Country Background Paper. Multidimensional Poverty in Mauritania

Transcription

1 Distr. LIMITED E/ESCWA/EDID/2017/CP.3 20 November 2017 ORIGINAL: ENGLISH Economic and Social Commission for Western Asia (ESCWA) Country Background Paper Multidimensional Poverty in Mauritania United Nations Beirut, 2017 Note: This document has been reproduced in the form in which it was received, without formal editing. The opinions expressed are those of the authors and do not necessarily reflect the views of ESCWA

2 Acknowledgments This paper has been prepared by the Multidimensional Poverty Team of the Economic Development and Globalization Division (EDID) of ESCWA. The team members are Khalid Abu-Ismail, Bilal Al-Kiswani, Dina Armanious, Verena Gantner, Sama El-Haj Sleiman, Ottavia Pesce, and Maya Ramadan. It serves as a country background paper to the Arab Multidimensional Poverty Report, a joint publication by the League of Arab States, ESCWA, UNICEF and Oxford Poverty and Human Development Initiative. The team members are grateful to Sabina Alkire and Bilal Malaeb from OPHI for their technical advice and collaboration on the construction of the regional Arab Multidimensional Poverty Index, which we apply in this paper using the household level data from the Mauritania MICS (2011).. 2

3 1. Introduction 1.1. Mauritania is a lower middle income country 1 in the Maghreb region of Western Africa. It is bordered by the Atlantic Ocean to the West, Western Sahara in the North, Algeria in the Northeast, Mali in the East and Southeast, and Senegal in the Southwest. Approximately 90% of the country is within the Sahara. Error! Reference source not found. shows some of the main socio-economic indicators for Mauritania. The Human Development Index (HDI) a measure of basic human development achievements in a country for Mauritania stood at in 2015, which puts the country in the low human development category, positioning it 157 th out of 188 countries and territories. Money metric poverty is high in Mauritania, with 31.0% of the population below the national poverty line in 2014 (the most recent year for which data is available). Table 1: Main socio-economic indicators for Mauritania Indicators Value (2015 unless otherwise indicated) Population 4,067,564 GDP (current US$) US$ 5.4 billion GNI per capita, Atlas method (current US$) US$ 1,230 Human Development Index (HDI 2 ) Life expectancy at birth 63.2 years Expected years of schooling 8.5 years Mean years of schooling 4.3 years GNI per Capita (2011 PPP$) 3,527 Human Development 2014 rank 157 (out of 188) Gender Development Index Inequality adjusted HDI Gini coefficient 32.4% (2014) Poverty headcount ratio at national poverty lines (% 31.0 (2014) of population) Gross enrolment ratio, primary (% of primary 102.5% school-age population) Sources: for population, GDP, GNI p.c., Gini Index, poverty headcount, gross enrolment ratio: World Bank World Development Indicators data accessed October For HDI, life expectancy, expected years of schooling, mean years of schooling, gender development index and inequality adjusted HDI: UNDP Human Development Reports accessed October The objective of the present paper is to provide in-depth analysis of the prevalence, distribution (geographical and by gender among other household socio-economic characteristics), and severity of multi-dimensional poverty in Mauritania. It is one of ten country profiles prepared by ESCWA as 1 Country classification corresponds to the Word Bank standards for the fiscal year 2017 as follows: lower middleincome economies are those with a GNI per capita, calculated using the World Bank Atlas method, between $1,026 and $4,035; upper middle-income economies are those with a GNI per capita between $4,036 and $12,475; high-income economies are those with a GNI per capita of $12,476 or more. 2 The HDI is a summary measure for assessing long-term progress in three basic dimensions of human development: a long and healthy life, access to knowledge and a decent standard of living. A long and healthy life is measured by life expectancy. Knowledge level is measured by mean years of education among the adult population, which is the average number of years of education received in a life-time by people aged 25 years and older; and access to learning and knowledge by expected years of schooling for children of school-entry age. The standard of living dimension is measured by GNI per capita. 3

