2022 Data Sources

IHME measures 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 of age 0–59 months. Projections to 2030 were modeled using an ensemble approach to forecast the prevalence of stunting, using SDI as a key driver in order to capture the effects of the COVID-19 pandemic on income per capita and education.

Estimates in Global Burden of Disease (GBD) 2020 leveraged several methodological advances including ensemble model predictions for severity-specific stunting prevalence and mean height-for-age z-scores (HAZ), further disaggregation of <5 age groups, and an improved distribution fitting model that focuses on HAZ scores of < -2 (i.e., under the range for stunting) rather than across the full range of HAZ scores. This led to improved estimates in a number of countries, notably including South Africa, the Democratic Republic of the Congo, India, and Pakistan. In addition, new data has improved estimates in a number of countries as well, including Pakistan.

The maternal mortality ratio (MMR) is defined as the number of maternal deaths among women ages 15–49 years during a given time period per 100,000 live births. It depicts the risk of maternal death relative to the number of live births and essentially captures the risk of death in a single pregnancy or a single live birth. Projections to 2030 were modeled using an ensemble approach to forecast MMR, using SDI as a key driver in order to capture the effects of the COVID-19 pandemic on income per capita and education.

Our analysis of direct and indirect maternal mortality in selected countries showed no significant relationship between direct mortality and indicators of the COVID-19 pandemic (i.e., COVID-19 infection incidence rate, COVID-19 mortality rate, changes in mobility). However, there was a significant effect of the COVID-19 pandemic on indirect maternal mortality. This effect on indirect maternal mortality was modeled using COVID-19 mortality rate as a covariate. This year, our estimates of excess indirect maternal mortality also include a correction for the proportion of deaths that are considered incidental, or unrelated to pregnancy status. Currently available data does not suggest a consistent relationship between the pandemic and indicators of maternal care (antenatal care, skilled birth attendance), and IHME has not incorporated an effect of the pandemic on these indicators.

IHME defines the under-5 mortality rate (U5MR) as the probability of death between birth and age 5. It is expressed as number of deaths per 1,000 live births. Projections were based on a combination of key drivers, including GBD risk factors, selected interventions (e.g., vaccines), and SDI. Additional short-term disruptions (2020–2021) from the COVID-19 pandemic incorporated the reductions seen in child deaths from infectious diseases (flu, respiratory syncytial virus, measles, pertussis) observed during the pandemic, driven primarily by social distancing and mask use, as well as increases in child deaths due directly to COVID-19. Most of the changes in U5MR estimates in the current Goalkeepers Report results came from new and additional input mortality data that IHME has incorporated since the GBD 2019 study, including estimates of excess mortality observed during the COVID-19 pandemic.

Wang, H., Paulson, K. R., Pease, S. A., Watson, S., Comfort, H., Zheng, P., Aravkin, A. Y., Bisignano, C., Barber, R. M., Alam, T., Fuller, J. E., May, E. A., Jones, D. P., Frisch, M. E., Abbafati, C., Adolph, C., Allorant, A., Amlag, J. O., Bang-Jensen, B. L., . . . Murray, C. J. L. (2022). Estimating excess mortality due to the COVID-19 pandemic: A systematic analysis of COVID-19-related mortality, 2020–21. The Lancet, 399(10334), 1513–1536. https://doi.org/10.1016/S0140-6736(21)02796-3

IHME defines the 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. Projections were based on a combination of key drivers, including GBD risk factors, selected interventions (e.g., vaccines), and SDI. Most of the changes in neonatal mortality estimates in this year’s Goalkeepers Report are the result of new data, including estimates of excess mortality observed during the COVID-19 pandemic.

