventions (including desirable policy reforms) and priorities for governments and their external partners; identify critical knowledge gaps; and help further shift the forced displacement agenda from humanitarian only to humanitarian + development. Team Leader: Xavier DevictorFragility, Conflict & Violence-CCSA-IBRDEmail: Xdevictor@worldbank.org

Energy Access in Africa This study will look into ways to promote broader access to energy in Africa to support economic growth and poverty reduction in Africa. Questions include: What’s the current state of affairs? How does the picture vary across countries (and rural and urban areas)? What are the most pressing challenges (for example, is it primarily about generation or transmission?), and what are the economic reasons for market failures in the identified bottleneck markets? Team Leader: Moussa BlimpoOffice of the Chief Economist, AfricaEmail: mblimpo@worldbank.org

Elvira MorellaEnergy & Extractives - GP Email: emorella@worldbank.org

Boosting African ProductivitySustaining growth in Sub-Saharan Africa in the midst of an uncertain global environment and headwinds from commodity prices requires efforts to boost productivity. This study will document the evolution and determi-nants of productivity in the region — overall and sectoral productivity. It will also examine whether international integration has an impact on the type of structural transformation that the region is experiencing.Team Leader: : Cesar CalderonOffice of the Chief Economist, AfricaEmail: ccalderon@worldbank.org

GP= Global PracticeCCSA = Cross Cutting Solutions Area

FROM THE OFFICE OF THE CHIEF ECONOMIST FOR AFRICA

10

Until most recently, nationally representative, disaggre-gated data on agriculture in Africa have been hard to come by. With the advent of a series of Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA)1 in six (and now even eight) African countries in the early 2010s, this has changed. Together, these six countries represent 40 percent of the population of Sub-Saharan Africa (SSA). They allow the revision of common wisdom on agriculture and its farmers’ livelihoods in Africa, which is the topic of the larger project led from the Chief Economist Office of the Africa Region, entitled: “Agriculture in Africa – Telling Fact from Myth.”2 Here, the findings on the involvement of women in agriculture are presented.

WHY DOES IT MATTER?Women are commonly considered to perform the bulk of work in African agriculture, with 60-80 percent, a commonly quoted figure. Combined with new evidence of a non-negligible gender gap in agricultural productivity, this has motivated increased attention to raising agricultural productivity among African women. Doing so is not only seen as important for empowering Africa’s women and improving the development outcomes of the next generation, but also for increasing Africa’s food supply, a key objective on the agenda of African and international policymakers (AGRA, 2012). With the new LSMS-ISA surveys it has now become possible to undertake a systematic review of the involvement of women in African agriculture and verify the premise of this argument.

FACT OR MYTH?Myth! The figure we arrive at is 40 percent - that is, the average female share of labor in crop production across the six countries is 40 percent (Figure 1). This is substantially less than in the much cited, though undocumented, 1972 quote, which holds that: “Few persons would argue against the estimate that women are responsible for 60-80 [percent] of the agricultural labor supplied on the continent of Africa.” It is also somewhat lower than FAO’s (2011) estimate of about 50 percent, based on agricultural employment categories only (as opposed to time use).

But there is variation across countries. At 56 percent, the

WOMEN PROVIDE MOST OF THE LABOR IN AFRICAN AGRICULTURE. FACT OR MYTH?

60

50

40

30

20

10

0

5652 52

37

2924

40

Uganda Tanzania Malawi Nigeria Ethiopia Niger Total Avg.*

estimated female share of agricultural labor is highest in Uganda, followed by Tanzania (52 percent) and Malawi (52 percent). Taking the female share in the total population as a natural benchmark, these are also the three countries where the female share in the population is slightly above half (52, 53 and 51) percent respectively. In contrast, women contribute less than a quarter of the overall amount of labor to crop production in Niger (24 percent) and only slightly more in Ethiopia (29 percent).

To be sure, even though the six countries represent 40 percent of SSA’s population and cover a wide variety of settings, they are not statistically representative of the continent either. At the same time, the overall headline number provides a useful antidote, with all country specific estimates below the 60-80 percent range, while underscoring the challenge of generalization given Africa’s diversity. Within country differences further exemplify this diversity, with the female share in agricultural labor in southern Nigeria estimated at 32 percent, compared to 51 percent in southern Nigeria, which is in line with expectations.

Figure 1: Female Share of Agricultural Labor (%) by Country

Note: * Population weighted Source; Authors’ Calculations

WHAT ELSE CAN WE SAY?The common thought has been that men allocate dispropor-tionately more of their time to (non-edible) cash crops, while women are believed to concentrate more on the production of staple and other food crops. Compared to the overall female

AGRICULTURE IN AFRICA TODAY - TELLING FACTS FROM MYTHS

1 http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/EXTLSMS/0,,contentMDK:23512006~pagePK:64168445~piPK:64168309~theSitePK:3358997,00.html2 http://www.worldbank.org/en/programs/africa-myths-and-facts#1

FROM THE OFFICE OF THE CHIEF ECONOMIST FOR AFRICA

11

60

50

40

30

20

10

0Tanzania Malawi Niger Northern Southern Ethiopia Total* Nigeria Nigeria

Total Non-Edible

share of agricultural labor, the female share allocated to non-edible crops is only slightly lower in Malawi and Uganda, while it is slightly higher in Tanzania and a lot higher in south-ern Nigeria (Figure 2). Overall, while there is some variation in the female share of agricultural labor across crop catego-ries within countries (especially Niger, Nigeria), these patterns cannot be generalized across countries.

Figure 2: Female Share of Agricultural Labor on the Production of Non-Edible Crops

Source; Authors’ Calculations

Turning to the crop production activities, land preparation is often considered a “male activity.” There are some signs of this, though only in Ethiopia and Niger. There is hardly any variation in female labor allocation across agricultural activities in Tanzania, Malawi and Nigeria3 (Figure 3). This is likely linked to the low degree of mechanized land preparation in SSA, with the exception of Ethiopia (and to some extent also in Niger), where the use of draught animals is more widespread (Sheahan and Barrett, 2014).

CAN WE TRUST THE FINDINGS?Two main concerns remain, which both relate to the source of our information about the labor input on each plot. First, if it is predominantly men who respond on the labor input of the different household members, and if they tend to underreport labor inputs provided by the female household members, this would lead one to underestimate the female labor share. The reverse may also hold, women underreporting labor input by men, as we know from other labor experiments (Bardasi, Beegle, Dillon and Serneels, 2011). Second, lack of knowledge about the labor input of the different household members may also bias the findings, if this results in systematic under (or over) reporting of female labor input. The direction of bias is a priori not clear, in either case.

Female Share Malawi Nigeria Prediction Prediction1. Prediction on the whole2. Respondent knows and Respondent Female 3. Respondent knows and Respondent Male4. Respondent does not know and Respondent Female5. Respondent does not know and Respondent Male

56%60%54%56%50%

32%24%27%36%38%

Total wealth (in billion USD) owned by the bottom ...Table 1: Predicted Household Female Labor Share in Agriculture, Controlling for Respondent Gender and Knowledge

3 For Uganda, the reported labor input was not disaggregated by activity domain.

Tanzania Malawi Niger Northern Southern Ethiopia Total* Nigeria Nigeria

Total Land Preparation

60

50

40

30

20

10

0

Figure 3: Female Share of Agricultural Labor in Land Preparation

Source; Authors’ Calculations

To examine the sensitivity of the findings to the gender and knowledge profile of the respondents, the effect of these characteristics on the female labor share in each household was first estimated (controlling for a host of other demographic, social and economic factors affecting the labor allocation by gender). Subsequent prediction of the female labor share for the extreme cases when all respondents are knowledgeable and female, as well as the case when all respondents are not knowledgeable and male, enabled us to put a confidence band on the results (Table 1). For Malawi, the predictions put the female share between 60 (all respondents female and knowledgeable) and 50 percent (all male and not knowledgeable), suggesting a downward bias among men. The opposite holds in Nigeria, where the corresponding band is 24 (all female and knowl-edgeable), suggesting an upward bias among men, and 38 (all male and not knowledgeable). While the profile of the respondent affects the estimates, the direction of the bias is not consistent and the magnitude not large enough to over-turn the overall finding that the female share in crop production has been much lower than commonly portrayed.