4 % background papers for the Arab Multidimensional Poverty Report 3 making use of the new Multidimensional Poverty Index proposed for the Arab States (Arab MPI) As shown in a rural-urban exodus in the past decades. The urban population share increased from 40% to 49% between 2004 and Most of the urban population (57.5%) is located in the capital, Noukachott (UNECA, 2017). The rapid urbanization put severe pressure on infrastructure and social services in urban areas (World Bank, 2013) Figure 1 below, Mauritania s GDP growth has been volatile in the 1990s. Starting in early 2000, GDP has increased constantly and the GDP per capita growth rate climbed from below zero to and steadily increased during the first half of 2000 s. GDP growth rates were on average higher in the 2000s than in the previous decades, despite several internal and external shocks to the Mauritanian economy such as two military coups in 2005 and The Mauritanian economy is characterized by a dual structure. One tier of the economy is very modern, mainly based on extractive industries and natural resources (mining, petroleum, fisheries) driving economic growth. However, due to the capital intensive nature of this sector, there is little impact of this sector on the domestic economy or the population. The second tier of the economy is a subsistence economy based on agriculture, livestock raising, and smallscale commercial activities that supports most of the population (World Bank 2011a, 2013) Mauritania has the highest population growth rate compared to the other North African countries (Algeria, Morocco, and Tunisia). The urban population growth rate is even higher as Mauritania has experienced a rural-urban exodus in the past decades. The urban population share increased from 40% to 49% between 2004 and Most of the urban population (57.5%) is located in the capital, Noukachott (UNECA, 2017). The rapid urbanization put severe pressure on infrastructure and social services in urban areas (World Bank, 2013). Figure 1: GDP, GDP p.c. and population growth (%) GDP and GDP p.c. annual growth (%) (5) (10) Source: World Bank (2017) GDP growth (annual %) GDP per capita growth (annual %) Population and urban population growth, annual (%) Population growth (annual %) Urban population growth (annual %) 3 Arab Multidimensional Poverty Report was launched in September 2017 as a joint publication of the League of Arab States Council of Arab Ministers for Social Affairs, the United Nation s Economic and Social Commission for Western Asia (ESCWA), the United Nations Children's Fund (UNICEF), and Oxford Poverty and Human Development Initiative (OPHI). 4

5 2. Methodology and Data 2.1. Multi-dimensional poverty measures multiple deprivations in basic services and capabilities, such as poor health, lack of education or illiteracy, and lacking access to safe drinking water. The multidimensional poverty approach complements monetary measures of poverty by considering these multiple deprivations and their overlap. The conceptual framework of multidimensional poverty measures draws from Sen s capability approach which states that development is realised not only through increased incomes and share in assets, but also through people s increased capabilities to lead lives that they have reason to value. Sen contends that capability deprivation is a more complete measure of poverty than income as it captures the aspects of poverty which may get lost or hidden in aggregate statistics (Sen 1985, 1999). In recent years, this conceptual framework was translated into practice to measure household poverty through the Multidimensional Poverty Index (MPI) The methodology of the MPI is based on the Alkire-Foster (AF) Method offering a comprehensive methodology for counting deprivation and analysing multidimensional poverty. The AF-methodology builds on the Foster-Greer-Thorbecke poverty measure, but it considers multiple dimensions. The AFmethodology includes two steps: first, it identifies the poor using a dual cut-off approach and by counting the simultaneous deprivations that a person or a household experiences across the different poverty indicators. And the second step is to aggregates this information into the adjusted headcount ratio (or MPI value) which can be decomposed and disaggregated geographically, by socio-economic characteristics, and by indicator Under the first step, to identify multidimensionally poor people, the AF-methodology uses a dual cutoff identification approach. The first cut-off sets a deprivation threshold for each indicator which determines whether a household or a person is considered as deprived or non-deprived in the respective indicator. After the cut-offs have been applied for each indicator, the deprivations of each person in all indicators are counted to calculate a deprivation score for that household or person. Weights are assigned to the indicators which reflect a normative value judgement to assess the relative importance of a given indicator as compared to the other indicators in constructing the deprivation score for a household or person. As a result, the deprivation score is a weighted sum of all deprivations. The second cut-off (the poverty cut-off) is set at a value say 20% or 30% against which the deprivation score is compared to in order to define and distinguish multidimensionally poor (those whose deprivation score is equal to or more than the poverty cut-off) from non-poor (whose deprivation score falls below the poverty cut-off) In the aggregation step of the AF Method, two indices are calculated; the headcount ratio and intensity of poverty. The headcount ratio (H) is the proportion of multidimensionally poor people to the total population. The headcount ratio is a useful measure to learn about the incidence of poverty, but it is insensitive to increases in the number of deprivations a poor person is deprived in. However, utilizing the information on the number of deprivations that poor people experience, the intensity of poverty can be calculated. The intensity of poverty (A), is the average deprivation score that multidimensionally poor people experience. The product of the poverty headcount and poverty intensity is the MPI, which adjusts the headcount for the average intensity of poverty that poor people experience The use of Multidimensional Poverty Index (MPI) to describe the application of AF Method was coined with the Global MPI launched in 2010 by OPHI and the United Nations Development Program (UNDP). However, the Global MPI has a major shortcoming: it is not very effective in capturing the less severe forms of poverty that characterise many Arab middle-income countries such as Jordan, Egypt or Morocco and thus underestimates the prevalence of less severe forms of multidimensional 5

