2019 Data Sources: Examining Inequality

The "Layers of Inequality" diagram is adapted from the United Nations Development Programme’s original version: United Nations Development Programme. What Does It Mean to Leave No One Behind? A UNDP discussion paper and framework for implementation. New York: UNDP, 2018 PDF discussion paper.

Narayan, Ambar, Roy Van der Weide, Alexandru Cojocaru, Christoph Lakner, Silvia Redaelli, Daniel Gerszon Mahler, Rakesh Gupta N. Ramasubbaiah, and Stefan Thewissen. Fair Progress?: Economic Mobility Across Generations Around the World. Washington, DC: World Bank, 2018. License: CC BY 3.0 IGO. E-book. https://openknowledge.worldbank.org/handle/10986/28428.

Revenga, Ana and Dooley, Meagan. “Inequality Beyond Neoliberalism: Policies for More Inclusive Growth.” In Beyond Neoliberalism: Insights from Emerging Markets, edited by Geoffrey Gertz and Homi Kharas, 29–42. Washington, DC: Brookings Institution, 2019. PDF chapter.

United Nations Development Programme and Oxford Poverty and Human Development Initiative. Global Multidimensional Poverty Index 2019: Illuminating Inequalities. UNDP and OPHI, 2019. Report.http://hdr.undp.org/sites/default/files/mpi_2019_publication.pdf. United Nations Development Programme. 2019 Human Development Report. PDF report. New York: UNDP, forthcoming. http://www.hdr.undp.org/.

World Bank. Poverty and Shared Prosperity 2018: Piecing Together the Poverty Puzzle. Washington, DC: World Bank, 2018. License: Creative Commons Attribution CC BY 3.0 IGO. E-book.

Narayan, Ambar, Roy Van der Weide, Alexandru Cojocaru, Christoph Lakner, Silvia Redaelli, Daniel Gerszon Mahler, Rakesh Gupta N. Ramasubbaiah, and Stefan Thewissen. Fair Progress?: Economic Mobility Across Generations Around the World. Washington, DC: World Bank, 2018. License: CC BY 3.0 IGO. E-book.https://openknowledge.worldbank.org/handle/10986/28428.

Revenga, Ana and Dooley, Meagan. “Inequality Beyond Neoliberalism: Policies for More Inclusive Growth.” In Beyond Neoliberalism: Insights from Emerging Markets, edited by Geoffrey Gertz and Homi Kharas, 29–42. Washington, DC: Brookings Institution, 2019.PDF chapter.

United Nations Development Programme and Oxford Poverty and Human Development Initiative. Global Multidimensional Poverty Index 2019: Illuminating Inequalities. UNDP and OPHI, 2019. Report. http://hdr.undp.org/sites/default/files/mpi_2019_publication.pdf. United Nations Development Programme. 2019 Human Development Report. PDF report. New York: UNDP, forthcoming.http://www.hdr.undp.org/.

World Bank. Poverty and Shared Prosperity 2018: Piecing Together the Poverty Puzzle. Washington, DC: World Bank, 2018. License: Creative Commons Attribution CC BY 3.0 IGO. E-book.

The Institute for Health Metrics and Evaluation (IHME) estimated under-five mortality and educational attainment at the 5x5km level for low- and middle-income countries, including trends for the period 2000 to 2017. These estimates are based on methods previously described in detail for under-five mortality (Golding et al., 2017) and education (Graetz et al., 2018). Countries were selected for inclusion in this analysis using the Socio-Demographic Index (SDI) published in the Global Burden of Disease study. The SDI is a measure of development that combines education, fertility, and income. We included all countries in the middle, low-middle, or low SDI quintiles, with several exceptions for reasons of geographic continuity or insufficient data. 97 countries are included. Major countries not included were Brazil, China, and Mexico.

In addition, IHME created future scenarios of these indicators to explore the likelihood that countries and subnational units, e.g., districts, will meet the targets for the SDGs. IHME prepared three future scenarios: a reference scenario based on past trends and relationships with key drivers, and alternative “progress” and “regress” scenarios to highlight the potential for faster progress and to examine the potential for reductions in inequalities in health and education.

