JEL: E - Macroeconomics and Monetary Economics/E.E6 - Macroeconomic Policy | Pollux - Fachinformationsdienst Politikwissenschaft

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Open Access#39162012

Doing Business Economy Profile 2013 : United States

World Bank; International Finance Corporation

This tenth edition of Doing Business sheds light on how easy or difficult it is for a local entrepreneur to open and run a small to medium-size business when complying with relevant regulations. It measures and tracks changes in regulations affecting eleven areas in the life cycle of a business: starting a business, dealing with construction permits, getting electricity, registering property, getting credit, protecting investors, paying taxes, trading across borders, enforcing contracts, resolving insolvency and employing workers. Doing Business presents quantitative indicators on business regulations and the protection of property rights that can be compared across 185 economies, from Afghanistan to Zimbabwe, over time. The indicators are used to analyze economic outcomes and identify what reforms have worked, where and why. This economy profile presents the Doing Business indicators for the United States. To allow useful comparison, it also provides data for other selected economies (comparator economies) for each indicator. The data in this report are current as of June 1, 2012 (except for the paying taxes indicators, which cover the period January - December 2011).

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Open Access#39172016

Memorias de Hacienda y del Tesoro y de la Nueva Granada y Colombia, siglo XIX

Castillo, José María del; Márquez, José Ignacio de; Soto, Francisco; Aranzazu, J. de; Pombo, Lino de; Calvo, Mariano; Hoyos, Jorge J; Cuervo, Rufino

Diallo, Amadou Bassirou

In this dissertation, we are mainly interested in the interactions between poverty and one of its greatest dimensions1, namely health. More specifically, we will focus on their inequalities: does poverty inequality have more effect on poverty than health level? Does health inequality matter to poverty? Poverty and health are two related concepts that both express human deprivation. Health is said to be one of the most important dimensions of poverty and vice-versa. That is, poverty implies poor health because of a low investment in health, a bad environment and sanitation and other living conditions due to poverty, a poor nutrition (thus a greater risk of illness), a limited access to, and use of, health care, a lower health education and investment in health, etc2. Conversely, poor health leads inevitably to poverty due to high opportunity costs occasioned by ill-health such as unemployment or limited employability (thus a loss of income and revenues), a lower productivity (due to loss of strength, skills and ability), a loss of motivation and energy (which lengthen the duration of job search), high health care expenditures (or catastrophic expenditures), etc3. But what are the degree of correlation and the direction of the causality between these two phenomena? Which causes which? This is a classic problem of simultaneity that has become a great challenge for economists. Worst, each of these phenomena (health and poverty) has many dimensions4. How to reconcile two multidimensional and simultaneous events? 1 Aside the income-related material deprivation. 2 Tenants of the ?Absolute Income? hypothesis for instance show that absolute income level of individual has positive impact on their health status (Preston, 1975; Deaton, 2003). Conversely, lack of income (and the poverty state it implies) leads unambiguously to poor health. For other authors, it is not the absolute level per se, but the relative level (i.e. comparably to others in the society) that impacts most health outcomes. This is the ?Relative Income? hypothesis (see van Doorslaer and Wagstaff, 2000, for a summary). 3 See Sen (1999) and more recently Marmot (2001) for more information. 4 Poverty could be seen as monetary poverty, human poverty, social poverty, etc. Identically, one talks of mental health, physical health, ?positive? and ?negative? health, etc. So a one-on-one causality could not possibly exits between the two, or will be hard to establish. We?ve chosen the first way of causality: that is, poverty (and inequality) causes poor health. As justification, we consider a life-cycle theory approach (Becker, 1962). An individual is born with a given stock of health. This stock is supposed to be adequate enough. During his life, this stock is submitted to depreciation due to health shocks and aging (Becker?s theory, 1962). We could think that the poorer you are, the more difficult is your capacity to invest in your health5. Empirically, many surveys (too numerous to be enumerated here) show that poor people6 do have worse health status (that is, high mortality and morbidity rates, poor access to health services, etc.). It has been established that poor children are less healthy worldwide, independently of the region or country considered. It is generally agreed that the best way to improve the health of the poor is through pro-poor growth policies and redistribution. Empirically, one of the major achievements of these last two decades in developing countries is the improvement in health status of populations (notably the drop in mortality rates and higher life expectations) following periods of (sustained) economic growth. However, is this relation always true? In some countries as we will see later in this thesis, while observing an improvement in the population?s welfare, the converse is observed in its health status, or vice versa. If health and poverty are so closely related, they should theoretically move in the same direction. But the fact that in some countries we observe opposite trends suggests that some dimensions of health and poverty are not or may not be indeed so closely related, as postulated, and that they may depend of other factors. 