Environmental quality index overview

EPA/600/R-14/305 | September 2014 | www.epa.gov/ord ENVIRONMENTAL QUALITY INDEX Overview Report Office of Research and Development National Exposure Research Laboratory Project Personnel Danelle T Lobdell, U.S Environmental Protection Agency (EPA), Office of Research and Development (ORD), National Health and Environmental Effects Research Laboratory (NHEERL) Jyotsna Jagai, University of Illinois at Chicago, Oak Ridge Institute for Science and Education (ORISE) Faculty Grantee Lynne C Messer, Portland State University, Support Contractor Kristen Rappazzo, University of North Carolina (UNC), Department of Epidemiology, ORISE Grantee Shannon Grabich, UNC, Department of Epidemiology, ORISE Grantee Christine L Gray, UNC, Department of Epidemiology, ORISE Grantee Kyle Messier, Student Services Contractor Genee Smith, Student Services Contractor Suzanne Pierson, Innovate!, Inc., Geographic Information Systems (GIS) Contractor Support Barbara Rosenbaum, Innovate!, Inc., GIS Contractor Support Mark Murphy, Innovate!, Inc., GIS Contractor Support Acknowledgments External Peer Reviewers Angel Hsu, Yale University, School of Forestry and Environmental Studies Paul D Juarez, University of Tennessee Health Science Center, Department of Preventive Medicine Peter H Langlois, Texas Department of State Health Services, Birth Defects Epidemiology and Surveillance Branch Internal Peer Reviewers Jane Gallagher, U.S EPA, ORD, NHEERL Thomas Brody, U.S EPA, Region Lisa Smith, U.S EPA, ORD, NHEERL This document has been reviewed by the U.S Environmental Protection Agency, Office of Research and Development, and approved for publication Mention of trade names or commercial products does not constitute endorsement or recommendation for use iv Table of Contents 1.0 Introduction Background Purpose Uses of Environmental Quality Index 2.0 Construction of the Environmental Quality Index Domain Identification Approach Summary of Activities Data Source Identification and Review Approach Summary of Activities Variable Construction Approach Summary of Activities 10 Data Reduction and Index Construction 10 Approach 10 Results 12 3.0 Discussion 13 Strengths and Limitations 13 Other Environmental Indices 13 Conclusions 13 4.0 References 15 Appendix I: County Maps of Environmental Quality Index A-1 Appendix II: Quality Assurance B-1 v List of Figures Figure Conceptual environmental quality—hazardous and beneficial aspects Figure Principal component analysis for the Environmental Quality Index (EQI) All counties included with four rural-urban continuum codes (RUCCs) 10 Figure Rural-urban continuum codes (RUCCs) for all counties in the United States 11 Figure Map of the Environmental Quality Index by rural-urban continuum codes (RUCCs) 11 vi List of Maps Map Environmental Quality Index by County, 2000-2005 NOTE: EQI valus suggest worse environmental quality, and lower EQI values suggest better environmental quality A-1 Map Air Domain Index by County, 2000-2005 A-2 Map Water Domain Index by County, 2000-2005 A-2 Map Land Domain Index by County, 2000-2005 A-3 Map Built Domain Index by County, 2000-2005 A-3 Map Sociodemographic Domain Index by County, 2000-2005 A-4 Map Environmental Quality Index Stratified by Rural-Urban Continuum Codes by County, 2000-2005 A-5 Map Air Domain Index Stratified by Rural Urban Continuum Codes by County, 2000-2005 A-5 Map Water Domain Index Stratified by Rural-Urban Continuum Codes by County, 2000-2005 A-6 Map 10 Land Domain Index Stratified by Rural-Urban Continuum Codes by County, 2000-2005 A-6 Map 11 Built Domain Index Stratified by Rural-Urban Continuum Codes by County, 2000-2005 A-7 Map 12 Sociodemographic Domain Index Stratified by Rural-Urban Continuum Codes by County, 2000-2005 A-7 vii List of Tables Table Sources of Data for Air, Water, Land, Built-Environment, and Sociodemographic Domains for Use in the Environmental Quality Index Table (continued) Sources of Data for Air, Water, Land, Built-Environment, and Sociodemographic Domains for Use in the Environmental Quality Index Table (continued) Sources of Data for Air, Water, Land, Built-Environment, and Sociodemographic Domains for Use in the Environmental Quality Index Table List of Variables by Domain Included in the Environmental Quality Index Table (continued) List