6 poverty. However, the AF-Method offers flexibility and it can be tailored to a variety of situations by selecting different dimensions, indicators of poverty within each dimension, and poverty cut offs In order to capture a broader spectrum of level and intensity of deprivation that better reflects the conditions of Arab countries, ESCWA and OPHI proposed an Arab MPI with two different levels: poverty and acute poverty. The Arab MPI is composed of three dimensions and twelve indicators. The education dimension has two indicators: school attendance and years of schooling. The health dimension includes three indicators: nutrition, child mortality, and early pregnancy combined with female genital mutilation. The living standard indicators are: access to electricity, improved sanitation facility, safe drinking water, clean cooking fuel, having suitable floor and roof, no overcrowding, and minimum assets of information, mobility, and livelihood (the deprivation cut-offs for the Arab MPI are presented in Table 2). Each of these indicators has two associated deprivation cut-offs, one reflects the deprivation of acute poverty which is similar (but not identical) to the global MPI. And the other, a higher cut-off denoting a slightly higher standard to measure poverty which is inclusive of acute poverty. While the cut offs usually vary across indicators for acute poverty and poverty, in case of the aggregate score for identifying a poor household, the cut off is the same. A household is considered acutely poor or poor if its total level of deprivation (total of weighted deprivations in all indicators) is higher than one-third of the total possible deprivation (k=33.3%). Similar to the Global MPI, the Arab MPI assigns equal weights to the three dimensions (one third), and indicators within each dimension are equally weighted. To obtain the set of multidimensionally poor people only, all information of deprivation of non-poor persons is censored from the data. Thus, the focus of the MPI measure is purely on the profile of the multidimensionally poor people and the indicators/dimensions in which they are deprived The MPI can be decomposed by population sub-groups, such as sub-national regions, or any socioeconomic characteristic of a household that is available from the data. Another feature of the MPI is that it can be decomposed to show how much each indicator contributes to poverty. Furthermore, the MPI can also give insight into the percentage of people that are deprived in multiple indicators, but below the poverty cut-off. This percentage of the population is considered vulnerable to poverty. In the case of the Arab MPI, population whose deprivation score is between % is considered as vulnerable to poverty. On the other side of the scale, the MPI can also give insight into how many people are deprived in for example more than half of all the weighted indicators. This percentage share of the population is considered to be in severe poverty. In the Arab MPI, poor people who are deprived in 50% or more of the indicators are considered as severely poor The results of this paper are based on data from the Multiple Indicator Cluster Survey (MICS), a survey conducted by countries with the support of UNICEF 5. The survey for Mauritania, conducted in 2011, covers 59,993 individuals. It provides data on education, health and working status for all members of the household; nutrition status of children and women; child mortality; housing conditions (availability of safe drinking water, sanitation facilities, electricity, cooking fuel etc.); and information on ownership of assets (refrigerator, motorbike, cattle, radio, TV etc.). Some of the information in this country profile is reported by head of household, which is the individual in the household who identified themselves or was identified as such in the survey. 4 See Alkire et al, 2016 for more details 5 For more information see 6

7 Living Conditions Health Education Table 2: Deprivation definitions and indicator weights Dimensio n Indicator Acute poverty if Poverty if Weight Years of No household member has No household member has completed 1/6 Schooling completed primary schooling 6. secondary schooling. School Any child of primary school age is Any school-age child is not attending 1/6 Attendance not attending school. school or is 2 years or more behind the right school grade. Child Mortality Nutrition FGM/Early Pregnancy Any child less than 60 months has died in the family during the 59 months prior to the survey. Any child (0-59 months) is stunted (height for age < -2) or any adult is malnourished (BMI < 18.5). A woman less than 28 years old got her first pregnancy before 18 years old and has undergone a female genital mutilation (FGM). Same as acute poverty 1/9 Any child (0-59 months) is stunted (height for age < -2) or any child is wasted (weight for height < -2) or any adult is malnourished (BMI < 18.5). A woman less than 28 years old either got her first pregnancy before being 18 years old or has undergone a female genital mutilation (FGM). Electricity Household has no electricity. Same as acute poverty 1/21 Sanitation Household sanitation is not improved, according to MDG guidelines, or it is improved but shared with other household. Same as acute poverty 1/21 Water Floor/Roof Cooking Fuel Overcrowdi ng Household does not have access to safe drinking water, according to MDG guidelines, or safe drinking water is 30-minutes roundtrip walk or more away from home. Floor is earth, sand, dung or roof is not available or made of thatch, palm leaf or sod Household cooks with solid fuels: wood, charcoal, crop residues or dung or no food is cooked in the household. Household has 4 or more people per sleeping room. Household does not have piped water into dwelling or yard. Floor is earth, sand, dung, rudimentary (woodplanks/bamboo/reeds/grass/ca nes), cement floor (not slab or tiles/asphalt strips) or roof is not available or made of thatch, palm leaf, sod, rustic mat, palm, bamboo, wood plank, cardboard. Household cooks with solid fuels: wood, charcoal, crop residues or dung or no food is cooked in the household or does not have a separate room for cooking. Household has 3 or more people per sleeping room. 1/9 1/9 1/21 1/21 1/21 1/21 6 According to UNESCO guidelines, the definition of primary schooling and secondary schooling is country-specific. In Mauretania, primary education consists of 6 years of schooling, middle education consists of 4 years of schooling and secondary education consists of 3 years (13 years in total). The entry age to primary schooling is six years. 7