1. The Purpose of the Study. 5 Another justification is that many authors have studied the problem the other way. Schultz and Tansel (1992, 1997) for instance showed that ill-health causes a loss of revenues in rural Cote d?Ivoire. Audibert, Mathonnat et al. (2003) also showed that malaria caused a loss of earnings of rural cotton producers in Cote d?Ivoire. 6 Usually defined from some income or expenditure-related metric or some assets-based metric. The ultimate goal of our dissertation in its essence is to measure inequality in health7 in developing countries using mainly Demographic and Health Surveys (DHS, henceforth)8. It deals with interactions between poverty and one of its greatest dimensions, putting aside the income-related material deprivation, namely health. It therefore measures inequality in health status and access to health and discusses which policies should be implemented to correct these inequalities. That is, it aims to see how much rich people are better off and benefit from health interventions, as compared to the poor, and how to reduce such an inequality. The present dissertation contains four papers that are related to these questions. Our main hypothesis (that will be tested) is that poverty impacts health through inequality effects9. Graphically, we can lay these simple relationships as: The dashed line in the figure above suggests that income inequality could impact health directly. But we consider that this direct effect is rather small or negligible, as compared to the indirect effect through inequality in health. Therefore, inequality in health is central to our discussion. To measure inequalities in health, we face three challenges: 7 And corollary health sanitation (access to safe water, toilet and electricity). Though electricity is more a measure of economic development that health measure per se, we add it here as a control for sanitation and nutrition: for example women could read more carefully the drugs? notices, or warm more quickly foods; more generally, electricity often improves the mental and material wellbeing of households. It also conditions health facility?s performance. 8 And potentially other surveys. In this case, we mention explicitly the survey(s). 9 The other important factor that could impact health is the performance of the health system. This is discussed in the Chapter 3. Health Assets Inequality Health Inequality Poverty (Assets Index) - measuring welfare (income metric) and subsequently inequality in welfare, - measuring health, - and measuring inequality in health. The measurements can be conducted using two approaches (Sahn, 2003): - Directly by ranking the households or individuals vis-à-vis their performance in the health indicator; we thus have a direct measure of inequality in health. This is suitable when the health indicator is continuous (such as weight, height or body mass index). According to Prof. David E. Sahn, that approach ?which has been referred to as the univariate approach to measuring pure health inequality, involves making comparisons of cardinal or scalar indicators of health inequality and distributions of health, regardless of whether health is correlated with welfare measured along other dimensions?. - Indirectly by finding a scaling measure such as consumption or income or another indicator (assets index for instance)10 that would help ranking the households or individuals (from the poorest to the richest), and see what are their performance in the health variable of interest. We are therefore measuring an indirect health inequality. The indirect method is mostly suitable when the health indicator is dichotomous (for example whether the individual has got diarrhoea last 2 weeks, or ?have the child been vaccinated?, or ?place of delivery?) or is a rate (such as child mortality). Again, quoting Prof. Sahn, ?making comparisons of health across populations with different social and economic characteristics is often referred to in the literature as following the so-called `gradient? or `socioeconomic? approach to health inequality. Much of the motivation for this work on the gradient approach to health inequality arises out of fundamental concerns over social and economic justice. The roots of the gradient will often arise from various types of discrimination, prejudice, and other legal, social, and economic norms that may contribute to stratification and fragmentation, and subsequently inequality in access to material resources and various correlated welfare outcomes?. While the first method would appear quickly limited for dummy or limited categorical health variables because of the small variability in the population, the second approach could also be 10 Or more generally any other socioeconomic gradient such as education, gender or location. impossible when no information is available to scale the units of observation in terms of welfare. We?ll be mostly focusing on the second approach, as did many health economists, and also due to the nature of the DHS datasets in hand and the indicators that we are investigating. 