To generate the reference scenarios for under-five mortality and educational attainment at the 5x5km level, IHME computed the annualized rate of change (AROC) over the period 2000 to 2017 for each 5x5km grid cell. For educational attainment, IHME computed the AROC using a half-logit transformation which bounds mean years of education between zero and 18 years and also captures the larger AROC observed at lower mean levels of education. Each 5x5km grid cell’s AROC was used to produce a preliminary prediction of under-five mortality and educational attainment for all grid cells from 2018 to 2100. The preliminary 5x5km level estimates were then scaled to national-level reference scenarios of under-five mortality and educational attainment. Those national-level reference scenarios incorporate more extensive data and methods into the future projections; e.g., for under-five mortality they incorporate estimated future trends in drivers such as income per capita, risk factors such as childhood malnutrition, and interventions such as vaccine coverage (Foreman et al., 2018).

To generate the progress and regress scenarios, IHME determined the 85th and 15th percentiles of the observed district-level AROC for the period 2000 to 2017. The AROCs are applied to all districts into the future unless the district-specific reference scenario is better than the progress scenario or the district-specific reference scenario is worse than the regress scenario. In those cases, the reference scenario replaces the alternative progress and regress scenarios.

Foreman, Kyle J., Neal Marquez, Andrew Dolgert, Kai Fukutaki, Nancy Fullman, Madeline McGaughey, Martin A. Pletcher, et al. “Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016–40 for 195 countries and territories.” The Lancet 392, no. 10159 (November 10, 2018): 2052–2090. https://doi.org/10.1016/S0140-6736(18)31694-5.

Golding, Nick, Roy Burstein, Joshua Longbottom, Annie J. Browne, Nancy Fullman, Aaron Osgood-Zimmerman, Lucas Earl, et al. “Mapping under-5 and neonatal mortality in Africa, 2000–15: a baseline analysis for the Sustainable Development Goals.” The Lancet 390, no. 10108 (November 11, 2017): 2171–2182.https://doi.org/10.1016/S0140-6736(18)31694-5.

Graetz, Nicholas, Joseph Friedman, Aaron Osgood-Zimmerman, Roy Burstein, Molly H. Biehl, Chloe Shields, Jonathan F. Mosser, et al. “Mapping local variation in educational attainment across Africa.” Nature 555: 48–53 (February 28, 2018). https://doi.org/10.1016/S0140-6736(17)31758-0.

Sources for the data points cited in the illustration “The Gender Gap” are the following:

The estimate of gender gaps in education was provided by IHME.

The early marriage reference is from UNICEF Global Database, which is based on the Demographic and Health Surveys, the Multiple Indicator Cluster Surveys, and various national censuses and surveys, 2000–2017:
UNICEF Data. “Child marriage.” UNICEF, June 2019.https://data.unicef.org/topic/child-protection/child-marriage/.

Information about unpaid care work is drawn from:
ILO (International Labour Organization). Care Work and Care Jobs for the Future of Decent Work. Geneva: ILO, 2018.PDF report.

UN Women, Progress of the World's Women 2019-2020: Families in a Changing World. New York: UN Women, 2019.PDF report.

IHME provided the estimates for the chart comparing formal labor participation rates among females relative to mean years of schooling. For workforce participation, IHME estimates total employment to population ratios (proportion of the population that is employed) using spatio-temporal Gaussian process regression (ST-GPR) by five-year age groups and sex, for the age range 15 to 69 years. Input data comes from household census, survey, and ILO tabulations. To be considered employed, respondents must report having worked at least one hour in the previous seven days for wages, in self-employment, as an apprentice, or for a family business, or else they must report being temporarily absent from such a position in the preceding week. IHME then uses ST-GPR to model the proportion of the employed population that are informal workers as defined by the ILO using data from ILO tabulations. Results are used to determine the proportion of the total population employed in formal work. IHME estimated mean years of schooling among females for the age range 15 to 69 years. The figure is shown in log-scale.

Additional references:

Aslan, Goksu, Corinne Deléchat, Monique Newiak, and Fan Yang. “Inequality in Financial Inclusion and Income Inequality.” Working Paper No. 17/236. Washington, DC: International Monetary Fund, November 8, 2017.PDF.

Gonzales, Christine, Sonali Jain-Chandra, Kalpana Kochhar, Monique Newiak, and Tlek Zeinullayev. “Catalyst for Change: Empowering Women and Tackling Income Inequality.” Staff Discussion Notes No. 15/20. Washington, DC: International Monetary Fund, October 22, 2015.PDF.

