Cameroon Poverty Assessment 2024 Working Out of Poverty: Building Resilience and Inclusive Growth for Cameroon’s Future December 2024 Acknowledgements T © 2025 International Bank his poverty assessment relies on survey data collected for Reconstruction and by the Institut National de la Statistique du Cameroun Development / The World Bank (Cameroon National Institute of Statistics, INS) in 1818 H Street NW, Washington DC collaboration with the World Bank. The fifth Enquête 20433 Camerounaise Auprès des Ménages (Cameroon Telephone: 202-473-1000 Household Survey, ECAM-5) and corresponding bridge survey – Internet: www.worldbank.org collected in 2021 and 2022 – provide Cameroon’s first poverty estimates since 2014. Using these data, INS launched the official Rights and Permissions poverty estimates for 2021/22 in April 2024. The material in this work is subject The poverty assessment was led by Jonathan Lain (Senior to copyright. Because The World Economist, EAWPV), Rayner Tabetando (Economist, EAWPV), Bank encourages dissemination and Clarence Tsimpo Nkengne (Senior Economist, EAWPV). The of its knowledge, this work may be core team working on the poverty assessment also included reproduced, in whole or in part, for noncommercial purposes as long Walker Kosmidou-Bradley (Geographer, EAWPV). as full attribution to this work is given. The report was prepared with close guidance from Johannes Hoogeveen (Senior Economist, EAWPV) and under the overall Any queries on rights and licenses, direction of Johan Mistiaen (Practice Manager, EAWPV), Clelia including subsidiary rights, should be addressed to World Rontoyanni (Program Leader, EAWDR), and Cheick Kante Bank Publications, The World (Country Director, AWCC1). Further strategic direction was pro- Bank Group, 1818 H Street NW, vided by Abebe Adugna (Regional Director, EAWDR) and Sandeep Washington, DC 20433, USA; Mahajan (Practice Manager, EAWM2). fax: 202-522-2625; e-mail: pubrights@worldbank.org. The analysis hinges on calculating the consumption aggregate and poverty line using ECAM-5 data. Initial work on these calcula- This work is a product of the staff tions was implemented by Martin Mba (Consultant, EAWPV) with of the World Bank with external guidance from Prospere R. Backiny-Yetna (Senior Economist, contributions. The findings, EAWPV). A crucial step in improving the calculations was the interpretations, and conclusions Quality Enhancement Review (QER), undertaken by Elizabeth expressed in this work do not necessarily reflect the views of The Foster (Economist, EAWPV). World Bank, its Board of Executive Many sections of the report benefited from specific analytical Directors, or the governments that they represent. The World Bank inputs. Amina Coulibaly (Senior Economist, EAWM2) and Francis does not guarantee the accuracy of Ngomba Bodi (Economist, EAWM2) provided macroeconomic the data included in this work. estimates and forecasts for Chapter 1 and 2. Jade Mali Mizutani (Senior Public Sector Specialist, EAWG2) contributed vital infor- mation on governance and decentralization in Cameroon for Chapters 1 and 8. Johanna Belanger (Consultant, EAWPV) and Caroline Gevaert (Consultant, GFDRR) provided the background analysis on conflict and displacement used in Chapter 1. Sandra Segovia (Consultant, EPVGE) constructed the small-area poverty map for Chapter 3. Advice on decomposing urban growth for Chapter 3 was provided by Michele Gragnolati (Practice Manager, Cameroon Working Out of Poverty: Building Resilience and Inclusive Growth for Cameroon’s Future HMNHN), Emi Suzuki (Demographer, DECID), and Matthew Wai-Poi (Lead Economist, EEAPV) while Grace Doherty (Consultant, EAWPV) conducted background analysis on urbanization in Cameroon. Key refinements to the social assistance and livelihoods analyses that feature in Chapters 4, 5, and 8 were provided by Ioana Botea (Senior Social Protection Specialist, HAWS3). Eulalie Saïsset (Researcher, Sciences Po) and Aiga Stokenberga (Senior Transport Economist, ILCT1) provided analysis on intra-city transport for Chapter 7. Clarice Yemelong (Intern, EAWPV) conducted analysis of school quality for Chapter 7. Ian Houts (Consultant, ESAPV) conducted the Commitment to Equity analysis used in Chapter 8. The report benefited from detailed and thoughtful peer review comments from Nabil M. Chaherli (Sector Leader, SAWDR), Yeon Soo Kim (Senior Economist, EPVGE), Obert Pimhidzai (Lead Economist, EECPV), and Robert Utz (Lead Economist, EAWM2). Crucial administrative and operational support was provided during the report’s production by Etsehiwot Albert (Program Assistant, EAWM1), Hilary Fuh Cham (Team Assistant, AWCC1), Santosh Kumar Sahoo (Senior Operations Assistant, EAWPV), Arlette Sourou (Senior Program Assistant, EAEPV), and Senait Yifru (Operations Officer, EAWPV). The team is also grateful to Georgia Christina Kosmidou-Bradley (Regional Economist, United Nations High Commissioner for Refugees), Ibrahima Sarr (Economist, United Nations High Commissioner for Refugees), Hamish Baverstock (Program Officer, International Organization for Migration) for helping access data on conflict and dis- placement in Cameroon. The cover photo is credited to Blaise Blaizo. Elizaveta Tarasova prepared the graphical design and layout of this publication. We thank Litera for translation services. 3 Cameroon Poverty Assessment 2024 Contents Executive Summary. Working Out of Poverty: Building Resilience and Inclusive Growth for Cameroon’s Future ........ 7 ES1. Poverty reduction in Cameroon has been languishing for 20 years............................................................................ 7 ES2. Cameroon’s failure to reduce poverty is especially striking, given its enormous potential........................................... 10 ES3. Cameroon’s development challenges are diverse and its poverty profile is changing, making policy action even more urgent..................................................................................................................................................................... 11 ES4. With the right mix of policies, Cameroon can harness its poverty-reducing potential.................................................. 14 ES5. Data remain essential for guiding poverty‑reducing policies and promoting good governance.................................... 19 Chapter 1. Introduction: Cameroon faces diverse and changing development challenges.............................. 21 1.1. Cameroon’s growth is below the country’s potential, constraining living standards.................................................... 21 1.2. Structural changes in Cameroon’s economy underline the challenge of making growth inclusive............................... 24 1.3. Cameroon is a country of extraordinary diversity....................................................................................................... 26 1.4. New shocks are hitting the population unevenly....................................................................................................... 26 1.5. Microdata hold the key to designing granular poverty-reducing policies..................................................................... 31 1.6. Structure of the poverty assessment........................................................................................................................ 33 Chapter 2. Poverty reduction has stagnated in Cameroon and inequality remains high.................................... 35 2.1. New data make it possible to construct Cameroon’s latest poverty estimates in line with best practice, while also tracking trends ....................................................................................................................................................... 35 2.2. Cameroon has adopted international best practices for poverty measurement.......................................................... 37 2.3. Poverty reduction in Cameroon is languishing, with poverty starting to rise in urban areas......................................... 41 2.4. International comparisons suggest that poverty in Cameroon is lower than in neighboring countries, but it is not fulfilling its poverty-reduction potential..................................................................................................................... 43 2.5. The current policy mix is not set to deliver rapid poverty reduction............................................................................ 44 2.6. Inequality in Cameroon is higher than it was two decades ago and exceeds the level in many peer countries............. 45 2.7. Tackling poverty relies on understanding its deep drivers........................................................................................... 46 Chapter 3. Growth and structural change are not helping the poorest, so some Cameroonians risk being left behind ................................................................................................................................................................. 49 3.1. Growth has not been reaching the poorest Cameroonians........................................................................................ 49 3.2. Urbanization is not the poverty-reducing force it once was........................................................................................ 51 3.3. Cameroon is highly spatially unequal and some regions risk being left behind........................................................... 56 3.4. Going beyond the spatial lens can provide a more comprehensive picture of Cameroon’s poverty profile................... 60 3.5. Understanding short-term poverty dynamics can complement analysis of long-term trends...................................... 63 Chapter 4. Widespread shocks could leave even more Cameroonians at risk of falling into poverty............ 67 4.1. Cameroonians who are close to the poverty line could be vulnerable to falling below it............................................. 67 4.2. Many non-poor Cameroonians are at risk of falling into poverty................................................................................. 69 4.3. Shocks affect poor and vulnerable households........................................................................................................ 70 4.4. When confronting shocks, Cameroonians adopt coping strategies which could reduce their welfare in the long run... 73 4.5. With widespread vulnerability and prevalent shocks, social assistance may need to be enhanced............................. 75 4.6. While social assistance can help protect households in the short run, long-run prospects for poverty reduction depend on human capital and livelihoods................................................................................................................ 77 4 Cameroon Working Out of Poverty: Building Resilience and Inclusive Growth for Cameroon’s Future Chapter 5. Despite gains in human capital and basic infrastructure, rural and northern Cameroonians remain far behind the rest of the country....................................................................................................... 79 5.1. Non-monetary indicators of well-being offer a more comprehensive understanding of poverty in Cameroon.............. 79 5.2. Cameroon is making overall progress on many non-monetary welfare indicators....................................................... 81 5.3. The latest snapshot of multidimensional poverty confirms that some Cameroonians are being left behind................. 86 5.4. Boosting educational enrolment, sanitation, and electricity access could offer pathways out of poverty..................... 91 5.5. Monetary poverty and deprivation in terms of education and basic infrastructure overlap more in Cameroon’s lagging areas...................................................................................................................................................................... 93 5.6. Out-of-pocket expenses for education and health could stop poorer Cameroonians from investing in human capital. 95 5.7. New non-monetary deprivations confront Cameroon’s growing urban population...................................................... 95 5.8. Boosting livelihood opportunities can help take advantage of investments in human capital...................................... 97 Chapter 6. Cameroon’s changing labor market is not yet lifting people out of poverty....................................... 101 6.1. The share of Cameroonians who are working has declined....................................................................................... 101 6.2. The labor market’s shift from agriculture to services is yielding mixed results for poverty reduction............................. 106 6.3. Women and young people face additional challenges in Cameroon’s labor market.................................................... 112 6.4. Boosting agricultural productivity remains crucial for lifting Cameroonians out of poverty, especially as climate change threatens the country’s natural capital......................................................................................................... 115 6.5. Human capital and livelihoods need the bedrock of infrastructure............................................................................ 120 Chapter 7. Poor and vulnerable Cameroonians face extra obstacles accessing markets and services....... 123 7.1. Geospatial data can shine a light on Cameroonians’ access to services and markets................................................ 123 7.2. Physical access to services remains a challenge for Cameroonians in remote and rural areas................................... 124 7.3. Long travel times in Cameroon’s remote and rural areas reflect the limitations of the road network............................ 129 7.4. Congestion could hamper access to markets and services in Cameroon’s cities....................................................... 131 7.5. Digital infrastructure remains out of reach for many poor and vulnerable Cameroonians........................................... 131 7.6. Lack of formal identification may further limit access to services.............................................................................. 133 7.7. The analysis can be synthesized to provide a framework for policy action.................................................................. 135 Chapter 8. With the right policies, Cameroon can harness its huge poverty-reducing potential..................... 137 8.1. Cameroon has huge poverty-reducing potential, but it urgently needs to boost the process....................................... 138 8.2. Promoting peace and security is a precondition for poverty reduction....................................................................... 139 8.3. Lifting Cameroonians out of poverty hinges on making growth quicker and more inclusive and creating productive jobs........................................................................................................................................................................ 140 8.4. Channeling government spending towards social assistance and investing in digital infrastructure could benefit Cameroonians across the country........................................................................................................................... 145 8.5. Reinforcing decentralization could allow Cameroon to tailor local policies for local needs......................................... 147 8.6. Lagging regions still need investment in human capital, agricultural productivity, and the underlying bedrock of basic infrastructure.......................................................................................................................................................... 148 8.7. Addressing congestion and overcrowding and supporting integration of migrants could help realize the poverty- reducing potential of urbanization............................................................................................................................ 150 8.8. Given Cameroon’s diverse and changing development challenges, data will remain crucial for guiding poverty- reducing policies and building good governance....................................................................................................... 152 References................................................................................................................................................................. 155 5 Cameroon Poverty Assessment 2024 List of acronyms and abbreviations ACLED Armed Conflict Location IRIS International Recommendations and Event Data Project on Internally Displaced Person BGMF Bill and Melinda Gates Statistics Foundation JRC Joint Research Centre BRT Bus Rapid Transit MIS Malaria Indicators Survey BUCREP Bureau Central de Recensement MPI Multidimensional Poverty Index et d'Etude de la population au MPM Multidimensional Poverty Cameroun (Central Bureau of the Measure Census and Population Studies) NDS30 National Development Strategy CAPI Computer-Assisted Personal 2020-2030 Interviewing NGO Non-Governmental Organization CEEAC Communauté économique OEC Observatory of Economic des États de l’Afrique centrale Complexity (Economic Community of Central OECD Organisation for Economic African States) Co-operation and Development CEM Country Economic Memoradum OPHI Oxford Poverty and Human CEMAC Communauté économique et Development Initiative monétaire de l'Afrique centrale PFS Projet Filets Sociaux (Safety Nets (Economic and Monetary Projects) Community of Central Africa) PNACC Plan National d’Adaptation CEQ Commitment to Equity au Changement Climatique CGCTD Code Général des Collectivités (National Adaptation to Climate Territoriales Décentralisées Change Plan) (General Code of Decentralized PPP Purchasing Power Parity Territorial Collectivities) PSUP Participatory Slum Upgrading CIA Central Intelligence Agency Programme CIESIN Center for International Earth SMIG Salaire Minimum Science Information Network Interprofessionnel Garanti CPI Consumer Price Index (Guaranteed Interprofessional DHS Demographic and Health Survey Minimum Wage) ECAM Enquête Camerounaise Auprès SOE State-Owned Enterprise des Ménages (Cameroon UN DESA United Nations Department Household Survey) of Economic and Social Affairs EESI Enquête sur l’Emploi et le Sectuer UNDP United Nations Development Informel (Survey on Employment Programme and the Informal Sector) UNHCR United Nations High FEWS NET Famine Early Warning Systems Commissioner for Refugees Network UN-HABITAT United Nations Human GDP Gross Domestic Product Settlements Programme GNI Gross National Income USD United States Dollars GRID3 Geo-Referenced Infrastructure VAT Value-Added Tax and Demographic Data for WAEMU West African Economic Development and Monetary Union HCI Human Capital Index WASH Water, Sanitation, and Hygiene IDP Internally Displaced Person WDIs World Development Indicators ILO International Labour Organisation WFP World Food Program IMF International Monetary Fund WHO World Health Organization INS Institut National de la Statistique WITS World Integrated Trade Solution du Cameroun (Cameroon WTO World Trade Organization National Institute of Statistics) XAF Central African CFA franc IOM International Organization XGBoost Extreme Gradient Boosting for Migration 6 Executive Summary Working Out of Poverty: Building Resilience and Inclusive Growth for Cameroon’s Future Executive Summary Working Out of Poverty: Building Resilience and Inclusive Growth for Cameroon’s Future T his report – Cameroon’s first ever official poverty assessment – draws on the country’s latest microdata to suggest policies that can ignite poverty reduction at a pivotal moment. Around 4 in 10 Cameroonians live below the national poverty line – a situation that has changed little for 20 years. Combined with population growth, the number of poor Cameroonians is rising, and now exceeds 10 million people. Growth, while stable, has been slow, with real gross domestic product (GDP) per capita lower today than it was in the 1980s. Productive jobs are scarce, so what little growth is achieved is not reaching the poor and vulnerable. With its geographical advantages, natural capital, rapid urbanization, and a young population with improving human capital outcomes, Cameroon has the potential to address its growing development challenges, but the need for policy reform is urgent. This report provides the latest trends in poverty in Cameroon, assessing its key drivers, and proposing countervailing policies. Alongside core poverty and inequality diagnostics, the report examines the role of shocks, human capital, livelihoods, and access to markets and services in depth. The report draws on the latest microdata collected in Cameroon, including the fifth Enquête Camerounaise Auprès des Ménages (Cameroon Household Survey, ECAM‑5) implemented in 2021/22. These household survey data are combined with other innovative data sources, including granular geo- spatial data. This Executive Summary highlights the poverty assessment’s key findings and outlines the policies that can help Cameroon harness its potential before its development challenges grow too large. ES1. Poverty reduction in Cameroon has been languishing for 20 years Around 4 in 10 Cameroonians live below the national poverty line, and this situ- ation has changed little in two decades. The fifth Enquête Camerounaise Auprès des Ménages (Cameroon Household Survey, ECAM‑5), collected between October 2021 and September 2022, reveals that 37.7 percent of Cameroonians lived below the national poverty line of 296,691 XAF per person per year, which corresponds to 7 Cameroon Poverty Assessment 2024 3.04 USD 2017 in Purchasing Power Parity (PPP) terms (Figure 1). ECAM‑5 provides the latest snapshot of poverty and welfare in Cameroon, employing international best practices. A special “bridge survey” collected in 2021 indicates that poverty has been virtually unchanged over the course of the 2000s and 2010s, moving only slightly from 40.2 percent in 2001 to 38.6 percent in 2021.(1) The 2021 bridge survey was designed to maintain exactly the same methodology as previous ECAMs from 2001, 2007, and 2014, so that poverty trends can be constructed. These estimates were officially launched by the Institut National de la Statistique du Cameroun (Cameroon’s National Institute for Statistics, INS) in April 2024 (INS, 2024). Figure 1. Poverty rate and absolute number of poor in Cameroon using national poverty line, by urban-rural, 2001-2022 Panel A: Poverty rate Panel B: Number of poor 70 12 10.3 56.8 58.3 56.3 10.1 55.0 Number of poor (millions) 60 52.1 10 Poverty rate (percent) 8.1 8.2 50 40.2 7.1 7.3 7.0 39.9 37.5 38.6 37.7 8 6.2 6.4 40 5.3 6 30 21.6 17.9 16.5 4 3.1 20 12.2 2.1 8.9 10 2 1.0 0.8 0.8 0 0 ) ) ) ) ) ) ) ) ) ) ey -2 -3 -4 ey -5 -2 -3 -4 -5 AM AM AM AM AM AM AM AM rv rv su su C C C C C C C C ge ge (E (E (E (E (E (E (E (E rid rid 01 07 14 2 01 07 14 2 /2 /2 (B (B 20 20 20 20 20 20 21 21 21 21 20 20 20 20 Total Urban Rural Total Urban Rural Note: Different urban-rural definitions applied in ECAM‑5 compared with previous surveys. Consumption is spatially deflated and, where relevant, temporally deflated to compare with national poverty lines. Estimates from 2001, 2007, 2014, and the 2021 bridge survey are comparable. 2021/22 estimates from ECAM‑5 represent latest best estimates but cannot be compared with previous surveys. Source: ECAM‑2, ECAM‑3, ECAM‑4, ECAM‑5, 2021 bridge survey, and World Bank estimates. With rapid population growth, the number of poor Cameroonians is rising, and now exceeds more than 10  million people. Given the pace of population growth and stagnant poverty reduction, the absolute number of people living below the national poverty line increased by about two-thirds between 2001 and 2021, rising from 6.2 million to 10.3 million. Since many of the measures put in place to curtail the COVID-19 crisis had been removed by the time data were collected in 2021, this likely reflects a continuation of long-term trends rather than a sudden and short-lived uptick in the number of poor people. The scale of Cameroon’s poverty-reduction challenge is growing year by year. 1. This change is not statistically significant, even at the 10 percent level. 8 Executive Summary Working Out of Poverty: Building Resilience and Inclusive Growth for Cameroon’s Future Cameroon’s flatlining poverty rate reflects its ponderous growth performance; gross domestic product (GDP) per capita is lower today than it was in the 1980s. Between 1985 and 1993, Cameroon suffered a sustained economic decline as the prices of key export commodities – especially oil, cocoa, and coffee – fell precipitously. Following this period of backsliding, the government reined in government spending and the Central African CFA franc was devalued in 1994. More stable and resilient economic growth ensued, even during the COVID-19 crisis. However, despite this stability, economic growth has been too slow, especially relative to population growth, to recoup previous losses. Moreover, many countries – Cameroon’s “aspirational peers” – that had similar GDP per capita levels to Cameroon in the 1990s have subsequently grown much faster.(2) Global evidence demonstrates that growth is tantamount to a precondition for poverty reduction (Kenny & Gehan, 2023). In Cameroon, basic decompositions show that what little poverty reduction has been achieved is largely down to growth rather than redistribution. Therefore, slow growth is proving a binding constraint on lifting Cameroonians out of poverty. Cameroon’s growth process is not only slow, but also non-inclusive, with the poorest third of Cameroonians seeing no consumption growth since the turn of the millennium. Most poor Cameroonians have not experienced any improvement in their living standards. Relatedly, Cameroon is more unequal than it was two decades ago. While the Gini coefficient fell from 44.0 in 2014 to 42.9 in 2021, it remains above its earlier level, 40.4, in 2001. This also means that inequality in Cameroon is significantly higher than in its aspirational peers. Growth is not reaching poor people because the labor market is not deliver- ing enough productive jobs for the country’s growing population: the share of Cameroonians who are working dropped by one quarter in the last 15 years. Since time is poor people’s main asset, labor is the main vehicle for spreading the pro- ceeds of growth (Fields, 2011). Yet between 2007 and 2021, the share of working-age Cameroonians who were working dropped from 80.3 percent to 60.2 percent. While this is partly a product of Cameroon urbanizing (see below) and the working share being lower in urban areas, the decline was witnessed in towns and cities too: urban labor markets are also becoming saturated.(3) Population growth is deepening the jobs challenge for Cameroon’s young people, as almost 2  million young Cameroonians will come of working age in the next decade. Between 2023 and 2033, the number of 15-24-year-olds in Cameroon is set to rise from 5.7 million to 7.4 million. Even if the share of young people who are working remains at its current low levels, this implies that 2.4 million young people will need jobs by 2033. Without productive livelihoods, Cameroon may fail to make the most of its demographic dividend. 2. The aspirational peers for Cameroon used in this report are Bangladesh, Kenya, Morocco, and Vietnam. 3. This significant drop in labor force participation appears in both the ECAMs and in the Enquête sur l’Emploi et le Sectuer Informel (Survey on Employment and the Informal Sector, EESI). Since these data were collected after the most serious measures used to contain the COVID-19 crisis were lifted, the situation in 2021 is likely to reflect long-term trends rather than the short-run effects of the pandemic. 9 Cameroon Poverty Assessment 2024 Despite livelihoods shifting out of agriculture, jobs in Cameroon remain insuf- ficiently productive to lift people out of poverty. Jobs have shifted rapidly from agriculture to services: the share of working Cameroonians engaged ­ primarily­in agri- culture dropped from 57.0 percent in 2007 to 42.4 percent in 2021. While productivity is higher in services than agriculture on average, Cameroonian workers appear to have been shifting to low-productivity service-sector jobs – in sub-sectors including com- merce, transport and communication, and personal services – which are less likely to entail an exit from poverty. At the same time, wage-employment – which is associated with escaping poverty – remains rare. Between 2007 and 2021, the share of working Cameroonians holding wage jobs rose slightly from 17.0 percent to 23.6 percent, but self-employment, mostly in informal activities, continues to dominate the labor market. Additionally, around one quarter of wage jobs are provided by the public sector. Cameroonians themselves recognize that lack of productive jobs is holding back poverty reduction, signaling their frustration. About two-thirds of Cameroonians live in a household that describes itself as either poor or very poor and around 6 in 10 Cameroonians have a very bad or quite bad perception of the economy, according to ECAM‑5 and the 2022 Afrobarometer survey. Relatedly, Cameroonians have low trust in some of the country’s key institutions. More than 6  in  10 Cameroonians had no trust or just a little trust in the parliament, in local governments, and in the judiciary. When asked about their policy priorities, jobs are by far the most commonly mentioned issue: about half of Cameroonians believe lack of jobs is the main cause of poverty in the country. The population is therefore well aware of Cameroon’s poverty-reduction challenge and the need for productive livelihoods. Projections suggest that “business-as-usual” policies will not reduce poverty. Combining ECAM‑5 with macroeconomic forecasts to project poverty indicates that – absent policy reforms – the poverty rate will rise slightly in the coming five years, reaching 40.0 percent by 2025. This means Cameroon would be missing the objectives set out in its National Development Strategy 2020-2030 (NDS30), as it nears the halfway mark. Similarly, the country’s Vision 2035 target to alleviate poverty and become an upper middle-income country by 2035 is also off track. The time remaining to realize these ambitions is running out. ES2. Cameroon’s failure to reduce poverty is especially striking, given its enormous potential Cameroon’s natural capital – its geography and natural resources – as well as its relative political stability should provide the ingredients for rapid, poverty-reducing growth. Cameroon’s growth and poverty-reduction performance belie its potential. The country is favorably situated as a potential gateway between Western and Central Africa, with a coastline that extends for more than 400 kilometers, and Douala placed as a natural economic hub. This could allow trade to flourish, a position reinforced by its membership of the Communauté économique et monétaire de l’Afrique centrale (Economic and Monetary Community of Central Africa, CEMAC). Additionally, the coun- try is endowed with sizeable natural capital: the stock of natural assets on which human wellbeing depends (World Bank, 2021). Alongside oil and natural gas, Cameroon is endowed with other minerals – including gold, iron, and manganese – fertile land, and 10 Executive Summary Working Out of Poverty: Building Resilience and Inclusive Growth for Cameroon’s Future rich ecological diversity within the Congo Basin. Finally, despite growing conflict in some parts of the country, Cameroon has not experienced the same turbulent political changes endured by some of its neighbors. These conditions should in theory provide the foundations for inclusive economic growth and, in turn, poverty reduction. Urbanization is advancing at pace in Cameroon, which could provide the right conditions for structural transformation, agglomeration effects, and improved service delivery. Over the last three decades, the share of Cameroonians living in urban areas has increased by around one half, rising from 40.8  percent in 1992 to 58.7 percent in 2022 (United Nations Population Division, 2019). This leaves Cameroon as one of the most urbanized countries in the region. In theory, the growth of urban centers can accelerate growth, job creation, and poverty reduction through agglom- eration effects: these include sharing public goods, knowledge spillovers, closer proximity to consumers, and better matching of workers and employers (Bolter & Robey, 2020). It may be easier for Cameroonians to reach schools, health centers, and other key services when they are physically closer. Global evidence demonstrates the potential benefits of higher density and lower distances to reach urban spaces for poverty reduction (World Bank, 2009). However, as discussed below, congestion and overcrowding may be hampering the economic potential of Cameroon’s towns and cities, while the urban poor face additional deprivations. With continuing population growth, Cameroon’s people present the country with a huge opportunity. Around 7  in  10 Cameroonians are aged less than 30. As such, the number of Cameroonians of working age, or soon to come of working age, is high relative to the number of dependents. This presents the country with a significant demographic dividend, providing young Cameroonians can find productive livelihoods to fulfil their potential. The potential of Cameroon’s people has been strengthened by substantial pro- gress on one keystone of poverty reduction: human capital. Outcomes for human capital – the knowledge, skills, and health that people accumulate throughout their lives, which enable them to “realize their potential as productive members of society” – have been improving (World Bank, 2018). This can boost long-run, intergenerational prospects for poverty reduction. While there is still a long way to go in Cameroon’s northern regions and rural areas and questions remain around the quality of schools and health facilities, educational enrolment and attainment, maternal and infant health outcomes, and access to water, sanitation, and electricity have all improved in the last two decades. Nevertheless, gains in human capital outcomes will not be fully realized if the livelihoods that can take advantage of growing productive potential are not available. ES3. Cameroon’s development challenges are diverse and its poverty profile is changing, making policy action even more urgent Cameroon is intrinsically diverse along many dimensions – including agro-ecology, ethnicity, and language – so ensuring growth reaches all types of Cameroonians has always been difficult. In terms of physical geography, the country is split into five 11 Cameroon Poverty Assessment 2024 agro-ecological zones, each with distinct climate, topography, soil, and vegetation (Perini, Nfor, Camin, Pianezze, & Piasentier, 2021). This influences the dominant agri- cultural practices in different parts of the country. Cameroon also displays staggering ethnic diversity, being home to around 250 different ethnic groups (World Bank, 2016). Relatedly, the country has around 250 local languages as well as two official languages, with the Sud-Ouest and Nord-Ouest region – on the border with Nigeria – being anglo- phone and the rest of the country being predominantly francophone (World Bank, 2017). Given these cross-cutting dimensions of diversity, Cameroon faces a particularly acute challenge ensuring that all types of Cameroonians are reached by growth, notwith- standing the need to ensure that growth reaches the poor and vulnerable. While Cameroon’s young and growing population presents a big opportunity, the dangers of labor market disappointment and overwhelmed services loom. Ensuring jobs are available for Cameroon’s large youth population not only matters for poverty reduction, but – as global evidence suggests – is also important for mitigating the risk of violence and political instability (Azeng & Yogo, 2013; Demeke, 2022). Young people without livelihood opportunities are more susceptible to being drawn into armed groups instead (Cramer, 2010). At the same time, investment in health and education facilities as well as in basic infrastructure needs to at least keep pace with population growth, to ensure they do not become overrun by new users. Urbanization carries potential, but also risks: 3 in 10 poor Cameroonians now live in urban areas and the urban poor face congestion, pollution, and overcrowding. Urban poverty in Cameroon had been gradually declining in the 2000s, but these gains have subsequently been wiped out. Between 2014 and 2021, the poverty rate and the number of poor people living in urban areas approximately doubled. By 2021/22, 3.1 million poor Cameroonians lived in urban areas. The notion that poverty in Cameroon is purely a rural phenomenon is no longer true. New deprivations afflict the urban poor. Pollution may increasingly blight human capital development in line with global trends: in 2019, 12.7 percent of all deaths in Cameroon could be attributed to the effects of air pollution (Ritche & Roser, 2021). Overcrowding is far more prevalent for the urban poor – of which 61.3 percent live in overcrowded accommodation – than the rural poor – of which 48.0 percent live in overcrowded accommodation.(4) With average commuting times of 2-3 hours in Douala and Yaoundé, congestion is also putting the brakes on productivity. This may be causing urban dwellers to drop out of the labor force entirely as it is simply not profitable to go to work. This could disproportionately affect the urban poor, as they are more likely to live on the peripheries of large cities, which require especially long commutes. Urbanization is increasingly being driven by rural-urban migration. Using pop- ulation projections and geospatial data to decompose urban population growth into (1) natural population growth, (2) reclassification of previously rural areas to urban, and (3) rural-urban migration, the latter clearly made a larger contribution in the 2010s than in the 2000s. Alongside the pursuit of economic opportunities, rising conflict and forced displacement could be pushing people to leave affected areas and move to more secure towns and cities. The rise of rural-urban migration influences the types of policies that can help urbanization yield inclusive growth and poverty reduction. 4. Overcrowding is defined as housing where there are more than two people per room, excluding kitchens, bathrooms, corridors, and balconies (Nkosi, Haman, Naicker, & Mathee, 2019). 12 Executive Summary Working Out of Poverty: Building Resilience and Inclusive Growth for Cameroon’s Future Even with urban poverty rising, poverty remains concentrated in rural areas and in Cameroon’s north: about two-thirds of poor Cameroonians live in the Extrême-Nord, Nord, and Nord-Ouest regions. The combined poverty rate for these three regions is six times higher than in Yaoundé and Douala (Figure 2). Between 2001 and 2014, the poverty rates for the northern regions and the rest of the country were diverging and have shown little sign of meaningful convergence since then. Also, despite progress in recent years, non-monetary poverty – lower human capital and less basic infrastructure – is also more widespread in these lagging regions. This has a direct link to urbanization. Lower living standards in remote and rural areas in northern Cameroon push people to migrate to towns and cities in pursuit of a better life. Figure 2. Poverty and the absolute number of poor in Cameroon’s regions, 2021/22 Poverty rate (percent) Absolute number of poor (thousands) 8.3 - 14.9 141 - 276 15.0 - 20.4 277 - 341 20.5 - 41.5 342 - 704 41.6 - 61.1 705 - 779 61.2 - 69.2 780 - 3,458 Note: Consumption is spatially deflated and temporally deflated to compare with the national poverty line. Source: Humanitarian Data Exchange and GRID-3 (for shapefiles), ECAM‑5, and World Bank estimates. Climate shocks – which reflect a deterioration Cameroon’s natural capital – and conflict shocks are proliferating; they disproportionately affect the country’s lagging regions. These types of shocks can deepen deprivation for those who are already poor or push the millions of Cameroonians just above the poverty line below it. Moreover, with insufficient social protection, some of the coping strategies that Cameroonians adopt in response to shocks – including selling assets or reducing food consumption – could have long-run consequences for their physical and human capital. As climate change advances, the Sudano-Sahelian north – where poverty is already widespread – is expected to be the most vulnerable to climate-related shocks, especially drought (World Bank, 2017). Additionally, exposure to fluvial and pluvial flood events is high in the Extrême-Nord and Nord-Ouest, although exposure is also high 13 Cameroon Poverty Assessment 2024 in the Sud-Ouest, Littoral, and Sud regions, where poverty is lower. Conflict shocks have largely been concentrated in the Extrême-Nord – precipitated by the activities of Boko Haram – and in the Nord-Ouest and Sud-Ouest – where longstanding sepa- ratist tendencies have escalated into armed conflict (Jedwab, Blankespoor, Masaki, & Rodríguez-Castelán, 2021; World Bank, 2021). Therefore, as the threat of these shocks grows, so too could the scale of Cameroon’s poverty reduction challenge in Cameroon’s lagging regions. Poor households across Cameroon are at a growing risk of being excluded by lack of formal identification and limits to digital infrastructure. Formal identification is needed to access certain government services. For example, at the time of writing, children (in theory) need birth certificates in order to receive their primary school leaving certificate and advance to secondary education. Yet only around half of children aged less than 15 reported having a birth certificate in 2021/22, with this share being even lower among those from poor households. This has direct implications for their primary school enrolment, even after controlling for household consumption and other key location and household characteristics. Recent reforms aim to alleviate this issue: the 2024 Loi sur l'organisation du système d'enregistrement des faits d'état civil au Cameroun (Law on Civil Registration Status) introduces both material and organizational improvements to Cameroon’s civil registration system, including digitization and computerization, and establishes a system of unique personal identi- fication numbers. Turning to digital infrastructure, mobile phones and the internet are increasingly important for spreading information between markets, supplying farmers with data on climate-related shocks, and providing health and education services (Hong, 2023). Yet poor and vulnerable households are far less likely to have access to them: the poorest 40 percent of Cameroonians are half as likely to have a mobile phone and seven times less likely to use the internet than the richest 60 percent. In this way, technological advance could perpetuate inequality in Cameroon. ES4. With the right mix of policies, Cameroon can harness its poverty-reducing potential Given the diverse nature of Cameroon’s development challenges, ­ poverty‑ reducing policies need to be tailored for different people in different parts of the country. Some poverty-reducing policies are cross-cutting, helping to alleviate constraints on poverty holistically, for all Cameroonians. This includes reforms to boost inclusive growth and create jobs, as well as expanding social assistance, formal identification, and digital infrastructure. Yet there are some policies that need to be tailored to the specific development challenges that different Cameroonians face. In broad terms, decentralizing policymaking can help achieve this by allowing local policies to address local issues. The report also proffers specialized measures for two particular areas: (1) lagging regions and (2) urban areas. A schematic representation of the report’s main policy suggestions is provided in Figure 3. 14 Executive Summary Working Out of Poverty: Building Resilience and Inclusive Growth for Cameroon’s Future Figure 3. Policies to harness Cameroon’s poverty-reducing potential Promoting peace, security, and good governance Policies to make growth faster and more inclusive and create jobs Improving the environment for private investment Harnessing international trade Unlocking fiscal space for pro-poor spending Cross-cutting support for all Cameroonians Institutionalizing and expanding social assistance Increasing access to digital infrastructure Addressing gaps in formal identification Reinforcing decentralization Special attention for lagging regions Tailored solutions for urban areas Continued investment in human capital Improving urban planning Boosting agriculture’s productivity and resilience Developing intra-city transport to climate-related shocks Transforming housing policy Upgrading access to roads and electricity Providing training for rural-urban migrants Source: World Bank. Peace and security underpin all efforts to reduce poverty in Cameroon. Conflict and violence have intensified dramatically in Cameroon’s Extrême-Nord, Nord-Ouest, and Sud-Ouest regions, and conflict has the potential to spread. Many of Cameroon’s neighbors also endure conflict and fragility, which could increasingly spill over the border. The causal links between conflict, displacement, and poverty are complex. However, globally, poverty is progressively becoming more concentrated in fragile and conflict-affected settings (Corral, Irwin, Krishnan, Mahler, & Vishwanath, 2020). In many parts of Cameroon, especially in the country’s lagging regions, poverty and conflict clearly overlap. Alongside the direct death and injury brought about by conflict, it also interrupts human capital development, hampers livelihoods, and destroys infrastructure. Progress on any of the poverty-reducing policies recommended below can be quickly reversed by conflict and violence. Therefore, strengthening peace and security remains critical for helping Cameroonians exit poverty. Reforms to encourage private sector investment, harness international trade, and unlock fiscal resources for pro-poor spending are an urgent priority; they can make growth quicker and – through job creation – more inclusive. While a full diagnostic on the drivers of growth is forthcoming in the World Bank’s Country Economic Memorandum (CEM), the efficacy of several core growth-accelerating policies is already clear.(5) First, Cameroon can build on previous efforts to improve the environment for private sector investment. This means addressing issues such as uneven application of taxes across sectors, regulatory barriers, and weak property ­ specially for land (Amoretti & Maur, 2022). Similarly, scaling back the subsidies rights, e 5. These recommendations draw directly from the preliminary analytical scan conducted as part of the CEM. 15 Cameroon Poverty Assessment 2024 and other support that state-owned enterprises (SOEs) receive could help private busi- nesses expand and thrive in SOE-dominated sectors (Coulibaly, Benlamine, & Piazza, 2022). Second, Cameroon can better exploit its natural advantages for international trade. By diversifying its exports beyond oil, gas, and other primary products, value can be added and jobs can be created in Cameroon itself rather than elsewhere. This can be achieved by boosting market connectivity through investments in infrastructure and reducing both tariff and non-tariff barriers to trade, making entry and exit points like Douala more efficient. Third, Cameroon can maintain momentum on fiscal reform to unlock the resources needed for pro-poor policies. This process has already begun, with reductions in fuel subsidies in 2014, 2016, 2023, and 2024. However, efforts to cushion the effects of these subsidy reforms may not be reaching the poor and vulnerable. For example, the government’s compensatory policy of raising minimum wages and public sector pay will not be felt by most workers from poor households, as they are concentrated in informal, self-employment jobs and enforcement of minimum wages is imperfect. Maximizing the effects on inclusive growth and poverty reduction will rely on ensuring that fiscal savings are channeled towards programs that help the poorest Cameroonians. Institutionalizing and expanding social assistance, investing in digital infrastruc- ture, and addressing gaps in formal identification could also benefit Cameroonians across the country. While generating inclusive growth through productive jobs is the key to sustainable poverty reduction in the long run, there are other policy levers that can start lifting people out of poverty now. One of the most direct ways to channel fiscal savings to the poor would be to expand Cameroon’s social assistance system. Social assistance programs – including safety nets initiatives under the Projet Filets Sociaux (Safety Nets Project, PFS) – appear to be progressive, reaching poorer households, but they are currently dwarfed by the extent of poverty and vulnerability in Cameroon. The government increased its contribution to PFS from less than 5 percent of the total budget in the first phase (2013‑2018) to 60 percent in the second phase (2019- 2022). Yet project coverage remains limited: only 2.6 percent of all Cameroonians and 4.2 percent of poor Cameroonians lived in a household receiving cash transfers in 2021/22. Despite increased government commitment, social assistance remains highly dependent on donor support and concessional lending and more work is needed to fully institutionalize such programs. The long-run gains of social assistance can be boosted by combining cash transfers with training and support to livelihood opportuni- ties or targeted investments in health and education services through so-called “cash plus” programs (Gentilini, 2016; Banerjee, Karlan, Darko Osei, Trachtman, & Udry, 2020; Gilligan, et al., 2020; Loeser, Özler, & Premand, 2021). This is already happening through PFS, which not only includes a cash-for-work component but also provides support for early childhood development and climate adaptation to those receiving regular cash transfers (World Bank, 2022). Providing such programs may be easier if, in tandem, the government can address gaps in formal identification and digital infrastructure. In part, this could entail reducing bureaucratic limits linked to formal identification on who can and cannot access education, health, and other services, to prevent the poor and vulnerable being locked out. At the same time, the government can bolster access to mobile phones and the internet by simultaneously promoting private investment in telecommunications and developing the skills required to use these tools effectively (Vora &  Dolan, 2022). Building these foundations stands to benefit all poor and vulnerable Cameroonians, regardless of where they live. 16 Executive Summary Working Out of Poverty: Building Resilience and Inclusive Growth for Cameroon’s Future Reinforcing efforts to decentralize policymaking could help Cameroon meet changing local development needs. Spurred by rising conflict in the Nord-Ouest and Sud-Ouest regions and persistent regional inequality, there has been renewed empha- sis on decentralizing policy in Cameroon in recent years. To this end, Law n°2019/024 instituted the Code Général des Collectivités Territoriales Décentralisées (General Code of Decentralized Territorial Collectivities, CGCTD) in 2019, which extended competencies to regions and communes and set a minimum amount – 15  percent of the total budget – that should be transferred from the central government to local governments (Fall, Frisa, & Nkounga, 2021). In theory, decentralization can help ensure that provision of local services matches local needs, improve accountability, and reduce tensions between different regions, especially between anglophone regions and the central authorities (World Bank, 2012; Myerson, 2021). In practice, however, at least three factors constrain the success of decentralization in Cameroon. First, political constraints remain: the central government maintains tight supervision of local governments through governors and prefects (Fall, Hilger, Vaillancourt, Perrot, & Daller, 2020). Second, administrative constraints remain: the competencies devolved to local governments are only defined in very broad terms and capacity for region- and commune-level authorities is limited. Third, fiscal constraints remain: local govern- ments’ own revenue-raising powers are limited so they rely on the central government for funding, but allocations to local governments are well below the 15-percent target and the final formula that determines funding for each region and commune is unclear (Fall, Mituzani, & Vaillancourt, 2023). The November 2024 local taxation law could help reduce these constraints by strengthening the financial autonomy of local authorities. This law introduces a local development tax, deducted from the base salary of workers in both the public and private sectors, ranging from 3,000 to 30,000 XAF per year. The revenue generated by this tax will bolster municipalities' capacity to deliver essen- tial services to the population, such as public lighting, sanitation, waste collection, ambulance services, water supply, and electrification. Making decentralization work is crucial for effecting the bespoke policies needed for Cameroon’s lagging regions and its growing urban areas. Cameroon’s lagging regions still need investment in three basic building blocks of poverty reduction: first, there is still much room for improvement on human capital outcomes. Improving health and education outcomes, especially in the early part of people’s lives can support long-run, intergenerational poverty reduction by augmenting people’s productive potential (Bhula, Mahoney, & Murphy, 2020; Holla, Bendini, Dinarte, & Trako, 2021). While some human capital outcomes in rural areas and northern Cameroon may be starting to catch up, they remain behind the rest of the country. Geospatial data reveal that many Cameroonian children in remote and rural areas simply cannot reach schools so either new facilities are needed or better roads are required to reach existing facilities. Low school quality could also inhibit learning: in rural areas, schools have higher student-teacher ratios and are less likely to have adequate water, latrines, and electricity. In addition, improved vocational and technical education – with a focus on skills that can make self-employment more productive – is crucial for helping young people succeed in Cameroon’s labor market; an issue which is reflected directly in the NDS30.