Impact of out-of-pocket health payments on poverty and alignment of public and external health financing in Guinea Health, Nutrition, and Population Global Practice June 2024 1 © 2024 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW Washington DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. 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Any queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: pubrights@worldbank.org Contact: Teegwendé Valérie Porgo tporgo@worldbank.org 2 Impact of out-of-pocket health payments on poverty and alignment of public and external health financing in Guinea Health, Nutrition, and Population Global Practice Yao Thibaut Kpegli Teegwendé Valérie Porgo Zénab Konkobo Kouanda June 2024 3 TABLE OF CONTENTS 1 Introduction................................................................................................................................................................................8 2 Methods.....................................................................................................................................................................................10 2.1 Design and population..........................................................................................................................................................................10 2.2 Impact of household expenditure on extreme poverty.............................................................................................................10 2.2.1 Data..................................................................................................................................................................................................10 2.2.2 Measuring the impact of expenditure categories on poverty measures.....................................................................12 2.3 Alignment of public and external financing with the impact of out-of-pocket health payments...................................13 2.3.1 Data..................................................................................................................................................................................................13 2.3.2 Concentration indices................................................................................................................................................................13 3 Results........................................................................................................................................................................................16 3.1 Impact of out-of-pocket health payments on extreme poverty...............................................................................................16 3.2 Relative impact of expenditure categories on extreme povety................................................................................................24 3.3 Alignment of public and external health financing with the impact of out-of-pocket health payments on extreme poverty...............................................................................................................................................................................24 4 Discussion..................................................................................................................................................................................27 4.1 Limitations.................................................................................................................................................................................................29 5 Conclusions...............................................................................................................................................................................32 6 Appendices...............................................................................................................................................................................34 6.1 Appendix 1: Detailed results of poverty measures and impacts for all sectors...................................................................34 6.2 Appendix 2:Technical note...................................................................................................................................................................46 6.3 Appendix 3: Sector rankings (comparison test)............................................................................................................................51 6.4 Appendix 4: Share of food and non-food expenditure in total consumer expenditure (%)............................................52 7 References.................................................................................................................................................................................56 4 LIST OF TABLES Table 1: Percentage point increase in the prevalence of extreme poverty according to expenditure categories...................17 Table 2: Percentage point increase in the normalized gap of extreme poverty according to expenditure categories..........19 Table 3: Percentage point increase in the severity of extreme poverty according to expenditure categories........................21 Table 4: Percentage point increase in the extreme poverty severity in subgroups as a result of out-of-pocket health payments................................................................................................................................................................................................23 Table 5: Concentration indices........................................................................................................................................................................25 Table 6: Financial commitments to health over the 2020-2024 period and regional contribution to the national impact of out-of-pocket health payments on extreme poverty in 2018-2019..................................................................................25 Table A1.1: Poverty prevalence before and after expenditure categories...........................................................................................34 Table A1.2: Normalized poverty gap before and after expenditure categories.................................................................................36 Table A1.3: Poverty severity before and after expenditure categories.................................................................................................38 Table A1.4: Percentage point increase in the prevalence of extreme poverty according to expenditure categories............40 Table A1.5: Percentage point increase in normalized poverty gap of extreme poverty according to expenditure categories....42 Table A1.6: Percentage point increase in the severity of extreme poverty according to expenditure categories..................44 Table A3.1: Sector rankings (comparison test)...........................................................................................................................................51 Table A4.1: Share of food and non-food expenditure in total consumer expenditure (%)........................................................52 FIGURES Figure A2.1: Financing concentration curve and index as a function of poverty prevalence.........................................................48 Figure A2.2: Curve and concentration index as a function of normalized poverty gap..................................................................49 Figure A2.3: Financing concentration curve and index as a function of poverty severity...............................................................50 ABBREVIATIONS GDP: Gross Domestic Product GFF: Global Financing Facility GNF: Guinean Franc LSMS: Living Standards Measurement Study OOP: Out-Of-Pocket Health Payment SDG: Sustainable Development Goal UHC: Universal Health Coverage 5 ABSTRACT There has been limited data on the impact of out-of-pocket health payments (OOPs) on extreme poverty in Guinea. Yet, these data are necessary for making informed decisions in the fight against poverty. This paper aimed to (i) measure the impact of OOPs on extreme poverty in Guinea, (ii) compare this impact with that of other household expenditure categories, and (iii) assess the alignment of health financing with this impact. The paper utilized data from the 2018–19 Living Standards Measurement Study and the 2020–24 health financial commitments of the State and its partners, and is based on the impoverishing expenditure method.The three most common poverty measures were used: (i) prevalence, which refers to the number of people in a given population living below a given poverty line; (ii) normalized gap, which estimates the amount needed to eradicate poverty in a given population; and (iii) severity, which indicates whether the gaps between the income of the poor and the poverty line in a given population are equal. OOPs increased the prevalence, normalized gap, and severity of extreme poverty by 2.1 percentage points (pp), 0.7 pp (or 66 million international dollars), and 0.3 pp, respectively. The groups most affected by the extreme poverty severity increase were Faranah (0.7 pp), N’Zérékoré (0.4 pp), Kindia (0.4 pp), and Labé (0.4 pp); as well as individuals aged ≥60 years (0.5 pp) and <1 year (0.4 pp). OOPs had an equal or greater impact than other sectors that receive more funding such as education (prevalence: 0.4 pp; normalized gap: 0.1 pp; and severity: 0.0 pp). The concentration indexes of health financing with respect to the impact of OOPs on extreme poverty were -0.35 (prevalence), -0.34 (normalized gap), and -0.41 (severity), indicating a misalignment. Guinea urgently needs universal health coverage, which will necessi- tate improved allocative efficiency. Groups most affected by the extreme poverty severity increase could serve as priority groups. Keywords: Household expenditure; financial protection; prevalence, normalized gap, and severity of extreme poverty; alignment; universal health coverage ACKNOWLEDGEMENTS This paper was conducted by Yao Thibaut Kpegli (Africa Fellow/Consultant, World Bank), Teegwendé Valérie Porgo (Health Specialist,World Bank and Visiting Scientist, Harvard University), and Zénab Konko- bo Kouanda (Senior Health Specialist, World Bank). It was reviewed by Khaïté Sall (Secretary General, Ministry of Health and Public Hygiene, Guinea), Midiaou Bah (Advisor, Ministry of Health and Public Hygiene, Guinea), Diakité Souleymane (Director, Strategy and Development Office, Ministry of Health and Public Hygiene, Guinea), Sering Touray (Economist, World Bank), and Ismaёl Issifou (Associate Social Affairs Officer, United Nations Department of Economic and Social Affairs). The paper benefited from the contributions of Gaston Sorgho (Health, Nutrition, and Population Practice Manager, World Bank), Nestor Coffi (Country Manager for Guinea, World Bank at the time of the study), Elisé Wendlassida Miningou (Economist, World Bank), Roukiatou Nikiema (Professor of Economics, University of Norbert Zongo, Burkina Faso), Heidi Kaila (Economist, World Bank), Khadidja Malloum Boukar (Research Profes- sional, Public Health Regional Directorate of Quebec/CIUSSS Montreal), Ezechiel Abouro Djallo (Extend- ed-Term Consultant,World Bank), Freddy Essimbi Onana Essomba (Liaison Officer, Global Finance Facility [GFF]), Isidore Sieleunou (Senior Economist, GFF), Richard Quansah Amissah (Consultant, World Bank), Aissatou Ouedraogo (Economist,World Bank), and Hebatalla Elgazzar (Program Leader,World Bank).The authors express their sincere gratitude to all reviewers and contributors. 6 1 INTRODUCTION 7 1 Introduction 1. Access to health services is a universal right, according to Article 25 of the 1948 Universal Declaration of Human Rights.[1] The member countries of the United Nations recognize this right through the adoption of the Sustainable Development Goals (SDGs) in 2015.[2] Target 8 of SDG 03 emphasizes the need to «achieve universal health coverage [UHC], including financial risk protection, access to quality essential health-care services and access to safe, effective, quality, and affordable essential medicines and vaccines for all» by 2030.[2] A financial health risk is any difficulty faced by a person as a result of out-of-pocket health payments (OOPs);[3,4] i.e., the costs they have to bear themselves at the time of service provision. [5] A major financial risk is impoverishment, including extreme impoverishment.[3,4] 2. Guinea, a member country of the United Nations, incorporated the SDGs into its Interim Reference Program covering the period from 2022 to 2025.[6,7] Universal access to health services is at the heart of the fourth objective of the Interim Reference Program, which is «social action, employment, and employa- bility.»[7] Nonetheless, Guinea still faces challenges in achieving effective universal access to health. Indeed, half of the total health expenditure is borne by households (53.5 percent in 2021).[8] This situation can be attributed to low public funding and allocative and technical inefficiencies.[9,10] For example, in 2019, public funding accounted for only 21 percent of total health expenditure. Furthermore, in the same year, public funding, as a percentage of gross domestic product (GDP), was 0.9 percent for health, compared to 3.4 percent for general public administration and 2.2 percent for defense and security.[11-13] Thus, Guinea has not yet met the minimum requirement of allocating 5 percent of GDP to public health financing, which is considered essential for progressing towards UHC.[14] Additionally, Guinea falls below the average for sub-Saharan Africa, where public health funding accounted for 2 percent of the GDP in 2019.[11] 3. The impact of OOPs on extreme poverty, defined as individuals living below the food poverty line, in Guinea is not known.[15,16] In addition, data on the relative impact of different categories of household expenditure are lacking. Furthermore, there is limited data available concerning the alignment of public and external health financing with the impact of OOPs on extreme poverty.Yet, these data are necessary for making informed decisions in the fight against poverty, including appropriate financial allocations. 