United Nations Development Programme. Gender Inequality Index (GII). UNDP Human Development Report. UNDP, 2018. Accessed September 1, 2019. Explanation:http://hdr.undp.org/en/content/gender-inequality-index-gii. Data:http://hdr.undp.org/en/composite/GII.

The graphic in the story is derived from GDP and population estimates:
World Bank. “World Development Indicators.” World Bank, 2019. Accessed September 1, 2019.http://datatopics.worldbank.org/world-development-indicators/.

The estimated target of $86 per capita (expressed in 2012 U.S. dollars) in government health expenditure is drawn from:
Royal Institute of International Affairs, Working Group on Financing, John-Arne Røttingen, Trygve Ottersen, and Awo Ablo. Shared Responsibilities for Health: A Coherent Global Framework for Health Financing. Chatham House report. London: Seven Bridges Press, 2014.PDF.

Taskforce on Innovative International Financing for Health Systems. More money for health, and more health for the money. Final report. Geneva: International Health Partnership, 2009.PDF.

For more information, refer to the following sources:

Anand, Rahul, David Coady, Adil Mohommad, Vimal Thakoor, and James P. Walsh. “The Fiscal and Welfare Impacts of Reforming Fuel Subsidies in India.” IMF Working Paper No. 13/128. Washington, DC: International Monetary Fund, May 29, 2013.PDF.

Field, Erica, Rihini Pande, Natalia Rigol, Simone Schaner, and Charity Troyer Moore. “An Account of One’s Own: Can Targeting Benefits Payments Address Social Constraints to Female Labor Force Participation?” Unpublished manuscript. Last modified October 17, 2016.PDF.

Gelb, Alan and Anna Diofasi. “Ghostbusters: Linking Subsidy Reform and Biometric Identity in India.” CDG Policy Blogs. Center for Global Development, March 17, 2015. https://www.cgdev.org/blog/ghostbusters-linking-subsidy-reform-and-biometric-identification-india.

Ministry of Petroleum and Natural Gas, Government of India. “Estimated savings/benefits of Rs. 59,599 crore upto March, 2019 under ‘PAHAL’ scheme.” Press release. July 5, 2019.https://pib.gov.in/newsite/PrintRelease.aspx?relid=191283.

Mittal, Neeraj, Anit Mukherjee, and Alan Gelb. “Fuel Subsidy Reform in Developing Countries: Direct Benefit Transfer of LPG Cooking Gas Subsidy in India.” CDG Policy Paper 114. Washington, DC: Center for Global Development, December 21, 2017.PDF.

Muralidharan, Karthick, Paul Niehaus, and Sandip Sukhtankar. “Building State Capacity: Evidence from Biometric Smartcards in India.” American Economic Review, 106, No. 10 (October 2016): 2895–2929.https://www.aeaweb.org/articles/pdf/doi/10.1257/aer.20141346.

For more information about the 2015 drought in Ethiopia, see:

FEWS NET (Famine Early Warning Systems Network). Ethiopia Special Report: Illustrating the Extent and Severity of the 2015 Drought. Report. FEWS.NET, December 17, 2015.PDF.

For more information about the growth and resilience of Ethiopia’s agricultural sector, see:

Dorosh, Paul and Shahidur Rashid, “Ethiopia’s 2015 drought; No reason for a famine,” IFPRI Blog. International Food Policy Research Institute, December 14, 2015.http://www.ifpri.org/blog/ethiopias-2015-drought-no-reason-famine.

Bachewe, Fantu Nisrane, Guush Berhane, Bart Minten, and Alemayehu Seyoum Taffesse. “Agricultural Growth in Ethiopia (2004-2014): Evidence and Drivers.” ESSP II Working Paper 81. Washington, DC: International Food Policy Research Institute (IFPRI), 2015.PDF.

For additional global perspective, see:

Global Commission on Adaptation. 2019 Flagship Report. Report, forthcoming.https://gca.org/global-commission-on-adaptation/report.

The Intergovernmental Panel on Climate Change (IPCC). Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. Report, forthcoming.https://www.ipcc.ch/report/srccl/.

World Resources Institute. Creating a Sustainable Food Future: A Menu of Solutions to Feed Nearly 10 Billion People by 2050. Report, July 2019.https://wrr-food.wri.org/.