(6) More broadly, out-of-pocket expenses for health 6. The NDS30 sets the target of increasing the share of students engaged in vocational and technical training from 10 percent to 25 percent at the secondary level and from 18 percent to 35 percent at the tertiary level between 2020 and 2030. 17 Cameroon Poverty Assessment 2024 and education services could make investing in human capital unaffordable for poorer households. Second, boosting agricultural productivity, which has strong links to preserving and harnessing Cameroon’s natural capital, remains central to poverty reduction in lagging regions. Building human capital will only yield poverty reduction in the long run if livelihoods that can mobilize people’s productive potential are available. In Cameroon’s lagging regions, livelihoods are – and will continue to be – concentrated in agriculture. Cameroon can improve agriculture’s productivity by enhancing access to both input markets – for the seeds, fertilizers, tools, and labor that they need – and output markets – helping them to sell their produce both within and outside the country’s borders. Reinforcing land tenure can also incentivize farmers to invest in their land and reduce inefficiencies associated with land-related conflicts. Building shock resilience and helping farmers adopt technologies that could preserve natural capital will also be essential as Cameroon’s farmers grapple with the effects of climate change. Third, developing human capital and agricultural productivity in Cameroon’s lagging regions hinges on investing in basic infrastructure, including roads and electricity. In some parts of Cameroon, especially in the country’s north but also in the Adamaoua and Centre regions, it can take more than an hour for people to reach the formal road network.(7) Similarly, electricity access can underpin digital infrastructure, augment the quality of care and schooling in health and education facilities, and expand people’s livelihood opportunities (Ratledge, Cadamuro, de la Cuesta, Stigler, & Burke, 2022). The need to develop human capital, agricultural productivity, and basic infrastructure is not a new policy message. However, it is becoming increasingly urgent in Cameroon’s lagging regions to stop them being left behind. At the same time, new development challenges are growing in Cameroon’s towns and cities, which require enhancing urban planning, intra-city transport, housing policy, and the integration of rural-urban migrants. The policies needed to combat urban poverty are not the same as those needed to combat rural poverty. Congestion is choking agglomeration effects and the potential economic gains of urbanization. Yet better urban planning could improve the organization of where people live, work, and buy goods in towns and cities. Investing in intra-city transport could also help: for example, Douala’s Bus Rapid Transit (BRT) system, which is set to be launched in 2024, is forecast to reduce the time that the average Douala resident spends in traffic from 88 to 71 minutes per day (Saïsset, Fouchard, & Stokenberga, 2020). To ensure the urban poor live in adequate accommodation that is not overcrowded, citywide approaches to upgrade slums, regulate land use, and increase the stock of adequate housing are also essential (PSUP, 2015). Finally, experience from other countries suggests that training and information programs have helped rural-urban migrants’ integrate into urban communities, boosting their livelihoods and their welfare – this matters in Cameroon because migration is an increasingly important source of urban growth (IOM, 2019; Zhao, Tang, & Li, 2022). 7. The formal road network includes motorways, primary roads, secondary roads, and tertiary roads. 18 Executive Summary Working Out of Poverty: Building Resilience and Inclusive Growth for Cameroon’s Future ES5. Data remain essential for guiding poverty‑reducing policies and promoting good governance Data collection and analysis will need to keep pace and provide granular infor- mation on Cameroon’s diverse and changing development challenges. This report benefits hugely from high-quality survey data on household welfare collected by INS, which both provide a detailed snapshot of the latest poverty-reduction challenges and make it possible to analyze trends over the last two decades. However, with urbaniza- tion, climate change, and conflict, the policies that Cameroon needs to reduce poverty are in flux. This means data and analysis rapidly go out of date: seven years between ECAMs in the future may be too long. Investing in new surveys and censuses and coupling them with administrative and geospatial data can help strengthen the base of evidence needed for poli- cymaking, especially given the growing challenge of conflict and displacement. Alongside the ECAM series, other types of microdata are needed to rise to the devel- opment challenges that Cameroon faces. This includes additional data on Cameroon’s labor force and workers’ productivity – through the EESI series – and on the population at large – through a new census. Building the data landscape therefore requires close collaboration between INS and the Bureau Central de Recensement et d’Etude de la population au Cameroun (Central Bureau of the Census and Population Studies, BUCREP). Survey data can be enriched and disaggregated further by blending them with geospatial data, administrative data, and other innovative data sources. This is especially important for providing the granular, disaggregated data on which local policymaking depends. Such methods can also help ensure data cover those affected by conflict and forced displacement (Arai, Knippenberg, Meyer, & Witayangkurn, 2021; Eckman & Himelein, 2022). Moreover, upcoming surveys and censuses can also be refined to track IDPs and refugees better, drawing on guidance from the International Recommendations on IDP Statistics (IRIS) (EGRISS, 2022). This presents a critical area for future work as conflict and displacement proliferate in Cameroon. Data can also be a vehicle for accountability and good governance, ensuring policies benefit the poor and sustainably lift people out of poverty. As well as designing new policies, tracking the efficacy of the government’s policies and programs and Cameroon’s overall poverty-reducing performance helps bolster transparency and hold policymakers accountable. Data can provide a voice to the millions of Cameroonians still suffering from poverty, revealing what works and, for those policies that do not work, how to correct the course. Embracing data-driven policy will therefore be essential as Cameroon harnesses its enormous potential to generate inclusive growth and permanently lift its people out of poverty.  19 Cameroon Poverty Assessment 2024 Chapter 1. KEY MESSAGES ➜ Growth in Cameroon has proved resilient, but it is far below the country’s potential, leaving real gross domestic product per capita today at 3,724 USD in 2017 Purchasing Power Parity terms – lower than it was in the mid-1980s ➜ Cameroon’s growth has been driven by labor shifting from agriculture to services rather than rising sectoral productivity, and has relied on exporting primary products, which could make it less inclusive ➜ Urbanization has continued at pace, leaving Cameroon as one of the most urbanized countries in Sub-Saharan Africa and presenting new development and poverty-reduction challenges ➜ Cameroon is one of the most diverse countries in Sub-Saharan Africa – in terms of agro-ecology, ethnicity, and language – making it even more critical that improvements in living standards reach all Cameroonians ➜ Accelerating inflation fueled by domestic and international shocks, climate change, and conflict are hitting Cameroonians unevenly and could get worse, threatening to entrench spatial differences in prospects for poverty reduction ➜ Given Cameroon’s diverse and fluid development challenges, detailed microdata are needed to provide granular poverty-reduction policies: the country’s national statistical office has invested strongly in collecting the data required to analyze poverty and inequality today and track recent trends Introduction: Cameroon faces diverse and changing development challenges T his introductory chapter of the report – Cameroon’s first World Bank poverty assessment – sets the scene for the country’s fight against pov- erty by outlining its main development challenges. This provides crucial context for the detailed analysis of poverty and inequality in Cameroon in the subsequent chapters. First, the chapter charts Cameroon’s growth history, demonstrating how, despite its resilience, the country may be failing to live up to its economic potential. Second, the chapter considers how structural changes in the foundations of the Cameroonian economy – including the main growth sectors, key exports, and urbanization – could influence growth’s inclusivity; the extent to which it is spreading to the poor and vulnerable. Third, the chapter highlights the many dimensions of diversity in Cameroon and explains how new shocks – including inflation, climate change, and conflict – could disproportionately affect certain groups, including the poor and vulnerable. Finally, the chapter explains how this poverty assessment can leverage new microdata to help design policies that can invigorate inclusive growth and lift Cameroonians out of poverty. 1.1. Cameroon’s growth is below the country’s potential, constraining living standards Cameroon has many ingredients that could promote rapid economic growth. Cameroon’s population of around 27.9 million people stretches across an area of 475,000 square kilometers, in a strategically-positioned country blessed with natural resources and relative political stability. The country is favorably situated as a potential gateway between Western and Central Africa – sharing borders with Nigeria, Chad, the Central African Republic (CAR), Equatorial Guinea, and the Republic of Congo – while the coastline extends for more than 400 kilometers. This could allow trade to flourish, a position reinforced by its membership of the Communauté économique et monétaire de l’Afrique centrale (Economic and Monetary Community of Central Africa, 21 Cameroon Poverty Assessment 2024 CEMAC).(8) Additionally, alongside oil and natural gas, Cameroon is endowed with other minerals – including gold, iron, and manganese – fertile land, and rich ecological diversity within the Congo Basin. Finally, despite growing conflict in some parts of the country (described below), Cameroon has not experienced the same turbulent political changes endured by some of its neighbors. These conditions should provide the foundations for economic growth and, in turn, poverty reduction. Yet Cameroon has not been meeting its economic potential, despite seeing pos- itive and resilient growth in the last three decades. Following a period of relatively strong growth between independence in 1960 and the start of the 1980s, Cameroon suffered a sustained economic decline between 1985 and 1993, as the prices of key commodities – especially oil, cocoa, and coffee – fell precipitously (Figure 4). Following this period of backsliding, the government opted for a structural adjustment program which sought to rein in government spending while the Central African CFA franc was devalued in 1994. Since then, growth has stabilized, proving relatively resilient in the last three decades: even at the peak of the COVID-19 crisis in 2020 the overall economy continued to expand, even if it contracted in per capita terms. Yet growth has not proved sufficient to recoup the per capita gross domestic product (GDP) levels of the mid-1980s. Figure 4. GDP per capita in Cameroon, 1960 to present day 45 2,000 Real GDP per capita (2015 USD) Real GDP (billions of 2015 USD) 40 1,800 35 1,600 30 1,400 1,200 25 1,000 20 800 15 600 10 400 5 200 0 0 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017 2020 Commodity price shocks CFA franc devaluation Real GDP Real GDP per capita Note: GDP expressed in USD 2015 terms to allow for a longer time series. Source: World Development Indicators (WDIs) and World Bank estimates. Given Cameroon’s ponderous growth record, living standards in peer countries have grown quicker and pulled away in the last three decades. With GDP per capita at 3,724 USD in 2017 Purchasing Power Parity (PPP) terms in 2022, Cameroon is a lower middle-income country (Figure 5). Growth has not yet been enough to meet the country’s ambition to achieve upper middle-income country status. Looking across the continent, Cameroon’s GDP per capita is close to the averages for Western and Central Africa, Sub-Saharan Africa, and CEMAC, although it is much lower than in Côte d’Ivoire, 8. CEMAC is set to be merged with the Communauté économique des États de l’Afrique centrale (Economic Community of Central African States, CEEAC), which includes Angola, Burundi, the Democratic Republic of the Congo, Rwanda, and São Tomé and Principe. 22 Chapter 1 Introduction: Cameroon faces diverse and changing development challenges Ghana, and Nigeria – its “structural peers”, whose economies have fundamentals that are similar to the Cameroonian economy. Moreover, some “aspirational peer” countries – whose living standards were similar to Cameroon in 1990 – have enjoyed far stronger growth than Cameroon in the last three decades so in those countries GDP per capita has risen much faster.(9) This demonstrates that Cameroon has not been living up to its economic potential. Subsequent chapters of this report will demonstrate the implications this growth record has had for poverty reduction. Figure 5. GDP per capita in Cameroon and comparator countries Panel A: Snapshot for 2022 Real GDP per capita (USD 2017 PPP) 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 Cameroon Central African Republic CEMAC Congo, Rep. Gabon Equatorial Guinea Chad Groups Aspirational Structural Côte d'Ivoire peers Ghana Nigeria Bangladesh Kenya peers Morocco Vietnam Africa Western and Central Sub-Saharan Africa Lower middle income countries Panel B: Recent trends for aspirational peers 12,000 GDP per capita ( USD 2017 PPP) 10,000 8,000 6,000 4,000 2,000 0 Cameroon Bangladesh Kenya Morocco Vietnam Note: CEMAC = Communauté économique et monétaire de l’Afrique centrale (Economic and Monetary Community of Central Africa). GDP expressed in USD 2017 PPP terms to facilitate cross-country comparisons. Lower middle income countries have gross national income (GNI) per capita of 1,136 to 4,465 USD 2017 PPP. Source: World Development Indicators (WDIs) and World Bank estimates. 9. The aspirational peer countries are Bangladesh, Kenya, Morocco, and Vietnam. Both the structural and aspirational peers are being used for other World Bank analytical work in Cameroon, including the upcoming Country Economic Memorandum (CEM) These selected countries are not exactly the same as those used in the National Development Strategy (NDS30). 23 Cameroon Poverty Assessment 2024 1.2. Structural changes in Cameroon’s economy underline the challenge of making growth inclusive Growth has been concentrated in the service sector, but this has mainly been due to shifts in labor rather than growing productivity, limiting prospects for inclusiv- ity. While agriculture and industry remain important components of GDP, the service sector has been the largest contributor to real GDP growth over the last three decades (Panels A and B of Figure 6). Yet over the same period, productivity – value-added per worker – has dropped in services (Panel C of Figure 6). This is because a growing share of Cameroonian workers are engaged in services, so the proceeds of service-sector growth are being spread around more thinly (Panel D of Figure 6). All other things equal, this suggests that the new service-sector jobs in which Cameroonians are engaging are relatively informal and low productivity, limiting the inclusivity of growth and the extent to which they can lift households out of poverty. The role of Cameroon’s labor market in poverty reduction is explored in more detail in Chapter 6. Figure 6. Decomposing growth in Cameroon over the last three decades Panel A: Share of GDP (percent) Panel B: Sectoral growth contributions 10 Real GDP growth (percent) Agriculture; 18.5 8 6 4 2 0 Services; -2 55.4 Industry; 26.0 -4 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 Agriculture Industry Services Total Panel C: Labor productivity Panel D: Share of employment Labor productivity (thousands, Share of employment (percent) 12 100 10 constant 2015 USD) 80 8 60 6 40 4 2 20 0 0 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 Agriculture Industry Services Agriculture Industry Services Note: Net taxes excluded from calculations for parsimony, so growth is calculated using value added from agriculture, industry, and services. Source: National Accounts, World Development Indicators (WDIs), and World Bank estimates. 24 Chapter 1 Introduction: Cameroon faces diverse and changing development challenges Relatedly, Cameroon’s exports are concentrated in primary goods, so value is not being added and processing and manufacturing jobs are not being created, before these products leave the country. Crude petroleum and petroleum gas are the two most important export products, comprising 37.9 percent and 16.2 percent of total exports respectively (Figure 7). Cocoa products, timber, fold, bananas, and raw aluminum comprise some of the country’s other key exports. Despite their high value and importance for the economy, these products are unlikely to add significant numbers of high-productivity jobs, because much of the processing and manufactur- ing is currently being done in other countries. The prices of some of these also vary dramatically over time, leaving Cameroon’s economy exposed to the ebb and flow of global demand. Figure 7. Main exports in Cameroon Share of exports (percent) Raw aluminum; 2.0 Cocoa butter; 2.0 Others; 9.2 Bananas; 2.2 Rough wood; 4.0 Gold; 6.3 Crude petroleum; 37.9 Sawn wood; 8.9 Petroleum gas; 16.2 Cocoa butter; 11.4 Note: Breakdown shown for products comprising at least 90 percent of Cameroon’s total exports. Source: Observatory of Economic Complexity (OEC) and World Bank estimates. Cameroon’s overall population is growing, which could put pressure on the labor market and on service delivery. Cameroon’s population is growing about 2.6 percent per year, slightly higher than the average for Western and Central Africa (2.5 percent) and significantly higher than the average for lower middle income countries (1.0 per- cent) (United Nations Population Division, 2022). Relatedly, Cameroon’s population is extremely young, with 7 in 10 Cameroonians being aged less than 30. As Chapters 6 and 7 of this report discuss, this means the need for productive livelihoods is growing, as Cameroon seeks to take advantage of its demographic dividend, while the challenge of providing health, education, and other services is also ballooning (INS, 2022). Concomitant with changing employment patterns, urbanization is continuing at pace in Cameroon, rapidly bringing new development challenges to the fore. Over the last three decades, the share of Cameroonians living in urban areas has increased by around one half, rising from 40.8 percent in 1992 to 58.7 percent in 2022 (United Nations Population Division, 2019). This leaves Cameroon as one of the most urbanized countries in the region: the average urban population share for Sub-Saharan Africa in 2022 was 42.4  percent. Urbanization on this scale brings opportunities through agglomeration effects but also new development challenges for policy makers, espe- cially in terms of informal housing, access to services, and urban jobs (Gough, Esson, 25 Cameroon Poverty Assessment 2024 Andreasen, Yemmafouo, & Yankson, 2013). The specific dynamics of urbanization in Cameroon and the role this can play in poverty reduction are explored in more detail in Chapter 3: profiles of poverty and vulnerability may look very different for those living in rural areas, those living in urban areas, and those that move between the two. 1.3. Cameroon is a country of extraordinary diversity Cameroon is one of the most diverse countries in Africa, highlighting the impor- tance of generating inclusive growth. In terms of physical geography, the country is split into five agro-ecological zones, each with distinct climate, topography, soil, and vegetation, which map approximately onto the country’s 10 administrative regions (Perini, Nfor, Camin, Pianezze, & Piasentier, 2021).(10) Cameroon also displays stag- gering ethnic diversity, being home to around 250 different ethnic groups (World Bank, 2016). Relatedly, the country has around 250 local languages as well as two official languages, with the Sud-Ouest and Nord-Ouest region – on the border with Nigeria – being anglophone and the rest of the country being predominantly francophone (World Bank, 2017). Given these cross-cutting dimensions of diversity, Cameroon faces a particularly acute challenge to ensure all types of Cameroonians are reached by growth, notwithstanding the need to ensure growth reaches the poor and vulnerable. This underlines the fact that making growth inclusive is critical. 1.4. New shocks are hitting the population unevenly In an already diverse country, new shocks are hitting Cameroonians unevenly: accelerating inflation erodes purchasing power, especially for the poor and vulner- able. Year-on-year Consumer Price Index (CPI) inflation stood at 6.2 percent in 2022 in Cameroon. This is below the Western and Central African average CPI inflation rate of 8.0 percent, but it marks a significant acceleration when compared with previous years in Cameroon (Figure 8). This acceleration in prices has been driven in part by the impact of international crises, including Russia’s invasion of Ukraine, on global commodity markets. Moreover, prices for food items rose even quicker: year-on-year food price inflation peaked at 16.4 percent in November 2022, and remained in double digits for much of 2023. This is likely to have a disproportionate effect on the poor and vulnerable, who devote a larger share of their consumption to food (see Chapter 2). 10. The Nord and Extrême-Nord regions correspond approximately to the Sudano-Sahelian zone. Adamaoua and much of the Est regions correspond approximately to much of the Guinea High Savannah zone. The Centre and Sud regions correspond approximately to the Bimodal Humid Forests zone. The Littoral and Sud-Ouest regions correspond approximately to the Monomodal Humid Forests zone. The Ouest and Nord-Ouest regions correspond approximately to the Western High Plateau zone. 26 Chapter 1 Introduction: Cameroon faces diverse and changing development challenges Figure 8. Inflation in Cameroon, 2020-2023 18 Year-on-year change in price index 16 14 12 (percent) 10 8 6 4 2 0 Jan-20 Mar-20 May-20 Jul-20 Sep-20 Nov-20 Jan-21 Mar-21 May-21 Jul-21 Sep-21 Nov-21 Jan-22 Mar-22 May-22 Jul-22 Sep-22 Nov-22 Jan-23 Mar-23 May-23 Jul-23 Sep-23 Nov-23 Overall Food Source: Institut National de la Statistique du Cameroun (INS) and World Bank estimates. Climate-related hazards increasingly threaten poverty reduction, having differ- ent effects across Cameroon’s agro-ecological zones. Average annual temperatures have already risen in Cameroon, climbing by about 0.9 °C  between the 1970s and today, while average annual rainfall has declined  (World Bank, 2022). As climate change advances, the Sudano-Sahelian north is expected to be the most vulnerable to climate-related shocks – especially drought – followed by coastal areas – where flooding and sea-level rise are the main threats (World Bank, 2017). While Cameroon is already preparing adaptation measures through its Plan National d’Adaptation au Changement Climatique (National Adaptation to Climate Change Plan, PNACC), effecting these measures – such as building seawalls, climate-proofing infrastructure, or propagating drought-resistant crops – will require hard choices and could prove costly. Without concerted countervailing policies, the effects of different types of climate-related shocks could worsen Cameroon’s prospects for poverty reduction in the future. Conflict has been rising in Cameroon and while the effects have largely been concentrated in the Extrême-Nord, Nord-Ouest, and Sud-Ouest regions, the crisis could spread. In the Extrême-Nord, insecurity has been precipitated by the spread of Boko Haram, which has been carrying out violent attacks in Cameroon since 2014 (Jedwab, Blankespoor, Masaki, & Rodríguez-Castelán, 2021). Meanwhile, longstanding separatist tendencies in the anglophone Nord-Ouest and Sud-Ouest regions erupted, when peaceful protests in 2016 quickly escalated into full-scale armed conflict (World Bank, 2021). This is reflected in monthly data on the number of conflict events and fatalities in these three regions compared with the remaining seven regions in Cameroon (Figure 9). Some departments within the Extrême-Nord, Nord-Ouest, and Sud-Ouest regions have been particularly affected by battles, explosions and remote violence, and violence against civilians. Beyond the direct loss of human life, conflict can disrupt the livelihoods and the investments in physical and human capital needed for poverty reduction. Indeed, these effects can extend well beyond short-term losses: the effects on health and education can have intergenerational consequences while conflict also 27 Cameroon Poverty Assessment 2024 impedes the implementation of poverty-reducing policies (Akresh, Bhalotra, Leone, & Osili, 2012). It is therefore unsurprising that, at the global level, poverty is increasingly becoming concentrated in fragile and conflict-affected settings (Corral, Irwin, Krishnan, Mahler, & Vishwanath, 2020). Conflict could disproportionately hold back poverty reduction in the Extrême-Nord, Nord-Ouest, and Sud-Ouest regions, widening spatial inequality in Cameroon, and its effects could spread to neighboring regions too. Figure 9. Conflict in Cameroon, 2009-2023 Panel A: All conflict events Panel B: Fatalities Total number of fatalities in each month 140 Total number of conflict events 700 120 600 100 500 in each month 80 400 60 300 40 200 20 100 0 0 Jan-09 May-10 Sep-11 Jan-12 May-13 Sep-23 Sep-14 Jan-15 May-16 Sep-17 Jan-18 May-19 Sep-20 Jan-21 May-22 Sep-23 Jan-09 May-10 Sep-11 Jan-12 May-13 Sep-14 Jan-15 May-16 Sep-17 Jan-18 May-19 Sep-20 Jan-21 May-22 Extrême-Nord Nord-Ouest Extrême-Nord Nord-Ouest Sud-Ouest All other regions Sud-Ouest All other regions Panel C: Heat map of battles, explosions and Panel D: Heat map of battles, explosions and remote violence, and violence against civilians remote violence, and violence against civilians in the Extrême-Nord in the Nord-Ouest and Sud-Ouest Note: In Panel A, conflict events include battles, explosions/remote violence, protests, riots, strategic developments, and violence against civilians. Source: Armed Conflict Location and Event Data Project (ACLED) and World Bank estimates. 28 Chapter 1 Introduction: Cameroon faces diverse and changing development challenges Displacement both within Cameroon and across its borders has increased in line with conflict. The number of internally displaced persons (IDPs) has grown rapidly in recent years. By 2022, there were around 1.0 million IDPs in Cameroon, 97.7 percent of whom had been displaced by conflict, with the small remainder being displaced by natural disasters (Figure 10). Refugees have also been entering Cameroon from neigh- boring countries, especially from Nigeria, Chad, and CAR. Data from the Ministère de l’Education de Base show the link between conflict and displacement: concentration of internally displaced primary school children is highest in the Extrême-Nord, Nord-Ouest, and Sud-Ouest regions –exactly the same regions where conflict is most widespread (Figure 11). Global evidence reveals the severe impacts that forced displacement can have on monetary and non-monetary welfare, as it causes people to lose assets, interrupts investment in human capital, and constrains livelihood opportunities (Pape & Sharma, 2019). Like conflict, displacement will not affect all Cameroonians, and hence their prospects for exiting and remaining out of poverty, equally. Figure 10. Recent trends in forced displacement in Cameroon Panel A: Number of IDPs and refugees, Panel B: Total flows of refugees into Cameroon, 2014-2022 2001-2022 1,600 Number of IDPs and refugees (thousands) 1,400 1,200 1,000 800 600 400 200 Number of persons - displaced 500 - 1,000 2014 2015 2016 2017 2018 2019 2020 2021 2022 1,001 - 50,000 50,001 - 100,000 100,001 - 150,000 IDPs Refugees > 150,000 Note: IDPs = internally displaced persons. Panel B excludes total refugee flows of less than 500 people. Source: Internal Displacement Monitoring Centre (IDMC), United Nations High Commissioner for Refugees (UNHCR), and World Bank estimates. Cameroon was not spared the effects of the COVID-19 crisis, although its impacts were muted compared with other countries. The first COVID-19 case was recorded in Cameroon on 8th March 2020, and by the end of 2023, almost 130,000 cases had been recorded in the country. At the start of the pandemic, Cameroon adopted strict meas- ures to control the spread of the virus, which would have disrupted economic activity and people’s livelihoods, including social distancing and lockdowns – land, sea, and air borders were closed on 18th March 2020 (Bonnechère, Sankoh, Samadoulougou, Yombi, & Kirakoya-Samadoulougou, 2021). However, many of these measures had been rolled back by 2021 and, unlike many of its neighbors, Cameroon was able 29 Cameroon Poverty Assessment 2024 to maintain positive economic growth through the peak of the COVID-19 crisis. The data on which this report relies, described below, were therefore collected after the pandemic’s most serious direct economic effects had started to abate. Figure 11. Number of internally displaced children and refugee children in primary schools across Cameroon Source: Ministère de l’Education de Base and World Bank estimates. 30 Chapter 1 Introduction: Cameroon faces diverse and changing development challenges 1.5. Microdata hold the key to designing granular poverty- reducing policies Macro-level data on the economy are crucial for guiding policy, but given Cameroon’s diverse and fluid development challenges, they can only go so far; microdata are needed to go further. Global evidence demonstrates that reducing poverty without growth is extremely difficult, so macroeconomic policies that can accelerate growth are fundamental for lifting people above the poverty line (Kenny & Gehan, 2023). Yet growth alone is not enough.(11) Generating inclusive growth, where the proceeds reach even the poorest households, is the only way to guarantee lifting people out of poverty. This is particularly crucial for Cameroon, given the country’s many dimensions of underlying diversity – across agro-ecological zones, ethnic groups, and linguistic groups – and the differential effects of urbanization, rising prices, cli- mate change, and conflict. Household- and individual-level data can help to profile who is trapped in poverty, where they live, and the specific constraints they face. This information is vital for designing, targeting, implementing, monitoring, and evaluating poverty-reducing policies. Assessing spatial inequality across Cameroon is especially important given the country’s renewed emphasis on decentralization. Spurred by rising conflict in the Nord-Ouest and Sud-Ouest regions and persistent regional inequality, Cameroon has tried to extend the powers of region- and commune-level governments in recent years. In particular, in 2019, Law n°2019/024 instituted the Code Général des Collectivités Territoriales Décentralisées (General Code of Decentralized Territorial Collectivities, CGCTD), moving competencies to regions and communes and setting a minimum amount – 15 percent of the total budget – that should be transferred from the central government to local governments (Fall, Frisa, & Nkounga, 2021). In theory, decentral- ization can help ensure that provision of local services matches local needs, improve accountability, and reduce tensions between different regions, especially between anglophone regions and the central authorities – as discussed in Chapter 8 – several factors may be constraining decentralization’s success in practice (World Bank, 2012; Myerson, 2021). Tracking the performance of local governments and guiding budget allocations from the central government relies on geographically-disaggregated data. This poverty assessment therefore takes an explicitly spatial lens, demonstrating, where possible, how monetary and non-monetary indicators of welfare have fared at the region level or lower. This poverty assessment can help track Cameroon’s progress on its National Development Strategy 2020-2030 (NDS30). In 2020, Cameroon set out a new national development strategy in the NDS30, which seeks to: (1) boost growth; (2) raise living conditions, improve access to services, and reduce poverty; (3) strengthen environ- mental management, including climate change adaptation and mitigation; and (4) promote good governance (Ministry of Economy, Planning and Regional Development, 2020). These objectives chime with Cameroon’s ambition to alleviate poverty and become an upper middle-income country by 2035 – its Vision 2035 initiative (Ministry 11. Globally, convergence in poverty rates has not been commensurate with convergence in average living standards (Ravallion, 2012). This may be because labor markets are becoming less effective at sharing the proceeds of growth while democratic institutions have been weakened and are not supporting redistributive policies as they did in the past (Pande & Enevoldsen, 2021). 31 Cameroon Poverty Assessment 2024 of Economy, Planning and Regional Development, 2009). Cameroon is already effecting reforms to try and meet the objectives of NDS30, including efforts in 2023 to reduce fuel subsidies and expand tax revenue to unlock spending on health, education, and other pro-poor interventions (World Bank, 2023).(12) Nevertheless, as Cameroon nears the halfway mark for NDS30, the analysis in this poverty assessment could provide critical guidance for tweaking policies and programs to help meet the strategy’s objectives. In 2021 and 2022, the data needed to measure and analyze poverty and inequal- ity were collected for the first time in seven years. The fifth Enquête Camerounaise Auprès des Ménages (Cameroon Household Survey, ECAM‑5), implemented between October 2021 and September 2022, was the first household survey suitable for detailed poverty and welfare measurement since 2014’s ECAM‑4. The survey was effected by the Institut National de la Statistique du Cameroun (Cameroon’s National Institute for Statistics, INS) with support from the World Bank. Significant improvements were made to ECAM‑5, allowing the survey to collect comprehensive information on household consumption in accordance with international best practices, following the standards now being applied across the West African Economic and Monetary Union (WAEMU) and CEMAC regions: this makes it possible to construct reliable poverty estimates for Cameroon. Yet ECAM‑5 also collected detailed information on education, basic infrastructure, agriculture and other livelihoods, and households’ experience of shocks, enabling the analysis to be extended to vital non-monetary metrics, alongside monetary consumption and poverty. Given significant methodological improvements, ECAM‑5 cannot be compared with previous ECAMs, but INS implemented a smaller “bridge survey” to enable monetary and non-monetary welfare trends up until 2021 to be constructed. Between ECAM‑4 and ECAM‑5, vital improvements were made not only to the survey questionnaire – especially the module used to collect information on consumption – but also the underlying sampling approach (as discussed in detail in Chapter 2). These improvements render ECAM‑5 and ECAM‑4 technically incomparable. Fortunately, however, INS also implemented a bridge survey, with a smaller sample, between October and December 2021, which maintained exactly the same methodology as previous ECAMs. This makes it possible to construct trends in monetary poverty, and other key welfare metrics, between 2014 and 2021. Assessing these long-run trends underpins the narrative in this report. Therefore, while ECAM‑5 forms the backbone of this report – providing the latest snapshot of monetary and non-monetary poverty in Cameroon – most of the analysis that involves assessing time trends uses the bridge survey. The report’s analysis is strengthened by going beyond traditional household-level microdata. In particular, the report draws on various geospatial data sources, which help examine the significant diversity in the development challenges faced across Cameroon. This includes taking a more granular approach to analyzing the relationships between poverty and urbanization, access to services, and climate-related shocks. 12. These policy decisions are discussed in more detail in Chapter 8. 32 Chapter 1 Introduction: Cameroon faces diverse and changing development challenges 1.6. Structure of the poverty assessment The poverty assessment is structured as follows. Chapter 2 reports the latest estimates of and trends in headline poverty and inequality. Chapter 3 considers the overall drivers of poverty, decomposing the changes in poverty witnessed in the last two decades and profiling which types of households are more at risk of being below the poverty line. Chapter 4 goes beyond current poverty levels, by investigating households’ vulnerability to shocks and stresses, which could leave them poor in the future. Chapter 5 explores non-monetary poverty metrics, including human capital. Chapter 6 focuses on the livelihood strategies available to Cameroonian households and examines whether they can sustainably lift Cameroonian households out of poverty. Chapter 7 uses geospatial data to pinpoint the role that access to services can play in poverty reduction. Chapter 8 synthesizes the evidence from the previous chapters and outlines the policies that could help Cameroon fulfil its poverty-reduction potential.  33 Cameroon Poverty Assessment 2024 Chapter 2. KEY MESSAGES ➜ In 2021/22, 37.7 percent of Cameroonians lived below the national poverty line, with minimal poverty reduction being achieved over the past two decades ➜ With poverty reduction stagnating and the population growing, the number of Cameroonians living in poverty increased by two-thirds between 2001 and 2021 and now exceeds 10 million people ➜ Poverty is still concentrated in rural areas, but urban poverty is now on the rise: between 2014 and 2021, the urban poverty rate jumped from 8.9 percent to 16.5 percent ➜ Cameroon’s poverty rate is lower than many regional comparators, but some countries have achieved lower poverty with similar levels of gross domestic product per capita, emphasizing the need to make growth inclusive and promote equity ➜ The Gini coefficient dropped slightly from 44.0 to 42.9 between 2014 and 2021, but overall inequality remains higher than it was in 2001 and continues to be above the level observed in aspirational peers ➜ The current mix of policies is not predicted to deliver rapid poverty reduction, underlining the need for policy reform Poverty reduction has stagnated in Cameroon and inequality remains high T his chapter of the poverty assessment presents Cameroon’s headline statistics on poverty and inequality. The chapter shows the latest estimates of poverty and inequality as well as the overall trends over the last two dec- ades. First, the chapter explains how measures of welfare and poverty are constructed in Cameroon using data collected in 2021 and 2022 through ECAM‑5 for the most recent best estimates and previous surveys for establishing trends. Second, the chapter tracks Cameroon’s progress on poverty reduction, showing where this leaves the country compared to its peers. Third, the chapter forecasts how poverty in Cameroon could evolve over the next decade given the current mix of policies. Finally, the chapter considers how inequality is changing in Cameroon, providing a first hint at what could be driving the country’s poverty-reduction story. 2.1. New data make it possible to construct Cameroon’s latest poverty estimates in line with best practice, while also tracking trends The poverty assessment uses ECAM‑5 to provide the latest best estimates of poverty and welfare in Cameroon alongside a specialized “bridge survey”, which maintains the survey methodology previously used in Cameroon in order to estimate trends. In particular, ECAM‑5 adopts the best international standards for measuring household consumption, the foundation of poverty measurement in Cameroon (see below). As such, the latest snapshot estimates for poverty and its drivers in this report are taken from ECAM‑5. However, making the improvements needed to conform to the latest international survey standards means that ECAM‑5 cannot be compared to previous ECAMs collected in 2001, 2007, and 2014. As such a bridge survey was implemented in 2021, which maintained exactly the same methodology as the previous ECAMs. Thus, the bridge survey is used whenever trends for poverty and its drivers are considered in this report. This explains why there are two poverty estimates presented for 2021/22: one which conforms to the latest best international practices (from ECAM‑5) and one 35 Cameroon Poverty Assessment 2024 which can be compared to previous ECAMs to construct poverty trends (from the 2021 bridge survey). Further details of these two surveys are provided in Box 1. Box 1. Surveys used for poverty measurement in Cameroon The most recent estimates of poverty and welfare in Cameroon draw on high-quality data collected in 2021 and 2022 through ECAM-5. This survey, which was implemented by INS with support from the World Bank, provides the first official estimates of poverty and welfare in Cameroon since 2014. Data collection was implemented in three waves – October-December 2021, March-May 2022, and July-September 2022 – so the information covers different seasons. To monitor and hence maintain high standards during the fieldwork, the data were collected through Computer-Assisted Personal Interviewing (CAPI). Alongside the detailed record of household consumption (described below), ECAM-5 also collected information on education, health, basic infrastructure, agriculture and other livelihoods, and households’ experience of shocks. This underpins the deep dives into potential constraints on poverty reduction that feature in the later chapters of this report. ECAM-5’s sampling strategy allows results to be geographically disaggregated, which is cru- cial given Cameroon’s spatial diversity. In particular, the sample was stratified at two levels: 12 regions – comprising Cameroon’s 10 administrative regions with Douala and Yaoundé being counted separately – and urban-rural (Table 1). The overall planned sample was 13,356 households, but – after some areas could not be covered due to insecurity, some households could not be reached, and some households had to be dropped due to incomplete information on consumption – the final sample available for analysis is 10,546 households. This sampling strategy makes it possible to produce poverty estimates with reasonable margins of error at the region and urban-rural levels. This is essential for assessing the different poverty-reduction challenges faced across Cameroon, and providing tailored policy guidance. Table 1. ECAM-5 sample by wave, urban-rural, and region First wave Second wave Third wave Region Total Rural Urban Rural Urban Rural Urban Adamaoua 144 93 144 93 158 106 738 Centre 189 96 171 101 194 87 838 Douala 459 460 499 1,418 Est 140 87 144 86 144 82 683 Extrême-Nord 296 142 303 133 316 134 1,324 Littoral 112 111 105 113 119 115 675 Nord 236 114 242 116 233 119 1,060 Nord-Ouest 87 86 118 77 101 82 551 Ouest 182 113 190 116 187 113 901 Sud 131 79 139 83 133 81 646 Sud-Ouest 90 115 79 117 73 100 574 Yaoundé 363 403 372 1,138 Total 1,607 1,858 1,635 1,898 1,658 1,890 10,546 Note: Actual sample used for analysis rather than target sample shown. Source: ECAM-5 and World Bank estimates. 36 Chapter 2 Poverty reduction has stagnated in Cameroon and inequality remains high Vital improvements were made to the methodology in ECAM-5, but these render its results incomparable to Cameroon’s previous poverty and welfare estimates. The main improvements focused on altering the consumption module of the questionnaire, bringing it in line with regional and global best practices. In ECAM-4 and in previous ECAMs, the consumption module focused on actual expenditure on food items over the past 15 days. However, ECAM-5 captures different modes of acquisition for consumption – including gifts and own production – and uses a shorter recall of seven days, limiting recall bias. Additionally, ECAM-5 was conducted in three waves throughout the year to reflect seasonality, whereas ECAM-4 and other previous ECAMs were only implemented in three months between October and December of that year. Finally, the sampling frame was renewed using updated enumeration areas, delineated in preparation for Cameroon’s upcoming census, as well as an updated urban-rural definition. While these changes raise the methodological standards applied in ECAM-5 and increase data quality, they leave it technically incomparable to earlier ECAMs. Fortunately, alongside ECAM-5, INS also implemented a smaller “bridge survey”, using the pre- vious methodology, making it possible to assess poverty trends. The bridge survey, implemented between October and December 2021, adopts the same methodology as ECAM-4. This means that trends in monetary poverty and other non-monetary welfare metrics can be constructed for the last two decades, using 2001’s ECAM-2, 2007’s ECAM-3, 2014’s ECAM-4, and the bridge survey from 2021. In this report, all trends are reported using these surveys. However, the latest best estimates and any analyses that can be conducted with one cross section of data are produced using ECAM-5, given its methodological superiority and its larger sample than the 2021 bridge survey. Data collection for ECAM-5 and the 2021 bridge survey was implemented after many of the measures designed to counter the spread of COVID-19 had been removed. As such, the report interprets the data from 2021 as an extension of the long-term trends constructed for 2001, 2007, and 2014 using ECAM-2, ECAM-3, and ECAM-4, rather capturing a sudden and fleeting deterioration of living standards resulting from the pandemic. 2.2. Cameroon has adopted international best practices for poverty measurement Poverty measurement in Cameroon hinges on consumption rather than income. This means Cameroon matches the best practices adopted by other countries with sim- ilar levels of economic development. When informal jobs and subsistence activities are prevalent, measuring income is difficult, especially as income may be more exposed to shocks (Mancini & Vecchi, 2022). Moreover, any differences between consumption and income are likely to be small when few people have savings, as is the case in Cameroon (IMF, 2018). The ECAM‑5 questionnaire records detailed information on the food items that Cameroonian households consume at home, coming from (1) purchases, (2) own production, (3) gifts, and (4) other sources, such as celebrations. The questionnaire also records the value of (5) meals consumed outside the home. Additionally, the survey collects information on expenditures on (6) education, (7) health, (8) durable goods, (9) housing, and (10) other non-food items such as transport, clothing, and fuel from other modules.