2. Strategy, Methods and Structure. Measuring wealth-related inequality in health implies in the first stage defining and characterizing the poor. Who are indeed the poor? Traditionally, monetary measures (income or consumption) have been used to distinguish households or people into ?rich? and ?poor? classes. Indeed, it is agreed that the ?incomemetric? approach is one of the best ways to measure welfare11. However, it sometimes, if not often, happens that we lack this essential information in household survey datasets. Especially in our case, the DHS datasets do not have income nor consumption information. Then, how to characterize the poor in this situation? For a long time, economists have eluded the question. But soon, it became evident that an alternative measure is needed to strengthen the ?poverty debate?. In the first part of our dissertation, we start by providing a theoretical framework to find a proxy for wellbeing, in the case where consumption or income-related data are missing, namely by discussing the use of assets as such a proxy. The first part of this thesis is relatively long, as compared to the second. However, this is justified, due to its purpose. The goal of the first part of the dissertation is to participate to the research agenda on poverty. It attempts to measure it in a ?non traditional?12 way. 11 There is a consensus in the economic literature that income is more suitable to measure wealth or welfare in developed countries while consumption is more adequate for developing ones due to various reasons such as irregularity of incomes for informal sector, seasonality, prices, recall periods, trustworthy, etc. (see Deaton 1998 for detail). 12 i.e. a non monetary way. The main rationale for this first part therefore is thus to find a new, non monetary measure to characterize in best, life conditions, welfare and then the poor. This measure is referred to as the ?assets index?. Indeed, as the majority of developing countries are engaged more and more in fighting poverty, inequality and deprivation, more and more information on the state of poverty13 is needed. If in almost all these countries, many household surveys have been implemented to collect information on socioeconomic indicators, the major indicator that is needed to analyze poverty (namely income or consumption data) is unfortunately not often collected due to various reasons (time, cost, periodicity, etc.). Even, if they were collected, the quality of the data is often poor. Therefore, economists tend to rely more on other indicators to compensate for the absence of monetary measures. One of the indicators often used are the assets owned by households. The question arose then how to use these assets to characterize the poor in this context? How to weight each of them? In a first attempt, many economists built a simple linear index by assigning arbitrary weights to the assets14. In a seminal paper, Filmer and Pritchett (2001) propose to construct the so-called ?assets index? which could be used as a proxy for consumption or income. It is commonly agreed that their methodology follows a ?scientific? approach, thus is more credible. In their case, they use a Principal Component Analysis (PCA, henceforth) to build their assets index. Since, many other economists have followed in their footsteps which we label in our dissertation, the ?material? poverty approach (as opposed to the monetary one) since it is based on materials (goods and assets) owned by the households or individuals. Because of the importance of the subject (poverty) and because the method is pretty new and original, this first part of our thesis is as said quite long as compared to the second one and has two papers which focus mainly on poverty and inequality issues and their connections with economic growth. In this part, we start by presenting a methodology of measuring non monetary (material) poverty, when a consumption or income data is not available. We show how one can obtain robust results using assets or wealth variables. Once the method is clearly 13 And more generally welfare. 14 For example a television is given a weight of 100, a radio 50, and so on. But this is clearly not a `scientific? way to proceed, as there is no rational ground in giving such weights. tested and validated, it is then confronted to real data. We show that the index shares basically the same properties with monetary metrics and roughly scales households in the same way as does the consumption or income variables. We discuss the advantages and also the limitations of using the assets index. The important thing to bear in mind is that, once it is obtained, it could be used to rank the observational units by wealth or welfare level. - The first chapter defines in a first section poverty and how it is usually measured (by the income metric approach). We discuss the limitations of the use of income/expenditure and what could be alternative measures. We then propose in section 2 the assets metric as a proxy for poverty measurement and test the material poverty approach on international datasets collected by the DHS program. We explore the material poverty and inequality nexus in the world and how Sub-Saharan Africa (SSA)15 compares with other regions. We show, using that index and DHS data, that poverty, at least from an assets point of view, was also decreasing in SSA as well as in other regions of the world. This result contrasts with other findings such as Ravallion and Chen (2001) or Sala-i-Martin (2002) that show that, while other regions of the world are experiencing a decline in their (monetary) poverty rates, SSA is lagging behind, with rates starting to rise over the last decade. Therefore, two different measures of welfare could yield opposite results and messages in terms of policies to implement to combat poverty. Moreover, the method we use not only allows observing changes over time for each country, but also provides a natural ranking among countries (from the poorest to the richest). In this chapter, aside the measure of welfare and poverty, we also discuss in a final section the impact of demographic transition on economic growth and therefore on poverty. Indeed, demographic transition is a new phenomenon that is occurring in developing countries, especially African ones. Its negligence could lead to underestimating poverty measures (both material and monetary) by underestimating real economic growth rates. We show that changes in the composition and the size of households put an extra-pressure on the development process. While traditional authors have not considered the impact of these 15 SSA