IHME measured stunting prevalence as height-for-age more than two standard deviations below the reference median on the height-age growth curve based on WHO 2006 growth standards for children 0-59 months:

Bachewe, Fantu Nisrane, Guush Berhane, Bart Minten, and Alemayehu Seyoum Taffesse. “Agricultural Growth in Ethiopia (2004-2014): Evidence and Drivers.” ESSP II Working Paper 81. Washington, DC: International Food Policy Research Institute (IFPRI), 2015.http://ebrary.ifpri.org/utils/getdownloaditem/collection/p15738coll2/id/129782/filename/129993.pdf/mapsto/pdf.

Estimates are based on the GBD 2017, with forecasts for 2018–2030. For a detailed description of methods, see:

GBD 2017 Risk Factor Collaborators. “Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017.” The Lancet 392, no. 10159 (November 10, 2018): 2052–2090.https://doi.org/10.1016/S0140-6736(18)32225-6.

Peru and Nepal research and charts were provided by SickKids Stunting Reduction Exemplars Research Team in 2019.

IHME defines a maternal death as any death of a woman while pregnant or within one year of the end of pregnancy, irrespective of the duration and site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management, but not from accidental or incidental causes. Ages include 10 to 54 years. Estimates are based on the Global Burden of Disease study (GBD) 2017, with forecasts for 2018–2030:

Institute for Health Metrics and Evaluation (IHME). Findings from the Global Burden of Disease Study 2017. Booklet. Seattle, WA: IHME, 2018.PDF.

For a detailed description of methods, see:

GBD 2017 Causes of Death Collaborators. “Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017.” The Lancet 392, no. 10159 (November 10, 2018): 1736–1788.https://doi.org/10.1016/S0140-6736(18)32203-7.

For more information about group antenatal care, please refer to:

World Health Organization Reproductive Health Library. “WHO recommendations on group antenatal care.” WHO, March 28, 2018.https://extranet.who.int/rhl/topics/improving-health-system-performance/implementation-strategies/who-recommendation-group-antenatal-care.

The source for maternal mortality data in China is from:

National Health Commission. China Health Statistical Yearbook, 2018. Beijing: China Union Medical University Press, 2019.

IHME measures the sum of the prevalence of the 15 NTDs per 100,000 that are currently measured in the Global Burden of Disease study: human African trypanosomiasis, Chagas disease, cystic echinococcosis, cysticercosis, dengue, food-borne trematodiases, Guinea worm, soil-transmitted helminths, leishmaniasis, leprosy, lymphatic filariasis, onchocerciasis, rabies, schistosomiasis, hookworm, trichuriasis, ascariasis, and trachoma. Estimates are based on the GBD 2017, with forecasts for 2018–2030. For a detailed description of methods, see:

GBD 2017 Causes of Death Collaborators. “Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017.” The Lancet 392, no. 10159 (November 10, 2018): 1736–1788.https://doi.org/10.1016/S0140-6736(18)32203-7.

GBD 2017 SDG Collaborators. “Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017.” The Lancet 392, no. 10159 (November 10, 2018): 2091–2138.https://doi.org/10.1016/S0140-6736(18)32281-5.

The map of onchocerciasis elimination programs in Africa is from:

Expanded Special Project for Elimination of Neglected Tropical Diseases. “Onchocerciasis.” ESPEN.Accessed September 1, 2019.http://espen.afro.who.int/diseases/onchocerciasis.

The bar chart, based on WHO data, is adapted from:

Uniting to Combat Neglected Tropical Diseases. “River blindness (onchocerciasis).” Uniting to Combat NTDs. Accessed September 1, 2019.https://unitingtocombatntds.org/ntds/onchocerciasis/.

Extreme poverty rates measure the fraction of a country’s population that is estimated to live on less than $1.90 per day, measured in 2011 purchasing power parity (PPP) adjusted dollars. Nationally representative data on extreme poverty rates was extracted from the World Bank for the time period 1980 to 2018. However, this data does not provide a complete time series of poverty rates for every country. To estimate a complete time series for all countries, we used a method developed and widely used by the Global Burden of Disease study: spatio-temporal Gaussian process regression (ST-GPR). ST-GPR was chosen because it makes predictions by building from data when available and borrowing strength across time, geography, and predictive covariates (GDP per capita, female education, and kilocalorie consumption) when data was not available. Forecasted poverty estimates were calculated for 2018 to 2030 by estimating the year-over-year change in the poverty rate using an ensemble model. For more information, see:

Institute for Health Metrics and Evaluation (IHME). “Methods for estimating past and future extreme poverty.” IHME, August 6, 2019.http://www.healthdata.org/methods-estimating-past-and-future-extreme-poverty.