8 Assets 7 Household has either not access to information or has access to information but no access to easy mobility and no access to livelihood assets. Household has either less than two assets for accessing information, or has more than one information asset but less than two mobility assets and less than two livelihood assets. 1/21 7 The assets of Information are: phone (mobile or fixed), radio, TV, internet, computer. The assets of Mobility are: bicycle, motorbike, motorboat, car, truck or animal wheel cart. The assets of Livelihood are: refrigerator, agricultural land, AC, water heater, livestock (at least one cattle or at least one horse or at least two goats or at least two sheep, or at least 10 chickens). 8

9 3. Poverty Analysis 3.1. Incidence of Deprivation in the indicators of the Arab MPI Figure 2: Incidence of Deprivation for each indicator (total population) Assets Overcrowding Cooking fuel Floor/Roof Water Sanitation Electricity FGM/Early pregnancy Nutrition Child Mortality School Attendance Years of Schooling First, we examine the prevalence of deprivation among the total population in the MPI indicators using the respective cut-off points for poverty and acute poverty as shown in Figure 2. This percentage share is also called the uncensored (or raw) headcount ratio, as it considers the deprivations of the total population before identifying the poor At acute poverty, the indicators with the highest headcount ratios are sanitation (68.9%), electricity (62.0%) and cooking fuel (61.7%). All indicators of the living standard dimensions show very high headcount ratios, less so for the assets indicator. This finding indicates that the majority of Mauritians 60%-70% do not have access to improved sanitation, water and/or are not connected to electricity. In the health dimension, the nutrition indicator shows the highest headcount ratio. The education dimension shows that both school attendance and years of schooling are of concern. Almost half of the population (46.4%) live in a household where no member has completed primary education Moving from acute poverty to poverty, the headcount ratios increases largely. A staggering 93.6% of the population are deprived in the floor/roof indicator. All indicators of the living standard dimensions show headcount ratios above 62%. In the health dimension, almost half of the population is deprived in the early pregnancy/fgm indicator (i.e. lives in a household where a least one woman less than 28 years old either got her first pregnancy before the age of 18 years old or has undergone female genital mutilation). The deprivation rates in the education indicators are also very high, 89.9% of the population live in a household where no member has completed secondary education, while almost two thirds of the population (63.3%) live in a household where at least one school-age child is not attending school or attending a school grade that is two or more years behind the school grade appropriate for age Uncensored Headcount (%) Poverty Acute Poverty The indicators with the main differences in deprivation headcount between acute poverty and poverty are assets, years of schooling, school attendance, and FGM/Early pregnancy. While acute poverty defines deprivation in FGM/early pregnancy as living in a household where at least one woman less 9

10 than 28 years old got her first pregnancy before 18 years old and has undergone a female genital mutilation (FGM), poverty defines it as either early pregnancy or FGM. This difference drives a large jump in the indicator between the two levels, from 12.3% deprived for acute poverty to 46.4% deprived for poverty The spider diagram in Figure 3 shows the uncensored headcount for the different Arab MPI indicators for urban and for rural areas. At acute poverty, the biggest differences in headcount between urban and rural population (with the rural population being significantly more deprived than the urban one) are in electricity, cooking fuel, floor/roof, education years, and sanitation. At poverty, the biggest differences in deprivation headcount between urban and rural population are in electricity, cocking fuel, sanitation and years of schooling. As presented in Table 2 the deprivation cut-off for electricity and sanitation is the same for both poverty and acute poverty. The rural population is significantly more deprived of electricity than the urban one, indicating a wide disparity in the availability of electricity by area in Mauritania. Assets is the only indicator in which the urban population is more deprived than the rural population (at acute poverty) in Mauritania 8. Figure 3 also shows that when moving from acute poverty to poverty, the incidence of deprivation in the education dimension increases a lot as well as the differences between rural and urban population. The nutrition indicator shows high headcount ratios for both urban and rural population, although the rural population is more deprived. Figure 3: Deprivation by indicator (% of population) for acute poverty and poverty, for urban, rural and total population. Acute Poverty Poverty Overcrowding Cooking fuel Assets Years of Schooling School Attendance Child Mortali Nutrition Overcrowding Cooking fuel Assets Years of Schooling School Attendance Child Mortali Nutrition Floor/Roof FGM/Early pregnancy Floor/Roof FGM/Early pregnancy Water Sanitation Electricity Water Sanitation Electricity Urban Rural Urban Rural 3.2. Incidence of Censored Deprivation in each of the Arab MPI indicators 8 This latter result, which may be puzzling at first, as we generally expect urban population to be less deprived than the rural one. However, especially in a country like Mauritania where a large share of the population lives in rural areas, the fact that urban dwellers generally do not own agricultural land, livestock or other assets as defined in the MPI can explain why the urban population is more deprived in assets than the rural. 10