of Variables by Domain Included in the Environmental Quality Index Table (continued) List of Variables by Domain Included in the Environmental Quality Index Table Weights for Each Domain’s Contribution to the Environmental Quality Index for 3141 U.S Counties (2000-2005) and for the Counties Stratified by Their Rural-Urban Status (RUCC code) 12 viii 1.0 Introduction A better way to calculate overall environmental quality is needed for researchers who study the environment and its effects on human health This report is an overview of how the environmental quality index (EQI) was developed for all counties in the United States for the period 20002005 The EQI represents five areas (called “domains”) of the environment ([1] air, [2] water, [3] land, [4] built, and [5] sociodemographic) In addition to the EQI, there is an index for each of the five domains The EQI accounts for environmental differences between urban and rural areas by grouping counties into one of four rural-urban continuum codes (RUCCs), ranging from highly urban to rural-isolated areas The EQI was developed in four steps: (1) The five domains were identified, (2) data for each of the five domains were located and reviewed, (3) environmental variables were developed from the data sources, and (4) data were combined in each of the environmental domains; then these domain indices were used to create the overall EQI The EQI relied on data sources that are mostly available to the public The approach to creating the EQI is outlined, so others can repeat the steps for their own unique areas of interest This report gives an overview of the EQI A companion report, Creating an Overall Environmental Quality Index, Technical Report, provides the detailed methodology and results The variables, EQI, domain-specific indices, and EQI stratified by rural-urban data are available publically at the U.S Environmental Protection Agency’s (EPA’s) Environmental Dataset Gateway Also, an interactive map of the EQI is available at EPA’s GeoPlatform Background The assessment of environmental exposures for human health is changing, and new methods constantly are being developed Exposures (both good and bad) that affect human health happen at the same time, but understanding their combined impact is difficult For example, negative environmental features, such as landfills and industrial plants, often are located in neighborhoods with a high percentage of minority and poor residents.[1-7] On the other hand, highincome neighborhoods often have features that promote health, such as parks, health clubs, and well-stocked grocery stores.[8,9] Yet, no single exposure can be held responsible for good or poor health It is not just good quality air or high income that produces health because many other exposures promote good health as well Figure Conceptual environmental quality—hazardous and beneficial aspects One limitation to current methods in environmental health research is the focus on single-exposure types Welldesigned environmental health studies face a trade-off: Either researchers can collect a lot of high-quality data on only a few participants because collecting detailed exposure data is expensive and time-consuming, or researchers can collect less-detailed exposure data on a larger number of study participants because, the more participants in a study, the more expensive it is to conduct This trade-off makes it impossible to account for many exposures that study participants might experience in addition to the main exposures of interest An index that summarizes many variables into a single variable is one approach that could improve statistical efficiency and still account for many environmental exposures at once The index then could be used to identify areas with different levels of environmental quality Clusters of negative environmental exposures could be identified and linked to health outcomes Conceptually, an EQI accounts for the multiple domains of the environment that encompass an area where humans interact (see Figure 1) These domains include chemical, natural, built, and sociodemographic environments that have both positive and negative influences on health People move in and out of these positive and negative influences Also, the positive and negative influences may even be co-located As a