2023 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 0–59 months. Estimates used several methods improvements, including ensemble model predictions for severity-specific stunting prevalence and mean height-for-age z-scores (HAZ) and further disaggregation of <5 age groups. This led to improved estimates in a number of countries, including South Africa, Democratic Republic of the Congo, India, and Pakistan. In addition, new data has improved estimates in a number of countries as well, including Pakistan.

To project stunting prevalence to 2030, we first projected overall risk-weighted prevalence of HAZ using the summary exposure value (SEV) with an ensemble modelling approach. We used a cascading random spline model to estimate age-specific stunting prevalence from the SEV. To optimize model configuration, we trained models on historical stunting estimates from 1990 to 2014 and used each model version to predict prevalence from SEVs for 2015 to 2021. We then used the best model to fit the full set of SEV and prevalence estimates from 1990 to 2021, and input corresponding SEV forecasts and SDI projections, to generate stunting prevalence projections through 2030.

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 during the same time period. It depicts the risk of maternal death relative to the number of live births and essentially captures the risk of death in pregnancy. Projections to 2030 were modeled using an ensemble approach to forecast MMR, using SDI as a key driver.

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). There was, however, a significant relationship between the COVID-19 pandemic and indirect maternal mortality. This relationship with indirect maternal mortality was modeled using COVID-19 mortality rate as a covariate. Our estimates of excess indirect maternal mortality related to COVID-19 were corrected to remove incidental COVID-19 deaths in pregnant or postpartum women that were not due to pregnancy. We employed the same general method and pandemic-year cause of death this year as in the 2022 Goalkeepers Report, but we incorporated more years of prepandemic data to estimate secular trends.

The under-5 mortality rate is 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 Global Burden of Disease (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 [RSV], measles, pertussis) observed during the pandemic, driven primarily by social distancing and mask use. We also incorporate increases in malaria deaths due to service disruption, as well as child deaths due directly and indirectly to COVID-19. Most of the changes in U5MR estimates in the current Goalkeepers Report results came from new and additional input mortality data we have 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., et al. (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., et al. (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 antiretroviral therapy (ART), prevention of maternal-to-child transmission (PMTCT) coverage, and transmission rate as inputs into a modified version of Avenir Health’s Spectrum software (Mahy et al, 2017). Adult ART is forecasted using location-specific rates of change, capping forecasted coverage using CD4 count-specific coverage caps developed for allocation of ART in GBD estimation. 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, et al. (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., et al. (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., et al. (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–e651. https://doi.org/10.1016/S2352-3018(21)00152-1

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. GBD estimates in the 2022 Goalkeepers Report incorporated methodological improvements to better capture the quality of case notification data. We refined this approach for the current Goalkeepers Report. This refinement is mainly reflected in the time trends of countries in North Africa and the Middle East. Additionally, refinements were made to the model to better follow the data, which resulted in changes in the time trend in some countries, including Malawi and Botswana.

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 prepandemic time periods and modeling assumptions used in the current studies, we did 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, we take into account updated reports regarding pandemic-related disruptions to malaria interventions and effective treatment with an antimalarial drug (which includes insecticide-treated bed nets (ITN), indoor residual spraying, antimalarial treatment, and drug effectiveness). These reports were used to apply an adjustment to estimates of effective antimalarial treatment coverage, which were then utilized to produce estimates of malaria prevalence and subsequently incidence. Projections to 2030 were derived using an ensemble model. First, coverage of artemisinin-based combination therapy (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 pulse surveys, which were used to adjust 2020 and 2021 incidence results, were applied to only 33 countries in Africa at this time 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 impacted care-seeking.

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 measures the sum of the prevalence of 15 neglected tropical diseases (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. In the 2022 Goalkeepers Report, IHME applied an adjustment to the dengue estimates to account for COVID-19 disruptions from Chen et al (2022). Based on an updated literature review and due to data gaps, lags in availability, and challenges in accounting for the likely disruptions to NTD surveillance during the pandemic, we did not estimate a COVID-19 effect on dengue this year, or similar to last year, an effect on other NTDs. 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 (Hollingsworth et al., 2021). While modeling studies can characterize potential disruptions under different scenarios, reliable data to quantify the true magnitude of pandemic effects on NTD epidemiology is sparse.