(13) By combining these components to produce an overall “consumption aggregate”, ECAM‑5 provides a comprehensive picture of household consumption in 13. The “use value” of durable goods is calculated using the purchase price, estimated depreciation, and estimated current sale price; this makes it possible to assess the value of each durable good during the year of the survey. The value of housing is estimated as the amount paid in rent for those who are renting, but a measure of “imputed rent” based on household characteristics is constructed for those who are not renting. See Deaton and Zaidi (2002) for further details. 37 Cameroon Poverty Assessment 2024 Cameroon. Consumption on various food and non-food items was also collected in previous ECAMs and in the bridge survey, although the specific questions capturing this information differed from ECAM‑5 (INS, 2019). Consumption is temporally and spatially deflated and adjusted for differences in household size to make comparisons between different households across Cameroon and with a single, national poverty line. Given the accelerating inflation facing Cameroon described in Chapter 1, the households covered in ECAM‑5 may have faced different prices depending on when they were interviewed. A separate price survey, effected alongside ECAM‑5, is therefore used to construct temporal price indices to account for this.(14) Similarly, the unit prices of the food items recorded in ECAM‑5 itself are used to construct a spatial price index to account for the fact that households in different parts of the country face different prices.(15) To correct for differences in household composition, the household-level consumption aggregate is divided by household size, placing it in per capita terms. Similar adjustments were made for the bridge survey and for the previous ECAMs, although their consumption aggregates were not temporally deflated – given the short fieldwork duration – and the household-level consumption aggregate was divided by so called “adult equiva- lents” – where the adjustment factor accounts for adults and children having different consumption needs – rather than total household size per se. Consumption is higher and more concentrated in non-food items in urban areas and in Douala and Yaoundé. Overall, mean deflated consumption per capita was about 500,000 XAF in Cameroon in 2021/22 (Figure 12). Yet overall consumption was higher in urban areas than rural areas and in Yaoundé and Douala than in other regions: these differences are statistically significant when formally tested using simple regressions clustered at the enumeration area level. About half (50.5 percent) of the average Cameroonian’s consumption basket was devoted to food, of which 12.7  percent came from meals consumed outside the home. The share devoted to non-food items was higher for urban dwellers than for rural dwellers and for those living in Yaoundé and Douala compared to other regions. Looking across the deciles of the real consumption distribution, the share of consumption devoted to non-food items was also significantly higher for richer Cameroonians, meaning that the consumption patterns observed in ECAM‑5 are consistent with Engel’s law (Anker, 2011). 14. A separate series of temporal deflators were constructed for each “domain”, where the domain is the combination of urban-rural and Cameroon’s seven agroecological zones. The seven agroeco- logical zones comprise the following groupings of Cameroon’s regions: (1) Yaoundé, (2) Douala, (3) Centre/Sud, (4) Adamaoua/Est, (5) Extrême-Nord/Nord, (6) Littoral/Sud-Ouest, and (7) Ouest/ Nord-Ouest. Since Yaoundé and Douala are entirely urban, this means there are 12 domains in total. 15. The spatial price indices are also constructed at the domain level. They are created by establishing poverty lines, using the methodology described below, for each of the domains and then dividing the domain-specific poverty lines by the national poverty line. 38 Chapter 2 Poverty reduction has stagnated in Cameroon and inequality remains high Figure 12. Consumption patterns in Cameroon by urban-rural, consumption decile, and region Panel A: By urban-rural Panel B: By consumption decile Share of the consumption basket (percent) Share of the consumption basket (percent) 100 700,000 100 1,500,000 Total consumption per person per year Total consumption per person per year 90 90 600,000 1,300,000 80 80 70 500,000 70 1,100,000 (deflated XAF) (deflated XAF) 60 60 900,000 400,000 50 50 300,000 40 700,000 40 30 200,000 30 500,000 20 20 100,000 300,000 10 10 0 0 0 100,000 Rural Urban Total 8 ch 9 t 6 7 4 5 t 2 3 es es or Ri Po Panel C: By region Share of the consumption basket (percent) Total consumption per person per year 100 800,000 80 600,000 60 (deflated XAF) 400,000 40 200,000 20 0 0 ua re la t d l d st st d st é ra Es nd or or Su ou ue ue ue nt ao to N N ou D O O Ce O am Lit e- d- d- Ya m Ad or Su trê N Ex Food - purchased Food - meals outside the home Housing Food - own production Education Other non-food Food - gifts Health Total consumption Food - other Durables Note: The “Food – other” category includes alcoholic beverages recorded in the non-food section of the questionnaire and food consumed at festivals and celebrations. Source: ECAM‑5 and World Bank estimates. A new national poverty line – 296,691 XAF per person per year – was constructed for ECAM‑5 using a “cost of basic needs” approach, which estimates how much money would be needed to maintain a basic level of welfare. The national line was created in three main steps. First, a typical basket of food items consumed by households lying between the 2nd and 6th decile of the consumption distribution – the so called “reference population” – was constructed.(16) This reveals how much it costs for the reference population to buy their food. Second, this initial basket was converted into calories using information on the nutritional value of the food items it contains 16. This basket contains 66 food items, which cover at least 85 percent of the total food basket, excluding meals consumed outside the home. 39 Cameroon Poverty Assessment 2024 and then scaled up to provide 2,300 calories per person per day.(17) This produces a “food poverty line” of 196,802 XAF per person per year: this is how much households would need to spend to meet their daily caloric requirements if they devoted their entire consumption basket to food. Third, an allowance is added for non-food items, based on the spending patterns of those households whose consumption levels are close to the food poverty line.(18) This produces a non-food poverty line of 99,889 XAF per person per year. Adding together the food and non-food poverty line produces an overall national poverty line of 296,691 XAF per person per year, which corresponds roughly to 3.04 USD 2017 PPP per person per day. Since consumption is deflated temporally and spatially, only one single poverty line is needed for the entire country. The trends analysis uses existing national poverty lines, which have been adjusted broadly in line with inflation, making comparisons across time straightforward. Between 2001 and 2014, the national poverty line and the food component of CPI both increased by about the same amount, a little under 50 percent (Figure 13). This means that the national poverty lines for previous ECAMs and for the bridge survey closely reflect changes in purchasing power over time. As such, no further adjustments are needed for these existing surveys and their corresponding poverty lines to track poverty trends over time.(19) Since the consumption aggregates for the previous surveys were expressed in adult equivalent terms, so too are the national poverty lines. Figure 13. Evolution of prices and the national poverty line in Cameroon, 2001-2021 CPI and national poverty line (2001 = 100) 180 165.3 160 146.1 140 115.9 120 100.0 100 80 60 40 20 0 Overall CPI Food CPI National poverty line Note: Overall CPI, food CPI, and the national poverty lines all rebased to 100 in 2001 to make their evolution over time easier to trace. Source: ECAM‑2, ECAM‑3, ECAM‑4, 2021 bridge survey, and World Bank estimates. 17. Before scaling up, the food basket for the reference population yielded 1,302 calories per person per day. 18. The non-food component of the poverty line is constructed using a version of the lower Ravallion method. Following Ravallion (1998), the overall poverty line is set at (2 – a)bf where a is the average food share of those households who can just afford basic food needs (and bf is the food poverty line). For Cameroon, the parameter a is estimated by taking the average food share of households whose total temporally-deflated consumption per capita is within 10 percent of the cost of the food basket. 19. The national poverty lines for ECAM‑2, ECAM‑3, ECAM‑4, and the 2021 bridge survey were 232,547 XAF, 269,443 XAF, 339,715 XAF, and 384,453 XAF per adult equivalent per year. 40 Chapter 2 Poverty reduction has stagnated in Cameroon and inequality remains high 2.3. Poverty reduction in Cameroon is languishing, with poverty starting to rise in urban areas Around 4 in 10 Cameroonians live below the national poverty line, a situation that has changed little over the past two decades. The latest best estimates from the 2021/22 ECAM‑5 suggest that 37.7 percent of Cameroonians live below the national poverty line (Figure 14). Previous ECAMs and the bridge survey reveal that this share – known as the “poverty rate” – fell only very slightly in the 2000s and 2010s, moving from 40.2 percent in 2001 to 38.6 percent in 2021, a change which is not statistically significant even at the 10 percent level. These estimates were officially launched by INS in April 2024 (INS, 2024). Figure 14. Poverty rate and absolute number of poor in Cameroon using national poverty line, 2001-2022 Panel A: Poverty rate Panel B: Absolute number of poor 45 Number of poor (millions) 12 Poverty rate (percent) 40 35 10 30 8 25 6 40.2 39.9 20 38.6 37.7 37.5 10.3 10.1 15 4 8.1 7.1 6.2 10 5 2 0 0 ) ) ) ) ) ) ) ) ) ) ey ey -2 -3 -4 -5 -2 -3 -4 -5 AM AM AM AM AM AM AM AM rv rv su su C C C C C C C C ge ge (E (E (E (E (E (E (E (E rid rid 01 07 14 2 01 07 14 2 /2 /2 (B (B 20 20 20 20 20 20 21 21 21 21 20 20 20 20 Note: Consumption is spatially deflated and, where relevant, temporally deflated to compare with national poverty lines. Estimates from 2001, 2007, 2014, and the 2021 bridge survey are comparable. 2021/22 estimates from ECAM‑5 represent latest best estimates but cannot be compared with previous surveys. Source: ECAM‑2, ECAM‑3, ECAM‑4, ECAM‑5, 2021 bridge survey, and World Bank estimates. With the population continuing to grow, the number of people living below the national poverty line has increased by around two-thirds in the last two decades. More than 10 million Cameroonians lived below the national poverty line in 2021/22. This reflects a steady increase in the 2000s and 2010s. While poverty remains concentrated in rural areas, it is – for the first time – on the rise in urban areas. The latest estimates from ECAM‑5 suggest that as many as 21.6 percent of those living in urban areas – about 3.1 million people – were below the national poverty line in 2021/22, compared with 56.3  percent of those living in rural areas – about 7.0  million people (Figure 15). This means about 7  in  10 poor Cameroonians live in rural areas. Nevertheless, any reduction in urban poverty achieved between 2001 and 2014 was largely reversed by 2021 as the poverty rate and the absolute number of poor for urban areas roughly doubled between 2014 and 2021. For the first time in the Cameroon’s recent history, poverty is becoming an increasingly urban phenomenon. The notion that poverty in Cameroon is purely a rural phenomenon is no longer correct. The drivers behind this vital change in the country’s poverty profile are explored in further detail in Chapter  3. 41 Cameroon Poverty Assessment 2024 Figure 15. Poverty rate and absolute number of poor in Cameroon using national poverty line, by urban- rural, 2001-2022 Panel A: Urban poverty rate Panel C: Rural poverty rate 58.3 56.8 70 70 56.3 Poverty rate (percent) Poverty rate (percent) 55.0 52.1 60 60 50 50 40 40 21.6 17.9 30 30 16.5 12.2 20 20 8.9 10 10 0 0 ) ) ) y) ) ) ) ) ) ) -2 -3 -4 -5 -2 -3 -4 ey -5 ve AM AM AM AM AM AM AM AM rv ur su s C C C C C C C C ge ge (E (E (E (E (E (E (E (E rid rid 01 07 14 2 01 07 14 2 /2 /2 (B (B 20 20 20 20 20 20 21 21 21 21 20 20 20 20 Panel B: Absolute number of urban poor Number of poor (millions) Panel D: Absolute number of rural poor 10 10 8.2 Number of poor (millions) 7.3 7.0 8 8 6.4 5.3 6 6 3.1 4 4 2.1 2 1.0 0.8 0.8 2 0 0 ) ) ) ) ) ) ) ) ) ) -2 -3 -4 ey -5 -2 -3 -4 ey -5 AM AM AM AM AM AM AM AM rv rv su su C C C C C C C C ge ge (E (E (E (E (E (E (E (E rid rid 01 07 14 2 01 07 14 2 /2 /2 (B (B 20 20 20 20 20 20 21 21 21 21 20 20 20 20 Note: Different urban-rural definitions applied in ECAM‑5 compared with previous surveys. Consumption is spatially deflated and, where relevant, temporally deflated to compare with national poverty lines. Estimates from 2001, 2007, 2014, and the 2021 bridge survey are comparable. 2021/22 estimates from ECAM‑5 represent latest best estimates but cannot be compared with previous surveys. Source: ECAM‑2, ECAM‑3, ECAM‑4, ECAM‑5, 2021 bridge survey, and World Bank estimates. Poverty has also remained deep in Cameroon, so vast resources would be needed to lift everyone above the poverty line. Looking at the poverty rate and the absolute number of poor alone does not reveal how far below the poverty line households are. It is therefore useful to calculate the “poverty gap index”, which measures the average difference between poor households’ consumption and the poverty line.(20) In 2021/22, the poverty gap index for Cameroon was 0.13 (see Annex 2.1). Multiplying this by the poverty line and by Cameroon’s population provides the theoretical cost of eliminating ­poverty, if the poor could be perfectly targeted without administrative costs: this amounts to around 1 trillion XAF per year, or nearly 4 billion USD 2017 PPP. 20. The “squared poverty gap index” is also reported in Annex 2.1. This considers inequality among the poor: it is improved with transfers from those just below the poverty line to those a long way below it. 42 Chapter 2 Poverty reduction has stagnated in Cameroon and inequality remains high 2.4. International comparisons suggest that poverty in Cameroon is lower than in neighboring countries, but it is not fulfilling its poverty-reduction potential Applying the global methodology indicates that about one quarter of Cameroonians live below the international extreme poverty line of 2.15 USD 2017 PPP per person per day. The global methodology, which can be straightforwardly applied to ECAM‑5, differs from the national methodology described above insofar as (1) no spatial defla- tion is applied, (2) temporal deflation across the survey’s duration uses CPI data rather than the regional price survey, (3) consumption is deflated back to 2017 terms, and (4) consumption is then converted to 2017 USD PPP terms using the relevant conversion factors. Employing this approach reveals that 23.0 percent of Cameroonians lived below the international extreme poverty line of 2.15 USD 2017 PPP per person per day. Applying the international poverty methodology confirms the story of Cameroon’s poverty stagnation, with poverty at the 2.15 USD 2017 PPP per person per day line in 2021 being around the same as it was in 2001 (see Annex 2.2). Cameroon has a lower poverty rate than regional comparators, but some countries have achieved lower poverty with similar GDP per capita. Cameroon’s international poverty rate of 23.0 percent at the 2.15 USD 2017 PPP line is the second lowest in CEMAC and is well below the average for Sub-Saharan Africa of 35.4 percent (Figure 16).(21) At the same time, all of Cameroon’s aspirational peers except Kenya have achieved lower poverty rates, and the average poverty rate for lower middle income countries is about half the level for Cameroon. Moreover, while Cameroon is roughly on the global trend for the relationship between GDP per capita and poverty, several countries have reached lower poverty with similar or even lower GDP per capita levels. This implies that policies that foster inclusive growth – rather than just growth per se – and promote equity are especially important for confronting Cameroon’s poverty-reduction challenge. 21. Poverty estimates for Equatorial Guinea are not available in the World Bank’s Poverty and Inequality Platform. 43 Cameroon Poverty Assessment 2024 Figure 16. International comparisons of the poverty rate at the international extreme poverty line and GDP per capita estimates Panel A: Poverty rate in Cameroon and comparators Poverty rate (percent) 0 10 20 30 40 50 60 70 Cameroon 23.0 Central African Republic 65.7 CEMAC Congo, Rep. 35.4 Gabon 2.5 Chad 30.9 Côte d’Ivoire 11.5 Structural peers Ghana 25.2 Nigeria 30.9 Bangladesh 9.6 Groups Aspirational Kenya 36.1 peers Morocco 1.4 Vietnam 0.7 Sub-Saharan Africa 35.4 Lower middle income countries 10.9 Panel B: Global comparison of the poverty rate and GDP per capita 90 80 70 Poverty rate (percent) 60 50 40 30 Cameroon 20 10 0 100 1,000 10,000 100,000 1,000,000 Real GDP per capita (USD 2017 PPP) Sub-Saharan Africa Other countries Note: Poverty calculated using the international extreme poverty line of 2.15 USD 2017 PPP per person per day. CEMAC = Communauté économique et monétaire de l’Afrique centrale (Economic and Monetary Community of Central Africa). GDP expressed in USD 2017 PPP terms to facilitate cross-country comparisons. Lower middle income countries have gross national income (GNI) per capita of 1,136 to 4,465 USD 2017 PPP. Source: World Development Indicators (WDIs), Poverty and Inequality Platform, ECAM‑5, and World Bank estimates. 2.5. The current policy mix is not set to deliver rapid poverty reduction Poverty is projected to rise over the next five years under current policies. This is done using a macro-micro simulation model, which combines forecasts for sec- tor-level economic growth, population growth, and inflation with ECAM‑5 to project the 44 Chapter 2 Poverty reduction has stagnated in Cameroon and inequality remains high distribution of consumption, and hence poverty.(22) The year in which data collection for ECAM‑5 started (2021) is taken as the base year and then subsequent years are now- casted – up until 2024 – and forecasted – up until 2026. The poverty rate at the national line is projected to increase, climbing to 40.0 percent by 2025 (Figure 17). Looking at the macroeconomic forecasts underpinning these results, this is largely because nominal per capita growth in agriculture and industry is set to be too low relative to overall price inflation; especially relative to inflation in the prices of food items, which are make up a larger share of the consumption baskets of poor and vulnerable Cameroonians. Nominal per capita growth in services is set to be only just above inflation but this may not be true for all service-sector workers, given the wide heterogeneity and constraints on productivity growth in that sector (discussed in more detail in Chapter 6). As such, policy reforms to invigorate more growth, and more inclusive growth, would be needed to reduce poverty. Figure 17. Poverty forecasts for Cameroon by urban-rural, 2021-2026 70 57.7 58.8 59.5 59.3 56.3 56.1 60 Poverty rate (percent) 50 38.7 39.6 40.0 40.0 37.7 37.4 40 30 21.6 22.3 23.0 23.1 23.4 21.3 20 10 0 2021 2022 2023 2024 2025 2026 Total Urban Rural Note: Consumption is spatially deflated and temporally deflated to compare with national poverty line for 2021/22. Source: ECAM‑5 and World Bank estimates. 2.6. Inequality in Cameroon is higher than it was two decades ago and exceeds the level in many peer countries Despite a small drop between 2014 and 2021, inequality in Cameroon, as meas- ured by the Gini coefficient, is higher than it was two decades ago and is above the level observed in aspirational peers. Applying the national methodology – where consumption is spatially deflated before calculating inequality statistics – shows that the Gini coefficient fell slightly from 44.0 in 2014 to 42.9 in 2021 (Figure 18). Yet this still marks a significant uptick in overall inequality since 2001, when the Gini coeffi- cient was 40.4. Applying the international methodology – where consumption is not spatially deflated – suggests that Cameroon’s Gini coefficient is moderate compared 22. This is the approach being applied for the Macro-Poverty Outlooks for Cameroon and several other countries in Western and Central Africa. Further details can be found in Foster and Inchauste (2024). 45 Cameroon Poverty Assessment 2024 with regional comparators, but well above its aspirational peers. This reinforces the idea that addressing equity will be crucial for realizing Cameroon’s hope of lifting its people out of poverty. Figure 18. Gini coefficient in Cameroon and comparator countries Panel A: Gini coefficient trends using Panel B: Latest Gini coefficient using national methodology international methodology Gini coefficient 0 20 40 60 50 44.0 42.9 Cameroon 42.2 45 40.4 39.0 40.1 40 Central African Republic 43.0 Gini coefficient 35 CEMAC Congo, Rep. 48.9 30 25 Gabon 38.0 20 Chad 37.5 15 Côte d’Ivoire 37.2 Structural 10 peers 5 Ghana 43.5 0 Nigeria 35.1 Bangladesh 31.8 ) ) ) ) ) -2 -3 -4 ey -5 Aspirational AM AM AM AM rv Kenya su 38.7 C C C C peers ge (E (E (E (E rid Morocco 01 07 14 2 39.5 /2 (B 20 20 20 21 21 Vietnam 20 36.8 20 Note: For the national methodology applied in Panel A, consumption is spatially deflated before calculating the Gini coefficient. For the international methodology applied in Panel B, consumption is not spatially deflated before calculating the Gini coefficient. 2021/22 estimates from ECAM‑5 represent latest best estimates but cannot be compared with previous surveys. For international comparisons, latest available estimates from each country are used. Source: ECAM‑2, ECAM‑3, ECAM‑4, ECAM‑5, 2021 bridge survey, Poverty and Inequality Platform, and World Bank estimates. 2.7. Tackling poverty relies on understanding its deep drivers While assessing the latest snapshot of poverty and inequality in Cameroon as well as recent trends provides a useful yardstick, developing countervailing policies hinges on determining poverty’s drivers. This chapter has highlighted that Cameroon can do much more to lift people out of poverty, as little progress has been made on poverty reduction since 2001. The chapter has also hinted at the types of policies on which poverty reduction may increasingly rely – especially focusing on both the urban and rural poor and addressing issues of equity. Yet fully assessing the factors that have shaped poverty in Cameroon can provide clearer policy guidance. The next chapter turns to examine poverty’s deep drivers, first by decomposing the changes in poverty witnessed over the last two decades and then by providing a more granular profile of Cameroon’s poor population.  46 Chapter 2 Poverty reduction has stagnated in Cameroon and inequality remains high Annex 2.1. Poverty gap index and squared poverty gap index in Cameroon Figure 19. Poverty gap index and squared poverty gap index in Cameroon, 2001-2022 Panel A: Poverty gap index Panel B: Squared poverty gap index 0.18 0.09 Squared poverty gap index 0.16 0.08 Poverty gap index 0.14 0.07 0.12 0.06 0.10 0.05 0.08 0.04 0.06 0.03 0.04 0.02 0.02 0.01 0.00 0.00 ) ) ) ) ) ) ) ) ) ) -2 -3 -4 ey -5 -2 -3 -4 ey -5 AM AM AM AM AM AM AM AM rv rv su su C C C C C C C C ge ge (E (E (E (E (E (E (E (E rid rid 01 07 14 2 01 07 14 2 /2 /2 (B (B 20 20 20 20 20 20 21 21 21 21 20 20 20 20 Note: Consumption is spatially deflated and, where relevant, temporally deflated to compare with national poverty lines. Estimates from 2001, 2007, 2014, and the 2021 bridge survey are comparable. 2021/22 estimates from ECAM‑5 represent latest best estimates but cannot be compared with previous surveys. Source: ECAM‑2, ECAM‑3, ECAM‑4, ECAM‑5, 2021 bridge survey, and World Bank estimates. Annex 2.2. Poverty trends using the international methodology Figure 20. Poverty rate in Cameroon using international poverty methodology, 2001-2022 35 Poverty rate at the international poverty 31.2 30 25.7 25.7 24.7 25 23.0 line (percent) 20 15 10 5 0 2001 (ECAM-2) 2007 (ECAM-3) 2014 (ECAM-4) 2021 2021/22 (Bridge survey) (ECAM-5) Note: Poverty calculated using the international extreme poverty line of 2.15 USD 2017 PPP per person per day. Estimates for 2001, 2007, and 2014 are taken directly from the World Bank Poverty and Inequality Platform. Source: ECAM‑5, 2021 bridge survey, Poverty and Inequality Platform, and World Bank estimates. 47 Cameroon Poverty Assessment 2024 Chapter 3. KEY MESSAGES ➜ Consumption growth was negative for the poorest 30  percent of Cameroonians between 2001 and 2021: growth’s lack of inclusivity is holding poverty reduction back ➜ Urbanization is not delivering the poverty reduction gains it was once, as rural-urban migration has accelerated but such migrants fare worse than other urban residents ➜ Spatial inequality in Cameroon is vast, with the poverty rate being around six times higher in its northern regions than in Douala and Yaoundé ➜ Overall stagnation in the national poverty rate over the last two decades masks significant divergence in the poverty rate between Cameroon’s poorest and richest regions, meaning that some regions risk being left behind ➜ “Pockets of poverty” persist even within Cameroon’s better off regions ➜ Echoing global evidence, Cameroonians are more exposed to poverty in larger households whose heads are illiterate and primarily engaged in agriculture: this emphasizes the importance of building human capital and providing livelihoods opportunities Growth and structural change are not helping the poorest, so some Cameroonians risk being left behind T his chapter considers the overall drivers of Cameroon’s poverty reduc- tion – or lack of it – in the past two decades. First, the chapter considers why Cameroon’s relatively resilient growth has not translated into poverty reduction, looking directly at who has benefited most from the proceeds of growth in recent years. Second, the chapter seeks to understand the mechanics behind one of the most striking results from Chapter 2 – the large uptick in urban poverty – decomposing the relentless urbanization process underway in Cameroon. Third, the chapter extends the spatial lens on poverty in Cameroon to look at how the country’s different regions are faring, as well as focusing on smaller areas within those regions: this is essential for directing poverty-reducing policies to the right parts of Cameroon. Finally, the chapter constructs “poverty profiles” for Cameroon, examining the relationship between poverty and key household- and individual-level characteristics, which not only help with policy targeting but also ascertaining which constraints on poverty reduction should be alleviated. Providing this overview of pov- erty’s underlying drivers helps motivate the deep dives into shocks, non-monetary poverty, livelihoods, and access to services that come in the subsequent chapters. 3.1. Growth has not been reaching the poorest Cameroonians Over the last two decades consumption fell for the poorest Cameroonians, as growth mainly benefited the rich. This can be seen by constructing “growth incidence curves”, which show how much consumption increased each year for Cameroonians in different deciles of the consumption distribution (Figure 21).(23) Between 2001 and 2021, annualized consumption growth was negative for the poorest 30 percent of Cameroonians, meaning that they were slipping further behind. This explains why inequality, as measured by the Gini coefficient, is higher today than it was in 2001 (as shown in Chapter 2). The richest 70 percent of Cameroonians experienced positive 23. This uses ECAM‑2, ECAM‑3, ECAM‑4, and the 2021 bridge survey. Consumption is deflated to 2001 terms using the national poverty line from each year to facilitate comparisons between years. 49 Cameroon Poverty Assessment 2024 consumption growth between 2001 and 2021 and, overall, consumption growth was higher for the richest. What little poverty reduction did occur during these two decades is due to the very slight increase in consumption for households around the 4th decile, as these are the households are close to the national poverty line. Yet these house- holds around the national poverty line did not experience positive consumption growth between 2014 and 2021, which explains the slight uptick in the national poverty rate over that period. Figure 21. Growth incidence curves for Cameroon, 2001-2021 2 Annualized real consumption 1 growth (percent) 0 1 2 3 4 5 6 7 8 9 10 -1 -2 Decile of the real consumption distribution 2001-2021 2001-2014 2014-2021 Note: Consumption deflated using national poverty lines from each survey year. Source: ECAM‑2, ECAM‑3, ECAM‑4, 2021 bridge survey, and World Bank estimates. Relatedly, while overall growth could have driven faster poverty reduction, it is being held back by rising inequality. This can be seen by using a Datt-Ravallion decomposition to disaggregate any changes in poverty into a component due to growth in average consumption and a component due to the changing shape of the consump- tion distribution (Datt & Ravallion, 1992). Between 2001 and 2021, if growth had been distribution evenly, poverty at the national line would have fallen by 4.1  percentage points, but changes in the distribution of consumption alone would actually have raised poverty by 2.5  percentage points over the same period, offsetting the contribution of growth (Figure 22). Distributional changes also pulled in the opposite direction to growth even between 2014 and 2021: slight redistribution helped offset negative average consumption growth. This confirms one of the key emerging messages from Chapters 1 and 2: growth alone is not proving enough to lift Cameroonians out of poverty, because it is not reaching the right people. 50 Chapter 3 Growth and structural change are not helping the poorest, so some Cameroonians risk being left behind Figure 22. Datt-Ravallion decompositions for Cameroon, 2001-2021 6 Change in the poverty rate 4 (percentage points) 2 0 -2 -4 -6 -8 2001-2021 2001-2014 2014-2021 Growth Redistribution Total Note: Consumption deflated using national poverty lines from each survey year. Source: ECAM‑2, ECAM‑3, ECAM‑4, 2021 bridge survey, and World Bank estimates. 3.2. Urbanization is not the poverty-reducing force it once was The shift in Cameroon’s population from rural to urban areas put downward pres- sure on poverty in the 2000s and early 2010s, but this effect has since waned. To see this, Ravallion-Huppi decompositions can break down changes in poverty into popula- tion shifts between different groups – which means urbanization when the groups are urban and rural areas – and changes in the poverty rate within those groups (Ravallion & Huppi, 1991). Between 2001 and 2014, the small amount of poverty reduction that Cameroon achieved was associated with the population shifting from rural towards urban areas: all other things equal urban areas were less poor than rural areas, so a relative increase in the urban population helped to reduce poverty (Figure 23). Yet since 2014, this has been more than outweighed by poverty within rural and within urban areas rising, wiping out any gains from urbanization. Rural poverty was rising anyway between 2001 and 2014 and continued to do so up to 2021; as Chapter 2 shows, the big change was that urban poverty itself began to rise between 2014 and 2021 too. A similar effect is observed when looking at the main activity in which the household head engaged.(24) The concomitant shift from agricultural jobs to non-agricultural jobs helped pulled down the poverty rate, but this was ultimately offset by poverty rising within these different employment sectors. 24. In 2021/22, 41.4 percent of Cameroonians lived in a household headed by an agricultural worker, 10.8 percent lived in household headed by an industry worker, 12.1 percent lived in a household headed by a commerce worker (buying and selling), 25.4 percent lived in a household headed by someone working in other services, and 10.3 percent lived in a household where the head was not working. 51 Cameroon Poverty Assessment 2024 Figure 23. Ravallion-Huppi decompositions for Cameroon, 2001-2021 Panel A: Disaggregation by urban- Panel B: Disaggregation by household rural head's main activity 6 6 Change in the poverty rate Change in the poverty rate 4 4 2 2 (percent) (percent) 0 0 -2 -2 -4 -4 -6 -6 1 7 4 1 1 7 4 1 02 00 01 02 02 00 01 02 -2 -2 -2 -2 -2 -2 -2 -2 01 01 07 14 01 01 07 14 20 20 20 20 20 20 20 20 Interaction effect Population-shift effect Interaction effect Population-shift effect Intra-sectoral effect Total change in poverty Intra-sectoral effect Total change in poverty Note: Consumption deflated using national poverty lines from each survey year. Disaggregation in Panel A splits the population by urban and rural areas. Disaggregation in Panel B splits the population according to the primary activity of the household head: not working, agriculture, industry, commerce, or other services. Source: ECAM‑2, ECAM‑3, ECAM‑4, 2021 bridge survey, and World Bank estimates. The changing mechanisms behind urbanization, and especially accelerating rural-urban migration, could explain why it is proving less of a poverty-reducing force for Cameroon today. Urbanization can be driven by three components: (1) natural population growth within urban areas, (2) reclassification of previously rural areas to urban, and (3) rural-urban migration. As Box 2 shows, the rural-urban migration channel appears to have accelerated over the last two decades. A priori, those moving to cities may initially struggle to access to same services and jobs as other urban dwellers, they may start off with lower human capital and assets having come from poorer rural areas to in the first place, and they may lack the social networks that they once had in rural areas and which other urban dwellers enjoy (Lall, Selod, & Shalizi, 2006). Box 2. Decomposing urbanization in Cameroon Urbanization can be driven by three distinct channels. First, natural population growth in urban areas can outstrip natural population growth in rural areas. Second, areas that used to be rural can become more densely populated, or acquire other characteristics of urban areas, leading them to be reclassified as urban. Third, people may migrate from rural to urban areas. Following Wai-Poi et al. (2018), the absolute change in the urban population can be decomposed into these three constituent parts: Δ (Urban population) = (Natural population growth) + (Reclassification) + (Net migration) While direct estimates of these components are not available for Cameroon, proxies can be estab- lished to help decompose the country’s urbanization trends; natural population growth can be proxied using projections. Without a census, natural population growth for urban and for rural areas cannot be estimated directly. However, the Population Division in the United Nations Department of Economic and Social Affairs produces frequent projections of the crude birth and death rate at the national level for all countries including Cameroon (United Nations Population Division, 2022). While projections for urban Cameroon alone are not available, these national-level projections can be modified to make 52 Chapter 3 Growth and structural change are not helping the poorest, so some Cameroonians risk being left behind them better reflect Cameroon’s urban areas by using information on the relative level of natural population growth in urban and rural areas in neighboring countries (UN DESA, 2022). The extent of reclassification can be estimated using geospatial data. For the early ECAMs and bridge survey on which estimating trends in this poverty assessment relies, urban classifications are applied at the settlement level, then aggregated up to the enumeration area level, where needed. Specifically, urban settlements have at least 10,000 inhabitants. Unfortunately, given the lack of census data in Cameroon, it is not possible to construct information on how the urban status of settlements and enumeration areas has evolved over time to quantify the extent of reclassification. However, an alternative approach can be established by using gridded geospatial data, where each one-kilometer grid cell is assigned as urban or rural based on information on its population and population density (JRC, European Commission, and CIESIN, 2021). The extent of reclassification can then be established by looking at the population, at a given point in time, in grid cells that were subsequently reclassified as urban as they became more and more densely population. Rural-urban migration is the residual after subtracting natural population growth and reclassification from the actual changes in the urban population that Cameroon experienced. The actual change in the urban population is also taken from United Nations projections; these appear to qualitatively match what is seen in ECAM‑2, ECAM-3, ECAM-4, and the 2021 bridge survey. Among the reclassified, it is also possible to isolate the effects of pure reclassification, natural growth, and rural-urban migration into areas reclassified as urban. This helps account for interactions between the three components of urban growth. It is also possible to triangulate the growing role of rural-urban migration using survey data. In particu- lar, ECAM-3, ECAM-4, and the 2021 bridge survey contained the same questions regarding whether each household member had lived in another arrondissement before and, if so, whether they had been in a “village” (approximating rural areas) or a “town” (approximating urban areas). These questions can therefore be used to assess the number of new urban residents (between each survey year) that were rural-urban migrants. Both the decomposition approach and survey data suggest a growing role for rural-urban migration for urbanization in Cameroon. Since 2000, the share of new urban residents coming from migration has increased (Figure 24). This arises even though overall natural population growth is relatively fast in Cameroon. The fact that two entirely different methodologies using entirely different assumptions and underlying data produce such qualitatively similar adds robustness to these findings. Figure 24. Decomposing the absolute change in the urban population in Cameroon, 2001-2021 Panel A: Decomposition approach Panel B: Additional urban population 100 Share of urban growth 100 Share of additional urban residents 80 90 (percent) 60 80 40 70 60 (percent) 20 50 0 2000-2010 2010-2020 40 Rural-urban migration into pre-existing 30 urban areas 20 Rural-urban migration into reclassified 10 areas 0 Natural growth among the reclassified 2007-2014 2014-2021 Pure reclassification Urban stayer Urban-urban Rural-urban Natural growth migrant migrant Note: In Panel A overall population changes are calibrated to match changing WDIs. Source: WDIs, UN DESA, JRC, European Commission, CIESIN, ECAM-3, ECAM-4, 2021 bridge survey, and World Bank estimates. 53 Cameroon Poverty Assessment 2024 Rural-urban migrants have higher poverty levels than other urban dwellers, but this is mainly because they fare worse than urban-urban migrants. In 2021/22, around 24.3  percent of rural-urban migrants lived below the poverty line (Figure 25).(25) This means that they are faring much better than the average rural dweller, but slightly worse than other urban dwellers among whom 21.1 percent lived below the poverty line.(26) However, this difference arises largely because poverty is so much lower among urban-urban migrants, of whom only 12.3 percent lived below the poverty line. Therefore, migration per se is not resulting in higher urban poverty, but rural-urban migration could be part of the story; to unpack this, rural-urban migrants’ access to basic services, human capital, and livelihoods will be considered in subsequent chapters. Box 3 also provides a basic profile of rural-urban migrants, and other types of internal migrants, in Cameroon. Figure 25. Poverty by migration status in Cameroon, 2021/22 Poverty rate (percent) 0 10 20 30 40 50 60 70 Always rural 64.1 Rural-rural migrant 46.9 Urban-rural migrant 29.5 Always urban 26.0 Urban-urban migrant 12.3 Always urban and urban-urban migrant combined 21.1 Rural-urban migrant 24.3 Note: Consumption is spatially deflated and temporally deflated to compare with the national poverty line. International migrants excluded. Source: ECAM‑5 and World Bank estimates. Box 3. Profiling rural-urban migrants in Cameroon Rural-urban migrants are older and more likely to be literate compared with those who stayed in place in rural areas but also compared with who always lived in urban areas. In 2021/22, rural-urban migrants were aged 32.6 years on average and, restricting the sample to those aged 14 or more, around 81.8 percent were literate (Table 2). Only urban-urban migrants have a higher literacy rate than rural-urban migrants. This suggests that at least some rural-urban migrants are moving to towns and cities to seek livelihood opportunities, which could make more direct use of their skills than the agricultural activities that dominate in rural areas. 25. The ECAM‑5 questionnaire asked whether individuals had lived in a different arrondissement in the past for more than 6 months. The details of the main place they previously lived were then captured, including whether it was a “village” – which approximately corresponds to rural areas – or a “town” – which approximately corresponds to urban areas. 26. The difference in the poverty rate between rural-urban migrants and all other urban dwellers is statistically significant at the 10 percent level (p=0.070). However, the difference between rural-ur- ban migrants and those who had always lived in urban areas is not statistically significant at the 10 percent level (p=0.409). 54 Chapter 3 Growth and structural change are not helping the poorest, so some Cameroonians risk being left behind Table 2. Sex, age, and literacy among internal migrants in Cameroon Always Rural-rural Urban-rural Always Urban-urban Rural-urban rural migrant migrant urban migrant migrant Male (percent) 48.2 40.4 51.0 47.9 46.9 43.7 Age (mean) 17.2 33.3 31.8 15.2 30.0 32.6 Literate (percent) 48.4 54.0 81.2 76.5 91.6 81.8 Note: Literacy only reported for those aged 14 or more. Source: ECAM-5 and World Bank estimates. Seeking economic opportunities, pursuing education, and escaping conflict are more common migration drivers for rural-urban migrants than for other internal migrants. Around 27.5 percent of rural-urban migrants moved for economic opportunities and 9.5 percent did so for schooling (Figure 26): these shares are higher than rural-rural migrant and urban-rural migrants, but slightly lower than urban-urban migrants. Around 10.9 percent of rural-urban migrants moved due to conflict or political instability, more than any other type of internal migrant – this reflects the impact of the anglophone crisis and Boko Haram’s activities in Cameroon’s north. While “family reasons” is the most widespread driver for all types of internal migrants, this could reflect household members fol- lowing those who move for reasons associated with economic opportunities, education, or conflict. Figure 26. Reasons for migration among internal migrants and location of urban dwellers in Cameroon. 2021/22 Panel A: Reasons for migrating Panel B: Region breakdown Share of urban residents (percent) 100 100 Share of migrants (percent) 90 90 80 80 70 70 59.7 54.4 56.1 60 50 60 40 50 30 40 18.0 22.3 17.4 20 30 10 20 0 10 22.3 23.3 26.5 Rural-rural Urban-rural Urban-urban Rural-urban 0 migrants migrants migrants migrants Always urban Urban-urban Rural-urban Family reasons Economic opportunities migrant migrant Education Conflict or instability Other Douala Yaoundé Other urban Source: ECAM-5 and World Bank estimates. Rural-urban migrants are also disproportionately more likely to live in Douala than urban-urban migrants and those who always lived in urban areas. Around 26.5 percent of rural-urban migrants lived in Douala in 2021/22. This may reflect their pursuit of economic opportunities, which are more likely to be found there. Meanwhile, urban-urban migrants are relatively more likely to move to Yaoundé. Urban areas are also highly heterogeneous: Cameroonians living on the periph- ery of urban settlements or in semi-urban areas are more exposed to poverty than those in urban centers. Using geospatial data from WorldPop, it is possible to distinguish different types of enumeration areas in ECAM‑5 based on their population and population density. These alternative measures of the urban-rural continuum underline that poverty is not the same for all urban dwellers (Figure 27).(27) In urban 27. These alternative urban classifications were first constructed for each “settlement” – a contiguous built-up area where people live. From the settlement-level data, the classifications where then mapped to each “population point” in Cameroon, meaning the corners of a 100 meter by 100 meter grid that covers the whole country. Information for each population point could then be mapped to ECAM‑5’s enumeration areas. Further details on the settlement-level classification are provided in Annex 3.1. 55 Cameroon Poverty Assessment 2024 centers – where settlements have at least 50,000 people and the population density is at least 1,500 inhabitants per square kilometer – around 15.1 percent of people lived in poverty in 2021/22, lower than the average for all urban areas. Yet for dense urban clusters – where settlements have between 5,000 and 50,000 people and population density is still at least 1,500 inhabitants per square kilometer – the poverty rate is more than double that, at 32.8 percent.(28) This reinforces the message that density and distance matter for poverty reduction, even within areas that are administratively classified as urban. Alongside understanding what is driving urbanization, it is therefore also vital for policymakers to consider the shape and structure of new and growing urban settlements to design poverty-reducing policies. Figure 27. Poverty by alternative urban classifications in Cameroon, 2021/22 80 65.8 Poverty rate (percent) 60 55.7 40 32.8 20 15.1 0 Urban center Dense urban cluster Semi-dense urban cluster Rural cluster Note: Consumption is spatially deflated and temporally deflated to compare with the national poverty line. Information is mapped from geospatial data to household level by using a population-weighted mode to collapse to the enumeration area level. See Annex 3.1 for details of urban classifications. Source: ECAM‑5 and World Bank estimates. 3.3. Cameroon is highly spatially unequal and some regions risk being left behind Alongside the changing of nature of urbanization, understanding differences between regions, departments, and arrondissements is critical for assessing why poverty reduction in Cameroon is lagging. As the previous section shows, devising poverty-reduction policies that can support both rural and urban areas is clearly a growing priority for Cameroon. Yet the different poverty-reduction challenges faced by Cameroon’s different administrative areas presents another crucial spatial dimension, which can be assessed using the same household survey data alongside geospatial data to allow for more granular estimates. This is especially important given Cameroon’s drive to decentralize certain government functions to its 10 regions (Administrative 1 level), as well as its departments (Administrative 2 level) and arrondissements (Administrative 3 level). Density and distance could constrain economic activity and poverty reduction in more remote areas of Cameroon. Adopting the language of the 2009 World Development Report, density and distance can strongly influence prospects for poverty reduction (World Bank, 2009). Density, generally a feature of urban spaces, can help achieve agglomeration effects, including include sharing public goods, knowledge spill- 28. Around 91.7 percent of the population in enumeration areas that are predominantly urban clusters are classified as urban in ECAM‑5, compared with 78.6 percent of the population in enumeration areas that are predominantly dense urban clusters. 56 Chapter 3 Growth and structural change are not helping the poorest, so some Cameroonians risk being left behind overs, closer proximity to consumers, and better matching of workers and employers (Bolter & Robey, 2020). Having services and markets concentrated in one place helps invest in human capital and access livelihood opportunities. More distance from these kinds of dense spaces can make accessing services and markets more difficult. This provides a useful framework for interpreting the inter-region and inter-arrondissement differences observed in this chapter and subsequent chapters of this report. Spatial inequality is vast, with poverty being disproportionately concentrated in Cameroon’s northern regions. In 2021/22, the share of people living below the poverty line was highest in the Extrême-Nord region, at 69.2 percent, followed closely by the Nord-Ouest region (66.8 percent) and Nord region (61.1 percent). These poverty rates are around three times higher than the rest of the country and around six times higher than in Douala and Yaoundé, where the poverty rates are 8.3 percent and 10.8 percent respectively (Figure 28). Similar patterns emerge in terms of the absolute number of poor people living in each region. This highlights the wide spatial inequalities between Cameroon’s regions. Figure 28. Poverty and the absolute number of poor in Cameroon’s regions, 2021/22 Poverty rate (percent) Absolute number of poor (thousands) 8.3 - 14.9 141 - 276 15.0 - 20.4 277 - 341 20.5 - 41.5 342 - 704 41.6 - 61.1 705 - 779 61.2 - 69.2 780 - 3,458 Note: Consumption is spatially deflated and temporally deflated to compare with the national poverty line. Source: Humanitarian Data Exchange and GRID-3 (for shapefiles), ECAM‑5, and World Bank estimates. For much of the last two decades, Cameroon’s poorest regions were diverging from the rest, placing them at risk of being left behind. While the 2021 bridge survey was not designed to produce region-level results, it is still possible to review how poverty fared in Cameroon’s regions between 2001 and 2014. Tentative results can 57 Cameroon Poverty Assessment 2024 be presented for 2021 when combing sets of regions to increase the sample size.