result, the EQI examines both adverse health outcomes and protective health events Purpose A better estimate of overall environmental quality is needed It will improve the understanding of the relationship between environmental conditions and human health Thus, an EQI was developed for all counties in the United States The EQI uses indicators from the chemical, natural, built, and social environment The EQI is composed of five environmental domains: (1) air, (2) water, (3) land, (4) built, and (5) sociodemographic Uses of EQI The EQI was designed to be used in two main ways: (1) to represent “environmental quality” in research designed to assess the relationship between environmental quality and human health outcomes and (2) as a variable to account for surrounding conditions for researchers interested in a specific environmental exposure (e.g., exposure to pesticides) and human health outcomes (e.g., cancer) However, other uses of the data are expected by different end users, such as local, county, State and Federal governments, nongovernmental organizations, and academic institutions The EQI holds promise for improving environmental estimation in public health because it describes the surrounding county-level conditions to which residents are exposed Use of the EQI will help public health researchers investigate the cumulative impact of many diverse environmental domains The EQI was developed to help understand which domains (air, water, etc.) contribute the most to the overall environment It also may be important for policymakers and environmental health workers to have information specific to the domains Thus, domain-specific indices also were created Each domain-specific index can be helpful to understand which domain is making the biggest contribution to the total environment in that particular county This also can be expanded to understanding environmental differences by urban or rural status In addition, researchers can use the EQI to control for environmental quality in their studies of specific exposures on health outcomes, adding environmental context to isolated exposures Another potential use of the EQI is for the comparison of county environmental quality across the United States The EQI can be used to identify counties having a greater burden of poor health because of poor environmental quality and to see the important environmental domains contributing to an individual county’s environmental quality With the EQI currently at county level, environmental injustice may be difficult to tease out; however, the methods applied may be used to make local EQIs for smaller geographical areas 12 Finally, figure shows the 10 countries whose rankings improve the most and the 10 countries whose rankings deteriorate the most if the quality-adjusted education index is used to assess countries’ education systems instead of the education index It should be noted that, while one Arab country is among the countries with the highest 10 rank deteriorations (Saudi Arabia), no Arab country is among the countries with the highest 10 rank improvements This is not surprising, given the gaps in education in the region, both from a quantity and a quality perspective, and the urgent need for Arab countries to take action to improve the quality of education delivered to their citizens Source: Author’s calculations on the basis of UNDP and World Bank data 29 28 26 25 25 22 22 21 -18 -20 -20 -20 -21 -22 -23 -28 -54 -31 20 37 Figure The ten countries whose rankings improve the most compared with their rankings under the education index, and the ten countries whose rankings deteriorate the most compared with their rankings under the education index, 2019 13 Conclusion The quality-adjusted education index proposed in this paper can be viewed as a revised HDI education index that incorporates a measure of education quality by taking into account harmonized international student test scores The proposed index has a strong conceptual reasoning grounded in the capability approach It is also relatively easy to calculate as the indicators informing that index are regularly updated by the UNDP Human Development Data Center and the World Bank In order to assess the capacity of the proposed index to capture the quality of education, the paper introduces two additional input