Wang, H., Paulson, K. R., Pease, S. A., Watson, S., Comfort, H., Zheng, P., Aravkin, A. Y., Bisignano, C., Barber, R. M., Alam, T., Fuller, J. E., May, E. A., Jones, D. P., Frisch, M. E., Abbafati, C., Adolph, C., Allorant, A., Amlag, J. O., Bang-Jensen, B. L., . . . Murray, C. J. L. (2022). Estimating excess mortality due to the COVID-19 pandemic: A systematic analysis of COVID-19-related mortality, 2020–21. The Lancet, 399:(10334), 1513–1536. https://doi.org/10.1016/S0140-6736(21)02796-3

IHME estimates the HIV rate as new HIV infections per 1,000 population. Forecasts of HIV incidence were based on forecasted ART, prevention of maternal-to-child transmission (PMTCT) coverage, and incidence as inputs into a modified version of Avenir Health’s Spectrum software. Adult ART is forecasted using the expected spending on HIV curative care—which in turn was forecasted based on income per capita, including the effect of the COVID-19 pandemic—and ART prices. GBD estimates incorporated methodological changes to cause of death data for HIV as well as the adjustment of incidence estimates, to be consistent with vital registration data.

Mahy, M., Penazzato, M., Ciaranello, A., Mofenson, L., Yiannoustsos, C., Davies, M-A., &Stover, J. (2017). Improving estimates of children living with HIV from the Spectrum AIDS Impact Model. AIDS, 31(Suppl 1), S13 - S22. https://doi.org/10.1097/QAD.0000000000001306

Eaton, J. W., Brown, T., Puckett, R., Glaubius, R., Mutai, K., Bao, L., Salomon, J. A., Stover, J., Mahy, M., & Hallett, T. B. (2019). The Estimation and Projection Package Age-Sex Model and the r-hybrid model: New tools for estimating HIV incidence trends in sub-Saharan Africa. AIDS, 33(Suppl 3), S235–S44. https://doi.org/10.1097/QAD.0000000000002437

Jahagirdar, D., Walters, M. K., Novotney, A., Brewer, E. D., Frank, T. D., Carter, A., Biehl, M. H., Abbastabar, H., Abhilash, E. S., Abu-Gharbieh, E., Abu-Raddad, L. J., Adekanmbi, V., Adeyinka, D. A., Adnani, Q. E. S., Afzal, S., Aghababaei, S., Ahinkorah, B. O., Ahmad, S., Ahmadi, K., & Kyu, H. H. (2021). Global, regional, and national sex-specific burden and control of the HIV epidemic, 1990–2019, for 204 countries and territories: the Global Burden of Diseases Study 2019. The Lancet HIV, 8(10), e633-651. https://doi.org/10.1016/S2352-3018(21)00152-1

IHME estimates new and relapse tuberculosis (TB) 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. GBD estimates in this round incorporate methodological improvements in using case notification data.

IHME evaluated the literature on COVID-19 disruptions to TB incidence and identified three types of studies: studies reporting raw data on diagnosis and treatment in 2020, studies reporting on service disruption from new surveys, and studies reporting on models of TB impacts using notification data or theoretical COVID scenarios. Due to the lack of counterfactual data in pre-pandemic time periods and modeling assumptions used in the current studies, IHME could not estimate an additional disruption in TB incidence due to COVID-19. IHME will continue to evaluate and analyze as more data is released. In addition to historical trends, projections to 2030 were modeled using an ensemble approach to forecast the incidence of TB, using SDI as a key driver in order to capture the effects of the COVID-19 pandemic on income per capita and education

IHME estimates the malaria rate as the number of new cases per 1,000 population. To estimate malaria incidence in 2020 and 2021, IHME takes into account updated reports regarding pandemic-related disruptions to malaria interventions and effective treatment with an antimalarial drug (which includes ITN, indoor residual spraying, antimalarial treatment, and drug effectiveness). These reports were used to apply an adjustment to estimates of antimalarial treatment coverage, which were then used to produce estimates of malaria incidence. Projections to 2030 were derived using an ensemble model. First, coverage of ACT and ITNs is forecast as a function of the SDI, which is predicted in turn by projections of income per capita and education. For countries where there exists available data on intervention coverage, malaria incidence is forecasted through 2030 using an ensemble approach, incorporating past trends and forecasts of ACT and ITN coverage to produce the projections. For countries where there is no available data on ACT or ITN coverage, an ensemble approach is used based on past trends in incidence as well as projections of SDI, which incorporates the effects of the COVID-19 pandemic through income per capita and education.