countries are Benin, Burkina Faso, Central African Republic, Cameroon, Chad, Comoros, Republic of Congo, Côte d?Ivoire, Ethiopia, Gabon, Ghana, Guinea, Kenya, Lesotho, Madagascar, Malawi, Mali, Mauritania, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, South Africa, Tanzania, Togo, Uganda, Zambia and Zimbabwe. The ?rest of the world? is represented by Armenia, Bangladesh, Bolivia, Brazil, Colombia, Dominican Republic, Egypt, Guatemala, Haiti, Honduras, India, Indonesia, Kazakhstan, Kyrgyz Republic, Moldova, Morocco, Nepal, Nicaragua, Pakistan, Paraguay, Peru, Philippines, Turkey, Uzbekistan, Vietnam and Yemen. changes, we show that taking this into account implies higher economic growth rates than those actually observed or forecasted. - Once the assets index approach is established and tested on international data, the question arose how it performs as compared to the monetary metric. Indeed, if monetary measures remain the reference, then our assets index should share some common properties with them. The second chapter assesses the trends in material poverty in Ghana from the assets perspective using the Core Welfare Indicators Questionnaires Surveys (CWIQ). It then compared these trends with the monetary poverty over roughly the same period. We show that the assets index could be used and yields the same consistent results as using other welfare variable (such as income, consumption or expenditure). Therefore, using two consecutive CWIQ surveys, we find that material poverty in Ghana has decreased roughly by the same magnitude as monetary one, as found in other studies by other authors such as Coulombe and McKay (2007) using Ghanaian GLSS16 consumption data. Thus, this chapter could thus be viewed as providing the proof that the material and the monetary approaches could be equivalent. The second part of our dissertation seeks how to define and measure health and inequality in health. While the definition of health is not obvious, we propose to measure it with child mortality rates. Our main rationale in doing so is that low child mortality generates, ceteris paribus, higher life expectancy17, thus is an adequate measure of a population?s health. This may not be true in areas devastated by wars, famines, and HIV and other pandemics where child mortality could be high (in this case, the best measure should be life expectancy by age groups). Also, the reader should bear in mind that in fact, child mortality could be itself is a good indicator for measuring the (success of the) economic development level of a society as a whole (Sen, 1995), mainly because in developing countries, child mortality is highly correlated to factors linked to the level of development such as access to safe water, sanitation, vaccination coverage, access to health care, etc. - In the third chapter, we focus on measuring overall population?s health. For this, we estimate child mortality in SSA and compare it to the rest of the world. We explore the 16 Ghana Living Standard Surveys. 17 By construction, life expectancy at birth is highly correlated and sensitive to child mortality (it is based on child mortality rates for various cohorts). Lower child mortality rates lead to higher life expectancy and vice versa. determinants of child mortality using mainly a Weibull model and DHS data with socioeconomic variables18 as one of our major covariates. The use of the assets index information is to see how these quintiles behave in a multivariate regression framework of child mortality (i.e. how they affect child mortality). We find, among others, that mother?s education and access to health care and sanitation are one of the strongest predictors for child survival. Controlling for education and other factors, family?s wealth and the area of residency do not really matter for child survival in SSA, contrasting with results found elsewhere. - The fourth and last chapter answers the ultimate goal of this dissertation, that is, the scope of health inequalities in the developing world, particularly in SSA. It uses the factor analysis (FA) method of Chapter 1 to rank household according to their economic gradient status19 and then studies inequalities in various health indicators in relation with these groups. The intention is to analyze inequality rates between rich and poor for various health variables. In this chapter, we concentrate solely on inequality issues in health and health-related infrastructures and services. Mainly, we analyze inequality in access to sanitation infrastructures (water and electricity20) and various health status and access to health indicators (such as child death, child anthropometry, medically assisted delivery and vaccination coverage) using a Gini and Marginal Gini Income Elasticity approach (GIE and MGIE, henceforth) on one hand, and the Concentration Index (CI) approach on the other. Results show that, while almost all countries have made great efforts in improving coverage in, and access to, these indicators, almost all the gains have been captured by the better-offs of the society, especially in SSA. We extend the analysis to compare GIE estimates to those of CI and find consistent results yielding quite similar messages. 18 Quintiles groups derived from an assets index. 19 By grouping usually households in 5 quintiles from poorer to richer ones. 20 On the rationale of using electricity, see footnote 7 above. 