4. The objectives of this paper were therefore to (i) measure the impact of OOPs on extreme poverty in Guinea, (ii) compare the impact of OOPs on extreme poverty with that of other household expenditure categories, and (iii) assess the alignment of public and external health financing with the impact of OOPs on extreme poverty. 8 2 METHODS 9 2 Methods 2.1 Design and population 5. We conducted a retrospective cohort study. All residents of Guinea were considered eligible. The analysis was based on the 2018-2019 Guinea Living Standards Measurement Study (LSMS), which used a nationally representative sample of the population.[16] Individuals with no recorded consumption expenditure were excluded. 2.2 2.2 Impact of household expenditure on extreme poverty 2.2.1 Data 6. Expenditure data were extracted from Guinea’s 2018-2019 LSMS.[16] Total consumption expenditure in the LSMS was broken down into food and non-food expenditures. Non-food expenditure categories included (i) health; (ii) clothing, footwear, and personal care (referred to as clothing in the remainder of the report); (iii) transport; (iv) housing; (v) energy; (vi) communication; (vii) durable goods; (viii) education; (ix) alcoholic beverages; (x) jewelry; and (xi) the other category.[16] 7. We examined all categories and reported the results for all categories in Appendix 1. In the main text, however, we reported the results for the five categories that had the greatest impact on each measure of extreme poverty (prevalence, normalized gap, and severity). Interestingly, these five categories (health, cloth- ing, housing, transport, and energy) remained consistent across all three poverty measures. In the following sections, we described total consumption expenditure and these five categories. a. Total consumer expenditure 8. Total consumption expenditure was used as the income of individuals.[9,15] This consumption expenditure corresponded to the monetary value of goods and services consumed. It included (i) food consumption, (ii) non-food consumption in non-durable goods and services, and (iii) usage value of durable goods. Total consumption expenditure data were available at the household level. Data were downscaled to the indivi- dual level by dividing total household consumption expenditure by household size, resulting in per capita expenditure.[9,15] b. Health expenses 9. All health expenses were considered OOPs since only 0.3 percent of Guineans had some form of prepay- ment mechanism according to the LSMS.[9,16] Health expenses included the cost of (i) consultation with a general practitioner, (ii) consultation with a specialist, (iii) consultation with a dentist, (iv) consultation with a healer, (v) medical examinations, (vi) non-hospital drugs, and (vii) hospitalization. 10. The costs associated with general practitioner consultations, specialist consultations, dental consultations, healer consultations, medical examinations, and drug purchases were recorded as quarterly data.They were annualized by multiplying them by four. 10 11. Hospitalization costs were available for the last hospitalization in the 12 months prior to the survey. In addition, data on the number of hospitalizations during the same period were available. Thus, the annual hospital expenditure was estimated by multiplying the hospital charges for the last hospitalization (in the last 12 months) by the number of hospitalizations in the last 12 months prior to the survey. c. Expenditure on clothing 12. Clothing expenditure included all costs related to (i) clothing and underwear, (ii) shoes, (iii) hairdressing, and (iv) accessories (make-up products, soap, shampoo, toothpaste, sanitary towels, razor, cotton, etc.). The data on clothing expenditure were collected annually at the household level. They were divided by household size to obtain individual-level data. d. Housing expenses 13. Housing expenses included rent and maintenance costs. During the survey, when a household owned its dwelling or occupied it free of charge, a model was used to estimate rent expenditure according to dwel- ling characteristics.1 Data on housing expenses were collected annually at the household level and divided by household size to obtain data at the individual level.We excluded the cost of durable goods in housing (land or home acquisition, materials for construction or major housing repairs, etc.).[16] e. Transportation expenses 14. Transport expenses included the cost of (i) transport by cab, bus, train, truck, motorcycle cab, animal trac- tion, moto/tricycle, boat, pirogue, pinasse, and plane; (ii) maintenance; and (iii) other (fuel, insurance, spare parts, and repair costs, etc.).Transport data were collected annually at the household level and divided by household size to obtain data at the individual level. f. Energy expenses 15. Energy expenses included the cost of (i) subscription to the electricity distribution network; (ii) electri- city bill; (iii) electric lamps, storm lamps, and torches; (iv) batteries and light bulbs; (v) fuel for domestic generator; (vi) kerosene; (vii) charcoal/mineral coal; (viii) purchased firewood; (ix) candles; (x) matches; (xi) domestic gas; and (xii) collected firewood. Energy data were collected annually at the household level; they were divided by household size to obtain data at the individual level. 1 The model involves regressing the logarithm of rent on various dwelling characteristics.[17] These characteristics typically include the following: type of housing, number of rooms, wall material, roof material, floor material, toilet type, electricity, running water, garbage disposal, sewage disposal, and other community va- riables such as distance to the road, neighborhood, municipality, etc.[17] The model is estimated using a stepwise procedure, which gradually introduces variables into the model and retains only those that are significant.[17] 11 2.2.2 Measuring the impact of expenditure categories on poverty measures 16. Poverty is commonly assessed using three measures: (i) prevalence, (ii) normalized gap, and (iii) severity.[15-19] Prevalence of poverty refers to the number of people in a given population living below a specified poverty line.[15-19] It therefore represents the number of people who need financial assistance to eradicate poverty. [15-19] The normalized gap of poverty indicates the average gap between the income of individuals in a given population and the poverty line.[15-19] It is used to estimate the average amount to be disbursed per poor person (and therefore the total amount required, also known as the total economic effort) to eradicate poverty in the said population.[15-19] Poverty severity indicates whether the gaps between the income of the poor and the poverty line in a given population are equal.[15-19] If the gaps are equal, all poor individuals have equal priority in terms of benefiting from aid policies. In such cases, if there is a specific amount to be dis- tributed, it should be divided equally among the poor, even if it does not cover the entire gap between their income and the poverty line. However, if these gaps are not equal, the poor with the largest gaps should be prioritized in terms of benefiting from aid policy.[15-19] Poverty severity is useful for identifying priority groups, particularly when it is impossible to meet the total economic effort in the short term.[15-19] 17. The impact of household expenditure on the three measures of extreme poverty was calculated using the impoverishing expenditure method.[15,20] This method involves determining whether an individual’s income, after accounting for various categories of expenditure, is sufficient to cover their basic needs. We com- pared measures of extreme poverty before and after expenditures according to the various expenditure categories. The annual extreme poverty line per person, according to the 2018-2019 LSMS, was 3,068,265 Guinean francs (GNF) or 828 international dollars (Purchasing Power Parity of 2019; $).[11] It corresponded to the annual monetary value required for an individual to buy a daily basket of food items providing 2,300 calories.[16] The extreme poverty line, representing basic food needs, remained constant both before and after deducting various non-food expenditures from incomes.[15] 18. We estimated the prevalence of extreme poverty by calculating the proportion of the total population with consumption below the extreme poverty line. To assess the normalized gap of extreme poverty, we first calculated the extreme poverty gap for each individual; i.e., the difference between the extreme poverty line and their consumption. For individuals not in extreme poverty, this gap was zero. Next, for each individual, the ratio of the poverty gap to the extreme poverty line was calculated. Finally, these ratios were averaged across the population. For the severity of extreme poverty, the square of the ratio of the poverty gap to the extreme poverty line was calculated for each individual (poor and non-poor).These measures were then av- eraged across the total population. For the three measures of extreme poverty, we performed the analyses at the national level and according to region, age, sex, and place of residence. Moreover, since the severity of poverty helps to identify priority groups, we examined the impact of OOPs on subgroups defined by region, age, and sex.[17] 19. The three measures of poverty and the impact of expenditure on these measures are averages. The or- dinary least squares method was used to estimate these averages. The regression equations include only intercept terms. For the prevalence of poverty, the dependent variable is represented by a dummy variable indicating whether an individual is extremely poor. In the case of the normalized poverty gap, the dependent variable is the ratio of the poverty gap to the extreme poverty line. For the severity of extreme poverty, the dependent variable is the square of the ratio of the poverty gap to the extreme poverty line.The estimated values of the intercepts correspond to the estimates of our poverty measures. Impacts are estimated using a similar regression approach.Appendix 2.1 presents the mathematical formulas used to estimate these im- pacts.We also followed the same regression procedure to calculate the share of each expenditure category in the total expenditure. 12 20. In the LSMS, it is highly likely that consumption expenditure among individuals within the same household is not independent.[21] This lack of independence would render invalid the standard formula for calculating the variance-covariance matrix associated with the averages obtained using the ordinary least squares method. The standard formula would thus lead to very low variances and, consequently, narrow and potentially biased confidence intervals, high Student’s t-tests, and low p-values.[21] To circumvent this problem, we employed a more general form of the variance-covariance matrix that accommodates the absence of independence between the consumption expenditures of individuals within the same household.[21] This matrix was also used to calculate the confidence intervals associated with the means and the Student’s t-tests to assess the differences between the means. 21. The ranking of categories based on their share and their impact on poverty was done in two stages (Ap- pendix 3). In the first stage, categories were ranked, without statistical testing, in descending order of their contribution to poverty measures. In the second stage, joint tests of equality of impacts were carried out for consecutive categories. If equality between impacts was accepted, the categories were considered to have the same rank (ranked ex aequo) from a statistical point of view. Nonetheless, if equality was rejected, the last category to enter the test was ranked next. The equality between this category’s share/impact and that of the next category was tested until the equality was rejected.The process was repeated until all categories had been ranked. 2.3 Alignment of public and external financing with the impact of out-of-pocket health payments 2.3.1 Data 22. The data was obtained from the mapping of health financing commitments conducted by Guinea’s Ministry of Health and Public Hygiene, the World Bank, and the Global Financing Facility (GFF) from August 2021 to April 2022.[22] This mapping exercise captured the commitments of the State and its development partners for the implementation of the National Health Development Plan during the period 2020-24.These financial commitments covered all three levels of health care: primary, secondary, and tertiary. In total, 78 percent of the financial commitments were allocated to the central level (mostly for operating expenses), while 22 percent were allocated to the regions.This 22 percent was therefore included in the analysis. 2.3.2 Concentration indices 23. Concentration indices have been used to analyze the alignment of public and external financing with the im- pact of OOPs on extreme poverty.[23,24] A concentration index measures the percentage difference between the actual distribution of financing and the reference or perfect alignment distribution. In the perfect align- ment scenario, the percentage of financial commitment allocated to each region is equal to its percentage contribution to the national impact of OOPs on each measure of extreme poverty. 13 24. A concentration index was calculated for each measure of extreme poverty.[23,24] The calculation was carried out in eight steps. First, we calculated the percentage of financial commitments from the State and its partners for each region. Second, we calculated each region’s percentage contribution to the national impact of OOPs. This was calculated by applying the following formula: [(impact on poverty measure at regional level/impact on poverty measure at national level) x share of Guinean population residing in the region].[25] Third, regions were ranked in ascending order based on their contribution to the national impact of poverty measurement. Fourth, fractional ranks, representing cumulative contributions to the national impact for each poverty measure, were calculated. Fifth, the cumulative percentages of financial commitments were calculated. Sixth, the actual distribution of financing was visualized through a curve (known as the concentration curve; Appendices 2.2 and 2.3). This curve plotted cumulative contributions to the national impact of the poverty measure on the x-axis and cumulative financial commitments on the y-axis. Seventh, we calculated the air gap; i.e., the difference between the reference air (0.5) and the actual air. Eighth, the index was calculated as the ratio of the air gap to the reference air.