11 The prevalence of deprivation in Table 3Error! Reference source not found. compares the incidence of uncensored and censored deprivations. As we saw above, the uncensored headcount ratios give the percentage of population who is deprived in an indicator regardless of their multidimensional poverty status. While the censored headcount ratio measures the proportion of the population who is identified as multidimensionally poor, according to the selected poverty (and acute poverty) cut-off point (set here at k=33.3%), and deprived of each of the indicators. By definition, the uncensored headcount ratio of an indicator is equal to or higher than the censored headcount of that indicator (Alkire et al. 2015). Assessing the difference between censored and uncensored headcount ratios allows the assessment of the extent of overlap between deprivation and multidimensional poverty. Table 3: Uncensored and Censored Deprivation Headcount Ratios % of total population deprived in Acute Poverty % of poor multidimensionall y people and deprived in % of total population deprived in Poverty % of multidimensionally poor people and deprived in Years of Schooling School attendance Child Mortality Child Nutrition FGM/Early Pregnancy Electricity Sanitation Water Floor/Roof Cooking Fuel Overcrowding Assets At acute poverty, the difference between the two headcount ratios are smallest in the indicators of the education and health dimension. The indicators child mortality and FGM/Early pregnancy show the smallest difference, which means that most of the population that is deprived in those two indicators is also identified as multidimensionally poor. The indicators of the living standard dimension show a higher gap between the two headcounts. Thus, deprivation in the living standard indicators is not only found among the multidimensionally poor, but is also widespread among the non-poor population. However, moving to Poverty, the differences between the two headcount ratios decreases also among the indicators of the living standard dimension Multidimensional Poverty Headcount, Intensity and MPI In Mauritania, 51.6% of the population suffers from acute multidimensional poverty and a staggering 89.1% of the population suffers from multidimensional poverty (Table 3). The intensity of poverty is 11

12 extremely high, at 52.2% for acute poverty and 63.7% for poverty. Headcount poverty and intensity of deprivation are much higher in rural 9 than in urban areas. The poverty headcount varies more significantly between rural and urban areas than the intensity of poverty does. This is especially true for acute poverty: people in rural areas are 2.8 times more likely to be acutely poor than those in urban areas. The MPI value, which ranges from 0-1, is high at national level, at for acute poverty and for poverty. Table 4: Headcount poverty, intensity and poverty value at national level and in urban and rural areas Acute poverty Headcount (%) Intensity (%) Multidimensional Poverty Index (MPI) (HxA) (%) Total Urban Rural Poverty Headcount (%) Intensity (%) Multidimensional Poverty Index (MPI) (HxA) (%) Total Urban Rural As shown in Figure 4, the areas of Dakhlett Nouadibou on the North coast, the North region of Tirsezemour that borders with Morocco and Mali, and the capital Nouakchott are the least affected by multidimensional poverty, while areas in the South of the country such as Hodh Garby, Guidimagha, Gorgol are most affected by poverty 10. In these States, acute poverty affects over 76% of the population and poverty affects over 97.8% of the population. States in the South of the country tend to have a higher poverty headcount. The areas least affected by acute poverty in Mauritania have nonetheless a high poverty headcount: the minimum is 12.1% in Dakhlett Nouadibou. 5 out of the 15 states with the highest acute poverty headcount of all the 10 Arab Countries examined in the Arab Multidimensional Poverty Report are located in Mauritania. 9 The definition of rural and urban areas follows the national definitions used in the MICS. 10 The MICS 2011 Survey was designed to provide statistically representative estimates at the national level, in urban and rural areas, and at the wilayat (governorate) level. 12

13 Figure 4: Headcount poverty in areas of Mauritania (%) for acute poverty and poverty Hodh Gharby Guidimagha Gorgol Hodh Charghy Assaba Tagant Brakna Adrar Trarza Nouakchott Tirs-ezemour Dakhlett Nouadibou Headcount (%) Poverty Acute Poverty 3.5. Table 5 shows the distribution of the national population and of acutely poor and poor people across of Mauritania. The last two columns of the table calculate the ratio of acutely poor and poor people over the total population. States with a ratio above 1 carry a disproportionate amount of multidimensionally poor people relative to their share of national population. This is the case, at the bottom of the table, for the areas of Hodh Gharby, Guidimagha and Gorgol, which have the highest ratios. At the other end of the scale, Dakhlett Nouadibou, Tirs-ezemour and the capital Nouakchott have the lowest ratios. The geographical disparity of poverty across states is considerable, with ratios ranging from a maximum of 1.6 (Hodh Gharby) to a minimum of 0.2 (Dakhlett Nouadibou ) for acute poverty. Table 5: Population and headcount poverty shares across Mauritania states Share of survey population (%) (1) Share of acutely poor populatio n (%) (2) Share of poor populatio n (%) (3) 2/1 3/1 Dakhlett Nouadibou Tirs-ezemour Nouakchott Trarza Adrar Brakna Tagant