indicators that are believed to affect the quality of education, namely the pupil-teacher ratio and the number of scientific and engineering articles published per 1,000 people As expected, the results show a positive relationship between the quality-adjusted education index and income, and between the quantity and quality of education However, the presence of certain outlier countries on the charts indicates that there is wide variation in the quality of countries’ schooling systems Indeed, the low quality-adjusted education index scores achieved by some countries relative to their income levels suggest that there are significant deficiencies in their education systems Deficiencies are also apparent in countries in which students perform poorly in harmonized tests despite the fact that those countries are characterized by high years of schooling A positive correlation is also apparent between the quality-adjusted education index and the inverted pupil-teacher ratio index That correlation becomes weaker when class sizes become very small, however, suggesting that it is not very effective to reduce still further the number of students in classes in which each student is already receiving enough attention from his or her teacher Lastly, it should be emphasized that most countries, including those in the Arab region, not experience significant ranking losses/improvements after adjustments are made for the quality of education This is to be anticipated given the strong correlation between the quality-adjusted education and education indices Of concern is the fact that no Arab country is among the top global performers in terms of education: Arab countries’ low rankings in the quality-adjusted education index make it clear that countries in the region must more to address educational gaps, not only by increasing enrolment, but also by investing more in initiatives to improve the quality of schooling 14 Annex A Quality of education index Harmonized test scores The harmonized test scores developed by Patrinos and Angrist (2018) 26 provide a standardized measurement of learning outcomes across countries and time on the basis of scores obtained in a number of key international student achievement tests 27 The achievement tests that provide input to the harmonized test scores index have allowed temporal comparability since the late 1990s A conversion factor is formulated by comparing test scores for countries that undertake both international standardized achievement tests (such as TIMSS, PISA, PIRLS) and a regional counterpart (such as SACMEQ, LLECE, and PASEC.) within a given time frame for a specific subject and educational level (primary or secondary) Those countries are dubbed “doubloon countries” The objective of the linking approach is to summarize the discrepancies in test difficulty between two given tests The authors rely on classical test theory, as developed by Holland and Hoskens 28 and assume constant group 26 27 28 ability, meaning that discrepancies in score distribution between two distinct assessments X and Y are entirely attributed to the test themselves The ratio linking approach entails indexing the difference in mean scores in the anchored test (X) and scale of reference (Y) test, as shown in equation (1) 𝑝𝑝𝑝𝑝 (1) 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌 (𝑋𝑋) = 𝑦𝑦 = 𝜇𝜇(𝑌𝑌) 𝜇𝜇(𝑋𝑋) 𝑥𝑥 To address the potential sensitivity of the ratio to the scales of tests and ensure that the ratio reflects the difference in test difficulty rather than scales, the tests used have means and standard deviations of 500 and 100, respectively To compute the conversion rate, mean scores for each of the tests is first calculated for a given round, subject and level Tests are considered to be conducted in the same round if they are five years apart and rounds are selected as close as possible to each other Hence, for a given country i in the set of doubloon countries, a fixed conversion rate (𝑑𝑑𝑠𝑠𝑠𝑠 ) from test X to Y for a given subject and schooling level is computed as in equation This conversion rate is then applied to a country j that undertakes test X but not Y Patrinos and Angrist, “Global Dataset on Education Quality: A Review and Update (2000–2017)” The tests included are the following: Trends in International Maths