Due to reporting lags, there is still relatively little data to inform pandemic-related impacts on malaria incidence. The WHO global pulse surveys, which were used to adjust 2020 and 2021 incidence results, were applied only to countries in sub-Saharan Africa due to a lack of comparable method for applying the adjustment to other regions arising from the difference in incidence estimation. Furthermore, although those pulse surveys currently allow us to begin trying to capture malaria pandemic-related impacts, the surveys were completed by national-level health officials and capture only their individual assessment of how the pandemic has affected care seeking.

World Health Organization.(2020, August). Pulse survey on continuity of essential health services during the COVID-19 pandemic: Interim report, 27 August 2020. Accessed November 2021. https://www.who.int/publications/i/item/WHO-2019-nCoV-EHS_continuity-survey-2020.1

World Health Organization. (2021, April). Second round of the national pulse survey on continuity of essential health services during the COVID-19 pandemic: January-March 2021 (Interim report). Accessed November 2021. https://www.who.int/publications/i/item/WHO-2019-nCoV-EHS-continuity-survey-2021.1

IHME measures the sum of the prevalence of 15 NTDs per 100,000 that are currently measured in the annual Global Burden of Disease study: human African trypanosomiasis, Chagas disease, cystic echinococcosis, cysticercosis, dengue, food-borne trematodiases, Guinea worm, soil-transmitted helminths (STH, comprising hookworm, trichuriasis, and ascariasis), leishmaniasis, leprosy, lymphatic filariasis, onchocerciasis, rabies, schistosomiasis, and trachoma. Since the 2020 Goalkeepers Report, changes in historical trends in this indicator reflect updates to the estimated prevalence of each NTD made for the GBD 2020 study. Specifically, changes in the summary NTD prevalence indicator between the 2020 Goalkeepers Report and these estimates largely reflect the addition of new data to STH models, especially in Latin America and South Asia.

In the 2021 Goalkeepers Report, IHME did not estimate a COVID-19 effect on this indicator, due to limited surveillance and control program data availability. Modeling studies and available data suggest that the COVID pandemic likely resulted in disruptions to NTD epidemiology, though these disruptions are likely to vary by disease and location and may be variably amenable to mitigation through increased control efforts. Although modeling studies can characterize potential disruptions under various scenarios, reliable data to quantify the true magnitude of pandemic effects on NTD epidemiology are sparse.

For this year’s report, IHME searched for published and gray literature quantifying the impact of the COVID-19 pandemic on NTD prevalence. Due to data gaps, lags in availability, and challenges in accounting for the likely disruptions to NTD surveillance during the pandemic, IHME found evidence to support adjustment for COVID-19 disruptions only for dengue. IHME adjusted dengue estimates in 2020 and 2021 using country-specific estimates of COVID disruptions from Chen et al. (2022), including updated estimates for 2021 graciously provided by the study authors via personal communication. For 2020, IHME adjusted only the proportion of cases occurring in April through December, reflecting the timing of the start of the pandemic; for 2021, IHME adjusted the full year. IHME excluded Brazil from the country-specific analysis due to data inconsistencies. For countries not estimated directly by this analysis, IHME applied regional or global disruption ratios. Projections to 2030 used an ensemble model, driven both by trends in the past as well as projections of SDI, which incorporated disruptions from the COVID-19 pandemic on income per capita and education.

Hollingsworth, T. D., Mwinzi, P., Vasconcelos, A., & de Vlas, S. J. (2021). Evaluating the potential impact of interruptions to neglected tropical disease programmes due to COVID-19. Transactions of The Royal Society of Tropical Medicine and Hygiene, 115(3), 201–204. https://doi.org/10.1093/trstmh/trab023

Chen, Y., Li, N., Lourenço, J., Wang, L., Cazelles, B., Dong, L., Li, B., Liu, Y., Jit, M., Bosse, N. I., Abbot, S., Velayudhan, R., Wilder-Smith, A., Tian, H., & Brady, O. J. (2022). Measuring the effects of COVID-19-related disruption on dengue transmission in southeast Asia and Latin America: A statistical modelling study. The Lancet Infectious Diseases, 22(5), 657–667. https://doi.org/10.1016/S1473-3099(22)00025-1

IHME estimates the proportion of women of reproductive age (15–49 years) who have their need for family planning satisfied with modern contraceptive methods. Modern contraceptive methods include the current use of male and female sterilization, male and female condoms, diaphragms, cervical caps, sponges, spermicidal agents, oral hormonal pills, patches, rings, implants, injections, intrauterine devices (IUDs), and emergency contraceptives. Projections to 2030 used an ensemble model, based both on past trends and using SDI as a key driver, which incorporates projections of income per capita and education and the effects of the COVID-19 pandemic.