3. Results and Policy Implications. As said above, the major goal in conducting this thesis research is to analyze inequality in health status, health care and health-related services using DHS data. To reach our objective, we follow two intermediate steps: - For assets poverty, results show that assets poverty and inequality are decreasing in every region of the world, including Sub-Saharan Africa. This tends to support our hypothesis that, contrary to common beliefs, African households use assets and building ownerships as saving tools and buffer to economic shocks. The first paper also shows however that the demographic transition actually occurring in developing countries could impede on economic growth and trigger a bullet on policies aiming at combating poverty. - Our third paper shows that child mortality is decreasing in all parts the world. However, the 1990s and early 2000s have been a lost decade for the African continent where many countries have witnessed an increase in rates that is mostly attributable among other factors to the economic and financial turmoils of the 1990s and early 2000s and the HIV epidemic. Our hypothesis is that these phenomena have destabilized the organization of the health care system, cut its funding and hampered its performance. High levels of health inequality can also be part of the puzzle. Coming back to the particular case of HIV/AIDS, the reader should observe that it affects more and more the less poor so that it can also lead to a decline in assets inequality (richer people are dying) along with an increase in child mortality and thus explain in great part our paradox. This setback (the rise in mortality over recent periods despite poverty reduction) will make impossible for these countries to reach the millennium development goals, at least for child mortality. The conclusion to this is that African population?s health has been stagnant over the period 1990-2005. Regression analysis reveals no strong correlation between our measure of welfare (assets index) and child mortality. More important are mothers? education and access to health care and sanitation services. - Finally, our inequality estimates show that they are quite high for all indicators considered. For ill-health indicators (child malnutrition and death), rates are excessively concentrated in poor and rural groups. Concerning access to health care services, rich and urban groups tend to be more favoured than poor and urban ones. But the high level of inequality tends to be reducing at the margin over time, as the poor have increasing access. Finally for access to sanitation services, results show that while the majority of countries have made substantial efforts to increase coverage on the first two periods, the rich and urban classes have benefited more and inequality (which is at high levels) tends to rise at the margin over time, especially for the poor. More preoccupying is the fact that rates are falling between 1995-2000 and 2000-2005, probably because of the privatization of these services and the new costs they impose on households. Overall, inequality in all variables considered is more pronounced in SSA than the rest of the world (expect for death and malnutrition). The sub-continent is still disadvantaged in terms of access to services or ill-health. Where to go from here? In the African sub-continent, we have the following picture: a decreasing (material) poverty and inequality but coupled with a stagnant child mortality situation, a stagnant or increasing malnutrition. This is mostly due to high levels of, and an increasing inequality at the margin in access to sanitation and electricity services coupled with a decreasing access to these services. Thus, despite the fact that we observe a decreasing inequality at the margin in access to health care (even though the average level of inequality is still high) the missing link in health-related services coupled with an overall high inequality in these two types of services hugely impact child health and survival. Therefore, as access to health care services and health-related sanitation services is essential to child survival, our findings call for vigorous policies to promote access of the poor groups and rural areas to these services. African Governments should continue to favour access of the poor to health care and reverse the inequality trends in access to water, sanitation and electricity. This is vital for the health of the population and for the development of Africa. Funding can come from various sources: the Government Budget, International Assistance but also from households themselves (since the first part of our thesis has demonstrated that they are getting richer (and various surveys show that they are willing to pay for quality health care), an adequate fees policy could benefit to the health care system). Measures should be put in place to strengthen the performance of the health system and to mitigate the negative effects of macroeconomic imbalances, economic crises and HIV/AIDS. Only on these conditions the Sub-Continent could hope to eradicate poverty and promote health for all. 4. Contribution of this Thesis. This thesis seeks to analyze empirically the inequality in health and access to health in SSA and how this region compared to the rest of the world. To do so, it develops a new method to characterize poor households and to analyze assets-based poverty, when the monetary measure is unavailable. Such a method is indeed necessary as almost all developing countries have collected many surveys that lack the consumption or income information. Once a poverty measure and a correct measure of health have been found, and their core determinants clearly established, we then proceed to the health inequality analysis, along with its determinants, using two methodologies: the traditional CI and the more recent GIE approaches. These approaches have been the mostly used to explore the inequality in health and access to health these last years. Though already studied in the literature, and sometimes applied on DHS or some groups of DHS datasets, our dissertation differs in its purpose and scope and its large scale. No paper to our knowledge used the totally to-date freely available DHS datasets to study poverty and inequality topics and provide basic statistics. Our main contribution is to shed a new light on the welfare-inequality-health