The Report: Amparo Palacios-Lopez, Luc Christiaensen and Talip Kilic, 2015, “How Much of the Labor in African Agriculture is Provided by Women?” Policy Research Working Paper 7282, World Bank, Washington, D.C.

FROM THE OFFICE OF THE CHIEF ECONOMIST FOR AFRICA

12

WHAT NOW?The lower than expected female labor shares would not support a disproportionate focus on female farmers to boost crop production in the aggregate. In addition, it is useful to disentangle a bit further the popular argument that African agricultural output could be boosted substantially by policies that close the gender productivity gap, given that women do most of the work. The productivity gaps are calculated based on differences in land productivity between male- and female-managed plots. They are not based on differences in returns to male and female time spent on crops within the household (the metric revisited in this paper). With female-managed plots seldom exceeding 25 percent of the plot population, full elimination of the gender gap in land productivity (estimated at 25 percent at most)4 would increase aggregate crop output by no more than 6.25 percent (and often less). Keeping the metrics straight is important.

That said, there may obviously be many other good reasons to close the gender productivity gap in agricultural production, such as empowering African women and, by extension, human capital of the next generation. The costs and benefits of closing the gender productivity gaps in agriculture must then be weighed against the costs and benefits from other interventions to empower women. More systematic data collection on female engagement in other agriculture related activities, such as livestock keeping and food processing, may further reveal important time savings and income opportunities. The new survey rounds supported under the LSMS-ISA initiative are making useful steps in this direction, creating a promising information base for obtaining further insights on gender and agriculture in Sub-Saharan Africa.

4 The estimated gender gap in land productivity (defined as the value of agricultural output per unit of cultivated area), based on the LSMS-ISA data, between male and female managed plots (or between male and female individualoperators in the case of Ethiopia), stand at 23 percent for Ethiopia (Aguilar et al., 2015), 25 percent for Malawi (Kilic et al., 2015), 18 percent for Niger (Backiny-Yetna and McGee (2015), 4 percent for northern Nigeria and 24 percent for southern Nigeria (Oseni et al., 2015), and 8 percent for Tanzania (Slavchevska, 2015).

Challenges and Opportunities of Mobile Phone-Based Data Collec-tion: Evidence from South SudanOBJECTIVEBetween 2005 and 2011, mobile cellular subscriptions in Africa increased from 87 million to 433 million. Accompanying this growth, there has been a proliferation of mobile applications, including digital money transfers and payments, citizen polling surveillance, remote health care consultation and diagnosis, and transmission of timely market information to farmers. This study examined the opportunities and challenges of using mobile phones as a research tool, based on the experience of the South Sudan Experimental Phone Survey (SSEP).

SOUTH SUDAN EXPERIMENTAL PHONE SURVEYIn early 2010, South Sudan was a year away from independ-ence and there was a need for comprehensive baseline data that could inform future policy. Given the nation’s poor infra-structure, limited road network, and high transportation costs, mobile phones were a costeffective solution to collecting high-frequency data. Mobile ownership per household in South Sudan increased from 2 percent in 2006 to 18 percent in 2010, with urban households having two phones on average.

METHODOLOGYA sample of households in urban centers in South Sudan was given mobile phones, whether they had previously owned a phone or not. Participants were called once a month for four consecutive months and asked questions. Upon completion of the survey, the participants were distributed airtime credit. The participants can be split into four categories: those who received a Nokia phone and SSP 5 of credit, a Nokia phone and SSP 10 of credit, a solar-rechargeable phone and SSP 5 of credit, and a solarrechargeable phone and SSP 10 of airtime credit.

RESULTSOverall, the response rate declined from 68 percent during the first round of the survey (December 2010), to 52 percent in round 4 (figure1).

Respondents can be categorized into four response categories: “full compliers,” who completed all four rounds of the survey; “intermittent compliers,” who completed between one and three surveys intermittently; “drop-outs,” who dropped out after successful completion of one or more surveys; and “non-compliers,” who did not complete a single survey.

Figure 1. Response Rates by Month, Type of Phone, and Incentive

Approximately 17 percent of the sample were non-compliers, while 31 percent were full compliers. Approximately half of all households (52 percent) completed between one and three surveys over the data collection period. Of these households, close to half (24 percent of the sample) dropped out after at least one successfully completed interview, 7 percent after round 1, 6 percent after round 2, and 10 percent after round 3.

Of respondents who received Nokia phones, those offered the SSP 10 credit were about 10 percent less likely in each round to complete the survey compared with respondents offered the SSP 5 credit. There was not a similar pattern among respondents using solar-rechargeable phones. Nokia and solar phone users completed surveys with the same frequency: among 4,028 possible completed surveys across all four survey rounds, the overall response rate for users of both phones was 59 percent. This suggests that the constraint of having to charge the Nokia phone using an electrical outlet did not influence survey completion.

Three prohibit regressions were used to distinguish factors associated with successful completion of the survey. Partici-pants who completed all four survey rounds were more likely to be over age 30 years and more likely to have access to an alternative phone. Gender, the type of phone, and the incen-tive value were not associated with the probability that a respondent would complete all four rounds of the survey. The very strong geographical pattern associated with completing all four survey rounds suggests that the differential reliability of network coverage across the 10 urban centers was a strong determinant of response rates. Poverty status was not associated

FROM THE OFFICE OF THE CHIEF ECONOMIST FOR AFRICA

13

POLICY AND SOCIAL IMPACT ANALYSIS

100

90

80

70

60

50

40

30

20

10

0Dec Jan Feb Mar

[Res

pons

e Rat

e (%

)]

with survey completion. Participants who did not complete a single survey were less likely to be female, less likely to have access to an alternative phone, and more likely to have been offered a pre-paid credit of SSP 10.

CHALLENGESMajor challenges related to reaching the correct respondent involved an unreachable number (37 percent of calls) and no network coverage (18 percent). Problems with the network increased over the course of the four month period, peaking at 32 percent of all dialed numbers in March. Calls to Bentiu and Torit in December and January, and to Aweil and Bor in March were cited as being particularly and consistently problematic because of poor network connectivity. The fraction of unreachable numbers was especially high in February, when 47 percent of all dialed numbers were unreachable (figure 2). In addition, the call center reported that respondents complained about faulty batteries (solar phones) and the cost of recharging batteries (Nokia phones).

Other challenges with survey implementation were related to content. Respondents were wary of questions focused on governance and leadership, and needed reassurance on confidentiality. Questions on food security, access to medicines and health care, frequency of illness, and personal security

yielded consistent response patterns.

RECOMMENDATIONSFuture studies using mobile data collection might mitigate non-response by:

1. Sending simple reminders (possibly using text messages(SMS)) to reduce the number of intermittent compliers.

2. Targeting older, female household members could reduce the number of non-compliers, but such targeting would not be desirable for surveys seeking a representative sample of individuals, for example, labor force surveys.

3. Having multiple options to call respondents by recording an alternative contact number (on a different mobile network), to reduce disruption caused by network coverage.

In addition, it was noticed that larger incentives in the form of pre-paid calling credit did not work to encourage survey completion. In fact, participation rates were slightly lower forthose who received the greater incentive. Finally, partnering with the national statistics bureau for accurate sampling and implementation of the survey was critical to the success of the SSEP project.