This is the most recent data available for select countries, ranging from 2005–2016; for additional information see:

Food and Agriculture Organization of the United Nations (FAO). Rural Livelihoods Information System (RuLIS), by indicator.” FAO. Accessed July 2019. http://www.fao.org/in-action/rural-livelihoods-dataset-rulis/data/by-indicator/en/.

For methodology, see:

Food and Agriculture Organization of the United Nations (FAO). Rural Livelihoods Information System (RuLIS): Technical notes on concepts and definitions used for the indicators derived from household surveys. Report. Rome: FAO, 2018.PDF.

IHME defines under-five mortality rate as the probability of death between birth and age five. It is expressed as number of deaths per 1,000 live births. Estimates are based on preliminary GBD 2019 findings, with forecasts for 2019–2030. For a detailed description of methods, see:

GBD 2017 Mortality Collaborators. “Global, regional, and national age-sex-specific mortality and life expectancy, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017.” The Lancet 392, no. 10159 (November 10, 2018): 1684–1735.https://doi.org/10.1016/S0140-6736(18)31891-9.

IHME defines neonatal mortality rate as the probability of death in the first 28 completed days of life. It is expressed as the number of deaths per 1,000 live births. A detailed description of methods is available at IHME defines neonatal mortality as the number of deaths during the first 28 completed days of life per 1,000 live births in a given year or period. Estimates are based on preliminary GBD 2019 findings, with forecasts for 2019-2030. For a detailed description of methods, see:

GBD 2017 Mortality Collaborators. “Global, regional, and national age-sex-specific mortality and life expectancy, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017.” The Lancet 392, no. 10159 (November 10, 2018): 1684–1735.https://doi.org/10.1016/S0140-6736(18)31891-9.

IHME estimates the age-standardized rate of new HIV infections per 1000 population. Estimates are based on the GBD 2017, with forecasts for 2018–2030. For more detailed description of methods, see:

GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. “Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017.” The Lancet 392, no. 10159 (November 10, 2018): 1789–1858.https://doi.org/10.1016/S0140-6736(18)32279-7.

IHME estimates new and relapse tuberculosis cases diagnosed within a given calendar year (incidence) using data from prevalence surveys, case notifications, and cause-specific mortality estimates as inputs to a statistical model that enforces internal consistency among the estimates. Estimates are based on the GBD 2017, with forecasts for 2018–2030. For a detailed description of methods, see:

GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. “Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017.” The Lancet 392, no. 10159 (November 10, 2018): 1789–1858.https://doi.org/10.1016/S0140-6736(18)32279-7.

IHME estimates the age-standardized rate of malaria cases per 1000 population. Estimates are based on the GBD 2017, with forecasts for 2018–2030. For a detailed description of methods, see:

GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. “Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017.” The Lancet 392, no. 10159 (November 10, 2018): 1789–1858.https://doi.org/10.1016/S0140-6736(18)32279-7.

Weiss, Daniel J., Tim C. D. Lucas, Michele Nguyen, Anita K. Nandi, Donal Bisanzio, and Katherine E. Battle. “Mapping the global prevalence, incidence, and mortality of Plasmodium falciparum, 2000–17: a spatial and temporal modelling study.” The Lancet 394, no. 10195 (July 27, 2018): 322–331.https://doi.org/10.1016/S0140-6736(19)31097-9.

The socio-demographic index (SDI) is a summary measure produced by IHME that identifies where countries or other geographic areas sit on the spectrum of development. Expressed on a scale of 0 to 1, SDI is a composite average of the rankings of the incomes per capita, average educational attainment, and fertility rates of all areas in the GBD study.

IHME estimates the proportion of women of reproductive age (15–49 years) who have their need for family planning satisfied with modern methods. Modern contraceptive methods include the current use of male or female sterilization, male or female condoms, diaphragms, spermicide foam or jelly, oral hormonal pills, implants, injections, intrauterine devices (IUDs), or emergency contraceptives. Estimates are based on preliminary GBD 2019 findings, with forecasts for 2019–2030. For a detailed description of methods, see:

GBD 2017 SDG Collaborators. “Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017.” The Lancet 392, no. 10159 (November 10, 2018): 2091–2138.https://doi.org/10.1016/S0140-6736(18)32281-5.

