STUDY LABOUR AND SOCIAL JUSTICE ACCESS TO HEALTH SERVICES A Key Demand of Informal Labour in Africa – Findings from Representative Country Surveys in Sub-Saharan Africa Rudolf Traub-Merz and Manfred Öhm February 2021 Obtaining improved medical care is a key demand of the informally employed in Africa. They rank health services higher than other essential state services. This holds true – with minor exceptions – across income groups, geographical location, gender, age and education. Access to health care is stratified by social inequality. The informally employed have little trust that their governments will provide them with improved health services. Nevertheless they show a strong interest in health insurance schemes and are willing to pay a premium. The report presents findings of country-wide, representative surveys jointly conducted by FES(lead agent), ILO and DIEGDI. The polls cover Kenya (2018), Benin(2018), Senegal (2019), Zambia(2019) and Ivory Coast(2020).

LABOUR AND SOCIAL JUSTICE ACCESS TO HEALTH SERVICES A Key Demand of Informal Labour in Africa – Findings from Representative Country Surveys in Sub-Saharan Africa

 Content EXECUTIVE SUMMARY  2 1 INTRODUCTION  5 RESULTS OF THE SURVEY 2 DEMANDS FOR BETTER STATE SERVICES  9 2.1 Demands for better state services: first 9 2.2 Demand for better state services: second 9 2.3 Priority voting by urban–rural 11 2.4 Priorities by 11 2.5 Priorities by 11 2.6 Priorities by 11 2.7 16  OF MEDICAL CARE – A MATTER OF INEQUALITY?  18 3.1 Use of medical care – inequality by 18 3.2 Use of medical care – inequality by urban–rural 20 3.3 Use of medical care – inequality by 20 3.4 Use of medical care – inequality by 21 a 3.5 22 a 4 PAYING FOR MEDICAL TREATMENT  24 4.1 Sources of financing medical 24 4.2 Sources of financing medical care, by urban–rural 25 4.3 Sources of financing medical care, by 27 a 4.4 Sources of financing medical care, by 27 a 4.5 28  GOVERNMENTS WILLING AND ABLE TO DELIVER BETTER STATE SERVICES?  30 5.1 Introductory 30 5.2 On the willingness and capacity of the state 31  INFORMALLY EMPLOYED PEOPLE WILLING TO PARTICIPATE IN A HEALTH INSURANCE SCHEME AND PAY A PREMIUM?  33 a 6.1 Membership of health insurance 33 a 6.2 Interest in joining a health insurance 34 a 6.3 Readiness to pay a premium for a health insurance 35 a 7 CONCLUSION  38 a  I: a ranking of state 41 a Appendix II: Technical notes 42 a Appendix III: Statistical computation 45 a List of Figures 49 a List of Tables 50 a

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH SERVICES EXECUTIVE SUMMARY Health protection is a key pillar of social protection. Access to health is a human need and, as we show in this paper, in strong social demand. Citizens expect governments to improve access to health care systems. The Friedrich-Ebert-Stiftung(FES, lead agent) in collaboration with the International Labour Office(ILO) and the German Development Institute(DIE) conducted national representative opinion polls in Kenya(October 2018), Benin(December 2018), Senegal(May 2019), Zambia(August 2019) and Ivory Coast(May 2020) to improve our knowledge of the strategies of the informally employed 1 to overcome shortcomings in social security provision. Informal labour constitutes 80 to 90 per cent of the labour markets in the survey countries and lack access to the formal social security set up for actors in the formal economy. The survey results are thus important for political decision-makers who would like to design social policies that reach out to people hitherto ignored. The survey refers to SDG goals 1.3(social protection) and, in particular, 3.8, which calls for universal health coverage (UHC),»including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all«. The governments of all survey countries have pledged to implement the SDG agenda and universal health coverage can be identified as the key instrument for measuring social progress in reforming health services. Access to health for all is also a key element of the implementation of social protection floors, called for by the International Labour Organization(Recommendation 202, 2012), and is central to the African Union´s African Health Strategy 2016–2030. The present publication focusses on the degree of importance the informally employed attach to access to decent health services in comparison with other essential state services, as well as perceptions of access to and the availability of medical care. It assesses the financial risk that results from a person’s falling sick by looking at the financial resources that patients may use to cover the cost of medical treatment. It then assesses the hopes invested by the 1 For a definition of informal employment, see Appendix II. informally employed in the government’s determination to improve services in the future and provides a view of the extent to which political regimes are deemed to be legitimate. Furthermore, it explores the interest of the informally employed in improving their financial predicament when health problems strike by inquiring into their willingness to join a health insurance scheme and preparedness to pay a premium. 2 The key findings are summarised as follows:  HEALTH SERVICES IS A NATIONAL DEMAND Health care takes top place in the ranking of demands for state services, closely followed by» better schools and education«. The call for» better health services« remains at the top whether we look at the living environment(urban versus rural), at disparate income clusters or at demographic variables. In the five surveyed countries, 47 to 71 per cent identify improved health as their first or second most important need. The call for better health cuts across social and spatial cleavages and can thus be called a national priority.  OF MEDICAL CARE IS STRATIFIED BY COUNTRY, INCOME AND RESIDENCE The use of medical care in the survey countries is marked by large discrepancies. In Senegal and Benin, around 30 per cent of respondents scarcely seek medical treatment when falling sick, while in Zambia and Kenya the proportion in this group is some 10 per cent. The Ivory Coast falls in-between but there are still 24 per cent who mainly have to cope without treatment if a health shock strikes. In Senegal, Benin and Ivory Coast, an urban–rural divide and income disparities combine to generate huge ­discrepancies 2 Other parts of the interviews, such as respondents’ assessments of the quality of medical services, views on taxation, state–citizen relations, membership of groups, and views on trade unions will be published in separate reports. 2

Executive summary in resort to medical care. In Kenya and Zambia the use of medical care by urban and rural residents is fairly balanced and disparities are primarily based on income. Gender, however, is not a statistically relevant dimension of the use of medical care. behalf of the populace and set the administrative machinery of the state in motion. The ruling regime’s l­egitimacy is challenged if half or more of the people see their political leaders as unwilling to improve services.  OF MEDICAL BILLS DIFFER BETWEEN THE COUNTRIES – KEY INDICATORS ARE FREE SERVICES AND PAYMENT BY INCURRING DEBTS Availability of free health services and membership of health insurance schemes are decisive factors in determining the degree to which sickness and medical treatment become a financial risk for households. Where there is a free primary health system, as in Zambia, few people are forced to sell property or take out a loan to mobilise funds for treatment. In countries with no large-scale schemes for free medical treatment, incurring debts or selling assets becomes a dire reality for many people. Membership in a health insurance provider produces the opposite effects: it enables the use of medical treatment with less risk of becoming indebted.  IS A STRONG FACTOR IN DETERMINING USE OF MEDICAL SERVICES  FOR HEALTH INSURANCE COVERAGE AND WILLINGNESS TO PAY A PREMIUM A low level of trust in government performance forces people to undertake their own»social investment« to cope with life’s exigencies. This has not yet strongly manifested itself in membership of health protection schemes. With the exception of Kenya, where membership stands at 22 per cent, the other survey countries exhibit negligible coverage. A clear majority of respondents in all countries, however, declared their interest in joining a scheme. Nearly all are aware that membership comes at a cost and are willing to paying a premium at regular intervals. Applying several reality checks by comparing the amounts respondents were willing to pay as premiums against various thresholds, such as current fees in existing schemes, we can identify three groups:(i) those prepared to pay a premium above what existing schemes charge;(ii) those who are ready to contribute substantially compared with existing fee levels; and(iii) those willing to pay well below the entry level of existing schemes. Linking the use of medical care to patients’ earnings yields a strong finding: the lower the income the higher the likeliness of incurring debts. Income is a strong determinant of access to medical care and poverty prevents people from looking after their health. It is difficult to assess the adequacy of respondents’ assertions regarding possible premium payments. In any case, the survey confirms that a majority of people have a positive attitude towards joining a health scheme and are aware that membership entails paying a premium.  INSURANCE AND FREE SERVICES CONTRIBUTE TO DELINK THE USE OF MEDICAL SERVICES FROM POVERTY Free health services and health insurance coverage improve the use of medical services. Both help to delink the use of medical care from poverty. Our data provide sufficient evidence for this connection for Kenya and Zambia, but fall short of strong statistical proof for Benin, Senegal and Ivory Coast because of the poor development of these financial tools in these countries.  MUCH TRUST IN GOVERNMENT TO PROVIDE BETTER STATE SERVICES While there is wide dissatisfaction with the ways in which the use of medical services is organised, not much hope exists that the governments of the various countries will improve the situation for the better and provide more services in the future. While a majority of respondents express trust in the capacity of state institutions to improve services, many doubt the willingness of political leaders to act on RECOMMENDATIONS: SIGNIFICANCE FOR PUBLIC POLICY DEVELOPMENT Our opinion poll reflects people’s views on aspects of their social reality, but does not provide answers on how to change the social situation. Policymakers have to evaluate the call for better health services within a wider framework of social realities and have to weigh different approaches in terms of their suitability to provide a lasting solution. Nevertheless, our findings have strong relevance for policymakers in public services, government and international organisations. They also provide evidence on which direction to go in and the appropriateness of social policies pertaining to public health services. IMPROVE ACCESS TO HEALTH SERVICES – Provide access to basic medical services outside employment relations: In the formal economy, access to social security is employment-based and costs are shared between employer and employee. Attempts to enforce a similar link in the informal economy have not been suc3

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH SERVICES cessful and governments, employers and trade unions are well advised to accept that access to social security cannot be linked to employment relations beyond the formal economy. Access to health is a human right, not just an entitlement for those with a workplace. Thus it must be attached to the individual, whether employed or not. Governments should cease to be constrained by the notion that the default basis for social security provision is a work contract. – Universal coverage needs tax-funding: Introducing universal health insurance or providing free basic medical care are key systems in widening access to medical care for all. No matter which path taken, it comes with a burden on public funds. Large segments of the population are poor or extremely poor, and do not have the means to share in the cost of medical treatment. Universal coverage implies that groups without adequate income receive free or subsidised access to health services, whether in the form of non-contributory health insurance coverage or a policy without user fees. – Hybrid forms of financing medical services: Large groups in society are neither poor nor rich or well-off; we might refer to them as the»non-poor«. Our poll confirms their interest in social protection schemes and their willingness to contribute in paying premiums. Access to health services may thus be based on three tiers: free medical treatment for the poor; contributory schemes for the non-poor, which may or may not include elements of subsidies; and continuation of shared-contribution schemes in the formal economy with access to higher quality medical services. Universality of access thus focusses on basic medical services, while access to higher standard medical services is reserved for those who can afford it. Expanding the medical services included in a primary health care package and reducing the gap between primary and higher standard services shall be the impetus of future health policies. 3 INVESTMENT IN HEALTH CARE REDUCES SOCIAL INEQUALITY Use of medical care is strongly linked to income inequality, even within the informal economy. Governments have opportunities to change the modes of income distribution, but key instruments, such as increasing the minimum wage or adjusting taxation, hardly reach the informally employed. Investing in better access to health care is an alternative approach to reducing social inequality. If people have improved access to health services, they are less under threat of having to sell productive property or becoming indebted if they need medical treatment. Negative spillovers of health expenditures threaten the investment potential for small 3 The preference for hybrid systems, including additional aspects of the management of health insurance schemes, is argued in Jürgen Schwettmann, Extending health coverage to the informal economy, FES Briefing paper, September 2017. business, weaken people’s mental or physical ability to work, or force families to choose between school fees and medical treatment. Granting safe access to health services overcomes one of the factors that keep people in poverty. INVESTMENT IN HEALTH CARE REDUCES GOVERNMENTS’ LEGITIMACY DEFICITS The large number of people who identify health services as a key concern and do not believe that the political executive will act on their behalf and improve service deliverables should be of major concern to governments that care about their legitimacy. Because of its wide scattering effects, a focus on provisions for universal access to improved health services may easily become a major strategy to improve the image of political decision-makers. 4 UNIVERSAL HEALTH COVERAGE SHOULD BECOME OR REMAIN A TOP PRIORITY ON NATIONAL AND INTERNATIONAL POLICY AGENDAS Social protection and universal health coverage are already binding elements of international policy frameworks. SDG 3.8, ILO Recommendation No. 202, as well as the African Union´s African Health Strategy focus on the provision of health coverage. The findings of the present study confirm that universal health coverage should be prioritised on national and international policy agendas. National or international policy initiatives or international cooperation agreements with the African continent should always favour inclusive and sustainable development and embrace universal health coverage as a top priority, in particular with regard to those in the informal economy. We conclude our study with the following statement: »The establishment of at least a basic level of social protection is a necessary pre-condition for enabling people to exit from poverty, for the creation of social cohesion, for the development of a productive and employable workforce and hence for the creation of the necessary basis for economic growth and rising welfare levels for all. It is an important step towards the realization of the human right to social security, and to state-building.« 5 4 This report looks primarily at the use of health services and, with a few exceptions, ignores the supply side. It goes without saying that universal health coverage is not possible without substantive investments in provisions for medical services, including staff. A cost-free visit to a health facility becomes meaningless if there are no medical staff to take care of patients. 5 Jürgen Schwettmann, Extending health coverage to the informal economy, FES briefing paper, September 2017. 4

Introduction 1 INTRODUCTION 6 Health protection is a key pillar of social protection. Access to health care is simply a human need and, as we show in this paper, in strong social demand. Citizens expect governments to improve access to health care systems. 6 Over many years, development discourses, as well as national and international policy initiatives, such as the G20 Compact with Africa, were economy-oriented and emphasised the need for economic growth, foreign direct investment and employment creation. Social policies were merely runners-up, needing economic growth beforehand to fill the tax coffers of the state before governments could implement social programmes. A rethink of priorities is under way, and in multilateral agreements such as the United Nation’s Agenda 2030, social policies are climbing up the ladder. African initiatives such as the African Union’s Agenda 2063, as well as initiatives at the level of Africa’s regional economic communities are coming forward with more people-centred ideas of inclusive social development, which include social security standards. 7 Today, the affordability of health care is high on the agenda of debates on social development. A major push some years ago came from the ILO’s Social Protection Floors Recommendation(2012, No. 202) which provides»guidance to member states in building comprehensive social security systems«. Instead of opting for»elaborate schemes«, which remain unaffordable for the foreseeable future in many countries, the ILO prioritises»the establishment of national floors of social protection accessible to all in need«. It campaigns for a basic social floor for all, which in addition to child benefits to keep children in school and some modest social assistance for the active population, includes»access to essential health care« and»universal pensions for the elderly and the disabled«(see https://www.social-protection. org/gimi/ShowMainPage.action). Access to health and social protection floors are upholding Agenda 2030 on which the United Nations agreed in 2015 6 The authors would like to express their gratitude to Florence Bonnet and Christoph Strupat, both from the survey project team, and to Reinhard Bahnmüller and Volker Winterfeld, who provided very helpful comments on earlier drafts of this report. 7 Agenda 2063: First Ten-Year Implementation Plan 2014–2023. Available at: www.au.int as sustainable development goals(SDG). SDG 1.3 calls for social protection systems for all and demands the implementation of»nationally appropriate social protection systems and measures for all, including floors«, while SDG 3.8 concerns achieving universal health coverage,»including financial risk protection, access to quality essential healthcare services and access to safe, effective, quality and affordable essential medicines and vaccines for all«. 8 Universal health coverage(UHC) has become the centre piece for measuring social progress in reforming health services.»Universal Health Coverage(UHC) means that all individuals and communities receive the health services they need without suffering financial hardship. It includes the full spectrum of essential, quality health services, from health promotion to prevention, treatment, rehabilitation and palliative care. UHC enables everyone to access the services that address the most important causes of disease and death, and ensures that the quality of those services is good enough to improve the health of the people who receive them.« 9 Societies have various ways to organise access to social security. In marked-based economies, labour markets and social security schemes are intertwined systems. Social security contributions are connected to employment relations and a formula is applied which allows sharing of premium payments for employers and employees. While, for employees, formal employment relations include social security coverage, informal employment usually becomes synonymous to non-access to formal social security schemes. The employee-employer relationship is not the norm in most developing countries and the vast majority of people in employment are either own-account workers or contributing family workers. They are faced with the absence of legal coverage due to the lack of legal recognition of their activities or by non-implementation of responsibilities. The state and public institutions are being called upon to broaden the concept by adding tax-funded security programmes for groups not covered by contributory schemes. 8 See https://sdgs.un.org/goals. 9 WHO, Universal Health Coverage, Information Note, 9. April 2019, available at: www.who.int/publications/i/item/universal-health-coverage(accessed 15.12.2020). 5