FROM THE OFFICE OF THE CHIEF ECONOMIST FOR AFRICA

14

By Gabriel Demombynes, Paul Gubbins & Alessandro Romeo

Figure 2. Calling E�ciency by Indicator

FROM THE OFFICE OF THE CHIEF ECONOMIST FOR AFRICA

15

BRIDGE THE GAP BETWEEN RESEARCH AND POLICY, ONE PANEL DISCUSSION (AND 145 STUDIES) AT A TIMEWhat’s the secret to effectively bridging the gap between research and policy? Relationships, relationships, relationships. David Evans (Senior Economist, AFRCE) shares the insights from a recent panel discussion and several studies on how to more effectively bring evidence into policy.

something of great scientific interest and something of great policy impact.” Sometimes we may decide to trade off some of one to have more of the other, but not always.

How do you get these relationships? If you can leverage existing relationships (for instance, you went to school with a policymaker), then great. Or you can reach out directly. But local academics, both at universities and policy think tanks, may have policy connections as well as local insights, and can be great partners in building quality research with policy influence.

How can research findings be brought to scale?If you want research findings to scale, expect a lot of additional, non-academic work. Michael Kremer (Gates Professor of Developing Societies at Harvard University) highlighted that researchers are often ignorant of the major effort required to go from a small, carefully evaluated pilot to a well-implemented project at scale (emphasis is on well implemented, although bringing any project to scale is non-trivial). I sometimes indulge this fantasy that if I can effectively communicate great findings to a policymaker (essentially, give an awesome presentation or design a radical policy brief), she’ll go and implement those findings at scale and we’ll both feel good. Not so easy. But there are successes. One example is deworming, going from a paper in Econometrica [2] to the Deworm the World Initiative [3], reaching millions of kids in Kenya and India.

STUDIES AND COMMENTARY[1] Blimpo, Moussa Pouguinimpo; Evans, David; Lahire, Nathalie. 2015. Parental Human Capital and Effective School Management: Evidence from The Gambia. Policy Research Working Paper; no. WPS 7238; Impact Evaluation Series. World Bank Group, Washington, D.C.[2] Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities, Edward Miguel and Michael Kremer, Econometrica Vol. 72, No. 1 (Jan., 2004), pp. 159-217. Published by: The Econometric Society Stable URL: http://www.jstor.org/stable/3598853[3] Deworm the World Initiative -http://www.evidenceaction.org/dewormtheworld/

In July, I participated in a series of events in Nairobi: the East Africa Evidence Summit, an Agriculture in Africa event, and a policy forum with Kenya’s Vision 2030. The events included active discussions on how to more effectively bring evidence into policy. The conversations included a wide range of views, from representatives of the Kenyan and Tanzanian governments, non-government organizations, Kenyan and Ugandan academics from universities and think tanks, and researchers from outside the region. Here are a few takeaways:

Relationships mean more than just convincing the government to support your randomized controlled trial. What are some of the charac-teristics of those relationships?The successes come from ongoing relationships between policymakers and researchers. Here is what these relationships look like in practice:

Hold lots of meetings with the policymakers at each stage, especially in the beginning, including with a range of actors within whatever organization you are partnering with, so that everyone is aware of the project,

everyone knows what value the research-ers are offering that the organiza-tion doesn’t already have, and everyone knows when results will be available. (Then hold more meetings with new policymakers when these policymak-ers leave.)

Be game to support the policymakers in other work. One NGO repre-sentative highlighted how the researchers had helped them look at other issues beyond their high-academic-return research project. I had this experience in the Gambia, where the government highlighted a concern about teacher knowledge, and we added a teacher content test to our end-line (which ended up making the paper [1] better anyway).

Co-generate questions. Sometimes researchers have a great idea and just want to get a partner on board to help them implement it. But for a greater opportunity to influence policy, researchers and scientists can sit together, as Nava Ashraf (Associate Professor in the Negotiations, Organi-zations, and Markets Unit at Harvard Business School) has put it in the past, “to decide on the questions that have that beautiful area of overlap between

CONVERSATIONS

1

2

3

4

STUDIES AND COMMENTARY[4] Heckle and Chide: Results of a Randomized Road Safety Intervention in Kenya, James Habyarimanaa, William Jack, a Georgetown University, Public Policy Institute, Washington, D.C., USA Georgetown University, Department of Economics, Washington, D.C.[5] This chapter first appeared as NBER working paper w18378, State vs Consumer Regula-tion: An Evaluation of Two Road Safety Interventions in Kenya, James Habyarimana, William Jack[6] Research impact on policymaking is often understood in instrumentalist terms, but more often plays symbolic role, July 17, 2014 - from “Impact Blog” - London School of Economics[7] Oliver et al A systematic review of barriers to and facilitators of the use of evidence by policymakers Kathryn Oliver, Simon Innvar, Theo Lorenc, Jenny Woodman and James Thomas[8] Chioda, Laura; de la Torre, Augusto; Maloney, William F. 2013. Toward a Conceptual Frame-work for the Knowledge Bank. Policy Research Working Paper; no. WPS 6623. Washington, D.C., World Bank

A second example is a program putting stickers inside Kenyan minivans, encouraging riders to speak up against unsafe driving. Habyarimana & Jack carried out two trials (one [4], two [5]), and now the program is being scaled up nationwide as well as tested in neighboring countries.

Is it okay if not every piece of research changes the world?I certainly hope so, given some of the research out there. David Ameyaw (Director, Strategy, Monitoring & Evaluation at the Alliance for a Green Revolution in Africa) made the point that some research adds to our collective knowledge rather than transforming a specific, immediate policy. Many countries that are now considering cash transfer programs are likely not basing that on a single study but on the fact that extensive evidence supports them. Christina Boswell ( Professor of Politics, University of Edinburgh) distinguishes this “enlightenment” function (accumulated research shines a light) from the “instrumentalist” function (where a specific item of research is used to solve a specific policy problem) [6].

It’s tempting to see this as a cop-out – an excuse to just do your research and hope in vain that it will percolate slowly into the policy world via academic journals. But it doesn’t become part of policymakers’ collective knowledge unless they know about it, and Ameyaw underlined that every piece of research they produce is presented and disseminated extensively. If your research doesn’t directly inform an upcoming policy decision, still make sure that it gets out broadly, so that it can be a part of future policy decisions. This is where clever policy briefs and brilliant presentations come in.

Ok, so that’s what some people said in panels. Is there some real evidence on this? Last year, Oliver et al. published a systematic review of “barriers to and facilitators of the use of evidence by policymakers,” across a range of fields [7]. These include 145 studies from 59 countries. Thirty-three of the studies are from low- and middle-income countries. Many of the studies are of perceptions, and many have a mixed population of policymakers, policy advisors, researchers themselves, and others. What they found is consistent with what came out of last week’s discussions: relationships are key. But clarity of presentation and creating easy access to research are also important.

What’s the government’s role in all this?Most of the discussion above puts the onus on researchers. But governments aren’t powerless in this process. Gungu Mibavu (Ministry of Agriculture, Tanzania) discussed how they have formed a policy analysis group to share research priorities with local researchers. Michael Kremer highlighted the value of governments hiring individuals with impact evaluation and research expertise, who can translate the evidence into policy. This role is like the “hinge” actors that Chioda et al. (2013) [8] call for more of in

TOP 5 FACILITATORS OF EVIDENCE USE BY POLICYMAKERS

Source: Minorly adapted from Oliver et al. (2014)

FROM THE OFFICE OF THE CHIEF ECONOMIST FOR AFRICA

16

Availability of and access to

research, or better dissemination

65 studies

Collaborationwith researchers

49 studies

Clarity/relevance

/reliability ofresearch findings

46 studies

Relationship with

policymakers

39 studies

Relationship with researchers

37 studies

Taken together, Collaboration and the relationships with policy makers and researchers lead the pack: “Contact, collaboration and relationships are a major facilitator of evidence use, reported in over two thirds of all studies.”