Our analysis of PMA surveys and the smartphone-based follow-up survey referenced above does not show a consistent, significant reduction in contraception use due to the pandemic. As a result, IHME did not incorporate a short-term effect on the family planning indicator. Changes to the historical estimates can be attributed to methodological updates and the addition of new data sources, including the Generations and Gender Programme surveys. They switched from modeling the demand that is satisfied with modern methods directly for all women to modeling the three underlying components of the indicator separately for partnered and unpartnered women: any contraceptive use, proportion of use that is modern, and proportion of non-use that is unmet need. This modeling approach better aligns with data restrictions such as only surveying partnered (married or in-union) women and allows us to construct the full range of family planning indicators.

The universal health coverage (UHC) effective coverage index is a metric composed of 23 effective coverage indicators that cover population-age groups across the entire life course (maternal and newborn age groups, children under age 5, youths ages 5–19 years, adults ages 20–64, and adults ages 65 years old and older). These indicators fall within several health service domains: promotion, prevention, and treatment.

Health system promotion indicators include met need for family planning with modern contraception.

Health system prevention indicators include the proportion of children receiving the third dose of the diphtheria-tetanus-pertussis vaccine and children receiving the first dose of measles-containing vaccine. Antenatal care for mothers and antenatal care for newborns are considered indicators of health system prevention and treatment of diseases affecting maternal and child health.

Indicators of treatment of communicable diseases are the mortality-to-incidence (MI) ratios for lower respiratory infections, diarrhea, and tuberculosis, as well as coverage of ART among those with HIV/AIDS. Indicators of treatment of noncommunicable diseases include MI ratios for acute lymphoid leukemia, appendicitis, paralytic ileus and intestinal obstruction, cervical cancer, breast cancer, uterine cancer, and colorectal cancer. Indicators of treatment of noncommunicable diseases also include mortality-to-prevalence (MP) ratios for stroke, chronic kidney disease, epilepsy, asthma, chronic obstructive pulmonary disease, diabetes, and the risk-standardized death rate due to ischemic heart disease.

To produce forecasts of the UHC index from 2022 to 2030, a meta-stochastic frontier model for UHC was fit, using total health spending per capita projections as the independent variable. Country- and year-specific inefficiencies were then extracted from the model and forecasted to 2030 using a linear regression with exponential weights across time for each country level. These forecasted inefficiencies, along with forecasted estimates of total health spending per capita, were substituted into the previously fit frontier to obtain forecasted UHC for all countries for 2022–2030.

Short-term effects due to the pandemic were included in our final results with some exceptions. ART coverage scores and met demand for family planning were not adjusted, due to limitations in data as described in previous indicator sections. Adjustments for vaccine delivery are described in the Vaccines section. For other indicators (19 out of 23), in the absence of data to inform the correspondence between reductions in utilization and reductions in coverage, IHME applied 25% of the reduction in monthly missed medical visits (excluding routine services).

IHME measures the age-standardized prevalence of any current use of smoked tobacco among those age 15 and older. IHME collates information from available representative surveys that include questions about self-reported current use of tobacco and information on the type of tobacco product smoked (including cigarettes, cigars, pipes, hookahs, and local products). IHME converts all data to its standard definition of any current smoking within the last 30 days, so that meaningful comparisons can be made across locations and over time. Estimates this year are higher than last year to reflect the update in the indicator from daily smoking to any smoking within the last 30 days, to better align with the SDG definition. Projections to 2030 used SDI as a key driver, which incorporates projections of income per capita, education, and the effect of the COVID-19 pandemic.

World Health Organization. (2021). WHO global report on trends in prevalence of tobacco use 2000-2025. (4th ed.).https://www.who.int/publications/i/item/9789240039322. Licence: CC BY-NC-SA 3.0 IGO.