nexus in Africa, how it evolves over time and how it compares to other regions around the world, using all available information. It also put numbers on various important socioeconomic indicators such as poverty, inequality, child health and mortality, access to health-related infrastructures, etc., for developing countries, especially African ones. As we sometimes lack these important information, this thesis proves finally to be a very useful exercise. ; Cette thèse part d'un postulat simple : « l'amélioration du niveau de vie s'accompagne de l'amélioration de l'état de santé générale d'une population » et teste sa validité dans le contexte de l'Afrique au Sud du Sahara (ASS). Si cette hypothèse se vérifie en général dans le contexte de l'ASS en ce qui concerne le niveau (plus le pays est riche, plus sa population est en bonne santé), il l'est moins en ce qui concerne les dynamiques, du moins à court et moyen terme. Notamment, les pays qui connaissent une amélioration tendancielle de bien-être matériel ne connaissent pas forcément une amélioration de la santé de leurs populations. Ceci constitue un paradoxe qui viendrait invalider notre postulat. En écartant tout effet de retard ou de rattrapage qui pourrait l'expliquer car nous travaillons sur une période de 15 ans réparties en 3 sous-périodes (1990-1995, 1995-2000 et 2000-2005), nous expliquons ce paradoxe, toutes choses égales par ailleurs, par deux canaux principaux qui peuvent interagir : - la performance du système de santé et - l'inégalité en santé. Si le premier est plus évident mais aussi plus difficile à prouver empiriquement du fait du manque de données sur des séries longues, ou du fait que ces données sont trop agrégées et éparses, le second canal est testable avec des bases de données adéquates qui, elles, sont disponibles au niveau microéconomique (ménages). Les bases de données que nous avons privilégiées sont les Enquêtes Démographiques et de Santé (EDS) du fait de leur comparabilité dans l'espace et le temps (mêmes noms de variables standardisées, même méthodologie d'enquête, mêmes modules, etc.). Ces atouts sont d'autant plus importants que les comparaisons de pauvreté et de bien-être basées sur les enquêtes de revenus ou de consommation butent sur de sérieux problèmes à savoir la comparabilité de ces enquêtes (méthodologies différentes, périodes de rappel différents, prix souvent non collectés de la même manière, etc.). Pour montrer ces effets de l'inégalité de santé sur les niveaux et les tendances de la santé des populations et la pauvreté et le bien-être, nous avons axé notre recherche autour de 3 axes principaux : 1- Comment mesurer le niveau de richesse et donc le bien-être des ménages en l'absence d'information sur la consommation et le revenu ? Les chapitres 1 et 2 de notre thèse se penchent sur cette question. Nous avons privilégié, à l'instar de plus en plus d'économistes, l'utilisation des biens des ménages et les méthodes de l'analyse factorielle et d'analyse en composantes principales pour construire un indice de richesse. Cet indice de richesse est pris comme un substitut du revenu ou de la consommation et sert donc de proxy pour la mesure du bien-être. Bien qu'il comporte quelques lacunes (notamment le fait qu'il ne concerne que les biens matériels et durables du ménage alors que la consommation ou le revenu sont des concepts plus globaux de bien-être, il ne prend pas en compte les préférences des ménages, il ne comporte aucune notion de valeur car le prix n'est pas pris en compte, de telle façon qu'une petite télévision en noir blanc vieille de vingt ans est mise au même niveau qu'un grand écran plasma flambant neuf, etc.), il n'en demeure pas moins que d'un côté, avec les EDS, il n'y a pas moyen de faire autrement en l'état actuel des choses, mais aussi et surtout parce que ces données permettent d'éviter les problèmes évoqués plus haut, notamment celui de la comparabilité des données pour faire de la comparaison spatiale et inter-temporelle des données en matière de pauvreté. Dans le premier chapitre, en nous basant sur cet indice et une ligne de pauvreté définie a priori à 60% pour la première observation dans notre échantillon (Benin, 1996), et en utilisant les données EDS et une analyse en composantes principales (ACP), nous avons pu mesurer la tendance de la pauvreté dite « matérielle » (en opposition à la pauvreté monétaire, basée sur la métrique monétaire). Cette méthode qui est privilégiée par des auteurs comme Sahn et Stifel est d'autant plus intéressante qu'elle donne non seulement les tendances de la pauvreté dans chaque pays, mais elle permet aussi une classification naturelle de ces pays par ordre de grandeur de pauvreté. Cependant, dans la mesure où les biens des ménages et la dépenses de consommation sont disponibles, l'analyste devrait estimer les deux types de pauvreté (matérielle via l'indice de richesse et monétaire via le revenu ou la consommation) car les études montrent souvent que les biens matériels et la consommation ou le revenu ne sont pas très bien corrélés, et donc le choix de l'indicateur de bien-être est crucial en termes de politiques économique et de santé. En effet, si l'indicateur sous-estime le vrai niveau de pauvreté ou d'inégalité (ou les surestime), les dépenses publiques qui en résultent peuvent être plus ou moins surévaluées, de même que les réponses apportées se révéler inadéquates. Donc dans la mesure du possible, il conviendrait de se pencher sur la question du choix de l'indicateur. Les résultats de notre méthodologie montrent que l'ASS reste la région la plus pauvre du monde en termes de possession d'actifs. La région orientale de l'ASS est la plus pauvre au monde (75%) suivie de l'Asie du Sud (64%), le Sud de l'ASS (61%), l'Afrique Centrale (57%), l'Afrique de l'Ouest (55%), l'Asie de l'Ouest (40%), l'Asie du Sud-Est (19%), l'Amérique Latine (18%), les Caraïbes (17%), l'Afrique