(29) Taking the Extrême-Nord, Nord-Ouest, and Nord regions together, it emerges that the poverty rate in Cameroon’s northern regions increased by around one-third between 2001 and 2014, jumping from 53.9 percent to 67.9 percent (Figure 29). Over the same period, poverty fell for Cameroon’s other regions, dropping from 32.3 percent in 2001 to 18.7 percent in 2014. The full set of region-level poverty rates for Cameroon for 2001, 2007, and 2014 is shown in Annex 3.2. Suggestive evidence from the 2021 bridge survey indicates that poverty may have risen faster for Cameroon’s non-northern regions between 2014 and 2021, but nowhere near enough to make up the widening gap of the last two decades.(30) Thus, the stagnation of Cameroon’s national-level poverty rate masks significant divergence between the country’s northern and non-northern regions, which are changing the spatial distribution of poverty in the country. Poverty-reducing policies must there account carefully for the scale and nature of different regions’ development challenges. Figure 29. Evolution of poverty in Cameroon’s northern regions, 2001-2021 80 Poverty rate (percent) 60 40 20 0 2001 2007 2014 2021 Extrême-Nord Nord Nord-Ouest All northern regions All other regions Note: Consumption is spatially deflated and temporally deflated to compare with the national poverty line. The 2021 bridge survey was not designed to be representative at the region level. Source: ECAM‑2, ECAM‑3, ECAM‑4, 2021 bridge survey, and World Bank estimates. There is a clear link between regional divergence and growing rural-urban migra- tion. As regions fall behind, the incentive to migrate increases because potential gains to living standards from moving are higher.(31) Insofar as it is disproportionately rural regions where poverty has been rising and disproportionately urban regions where poverty has been falling, this type of inter-regional movement will contribute to urban- ization. However, since urban poverty started to increase between 2014 and 2021, the potential gains from this type of migration may be starting to wane. 29. Combining regions to expand the sample size helps to reduce the standard errors of the estimates but, strictly, the 2021 bridge survey was not designed to produce reliable estimates at the region level. 30. Forthcoming analysis using night-time lights data produced for Cameroon’s upcoming CEM suggests that GDP per capita may have declined after 2014 in those regions most affected by conflict – the Extrême-Nord, Nord-Ouest, and Sud-Ouest. 31. This follows the basic logic of the Harris-Todaro model of rural-urban migration (Harris & Todaro, 1970). 58 Chapter 3 Growth and structural change are not helping the poorest, so some Cameroonians risk being left behind Even within some of Cameroon’s better off regions, “pockets of poverty” persist, which have profound implications for targeting poverty-reducing policies. This can be seen by creating an arrondissement-level poverty map for Cameroon, using geospatial data and machine learning techniques (see Box 4). As such, while designing policies to meet specific regions’ needs marks an important step for reducing poverty, geographical targeting for pro-poor programs can also benefit from more granular information on where poor people live. Box 4. An arrondissement-level poverty map for Cameroon Small-area poverty maps can provide Cameroon’s policymakers with more disaggregated information on where poverty is concentrated, helping to target pro-poor programs. ECAM-5 yields reliable poverty estimates at the region level. Yet there could be variation in living conditions within regions. Understanding this intra-region variation can be useful for tailoring policies and selecting the right areas for interventions to boost monetary consumption, human capital, and livelihoods, including social assistance programs. Small-area poverty maps show the concentration of poverty at lower administrative levels – for Cameroon this means constructing poverty estimates for arrondissements and departments. Cameroon’s small-area poverty maps are constructed using extensive geospatial data in combina- tion with innovative machine learning techniques. The basic idea of this poverty mapping approach is to estimate a model linking a range of detailed geospatial data – which cover the whole country and are relia- ble even for small areas – with estimates of poverty from ECAM-5. The geospatial data used for Cameroon’s poverty map are available for each “population point” in Cameroon, meaning the corners of a 100 meter by 100 meter grid that covers the whole country. The geospatial data include information on: population Figure 30. Arrondissement-level poverty map for density; exposure to floods and extreme heat; expo- Cameroon sure to conflict; and travel times to different health and education facilities, markets, towns (Yaoundé Poverty rate (percent) and Douala), and border crossings (as explained in more detail in Chapter 6). The geospatial data are 0.0 - 20.0 collapsed to the arrondissement level and then 20.1 - 40.0 merged with the household survey data. The model 40.1 - 60.0 linking the two types of data is estimated using the 60.1 - 80.0 Extreme Gradient Boosting (XGBoost) supervised machine learning algorithm – this method has 80.1 - 100.0 proved successful in constructing small-area pov- erty estimates in other similar settings (Friedman, 2001; Chen & Guestrin, 2016; Corral, Henderson, & Segovia, 2023). The arrondissement-level poverty maps suggest that there is significant variation within Cameroon’s regions in the extent of poverty. Poverty is generally more concentrated in Cameroon’s northern arrondissements, echoing the main message of the region-level breakdown. Yet there are some arrondissements with high poverty rates in regions where the overall poverty rate is relatively low, especially the Centre, Ouest, and Sud- Ouest regions (Figure 30). This creates so-called “pockets of poverty”. As such, granular geographical targeting, going below the region level, could help ensure poverty-reducing programs reach the poor. Source: ECAM-5 and World Bank estimates. 59 Cameroon Poverty Assessment 2024 Three robustness checks indicate that the methodology used to construct small-area poverty maps for Cameroon is valid. First, the direct estimates from ECAM-5 for each arrondissement – which are not reliable on their own, but are still informative – correlate very strongly with the arrondissement-level estimates produced using the XGBoost algorithm. The correlation between the direct estimates and the estimates fitted using XGBoost is 0.91, while the Spearman rank correlation is 0.90. Second, when the arrondissement-level estimates from XGBoost are aggregated to the region level and compared with the direct ECAM-5 estimates, the ranking of the regions is maintained. Indeed, the resulting region-level point estimates from XGBoost fall within the 95 percent confidence intervals of the direct estimates from ECAM-5 for all regions except Littoral, Sud-Ouest, and Sud. Finally, changing the way in which the variables are selected has little effect on the results. For the estimates presented in Figure 30, the variables were first pre-selected to try and focus on those that, a priori, could theoretically drive poverty and to avoid including multiple variables capturing the same information. However, omitting this pre-selection step and feeding all of the available geospatial variables into the XGBoost algorithm leaves the main findings virtually unchanged. 3.4. Going beyond the spatial lens can provide a more comprehensive picture of Cameroon’s poverty profile Examining which types of households are most likely to be poor, beyond just knowing where the poor live, can further refine the design and implementation of poverty-reducing policies. The previous section identifies the needs of different parts of Cameroon, which helps to target poverty-reducing policies to the right areas. Yet this type of targeting may be improved further by looking at the correlates between poverty and household- and individual-level characteristics to build a profile of who is most exposed to dropping below the poverty line. This is important for supporting the right households in the right parts of the country. Moreover, these types of correlations can reveal what the main constraints on poverty reduction might be – be they related to household demographics, human capital, livelihoods, or other factors – so that the right interventions can be tailored to support Cameroon’s poor accordingly. This section therefore first considers the simple correlations between poverty and a host of household- and individual-level characteristics, then extends this analysis to basic regressions to reveal the partial correlations. Echoing the results from many other countries, larger households whose heads are illiterate and who work in agriculture are more likely to be poor. Households are often larger due to higher fertility and polygamy, both of which have been identified as strong correlates of poverty in other countries (Figure 31).(32) Indeed, the individu- al-level results below confirm the link between poverty and polygamy in Cameroon. Further, those living in households with illiterate household heads are almost three times as likely as those in households with literate heads to live in poverty. Finally, Cameroonians in households whose heads work in agriculture are around twice as likely to live in poverty compared with households whose heads work primarily engage in other activities or do not work at all. These results foreshadow some of the deeper analysis into human capital in Chapter 5 and livelihoods in Chapter 6. 32. World Bank (2021) and World Bank (2023) provide examples for Chad and the Central African Republic. 60 Chapter 3 Growth and structural change are not helping the poorest, so some Cameroonians risk being left behind Figure 31. Poverty in Cameroon by household size and household head characteristics, 2021/22 100 90 Poverty rate (percent) 80 70 60 50 40 30 20 10 0 1 2-4 5-9 10-19 20+ Female Male Illiterate Not working Agriculture Industry Commerce Other services Literate Household size Household Household Household head activity head sex head literacy Note: Consumption is spatially deflated and temporally deflated to compare with the national poverty line. Source: ECAM‑5 and World Bank estimates. The relationships between household size, household head literacy, household head livelihoods, and poverty remain strong even when controlling for other house- hold characteristics, highlighting potential pathways out of poverty. Since different household characteristics may be correlated with each other, it is important to check whether their relationship with poverty holds when considering all such characteristics jointly. This can be done by regressing poverty on all potential correlates of poverty at the same time as well as other control variables including households’ locations and the non-monetary deprivations that underpin Chapter 5 (Table 3).(33) A similar model is run with log of deflated consumption per capita as the dependent variable, for which the results from this model are reported in Annex 3.3. This also helps ascertain whether there are differences in poverty within regions and urban and rural areas in terms of household characteristics (as opposed to household characteristics simply differing between regions). First, it emerges that fertility remains an important factor for poverty reduction in Cameroon, just as the simple correlations in Figure 31 suggest. This reso- nates with global evidence suggesting that larger households’ investments in children’s human capital are spread more thinly as well as the well-documented association between girls’ education, fertility, and poverty (Ainsworth, Beegle, & Nyamete, 1996; Beegle & Christiaensen, 2019). Second, the clear relationship between household head literacy and poverty underlines the importance of not only attending school but also learning whilst in school to reap the full benefits. Third, the fact that poverty remains higher among households whose heads work primarily agriculture, even after controlling for household location, underlines the importance of raising agricultural productivity but also helping those who switch from agriculture to services find good jobs when they get there. This presents a stark challenge because the acceleration of 33. Specifically, this is a regression in which the dependent variable takes 1 if the household is below the national poverty line and 0 otherwise. It is therefore a linear probability model, so its coefficients can be read directly as marginal effects. The standard errors are clustered at the enumeration area level, minimizing the impact of heteroskedasticity on inference. The results are qualitatively similar if a probit model is estimates and the marginal effects at the mean are estimated. 61 Cameroon Poverty Assessment 2024 rural-urban migration described above could lead to increased competition for jobs in urban areas, but also because overall labor productivity has been falling in services (as shown in Chapter 1). Table 3. Regression of poverty status on household characteristics Adding location Adding non-monetary Main variables only controls deprivations 0.0062 0.0093** 0.0124*** Household size (0.0044) (0.0043) (0.0045) Number of household 0.0467*** 0.0372*** 0.0286*** dependents (0.0057) (0.0057) (0.0057) -0.0035 -0.0225* -0.0295** Household head male (0.0134) (0.0131) (0.0128) -0.2441*** -0.1333*** -0.0868*** Household head is literate (0.0166) (0.0169) (0.0201) Household head primarily works 0.2062*** 0.1369*** 0.0872*** in agriculture (0.0216) (0.0205) (0.0199) Household head primarily works 0.0322 0.0423* 0.0455* in industry (0.0275) (0.0247) (0.0238) Household head primarily works -0.0627** -0.0405* -0.0271 in commerce (0.0245) (0.0222) (0.0216) Household head primarily works -0.0344 -0.0086 0.0075 in other services (0.0211) (0.0190) (0.0179) N 10,546 10,546 10,546 R-squared 0.2983 0.3597 0.3882 Notes: Dependent variable is a binary variable taking 1 if the household is below the national poverty line and 0 otherwise. This is a linear probability model so the coefficients can be read directly as marginal effects. Standard errors clustered at the enumeration area level are in parentheses. Location controls comprise a dummy for urban areas and region fixed effects. Non-monetary deprivations are outlined in Chapter 5. * p<0.10, ** p<0.05, *** p<0.01. Source: ECAM‑5 and World Bank estimates. Profiling poverty according to individual-level characteristics confirms the strong link with education and suggests that gender differences may arise in certain households. Since welfare and poverty are measured at the household level, individual-level poverty profiles should be interpreted with some caution because it is not possible to track intra-household allocation of consumption. Nevertheless, the individual-level differences in poverty according to education echo the household-level differences: school-age children who are not enrolled are around twice as likely to be living in poverty as those who are enrolled, while illiterate Cameroonians are more than twice as likely to be living in poverty as those who are literate, when focusing on those aged 14 years or more (Figure 32). On average, gender gaps in exposure to poverty appear to be small, but this masks substantial differences if the results are disaggregated according to age and marital status. First, women appear to be more exposed to poverty then men between the ages of 20 to 44 – this is when childbearing and childrearing responsibilities might have the largest impact on income-generating opportunities. Second, and relatedly, women who are divorced, separated, or widowed are more likely to live in poverty than men who are divorced, separated, or widowered, as they retain responsibility for caring for their family despite lacking the assets or spousal income they have had before. Additionally, poverty is higher for those who are polygamously married for both sexes and especially for polygamously-married women. 62 Chapter 3 Growth and structural change are not helping the poorest, so some Cameroonians risk being left behind Figure 32. Poverty in Cameroon by individual-level characteristics, 2021/22 Panel A: Overall individual characteristics 80 70 Poverty rate (percent) 60 50 40 30 20 10 0 Female Male 0-14 15-64 65+ Unenrolled Enrolled Illiterate Literate Sex Age Enrolled (amongs Literacy (among chool-age children) those aged 14+) Panel B: By sex and age Panel C: By sex and marital status 60 70 Poverty rate (percent) 60 50 50 Poverty rate (percent) 40 40 30 30 20 10 20 0 10 d ar r ed d s) s) m eve rie te ou ou ow ra am am /n pa id 0 yg og W se n io ol on or un (p + (m 4 65-64 70-69 60-59 50-49 55-54 45-44 40-39 35-34 25-24 30-29 20-19 15-14 105-9 4 ed 75 al d -7 0- rie rm d rc rie ivo ar fo ar M In D Age (years) M Female Male Female Male Note: Consumption is spatially deflated and temporally deflated to compare with the national poverty line. In Panel A, school-age means those aged between 6 and 13. Panel C restricted to people aged 15+. Source: ECAM‑5 and World Bank estimates. 3.5. Understanding short-term poverty dynamics can complement analysis of long-term trends Alongside the long-term trends and drivers of poverty, policymakers also need to understand the mechanics of short-run shocks, uncertainty, and vulnerability. The previous chapters hint at the overall structural constraints that have held back poverty reduction in Cameroon over the last two decades. Despite the stagnation of Cameroon’s national poverty rate, there have been important differences over this period in terms of urbanization, spatial inequality, and the role of human capital and livelihoods, the latter of which will be explored in more detail in Chapters 5 and 6. Yet there are also short-term dynamics that can cause poverty to fluctuate at the household 63 Cameroon Poverty Assessment 2024 level. Shocks – including those related to prices, climate, and conflict described in Chapter 4 – can push non-poor households below the poverty line rapidly. This can quickly reverse any gains in poverty reduction. As such, the next chapter considers Cameroonians’ vulnerability to shocks, providing policymakers with the information needed to ensure non-poor households can achieve economic security.  Annex 3.1. Alternative urban classifications using geospatial data Table 4. Population and population density criteria for defining alternative urban classifications Minimum population size of the cluster of cells No minimum (settlement size) population size criterion ≥50,000 50,000-5,000 5,000-500 (not an entity) ≥1,500 Urban center Dense urban cluster Population density of cells (inhabitants per squared kilometer) Semi-dense urban Rural cluster Suburban or 300-1,500 cluster peri-urban grid cell Low density rural 50-300 grid cell Very low density <50 rural grid cell Source: JRC, European Commission, and CIESIN. Annex 3.2. Region-level poverty rates, 2001-2014 Table 5. Region-level poverty rates at the national poverty line, 2001-2014 Change between 2001 2007 2014 2001 and 2014 Adamaoua 48.4 53.0 47.1 -1.2 Centre 48.2 41.2 30.3 -17.9 Douala 10.9 5.5 4.2 -6.7 Est 44.0 50.4 30.0 -14.0 Extrême-Nord 56.3 65.9 74.3 18.0 Littoral 35.5 31.1 19.5 -16.0 Nord 50.1 63.7 67.9 17.8 Nord-Ouest 52.5 51.0 55.3 2.8 Ouest 40.3 28.9 21.7 -18.7 Sud 31.5 29.3 34.1 2.5 Sud-Ouest 33.8 27.5 18.2 -15.6 Yaoundé 13.3 5.9 5.4 -8.0 Note: Consumption is spatially deflated and temporally deflated to compare with the national poverty line. Source: ECAM‑2, ECAM‑3, ECAM‑4, and World Bank estimates. 64 Chapter 3 Growth and structural change are not helping the poorest, so some Cameroonians risk being left behind Annex 3.3. Alternative poverty profile results Table 6. Regression of consumption on household characteristics Adding non- Adding location Main variables only monetary controls deprivations -0.0196*** -0.0266*** -0.0324*** Household size (0.0058) (0.0057) (0.0058) Number of household -0.0778*** -0.0619*** -0.0503*** dependents (0.0082) (0.0082) (0.0080) 0.0192 0.0417** 0.0507*** Household head male (0.0185) (0.0179) (0.0172) 0.3879*** 0.2314*** 0.1601*** Household head is literate (0.0220) (0.0230) (0.0253) Household head primarily -0.3239*** -0.2022*** -0.1305*** works in agriculture (0.0291) (0.0275) (0.0256) Household head primarily -0.0305 -0.0423 -0.0468 works in industry (0.0364) (0.0319) (0.0305) Household head primarily 0.0799** 0.0503* 0.0311 works in commerce (0.0324) (0.0293) (0.0278) Household head primarily 0.0769*** 0.0382 0.0168 works in other services (0.0295) (0.0257) (0.0242) N 10,546 10,546 10,546 R-squared 0.3988 0.4695 0.4960 Notes: Dependent variable is the log of deflated consumption per capita. Standard errors clustered at the enumeration area level are in parentheses. Location controls comprise a dummy for urban areas and region fixed effects. Non-monetary deprivations are outlined in Chapter 5. * p<0.10, ** p<0.05, *** p<0.01. Source: ECAM‑5 and World Bank estimates. 65 Cameroon Poverty Assessment 2024 Chapter 4. KEY MESSAGES ➜ Around 1 in 5 Cameroonians have consumption levels between 1 and 1.5 times the poverty line: they are one shock away from poverty ➜ About 6 in 10 Cameroonians live in households that experienced a negative shock in the last three years, including those related to health, prices, climate change, and conflict ➜ Exposure to climate- and conflict-related shocks and poverty overlap ➜ In response to shocks, households adopt coping strategies that could weaken their financial, physical, and human capital, limiting their long- run prospects for escaping poverty ➜ Social assistance is currently dwarfed by the extent of poverty in Cameroon: just 2.6 percent of Cameroonians lived in a household receiving cash transfers in 2021/22 ➜ Despite being small, social assistance programs are progressive, demonstrating their potential as a vehicle for the government to support the poor and vulnerable Widespread shocks could leave even more Cameroonians at risk of falling into poverty T his chapter considers Cameroonians’ vulnerability to falling into poverty when shocks hit. Shocks related to conflict, climate, and prices – described in Chapter 1 – could all influence households’ incomes, pushing non-poor households into poverty, or deepening the deprivation of those who are already poor. The risk of these shocks striking can trap households in poverty by discouraging investments in human and physical capital as well as certain livelihood opportunities. To explore how shocks and uncertainty could be holding back poverty reduction in Cameroon, this chapter first considers which non-poor households could be at risk of falling into poverty, by looking at those who are just above the poverty line. Second, the chapter examines the prevalence of different types of shocks that Cameroonians experience, highlighting the particular threat posed by climate and conflict shocks using geospatial data. Third, the chapter explores the connection between shocks and negative coping strategies, which could impact the welfare of Cameroonian households both now and in the future. Lastly, the section evaluates the current state of social assistance in Cameroon – one of the key policy levers for protecting households against shocks and uncertainty. 4.1. Cameroonians who are close to the poverty line could be vulnerable to falling below it With the climate changing, conflict proliferating, and prices rising, policymakers need to know which households could be vulnerable to falling into poverty when shocks hit. Chapter 1 demonstrates how climate, conflict, and price shocks increas- ingly impact the Cameroonian economy. All of these shocks could feed through to household income and consumption. Households’ poverty status provides a snapshot of their consumption levels relative to the poverty line at the time of the survey, but many non-poor households could be vulnerable to falling into poverty in the future. Shocks are likely to threaten some households with poverty more than others. Those who are just above the poverty line could be just one shock away from falling into poverty while the very richest households are unlikely to fall into poverty even if they are hit by the same 67 Cameroon Poverty Assessment 2024 shock. Moreover, some households may adopt negative coping strategies if they are hit by shocks – including reduced investment in human and physical capital – which may have long-run consequences for their welfare. At the same time, households may avoid high-risk, high-return livelihood activities, if they are exposed to shocks (Dercon, 2002). By identifying which households are vulnerable to which types of shocks, policymakers may be able to anticipate and effect countervailing policies before more households fall into poverty: this is especially important for the design of social protection programs. Vulnerability is measured by identifying households that are just above the poverty line. The ECAM‑5 data from 2021/22 show that the distribution of consumption around the poverty line was relatively flat in Cameroon (Figure 33). This means that many non-poor households are clustered just above the poverty line, having consump- tion levels that could easily fall below the poverty line were shocks to materialize. Following the approach taken by other countries and adapting the approach taken by INS, the analysis that follows defines vulnerability using multiples of the poverty line: this captures consumption levels for those households who are non-poor but have a significant chance of falling back into poverty.(34),(35) A “vulnerability line” is set at 1.5 times the poverty line (445,036 XAF per person per year): those between the poverty line and the vulnerability line are classed as “vulnerable”. Those above the vulnerability line are classed as “economically secure”.(36) Figure 33. Distribution of consumption, poverty, and vulnerability in Cameroon, 2021/22 Note: Consumption is spatially deflated and temporally deflated to compare with the national poverty line and vulnerability line. Vulnerability line set at 1.5 times the poverty line. Source: ECAM‑5 and World Bank estimates. 34. Panel data from other countries has shown that households with consumption levels between 1 and 1.5 times the poverty line have at least a 10 percent chance of falling back into poverty each year (see, for example, “Aspiring Indonesia – Expanding the Middle Class” (World Bank, 2019)). This approach was also adopted for the 2022 Nigeria poverty assessment (Lain & Vishwanath, 2022). 35. INS use 1.25 and 1.5 times the poverty line to consider vulnerability. 36. Others have taken a different approach to the one used in this chapter by decomposing the variance of consumption to measure the extent of vulnerability. For further details see Pritchett, Suryahadi, and Sumarto (2000), Günter and Harttgen (2009), and Gao, Vinha, and Skoufias (2021). 68 Chapter 4 Widespread shocks could leave even more Cameroonians at risk of falling into poverty 4.2. Many non-poor Cameroonians are at risk of falling into poverty Around 1 in 5 Cameroonians are not currently poor but are vulnerable to falling into poverty. Using the multiples of the poverty line described above, 21.6 percent of Cameroonians were non-poor but were vulnerable to falling into poverty – with consumption between 1 and 1.5 times the poverty line – in 2021/22 (Figure 34). This means that around one-third of non-poor Cameroonians –were vulnerable. The share of Cameroonians who are vulnerable fell slightly more than poverty between 2001 and 2021. Over that period, the share of Cameroonians who were vulnerable dropped by about 3.8 percentage points. This resonates with the growth incidence curves shown in Chapter 3: consumption growth in the middle of the consumption distribution – where the vulnerable are situated – has been stronger than for the bottom 30 percent – where the poor are situated. Figure 34. Poverty and vulnerability in Cameroon, 2001-2022 100 90 Share of the population (percent) 80 70 60 50 40 30 20 10 0 2001 (ECAM-2) 2007 (ECAM-3) 2014 (ECAM-4) 2021 2021/22 (Bridge survey) (ECAM-5) Poor Vulnerable Economically secure Note: Consumption is spatially deflated and, where relevant, temporally deflated to compare with the national poverty and vulnerability lines. Vulnerability line set at 1.5 times the poverty line. Estimates from 2001, 2007, 2014, and the 2021 bridge survey are comparable. 2021/22 estimates from ECAM‑5 represent latest best estimates but cannot be compared with previous surveys. Source: ECAM‑2, ECAM‑3, ECAM‑4, ECAM‑5, 2021 bridge survey, and World Bank estimates. Vulnerability affects both rural and urban areas and all regions of Cameroon; even those where poverty is relatively low. Vulnerability is distributed more evenly than poverty across different parts of Cameroon. While poverty is significantly higher in rural areas than urban areas – as Chapter 2 shows – around 22.2 percent of urban dwellers are vulnerable compared with 20.8  percent of rural dwellers (Figure  35). Similarly, even in regions where poverty is relatively low, a significant share of the population are vulnerable. In Douala and Yaoundé – the regions with the lowest poverty rates – 17.4 percent and 17.5 percent of the respective populations are vulnerable. This demonstrates the precarious nature of poverty reduction in Cameroon and the profound risk posed by shocks, even in places where current poverty is rarer. 69 Cameroon Poverty Assessment 2024 Figure 35. Poverty and vulnerability in Cameroon by urban-rural and region, 2021/22 100 Share of the population (percent) 90 80 70 60 50 40 30 20 10 0 National Urban Rural Douala Yaoundé Sud Centre Sud-Ouest Littoral Nord Extrême-Nord Ouest Est Adamaoua Nord-Ouest Urban-rural Regions Poor Vulnerable Economically secure Note: Consumption is spatially deflated and temporally deflated to compare with national poverty line and vulnerability line. Vulnerability line set at 1.5 times the poverty line. Source: ECAM‑5 and World Bank estimates. 4.3. Shocks affect poor and vulnerable households Around 6 in 10 Cameroonians suffered a negative shock in the past three years, with health shocks being the most prevalent. Taking all types of shocks together, 59.7 percent of Cameroonians lived in a households that had been affected in the three years prior to ECAM‑5 (Figure 36). Health shocks were the most common, having been experienced by 33.2 percent of Cameroonians.(37) However, climate shocks, food price shocks, and security shocks are also relatively widespread, having affected 10.6 per- cent, 14.1 percent, and 18.6 percent of Cameroonians respectively in the three years prior to the survey. This means that the macro-level shocks presented in Chapter 1 are being felt by Cameroonian households themselves. 37. For parsimony, different types of shocks in the ECAM‑5 data were grouped. Climate shocks include: droughts or irregular rains; floods; fires; and landslides. Household income shocks include: increased rate of crop disease; increased rate of animal disease; attacks by locusts or other pests; drop in price of agricultural products; increase in price of agricultural inputs; end of regular transfers from other households; loss of non-agricultural income; bankruptcy of a non-agricultural business; loss of salary; and loss of salaried job. Security shocks include: theft of money, assets, harvest, or animals; farmer-herder conflict; and armed conflict, violence, or insecurity. Health shocks include: serious illness or accident; death of a household member. 70 Chapter 4 Widespread shocks could leave even more Cameroonians at risk of falling into poverty Figure 36. Shocks experienced in Cameroon by vulnerability status, 2021/22 80 Share of the population (percent) 70 59.7 60 50 40 33.2 30 18.6 20 14.1 15.2 10.6 10 3.9 6.1 0 Climate Food price Income Security Health COVID-19 Other Any Poor Vulnerable Economically secure Total Note: Consumption is spatially deflated and temporally deflated to compare with national poverty line and vulnerability line to establish vulnerability status. Vulnerability line set at 1.5 times the poverty line. Shocks reported for the three years prior to ECAM‑5. Source: ECAM‑5 and World Bank estimates. Poor and vulnerable households were more exposed to climate- and security-­ related shocks. The share of people in poor households experiencing climate-related shocks in the three years prior to ECAM‑5 was more than three times higher than the share in economically secure households. Similarly, the share of people in poor households experiencing security-related shocks was about 1.5 times higher than the share in economically secure households. This may partly reflect the fact that climate shocks and conflict are concentrated in parts of Cameroon where poverty is higher: this is explored using geospatial data in Box 5. Additionally, as Chapter 6 shows, poor Cameroonians are also more likely to engage in agricultural livelihoods, which could increase their exposure to climate-related shocks. Box 5. Geographical profile of climate and conflict shocks and poverty in Cameroon Household survey data suggest that poor and vulnerable Cameroonians are disproportion- ately exposed to conflict and climate shocks. Establishing causality in the relationships between conflict, climate, and poverty is complex. Yet Cameroonians in households classed as poor and vulnerable in ECAM-5 were more likely than Cameroonians in economically secure households to have experienced conflict and climate shocks in the three years prior to the survey. Given the associations between conflict, climate, and poverty, there are some households that suffer both from low consumption and from high exposure to climate and conflict shocks. These households are more likely to fall into poverty – if they are vulnerable – or deeper into poverty – if they are already poor. Identifying these households can help policymakers target countervailing policies. Geospatial data can help identify areas where poverty is concentrated and exposure to climate and conflict shocks is high. The backbone for this analysis is the arrondissement-level poverty map presented in Chapter 3. This can be coupled with geospatial data. Information on conflict and climate for each “population point” in Cameroon – the corners of a 100 meter by 100 meter grid covering the whole country – are collapsed to the arrondissement level; weighted using the population at each point. Exposure to three types of climate shock is included in the analysis. First, the share of the population that have a 1 in 20 chance of being exposed to a fluvial flood of at least 71 Cameroon Poverty Assessment 2024 30 centimeter in each arrondissement is constructed using Fathom 2 data. Second, the method for fluvial floods can be repeated for pluvial floods. Third, the share of the population with at least a 1 in 20 chance of being exposed to extreme heat – a Wet Bulb Globe Temperature of at least 32°C – is calculated (De Ridder, Lauwaet, Hooyberghs, & Lefebre, 2017). For conflict, a specialized conflict index was constructed, which draws on ACELD data and then makes adjustments according to how close each population point was to each conflict event and long ago the conflict event took place (see Gevaert (2023) for further details). This is collapsed to the arrondissement level using a simple (weighted) mean. The overlap between poverty and climate and conflict exposure can be illustrated with two-way choropleth maps. The Extrême-Nord and Nord-Ouest regions are faced with both high poverty and high exposure to climate and conflict shocks, yet there are still areas with relatively low poverty that are exposed. First, looking at flooding, high exposure to floods (both pluvial and fluvial) is scattered across the very north and much of the west of Cameroon: this overlaps strongly with poverty in the Extrême-Nord and Nord-Ouest regions (Figure 37). Nevertheless, there are several poor arrondissements, especially in the Nord region, where exposure to flooding is relatively low. Equally, Cameroonians in the Littoral, Sud, and Sud-Ouest are more exposed to flooding, even if poverty there is lower. Second, turning to extreme heat, most arrondissements where extreme heat is a threat have high poverty rates. Again, however, there are several arrondissements, especially in the Nord region, where poverty is high but exposure to extreme heat exposure is relatively low. Third, conflict and poverty mainly overlap in the Extreme-Nord and Nord-Ouest regions. Yet exposure to conflict is also high in the Sud-Ouest region too, even if poverty there is relatively low. Shocks disproportionately affect the poor and vulnerable, but better-off Cameroonians are not spared. Figure 37. Overlap between poverty and exposure to climate and conflict shocks Panel A: Fluvial floods Panel B: Pluvial floods Higher poverty Higher poverty 11.1% 10.6% 11.7% 10.0% 12.8% 10.6% 11.7% 10.3% 11.4% 11.4% 10.0% 11.9% 10.6% 12.5% 10.3% 11.9% 10.6% 10.8% Higher exposure Higher exposure Panel C: Extreme heat Panel D: Conflict index Higher poverty Higher poverty 16.4% 0.0% 16.9% 11.9% 7.5% 13.9% 30.6% 0.0% 2.8% 18.9% 3.1% 11.4% 32.8% 0.0% 0.6% 20.0% 5.3% 8.1% Higher exposure Higher exposure Source: Fathom 2 (for Panels A and B), Wet Bulb Globe Temperature (for Panel C), ACLED and Gevaert (2023) (for Panel D), and World Bank estimates. 72 Chapter 4 Widespread shocks could leave even more Cameroonians at risk of falling into poverty 4.4. When confronting shocks, Cameroonians adopt coping strategies which could reduce their welfare in the long run When shocks materialize, household income, assets, and food purchases typically fall. This occurs across various types of shocks including those related to climate (droughts and floods), food prices, and conflict (Figure 38). The only exception is a small share of households – representing around 1 in 5 shock-hit Cameroonians – that appear to increase food purchases in response to shocks, especially those related to climate. This may be because floods and droughts make it harder to grow food at home so some households need to buy extra food in to maintain their consumption levels. Figure 38. Impact of shocks on income, assets, and food purchases in Cameroon, 2021/22 Panel A: Drought Panel B: Floods Share of drought-affected people Share of flood-affected people (percent) (percent) 0 50 100 0 50 100 Income Income Assets Assets Food purchased Food purchased Increased Unchanged Decreased Increased Unchanged Decreased Panel C: Food prices Panel D: Conflict Share of food price shock-affected Share of conflict-affected people people (percent) (percent) 0 50 100 0 50 100 Income Income Assets Assets Food purchased Food purchased Increased Unchanged Decreased Increased Unchanged Decreased Note: Each graph focuses on the sub-sample of people living in households that experienced each shock. The small minority of households reporting that the shock did not concern income, assets, or food purchases excluded from each graph. Shocks reported for the three years prior to ECAM‑5. Source: ECAM‑5 and World Bank estimates. Drawing down savings, selling or renting assets, and reducing food consumption are among the most common coping strategies that Cameroonian households deploy in response to shocks; all of these strategies could have long-run con- sequences for welfare. Among Cameroonians living in shock-affected households, 46.7  percent coped by relying on savings, 9.8  percent sold or rented assets, while 17.3 percent reduced food consumption (Figure 39).(38) These coping strategies were prevalent for poor, vulnerable, and economically secure households. As well as impact- ing households in the short run, these coping strategies could have long-run effects on households’ welfare too. For example, unless they can be replenished, drawing 38. The analysis of coping strategies focuses on the primary coping strategy that each shock-hit house- hold deploys. 73 Cameroon Poverty Assessment 2024 down savings and selling assets could weaken households' future financial stability, potentially limiting their investments in physical and human capital and constraining future earnings. Similarly, reduced food consumption not only marks a welfare loss today, but could lead to malnutrition and stunting, with possible repercussions for educational attainment and long-run human capital development (World Bank, 2018). Figure 39. Coping strategies deployed by shock-affected households in Cameroon by vulnerability status, 2021/22 Share of shock hit population (percent) 0 10 20 30 40 50 60 Relied on savings 46.7 Received assistance from friends/family members 25.9 Received assistance from the government or NGOs 2.2 Sold/rented out assets 9.8 Engaged in additional work 2.6 Migrated 2.7 Reduced food consumption 17.3 Reduced education/health expenditure 2.0 Relied on loan/credit 3.0 Others 9.2 No strategy 25.4 Poor Vulnerable Economically secure Total Note: NGOs = non-governmental organizations. Consumption is spatially deflated and temporally deflated to compare with national poverty line and vulnerability line to establish vulnerability status. Vulnerability line set at 1.5 times the poverty line. Shocks reported for the three years prior to ECAM‑5. Chart focuses on shock-affected households and on the primary coping strategy. Source: ECAM‑5 and World Bank estimates. Cameroonians rely much more on friends and family than they do on the govern- ment or non-governmental organizations (NGOs) when shocks hit. Among people living in shock-affected households, about 25.9 percent relied on the support of family or friends while just 2.2 percent received assistance from the government or NGOs. This emphasizes the crucial role that social networks play in cushioning the effects of shocks. Even though poverty and vulnerability are widespread and conflict may be eroding social cohesion in some parts of the country, Cameroonians endeavor to support one another during difficult times. However, Cameroonian households do not typically rely on the government when faced with shocks: this foreshadows the profile of social assistance programs in Cameroon, discussed below. Echoing patterns from other countries, agricultural households are more likely to sell or rent their assets in response to shocks. When the coping strategies are 74 Chapter 4 Widespread shocks could leave even more Cameroonians at risk of falling into poverty broken down according to the main occupation of the household head, Cameroonians living in households where the head works in agriculture are about three times more likely than other households to sell or rent their assets (Figure 40). These types of “distress sales” of agricultural assets – especially land – have been documented in similar settings and, by limiting current and future agricultural production, could pose a serious threat to poverty reduction (Tabetando, Fani, Ragasa, & Michuda, 2023).(39) This emphasizes the link between livelihoods and resilience, especially resilience to climate-related shocks, which is explored further in Chapter 6. Figure 40. Coping strategies deployed by shock-affected households in Cameroon by main activity of the household head, 2021/22 Share of shock hit population (percent) 0 10 20 30 40 50 60 Relied on savings Received assistance from friends/family members Received assistance from the government or NGOs Sold/rented out assets Engaged in additional work Migrated Reduced food consumption Reduced education/health expenditure Relied on loan/credit Other No strategy Agriculture Industry Commerce Other services Not working Note: NGOs = non-governmental organizations. Shocks reported for the three years prior to ECAM‑5. Chart focuses on shock- affected households and on the primary coping strategy. Source: ECAM‑5 and World Bank estimates. 4.5. With widespread vulnerability and prevalent shocks, social assistance may need to be enhanced With around 6 in 10 Cameroonians being either poor or vulnerable and shocks being widespread, understanding the extent to which the government can support households in times of crisis is vital. In particular, social protection – and in particular, social assistance programs – can help safeguard households against the effects of shocks and stop them resorting to coping strategies that could have negative short- and long-run consequences for welfare. ECAM‑5 collected detailed information on 39. It also emerges that households headed by those who are not working are almost twice as likely to rely on support from friends and family when shocks occur. This may reflect these households having older household heads who have had time to build larger familial or social networks or such households lacking their own savings and assets to draw down or sell when shocks hit. 75 Cameroon Poverty Assessment 2024 access to various social assistance programs in the 12 months prior to the survey: this includes food transfers, cash transfers, in kind care for infants and pregnant women, and distribution of mosquito nets. The share of Cameroonians receiving cash or food assistance is dwarfed by the prevalence of poverty, vulnerability, and economic security. In 2021/22, around 3.6 percent of Cameroonians lived in a household that had received food assistance in the past 12 months and 2.6 percent lived in a household that had received cash transfers, including receipts through the Projet Filets Sociaux (Safety Nets Project, PFS) (Figure 41).(40),(41) This is far below the share of Cameroonians living in poverty (37.7 per- cent) and even further below the share of Cameroonians who are poor or vulnerable (59.2 percent). Consequently, the share of Cameroonians living below the poverty that received such support was also low: just 5.6 percent of poor Cameroonians received food assistance and 4.2 percent of poor Cameroonians received cash transfers. The share of Cameroonians receiving other types of social assistance was higher, with 10.4 percent living in a household receiving care for infants or pregnant women and as many as 19.7 percent living in a household that had received mosquito nets. These types of in-kind transfers are important for building health and broader human capital outcomes, but they do not address poor Cameroonians’ immediate lack of access to the food and non-food items they need. Figure 41. Receipts of social assistance programs in Cameroon by consumption decile, 2021/22 60 a beneficiary household (percent) Share of the population living in 50 40 30 29.1 20 19.7 10 10.4 3.6 0 2.6 1 2 3 4 5 6 7 8 9 10 Total Decile of the real consumption distribution Food Cash transfer Mosquito nets Care for infants and pregnant women All Notes: Consumption deflated spatially and temporally. “Food” category includes donations of cereals, flour or semolina, food for school children, food for work, and nutritional supplements for malnourished children. “Cash transfers” include public works program. Source: ECAM‑5 and World Bank estimates. 40. Given their small size and that both are provided by PFS, the analysis combines cash for work and cash transfers for parsimony. 41. Households do not report the organization from which the social assistance was received. Therefore, both food assistance and cash transfers could reflect the activities of local and international NGOs as well as the government. In 2021 and 2022, administrative data indicate that the World Food Program (WFP) reached more than 150,000 beneficiaries through cash transfers. In 2019, PFS reached around 529,000 people, and expanded only very slightly in subsequent years (World Bank, 2022). Adding these two figures (150,000 and 529,000) and dividing by the population of Cameroon in 2020 (26.5 million) suggests that around 2.6 percent of the population would have been reached. This is virtually identical to the estimate coming from ECAM‑5. 76 Chapter 4 Widespread shocks could leave even more Cameroonians at risk of falling into poverty While social assistance programs are not sufficient to address current poverty levels in Cameroon, they are at least reaching poorer households more than richer ones. Cameroonians in the bottom 40  percent of the consumption distribution are more than twice as likely to receive food assistance and cash transfers than those in the top 60  percent. This demonstrates the progressivity of social assistance in Cameroon: while programs are not always targeted to the poorest Cameroonians and targeting methods may be imperfect, social assistance is at least more likely to reach poorer households than subsidies for fuel and electricity, as discussed in more detail in Chapter 8.(42) Scaling up and enhancing social assistance in Cameroon could, therefore, provide an effective vehicle for the government to reach the poor and vulnerable and protect them from potentially devastating shocks. 4.6. While social assistance can help protect households in the short run, long-run prospects for poverty reduction depend on human capital and livelihoods This chapter demonstrates how widespread exposure to shocks could trap poor households in poverty and leave vulnerable households on the brink. Even if house- holds are not poor today, they could be one shock away from poverty in the future. Currently, shocks lead households to adopt negative coping strategies – drawing down their financial, physical, and human capital – which could limit their long-run welfare. Social assistance systems are progressive, but they are too small to protect household meaningfully so expanding and enhancing them remains a priority, as discussed in more detail in Chapter 8. With conflict proliferating in some parts of the country and climate-related shocks set to worsen, the need to protect households from shocks is urgent. Yet addressing short-run variation through social assistance may not be enough to provide a sustainable pathway to inclusive growth and hence poverty reduction. That requires building human capital and then harnessing people’s productive potential through profitable livelihoods – this is where the report turns in the next two chapters.  42. While the sample sizes do not permit more detailed analysis of who among the poor receives social assistance, it emerges that poor rural dwellers are slightly more likely to receive social assistance than poor urban dwellers. 