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH SERVICES Tax-funded social security programmes have been established as pilot programmes in many countries to target groups of the poorest or at least those unable to work. This leaves uncovered the majority who work in informal employment. If people are outside formal employment relations and have no access to tax-funded schemes, they depend for social help on self-organized membership organisation or family. In non-industrialised countries, reciprocity and solidarity at the community or household level provide the only social security platform for a majority. 10 ­informal employment 11 perceive the importance of health services for their own well-being and how they assess their access to medical treatment. The study looks at the views of consumers and patients without advancing an evaluation of the various components of the health sector. The study thus is not intended as a judgement on the progress of reform implementation. However, in comparing views across borders, conclusions can be drawn on the levels of criticism and satisfaction of respondents with current health systems. In recent years, many governments of African countries have made high-profile political commitments to achieving universal health coverage(UHC) nationwide over the coming years. The policy approach is also supported by the African Health Strategy 2016–2030, envisaged by the African Union as a strategic framework for member states to engage in all aspects of health care provision. Taking it from there, universal health coverage can be identified as the unifying platform for African countries’ health system development. This includes Benin, Senegal, Ivory Coast, Kenya and Zambia, the five countries included in this report on access to health care. They have pledged to achieve the aims of universal health coverage and set in motion various reforms to ensure progress with this agenda. In Benin, the UHC programme is being implemented within the framework of the ARCH programme( Assurance pour le Renforcement du Capital Humain), In Senegal and Ivory Coast it is known as Couverture Maladie Universelle(CMU), in Kenya as the Afya Care Card, while in Zambia it has no specific name and is a package for primary health services provided free of charge to all. The programmes are at various stages of implementation. Some are still in the pilot phase, while others are already being implemented country-wide. All UHC policies are aimed at reducing»OOPS« – out-of-pocket payments for health services – and try to combine in various arrangements free services for certain types of treatment and access to health services based on insurance coverage. With subsidies from central government, they depend on adequate separation of those who qualify for subsidies from those who do not. The supply side is fraught with challenges, including inadequate staffing, medical supplies and technical equipment, and adequate management of risk pooling. Implementing universal health coverage is a huge task. Countries in Latin America and Asia have made major strides in opening up their health systems for the use of all. Many African countries are now on the move as well and it may well be that implementing universal health coverage will be the paramount social policy for the coming decade. Public policymakers should indeed prioritise access to health services for all. This paper aims at supporting universal health coverage as a major social security reform. It looks at how people in 10 On the state of affairs in social security coverage, see ILO World Social Protection Report 2017–19. Statistical Annexes. The paper presents data and explanations based on empirical surveys conducted in five African countries: Benin(December 2018), Kenya(October 2018), Senegal(May 2019), Zambia(August 2019) and Ivory Coast(May 2020). The study was done in collaboration between the FriedrichEbert-Stiftung(FES, the lead agent), the International Labour Office(ILO) and the German Institute for Development (DIE). The study focusses on the informally employed, who constitute the vast bulk of employment in the five survey countries. The polls were conducted with a country-wide representative sample and by using the same research protocol, allowing cross-border comparison of findings. 12 It is certainly true that interpretation of data always has to be done in light of differences between national public health systems, cultural factors, and local political and economic trajectories as they might influence interviewees’ responses. Nevertheless, intercountry comparison allows identification of the factors that favour or impede specific development trends. The evidence gathered proves the importance of access to health care to citizens across countries, income groups and different living conditions. Considering the current COVID-19 pandemic this is unlikely to be surprising. The surveys were conducted before the pandemic emerged, however, in 2018 and 2019, and already showed a strong demand for access to health care, which is likely to have grown in the meantime. The interviews covered a number of themes closely related to people’s exposure to health risk and the handling of medical treatment. 13 Section 2 introduces a ranking order to determine the demand for better health services within a list of state responsibilities; Section 3 discusses perceptions of the availability of medical treatment in terms of frequency of use, while Section 4 looks at financing risks and the means that people mobilise to pay for health care. Section 5 takes up trust in government and other institutions and evaluates people’s confidence in whether public insti11 Informal employment is made up of four employment groups: employers; employees; own-account workers; and family-support workers. For an operational definition, see Appendix II. For a statistical overview of informal employment globally, see ILO, Women and men in the informal economy: A statistical picture, 2018. 12 See Appendix II for technical notes on research methodologies, including a definition of informal employment. 13 The poll covered additional themes, such as self-organisation and views on trade unions the results of which are published in other reports on the survey project. 6

tutions and o­ rganisations are prepared to promote their declared(health) needs. Section 6 closes the empirical overview by looking at membership of health insurance, the numbers of people who remain uncovered, reasons for not joining, and the willingness and ability of those who want to join to contribute financially to a health scheme. The»realism« of people’s readiness to pay regular fees is argued in terms of the cost of available schemes. Section 7 concludes with a summary of the findings and recommendations for social policy adjustments. Introduction 7

RESULTS OF THE SURVEY

Demands for better state services 2 DEMANDS FOR BETTER STATE SERVICES To construct a ranking of people’s expectations for better state services, we selected eight services that we consider »key responsibilities« of the state to society. The services from which respondents were supposed to select included education, health, water supply, roads and bridges, electricity supply, pensions, food supplies in times of crisis, and police services. 14 Respondents were first asked to assess in each case whether the government should improve a particular service. Thereafter, they were asked to rank the services as first, second or third priorities. ­pensions for the elderly« find themselves at the bottom of the demand hierarchy. – All five countries produce a middle cluster consisting of various infrastructural components:»better water supply«,»better food programmes in times of crisis«, »better roads and bridges« and»better electricity supply«. The national ranking of these services varies between countries, but none of them is ranked first or last.  FOR BETTER STATE SERVICES: FIRST PRIORITY Figure 1 provides an overview of respondents’ answers regarding their highest priority in the list. 15 The data show similarities and differences between the countries and clearly allocate an outstanding role for» better health services« and» better schools and education« as top priorities for the informally employed. – In all five countries,»better health« is ranked first or second; in Benin, Senegal and Ivory Coast»better health« is the top runner by far; in Kenya and Zambia it is relegated to second place by a narrow margin; on average, 33.9 per cent of respondents in the five countries picked»better health services« and made it their top choice. – Similar prominence is given to»better schools and education«. In Kenya and Zambia, it moves ahead of health care and comes top, in Senegal and Ivory Coast it is ranked second, and only in Benin is it»downgraded« to third place. The wide gaps between the three clusters should be noted. Together, the top two(health and education) always garner at least 52 per cent, going up to 78 per cent, while the bottom two(police services and pensions) always fall below seven per cent. Countries have similar priorities concerning what is needed first and which state services have to wait if government cannot focus on all of them at the same time. Differences between countries, however, remain substantial when we look at the positioning of the various infrastructural services. 16  FOR BETTER STATE SERVICES: SECOND PRIORITY A look at the second priority offers an opportunity to test the ranking of the first vote. Figure 2 shows that the priorities of the first choice are reproduced when going for the second.»Better health« remains ahead in all countries, with»better schools« just a step behind. At the bottom of the list again come»better police« and»better pensions«, with the exception of Senegal, where the police fare a little better, rated ahead of»better roads and bridges«. The middle cluster is made up mostly of infrastructural components again. – The bottom end of the ranking is also unambiguous. In all countries,» better police services« and» better The strong conformity between the first and second choices implies that many of those who voted for»better health« as front runner in the first place, picked»better 14 See Annex 1 for the reason why we selected these eight services. 15 In presenting the results, we have used the unweighted five-country average and positioned the individual services according to their share, with the highest to the left and the lowest to right. Arranging data presentation in this way helps to highlight similarities between countries, as well as national differences or peculiarities. 16 These findings are confirmed by the Chi²-testing, which discloses significant differences in voting between the countries. However, variations are at best small or of medium relevance(see statistical computation in Appendix III); beyond the significant differences, there are many similarities in the voting pattern of the four countries. 9

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH SERVICES F 5 i 0 g % ure 1 Five countries – demand for better state services according to first priority 45% 43.6% 40% 35% 33.5% 30% 25% 20% 28.4% 23.0% 15% 10% 5% 0% BENIN KENYA Better health services Better food programs in times of crisis Better pensions for the elderly Note: See Appendix III for statistical computations. SENEGAL ZAMBIA Better schools and education Better roads and bridges Better police services Figure 2 Five countries – demand for better state services according to second priority 35% 30% 26.7% 25% 31.1% 27.1% 24.2% 41.2% 33.9% IVORY COAST 5-country average Better water supply Better electricity supply 28.0% 27.4% 20% 15% 10% 5% 0% BENIN KENYA Better health services Better food programs in times of crisis Better pensions for the elderly Note: See Appendix III for statistical computations. SENEGAL ZAMBIA Better schools and education Better roads and bridges Better police services 10 IVORY COAST 5-country average Better water supply Better electricity supply

Demands for better state services schools« as top choice in the second round, and vice versa. This voting behaviour emphasises again the exceptional place of health and education when it comes to demands for state services. 17  VOTING BY URBAN– RURAL RESIDENCE Priorities in demands for particular services reflect a mixture of needs and deficits. If a service is important to a person and its availability is unsatisfactory, demand for it will be heightened. Rural and urban areas differ in the supply of state services. Cities with high population densities are usually provided with physical infrastructure before remote areas are served. In general, access to public goods such as roads, water and electricity improves with population density. So does access to health facilities. On the other hand, if consumption of goods or services is not free but comes at a price, low-income groups may not have access even if they live in reasonable proximity to supply. Figure 3 illustrates the priority profiles in correlation with the urban or rural background of respondents. There are similarities and discrepancies, depending on the issue concerned.»Better police services« and»better pensions« tend to have a low ranking even though urban inhabitants want them slightly more than rural people. »Water« is always more important for rural areas, as are »roads and better bridges«, while»better schools« are more in demand in urban locations, with the exception of Kenya. Within a country, the living environment is an important factor influencing priorities. All five countries show a statistically significant discrepancy between what urban dwellers demand in terms of better state services and what rural inhabitants prefer. Urban–rural differences are most strongly expressed in Ivory Coast, followed by Zambia and Senegal(see Cramer-V in Appendix III). Urban–rural comparison refers to the overall distribution of listed services. What if we separate»better health« and look at it as a single issue? Is it part of an urban–rural divide as well? For three countries(Benin, Senegal, Zambia) we cannot observe a gap. Residents in rural areas rate the importance of»better health« in same way as residents in urban locations. Kenya and, to some extent, Ivory Coast diverge from this uniformity in that they manifest an urban–rural drift. To some extent, rural residents regroup their priorities by shifting their emphasis from»health services« to»better water supply«.  BY AGE It is rather difficult to predict the impact of age on the need for better state services. The older generation may be more interested in better police services, better health services, better water supply and good pensions. The younger age groups may set their priorities on education, electricity connection and transport infrastructure to improve their(social) mobility. Where the old and the young live together in a household, continual discussions on state service deficits may have a balancing effect, however. A look at Figure 4, which illustrates demand profiles, discloses considerable conformity between age groups. The three-cluster model of putting health services and better education first, police services at the bottom and physical infrastructure projects in the middle is widely reproduced by all age groups. Variations occur as regards the importance of pensions, which the elderly in Kenya, Zambia and Ivory Coast find more relevant, or the water supply, expression of the importance of which diminishes with the age of respondents in Benin and Zambia. Other services are positively linked to age in one country, while in another country there is no effect or the correlation is negative. Overall, the age factor does not strongly influence the priority profiles in Benin and Senegal, while in Kenya, Zambia and Ivory Coast there is a link, albeit fairly modest. Beyond the voting profile on all services, a closer look at »better health services« and»better pensions« is revealing (see Figure 5). Our assumption that the old shift their priority from health to pension is confirmed for Kenya, Zambia and Ivory Coast, but not for Senegal and Benin. The effect is statistically strong in Kenya, where the oldest group shifts their priority to such an extent that the call for better pensions even outstrips the demand for better health services. The old may be prone to more illness, but their need for monetary support from pensions overshadows other needs.  BY GENDER Could gender be a factor influencing respondents’ health priorities? Figure 6 provides an answer from our survey data. If we compare male and female respondents in terms of their preference for»better health services« as their top priority, we see no meaningful differences, with the exception of Kenya. There, significantly more male respondents than female opt in favour of better health care as first priority. The effect is fairly small, however. Overall, gender is not a variable that explains a preference for health care. 17 The findings are confirmed by the Chi²-testing, which discloses significant differences in the distribution of second choice votes between all countries. As previously, the variations are at best small or of medium weight(see computation in Appendix III), indicating many similarities in the voting patterns of the four countries.  BY INCOME Income is another factor concerning which it is difficult to assess how it is linked to demands for better state services. The poor are likely to depend on state provision to satisfy basic social needs, whereas the rich are more likely to be in 11

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH SERVICES Figure 3 Five countries – demand for better state services(first priority) by urban–rural residence 44.7%42.4% Better health services 25.8% 15.5% Better schools and education 11.2% 5.6% Better water supply SENEGAL 12.3%14.3% 2.3% 4.9% 7.0% 1.0% 3.4% 1.9% Better food Better roads and programs in times bridges of crisis urban rural Better electricity Better pensions for supply the elderly 5.0% 2.8% Better police services 22.4%23.4% Better health services 32.2% 20.0% Better schools and education 16.4% 9.7% Better water supply ZAMBIA 20.7% 13.7% 12.5% 5.2% 9.3% 2.7% 5.8% 3.9% Better food Better roads and programs in times bridges of crisis urban rural Better electricity Better pensions for supply the elderly 1.7% 0.4% Better police services 34.9% 25.4% Better health services 31.6%33.4% Better schools and education 14.6% 7.5% Better water supply KENYA 6.4% 5.4% 8.3% 10.4% 2.1% 4.7% 3.2% 3.3% 5.9% 2.7% Better food Better roads and programs in times bridges of crisis urban rural Better electricity Better pensions for supply the elderly Better police services 35 3 .3 5. % 3% 31 3 . 1 9 . % 9% 24 2 . 4 7 . % 7% 14 1 . 4 0 .0 % % 2 2 5 5 .2 .2 % % 1 1 6 6 .3 .3 % % Be B t e te tt r e h r e h a e l a th lth Be B t e t t e t r er sc s h ch o oo ls ls a a n n d d se s r e v r i v ce ic s es ed ed u u ca ca ti t o io n n B B e e t tt e e r r w w a a t t e e r r s s u u p p p p ly ly B ENIN 3 3 . . 6 6 % % 3 3 . . 0 0 % % 1 1 4 4 . . 2 2 % % 9 9. . 9 9 % % 9 9 .1 .1 % % 5 5 .7 .7 % % 3 3 .2 . % 2% 1. 1 4 . % 4% 1. 1 2 . % 2% 1. 1 1% .1% B B e e t t t t er food p p r r o o g g r r a a m m s in time s o o f f crisis Bette e r r r r o o a a d d s s a a n n d d b b r r i i d d g g e e s s urb an rur r a a l l B B e e t tt e e r r e e le le c c tr t i r c ic it i y ty B B e e tt t e t r er p p en en si s o i n o s ns fo f r or s s u u p p p p ly ly th th e e el e d l e d r e l r y ly Be B t e te tt r e p r o p l o ic l e ice se s r e v r ic v e ic s es 43.8% 38.1% Better health services 32.3% 21.2% Better schools and education 13.5% 4.4% Better water supply IVORY COAST 6.4% 3.3% 10.9% 5.9% 8.9% 1.9% 3.1% 3.1% Better food programs in times of crisis Better roads and bridges urban rural Better electricity Better pensions for supply the elderly 2.2% 0.9% Better police services Note: See Appendix III for statistical computations. 12