HME defines the UHC index to be the coverage of nine tracer interventions and risk-standardized death rates from 32 causes amenable to personal health care. Tracer interventions include vaccination coverage (coverage of three doses of DPT, measles vaccine, and three doses of the oral polio vaccine or inactivated polio vaccine); met need for modern contraception; antenatal care coverage (one and four visits); skilled birth attendant coverage; in-facility delivery rates; and coverage of antiretroviral therapy among people living with HIV. The 32 causes amenable to personal health care include tuberculosis, diarrheal diseases, lower respiratory infections, upper respiratory infections, diphtheria, whooping cough, tetanus, measles, maternal disorders, neonatal disorders, colon and rectal cancer, non-melanoma cancer, breast cancer, cervical cancer, uterine cancer, testicular cancer, Hodgkin’s lymphoma, leukemia, rheumatic heart disease, ischemic heart disease, cerebrovascular disease, hypertensive heart disease, peptic ulcer disease, appendicitis, hernia, gallbladder and biliary diseases, epilepsy, diabetes, chronic kidney disease, congenital heart anomalies, and adverse effects of medical treatment.

IHME then scaled the 41 inputs on a scale of 0 to 100, with 0 reflecting the worst levels observed between 1990 to 2016 and 100 reflecting the best observed. They took the arithmetic mean of these 41 scaled indicators to capture a wide range of essential health services pertaining to reproductive, maternal, newborn, and child health; infectious diseases; noncommunicable diseases; and service capacity and access. Estimates are based on the GBD 2017, with forecasts for 2018–2030. For a detailed description of methods, see:

GBD 2017 SDG Collaborators. “Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017.” The Lancet 392, no. 10159 (November 10, 2018): 2091–2138.https://doi.org/10.1016/S0140-6736(18)32281-5.

IHME measures current use of smoked tobacco. IHME collates information from all available surveys that include questions about frequency of tobacco use (e.g., daily, occasional), either currently or within the last 30 days, and information on the type of tobacco product smoked (including cigarettes, cigars, pipes, hookah, as well as local products). IHME converts all data to its standard definition so that meaningful comparisons can be made across locations and over time. Estimates are based on preliminary GBD 2019 findings, with forecasts for 2019–2030. For a detailed description of methods, see:

GBD 2017 Risk Factor Collaborators. “Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017.” The Lancet 392, no. 10159 (November 10, 2018): 2052–2090.https://doi.org/10.1016/S0140-6736(18)32225-6.

HME’s measurement of immunization coverage reports on the coverage of the following vaccines separately: three-dose diphtheria-tetanus-pertussis (DTP3), measles second dose (MCV2), and three-dose pneumococcal conjugate vaccine (PCV3). Estimates are based on preliminary GBD 2019 findings, with forecasts for 2019–2030. For a detailed description of methods, see:

GBD 2017 SDG Collaborators. “Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017.” The Lancet 392, no. 10159 (November 10, 2018): 2091–2138.https://doi.org/10.1016/S0140-6736(18)32281-5.

Estimates of local patterns of vaccine coverage from IHME are produced using geospatial modeling methods described in:

Mosser, Jonathan F., William Gagne-Maynard, Puja C. Rao, Aaron Osgood-Zimmerman, Nancy Fullman, and Nicholas Graetz. “Mapping diphtheria-pertussis-tetanus vaccine coverage in Africa, 2000–2016: a spatial and temporal modelling study.” The Lancet 393, no. 10183 (May 4, 2019): 1843–1855.https://doi.org/10.1016/S0140-6736(19)30226-0.

At the time the 2019 Goalkeepers Report was published, measles estimates were not yet published.

The UNESCO projections are based on annualized differences between the average Analysis Programme of CONFEMEN Education Systems (PASEC) scores in 2006 versus 2014. Note that the tests administered in each year were not psychometrically comparable, though they covered largely similar content. The UNESCO Institute for Statistics (UIS) applied an equivalent definition of minimum proficiency across the assessments in each year to create a harmonized performance scale that has not been empirically validated given the design of the assessments.