77 Cameroon Poverty Assessment 2024 Chapter 5. KEY MESSAGES ➜ There are many key dimensions of welfare that the monetary value of consumption does not reflect ➜ Nationally, Cameroon has achieved progress on key non-monetary welfare metrics including access to electricity and water, educational enrolment, and several health outcomes ➜ Rural areas and the Extrême-Nord, Nord, and Nord-Ouest regions face much more widespread deprivation in terms of education and basic infrastructure than the rest of the country ➜ Echoing global evidence, monetary poverty is strongly correlated with lack of access to sanitation and electricity ➜ The overlap between different monetary and non-monetary poverty dimensions is largest in rural areas and northern regions, so interventions jointly addressing lack of education, weak basic infrastructure, and low incomes can be targeted to the same households in these areas ➜ Out-of-pocket health and education expenses could deter poorer Cameroonians from investing in human capital ➜ Urban dwellers face additional deprivations, including overcrowding and pollution ➜ Limited livelihood opportunities – reflecting low access to input and output markets and other structural constraints on job creation – may explain why gains in human capital and basic infrastructure are not yet yielding significant monetary poverty reduction Despite gains in human capital and basic infrastructure, rural and northern Cameroonians remain far behind the rest of the country T his chapter goes beyond monetary poverty to provide a more com- prehensive picture of Cameroon’s poverty-reduction challenge. While monetary consumption remains the most direct measure of households’ welfare, there is growing acknowledgment that non-monetary indicators are needed to assess overall living standards. Additionally, understanding the interplay between monetary and non-monetary dimensions of poverty can help to identify constraints on monetary poverty reduction and potential strategies for poverty alleviation. First, this chapter begins by reviewing trends in non-monetary welfare over time in Cameroon, demonstrating the clear progress that the country has made on access to electricity and water, educational enrolment, and health outcomes at the national level in the last two decades. Second, the chapter focuses on a subset of non-monetary indicators to construct an adapted version of the World Bank's Multidimensional Poverty Measure (MPM), profiling multidimensional poverty at the subnational level. Third, the chapter explores the interactions between different aspects of monetary and non-monetary poverty, offering insights into potential avenues for poverty-reducing policies. Fourth, the chapter shows how out-of-pocket expenses for health and education could be further holding back human capital investment for poorer Cameroonians. Finally, the chapter considers the growing non-monetary deprivations faced by those living in Cameroon’s towns and cities. 5.1. Non-monetary indicators of well-being offer a more comprehensive understanding of poverty in Cameroon The concept of poverty is evolving to encompass multiple non-monetary dimen- sions. Even households that are above the monetary poverty line may encounter other constraints on their welfare including lack of access to education and health services or weak basic infrastructure. Indeed, in participatory studies, poor and vulnerable individuals themselves report that non-monetary factors like health, education, and security significantly impact their well-being beyond income and consumption (see, for example, Moreno (2017)). Many of these elements are “public goods”, which are 79 Cameroon Poverty Assessment 2024 often provided by the government. They may not be available to buy in the market, so measuring monetary income or consumption alone may be insufficient to capture them properly (Bourguignon & Chakravarty, 2003). Additionally, global evidence reveals the strong links between human capital, housing, basic infrastructure, and monetary poverty (Nguyen, Yoshida, Wu, & Narayan, 2020). Consequently, multidimensional poverty measurement is gaining traction with national governments and international organizations. For example, the governments of Mexico and Colombia have incorpo- rated multidimensional poverty into their national development plans while the United Nations Development Programme (UNDP) uses the Human Development Index to track countries’ progress (Ferreira & Lugo, 2013). The chapter adopts the World Bank’s Multidimensional Poverty Measure (MPM) but also enhances the analysis with additional indicators taken from surveys beyond the ECAMs. The construction of the MPM and the additional data sources that complement the ECAMs are described in Box 6. In particular, the chapter uses the Demographic and Health Surveys (DHSs) and Malaria Indicators Survey (MIS) to triangulate non-monetary poverty trends and offer additional health indicators that are not available in the ECAMs. Box 6. Non-monetary welfare metrics for Cameroon The first part of the chapter considers trends in a wide range of non-monetary welfare metrics, taken from the ECAMs and other data sources. The ECAMs and the bridge survey make it possible to construct trends for several non-monetary welfare measures for the last two decades. Yet to reinforce and complement the messages coming from the ECAMs and the bridge survey, the chapter also draws on the Demographic and Health Surveys (DHSs) from 2004, 2011, and 2018, as well as the Malaria Indicators Survey (MIS) from 2022. These surveys provide additional information on health outcomes that is not available in the ECAMs. They also help to validate the trends observed in the ECAMs. These trends are constructed for both household- and individual-level indicators. The second part of the chapter then employs an adapted version of the World Bank’s MPM, which comprises six indicators organized under three dimensions. The three dimensions cover monetary poverty, education, and basic infrastructure. The World Bank’s MPM therefore stands out from other multidimensional poverty measures by incorporating both non-monetary and monetary poverty. As such, the MPM can only be constructed in surveys that have an adequate measure of monetary poverty: for Cameroon, this means the ECAMs. The MPM is constructed at the household level. The indicators that are used as well as the two adaptations required to make them work for Cameroon are described in Table 7. First, the monetary poverty indicator was adapted to use Cameroon’s national poverty methodology. This is because – unlike the international methodology – the national poverty methodology applies spatial deflation, making it possible to compare different households within Cameroon: this is essential when looking at interactions between different elements of monetary and non-monetary poverty. Second, the educational attainment indicator was adapted because educational attainment is not captured in ECAM-5 for individuals aged 25 years or more. Therefore, to construct an alternative proxy, households are considered deprived on the educational attainment metric if there is no adult in the household that is both literate and has attended school. 80 Chapter 5 Widespread shocks could leave even more Cameroonians at risk of falling into poverty Table 7. Estimating Multidimensional Poverty for Cameroon Dimension Indicator in global version of MPM Adapted for Cameroon? Weight Daily consumption or income is Use national poverty methodology which Monetary less than 2.15 USD 2017 PPP per includes spatial deflation and uses a line 1/3 person of 296,691 XAF per person per year At least one school-age child up to the age of Grade 8 is not enrolled Unchanged 1/6 in school Education No adult in the household (age of No adult in the household (age of Grade Grade 9 or above) has completed 9 or above) that is both literate and has 1/6 primary education attended school The household lacks access to Unchanged 1/9 limited-standard drinking water Basic The household lacks access to Unchanged 1/9 infrastructure limited-standard sanitation The household has no access to Unchanged 1/9 electricity Source: World Bank (2018). The MPM combines the six indicators on monetary poverty, education, and basic infrastructure to produce a binary variable indicating which households are multidimensionally poor. To do this, the MPM aggregates information across the three dimensions, then applies a specific threshold that determines whether a household is multidimensionally poor. When aggregating across indicators, the MPM first gives equal weight (one-third) to each of the dimensions – monetary poverty, educa- tion, and basic infrastructure – and then equal weight to each indicator within those dimensions. Households are considered multidimensionally poor if they are deprived in indicators whose weight adds up to one-third or more. Since the monetary poverty dimension is measured using only one indicator, anyone who is monetarily poor is automatically also multidimensionally poor. This means multidimensionally poverty is always weakly higher than monetary poverty. Non-monetary poverty overlaps significantly with human capital. Human capital can be defined as the knowledge, skills, and health that people accumulate throughout their lives, which enable them to “realize their potential as productive members of society” (World Bank, 2018). Many of the indicators reported in the remainder of this chapter are either directly relevant for human capital – such as education and health outcomes – or capture factors that could be instrumental in building human capital. For example, access to clean water and sanitation strongly influences individuals’ health outcomes. Since human capital is about people’s current and future productive poten- tial, human capital outcomes have long-term consequences for poverty reduction. 5.2. Cameroon is making overall progress on many non-monetary welfare indicators While there is still room for improvement. educational enrolment and attainment are rising across Cameroon. Using ECAM‑2, ECAM‑3, ECAM‑4, and the 2021 bridge survey to construct trends, both the primary school enrolment rate and the secondary school enrolment rate rose steadily throughout the past two decades (Panel A of Figure 81 Cameroon Poverty Assessment 2024 42).(43),(44) Results from the DHSs, reported in Annex 5.1, reveal a similar trend, at least for secondary school attendance. Nevertheless, around 2 in 10 primary school-age children were not in enrolled in primary school and around 4 in 10 secondary school- age children were not enrolled in secondary school in 2021. Primary and secondary school attainment have also increased steadily in Cameroon, according to DHS and MIS data (Panel B of Figure 42).(45) However, around one-third of 15-49-year-old women and one-quarter of 15-49-year-old men had not attained primary education in 2022. Therefore, there is still work to do to ensure all Cameroonian children go to and stay in school. Figure 42. Trends in educational enrolment and attainment in Cameroon, 2001-2022 Panel A: Enrolment Panel B: Attainment Attainment rate among 15-49-year-olds (percent) 90 90 Enrolment rate (percent) 80 80 70 60 70 50 60 40 50 30 20 40 10 30 0 20 ) ) ) ) ) -2 -3 -4 ey -5 10 AM AM AM AM rv su C C C C 0 ge (E (E (E (E rid 01 07 14 2 2004 2011 2018 2022 /2 (B 20 20 20 21 21 DHS DHS DHS MIS 20 20 Girls - Primary Boys - Primary Women - Primary Men - Primary Girls - Secondary Boys - Secondary Women - Secondary Men - Secondary Note: Attendance statistics from the DHSs and MIS reported in Annex 5.1. Source: ECAM‑2, ECAM‑3, ECAM‑4, 2021 bridge survey, and ECAM‑5 for Panel A, 2004 DHS, 2011 DHS, 2018 DHS, and 2022 MIS for Panel B, and World Bank estimates. Sex-based differences persist for educational outcomes: this presents a clear challenge on which policymakers should focus. These sex-based gaps are signifi- cantly larger for educational attainment than for educational enrolment, so they may naturally reduce over time as the current cohort of primary and secondary school students come of age. However, for primary and secondary enrolment there are still 43. The DHSs report attendance rather than enrolment. The DHSs suggest that progress has been flatter for primary educational attendance than the ECAMs suggest for primary educational enrolment. However, both the ECAM series and the DHS series place the primary school enrolment and attendance rates for 2021/22 at around 8 in 10 primary-school-age children. 44. Technical issues with the questionnaire skip logic explain the differences in the enrolment rates estimated using ECAM‑5 and the 2021 bridge survey. The latter maintains more consistency with previous ECAMs. 45. The highest level of schooling completed was only recorded for those aged 6-24 years in some ECAMs, so the DHS and MIS data are preferred to tracking educational attainment over time. 82 Chapter 5 Widespread shocks could leave even more Cameroonians at risk of falling into poverty slight differences between boys and girls, according to the 2021 bridge survey and the 2018 DHS. Ensuring Cameroonian boys and girls have equal access to education is therefore another key area of improvement for building human capital. Nevertheless, the quality of education remains a concern, even as enrolment and attainment rise. Realizing the full benefits of rising educational enrolment and attainment hinges on ensuring children learn while they are at school. This depends on the quality of schooling they receive. As discussed in more detail in Chapter  7, student-teacher ratios remain high and many schools lack access to electricity, water, and sanitation at the primary level. Low school quality could partly hamper the gains of rising enrolment and attainment. Health indicators for young children have also improved in Cameroon in the last two decades. Between 2004 and 2018, Cameroon witnessed better antenatal care, growing prevalence of vaccination, reduced stunting, and a particularly significant drop in the infant mortality rate, which fell by nearly 40 percent from 148 deaths per 1000 births to 90 deaths per 1000 births (Figure 43). Once again, there is still work to do to ensure antenatal care, vaccinations, and other children’s health services are available for all Cameroonian households, but – all other things equal – these gains should help build human capital for future cohorts of working-age people. Figure 43. Trends in maternal and children’s health outcomes in Cameroon, 2004-2022 Panel A: Antenatal care from a skilled provider Panel B: Received all eight basic vaccinations Share of women who had a live birth Share of children aged 12-23 months 100 60 90 80 50 70 40 (percent) (percent) 60 50 30 40 30 20 20 10 10 0 0 2004 2011 2018 2022 2004 2011 2018 DHS DHS DHS MIS DHS DHS DHS Panel C: Children stunted Panel D: Under-five mortality rate 40 160 Share of children (percent) 35 140 Probability of mortality 30 120 per 1000 births 25 100 20 80 15 60 10 40 5 20 0 0 2004 2011 2018 2004 2011 2018 DHS DHS DHS DHS DHS DHS Source: 2004 DHS, 2011 DHS, 2018 DHS, 2022 MIS, and World Bank estimates. 83 Cameroon Poverty Assessment 2024 Alongside advances for health and education, Cameroonians’ access to basic infrastructure has increased in recent years, although advances in sanitation access have slowed. Between 2001 and 2021, the share of the population with access to an improved drinking water source rose from about 67.1 percent to 81.9 percent, while the share with access to electricity 48.3  percent to 65.8  percent (Figure 44). While the ECAMs’ questions on sanitation access changed in 2014, making it impossi- ble to construct trends for the whole decade, the DHSs suggest that sanitation access improved over roughly the same period (see Annex 5.1). However, most of these gains for sanitation were achieved between 2004 and 2011, and the ECAM‑4 and the 2021 bridge survey also suggest there was no progress between 2014 and 2021. Therefore, renewed efforts may be needed to ensure sanitation keeps pace with Cameroon’s overall progress on other non-monetary welfare metrics. Figure 44. Trends in access to basic infrastructure in Cameroon, 2001-2022 90 80 Share of the population (percent) 70 60 50 40 30 20 10 0 2001 2007 2014 2021 2021/22 (ECAM-2) (ECAM-3) (ECAM-4) (Bridge survey) (ECAM-5) Improved water Improved sanitation Electricity Note: Basic infrastructure statistics from the DHSs and MIS reported in Annex 5.1. Source: ECAM‑2, ECAM‑3, ECAM‑4, 2021 bridge survey, ECAM‑5, and World Bank estimates. In urban areas, progress on some non-monetary measures of well-being is leveling off as they hit their maximum while some indicators are showing signs of decline. Across all of the non-monetary welfare indicators considered, the latest data suggest urban areas outperform rural areas, just as with monetary poverty (Figure 45). This is shown in more detail below, using the indicators comprising the World Bank’s MPM. However, the gains for certain non-monetary welfare metrics appear to be flattening off in urban areas more than in rural areas. In part, this may be because a high share of urban dwellers can already access key services, so reaching the last few urban dwellers who lack these services is a challenge. This may be the case for access to improved drinking water and electricity – they may be approaching their theoretical maximum, beyond which significant effort is needed to reach the remaining households. However, some indicators have not only levelled off, but in fact gone backwards in urban areas in the last decade. For example, primary, and especially secondary, enrolment rates appear to have declined for urban dwellers between 84 Chapter 5 Widespread shocks could leave even more Cameroonians at risk of falling into poverty 2014 and 2021.(46) These enrolment trends therefore echo the recent uptick in urban monetary poverty observed in Chapter 2 and the growing role of rural-urban migration described in Chapter 3: as migrant households move to towns and cities, it may take time for their children to integrate into schools and schools may become saturated as the urban population grows. Figure 45. Trends in access to water, access to electricity, and educational enrolment in Cameroon by urban-rural, 2001-2022 Panel A: Improved water access Panel B: Electricity access 100 Share of the population (percent) Share of the population (percent) 100 90 90 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 0 ) ) ) ) ) ) ) ) ) ) -2 -3 -4 ey -5 -2 -3 -4 ey -5 AM AM AM AM AM AM AM AM rv rv su su C C C C C C C C ge ge (E (E (E (E (E (E (E (E rid rid 01 07 14 2 01 07 14 2 /2 /2 (B (B 20 20 20 20 20 20 21 21 21 21 20 20 20 20 Total Urban Rural Total Urban Rural Panel C: Primary school enrolment Panel D: Secondary school enrolment 90 80 Share of secondary school-age children Share of primary school-age children 80 70 70 60 60 50 (percent) (percent) 50 40 40 30 30 20 20 10 10 0 0 ) ) ) y) ) ) ) ) ) ) -2 -3 -4 -5 -2 -3 -4 ey -5 e AM AM AM AM AM AM AM AM rv rv su su C C C C C C C C ge ge (E (E (E (E (E (E (E (E rid rid 01 07 14 2 01 07 14 2 /2 /2 (B (B 20 20 20 20 20 20 21 21 21 21 20 20 20 20 Total Urban Rural Total Urban Rural Note: Water, electricity, and educational attendance statistics from the DHSs and MIS reported in Annex 5.1. Source: ECAM‑2, ECAM‑3, ECAM‑4, 2021 bridge survey, ECAM‑5, and World Bank estimates. 46. These changes between 2014 and 2021 are not revealed in the DHSs because attendance rates are only reported in 2004 and 2018. 85 Cameroon Poverty Assessment 2024 5.3. The latest snapshot of multidimensional poverty confirms that some Cameroonians are being left behind To provide a more holistic picture of multidimensional poverty in Cameroon, the remainder of this chapter focuses on the World Bank’s MPM using ECAM‑5. This includes exploring separately the indicators and dimensions – monetary poverty, education, and basic infrastructure – that are used to construct the MPM. By using ECAM‑5, the analysis incorporates non-monetary welfare metrics alongside the latest best estimate of monetary poverty. The indicators that follow are all expressed as deprivations, meaning that the higher they are, the more widespread the deprivation on that indicator. Notwithstanding the trends observed above, non-monetary poverty is more widespread in rural areas than urban areas, with the gap being largest for sanita- tion and electricity. In 2021/22, the share of rural dwellers lacking access to improved sanitation was almost four times higher in rural areas than in urban areas, while the share lacking access to electricity was almost five times higher (Figure 46). This is significantly larger than the rural-urban gap for monetary poverty (about 2.6 times higher in rural areas). Even if progress on non-monetary welfare indicators is slowing down in urban areas, the largest opportunities for improvements, and hence policy focus, should remain in rural areas. Figure 46. Indicators of non-monetary poverty in Cameroon by urban-rural, 2021/22 Share of the population deprived (percent) Monetary poor 80 70 60 Electricity 50 Eductional 40 enrolment 30 20 10 0 Sanitation Educational attainment Water Total Urban Rural Note: Monetary and non-monetary deprivations created by adapting the World Bank’s Multidimensional Poverty Measure. Further details provided in Box 6. Source: ECAM‑5 and World Bank estimates. As with monetary poverty, rural-urban migrants are slightly more likely to be education deprived than other urban dwellers, but they are still far less deprived 86 Chapter 5 Widespread shocks could leave even more Cameroonians at risk of falling into poverty than rural dwellers. The slight differences in the educational enrolment and educa- tional attainment indicators between rural-urban migrants and other urban dwellers are largely driven by rural-urban migrants faring worse than urban-urban migrants. The gap between rural-urban migrants and other urban dwellers is largest for the enrolment deprivation. This suggests that ensuring children can attend schools in the towns and cities to which they have migrated for some rural-urban migrants (Figure 47).(47) However, there is no clear evidence that rural-urban migrants are faring worse than other urban dwellers in terms of access to basic infrastructure. Moreover, outcomes for rural-urban migrants are much better than those staying in rural areas across all of the MPM’s welfare metrics: this may partly explain why households choose to move in the first place. Figure 47. Indicators of non-monetary poverty in Cameroon by migration status, 2021/22 Share of the population deprived (percent) Monetary poor 80 60 Electricity Eductional 40 enrolment 20 0 Sanitation Educational attainment Water Rural Rural-urban migrant Other urban Note: Monetary and non-monetary deprivations created by adapting the World Bank’s Multidimensional Poverty Measure. Further details provided in Box 6. Source: ECAM‑5 and World Bank estimates. The large gaps between the Extrême-Nord, Nord, and Nord-Ouest regions and the rest of Cameroon extend to non-monetary indicators. The gaps between these three regions and the rest of the country appear to be especially large for access to improved sanitation and electricity (Figure  48). Taking these three northern regions together, about 66.7 percent of people lacked access to improved sanitation compared to 20.2  percent for the rest of Cameroon in 2021/22. Similarly, about 74.5  percent lacked access to electricity in the Extrême-Nord, Nord, and Nord-Ouest regions, compared to 20.3 percent for all other regions. This suggests that addressing deficits in basic infrastructure may be especially important for poverty reduction in Cameroon’s lagging regions. 47. As with the monetary poverty results, migration status is being defined at the individual level while the non-monetary deprivations are being defined at the household level. 87 Cameroon Poverty Assessment 2024 Figure 48. Indicators of non-monetary poverty in Cameroon separating out the Extreme-Nord, Nord, and Nord-Ouest regions, 2021/22 Share of the population deprived (percent) Monetary poor 80 70 60 50 Eductional Electricity 40 enrolment 30 20 10 0 Sanitation Educational attainment Water Extrême-Nord Nord Nord-Ouest All other regions Note: Monetary and non-monetary deprivations created by adapting the World Bank’s Multidimensional Poverty Measure. Further details provided in Box 6. Source: ECAM‑5 and World Bank estimates. Rural-urban differences in multidimensional poverty echo rural-urban differ- ences in monetary poverty. Using the adapted version of the World Bank’s MPM, described in Box 6, around 46.2 percent of Cameroonians were multidimensionally poor in 2021/22 (Figure 49).(48) Multidimensional poverty is higher than monetary poverty by construction: a household that is monetarily poor will automatically be classified as multidimensionally poor, yet there are also some households who suffer education and basic infrastructure deprivations that will be classified as multidimensionally poor too. The gap between rural and urban areas in terms of multidimensional poverty is slightly larger than for monetary poverty. Around 70.2  percent of rural-dwelling Cameroonians were multidimensionally poor in 2021/22, 2.8  times higher than the share for urban-dwelling Cameroonians, of whom 25.5 percent were multidimension- ally poor. 48. Multidimensional poverty is higher than monetary poverty by construction. Someone who is mone- tarily poor will automatically be classed as multidimensionally poor, yet there are also some 88 Chapter 5 Widespread shocks could leave even more Cameroonians at risk of falling into poverty Figure 49. Multidimensional poverty in Cameroon by urban-rural and migration status, 2021/22 Share of the population monetarily and multidimensionally poor (percent) 90 80 70 60 50 40 30 20 10 0 Urban Rural Always rural Rural-rural migrant Urban-rural migrant Always urban Urban-urban migrant Always urban and urban- urban migrant combined Rural-urban migrant Total Urban-rural Migration status Multidimensional Monetary Note: Multidimensional poverty created using an adapted version of the World Bank’s Multidimensional Poverty Measure. Further details provided in Box 6. Source: ECAM‑5 and World Bank estimates. The difference between rural-urban migrants and other urban dwellers in terms of multidimensional poverty is about the same as the difference observed for monetary poverty alone. Around 29.7 percent of rural-urban migrants lived in mul- tidimensionally poor households in 2021/22 compared with 24.6 percent of all other urban dwellers. As with monetary poverty, this gap is largely driven by urban-urban migrants – and not those who always lived in urban areas – faring better than rural-ur- ban migrants. Multidimensional poverty is highest in Cameroon’s northern regions, but the gap between multidimensional and monetary poverty is widest in the Est, Sud-Ouest, and Sud regions. In the Extrême-Nord, Nord, and Nord-Ouest regions, multidimen- sional poverty stood at 82.1 percent, 75.3 percent, and 71.8 percent respectively in 2021/22 (Figure 50). This directly mirrors the patterns seen for monetary poverty. However, the relative gap between monetary and multidimensional poverty – which shows how many households are affected by education and basic infrastructure deprivations but who are not monetarily poor – was largest in the Est, Sud-Ouest, and Sud regions. These regions require specific, tailored programs, using data on education and basic infrastructure alongside data on monetary poverty, to address the additional non-monetary deprivations they face. 89 Cameroon Poverty Assessment 2024 Figure 50. Multidimensional poverty in Cameroon by region, 2021/22 Panel A: Map of multidimensional poverty rates by region Multidimensional poverty rate (percent) 9.4 - 20.5 20.6 - 27.2 27.3 - 60.3 60.4 - 71.8 71.9 - 82.1 Panel B: Multidimensional and monetary poverty rates by region Share of the population monetaryily and 90 multidimensionally poor (percent) 80 70 60 50 40 30 20 10 0 Multidimensional Monetary Note: Multidimensional poverty created using an adapted version of the World Bank’s Multidimensional Poverty Measure. Further details provided in Box 6. Source: Humanitarian Data Exchange and GRID-3 (for shapefiles), ECAM‑5, and World Bank estimates. 90 Chapter 5 Widespread shocks could leave even more Cameroonians at risk of falling into poverty 5.4. Boosting educational enrolment, sanitation, and electricity access could offer pathways out of poverty Nationally, there is a clear correlation between non-monetary deprivations – espe- cially sanitation and electricity – and monetary poverty. For all five of the non-mone- tary poverty metrics included in the World Bank’s MPM, those living in households that were deprived were also more likely to live in monetary poverty (Figure 51). However, this correlation was strongest for sanitation and electricity. Cameroonians deprived in terms of sanitation and electricity were about 3.1 and 3.5 times more likely to be monetarily poor than those who were not deprived on these metrics. This hints at the role basic infrastructure plays in poverty reduction. There was also a relatively strong correlation between monetary poverty and educational enrolment: Cameroonians living in households deprived in terms of educational enrolment were about 2.2 times more likely to live below the national monetary poverty line compared to those not deprived. Figure 51. Monetary poverty rate cut by different indicators of non-monetary in Cameroon, 2021/22 80 70 Poverty rate (percent) 60 50 40 30 20 10 0 Educational Educational Water Sanitation Electricity enrolment attainment Not deprived Deprived Note: Monetary and non-monetary deprivations created by adapting the World Bank’s Multidimensional Poverty Measure. Further details provided in Box 6. Source: ECAM‑5 and World Bank estimates. The relationships between monetary poverty, sanitation, and electricity remain strong, even when controlling for other household characteristics. These results emerge from adding each of the five non-monetary deprivation indicators from the World Bank’s MPM to the poverty profile regressions estimated in Chapter 3 – the last column of Table 8 includes both location- and household-level characteristics. These results suggest that for two Cameroonians who have otherwise similar charac- teristics, those who lack access to improved sanitation are 11.6 percent more likely to be monetarily poor and those who lack access to electricity are 15.6 percent more likely to be monetarily poor. These results chime with global evidence. For example, electrification has been shown to expand access to new livelihood opportunities and help households build assets in countries at similar development levels to Cameroon ­ anitation, (Ratledge, Cadamuro, de la Cuesta, Stigler, & Burke, 2022). Similarly, water, s and hygiene (WASH) programs help create better conditions for human capital devel- opment: for example, diarrheal disease – almost 90 percent of which can be attributed 91 Cameroon Poverty Assessment 2024 to suboptimal WASH – has been shown to be the largest cause of morbidity and mortality for children aged less than five (Ramesh, Blanchet, Ensink, & Roberts, 2015). The partial correlation between educational enrolment and monetary poverty was also relatively strong in these regressions, with those who are deprived in terms of educational enrolment 7.7 percent more likely to be monetarily poor.(49) Table 8. Regression of poverty status on non-monetary poverty measures Basic household Location No controls All controls controls controls Educational enrolment 0.2498*** 0.1028*** 0.2100*** 0.0774*** deprivation (0.0196) (0.0186) (0.0185) (0.0181) Educational attainment 0.0214 -0.0098 -0.0059 -0.0110 deprivation (0.0176) (0.0203) (0.0171) (0.0194) 0.2622*** 0.1943*** 0.1844*** 0.1556*** Electricity deprivation (0.0242) (0.0243) (0.0243) (0.0242) 0.0606*** 0.0478** 0.0486*** 0.0377** Drinking water deprivation (0.0187) (0.0188) (0.0177) (0.0173) 0.1647*** 0.1206*** 0.1297*** 0.1160*** Sanitation deprivation (0.0196) (0.0204) (0.0187) (0.0186) N 10,546 10,546 10,546 10,546 R-squared 0.2958 0.3587 0.3316 0.3882 Notes: Dependent variable is a binary variable taking 1 if the household is below the national poverty line and 0 otherwise. This is a linear probability model so the coefficients can be read directly as marginal effects. Standard errors clustered at the enumeration area level are in parentheses. Basic household controls include household size, number of dependents, and the sex, age, occupation, and work of the household head. Location controls comprise a dummy for urban areas and region fixed effects. * p<0.10, ** p<0.05, *** p<0.01. Source: ECAM‑5 and World Bank estimates. Alleviating constraints on sanitation and electricity could help Cameroonians – especially those in lagging regions and rural areas – escape and stay out of poverty. The regressions above and global evidence underscore the poverty-reduction possibilities of investing in sanitation and electricity. These are also the two non-mone- tary poverty measures where Cameroon’s rural areas and northern regions are furthest behind the rest of the country. Therefore, investing in sanitation and electricity in rural and northern parts of Cameroon could offer especially large “bang for buck” in terms of multidimensional poverty reduction. 49. The partial correlation between the educational attainment deprivation and monetary poverty is no longer statistically significant in these regressions, despite having a clear raw correlation. This is because educational attainment is collinear with the other non-monetary deprivations and control variables included in the regressions. 92 Chapter 5 Widespread shocks could leave even more Cameroonians at risk of falling into poverty 5.5. Monetary poverty and deprivation in terms of education and basic infrastructure overlap more in Cameroon’s lagging areas Many Cameroonians face multiple types of deprivation, confronting monetary poverty, low education outcomes, and lack of access to basic infrastructure at the same time. This can be shown by constructing measures of deprivation for each of the dimensions that used to calculate the World Bank’s MPM. Households are considered deprived on a particular dimension if they are deprived in at least one of the indicators falling under that dimension. Around 18.8 percent of Cameroonians were deprived across all three dimensions – monetary poverty, education, and basic infrastructure – and a further 20.2 percent were deprived in two out of the three dimensions (Figure 52). Facing multiple dimensions of poverty at the same time deepens households’ deprivation. Figure 52. Overlaps between different dimensions of non-monetary poverty in Cameroon, 2021/22 1.9 5.6 4.0 18.8 11.4 6.9 13.8 Monetary poverty Infrastructure Education Note: Numbers shown are percentages. Monetary and non-monetary deprivations created by adapting the World Bank’s Multidimensional Poverty Measure. Further details provided in Box 6. Size of circles in Venn diagrams not perfectly to scale. Deprivation in a given dimension means that household is deprived in at least one indicator within that dimension. Source: ECAM‑5 and World Bank estimates. Different poverty dimensions overlap more in rural areas and in northern regions than in the rest of Cameroon. In rural areas, as many as 33.9  percent of people are deprived across all three poverty dimensions – monetary poverty, education, and basic infrastructure – compared with just 5.7 percent in urban areas (Figure 53). The overlap between different deprivations is even more prevalent for Cameroonians in the Extrême-Nord, Nord, and Nord-Ouest regions, among whom 40.3 percent are deprived across all three poverty dimensions, compared to 10.8 percent for Cameroon’s other regions. This not only deepens deprivation for rural areas and northern regions today, but – with lower human capital development – could mean people’s productive poten- tial is constrained and future generations may also remain trapped in poverty too. 93 Cameroon Poverty Assessment 2024 Figure 53. Overlaps between different dimensions of non-monetary poverty in Cameroon by urban-rural and separating out the Extrême-Nord, Nord, and Nord-Ouest regions, 2021/22 Panel A: Urban only Panel B: Rural only 0.8 2.8 1.9 8.0 2.9 5.8 5.7 33.9 18.8 5.0 3.0 11.4 8.8 18.4 Panel C: Extrême-Nord, Nord, Panel D: All other regions and Nord-Ouest only 1.9 3.7 2.0 3.2 6.3 2.0 4.2 10.8 40.3 9.0 5.9 17.9 9.7 14.8 8.9 Monetary poverty Infrastructure Education Note: Numbers shown are percentages. Monetary and non-monetary deprivations created by adapting the World Bank’s Multidimensional Poverty Measure. Further details provided in Box 6. Size of circles in Venn diagrams not perfectly to scale. Deprivation in a given dimension means that household is deprived in at least one indicator within that dimension. Source: ECAM‑5 and World Bank estimates. The larger overlaps between different poverty dimensions in rural areas and northern regions also confirm the continued value of investing in education and basic infrastructure in those parts of Cameroon. First, for these households, it may be possible to “bundle up” interventions, jointly addressing different elements of mon- etary and non-monetary poverty at the same time. This includes programs that provide support for education and health services alongside cash or other in-kind transfers to households to enhance their incomes and livelihoods. Secondly, in rural and northern areas, interventions that universally improve education and basic infrastructure are more likely to reach monetarily poor people; this not only addresses their non-monetary poverty but also, given the results shown in Table 8, could help them escape monetary poverty too. By contrast, in urban areas, tailored interventions which try to address specific dimensions of poverty for specific groups of people will be needed. Thus, these results emphasize the importance of adapting policies for the different development challenges that Cameroonians in different parts of the country confront. 94 Chapter 5 Widespread shocks could leave even more Cameroonians at risk of falling into poverty 5.6. Out-of-pocket expenses for education and health could stop poorer Cameroonians from investing in human capital Out-of-pocket expenses for education and health services could be a barrier for Cameroonians from poorer households. As shown in Chapter 2, both poor and non-poor household divert some of their consumption to out-of-pocket spending on education and health. On average, about 4.1 percent of the consumption basket goes to spending on education and 5.1 percent goes to spending on health. Richer households generally devote a larger share of their consumption to education, but poor households still pay out of pocket. Digging deeper into what these expenditures comprise, enrolment fees are the largest single education expenditure, but books and uniforms take up a sizeable share of household expenditure, especially for poorer households (Figure 54). For health, out-of-pocket expenditures mainly go to pay for medicines – this is the case for households right across the consumption distribution. These out-of-pocket expenditures may prove too high for some households. This resonates with the large overlap between the education dimension and the monetary dimension of the MPM in rural areas and in northern Cameroon. Figure 54. Share of the overall consumption basket going to different education and health items by consumption decile, 2021/22 Panel A: Education Panel B: Health Share of the overall consumption basket Share of the overall consumption basket 6 6 5 5 4 4 3 3 (percent) (percent) 2 2 1 1 0 0 1 2 3 4 5 6 7 8 9 10 Total Total 1 2 3 4 5 6 7 8 9 10 Decile of the real consumption Decile of the real consumption distribution distribution Enrolment fee Ongoing fees Books Other materials Consultations Medical exams Uniforms Canteen fees Medication Vaccination Transport Other fees Other Source: ECAM‑5 and World Bank estimates. 5.7. New non-monetary deprivations confront Cameroon’s growing urban population Alongside the standard indicators considered above, Cameroon’s urban dwellers – and especially the urban poor – could face additional hardships; for one, their housing may be overcrowded. When household members are crammed into small accommodation where space is lacking, they become more susceptible to infectious diseases, they may find it harder to rest, and children may not have quiet areas to 95 Cameroon Poverty Assessment 2024 concentrate on schoolwork (UN-HABITAT, 2007). This can hold back human capital development. Overcrowding – defined as housing where there are more than two people per room, excluding kitchens, bathrooms, corridors, and balconies – poses a dispro- portionate challenge for Cameroon’s urban poor (Nkosi, Haman, Naicker, & Mathee, 2019). While he overall share of people living in overcrowded accommodation is similar in rural and urban areas, overcrowding is far more prevalent for the urban poor – of which 61.3 percent live in overcrowded accommodation – than the rural poor – of which 48.0 percent live in overcrowded accommodation (Figure 55). Suggestive evidence from ECAM‑5 on the shocks that Cameroonians face indicates that overcrowding may indeed influence the spread of infectious diseases in urban areas. For example, while relatively few households reported being affected by COVID-19 in the three years prior to the survey overall, the share of Cameroonians living in a COVID-19 affected households was approximately double in urban areas compared to rural areas.(50) As urbanization advances, overcrowding – and broader problems associated with informal housing – could become a growing problem for poor and vulnerable Cameroonians. Figure 55. Overcrowding by urban-rural and monetary poverty status in Cameroon, 2021/22 70 60 Share of the population living in overcowded housing (percent) 50 40 30 20 10 0 Urban Rural Total Poor Non-poor Note: Consumption deflated spatially and temporally to compare with national poverty line. Source: ECAM‑5 and World Bank estimates. Pollution may further blight human capital development in Cameroon’s cities. Air pollution is a growing global health concern, constraining cognitive development and labor productivity as well as leading to premature deaths (Karakulah, Lange, Awe, & Chonabayashi, 2021). In 2019, 12.7 percent of all deaths in Cameroon, from any cause, were attributed to the effects of indoor or outdoor air pollution (Ritche & Roser, 2021). Poor air quality in Cameroon’s towns and cities means this could get worse as people move to urban areas. In Douala, for example, air quality – as measured by the concentration of particulates in the air – consistently exceeded World Health Organization (WHO) guidelines throughout 2023, especially in the dry season in December, January, and February (Figure 56). While the effects of pollution are difficult 50. The share of Cameroonians living a household affected by COVID-19 in the three years prior to COVID-19 was 5.0 percent in urban areas and 2.5 percent in rural areas. 96 Chapter 5 Widespread shocks could leave even more Cameroonians at risk of falling into poverty to measure, especially with standard household survey data, their cost to human capital and long-run welfare could escalate and intensify as urbanization progresses. Figure 56. Air pollution in Douala in 2023 50 45 40 PM2.5 concentration (µg/m³) 35 30 25 20 15 10 5 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Level in Douala WHO guide line level Note: WHO = World Health Organization. Source: IQAir and World Bank estimates. 5.8. Boosting livelihood opportunities can help take advantage of investments in human capital Investments in human capital will only pay off if the jobs that can harness people’s productive potential are available, so understanding Cameroon’s labor market is vital. At the national level, Cameroon has made clear progress on human capital and basic infrastructure measures. However, this has not translated into national-level poverty reduction, despite the evidence from Cameroon and other countries showing that human capital, basic infrastructure, and monetary poverty are highly correlated. This presents something of a puzzle. In part, this could be because the development of human capital and basic infrastructure is starting to level off in urban areas and, as Chapter 3 shows, this is where Cameroonians are increasingly becoming concentrated. Overcrowding and pollution also present new, urban-specific threats to human capital development. That being said, urban areas outperform rural areas on non-monetary poverty metrics, so this is unlikely to be the full story. Additionally, there are some regions in Cameroon where deprivation in terms of monetary poverty, education, and basic infrastructure pile up, deepening people’s deprivation – without renewed efforts to improve non-monetary welfare outcomes in these areas, intergenerational poverty traps may persist. However, obtaining a more holistic picture of the relationship between human capital development and monetary welfare depends on understanding how people convert their productive potential into income generation. This means looking at people’s livelihoods and the factors that determine what people do for work. This is the focus of the next chapter.  97 Cameroon Poverty Assessment 2024 Annex 5.1. Selected trends for non-monetary welfare metrics from the Demographic and Health Surveys and Malaria Indicators Survey Figure 57. Educational attendance rates in Cameroon, 2004-2018 90 80 70 Enrolment rate (percent) 60 50 40 30 20 10 0 2004 DHS 2018 DHS Girls - Primary Girls - Secondary Boys - Primary Boys - Secondary Note: DHSs and MIS report net attendance rate, not enrolment. Source: 2004 DHS, 2011 DHS, 2018 DHS, 2022 MIS, and World Bank estimates. Figure 58. Trends in access to basic infrastructure in Cameroon, 2004-2022 90 Share of the population (percent) 80 70 60 50 40 30 20 10 0 2004 DHS 2011 DHS 2018 DHS 2022 MIS Improved water Improved sanitation Electricity Source: 2004 DHS, 2011 DHS, 2018 DHS, 2022 MIS, and World Bank estimates. 98 Chapter 5 Widespread shocks could leave even more Cameroonians at risk of falling into poverty Figure 59. Trends in access to water, access to electricity, and educational attendance in Cameroon by urban-rural, 2004-2022 Panel A: Improved water access Panel B: Electricity access 100 100 Share of the population (percent) Share of the population (percent) 90 90 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 0 2004 2011 2018 2022 2004 2011 2018 2022 DHS DHS DHS MIS DHS DHS DHS MIS Total Urban Rural Total Urban Rural Panel C: Primary school attendance Panel D: Secondary school enrolment Share of primary school-age children (percent) 100 100 Share of secondary school-age children 90 90 80 80 70 70 60 60 (percent) 50 50 40 40 30 30 20 20 10 10 0 0 2004 DHS 2018 DHS 2004 DHS 2018 DHS Total Urban Rural Total Urban Rural Source: 2004 DHS, 2011 DHS, 2018 DHS, 2022 MIS, and World Bank estimates. 99 Cameroon Poverty Assessment 2024 Chapter 6. KEY MESSAGES ➜ Cameroon has witnessed a dramatic decline in the share of people who are working in the last two decades ➜ The decline in the share of people who are working could be linked to urbanization, as urban labor markets may be becoming increasingly saturated with new participants ➜ The specific activities in which Cameroonians engage is more closely related to poverty than whether they work: non-agricultural, wage jobs are most associated with escaping poverty ➜ The shift from agriculture to services is not yet yielding poverty reduction, because the specific service-sector jobs Cameroonians are taking on are not productive enough ➜ While wage jobs are slowly becoming more prevalent, many lack markers of formality like leave policies and pay slips; about one quarter of wage jobs are in the public sector ➜ Women and young people face added challenges in accessing the jobs most associated with exiting poverty ➜ Boosting agricultural livelihoods – by improving access to input and output markets – remains a crucial priority for poverty reduction, especially in Cameroon’s lagging regions Cameroon’s changing labor market is not yet lifting people out of poverty T his chapter explores how Cameroon’s changing livelihood opportu- nities are shaping the country’s poverty-reduction story. As Chapter 5 demonstrates, human capital has a vital role to play in eliminating poverty in Cameroon. However, this cannot work in a vacuum: people need liveli- hoods that can take advantage of their growing productive potential. This chapter examines three especially important elements of Cameroon’s labor market. First, the chapter investigates one striking trend from the last two decades – namely the precipitous decline in the share of people working – linking this to the urbanization process discussed in Chapter 3. Second, the chapter notes that what people do may matter more than whether they work for exiting poverty and reviews how the types of jobs in which Cameroonian’s engage has changed over the last two decades. Finally, the chapter dives deep into farm livelihoods: even as Cameroon urbanizes, agriculture will remain a vital vehicle for lifting those in the country’s lagging regions out of poverty. 6.1. The share of Cameroonians who are working has declined The challenge of creating jobs for Cameroonians – and especially young Cameroonians – is set to grow. Cameroon’s population is growing at about 2.6 percent per year and 7 in 10 Cameroonians are aged less than 30. Cameroon’s working-age and youth populations are projected to keep on expanding for at least the next decade. According to the latest United Nations projections, the number of 15-64-year-olds in Cameroon is set to rise from 15.9 million to 21.4 million between 2023 and 2033, while the number of 15-24-year-olds in Cameroon is set to rise from 5.7 million to 7.4 million over the same period (Figure 60). Even if the share of young people who are working remains at its current low levels (see below), this implies that 2.4 million young people will need jobs by 2033, up from 1.8 million in 2023. This demonstrates the importance of job creation for Cameroon to make the most of its demographic dividend. 101 Cameroon Poverty Assessment 2024 Figure 60. Population pyramids for Cameroon in 2023 and 2033 100+ 95-100 90-94 85-89 80-84 75-79 70-74 65-69 60-64 Age (years) 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14 5-9 0-4 3,000 2,000 1,000 0 1,000 2,000 3,000 Thousands of people Female 2023 Male 2023 Female 2033 Male 2033 Source: United Nations, Department of Economic and Social Affairs, Population Division and World Bank estimates. Jobs have not been keeping pace with population growth, as the overall share of Cameroonians who are working has been falling. This trend emerges from mul- tiple data sources. The ECAMs and 2021 bridge survey suggest that the proportion of the working-age population(51) who were working fell from 80.3  percent in 2007 to 60.2  percent in 2021 (Figure  61).(52),(53) Similarly, the Enquête sur l’Emploi et le Sectuer Informel (Survey on Employment and the Informal Sector, EESI) indicate that the “labor force participation rate” – the share of working-age people who are in the labor force – fell from 78.2  percent in 2005 to 54.2  percent by 2021.