2018 Data Sources: Is Poverty Inevitable?

Most low-income countries in sub-Saharan Africa still aren’t collecting data on agricultural productivity and income, because doing so is unusually expensive and labor intensive. Working with a group of donors, U.N. agencies, and countries, our foundation is helping to ramp up efficient agricultural surveys in the countries where there are gaps, with the goal that all countries are regularly funding high-quality surveys in the next decade. This support will enable these countries to continually adjust investments and policies based on evidence about what works.

All data provided by the Institute for Health Metrics and Evaluation (IHME), 2018. Brief methodological notes are provided below. For more information, please visit IHME's website. All regional classifications follow IHME super-regions, which are regions grouped on the basis of cause of death patterns

Extreme poverty raes measure the fraction of a country’s population estimated to live below $1.90 per day, measured in purchasing power parity (PPP) adjusted dollars. National estimates were extracted for 1980 and 2016 from the World Bank. Spatio-temporal Gaussian process regression was used to estimate a complete time series for all countries using three covariates (GDP per capita, education, and fertility) predictive of poverty. National poverty estimates were estimated for 2017 to 2050 by estimating the year-over-year change in the poverty rate using an ensemble model. For more information, please see IHME’s technical note on “Methods for estimating past and future extreme poverty.”

Population estimates are based on a systematic analysis of data on population, mortality, fertility, and migration using a Bayesian statistical model. Projections of mortality and fertility contain a causal component that reflects key drivers and a component that captures residual variation that is correlated in time. For mortality, the causal component includes risks and interventions as well as more distal drivers such as income. For fertility, women’s educational attainment and the fraction of women who have their need for family planning met with modern contraception methods are included.

Estimates of human capital stock incorporate three components: educational attainment measured as the average years of schooling; learning or education quality as measured by standardized tests; and functional health status measured as the weighted prevalence of seven health conditions shown to be related to productivity, including stunting. The effect of changes in human capital stock on changes in GDP per capita were estimated using a growth regression and used to model the effect of different future scenarios.

The World Bank’s Human Capital Project will release a Human Capital Index October 11.

Human Capital and Population Growth data chart “Projected Population in Sub-Saharan Africa” from Track20 Project, 2018. The “U.N. projection” is aligned with the World Population Prospects 2017 revision, medium variant for sub-Saharan Africa. The projected impact of addressing unwanted fertility is estimated by assuming the total fertility rate (TFR) declines rapidly in a 5-year period by overall level of excess fertility based upon the weighted average from 39 Demographic and Health Surveys. The “shift away from early births” scenario keeps the TFR decline consistent with the U.N. medium variant but adjusts age distribution of births over a 5-year period to mimic current age distribution of births for Asia, where there are few adolescent births, and most births are concentrated in age groups above the age of 25.

Three Future Scenarios for Zimbabwe’s HIV Epidemic data chart “Up to 364K New Cases of HIV Could be Averted Among 15-29-Year-Olds” by Leo Beacroft and Professor Tim Hallett of Imperial College using the model from Smith et. al., The Lancet HIV, July 2016, 3(7) e289-e296, transferring the analysis from South Africa to Zimbabwe.

Moving from Enrollment to Learning data chart “Percentage of children and adolescents expected to achieve minimum proficiency level in math and reading” adapted from UNESCO Institute for Statistics, “More than One-Half of Children and Adolescents Are Not Learning Worldwide”, Fact Sheet No. 46, September 2017. The figure represents the combined proportion of children and adolescents of primary and lower-secondary age who are expected to achieve minimum proficiency level in reading and math by the time they finish primary or lower-secondary school, respectively.

“Vietnam scores as well as high-income countries on international tests” data chart adapted from Dang, H.H., and Glewwe, P.W., “Well Begun, but Aiming Higher: A Review of Vietnam’s Education Trends in the Past 20 Years and Emerging Challenges,” The Journal of Development Studies, 2018, 54(7): 1171-1195. Data shared by the authors.

Agriculture and Poverty Reduction data chart “Percentage of Population Living in Poverty” by International Food Policy Research Institute (IFPRI), 2018, using IFPRI’s Rural Investment and Policy Analysis (RIAPA) model. The Ghana RIAPA model uses a 2013 social accounting matrix to align with the 2012/13 Ghana Living Standards Survey. Ghana’s national poverty threshold is used, which defines poverty as an individual considered unable to meet all their food and nonfood needs, which was set at 1,314 Ghanaian cedi per adult per year for 2013. The “current projection” scenario assumes 2006-2013 agricultural and national trends continue until 2030. The “doubling productivity” scenario increases total factor productivity growth across all crops, livestock, and fisheries until labor productivity level doubles by 2030 relative to 2016. Poverty impacts are measured using survey-based microsimulation analysis.

All data is provided by IHME, 2018. Moderate poverty rates measure the fraction of a country’s population estimated to live below $3.20 per day, measured in purchasing power parity (PPP) adjusted dollars. For more information, please see description above under “Poverty estimates.”

Munoz Boudet, A., Buitrago, P., Leroy De La Briere, B., Newhouse, D., Rubiano Matulevich, E., Scott, K., Suarez Becerra, P., Gender differences in poverty and household composition through the life-cycle : a global perspective. Policy Research working paper; no. WPS 8360. World Bank Group, 2018.

UN Women, Progress of the World’s Women 2015–2016: Transforming Economies, Realizing Rights, 2015.

2005 and 2008: International Monetary Fund, Financial Access Survey.
2011–2017: World Bank, Global Financial Inclusion (Global Findex) Database.
2018–2030: World Bank. An annualized average conversion rate of non-included adults was calculated based upon existing 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 upon Manyika, J., Lund, S., Singer, M., White, O., and Berry, C., “Digital finance for all: Powering inclusive growth in emerging economies,”McKinsey Global Institute, September 2016.

UN Women, Progress of the World’s Women 2015–2016: Transforming Economies, Realizing Rights, 2015.

UNESCO Institute for Statistics, SDG 4 Data Book: Global Education Indicators 2018, 2018.

IHME measures the sum of the prevalence of 15 NTDs per 100,000, 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.

IHME defines the indicatorby a UHC index of the coverage of ninetracer interventions and risk-standardized death rates from 32 causes amenable to personal healthcare. 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 needfor 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 healthcare 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 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.

IHME’s measurement of immunization coverage reports on the coverage ofthe following vaccinesseparately: three-dose diphtheria-tetanus-pertussis (DTP3), measles second dose (MCV2), and three-dose pneumococcalconjugate vaccine (PCV3).

IHME measured households with piped sanitation (with a sewer connection); households with improved sanitation without a sewerconnection (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.