[26] Appendices 2.1 and 2.4 present the mathematical formulas for the index and the concentration curve. 25. A negative concentration index indicates that regions with lower contributions to national extreme poverty receive large shares of financial commitments compared to the perfect alignment scenario, while a positive concentration index suggests that regions with higher contributions to national ex- treme poverty receive smaller shares of financial commitments. 26. The confidence level chosen was 95 percent. Statistical analyses were conducted using STATA soft- ware (StataCorp LLC, College Station,TX, version 14.2). 14 3 RESULTS 15 3 Results 27. The LSMS involved 41,449 individuals from 8,256 households. We excluded 13 households where total consumption expenditure was missing. Among these 13 households, 11 were single-person households, while the other two were households with two people each. Thus, a total of 15 indi- viduals (0.04 percent) were excluded from the analysis. The study population therefore comprised 41,434 individuals, representing the Guinean population in 2018-2019, estimated at 12,083,286 indi- viduals.[17] 3.1 Impact of out-of-pocket health payments on extreme poverty 28. Before and after OOPs, the prevalence of extreme poverty was 13.4 percent and 15.5 percent, res- pectively (Table A1.1). Thus, OOPs increased the prevalence of poverty by 2.1 percentage points (pp; Table 1). This impact varied across regions (p-value < 0.0001), with the highest increases observed in N’Zérékoré (4.2 pp), Labé (3.4 pp), Faranah (2.6 pp), and Kindia (2.6 pp). It was smaller in the capital Conakry (0.3 pp). It did not vary significantly with gender (p-value = 0.3117). It varied with age (p-value < 0.0001,Table 1). It was higher in people between the ages of 41 and 59 years (3.3 pp), 60 years and over (3.0 pp), between 35 and 40 years (2.8 pp), and less than 1 year (2.8 pp). It was higher in rural areas (2.8 pp) than in urban areas (0.7 pp; p-value < 0.0001). 29. Before and after OOPs, the normalized gap of extreme poverty was 2.9 percent and 3.6 percent, res- pectively (Table A1.2).Thus, OOPs increased the normalized gap of poverty by 0.7 percentage points (pp;Table 2).This impact varied across regions (p-value < 0.0001), with the highest increases observed in Faranah (1.4 pp), N’Zérékoré (1.1 pp), Labé (1.0 pp), and Kindia (0.9 pp). It was zero in the capital Conakry (0.0 pp). It was higher for women (0.7 pp) than for men (0.6 pp; p-value = 0.0196). It also varied with age (p-value < 0.0001, Table 1). It was higher in people aged 60 years and over (1.1 pp), between 41 and 59 years (0.9 pp), and less than 1 year (0.9 pp). It was higher in rural areas (0.9 pp) than in urban areas (0.2 pp; p-value < 0.0001). 30. Before and after OOPs, the severity of extreme poverty was 1.0 percent and 1.3 percent, respectively (Table A1.3).Therefore, OOPs increased the severity of extreme poverty by 0.3 pp (Table 3).This im- pact varied according to region (p-value < 0.0001). It was highest in Faranah (0.7 pp), N’Zérékoré (0.4 pp), Kindia (0.4 pp), and Labé (0.4 pp). It was zero in the capital Conakry. It did not vary significantly with gender alone (p-value = 0.3094), but varied with age (p-value < 0.0001,Table 3). It was higher in people aged 60 and over (0.5 pp), between 35 and 40 years (0.4 pp), and less than 1 year (0.4 pp).The most affected subgroups were males under 1 year in Faranah (2.1 pp), females between 35 and 40 years in Faranah (1.9 pp), males under 1 year in N’Zérékoré (1.5 pp), and males 60 years and over in Faranah (1.4 pp;Table 4).The impact in the other subgroups was below 1. It was higher in rural areas (0.4 pp) than in urban areas (0.1 pp; p-value < 0.0001). 16 Table 1: Percentage point increase in the prevalence of extreme poverty according to expenditure categories Health Clothing Transport Housing Energy All % 2.1*** 4.3*** 3.2*** 2.0*** 2.2*** CI [1.8; 2.5] [3.7; 4.9] [2.6; 3.8] [1.6; 2.5] [1.8; 2.6] N 256,752 516,132 381,784 247,500 265,625 Regions Boké % 2.2*** 3.2*** 3.4*** 2.5*** 2.6*** CI [1.0; 3.4] [1.5; 4.9] [1.7; 5.1] [1.1; 3.9] [1.1; 4.0] N 27,691 39,556 42,667 31,511 31,920 Conakry % 0.3** 0.6 0.7 0.4 0.3 CI [0.1; 0.5] [-0.1; 1.3] [-0.1; 1.5] [-0.1; 1.0] [-0.2; 0.9] N 5,414 11,369 13,228 8,252 6,393 Faranah % 2.6*** 5.3*** 3.3*** 2.9*** 2.9*** CI [1.7; 3.5] [3.4; 7.2] [1.8; 4.7] [1.6; 4.1] [1.6; 4.2] N 28,001 57,597 35,413 30,894 31,558 Kankan % 0.8*** 1.7*** 2.9*** 0.6* 0.7* CI [0.3; 1.2] [0.7; 2.7] [1.3; 4.5] [0.0; 1.1] [0.1; 1.4] N 17,316 38,159 65,541 12,670 16,687 Kindia % 2.6*** 4.5*** 3.2*** 2.9*** 3.0*** CI [1.7; 3.5] [3.0; 6.0] [1.9; 4.5] [1.7; 4.1] [1.8; 4.3] N 46,866 80,975 57,508 52,333 54,383 Labé % 3.4*** 9.7*** 3.4*** 4.8*** 4.3*** CI [2.3; 4.5] [7.1; 12.3] [1.8; 5.1] [2.9; 6.7] [2.6; 6.1] N 38,744 110,624 39,004 54,571 49,582 Mamou % 2.1*** 4.0** 1.3* 2.1** 2.0** CI [0.9; 3.3] [1.4; 6.6] [0.2; 2.4] [0.7; 3.5] [0.5; 3.4] N 17,337 33,572 10,862 17,703 16,662 N’Zérékoré % 4.2*** 8.0*** 6.5*** 2.2*** 3.2*** CI [2.8; 5.5] [5.7; 10.3] [4.1; 8.9] [0.9; 3.5] [1.6; 4.9] N 75,383 144,281 117,561 39,566 58,440 Age (years) 0 % 2.8** 8.7*** 7.2*** 4.3** 6.0*** CI [0.8; 4.8] [4.9; 12.6] [3.6; 10.8] [1.4; 7.2] [2.5; 9.4] N 2,741 8,527 7,028 4,198 5,839 1-4 % 2.6*** 5.2*** 4.4*** 2.2*** 2.5*** CI [2.0; 3.2] [4.2; 6.2] [3.4; 5.4] [1.6; 2.9] [1.8; 3.3] N 47,015 95,465 79,453 40,923 45,787 5-9 % 2.0*** 4.8*** 3.8*** 2.6*** 2.3*** CI [1.4; 2.5] [4.0; 5.6] [3.0; 4.6] [1.9; 3.2] [1.8; 2.9] N 42,648 103,265 81,888 55,442 50,324 10 - 14 % 1.6*** 4.6*** 3.7*** 2.2*** 2.4*** CI [1.1; 2.1] [3.6; 5.5] [2.7; 4.6] [1.5; 2.8] [1.7; 3.1] N 22,075 63,863 51,072 30,125 32,859 15 - 19 % 1.2*** 3.5*** 2.1*** 1.6*** 2.1*** CI [0.7; 1.6] [2.5; 4.5] [1.4; 2.9] [1.0; 2.2] [1.3; 2.8] N 12,590 38,254 23,107 17,011 22,517 17 20 - 24 % 1.1*** 2.5*** 1.9*** 1.0*** 1.1*** CI [0.6; 1.5] [1.8; 3.3] [1.2; 2.6] [0.5; 1.5] [0.6; 1.7] N 9,339 21,994 16,598 8,664 9,902 25 - 34 % 1.7*** 2.9*** 2.0*** 1.3*** 1.5*** CI [1.3; 2.2] [2.3; 3.5] [1.5; 2.5] [0.9; 1.6] [1.1; 2.0] N 28,558 48,613 33,524 20,784 25,246 35 - 40 % 2.8*** 4.5*** 3.5*** 2.3*** 2.4*** CI [2.1; 3.5] [3.5; 5.4] [2.6; 4.3] [1.6; 2.9] [1.7; 3.1] N 25,042 40,408 31,179 20,359 21,726 41 - 59 % 3.3*** 5.1*** 3.0*** 2.6*** 2.6*** CI [2.6; 3.9] [4.1; 6.1] [2.2; 3.7] [1.9; 3.2] [1.9; 3.2] N 45,043 69,649 40,725 35,513 35,395 60 and over % 3.0*** 3.7*** 2.4*** 2.0*** 2.2*** CI [2.2; 3.9] [2.6; 4.7] [1.5; 3.3] [1.3; 2.8] [1.4; 3.1] N 21,701 26,094 17,212 14,481 16,029 Sex Male % 2.0*** 4.0*** 3.1*** 1.8*** 2.0*** CI [1.7; 2.4] [3.4; 4.6] [2.5; 3.7] [1.4; 2.2] [1.6; 2.4] N 116,753 230,591 176,942 105,321 115,040 Female % 2.2*** 4.5*** 3.2*** 2.2*** 2.4*** CI [1.8; 2.6] [3.8; 5.2] [2.6; 3.9] [1.8; 2.7] [1.9; 2.9] N 139,999 285,541 204,842 142,179 150,585 Residence Urban % 0.7*** 1.1*** 0.9*** 0.8*** 0.5** CI [0.5; 0.9] [0.7; 1.5] [0.5; 1.4] [0.4; 1.2] [0.2; 0.8] N 29,569 47,063 40,254 34,742 20,290 Rural % 2.8*** 6.0*** 4.4*** 2.7*** 3.1*** CI [2.3; 3.3] [5.1; 6.9] [3.5; 5.3] [2.1; 3.3] [2.5; 3.8] N 219,823 469,069 341,530 212,758 245,335 CI: 95% confidence intervals; *: p < 0.05, **: p < 0.01, ***: p < 0.001 18 Table 2: Percentage point increase in the normalized gap of extreme poverty according to expenditure categories Health Clothing Transport Housing Energy All % 0.7*** 1.3*** 0.6*** 0.7*** 0.7*** CI [0.6; 0.7] [1.2; 1.4] [0.6; 0.7] [0.6; 0.8] [0.6; 0.7] Regions Boké % 0.6*** 1.1*** 0.7*** 0.9*** 0.9*** CI [0.4; 0.8] [0.8; 1.4] [0.5; 1.0] [0.7; 1.2] [0.7; 1.2] Conakry % 0.0** 0.1* 0.1* 0.1* 0.0* CI [0.0; 0.1] [0.0; 0.2] [0.0; 0.1] [0.0; 0.2] [0.0; 0.1] Faranah % 1.4*** 2.6*** 1.0*** 1.4*** 1.3*** CI [1.1; 1.6] [2.2; 3.0] [0.7; 1.3] [1.2; 1.6] [1.1; 1.6] Kankan % 0.2*** 0.3*** 0.3*** 0.2*** 0.1*** CI [0.1; 0.3] [0.2; 0.4] [0.2; 0.5] [0.1; 0.2] [0.1; 0.2] Kindia % 0.9*** 1.6*** 1.2*** 1.1*** 1.0*** CI [0.8; 1.1] [1.3; 1.8] [0.9; 1.4] [1.0; 1.3] [0.9; 1.2] Labé % 1.0*** 2.7*** 0.7*** 1.5*** 1.3*** CI [0.8; 1.1] [2.4; 3.1] [0.5; 0.9] [1.3; 1.7] [1.1; 1.4] Mamou % 0.5*** 1.0*** 0.4*** 0.5*** 0.4*** CI [0.4; 0.7] [0.7; 1.3] [0.2; 0.6] [0.4; 0.7] [0.3; 0.5] N’Zérékoré % 1.1*** 2.2*** 1.0*** 0.6*** 0.8*** CI [0.9; 1.3] [1.8; 2.6] [0.7; 1.3] [0.5; 0.7] [0.7; 1.0] Age (years) 0 % 0.9*** 2.2*** 1.3*** 1.0*** 1.1*** CI [0.5; 1.3] [1.6; 2.8] [0.7; 1.8] [0.7; 1.3] [0.8; 1.5] 1-4 % 0.8*** 1.5*** 0.9*** 0.8*** 0.8*** CI [0.7; 0.9] [1.4; 1.7] [0.7; 1.0] [0.7; 0.9] [0.7; 0.9] 5-9 % 0.6*** 1.5*** 0.8*** 0.8*** 0.8*** CI [0.5; 0.6] [1.4; 1.7] [0.7; 0.9] [0.8; 0.9] [0.7; 0.9] 10 - 14 % 0.6*** 1.6*** 0.8*** 0.8*** 0.8*** CI [0.5; 0.6] [1.4; 1.8] [0.6; 0.9] [0.7; 0.9] [0.7; 0.8] 15 - 19 % 0.4*** 1.1*** 0.5*** 0.6*** 0.5*** CI [0.3; 0.5] [1.0; 1.3] [0.4; 0.6] [0.5; 0.7] [0.5; 0.6] 20 - 24 % 0.3*** 0.6*** 0.3*** 0.4*** 0.4*** CI [0.2; 0.4] [0.5; 0.8] [0.2; 0.4] [0.3; 0.4] [0.3; 0.4] 25 - 34 % 0.6*** 1.0*** 0.5*** 0.5*** 0.5*** CI [0.5; 0.7] [0.8; 1.1] [0.4; 0.6] [0.5; 0.6] [0.5; 0.6] 35 - 40 % 0.9*** 1.3*** 0.6*** 0.7*** 0.6*** CI [0.7; 1.0] [1.1; 1.4] [0.5; 0.7] [0.6; 0.7] [0.6; 0.7] 41 - 59 % 0.9*** 1.3*** 0.6*** 0.7*** 0.7*** CI [0.7; 1.0] [1.2; 1.5] [0.5; 0.7] [0.6; 0.8] [0.6; 0.8] 60 and over % 1.1*** 1.2*** 0.5*** 0.8*** 0.7*** CI [0.9; 1.2] [1.1; 1.4] [0.3; 0.6] [0.7; 0.9] [0.6; 0.8] 19 Sex Male % 0.6*** 1.3*** 0.6*** 0.7*** 0.6*** CI [0.6; 0.7] [1.1; 1.4] [0.6; 0.7] [0.6; 0.7] [0.6; 0.7] Female % 0.7*** 1.4*** 0.6*** 0.7*** 0.7*** CI [0.6; 0.8] [1.2; 1.5] [0.6; 0.7] [0.7; 0.8] [0.6; 0.8] Residence Urban % 0.2*** 0.3*** 0.1*** 0.2*** 0.1*** CI [0.1; 0.2] [0.2; 0.3] [0.1; 0.2] [0.1; 0.2] [0.1; 0.1] Rural % 0.9*** 1.9*** 0.9*** 1.0*** 1.0*** CI [0.8; 1.0] [1.7; 2.0] [0.8; 1.0] [0.9; 1.1] [0.9; 1.1] CI: 95% confidence intervals; *: p < 0.05, **: p < 0.01, ***: p < 0.001 20 Table 3: Percentage point increase in the severity of extreme poverty according to expenditure categories Health Clothing Transport Housing Energy All % 0.3*** 0.5*** 0.2*** 0.3*** 0.3*** CI [0.2; 0.3] [0.5; 0.6] [0.2; 0.2] [0.3; 0.3] [0.2; 0.3] Regions Boké % 0.2*** 0.5*** 0.2*** 0.4*** 0.4*** CI [0.1; 0.3] [0.3; 0.6] [0.1; 0.3] [0.3; 0.5] [0.3; 0.5] Conakry % 0.0* 0.0 0.0 0.0* 0.0 CI [0.0; 0.0] [-0.0; 0.1] [-0.0; 0.0] [0.0; 0.0] [-0.0; 0.0] Faranah % 0.7*** 1.2*** 0.4*** 0.6*** 0.6*** CI [0.5; 0.9] [1.0; 1.5] [0.2; 0.6] [0.5; 0.8] [0.5; 0.7] Kankan % 0.1*** 0.1*** 0.1** 0.0*** 0.0*** CI [0.0; 0.1] [0.0; 0.1] [0.0; 0.1] [0.0; 0.1] [0.0; 0.0] Kindia % 0.4*** 0.6*** 0.4*** 0.5*** 0.4*** CI [0.3; 0.5] [0.5; 0.7] [0.3; 0.6] [0.4; 0.6] [0.3; 0.5] Labé % 0.4*** 1.2*** 0.2*** 0.6*** 0.5*** CI [0.3; 0.5] [0.9; 1.4] [0.1; 0.3] [0.5; 0.7] [0.4; 0.7] Mamou % 0.2*** 0.4*** 0.1*** 0.2*** 0.2*** CI [0.1; 0.3] [0.3; 0.6] [0.1; 0.2] [0.1; 0.3] [0.1; 0.2] N’Zérékoré % 0.4*** 0.9*** 0.3*** 0.2*** 0.3*** CI [0.3; 0.5] [0.7; 1.1] [0.2; 0.4] [0.2; 0.3] [0.3; 0.4] Age (years) 0 % 0.4*** 0.9*** 0.3*** 0.5*** 0.5*** CI [0.2; 0.6] [0.6; 1.2] [0.2; 0.5] [0.3; 0.6] [0.3; 0.6] 1-4 % 0.3*** 0.6*** 0.3*** 0.3*** 0.3*** CI [0.3; 0.4] [0.5; 0.7] [0.2; 0.3] [0.3; 0.4] [0.3; 0.4] 5-9 % 0.2*** 0.6*** 0.3*** 0.3*** 0.3*** CI [0.2; 0.3] [0.6; 0.7] [0.2; 0.3] [0.3; 0.4] [0.3; 0.4] 10 - 14 % 0.2*** 0.7*** 0.3*** 0.3*** 0.3*** CI [0.2; 0.3] [0.6; 0.8] [0.2; 0.3] [0.3; 0.4] [0.3; 0.4] 15 - 19 % 0.2*** 0.5*** 0.2*** 0.2*** 0.2*** CI [0.1; 0.2] [0.4; 0.5] [0.1; 0.2] [0.2; 0.3] [0.2; 0.3] 20 - 24 % 0.1*** 0.2*** 0.1*** 0.1*** 0.1*** CI [0.1; 0.2] [0.2; 0.3] [0.1; 0.1] [0.1; 0.2] [0.1; 0.2] 25 - 34 % 0.3*** 0.4*** 0.2*** 0.2*** 0.2*** CI [0.2; 0.3] [0.3; 0.5] [0.1; 0.2] [0.2; 0.2] [0.2; 0.2] 35 - 40 % 0.4*** 0.5*** 0.2*** 0.3*** 0.3*** CI [0.3; 0.5] [0.4; 0.6] [0.1; 0.2] [0.2; 0.3] [0.2; 0.3] 41 - 59 % 0.3*** 0.5*** 0.2*** 0.3*** 0.3*** CI [0.3; 0.4] [0.5; 0.6] [0.2; 0.2] [0.2; 0.3] [0.2; 0.3] 60 and over % 0.5*** 0.5*** 0.2*** 0.3*** 0.3*** CI [0.4; 0.6] [0.4; 0.6] [0.1; 0.3] [0.3; 0.4] [0.2; 0.4] 21 Sex Male % 0.3*** 0.5*** 0.2*** 0.3*** 0.3*** CI [0.2; 0.3] [0.5; 0.6] [0.2; 0.2] [0.2; 0.3] [0.2; 0.3] Female % 0.3*** 0.6*** 0.2*** 0.3*** 0.3*** CI [0.3; 0.3] [0.5; 0.6] [0.2; 0.2] [0.3; 0.3] [0.3; 0.3] Residence Urban % 0.1*** 0.1*** 0.0*** 0.0*** 0.0*** CI [0.0; 0.1] [0.1; 0.1] [0.0; 0.0] [0.0; 0.1] [0.0; 0.0] Rural % 0.4*** 0.8*** 0.3*** 0.4*** 0.4*** CI [0.3; 0.4] [0.7; 0.9] [0.2; 0.3] [0.4; 0.5] [0.4; 0.5] CI: 95% confidence intervals; *: p < 0.05, **: p < 0.01, ***: p < 0.001 22 Table 4: Percentage point increase in the extreme poverty severity in subgroups as a result of out-of-pocket health payments Age (years) Sex Boké Conakry Faranah Kankan Kindia Labé Mamou N’Zérékoré All 0 M % 0.25 0.05 2.00** 0.00 0.20 0.26 0.87 1.43 0.52** CI [-0.21; 0.70] [-0.05; 0.16] [0.74; 3.25] [-0.01; 0.01] [-0.13; 0.54] [-0.13; 0.65] [-0.53; 2.26] [-0.95; 3.81] [0.17; 0.87] 0 F % 0.62 0.00 0.50* 0.22 0.11 0.71* 0.00 0.57 0.32** CI [-0.52; 1.75] [-0.01; 0.01] [0.06; 0.95] [-0.20; 0.64] [-0.06; 0.28] [0.12; 1.30] [-0.01; 0.01] [-0.37; 1.51] [0.11; 0.53] 1–4 M % 0.26*** 0.00 0.92*** 0.06** 0.47*** 0.34*** 0.37* 0.54*** 0.33*** CI [0.12; 0.40] [-0.01; 0.01] [0.51; 1.33] [0.02; 0.11] [0.27; 0.67] [0.18; 0.50] [0.02; 0.71] [0.22; 0.86] [0.25; 0.40] 1-4 F % 0.23*** 0.08 0.88*** 0.04* 0.39*** 0.31*** 0.16* 0.55*** 0.30*** CI [0.10; 0.36] [-0.06; 0.23] [0.48; 1.29] [0.01; 0.07] [0.23; 0.54] [0.16; 0.45] [0.02; 0.29] [0.34; 0.77] [0.24; 0.36] 5-9 M % 0.23*** 0.00 0.44*** 0.06* 0.35*** 0.27*** 0.15*** 0.25*** 0.21*** CI [0.11; 0.35] 0.01] [-0.01; [0.26; 0.63] [0.01; 0.10] [0.17; 0.54] [0.14; 0.40] [0.07; 0.23] [0.14; 0.36] [0.17; 0.26] 5-9 F % 0.09** 0.01 0.65*** 0.04* 0.34*** 0.29*** 0.12* 0.36*** 0.22*** CI [0.03; 0.16] [-0.01; 0.02] [0.39; 0.92] [0.00; 0.09] [0.17; 0.51] [0.16; 0.43] [0.03; 0.20] [0.19; 0.52] [0.17; 0.27] 10 - 14 M % 0.16* 0.00 0.56*** 0.05 0.36** 0.56** 0.11** 0.30** 0.26*** CI [0.02; 0.30] [-0.01; 0.00] [0.26; 0.86] [-0.01; 0.10] [0.11; 0.61] [0.20; 0.91] [0.03; 0.19] [0.10; 0.51] [0.18; 0.33] 10 - 14 F % 0.09* 0.00 0.61*** 0.03* 0.39* 0.23*** 0.41* 0.33*** 0.24*** CI [0.01; 0.16] 0.00] [-0.01; [0.28; 0.94] [0.00; 0.06] [0.06; 0.71] [0.10; 0.36] [0.07; 0.74] [0.15; 0.52] [0.16; 0.31] 15 - 19 M % 0.09* 0.00 0.49** 0.06 0.17* 0.08 0.20* 0.19*** 0.14*** CI [0.01; 0.17] [-0.01; 0.01] [0.15; 0.83] [-0.01; 0.12] [0.03; 0.30] [-0.01; 0.16] [0.01; 