(54),(55) As the analysis on 15-24-year-olds below demonstrates, these trends do not appear to be a purely a product of young people spending longer in e ­ ducation. Since both 51. The working-age population is defined as those aged 15-64 years, unless otherwise specified. 52. The questionnaire for ECAM‑3, ECAM‑4, and the 2021 bridge survey had a different labor module from the questionnaire for ECAM‑2. Therefore, it is not possible to draw trends from 2001 to 2007 for key labor market variables. 53. The ECAM questionnaires do not explicitly distinguish between subsistence farmers and those com- mercial farmers who sell at least half of their produce. Therefore, the analysis focuses on the share of people “working” rather than the share of people who are “employed”. Strictly speaking, “employment” refers to working for pay or profit, as per the standards set out in the 19th International Conference of Labour Statisticians (ILO, 2013). 54. The labor force comprises everyone who is either working or unemployed. The unemployed are those who are not working, but who are actively searching for work. 55. EESI uses a slightly different definition for the working-age population, including everyone aged 15 or more. This explains why its estimates for the labor force participation rate in 2001 can be lower than the estimate for the share of people working coming from the bridge survey. 102 Chapter 6 Cameroon’s changing labor market is not yet lifting people out of poverty the 2021 bridge survey and the 2021 EESI were collected after many the measures Cameroon used to contain COVID-19 had been removed, the declining working share between 2014 and 2021 is interpreted as an extension of the decline that was already taking place between 2007 and 2014.(56) Therefore, these data indicate a dramatic decline in the share of Cameroonians who are engaged in the labor market over the last two decades.(57) Figure 61. Cameroon’s falling working share and labor force participation rate Panel A: Working share from Panel B: Labor force participation ECAMs and 2021 bridge survey rate from EESI Share of the working-age population (percent) 100 100 90 90 Share of the population aged 15+ 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 0 ) ) ) ) ) 05 07 09 11 13 15 17 19 21 -2 -3 -4 ey -5 20 20 20 20 20 20 20 20 20 AM AM AM AM rv su C C C C ge (E (E (E (E rid 01 07 14 2 /2 (B 20 20 20 21 21 20 20 Total Urban Rural Total Urban Rural Note: Share of people working among working-age population (those aged 15-64 years) reported in Panel A. Labor force participation rate for those aged 15+ years reported in Panel B. In Panel A, comparisons are only possible between ECAM‑3, ECAM‑4, and the 2021 bridge survey. Source: ECAM‑2, ECAM‑3, ECAM‑4, ECAM‑5, 2021 bridge survey (for Panel A), EESI 2005, EESI 2021 (for Panel B), and World Bank estimates. The drop in the share of Cameroonians who are working has occurred in both rural and urban areas, but these patterns could still be consistent with urbani- zation and accelerating rural-urban migration. Throughout the last two decades, the share of people working has been higher in rural areas than in urban areas in Cameroon. Therefore, all other things equal, the persistent urbanization discussed 56. Indeed, by 2021, neighboring Nigeria had witnessed an increase in the share of people working as households tried to bolster their incomes to cope with the economic effects of the COVID-19 crisis (Lain, et al., 2021). 57. The International Labor Organization (ILO) modelled estimates suggest the employment-to-popula- tion ratio fell from 78.2 percent in 2005 to 67.9 percent in 2021. These estimates include everyone aged 15 or more. 103 Cameroon Poverty Assessment 2024 in Chapter 3 should reduce the share of people working. Yet the situation appears to be slightly more complicated. If people with a higher propensity to work were simply moving from rural to urban areas, the share of people working would be expected to fall in rural areas (which is observed in the data) but also rise in urban areas, especially as – looking at data at a single point in time – rural-urban migrants are more likely to work than other urban residents (Figure 62). Yet while the drop has been less than in rural areas, the share of people working has also started to decline in urban areas too, especially between 2014 and 2021. This is the same period over which poverty started to increase in urban areas too. This could therefore be indicative of growing saturation in the urban labor market: as rural-urban migration has accelerated, ensuring there are enough jobs for new urban dwellers may have become more challenging. Figure 62. Labor market status in Cameroon by migration status, 2021/22 Share of the working-age population Unemployment rate (percent) 100 8 90 7 80 6 70 60 5 (percent) 50 4 40 3 30 2 20 10 1 0 0 Always urban Rural-to-urban Always rural Rural-to-rural migrants Urban-to-rural migrants Urban-to-urban migrants migrants Rural Urban Employed (LHS) Unemployed (LHS) Out of the labor force (LHS) Unemployment rate (RHS) Note: Working-age population defined as those aged 15-64. International migrants excluded. Source: ECAM‑5 and World Bank estimates. The drop in the share of people working has been especially profound for young Cameroonians. Between 2007 and 2021, the share of young Cameroonians who were working dropped by around half, from 64.4 percent to 32.5 percent (Figure 63). The lack of labor market opportunities is therefore disproportionately affecting Cameroon’s growing population of young people, who may be trying to gain their first job. Differences between women’s and men’s likelihood of working persisted between 2007 and 2021. The additional constraints women and young people face in Cameroon’s labor market are explored in more detail below. 104 Chapter 6 Cameroon’s changing labor market is not yet lifting people out of poverty Figure 63. Trends in the share of people working in Cameroon by age and sex Panel A: By age Panel B: By sex Share of the working-age population (percent) Share of the working-age population (percent) 100 90 90 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 0 ) ) ) ) ) ) ) ) ) ) -2 -3 -4 ey -5 -2 -3 -4 ey -5 AM AM AM AM AM AM AM AM rv rv su su C C C C C C C C ge ge (E (E (E (E (E (E (E (E rid rid 01 07 14 2 01 07 14 2 /2 /2 (B (B 20 20 20 20 20 20 21 21 21 21 20 20 20 20 Total Youth Non-youth Total Male Female Note: In Panel A, youth is those aged 15-24 and non-youth is those aged 25-64. Comparisons are only possible between ECAM‑3, ECAM‑4, and the 2021 bridge survey. Source: ECAM‑2, ECAM‑3, ECAM‑4, ECAM‑5, 2021 bridge survey and World Bank estimates. Having a job is not, on its own, a guarantee of escaping poverty; this could explain why the poverty rate has remained stagnant despite these dramatic shifts in the share of people working. In 2021/22, the differences in the share of working-age Cameroonians who were working across different deciles of the consumption distribu- tion were minimal (Figure 64). Around 64.0 percent of working-age Cameroonians in the bottom 40 percent of the consumption distribution were working compared with 63.4  percent of working-age Cameroonians in the top 60. It does, however, appear that Cameroonians from richer households are more likely to be unemployed. This could be because they have more chance of getting high-quality jobs – or at least they aspire to do so – for which it may be necessary to wait and queue and because better-off households may be able to support them during their job search. Yet, overall, working alone does not necessarily offer Cameroonians a pathway out of poverty: in-work poverty is widespread. This indicates that the type of work that Cameroonians do matters more for escaping poverty. 105 Cameroon Poverty Assessment 2024 Figure 64. Labor market status in Cameroon by consumption decile, 2021/22 100 6 Unemployment rate (percent) Share of the working-age 80 5 population (percent) 4 60 3 40 2 20 1 0 0 1 2 3 4 5 6 7 8 9 10 Decile of the real consumption distribution Total Employed (LHS) Unemployed (LHS) Out of the labor force (LHS) Unemployment rate (RHS) Note: Working-age population defined as those aged 15-64. Consumption deflated spatially and temporally to construct deciles. Source: ECAM‑5 and World Bank estimates. 6.2. The labor market’s shift from agriculture to services is yielding mixed results for poverty reduction The share of workers engaged in agriculture has declined rapidly in the past two decades. The share of workers employed in agriculture dropped from 57.0 percent in 2007 to 42.4 percent in 2021, a decline of around one quarter (Figure 65).(58) This resonates with the rapid pace of urbanization witnessed in Cameroon over the same period: as people are moving to urban areas, their chances of primarily working in agriculture is lower. The shift away from agriculture has been reasonably evenly split between industry, commerce (that is, buying and selling), and other non-commerce services. The share of employed Cameroonians primarily employed in these activities rose by 5.1  percentage points, 5.0  percentage points, and 4.5  percentage points between 2007 and 2021. Figure 65. Shifting primary activities among working Cameroonians 100 Share of working people 80 60 (percent) 40 20 0 2001 (ECAM-2) 2007 (ECAM-3) 2014 (ECAM-4) 2021 2021/22 (Bridge survey) (ECAM-5) Agriculture Industry Commerce Other services Note: Sample restricted to working Cameroonians aged 15-64. Comparisons are only possible between ECAM‑3, ECAM‑4, and the 2021 bridge survey. Source: ECAM‑2, ECAM‑3, ECAM‑4, ECAM‑5, 2021 bridge survey (for Panel A), EESI 2005, EESI 2021 (for Panel B), and World Bank estimates. 58. These survey estimates are very close to the ILO modelled estimates reported in Chapter 1, which suggest that the share of workers engaged in agriculture dropped from 59.8 percent in 2007 to 42.6 percent in 2021. 106 Chapter 6 Cameroon’s changing labor market is not yet lifting people out of poverty Jobs outside of agriculture are more likely to be held by Cameroonians from non-poor households, which begs the question of why the shift from agriculture to services has not helped reduce poverty more. Around 65.3 percent of Cameroonian workers from households in the bottom 40 percent of the real consumption distribution work in agriculture compared with 25.9 percent of those from households in the top 60 (Figure 66). All other things equal, working outside of agriculture therefore appears to be associated with escaping poverty. This chimes with the macroeconomic evidence from Chapter 1 showing that average labor productivity is higher in services (taking commerce and other services together) and industry. Given these patterns, the fact that the shift from agriculture to services has not resulted in more poverty reduction remains something of a puzzle. Figure 66. Primary labor market activity in Cameroon by consumption decile, 2021/22 100 90 Share of working people (percent) 80 70 60 50 40 30 20 10 0 1 2 3 4 5 6 7 8 9 10 Decile of the real consumption distribution Total Agriculture Industry Commerce Other services Note: Sample restricted to working Cameroonians aged 15-64. Consumption deflated spatially and temporally to construct deciles. Source: ECAM‑5 and World Bank estimates. The shift from agriculture to services may not be yielding more poverty reduction because the specific types of service-sector jobs that workers are taking on are relatively less productive. Some types of service-sector jobs are clearly concentrated in the higher deciles: for example, workers from households in the top 60 percent of the consumption distribution are almost four times as likely to work in education and health services as those from the bottom 40 (Figure 67).(59) Yet the split across the deciles is far more even for other sub-sectors, including commerce, transport and communication, and personal services. Therefore, getting a job outside of agriculture is not enough per se: workers need jobs that are productive enough to lift them out of poverty. 59. Jobs in education and health services are more likely to be in the public sector. 107 Cameroon Poverty Assessment 2024 Figure 67. Detailed information on primary labor market activity in Cameroon by consumption decile, 2021/22 100 90 80 Share of working people (percent) 70 60 50 40 30 20 10 0 1 2 3 4 5 6 7 8 9 10 Decile of the real consumption distribution Total Agriculture Livestock, fishing, and forestry Extractives Manufacturing and other industry Construction Commerce Restaurants and accommodation Transport and communications Education and health Personal services Other services Note: Sample restricted to working Cameroonians aged 15-64. Consumption deflated spatially and temporally to construct deciles. Source: ECAM‑5 and World Bank estimates. Relatedly, rural-urban migrants are more likely to hold the types of service sec- tor jobs that are less associated with escaping poverty. In particular, rural-urban migrants are more likely than both urban-urban migrants and those that have always lived in urban areas to hold jobs in commerce, transport and communications, and personal services (Figure 68). These are the non-agricultural jobs that are relatively less likely to be found among workers in higher deciles of the consumption distribution. As such, those newly arriving in urban areas from rural areas are less likely to obtain the non-agricultural jobs that could lift them out of poverty. 108 Chapter 6 Cameroon’s changing labor market is not yet lifting people out of poverty Figure 68. Detailed information on primary labor market activity in Cameroon by migration status, 2021/22 100 90 80 Share of working people (percent) 70 60 50 40 30 20 10 0 migrants Rural-to-rural Urban-to-rural migrants Always rural Always urban Urban-to-urban migrants Rural-to-urban migrants Rural Urban Agriculture Livestock, fishing, and forestry Extractives Manufacturing and other industry Construction Commerce Restaurants and accommodation Transport and communications Education and health Personal services Other services Note: Sample restricted to working Cameroonians aged 15-64. International migrants excluded. Source: ECAM‑5 and World Bank estimates. While the shift from agriculture to services is not directly delivering poverty reduction, wage jobs remain scarce although they are gradually becoming more widespread. Wage jobs are clearly associated with exiting poverty, being concentrated among workers from richer households (Panel A of Figure 69).(60) Even though the share of people who were working dropped over the last two decades, among those who were working, wage jobs became more prevalent (Panel B of Figure 69). Between 2007 and 2021, the share of working Cameroonians holding wage jobs rose from 17.0 percent to 23.6 percent. Sustaining this drive towards quality jobs could provide an avenue for lifting Cameroonians out of poverty in the future. 60. In broad terms, the wage-employed do not have decision-making power over the establishment where they work (and its capital). Their payment is received with some regularity, and it is not directly dependent on the revenue of the establishment where they work. In the ECAM series, wage work is identified using the question on jobs’ “socio-professional category”. Those declaring themselves to be (1) “Senior manager, engineer, and similar”, (2) “Middle manager, supervisor”, (3) “Employee/ skilled worker”, (4) “Employee/semi-skilled worker”, and (5) “Manual workers” are categorized as wage workers. 109 Cameroon Poverty Assessment 2024 Figure 69. Job type in Cameroon by consumption decile and over time Panel A: Comparison across Panel B: Trends in wage work consumption deciles Share of working people (percent) 100 Share of working people primarily 35 engaged in wage jobs (percent) 90 80 30 70 25 60 50 20 40 15 30 20 10 10 5 0 0 1 2 3 4 5 6 7 8 9 10 ) ) ) ) ) -2 -3 -4 ey -5 AM AM AM AM rv su Decile of the real consumption C C C C ge (E (E (E (E distribution rid 01 07 14 2 /2 (B 20 20 20 21 Wage-employed Self-employed Other 21 20 20 Note: Sample restricted to working Cameroonians aged 15-64 in both Panels. Consumption deflated spatially and temporally to construct deciles. In Panel B, comparisons are only possible between ECAM‑3, ECAM‑4, and the 2021 bridge survey. Source: ECAM‑2, ECAM‑3, ECAM‑4, ECAM‑5, 2021 bridge survey, and World Bank estimates. Not all wage jobs in Cameroon come with non-pecuniary benefits such as paid leave and formal pay slips. Among those Cameroonians primarily employed in wage jobs, 24.5  percent had paid leave, 22.2  percent had sick leave, 14.4  percent had parental leave, and 38.7 percent had formal pay slips in 2021/22 (Figure 70). These non-pecuniary benefits that are typically associated with wage jobs in high-income countries are therefore far from ubiquitous. Moreover, they are even less common for wage workers coming from households in lower deciles of the consumption distribution: accessing non-pecuniary job benefits is even more difficult for the poor and vulnerable. Figure 70. Non-pecuniary job benefits among wage workers in Cameroon by consumption decile, 2021/22 60 Share of wage-employed people 50 38.7 40 (percent) 30 24.5 22.2 20 14.4 10 0 Annual leave Sick leave Parental leave Pay slip 1 2 3 4 5 6 7 8 9 10 Total Note: Sample restricted to wage-employed Cameroonians aged 15-64. Consumption deflated spatially and temporally to construct deciles. Source: ECAM‑5 and World Bank estimates. 110 Chapter 6 Cameroon’s changing labor market is not yet lifting people out of poverty The public sector still accounts for a significant share of Cameroon’s wage jobs. In 2021/22, 25.6 percent of wage jobs were provided by the public sector (Figure 71). This share dropped slightly between 2007 and 2021, but public institutions remain an important provider of wage-employment. Cameroon also has a larger share of overall employment in the public sector than many neighboring countries, although some of its aspirational peers have even larger shares of employment provided by the state. Figure 71. Public sector work in Cameroon Panel A: Share of wage jobs Panel B: Share of total employment provided by the public sector in the public sector Share of wage-employed people (percent) 35 Share of employment (percent) 30 0 5 10 15 25 Cameroon 6.7 20 Chad 3.5 15 Structural peers Ghana 6.7 10 Nigeria 2.3 5 Bangladesh 5.5 Aspirational peers 0 Kenya 5.6 ) ) ) ) ) -2 -3 -4 ey -5 AM AM AM AM rv su C C C C Morocco 11.1 ge (E (E (E (E rid 01 07 14 2 /2 (B 20 20 20 21 Vietnam 8.0 21 20 20 Note: Sample restricted to wage-employed Cameroonians aged 15-64. In Panel A, comparisons are only possible between ECAM‑3, ECAM‑4, and the 2021 bridge survey. Latest available year of data used for Panel B. Source: ECAM‑2, ECAM‑3, ECAM‑4, ECAM‑5, and 2021 bridge survey (for Panel A), ILOSTAT (for Panel B), and World Bank estimates. Minimum wage legislation is unlikely to affect the poorest Cameroonian workers directly. Raising minimum wages is sometimes used to try and compensate house- holds and cushion the short-term effects of fiscal reforms, including reducing subsidies for fuel, electricity, and other goods. However, this is unlikely to have significant effects on the income of poor households for two key reasons. First, as shown above, the wage jobs that are subject to minimum wage legislation are rare, especially for workers from poorer households. Poor workers are concentrated in self-employment jobs, where minimum wages will not make a direct difference. Second, even among wage workers, enforcement appears to be highly imperfect. For wage workers that reported earnings, around one quarter earned less than 36,270 XAF per month, which was the Salaire Minimum Interprofessionnel Garanti (Guaranteed Interprofessional Minimum Wage, SMIG) when the ECAM‑5 data were collected in 2021/22.(61) The share of wage workers from poor households earning less than the SMIG was closer to one half. This 61. The SMIG was revised through a new decree in 2023. In this decree, different minimum wages were set for public sector wage workers, private sector wage workers in agriculture, and other private sector wage workers. Excluding public sector workers and private sector wage workers in agriculture from the analysis using ECAM‑5 for 2021/22 does not substantially alter the results. 111 Cameroon Poverty Assessment 2024 lack of enforcement further limits the extent to which minimum wage legislation can reach poor and vulnerable Cameroonians.(62) Even with the shifts among working people towards services and wage jobs, there are fundamentally not enough productive jobs for Cameroon’s growing population. As Cameroon’s working-age population has expanded, the group that has grown the most is those who are not working (Figure 72). This could further explain why the shift towards services and wage jobs has not yielded more poverty reduction. Figure 72. Trends in absolute number of workers by primary activity and primary job type over time Panel A: Activity Panel B: Job type 16 16 Working-age people (millions) Working-age people (milions) 14 14 12 12 10 10 8 8 6 6 4 4 2 2 0 0 ) ) ) ) ) ) ) ) ) ) -2 -3 -4 ey -5 -2 -3 -4 ey -5 AM AM AM AM AM AM AM AM rv rv su su C C C C C C C C ge ge (E (E (E (E (E (E (E (E rid rid 01 07 14 2 01 07 14 2 /2 /2 (B (B 20 20 20 20 20 20 21 21 21 21 20 20 20 20 Agriculture Industry Wage-employed Self-employed Commerce Other services Other Not employed Not employed Note: Sample restricted to Cameroonians aged 15-64. Comparisons are only possible between ECAM‑3, ECAM‑4, and the 2021 bridge survey. Source: ECAM‑2, ECAM‑3, ECAM‑4, ECAM‑5, and 2021 bridge survey and World Bank estimates. 6.3. Women and young people face additional challenges in Cameroon’s labor market Women are less likely to be working than men, and those that are working are less likely to hold wage jobs and jobs outside of agriculture. In 2021/22, around 55.6 percent of working-age women were working compared with 73.0 percent of working-age men (Figure 73). Sex-based differences also extend to the types of work women and men do. Working men were almost twice as likely as working women to 62. According to classical labor market theory, minimum wages could reduce the level of employment by raising the price of labor (the wage) above its competitive level and restricting the amount of labor demanded by employers. However, in monopsony labor markets – where employers as the buyers of labor can control wage and employment levels – this will not necessarily be the case. In monopsony labor markets, raising minimum wages could in theory increase employment levels. 112 Chapter 6 Cameroon’s changing labor market is not yet lifting people out of poverty hold wage jobs – those jobs that are most able to lift people out of poverty. Working men were also more likely to hold non-agricultural jobs: the share of working women holding jobs in agriculture was 45.0 percent in 2021/22, compared with 38.8 percent for men. Ensuring that everyone can access good jobs – regardless of sex – therefore presents a crucial policy priority for ensuring the proceeds of growth reach all Cameroonians and lift them out of poverty. Figure 73. Labor market outcomes in Cameroon by sex, 2021/22 Panel A: Labor market Panel B: Primary job Panel C: Primary status type activity 100 5 100 100 90 Share of the working-age population (percent) 90 90 80 4 80 80 Share of working people (percent) Share of working people (percent) Unemployment rate (percent) 70 70 70 60 3 60 60 50 50 50 40 2 40 40 30 30 30 20 1 20 20 10 10 10 0 0 0 0 Women Men Women Men Women Men Out of the labor force (LHS) Other Other services Unemployed (LHS) Self-employed Commerce Employed (LHS) Wage-employed Industry Unemployment rate (RHS) Agriculture Note: Sample restricted to working-age Cameroonians aged 15-64 in Panel A and working Cameroonians of working age in Panels B and C. Source: ECAM‑5 and World Bank estimates. Urbanization could exacerbate some elements of sex-based differences in the labor market. In particular, the gap between the share of people working is larger in urban areas (19.4 percentage points) than in rural areas (14.8 percentage points, see Figure 74). As more and more Cameroonian households live in towns and cities, this underlines the urgency of ensuring both women and men have access to good livelihood opportunities. 113 Cameroon Poverty Assessment 2024 Figure 74. Share of women and men who are working in urban and rural areas, 2021/22 90 77.6 Share of the working-age population 80 69.8 70 62.8 60 50.4 50 (percent) 40 30 20 10 0 Urban Rural Women Men Note: Sample restricted to working-age Cameroonians aged 15-64. ECAM‑5 and World Bank estimates. Young people also appear to have lower access to non-agricultural wage jobs. The share of people working was certainly lower for young people – those aged 15-24 years – compared with the rest of the population in 2021/22, although this was partly because many are still in full-time education (Figure 75). Nevertheless, the open unemployment rate – which captures those not working but actively searching for work – was more than double for young people than the rest of the population. Evidence from ECAM‑4 and the bridge survey also shows that the share of 15-24-year-olds not working and not in education grew more – from 15.0 percent to 26.2 percent – than the share who were not working and in education – from 38.9 percent to 41.3 percent – between 2014 and 2021. Moreover, among those working, slightly fewer young people held wage jobs in 2021/22 (26.2 percent) compared with the non-youth population (29.5 percent).(63) At the same time, young people were disproportionately concentrated in agriculture and did attain the other service sector jobs most associated with escaping poverty. With population growth continuing at pace and with people moving to urban areas, these results herald a potential risk for Cameroon: a large youth population, frustrated with their labor market outcomes. This is important not only for poverty reduction, but – as global evidence suggests – reducing the risk of violence and political instability (Azeng & Yogo, 2013; Demeke, 2022). 63. The “other” category includes apprentices and trainees, which is why it is so large for youth. 114 Chapter 6 Cameroon’s changing labor market is not yet lifting people out of poverty Figure 75. Labor market outcomes in Cameroon by age, 2021/22 Panel A: Labor market Panel B: Primary job Panel C: Primary status type activity 100 8 100 100 Share of the working-age population (percent) 90 90 90 7 Share of working people (percent) Share of working people (percent) 80 80 80 Unemployment rate (percent) 6 70 70 70 5 60 60 60 50 4 50 50 40 40 40 3 30 30 30 2 20 20 20 1 10 10 10 0 0 0 0 Youth Non-youth Youth Non-youth Youth Non-youth Out of the labor force, not Other Other services in education (LHS) Self-employed Commerce Out of the labor force, in education (LHS) Wage-employed Industry Unemployed (LHS) Agriculture Employed (LHS) Unemployment rate (RHS) Note: Youth is those aged 15-24. Sample restricted to working-age Cameroonians aged 15-64 in Panel A and working Cameroonians of working age in Panels B and C. For Panel A, educational enrolment is only available for youth. Source: ECAM‑5 and World Bank estimates. 6.4. Boosting agricultural productivity remains crucial for lifting Cameroonians out of poverty, especially as climate change threatens the country’s natural capital Even though work is shifting into services, agriculture still has a vital role to play in reducing poverty, especially in Cameroon’s lagging regions. While it is certainly true that urban poverty is growing and workers are overall shifting into the service sector, the poor remain disproportionately concentrated in rural areas and in agricultural jobs. Moreover, agriculture is a particularly important employer in those regions that are at risk of falling behind, where conflict is proliferating, and where poverty has been getting worse, especially in the north of Cameroon (Figure 76). As such, focusing policy on agricultural productivity remains vital for poverty reduction. 115 Cameroon Poverty Assessment 2024 Figure 76. Share of working Cameroonians primarily Agriculture is still a significant engaged in agriculture by region, 2021/22 employer in less dense urban areas, including on the periphery of towns and Share of workers engaged cities. While agriculture dominates in rural in agriculture (percent) areas, it remains important for some urban 1.8 - 38.9 dwellers too. Using the alternative measure 39.0 - 48.4 of the urban-rural continuum outlined in 48.5 - 53.1 Chapter  3, it emerges that a significant 53.2 - 60.4 share of working people were primarily 60.5 - 64.3 engaged in agriculture in dense urban clus- ters (38.2 percent) and semi-dense urban clusters (78.2 percent) in 2021/22 (Figure 77). Some households in these areas may continue to use agriculture as a personal safety net if productive non-agricultural jobs in urban areas are scarce. Moreover, since agriculture requires land, this could reflect the extent of sprawl on the edge of Cameroon’s urban areas: this lack of den- sification could inhibit the agglomeration effects that allow economic activity and productive job creation in towns and cities to flourish. Note: Sample restricted to working Cameroonians aged 15-64. Source: Humanitarian Data Exchange and GRID-3 (for shapefiles), ECAM‑5, and World Bank estimates. Figure 77. Primary labor market activity in Cameroon by alternative urban classifications, 2021/22 100 Share of working people (percent) 90 80 70 60 50 40 30 20 10 0 Urban center Dense urban cluster Semi-dense urban Rural cluster cluster Agriculture Industry Commerce Other services Note: Information is mapped from geospatial data to household level by using a population-weighted mode to collapse to the enumeration area level. See Annex 3.1 in Chapter 3 for details of urban classifications. Source: ECAM‑5 and World Bank estimates. 116 Chapter 6 Cameroon’s changing labor market is not yet lifting people out of poverty Global evidence suggests that agriculture in Cameroon is performing moderately, but there could be room for improvement. Cameroon has the third highest fertilizer use in the CEMAC region, but it is below its structural peers, aspirational peers, and the averages for Western and Central Africa and Sub-Saharan Africa (Panel A of Figure 78). Additionally, agricultural labor productivity – calculated using the employment levels and value added in each sector – appears to be rising having increased by around three-quarters in the last two decades, but it is still significantly below the levels for Cameroon’s aspirational peers (Panel B of Figure 78). Figure 78. Comparing agriculture in Cameroon to other countries Panel A: Fertilizer use Fertilizer consumption (kilograms per hectare of arable land) 0 50 100 150 200 250 300 350 400 450 Cameroon 13.4 Central African Republic 0.2 Congo, Rep. 9.7 CEMAC Gabon 28.3 Equatorial Guinea 18.4 Chad 2.2 Côte d’Ivoire 45.9 Aspirational Structural peers Ghana 37.4 Nigeria 18.6 Bangladesh 384.2 Kenya 60.7 peers Morocco 55.3 Vietnam 427.6 Groups Africa Western and Central 15.4 Sub-Saharan Africa 22.6 Panel B: Agricultural productivity 7,000 6,000 (constant 2015 USD per worker) Agricultural labor productivity 5,000 4,000 3,000 2,000 1,000 0 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Cameroon Kenya Morocco Vietnam Note: Bangladesh excluded from Panel B. Source: WDIs and World Bank estimates. 117 Cameroon Poverty Assessment 2024 There is huge diversity in agricultural livelihoods across Cameroon, underlin- ing the need for tailored policies that can support inclusive growth as climate change threatens the country’s natural capital. Echoing the agroecological differ- ences outlined in Chapter 1, the dominant crops, livestock, fishing, and other natural resource-oriented livelihoods vary across Cameroon (Figure 79 with details in Annex 6.1). Some of these livelihood practices are more exposed to shocks than others. For example, climate shocks could pose a particular threat to rainfed crops in the north of the country – both staple and cash crops – as this is where the rise of extreme heat and drought is predicted to be most severe. As such, policies to support climate adaption and help preserve Cameroon’s natural capital need to be tailored for different livelihoods in different parts of the country. Figure 79. Livelihood zones in Cameroon Urban Western Cross-Border Trade Coastal Lom-Pangar Grassy Savannah Dense Forest of the South-East Degraded Forest of the Center-South Sanaga-Mbam Plain Mount Cameroon Forest Western Highlands Tikar Plain Adamawa High Plateau Faro-Mayo Rey Lowlands Benue Plain Mandara Mountains Piedmont Duck's Beak River Logone Flood Plain Note: Details on livelihood zones provided in Annex 6.1. Source: FEWS NET and World Bank estimates. While some agricultural inputs appear to be more difficult for poorer Cameroonian farmers to access, this is not the case for fertilizers, phytosanitary products, and plows: access to inputs is not solely about pecuniary constraints. Regardless of 118 Chapter 6 Cameroon’s changing labor market is not yet lifting people out of poverty households’ position in the consumption distribution, use of inputs was relatively low. For example, on average, only 37.3 percent of agricultural households used labor from outside the household for preparing plots – the most common activity for which non-household labor was hired. This in itself suggests input markets may be imperfect or incomplete. Notwithstanding this overall finding, the share of agricultural households – those that cultivated plots in the previous agricultural cycle – which have land titles, which have access to irrigation, and which use labor from outside the household was generally higher for those from higher consumption deciles (Figure 80). However, some inputs – namely fertilizers, phytosanitary products (that is pesticides, fungicides, and herbicides), and animal-drawn or motorized plows – are used more prevalently by poorer agricultural households. This does not solely appear to be a product of poorer households having larger farms, which would require such inputs.(64) This could partly reflect the overlap between different livelihood zones – in which different crops needing different inputs – and areas of the country where poorer households are located. It therefore appears that pecuniary constraints are not necessarily binding for all inputs, especially fertilizers and phytosanitary products. Expanding their take-up could be about ensuring farmers are actually reached by input markets, making sure complementary inputs are available (especially as irrigation remains rare), or providing information and training about potential benefits, alongside addressing issues of affordability. Accessing some inputs – such as irrigation and new types of fertilizer – could be especially important for supporting agriculture’s resilience to climate-related shocks. Figure 80. Access to agricultural inputs in Cameroon Panel A: Non-labor inputs Panel B: Non-household labor Share of agricultural households using this type Share of agricultural households with access 60 60 of non-household labor (percent) 50 50 to each input (percent) 40 40 30 30 20 20 10 10 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 Decile of the real consumption Decile of the real consumption distribution distribution Land title Fertilizer Preparation Maintaining Irrigation Phytosanitary products Harvesting Animal-drawn or motorized plow Note: Consumption deflated spatially and temporally to construct deciles. Sample restricted to those households that cultivated plots in the previous agricultural cycle. Household weights applied, so statistics report the share of households. Source: ECAM‑5 and World Bank estimates. 64. The mean total plot area for agricultural households in the bottom 40 percent of the consumption distribution is 15,157 square meters compared with 15,712 square meters for those from the top 60. 119 Cameroon Poverty Assessment 2024 Difficulties accessing output markets could be constraining agricultural pro- ductivity for some farmers. Among agricultural households, around three-quarters (72.1 percent) had tried or were planning to sell their produce in 2021/22. Of those household that had tried or were planning to sell their agricultural output, 31.2 percent reported encountering difficulties doing so. Output prices being too low was by far the most common reason listed, but farmers also complained about markets being too far away and being served by bad roads (Figure 81). These results underline the importance of infrastructure as a vehicle for boosting agricultural productivity and escaping poverty, as discussed in more detail in the next chapter. Figure 81. Difficulties encountered by agricultural households in Cameroon when trying to sell their produce 25 20.9 that are selling produce (pecent) Share of agricultural households 20 15 10.8 10.0 10 9.0 7.3 7.4 5 0 Roads too far Markets Transport costs Bad roads Insufficient Prices too far too high customers too low Note: Sample restricted to agricultural households that had tried or were planning to sell their produce. Household weights applied, so statistics report the share of households. Source: ECAM‑5 and World Bank estimates. 6.5. Human capital and livelihoods need the bedrock of infrastructure Building human capital and unleashing the livelihoods required to lift Cameroonians out of poverty depends on the foundations of infrastructure. Changing livelihoods patterns are reflecting the structural dynamics witnessed across the Cameroonian economy, with overall employment dropping dramatically and activity shifting out of agriculture. Even though non-agricultural jobs are more productive on average and a larger share of Cameroonians are gaining wage jobs, these changes are not yielding a significant impact on poverty reduction yet. Boosting infrastructure in both rural and urban areas – as explored in Chapter 7 – could hold the key to unlocking this potential, both by enhancing access to human capital and helping Cameroonians find the jobs that can take advantage of their productive potential.  120 Chapter 6 Cameroon’s changing labor market is not yet lifting people out of poverty Annex 6.1. Detailed information on livelihood zones in Cameroon Table 9. Description of different livelihood zones Livelihood zone name Description Cattle, goats and sheep, fishing, irrigated rice, maize, sorghum, and cross- River Logone Flood Plain border trad’ Duck's Beak Cotton, pigs, poultry, cattle, rainfed sorghum, pulses Piedmont Surplus off-season sorghum, market gardening, livestock, trade Mandara Mountains Potatoes, onions, garlic, maize, soya, tubers, cross-border trade Benue Plain Groundnuts, cotton, maize, irrigated rice, onions, cattle, fishing Faro-Mayo Rey Lowlands Maize, yams, cotton, soya, groundnuts Adamawa High Plateau Cattle, maize, cassava, yams, sweet potatoes, beans, honey Tikar Plain Maize, irrigated rice, robusta coffee, fishing, livestock Maize, market gardening, beans, potatoes, egg production, tubers, arabica Western Highlands coffee Mount Cameroon Forest Cocoa, palm oil, robusta coffee, rubber, plantain, tubers, pepper, snails Cocoa, plantain, pineapple, market gardening, cassava, yellow yams, Sanaga-Mbam Plain smallstock, poultry Degraded Forest of the Cocoa, pineapple, cassava, maize, market-gardening, small livestock and Center-South poultry Dense Forest of the Cassava, plantain, macabo corms, cocoa, robusta coffee, palm oil, forest wild South-East foods, smallstock, poultry, game Lom-Pangar Grassy Cattle, cassava, maize, groundnuts, fishing, artisanal mining Savannah Artisanal sea-fishing, shrimps, informal cross-border trade, forest gnetum Coastal leaves, palm oil, fresh and processed cassava, coconuts Western Cross-Border Tapioca, palm oil, tomatoes, rice, cocoa, cattle, gnetum leaves and other wild Trade forest products Urban N/A Source: FEWS NET. 121 Cameroon Poverty Assessment 2024 Chapter 7. KEY MESSAGES ➜ Physical access to education and health services remains a constraint for some Cameroonians, especially in remote and rural areas ➜ Even if children can reach school, questions around quality remain: schools in rural areas and in northern Cameroon have higher student- teacher ratios and are less likely to have adequate latrines, water supply, and electricity ➜ Constraints on physical access partly reflect the limitations of Cameroon’s road network ➜ Urban dwellers – especially women and the urban poor – increasingly confront traffic congestion when trying to reach jobs and services ➜ However, physical access is not a binding constraint for everyone, so other factors could prevent Cameroonians from reaching markets and services ➜ Digital infrastructure – including mobile phones and the internet – remains out of reach for many poor and vulnerable Cameroonians ➜ Lack of formal identification disproportionately excludes poor and vulnerable Cameroonians from schooling Poor and vulnerable Cameroonians face extra obstacles accessing markets and services T his chapter considers how access to services and markets may influence Cameroon’s pathway to poverty reduction. Previous chapters have demon- strated the strong links between human capital, livelihoods, and poverty. Yet underlying human capital and livelihoods is the bedrock of physical and digital infrastructure, which determines whether people can access the services and markets they need. To explore these issues further, the chapter first uses geospatial data to assess whether Cameroonians can actually reach education and health facilities, demonstrating how physical access influences human capital outcomes; this includes the growing congestion that urban dwellers confront. Second, the chapter explores whether poor and vulnerable Cameroonians face dispropor- tionate constraints in accessing digital infrastructure, including mobile phones and the internet. Finally, the chapter considers the specific role of formal identification in accessing services, focusing specifically on school enrolment. 7.1. Geospatial data can shine a light on Cameroonians’ access to services and markets The analysis combines several sources of geospatial data to complement the household survey data used throughout this report. Assessing physical access relies on knowing where people live and where the facilities they need are located. This goes beyond what is possible with the ECAMs and other household surveys alone, as the most granular estimates that they are designed to produce are at the region level. Geospatial data can help address this data gap. Information on where Cameroonians live is taken from WorldPop, which provides so-called “population points”: the popula- tion at each point on a 100 meter by 100 meter grid for the whole country (Bondarenko, Kerr, Sorichetta, Tatem, & WorldPop, 2020). Information on where primary schools are located, as well as their basic characteristics, is provided directly by the Ministè’e de l'Education de Base (Ministry of Basic Education). The locations of health facilities are taken from WHO and Bill and Melinda Gates Foundation (BGMF) data. Combining 123 Cameroon Poverty Assessment 2024 these data sources provides a more precise picture of Cameroonians’ physical access to services. An innovative mobility model can be used to refine estimates of physical access, casting it in terms of travel time. Geospatial data on where people live and where facilities are located show the distance people need to travel to reach certain facilities. Yet a more relevant metric for assessing physical access is the time people would need to travel, which could be influenced by topography, physical barriers such as rivers, or differences in the quality of paths or roads. Using information on these geographical feature, specialized mobility models – known as “friction surface” models – can more precisely estimate the time it takes for people to reach facilities both on foot and through mixed transport methods, that is using vehicles as well as walking. This is particularly useful when survey-based estimates of the time or distance needed to reach schools, health facilities, markets, and other key destinations are not available. The specific friction surface model applied in this chapter is based on analysis by Kosmidou-Bradley and Blankespoor (2019) for Afghanistan. Geospatial data on physical access can be combined with ECAM‑5 to assess how access influences outcomes. To do this, travel-time data for each population point can be calculated using the friction surface models and then collapsed down to the enumeration area level using a map of enumeration area boundaries. This enumeration area-level information on the time it takes to reach facilities can then be merged with ECAM‑5.(65) 7.2. Physical access to services remains a challenge for Cameroonians in remote and rural areas Many primary-school-age Cameroonians live prohibitively far away from their nearest primary school. About 31.1 percent of primary-school-age Cameroonians live more than one hour on foot away from their nearest primary school and as many as 51.1 percent live more than 30 minutes away (Figure 82). Global standards for school access place maximum travel times somewhere between 30 minutes and one hour.(66) Even with mixed transport methods, as many as 19.0 percent of primary-school-age Cameroonians live more than one hour away from their nearest primary school and 36.3 percent live more than 30 minutes away. The median travel time on foot is 31 minutes while the median travel time using mixed transport methods is 18 minutes. Taken together, this suggests that the majority of Cameroonian children can reach their nearest primary school, but a significant minority cannot. Travel times to the nearest public primary school are also shorter, on average, than travel times to the nearest private primary school, emphasizing the importance of the government in ensuring children are able to access the education they need. 65. A similar approach was used in Chapter 3 to construct alternative urban-rural definitions for each enumeration area using WorldPop data. 66. In many countries, 3 kilometers is seen as the maximum distance children should need to walk to reach school. The mobility model suggests that it takes primary-school-age children between 30 minutes and one hour to walk this distance on average (Theunynck, 2009). 124 Chapter 7 Poor and vulnerable Cameroonians face extra obstacles accessing markets and services Figure 82. Time taken to reach the nearest primary school in Cameroon 100 Share of primary-school-age population 90 80 70 60 50 (percent) 40 30 20 10 0 All Public Private All Public Private On foot Mixed transport methods <15 minutes 15-30 minutes 30-60 minutes 1-1.5 hours 1.5-2 hours 2-3 hours >3 hours Note: Estimates cover primary-school-age children (those aged 6-11). Source: Ministère de l'Education de Base, WorldPop, and World Bank estimates. Physical access influences the chances of children being enrolled in school, even after accounting for household and location characteristics. To test this, the friction surface model estimates of travel times can be merged into the ECAM‑5 data, and then enrolment in primary-school can be regressed on travel times to the nearest primary alongside the same household and location controls used for the poverty profiles in Chapter 3.(67) The log of consumption is also included as a control to ensure the results are not simply a byproduct of both access and enrollment being correlated with monetary welfare. Using this approach, it emerges that primary-school children in households that are more than 30 minutes away from the nearest primary school on foot are 8.1 percentage points less likely to be enrolled in primary school, even after controlling for household and location characteristics and household consumption (Table 10). This suggests that physical access is a binding constraint on enrolment for some Cameroonian children. Table 10. Regression of primary school enrolment on time taken to reach the nearest primary school on foot Adding Adding Adding No controls location household consumption controls controls -0.1661*** -0.1053*** -0.0830** -0.0808** >30 minutes from nearest primary school (0.0421) (0.0407) (0.0361) (0.0369) N 8,500 8,500 8,500 8,500 R-squared 0.0159 0.1105 0.1407 0.1423 Note: Sample restricted to primary-school-age children (those aged 6-11). Dependent variable is a binary variable taking 1 if the child is enrolled in primary school and 0 otherwise. Standard errors clustered at the enumeration area level are in parentheses. * p<0.10, ** p<0.05, *** p<0.01. Source: ECAM‑5, Ministère de l'Education de Base, WorldPop, and World Bank estimates. 67. These regressions have a binary variable taking 1 if the child is enrolled and 0 otherwise on the left-hand-side. They are therefore linear probability models, so the coefficients can be read directly as marginal effects. The standard errors are clustered at the enumeration area level to minimize the effect of heteroskedasticity. 125 Cameroon Poverty Assessment 2024 Figure 83. Share of children in each Physical access to schools is uneven arrondissement living more than one hour on foot from their nearest primary school across Cameroon: it is mainly a prob- lem in remote and rural areas. In and around large urban centers, like Douala Share of children >1 hour's walk to primary school and Yaoundé, most Cameroonian children (percent) can reach primary schools, even on foot 0.0 - 20 (Figure  83). All other things equal this 20.1 - 40 presents one advantage of urbanization: 40.1 - 60 people are physically closer to the services 60.1 - 80 they need. However, in some pockets of 80.1 - 100 Cameroon –  especially in the Est, Nord- Ouest, and Sud-Ouest regions – it takes a prohibitively long time for many households to get to school. For these communities, physical access could represent a binding constraint on enrolment, attainment, and learning, having long-term consequences on human capital development and poverty reduction. Even if children can reach primary schools, school quality could limit learn- ing. Data from the Ministè’e de l'Education de Base reveal that there are around 47 primary school students per teacher in Note: Estimates cover primary-school-age children Cameroon, above the averages for Sub- (those aged 6-11). Saharan Africa (37) and for lower middle Source: Ministère de l'Education de Base, WorldPop, and World Bank estimates. income countries  (29, see Figure  84).(68) Additionally, just 50.4  percent of primary schools provided drinking water, 72.5 percent provided basic latrines, and 34.0 per- cent had functional electricity. This could weaken the learning environment and deter children from attending schools that lack these elements of basic infrastructure. These issues were more widespread in rural areas and in Cameroon’s northern regions, which – as Chapter 5 shows – are the areas of Cameroon where most work is needed to improve enrolment. Therefore, improving school quality as well as school coverage and physical access represents an important component of ensuring children attend and stay in school. 