FROM THE OFFICE OF THE CHIEF ECONOMIST FOR AFRICA

17

STUDIES AND COMMENTARY[9] The Marketplace of Ideas for Policy Change: Who do Developing World Leaders Listen to and Why?[10] Which bits of advice do developing country decision makers actually listen to? Great new research May 12, 2015 - from blog site “From Poverty to Power”[11] Five principles for the practice of knowledge exchange in environmental management, M.S. Reeda, L.C. Stringerb, I. Fazeyc, A.C. Evelyd, J.H.J. Kruijsene [12] What Do Policymakers Want From Us? Results of a Survey of Current and Former Senior National Security Decision-makers1 International Studies Quarterly, Vol. 58, No. 4 (December 2014): [forthcoming] Paul C. Avey MIT, Michael C. Desch, University of Notre Dame[13 Do’s and don’ts on research -> policy and the state of Development Studies in Ireland, September 10, 2012 - from blog site “From Poverty to Power”[14] Everybody wants to save the world, March 3, 2015 by Tseen Khoo- from blog site “The Research Whisperer”[15] What is the evidence on evidence –informed policy making, Authors: Kirsty Newman, Antonio Capillo, Akin Famurewa, Chandrika Nath and Willie Siyanbola. Published by: INASP, 60 St Aldates, Oxford OX1 1ST, United Kingdom[16] What is the evidence for evidence-based policy making? Pretty thin, actually. February 27, 2013 - from blog site “From Poverty to Power”

the World Bank, people who can “communicate and operate well across different knowledge communities — academics, policymakers, practitioners.”

If you’re a researcher, and you want your research to seriously affect policy and practice, then it’s time to start investing in long-term relationships that bring real value to govern-ments and other practitioners, in addition to all those hot journal publications. The results can be transformative. Bonus reading:

AidData has a recent report (executive summary [9]) on a survey of developing world policymakers and what reports they pay attention to. Two key findings: studies are more likely to influence policy if they focus on a specific country (rather than a region), and prescriptive analysis tends to be more influential than descriptive analysis. Duncan Green summarizes in a blog post [10].

Reed & Evely write about “How can your research have more impact? Five key principles and practical tips for effective knowledge exchange” on the London School of Economics Impact Blog, based on their 2014 publication [11] on knowledge exchange in environmental management. Once again: “To ensure this is useful knowledge and has impact for those who need it, relationships must be built: two-way, long-term, trusting relationships between researchers and the people who need the new knowledge we are generating.”

A survey of national security policymakers on what they want from international relations scholars, by Avey & Desch (2014) [12]. In that context, newspapers and classified reports were selected as the most important information sources. (Blogs fare more poorly.)

Green’s do’s and don’ts on getting research into policy [13].

Tseen Khoo has a nice summary of advice on The Research Whisperer. [14]

What is the evidence on evidence-informed policy making? A 2013 report by Newman et al [15]. Also summarized by Green [16]. They provide a “theory of change” diagram which captures the importance of relationships but misses the value of co-generating research questions.

TOP 5 BARRIERS OF EVIDENCE USE BY POLICYMAKERS

Source: Minorly adapted from Oliver et al. (2014)

Availability and access to

research

63 studies

Clarity/relevance /reliability of

research findings

54 studies

No time or opportunity

to use research

42 studies

Policymaker research skills

26 studies

Costs

25 studies

The fourth barrier, policymaker research skills, reflects another point that came up in the panels last week, and it also points to what governments can do.

ADVISORY SERVICES AND ANALYTICS

18

Global Practice Team Leader Project ID Project Name Country

AFCS1 Kumar,Praveen P149550 Fourth Econ Brief-Financial Inclusion Zambia

Kumar,Praveen P149551 Third Economic Brief-Trade Zambia

AFRCE Chuhan-Pole,Punam P148422 Impact of Mining on Local Communities Africa

Evans,David P152983 Economic Impact of the Ebola Epidemic Africa

Chakravarty,Shubha P151705 Lesotho Gender Study Lesotho

GGOPR Brar,Parminder P. S. P148696 IGFT Study Ethiopia

Brar,Parminder P. S. P149510 Ethiopia PEFA 2014 Regional Reports Ethiopia

Brar,Parminder P. S. P149693 Ethiopia PEFA 2014 - Federal Report Ethiopia

Brintet,Eric P146122 SENEGAL: Support Transparency Code Senegal

Oyeyiola,Adenike Sherifat P144620 SS-Asess of PFM in Counties and Payams South Sudan

Fritz,Verena Maria P147992 SD-Governace Review Sudan

GGOPS Traore,Cheick P144348 Boosting Budget Execution & Procurement Africa

Larbi,George Addo P149788 Kenya: Political Economy of Devolution Kenya

GHN07 Barroy,Helene P147553 Social Sector PER Congo, Democratic Republic of

GMFD1 Aslan,Cigdem P144758 DeMPA Chad Chad

Kiringai,Jane Wangui P148336 Ethiopia PER Ethiopia

Mungunasi,Emmanuel A. P155544 Tanzania PER 2014 ESW Tanzania

GSP07 Ringold,Dena P155775 Jobs Assessment and Strategy Development Nigeria

Ringold,Dena P155776 Review of ICT and Jobs Nigeria

GTCD1 Buba,Johanne P147941 Inclusive Markets Nigeria

Not assigned Berg,Birgitte Gunhild P156639 Bank Financing of SMEs in Kenya Africa

*As of September 28, 2015

SORTED BY GLOBAL PRACTICE*ECONOMIC SECTOR WORK (ESW) DELIVERED IN FY16

ADVISORY SERVICES AND ANALYTICS

19

Global Practice Team Leader Project ID Project Name Country

Southern AFR 1 Purfield,Catriona Mary P154853 Potential country-level policy notes Southern Africa

Office of the Chief Economist Christiaensen,Luc P145705 Agriculture in Africa - Myths and Facts Africa

Goldstein,Markus P. P144114 Republic of Congo Gender Assessment 2013 Congo, Republic of

Sub-National Business - WB Mici,Trimor P148743 Doing Business in South Africa South Africa

Education Fasih,Tazeen P143477 ET Skills for Competitiveness and Growth Ethiopia

Craig,Helen J. P133772 KE STEP Skills Measurement Study Kenya

Craig,Helen J. P149334 Stocktaking Youth Employment Activities Kenya

Craig,Helen J. P150019 Teacher Policies in Kenya (SABER) Kenya

Craig,Helen J. P150025 Value Chain Analysis Kenya

Blom,Andreas P150609 Higher Education Kenya

Caillaud,Fadila P147611 MG - Education and Health PER Notes Madagascar

Tanaka,Nobuyuki P148735 Malawi Primary Education PET-QSD study Malawi

Tanaka,Nobuyuki P153416 Higher Education Study Dissemination Malawi

Nellemann,Soren P153250 Sudan Tertiary Education Diagnostic Sudan

Naudeau,Sophie P147519 Zimbabwe HD ESW Zimbabwe

Devercelli,Amanda Epstein P144498 ELP - Regional Activities Africa

Devercelli,Amanda Epstein P144501 ELP- Country Activities Africa

Sosale,Shobhana P143140 CM--Skills Development Cameroon

Darvas,Peter P123857 ZR-Skills Development Study Congo, Democratic Republic of

Lahire,Nathalie P133417 Towards a tertiary education strategy Guinea

Di Gropello,Emanuela P155946 HD PER -- Policy Notes 2 Guinea

Di Gropello,Emanuela P155653 Education Resilience Assessment Mali

Appasamy,Irajen P148688 Nigeria Skills and Competitiveness Nigeria

Seck,Atou P143517 Senegal Education PER Senegal

Energy & Extractives Banerjee,Sudeshna Ghosh P132627 Power and Mining in Africa Africa

O'Sullivan,Kyran P133675 Powering Kenya's Future Kenya

Lokanc,Martin P151208 Fifth Econ Brief-Mining Zambia

Arrobas,Daniele La Porta P147613 Liberia Sustainable and Inclusive Natura Liberia