and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), Programme for International Student Assessment (PISA), Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ), Program of Analysis of Education Systems (PASEC), Latin American Laboratory for Assessment of the Quality of Education (LLECE), Pacific Islands Literacy and Numeracy Assessment (PILNA), nationallyrepresentative Early Grade Reading Assessments (EGRA), and non-nationally-representative Early Grade Reading Assessments (EGRANR) Paul Holland and Machteld Hoskens, “Classical test theory as a first-order item response theory: Application to true-score prediction from a possibly nonparallel test”, Psychometrika 68.1, pp 123–149 (2003) 15 (2) 𝑑𝑑𝑠𝑠𝑠𝑠 = 𝑟𝑟 𝜇𝜇(𝑋𝑋𝑟𝑟𝑟𝑟𝑟𝑟 ) ∑ 𝑟𝑟 𝑟𝑟=1 𝜇𝜇(𝑌𝑌𝑟𝑟𝑟𝑟𝑟𝑟 ) where r is the testing rounds over the time period, s is the subject and l is schooling level This conversion rate is then applied to a given country j that participates in test X but not test Y to produce a harmonized score: (3) 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑌𝑌𝑝𝑝𝑝𝑝 �𝑋𝑋𝑗𝑗𝑗𝑗𝑗𝑗𝑗𝑗 � = 𝑌𝑌𝑗𝑗𝑗𝑗𝑗𝑗𝑗𝑗 = 𝑋𝑋𝑗𝑗𝑗𝑗𝑗𝑗𝑗𝑗 𝑑𝑑𝑠𝑠𝑠𝑠 where y is the official year of anchored test X To get aggregate scores, the average is taken first across subjects then across levels, given both equal weights The database includes 164 countries and territories over the period 2000–2017 A caveat of the linking method is that it could result in varying mean scores Nonetheless, ranks and relative performance are fairly robust For more details on the methodology of harmonized test scores, refer to Patrinos and Angrist (2018) 29 country was 581, so the maximum value was set at 581 Ranks are then calculated for the conventional and quality-adjusted education index (annex 2) B Other indices Two other quality indices are formulated to check the validity of the quality of education index derived from standardized test scores The first is the pupil-teacher ratio in primary education, and the second is the number of scientific and engineering articles published (per 1,000 people) To formulate the two indices, we follow the below steps: Quality of education adjustment ratio To calculate the quality of education adjustment ratio, we standardize country scores on harmonized student international test scores using the following formula: 𝐻𝐻𝐻𝐻𝐻𝐻𝑖𝑖 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 The maximum is set based on the maximum observed value in raw data for test scores IN our analysis, the highest value scored by a 29 Missing values are replaced by the average for the nearest 3–5 years To deal with outliers and obtain a smoother distribution, a log transformation of the indicators is taken We standardize country scores using the min-max formula The minimum and maximum values are chosen on the basis of the HDI methodology The minimum (maximum) is set based on the minimum (maximum) observed value in raw data for both indices The data shows that the lowest (highest) value scored by a country on the pupilteacher ratio over the period 1990–2018 was 5.2 (100.2) Thus, the minimum (maximum) values were set at (104) For the number of publications (per 1,000 people), the lowest (highest) value over the period 1990–2018 was (2.7), and hence, the minimum (maximum) values were set at (2.8) Patrinos and Angrist, “Global Dataset on Education Quality: A Review and Update (2000–2017)” 16 Annex Country Education index Pupil-teacher ratio index Pupil-teacher ratio index rescaled Publications index (per 1,000 people) Harmonized test score index Quality-adjusted education index Education index rank Quality-adjusted education index rank Quality-adjusted education index –education index Quality-adjusted education index, World Bank Human Capital Project harmonized test score index and education index scores and rankings (189 countries and territories) Finland 0.93 0.32 0.68 0.96 0.83 0.85 -2 Germany 0.95 0.29 0.71 0.91 0.77 0.84 Singapore 0.84 0.36 0.64 0.96 0.98 0.83 31 -28 Hong Kong, China 0.88 0.32 0.68 0.88 0.83 22 -18 United Kingdom of Great Britain and Northern Ireland 0.93 0.39 0.61 0.92 0.78 0.83 -1 New Zealand 0.93 0.35 0.65 0.94 0.78 0.83 Ireland 0.92 0.38 0.62 0.92 0.78 0.83 7 Canada 0.90 0.94 0.83 0.82 17 -9 Estonia 0.88 0.27 0.73 0.89 0.86 0.82 21 -12 Norway 0.93 0.19 0.81 0.97 0.76 0.82 10 Denmark 0.92 0.25 0.75 0.98 0.77 0.82 11 Australia 0.92 0.97 