14 Assaba Hodh Charghy Gorgol Guidimagha Hodh Gharby Someone is defined as poor if he or she is deprived in at least one third of the weighted indicators. Following OPHI s definition, individuals are vulnerable to poverty when they are deprived in 20% 33.3% of the weighted indicators. Individuals are defined as in Severe Poverty when they are deprived in 50% or more of the indicators. 11 As shown in Figure 5, in Mauritania, at acute poverty, a significant 26.2% are severely poor. This implies that, at acute poverty, more than one quarter of the population suffers from a deprivation level higher than 50% of the total possible deprivation. For poverty, the share of severely poor is more than two-thirds at 69.2% % are vulnerable to falling into acute poverty (experiencing a deprivation level between 20% and 33% of total possible deprivation), while 7.3% are vulnerable to falling into poverty. Figure 5: Vulnerable and Severely Poor Population for Acute Poverty and Poverty (%) Poverty Acute Poverty Headcount (%) Severity Vulnerability 3.8. The percentage contribution of each of the three dimensions to the MPI 12 for acute poverty and poverty is a useful summary indicator. As shown in Figure 6, at acute poverty, the living standards dimension contributes nearly half of total deprivation (46.0%). At poverty the contribution of living standards deprivations decreases while the contribution of deprivations in the education dimension increases to 43.3%. The contribution of deprivations in health is the smallest and slightly increase as we move from acute poverty to poverty. Figure 6: Contribution of Dimensions to Acute Poverty and Poverty (%) Poverty Education Acute Poverty Health Living Standard 0% 20% 40% 60% 80% 100% Percentage Contribution of Dimensions to Poverty Value 11 Alkire et al. (2016) 12 Refer to the technical note of the Human Development Report 2014 for a complete explanation of how the percentage contribution of each dimension is calculated. 14

15 Urban Rural 3.9. As shown in Figure 7, while the same pattern as above for education and living standards contribution to MPI is found in urban and rural areas as we move from acute poverty to poverty, the contribution of the living standard dimension is higher in rural areas than in urban areas. In urban areas, education and health contributes more to poverty than in rural areas. Figure 7: Contribution of Dimensions to Acute Poverty and Poverty by Rural and Urban Areas (%) Poverty Acute Poverty Poverty Acute Poverty Percentage Contribution of Dimensions to Poverty Value Education Health Living Standard Figure 8 shows the percentage contribution of each indicator to acute poverty and poverty. Years of schooling make the highest contribution at both levels, followed by school attendance. This means that education should be a priority area for poverty-reduction interventions in the country. At acute poverty, the third indicator with the highest contribution to poverty is nutrition, meaning that stunting and malnourishment should be given due consideration in Mauritania. Several indicators from the living standard dimension such as cooking fuel, electricity, and roof/floor also show a high contribution to acute poverty. At poverty, FGM/early pregnancy is the third most significant contributor to deprivation, followed by nutrition and roof/floor. Figure 8: Percentage Contribution of Indicators to Acute Poverty and Poverty (A) Acute Poverty (B) Poverty 15

16 Headcount (%) Overcrow ding, 5.8 Assets, 2.8 Cooking Fuel, 8.0 Overcrowdi ng, 5.6 Cooking Fuel, 5.1 Assets, 5.6 Roof / Floor, 7.1 Sanitation, 8.1 Electricity, 7.9 Water, 6.4 FGM/Early pregnancy, 4.2 Years of schooling, 24.9 School Attendanc e, 13.4 Child Mortality, Nutrition, Roof / Floor, 7.2 Water, 5.6 Sanitation, 5.6 Electricity, 5.1 FGM/Early pregnancy, 8.7 Nutrition, 6.9 Years of schooling, 25.0 School Attendance, 18.3 Child Mortality, Inequality in Deprivation Figure 9 shows the difference in incidence of multidimensional poverty between male-headed households (MHH) and female-headed households (FHH). In Mauritania, FHH have a slightly higher poverty headcount at both levels of poverty. At poverty, the difference between the two groups is minor. Figure 9: Poverty headcount by gender of household head (%) Total FHH MHH Acute Poverty Poverty 4.1. Error! Reference source not found. shows the contribution of each dimension to poverty by the gender of the household head. In Mauritania, while the levels are close, education makes a slightly higher contribution in FHHs than in MHHs at both levels of poverty, but the health dimension makes a lower contribution in FHHs at both levels of poverty. Living standards contribute more to FHHs deprivation than they do to that of MHHs. 16