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., et al. (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 or female sterilization, male or 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 as well as utilizing 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 other pandemic era surveys do not show a consistent, significant reduction in contraception use due to the pandemic. As a result, we did not incorporate a pandemic effect on the family planning indicator. Changes to the historical estimates can be attributed to methodological updates and the addition of new data from eight countries: Pakistan, India, Vietnam, Madagascar, Nigeria, Fiji, Uzbekistan, and Cambodia. We model demand satisfied via three underlying components of the indicator—any contraceptive use, proportion of use that is modern, and the proportion of non-use that is unmet need—separately for partnered and unpartnered women. This modelling approach 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. In prior iterations we had constrained modern contraceptive prevalence to the sum of all modern methods, but this year we estimate modern contraceptive prevalence as a proportion of all use directly.

Performance Monitoring for Action. (2023). Available Datasets [Data set]. https://www.pmadata.org/data

IHME measures the age-standardized prevalence of any current use of smoked tobacco among those aged 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, as well as 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.

IHME estimates the proportion of population with access to safely managed sanitation. As defined by the Joint Monitoring Programme (JMP), safely managed facilities must meet three criteria: 1) is not shared with multiple households, 2) is an improved sanitation facility, and 3) wastewater is disposed of safely (World Health Organization 2021). 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 (World Health Organization 2021). Safely managed treated wastewater must have received at least secondary treatment (World Health Organization 2021). 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.

For the 2023 Goalkeepers Report, we developed models to estimate two components of safely managed sanitation: 1) the proportion of sewer-connected facilities that are safely managed and 2) the proportion of improved, nonsewer facilities that are safely managed. For both components we selected the final model from a collection of candidate models based on out-of-sample root-mean-squared error (RMSE) estimated by cross-validation. Candidate models varied in model type (MR-BRT Bayesian spline cascade models versus shape constrained additive models), and predictive covariates (SDI, lag distributed income per capita [LDI], and both linear and log transformations). For the Bayesian spline cascade models, we tested models that varied in the strength of the priors used in the spline cascade.

Data for estimating the proportion of sewer-connected facilities that are safely managed were extracted from Eurostat, Aquastat, and the Organization for Economic Co-operation and Development (OECD). The resulting 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.

Data for estimating the proportion of improved, non sewer facilities that are safely managed were extracted from Multiple Indicator Cluster Surveys (MICS), Demographic and Health Surveys (DHS), national surveys (in Canada and Norway), and Eurostat. Crosswalks were performed to estimate toilet type and wastewater treatment where data was unknown within the MICS and DHS microdata. The resulting estimates from this model were multiplied by the IHME estimates of the proportion of the population with improved, nonsewer-connected facilities to estimate the proportion of the population with safely managed improved non sewer facilities.

We estimated the proportion of the total population with safely managed sanitation as the sum of the proportion of the population with safely managed sewer-connected facilities and the proportion of the population with safely managed improved nonsewer facilities.

World Health Organization & UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP). (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

IHME 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). IHME estimated the pandemic era (2020–2022) effects on vaccine coverage via administrative data coverage. To estimate disruptions in vaccine coverage during the COVID pandemic, IHME used administrative vaccine coverage data collected through the 2023 Joint Reporting Form. First, they 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 through 2022 for all countries due to the COVID pandemic. Second, they 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, they 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, they used these estimated disruptions in administrative coverage to generate as covariates in the final ST-GPR coverage models, which were fit to survey data and bias-adjusted administrative data. If administrative data was missing for 2020 through 2022, IHME 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 the use of the magnitude of coverage disruptions implied by administrative data, while still adjusting for bias in this data.