Demands for better state services Figure 4 Five countries – demand for better state services(first priority), by age groups 60% 50% 40% 30% 20% 10% 0% 35% 30% 25% 20% 15% 10% 5% 0% 40% 35% 30% 25% 20% 15% 10% 5% 0% 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% SENEGAL Better health services Better schools and education Better water supply Better food Better roads and programs in times bridges of crisis Better electricity supply Better pensions for the elderly age 15-24 age 25-34 age 35-44 age 45-54 age 55-64 age 65 a.m. ZAMBIA Better police services Better health services Better schools and education Better water supply Better food Better roads and programs in times bridges of crisis Better electricity supply Better pensions for the elderly age 15-24 age 25-34 age 35-44 age 45-54 age 55-64 age 65 a.m. KENYA Better police services Better health services Better schools and education Better water supply Better food Better roads and programs in times bridges of crisis Better electricity supply Better pensions for the elderly age 15-24 age 25-34 age 35-44 age 45-54 BENIN age 55-64 age 65 a.m. Better police services Better health services Better schools and education Better water supply Better food Better roads and programs in times bridges of crisis Better electricity supply Better pensions for the elderly age 15-24 age 25-34 age 35-44 age 45-54 IVORY COAST age 55-64 age 65 a.m. Better police services Better health services Better schools and education Better water supply Better food Better roads and programs in times bridges of crisis Better electricity supply Better pensions for the elderly age 15-24 age 25-34 age 35-44 age 45-54 age 55-64 age 65 a.m. Better police services Note: See Appendix III for statistical computations. 13

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH SERVICES Figure 5 Five countries – demand for better health services versus pensions(first priority), by age groups 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% better health better pension SENEGAL better health better pension ZAMBIA better health better pension KENYA better health better pension BENIN better health better pension IVORY COAST age 15-24 years age 25-34 years Note: See Appendix III for statistical computations. age 35-44 years age 45-54 years age 55-64 years age 65 a.m. years Figure 6 Five countries – demand for better health services(first priority), by gender 45.4% 42.0% 22.4% 23.3% 31.2% 25.9% 34.5% 32.8% 43.1% 39.4% SENEGAL ZAMBIA Note: See Appendix III for statistical computations. KENYA male female 14 BENIN IVORY COAST

Demands for better state services Figure 7 Five countries – demand for better state services(first priority), by income 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% 40% 35% 30% 25% 20% 15% 10% 5% 0% 40% 35% 30% 25% 20% 15% 10% 5% 0% 40% 35% 30% 25% 20% 15% 10% 5% 0% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% SENEGAL Better health services Better schools and education Better water supply Better food Better roads and programs in times bridges of crisis Better electricity supply less 0.5 MW 0.5 MWZ 1 A M M W BIA >1 MW-2 MW>2 MW Better pensions for the elderly Better police services Better health services Better schools and education Better water supply Better food Better roads and programs in times bridges of crisis Better electricity supply less 0.5 MW 0.5 MW-1 MW>1 MW-2 MW>2 MW KENYA Better pensions for the elderly Better police services Better health services Better schools and education Better water supply Better food Better roads and programs in times bridges of crisis Better electricity supply less 0.5 MW 0.5 MW-1 MW>1 MW-2 MW BENIN >2 MW Better pensions for the elderly Better police services Better health services Better schools and education Better water supply Better food Better roads and programs in times bridges of crisis less 0.5 MW 0.5 MW-1 MW>1 MW-2 MW IVORY COAST Better electricity supply >2 MW Better pensions for the elderly Better police services Better health services Better schools and education Better water supply Better food Better roads and programs in times bridges of crisis less 0.5 MW 0.5 MW-1 MW>1 MW-2 MW Better electricity supply >2 MW Better pensions for the elderly Better police services Note: MW is statutory minimum wage. See Appendix III for statistical computations. 15

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH SERVICES a position to purchase private services. In between are the »non-poor«, who may be able to tap alternative resources where the state fails to deliver in some areas(for example, electricity), but may find it difficult to do so in other areas (such as health care). There may also be people who classify some duties as moral duties of the state towards all citizens and identify a particular service as a top priority, independently of their individual situation. To test the impact of income on priorities we grouped respondents into four income classes: the»extreme poor« (monthly income less than half of the statutory minimum wage(MW)), the»moderately poor«(monthly income between half and full minimum wage), the»non-poor« (monthly income between minimum wage and twice the minimum wage) and the»well-off«(monthly income higher than twice the minimum wage). The findings are shown in Figure 7. It is difficult to argue the case for a link between income and service priorities merely by looking at profiles. In Benin, many service demands are sensitive to income. The demand for»better schools« and»better roads« increases with income, while demand for better health and water diminishes with higher earnings. Income and priorities are linked but the statistical significance of this correlation is low. Kenya’s priority profile is more ambiguous. With higher incomes, the demand for»better roads and bridges« increases, while the demand for»better schools and education« diminishes. 18 Demand for other services, however, is not connected to income, and the Chi²-test finds no link between income and service priorities. Senegal has only one service domain that reacts sensitively to variations in income(»water supply«) – all other areas are articulated independently of earnings. The Chi²-test again observes no dependency(see Appendix III). Zambia exhibits the widest fluctuations between service needs and income classes. The links, however, are not manifested in a continuous manner and instead of a gradual change there are fluctuations within service categories without a clear tendency. The Chi²-test confirms this unstructured picture and observes no dependency between the variables. In Senegal, we again find variations in demand for services according to income, but no strong tendency can be observed. There is no statistical significance between service priority and income. Overall, no dominant disparities in priorities between income classes can be observed across borders.»Health care« as a single issue does show some income dependency in Zambia and Benin, but it is fairly minor. In Kenya, Senegal and Ivory Coast, the demand for»better health services« is not dependent on what people earn. Reasons why»health service« and»schools and education« top the list of state services easily come into mind. In many African countries, education is identified as the number one vehicle for social advancement, ranked higher than private property or inheritance. The combination of educational achievements and access to state employment has been the common strategy for social careers during decolonisation and in the early decades thereafter. Obtaining educational qualifications is the easiest way for many to improve their chances on the labour market and climb the social ladder. The high demand for more educational infrastructure expresses the hope of large segments of the population that they may be able to improve living standards for themselves or for their children, if only access to education is improved. 19 The top ranking of»better health services« may equally be reflected in calls for improved living conditions. Here, we cannot break down the call for»better health services« into different components, such as»access to health insurance« or»better access to existing health facilities«. That will be done further on in the ongoing data analysis. It is important to note that the call for»better health services« far outstrips the demand for»better pensions for the elderly«. On average, 11 times more people identify»better health care« as their top need than»better pensions«. One explanation may be that in the articulation of social demands, the urgency of social problems outstrips future needs. Sickness, its treatment and cost are daily problems for many and draw more attention than future income after retirement. We will, however, test this argument below when looking at the hierarchy of needs in relation to respondents’ age. Two additional arguments may be mentioned here to explain the preponderance of»health services« over»retirement benefits«. Given the demographic structure of many African societies only a small percentage of the population are in the age cohort of legal retirement. Half of the population is below 20 years of age. Because the number of oldage people is still fairly low, finding economic resources for their support is less of a priority than in aging societies. A further argument emphasises the role of the family. Many people still live in extended families in which younger members traditionally take care of the older ones. Extended families can be seen as an alternative»insurance scheme« for retirement benefits, which is not integrated into the monetary economy through premium payments 18 This may imply that the demand for more public schools declines while the demand for private schools increases. 19 There is indeed a positive relationship between higher level of education and access to formal employment. See ILO(2018) Women and men in the informal economy: A statistical picture. 16

or other specialised schemes. Retirement benefits within extended families are provided in non-monetary forms, such as direct food and housing deliverables. Health services, on the other hand, are mainly integrated into the market economy and have to be paid for in cash. No or low cash income hinders access to health services, but it does not challenge access to food and housing to the same extent. The preponderance of»better health services« thus acknowledges the fact that access to better health care cannot be achieved through traditional means of social reproduction, such as living in extended families, as is the case with access to food and housing after retirement. We tested four factors in relation to first priorities and found a small to medium link to the urban–rural living environment; no or only a small link to age; no link to gender; and no or only a small link to income inequality. If we assume that these factors are strong components in socio-economic class formation, we can conclude that internal stratification within the informal labour force has not emerged to such an extent that giving priority to better state services depends on social affiliation. Income inequality can be taken as a key reference to underscore this argument. While we did not observe strong discrepancies in priorities between income classes, we cannot assert with confidence what would happen when the general level of income increases and income disparities widen further. It may be that with higher earnings and intensified inequalities, the demand structure of high-income earners would shift away from that of lower income earners. At the current level of income inequality, however, differences are modest and income is mainly a negligible factor when searching for disparities in priority setting. If socio-economic factors are not yet at work to give rise to a differentiated need structure, we may consider that our ranking is a typical reflection of the informal labour force as a whole. As this segment of the labour market dominates the economy, encompassing between 80 and 90 per cent of total employment, the vote profile can be generalised and assessed as a reflection of general significance within a country. Demands for better state services 17

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH SERVICES 3 USE OF MEDICAL CARE – A MATTER OF INEQUALITY?  OF MEDICAL CARE – INEQUALITY BY COUNTRIES Equality of access may be seen as a cornerstone of the health policy of a country committed to building a health care system for all social groups. Medical care should be made accessible to all who are sick and need treatment. To assess accessibility, we have to look at two criteria: are health services set up with a geographical density such that time and travel costs do not become prohibitive for people living distant from health care facilities? Are health services open even to those who are unable to pay for them? Access to medical services is a question of supply and demand. If health care is provided for a fee and charges are above what poorer segments in society can afford, medical care may be materially available but remain out of their financial reach. The term»access« blends»availability of health care« and»ability to pay for health care«. Some aspects of access concern supply factors, while others reflect the social conditions of demand. Equality of access does not mean equality of opportunity, but equality of treatment. In view of limited financial resources equality of access cannot include all forms of medical treatment. Subject to the economic development stage of non-industrialised countries, the reference here is to primary basic care, a health care concept bringing basic medical treatment within reach of the poor. In order to obtain an understanding of how medical treatment is»consumed« we asked interviewees about the frequency with which they were able to obtain medical care. 20 The question was phrased as follows:»Over the past year how often, if ever, have you or anyone in your family gone without medicine or medical treatment?« 21 22 20 We limit our analysis here to one aspect in evaluating use of medical services. A more detailed paper which looks at the assessment of quality of services and compares public with private health facilities will be published at a later date. 21 This question is part of a five-sequence question which includes food, clean water, cooking fuel and cash income and is used to construct an index of lived poverty. We follow here the AfroBarometer(AB) approach and acknowledge its work in this area. 22 Due to the unclear answer option for households with no cases of sickness, their responses were ignored in the data analysis. The question referred not to the respondent alone but included all members of the family. The question does not ask about the frequency of illness or the number of visits or non-visits to medical facilities. Instead, it requested that interviewees weight the incidence occurrence of a need for treatment and for receiving treatment. We thus obtain the respondent’s personal assessment of whether medical care is available when needed. Respondents could voice their opinion on the actual number of cases of treatment or just articulate a gut feeling. Either way, the question throws light on their perception concerning whether people believe that they are able to use health services when needed. The answers to our question indicate huge differences and make it possible to group the five countries into two camps and a middle position(Figure 8). A large majority – nearly 70 per cent – in Kenya and Zambia stated that they mostly or always go for medical care when needed. Only about 10 per cent declared that they have to manage their medical problems without(or mostly without) medical care. In Senegal and Benin, between 24 and 32 per cent of people face a situation that prevents them mostly or always from getting medical care when needed. The share of those who have always or mostly»gone without medical care« in Benin and Senegal is nearly three times higher than in Kenya and Zambia. Ivory Coast falls in the middle. The share of those who feel prevented from using medical care(23.9 per cent) is substantially lower than in Senegal and Benin but still double the level in Kenya and Zambia. 23 To trace differences at levels lower than the national average we looked at subgroups by living environment and income. Medical facilities are usually concentrated in areas of high population density and we can expect respondents in urban areas to make more frequent visits to medical facilities than rural residents. The same may be the case in relation to income. People with higher incomes are less likely to avoid medical care because of cost implications than poor people, who cannot afford the expense. 23 The T-test confirms significant differences between Senegal and Benin on one hand and Kenya and Zambia on the other. See table at the bottom of Figure 8. 18

Figure 8 Five countries – use of medical care 68.1% Use of medical care –a matter of inequality? 'gone without medical care' 68.3% 34.9% 34.8% 30.2% 19.4% 12.5% 21.7% 10.0% 39.6% 32.4% 28.0% 46.9% 29.2% 23.9% SENEGAL ZAMBIA never+just once or twice KENYA several times BENIN many times+always Question:»Over the past year how often, if ever, have you or anyone in your family gone without medicine or medical treatment?« Note: Data on household with cases of sickness during last 12 months. See Appendix III for statistical computations. IVORY COAST Figure 9 Five countries – use of medical care by urban–rural residence 69.8% 69.8% 66.9% 66.9% 69.6% 69.6% 67.8% 67.8% 43.5% 43.5% 36.3% 36.3% 26.5% 24.1% 26.5% 24.1% 13.6% 13.6% 11.8% 11.8% 9.5% 9.5% 50.3% 50.3% 55.6% 55.6% 24.7% 24.7% 38.2% 38.2% 31.7% 31.7% 10.2% 10.2% 37.9% 37.9% 31.2% 31.2% 16.8% 16.8% Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban SENEGAL Rural Urban ZAMBIA Rural Urban KENYA Rural Urban BENIN Rural Urb IV an ORY COA R S u T ral SENEGAL ZAM re B g IA ular use of medical c K a E re NYA low use of medica B l E c N ar IN e IVORY COAST regular use of medical care low use of medical care Note: Data on households with cases of sickness during past 12 months. Definitions: regular use of medical care=»gone without medical care«: never or just once or twice; low use of medical care=»gone without medical care«: many times, or always. See Appendix III for statistical computations. 19