68. These global estimates are taken from the WDIs, which are themselves based on UNESCO Institute for Statistics data. These data report a primary school student-teacher ratio for Cameroon of 45 – about the same as the data from Ministère de l'Education de Base. 126 Chapter 7 Poor and vulnerable Cameroonians face extra obstacles accessing markets and services Figure 84. Student-teacher ratios and access to basic infrastructure in Cameroon’s primary schools Panel A: Student-teacher ratio by urban-rural Panel B: Basic infrastructure by urban-rural Share of schools (percent) 60 100 Student-teacher ratio 50 40 50 30 20 10 0 0 Water Latrines Electricity Total Urban Rural Total Urban Rural Panel C: Student-teacher ratio by region Panel D: Share of primary schools with electricity (percent) Student-teacher ratio Share of primary schools with electricity (percent) 20.0 - 40.0 0.0 - 10.0 40.1 - 60.0 10.1 - 20.0 60.1 - 80.0 20.1 - 30.0 80.1 - 100.0 30.1 - 40.0 40.1 - 100.0 Source: Ministère de l'Education de Base and World Bank estimates. Most Cameroonians can access some form of health facility, although hospitals are harder to reach. About 22.7 percent of Cameroonians live more than one hour on foot away from their nearest health facility and 37.5  percent live more than 30 minutes away (Figure 85). Allowing for mixed transport methods, just 9.3 percent of the population live an hour away from their nearest health facility and 17.1 percent live more than 30 minutes away. This means most Cameroonians can find a way to reach some part of the health system, including clinics and health posts.(69) However, hospitals appear to have more of an access challenge, with 17.5 percent living more than one hour away and 31.2 percent living more than 30 minutes away, even when using mixed transport methods. 69. This does not include pharmacies. 127 Cameroon Poverty Assessment 2024 Figure 85. Time taken to reach health facilities in Cameroon 100 Share of the population (percent) 90 80 70 60 50 40 30 20 10 0 All Hospitals only All Hospitals only On foot Mixed transport methods <15 min. 15-30 min. 30-60 min. 1-1.5 hours 1.5-2 hours 2-3 hours >3 hours Note: Estimates cover population of all ages. Source: Bill and Melinda Gates Foundation, WorldPop, and World Bank estimates. There are relatively few areas in Cameroon where people cannot access at least some part of the health system, but this is not the case for hospitals, for which large swathes of the country live prohibitively far away. The arrondissements where reaching hospitals is more difficult are mainly concentrated in northern Cameroon and in the Est region (Figure 86). As such, while physical access is not a binding constraint for basic health services, it may prevent some Cameroonians in remote and rural areas from accessing more complex health services. Figure 86. Share of the population in each arrondissement living more than one hour from their nearest health facility by mixed transport methods Panel A: Share of the population living more Panel B: Share of the population living more than one hour from any health facility (percent) than one hour from a hospital (percent) Share of people >1 hour's Share of people >1 hour's travel to any health facility travel to hospital (percent) (percent) 0.0 - 20.0 0.0 - 20.0 20.1 - 40.0 20.1 - 40.0 40.1 - 60.0 40.1 - 60.0 60.1 - 80.0 60.1 - 80.0 80.1 - 100.0 80.1 - 100.0 Note: Estimates cover population of all ages. Source: World Health Organization (WHO), Bill and Melinda Gates Foundation, WorldPop, and World Bank estimates. 128 Chapter 7 Poor and vulnerable Cameroonians face extra obstacles accessing markets and services 7.3. Long travel times in Cameroon’s remote and rural areas reflect the limitations of the road network International comparisons suggest that Cameroon’s overall road coverage and quality, which underpin access to services and markets, could be improved. The friction surface models applied in the previous section already hint that the coverage and quality of Cameroon’s roads may be preventing people from reaching education and health facilities in certain parts of the country, given the long travel times some people face even when travelling by vehicle. Moreover, Chapter 6 showed that many farmers directly report that bad roads limit their ability to bring their output to mar- ket. International comparisons further indicate that roads be constraining physical access. The Road Quality Index, which determines road quality by conducting surveys with business leaders in all participating countries, places road quality in Cameroon below its structural and aspirational peers (Panel A of Figure 87). Indeed, in 2019, only around 6.6 percent of the country’s road network was estimated to be paved (CIA, 2024). Relatedly, World Health Organization statistics also suggest Cameroon’s roads are relatively dangerous: there are 30.2 road traffic deaths per 100,000 people in Cameroon, higher than the most of its structural and aspirational peers (Panel B of Figure 87). Figure 87. Metrics of road quality in Cameroon and comparator countries Panel A: Road Quality Index Panel B: Road traffic deaths 5 40 4.5 35 4 30 25 Estimated road traffic death rate Road Quality Index 3.5 3 20 (per 100,000 people) 2.5 15 2 10 1.5 5 1 0 Morocco Cameroon Chad Bangladesh Kenya Vietnam CentralAfrican Republic Congo, Rep. Gabon Equatorial Guinea Côte d’Ivoire Ghana Nigeria 0.5 0 Cameroon Côte d’Ivoire Bangladesh Nigeria Kenya Morocco Vietnam Ghana Structural Aspirational CEMAC Structural Aspirational peers peers peers peers Source: World Population Review (for Panel A), World Health Organization (for Panel B), and World Bank estimates. More granular analysis of geospatial data helps identify where the road network is most lacking. The same friction surface models used to examine how long it takes for Cameroonians to reach education and health facilities can also assess how long it takes to reach elements of the country’s formal road network: this means all motorways, 129 Cameroon Poverty Assessment 2024 primary roads, secondary roads, and tertiary roads.(70) Most Cameroonians can reach the formal road network relatively easily: the median time it takes to reach such a road is only around two minutes when allowing for mixed transport methods and still less than five minutes when restricting to travel on foot.(71) However, there are significant areas in the north of the country, but also in the Adamaoua and Centre regions, where the formal road network is more than one hour away, even using mixed transport methods (Panel A of Figure 88). This bears out when comparing the location of motorways, primary roads, secondary roads, and tertiary roads with data on population density (Panel B of Figure 88). These areas have less dense population, but there are still some people living there. Addressing these gaps in the road network’s coverage or upgrading the informal paths and roads that exist in these parts of the country could therefore be a key ingredient for improving access to services and markets. Figure 88. Road access in Cameroon Panel A: Mean number of minutes it takes to Panel B: Population and road network reach the formal road network Mean number of minutes Settlement population to reach the formal road network Low High 0-5 5 - 10 Road network 10 - 30 Primary 30 - 60 Secondary 60+ Tertiary Note: Estimates in Panel A cover population of all ages. Panel B covers motorways, primary roads, secondary roads, and tertiary roads, as defined by OpenStreetMap. Source: OpenStreetMap, WorldPop, and World Bank estimates. 70. The definitions of motorways, primary roads, secondary roads, and tertiary roads follow those applied on OpenStreetMap. 71. There are some less formal roads and paths that are not captured in OpenStreetMap, on which vehi- cles could potentially drive, which explains the difference between the on foot and mixed transport methods results. 130 Chapter 7 Poor and vulnerable Cameroonians face extra obstacles accessing markets and services 7.4. Congestion could hamper access to markets and services in Cameroon’s cities The travel time estimates above do not account for traffic congestion. Even when allowing for mixed transport methods, the friction surfaced models are based on the assumption that paths, tracks, and roads are clear. Therefore, while the picture these estimates paint of low access in remote and rural areas is accurate, there may be additional transport constraints in urban areas. Traffic congestion makes reaching markets and services increasingly difficult in Cameroon’s largest cities, especially for women and those from poor households. Case studies from Douala and Yaoundé reveal one-way commuting times of 2-3 hours each working day, with these travel times set to worsen in the absence of profound improvements to the transport network (Taillandier, 2022; Tatah, et al., 2022). This creates a huge drag on productivity and could even explain why people are dropping out of the labor force as urbanization advances, as shown in Chapter 6: it may simply not be profitable to travel for several hours per day to reach work. Women may suffer disproportionately from the effects of traffic congestion, given their mobility needs (Guerrero Gámez, Portabales González, Dominguez Gonzalez, & Bello, 2020). For example, they may bear more of the burden for purchasing the items the household: in Douala women account for around 8 in 10 trips to markets (Saïsset, Fouchard, & Stokenberga, 2020). Moreover, congestion’s impact on commuting times is likely to affect poor urban dwellers the most, as they are disproportionately concentrated in less dense parts of the city, typically on the periphery of metropolitan areas (as shown in Chapter 3). Congestion could therefore trap the urban poor in poverty. While physical access restricts access in remote and rural areas and conges- tion may worsen in large cities, other factors may prevent poor and vulnerable Cameroonians in the rest of the country from reaching markets and services. With Cameroon urbanizing at pace, a growing share of the population will be situated closer to education and health facilities and to major markets. Physical access mainly appears to be a constraint on using services in the remote and rural areas from which people are migrating. The subsequent sections therefore consider other factors that could influence whether Cameroonians can access services, including digital infrastructure, formal identification, and affordability. 7.5. Digital infrastructure remains out of reach for many poor and vulnerable Cameroonians Cameroon’s digital infrastructure lags comparator countries. Mobile phones and the internet are increasingly important for accessing markets and services. For exam- ple, they can improve the flow of information between market participants helping farms, non-farm enterprises, and other workers reach their consumers, supply the right products, and set the right prices (Jensen, 2007). The COVID-19 pandemic also demonstrated opportunities for remote learning for households with access to the requisite technology while digital skills are increasingly in demand among employers (Munoz-Najar, et al., 2022). In 2021, there were around 80 mobile phone subscriptions per 100 people in Cameroon and 45.6 percent of Cameroonians used the internet 131 Cameroon Poverty Assessment 2024 (Figure 89). This places Cameroon behind many of its structural and aspirational peers. The digital infrastructure that could support access to services and markets is therefore lacking for many Cameroonians. Figure 89. Digital infrastructure in Cameroon and comparator countries 180 100 Share of the population with internet access 160 90 Mobile subscriptions (per 100 people) 140 80 120 70 100 60 50 80 40 60 30 (percent) 40 20 20 10 0 0 Cameroon Central African Equatorial Guinea Chad Côte d'Ivoire Nigeria Bangladesh Kenya Morocco Vietnam Republic Congo, Rep. Gabon Ghana CEMAC Structural peers Aspirational peers Mobile subscriptions (LHS) Internet users (RHS) Source: WDIs and World Bank estimates. Access to digital infrastructure is lower for Cameroonians from poorer house- holds and for women and girls. Mobile phone ownership is much less prevalent among poorer Cameroonians. The share of Cameroonians from the bottom 40 percent of the consumption distribution who own a mobile phone is about half the share from the top 60 percent (Panel A of Figure 90).(72) The divide is even starker for internet access. The share of Cameroonians from the bottom 40 percent of the consumption distribution with internet access is about seven times lower than the share from the top 60 percent (Panel B of Figure 90). It also emerges that women and girls also have lower access to digital infrastructure than men and boys. Insofar as digital infrastructure helps people access services and markets, these gaps between different segments of the population mark a vital and growing constraint on ensuring growth is inclusive and lifting people out of poverty. 72. Questions on mobile phone ownership and internet access were only asked to those respondents aged 10 years or more. 132 Chapter 7 Poor and vulnerable Cameroonians face extra obstacles accessing markets and services Figure 90. Mobile phone ownership and internet access in Cameroon by consumption decile and sex, 2021/22 Panel A: By consumption decile Panel B: By sex Share of the population aged Share of the population aged 100 60 80 50 10+ (percent) 10+ (percent) 40 60 30 40 20 20 10 0 0 Owns mobile Internet access Owns mobile Internet phone phone access 1 2 3 4 5 6 7 8 9 10 Women and girls Men and boys Note: Sample restricted to those aged 10 or more. Source: ECAM‑5 and World Bank estimates. 7.6. Lack of formal identification may further limit access to services Formal identification is needed to benefit from certain services provided by the government. This not only includes social protection programs, discussed in Chapter 4, but also education and health. In particular, formal identification – typically in the form of a birth certificate – is needed to receive primary school leaving certificates, which are required if Cameroonians wish to continue their education on to the secondary level. Enrolment in primary education is lower among those without birth certificates, underlining the importance of formal identification. Since a birth certificate is needed to attain a primary school leaving certificate, there is a clear mechanism linking formal identification and enrolment, insofar as individuals are less likely to be enrolled if they cannot receive their school leaving certificate at the end of their studies. It is therefore unsurprising that the net primary school enrolment is signifi- cantly higher among children with birth certificates (83.3 percent) than those without (53.7 percent). However, this difference could be because enrolment and having a birth certificate are both correlated with other individual and household characteristics – in particular, they may both be proxying for households’ overall welfare levels. To check this possibility, enrolment is regressed on ownership of a birth certificate as well as a series of individual, household, and location characteristics.(73) These regressions also control directly for monetary welfare by including the log of household consumption. Using the specification with a full set of controls (the last column in Table 11) reveals that for primary-school-age children with otherwise equivalent characteristics, those with birth certificates are 15.8 percentage points more likely to be enrolled in primary school. As such, formal identification appears to have a profound impact on whether people access services. This underlines the importance of recent initiatives to reform 73. The individual characteristics included are the age and sex of the child. The household and location characteristics are the same as those used for the poverty profile in Chapter 3. 133 Cameroon Poverty Assessment 2024 and modernize identification systems in Cameroon through the 2024 Loi sur l'organ- isation du système d'enregistrement des faits d'état civil au Cameroun (Law on Civil Registration Status), discussed in more detail in Chapter 8. Table 11. Regression of primary school enrolment on having a birth certificate Adding basic Adding Adding child Adding No controls household location age and sex consumption controls controls Child has a birth 0.2959*** 0.2850*** 0.1783*** 0.1590*** 0.1579*** certificate (0.0186) (0.0185) (0.0168) (0.0166) (0.0166) N 8,630 8,630 8,630 8,630 8,630 R-squared 0.1028 0.1611 0.2066 0.2204 0.2205 Note: Sample restricted to primary-school-age children (those aged 6-11). Dependent variable is a binary variable taking 1 if the child is enrolled in primary school and 0 otherwise. Standard errors clustered at the enumeration area level are in parentheses. * p<0.10, ** p<0.05, *** p<0.01. Source: ECAM‑5 and World Bank estimates. Formal identification is less prevalent for poorer Cameroonians living in rural areas. The share of Cameroonians holding birth certificates from households in the bottom 40 percent of the consumption distribution is about half the share from those in the top 60 percent (Figure 91).(74) A similar gap exists between rural and urban areas, although more than a third of urban dwellers do not have birth certificates either. As such, the absence of formal identification could compound the constraints on reaching services imposed by lack of physical access and weak infrastructure or create a new problem for those in urban areas. Figure 91. Ownership of birth certificates in Cameroon by consumption decile and urban-rural, 2021/22 Panel A: By consumption decile Panel B: By urban-rural Share of the population aged less than 15 Share of the population aged less than 15 80 70 70 with a birth certificate (percent) with a birth certificate (percent) 60 60 50 50 40 40 30 20 30 10 20 0 10 1 2 3 4 5 6 7 8 9 10 Total 0 Decile of the real consumption distribution Urban Rural Note: Sample restricted to those aged less than 15. Source: ECAM‑5 and World Bank estimates. 74. Questions on birth certificates were only asked for those aged less than 15 years. 134 Chapter 7 Poor and vulnerable Cameroonians face extra obstacles accessing markets and services 7.7. The analysis can be synthesized to provide a framework for policy action This chapter has explored several factors that could be stopping Cameroonians reaching the markets and services they need, all of which present options for poverty-reducing policies. For some Cameroonians in remote and rural areas, phys- ical access to markets and services remains a problem, compounded by limits to the country’s road network. Yet as the country urbanizes, congestion poses a growing constraint for those living in towns and cities. Moreover, lack of digital infrastructure and formal identification could hamper access across all parts of Cameroon. Addressing these issues could help build human capital – as discussed in Chapter 5 – and enhance livelihood opportunities – as discussed in Chapter 6. The final chapter of the report tries to synthesize the policy messages from across the report, outlining how Cameroon can seize its potential, improve on the last two decades, and sustainably lift people out of poverty.  135 Cameroon Poverty Assessment 2024 Chapter 8. KEY MESSAGES ➜ Cameroon has enormous poverty-reducing potential, but given poverty’s persistence and the growing threat of climate, conflict, and other shocks, it needs to act now ➜ Poverty reduction hinges on making growth quicker and more inclusive; reforms to encourage private sector investment, harness international trade, and unlock fiscal resources for pro-poor spending can help ➜ Creating productive jobs is the main vehicle for making growth more inclusive, and represents the country’s top policy priority according to Cameroonians themselves ➜ Institutionalizing and expanding social assistance, investing in new digital infrastructure, and addressing gaps in formal identification could benefit households across Cameroon ➜ Yet policies must also be tailored for different parts of Cameroon; reinforcing decentralization may help achieve this ➜ The country’s lagging regions need additional attention for three basic building blocks of poverty reduction – human capital, agricultural productivity, and basic infrastructure ➜ Emerging challenges in towns and cities require better urban planning, improving intra-city transport, revitalizing housing policy, and integrating rural-urban migrants ➜ Given Cameroon’s diverse and changing development challenges, data will remain critical for guiding poverty-reducing policies and building good governance With the right policies, Cameroon can harness its huge poverty-reducing potential T his final chapter of the poverty assessment synthesizes the main mes- sages of the previous chapters and describes the policy options that could lift Cameroonians out of poverty. These policy messages come at a crucial time for Cameroon, as the country nears the halfway point of NDS30, opening a key window for reform. First, the chapter emphasizes the urgent need to act now for Cameroon to seize its potential for lifting people out of poverty. Second, the chapter considers the cross-cutting policies that stand to benefit all Cameroonians. This includes macroeconomic reforms to accelerate growth and create productive jobs as well as interventions to improve social assistance, digital infrastructure, and formal identification. Third, recognizing the diversity of Cameroon’s development challenges, the chapter focuses on the policies needed to lift people out of poverty in the country’s lagging regions – those who risk being left behind. Fourth, the chapter proposes policies that can help Cameroon maximize the benefits of urbanization and address urban poverty. Finally, the chapter reinforces the crucial role of collecting and analyzing data for poverty reduction in Cameroon – for designing, targeting, monitoring, and evaluating policies and for promoting accountability and good governance. Figure 92 summarizes the chapter’s main policy recommendations. 137 Cameroon Poverty Assessment 2024 Figure 92. Policies to harness Cameroon’s poverty-reducing potential Promoting peace, security, and good governance Policies to make growth faster and more inclusive and create jobs Improving the environment for private investment Harnessing international trade Unlocking fiscal space for pro-poor spending Cross-cutting support for all Cameroonians Institutionalizing and expanding social assistance Increasing access to digital infrastructure Addressing gaps in formal identification Reinforcing decentralization Special attention for lagging regions Tailored solutions for urban areas Continued investment in human capital Improving urban planning Boosting agriculture’s productivity and resilience Developing intra-city transport to climate-related shocks Transforming housing policy Upgrading access to roads and electricity Providing training for rural-urban migrants Source: World Bank. 8.1. Cameroon has huge poverty-reducing potential, but it urgently needs to boost the process Through its natural capital and human capital, Cameroon has enormous potential for successfully and sustainably reducing poverty. Cameroon’s geography – situated at the gateway between Western and Central Africa – and its natural resources alone equip the country with the ingredients of rapid growth. This is coupled with relative political stability, despite the recent uptick in conflict in the Extrême-Nord, Nord-Ouest, and Sud-Ouest regions. Indeed, growth in Cameroon has at least proved resilient, if not rapid, in recent years, maintaining momentum even during the peak of the COVID- 19 crisis. Yet even more important for Cameroon’s poverty reduction prospects are its people. The country has a sizeable demographic dividend to exploit: with around 7 in 10 Cameroonians being aged less than 30, young workers or workers-to-be are in high supply. Moreover, while there is still room for improvement, Cameroon has enjoyed marked progress for key human capital outcomes in the past two decades, demonstrating Cameroonians’ growing productive potential. However, with stagnant poverty reduction since the turn of the millennium, an expanding population, and growing shocks, Cameroon urgently needs policies to lift people out of poverty. Around 4 in 10 Cameroonians live below the national poverty line, and this situation has changed little in two decades. Coupled with pop- ulation growth, this means the number of poor Cameroonians has been rising and now exceeds 10  million people. The latest projections indicate that this situation is 138 Chapter 8 With the right policies, Cameroon can harness its huge poverty-reducing potential unlikely to change substantially with the current policy mix. Moreover, conflict and climate-related shocks have worsened in certain parts of the country and could push or trap even more people in poverty. The frequency and intensity of climate-related shocks is projected to increase. Even among non-poor households, many are only just above the poverty line, leaving them vulnerable to falling below it. As such, delaying policy reforms and programs to reduce poverty could make the scale of the challenge even larger. As Cameroon urbanizes, its development challenges are changing, so policies need to be adjusted accordingly. Alongside the changing profile of conflict and cli- mate shocks, several other “megatrends” are altering the mix of policies that Cameroon requires to reduce poverty. In particular, the share of Cameroonians living in urban areas has risen by around one half in the last two decades, leaving Cameroon as one of the most urbanized countries in Sub-Saharan Africa. An increasing proportion of urban growth can also be attributed to rural-urban migration, rather than rural areas being reclassified as urban or natural population growth being faster in towns and cities. Cameroon’s labor market is also experiencing changes that are closely linked to urbanization. The share of working-age Cameroonians who are working fell by a staggering 20.0 percentage points between 2007 and 2021. In part this is a byproduct of the share of people working being lower in urban areas than in rural areas, but urban labor markets are also showing signs of saturation, being unable to absorb new migrant workers. At the same time, those who are working are increasingly becoming concentrated in services rather than agriculture. With the profile of poverty changing in Cameroon, “business-as-usual” policies will not work. Given the diverse nature of Cameroon’s development challenges, poverty-reduc- ing policies need to be tailored for different people in different parts of the country. Some poverty-reducing policies are cross-cutting, helping to alleviate constraints on poverty holistically. Yet there are some policies that need to be tailored to the spe- cific development challenges that different types of Cameroonians face. Therefore, alongside cross-cutting solutions, the chapter considers specialized measures to (1) support poverty reduction in Cameroon’s lagging regions and (2) tackle growing concerns around urban poverty. 8.2. Promoting peace and security is a precondition for poverty reduction Peace and security underpin all efforts to reduce poverty in Cameroon, so address- ing the crisis in the Extrême-Nord, Nord-Ouest, and Sud-Ouest regions is vital. In the past two decades, Cameroon has faced increasing conflict and violence. While this has largely been concentrated in the Extrême-Nord, Nord-Ouest, and Sud-Ouest regions, it has the potential to spread. Many of Cameroon’s neighbors also endure conflict and fragility, which could spill over the border. The causal links between con- flict, displacement, and poverty are complex. However, globally, poverty is increasingly becoming concentrated in fragile and conflict-affected settings (Corral, Irwin, Krishnan, Mahler, & Vishwanath, 2020). In many areas, especially in Cameroon’s lagging regions, poverty and conflict clearly overlap. Alongside the direct death and injury brought about by conflict, it also interrupts human capital development, hampers livelihoods, and 139 Cameroon Poverty Assessment 2024 destroys infrastructure. Progress on any of the policies recommended below can be quickly reversed by conflict and violence. Therefore, strengthening peace and security remains critical for helping Cameroonians exit poverty. 8.3. Lifting Cameroonians out of poverty hinges on making growth quicker and more inclusive and creating productive jobs Cameroon needs to accelerate overall growth to lift people out of poverty. Even though Cameroon has generally achieved positive economic growth since the 1990s, GDP per capita remains lower today than it was in the 1980s, when a spate of commod- ity price shocks rocked the economy. Moreover, many countries whose living standards were similar to Cameroon in 1990 have grown and reduced poverty much faster since then. As Chapter 3 shows, what little poverty reduction Cameroon did achieve in the last two decades can be attributed more to overall growth than to changes in the distribution of consumption. Therefore, speeding up growth remains a key foundation for poverty reduction. However, as well as making growth quicker, it also needs to be made more inclusive, which depends on creating productive livelihoods opportunities for all Cameroonians. People in the bottom third of the consumption distribution have not seen any of the proceeds of growth in the last two decades – if anything their consumption levels dropped. Relatedly, inequality in Cameroon is higher today than it was 20 years ago and is above the level observed in its aspirational peers. Since the main asset of poor and vulnerable households is their labor, jobs stand as the main vehicle for sharing the proceeds of growth (Fields, 2011). Even though labor productivity in services is higher on average than in agriculture, the shift in jobs from agriculture to services is not helping people exit poverty. This is because the specific types of service sector jobs towards which Cameroonians are shifting are not suffi- ciently productive. Many are in commerce, transport and communication, and personal services – sub-sectors which are not associated with exiting poverty. Alongside the direct economic effects, lack of productive jobs could also fuel frustration and potentially conflict. Evidence from across Sub-Saharan Africa demonstrates that people – especially young people – without productive livelihoods are more susceptible to be drawn into conflict (Cramer, 2010; UNDP, 2023). While conflict remains localized in Cameroon it could still be exacerbated by low employment prospects, especially as young people – and women – face additional constraints in finding productive jobs. Moreover, across the country, Cameroonians themselves report that jobs are by far their biggest policy priority, as shown in Box 7. When com- bined with low trust in Cameroon’s institutions and a perception that the country is underperforming economically, frustration around economic opportunities appears to be high among Cameroonians. To achieve faster and more inclusive growth that generates productive jobs, three streams of macro-fiscal reforms could help; first, Cameroon can improve the environment for private investment. Despite some reforms, Cameroon’s business environment is less supportive of investment than other countries in the region and 140 Chapter 8 With the right policies, Cameroon can harness its huge poverty-reducing potential its aspirational peers. In particular, taxes that are applied unevenly across sectors, regulatory barriers, and weak property rights – especially for land – weaken competi- tion and disincentivize private sector investment (Amoretti & Maur, 2022). Addressing these issues could support firm entry, firm growth, and the creation of productive jobs. Additionally, some industries are dominated by state-owned enterprises (SOEs), especially transport, banking, agriculture, utilities, manufacturing, and oil and gas. Scaling back the subsidies and other support that SOEs receive could help private businesses expand and thrive in these sectors (Coulibaly, Benlamine, & Piazza, 2022). Second, Cameroon can harness its natural advantages in international trade. Given its geography and its abundance of natural resources, trade could be a much larger engine of growth for Cameroon’s economy. Douala in particular acts as a natural trade hub for Central Africa. Yet exports are currently dominated by gas and oil alongside other mineral products, for which value is added – and hence jobs are created – outside of Cameroon. Diversifying the products that Cameroon exports could therefore support inclusive growth and job creation. In part, this depends on improving the infrastructure needed to link domestic producers with markets inside and outside of Cameroon better – this is discussed in more detail below. Yet reforming trade policies, including tariffs, could also help. In particular, high import tariffs can thwart export diversification, as they may result in retaliatory tariffs by other countries, distort global transport patterns, reduce incentives for domestic producers to make their goods competitive on international markets, and make intermediate goods that exporters need more expensive (Hayakawa, Ishikawa, & Tarui, 2020; WTO, 2023; Boer & Rieth, 2024). Cameroon currently has relatively high tariffs compared to regional peers and lower middle income countries and, despite being part of CEMAC, deviates from the trade bloc’s agreed tariffs for around 300 items (WITS, 2024). At the same time, entry points like Douala can be made more efficient, by reducing non-tariff barriers (World Bank, 2015). Growing and diversifying exports in this way can help expedite growth and – by creating jobs – make it more inclusive. Third, maintaining momentum on fiscal reform can unlock the resources needed for pro-poor policies. Investments in human capital, infrastructure, and social assis- tance – the need for which is outlined below – will not come entirely from the private sector, so require significant government spending. The resources to finance this spending have to come from somewhere. Since Cameroon’s tax-to-GDP is low relative to other countries in the region, efforts to broaden the tax base in progressive ways could help (OECD, African Union Commission, and African Tax Administration Forum, 2023). Yet Cameroon can also create fiscal space by diverting spending away from regressive programs and subsidies. As Box 8 demonstrates, subsidies for fuel and electricity disproportionately benefit richer Cameroonians, a situation which is common across low and middle income countries (Breton & Mirzapour, 2016; Calvo-Gonzalez, Cunha, & Trezzi, 2017). However, even though richer households benefit more, households across the consumption distribution will feel the effects of inflationary pressure on their purchasing power, necessitating countervailing policies to cushion the impacts for the poor and vulnerable as subsidies are reduced. Cameroon has already started these reforms, reducing fuel subsidies in 2014, 2016, 2023, and most recently in February 2024. However, policies designed to offset the effects of subsidies may not reach the poorest Cameroonians. For example, the government’s compensatory policy of raising minimum wages and public sector pay will not be felt by most workers from poor households, as they are concentrated in informal, self-employment jobs and 141 Cameroon Poverty Assessment 2024 enforcement of minimum wages is imperfect. Therefore, maintaining the pace of fiscal reforms and ensuring fiscal savings are channeled towards programs that can help the poorest Cameroonians is an essential policy priority. The subsequent sections of this chapter outline which programs can most support the poor, tailoring these efforts for different parts of the country. Box 7. What are Cameroonians’ policy priorities? Asking Cameroonians explicitly what they think of current policies complements this report’s analysis. This provides an alternative – and potentially more direct – perspective on potential poverty-reducing policies. Successful policy reform rests on political support and buy-in from the population. Questions on subjective poverty, trust, and perceptions of which policy issues should be prioritized were asked not only in ECAM-5 but also in the Afrobarometer survey, of which the last round was conducted in April and May of 2022. Most Cameroonians describe themselves as poor or very poor and have a negative perception of the country’s economy. About two-thirds of Cameroonians live in a household describing itself as either poor or very poor, with this share being higher in rural areas than in urban areas (Figure 93). Virtually no Cameroonians regard themselves as rich. About 6 in 10 Cameroonians have a very bad or quite bad perception of the economy. Thus, the urgent need to generate inclusive growth and reduce poverty resonates strongly with Cameroon’s population. Figure 93. Perceptions of poverty and the economy in Cameroon Panel A: Subjective poverty Panel B: Perception of the economy Share of the population 100 100 Very good 80 Share of respondents 80 (percent) 60 Quite good (percent) 40 60 Neither good 20 40 nor bad 0 Quite bad Total Urban Rural 20 Very bad Very poor Poor Average Rich 0 Note: Individual-level weights applied in Panel A, so the results present the share of the whole population. Panel B shows unweighted share of Afrobarometer respondents. “Do not know” responses excluded. Source: ECAM-5 (for Panel A), 2022 Afrobarometer (for Panel B), and World Bank estimates. Against this backdrop, Cameroonians have low trust in the country’s key institutions, making reforms to promote inclusive growth and poverty reduction even more important. More than 6 in 10 Cameroonians had no trust or just a little trust in the parliament, in local governments, and in the judiciary (Figure 94). Moreover, institutional trust was lower in urban areas, so urbanization could make this worse. This adds even more urgency to the need for reforms that can help share the proceeds of growth and lift people out of poverty; such policies may build trust. Without this, evidence from other countries suggests that more difficult reforms – such as reducing subsidies and broadening the tax base – are made even harder (Chelminski, 2018; Rose & Plant, 2021). 142 Chapter 8 With the right policies, Cameroon can harness its huge poverty-reducing potential Figure 94. Trust in key institutions in Cameroon 100 Share of respondents (percent) 90 80 70 60 50 40 30 20 10 0 Total Urban Rural Total Urban Rural Total Urban Rural Total Urban Rural President Parliament Local government Judiciary No trust Just a little trust Partial trust A lot of trust Note: Results show unweighted share of Afrobarometer respondents. “Do not know” responses excluded. Source: 2022 Afrobarometer and World Bank estimates. Jobs are Cameroonians’ number one policy priority. Around half of Cameroonians believe that lack of jobs is the main cause of poverty in Cameroon, significantly more than any other single factor (Figure 95). Relatedly, job creation is by far the most prevalent policy suggested by Cameroonian households to address poverty. This echoes one of this report’s key messages: generating productive jobs will be crucial for sharing the proceeds of growth and lifting Cameroonians out of poverty. Figure 95. Cameroonians’ main policy priorities Panel A: Perceived causes of Panel B: Poverty-reducing policy poverty priorities Share of households (percent) Share of households (percent) 70 60 60 50 50 40 40 30 30 20 20 10 10 0 0 bs ng n on s rs th oad io to jo ivi ati pt fac fl uc r of ru of to ed or ck er ck os of La C La C O ck La Total Urban Rural Total Urban Rural Note: Household-level weights applied, so the results present the share of households. Results show what each household believes is the primary cause of poverty in Cameroon (Panel A) and their main policy priority (Panel B). Source: ECAM-5 and World Bank estimates. 143 Cameroon Poverty Assessment 2024 Box 8. Fiscal incidence analysis for Cameroon Cameroon has recently conducted a Commitment to Equity (CEQ) assessment to examine how the country's fiscal system affects different households across the welfare distribution. This approach – for which the application to Cameroon is described in Houts et al. (Forthcoming) – involves mapping administrative budget data into ECAM-5 to see how taxes and government spending impact different households, depending on whether they are covered by different programs (Lustig, 2022). On the taxation side, this includes value-added tax (VAT), income tax, and customs duties paid on imported goods. On the spending side, this includes cash transfers, including from PFS, and subsidies for fuel and electricity. When estimating the effects of the fiscal system on inequality, in-kind transfers in the form of health and education spending are also included. However, these are not included when estimating the impacts on poverty, because these elements of human capital spending do not directly affect current consumption, the metric in which poverty is cast. According to the CEQ analysis, Cameroon’s fiscal system moderately reduces inequality. The Gini coefficient for final consumption is 3.3 points lower than the Gini coefficient would be absent any tax or spending measures. This difference is largely driven by the equalizing effects of in-kind transfers, that is, health and education spending. Even though Cameroon’s fiscal system successfully redistributes income, it does so less than other countries in which a CEQ has been conducted. However, Cameroon’s fiscal system impoverishes more people than it lifts out of poverty. The CEQ analysis indicates that the fiscal system increases the poverty rate by around 2.1 percentage points. This is because a large share of revenue raising comes from indirect taxes, which are levied on both rich and poor households: Cameroon’s VAT rate is one of the highest in Africa and the exceptions to the CEMAC standard tariffs leave customs duties higher too. Moreover, government revenue is directed towards regressive subsidies for fuel and electricity that disproportionately benefit richer households – as they devote more of their consumption to these goods – both directly and through the knock-on effects on transport costs (Figure 96). Cash transfers are simply too small to offset this. Channeling government resources away from regressive subsidies, as recent reforms aim to do, would offer a much better chance of inclusive growth and poverty reduction, providing the savings are channeled towards pro-poor programs. Figure 96. Share of consumption devoted to fuel, transport, and electricity by consumption decile 12 Share of consumption (percent) 10 8 6 4 2 0 1 2 3 4 5 6 7 8 9 10 Total Decile of the real consumption distribution Fuel for vehicles or generators Transport Electricity Source: ECAM-5 and World Bank estimates. 144 Chapter 8 With the right policies, Cameroon can harness its huge poverty-reducing potential 8.4. Channeling government spending towards social assistance and investing in digital infrastructure could benefit Cameroonians across the country Since social assistance measures are better targeted to poor households than sub- sidies, but are currently dwarfed by the extent of poverty in Cameroon, the social assistance system could be institutionalized and coverage could be expanded. While long-run, sustainable poverty reduction hinges on inclusive growth and creating productive growth, social protection can help households weather shocks and boost welfare in the short run. The government’s contribution to PFS increased from less than 5 percent in the first phase (2013-2018) to 60 percent in the second phase (2019-2022). Yet coverage remains limited: only around 2.6 percent of all Cameroonians and 4.2 per- cent of poor Cameroonians lived in a household receiving cash transfers in 2021/22. Even among this small share, not all transfers were provided by the government through PFS, as non-government actors – including the World Food Program (WFP) – were also distributing cash transfers during this period. Even PFS is currently dependent on donor support and concessional lending, so further effort is needed to institutionalize social assistance in Cameroon, including by building the government structures to house social assistance programs and ensuring long-term and predictable budget is available (World Bank, 2023). At the same time, despite being small, social assistance measures in Cameroon have proved successful in reaching poorer households more than richer ones: they are significantly more progressive than fuel and electricity subsidies. This demonstrates their potential as a tool for redistribution to make growth more inclusive. However, existing social assistance programs are still reaching many non-poor house- holds, so targeting methods would likely need to be improved if measures are scaled up. A unified social registry would be a key steppingstone for enhancing social assistance in Cameroon by improving the targeting, efficiency, and coordination of support for vulnerable populations. This aligns with ongoing efforts by the government and develop- ment partners to strengthen Cameroon’s social protection systems. Expanding access to formal identification and building digital infrastructure could support this. Equally, using innovative techniques involving geospatial data – as the small-area poverty maps in Chapter 3 show – could facilitate geographical targeting. Social assistance can offer both short- and long-run benefits, especially when combined with other pro-poor interventions. In the short run, social assistance can increase household consumption for those facing extreme forms of deprivation and stabilize incomes for those facing shocks. Yet it can also have broader, more sustaina- ble benefits. First, by protecting households against shocks, it can enable households to engage in higher-risk, higher-reward livelihood activities that hinge on investing in physical capital (Bowen, et al., 2020). Second, they can help households invest in human capital, building their members’ future productive potential (Carneiro, et al., 2021). Providing cash (or food) alongside training and support to livelihood opportuni- ties or targeted investments in health and education services – through so-called “cash plus” programs – could double down on these types of long-run benefits (Gentilini, 2016; Banerjee, Karlan, Darko Osei, Trachtman, & Udry, 2020; Gilligan, et al., 2020; Loeser, Özler, & Premand, 2021). This is already happening through PFS, which not only includes a cash-for-work component but also provides training and other support for early childhood development and climate adaptation to those receiving regular 145 Cameroon Poverty Assessment 2024 cash transfers (World Bank, 2022).(75) Third, social assistance could even reduce the incidence of conflict (Fetzer, 2020). This would help alleviate a significant barrier to poverty reduction in the Extrême-Nord, Nord-Ouest, and Sud-Ouest regions. Investing in digital infrastructure could reduce inequality and provide additional pathways to poverty reduction. Strong digital infrastructure offers many potential benefits: it can help spread information between markets, supply farmers with data on climate-related shocks so they can plan, provide remote education and health services, increase the government’s administrative reach for social protection programs, and promote financial inclusion (Hong, 2023). However, many Cameroonians, both in rural and urban areas, currently lack mobile phones and access to the internet. Access is most lacking for the poorest Cameroonians. Therefore, the numerous benefits of digital infrastructure could be exacerbating inequality (Gandhi, 2019). Predicating pro-poor programs on people having digital access risks entrenching inequality further. While exploiting the opportunities of mobile phones and the internet, the government could also play an active role in ensuring all Cameroonians have access, by simultaneously promoting private investment in telecommunications and building the skills required to use these tools effectively (Vora & Dolan, 2022). Relatedly, addressing gaps in formal identification will be crucial for building the foundations of pro-poor programs. Around half of Cameroonian children do not have birth certificates. Yet, at the time of writing, birth certificates are officially required to receive a primary school leaving certificate and continue education at the secondary level. Data on enrolment suggest this is a binding constraint on going to school, even after controlling for key individual and household characteristics. Placing such strict bureaucratic limits linked to formal identification on who can and cannot access education, health, and other services risks excluding the poor and vulnerable and increasing inequality, so should be reduced or eliminated. Contemporaneously, access to formal identification could be broadened to help increase the government’s administrative reach – insofar as social assistance programs can be used to give people formal identification, this could represent an additional long-run benefit of expanding social assistance. Recent government initiatives could help to improve formal identifi- cation in Cameroon: the 2024 Loi sur l'organisation du système d'enregistrement des faits d'état civil au Cameroun (Law on Civil Registration Status), seeks to modernize Cameroon’s civil registration system to meet international standards. It introduces key enhancements, such as digitization, computerization, and the creation of a unique personal identification number. 75. Monitoring data from the pilot of PFS – conducted in Soulédé-Roua commune in the Extrême-Nord region and in Ndop commune in the Nord-Ouest region – reveal that spending on education and health services as well as agricultural investments were among the most common uses of the cash transfers (World Bank, 2018). 