0.39] [0.09; 0.29] [0.09; 0.18] 15 - 19 F % 0.10 0.00 0.41** 0.05 0.32* 0.15** 0.14 0.43** 0.18*** CI [-0.06; 0.26] [-0.01; 0.01] [0.13; 0.70] [-0.04; 0.13] [0.06; 0.59] [0.05; 0.26] [-0.02; 0.30] [0.17; 0.70] [0.12; 0.24] 20 - 24 M % 0.11 0.00 0.20* 0.00 0.03 0.01 0.09 0.18 0.06** CI [-0.10; 0.31] [-0.01; 0.01] [0.02; 0.38] [-0.01; 0.01] [-0.01; 0.07] [-0.01; 0.03] [-0.07; 0.25] [-0.07; 0.44] [0.02; 0.10] 20 – 24 F % 0.11 0.00 0.68*** 0.01 0.14** 0.41* 0.09 0.23* 0.15*** CI [-0.03; 0.26] [-0.01; 0.01] [0.30; 1.07] [-0.01; 0.02] [0.04; 0.25] [0.07; 0.75] [-0.05; 0.23] [0.05; 0.40] [0.10; 0.21] 25 - 34 M % 0.01 0.00 0.39** 0.00 0.32 0.06 0.02 0.27* 0.11*** CI 0.03] [-0.01; [-0.01; 0.01] [0.12; 0.65] [-0.01; 0.00] [-0.02; 0.66] [-0.02; 0.14] [-0.02; 0.05] [0.04; 0.50] [0.05; 0.16] 25 - 34 F % 0.35* 0.04 0.59*** 0.06** 0.61*** 0.57*** 0.38** 0.61*** 0.36*** CI [0.04; 0.67] [-0.03; 0.11] [0.37; 0.80] [0.02; 0.11] [0.27; 0.94] [0.28; 0.86] [0.13; 0.62] [0.37; 0.85] [0.28; 0.44] 35 - 40 M % 0.63 0.00 0.57*** 0.01 0.41* 0.21 0.57* 0.44** 0.28*** CI [-0.28; 1.54] [-0.01; 0.01] [0.30; 0.84] [-0.01; 0.03] [0.07; 0.75] [-0.12; 0.54] [0.05; 1.09] [0.15; 0.74] [0.15; 0.41] 35 - 40 F % 0.34** 0.00 1.83** 0.12* 0.53** 0.75*** 0.25 0.40** 0.46*** CI [0.09; 0.58] 0.00] [-0.01; [0.73; 2.94] [0.02; 0.22] [0.17; 0.88] [0.40; 1.11] [-0.02; 0.52] [0.15; 0.64] [0.32; 0.60] 41 - 59 M % 0.27* 0.01 0.98*** 0.10* 0.46*** 0.63** 0.30** 0.64*** 0.38*** CI [0.06; 0.48] 0.02] [-0.01; [0.54; 1.43] [0.01; 0.18] [0.26; 0.67] [0.18; 1.08] [0.09; 0.50] [0.33; 0.94] [0.30; 0.47] 41 - 59 F % 0.12* 0.00 0.45** 0.05 0.48*** 0.55*** 0.18* 0.34** 0.28*** CI [0.01; 0.23] 0.00] [-0.01; [0.13; 0.78] [-0.01; 0.11] [0.25; 0.70] [0.29; 0.81] [0.04; 0.32] [0.13; 0.54] [0.21; 0.36] 60 and over M % 0.88* 0.10 1.39* 0.09 0.73*** 0.66** 0.39* 0.45** 0.56*** CI [0.19; 1.58] [-0.09; 0.29] [0.32; 2.45] [-0.01; 0.19] [0.35; 1.12] [0.24; 1.08] [0.05; 0.73] [0.11; 0.79] [0.39; 0.73] 60 and over F % 0.08 0.04 0.78*** 0.16 0.55** 0.39*** 0.19 0.59* 0.35*** CI [-0.03; 0.18] [-0.04; 0.13] [0.37; 1.20] [-0.05; 0.37] [0.15; 0.95] [0.20; 0.58] [-0.06; 0.43] [0.05; 1.13] [0.24; 0.47] CI: 95% confidence intervals; *: p < 0.05, **: p < 0.01, ***: p < 0.001; M: male, F: female 23 3.2 Relative impact of expenditure categories on extreme poverty 31. The share of income for each category was, in descending order, 9.4 percent for clothing, 7.1 percent for transport, 5.4 percent for housing, 4.5 percent for energy, and 4.2 percent for health (all p-values = 0.0001; Appendix 4).The share for health was greater for extremely poor individuals (4.8 percent) than for non-ex- tremely poor individuals (4.1 percent). 32. The five highest increases in the prevalence of extreme poverty were observed for (i) clothing (4.3 pp), (ii) transport (3.2 pp), (iii) energy (2.2 pp), (iv) health (2.1 pp), and (v) housing (2.0 pp;Table 1). Statistically, the categories, ranked in ascending order based on their impact, are as follows: (i) clothing, (ii) transport, and (iii) energy, health, and housing ex aequo (p-values are presented in Appendix 3). 33. The five highest increases in the normalized gap of extreme poverty were observed for (i) clothing (1.3 pp), (ii) health (0.7 pp), (iii) housing (0.7 pp), (iv) energy (0.7 pp), and (v) transport (0.6 pp; Table 2). Statistically, the order of categories in ascending order was as follows: (i) clothing and (ii) energy, health, housing, and transport ex aequo. 34. The five highest increases in the severity of extreme poverty were observed for (i) clothing (0.5 pp), (ii) health (0.3 pp), (iii) housing (0.3 pp), (iv) energy (0.3), and (v) transport (0.2 pp; Table 3). Statistically, the order of categories in ascending order was as follows: (i) clothing; (ii) energy, health, and housing ex aequo; and (iii) transport. 3.3 Alignment of public and external health financing with the impact of out-of-pocket health payments on extreme poverty 35. For the prevalence of extreme poverty, the concentration index was -0.35 (Table 5). Two regions had financing percentages significantly lower than their contribution to the national extreme poverty (p-va- lues < 0.0002). These were, in descending order of contribution, (i) N’Zérékoré (financing: 15.0 percent; contribution: 29.4 percent) and (ii) Labé ( 8.6 percent; 15.1 percent; Table 6). In contrast, two regions had significantly higher percentages of financing than their contributions (p-values < 0.0001). These were, in descending order of contributions, (i) Kankan (18.8 percent vs. 6.7 percent) and (ii) Conakry (17.8 percent vs. 2.1 percent). Financing and contribution percentages were not significantly different for the remaining four regions (p-values > 0.2173). 36. For the normalized gap of extreme poverty, the concentration index was -0.34 (Table 5). Four regions had percentages of financing significantly lower than their contributions to the national extreme poverty (p-va- lues < 0.0002). These were, in descending order of contributions, (i) N’Zérékoré (financing: 15.0 percent; contribution: 24.2 percent), (ii) Kindia (14.9 percent; 21.2 percent), (iii) Faranah (8.6 percent; 18.5 percent), and (iv) Labé (8.6 percent; 14.1 percent; Table 6). In contrast, two regions had percentages of financing higher than their contributions (p-values < 0.0001). These were, in descending order of contributions, (i) Kankan (18.8 percent; 5.8 percent) and (ii) Conakry (17.8 percent; 1.2 percent). Financing and contribution percentages were not significantly different for the remaining two regions (p-values > 0.3257). 24 37. For the severity of extreme poverty, the concentration index was -0.41 (Table 5). Four regions had percen- tages of financing significantly below their contributions to the national extreme poverty (p-values < 0.036). These were, in descending order of contributions, (i) N’Zérékoré (financing: 15.0 percent; contribution: 23.6 percent), (ii) Faranah (8.6 percent; 23.0 percent), (iii) Kindia (14.9 percent; 21.6 percent), and (iv) Labé (8.6 percent; 13.4 percent;Table 6). In contrast, two regions had percentages of financing higher than their contributions (p-values < 0.0001).These were, in descending order of contributions, (i) Kankan (18.8 percent; 3.4 percent) and (ii) Conakry (17.8 percent; 0.8 percent). Financing and contribution percentages were not significantly different for the remaining two regions (p-values > 0.4219). Table 5: Concentration indices Extreme poverty measurement Public financing External financing Total financing Prevalence -0.28 -0.41 -0.35 Normalized gap -0.27 -0.39 -0.34 Severity -0.31 -0.47 -0.41 Table 6: Financial commitments to health over the 2020-2024 period and regional contribution to the national impact of out-of-pocket health payments on extreme poverty in 2018-2019 Region Population 2018-2019* Financial commitment (%) Contribution to the impact of OOPs on extreme poverty (%) N % Public External Total Prevalence Normalized gap Severity Boké 1,249,498 10.3 11.1 5.8 9.5 10.8 9.3 8.4 Conakry 1,909,716 15.8 13.5 26.9 17.8 2.1 1.2 0.8 Faranah 1,083,384 9.0 12.3 6.7 8.6 10.9 18.5 23.0 Kankan 2,255,584 18.7 13.5 20.5 18.8 6.7 5.8 3.4 Kindia 1,793,924 14.8 12.9 15.5 14.9 18.3 21.2 21.6 Labé 1,140,304 9.4 11.0 4.7 8.6 15.1 14.1 13.4 Mamou 839,158 6.9 8.2 5.5 6.7 6.8 5.8 5.8 N’Zérékoré 1,811,718 15.0 17.5 14.4 15.0 29.4 24.2 23.6 Total 12,083,286 100 100 100 100 100 100 100 *As estimated in the Living Standards Measurement Study; OOPs: out-of-pocket health payments 25 4 DISCUSSION 26 4 Discussion 38. This paper showed that, over the 2018-2019 period, OOPs increased the prevalence, normalized gap, and severity of extreme poverty by 2.1 pp, 0.7 pp, and 0.3 pp, respectively, in Guinea.The impact of OOPs on the prevalence of extreme poverty varied by region, age, and place of residence. Specifically, the regions of N’Zé- rékoré, Labé, Faranah, and Kindia experienced an above-average increase, while the increase was nearly zero in Conakry. Furthermore, people aged 41 years and over had the highest increases.Additionally, rural areas ex- perienced a higher increase than urban areas.The impact of OOPs on the normalized gap of extreme poverty varied by region, gender, age, and place of residence. Specifically, the regions of Faranah, N’Zérékoré, Kindia, and Labé experienced an above-average increase, while the increase was zero in Conakry.The increase was higher among women. Furthermore, people aged 60 and over and children under 1 year had the highest increases. Additionally, rural areas experienced a higher increase than urban areas.The impact of OOPs on the severity of extreme poverty varied by region, age, and place of residence. The regions of Faranah, N’Zérékoré, Labé, and Kindia had an above-average increase, while the increase was zero in Conakry. Moreover, people aged 60 and over and children under 1 year had the highest increases.Additionally, rural areas experienced a higher in- crease than urban areas.The most affected subgroups were males under 1 year in Faranah, females aged 35 to 40 in Faranah, males under 1 year in N’Zérékoré, and males aged 60 years and over in Faranah.The paper also showed that health was among the five expenditure categories with the greatest impact on extreme poverty, ranking third, second, and second, respectively, for the prevalence, normalized gap, and severity of extreme po- verty. In addition, the paper showed that the financial commitments of the State and its development partners to health were not aligned with the impact of OOPs on the three measures of extreme poverty. 39. Porgo et al. estimated that OOPs led to a 4-percentage point increase in the prevalence of poverty, based on a food and non-food threshold poverty line, in the general population during 2018-2019, representing 477,288 individuals.[9] Moreover, 13 percent of the Guinean population had catastrophic health expenditure in 2018- 2019; i.e., their health expenditure represented 10 percent or more of their income.[9] In this paper, we have estimated the number of individuals who face extreme poverty (living below the food poverty line) due to OOPs (256,752) and the impact of OOPs on total economic effort in Guinea. Before OOPs, the normalized gap of extreme poverty was 2.9 percent.This normalized gap of extreme poverty corresponds to an average amount of GNF 671,243 ($181) required to be disbursed per extreme poor person to eradicate extreme poverty.2 It corresponds to a total economic effort of GNF 1,088 billion ($294 million).3 The 0.7 pp increase implies that due to OOPs alone, the average amount per extreme poor person rises to GNF 709,760 ($192), while the total economic effort rises to GNF 1,332 billion ($360 million).This translates to increases of GNF 38,517 ($11) per extreme poor person and GNF 245 billion ($66 million) in the total economic effort.The additional total economic effort required as a result of OOPs corresponds to 0.2 percent of Guinea’s GDP in 2019 and 22 percent (one-fifth) of GDP allocated to health in the same year (0.9 percent). 40. We also assessed the impact of OOPs on the severity of extreme poverty, with the aim of prioritizing vulne- rable groups if it was not feasible to achieve the total economic effort. If Guinea were in an egalitarian (or reference) situation, all the extreme poor would have needed the same average amount of $181 in 2018-2019 to escape extreme poverty. The severity of extreme poverty in this reference situation would be 0.6 pp.4 In reality, the severity of extreme poverty was 1.0 pp before OOPs and 1.3 pp after OOPs. This indicates that before OOPs, there were inequalities in the needs (the amount required to escape extreme poverty) among the extreme poor. OOPs exacerbated these inequalities to the detriment of the poorest individuals. These needs ranged from GNF 351 ($0.1) to GNF 2.46 million ($664) before OOPs and from GNF 351 ($0.1) to GNF 2.75 million ($742) after OOPs. 2 This is based on the formula:[7] [(normalized gap/prevalence of extreme poverty) x (extreme poverty line)] 3 This is based on the formula: (normalized gap of extreme poverty x extreme poverty line x total population) 4 This is based on the formula: the formula [prevalence of poverty x ((average amount/poverty threshold)^2)] 27 41. In terms of the relative impact of household expenditure categories, health, despite not being among the five categories with the largest shares of income, had one of the largest impacts on the three measures of extreme poverty. For example, for all three poverty measures, the health category had a greater impact than education (prevalence: 2.1 pp versus 0.4 pp; normalized gap: 0.7 pp versus 0.1 pp; and severity: 0.3 pp versus 0.0 pp).The variation in impact between health and education could be attributed to the difference in public budget allocation. In 2017, only 4.2 percent of the state’s domestic budget was allocated to health, whereas education received a higher share of 15.2 percent.[12] 42. Furthermore, the negative values of the concentration indices suggests that the financial commitments of the State and its partners over the period 2020-2024 did not target regions that contributed most to the prevalence, normalized gap, and severity of extreme poverty caused by OOPs in 2018-2019. The regions of N’Zérékoré, Faranah, Kindia, and Labé received merely half (47 percent) of the total financing, even though they contributed most to the impact of expenditure on extreme poverty in terms of normalized gap (78 percent) and severity (82 percent). In contrast, the regions of Conakry and Kankan received more than one- third (37 percent) of the total financing, even though they contributed to a smaller proportion of the impact of expenditure on extreme poverty in terms of normalized gap (7 percent) and severity (4 percent). It should also be noted that 78 percent of the financial commitments were intended primarily for operating expenses at the central level and therefore not channeled to the prefectural and peripheral levels.[22] 43. Our findings regarding the alignment of financial commitments with the impact of OOPs on poverty are consistent with the World Bank’s Public Expenditure Review.[13] The review revealed that in 2019, 30 percent of State (domestic) expenditure on health favored Conakry, although according to the LSMS, Conakry ac- counted for only 16 percent of the Guinean population that same year.