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH SERVICES  OF MEDICAL CARE – INEQUALITY BY URBAN–RURAL RESIDENCE the case for 31 to 38 per cent. Around a third of rural residents often have to cope with illness without benefitting from medical treatment. 26 The residence factor confirms the need to divide our survey countries into two camps. In Kenya and Zambia, location proves to be of minor or no importance. 24 Irrespective of where respondents live, most of them can avail themselves of medical care when needed(see Figure 9). This contradicts our common understanding that rural dwellers experience more difficulties in accessing medical treatment. 25 Judging from our poll data, Kenya and Zambia appear to have overcome a rural–urban divide in this regard. The T-test confirms the polarisation: in Kenya and Zambia, differences between urban and rural residents in the use of medical care are below statistical significance, while they are clearly manifest in Senegal, Benin and Ivory Coast(see Appendix III).  OF MEDICAL CARE – INEQUALITY BY GENDER In Benin, Senegal and Ivory Coast, the living environment becomes a distorting factor. While in urban locations, some 17 to 25 per cent lament low use, for rural dwellers this is 24 We have simplified the data appraisal by putting the five-answer options into two groups. If people have»never« or»only once or twice« gone without medicine or medical treatment, we call it»regular use of medical care«; if people have»mostly« or»always« gone without medicine or medical treatment, we call it»low use of medical care«. The middle category of»several times« is ignored. 25 See, for example, Xenia Scheil-Adlung(ed.), Global evidence on inequities in rural health protection: new data on rural deficits in health coverage for 174 countries. International Labour Office, ESS Document No. 47, 2015. Is gender a factor in the use of medical services? To obtain an understanding of the gender sensitivity of people’s use of medical services, we divided our sample into male- and female-led households. The head of household answered the questions for all members without giving separate accounts for male and female members. If we assume that both male and female heads are even-handed when assessing cases of sickness in their households, the responses can be taken as an approximation for assessing the gender-related use of medical services. 26 The Chi² test confirms a statistically worse level of use of medical care for the rural population in Senegal and Benin(see Appendix III). Figure 10 Five countries – use of medical care by gender 12.8% 12.3% 31.4% 28.7% 10.7% 9.4% 32.7% 31.9% 25.7% 19.9% 31.4% 39.4% 67.8% 68.2% 65.6% 70.8% 40.4% 37.7% 43.5% 54.4% Male Female SENEGAL Male Female ZAMBIA Note: Data on household with cases of sickness over the past 12 months. See Appendix III for statistical computations. Male Female KENYA use regular use low Male Female BENIN 20 Male Female IVORY COAST

Use of medical care –a matter of inequality? Figure 10 reproduces responses according to the sex of the head of household. No country shows a significant disparity in the use of medical care between male-led and female-led households. Senegal and Ivory Coast just fall short of the significance level, while the other three countries show a high level of conformity. We thus cannot establish gender as an impediment to using medical services.  OF MEDICAL CARE – INEQUALITY BY INCOME A look at the impact of respondents’ earnings on use of medical care reveals a slightly modified pattern of inequality. Adopting the income clusters explained above(»extreme poor«,»moderate poor«,»non-poor« and»welloff«) we arrive at a positive correlation in four countries. In Senegal, Kenya, Benin and Ivory Coast, the use of medical care increases if income rises. The effect is statistically negligible in Zambia. Zambia is thus the only country in which income does not decide whether medical treatment is available or not(see Figure 11). Many of the poor in Senegal and Benin are in a miserable situation. Around 40 per cent of households with an income below 50 per cent of the official minimum wage hardly use medical treatment(Benin 42.9 per cent; Senegal 39.5 per cent). For Ivory Coast, the situation is only ­slightly better. A quarter of households report that medical treatment is beyond their reach. In Kenya and Zambia, this share is considerably lower, at 13.7 and 13 per cent, respectively. Also striking is the fact that a decent or fairly high income is by itself not a sufficient condition to secure medical care. In Kenya and Zambia, only 90.5 and 72.7 per cent, respectively, of the highest income segment report good use of medical treatment at all times. In Benin and Senegal, less than half of the»well-off« answer positively. The »well-off« in Senegal fare a little better: 61.5 per cent report resorting to medical treatment whenever needed. Income indeed facilitates the use of medical care. However, that is not the case for all and a significant number of fairly well-off people are still left without medical care. We have to emphasise again that our evaluation measures the use of medical care, not the quality of medical treatment or satisfaction with it. It may well be that the quality of medical treatment equally depends on income and people’s capacity to pay for lower or higher standards of treatment. If so, inequality arising from income differentials could be even higher. We can link the urban–rural residence factor to income differences and measure how the combination of the two aggraFigure 11 Five countries – use of medical care, by income 100% 90% 90.5% 80% 70% 60% 50% 40% 72.7% 66.4% 45.1% 39.5% 59.8% 49.4% 42.9% 61.3% 51.5% 30% 26.5% 20% 19.0% 21.2% 13.0% 28.1% 13.7% 22.4% 25.3% 15.1% 10% 0% regular use low use SENEGAL 2.4% regular use low use ZAMBIA less 0.5 MW regular use low use regular use low use KENYA BENIN >0.5 MW-1 MW>1 MW-2 MW>2 MW regular use low use IVORY COAST Note: Data on households with cases of illness during past 12 months. See Appendix III for statistical computations. 21

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH SERVICES vates inequality. Their combined effect can be ­demonstrated by comparing the number of respondents regularly using medical care in different income groups, additionally separated by location. A figure of 1.0 would imply that the urban well-off and the rural poor have similar access to medical care and neither the living environment nor income inequality play a discriminatory role. The more this ratio increases, the more inequality enters the system. Table 1 provides findings limited to a comparison of the »urban well-off«(income group 4) to the»rural poor«(income group 1). We have seen that in Kenya and Zambia location is of minor importance, and inequality is almost exclusively determined by income differences. In Benin and Senegal, living environment and income differences combine to enlarge inequality substantially. In Ivory Coast, urban–rural residence and income contribute in modestly to inequality in using medical care. Our data show a fairly low discrimination factor of 1.17 for Zambia. The rural poor have only a 17 per cent lower chance of using medical care in a regular manner than the urban well-off. The ratios for Ivory Coast and Kenya, at 1.48 and 1.67, is still fairly balanced. The health environment in Senegal is already highly discriminatory(ratio 2.46), whereas Benin(ratio 2.86) is the worst player in our five-country comparison. Certainly, we did not control the two groups with regard to their size and the relationship of income boundaries to the statutory minimum wage may produce a bias. These methodological considerations, however, do not challenge our main statements: – all five countries filter access to medical treatment by income and produce discrimination based on earnings; – the living environment is an additional discriminatory factor in Benin and Senegal, modestly present in Ivory Coast, but hardly relevant for Kenya and Zambia; – overall, discrimination in Benin and Senegal is much higher than in Kenya and Zambia, with Ivory Coast finding itself in the middle. The large differences in the use of medical care by the informally employed in the various countries deserve comment. Even though this is not the place to compare respondents’ perceptions in detail with facts on existing or missing health facilities, a simple look at WHO statistics immediately indicates national disparities in the supply of medical services which mirror our respondents’ perspectives. Benin and Senegal are the weakest performers with regard to all indicators listed in Table 2. They spend less of their GDP on health care, which also results in lower per Table 1 Five countries – measuring inequality of»good use« of medical care(selected groups) Good use of ­medical care Senegal Zambia Kenya Benin Ivory Coast 1. Urban – High-Income Group 4 2. Rural – Low-Income Group 1 53.3% 21.7% 76.0% 65.0% 100.0% 59.8% 63.6% 22.2% 68.7% 46.4% 3. Inequality 2.46 1.17 1.67 2.86 1.48 factor(1./2.) 4. Better access: urban high income vs rural low income 146% 17% 67% Note:* 2018;** 2016;*** 2014. Source: https://apps.who.int/nha/database/ViewData/Indicators/en; https://apps.who.int/gho/data/node.main; www.who.int/data/gho/data/themes/topics/indicator-groups/indicator-group-details/GHO/current-health-expenditure-(che) 22 186% 48%

Use of medical care –a matter of inequality? Table 2 Five countries – health expenditures(various indicators, 2017) Benin Kenya Senegal Zambia Ivory Coast I. Current health expenditure as% 3.72 4.80 4.13 4.47 4.45 of GDP II. Current health expenditure(CHE) 85 158 143 180 176 per capita in PPP in international$ III. Primary health care(PHC) expenditure as% of CHE(2018) n. a. 73 66 79 80 IV. Primary health care(PHC) per capita in$US(PPP n. a. 116 92 140 140 V. Medical doctors (per 10,000 population) 0.791* 1.565* 0.691 1.628** 2.314*** VI. Nursing and midwifery personnel (per 10,000 population) 3.888* 11.656* 3.127 13.376* 6.048* VII. Out-of-pocket payments(OOP) 45.0 24.0 52.4 11.8 39.4 as% of total health spending Note:* 2018;** 2016;*** 2014. Source: https://apps.who.int/nha/database/ViewData/Indicators/en; https://apps.who.int/gho/data/node.main; www.who.int/data/gho/data/themes/topics/indicator-groups/indicator-group-details/GHO/current-health-expenditure-(che) capita expenditure. They have the lowest numbers of medical staff, including both medical doctors and nursing and midwifery personnel. Most importantly, out-of-pocket payments(OOPs), which measure households’ share of health expenditure and serve as an indicator of family financial burdens, are highest there. Kenya, Zambia 27 and Ivory Coast fare better in all regards. They employ more medical staff, spent a higher percentage of the budget for health care and give primary health care a stronger focus. In the end, this translates into lower levels of OOPs. 28 Senegal selected»better health services« as their highest priority, while in Kenya and Zambia health is»downgraded« to second position behind»better education«. These disparate valuations correspond to the»supply indicators« in Table 1. We may therefore assume that Kenyans and Zambians switch priorities from»better health« to»better education« because their national governments invest more in primary health care while Beninese and Senegalese opt for»better health services« as top priority because health services in their countries are more wanting. At this point we are able to reassess a variation between countries that we noted above. Respondents in Benin and 27 Zambia under the National Health Care Package(NHCP) offers basic healthcare packages at the primary(district) level free of charge. Capacity constraints and funding shortages do not always allow unlimited access to medical care under NHCP. For details, see: World Health Organisation Zambia, WHO Country Cooperation Strategy 2017–2021, available at: https://apps.who.int/iris/bitstream/handle/10665/273149/ccs-zmb-eng.pdf?ua=1(accessed 23.5.2020) 28 Other indicators could be used to measure the quality of medical care and its coverage, many of which are available at the WHO. We use these indicators only to counter-check the supply profile with our perception profile and can confirm that the views on accessibility of health care in the five survey countries correspond to the supply factors at macro level. The WHO estimates that 45 medical workers per 10,000 people are needed to meet the SDG target of universal health coverage. This implies that all survey countries still have to go some way before they can fulfil this indicator. 23

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH SERVICES 4 PAYING FOR MEDICAL TREATMENT  OF FINANCING MEDICAL CARE People may refrain from visiting medical facilities for various reasons. But what happens if they do go for medical treatment? If health services are provided free of charge, there are still incidental costs: payments for transportation, period of absence from home, out-of-pocket payments for food and other factors, and other things. However, if health services are paid for directly by users themselves, the availability of sufficient funding may become a major concern. To obtain an understanding of how onerous the costs of medical treatment may become we asked the head of the household»How did you or your family find the money to pay for this treatment?« Because we did not ask for the exact amount that patients paid we are not able to relate health spending to household income. Identifying the source of funding, however, still allows us to draw conclusions about the financial burden people have to cope with when seeking treatment. We grouped the ways of paying health bills into various sources. Patients may use health services without paying for them directly, either because no costs are charged or payments are made from another source, such as health insurance. If households themselves have to settle a health bill, three main options are available: people may settle bills by using(part or all of) their savings. If they do not have funds available, they may sell some of their possessions, such as cattle, tools, advance sales of produce, jewellery, household equipment, means of transport. Alternatively, they could approach friends, relatives, neighbours, money lenders, banks or others to obtain a loan. A further option is monetary assistance in the form of a donation. One may challenge the rationale of combining access to traditional forms of solidarity with obtaining funds from market economy operators. But traditional forms of solidarity are built on reciprocity and, while they may allow more leeway in repayment than market operators, there is nevertheless pressure to mobilise one’s own resources when the need arises for others. Figure 12 Four countries – sources of finance for medical treatment IVORY COAST 6.8% 57.1% 27.1% 0.4% 8.6% KENYA 20.8% 33.9% 31.0% 1.2% 13.1% ZAMBIA 49.1% 26.0% 12.2% 1.0% 11.7% SENEGAL 2.0% 41.5% 47.9% 1.1% 7.6% 0% 10% no cost charged Note: No data available for Benin. 20% savings 30% 40% 50% 60% 70% 80% sale of assets or taking loan(all forms) assistance(government, employer, co-workers) 90% other means 24 100%

Paying for medical treatment Various sources for paying health bills are presented in Figure 12. Patients in Zambia appear to be in a»socially privileged« situation in terms of financing health care costs. Nearly half of them receive medical care without having to cover the cost. Some 12 per cent of Zambians are»less privileged« in that they have to obtain cash by either selling some of their possessions or asking others for a loan. Senegal is the counter-example. A mere 2 per cent of patients receive free health services, while 47.9 per cent realise funds by selling property or going into debt. Kenya and Ivory Coast find themselves in a similar situation, in that 31 per cent and 27.1 per cent, respectively, have to look for external funds by borrowing or selling assets. The countries differ in how other sources are mobilised, however. Respondents from Ivory Coast rely mainly (57.1 per cent) on savings, while free medical treatment is scarcely available to anybody(6.8 per cent). Kenyans rely less on savings(33.9 per cent) and have better access to treatment without having to bear the cost(20.8 per cent). Our findings are fully supported by WHO figures for outof-pocket payments(OOP) as presented in Table 1, which groups Zambia ahead of Kenya and Ivory Coast with regard to lower personal payments for medical treatment. Benin and Senegal trail the others by some distance. The high level of free services in Zambia reflects the policy introduced a few years ago which offers basic health-care packages at the primary(district) level free of charge. The fact that about 50 per cent of patients still pay for medical services points either to medical treatment beyond the level of primary health care or to capacity constraints and funding shortages that limit the use of free medical care. We can assume that the new health policy is a key reason why fewer patients in Zambia have to sell property or take out loans than in Kenya and Senegal. In Kenya, various programmes are at work, which explains while some 20 per cent of respondents report medical treatment without charge. A key reason is the fairly wide membership of health insurance schemes. Figure 13 shows the effects of membership of a health insurance scheme on the payment of health bills. The share of those who are forced to sell property or borrow money to pay for medical treatment declines from 33.6 to 21.4 per cent, while the share of those who consume medical services with no further costs increases from 17.1 to 35 per cent. Insurance cover eases the financial burden entailed by the use of medical care.  OF FINANCING MEDICAL CARE, BY URBAN–RURAL RESIDENCE We have grouped respondents according to their urban or rural residence to see whether the modes of payment Figure 13 Kenya – sources of finance for medical treatment by health insurance membership 35.0% 34.7% 30.8% 33.6% 17.1% 21.4% 13.3% 12.0% no cost charged 0.9% 1.3% savings sale of assets or taking loan assistance Health insurance No health insurance 25 other means