146 Chapter 8 With the right policies, Cameroon can harness its huge poverty-reducing potential 8.5. Reinforcing decentralization could allow Cameroon to tailor local policies for local needs Cameroon also needs bespoke poverty-reduction solutions for different parts of the country. The previous sections have considered macro-fiscal reforms and options for expanding social assistance, digital infrastructure, and formal identification that could lift Cameroonians across the country out of poverty. Yet given the diversity of the country’s development challenges, bespoke local policies may be needed. In theory, the renewed emphasis on decentralization in Cameroon could help tailor policies for local development challenges better. Momentum for decen- tralizing policymaking has been growing in recent years. Decentralization is seen as a way to alleviate conflict in the anglophone Nord-Ouest and Sud-Ouest regions and addressing regional inequality. In this vein, Law n°2019/024 instituted the Code Général des Collectivités Territoriales Décentralisées (General Code of Decentralized Territorial Collectivities, CGCTD) in 2019, which extended competencies to regions and communes and set a minimum amount – 15 percent of the total budget – to be transferred from the central government to local governments (Fall, Frisa, & Nkounga, 2021).The broad competencies devolved to local governments include promoting agriculture, natural resource management, protecting the environment, urban planning and housing, regional health, and secondary education. The 2019 law builds on the general framework for decentralization adopted in 2004 and the 2008 decree to replace provinces with regions. In theory, decentralization can help ensure that provision of local services matches local needs, improve accountability, and reduce tensions between different regions, especially between anglophone regions and the central authorities (World Bank, 2012; Myerson, 2021). More recent initiatives could enhance decentralization, but their full effects may not yet be felt. In particular, the November 2024 local taxation law seeks to strengthen the financial autonomy of local authorities. This law introduces a local development tax, deducted from the base salary of workers in both the public and private sectors, ranging from 3,000 to 30,000 XAF per year. The revenue generated by this tax will be used to bolster municipalities' capacity to deliver essential services to the population, such as public lighting, sanitation, waste collection, ambulance services, water supply, and electrification. In practice, at least three factors constrain decentralization in Cameroon. First, political constraints remain: the central government maintains tight supervision of local governments through governors and prefects, especially for budget decisions (Fall, Hilger, Vaillancourt, Perrot, & Daller, 2020). At the commune level, supervisory author- ities can even suspend or dissolve municipal councils and suspend mayors. Second, administrative constraints remain: the competencies devolved to local governments are only defined in very broad terms, so sub-functions – the regulation, provision, and financing of specific services – have not been clearly delineated. The capacity and autonomy of region- and commune-level authorities is therefore limited. Third, fiscal constraints remain: since local governments can decide on very few local taxes, they rely on the central government for funding. However, the allocations to local government are well below the target of 15 percent of the central government budget. In 2023, the share was just 4.0 percent (Fall, Mituzani, & Vaillancourt, 2023). Moreover, while NDS30 directly calls for reducing regional inequality, the specific form and parameters of the 147 Cameroon Poverty Assessment 2024 fiscal formula for allocating funding to regions and communes remains unclear.(76) Addressing these issues is vital for maximizing the benefits from decentralization. This is especially important for preparing bespoke policies for Cameroon’s lagging regions and its growing urban areas. 8.6. Lagging regions still need investment in human capital, agricultural productivity, and the underlying bedrock of basic infrastructure Remote and rural areas, especially in Cameroon’s northern regions, require addi- tional policy attention on three basic pillars of poverty reduction – human capital, agricultural productivity, and basic infrastructure – to avoid being left behind. Monetary and non-monetary poverty are significantly higher in the Extrême-Nord, Nord, and Nord-Ouest regions. Between 2001 and 2014, monetary poverty in these regions was also rising, leaving them even further behind the rest of the country. As the small- area poverty map demonstrates, there are also remote and rural areas outside these regions where poverty is highly concentrated. Therefore, these parts of Cameroon need renewed focus to overcome constraints on poverty that have been known about for decades (Bolch, Ceriani, & López-Calva, 2022; World Bank, 2022). Three key drivers of poverty reduction warrant particular attention: (1) human capital, (2) agricultural productivity, and (3) basic infrastructure. Cameroon’s lagging regions require continued investment in human capital to lift people out of poverty sustainably. Improving health and education outcomes, especially in the early part of people’s lives can support long-run, intergenerational poverty reduction by building people’s productive potential (Bhula, Mahoney, & Murphy, 2020; Holla, Bendini, Dinarte, & Trako, 2021). The poverty profiles in Chapter 3 suggest a clear association between literacy and poverty reduction in Cameroon, echoing results from other countries. These poverty profiles also reveal the strong link between water and sanitation outcomes, which could proxy for human capital, and poverty in Cameroon.(77) Even though rural areas have gradually been catching up, human capital outcomes in Cameroon’s lagging regions remain behind the rest of the country and need a boost. Five strategies can help develop human capital in Cameroon’s lagging regions: (1) improving physical access to education, (2) raising the quality of education and health facilities, (3) enhancing vocational and technical education, (4) addressing out-of-pocket expenses, and (5) combining human capital interventions with social 76. Recent cross-region evidence on the fiscal incidence of government programs is mixed. For example, in 2017, per capita allocations for health programs were generally higher in regions with lower under- five mortality rates (World Bank, 2022). Similarly, in 2016, school-level non-teacher allocations were highest in the Littoral (including Douala), Centre (including Yaoundé), and Sud-Ouest regions, where educational outcomes are better and monetary poverty is lower. On the other hand, in 2022, the share of primary schools receiving the paquet mininimum – a minimum package of school supplies – was generally higher in Cameroon’s northern regions where monetary and education deprivation is highest. 77. This chimes with global evidence: diarrheal diseases – 90 percent of which can be attributed to poor water, sanitation, and hygiene (WASH) outcomes – are the leading cause of morbidity and mortality amongst children under five (Ramesh, Blanchet, Ensink, & Roberts, 2015). 148 Chapter 8 With the right policies, Cameroon can harness its huge poverty-reducing potential assistance. As Chapter 7 demonstrates, many Cameroonian children in remote and rural areas cannot reach schools, regardless of whether they travel on foot or have access to other transport modes. Either new facilities are needed in those areas or the roads required to reach existing facilities need to be improved. However, boosting learning outcomes also requires investment in the quality of health and education facil- ities: schools in northern Cameroon have significantly larger student-teacher ratios and are less likely to have adequate drinking water, latrines, and electricity. Furthermore, improved vocational and technical education – with a focus on skills that can make self-employment more productive – is crucial for helping young people succeed in Cameroon’s labor market. This is directly reflected in the NDS30, which sets the target of expanding the share of students engaged in vocational and technical training from 10 percent to 25 percent at the secondary level and from 18 percent to 35 percent at the tertiary level between 2020 and 2030. More broadly, out-of-pocket expenses for health and education services could deter poorer households from investing in human capital, so options to reduce these prohibitive financial costs for the poor and vulnerable could be explored. Finally, since the overlap between education deprivations and monetary poverty is larger in rural areas and in Cameroon’s northern regions, there are clear opportunities to “bundle up” interventions that jointly try to increase consumption – including cash transfers – with human capital interventions. This can help support poverty reduction in both the short run and the long run. Since farming is central to livelihoods in Cameroon’s lagging regions but climate change poses a growing threat, improving access to agricultural input and output markets remains essential for lifting people out of poverty. Building human capital will only yield poverty reduction in the long run if livelihoods that can mobilize people’s productive potential are available. In northern Cameroon and in remote and rural areas, livelihoods are – and will continue to be – concentrated in agriculture. The specific types of agricultural activities in which Cameroonian farmers engage varies hugely across the country, so interventions will need to be tailored to account for this. Yet two broad principles apply for all farmers. First, input markets should be bolstered to ensure farmers can obtain the seeds, fertilizers, tools, and labor that they need. For some of these inputs, pecuniary constraints do not appear to be binding, so expanding their take-up relies on ensuring the infrastructure is in place to allow these inputs to reach farms, helping farmers access complementary inputs including irrigation, or providing information and training about potential benefits – this is where building digital infrastructure could be especially useful. This is especially important as farmers seek strategies that enable them to adapt to the growing threat of climate-related shocks. Reinforcing land tenure can also encourage investment in farming activities, help farmers access credit by providing collateral, and reduce inefficiencies associated with land-related conflicts. Second, many Cameroonian farmers also complain about lack of access to output markets. Addressing this depends not only on developing basic infrastructure to allow produce to reach domestic consumers but also adapting trade policies to create opportunities in external markets. Enhancing the bedrock of basic infrastructure – especially roads and electricity – is vital for Cameroon’s lagging regions. The overall quality of Cameroon’s roads and susceptibility to road traffic deaths is worse than in its structural and aspirational peers. Additionally, in some parts of Cameroon, especially in the country’s north but also in the Adamaoua and Centre regions, it can take more than an hour for people to reach the 149 Cameroon Poverty Assessment 2024 formal road network.(78) The road network must therefore be extended and upgraded to allow Cameroonians in lagging regions to reach the facilities needed for human capital development and to help farmers access input and output markets. Similarly, electricity access is a keystone for building digital infrastructure and can augment the quality of care and schooling in health and education facilities. Global evidence also directly shows how electrification can expand people’s livelihood opportunities (Ratledge, Cadamuro, de la Cuesta, Stigler, & Burke, 2022). More broadly, roads, electricity, and other infrastructure are essential for integrating Cameroon’s different regions. This can help ensure the spillover effects of economic activity in Douala, Yaoundé, and other major cities benefit Cameroonians at large. 8.7. Addressing congestion and overcrowding and supporting integration of migrants could help realize the poverty-reducing potential of urbanization Urbanization necessitates a new focus on eliminating poverty in Cameroon’s towns and cities. The notion that poverty in Cameroon is purely a rural phenomenon is no longer true. In 2021/22, around one third of poor Cameroonians – some 3.1 million people – lived in urban areas. The dynamics of urbanization suggest that urban poverty is unlikely to go away without concerted policy effort. Urbanization is increasingly being driven by rural-urban migration, with migrants seeking economic opportunities or being pushed out by conflict. This creates a direct link between what is happening in Cameroon’s lagging regions and the poverty profile of its towns and cities. Addressing congestion through urban planning and improved intra-city trans- port could help maximize the productivity benefits of urbanization. In theory, urban centers can accelerate growth, job creation, and poverty reduction through agglomeration effects, including sharing public goods, knowledge spillovers, closer proximity to consumers, and better matching of workers and employers (Bolter & Robey, 2020). Urban areas are denser, lessening distances to services and markets. Yet congestion interrupts these effects and drains productivity in Cameroon’s towns and cities. Commuters in Douala and Yaoundé report needing 2-3 hours each way to reach their place of work (Tatah, et al., 2022). In turn, this may explain why new service-sector jobs in urban areas have not been productive enough to lift people out of poverty. Strengthening urban planning policies could improve the organization of where people live, work, and buy goods in towns and cities. This could be linked to decentralization: efforts to decentralize policy decisions, especially to the commune level, may support urban planning by allowing policymakers in towns and cities to match local services with local needs. Investing in intra-city public transport may also reduce traffic congestion. For example, Douala’s Bus Rapid Transit (BRT) system, which is set to be launched in 2024, is forecast to reduce the time that the average Douala resident spends in traffic from 88 to 71 minutes per day – a sizeable gain, but still leaving room for improvement (Saïsset, Fouchard, & Stokenberga, 2020) (Figure 97). Enabling Cameroon’s towns and cities to function more effectively is crucial for generating – and making accessible – the productive jobs that urban Cameroonians need to escape poverty. 78. The formal road network includes motorways, primary roads, secondary roads, and tertiary roads. 150 Chapter 8 With the right policies, Cameroon can harness its huge poverty-reducing potential Figure 97. Average time spent in transport in Douala with and without the Douala Urban Mobility Project investment in the Douala Bus Rapid Transit system Panel A: Baseline Panel Panel B: With Douala Bus Rapid Transit Source: Saïsset, Fouchard, and Stokenberga (2020) and World Bank (2020). Housing policy can be better geared to addressing overcrowding and inade- quate living conditions for the urban poor. Cameroon’s urban poor endure certain deprivations more than the rural poor. For one, those in towns and cities suffer from the effects of outdoor air pollution more than rural dwellers, further reinforcing the importance of investing in public transportation and necessitating better regulation of emissions and waste disposal. Moreover, by some metrics, housing conditions are also worse for poor people in urban areas. Cameroon’s urban poor are more likely to live in overcrowded accommodation than its rural poor. Therefore, citywide approaches to upgrade slums, regulate land use, and increase the stock of adequate housing are essential (PSUP, 2015). Specialized information and training programs may help rural-urban migrants integrate quicker. With migration becoming an increasingly important driver of urban growth, ensuring migrants can integrate into the towns and cities to which they move is crucial for helping them find – and create – productive jobs and for boosting their mone- tary and non-monetary welfare. This resonates with the experience in Cameroon, where rural-urban migrants are more likely to end up in less productive types of service-sector jobs and their children are less likely to be enrolled in school than other urban dwellers. Many of the policies outlined above could act as vital building blocks for integration: digital infrastructure and formal identification can help migrants find out about and register for services, easing congestion can improve their access to productive jobs even if they move to the periphery of cities, while housing policy ensures they can find suitable accommodation. Yet there may still be gaps between rural-urban migrants and existing urban dwellers in terms of languages, skills, and broader information about urban living. In other countries, training and information programs have successfully been administered to help bridge these gaps and expedite migrants’ integration into urban communities (IOM, 2019; Zhao, Tang, & Li, 2022). 151 Cameroon Poverty Assessment 2024 8.8. Given Cameroon’s diverse and changing development challenges, data will remain crucial for guiding poverty- reducing policies and building good governance With Cameroon’s development challenges changing rapidly, collecting and analyzing data on households' living standards needs to keep pace. This poverty assessment benefited hugely from high-quality survey data on household welfare collected by INS, which both provided a detailed snapshot of the latest poverty-re- duction challenges and made it possible to analyze trends over the last two decades. However, with urbanization, climate change, and conflict – both within and outside of the country’s borders – the policies that Cameroon needs to reduce poverty are in flux. This means data and analysis rapidly go out of date: seven years between ECAMs may be too long. The selection, design, and targeting methods of recommended policies can change just as quickly. It is therefore vital that fresh data on household welfare are collected, analyzed, and made public in a timely way. Investing in new surveys and censuses can help strengthen the base of evidence needed for policymaking. Alongside the ECAM series, other types of microdata are needed to rise to the development challenges that Cameroon faces. In particular, since the EESI has only been collected three times in the last two decades, more frequent labor force survey data – including information on earnings in different jobs – could help guide much-needed policies for creating productive jobs. Moreover, a full population census has not been conducted in Cameroon since 2005. New census data would be crucial for tracking Cameroon’s population dynamics given the scale of urbanization and internal migration in recent years. Building the data landscape therefore requires close collaboration between INS and the Bureau Central de Recensement et d'Etude de la population au Cameroun (Central Bureau of the Census and Population Studies, BUCREP). Given the diversity of Cameroon’s development challenges and the push to decentralize policymaking, granular data – including from geospatial or admin- istrative sources – could help provide local governments with the evidence they need. As this chapter has demonstrated, the policies needed to lift Cameroonians out of poverty must be adapted for different regions and for urban and rural areas. Yet even within urban areas, no two towns or cities are the same and the dominant agricultural activities vary widely across different livelihood zones. If policymaking becomes increasingly decentralized, local governments require data that can tell them about local needs. By producing region-level statistics, household survey data are a useful starting point. However, as this poverty assessment shows, the analysis of these household survey data can be enriched and disaggregated further by blending them with geospatial data, administrative data (including data on primary schools used above), and other innovative data sources. Continuing to compile and harmonize additional administrative data from different arrondissements, departments, and regions – on services, markets, and infrastructure – can further help adapt policies for the distinct challenges that different parts of Cameroon confront. Efforts to collect data on conflict and forced displacement should be redoubled, especially for the Extrême-Nord, Nord-Ouest, and Sud-Ouest regions. Conflict threatens to interrupt survey data collection as enumerators cannot access dangerous areas and the infrastructure required to reach sampled households may be damaged. 152 Chapter 8 With the right policies, Cameroon can harness its huge poverty-reducing potential Additionally, traditional household surveys may miss people who are forcibly displaced by conflict because they live in specific locations – like camps – that are not in typical sample frames, they are still moving, or they settle in areas where people were not living before (Pape & Verme, 2023; Lain, Yama, & Hoogeveen, 2024). New methods that exploit geospatial data, work with humanitarian actors, and even use mobile phone records are being developed to make sure that those affected by conflict are included in surveys and censuses (Arai, Knippenberg, Meyer, & Witayangkurn, 2021; Eckman & Himelein, 2022). Upcoming surveys and censuses can also be refined to include questions that better track IDPs and refugees, including identifying those living in host communities and finding out from where they came and why; this can draw on guidance from the International Recommendations on IDP Statistics (IRIS) (EGRISS, 2022). This presents a critical area for future work as conflict and displacement pro- liferate in Cameroon. Data provides the foundations of good governance, ensuring that policies benefit the poor and sustainably lift Cameroonians out of poverty. As well as designing new policies, tracking the efficacy of the government’s policies and programs and Cameroon’s overall poverty-reducing performance helps bolster transparency and hold policymakers accountable. Data can provide a voice to the millions of Cameroonians still suffering from poverty, revealing what works and, for those policies that do not work, how to correct the course. Embracing data-driven policy will therefore be essen- tial as Cameroon harnesses its enormous potential to generate inclusive growth and permanently lift its people out of poverty.  153 References With the right policies, Cameroon can harness its huge poverty-reducing potential References Ainsworth, M., Beegle, K., & Nyamete, A. (1996). The impact of women's schooling on fertility and contraceptive use : a study of fourteen Sub-Saharan African countries. Washington DC: World Bank. Akresh, R., Bhalotra, S., Leone, M., & Osili, U. (2012). War and Stature: Growing up during the Nigerian Civil War. American Economic Review, 102(3), 273-277. doi:10.1257/aer.102.3.273 Amoretti, U., & Maur, J.-C. (2022). Creating markets in Cameroon: Unleashing private sector growth. Washington DC: World Bank. Retrieved from https://documents.worldbank. org/en/publication/documents-reports/documentdetail/099945102202316750/ idu0429f0760047b504b6c0920d0b854326d2e2f Anker, R. (2011). Engel’s Law Around the World 150 Years Later. Amherst Massachusetts: Political Economy Research Institute. Arai, A., Knippenberg, E., Meyer, M., & Witayangkurn, A. (2021). The hidden potential of call detail records in The Gambia. Data and Policy, 3(e9). doi:10.1017/dap.2021.7 Azeng, T., & Yogo, T. (2013). Youth Unemployment and Political Instability in Selected Developing Countries. Tunis: African Development Bank. Retrieved from https://www.afdb.org/fileadmin/ uploads/afdb/Documents/Publications/Working_Paper_171_-_Youth_Unemployment_and_ Political_Instability_in_Selected_Developing_Countries.pdf Banerjee, A., Karlan, D., Darko Osei, R., Trachtman, H., & Udry, C. (2020). Unpacking a Multi-Faceted Program to Build Sustainable Income for the Very Poor. Cambridge: National Bureau of Economic Research. Beegle, K., & Christiaensen, L. (2019). Accelerating Poverty Reduction in Africa. Washington DC: World Bank. doi:10.1596/978-1-4648-1232-3 Bhula, R., Mahoney, M., & Murphy, K. (2020). Conducting cost-effectiveness analysis (CEA). Cambridge: Abdul Latif Jameel Poverty Action Lab. Boer, L., & Rieth, M. (2024). The Macroeconomic Consequences of Import Tariffs and Trade Policy Uncertainty. Washington DC: IMF. Retrieved from https://www.imf.org/en/Publications/WP/Issues/2024/01/19/ The-Macroeconomic-Consequences-of-Import-Tariffs-and-Trade-Policy-Uncertainty-543877 Bolch, K., Ceriani, L., & López-Calva, L. F. (2022). The arithmetics and politics of domestic resource mobilization for poverty eradication. World Development, 149, 105691. doi:10.1016/j. worlddev.2021.105691 Bolter, K., & Robey, J. (2020). Agglomeration Economies: A Literature Review. Kalamazoo MI: Upjohn Institute: The Fund for our Economic Future. Retrieved from https://research.upjohn.org/cgi/ viewcontent.cgi?article=1256&context=reports Bondarenko, M., Kerr, D., Sorichetta, A., Tatem, A., & WorldPop. (2020). Census/projection- disaggregated gridded population datasets for 51 countries across sub-Saharan Africa in 2020 using building footprints. Southampton: University of Southampton. Bonnechère, B., Sankoh, O., Samadoulougou, S., Yombi, J., & Kirakoya-Samadoulougou, F. (2021). Surveillance of COVID-19 in Cameroon: Implications for policymakers and the healthcare system. Journal of Public Health in Africa, 12(2), 1415. doi:10.4081/jphia.2021.1415 Bourguignon, F., & Chakravarty, S. (2003). The measurement of multidimensional poverty. Journal of Economic Inequality, 1, 25-49. Retrieved from http://www.ophi.org.uk/wp-content/uploads/ Bourgignon-Chakravarty-2003.pdf 155 Cameroon Poverty Assessment 2024 Bowen, T., del Ninno, C., Andrews, C., Coll-Black, S., Gentilini, U., Johnson, K., . . . Williams, A. (2020). Adaptive Social Protection: Building Resilience to Shocks. Washington DC: World Bank. Retrieved from https://openknowledge.worldbank.org/server/api/core/ bitstreams/7ab2af13-08ca-5b10-b08b-268e6519eb15/content Breton, M., & Mirzapour, H. (2016). Welfare implications of reforming energy consumption subsidies. Energy Policy, 98, 232-240. doi:10.1016/j.enpol.2016.08.031 Calvo-Gonzalez, O., Cunha, B., & Trezzi, R. (2017). When Winners Feel Like Losers: Evidence from an Energy Subsidy Reform. World Bank Economic Review, 31(2), 329-350. doi:10.1093/wber/lhv058 Carneiro, P., Kraftman, L., Mason, G., Moore, L., Rasul, I., & Scott, M. (2021). The Impacts of a Multifaceted Prenatal Intervention on Human Capital Accumulation in Early Life. American Economic Review, 111(8), 2506-49. doi:10.1257/aer.20191726 Chelminski, K. (2018). Fossil Fuel Subsidy Reform in Indonesia: The Struggle for Successful Reform. In J. Skovgaard, & H. van Asselt, The Domestic Politics of Fossil Fuel Subsidies and Their Reform (pp. 193-211). Cambridge: Cambridge University Press. doi:10.1017/9781108241946.013 Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785- 794. doi:10.1145/2939672.2939785 CIA. (2024). Field Listing - Roadways. Retrieved from CIA: The World Factbook: https://www.cia.gov/ the-world-factbook/field/roadways/ Corral, P., Henderson, H., & Segovia, S. (2023). Poverty Mapping in the Age of Machine Learning. Washington DC: World Bank. Corral, P., Irwin, A., Krishnan, N., Mahler, D., & Vishwanath, T. (2020). Fragility and Conflict: On the Front Lines of the Fight Against Poverty. Washington DC: World Bank. Retrieved from http://hdl. handle.net/10986/33324 Coulibaly, A., Benlamine, M., & Piazza, R. (2022). State-Owned Enterprise and Sectoral Distortions: Growth and Budget Implications for Cameroon. Washington DC: International Monetary Fund. Retrieved from https://www.elibrary.imf.org/view/journals/002/2022/076/article-A001-en.xml Cramer, C. (2010). Unemployment and Participation in Violence. Washington DC: World Bank. Datt, G., & Ravallion, M. (1992). Growth and redistribution components of changes in poverty measures: A decomposition with applications to Brazil and India in the 1980s. Journal of Development Economics, 38(2), 275-295. doi:10.1016/0304-3878(92)90001-P De Ridder, K., Lauwaet, D., Hooyberghs, H., & Lefebre, F. (2017). Development of a hazard screening protocol for Extreme Heat. Washington DC: World Bank. Retrieved from https://datacatalog. worldbank.org/int/search/dataset/0040194/global-extreme-heat-hazard Deaton, A., & Zaidi, S. (2002). Guidelines for Constructing Consumption Aggregates for Welfare Analysis. Washington DC: World Bank. Demeke, Y. (2022). Youth unemployment and political instability: evidence from IGAD member countries. Cogent Economics and Finance, 10(1). doi:10.1080/23322039.2022.2079211 Dercon, S. (2002). Income Risk, Coping Strategies, and Safety Nets. World Bank Research Observer, 17(2), 141-116. doi:10.1093/wbro/17.2.141 Eckman, S., & Himelein, K. (2022). Innovative Sample Designs for Studies of Refugees and Internally Displaced Persons. In S. Pötzschke, & S. Rinken, Migration Research in a Digitized World: Using Innovative Technology to Tackle Methodological Challenges (pp. 15-34). Cham, Switzerland: Springer Cham. doi:10.1007/978-3-031-01319-5 EGRISS. (2022). Compilers’ Manual on Forced Displacement Statistics. Luxembourg: Expert Group on Refugee, Internally Displaced Persons, and Statelessness Statistics. Retrieved from https:// egrisstats.org/activities/compilers-manual/ Fall, M., Frisa, L., & Nkounga, O. (2021). Political Economy Analysis of Decentralization . Washington DC: World Bank. 156 References Working Out of Poverty: Building Resilience and Inclusive Growth for Cameroon’s Future Fall, M., Hilger, A., Vaillancourt, F., Perrot, V., & Daller, B. (2020). Deepening Decentralization for Service Delivery in Cameroon. Washington DC: World Bank. Fall, M., Mituzani, J., & Vaillancourt, F. (2023). Financing the regions of Cameroon: Technical note. Washington DC: World Bank. Ferreira, F., & Lugo, M. A. (2013). Multidimensional Poverty Analysis: Looking for a Middle Ground. World Bank Research Observer, 28, 220-235. doi:doi:10.1093/wbro/lks013 Fetzer, T. (2020). Can Workfare Programs Moderate Conflict? Evidence from India. Journal of the European Economic Association, 16(6), 3337-3375. doi:10.1093/jeea/jvz062 Fields, G. (2011). Labor market analysis for developing countries. Labour Economics, 18(Supplement 1), S16-S22. doi:10.1016/j.labeco.2011.09.005 Foster, E., & Inchauste, G. (2024). New Micro-Macro Simulation for Western and Central Africa Region. Washington DC: World Bank. Friedman, H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189-1232. doi:10.1214/aos/1013203451 Gandhi, D. (2019). Figure of the week: Gap in universal mobile phone and internet access in Africa. Retrieved from Brookings: https://www.brookings.edu/articles/ figure-of-the-week-gap-in-universal-mobile-phone-and-internet-access-in-africa/ Gao, J., Vinha, K., & Skoufias, E. (2021). Vulnerability Tool: A User's Manual. World Bank. Gentilini, U. (2016). Revisiting the 'Cash Versus Good' Devate: New Evidence for an Old Puzzle? World Bank Research Observer, 31(1), 135-167. Gevaert, C. (2023). Spatial Conflict Index Raster: Work Report. Washington DC: World Bank. Gilligan, D., Arrieta, A., Devereux, S., Hoddinott, J., Kebede, D., Ledlie, N., . . . A, T. (2020). Integrating Service Delivery with Cash Transfers to Improve Nutrition in Ethiopia: An Impact Evaluation of the IN-SCT Pilot Project in Oromia and Southern Nations, Nationalities, and Peoples’ Region. New York: UNICEF. Gough, K., Esson, J., Andreasen, M., Yemmafouo, A., & Yankson, P. (2013). State of the Art Report for RurbanAfrica Work Package 3: City Dynamics. Copenhagen: Department of Geosciences and Natural Resource Management, University of Copenhagen. Retrieved from https://repository.lboro.ac.uk/articles/report/ State_of_the_art_report_for_RurbanAfrica_work_package_3_city_dynamics/9484856 Guerrero Gámez, S., Portabales González, I., Dominguez Gonzalez, K., & Bello, G. M. (2020). Far too often, public transport leaves women on the roadside. Retrieved from Transport for Development World Bank Blog: https://blogs.worldbank.org/en/transport/ far-too-often-public-transport-leaves-women-roadside Günther, I., & Harttgen, K. (2009). Estimating households vulnerability to idiosyncratic and covariate shocks: A novel method applied in Madgascar. World Development, 37(7), 1222-1234. Harris, J., & Todaro, M. (1970). Migration, Unemployment and Development: A Two-Sector Analysis. American Economic Review, 60(1), 126-142. Retrieved from https://www.jstor.org/stable/1807860 Hayakawa, K., Ishikawa, J., & Tarui, N. (2020). What goes around comes around: Export-enhancing effects of import-tariff reductions. Journal of International Economics, 126, 103362. doi:10.1016/j. jinteco.2020.103362 Holla, A., Bendini, M., Dinarte, L., & Trako, I. (2021). Is Investment in Pre-Primary Eduction Too Low? Lessons from (Quasi) Experimental Evidence across Countries. Washington DC: World Bank. Hong, T. (2023). Why digital public infrastructre matters. Retrieved from Bill and Melinda Gates Foundation: https://www.gatesfoundation.org/ideas/articles/what-is-digital-public-infrastructure Houts, I., Lain, J., & Tabetando, R. (Forthcoming). The Impact of Fiscal Policy on the Income Distribution in Cameroon: 2022. Washington DC: World Bank. 157 Cameroon Poverty Assessment 2024 ILO. (2013). Report II: Statistics of work, employment and labor underutilization - 19th International Conference of Labour Statisticians. Geneva: International Labour Organization. Retrieved from https://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/publication/ wcms_220535.pdf IMF. (2018). Financial Inclusion in Cameroon. Washington DC: International Monetary Fund. Retrieved from https://www.elibrary.imf.org/view/journals/002/2018/256/article-A005-en.xml INS. (2019). Pauvreté et évolution du pouvoir d'achat des ménages. Yaoundé: Institut National de la Statistique du Cameroun. Retrieved from https://ins-cameroun.cm/wp-content/uploads/2019/09/ Pauvrete_et_evolution_du_pouvoir_d_achat_des_menages_ECAM4.pdf INS. (2022). Demographic Growth and Resilience in Cameroon: The Case of Education, Health, and Decent Work. Yaoundé: Institut National de la Statistique du Cameroun. Retrieved from https:// ins-cameroun.cm/wp-content/uploads/2023/06/Brochure-JMP-2022-ENG.pdf INS. (2024). Résultats de la 5ème Enquête Camerounaise Auprès des Ménages (ECAM‑5). Yaoundé: Institut National de la Statistique du Cameroun. Retrieved from https://ins-cameroun.cm/ document/resultats-de-la-5eme-enquete-camerounaise-aupres-des-menages-ecam5- communique-de-presse/ IOM. (2019). IOM Migrant Training Programming: Preparing and Equipping Migrants to Integrate in their New Communities. Geneva: International Migration Organization. Retrieved from https://www.iom. int/sites/g/files/tmzbdl486/files/our_work/DMM/Integration/Migrant-Training-Infosheet-2019.pdf Jedwab, R., Blankespoor, B., Masaki, T., & Rodríguez-Castelán, C. (2021). Technical Paper 3. Estimating the Spillover Economic Effects of Foreign Conflict: Evidence from Boko Haram. Washington DC: World Bank. Retrieved from https://documents1.worldbank.org/curated/ en/168241636640474130/pdf/Technical-Paper-3-Estimating-the-Spillover-Economic-Effects-of- Foreign-Conflict-Evidence-from-Boko-Haram.pdf Jensen, R. (2007). The Digital Provide: Information (Technology), Market Performance, and Welfare in the South Indian Fisheries Sector. Quarterly Journal of Economics, 122(3), 879-924. JRC, European Commission, and CIESIN. (2021). Documentation for the Global Human Settlement Layers (GHSL): Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid. Palisades, New York: Joint Research Centre, European Commission, and Center for International Earth Science Information Network. doi:10.7927/tg7r-n260 Karakulah, K., Lange, G.-M., Awe, Y., & Chonabayashi, S. (2021). Impact of Air Pollution on Human Capital. In W. Bank, The Changing Wealth of Nations 2021: Managing Assets for the Future (pp. 177-190). Washington DC: World Bank. doi:10.1596/978-1-4648-1590-4_ch8 Kenny, C., & Gehan, Z. (2023). Scenarios for Future Global Growth to 2050. Washington DC: Center for Global Development. Retrieved from https://www.cgdev.org/sites/default/files/scenarios-future- global-growth-2050.pdf Kosmidou-Bradley, W., & Blankespoor, B. (2019). Measuring Mobility in Afghanistan Using Time-Cost Raster Models : Methodology Note. Washington DC: World Bank. Lain, J., & Vishwanath, T. (2022). A Better Future for All Nigerians: Nigeria Poverty Assessment 2022. Washington DC: World Bank. Retrieved from http://documents.worldbank.org/curated/ en/099730003152232753/P17630107476630fa09c990da780535511c Lain, J., Vishwanath, T., Amankwah, A., Contreras-Gonzalez, I., Jenq, C., Lagrange, A. A., . . . Sagesaka, A. (2021). COVID-19 in Nigeria: Frontline data and pathways for policy. Washington DC: World Bank. Lain, J., Yama, G., & Hoogeveen, J. (2024). Comparing Internally Displaced Persons with Those Left Behind : Evidence from the Central African Republic. Washington DC: World Bank. Retrieved from https://documents.worldbank.org/en/publication/documents-reports/ documentdetail/099745503192422251/idu127a68af61372d14c471a1841e1555d6c7542 158 References Working Out of Poverty: Building Resilience and Inclusive Growth for Cameroon’s Future Lall, S., Selod, H., & Shalizi, Z. (2006). Rural-urban migration in developing countries : a survey of theoretical predictions and empirical findings. Washington DC: World Bank. Retrieved from https://documents.worldbank.org/en/publication/documents-reports/ documentdetail/416901468140979731/rural-urban-migration-in-developing-countries-a-survey- of-theoretical-predictions-and-empirical-findings Loeser, J., Özler, B., & Premand, P. (2021). What have we learned about cash transfers? Retrieved from World Bank Development Impact Blog: https://blogs.worldbank.org/impactevaluations/ what-have-we-learned-about-cash-transfers Lustig, N. (2022). Commitment to Equity Handbook. Washington DC: Brookings Institution. Mancini, G., & Vecchi, G. (2022). On the Construction of a Consumption Aggregate for Inequality and Poverty Analysis. Washington DC: World Bank. Ministry of Economy, Planning and Regional Development. (2009). Cameroon Vision 2035. Yaoundé: Ministry of Economy, Planning and Regional Development. Ministry of Economy, Planning and Regional Development. (2020). National Development Strategy 2020-2030: For structural transformation and inclusive development. Yaoundé: Ministry of Economy, Planning and Regional Development. Moreno, C. (2017). Defining MPI Dimensions through Participation: The Case of El Salvador. Oxford: OPHI. Retrieved from https://www.ophi.org.uk/wp-content/uploads/B49_El_Salvador_vs2_online.pdf Munoz-Najar, A., Gilberto Sanzana, A. G., Hasan, .., Cobo Romani, J. C., Azevedo, J. P., & Akmal, M. (2022). Remote Learning During COVID-19 : Lessons from Today, Principles for Tomorrow. Washington DC: World Bank. Retrieved from https://documents.worldbank. org/en/publication/documents-reports/documentdetail/160271637074230077/ remote-learning-during-covid-19-lessons-from-today-principles-for-tomorrow Myerson, R. (2021). Comments on Decentralization in Cameroon. Chicago: University of Chicago. Retrieved from https://home.uchicago.edu/~rmyerson/research/cameroon2021rbm.pdf Nguyen, M., Yoshida, N., Wu, H., & Narayan, A. (2020). Profiles of the new poor due to the COVID-19 pandemic. Washington DC: World Bank. Retrieved from https://pubdocs.worldbank.org/ en/767501596721696943/Profiles-of-the-new-poor-due-to-the-COVID-19-pandemic.pdf Nkosi, V., Haman, T., Naicker, N., & Mathee, A. (2019). Overcrowding and health in two impoverished suburbs of Johannesburg. BMC Public Health, 19(1358). doi:10.1186/s12889-019-7665-5 OECD, African Union Commission, and African Tax Administration Forum. (2023). Revenue Statistics in Africa 2023. Paris: Organisation for Economic Cooperation and Development. doi:10.1787/15bc5bc6-en-fr. Pande, R., & Enevoldsen, N. (2021). Growing Pains? A Comment on “Converging to Convergence”. Cambridge: National Bureau of Economic Research. Retrieved from https://www.nber.org/system/ files/working_papers/w29046/w29046.pdf Pape, U., & Sharma, A. (2019). Using Micro-Data to Inform Durable Solutions for IDPs : Volume A. Washington DC: World Bank. Pape, U., & Verme, P. (2023). Measuring Poverty in Forced Displacement Contexts. Washington DC: World Bank. Retrieved from https://documents.worldbank.org/en/publication/documents-reports/ documentdetail/099330002082336067/idu0f21f2e9f0f7b704c420a5af038bbbbb4f592 Patrinos, H., & Angrist, N. (2018). Global Dataset on Education Quality: A Review and Update (2000- 2017). Washington DC: World Bank. Retrieved from https://documents1.worldbank.org/curated/ en/390321538076747773/pdf/WPS8592.pdf Perini, M., Nfor, M., Camin, F., Pianezze, S., & Piasentier, E. (2021). Using Bioelements Isotope Ratios and Fatty Acid Composition to Deduce Beef Origin and Zebu Feeding Regime in Cameroon. Molecules, 26(8). doi:10.3390/molecules26082155 Pritchett, L., Suryahadi, A., & Sumarto, S. (2000). Quantifying vulnerability to poverty: A proposed measure, applied to Indonesia. Washington DC: The World Bank. 159 Cameroon Poverty Assessment 2024 PSUP. (2015). Cameroon Impact Story: Fostering political will to create a platform for cooperation between all key stakeholders. Nairobi: Participatory Slum Upgrading Programme - UN-HABITAT. Retrieved from https://www.mypsup.org/library_files/downloads/Cameroon%20Impact%20Story.pdf Ramesh, A., Blanchet, K., Ensink, J., & Roberts, B. (2015). Evidence on the Effectiveness of Water, Sanitation, and Hygiene (WASH) Interventions on Health Outcomes in Humanitarian Crises: A Systematic Review. PloS One, 10(9). Ratledge, N., Cadamuro, G., de la Cuesta, B., Stigler, M., & Burke, M. (2022). Using machine learning to assess the livelihood impact of electricity access. Nature, 611, 491-495. doi:10.1038/ s41586-022-05322-8 Ravallion, M. (1998). Poverty Lines in Theory and Practice. Washington DC: World Bank. doi:10.1596/0-8213-4226-6 Ravallion, M. (2012). Why Don't We See Poverty Convergence? American Economic Review, 102(1), 504-523. doi:10.1257/aer.102.1.504 Ravallion, M., & Huppi, M. (1991). Measuring Changes in Poverty: A Methodological Case Study of Indonesia during an Adjustment Period. World Bank Economic Review, 5(1), 57-82. Retrieved from http://www.jstor.org/stable/3989969 Ritche, H., & Roser, M. (2021). Air Pollution. Retrieved from Our World in Data: https://ourworldindata. org/air-pollution Rose, S., & Plant, M. (2021). Fuel Subsidy Reform in Fragile States. Washington DC: Center for Global Development. Retrieved from https://www.cgdev.org/publication/ fuel-subsidy-reform-fragile-states-forging-constructive-ifi-un-partnership Saïsset, E., Fouchard, B., & Stokenberga, A. (2020). Access Granted: Unlocking Opportunities through Better Urban Transport. Retrieved from Transport for Development World Bank Blog: https://blogs.worldbank. org/en/transport/access-granted-unlocking-opportunities-through-better-urban-transport Saïsset, E., Fouchard, B., & Stokenberga, A. (2020). Access Granted: Unlocking Opportunities through Better Urban Transport. Retrieved from Transport for Development World Bank Blog: https://blogs.worldbank. org/en/transport/access-granted-unlocking-opportunities-through-better-urban-transport Tabetando, R., Fani, D. C., Ragasa, C., & Michuda, A. (2023). Land market responses to weather shocks: evidence from rural Uganda and Kenya. European Review of Agricultural Economics, 50(3), 954-977. doi:doi.org/10.1093/erae/jbad005 Taillandier, F. (2022). Project Appraisal Document (PAD) - Douala Urban Mobility Project. Washington DC: World Bank. Retrieved from https://documents.worldbank. org/en/publication/documents-reports/documentdetail/099200105112212760/ p1677950c5e5bc0a092950bff3b8497a1b Tatah, L., Wasnyo, Y., Pearce, M., Oni, T., Foley, L., Mogo, E., . . . Assah, F. (2022). Travel Behaviour and Barriers to Active Travel among Adults in Yaoundé, Cameroon. Sustainability, 14(15). doi:10.3390/ su14159092 Tatah, L., Wasnyo, Y., Pearce, M., Oni, T., Foley, L., Mogo, E., . . . Assah, F. (2022). Travel Behaviour and Barriers to Active Travel among Adults in Yaoundé, Cameroon. Sustainability, 14, 9092. doi:doi. org/10.3390/su14159092 Theunynck, S. (2009). School Construction for Universal Primary Education in Africa: Should Communities Be Empowered to Build Their Schools? Washington DC: World Bank. UN DESA. (2022). Demographic Yearbook 2022. New York: United Nations, Department of Economic and Social Affairs. Retrieved from https://unstats.un.org/unsd/demographic-social/products/dyb/ dybsets/2022.pdf (2022). UN DESA Population Division. New York: United Nations, Department of Economic and Social Affairs, Population Division. Retrieved from https://population.un.org/wpp/ UNDP. (2023). Journey to Extremism in Africa: Pathways to Recruitment and Disengagement. New York: United Nations Development Programme. 160 References Working Out of Poverty: Building Resilience and Inclusive Growth for Cameroon’s Future UN-HABITAT. (2007). State of the World's Cities 2006/7 - Slums: Overcrowding or the “hidden homeless”. Nairobi: UN-HABITAT. United Nations Population Division. (2019). World Urbanization Prospects 2018: Highlights. New York: United Nations, Department of Economic and Social Affairs, Population Division. Retrieved from https://population.un.org/wup/publications/Files/WUP2018-Highlights.pdf United Nations Population Division. (2022). World Population Prospects 2022: Summary of Results. New York: United Nations, Department of Economic and Social Affairs, Population Division. Retrieved from https://www.un.org/development/desa/pd/sites/www.un.org.development.desa.pd/ files/wpp2022_summary_of_results.pdf Vora, P., & Dolan, J. (2022). Measuring digital infrastructure to maximize development outcomes and mitigate risks. Washington DC: Brookings. Retrieved from https://www.brookings.edu/wp-content/ uploads/2022/02/Good-Digital-Infrastructure.pdf Wai-Poi, M., Alatas, H., Chandrashekar, K., & Lain, J. (2018). The Different Faces of Urban Indonesia: Recent Urban Trends in Indonesia. Washington DC: World Bank. WITS. (2024). World Integrated Trade Solution. Retrieved from Cameroon trade statistics: https://wits. worldbank.org/CountryProfile/en/CMR World Bank. (2009). World Development Report 2009: Reshaping Economic Geography. Washington DC: World Bank. Retrieved from https://openknowledge.worldbank.org/entities/ publication/58557d74-baf0-5f97-a255-00482909810a World Bank. (2012). Cameroon - The Path to Fiscal Decentralization: Opportunities and Challenges. Washington DC: World Bank. Retrieved from https://documents.worldbank. org/en/publication/documents-reports/documentdetail/685841468239367086/ cameroon-the-path-to-fiscal-decentralization-opportunities-and-challenges World Bank. (2015). Revisiting the sources of growth: Enhancing the efficiency of the Port of Douala. Washington DC: World Bank. Retrieved from https://www.worldbank.org/en/news/press-release/2015/02/11/ cameroon-enhancing-the-efficiency-of-the-port-of-douala-for-a-more-sustainable-growth World Bank. (2016). Republic of Cameroon - Priorities for Ending Poverty and Boosting Shared Prosperity: Systematic Country Diagnostic. Washington DC: World Bank. Retrieved from https:// elibrary.worldbank.org/doi/abs/10.1596/24697 World Bank. (2017). Cameroon’s Diagnostic Report: Climate Change and Disaster Risk Management in Cameroon. Washington DC: World Bank. Retrieved from https://climateknowledgeportal. worldbank.org/country/cameroon/vulnerability World Bank. (2017). Country Partnership Framework for the Republic of Cameroon for the Period FY17-FY21. Washington DC: World Bank. Retrieved from https://documents. worldbank.org/en/publication/documents-reports/documentdetail/480711490925662402/ cameroon-country-partnership-framework-for-the-period-fy17-fy21 World Bank. (2018). Poverty and Shared Prosperity 2018: Piecing Together the Poverty Puzzle. Washington DC: World Bank. Retrieved from https://openknowledge.worldbank.org/bitstream/ handle/10986/30418/9781464813306.pdf World Bank. (2018). Project Paper for the Social Safety Net Project. Washington DC: World Bank. World Bank. (2018). The Human Capital Project: Frequently Asked Questions. Washington DC: World Bank. World Bank. (2018). World Development Report 2018: Learning to Realize Education's Promise. Washington DC: World Bank. doi:10.1596/978-1-4648-1096-1 World Bank. (2019). Aspiring Indonesia - Expanding the Middle Class. Jakarta: World Bank. Retrieved from https://www.worldbank.org/en/country/indonesia/publication/ aspiring-indonesia-expanding-the-middle-class World Bank. (2020). Urban Accessibility Analysis for Douala, Cameroon. Washington DC: World Bank. 161 Cameroon Poverty Assessment 2024 World Bank. (2021). Proceedings and Outcomes: Fifth Policy Forum on Natural Capital Accounting for Better Decision Making - Greening the Recovery. Washington DC: World Bank. Retrieved from https://documents1.worldbank.org/curated/en/942131638170615027/pdf/The-Fifth- Policy-Forum-Natural-Capital-Accounting-for-Better-Decision-Making-Greening-the-Recovery- Proceedings-and-Outcomes.pdf World Bank. (2021). Tchad: Évaluation de la pauvreté. Washington DC: World Bank. World Bank. (2021). The Socio-Political Crisis in the Northwest and Southwest Regions of Cameroon: Assessing the Economic and Social Impacts. Washington DC: World Bank. Retrieved from https:// documents1.worldbank.org/curated/en/795921624338364910/pdf/The-Socio-Political-Crisis- in-the-Northwest-and-Southwest-Regions-of-Cameroon-Assessing-the-Economic-and-Social- Impacts.pdf World Bank. (2022). Cameroon - Systematic Country Diagnostic : An Update. Washington DC: World Bank. Retrieved from https://documents.worldbank.org/en/publication/documents-reports/ documentdetail/446021657067054458/cameroon-systematic-country-diagnostic-an-update World Bank. (2022). Cameroon Country Climate and Development Report. Washington DC: World Bank. Retrieved from https://elibrary.worldbank.org/doi/abs/10.1596/38242 World Bank. (2022). Project Appraisal Document for the Adaptive Safety Nets and Economic Inclusion Project. Washington DC: World Bank. World Bank. (2023). Cameroon Macro-Poverty Outlook: October 2023. Washington DC: World Bank. Retrieved from https://thedocs.worldbank.org/en/doc/ bae48ff2fefc5a869546775b3f010735-0500062021/related/mpo-cmr.pdf World Bank. (2023). Central African Republic Poverty Assessment 2023: A Road Map Towards Poverty Reduction in the Central African Republic. Washington DC: World Bank. Retrieved from https://documents.worldbank.org/en/publication/documents-reports/ documentdetail/099111323121515851/p17739108d680e074088b608a00615bcba3 World Bank. (2023). Fuel Subsidy Reform in Cameroon: The Role of Social Safety Nets in Mitigating Impacts on Vulnerable Populations. Washington DC: World Bank. WTO. (2023). Trade Policy Review: Report by the Secretariat - Countries of the Central African Economic and Monetary Community (CEMAC). Geneva: World Trade Organization. Retrieved from https://www.wto.org/english/tratop_e/tpr_e/tp545_e.htm Zhao, C., Tang, M., & Li, H. (2022). The Effects of Vocational-Skills Training on Migrant Workers’ Willingness to Settle in Urban Areas in China. Sustainability, 14(19), 11914. Retrieved from 10.3390/su141911914  162 © 2025 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW, Washington DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org