[12,16] In addition, our results comple- ment the findings of the mapping of the health financing commitments, which highlighted the lack of equitable distribution of health financing based on key indicators of maternal health (skilled birth attendance, antenatal care by a skilled provider, and contraceptive prevalence rate), child and adolescent health (neonatal morta- lity rate, infant and child mortality rate, and adolescent childbearing rate), and chronic malnutrition among children under five in the different regions.[23] Furthermore, the regions of N’Zérékoré, Faranah, Kindia, and Labé, which were disadvantaged in terms of the distribution of financial commitments, were also the regions in which OOPs increased the inequality of needs (extreme poverty severity) the most. 44. The impact of OOPs on extreme poverty highlighted in this paper could have been avoided or reduced through measures such as UHC. In a context of limited financial resources, UHC focuses on targeting the most vulne- rable, instead of financing all health services for the entire population.[27] Priority should be given to the groups most affected, based on the severity of extreme poverty, and the most disadvantaged regions, in terms of the dis- tribution of financial commitments, as identified in this paper.This will not only benefit the most vulnerable but also reduce the inefficiencies in the health sector,[10] ultimately conserving resources that could be redirected to benefit a larger population. Nonetheless, it will be equally important for the Government to allocate additional resources to the health sector. Firstly, it was estimated that Guinea had the fiscal capacity to increase its public health expenditure by 14.4 percent over the period 2000-2018.[10] Secondly, similar to the health sector, ineffi- ciencies exist in the utilization of public education resources in Guinea,[13] and it is likely that such inefficiencies extend to other sectors as well. Research is needed to identify the sources of inefficiencies in the utilization of the national budget and to devise strategies for their reduction, allowing for the reallocation of saved resources across sectors in alignment with priorities, including UHC.[13] Finally, Guinea could explore innovative health financing mechanisms, such as taxes on harmful goods (tobacco, alcohol, and sugar), mobile phone transaction taxes, and levies on air tickets and mineral mining, based on rigorous feasibility studies.[28,29] 28 45. It should also be noted that the health status of the population contributes significantly to human capital accumulation and the productivity of the workforce, ultimately impacting the country’s overall production (GDP).[30,31] Thus, investing in UHC will not only improve the living conditions of the Guinean population and alleviate the overall economic burden associated with eradicating extreme poverty, but will also contribute significantly to Guinea’s economic development.[30] 4.1 Limitations 46. This paper has seven main potential limitations. First, for all three objectives, data were extracted from the LSMS, which might be prone to measurement errors. On one hand, respondents to the LSMS may have over-reported their income due to issues of social desirability, potentially leading to underestimations of the prevalence, normalized gap, and severity of poverty before and after expenditure categorization. On the other hand, using total household consumption as a measure of household income may have underestimated household income, as income comprises both consumption and savings.[33] This could have led to an overes- timation of the prevalence, normalized gap, and severity of poverty before and after expenditure categoriza- tion. Nevertheless, it is highly unlikely that measurement errors in the LSMS significantly changed the results of the paper.The LSMS follows well-developed methods to elicit household expenditure, providing adequate estimations of household income.[17] Additionally, any potential overestimation or underestimation of income would affect each poverty measure proportionally, both before and after expenditure. 47. Second, with regard to health expenditure, for all three objectives, respondents could report multiple hospi- talizations for different reasons. However, only expenditures related to the last hospitalization were available. We therefore estimated annual hospital expenditure by multiplying expenditure on the last health problem leading to hospitalization by the number of hospitalizations. This estimate of annual hospital expenditure could be biased (upwards or downwards) for individuals with more than one hospitalization. Nevertheless, this potential bias would not significantly affect our results for health category impact (objective 1) and rel- ative impact (objective 2). In fact, only 1.08 percent of the extreme poor (after OOPs) had declared having had more than one hospitalization. This percentage was broken down as follows: two hospitalizations (0.81 percent), three hospitalizations (0.19 percent), and more than three hospitalizations (0.08 percent).Addition- ally, only 0.66 percent of the population (after OOPs) had more than one hospitalization. This percentage was broken down as follows: two hospitalizations (0.47 percent), three hospitalizations (0.15 percent), and more than three hospitalizations (0.04 percent).The fact that the impact of the health category (objective 1) is not significantly affected by this potential bias implies that the alignment results (objective 3) should not be significantly affected either. 48. Third, for all three objectives, we considered all health expenditures as OOPs.This could have led to an over- estimation of the prevalence, normalized gap, and severity of poverty after OOPs.This overestimation would have resulted in an overestimation of the impact of health expenditure and its relative impact (objectives 1 and 2). Moreover, if the rate of overestimation of the impact of OOPs is identical for all regions, the alignment results for objective 3 will not be impacted; however, if it differs from region to region, the alignment results will be impacted. Nevertheless, only 0.3 percent of individuals had some form of prepayment mechanism, which strongly minimizes the positivity of these biases for all objectives. 29 49. Fourth, the OOPs analyzed did not include any indirect health expenditures, yet it is plausible that a portion of transport expenditure is related to health. For objectives 1 and 2, this could have led to an underesti- mation of the health sector’s impact on OOPs. For objective 3, if the underestimation of the impact of the health sector is consistent across regions, the misalignment found will not be affected. If the underestimation of the health sector’s impact varies across regions, the misalignment will be either overestimated (if the health sector’s impact is more underestimated in regions with higher financing) or underestimated (if the underestimation is more pronounced in regions with lower financing). Nonetheless, the impact of indirect health expenditure on the paper’s results would not be significant because the majority (79 percent) of the population resides within the World Health Organization’s recommended distance of five kilometers from health facilities and only 3.3 percent of sick people cited distance as the reason for not seeking healthcare. Moreover, even in the worst-case scenario where indirect health expenditure related to transport is high, the conclusions of the paper would be reinforced. 50. Fifth, for all three objectives, it is highly likely that the consumption expenditure of individuals within the same LSMS household is not independent.[21] Failure to take account of this lack of independence (in the case of the LSMS, where the ratio of number of individuals to number of households is fairly large, greater than 5), would have led to a significant underestimation of the standard deviations associated with the means, and therefore favored conclusions of significant differences in means (objectives 1 and 2).To circumvent this pro- blem, we employed the more general form of the variance-covariance matrix that accounts for the absence of independence between the consumption expenditures of individuals within the same household.[21] 51. Sixth, for objective 3, the response rate from state structures and partners to the mapping of commitments was 67 percent. As a result, total financial commitments were underestimated. Another source of unde- restimation of financial commitments is linked to the fact that state structures and partners sometimes lack visibility on the amount of all financial commitments planned beyond one year.[22] Nevertheless, these underestimations should not strongly affect our results for two reasons: (i) the 67 percent of respondents included the main partners who finance health (Global Fund to Fight AIDS, Tuberculosis and Malaria, World Bank, United States Agency for International Development, etc.)[22] and (ii) the rate of underestimation is probably similar between the different partners. Indeed, there is no reason to believe that some partners would underestimate their financial commitments more than others. Additionally, there is no reason to be- lieve that public financial commitments are over- or underestimated overall or for specific regions. Moreover, if public financial commitments were biased, the bias would probably be in favor of the capital Conakry, due to the greater visibility of commitments in this region. On the contrary, the results obtained with the State’s commitments are similar to those of the partners. For example, 13.5 percent of the State’s financial commit- ments went to Conakry (the region that contributes least to the impact on the three measures of extreme poverty) and 17.5 percent went to N’Zérékoré (the region that contributes most to the impact on the three measures of extreme poverty). Furthermore, an important property of concentration analysis is that it is relative; i.e., based on financing percentages and not on financing amounts.[23,24] Therefore, underestimations in the amount of financing are not expected to significantly affect the results of our concentration analysis. 52. Seventh, for objective 3, it was impossible to assess the equity of the distribution of State and partner financ- ing among individuals within each region. Indeed, to make such an assessment, we needed detailed informa- tion on the amount of public and external financing allocated to each individual within their respective region of residence. However, despite this limitation, we were still able to identify the most disadvantaged and most favored regions by comparing their contribution with their percentage share of public and external financing. 30 5 CONCLUSIONS 31 5 Conclusions 53. In 2018-2019, due to OOPs, 256,752 individuals in Guinea fell into extreme poverty, and the total economic effort to eradicate extreme poverty increased by $66 million ($11 per extremely poor person or 0.2 percent of the country’s GDP). Consequently, the highest amount per person needed to escape extreme poverty increased from $664 to $742.The regions most affected by the extreme poverty severity increase were Faranah, N’Zérékoré, Kindia, and Labé, as well as individuals aged ≥60 years and <1 year. Although health was not among the top five income categories, it ranked third, second, and second in terms of its impact on the prevalence, normalized gap, and severity of extre- me poverty, respectively. Moreover, the financial commitments of the State and its development partners for the period 2020-2024 were not aligned with the impact of OOPs on extreme poverty in 2018-2019. The regions of N’Zérékoré, Faranah, Kindia, and Labé received only half of the total financing despite contributing most to the impact of OOPs on extreme poverty in terms of norma- lized gap and severity. Health should therefore be given greater priority in the fight against poverty. The establishment of UHC in Guinea is an urgent measure to develop the country’s human capital and contribute to its economic development.The data from this paper can be combined with health indicators to effectively target UHC beneficiaries in Guinea. 32 6 APPENDICES 33 6.1 Appendix 1: Detailed results of poverty measures and impacts for all sectors Appendix 1.1: Poverty measures before and after expenditure categories Table A1.1: Poverty prevalence before and after expenditure categories Before After expenditures of : Alcoholic beverages Communication Other category Durable goods Transport Education Clothing Housing Jewelry Energy Health All % 13.4*** 15.5*** 17.7*** 16.6*** 15.5*** 15.6*** 15.1*** 14.8*** 13.8*** 13.6*** 13.4*** 13.4*** CI [12.3; 14.5] [14.4; 16.7] [16.4; 18.9] [15.3; 17.8] [14.3; 16.6] [14.4; 16.8] [14.0; 16.3] [13.6; 16.0] [12.7; 15.0] [12.5; 14.7] [12.3; 14.5] [12.3; 14.6] Regions Boké % 12.8*** 15.1*** 16.0*** 16.2*** 15.4*** 15.4*** 14.7*** 14.0*** 13.5*** 12.8*** 12.8*** 12.8*** CI [9.5; 16.1] [11.6; 18.5] [12.4; 19.6] [12.6; 19.9] [11.9; 18.9] [11.9; 18.9] [11.2; 18.3] [10.5; 17.4] [10.2; 16.8] [9.5; 16.1] [9.5; 16.1] [9.5; 16.1] Conakry % 0.6* 0.9** 1.2* 1.3** 1.1* 1.0* 1.0* 1.1* 0.9** 0.6* 0.7* 0.6* CI [0.0; 1.3] [0.3; 1.6] [0.3; 2.2] [0.3; 2.3] [0.2; 1.9] [0.1; 1.8] [0.1; 1.8] [0.3; 2.0] [0.2; 1.6] [0.0; 1.3] [0.1; 1.4] [0.0; 1.3] Faranah % 26.4*** 29.0*** 31.7*** 29.7*** 29.3*** 29.3*** 28.1*** 28.7*** 26.9*** 26.9*** 26.4*** 26.4*** CI [21.9; 30.9] [24.6; 33.4] [27.2; 36.3] [25.1; 34.2] [24.7; 33.8] [24.8; 33.8] [23.5; 32.6] [24.2; 33.3] [22.5; 31.4] [22.4; 31.4] [21.9; 30.9] [21.9; 30.9] Kankan % 3.3*** 4.0*** 5.0*** 6.2*** 3.8*** 4.0*** 3.8*** 3.6*** 3.3*** 3.3*** 3.3*** 3.3*** CI [1.7; 4.8] [2.4; 5.6] [3.1; 6.8] [4.0; 8.4] [2.2; 5.5] [2.3; 5.7] [2.1; 5.4] [1.9; 5.2] [1.7; 4.8] [1.7; 4.8] [1.7; 4.8] [1.7; 4.8] Kindia % 22.7*** 25.3*** 27.2*** 25.9*** 25.6*** 25.7*** 25.1*** 24.1*** 23.0*** 23.0*** 22.7*** 22.7*** CI [19.2; 26.1] [21.9; 28.7] [23.6; 30.7] [22.4; 29.4] [22.1; 29.1] [22.2; 29.2] [21.6; 28.6] [20.6; 27.6] [19.6; 26.5] [19.5; 26.4] [19.2; 26.1] [19.2; 26.1] Labé % 30.3*** 33.7*** 40.0*** 33.7*** 35.0*** 34.6*** 33.5*** 33.5*** 31.0*** 30.3*** 30.3*** 30.3*** CI [25.6; 34.9] [29.2; 38.1] [35.4; 44.5] [29.1; 38.3] [30.4; 39.7] [30.0; 39.2] [28.9; 38.2] [28.8; 38.1] [26.5; 35.6] [25.6; 34.9] [25.6; 34.9] [25.6; 34.9] Mamou % 10.7*** 12.8*** 14.7*** 12.0*** 12.8*** 