du Nord (6%), l'Asie Centrale (2%) et l'Europe de l'Est (1%). Notre analyse nous montre que la pauvreté baisse dans l'ensemble des pays Africains au Sud du Sahara (sauf la Zambie), à l'instar des autres pays du monde dans l'échantillon. En effet, en considérant les trends, nous voyons que la moyenne de l'ASS passe de 63% de pauvreté matérielle entre 1990-1995 à 62% en 1995-2000 et 58% entre 2000 et 2005. La baisse est modeste et lente mais non négligeable et surtout, elle est en accélération sur les 2 dernières périodes. Mais elle demeure toutefois beaucoup plus marquée dans le reste du monde. Concomitamment à la baisse de la pauvreté, nous observons aussi une baisse de l'inégalité. Nous terminons ce chapitre par une réflexion sur l'effet de la transition démographique sur la croissance économique et la pauvreté en ASS et dans les autres pays en développement. En effet, la chute de la fertilité et de la mortalité couplées à un exode rural font que le nombre de famille se démultiplie du fait de la transition vers des tailles plus réduites. Ceci impose plus de contraintes (et donc peut avoir un impact négatif) sur la croissance économique et risque de sous-estimer le niveau réel de pauvreté. Il convient, une fois que la pauvreté matérielle et ses tendances ont été bien calculées avec les biens durables (et la transition économique prise si possible en compte), de tester la validité de cette méthode en la confrontant avec les résultats issus de l'analyse monétaire de la pauvreté. Les EDS ne comportant pas données d'information sur la consommation, nous nous sommes tournés vers une autre source de données. Dans le chapitre 2, nous avons testé la robustesse de notre méthode dans le cas particulier du Ghana, en utilisant les enquêtes du Questionnaire Unifié sur les Indicateurs de Base de Bienêtre (QUIBB), et en confrontant les résultats issus de la méthode ACP avec ceux issus de la méthode traditionnelle monétaire et trouvons grosso modo les mêmes résultats (10% de baisse avec la méthode monétaire traditionnelle et 7% avec notre méthode sur la période 1997- 2003). Ceci valide donc le fait que la méthode que nous proposons (à savoir, mesurer le bienêtre et la pauvreté par les biens durables des ménages) est tout aussi valide que la méthode plus traditionnelle utilisant des métriques monétaires. Une analyse fine dans le cas du Ghana montre que la baisse de la pauvreté est due à une croissance économique particulièrement pro-pauvre mais aussi à des dynamiques intra et intersectorielles (réallocation des gens des secteurs moins productifs vers ceux plus productifs) et aussi une forte migration des campagnes vers les villes. Nos simulations montrent que les migrants ruraux ont aussi bénéficié de cette croissance dans les villes où ils trouvent plus d'opportunités. 2- Une fois établie que la pauvreté est en recul en ASS, nous avons voulu mesurer la tendance de la santé de sa population (approximée par les taux de mortalité infantile et infanto-juvénile). Nous discutons dans le chapitre 3 de trois méthodes pour estimer et comparer les taux de mortalité des enfants : - la méthode des cohortes fictives (sur laquelle l'équipe de l'EDS se base pour estimer les taux « officiels » de mortalité), - la méthode non paramétrique (Kaplan et Meier) que privilégient un certain nombre d'économistes et - la méthode paramétrique (Weibull) de plus en plus utilisée pour sa souplesse et sa robustesse. Les deux premières méthodes ont tendance à sous-estimer le vrai niveau de mortalité et de ce fait nous avons privilégié le Weibull. De plus, avec cette dernière, nous pouvons évaluer l'effet de chaque variable spécifique (comme l'éducation ou l'accès à l'eau) sur le niveau de mortalité. Une étude des déterminants de cette mortalité montre qu'outre l'effet attendu de l'éducation des mères, l'accès aux infrastructures de santé (soins médicaux et surtout prénataux durant et lors de l'accouchement) et sanitaires (accès aux toilettes et dans une moindre mesure à l'eau potable) en sont les principaux facteurs. L'effet de richesse joue peu en ASS (mais pas dans le reste du monde), une fois que nous contrôlons pour le lieu de résidence (urbain) et le niveau d'éducation. Ce résultat nous surprend quelque peu, même s'il a été trouvé dans d'autres études. Ensuite, nous avons calculé la mortalité prédite des enfants. De toutes les régions du monde, l'ASS a le niveau de mortalité le plus élevé (par exemple en moyenne 107 décès pour la mortalité infantile contre 51 pour le reste du monde, soit plus du double). Ce résultat était toutefois attendu. Par contre nous avons été quelque peu surpris en ce qui concerne les tendances. Le constat est que sur les 15 ans, la mortalité des enfants a très peu ou pas du tout baissé dans le sous-continent africain (et est même en augmentation dans certains pays, alors qu'ils enregistrent une baisse de la pauvreté matérielle sur la même période). En moyenne, considérant les enfants de moins d'un an, les taux sont passés de 95%o à 89.5%o pour remonter à 91.5%o pour les 3 périodes 1990-1195, 1995-2000 et 2000-2005. Ainsi sur 15 ans, la mortalité infantile n'a baissé que de 3 points et demie en moyenne et surtout, elle remonte sur la période 1995-2005. Un examen des taux de malnutrition des enfants confirme ces tendances. On pourrait dire que ces résultats sont plutôt encourageants et normaux si on fait une analyse d'ensemble du sous-continent. En effet pour l'ensemble de l'ASS, cette légère baisse semble en conformité avec la baisse de 5 points des taux de pauvreté matérielle (63% en 1990-1995 à 58% en 2000-2005). Mais l'ordre de grandeur est faible en termes de magnitude, et surtout si compare au reste du monde où on observe une baisse de la mortalité beaucoup plus conséquente. Mais c'est l'arbre qui cache la forêt. Une analyse plus fine par pays montre en effet une situation plus contrastée. Notre postulat de départ nous dit que