IHME’s measurement of immunization coverage reports on the coverage of the following vaccines separately: DTP3, measles second dose (MCV2), and three-dose pneumococcal conjugate vaccine (PCV3). IHME estimated the short-term (2020–2021) effects via administrative data on vaccine doses. In the 2021 Goalkeepers Report, IHME used a two-step random-spline meta-regression model to estimate coverage disruptions, fit to monthly administrative data and using mobility disruptions as a predictor. In this year’s report, IHME estimated coverage disruptions due to the COVID pandemic directly within our modeling framework, in the same way that stockouts and other disruptions are accounted for in pre-pandemic years. This change was made for several reasons. First, full-year administrative data for both 2020 and 2021 are now available through WHO and UNICEF’s Joint Reporting Process, representing a more comprehensive annual data set than was available for last year’s report. Second, the availability of timely monthly coverage data has decreased throughout the pandemic. Third, though mobility disruptions were a strong predictor of coverage disruptions early in the pandemic, the reasons for ongoing vaccination service disruptions have become increasingly complex over time, including persistent supply disruptions, workforce shortages, and decreased care seeking. In this year’s report, therefore, IHME has adapted our modeling strategy to leverage the increasing amount of annual data and decrease the model’s reliance on mobility as a predictor of coverage disruptions.

To estimate disruptions in vaccine coverage, IHME used administrative vaccine coverage data collected through the 2022 Joint Reporting Form. First, IHME assembled a “shock-free” time series of administrative vaccine coverage data, omitting country-year-vaccine data points for which countries reported stockouts or for which other known service delivery disruptions made sudden decreases in vaccine coverage plausible. In this step, they omitted all data points from 2020 and 2021 for all countries due to the COVID pandemic. Second, IHME then fit spatiotemporal Gaussian process regression (ST-GPR) models to this “shock-free” administrative time series, producing estimates of expected administrative coverage in the absence of disruptions. Third, IHME compared the reported administrative coverage to these expectations, to estimate the magnitude of disruption implied by the administrative data for each country, vaccine, and year. Last, IHME used these estimated disruptions in administrative coverage to generate covariates in our final ST-GPR coverage models, which were fit to survey data and bias-adjusted administrative data. If administrative data was missing in 2020 or 2021, they imputed disruptions using vaccine- and year-specific distributions of observed disruptions in countries with available administrative data, propagating uncertainty throughout this imputation process. This approach allowed IHME to leverage the magnitude of coverage disruptions implied by administrative data, while still adjusting for bias in this data.

Causey, K., Fullman, N., Sorensen, R. J. D., Galles, N. C., Zheng, P., Aravkin, A., Danovaro-Holliday, M. C., Martínez-Piedra, R., Sohda, S. V., Velandia-Gonzáles, M. P., Gacic-Dobo, M., Castro, E., He, J., Schipp, M., Deen, A., Hay, S. I., Lim, S. S., & Mosser, J. F. (2021). Estimating global and regional disruptions to routine childhood vaccine coverage during the COVID-19 pandemic in 2020: A modelling study. The Lancet, 398(10299), 522–534. https://doi.org/10.1016/S0140-6736(21)01337-4

World Health Organization. (2022, February). Third round of the global pulse survey on continuity of essential health services during the COVID-19 pandemic (Interim report, November–December 2021). Accessed July 27, 2022. https://www.who.int/publications-detail-redirect/WHO-2019-nCoV-EHS_continuity-survey-2022.1

IHME estimates the proportion of population with access to safely managed sanitation. As defined by the Joint Monitoring Programme (JMP), a safely managed facility must meet three criteria: (i) is not shared with multiple households, (ii) is an improved sanitation facility, and (iii) its wastewater is disposed of safely. Safe wastewater disposal can consist of being treated and disposed of in situ, stored temporarily and treated off-site, or transported through a sewer and treated. Safely managed treated wastewater must have received at least secondary treatment. IHME measured households with piped sanitation (with a sewer connection or septic tank); households with improved sanitation but without a sewer connection (pit latrine, ventilated improved latrine, pit latrine with slab, composting toilet); 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, no facilities); and wastewater treatment type for sewer-connected households, as defined by the JMP for Water Supply and Sanitation. Two new models were developed for the 2021 Goalkeepers Report, those being the proportion of sewer-connected facilities that are safely managed and the proportion of improved, non-sewer facilities that are safely managed.