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH SERVICES for medical treatment vary according to patients’ living ­environment. Forms of payment vary, as shown in Figure 14. Discrepancies take various forms and can be summarised as follows: – Rural dwellers benefit more from the introduction of schemes that provide free access to medical treatment. While governments that have established free treatment schemes are not likely to give preference to rural people, NGO projects and confession-based initiatives may do so. – Urban dwellers have a higher share in financing medical expenditures from their own savings. The reason may be their slightly higher cash income and higher integration into the cash economy. – Urban and rural people have a similar level of mobilising funds from other sources but they follow different paths. Patients from rural areas sell possessions more often than patients from urban locations, while urban dwellers go for cash loans more often than rural residents. The key differences between urban and rural residents in settling their health bills appear to be linked to the mode of production. A majority of rural dwellers are peasants who own some land, farming equipment or agricultural produce, which they may sell to obtain the cash needed for medical treatment, while urban residents do have less property and are forced to go into debt when the need arises for medical treatment. Both modes of mobilising funds put financial strains on patients afterwards. Our data do not enable us to further qualify the negative effects of such mobilising strategies. The analysis cannot conclude whether accessing medical treatment confronts rural people with more»aftermath« stress than is the case for urban people. Our data clearly show, however, that the high level of financing health costs from the sale of assets and from obtaining loans is a heavy financial burden for both groups. Figure 14 Four countries – payment for medical treatment by sources of finance and urban-rural residence 2.7% 1.3% SENEGAL 42.0% 'no cost charged' 54.0% 16.6% 22.6% ZAMBIA Urban Rural 'own savings' KENYA 46.0% 37.3% 30.7% 22.8% 38.5% 31.9% SENEGAL 25.8% 8.2% SENEGAL 33.0% 28.4% SENEGAL ZAMBIA Urban Rural 'sale of assets' KENYA 2.8% 4.8% 6.5% 15.1% ZAMBIA Urban Rural 'taking loan' KENYA 11.8% 5.8% 26.0% 15.3% ZAMBIA Urban Rural KENYA 7.5% 6.1% IVORY COAST 56.8% 57.4% IVORY COAST 0.8% 1.1% IVORY COAST 28.4% 23.8% IVORY COAST 26

Paying for medical treatment Figure 15 Four countries – sources of financing medical care, by gender 48.5% 47.1% 15.0% 27.5% 9.9% 24.7% 33.6% 28.6% 28.2% 24.5% 40.8% 42.4% 1.9% Male 2.1% Female SENEGAL 45.0% 52.7% 35.7% 16.4% 32.1% 25.1% Male Female Male Female ZAMBIA KENYA no cost savings sale of possession or taking loan 57.1% 56.9% 6.0% 8.8% Male Female IVORY COAST  OF FINANCING MEDICAL CARE, BY GENDER  OF FINANCING MEDICAL CARE, BY INCOME Selling productive assets or plunging into debt to pay for medical treatment becomes an economic liability for the future and may bind households into a cycle of poverty. Are these threats contingent on whether it is a male- or a female-led household that is facing payments for medical treatment? The breakdown of the sample into male- and female-led households is shown in Figure 15. No significant differences are discernible in Senegal and Ivory Coast. In Zambia and Kenya female-led households appear to be in more favourable circumstances, in that they use medical care more frequently without paying for treatment and consequently are less forced into external financing from asset sales and loans. Our findings indicate that programmes and policies aimed at reducing out-of-pocket payments are beneficial for all households, but may favour female-led households in particular. Confirmation of this, however, would require a more detailed analysis than we have room for here. We have grouped respondents according to their level of income and have compared three modes of payment for medical treatment(free services, savings, external financing) and according to four income classes. Results are shown in Figure 16. In Senegal, there is no robust scheme that provides free medical treatment or insurance reimbursement, and nearly all patients, no matter what their income may be, seek resources to cover costs. Savings and external financing are counter-cyclical. With a higher income, the savings capacity increases and households cover more medical treatment from their own reserves and the need to mobilise funds from asset sales or indebtedness declines. In Zambia, provisions for free health services appear to be sensitive to income. Poor patients are less frequently charged for health services than the well-off. While this may be an intended effect of a social policy, it is not likely to be the outcome of the current system, which provides free services at district level to all. We can therefore assume 27

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH SERVICES that higher income patients who are not satisfied with the medical services on offer free of charge opt for other, paid forms of treatment. In Kenya, we observe more well-off patients who have access to free treatment. This effect results from membership of health protection schemes. More of the well-off population are covered by health insurance and thus do not have to look for resources when a health shock strikes. Ivory Coast shows a positive correlation between higher income and use of own savings, and a negative correlation between income and external financing. In all cases, the links are only modestly articulated, however. The income effect on the use of funds can be summarised as follows. Savings usually go up with income and the better-off are better equipped to pay for medical care from their reserves than the poor. This effect is discernible in all four countries. Better-off households are more likely to pay their health bills from savings than poor households. If costs are charged and savings are not available, selling property or looking for loans are the key alternatives to mobilise monetary means for medical treatment. In Senegal and Ivory Coast these forms of resource mobilisation are sensitive to income. The poor are more likely than the well-off to have to sell property or go into debt. In Zambia and Kenya, the same effect is not observed. We have no documentation on how many times the informally employed or their family members were sick; how many times they visited medical centres; how serious the sickness was in each case; whether the kind of treatment actually received corresponded to the treatment needed; Figure 16 Four countries – modes of payment for medical treatment, by income class 2 2 .4 .4 % % 2 2 .1 .1 % % 0 0 .9 .9 % % 2 2 .1 .1 % % SENEGAL SENEGAL 54.1% 54.1% 46.3% 46.3% 36.8% 36.8% 32.9% 32.9% ' ' n n o o c c o o s s t t c c h h a a r r g g e e d d ' ' 5 5 3 3 . . 3 3 % % 4 4 6 6 . . 9 9 % % 4 4 5 5 .6 .6 % % 3 3 7 7 . . 1 1 % % 2 2 4 4 . . 6 6% % 3 3 0 0. . 2 2 % % 1 1 9 9 . . 0 0 % % 1 1 8 8 . . 8 8% % 4.9% 6 6 . . 3 3 % % 6 6 . . 8 8 % % 8 8 . . 2 2 % % 4.9% ZAMBIA ZAMBIA KENYA KENYA less 0.5 MW less 0.5 MW >0.5 MW-1 MW>1 MW-2 MW >0.5 MW ' s 1 a M vi W ngs' >1 MW-2 MW 'savings' >2 MW >2 MW 40.4% 40.0% 40.4%40.0% 25.0% 21.0% 25.0% 21.0% 44.2% 4 4 1 1 . . 7 7% %44.2% 30.4%31.2% 30.4%31.2% IVORY COAST IVORY COAST 57.3%5 5 8 8 . . 9 9 % %5 5 9 9 . . 6 6 % % 57.3% 5 50 0 . . 0 0 % % SENEGAL SENEGAL 5 5 7 7 . . 5 5 % % 5 5 2 2 . . 6 6 % % 4 4 4 4 . . 1 1 % % 3 3 5 5 . . 2 2 % % SSEENNEEGGAALL ZAMBIA ZAMBIA KENYA KENYA less 0.5 MW>0.5 MW-1 MW>1 MW-2 MW less 0.5 MW ''ssaalle > e 0 oo .5 ff M aas W ssse e 1 tts M s oo W rr ttaakk > iinn 1 gg M ll W ooaa -2 nn M '' W >2 MW >2 MW IVORY COAST IVORY COAST 2 2 0 0 . . 0 0 % % 1 1 1 1 . . 8 8 % % 1 1 4 4 . . 1 1 % % 7 7 . . 0 0 % % 3 3 2 2 . . 0 0 % % 3 3 0 0 . . 4 4 % % 3 3 2 2 . . 3 3 % % 1 1 8 8 . . 6 6 % % 3 3 3 3 . . 6 6 % % 2 2 8 8 . . 2 2 % % 2 2 5 5 . . 8 8 % % 2 2 3 3 . . 3 3 % % ZZAAMMBBIIAA lleessss 00..55 MMWW>>00..55 MMWW--11 MMWW KKEENNYYAA >>11 MMWW--22 MMWW>>22 MMWW IIVVOORRYY CCOOAASSTT Note: Each of the four income groups refer to the statutory minimum wage(MW) 28

Paying for medical treatment Figure 17 Four countries – use of medical care if funding comes from asset sales or debt 'sale of possesion or taking loan' 59.8% 35.8% 12.1% 14.3% 27.1% 45.5% 20.0% 37.3% regular use of medical care low use of medical care SENEGAL regular use of medical care low use of medical care ZAMBIA regular use of medical care low use of medical care KENYA regular use of medical care low use of medical care IVORY COAST or whether patients»opted« for simple medical care because it was provided free instead of going for surgery, and intensive and costly medical treatment. Such information would be needed to perform a well-informed evaluation of the availability of quality medical care to all. Furthermore, we did not collect information about the levels of health bills and how medical expenditures are related to income. Such information again would be needed to carry out an elaborate evaluation of the affordability of quality medical care. Our data provide an insight into the self-assessed availability of medical care and financial consequences emanating from the payment of health bills. We argue that people who are forced to sell property or go into debt in order to source funds for medical treatment will tend to avoid visits to medical services whenever possible to prevent falling into dire financial straits. This effect is confirmed in Figure 17. In Zambia, selling assets or taking out loans are minor sources for funding medical treatment and have no impact on the intensity with which medical care is sought. In Kenya, the effect is already discernible. If the share of external financing(asset sales and loans) goes up, the use of medical services goes down. The same response can be observed in Ivory Coast and Senegal. In Senegal, 60 per cent of respondents who have to sell assets or go into debt declare that they hardly seek medical care at all. Many of the poor are forced to go without treatment not because medical services are not available, but because they do not have the means to pay for them. 29

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH SERVICES 5 ARE GOVERNMENTS WILLING AND ABLE TO DELIVER BETTER STATE SERVICES?  REMARKS Articulating a strong demand for a specific public good and requesting more of it does not imply that people in fact believe that their wish will be honoured and services delivered. Between identifying a deficit and hoping that the identified shortfall in the provision of a public good will soon be remedied two variables intervene. First, people must be convinced that decision-makers in relevant institutions are willing to use their positions and mobilise resources in order to deliver better state services. Second, people must be convinced that leaders of state institutions are not only willing, but also have the technical and financial capacity to produce and deliver. Political willingness and organisational capacity must both be on hand in order to meet people’s demands and provide improved state services. Assessing capacity and appraising willingness are different ways of looking at the state. Capacity characterises the anchoring of an institution in relation to others, its endowments with rights and duties, and whether it commands the means and skills to do a certain job. Willingness, in our context, characterises the preparedness of political leaders to decide on and take steps for implementation. It is a ­psychological category with regard to individual motivation. It refers to group behaviour if a collective rather than an individual is in charge. To obtain some insight into the belief of the informally employed in the willingness and capacity of political decision-makers to deliver services, we posed two questions to respondents: Q86:»How do you think that the following organisations/ institutions care about the top priorities you highlighted?« (Reference was to the selection of state services as first, second and third priority). Q87:»Assuming the following actors are willing to work towards tackling the top priorities you highlighted, do you think that they can make a significant difference and change the situation for the better through their actions?« 29 29 In the interview, the two questions directly followed the questions on priorities in the demand for better state services. We can therefore assume that respondents still had the priorities they previously selected and were able to refer the two new questions to their own priority ranking. Table 3 Willingness and capacities of institutions to deliver services – a four-field matrix Leaders care about priority demands Institutions have the capacity to deliver GROUP I: Leaders are willing and institutions have the capacity Leaders do not care about priority ­demands Group III: Leaders are not willing but institutions do have the capacity(could deliver) Institutions have no capacity to deliver Group II: Leaders are willing but institutions have no capacity Group IV: Leaders are not willing and institutions have no capacity KEINE ANGABE 30

Are governments willing and able to deliver better state services? In linking the two questions, we identified four groups of answers(see Table 3): Group I: Respondents believe that the relevant leaders are WILLING to provide the services needed and the responsible institutions HAVE the technical capacity to produce them. People in this group are optimistic and they believe in responsive(competent) leadership. Group II: Respondents believe that the relevant leaders are WILLING to provide needed services, but the responsible institutions do NOT have the technical capacity to deliver them. People in this group are pessimistic but do not blame the institutions because of this handicap. Leaders are willing but institutions suffer from a shortage of resources. Group III: Respondents believe that the relevant leaders are NOT WILLING to provide the services needed, even though the responsible institutions HAVE the technical capacity to deliver them. People in this group blame those who run the institutions for their lack of interest, as capacities are available but they are unwilling to use them for the benefit of the respondents. Group IV: Respondents believe that the relevant leaders are NOT WILLING to provide the needed services, and the responsible institutions do NOT HAVE the capacity to deliver. People in this group believe that the respective leaders are irrelevant and state policies(at least with regard to activities aimed at producing the requested services) have no relevance for their everyday lives. Before looking at the answers we have to clarify the type of institutions that we consider relevant to producing and supplying services. Earlier we had asked about better state services, implying that state services are produced by state actors. In our listing of institutions,»the president«,»national government« and»local government« qualify as the »state executive«. We ignore» national parliament« and »political parties« as they are components of a modern state structure, but not concerned with the execution of projects. We also ignore institutions and organisations that may play a role in implementing projects, such as»traditional leaders« or»national and international NGOs«, which in a legal-technical sense are not state institutions, even though they may step in to provide services when state institutions fail to do so.  THE WILLINGNESS AND CAPACITY OF THE STATE EXECUTIVE » The president«, the» national government« and» local government« are the key decision-making organs to set in motion the technical departments and agencies of the state in producing more and better state services. Our respondents’ views on the willingness of the three actors that we summarily call the»state executive« to mobilise the state machinery for the production of services are revealing in several regards(see Figure 18): – Trust in the capacity of responsible institutions(groups I+ III): In all five countries, a large majority of people are convinced that the responsible institutions have the technical capacity to produce and deliver better state services. In Benin this positive view is shared by 69.5 per cent of people, in Senegal by 73 per cent, in Zambia by 74.1 per cent, in Kenya by 74.7 per cent and in Ivory Coast by 78.1 per cent. Those who doubt the capabilities of the responsible institutions constitute a clear minority. The optimistic view clearly prevails. – Trust in the willingness of leaders(groups I+ II): Here, respondents draw a more pessimistic picture. On average, only about half of the people believe that the key agents of the state have the will to provide better services(Zambia 40.7 per cent; Benin 46 per cent; Ivory Coast 49.8 per cent; Senegal 53.9 per cent; Kenya 55 per cent). – Leaders are willing, but responsible institutions have no capacity(group II): None of the five countries has a significant number of people who subscribe to the idea that the state executive state is willing, but is handicapped by institutions that cannot deliver. Only 5 per cent or less of respondents answer in this way. People believe that if state institutions are willing to perform, they are able to deliver. Our four-case matrix is thus de facto reduced to a three-group model. – Responsible institutions have the capacity, but leaders are not willing(group III): A further group comprises those who believe that state institutions do have the technical and financial capacity, but leaders are not prepared to act. Leaders could improve service delivery if they were willing, but they have no interest in doing so. This group encompasses 23.2 per cent of respondents in Senegal, 24.4 per cent in Kenya, 29.1 per cent in Benin, 33.1 per cent in Ivory Coast and, highest of all, 36.4 per cent in Zambia. – Leaders are not willing and institutions lack capacity (group IV): Another significant group, to which 16 to 30 per cent of respondents subscribe, are those who see state actors as willing but unable to perform due to resource constraints. With the exception of Benin, where people give the same ratings to all three components of the political executive, the other countries have a more positive assessment of the president, ahead of the national government, which in turn fares better than local government with regard to their willingness to perform and implementation capacity. The positive finding is that a broad majority of respondents (groups I+ III) believe that the state institutions in principle have the capacity to deliver improved health services. This may not be surprising. None of the services on our list are new and state institutions have proven time and again that 31