12.7*** 12.9*** 12.8*** 11.1*** 11.1*** 10.7*** 10.7*** CI [7.4; 14.1] [9.3; 16.3] [10.6; 18.8] [8.5; 15.5] [9.2; 16.4] [9.1; 16.3] [9.2; 16.6] [9.1; 16.4] [7.7; 14.5] [7.7; 14.6] [7.4; 14.1] [7.4; 14.1] N’Zérékoré % 13.6*** 17.7*** 21.5*** 20.1*** 15.8*** 16.8*** 16.4*** 15.4*** 14.4*** 14.0*** 13.6*** 13.8*** CI [10.6; 16.5] [14.7; 20.8] [18.0; 25.0] [16.5; 23.6] [12.6; 18.9] [13.6; 20.1] [13.2; 19.6] [12.2; 18.5] [11.4; 17.4] [11.0; 17.0] [10.6; 16.5] [10.9; 16.8] 34 Age (years) 0 % 17.2*** 20.0*** 25.9*** 24.4*** 21.5*** 23.2*** 22.0*** 19.8*** 17.2*** 17.7*** 17.2*** 17.2*** CI [11.8; 22.6] [14.3; 25.6] [19.7; 32.1] [18.2; 30.5] [15.6; 27.4] [17.1; 29.2] [16.1; 28.0] [14.1; 25.4] [11.8; 22.6] [12.3; 23.2] [11.8; 22.6] [11.8; 22.6] 1-4 % 15.3*** 17.9*** 20.6*** 19.7*** 17.6*** 17.8*** 17.2*** 16.9*** 15.4*** 15.5*** 15.4*** 15.4*** CI [13.8; 16.9] [16.3; 19.5] [18.8; 22.3] [17.9; 21.5] [15.9; 19.2] [16.2; 19.5] [15.6; 18.9] [15.3; 18.6] [13.8; 17.0] [14.0; 17.1] [13.8; 16.9] [13.8; 16.9] 5-9 % 16.2*** 18.2*** 21.0*** 20.0*** 18.8*** 18.5*** 18.0*** 17.8*** 17.0*** 16.4*** 16.2*** 16.2*** CI [14.6; 17.8] [16.6; 19.8] [19.3; 22.7] [18.3; 21.7] [17.1; 20.4] [16.9; 20.2] [16.4; 19.7] [16.2; 19.4] [15.5; 18.6] [14.8; 18.0] [14.7; 17.8] [14.7; 17.8] 10 - 14 % 17.1*** 18.6*** 21.6*** 20.7*** 19.2*** 19.4*** 19.2*** 18.7*** 18.5*** 17.3*** 17.1*** 17.1*** CI [15.0; 19.1] [16.6; 20.7] [19.5; 23.8] [18.5; 22.9] [17.1; 21.3] [17.3; 21.5] [17.1; 21.3] [16.6; 20.8] [16.4; 20.6] [15.2; 19.3] [15.0; 19.1] [15.0; 19.2] 15 - 19 % 11.5*** 12.6*** 15.0*** 13.6*** 13.0*** 13.5*** 13.0*** 12.7*** 12.4*** 11.6*** 11.5*** 11.5*** CI [9.9; 13.1] [11.0; 14.3] [13.2; 16.8] [11.9; 15.3] [11.3; 14.7] [11.8; 15.3] [11.3; 14.7] [11.0; 14.3] [10.7; 14.0] [10.0; 13.2] [9.9; 13.1] [9.9; 13.1] 20 - 24 % 7.0*** 8.1*** 9.5*** 8.9*** 8.0*** 8.1*** 8.2*** 7.6*** 7.3*** 7.1*** 7.0*** 7.1*** CI [5.6; 8.4] [6.6; 9.6] [8.0; 11.1] [7.4; 10.5] [6.5; 9.5] [6.6; 9.6] [6.8; 9.7] [6.1; 9.0] [5.8; 8.7] [5.7; 8.6] [5.6; 8.4] [5.6; 8.5] 25 - 34 % 9.7*** 11.4*** 12.6*** 11.7*** 11.0*** 11.2*** 10.9*** 10.9*** 9.7*** 9.9*** 9.7*** 9.8*** CI [8.6; 10.8] [10.2; 12.6] [11.4; 13.9] [10.5; 13.0] [9.8; 12.1] [10.0; 12.4] [9.7; 12.1] [9.7; 12.1] [8.6; 10.9] [8.8; 11.0] [8.6; 10.9] [8.6; 10.9] 35 - 40 % 12.1*** 14.9*** 16.6*** 15.6*** 14.4*** 14.6*** 14.0*** 13.4*** 12.2*** 12.4*** 12.2*** 12.2*** CI [10.5; 13.7] [13.2; 16.6] [14.8; 18.4] [13.8; 17.4] [12.7; 16.1] [12.8; 16.3] [12.3; 15.7] [11.8; 15.1] [10.6; 13.8] [10.8; 14.0] [10.6; 13.8] [10.6; 13.8] 41 - 59 % 13.2*** 16.5*** 18.3*** 16.2*** 15.8*** 15.8*** 15.0*** 14.6*** 13.2*** 13.4*** 13.2*** 13.3*** CI [11.8; 14.7] [14.9; 18.0] [16.6; 19.9] [14.6; 17.7] [14.2; 17.3] [14.2; 17.3] [13.5; 16.6] [13.1; 16.1] [11.8; 14.7] [11.9; 14.8] [11.8; 14.7] [11.8; 14.7] 60 and over % 13.7*** 16.7*** 17.4*** 16.1*** 15.7*** 15.9*** 15.5*** 15.1*** 13.7*** 13.7*** 13.7*** 13.8*** CI [11.8; 15.6] [14.7; 18.8] [15.2; 19.5] [14.0; 18.2] [13.7; 17.8] [13.9; 18.0] [13.5; 17.6] [13.1; 17.1] [11.8; 15.6] [11.8; 15.7] [11.8; 15.6] [11.8; 15.7] Sex Male % 13.1*** 15.2*** 17.1*** 16.2*** 15.0*** 15.1*** 14.8*** 14.6*** 13.5*** 13.3*** 13.1*** 13.2*** CI [11.9; 14.3] [13.9; 16.4] [15.8; 18.4] [14.9; 17.5] [13.7; 16.2] [13.9; 16.4] [13.5; 16.0] [13.3; 15.8] [12.3; 14.7] [12.1; 14.5] [11.9; 14.3] [12.0; 14.4] Female % 13.7*** 15.9*** 18.2*** 16.9*** 15.9*** 16.1*** 15.5*** 15.0*** 14.1*** 13.8*** 13.7*** 13.7*** CI [12.5; 14.9] [14.7; 17.1] [16.9; 19.5] [15.6; 18.2] [14.7; 17.2] [14.8; 17.3] [14.3; 16.7] [13.8; 16.2] [12.9; 15.3] [12.6; 15.0] [12.5; 14.9] [12.5; 14.9] 95% confidence intervals:* p < 0.05,** p < 0.01,*** p < 0.001 35 Table A1.2: Normalized poverty gap before and after expenditure categories Before After expenditures of : Alcoholic beverages Communication Other category Durable goods Transport Education Clothing Housing Jewelry Energy All % 2.9*** Health 3.6*** 4.2*** 3.6*** 3.6*** 3.6*** 3.4*** 3.3*** 3.1*** 3.0*** 2.9*** 3.0*** CI [2.6; 3.3] [3.2; 4.0] [3.8; 4.7] [3.2; 4.0] [3.2; 4.0] [3.2; 4.0] [3.0; 3.8] [2.9; 3.7] [2.7; 3.4] [2.6; 3.3] [2.6; 3.3] [2.6; 3.3] Regions Boké % 2.4*** 3.0*** 3.6*** 3.2*** 3.4*** 3.4*** 2.8*** 2.8*** 2.6*** 2.5*** 2.4*** 2.4*** CI [1.7; 3.2] [2.2; 3.9] [2.6; 4.6] [2.3; 4.1] [2.4; 4.3] [2.4; 4.3] [1.9; 3.6] [1.9; 3.6] [1.8; 3.4] [1.7; 3.3] [1.7; 3.2] [1.7; 3.2] Conakry % 0.1 0.1* 0.2* 0.1* 0.2* 0.1 0.1* 0.1* 0.1* 0.1 0.1 0.1 CI [-0.1; 0.2] [0.0; 0.2] [0.0; 0.3] [0.0; 0.2] [0.0; 0.3] [-0.1; 0.2] [0.0; 0.3] [0.0; 0.2] [0.0; 0.2] [-0.1; 0.2] [-0.1; 0.2] [-0.1; 0.2] Faranah % 6.3*** 7.6*** 8.9*** 7.3*** 7.6*** 7.6*** 7.0*** 7.2*** 6.4*** 6.4*** 6.3*** 6.3*** CI [4.8; 7.7] [6.0; 9.3] [7.1; 10.6] [5.7; 8.8] [6.1; 9.2] [6.1; 9.2] [5.4; 8.5] [5.7; 8.7] [5.0; 7.9] [4.9; 7.8] [4.8; 7.7] [4.8; 7.7] Kankan % 0.3*** 0.5*** 0.6*** 0.6*** 0.5*** 0.4*** 0.5*** 0.4*** 0.3*** 0.3*** 0.3*** 0.3*** CI [0.1; 0.5] [0.3; 0.7] [0.3; 0.9] [0.3; 0.9] [0.2; 0.7] [0.2; 0.7] [0.2; 0.7] [0.2; 0.6] [0.1; 0.5] [0.1; 0.5] [0.1; 0.5] [0.1; 0.5] Kindia % 4.8*** 5.7*** 6.4*** 6.0*** 5.9*** 5.8*** 5.7*** 5.2*** 5.0*** 4.9*** 4.8*** 4.8*** CI [3.8; 5.8] [4.7; 6.8] [5.2; 7.5] [4.9; 7.0] [4.9; 7.0] [4.8; 6.9] [4.6; 6.8] [4.2; 6.2] [4.0; 6.0] [3.9; 5.9] [3.8; 5.8] [3.8; 5.8] Labé % 7.8*** 8.8*** 10.5*** 8.5*** 9.2*** 9.0*** 8.8*** 8.6*** 8.0*** 7.8*** 7.8*** 7.8*** CI [5.4; 10.1] [6.4; 11.1] [8.0; 12.9] [6.1; 10.8] [6.8; 11.6] [6.6; 11.4] [6.4; 11.3] [6.2; 10.9] [5.6; 10.4] [5.5; 10.2] [5.4; 10.1] [5.4; 10.1] Mamou % 2.4*** 2.9*** 3.4*** 2.8*** 2.9*** 2.7*** 2.8*** 2.7*** 2.5*** 2.4*** 2.4*** 2.4*** CI [1.5; 3.2] [1.9; 3.9] [2.3; 4.5] [1.8; 3.7] [1.9; 3.9] [1.8; 3.7] [1.8; 3.8] [1.7; 3.8] [1.5; 3.4] [1.5; 3.3] [1.5; 3.3] [1.5; 3.2] N’Zérékoré % 3.0*** 4.0*** 5.1*** 3.9*** 3.5*** 3.8*** 3.5*** 3.3*** 3.1*** 3.0*** 3.0*** 3.1*** CI [2.2; 3.7] [3.1; 4.9] [4.1; 6.2] [3.1; 4.8] [2.7; 4.4] [2.9; 4.7] [2.6; 4.3] [2.5; 4.1] [2.4; 3.9] [2.2; 3.8] [2.2; 3.7] [2.3; 3.9] 36 Age (years) 0 % 3.9*** 4.8*** 6.1*** 5.2*** 4.9*** 5.0*** 4.6*** 4.8*** 3.9*** 3.9*** 3.9*** 3.9*** CI [2.4; 5.3] [3.1; 6.5] [4.2; 8.0] [3.6; 6.8] [3.2; 6.6] [3.3; 6.7] [3.0; 6.2] [3.1; 6.4] [2.4; 5.3] [2.5; 5.4] [2.4; 5.3] [2.5; 5.4] 1-4 % 3.3*** 4.1*** 4.9*** 4.2*** 4.1*** 4.1*** 3.8*** 3.8*** 3.4*** 3.4*** 3.3*** 3.4*** CI [2.9; 3.8] [3.6; 4.6] [4.3; 5.4] [3.7; 4.7] [3.7; 4.6] [3.7; 4.6] [3.4; 4.3] [3.3; 4.2] [2.9; 3.8] [3.0; 3.8] [2.9; 3.8] [2.9; 3.8] 5-9 % 3.5*** 4.1*** 5.1*** 4.3*** 4.4*** 4.3*** 4.0*** 3.9*** 3.7*** 3.6*** 3.5*** 3.5*** CI [3.0; 4.0] [3.6; 4.6] [4.5; 5.6] [3.8; 4.8] [3.8; 4.9] [3.8; 4.8] [3.5; 4.6] [3.4; 4.5] [3.2; 4.2] [3.1; 4.1] [3.0; 4.0] [3.0; 4.0] 10 - 14 % 4.0*** 4.6*** 5.6*** 4.8*** 4.8*** 4.8*** 4.7*** 4.5*** 4.5*** 4.1*** 4.0*** 4.1*** CI [3.2; 4.8] [3.8; 5.4] [4.7; 6.5] [4.0; 5.6] [4.0; 5.6] [4.0; 5.6] [3.8; 5.5] [3.7; 5.3] [3.7; 5.3] [3.3; 4.8] [3.3; 4.8] [3.3; 4.9] 15 - 19 % 2.6*** 3.0*** 3.7*** 3.1*** 3.2*** 3.1*** 3.1*** 2.9*** 2.9*** 2.6*** 2.6*** 2.6*** CI [2.0; 3.2] [2.4; 3.6] [3.1; 4.4] [2.5; 3.7] [2.6; 3.8] [2.5; 3.7] [2.5; 3.7] [2.3; 3.5] [2.3; 3.5] [2.0; 3.2] [2.0; 3.2] [2.0; 3.2] 20 - 24 % 1.4*** 1.8*** 2.1*** 1.8*** 1.8*** 1.8*** 1.8*** 1.6*** 1.5*** 1.5*** 1.5*** 1.5*** CI [1.1; 1.8] [1.4; 2.2] [1.6; 2.5] [1.4; 2.2] [1.4; 2.2] [1.4; 2.2] [1.3; 2.2] [1.2; 2.0] [1.2; 1.9] [1.1; 1.8] [1.1; 1.8] [1.1; 1.8] 25 - 34 % 2.0*** 2.6*** 3.0*** 2.5*** 2.5*** 2.5*** 2.3*** 2.3*** 2.0*** 2.0*** 2.0*** 2.0*** CI [1.7; 2.3] [2.3; 3.0] [2.6; 3.3] [2.2; 2.8] [2.2; 2.8] [2.2; 2.8] [2.0; 2.7] [2.0; 2.6] [1.7; 2.3] [1.8; 2.3] [1.7; 2.3] [1.7; 2.3] 35 - 40 % 2.7*** 3.5*** 4.0*** 3.3*** 3.4*** 3.3*** 3.1*** 3.0*** 2.7*** 2.7*** 2.7*** 2.7*** CI [2.2; 3.2] [3.0; 4.1] [3.4; 4.6] [2.8; 3.8] [2.8; 3.9] [2.8; 3.9] [2.6; 3.6] [2.5; 3.5] [2.2; 3.2] [2.2; 3.2] [2.2; 3.2] [2.2; 3.2] 41 - 59 % 2.8*** 3.7*** 4.1*** 3.4*** 3.5*** 3.5*** 3.3*** 3.2*** 2.8*** 2.8*** 2.8*** 2.8*** CI [2.4; 3.2] [3.2; 4.1] [3.7; 4.6] [3.0; 3.9] [3.1; 4.0] [3.1; 4.0] [2.9; 3.8] [2.7; 3.6] [2.4; 3.2] [2.4; 3.2] [2.4; 3.2] [2.4; 3.2] 60 and over % 2.9*** 4.0*** 4.2*** 3.4*** 3.7*** 3.6*** 3.5*** 3.3*** 2.9*** 3.0*** 2.9*** 2.9*** CI [2.4; 3.5] [3.3; 4.6] [3.5; 4.8] [2.8; 4.0] [3.1; 4.4] [3.0; 4.2] [2.9; 4.0] [2.7; 3.8] [2.4; 3.5] [2.4; 3.5] [2.4; 3.5] [2.4; 3.5] Sex Male % 2.9*** 3.5*** 4.2*** 3.6*** 3.6*** 3.5*** 3.4*** 3.3*** 3.0*** 2.9*** 2.9*** 2.9*** CI [2.5; 3.3] [3.1; 4.0] [3.7; 4.6] [3.1; 4.0] [3.1; 4.0] [3.1; 4.0] [2.9; 3.8] [2.8; 3.7] [2.6; 3.5] [2.5; 3.4] [2.5; 3.3] [2.5; 3.3] Female % 3.0*** 3.7*** 4.3*** 3.6*** 3.7*** 3.7*** 3.5*** 3.3*** 3.1*** 3.0*** 3.0*** 3.0*** CI [2.6; 3.3] [3.3; 4.0] [3.9; 4.7] [3.2; 4.0] [3.3; 4.1] [3.3; 4.0] [3.1; 3.8] [3.0; 3.7] [2.7; 3.4] [2.7; 3.3] [2.6; 3.3] [2.6; 3.3] 95% confidence intervals:* p < 0.05,** p < 0.01,*** p < 0.001 37 Table A1.3: Poverty severity before and after expenditure categories Before After expenditures of : Alcoholic beverages Communication Other category Durable goods Transport Education Clothing Housing Jewelry Energy Health All % 1.0*** 1.3*** 1.5*** 1.2*** 1.3*** 1.3*** 1.2*** 1.1*** 1.0*** 1.0*** 1.0*** 1.0*** CI [0.8; 1.2] [1.1; 1.5] [1.3; 1.7] [1.0; 1.4] [1.1; 1.5] [1.0; 1.5] [1.0; 1.4] [0.9; 1.3] [0.8; 1.2] [0.8; 1.2] [0.8; 1.2] [0.8; 1.2] Regions Boké % 0.7*** 0.9*** 1.1*** 0.9*** 1.1*** 1.1*** 0.8*** 0.8*** 0.7*** 0.7*** 0.7*** 0.7*** CI [0.4; 1.0] [0.6; 1.2] [0.7; 1.5] [0.6; 1.2] [0.7; 1.4] [0.7; 1.4] [0.5; 1.1] [0.5; 1.1] [0.4; 1.0] [0.4; 1.0] [0.4; 1.0] [0.4; 1.0] Conakry % 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 CI [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] Faranah % 2.0*** 2.7*** 3.2*** 2.4*** 2.6*** 2.6*** 2.3*** 2.4*** 2.1*** 2.0*** 2.0*** 2.0*** CI [1.4; 2.6] [2.0; 3.4] [2.4; 4.0] [1.8; 3.0] [1.9; 3.3] [1.9; 3.2] [1.7; 2.9] [1.8; 3.1] [1.5; 2.6] [1.4; 2.6] [1.4; 2.6] [1.4; 2.6] Kankan % 0.0** 0.1*** 0.1*** 0.1*** 0.1*** 0.1*** 0.1*** 0.1*** 0.0** 0.0** 0.0** 0.0** CI [0.0; 0.1] [0.0; 0.1] [0.1; 0.2] [0.0; 0.2] [0.0; 0.1] [0.0; 0.1] [0.0; 0.1] [0.0; 0.1] [0.0; 0.1] [0.0; 0.1] [0.0; 0.1] [0.0; 0.1] Kindia % 1.5*** 1.9*** 2.1*** 1.9*** 2.0*** 1.9*** 1.9*** 1.7*** 1.6*** 1.5*** 1.5*** 1.5*** CI [1.1; 1.9] [1.5; 2.4] [1.6; 2.6] [1.5; 2.4] [1.5; 2.5] [1.5; 2.4] [1.4; 2.4] [1.2; 2.1] [1.2; 2.0] [1.1; 1.9] [1.1; 1.9] [1.1; 1.9] Labé % 3.3*** 3.7*** 4.4*** 3.5*** 3.9*** 3.8*** 3.7*** 3.6*** 3.3*** 3.3*** 3.3*** 3.3*** CI [1.6; 4.9] [2.0; 5.3] [2.6; 6.2] [1.8; 5.1] [2.2; 5.6] [2.1; 5.5] [2.0; 5.5] [1.9; 5.2] [1.7; 5.0] [1.7; 4.9] [1.6; 4.9] [1.6; 4.9] Mamou % 0.7*** 0.9*** 1.1*** 0.8*** 0.9*** 0.8*** 0.9*** 0.9*** 0.7*** 0.7*** 0.7*** 0.7*** CI [0.4; 1.0] [0.5; 1.3] [0.6; 1.6] [0.5; 1.2] [0.5; 1.3] [0.5; 1.2] [0.5; 1.2] [0.5; 1.2] [0.4; 1.1] [0.4; 1.0] [0.4; 1.0] [0.4; 1.0] N’Zérékoré % 1.0*** 1.4*** 1.9*** 1.2*** 1.2*** 1.3*** 1.1*** 1.1*** 1.0*** 1.0*** 1.0*** 1.0*** CI [0.6; 1.3] [1.0; 1.8] [1.4; 2.3] [0.9; 1.6] [0.8; 1.5] [0.9; 1.7] [0.8; 1.5] [0.7; 1.4] [0.7; 1.3] [0.7; 1.3] [0.6; 1.3] [0.7; 1.3] Age (years) 0 % 1.2*** 1.6*** 2.1*** 1.5*** 1.6*** 1.6*** 1.4*** 1.6*** 1.2*** 1.2*** 1.2*** 1.2*** CI [0.7; 1.7] [0.9; 2.3] [1.3; 2.9] [0.9; 2.1] [0.9; 2.3] [1.0; 2.3] [0.8; 2.0] [0.9; 2.3] [0.7; 1.7] [0.7; 1.7] [0.7; 1.7] [0.7; 1.7] 1-4 % 1.1*** 1.5*** 1.7*** 1.4*** 1.5*** 1.5*** 1.3*** 1.3*** 1.1*** 1.1*** 1.1*** 1.1*** 38 CI [0.9; 1.3] [1.2; 1.7] [1.5; 2.0] [1.2; 1.6] [1.2; 1.7] [1.2; 1.7] [1.1; 1.5] [1.1; 1.5] [0.9; 1.3] [0.9; 1.3] [0.9; 1.3] [0.9; 1.3] 5-9 % 1.2*** 1.4*** 1.8*** 1.4*** 1.5*** 1.5*** 1.4*** 1.3*** 1.3*** 1.2*** 1.2*** 1.2*** CI [0.9; 1.5] [1.1; 1.7] [1.5; 2.2] [1.2; 1.7] [1.2; 1.8] [1.2; 1.8] [1.1; 1.7] [1.1; 1.6] [1.0; 1.5] [0.9; 1.5] [0.9; 1.5] [0.9; 1.5] 10 - 14 % 1.4*** 1.7*** 2.1*** 1.7*** 1.7*** 1.7*** 1.7*** 1.6*** 1.6*** 1.4*** 1.4*** 1.4*** CI [1.0; 1.8] [1.2; 2.1] [1.6; 2.6] [1.2; 2.1] [1.3; 2.2] [1.3; 2.2] [1.2; 2.1] [1.1; 2.0] [1.2; 2.0] [1.0; 1.9] [1.0; 1.9] [1.0; 1.9] 15 - 19 % 0.9*** 1.1*** 1.4*** 1.1*** 1.2*** 1.1*** 1.1*** 1.0*** 1.0*** 0.9*** 0.9*** 0.9*** CI [0.6; 1.3] [0.7; 1.4] [1.0; 1.8] [0.7; 1.4] [0.8; 1.5] [0.8; 1.5] [0.7; 1.5] [0.7; 1.4] [0.7; 1.4] [0.6; 1.3] [0.6; 1.3] [0.6; 1.3] 20 - 24 % 0.5*** 0.6*** 0.7*** 0.6*** 0.6*** 0.6*** 0.6*** 0.5*** 0.5*** 0.5*** 0.5*** 0.5*** CI [0.3; 0.6] [0.4; 0.8] [0.5; 0.9] [0.4; 0.7] [0.4; 0.8] [0.4; 0.8] [0.4; 0.7] [0.4; 0.7] [0.3; 0.6] [0.3; 0.6] [0.3; 0.6] [0.3; 0.6] 25 - 34 % 0.6*** 0.9*** 1.0*** 0.8*** 0.8*** 0.8*** 0.8*** 0.7*** 0.6*** 0.7*** 0.6*** 0.6*** CI [0.5; 0.8] [0.7; 1.1] [0.9; 1.2] [0.7; 0.9] [0.7; 1.0] [0.7; 1.0] [0.6; 0.9] [0.6; 0.9] [0.5; 0.8] [0.5; 0.8] [0.5; 0.8] [0.5; 0.8] 35 - 40 % 0.9*** 1.3*** 1.4*** 1.1*** 1.2*** 1.1*** 1.0*** 1.0*** 0.9*** 0.9*** 0.9*** 0.9*** CI [0.7; 1.1] [1.0; 1.6] [1.1; 1.7] [0.8; 1.3] [0.9; 1.4] [0.9; 1.4] [0.8; 1.3] [0.8; 1.3] [0.7; 1.1] [0.7; 1.1] [0.7; 1.1] [0.7; 1.1] 41 - 59 % 0.9*** 1.2*** 1.4*** 1.1*** 1.2*** 1.2*** 1.1*** 1.0*** 0.9*** 0.9*** 0.9*** 0.9*** CI [0.7; 1.1] [1.0; 1.5] [1.2; 1.7] [0.9; 1.3] [1.0; 1.4] [1.0; 1.4] [0.9; 1.3] [0.8; 1.2] [0.7; 1.1] [0.7; 1.1] [0.7; 1.1] [0.7; 1.1] 60 and over % 0.9*** 1.4*** 1.5*** 1.1*** 1.3*** 1.2*** 1.1*** 1.1*** 0.9*** 0.9*** 0.9*** 0.9*** CI [0.7; 1.2] [1.1; 1.7] [1.2; 1.8] [0.8; 1.4] [1.0; 1.6] [1.0; 1.5] [0.9; 1.4] [0.8; 1.3] [0.7; 1.2] [0.7; 1.2] [0.7; 1.2] [0.7; 1.2] Sex Male % 1.0*** 1.3*** 1.5*** 1.2*** 1.3*** 1.3*** 1.2*** 1.1*** 1.0*** 1.0*** 1.0*** 1.0*** CI [0.8; 1.2] [1.0; 1.5] [1.2; 1.8] [1.0; 1.4] [1.0; 1.5] [1.0; 1.5] [0.9; 1.4] [0.9; 1.4] [0.8; 1.3] [0.8; 1.2] [0.8; 1.2] [0.8; 1.2] Female % 1.0*** 1.3*** 1.5*** 1.2*** 1.3*** 1.3*** 1.2*** 1.1*** 1.0*** 1.0*** 1.0*** 1.0*** CI [0.8; 1.1] [1.1; 1.4] [1.3; 1.7] [1.0; 1.4] [1.1; 1.5] [1.1; 1.4] [1.0; 1.3] [0.9; 1.3] [0.8; 1.2] [0.8; 1.2] [0.8; 1.1] [0.8; 1.2] 95% confidence intervals:* p < 0.05.** p < 0.01.