SORTED BY GLOBAL PRACTICE*ESW DELIVERED IN FY15

ADVISORY SERVICES AND ANALYTICS

20

Global Practice Team Leader Project ID Project Name Country

Energy & Extractives Pelon,Remi P131522 Madagascar Mining Benefits Madagascar

Environment & Natural Resources Follea,Salimata D. P150544 Update Cote d'Ivoire 2010 CEA Cote d'Ivoire

Hughes,Nigel Ross P128056 3A: AFR Climate Insurance Africa

Cervigni,Raffaello P153755 Preparation of Background Studies Africa

Cervigni,Raffaello P153759 Preparation of Synthesis Report Africa

Damania,Richard P149584 Rwanda Infrastructure Project Diagnostic Rwanda

Glauber,Ann Jeannette P127165 TZ:Building Blocks= National CC Strategy Tanzania

Glauber,Ann Jeannette P145914 Tanzania Hydropower Assessment Sustainab Tanzania

Agriculture Sene,Manievel P143153 CM - Agric Sector Strat Options Analysis Cameroon

Chengula,Ladisy Komba P148649 Kenya Agriculture Sector Review 1 Kenya

Tchale,Hardwick P118371 3A:Review of Land Administration& Reform Africa

Tchale,Hardwick P143739 A Review of Land Administration Sudan

Pehu,Eija P132953 Linking women and the private sector Zambia

Pereira Goncalves, P153824 Technical Note on AML/CF in the CEMAC Central Africa Marilyne

Pereira Goncalves, P147917 AML/CFT Assessment of the DRC Congo, Marilyne Democratic Republic of

Van der Does de P147915 AML/CFT Assessment of Ethiopia Ethiopia Willebois,Emile J. M.

Finance & Markets Goffe,Valeriya P151852 Uganda - Review of Financial Sector Uganda

Governance Chirwa,Simon B. P146355 Procurement Value Chain Analysis Ethiopia Chenjerani

Agossou,Hugues P154699 FM and Procurement systems Review Madagascar

Larbi,George Addo P149796 Kenya Political Econ Agriculture Sector Kenya

Raballand,Gael J. R. F. P152541 Illicit traffiic and governance in Mali Mali

Fritz,Verena Maria P148524 SS-Governance Review for South Sudan South Sudan

Health, Nutrition & Population Jobanputra,Sangeeta P127177 3A: Population and Development in Africa Africa Raja

Diack,Aissatou P143786 Chad Health Country Status Report Chad

Shekar,Meera P155576 Nutrition Assessment for Resilience Mali

SORTED BY GLOBAL PRACTICE*ESW DELIVERED IN FY15

ADVISORY SERVICES AND ANALYTICS

21

Global Practice Team Leader Project ID Project Name Country

Urban, Rural & Social Development Parby,Jonas Ingemann P150440 BI:Urbanization and Economic Development Burundi

Toure,Boulel P132787 BJ Social Capital Study Benin

Pinto Moreira,Emmanuel P147410 PEMFAR Congo, Democratic Republic of

Tchana Tchana,Fulbert P147163 REPUBLIC OF CONGO - PEMFAR Congo, Republic of

Tchana Tchana,Fulbert P151455 Second Edition of the Economic Update Congo, Republic of

Treichel,Volker P148295 Cote d'Ivoire Growth and Competitiveness Cote d'Ivoire

Tsouck Ibounde,Rick Emery P127623 Gabon - Policy Notes Gabon

Damania,Richard P123521 Prioritizing Infrastructure Investment Ghana

Aykut,Dilek P133667 Ghana - Policy Note Ghana

Zafar,Ali P144401 Guinea Public Expenditure Review Guinea

Kiringai,Jane Wangui P148160 KE PER FY14 Kenya

Randa,John P148187 Kenya Economic Note Kenya

Graham,Errol George P133196 Liberia Policy Notes Liberia

Boakye,Daniel Kwabena P146541 Enhancing Regulations on Services Trade Liberia

Kubota,Keiko P146612 MG Reengagement Policy Notes Madagascar

Mbowe,Appolenia P147757 Malawi Policy Note Series Malawi

Blanco Armas,Enrique P130463 Mozambique Public Expenditure Review Mozambique

Blanco Armas,Enrique P151635 Policy Notes New Government Mozambique

Kida,Mizuho P144949 Niger Reform Plan Niger

Shatalov,Sergei I. P146536 Niger - Policy Notes Series 2 Niger

Zeikate,Signe P149261 DEMPA - Nigera, Cross River State Nigeria

Ishihara,Yoichiro P147369 Rwanda Economic Update FY14 Rwanda

Nishiuchi,Toru P151683 Rwanda Economic Update FY15

Cabral,Rodrigo Silveira P147175 Sao Tome and Principe MTDS Sao Tome Veiga and Principe

Munoz Moreno,Rafael P132465 SC-Programmatic Public Expenditure Rev Seychelles

Purfield,Catriona Mary P128715 ZA:Economics of South African Townships South Africa

Purfield,Catriona Mary P145547 SA - distributional impact of fiscal pol South Africa

Zacchia,Paolo B. P143976 SS Jobs & Livelihoods South Sudan

Im,Fernando Gabriel P143396 SW Growth Report Swaziland

Morisset,Jacques P151530 Economic Update Tanzania

Smith,Gregory P155102 Tanzania Second DeMPA Tanzania

Sebudde,Rachel K. P148040 Uganda Economic Update 4 Uganda

Sebudde,Rachel K. P151592 Uganda Economic Update(5th) Uganda

SORTED BY GLOBAL PRACTICE*ESW DELIVERED IN FY15

ADVISORY SERVICES AND ANALYTICS

22

Global Practice Team Leader Project ID Project Name Country

Poverty Dabalen,Andrew L. P143977 Vulnerability and Resilience - Sahel Africa

Batana,Yele Maweki P151548 TD & ML-Poverty impact of Elect and Irrg Africa

Mungai,Rose P151854 Analytics for Results in Africa Africa

MacWilliam,David Cal P118054 BJ-Poverty Assessment Benin

Belghith,Nadia Belhaj P147034 Burundi Vulnerability Assessment Burundi Hassine

Beidou,Abdoullahi P145994 Cote d'Ivoire Country Statistical Brief Cote d'Ivoire

Hill,Ruth P147357 Ethiopia Poverty Assessment Ethiopia

Molini,Vasco P151600 Ghana Work program (FY15) Ghana

Himelein,Kristen P152547 Economic Impact of Ebola Survey Liberia

Hoogeveen,Johannes G. P146530 Mali - Geography of Poverty Mali

Batana,Yele Maweki P148557 ML - Poverty and Gender Notes Mali

Hoogeveen,Johannes G. P151364 Political Economy of Poverty Reduction

Sulla,Victor P146743 MU Poverty Analysis Mauritius

Molini,Vasco P146523 NG - Programmatic Poverty Work Nigeria

Molini,Vasco P151590 Nigeria: Programmatic Poverty Work Nigeria

Bundervoet,Tom P147845 Rwanda Poverty Assessment Rwanda

Bundervoet,Tom P151669 Rwanda Jobs and Employment Study Rwanda

Dabalen,Andrew L. P127438 SN-Poverty and Gender Report Senegal

Himelein,Kristen P133316 SL - Poverty Assessment (FY13) Sierra Leone

Hill,Ruth P146822 Somaliland Poverty Assessment Somalia

Bandiera,Luca P126976 SD-South Sudan Poverty Notes (FY13) South Sudan

Ersado,Lire P147814 Full Poverty Assessment Sudan Belghith,Nadia Belhaj

Hassine P148501 Tanzania Poverty Assessment Tanzania

Katayama,Roy Shuji P132379 Zimbabwe Poverty Analysis Zimbabwe

Social Protection & Labor Pontara,Nicola P147501 South Sudan Youth Business Startup Study South Sudan

Del Ninno,Carlo P155654 Social Protection Review Mali

Urban, Rural & Social Development Parby,Jonas Ingemann P150440 BI:Urbanization and Economic Development Burundi