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH SERVICES Figure 18 Five countries – willingness and capacity of institutions to provide better state services IVORY COAST BENIN KENYA Political executive Local government National government The President Political executive Local government National government The President Political executive Local government National government The President Political executive Local government National government The President Political executive Local government National government The President 45.0% 2.8% 33.1% 34.1% 4.3% 35.3% 47.9% 1.9% 32.9% 53.1% 2.3% 31.1% 40.4% 5.7% 29.1% 40.0% 6.7% 24.7% 39.2% 6.1% 30.7% 41.9% 4.2% 31.8% 50.3% 4.7% 24.4% 45.0% 5.7% 25.0% 47.7% 4.9% 26.0% 58.2% 3.5% 22.2% 37.7% 3.0% 36.4% 31.4% 3.7% 38.8% 38.9% 2.1% 36.9% 42.8% 3.0% 33.6% 49.8% 4.1% 23.2% 41.8% 5.1% 23.2% 51.2% 3.7% 23.9% 56.5% 3.5% 22.3% willing+ capacity willing+ no capacity not willing+ capacity not willing+ no capacity ZAMBIA SENEGAL 19.1% 26.4% 17.4% 13.5% 24.9% 28.6% 24.0% 22.0% 20.6% 24.3% 21.5% 16.1% 22.9% 26.2% 22.0% 20.6% 22.9% 29.8% 21.2% 17.7% they can deliver more and better quality if leaders instruct them to do so. The large majority thus express no hostility towards the state as such. Overall, however, an alarming 50 per cent of respondents in Kenya and Senegal and an even clearer majority of 57 per cent in Ivory Coast, 60 per cent in Benin and 62 per cent in Zambia doubt that the state executive will improve state services in the areas of their priority needs(groups II+ III+ IV). About half of them believe that the responsible institutions have no capacity to do so(groups II+ IV: 24 to 31 per cent), while the other half believe that non-­performance is linked to leaders’ unwillingness to do their job(group III: 23 to 36 per cent). The supply gap with regard to services perceived by respondents corresponds to a perceived willingness gap on the part of leaders, which should certainly be a political concern for any government. 30 30 Most services on our list are services whose provision is strongly linked to central government. We therefore abstain here from interpreting the lower values for local government. The overall picture would not change substantively if we ignored the ratings for local government completely. 32

Are informally employed people willing to participate in a health insurance scheme and pay a premium? 6 ARE INFORMALLY EMPLOYED PEOPLE WILLING TO PARTICIPATE IN A HEALTH INSURANCE SCHEME AND PAY A PREMIUM? There are various ways of testing the seriousness with which respondents’ value better health services as a matter of importance. Ranking the urgency with which people call for a certain service is one way; looking at their actions aimed at improving an appalling situation with their own means is another. One more way is to join a health insurance scheme and pay regular premiums to reduce out-ofpocket payments when health shocks strike. Many countries have made membership of health insurance schemes mandatory for certain employment categories, but it remains voluntary in other cases. But if infrastructure for service providers is not available, even the willing will be frustrated.  OF HEALTH INSURANCE SCHEMES Figure 19 looks at the extent to which people in informal employment have joined a health insurance scheme. Only in Kenya are a significant number of informally employed people covered by health insurance. Taking public, private and micro-finance insurance together, 25.6 per cent enjoy health protection from insurance. For the other countries, these figures are 10.3 per cent(Côte d’Ivoire), 8.2 per cent (Senegal), 2.3 per cent(Benin) and 2.1 per cent(Zambia). Low insurance cover for Zambia may be partly caused by the availability of a tax-funded universal health care system at primary level, but health treatments at secondary level are charged to the patient. In general, we need to note for all countries their very low coverage rates. There are various ways of accounting for this low coverage. People may not value health insurance; they may not understand what an insurance scheme is and how it operates; they may not have access to health insurance because no scheme is on offer; or they may not be able to afford the membership premium. The data do show some disparities in coverage by gender (Figure 20). In all countries, women show a higher level of membership, which in Senegal and Benin is even statistically significant. Due to the generally low level of membership, however, we should refrain from putting much emphasis on these differences. They may disappear if average membership goes up substantially. Figure 19 Five countries – share of people in health insurance schemes 22.1% 5.3% 2.7% 3.8% 0.2% SENEGAL 1.3% 0.8% 0.6% 0.0% ZAMBIA Public health scheme Private health insurance 8.0% 3.2% 0.3% KENYA 4.6% 1.1% 1.0% 0.2% BENIN 7.3% 2.7% 1.8% 0.3% IVORY COAST Micro-insurance scheme Previously health insurance but dropped out 33

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH SERVICES Figure 20 Five countries – share of membership in health insurance, by gender 27.0% 24.0% 11.3% 5.4% SENEGAL 1.3% 2.8% ZAMBIA Note: See Appendix III for statistical computations KENYA Male Female 3.4% 0.8% BENIN Figure 21 Four countries – interest of non-members in joining a health insurance scheme, by gender 73.3% 75.1% 57.4% 58.9% 70.8% 70.4% 59.0% 53.4% 10.1% 10.4% IVORY COAST 81.3% 79.7% SENEGAL ZAMBIA KENYA Male Female Note: Question posed as yes/no alternative to persons who are not members of a health insurance scheme. See Appendix III for statistical computations. BENIN IVORY COAST  IN JOINING A HEALTH ­INSURANCE SCHEME To obtain a better understanding of what people know and think about health protection schemes we asked those who are not covered whether they were interested in joining one. If people were not interested, we asked them why, and also whether they would be willing to pay a premium and how much they would be willing to pay. The findings are revealing. In all five countries a clear majority of respondents who are not yet a member of a health insurance scheme are interested in joining one(see Figure 21). The informally employed in Ivory Coast, Senegal and Kenya show an overwhelming interest in joining, while in Benin and Zambia many are in favour but some 40 per cent show a marked reservation and are not willing to be covered by a health protection system. None of the countries has a statistically significant difference in interest by gender. 34

Are informally employed people willing to participate in a health insurance scheme and pay a premium? Figure 22 Five countries – reason for not joining a health insurance scheme 68.4% 68.5% 54.7% 59.8% 53.1% 24.3% 13.8% 19.1% 15.4% 13.8% 21.1% 18.5% 27.1% 16.9% 7.2% 8.9% 3.6% 2.4% 0.6% 2.9% SENEGAL ZAMBIA KENYA BENIN IVORY COAST I do not have enough financial resources I do not trust in health insurance scheme I do not know anything about the way they work or proceed Other Why would people not be willing to become a member in a protection scheme? We offered three main answer options: (i) I do not have the financial resources to make regular premium payments;(ii) I do not trust health insurance; and(iii) I have no knowledge of how such schemes work. As always, respondents could provide additional reasons if none of the three offered options reflected their view. The data show a similar profile of answers in all five countries(see Figure 22). A strong majority identified a lack of financial means as the key reason for opting against membership. Lack of trust in such schemes is highest in Senegal (24.3 per cent) and lowest in Zambia(8.9 per cent), while ignorance of their workings is indicated by 27.1 per cent in Ivory Coast, followed by Benin(21.1 per cent). If we argue the case that ignorance is primarily a matter of information and education, and that a well-designed public information campaign could reduce this group to a negligible level we are left with poverty and mistrust as the main arguments against health insurance schemes. 31  TO PAY A PREMIUM FOR A HEALTH INSURANCE SCHEME Some people might believe that membership of a health protection scheme is free of charge. To avoid such misconceptions, respondents were informed that membership 31 In cross-tabulating the question about the reasons for not wanting to join a health scheme with questions on income and education, it can be shown that poorer respondents rather tick the option indicating a lack of financial means, while respondents with a higher educational level tend to tick the option concerning mistrust. »means contributing to a scheme on a regular basis in order to receive a financial compensation when needed«. After explaining the financial operation of a scheme, we asked:»What premium amount would you be prepared to pay?«. Certainly, it is not possible to obtain carefully considered answers on the exact amount of money people are willing to pay during such an interview. A question on the level of premium could produce reasonable results only if a well-designed information campaign was under way on what a health protection schemes covers and was widely discussed. Our intention was(i) to re-confirm people’s readiness to join a health scheme even though it comes with a cost, and(ii) to establish an approximate level of what amount of premium respondents found reasonable in a spontaneous answer situation. On average, respondents were prepared to pay the following monthly amounts as premiums: Benin: 628.48 CFAFranc(CFA); Kenya: 234.60 Kenyan Shilling(KES); Senegal: 967.41 CFA; Zambia: 20.39 Zambian Kwacha(ZMW); and Ivory Coast: 1140.64 CFA. To compare these payments across borders, we calculated them as a share of the monthly statutory minimum wage. Furthermore, we compared the statements on premium payments to various thresholds, and calculated the share of those who were prepared to pay above a certain premium level. We selected two US dollars, three US dollars and five US dollars as thresholds. The findings are summarised in Figure 23. Respondents who are not yet members of a health protection scheme and show an interest in joining were not surprised that membership comes with a cost. With the excep35

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH SERVICES Figure 23 Five countries – declared premium payment by non-members in relation to various benchmarks 2.5% 70% 2.0% 1.5% 1.0% 0.5% 0.0% 1.57% 1.80% 32.23% 1.76% 1.36% 2.07% 60% 50% 40% 30% 18.12% 18.27% 21.79% 20% 13.64% 8.89% 9.67% 10% 3.85% BENIN 4.13% KENIA SENEGAL 4.52% ZAMBIA 0% IVORY COAST average premium% minimum wage share to pay US$2 a.m. share to pay US$3 a.m. share to pay US$5 a.m. tion of a few individuals who declared their unwillingness to pay anything, a large majority of respondents provided a figure that they were willing to contribute. In relation to the statutory minimum wage, the average amounts respondents mentioned were between 1.36 per cent in Zambia and 2.07 per cent Ivory Coast. The very similar amounts mentioned allow us to assume that there are similar attitudes to potential premium payments exists in all five countries. At first glance, 1.4 to 2.1 per cent of the minimum wage appears to be a rather low sum that people would be willing to contribute to a health protection scheme. However, we have to take into consideration that the benchmark set by the minimum wage is rather high. The minimum wage is a legal requirement usually ignored in informal employment and 70 to 80 per cent of respondents earn below this income reference. 32 Taking this into account the percentage of income many respondents are prepared to contribute to a health scheme goes up significantly, to two per cent to five per cent of their earnings. To obtain a better understanding of the financial significance, we converted the amounts declared by the respondents into US dollars and counted the number of respondents who were willing to pay above thresholds we set. Looking at the lower reference sum of two US dollars, the share of those willing to pay such an amount varies from 13.6 per cent(Benin) to 32.2 per cent(Kenya). If we apply higher benchmarks, the interest in a health scheme declines. The three US dollars benchmark leaves interest in 32 The FES will publish the data on the socio-economic situation in informal employment of the four countries at a later stage. joining a scheme still at 25 per cent in Kenya, while it declines to 16 per cent in Ivory Coast and more in the other countries. In all countries, the share of interest falls below 10 per cent if the premium is raised to five US dollars per month. Kenya is likely to be the front runner in our five-country group as it has the largest market for health care schemes. There are many private health insurance companies in operation and the government is the largest player, with its National Health Insurance Fund(NHIF). The fairly high number of insured persons may be an additional factor to help dissemination of information on the working procedures and benefits of protection schemes and may contribute to people’s interest in becoming a member. Exposing respondents’ statements to a»reality test«, we contrast declared premium payments with the costs of existing schemes. In Kenya, the NHIF has a detailed premium scale for employees, which starts at 150 KES(monthly) for a salary of no more than 5,999 KES and goes up to 1,700 KES for a salary of 100,000 KES or more. The lowest income class in our study(below 6,500 KES) declared its willingness to pay a monthly premium of 195.2 KES and would have to pay a NHIF premium of 150 KES to 300 KES(see Table 4). We can thus assume that the premium envisaged by the majority of our lowest income class would already qualify them for membership in the NHIF. The second lowest income group in our study(6,500 KES to 13,000 KES) is willing to pay an average of 230 KES, but would be charged by NHIF between 300 KES and 500 KES. Our respondents are therefore willing to pay between 77 and 46 per cent of what NHIF demands as a contribution. 36

Are informally employed people willing to participate in a health insurance scheme and pay a premium? The NHIF premium list also discloses the different coverage rates of employees and the self-employed. The self-employed are not grouped into income classes but charged a flat rate of 500 KES. This amount puts the cost outside the reach of most of our respondents. If we assume that the government puts a policy in place subsidising contributions at the level of 50 per cent, however, the NHIF premium would cost the self-employed a monthly 250 KES. Most self-employed in our study would be willing and able to join. A similar»reality check« for Benin produces similar results. While the premium our respondents are willing to pay varies between 566 CFA and 1,076 CFA, depending on their income, the amount charged by three reference schemes(mutual health insurance, CMPS, RAMU) varies between 850  CFA and 1,200 CFA. On average, respondents in Benin are willing to pay at least 50 per cent of the premium charged by existing health protection schemes(see Table 5). With a government subsidy of 50 per cent nearly all informally employed people would be interested in joining a contributory health protection scheme. We have to emphasise again that our findings can be taken only as a rough approximation. A substantive assessment of the actual acceptance of an insurance premium can be done only when people are presented with a particular protection scheme and a final price list. With our poll, we were able to explore only the interviewees’ general feelings about the value of health insurance schemes. The analysis confirms that membership is high on their social agenda. A majority in all five countries are willing to join, they are aware that membership comes with a cost and they are willing to pay a premium. The majority of those reluctant to join identify lack of income as the main reason. Finally, we can group our sample into three clusters.(i) Those who are willing to join but do not have the financial means. For them, coverage is possible only if membership comes free of charge and the premium is fully or mostly subsidised from public coffers.(ii) Those who are willing to join a scheme and have the means to contribute; not all members of this group may be able to fully cover the premium, but they are willing to share.(iii) Those who are not only willing to join but also have the means to hand to cover the premium in full, at least for a basic medical scheme. We have not established the reason why members of this group have not yet joined a scheme but we believe that difficulties accessing schemes and logistical reasons are key factors. Table 4 Kenya – comparing premiums for NHIF membership with premium payments respondents are willing to pay Monthly income of informally employed(KES) Income groups Less than 6500 6,50 0 –13,0 0 0 13,001–25,000 25,001–100,000 Willing payers 57.04% 29.55% 10.31% 3.09% Monthly premium(average) 195.19 229.55 265.27 289.39 % of MW(13,000) 1.50% 1.77% 204% 2.23% NHIF premium(KES, monthly) for employees 150–300 300–500 500–750 from 850 NHIF premium(KES monthly) for self-employed 500 500 500 500 Source: Data from FES survey; www.nhif.or.ke. Table 5 Benin – premium for membership in health schemes and declared premium payments Monthly income of informally employed(CFA) Income groups Less 20,000 20,001– 40,000 40,001–80,000 80,001–320,000 Willing payers 42.38% 31.14% 19.42% 7.06% Monthly premium(average) 566 595 1,076 543 % of MW(40.000) 1.42% 1.49% 2.69% 1.36% Monthly contribution for mutual health insurance 1,200 1,200 1,200 1,200 Monthly contributions for CMPS 850 850 850 850 Monthly contributions for RAMU 1,000 1,000 1,000 1,000 Source: Data from FES survey; personal communication. 37