*** p < 0.001 39 Appendix 1.2: Impact of expenditure sectors on poverty measures Table A1.4: Percentage point increase in the prevalence of extreme poverty according to expenditure categories Alcoholic beverages Communication Other category Durable goods Transport Education Clothing Housing Jewelry Energy Health All % 2.1*** 4.3*** 3.2*** 2.0*** 2.2*** 1.7*** 1.4*** 0.4*** 0.2** 0.0 0.1* CI [1.8; 2.5] [3.7; 4.9] [2.6; 3.8] [1.6; 2.5] [1.8; 2.6] [1.3; 2.1] [1.0; 1.8] [0.3; 0.6] [0.1; 0.3] [-0.1; 0.1] [0.0; 0.1] Regions Boké % 2.2*** 3.2*** 3.4*** 2.5*** 2.6*** 1.9* 1.1 0.6** 0.0 0.0 0.0 CI [1.0; 3.4] [1.5; 4.9] [1.7; 5.1] [1.1; 3.9] [1.1; 4.0] [0.5; 3.4] [-0.1; 2.3] [0.2; 1.0] [0.0; 0.1] [0.0; 0.1] [0.0; 0.1] Conakry % 0.3** 0.6 0.7 0.4 0.3 0.3 0.5 0.3** 0.0 0.0 0.1 CI [0.1; 0.5] [-0.1; 1.3] [-0.1; 1.5] [-0.1; 1.0] [-0.2; 0.9] [-0.2; 0.9] [-0.1; 1.1] [0.1; 0.4] [0.0; 0.1] [0.0; 0.1] [-0.1; 0.2] Faranah % 2.6*** 5.3*** 3.3*** 2.9*** 3.0*** 1.7** 2.3*** 0.5** 0.5 0.2 0.0 CI [1.7; 3.5] [3.4; 7.2] [1.8; 4.7] [1.6; 4.1] [1.6; 4.3] [0.6; 2.7] [1.2; 3.5] [0.2; 0.8] [-0.1; 1.1] [-0.2; 0.5] [0.0; 0.1] Kankan % 0.8*** 1.7*** 2.9*** 0.6* 0.7* 0.5* 0.3 0.0 0.0 0.0 0.0 CI [0.3; 1.2] [0.7; 2.7] [1.3; 4.5] [0.0; 1.1] [0.1; 1.4] [0.0; 1.0] [-0.1; 0.7] [-0.1; 0.1] [0.0; 0.1] [0.0; 0.1] [0.0; 0.1] Kindia % 2.6*** 4.5*** 3.2*** 2.9*** 3.0*** 2.4*** 1.4** 0.4*** 0.3 0.0 0.0 CI [1.7; 3.5] [3.0; 6.0] [1.9; 4.5] [1.7; 4.1] [1.8; 4.3] [1.3; 3.6] [0.4; 2.4] [0.2; 0.6] [-0.1; 0.7] [0.0; 0.1] [0.0; 0.1] Labé % 3.4*** 9.7*** 3.4*** 4.8*** 4.3*** 3.3*** 3.2*** 0.8** 0.0 0.0 0.0 CI [2.3; 4.5] [7.1; 12.3] [1.8; 5.1] [2.9; 6.7] [2.6; 6.1] [1.6; 4.9] [1.5; 4.9] [0.3; 1.3] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] Mamou % 2.1*** 4.0** 1.3* 2.1** 2.0** 2.2** 2.0** 0.4** 0.4 0.0 0.0 CI [0.9; 3.3] [1.4; 6.6] [0.2; 2.4] [0.7; 3.5] [0.5; 3.4] [0.6; 3.8] [0.6; 3.5] [0.1; 0.7] [-0.4; 1.2] [-0.1; 0.1] [-0.1; 0.1] N’Zérékoré % 4.2*** 8.0*** 6.5*** 2.2*** 3.2*** 2.8*** 1.8* 0.8* 0.4 0.0 0.3 CI [2.8; 5.5] [5.7; 10.3] [4.1; 8.9] [0.9; 3.5] [1.6; 4.9] [1.3; 4.3] [0.4; 3.1] [0.2; 1.5] [-0.1; 0.8] [-0.1; 0.1] [-0.1; 0.5] Age (years) 0 % 2.8** 8.7*** 7.2*** 4.3** 6.0*** 4.8** 2.6* 0.0 0.5 0.0 0.0 40 CI [0.8; 4.8] [4.9; 12.6] [3.6; 10.8] [1.4; 7.2] [2.5; 9.4] [1.7; 7.9] [0.6; 4.6] [-0.1; 0.1] [-0.3; 1.3] [-0.1; 0.1] [-0.1; 0.1] 1-4 % 2.6*** 5.2*** 4.4*** 2.2*** 2.5*** 1.9*** 1.6*** 0.1 0.2* 0.0 0.1 CI [2.0; 3.2] [4.2; 6.2] [3.4; 5.4] [1.6; 2.9] [1.8; 3.3] [1.3; 2.5] [1.0; 2.2] [-0.1; 0.1] [0.1; 0.4] [-0.1; 0.1] [-0.1; 0.1] 5-9 % 2.0*** 4.8*** 3.8*** 2.6*** 2.3*** 1.8*** 1.6*** 0.8*** 0.2** 0.0 0.0 CI [1.4; 2.5] [4.0; 5.6] [3.0; 4.6] [1.9; 3.2] [1.8; 2.9] [1.3; 2.3] [1.1; 2.1] [0.5; 1.1] [0.1; 0.4] [-0.1; 0.1] [-0.1; 0.1] 10 - 14 % 1.6*** 4.6*** 3.7*** 2.2*** 2.4*** 2.1*** 1.7*** 1.5*** 0.2 0.0 0.0 CI [1.1; 2.1] [3.6; 5.5] [2.7; 4.6] [1.5; 2.8] [1.7; 3.1] [1.4; 2.8] [1.1; 2.2] [0.9; 2.0] [-0.1; 0.4] [-0.1; 0.1] [-0.1; 0.1] 15 - 19 % 1.2*** 3.5*** 2.1*** 1.6*** 2.1*** 1.5*** 1.2*** 0.9*** 0.1 0.0 0.0 CI [0.7; 1.6] [2.5; 4.5] [1.4; 2.9] [1.0; 2.2] [1.3; 2.8] [0.9; 2.1] [0.6; 1.7] [0.5; 1.3] [-0.1; 0.3] [-0.1; 0.1] [-0.1; 0.1] 20 - 24 % 1.1*** 2.5*** 1.9*** 1.0*** 1.1*** 1.2*** 0.6** 0.3* 0.1 0.0 0.1 CI [0.6; 1.5] [1.8; 3.3] [1.2; 2.6] [0.5; 1.5] [0.6; 1.7] [0.7; 1.8] [0.2; 0.9] [0.1; 0.5] [-0.1; 0.3] [-0.1; 0.1] [-0.1; 0.2] 25 - 34 % 1.7*** 2.9*** 2.0*** 1.3*** 1.5*** 1.2*** 1.2*** 0.0 0.2** 0.0 0.1 CI [1.3; 2.2] [2.3; 3.5] [1.5; 2.5] [0.9; 1.6] [1.1; 2.0] [0.8; 1.6] [0.8; 1.6] [-0.1; 0.1] [0.1; 0.3] [-0.1; 0.1] [-0.1; 0.1] 35 - 40 % 2.8*** 4.5*** 3.5*** 2.3*** 2.4*** 1.9*** 1.3*** 0.0 0.2* 0.0 0.1 CI [2.1; 3.5] [3.5; 5.4] [2.6; 4.3] [1.6; 2.9] [1.7; 3.1] [1.2; 2.5] [0.8; 1.8] [-0.1; 0.1] [0.0; 0.4] [-0.1; 0.1] [-0.1; 0.1] 41 - 59 % 3.3*** 5.1*** 3.0*** 2.6*** 2.6*** 1.8*** 1.4*** 0.0 0.2* 0.0 0.1 CI [2.6; 3.9] [4.1; 6.1] [2.2; 3.7] [1.9; 3.2] [1.9; 3.2] [1.3; 2.4] [1.0; 1.9] [-0.1; 0.1] [0.0; 0.3] [-0.1; 0.1] [-0.1; 0.1] 60 and over % 3.0*** 3.7*** 2.4*** 2.0*** 2.3*** 1.8*** 1.4*** 0.0 0.0 0.0 0.1 CI [2.2; 3.9] [2.6; 4.7] [1.5; 3.3] [1.3; 2.8] [1.4; 3.1] [1.1; 2.6] [0.8; 2.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.2] Sex Male % 2.0*** 4.0*** 3.1*** 1.8*** 2.0*** 1.6*** 1.4*** 0.4*** 0.2** 0.0 0.1* CI [1.7; 2.4] [3.4; 4.6] [2.5; 3.7] [1.4; 2.2] [1.6; 2.4] [1.2; 2.0] [1.0; 1.8] [0.3; 0.6] [0.1; 0.3] [-0.1; 0.1] [0.0; 0.1] Female % 2.2*** 4.5*** 3.2*** 2.2*** 2.4*** 1.8*** 1.3*** 0.4*** 0.2** 0.0 0.0* CI [1.8; 2.6] [3.8; 5.2] [2.6; 3.9] [1.8; 2.7] [1.9; 2.9] [1.4; 2.3] [0.9; 1.7] [0.3; 0.6] [0.1; 0.2] [-0.1; 0.1] [0.0; 0.1] Confidence intervals:* p < 0.05.** p < 0.01.*** p < 0.001 41 Table A1.5: Percentage point increase in the normalized poverty gap of extreme poverty according to expenditure categories Alcoholic beverages Communication Other category Durable goods Transport Education Clothing Housing Jewelry Energy Health All % 0.7*** 1.3*** 0.6*** 0.7*** 0.7*** 0.5*** 0.4*** 0.1*** 0.0*** 0.0*** 0.0** CI [0.6; 0.7] [1.2; 1.4] [0.6; 0.7] [0.6; 0.8] [0.6; 0.7] [0.4; 0.5] [0.3; 0.4] [0.1; 0.1] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] Regions Boké % 0.6*** 1.2*** 0.7*** 0.9*** 0.9*** 0.3*** 0.3*** 0.2*** 0.1** 0.0 0.0 CI [0.4; 0.8] [0.9; 1.5] [0.5; 1.0] [0.7; 1.2] [0.7; 1.2] [0.2; 0.5] [0.2; 0.5] [0.1; 0.2] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] Conakry % 0.0** 0.1* 0.1* 0.1* 0.0* 0.1 0.1* 0.1** 0.0 0.0 0.0 CI [0.0; 0.1] [0.0; 0.2] [0.0; 0.1] [0.0; 0.2] [0.0; 0.1] [-0.1; 0.1] [0.0; 0.1] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] Faranah % 1.4*** 2.6*** 1.0*** 1.4*** 1.3*** 0.7*** 0.9*** 0.1*** 0.1** 0.0* 0.0* CI [1.1; 1.6] [2.2; 3.1] [0.7; 1.3] [1.2; 1.6] [1.1; 1.6] [0.5; 0.8] [0.6; 1.3] [0.1; 0.2] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] Kankan % 0.2*** 0.3*** 0.3*** 0.2*** 0.1*** 0.2*** 0.1*** 0.0* 0.0 0.0 0.0 CI [0.1; 0.3] [0.2; 0.4] [0.2; 0.5] [0.1; 0.2] [0.1; 0.2] [0.1; 0.3] [0.0; 0.2] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] Kindia % 0.9*** 1.6*** 1.2*** 1.1*** 1.0*** 0.9*** 0.4*** 0.2*** 0.1** 0.0 0.0 CI [0.8; 1.1] [1.3; 1.8] [0.9; 1.4] [1.0; 1.3] [0.9; 1.2] [0.7; 1.1] [0.3; 0.5] [0.1; 0.2] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] Labé % 1.0*** 2.8*** 0.7*** 1.5*** 1.3*** 1.1*** 0.8*** 0.2*** 0.1** 0.0 0.0 CI [0.8; 1.1] [2.4; 3.1] [0.5; 0.9] [1.3; 1.7] [1.1; 1.4] [0.9; 1.3] [0.6; 0.9] [0.2; 0.3] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] Mamou % 0.5*** 1.0*** 0.4*** 0.5*** 0.4*** 0.4*** 0.4*** 0.1*** 0.0 0.0 0.0 CI [0.4; 0.7] [0.7; 1.3] [0.2; 0.6] [0.4; 0.7] [0.3; 0.5] [0.3; 0.6] [0.2; 0.5] [0.1; 0.2] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] N’Zérékoré % 1.1*** 2.3*** 1.0*** 0.6*** 0.8*** 0.5*** 0.3*** 0.2*** 0.0** 0.0** 0.1* CI [0.9; 1.3] [1.8; 2.7] [0.7; 1.3] [0.5; 0.7] [0.7; 1.0] [0.4; 0.7] [0.2; 0.4] [0.1; 0.3] [0.0; 0.1] [-0.1; 0.1] [0.0; 0.3] Age (years) 0 % 0.9*** 2.2*** 1.3*** 1.0*** 1.1*** 0.7*** 0.9** 0.0 0.1* 0.0 0.0 CI [0.5; 1.3] [1.6; 2.8] [0.7; 1.8] [0.7; 1.3] [0.8; 1.5] [0.4; 1.0] [0.3; 1.4] [-0.1; 0.1] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] 1-4 % 0.8*** 1.6*** 0.9*** 0.8*** 0.8*** 0.5*** 0.4*** 0.0 0.1*** 0.0*** 0.0** CI [0.7; 0.9] [1.4; 1.7] [0.7; 1.0] [0.7; 0.9] [0.7; 0.9] [0.4; 0.6] [0.4; 0.5] [-0.1; 0.1] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] 42 5-9 % 0.6*** 1.6*** 0.8*** 0.8*** 0.8*** 0.5*** 0.4*** 0.2*** 0.1*** 0.0*** 0.0** CI [0.5; 0.6] [1.4; 1.7] [0.7; 0.9] [0.8; 0.9] [0.7; 0.9] [0.5; 0.6] [0.4; 0.5] [0.1; 0.2] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] 10 - 14 % 0.6*** 1.6*** 0.8*** 0.8*** 0.8*** 0.6*** 0.4*** 0.5*** 0.0*** 0.0** 0.0 CI [0.5; 0.6] [1.4; 1.8] [0.6; 0.9] [0.7; 0.9] [0.7; 0.8] [0.5; 0.7] [0.4; 0.5] [0.4; 0.6] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] 15 - 19 % 0.4*** 1.2*** 0.5*** 0.6*** 0.5*** 0.5*** 0.3*** 0.3*** 0.0*** 0.0* 0.0 CI [0.3; 0.5] [1.0; 1.3] [0.4; 0.6] [0.5; 0.7] [0.5; 0.6] [0.4; 0.6] [0.3; 0.4] [0.2; 0.3] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] 20 - 24 % 0.3*** 0.7*** 0.3*** 0.4*** 0.4*** 0.3*** 0.2*** 0.1*** 0.0** 0.0 0.0 CI [0.2; 0.4] [0.5; 0.8] [0.2; 0.4] [0.3; 0.4] [0.3; 0.4] [0.2; 0.4] [0.1; 0.2] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] 25 - 34 % 0.6*** 1.0*** 0.5*** 0.5*** 0.5*** 0.3*** 0.3*** 0.0 0.0*** 0.0** 0.0** CI [0.5; 0.7] [0.9; 1.1] [0.4; 0.6] [0.5; 0.6] [0.5; 0.6] [0.3; 0.4] [0.2; 0.4] [-0.1; 0.1] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] 35 - 40 % 0.9*** 1.3*** 0.6*** 0.7*** 0.6*** 0.4*** 0.3*** 0.0 0.0*** 0.0** 0.0* CI [0.7; 1.0] [1.1; 1.5] [0.5; 0.7] [0.6; 0.7] [0.6; 0.7] [0.3; 0.5] [0.3; 0.4] [-0.1; 0.1] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] 41 - 59 % 0.9*** 1.4*** 0.6*** 0.7*** 0.7*** 0.5*** 0.4*** 0.0 0.0*** 0.0** 0.0* CI [0.7; 1.0] [1.2; 1.5] [0.5; 0.7] [0.6; 0.8] [0.6; 0.8] [0.5; 0.6] [0.3; 0.4] [-0.1; 0.1] [0.0; 0.1] [-0.1; 0.1] [0.0; 0.1] 60 and over % 1.1*** 1.3*** 0.5*** 0.8*** 0.7*** 0.5*** 0.4*** 0.0 0.0** 0.0* 0.0* CI [0.9; 1.2] [1.1; 1.5] [0.3; 0.6] [0.7; 0.9] [0.6; 0.8] [0.4; 0.6] [0.3; 0.4] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] Sex Male % 0.6*** 1.3*** 0.6*** 0.7*** 0.6*** 0.5*** 0.4*** 0.1*** 0.0*** 0.0*** 0.0** CI [0.6; 0.7] [1.2; 1.4] [0.6; 0.7] [0.6; 0.7] [0.6; 0.7] [0.4; 0.5] [0.3; 0.4] [0.1; 0.2] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] Female % 0.7*** 1.4*** 0.6*** 0.7*** 0.7*** 0.5*** 0.4*** 0.1*** 0.0*** 0.0** 0.0** CI [0.6; 0.8] [1.3; 1.5] [0.6; 0.7] [0.7; 0.8] [0.6; 0.8] [0.4; 0.5] [0.3; 0.4] [0.1; 0.1] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] Confidence intervals:* p < 0.05.** p < 0.01.*** p < 0.001 43 Table A1.6: Percentage point increase in the severity of extreme poverty according to expenditure categories Alcoholic beverages Communication Other category Durable goods Transport Education Clothing Housing Jewelry Energy Health All % 0.3*** 0.6*** 0.2*** 0.3*** 0.3*** 0.2*** 0.1*** 0.0*** 0.0*** 0.0** 0.0* CI [0.2; 0.3] [0.5; 0.6] [0.2; 0.2] [0.3; 0.3] [0.2; 0.3] [0.2; 0.2] [0.1; 0.2] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] Regions Boké % 0.2*** 0.5*** 0.2*** 0.4*** 0.4*** 0.1*** 0.1*** 0.0*** 0.0** 0.0 0.0 CI [0.1; 0.3] [0.3; 0.6] [0.1; 0.3] [0.3; 0.5] [0.3; 0.5] [0.0; 0.1] [0.1; 0.2] [0.0; 0.1] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] Conakry % 0.0* 0.0 0.0 0.0* 0.0 0.0 0.0* 0.0** 0.0 0.0 0.0 CI [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] Faranah % 0.7*** 1.3*** 0.4*** 0.6*** 0.6*** 0.3*** 0.4*** 0.1*** 0.0** 0.0** 0.0* CI [0.5; 0.9] [1.0; 1.6] [0.2; 0.6] [0.5; 0.8] [0.5; 0.7] [0.2; 0.4] [0.2; 0.6] [0.0; 0.1] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] Kankan % 0.1*** 0.1*** 0.1** 0.0*** 0.0*** 0.0*** 0.0*** 0.0 0.0 0.0 0.0 CI [0.0; 0.1] [0.0; 0.1] [0.0; 0.1] [0.0; 0.1] [-0.1; 0.1] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] Kindia % 0.4*** 0.6*** 0.4*** 0.5*** 0.4*** 0.4*** 0.2*** 0.1*** 0.0* 0.0 0.0 CI [0.3; 0.5] [0.5; 0.7] [0.3; 0.6] [0.4; 0.6] [0.3; 0.5] [0.3; 0.5] [0.1; 0.2] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] Labé % 0.4*** 1.2*** 0.2*** 0.6*** 0.5*** 0.5*** 0.3*** 0.1*** 0.0* 0.0 0.0 CI [0.3; 0.5] [1.0; 1.4] [0.1; 0.3] [0.5; 0.7] [0.4; 0.7] [0.3; 0.6] [0.2; 0.4] [0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] Mamou % 0.2*** 0.4*** 0.1*** 0.2*** 0.2*** 0.2*** 0.2*** 0.0*** 0.0 0.0 0.0 CI [0.1; 0.3] [0.3; 0.6] [0.1; 0.2] [0.1; 0.3] [0.1; 0.2] [0.1; 0.3] [0.1; 0.3] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] N’Zérékoré % 0.4*** 0.9*** 0.3*** 0.2*** 0.3*** 0.2*** 0.1*** 0.1*** 0.0* 0.0* 0.1* CI [0.3; 0.5] [0.7; 1.2] [0.2; 0.4] [0.2; 0.3] [0.3; 0.4] [0.1; 0.3] [0.1; 0.1] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] [0.0; 0.1] Age (years) 0 % 0.4*** 0.9*** 0.3*** 0.5*** 0.5*** 0.2*** 0.4** 0.0 0.0 0.0 0.0 CI [0.2; 0.6] [0.6; 1.2] [0.2; 0.5] [0.3; 0.6] [0.3; 0.6] [0.1; 0.3] [0.1; 0.7] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] 1-4 % 0.3*** 0.6*** 0.3*** 0.3*** 0.3*** 0.2*** 0.2*** 0.0 0.0*** 0.0* 0.0* CI [0.3; 0.4] [0.6; 0.7] [0.2; 0.3] [0.3; 0.4] [0.3; 0.4] [0.2; 0.2] [0.1; 0.2] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] 44 5-9 % 0.2*** 0.7*** 0.3*** 0.3*** 0.3*** 0.2*** 0.2*** 0.1*** 0.0*** 0.0** 0.0* CI [0.2; 0.3] [0.6; 0.7] [0.2; 0.3] [0.3; 0.4] [0.3; 0.4] [0.2; 0.2] [0.1; 0.2] [0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] 10 - 14 % 0.2*** 0.7*** 0.3*** 0.3*** 0.3*** 0.3*** 0.2*** 0.2*** 0.0*** 0.0* 0.0 CI [0.2; 0.3] [0.6; 0.8] [0.2; 0.3] [0.3; 0.4] [0.3; 0.4] [0.2; 0.3] [0.1; 0.2] [0.1; 0.2] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] 15 - 19 % 0.2*** 0.5*** 0.2*** 0.2*** 0.2*** 0.2*** 0.1*** 0.1*** 0.0*** 0.0 0.0 CI [0.1; 0.2] [0.4; 0.6] [0.1; 0.2] [0.2; 0.3] [0.2; 0.3] [0.2; 0.3] [0.1; 0.1] [0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] 20 - 24 % 0.1*** 0.3*** 0.1*** 0.1*** 0.1*** 0.1*** 0.1*** 0.0** 0.0** 0.0 0.0 CI [0.1; 0.2] [0.2; 0.3] [0.1; 0.1] [0.1; 0.2] [0.1; 0.2] [0.1; 0.1] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] 25 - 34 % 0.3*** 0.4*** 0.2*** 0.2*** 0.2*** 0.1*** 0.1*** 0.0 0.0*** 0.0** 0.0* CI [0.2; 0.3] [0.3; 0.5] [0.1; 0.2] [0.2; 0.2] [0.2; 0.2] [0.1; 0.2] [0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] 35 - 40 % 0.4*** 0.5*** 0.2*** 0.3*** 0.3*** 0.2*** 0.1*** 0.0 0.0** 0.0* 0.0* CI [0.3; 0.5] [0.4; 0.6] [0.1; 0.2] [0.2; 0.3] [0.2; 0.3] [0.1; 0.2] [0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] 41 - 59 % 0.3*** 0.5*** 0.2*** 0.3*** 0.3*** 0.2*** 0.1*** 0.0 0.0*** 0.0** 0.0 CI [0.3; 0.4] [0.5; 0.6] [0.2; 0.2] [0.2; 0.3] [0.2; 0.3] [0.2; 0.2] [0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] 60 and over % 0.5*** 0.5*** 0.2*** 0.3*** 0.3*** 0.2*** 0.1*** 0.0 0.0** 0.0* 0.0 CI [0.4; 0.6] [0.4; 0.6] [0.1; 0.3] [0.3; 0.4] [0.2; 0.4] [0.2; 0.2] [0.1; 0.2] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] Sex Male % 0.3*** 0.5*** 0.2*** 0.3*** 0.3*** 0.2*** 0.1*** 0.1*** 0.0*** 0.0** 0.0* CI [0.2; 0.3] [0.5; 0.6] [0.2; 0.2] [0.2; 0.3] [0.2; 0.3] [0.2; 0.2] [0.1; 0.2] [0.0; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] Female % 0.3*** 0.6*** 0.2*** 0.3*** 0.3*** 0.2*** 0.1*** 0.0*** 0.0*** 0.0** 0.0* CI [0.3; 0.3] [0.5; 0.6] [0.2; 0.2] [0.3; 0.3] [0.3; 0.3] [0.2; 0.2] [0.1; 0.2] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] [-0.1; 0.1] Confidence intervals:* p < 0.05.** p < 0.01.*** p < 0.001 45 6.2 Appendix 2: Technical note Appendix 2.1: Formula for calculating the impact of expenditure sectors on the poverty prevalence, normalized poverty gap, and poverty severity We consider the individual as the unit of analysis. We note by the total number of individuals surveyed, the individual’s income and the weight of the individual. The index represents individuals. Note by the food poverty line. Formally, poverty prevalence , normalized poverty gap , and poverty severity , is defined as follows: [15-19] (1) with the convention . Noting that poverty measures are averages (expectation), they can be suitably estimated by linear regression. We estimate by the ordinary least squares method, based on the following model: (2) with the estimated coefficients, corresponding to poverty measures of type . Each individual is weighted by its weight during estimation. There are 8 regions in Guinea. Let us note by all regions, i.e. {Boké, Conakry, Faranah, Kankan, Kindia, Labé, Mamou, N’Zérékoré}. Let par denote the binary variable associated with a region . Poverty mea- sures in the 8 regions are calculated using the following model : (3) with the estimated coefficients, corresponding to the poverty measures of type of the region . For age and gender, an approach identical to equation (3) was used, integrating the binary variables associated with these socio-demographic characteristics. To assess the impact of the different expenditure sectors of (i) health; (ii) clothing; (iii) transport; (iv) housing; (v) energy; (vi) communication; (vii) durable goods; (viii) education; (ix) alcoholic beverages; (x) jewelry; and (xi) the category other; we use the impoverishing expenditure method.