17 Percentage contribution of dimensions to poverty value Figure 10: Contribution of each dimension to acute poverty and poverty by gender of the household head (%) 4.2. Figure 11 presents the distribution of education of the head of household in Mauritania. In 70% of households in Mauritania, the head has not received any formal education. Only 14% of heads of households in Mauritania have primary education Figure 11: Education level of household head (% of total population) None Primary Secondary General Secondary Technical University Acute Poverty Poverty Acute Poverty Poverty FHH Education Health Living Standard MHH 4.3. As shown in Figure 12, multidimensional poverty decreases dramatically as the education of the head of household increases, in particular for acute poverty. While 62.1% of people in a household whose head has no education are acutely poor, only 5.5% of people in a household whose head has a university education are. The trend is the same for poverty. However, the difference in poverty incidence between people living in a household where the head has no education and where the household has primary education is small. The poverty rate for people where the head of households has a secondary general and technical education is also very high at 77.0% and 76.1% respectively. At acute poverty, individuals from households where the household head has no education are 11 times more likely to be poor than those where the head has a university degree. While at poverty, they are 2.6 times more likely to be poor. This shows that using the very demanding thresholds of the poverty measure, multidimensional poverty is also widespread among those families where the head of the household has higher than primary education. 17

18 Headcount (%) Intensity (%) Headcount (%) Figure 12: Poverty headcount for acute poverty and poverty by education of the household head (%) None Primary Secondary General Secondary Technical University Acute Poverty Poverty 4.4. As shown in Figure 13, large households (with more than 8 members) show a higher incidence of poverty. Both the poverty headcount and intensity increases among larger households and more so at poverty. Figure 13: Poverty headcount (A) and intensity (B) for acute poverty and poverty by household size (%)d A B Acute Poverty Poverty "1-4" "5-7" "8+" Acute Poverty Poverty "1-4" "5-7" "8+" 4.5. The MICS survey also provides information about the Wealth Index (WI) of each household, which is an indicator of the economic wellbeing and living standards of a household. The WI measures the household s ownership of assets and the housing characteristics. As shown in Figure 14, this information allows us to map the incidence of poverty across the different wealth quintiles. The numbers illustrate the depth of inequality in Mauritania: while it is expected for multidimensional 18

19 Percentage contribution of dimensions to poverty value Percentage contribution of dimensions to poverty value poverty to have a different incidence in the highest (richest) and lowest (poorest) wealth quintiles of the population due to the overlap between the WI and some indicators of multidimensional poverty (in particular assets), the ratio between the top and bottom quintiles is staggering. Houses in the bottom quintile are over 1.7 times more likely to be poor, and almost 18.3 times more likely to be acutely poor than those in the top quintile. This result illustrates that, for poverty, inequality across the WI quintiles is lower than for acute poverty. Figure 14: Headcount poverty (%) by wealth quintiles Top Second Highest Middle Second Lowest Bottom Headcount (%) Acute Poverty Poverty 4.6. As shown in Figure 15, the contribution of living standards to overall deprivation declines as the wealth of the household increases. This is expected as the WI overlaps with the some of the indicators of the living standards dimension (for example assets ownership). As the contribution of living standards goes down with wealth, it is interesting to look at which dimension, education or health, fills the gap more. In Mauritania, while the contribution of health and education increases with wealth, health s contribution is larger at acute poverty and education s contribution is larger at poverty. Figure 15: Contribution of dimensions to multi-dimensional poverty by wealth quintiles A (Acute Poverty) B (Poverty) Poorest Second Middle Fourth Richest 0.0 Poorest Second Middle Fourth Richest Education Health Living Standard Education Health Living Standard 5. Policy considerations 5.1. In Mauritania, an extremely high share of the population suffers from acute poverty or poverty. 51.6% are acutely poor and 89.1% are poor. The intensity of poverty is high, at 52.2% for acute poverty and 19

20 63.7% for poverty. Mauritania urgently needs to continue its efforts to reduce poverty, and policies need to be wide-ranging to address the multiple dimensions of poverty Differences in the impact of poverty in rural and urban population in Mauritania are striking. People in rural areas of Mauritania are 2.8 times more likely to be acutely poor than people in urban areas. This implies that policy-reduction strategies should prioritise rural areas. The rural population needs to be connected to infrastructure providing basic services such as safe water, improved sanitation, or electricity. Furthermore, access to health and education services needs to be improved, as over half (61.8%) of the rural population do not have even one member who has completed primary education. Rural areas are also especially affected by food insecurity and malnutrition as they are highly dependent on climate conditions and vulnerable to environmental shocks such as draughts. World Bank (2013) estimated that around one quarter of all rural households were food insecure in late The high deficits in child nutrition also need to be tackled. Malnutrition is responsible for over one thirds of child deaths and the effects of malnutrition in childhood will adversely affect the country s productivity and prosperity on the long run (World Bank, 2011b) In Mauritania, at acute poverty, 26.2% are severely poor (suffer from a deprivation level higher than 50% of the total possible deprivation). At poverty, 69.2% are severely deprived. These numbers are extremely high and indicate that policies would need to address a level of poverty that is not only widespread across the country, but also severe and which encompasses shortcomings in basic needs for living The high contribution of schooling and nutrition to multidimensional poverty suggests that any poverty reduction strategy in Mauritania should focus on reducing child deprivation, in particular through better education and nutrition. In addition, policies in Mauritania should devote more attention to women s health and take measures to halt FGM and early pregnancies Geographic disparities are sharp in Mauritania, with some states exhibiting strikingly higher levels of poverty than the country s average. While these geographic differences point to the need for a targeted approach to poverty reduction, it is important to keep in mind that poverty is widespread all over Mauritania Inequality in multidimensional poverty between the highest and lowest wealth quintiles in Mauritania is sharp, suggesting an enormous gap in access to resources and capabilities between rich and poor households. While 93.5% of the bottom quintile population is acutely poor, this falls to 5.1% for the top quintile Given the wide reach and intensity of poverty and inequality in Mauritania, continued efforts to reduce poverty in all its forms are urgently needed. In order to address these challenges, the country is likely to require substantial external help from the development community. 20