World Health Organization. (2023). The Big Catch-Up: An Essential Immunization Recovery Plan for 2023 and Beyond. Accessed August 8, 2023. https://www.who.int/publications/i/item/9789240075511

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 or 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 (DTP) 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 scaled mortality-to-incidence (MI) ratios for lower respiratory infections, diarrhea, and tuberculosis, as well as coverage of antiretroviral therapy (ART) among those with HIV/AIDS. Indicators of treatment of noncommunicable diseases include scaled 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 scaled 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. The effective coverage indicators are weighted in the index according to the potential health gain that each country could achieve if it were to improve coverage of that indicator.

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 total health spending per capita estimates, were substituted into the previously fit frontier to obtain forecasted UHC for all countries for 2022–2030.

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 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, we applied 25 percent of the reduction in monthly missed health care visits (excluding routine services). Details of the estimation of missed health care visits are described in last year’s report.

Bill & Melinda Gates Foundation. (2022) 2022 Goalkeepers Report: The Future of Progress. https://www.gatesfoundation.org/goalkeepers/report/2022-report/

World Bank. Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population). [Data set]. Poverty and Inequality Platform: World Development Indicators. Accessed July 2023. https://data.worldbank.org/indicator/SI.POV.DDAY. License: CC BY-4.0.

For methodology, see:
World Bank. (2023). 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. (2023). 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 2023. https://www.fao.org/in-action/rural-livelihoods-dataset-rulis/data-application/data/en

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]. https://www.fao.org/3/cb4762en/cb4762en.pdf. License: CC BY-NC-SA 3.0 IGO.

Small food producers’ income growth for selected countries with at least two entries for small food producers’ income from 2005 to 2022. For all countries without data for 2014 and 2019, the earliest and most recent years were used to calculate income growth. Small food producers’ income growth is calculated per country and using years listed below: 

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.  https://www.fao.org/3/ca2813en/CA2813EN.pdf. License: CC BY-NC-SA 3.0 IGO.

World Bank, UNESCO Institutes for Statistics (UIS), UNICEF, the Foreign, Commonwealth & Development Office (FCDO), USAID, et al. (2022). The State of Global Learning Poverty: 2022 Update. https://www.unicef.org/media/122921/file/StateofLearningPoverty2022.pdf

Source for Learning Poverty 2022 simulations:
Azevedo, J., Demombynes, G. & Wong, Y.N. (2023, April 20). Why has the pandemic not sparked more concern for learning losses in Latin America? World Bank Blog. https://blogs.worldbank.org/education/why-hasnt-pandemic-sparked-more-concern-learning-losses-latin-america-perils-invisible

The chart is based on data from the United Nations Global SDG Database, the Government of India’s National Statistical Office, and the International Labour Organization. 

The data is the most recent available for 93 countries and territories (2001–2022). The age group is 15 or older where available (18 or older in Ghana). In a number of cases, data is for those ages 10 or older (n=13) or 12 or older (n=3). The data for Malaysia, Ireland, and Cambodia refers to individuals ages 15–64. In the case of Thailand (2015) and India (2019), it is for those ages 6 or older, and in the United Republic of Tanzania (2014) for those ages 5 or older. Data for Bulgaria, Denmark, Latvia, the Netherlands, Slovenia, and Spain correspond to time spent on unpaid care among those ages 20 to 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.

The regional average ratios are the averages of the ratios of the component countries, and the global average ratio is the average of the ratios of all countries included.

For further information on the country-level data, see:

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

Data for India and Madagascar are not currently available in the SDG data portal, so they come from:

• Government of India. (2020). Time Use in India-2019. Ministry of Statistics and Programme Implementation, National Statistical Office. https://mospi.gov.in/sites/default/files/publication_reports/Report_TUS_2019_0.pdf

• Government of India. (2020). Time Use in India-2019. Ministry of Statistics and Programme Implementation, National Statistical Office. 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 percent of households and poorest 40 percent of households, respectively.

Demirgüç-Kunt, A., Klapper, L., Singer, D. & Ansar, S. (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 2023. 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.