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH SERVICES 7 CONCLUSION The FES country surveys in Benin, Kenya, Senegal, Zambia and Ivory Coast were conducted as national representative opinion polls on the strategies used by informally employed people to cope with social security exigencies. The surveys were implemented to allow uniform cross-border comparison. They reflect the views of 70 to 90 per cent of the whole national labour force and thus the attitudes of a large majority of the economically active population. Based on a section of the poll, the publication at hand focusses on the importance of health services, as well as perceptions of the availability of and access to medical care. It shows the resources patients activate to cover the cost of medical treatment. It examines the hopes the informally employed put in their governments’ determination to improve services in the future. It explores people’s interest in joining a health insurance scheme and preparedness to pay a premium. 33 The key findings may be summarised as follows:  HEALTH SERVICES IS A NATIONAL DEMAND In the ranking of demands for state services‚ health care takes top place. While it is closely followed by»better schools and education«, the call for» better health services« remains in premier position regardless of whether we look at the living environment(urban versus rural), disparate income clusters or demographic variables. In the five survey countries, between 47 and 71 per cent identified improved health care as their first or second most important need. The call for better health care cuts across social and spatial cleavages and can thus be called a national priority. 30 per cent of respondents hardly look for medical treatment when falling sick, while in Zambia and Kenya only around 10 per cent do the same. Ivory Coast falls in-between but there are still 24 per cent who mostly have to cope without treatment if a health shock strikes. In Senegal, Benin and Ivory Coast, an urban–rural divide and income disparities combine to result in huge discrepancies in the use of medical care. In Kenya and Zambia the use of medical care by urban and rural residents is fairly balanced and disparities are based primarily on income. Gender, however, is not a statistically relevant dimension of the use of medical care.  OF MEDICAL BILLS DIFFER BETWEEN COUNTRIES – KEY INDICATORS ARE FREE SERVICES AND MEETING PAYMENTS BY GOING INTO DEBT The availability of free health services and membership of health insurance schemes are decisive factors in determining the degree to which sickness and medical treatment become a financial risk for households. Where there is a free primary health system, as in Zambia, only a few are forced to sell assets or take out a loan to mobilise funds for treatment. In countries with no large-scale schemes for free medical treatment, incurring debts or selling assets become a dire reality for large groups. Membership of a health insurance scheme produces the opposite effects: it enables resort to medical treatment with fewer risks of getting into debt.  OF MEDICAL CARE IS STRATIFIED BY COUNTRY, INCOME AND RESIDENCE The use of medical care in the survey countries is marked by large discrepancies. In Senegal and Benin, around 33 Other parts of the interviews, such as the respondents’ assessment of the quality of medical services, views on taxation, state–citizen relations, membership of groups, and views on trade unions will be published in separate reports.  IS A STRONG FACTOR IN DETERMINING USE OF MEDICAL SERVICES Linking the use of medical care to patients’ earnings provides a strong statement: the lower a person’s income the higher their likelihood of incurring debt. Income is a strong determinant of access to medical care and poverty prevents people from looking after their health. 38

Conclusion  INSURANCE AND FREE SERVICES CONTRIBUTE TO DELINK THE USE OF MEDICAL SERVICES FROM POVERTY Free health services and health insurance coverage improve the use of medical services. Both help to delink the use of medical care from poverty. Our data provide sufficient evidence for this connection for Kenya and Zambia, but fall short of strong statistical proof for Benin, Senegal and Ivory Coast due to the weak development of these financial tools in these countries. RECOMMENDATIONS: SIGNIFICANCE FOR PUBLIC POLICY DEVELOPMENT Our opinion poll reflects people’s views on aspects of their social reality, but does not provide answers on how to change it. Policymakers must respond to calls for better health services within a wider social framework and have to weigh different approaches in terms of their suitability for providing a lasting solution. Nevertheless, our findings have strong relevance for policymakers in public service, governments and international organisations and provide evidence on what direction to follow and the appropriateness of social policies pertaining to public health services.  MUCH TRUST IN GOVERNMENT TO PROVIDE BETTER STATE SERVICES IMPROVE ACCESS TO HEALTH SERVICES Besides broad dissatisfaction with how medical services are organised, there is little hope that governments in the various countries will improve the situation for the better and provide more services in the future. While a majority of respondents express trust in the capacity of state institutions to improve services, many doubt the willingness of political leaders to act on their behalf and set the administrative machinery of the state in motion. The legitimacy of the ruling regime is challenged if half or more of the people see their political leaders as unwilling to improve services.  FOR HEALTH INSURANCE COVERAGE AND WILLINGNESS TO PAY A PREMIUM A low level of trust in a government’s performance forces people to undertake their own»social investment« to cope with life’s exigencies. This is not yet strongly manifested by current membership of health protection schemes. With the exception of Kenya, where membership stands at 22 per cent, the other survey countries exhibit negligible coverage. However, a clear majority of respondents in all countries declared their interest in joining a scheme. Nearly all are aware that membership comes at a cost and are willing to pay a premium at regular intervals. Applying several»reality checks« by comparing the amount respondents were willing to pay as premium against various thresholds, such as current fees in existing schemes, we can identify three groups:(i) those prepared to pay a premium above what existing schemes charge;(ii) those who are ready to contribute substantially in relation to existing fee levels; and(iii) those who are ready to pay only substantially below the entry level of existing schemes. It is difficult to assess the adequacy of respondents’ assertions regarding possible premium payments. In any case, the survey confirms that a majority of people have a positive attitude towards joining a health scheme and are aware that membership entails paying a premium. – Provide access to basic medical services outside employment: In the formal economy access to social security is employment-based and costs are shared between employer and employee. Attempts to enforce a similar link in the informal economy have not been successful and governments, employers and trade unions are well-advised to accept that beyond the formal economy, access to social security cannot be linked to employment. Access to health care is a human right, however, not an entitlement confined to people with a workplace and thus must be available to all, whether employed or not. Governments should cease to be constrained by the notion that social security provision should be linked to a work contract. – Universal coverage needs tax-funding: Introducing universal health insurance or providing free medical care for basic medical services are key systems in widening access to medical care for all. No matter which path taken, it comes with a burden on public funds. Large segments of the population belong to the poor or the extreme poor who do not have the means to contribute to costs for medical treatment. Universal coverage implies that groups without adequate income get free or subsidised access to health services whether in the form of non-contributory coverage in a health insurance scheme or under a policy of no user-fees. – Hybrid forms of financing medical services: Large groups in society are neither poor nor well-off and so can be called the non-poor. Our survey confirms their interest in social protection schemes and their willingness to contribute by paying a premium. Access to health services may thus be based on three tiers: free medical treatment for the poor; contributory schemes for the non-poor, which may or may not include elements of subsidies; and continuation of shared-contribution schemes in the formal economy with access to higher medical standards. Universality of access thus focusses on basic medical services, while access to higher standards of medical services is r­eserved for those who can afford it. Expanding the medical services that are included in a primary health package 39

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH SERVICES and reducing the gap between primary and higher standard services should be the aim of future health policies. 34 INVESTMENT IN HEALTH CARE REDUCES SOCIAL INEQUALITY tiatives or international cooperation agreements with the African continent should always favour inclusive and sustainable development and embrace universal health coverage as a top priority, in particular with a view to those in the informal economy. We conclude our study with the following statement: Use of medical care is strongly linked to income inequality, even within the informal economy. Governments have opportunities to change the mode of income distribution, but key instruments, such as increasing the minimum wage or adjusting taxation, hardly reach the informally employed. Investing in better access to health care is an alternative approach to reducing social inequality. If people have improved access to health services, they are less likely to have to sell productive assets or go into debt if they need treatment. Negative spillovers of health expenditure threaten the investment potential for small businesses, weaken people’s mental or physical abilities to work or force families to choose between school fees and medical treatment. Granting safe access to health services eliminates one of the factors that keep people in poverty. »The establishment of at least a basic level of social protection is a necessary pre-condition for enabling people to exit from poverty; for the creation of social cohesion; for the development of a productive and employable workforce; and hence for the creation of the necessary basis for economic growth and rising welfare levels for all. It is an important step towards the realization of the human right to social security, and to state-building«. 36 INVESTMENT IN HEALTH CARE REDUCES GOVERNMENTS’ LEGITIMACY DEFICITS The large number of people who identify health services as a key concern, and do not believe that the political executive will act on their behalf and improve service deliverables, should be of major concern to a government that cares about its legitimacy. Because of its wide scattering effect, a focus on provisions for universal access to improved health services may easily become a major strategy to improve the image of political decision-makers. 35 UNIVERSAL HEALTH COVERAGE SHOULD BECOME OR REMAIN A TOP PRIORITY ON NATIONAL AND INTERNATIONAL POLICY AGENDAS Social protection and universal health coverage are already binding elements of international policy frameworks. SDG 3.8, ILO Recommendation No. 202 and the African Union’s African Health Strategy focus on the provision of health coverage. The findings of this study confirm that universal health coverage should be prioritised on national and international policy agendas. National or international policy ini34 The preference for hybrid systems, including additional aspects of the management of health insurance schemes, is argued in Jürgen Schwettmann, Extending health coverage to the informal economy, FES Briefing paper, September 2017. 35 This report looks primarily at the use of health services and with few exceptions ignores the supply side. It goes without saying that universal health coverage is not possible without substantive investments in provisions for medical services, including staff. A cost-free visit to a health facility becomes meaningless if there are no medical staff to take care of patients. 36 Jürgen Schwettmann, Extending health coverage to the informal economy, FES briefing paper, September 2017. 40

Conclusion APPENDIX I: ESTABLISHING A RANKING OF STATE SERVICES – METHODOLOGICAL CONSIDERATIONS In constructing a rank order of state services, we presented respondents with a list from which they were asked to select according to their priorities. We identified eight services, which we called»key responsibilities« of the state to society. Because the state is already engaged in producing such services, we did not ask about the importance of delivering these services, but about the importance of improving services to people in the various sectors. The issue was presented in terms of two questions. Question 1 asked:»If the government wants to improve services, what in your opinion are the sectors that the government should focus on?«: The following state responsibilities were mentioned: – schools and education – roads and bridges – police services – health services – electricity supply – water supply – old-age pensions – food programmes in times of crisis – services should be delimitable from others and have a clear»identity«. Two methodological arguments concerning the weakness of our approach to constructing a ranking order should be mentioned. First, asking respondents to select a single service as their key demand may not coincide with their»personal reality«, which may consist of many needs that they consider equally important. Our approach forces them to make a choice. However, a ranking order is a means of communication between those in need of services and providers of such services. When resources are in short supply and decisions are needed on what to focus on, a priority list helps. (2) Our approach to identifying priorities is appropriate when using a questionnaire. There are, however, other ways of identifying needs and linking demand to supply that may go deeper, as they involve elements of discourse. Participatory budgeting is one such instrument for identifying people’s demands regarding government services at local government level. Question 2 asked:»Please rank the sectors you have highlighted above(ranked 1, 2 and 3)». In selecting service areas, we took note of the most recent AfroBarometer surveys in 37 African countries(round 7), whose findings show that people treat democratic and political participatory rights as less important than social policy and access to physical infrastructure. Our approach differed from the AB method in two main regards. AB allows respondents to freely identify service areas and it was the duty of the interviewer to classify according to a pre-fixed list of options. We opted against a procedure that allowed respondents to name whatever they wanted but presented a final list of service options for selection. The AB included items that we would not call services to be primarily delivered by the state. It also included service categories of a broad character and had no clear operational meaning, such as»economic management«. To be considered for our ranking list, a service had to meet several criteria: – services should be dominant or exclusive»deliverables« of the state; – services should be directly»consumable« by private households; – services should be relevant to a majority of the population; – services should have relevance for the socio-economic environment of households; 41

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH SERVICES APPENDIX II: TECHNICAL NOTES Project team The survey project was realised as a joint project between the Friedrich-Ebert-Stiftung(FES) as lead institution, and the International Labour Office(ILO) and the German Development Institute(Deutsches Institut für Entwicklungspolitik – DIE) as cooperating partners. National survey institutes (NSI) that are part of the AfroBarometer network were the implementing partners in the survey countries. Additional technical support, including data management, was provided by the Institute for Development Studies(IDS), University of Nairobi. Members of these institutions met on various occasions to jointly develop the questionnaire and to agree on details of the survey protocol. Objectives of the survey The main objectives of the survey are to obtain a better understanding of the social situation of the informally employed with regard to health issues, views on trust in state and government, self-organisation and interest in trade unions. Operational definition of informal employment The ILO provides a definition of informal employment for three categories of workers: (i) are considered to have informal jobs if their employment relationship is, in law or in practice, not subject to national labour legislation, income taxation, social protection or entitlement to certain employment benefits(advance notice of dismissal, severance pay, paid annual or sick leave, etc.).« ation and own-use production of goods was excluded from employment and recognised as one of five forms of work. Our survey is aligned to these changes, and own-use production of goods including subsistence workers is therefore excluded from informal employment. To identify informal labour and its various categories, the survey used the following operational definitions: Informal farming, raising animals or fishing: economic activities whose products are»intended only or mainly for sale«. If they are intended only or mainly for family consumption, activities are listed as subsistence production and excluded from the survey. Informal employees: paid job with reference to an employer’s contribution to a public or private pension scheme. Reference is to a main job. If employers did not pay contributions, employees were grouped as informal. Informal employers and own-account workers: informality is defined by non-registration in the national registry, which is used for company taxation. Contributing family workers: defined, by default, as having an informal job because of the informal nature of jobs held by contributing family workers. In the case of multiple jobs: the main job is defined as the job in which the respondent usually works the highest number of hours for pay or profit. Only the main job was considered for identifying informal jobs; secondary jobs were not considered. (ii) and own-account workers are considered to be in informal employment when their economic units belong to the informal sector. The informal sector is a subset of household enterprises(not constituted as separate legal entities, independent of their owners) that produce goods or services for sale in the market, and that do not have a complete set of accounts and/or are not registered under national legislation.« (iii) family workers are, by definition, informally employed, regardless of whether they work in formal or informal sector enterprises.« 37 The definition of informal employment rests on the definition of employment. The definition of employment was changed in 2013 with the adoption of the 19th ICLS resolution I. Employment became more closely linked to remunerThe questionnaire The questionnaire originally consisted of 143 main questions, which can be broken down into several sections. The key groups are: Personal and sociographic data, such as age, sex, status within the household, education, respondents’ employment situation and income, household assets. Health issues: respondents’ experience with health services; respondents’ resources for financing medical treatment; health insurance, including reasons for joining / not joining. Trust in state / government: respondents’ expectations with regard to services provided by the state; respondents’ views on the state’s capacity and willingness to provide services; respondents’ views on paying taxes and fees in exchange for services; respondents’ views on social inequality, social justice and the role of social policy. 37 ILO, Interactions between workers’ organizations and workers in the informal economy: a compendium of practice(2019: 15). Self-organization and interest representation: where, why and how do respondents organise themselves in groups? Do 42