[15] The method involves calculating measures (prevalence, normalized gap, severity) of poverty before and after sector expenditure. Noting by a particular sector’s expenditure, the impact is formally calculated as follows: [15] (4) with Noting that the impact is also an average (expectation), it can be suitably estimated by linear re- gression. We estimate by the ordinary least squares method, based on the following model: (5) 46 Similarly, each individual is weighted by its weight . Impacts in the 8 regions are calculated using the following model: (6) For age and gender, an approach identical to equation (6) was used, integrating the binary variables associated with these socio-demographic characteristics. Confidence intervals and statistical tests are obtained using the ordinary least squares approach, taking into account the correlation between observations of individuals in the same household. Appendix 2.2: Procedure for displaying concentration curves Taking the (conditional) expectations from equations (5) and (6), and after a few manipulations, we obtain the following contribution   of the region   to the national impact of the poverty measure of type  : representing the share of the national population resident in the region  . with    For each region   we calculate the absolute rank   as the number of regions with a contribution less than or equal to that of the of the region : with   the indicator function taking the value 1 if the condition is true and 0 otherwise. The possible values of  are 1, 2, ...,8. For each region   we calculated the fractional rank  corresponding to the cumulative contributions to the   national impact for each poverty measure: Let’s denote by   the share of funding going to a region.Thus, the cumulative funding   of a region   is given by : The concentration curve is the locus of the set of points . 47 Appendix 2.3: Concentration curve results We report here the concentration curves corresponding to the concentration indices found in the main text (Table 5). Figure A2.1: Financing concentration curve and index as a function of poverty prevalence Panel (a): Public financing (Index = -0.28) Panel (b): External financing (Index = -0.41) 100 100 Concentration Concentration N’Zérékoré N’Zérékoré 45° 45° Cumulative % of external funding (2020-2024) Cumulative % of public funding (2020-2024) Kindia 80 80 Kindia Labé Labé Faranah 60 60 Faranah Boké Mamou Boké 40 40 Kankan Mamou Kankan Conakry 20 20 Conakry 0 20 40 60 80 100 0 20 40 60 80 100 Cumulative % of contribution to the national impact (2018-2019) Cumulative % of contribution to the national impact (2018-2019) Panel (c): Total financing (Index = -0.35) 100 Concentration N’Zérékoré 45° Cumulative % of total funding (2020-2024) Kindia 80 Labé Faranah 60 Boké Mamou 40 Kankan 20 Conakry 0 20 40 60 80 100 Cumulative % of contribution to the national impact (2018-2019) 48 Figure A2.2: Financing concentration curve and index as a function of normalized poverty gap Panel (a): Public financing (Index = -0.27) Panel (b): External financing (Index = -0.39) 100 100 Concentration Concentration N’Zérékoré N’Zérékoré 45° 45° Cumulative % of external funding (2020-2024) Cumulative % of total funding (2020-2024) Kindia Faranah 80 80 Kindia Labé Labé 60 60 Faranah Boké Boké Mamou Kankan 40 Mamou 40 Kankan Conakry 20 20 Conakry 0 20 40 60 80 100 0 20 40 60 80 100 Cumulative % of contribution to the national impact (2018-2019) Cumulative % of contribution to the national impact (2018-2019) Panel (c): Total financing (Index = -0.34) 100 Concentration N’Zérékoré 45° Cumulative % of external funding (2020-2024) Kindia 80 Faranah 60 Labé Boké 40 Kankan Mamou 20 Conakry 0 20 40 60 80 100 Cumulative % of contribution to the national impact (2018-2019) 49 Figure A2.3: Financing concentration curve and index as a function of poverty severity Panel (a): Public financing (Index = -0.31) Panel (b): External financing (Index = -0.47) 100 100 Concentration Concentration N’Zérékoré N’Zérékoré 45° 45° Cumulative % of external funding (2020-2024) Cumulative % of public funding (2020-2024) Kindia Faranah 80 80 Kindia Faranah 60 60 Labé Labé Boké Boké Mamou 40 40 Kankan Kankan Mamou Conakry 20 20 Conakry 0 20 40 60 80 100 0 20 40 60 80 100 Cumulative % of contribution to the national impact (2018-2019) Cumulative % of contribution to the national impact (2018-2019) Panel (c): Total financing (Index = -0.41) 100 Concentration N’Zérékoré 45° Cumulative % of total funding (2020-2024) Faranah 80 Kindia 60 Labé Boké 40 Mamou Kankan 20 Conakry 0 20 40 60 80 100 Cumulative % of contribution to the national impact (2018-2019) 50 Appendix 2.4: Procedure for calculating concentration indices The concentration index is obtained by calculating the deviation of the concentration curve from the 45° line. This straight line corresponds to the situation of perfect alignment in which (and therefore ) for each region .The concentration index corresponds to twice the difference between the area between the con- centration curve and the 45° line. Formally, using the trapezoidal integration method, the concentration index is given by: with the convention An important property of the concentration index is that it is a relative measure (based on percentages), i.e. it is invariant when the funding (in monetary units) of all regions is multiplied by the same positive constant. A (respectively ) indicates a situation in which regions with low contributions contributions are those that benefit most (respectively least) from healthcare funding. 6.3 Appendix 3: Sector rankings (comparison test) Table A3.1: Sector rankings (comparison test) Comparison p-value Conclusion Impact on poverty prevalence Clothing and transport 0.0002 Class 1: Clothing Transport and energy 0.0007 Class 2: Transport Energy and health 0.7127 Same class 3 Energy, health, housing 0.7054 Same class 3 Energy, health, housing and communications 0.0435 Class 3: Energy, health, and housing Impact on normalized poverty gap Clothing and housing 0.0000 Class 1: Clothing Housing and energy 0.2288 Same class 2 Housing, energy and health 0.2669 Same class 2 Housing, energy, health and transport 0.3700 Same class 2 Housing, energy, health, transport and communications 0.0000 Class 2: Housing, energy, health, and transport Impact on poverty severity Clothing and housing 0.0000 Class 1: Clothing Housing and health 0.4966 Same class Housing, health and energy 0.3832 Same class Housing, health, energy, transport 0.0000 Class 2: Housing, health, energy Transport and communication 0.2955 Class 3: Transport and communication 51 51 6.4 Appendix 4: Share of food and non-food expenditure in total consumer expenditure (%) Table A4.1: Share of food and non-food expenditure in total consumer expenditure (%) Food Non-food Alcoholic beverages Communication Other category Durable goods Transport Education Clothing Housing Jewelry Energy Health All % 57.9*** 9.4*** 7.1*** 5.4*** 4.5*** 4.7*** 4.2*** 4.0*** 1.5*** 0.4*** 0.1*** 0.4*** CI [57.5; 58.2] [9.2; 9.5] [6.9; 7.3] [5.3; 5.5] [4.4; 4.6] [4.6; 4.7] [4.1,4.3] [3.8; 4.1] [1.4; 1.5] [0.3; 0.4] [0.1,0.1] [0.4; 0.5] Quintile of consumption Quintile 1 % 59.9*** 10.0*** 4.8*** 5.8*** 5.7*** 4.2*** 4.8*** 2.9*** 0.9*** 0.3*** 0.1*** 0.3*** CI [59.0; 60.7] [9.6; 10.4] [4.4; 5.2] [5.6; 6.0] [5.4; 6.0] [3.9; 4.4] [4.5; 5.0] [2.7; 3.2] [0.8; 1.1] [0.3; 0.4] [0.1; 0.1] [0.2; 0.4] Quintile 2 % 58.7*** 9.5*** 7.0*** 5.1*** 5.0*** 4.5*** 4.8*** 3.3*** 1.0*** 0.4*** 0.1*** 0.3*** CI [57.9; 59.4] [9.2; 9.8] [6.6; 7.5] [4.9; 5.3] [4.8; 5.2] [4.3; 4.7] [4.6; 5.1] [3.1; 3.5] [0.9; 1.1] [0.3; 0.5] [0.1; 0.1] [0.2; 0.3] Quintile 3 % 58.9*** 9.4*** 7.1*** 5.0*** 4.4*** 4.7*** 4.4*** 3.5*** 1.5*** 0.4*** 0.1*** 0.4*** CI [58.1; 59.6] [9.1; 9.7] [6.7; 7.4] [4.8; 5.2] [4.2; 4.6] [4.5; 4.9] [4.2; 4.5] [3.3; 3.7] [1.3; 1.6] [0.3; 0.4] [0.1; 0.1] [0.3; 0.5] Quintile 4 % 57.9*** 8.9*** 7.4*** 5.2*** 4.0*** 5.0*** 3.9*** 4.5*** 1.7*** 0.3*** 0.1*** 0.4*** CI [57.1; 58.7] [8.6; 9.1] [7.1; 7.8] [5.0; 5.5] [3.9; 4.2] [4.8; 5.2] [3.7; 4.0] [4.1; 4.9] [1.6; 1.9] [0.3; 0.4] [0.1; 0.1] [0.4; 0.5] Quintile 5 % 54.1*** 9.1*** 9.1*** 6.0*** 3.6*** 4.9*** 3.2*** 5.7*** 2.1*** 0.4*** 0.1*** 0.7*** CI [53.4; 54.8] [8.9; 9.3] [8.7; 9.5] [5.8; 6.2] [3.4; 3.7] [4.8; 5.1] [3.1; 3.3] [5.3; 6.0] [1.9; 2.2] [0.3; 0.4] [0.1; 0.1] [0.6; 0.8] Regions Boké % 58.7*** 8.7*** 6.9*** 5.9*** 5.3*** 4.5*** 3.8*** 3.4*** 1.2*** 0.6*** 0.1*** 0.5*** CI [57.8; 59.5] [8.4; 9.1] [6.4; 7.4] [5.6; 6.2] [5.0; 5.6] [4.2; 4.9] [3.5; 4.0] [3.0; 3.8] [1.1; 1.4] [0.5; 0.8] [0.1; 0.1] [0.4; 0.5] Conakry % 51.6*** 9.4*** 8.1*** 7.9*** 2.9*** 5.7*** 3.5*** 5.5*** 3.8*** 0.3*** 0.1*** 0.7*** CI [50.8; 52.3] [9.1; 9.7] [7.6; 8.5] [7.7; 8.2] [2.8; 3.0] [5.5; 5.9] [3.3; 3.6] [5.2; 5.9] [3.5; 4.0] [0.2; 0.3] [0.1; 0.1] [0.6; 0.8] Faranah % 60.4*** 9.5*** 5.6*** 4.9*** 5.0*** 3.4*** 5.2*** 4.2*** 0.8*** 0.4*** 0.1*** 0.2*** CI [59.3; 61.4] [9.2; 9.9] [5.1; 6.1] [4.7; 5.1] [4.8; 5.3] [3.2; 3.7] [4.9; 5.5] [3.4; 4.9] [0.7; 0.9] [0.3; 0.4] [0.1; 0.1] [0.2; 0.3] Kankan % 57.2*** 8.8*** 9.4*** 4.5*** 5.2*** 5.0*** 4.1*** 4.0*** 0.6*** 0.4*** 0.1*** 0.4*** CI [56.3; 58.2] [8.5; 9.2] [8.9; 9.8] [4.3; 4.7] [4.9; 5.4] [4.8; 5.3] [3.9; 4.3] [3.8; 4.2] [0.5; 0.7] [0.3; 0.4] [0.1; 0.1] [0.3; 0.5] 52 Kindia % 59.2*** 8.4*** 6.6*** 6.1*** 4.6*** 5.0*** 3.8*** 3.7*** 1.4*** 0.5*** 0.1*** 0.3*** CI [58.4; 60.1] [8.1; 8.6] [6.2; 7.0] [5.9; 6.3] [4.4; 4.9] [4.8; 5.3] [3.6; 4.0] [3.4; 4.1] [1.3; 1.5] [0.4; 0.6] [0.1; 0.1] [0.2; 0.3] Labé % 64.3*** 9.4*** 4.1*** 5.3*** 4.4*** 4.1*** 3.3*** 3.8*** 0.9*** 0.2*** 0.1*** 0.1*** CI [63.3; 65.3] [9.1; 9.8] [3.6; 4.7] [5.1; 5.5] [4.1; 4.6] [3.8; 4.5] [3.1; 3.5] [3.4; 4.2] [0.8; 1.0] [0.1; 0.2] [0.1; 0.1] [0.1; 0.2] Mamou % 61.2*** 9.9*** 5.6*** 4.6*** 4.1*** 4.0*** 4.4*** 4.6*** 1.0*** 0.1*** 0.1*** 0.2*** CI [60.1; 62.3] [9.3; 10.5] [5.0; 6.3] [4.4; 4.9] [3.9; 4.3] [3.7; 4.4] [4.0; 4.8] [4.1; 5.1] [0.8; 1.1] [0.1; 0.2] [0.1; 0.1] [0.2; 0.3] N’Zérékoré % 56.5*** 11.1*** 7.3*** 3.7*** 4.9*** 4.1*** 5.8*** 2.7*** 1.2*** 0.4*** 0.1*** 0.8*** CI [55.5; 57.4] [10.6; 11.5] [6.7; 7.8] [3.5; 3.8] [4.6; 5.1] [3.9; 4.4] [5.5; 6.0] [2.4; 3.0] [1.1; 1.4] [0.3; 0.5] [0.1; 0.1] [0.6; 0.9] Age (years) 0 % 54.3*** 11.1*** 8.7*** 5.7*** 5.8*** 4.6*** 3.4*** 4.5*** 0.0 0.5*** 0.1*** 0.4*** CI [52.7; 55.9] [10.5; 11.8] [7.9; 9.6] [5.3; 6.1] [5.4; 6.2] [4.2; 5.0] [2.8; 3.9] [3.7; 5.3] [-0.1; 0.1] [0.3; 0.6] [0.1; 0.1] [0.3; 0.5] 1-4 % 58.4*** 9.6*** 7.5*** 5.2*** 4.7*** 4.2*** 4.3*** 3.7*** 0.2*** 0.4*** 0.1*** 0.4*** CI [58.0; 58.9] [9.4; 9.8] [7.2; 7.8] [5.1; 5.3] [4.6; 4.8] [4.1; 4.3] [4.1; 4.4] [3.6; 3.9] [0.1; 0.2] [0.3; 0.4] [0.1; 0.1] [0.4; 0.5] 5-9 % 59.2*** 9.2*** 6.8*** 5.1*** 4.5*** 4.2*** 3.6*** 3.8*** 2.4*** 0.4*** 0.1*** 0.4*** CI [58.7; 59.6] [9.0; 9.4] [6.6; 7.1] [5.0; 5.3] [4.4; 4.6] [4.1; 4.3] [3.5; 3.8] [3.5; 4.0] [2.3; 2.5] [0.3; 0.4] [0.1; 0.1] [0.3; 0.4] 10 - 14 % 59.0*** 9.2*** 6.5*** 5.1*** 4.3*** 4.5*** 3.5*** 3.8*** 3.9*** 0.3*** 0.1*** 0.4*** CI [58.3; 59.6] [9.0; 9.4] [6.2; 6.8] [5.0; 5.2] [4.2; 4.5] [4.4; 4.7] [3.3; 3.6] [3.6; 4.0] [3.7; 4.1] [0.2; 0.3] [0.1; 0.1] [0.3; 0.5] 15 - 19 % 57.6*** 9.2*** 6.9*** 5.4*** 4.3*** 5.3*** 3.4*** 3.9*** 3.5*** 0.3*** 0.1*** 0.5*** CI [57.0; 58.2] [9.0; 9.4] [6.6; 7.2] [5.2; 5.5] [4.2; 4.5] [5.1; 5.5] [3.2; 3.6] [3.8; 4.1] [3.3; 3.8] [0.2; 0.3] [0.1; 0.1] [0.4; 0.5] 20 - 24 % 56.4*** 9.4*** 7.7*** 5.5*** 4.3*** 5.4*** 3.8*** 4.2*** 2.2*** 0.4*** 0.1*** 0.5*** CI [55.8; 57.0] [9.2; 9.7] [7.3; 8.0] [5.3; 5.7] [4.1; 4.5] [5.2; 5.6] [3.6; 4.0] [3.9; 4.5] [1.9; 2.4] [0.3; 0.5] [0.1; 0.1] [0.5; 0.6] 25 - 34 % 56.2*** 9.7*** 7.7*** 5.7*** 4.5*** 4.9*** 4.4*** 4.3*** 0.5*** 0.5*** 0.1*** 0.5*** CI [55.7; 56.6] [9.5; 9.8] [7.5; 8.0] [5.6; 5.9] [4.4; 4.6] [4.8; 5.0] [4.2; 4.6] [4.1; 4.6] [0.4; 0.6] [0.4; 0.5] [0.1; 0.1] [0.5; 0.6] 35 - 40 % 57.0*** 9.7*** 7.3*** 5.6*** 4.7*** 4.6*** 5.0*** 3.9*** 0.0** 0.5*** 0.1*** 0.5*** CI [56.5; 57.6] [9.5; 9.9] [7.0; 7.6] [5.5; 5.8] [4.5; 4.8] [4.4; 4.7] [4.7; 5.2] [3.7; 4.1] [-0.1; 0.1] [0.4; 0.6] [0.1; 0.1] [0.4; 0.5] 41 - 59 % 57.8*** 9.2*** 6.9*** 5.6*** 4.7*** 4.8*** 5.1*** 4.0*** 0.0 0.4*** 0.1*** 0.4*** CI [57.3; 58.3] [9.0; 9.4] [6.6; 7.1] [5.5; 5.7] [4.5; 4.8] [4.7; 5.0] [4.9; 5.3] [3.8; 4.3] [-0.1; 0.1] [0.3; 0.4] [0.1; 0.1] [0.4; 0.5] 60 and over % 58.4*** 9.0*** 6.1*** 6.0*** 4.9*** 4.7*** 6.1*** 4.4*** 0.0 0.2*** 0.1*** 0.3*** CI [57.7; 59.1] [8.7; 9.2] [5.7; 6.4] [5.8; 6.2] [4.7; 5.1] [4.5; 4.9] [5.8; 6.5] [4.1; 4.8] [-0.1; 0.1] [0.2; 0.3] [0.1; 0.1] [0.3; 0.4] 53 Sex Male % 57.7*** 9.3*** 7.2*** 5.4*** 4.8*** 4.7*** 4.0*** 4.0*** 1.6*** 0.4*** 0.1*** 0.5*** CI [57.3; 58.1] [9.2; 9.5] [7.0; 7.4] [5.3; 5.5] [4.7; 4.9] [4.6; 4.8] [3.9; 4.2] [3.9; 4.2] [1.5; 1.7] [0.4; 0.4] [0.1; 0.1] [0.4; 0.5] Female % 58.1*** 9.4*** 7.0*** 5.5*** 4.8*** 4.6*** 4.4*** 3.9*** 1.3*** 0.3*** 0.1*** 0.4*** CI [57.7; 58.4] [9.3; 9.6] [6.8; 7.2] [5.4; 5.6] [4.7; 4.9] [4.5; 4.7] [4.3; 4.5] [3.8; 4.1] [1.2; 1.4] [0.3; 0.4] [0.1; 0.1] [0.4; 0.4] Extreme Poor % 60.5*** 10.2*** 3.9*** 6.1*** 5.7*** 4.1*** 4.8*** 2.8*** 0.9*** 0.3*** 0.1*** 0.3*** CI [59.4; 61.6] [9.7; 10.7] [3.4; 4.4] [5.8; 6.3] [5.4; 6.1] [3.7; 4.4] [4.5; 5.1] [2.5; 3.1] [0.8; 1.0] [0.2; 0.4] [0.1; 0.1] [0.2; 0.5] Non-extreme % 57.5*** 9.3*** 7.6*** 5.3*** 4.4*** 4.7*** 4.1*** 4.2*** 1.5*** 0.4*** 0.1*** 0.4*** Poor CI [57.1; 57.8] [9.1; 9.4] [7.4; 7.8] [5.2; 5.4] [4.3; 4.4] [4.6; 4.8] [4.0; 4.2] [4.0; 4.3] [1.5; 1.6] [0.3; 0.4] [0.1; 0.1] [0.4; 0.5] 95% confidence interval:* p < 0.05,** p < 0.01,*** p < 0.001 54 7 REFERENCES 55 7 References 1. 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