Fall,Madio P147762 Cote d'Ivoire Urbanization Review Cote d'Ivoire

Vemuru,Varalakshmi P152459 Horn of Africa Forced Displacement Study Eastern Africa

Ozlu,Mehmet Onur P146519 Ethiopia Urbanization Review Ethiopia

Parby,Jonas Ingemann P143696 Ghana - Urbanization Review Ghana

SORTED BY GLOBAL PRACTICE*ESW DELIVERED IN FY15

ADVISORY SERVICES AND ANALYTICS

23

Global Practice Team Leader Project ID Project Name Country

Urban, Rural & Social Development El-Arnaout,Sateh Chafic P143476 Nigeria Urbanization Review Nigeria

Rouhana,Salim P124695 Senegal Spatial Development Senegal

Rossiasco Uscategui, P131272 Connecting the Dots Africa Paula Andrea

Bance,Paul G. A. P147735 CAR - Pastoral Conflict Initiative Central African Republic

Finch,Christopher P150237 Social Accountability in Devolution Kenya

Trade & Competitiveness Dihel,Nora Carina P131406 Regional Trade-Health and Education Africa

Farole,Thomas P143446 Support to SACU Secretariat Africa

Isik,Gozde P145240 Natural Resources Regional Integration Africa

Hoffman,Barak Daniel P153309 Africa Competitiveness Report 2015 Africa

Maur,Jean-Christophe P145228 Benin: DTIS Update Benin

Dimitriyev,Steven R. P147309 Private Sector Development Policy Notes Comoros

Tchana Tchana,Fulbert P149800 Trade Facilitation and Intervention Congo, Republic of

Hoppe,Mombert P143233 MG DTIS Update Madagascar

Mukim,Megha P151750 Vendor Supplier Diagnostic Rwanda

Dimitriyev,Steven R. P151778 SC-PPP Outsourcing and PSD dialogue Seychelles

Goffe,Valeriya P147217 Uganda Local Content Multi-Sector Study Uganda

Maur,Jean-Christophe P132559 Regional Foodstaples Trade Western Africa

Transport & ICT Halewood,Naomi J. P147092 Ghana Digital Economy Strategy Ghana

Markland,James Robert P144039 Review of Road Construction Costs Ethiopia

Gorham,Roger P147972 ET Urban & Metropolitan Transport Review Ethiopia

Water Cervigni,Raffaello P126254 3A: Climate Vulnerability Infrastructure Africa

Heymans,Christiaan P132237 Assessment of prepaid systems Africa

Mutono,Samuel Dawuna P146046 Private Sector Performance in Delivering Uganda

Not assigned Tanase,Virginia P116160 SSATP-Legal instruments in TTF- Update Africa

Humphreys,Richard Martin P133269 Regional Intermodal Study - Pillar 1 Africa

Shekar,Meera P144425 Population and Development For The Sahel Africa

Marc,Alexandre P148420 West Africa Fragility and Security Africa

Diallo,Bella Lelouma P144043 BI-Use of Country System study Burundi

SORTED BY GLOBAL PRACTICE*ESW DELIVERED IN FY15

ADVISORY SERVICES AND ANALYTICS

24

Global Practice Team Leader Project ID Project Name Country

Not assigned Diallo,Bella Lelouma P144359 ROSC Burundi

Larizza,Marco P145324 Burundi Fiscal Decentralization Burundi

Hayatou,Mohamadou S P133072 Chad Value Chain Study and Analysis Chad

Vermehren,Andrea P133818 KM-Social Policy Notes Comoros

Dalil Essakali,Mohammed P144128 River and Urban Transport Review Congo, Democratic Republic of

Mousset,Cedric P144434 FSAP Democratic Republic of Congo Congo, Democratic Republic of

Diallo,Bella Lelouma P144041 CG-Use of Country System Congo, Republic of

Strobbe,Francesco P143270 FPD Policy Notes Ethiopia

Araya,Elsa P144704 Public Sector Staff Turnover Study Ethiopia

Issa,Djibrilla Adamou P133304 Ghana Competitiveness and Job Creation Ghana

Brintet,Eric P146776 Guinea-Bissau Joint PEFA with EU Guinea-Bissau

Muhula,Raymond P146745 Liberia: Decentralization Diagnostic Liberia

Byamugisha,Joseph P145181 Madagascar Extended GAC Review Madagascar

Vermehren,Andrea P146919 Next Generation Social Protection Madagascar

Le,Tuan Minh P133262 Malawi PER Malawi

Wee,Asbjorn Haland P146783 Revisiting local governance in the Sahel Mali

Fam,Maimouna Mbow P148756 Discrete ESW Mali

Samba,Fatou Fall P129733 MR-ROSC Accounting Mauritania

Humphreys,Richard Martin P133271 Defining an Asset Management Strategy Mauritius

Nombora,Dionisio Augusto P143053 MZ Governance Mozambique

Ronen Mevorach,Irit P144909 Namibia - ICR ROSC Namibia

Bove,Abel Paul Basile P147415 State Governance Benchmarking Nigeria

Workie,Netsanet Walelign P117060 RW-Health System Strengthening (FY11) Rwanda

Austin,Mark A. P145730 Rwanda Agriculture Policy Note Rwanda

Gamberoni,Elisa P132018 STP Diagnostic Trade Integration Study Sao Tome and Principe

Laure,Alexandre Hugo P132874 Sierra Leone Growth Pole Diagnostic Sierra Leone

Yilmaz,Serdar P133319 SL-Constraints to Service Delivery Sierra Leone

Mousley,Peter J. P128557 SO:Political Economy of PSD&Job Creation Somalia

Bandiera,Luca P127492 South Sudan Trade Regional Integration South Sudan

Fine,Gary J. P132930 Fostering Competitiveness and Growth South Sudan

Garcia,Andres F. P132934 The Private Sector and Job Creation South Sudan

SORTED BY GLOBAL PRACTICE*ESW DELIVERED IN FY15

ADVISORY SERVICES AND ANALYTICS

25

Global Practice Team Leader Project ID Project Name Country

Not assigned ElFadil,Yousif Mubarak P143067 Sudan/South Sudan Border Diagnostic South Sudan

Lufafa,Abel P143732 Land Administration Study South Sudan

Pontara,Nicola P148891 Social Protection Policy Dialogue South Sudan

Alamir,Mosllem Ahmed P127259 Sudan PER/State Development Sudan

Geiger,Michael Tobias P145798 Sudan DTIS Update Sudan

Kamuchwezi,Paul Kato P148664 Uganda ROSC A&A Uganda

Dihel,Nora Carina P132612 Diagnostic Trade Integration StudyZambia Zambia

Pierola Castro,Martha D. P144073 ZW - Competitiveness and Trade Work Zimbabwe

*As of September 29, 2015

SORTED BY GLOBAL PRACTICE*ESW DELIVERED IN FY15

ADVISORY SERVICES AND ANALYTICS

26

Country Team Leader Project ID Project Name

Africa Chuhan-Pole,Punam P148422 Impact of Mining on Local Communities

Evans,David P152983 Economic Impact of the Ebola Epidemic

Traore,Cheick P144348 Boosting Budget Execution & Procurement

Berg,Birgitte Gunhild P156639 Bank Financing of SMEs in Kenya

Chad Aslan,Cigdem P144758 DeMPA Chad

Congo, Democratic Republic of Barroy,Helene P147553 Social Sector PER

Ethiopia Brar,Parminder P. S. P148696 IGFT Study

Brar,Parminder P. S. P149510 Ethiopia PEFA 2014 Regional Reports

Brar,Parminder P. S. P149693 Ethiopia PEFA 2014 - Federal Report

Kiringai,Jane Wangui P148336 Ethiopia PER

Kenya Larbi,George Addo P149788 Kenya: Political Economy of Devolution

Lesotho Chakravarty,Shubha P151705 Lesotho Gender Study

Nigeria Ringold,Dena P155775 Jobs Assessment and Strategy Development

Ringold,Dena P155776 Review of ICT and Jobs

Buba,Johanne P147941 Inclusive Markets

Senegal Brintet,Eric P146122 SENEGAL: Support Transparency Code

South Sudan Oyeyiola,Adenike Sherifat P144620 SS-Asess of PFM in Counties and Payams