For more information, see:

UNESCO Institute for Statistics and Global Education Monitoring Report Team. Meeting Commitments: Are Countries on Track to Achieve SDG 4? 2019. https://unesdoc.unesco.org/ark:/48223/pf0000369009

UNESCO Institute for Statistics. SDG 4 Data Book: Global Education Indicators 2019. E-book. Montreal: UNESCO, 2019PDF.

The chart is adapted from:

UN Women, Progress of the World's Women 2019-2020: Families in a Changing World. New York: UN Women, 2019.PDF Report.

The data is the most recent available for 88 countries and territories (2001–2017). The age group is 15+ where available (18+ in Ghana). In a number of cases, data is for those ages 10+ or 12+. In the case of Thailand (2015) they are for those ages 6+, and in the United Republic of Tanzania (2014) for those ages 5+. Data for Bulgaria, Denmark, Latvia, the Netherlands, Slovenia, and Spain corresponds to time spent on unpaid care among those ages 20 to 74 only. In the case of Qatar, only urban areas are covered in the analysis. Differences across countries should be interpreted with caution, given heterogeneity across surveys and countries in definitions, methodology, and sample coverage. For further information on the country-level data, see:

United Nations Statistics Division. “Global SDG Indicators Database.” Updated August 6, 2019.https://unstats.un.org/sdgs/indicators/database/.

IHME measured households with piped sanitation (with a sewer connection); households with improved sanitation without a sewer connection (pit latrine, ventilated improved latrine, pit latrine with slab, composting toilet); and households without improved sanitation (flush toilet that is not piped to sewer or septic tank, pit latrine without a slab or open pit, bucket, hanging toilet or hanging latrine, shared facilities, no facilities), as defined by the Joint Monitoring Programme for Water Supply and Sanitation. Estimates are based on the GBD 2017, with forecasts for 2018–2030. For a detailed description of methods, see:

GBD 2017 Risk Factor Collaborators. “Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017.” The Lancet 392, no. 10159 (November 10, 2018): 2052–2090.https://doi.org/10.1016/S0140-6736(18)32225-6.

This data is the most recent available for 99 countries, collected in 2017 through the Global Findex surveys:

World Bank Group. “ID4D: Identification for Development.” Accessed July 2019.https://id4d.worldbank.org/.

For methodology, see:

ID4D. “Data Note – ID4D Global Dataset and ID4D-Findex Survey.” Revised August 29, 2018.PDF.

Demirgüç, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. “Survey methodology.” In The Global Findex database 2017 : measuring financial inclusion and the fintech revolution, 111–122. Washington, DC: World Bank, 2018.https://globalfindex.worldbank.org/sites/globalfindex/files/reports/2017%20Findex%20full%20report_survey%20methodology.pdf.

Sources for chart showing percentage of adults with a bank account are:

2005 and 2008:

International Monetary Fund. “IMF Data: Financial Access Survey (FAS).” Accessed September 1, 2019.https://data.imf.org/?sk=E5DCAB7E-A5CA-4892-A6EA-598B5463A34C.

2011–2017:

Demirgüç, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. The Global Findex database 2017 : measuring financial inclusion and the fintech revolution. Washington, DC: World Bank, 2018.https://globalfindex.worldbank.org/. World Bank. “Global Findex Database 2017.” World Bank. Accessed September 1, 2019.https://globalfindex.worldbank.org/.

2018–2030:

An annualized average conversion rate of non-included adults was calculated by the Findex team at the World Bank based on existing World Bank data for 2011, 2014, and 2017 and then applied to each country from 2018 to 2030. Weighted values were used for each country. The projections do not consider growth before 2011 and only use demand-side financial inclusion data. The gender gap remains flat because the available data from 2011, 2014, and 2017 does not show any change in the gender gap.

The “if we progress” scenario is based on:

Manyika, James, Susan Lund, Marc Singer, Olivia White, and Chris Berry. Digital Finance for All: Powering Inclusive Growth in Emerging Economies. McKinsey & Company, September 2016.PDF Report.

Images provided by Gates Archive, with the following additions in the sections noted:

“Examining Inequality”: photo courtesy of LightRocket via Getty Images/SOPA Images/Marcus Valance

“Climate Adaptation”: photo courtesy of Corbis via Getty Images/Art in All of Us/Eric Lafforgue

“Maternal Mortality”: photo courtesy of Jhpiego/Paul Joseph Brown