sur une période suffisamment longue, une amélioration de bien-être s'accompagne d'une amélioration de la santé. Or on constate que certains pays qui connaissent une baisse de la pauvreté matérielle connaissent également une recrudescence de la mortalité des enfants. Pour une même année, ce résultat peut être normal, traduisant un simple décalage pour que l'amélioration de bien-être se traduise par un meilleur état de santé de la population. Mais à moyen terme (période de 5 ans), nous observons la même absence d'effet. Nous sommes donc face à un paradoxe qu'il nous faut comprendre et tenter d'expliquer. Une des pistes pour comprendre ces résultats est d'analyser la performance des systèmes de santé en Afrique. Les facteurs qui expliquent notamment cette performance sont : des facteurs « classiques » comme la performance économique des périodes passées, les montants et l'allocation des dépenses de santé, l'organisation des systèmes de santé, la baisse de la fourniture de services de soins de santé (vaccination, assistance à la naissance, soins prénataux, soins curatifs, .), la malnutrition, le SIDA, les guerres, la fuite des cerveaux notamment du personnel médical, etc., à côté de facteurs plus « subtils » ou ténus car moins saisissables comme les crises financières des années 1990s qui ont plombé certaines des économies de la sous-région, la qualité des soins, la corruption et les dessous-de-table, l'instabilité de la croissance économique (même si elle est positive), etc. La seconde voie que nous examinons pour expliquer le manque de résultat en santé dans certains pays concerne l'inégalité en santé et ceci fait l'objet de notre dernier chapitre. 3- Expliquer l'absence de lien entre santé et pauvreté dans certains pays de l'ASS : l'effet de l'inégalité en santé. Dans le chapitre 4, nous émettons l'hypothèse que le fort niveau d'inégalité dans l'accès aux services de santé et d'assainissement couplé à la faible performance du système de santé (avec en toile de fond l'impact du Sida) peuvent servir à expliquer en partie notre paradoxe. Nous considérons deux types de services : - soins de santé (vaccination, assistance médicale à la naissance et traitement médical de la diarrhée) et - hygiène et assainissement (accès à l'eau potable et à l'électricité, accès aux toilettes propres). Le choix de ces services est motivé par le fait que le modèle Weibull dans le chapitre 3 nous montre que toutes choses égales par ailleurs, ils sont cruciaux pour la survie des enfants, en particulier en Afrique. Les niveaux d'accès montrent une baisse tendancielle des taux pour les services de santé (surtout pour la vaccination) et une légère augmentation de l'accès à l'électricité et dans une moindre mesure à l'eau potable. L'accès aux toilettes propres demeure un luxe réservé à une petite fraction de la population. Pour les calculs d'inégalité, nous considérons deux indicateurs: - l'indice de concentration (pour mesurer le niveau moyen d'inégalité) - et l'élasticité-revenu du Gini (inégalité « à la marge » quand le revenu d'un individu ou d'un groupe augmente d'un point de pourcentage). Globalement, les pays d'ASS ont un niveau d'inégalité beaucoup plus élevé comme on s'y attendait par rapport au reste du monde. Pour les tendances, nous remarquons que l'inégalité marginale s'accroît pour les services d'assainissement (eau, toilette et électricité), mais qu'elle diminue pour les soins de santé. En ce qui concerne l'inégalité moyenne, elle indique une disproportion dans l'accès des classes riches par rapport à celles pauvres. Même si les groupes pauvres « rattrapent » ceux riches dans la provision de certains services, cela se fait de façon trop lente. De fait, le haut niveau d'inégalité couplé à une recrudescence de cette inégalité à la marge pour certains services tendent à annihiler les effets positifs de la croissance économique et de la réduction de la pauvreté et maintiendraient la mortalité, la malnutrition et la morbidité des enfants en Afrique à des niveaux relativement élevés et plus particulièrement concentrées dans les groupes les plus pauvres. Tout ceci appelle à des politiques économiques, sociales et sanitaires pour renverser fortement les tendances de la mortalité des enfants. En particulier, nos résultats suggèrent qu'il faudrait que les pays Africains puissent entre autres : - accroître les services de soins de santé, notamment les soins préventifs comme les services essentiels à la santé de l'enfant dès sa naissance (vaccination, services prénataux et assistance à la naissance), les soins curatifs et les campagnes de sensibilisation. - renverser la tendance baissière dans la provision des services sanitaires (eau, électricité, environnement et assainissement, prise en charge des déchets, etc.). - améliorer la nutrition et l'environnement immédiat de ces enfants et les comportements des ménages (espacement des naissances, éducation des mères en matière de santé, etc.). - plus généralement comme le montrent d'autres études, il faudrait aussi améliorer la performance globale de leur système de santé en empêchant la fuite des cerveaux, en allouant un budget suffisant à la santé, en organisant mieux les différents organes, de même que les ciblages des politiques de santé, en empêchant la corruption, en améliorant la qualité (accueil, propreté des centres de soins, etc.), en équipant les centres en médicaments, vaccins, moyens de transport et de communication, etc. Intégrer si possible les systèmes plus traditionnels de soins (comme les matrones et les guérisseurs) et le secteur privé, de même qu'une meilleure organisation du système pharmaceutique. Ces politiques constituent un tout et doivent être mise en oeuvre rapidement, ou renforcées le cas échéant. A cette seule condition les pays Africains pourraient espérer rattraper leur retard dans les Objectifs du Millénaire.

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