Sudan Fritz,Verena Maria P147992 SD-Governace Review

Tanzania Mungunasi,Emmanuel A. P155544 Tanzania PER 2014 ESW

Zambia Kumar,Praveen P149550 Fourth Econ Brief-Financial Inclusion

Kumar,Praveen P149551 Third Economic Brief-Trade

*As of September 28, 2015

SORTED BY COUNTRY*ESW DELIVERED IN FY16

ADVISORY SERVICES AND ANALYTICS

27

Country Team Leader Project ID Project Name

Africa Christiaensen,Luc P145705 Agriculture in Africa - Myths and Facts

Devercelli,Amanda Epstein P144498 ELP - Regional Activities

Devercelli,Amanda Epstein P144501 ELP- Country Activities

Banerjee,Sudeshna Ghosh P132627 Power and Mining in Africa

Hughes,Nigel Ross P128056 3A: AFR Climate Insurance

Cervigni,Raffaello P153755 Preparation of Background Studies

Cervigni,Raffaello P153759 Preparation of Synthesis Report

Tchale,Hardwick P118371 3A:Review of Land Administration& Reform

Jobanputra,Sangeeta Raja P127177 3A: Population and Development in Africa

Dabalen,Andrew L. P143977 Vulnerability and Resilience - Sahel

Batana,Yele Maweki P151548 TD & ML-Poverty impact of Elect and Irrg

Mungai,Rose P151854 Analytics for Results in Africa

Rossiasco Uscategui, P131272 Connecting the Dots

Paula Andrea

Dihel,Nora Carina P131406 Regional Trade-Health and Education

Farole,Thomas P143446 Support to SACU Secretariat

Isik,Gozde P145240 Natural Resources Regional Integration

Hoffman,Barak Daniel P153309 Africa Competitiveness Report 2015

Cervigni,Raffaello P126254 3A: Climate Vulnerability Infrastructure

Heymans,Christiaan P132237 Assessment of prepaid systems

Tanase,Virginia P116160 SSATP-Legal instruments in TTF- Update

Humphreys,Richard Martin P133269 Regional Intermodal Study - Pillar 1

Shekar,Meera P144425 Population and Development For The Sahel

Marc,Alexandre P148420 West Africa Fragility and Security

Angola Gamberoni,Elisa P146753 Angola Economic Updates

Benin Toure,Boulel P132787 BJ Social Capital Study

MacWilliam,David Cal P118054 BJ-Poverty Assessment

Maur,Jean-Christophe P145228 Benin: DTIS Update

Burundi Belghith,Nadia Belhaj Hassine P147034 Burundi Vulnerability Assessment

Parby,Jonas Ingemann P150440 BI:Urbanization and Economic Development

Diallo,Bella Lelouma P144043 BI-Use of Country System study

Diallo,Bella Lelouma P144359 ROSC

Larizza,Marco P145324 Burundi Fiscal Decentralization

SORTED BY COUNTRY*ESW DELIVERED IN FY15

ADVISORY SERVICES AND ANALYTICS

28

Country Team Leader Project ID Project Name

Cameroon Sosale,Shobhana P143140 CM--Skills Development

Sene,Manievel P143153 CM - Agric Sector Strat Options Analysis

Central Africa Pereira Goncalves,Marilyne P153824 Technical Note on AML/CF in the CEMAC

Central African Republic Bance,Paul G. A. P147735 CAR - Pastoral Conflict Initiative

Chad Diack,Aissatou P143786 Chad Health Country Status Report

Hayatou,Mohamadou S P133072 Chad Value Chain Study and Analysis

Comoros Dimitriyev,Steven R. P147309 Private Sector Development Policy Notes

Vermehren,Andrea P133818 KM-Social Policy Notes

Congo, Democratic Republic of Darvas,Peter P123857 ZR-Skills Development Study

Pereira Goncalves,Marilyne P147917 AML/CFT Assessment of the DRC

Pinto Moreira,Emmanuel P147410 PEMFAR

Dalil Essakali,Mohammed P144128 River and Urban Transport Review

Mousset,Cedric P144434 FSAP Democratic Republic of Congo

Congo, Republic of Goldstein,Markus P. P144114 Republic of Congo Gender Assessment 2013

Tchana Tchana,Fulbert P147163 REPUBLIC OF CONGO - PEMFAR

Tchana Tchana,Fulbert P151455 Second Edition of the Economic Update

Tchana Tchana,Fulbert P149800 Trade Facilitation and Intervention

Diallo,Bella Lelouma P144041 CG-Use of Country System

Cote d'Ivoire Follea,Salimata D. P150544 Update Cote d'Ivoire 2010 CEA

Treichel,Volker P148295 Cote d'Ivoire Growth and Competitiveness

Beidou,Abdoullahi P145994 Cote d'Ivoire Country Statistical Brief

Fall,Madio P147762 Cote d'Ivoire Urbanization Review

Eastern Africa Vemuru,Varalakshmi P152459 Horn of Africa Forced Displacement Study

Ethiopia Fasih,Tazeen P143477 ET Skills for Competitiveness and Growth

Van der Does de Willebois, P147915 AML/CFT Assessment of Ethiopia Emile J. M.

Chirwa,Simon B. Chenjerani P146355 Procurement Value Chain Analysis

Hill,Ruth P147357 Ethiopia Poverty Assessment

Ozlu,Mehmet Onur P146519 Ethiopia Urbanization Review

Markland,James Robert P144039 Review of Road Construction Costs

Gorham,Roger P147972 ET Urban & Metropolitan Transport Review

Strobbe,Francesco P143270 FPD Policy Notes

Araya,Elsa P144704 Public Sector Staff Turnover Study

SORTED BY COUNTRY*ESW DELIVERED IN FY15

ADVISORY SERVICES AND ANALYTICS

29

Country Team Leader Project ID Project Name

Gabon Tsouck Ibounde,Rick Emery P127623 Gabon - Policy Notes

Ghana Damania,Richard P123521 Prioritizing Infrastructure Investment

Aykut,Dilek P133667 Ghana - Policy Note

Molini,Vasco P151600 Ghana Work program (FY15)

Parby,Jonas Ingemann P143696 Ghana - Urbanization Review

Halewood,Naomi J. P147092 Ghana Digital Economy Strategy

Issa,Djibrilla Adamou P133304 Ghana Competitiveness and Job Creation

Guinea Lahire,Nathalie P133417 Towards a tertiary education strategy

Di Gropello,Emanuela P155946 HD PER -- Policy Notes 2

Zafar,Ali P144401 Guinea Public Expenditure Review

Guinea-Bissau Brintet,Eric P146776 Guinea-Bissau Joint PEFA with EU

Kenya Craig,Helen J. P133772 KE STEP Skills Measurement Study

Craig,Helen J. P149334 Stocktaking Youth Employment Activities

Craig,Helen J. P150019 Teacher Policies in Kenya (SABER)

Craig,Helen J. P150025 Value Chain Analysis

Blom,Andreas P150609 Higher Education

O'Sullivan,Kyran P133675 Powering Kenya's Future

Chengula,Ladisy Komba P148649 Kenya Agriculture Sector Review 1

Larbi,George Addo P149796 Kenya Political Econ Agriculture Sector

Kiringai,Jane Wangui P148160 KE PER FY14

Randa,John P148187 Kenya Economic Note

Finch,Christopher P150237 Social Accountability in Devolution

Liberia Arrobas,Daniele La Porta P147613 Liberia Sustainable and Inclusive Natura