Appendix respondents feel that their interests are represented by their group? What are the respondents’ views on trade unions? With the outbreak of the Covid-19 pandemic, some questions were added on how people have been responding and on government lockdown policies. Sample design and sampling process The sample is designed as a representative cross-section of all informally employed citizens aged 15 or above in a given country. Every citizen who corresponds to the criteria of age and informal employment is selected randomly for interview. The selected sample is determined by random selection methods at every stage of sampling and the application of probability sampling based on population size. The sampling process is based on stratification of the country into regions. Regions are further classified as urban or rural. Primary sampling units(PSU) – sometimes referred to as enumeration areas(EAs) – are the smallest geographical unit for which reliable population data is obtainable. The primary sampling units are selected from each stratum based on its share of the national population, and further allocated based on the urban/rural divide of each stratum. A total of eight households were clustered in each enumeration area for logistical efficiency and to lower the cost of contacting the sample. The national sample of 1,200 households is large enough to make inferences about all informally employed persons who are 15 years of age or above, with an average margin of sampling error of no more than plus or minus 2.8 per cent at a 95 per cent confidence level. The sampling process is structured in four stages:(i) selection of enumeration areas;(ii) selection of sampling start-points; (iii) selection of households; and(iv) selection of random respondents for interview. This sampling method is applied across all survey countries as a standard design. The survey uses a standard questionnaire that contains identical or functionally equivalent questions. Because of this standardisation, responses can be compared across countries and over time. – Selecting enumeration areas(EA): Based on the latest and updated population census the national statistical offices randomly select enumeration areas for each stratum and respective rural/urban divide, based on probability proportional to size of population. For a sample of N=1,200 the statistical office randomly selects 150 enumeration areas for a given country survey – that is, 150 x 8= 1,200 interviews. – Selecting the sampling start-points(SSPs) for each enumeration area: Across the survey countries, no complete lists of households were available from which the sample could be randomly drawn. The next best method therefore is to use physical maps(provided by the office of statistics). A sampling start-point(SSP) 38 is 38 Random selection of a start-point uses a grid. A ruler is placed along the top of the map and another along the side. A table of random numbers is then used to select pairs of numbers, one for the top marked on the map and field teams travel as close as possible to it, or to housing settlements nearest to it. A second SSP is selected as a reserve or substitute in case the initial SSP is inappropriate or inaccessible. – Selecting the household – walking pattern of interview teams: The interviewers start walking away from the physical start-point, with interviewer 1 walking towards the sun; interviewer 2 in the opposite direction; interviewers 3 and 4 at a 90-degree angle to the right and left. With this walking pattern, all four directions are covered. By counting households on both sides of the walking path, household No. 5 is selected as the first household for the interview and household No. 15 for the second interview. 39  If the interview cannot take place because nobody is at home, or the interview starts but cannot be finished, the walk continues to the next household on the same side of the road or opposite(household No. 6), while the second interview is done in household No. 16.  If the interview is refused the walk continues in the same direction until household No. 15. The second interview would take place with household No. 25. – Identifying informally employed respondents for the interview: At the household level, each interview is done in two phases. Phase 1 of the interview is conducted with the household head or the most knowledgeable person living in the household. The most knowledgeable person is the one who is best informed about all the other members of the household. The household head(or most knowledgeable person) provides information on each member of the household(15 or older), based on which a list is drawn up to include all members who meet the criteria for informal employment. The respondent for the main part of the interview(phase 2) is randomly selected from the list of informally employed persons for interview. If the selected respondent is unavailable the fieldworker makes an appointment for a later time in the day for a second attempt. If the interview is unsuccessful after the second attempt, the fieldworker randomly selects another respondent who qualifies as informally employed within the same household for the interview. If the second respondent is unavailable or the interview is unsuccessful for whatever reason, the household is dropped and the fieldworker replaces it with another household. axis and one for the side axis, resulting in a random combination. A line is then drawn on the map horizontal to the number chosen on the side, and another line is drawn vertical to the number chosen on the top. The point on the map where these two lines intersect is the sampling start-point. Each X-Y pair of numbers from the random number table can be used only once. 39 Special rules were applied in the case of multi-storey buildings, widely scattered households and settlements within commercial farms. 43

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH SERVICES IMPLEMENTING THE SURVEY National survey institutes(NSI) National survey institutes(NSI) that are part of the AfroBarometer network and have long-standing experience in opinion polling are contracted to implement the survey. They follow the protocol for survey implementation laid down in the manual. National survey institutes are responsible for indigenising the questionnaire, translating it into local languages, programming the tablets, selecting and training the enumerators, conducting field pre-tests, drawing up the field plan, organising and supervising the interviews, controlling data quality and, finally, presenting the results for further use. The work of national survey institutes is guided by an external supervisor, whose duty is to ensure uniform application of research rules. Electronic data capture Data collection is done through computer-assisted personal interviewing(CAPI), using tablets. The tablets are provided with the software Survey-To-Go(STG) and loaded with the questionnaire. The interviewers read questions from the screen of handheld tablets to respondents. The programming of the tablets filters the questionnaire and the random selection of the informally employed member of the household for the second phase. Interviewers use the tablets page-by-page and find instructions on what to do each time. Coding of responses is done with a touch of the screen. Translation into local languages The survey uses English and French as primary survey questionnaire languages. They are in turn translated into the most widely spoken local languages in the countries. All respondents have the choice of language in which they prefer to be asked questions and provide responses in a language in which they feel at home conversing. Training of interviewers and pre-testing Training of field teams in preparation for the survey lasts five to seven days, during which the questionnaire is reviewed comprehensively and teams practice using the tablets. The training includes pre-testing and final refinement of the questionnaire. The fieldworkers’ practice interviews serve as pre-tests of all of the local-language versions of the questionnaire. All members of the teams, including the supervisors, administer at least two questionnaires during the practice/pre-test phase. In a feedback session, the experiences during pre-testing are discussed and scope is provided to amend the questionnaire. Interview teams Interview teams going into the field are made up of four interviewers/enumerators and one supervisor. The supervisor is the person to whom all interviewers report every day and address emerging problems. Field supervisors usually have at least an undergraduate degree, as well as experience in collecting data and managing teams of fieldworkers. Fieldworkers have some university education, a strong facility in local languages and an ability to relate to respondents in a respectful manner. The field team structure is fitted to the size of the sample (1,200 interviews). Interviewers try to complete four interviews per day or 16 per field team; each field team tries to cover two enumeration areas per day. At this rate, it takes eight field teams(32 interviewers x 4 interviews/ day= 128 interviews/day) 9.37 days or one-and-a half weeks, including rest and travel days, to complete a standard survey. This is feasible because each team has a hired vehicle dedicated to it during fieldwork. Field teams are covered by insurance during the period of fieldwork. 44

APPENDIX III: STATISTICAL COMPUTATION Reference is to figures in the main text. Figure 1 Five countries – demand for better state services according to first priority Chi²-testing for differences in first priority setting between countries Countries Senegal vs Zambia Senegal vs Kenya Senegal vs Benin Senegal vs Ivory Coast Zambia vs Kenya Zambia vs Benin Zambia vs Ivory Coast Kenya vs Benin Kenya vs Ivory Coast Benin vs Ivory Coast N 2378 2373 2383 2382 2365 2375 2377 2370 2375 2378 Sig. 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Figure 2 Five countries – demand for better state services according to second priority Chi²-testing for differences in first priority setting between countries Countries Senegal vs Zambia Senegal vs Kenya Senegal vs Benin Senegal vs Ivory Coast Zambia vs Kenya Zambia vs Benin Zambia vs Ivory Coast Kenya vs Benin Kenya vs Ivory Coast Benin vs Ivory Coast N 2378 2373 2383 2369 2365 2375 2361 2370 2356 2366 Sig. 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Figure 3 Five countries – demand for better state services(first priority) by urban–rural residence Chi²-testing for differences in first priority setting by urban-rural differences Country Benin Kenya Ivory Coast Senegal Zambia N 1190 1187 1188 1193 1189 Sig. 0.000 0.000 0.000 0.000 0.000 45 Appendix Cramer-V 0.264 0.252 0.319 0.197 0.221 0.282 0.264 0.222 0.154 0.214 Cramer-V 0.202 0.234 0.249 0.181 0.163 0.139 0.155 0.160 0.128 0.174 Cramer-V 0.190 0.163 0.277 0.237 0.263

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH A S P E P R E V N IC D E IX S Figure 4 Five countries – demand for better state services(first priority) by age groups Chi²-testing: differences in first priority setting – differences between age groups within countries Country Benin Kenya Ivory Coast Senegal Zambia N 1190 1187 1188 1188 1188 Sig. 0.334 0.000 0.008 0.518 0.019 Figure 5 Five countries – demand for better health services versus pensions(first priority) by age groups Chi²-testing: differences in priority setting between health and pensions Country Senegal Zambia Ivory Coast Kenya Benin N Sig. 550 0.366 329 0.001 526 0.001 376 0.000 426 0.341 Figure 6 Five countries – demand for better health services(first priority) by gender Chi²-testing for differences in priority for health(first priority) by gender Country Senegal Zambia Ivory Coast Kenya Benin N 1193 1189 1188 1187 1190 Sig. 0.246 0.720 0.198 0.045 0.537 Figure 7 Five countries – demand for better state services(first priority) by income Chi²-testing for differences in first priority setting by income groups Country Senegal Zambia Ivory Coast Kenya Benin N 1164 1147 1181 1132 1188 Sig. 0.141 0.044 0.466 0.270 0.014 46 Cramer-V 0.080 0.157 0.099 0.076 0.096 Cramer-V 0.099 0.257 0.194 0.397 0.115 Cramer-V 0.034 0.010 0.037 0.058 0.018 Cramer-V 0.090 0.098 0.077 0.085 0.103

Appendix Figure 8 Five countries – use of medical care Country Senegal Zambia Kenya Benin Ivory Coast Zambia vs Benin Range of values. Min=1; Max=3. N 810 504 511 694 548 2375 T-testing: differences in use of medical care between countries Countries Senegal vs Zambia Senegal vs Kenya Senegal vs Benin Senegal vs Ivory Coast Zambia vs Kenya Zambia vs Benin Zambia vs Ivory Coast Kenya vs Benin Kenya vs Ivory Coast Benin vs Ivory Coast Range of values. Min=1; Max=3. sig 0.000 0.000 0.557 0.000 0.522 0.000 0.000 0.000 0.000 0.001 Figure 9 Five countries – use of medical care by urban–rural residence Chi²-testing for differences in use of medical care by urban–rural residence Country N Senegal 810 Zambia 504 Ivory Coast 548 Kenya 511 Benin 694 Figure 10 Five countries – use of medical care by gender Chi²-testing for differences in use of medical care by gender Country N Senegal 810 Zambia 504 Ivory Coast 548 Kenya 511 Benin 694 47 Sig. 0.000 0.398 0.000 0.920 0.000 Sig. 0.056 0.986 0.059 0.444 0.638 Mean 1.953 1.444 1.417 1.928 1.77 0.000 Cramer-V 0.186 0.06 0.198 0.018 0.194 Cramer-V 0.084 0.008 0.102 0.06 0.04

FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH A S P E P R E V N IC D E IX S Figure 11 Five countries – use of medical care by income Chi²-testing for differences in use of medical care by income Country N Senegal 779 Zambia 473 Ivory Coast 548 Kenya 480 Benin 690 Sig. 0.000 0.305 0.01 0.000 0.000 Cramer-V 0.137 0.087 0.125 0.178 0.147 Note: Chi² calculation included cases for occasional use which are not shown in chart. Calculation based on four income classes. If data from seven income classes are used, significance parameters go up slightly without changing the character of interpretation. Figure 20 Five countries – share of membership of health insurance schemes by gender Chi²-testing for differences in health insurance membership by gender Country Senegal Zambia Ivory Coast Kenya Benin N 1193 1192 1200 1188 1190 Sig. 0.000 0.089 0.866 0.228 0.002 Cramer-V 0.107 0.049 0.005 0.035 0.088 Figure 21 Five countries – interest of non-members in joining a health insurance scheme by gender Chi²-testing for differences in interest of joining health insurance by gender Country Senegal Zambia Ivory Coast Kenya Benin N 1076 1072 1065 874 1156 Sig. 0.506 0.632 0.514 0.906 0.056 Cramer-V 0.020 0.015 0.020 0.004 0.056 48

List of Figures LIST OF FIGURES 10 Figure 1  countries – demand for better state services according to first priority 10 Figure 2  countries – demand for better state services according to second priority 12 Figure 3  countries – demand for better state services(first priority) by urban–rural residence 13 Figure 4  countries – demand for better state services(first priority), by age groups 14 Figure 5  countries – demand for better health services versus pensions(first priority), by age groups 14 Figure 6  countries – demand for better health services (first priority), by gender 15 Figure 7  countries – demand for better state services(first priority), by income 19 Figure 8  countries – use of medical care 19 Figure 9  countries – use of medical care by urban–rural residence 28 Figure 16  countries – modes of payment for medical treatment, by income class 29 Figure 17  countries – use of medical care if funding comes from asset sales or debt 32 Figure 18  countries – willingness and capacity of institutions to provide better state services 33 Figure 19 Five countries – share of people in health insurance schemes 34 Figure 20  countries – share of membership in health insurance, by gender 34 Figure 21  countries – interest of non-members in joining a health insurance scheme, by gender 35 Figure 22  countries – reason for not joining a health insurance scheme 36 Figure 23  countries – declared premium payment by non-members in relation to various benchmarks 20 Figure 10  countries – use of medical care by gender 21 Figure 11  countries – use of medical care, by income 24 Figure 12  countries – sources of finance for medical treatment 25 Figure 13  – sources of finance for medical treatment by health insurance membership 26 Figure 14 Four countries – payment for medical treatment by sources of finance and urban-rural residence 27 Figure 15  countries – sources of financing medical care, by gender 49

LIST OF TABLES 22 Table 1  countries – measuring inequality of»good use« of medical care(selected groups) 23 Table 2 Five countries – health expenditures 30 Table 3  and capacities of institutions to deliver services – a four-field matrix 37 Table 4  – comparing premiums for NHIF membership with premium payments respondents are willing to pay 37 Table 5  – premium for membership in health schemes and declared premium payments FRIEDRICH-EBERT-STIFTUNG – ACCESS TO HEALTH SERVICES 50

IMPRINT ABOUT THE AUTHORS IMPRINT Dr. Rudolf Traub-Merz(author) is a consultant and currently works as coordinator of the FES-ILO-DIE/GDI survey project on informal employment in countries in Sub-Saharan Africa. Previously he served as head of FES offices in Lagos, Harare, Manila, Shanghai and Moscow. Dr. Manfred Öhm(co-author) has headed the Sub-Saharan Africa department of the Friedrich-Ebert-Stiftung since 2013. He received his PhD from the Albert Ludwig University Freiburg. He researches and lectures on peace, security and the development of democracy in Africa. He is a political scientist. This publication is part of the project»Informal Employment, Social Security and Political Trust in Sub-Saharan Africa«. You can find further publications of this project at https://www. fes.de/referat-afrika/publikationen. Friedrich-Ebert-Stiftung| Africa Department Hiroshimastr. 17| 10785 Berlin| Germany Responsible: Dr Manfred Öhm| Head of the Africa Department Phone:+49-30-269-35-7456| Fax:+49-30-269-35-9217 http://www.fes.de/afrika To order publications: Janine.Kaliga@fes.de Commercial use of all media published by the FriedrichEbert-Stiftung(FES) is not permitted without the written consent of the FES. The views expressed in this publication are not necessarily those of the Friedrich-Ebert-Stiftung. ISBN 978-3-96250-823-4

ACCESS TO HEALTH SERVICES A Key Demand of Informal Labour in Africa – Findings from Representative Country Surveys in Sub-Saharan Africa Access to health and social protection floors uphold Agenda 2030, on»sustainable development goals«(SDG) of the United Nations. Many African countries have pledged to achieve the aims of universal health care(UHC) and to set in motion various reforms to advance this agenda. One of the key challenges is to delink employment and social security and to build an inclusive platform for all, not dependent on employment. This is the hope of the informally employed, who in many countries in Sub-Saharan Africa account for 80 to 90 per cent of all employment. Access to health care for them means joining insurance schemes or having free or subsidised health services. The report focusses on how important the informally employed perceive access to decent health services within a ranking of other essential state services, as well as on their perceptions of the availability of medical care. It assesses the financial risk that arises from falling sick by looking at the financial resources that patients may use to pay for medical treatment. It assesses the hopes the informally employed place in their governments’ determination to improve services in the future and offers a view on the extent to which political regimes are deemed to be legitimate. Furthermore, it explores the interest of the informally employed in joining a health insurance scheme and preparedness to pay a premium. The Friedrich-Ebert-Stiftung(FES) in cooperation with the International Labour Office(ILO) and the German Development Institute(DIE-GDI) launched a research project on Informal Employment, Social Security and Political Trust in Sub-Saharan Africa, which includes an opinion survey of views on social security with particular a focus on access to health services. The survey sheds light on the predicaments facing the informally employed when trying to obtain medical treatment and the importance they attribute to better health care. The opinion surveys were conducted as country-wide representative surveys with a uniform research protocol to allow cross-border comparisons. The report presents findings from the surveys carried out in Kenya(2018), Benin(2018), Senegal(2019), Zambia(2019) and Ivory Coast(2020). Further information on the topic can be found here: www.fes.de/en/africa-department