21 Appendix Table 1: Standard Errors and Confidence Intervals for multidimensional poverty indices using acute poverty definition by urban and rural areas Value Standard 95% confidence interval error Headcount Total Intensity Total MPI Total Headcount Urban Intensity Urban MPI Urban Headcount Rural Intensity Rural MPI Rural Table 2: Poverty: Standard Errors and Confidence Intervals for multidimensional poverty indices using poverty definition by urban and rural areas Value Standard 95% confidence interval error Headcount Total Intensity Total MPI Total Headcount Urban Intensity Urban MPI Urban Headcount Rural Intensity Rural MPI Rural Table 3: Standard Errors and Confidence Intervals for poverty headcount using acute poverty definition by different household characteristics Gender of the Head of Household Education of the Head of Household Household Size Value Standard error 95% confidence interval Female Male None Primary Secondary General Secondary Technical University "1-3" "4-7" "8+" Poorest

22 Wealth Quintile Second Middle Fourth Richest Table 4: Standard Errors and Confidence Intervals for poverty headcount using poverty definition by different household characteristics Value Standard error 95% confidence interval Gender of the Head of Household Education of the Head of Household Household Size Wealth Quintile Female Male None Primary Secondary General Secondary Technical University "1-3" "4-7" "8+" Poorest Second Middle Fourth Richest Table 5: Standard Errors and Confidence Interval for uncensored deprivation headcount of MPI indicators using the acute poverty definition Value Standard error 95% confidence interval Years of Schooling School attendance Child Mortality Child Nutrition FGM/Early Pregnancy Electricity Sanitation Water Floor/Roof Cooking Fuel Overcrowding Assets

23 Table 6: Standard Errors and Confidence Interval for uncensored deprivation headcount of MPI indicators using the poverty definition Value Standard error 95% confidence interval Years of Schooling School attendance Child Mortality Child Nutrition FGM/Early Pregnancy Electricity Sanitation Water Floor/Roof Cooking Fuel Overcrowding Assets Table 7: Standard Errors and Confidence Intervals for poverty headcount using acute poverty definition by State Value Standard error 95% confidence interval Hodh Charghy Hodh Gharby Assaba Gorgol Brakna Trarza Adrar Dakhlett Nouadibou Tagant Guidimagha Tirs-ezemour Nouakchott

24 Table 8: Standard Errors and Confidence Intervals for poverty headcount using poverty definition by State Value Standard error 95% confidence interval Hodh Charghy Hodh Gharby Assaba Gorgol Brakna Trarza Adrar Dakhlett Nouadibou Tagant Guidimagha Tirs-ezemour Nouakchott

25 References Alkire, S., Foster, J. E., Seth, S., Santos, M. E., Roche, J. M., and Ballon, P. (2015). Multidimensional Poverty Measurement and Analysis. Oxford: Oxford University Press, ch. 9. Alkire, S., C. Jindra,, G. Robles,, and A., Vaz, (2016). Multidimensional Poverty Index : Summer Brief Methodological Note and Results. OPHI Briefing 42. University of Oxford. Ballon, P., and J.Y., Duclos, (2015). Multidimensional Poverty in Mauritania and South Mauritania. OPHI working paper no. 93. University of Oxford. El Laithy, H. and K. Abu-Ismail (2008). Poverty Growth and Distribution in Lebanon. United Nations Development Programme: Lebanon. (2007). Poverty Growth and Distribution in Yemen. United Nations Development Programme: Yemen. (2005). Poverty Growth and Distribution in Syria. United Nations Development Programme: Syria. Milazzo, A., and D. van de Walle, (2015). Women left behind? Poverty and headship in Africa. World Bank Policy Research Working Paper 7331, June United Nations Economic Commission for Africa (2017) Country Profile 2016 Mauritania. ECA. Addis Ababa, Ethiopia World Bank (2011a) POVERTY REDUCTION STRATEGY PAPER AND JOINT STAFF ADVISORY NOTE. Report No.: MR. World Bank (2011b) Nutrition at a Glance: Mauritania. Available at: pdf World Bank (2013) COUNTRY PARTNERSHIP STRATEGY (FY ) FOR THE ISLAMIC REPUBLIC OF MAURITANIA. Report No: MR. 25