Graham,Errol George P133196 Liberia Policy Notes

Boakye,Daniel Kwabena P146541 Enhancing Regulations on Services Trade

Himelein,Kristen P152547 Economic Impact of Ebola Survey

Muhula,Raymond P146745 Liberia: Decentralization Diagnostic

Madagascar Caillaud,Fadila P147611 MG - Education and Health PER Notes

Pelon,Remi P131522 Madagascar Mining Benefits

Agossou,Hugues P154699 FM and Procurement systems Review

Kubota,Keiko P146612 MG Reengagement Policy Notes

Hoppe,Mombert P143233 MG DTIS Update

Byamugisha,Joseph P145181 Madagascar Extended GAC Review

SORTED BY COUNTRY*ESW DELIVERED IN FY15

ADVISORY SERVICES AND ANALYTICS

30

Country Team Leader Project ID Project Name

Madagascar Vermehren,Andrea P146919 Next Generation Social Protection

Malawi Tanaka,Nobuyuki P148735 Malawi Primary Education PET-QSD study

Tanaka,Nobuyuki P153416 Higher Education Study Dissemination

Mbowe,Appolenia P147757 Malawi Policy Note Series

Le,Tuan Minh P133262 Malawi PER

Mali Di Gropello,Emanuela P155653 Education Resilience Assessment

Raballand,Gael J. R. F. P152541 Illicit traffiic and governance in Mali

Shekar,Meera P155576 Nutrition Assessment for Resilience

Hoogeveen,Johannes G. P146530 Mali - Geography of Poverty

Batana,Yele Maweki P148557 ML - Poverty and Gender Notes

Hoogeveen,Johannes G. P151364 Political Economy of Poverty Reduction

Del Ninno,Carlo P155654 Social Protection Review

Wee,Asbjorn Haland P146783 Revisiting local governance in the Sahel

Fam,Maimouna Mbow P148756 Discrete ESW

Mauritania Samba,Fatou Fall P129733 MR-ROSC Accounting

Mauritius Sulla,Victor P146743 MU Poverty Analysis

Humphreys,Richard Martin P133271 Defining an Asset Management Strategy

Mozambique Blanco Armas,Enrique P130463 Mozambique Public Expenditure Review

Blanco Armas,Enrique P151635 Policy Notes New Government

Nombora,Dionisio Augusto P143053 MZ Governance

Namibia Ronen Mevorach,Irit P144909 Namibia - ICR ROSC

Niger Kida,Mizuho P144949 Niger Reform Plan

Shatalov,Sergei I. P146536 Niger - Policy Notes Series 2

Nigeria Appasamy,Irajen P148688 Nigeria Skills and Competitiveness

Zeikate,Signe P149261 DEMPA - Nigera, Cross River State

Molini,Vasco P146523 NG - Programmatic Poverty Work

Molini,Vasco P151590 Nigeria: Programmatic Poverty Work

El-Arnaout,Sateh Chafic P143476 Nigeria Urbanization Review

Bove,Abel Paul Basile P147415 State Governance Benchmarking

Rwanda Damania,Richard P149584 Rwanda Infrastructure Project Diagnostic

Ishihara,Yoichiro P147369 Rwanda Economic Update FY14

Nishiuchi,Toru P151683 Rwanda Economic Update FY15

Bundervoet,Tom P147845 Rwanda Poverty Assessment

SORTED BY COUNTRY*ESW DELIVERED IN FY15

ADVISORY SERVICES AND ANALYTICS

31

Country Team Leader Project ID Project Name

Rwanda Bundervoet,Tom P151669 Rwanda Jobs and Employment Study

Mukim,Megha P151750 Vendor Supplier Diagnostic

Workie,Netsanet Walelign P117060 RW-Health System Strengthening (FY11)

Austin,Mark A. P145730 Rwanda Agriculture Policy Note

Sao Tome and Principe Cabral,Rodrigo Silveira Veiga P147175 Sao Tome and Principe MTDS

Gamberoni,Elisa P132018 STP Diagnostic Trade Integration Study

Senegal Seck,Atou P143517 Senegal Education PER

Dabalen,Andrew L. P127438 SN-Poverty and Gender Report

Rouhana,Salim P124695 Senegal Spatial Development

Seychelles Munoz Moreno,Rafael P132465 SC-Programmatic Public Expenditure Rev

Dimitriyev,Steven R. P151778 SC-PPP Outsourcing and PSD dialogue

Sierra Leone Himelein,Kristen P133316 SL - Poverty Assessment (FY13)

Laure,Alexandre Hugo P132874 Sierra Leone Growth Pole Diagnostic

Yilmaz,Serdar P133319 SL-Constraints to Service Delivery

Somalia Hill,Ruth P146822 Somaliland Poverty Assessment

Mousley,Peter J. P128557 SO:Political Economy of PSD&Job Creation

South Africa Mici,Trimor P148743 Doing Business in South Africa

Purfield,Catriona Mary P128715 ZA:Economics of South African Townships

Purfield,Catriona Mary P145547 SA - distributional impact of fiscal pol

South Sudan Fritz,Verena Maria P148524 SS-Governance Review for South Sudan

Zacchia,Paolo B. P143976 SS Jobs & Livelihoods

Bandiera,Luca P126976 SD-South Sudan Poverty Notes (FY13)

Pontara,Nicola P147501 South Sudan Youth Business Startup Study

Bandiera,Luca P127492 South Sudan Trade Regional Integration

Fine,Gary J. P132930 Fostering Competitiveness and Growth

Garcia,Andres F. P132934 The Private Sector and Job Creation

ElFadil,Yousif Mubarak P143067 Sudan/South Sudan Border Diagnostic

Lufafa,Abel P143732 Land Administration Study

Pontara,Nicola P148891 Social Protection Policy Dialogue

Southern Africa Purfield,Catriona Mary P154853 Potential country-level policy notes

Sudan Nellemann,Soren P153250 Sudan Tertiary Education Diagnostic

Tchale,Hardwick P143739 A Review of Land Administration

Ersado,Lire P147814 Full Poverty Assessment

SORTED BY COUNTRY*ESW DELIVERED IN FY15

ADVISORY SERVICES AND ANALYTICS

32

Country Team Leader Project ID Project Name

Sudan Alamir,Mosllem Ahmed P127259 Sudan PER/State Development

Geiger,Michael Tobias P145798 Sudan DTIS Update

Swaziland Im,Fernando Gabriel P143396 SW Growth Report

Tanzania Glauber,Ann Jeannette P127165 TZ:Building Blocks= National CC Strategy

Glauber,Ann Jeannette P145914 Tanzania Hydropower Assessment Sustainab

Morisset,Jacques P151530 Economic Update

Smith,Gregory P155102 Tanzania Second DeMPA

Belghith,Nadia Belhaj Hassine P148501 Tanzania Poverty Assessment

Uganda Goffe,Valeriya P151852 Uganda - Review of Financial Sector

Sebudde,Rachel K. P148040 Uganda Economic Update 4

Sebudde,Rachel K. P151592 Uganda Economic Update(5th)

Goffe,Valeriya P147217 Uganda Local Content Multi-Sector Study

Mutono,Samuel Dawuna P146046 Private Sector Performance in Delivering

Kamuchwezi,Paul Kato P148664 Uganda ROSC A&A

Western Africa Maur,Jean-Christophe P132559 Regional Foodstaples Trade

Zambia Lokanc,Martin P151208 Fifth Econ Brief-Mining

Pehu,Eija P132953 Linking women and the private sector

Dihel,Nora Carina P132612 Diagnostic Trade Integration StudyZambia

Zimbabwe Naudeau,Sophie P147519 Zimbabwe HD ESW

Katayama,Roy Shuji P132379 Zimbabwe Poverty Analysis

Pierola Castro,Martha D. P144073 ZW - Competitiveness and Trade Work

*As of September 29, 2015

SORTED BY COUNTRY*ESW DELIVERED IN FY15