IHME used a meta-regression, Bayesian, regularized, trimmed (MR-BRT) spline cascade model, with SDI as a predictor, cascading on super-region and country to estimate the proportion of sewer-connected facilities that are safely managed. Using cross-validation, they selected this model from a collection of candidate models based on out-of-sample root-mean-square deviation (RMSE). The estimates from this model were multiplied by the existing IHME estimates of the proportion of the population with sewer-connected facilities to estimate the proportion of the population with safely managed sewer-connected facilities.

IHME used a shape constrained additive model, with lag-distributed income per capita (LDI) as a predictor and random effects on super-region and country to estimate the proportion of improved, non-sewer facilities that are safely managed. Using cross-validation, they selected this model from a collection of candidate models based on out-of-sample RMSE. The estimates from this model were multiplied by the IHME estimates of the proportion of the population with improved, non-sewer-connected facilities to estimate the proportion of the population with safely managed improved non-sewer facilities.

To estimate the proportion of the total population with safely managed sanitation, the proportion of the population with safely managed sewer-connected facilities were added to the proportion of the population with safely managed improved non-sewer facilities. IHME propagated uncertainty through all components of the modeling chain using posterior simulation in which all calculations were performed on 1,000 draws from the posterior distribution of each model. Projections to 2030 were modeled using an ensemble approach to forecast the summary exposure value of unsafe sanitation, using SDI as a key driver in order to capture the effects of the COVID-19 pandemic and projections of income per capita and education.

World Health Organization & UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene. (2021). Proportion of population using safely managed sanitation services [SDG indicator 6.2.1a metadata]. JMP. Accessed December 12, 2021. https://washdata.org/sites/default/files/2022-01/jmp-2021-metadata-sdg-621a.pdf

Zheng, P., Barber, R., Sorensen, R. J. D., Murray, C. J. L., & Aravkin, A. Y. (2021). Trimmed constrained mixed effects models: Formulations and algorithms. Journal of Computational and Graphical Statistics, 30(3), 544-556. https://doi.org/10.1080/10618600.2020.1868303

Poverty data is based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies comes primarily from the LIS (formerly Luxembourg Income Study) database.

For 2019–2022 estimates, extreme poverty is measured as the headcount ratio of people living on less than US$1.90 per day. 2018 is the last year with official global poverty estimates. Baseline and pessimistic projections utilize growth forecasts based on April 2022 Macro Poverty Outlook data sets from the Poverty and Inequality Platform database. The baseline scenario distributes the impacts of the COVID-19 pandemic, rising inflation, and the conflict in Ukraine equally to all households. The pessimistic scenario includes the disproportionate impact of rising food prices on the bottom 40% compared to the top 60% over the baseline scenario. Official poverty estimates are available for East Asia and Pacific, Europe and Central Asia, Latin America and Caribbean, sub-Saharan Africa, and the rest of the world for up to 2019, and for Middle East and North Africa up to 2018. Official South Asia estimates are only available up to 2014. Regions are categorized using the Poverty and Inequality Platform definition.

Luxembourg Income Study Database (LIS). https://www.lisdatacenter.org/

World Bank. Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population) [Data set]. Poverty and Inequality Platform: World Development Indicators. Accessed June 2022.  https://data.worldbank.org/indicator/SI.POV.DDAY. License: CC BY-4.0.

2019–2022 Estimates

Lakner, C., Mahler, D. G., Negre, M., & Prydz, E. B. (2022). How much does reducing inequality matter for global poverty? Journal of Economic Equality. https://doi.org/10.1007/s10888-021-09510-w

World Bank. Macro Poverty Outlook [Data set]. Poverty and Inequality Platform: World Development Indicators. Accessed July 2022. https://www.worldbank.org/en/publication/macro-poverty-outlook. Headcount ratio provided by the World Bank upon request.

For methodology, see:

World Bank. (2022). Poverty and Inequality Platform Methodology Handbook. https://worldbank.github.io/PIP-Methodology/

The FAO computation on national survey data (RuLIS Project) and official estimates were computed with the support of the 50x2030 Initiative.

50x2030. (2022). A partnership for data-smart agriculture. https://www.50x2030.org/

Food and Agriculture Organization of the United Nations (FAO). Average annual income from agriculture, PPP (constant 2011 international USD) [Data set]. RuLIS - Rural Livelihoods Information System. FAO. Accessed June 2022. www.fao.org/in-action/rural-livelihoods-dataset-rulis/
The most recent year available was used for selected countries, ranging from 2005 through 2020.

Food and Agriculture Organization of the United Nations (FAO). (2021). Use of AGRISurvey data for computing SDG’s and national indicators: Experience in three countries [Country brief]. www.fao.org/3/cb4762en/cb4762en.pdf. License: CC BY-NC-SA 3.0 IGO. 

For methodology, see:

Food and Agriculture Organization of the United Nations (FAO). (2018). Rural Livelihoods Information System (RuLIS): Technical notes on concepts and definitions used for the indicators derived from household surveys [Report]. FAO. www.fao.org/3/ca2813en/CA2813EN.pdf. Licence: CC BY-NC-SA 3.0 IGO

UNESCO Institute for Statistics (UIS). Sustainable Development Goal 4. UIS. Data accessed June 2022. http://sdg4-data.uis.unesco.org/

Source for Learning Poverty 2019 data:

World Bank & UNESCO Institute for Statistics. (2019). Historical data and sub-components [Data set]. Learning Poverty Database. https://datacatalog.worldbank.org/search/dataset/0038947

Source for Learning Poverty 2022 simulations:

2022 simulation results taken from Azevedo, J. P., Demombynes, G., & Wong, Y. N. 2022. Why has the pandemic not sparked more concern for learning losses in Latin America? World Bank Blogs: Education for Global Development (forthcoming).

The chart is based on data from the United Nations Global Sustainable Development Goals Database, the Government of India’s National Sample Survey Office, and the International Labour Organization.

The data is the most recent available for 92 countries and territories (2001–2019). The age group is 15 and older where available (18 and older in Ghana). In a number of cases, data are for those ages 10 and older (n=13) or 12 and older (n=3). The data for Malaysia, Ireland, and Cambodia refers to individuals ages 15 through 64. In the case of Thailand (2015) and India (2019), data covers those ages 6 and older, and in the United Republic of Tanzania (2014) those ages 5 and older. Data for Bulgaria, Denmark, Latvia, the Netherlands, Slovenia, and Spain corresponds to time spent on unpaid care among those ages 20 through 74 only. Differences across countries should be interpreted with caution, given heterogeneity across surveys and countries in definitions, methodology, and sample coverage. Time-diary data often excludes supervisory responsibilities, leading to underestimation of the time constraints of care.

For further information on the country-level data excluding India and Madagascar, see:

United Nations Statistics Division. (2022, May). Global SDG Indicators Data Platform. https://unstats.un.org/sdgs/dataportal

Data for India and Madagascar is available from:

Ministry of Statistics and Programme Implementation. (2019). Time Use Survey Report. Government of India. http://164.100.161.63/download-reports

Addati, L., Cattaneo, U., Esquivel, V., & Valarino, I. (2018). Care Work and Care Jobs for the Future of Decent Work. Geneva: International Labour Organization. https://www.ilo.org/global/publications/books/WCMS_633135/lang--en/index.htm

The “Income” comparison refers to what the World Bank calculates as account ownership of the richest 60% of households and poorest 40% of households, respectively.

Demirgüç-Kunt, A., Klapper, L., Singer, D., and S. Ansar. (2022). The Global Findex database 2021: Financial inclusion, digital payments, and resilience in the age of COVID-19. Washington, DC: World Bank. https://openknowledge.worldbank.org/handle/10986/37578 License: CC BY 3.0 IGO.

World Bank. (2022). Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+) [Data set]. Global Findex Database. Accessed June 2022. https://data.worldbank.org/indicator/FX.OWN.TOTL.ZS License: CC BY-4.0.

For methodology, see:

World Bank. (2022). Survey Methodology. In The Global Findex database 2021: Financial inclusion, digital payments, and resilience in the age of COVID-19 (pp. 181–197). Washington, DC: World Bank. https://thedocs.worldbank.org/en/doc/f3ee545aac6879c27f8acb61abc4b6f8-0050062022/original/Findex-2021-Methodology.pdf License: CC BY-4.0.