0.76 0.82 12 Sweden 0.92 0.29 0.71 0.96 0.78 0.82 10 13 Slovenia 0.91 0.39 0.61 0.93 0.78 0.82 12 14 Netherlands 0.91 0.28 0.72 0.95 0.78 0.82 11 15 Country Education index Pupil-teacher ratio index Pupil-teacher ratio index rescaled Publications index (per 1,000 people) Harmonized test score index Quality-adjusted education index Education index rank Quality-adjusted education index rank Quality-adjusted education index –education index 17 Belgium 0.90 0.27 0.73 0.92 0.77 0.80 13 16 Republic of Korea 0.87 0.39 0.61 0.91 0.84 0.80 24 17 -7 Switzerland 0.90 0.23 0.77 0.99 0.76 0.80 14 18 Iceland 0.93 0.23 0.77 0.96 0.70 0.79 19 16 Poland 0.87 0.23 0.77 0.87 0.82 0.79 23 20 -3 United States of America 0.90 0.35 0.65 0.91 0.75 0.79 14 21 Japan 0.85 0.39 0.61 0.85 0.84 0.78 30 22 -8 Czechia 0.89 0.44 0.56 0.92 0.75 0.78 18 23 Lithuania 0.90 0.31 0.69 0.85 0.69 0.76 16 24 Latvia 0.88 0.27 0.73 0.84 0.72 0.76 19 25 Austria 0.86 0.24 0.76 0.92 0.74 0.75 25 26 Israel 0.88 0.29 0.71 0.92 0.64 0.73 19 27 Spain 0.83 0.32 0.68 0.90 0.73 0.72 33 28 -5 France 0.82 0.42 0.58 0.88 0.75 0.72 41 29 -12 Cyprus 0.83 0.30 0.70 0.88 0.72 0.72 36 30 -6 Liechtenstein 0.83 0.15 0.85 0.85 0.69 0.70 34 31 -3 Russian Federation 0.82 0.46 0.54 0.81 0.70 0.70 39 32 -7 Belarus 0.84 0.44 0.56 0.63 0.67 0.70 32 33 Hungary 0.82 0.26 0.74 0.83 0.69 0.70 40 34 -6 Slovakia 0.83 0.37 0.63 0.88 0.66 0.69 37 35 -2 Greece 0.85 0.21 0.79 0.88 0.60 0.68 29 36 Luxembourg 0.81 0.17 0.83 0.92 0.68 0.68 43 37 -6 Palau 0.86 0.76 0.58 0.68 27 38 11 Country Education index Pupil-teacher ratio index Pupil-teacher ratio index rescaled Publications index (per 1,000 people) Harmonized test score index Quality-adjusted education index Education index rank Quality-adjusted education index rank Quality-adjusted education index –education index 18 Italy 0.79 0.28 0.72 0.90 0.68 0.67 48 39 -9 Croatia 0.80 0.33 0.67 0.88 0.67 0.67 45 40 -5 Malta 0.82 0.32 0.68 0.86 0.62 0.67 38 41 Portugal 0.77 0.31 0.69 0.92 0.74 0.67 57 42 -15 Ukraine 0.80 0.31 0.69 0.71 0.63 0.66 47 43 -4 Chile 0.81 0.43 0.57 0.76 0.54 0.63 42 44 United Arab Emirates 0.80 0.47 0.53 0.75 0.52 0.62 46 45 -1 Serbia 0.78 0.35 0.65 0.83 0.56 0.61 51 46 -5 Barbados 0.78 0.34 0.66 0.64 0.55 0.61 52 47 -5 Montenegro 0.80 0.77 0.48 0.60 44 48 Turkey 0.73 0.43 0.57 0.77 0.63 0.60 69 49 -20 Argentina 0.86 0.69 0.38 0.60 28 50 22 Mauritius 0.74 0.39 0.61 0.61 0.61 0.60 68 51 -17 Bahrain 0.77 0.29 0.71 0.69 0.54 0.60 55 52 -3 Uzbekistan 0.73 0.48 0.52 0.34 0.62 0.59 71 53 -18 Kazakhstan 0.83 0.45 0.55 0.64 0.41 0.59 35 54 19 Georgia 0.86 0.19 0.81 0.65 0.35 0.59 26 55 29 Bulgaria 0.78 0.40 0.60 0.79 0.50 0.59 53 56 Andorra 0.72 0.24 0.76 0.52 0.63 0.59 77 57 -20 Romania 0.77 0.44 0.56 0.80 0.50 0.58 58 58 Seychelles 0.73 0.35 0.65 0.60 0.58 0.58 72 59 -13 Uruguay 0.76 0.27 0.73 0.71 0.49 0.57 59 60 Trinidad and Tobago 0.73 0.66 0.56 0.57 72 61 -11 Armenia 0.74 0.37 0.63 0.67 0.51 0.56 64 62 -2 Country Education index Pupil-teacher ratio index Pupil-teacher ratio index rescaled Publications index (per 1,000 people) Harmonized test score index Quality-adjusted education index Education index rank Quality-adjusted education index rank Quality-adjusted education index –education index 19 Viet Nam 0.63 0.46 0.54 0.51 0.78 0.56 117 63 -54 Iran (Islamic Republic of) 0.75 0.55 0.45 0.82 0.47 0.56 61 64 Bahamas 0.74 0.45 0.55 0.53 0.50 0.56 66 65 -1 Malaysia 0.73 0.28 0.72 0.84 0.52 0.56 74 66 -8 Albania 0.75 0.41 0.59 0.55 0.48 0.56 62 67 Cuba 0.79 0.20 0.80 0.59 0.38 0.55 49 68 19 Mongolia 0.74 0.59 0.41 0.51 0.48 0.55 67 69 Saudi Arabia 0.79 0.33 0.67 0.74 0.35 0.54 50 70 20 Costa Rica 0.73 0.29 0.71 0.61 0.46 0.53 75 71 -4 Republic of Moldova 0.71 0.42 0.58 0.58 0.49 0.53 82 72 -10 Kyrgyzstan 0.73 0.53 0.47 0.42 0.43 0.53 69 73 Peru 0.74 0.41 0.59 0.53 0.41 0.53 65 74 Brunei Darussalam 0.70 0.22 0.78 0.83 0.49 0.53 87 75 -12 Oman 0.72 0.22 0.78 0.67 0.44 0.52 78 76 -2 Grenada 0.77 0.39 0.61 0.77 0.34 0.52 55 77 22 Mexico 0.70 0.56 0.44 0.64 0.46 0.52 84 78 -6 Tonga 0.77 0.50 0.50 0.52 0.30 0.51 54 79 25 Sri Lanka 0.75 0.48 0.52 0.55 0.35 0.51 62 80 18 Gabon 0.65 0.52 0.48 0.46 0.55 0.51 112 81 -31 Azerbaijan 0.71 0.37 0.63 0.57 0.41 0.51 80 82 Bosnia and Herzegovina 0.71 0.40 0.60 0.69 0.41 0.51 81 83 Ecuador 0.70 0.52 0.48 0.63 0.43 0.51 86 84 -2 Fiji 0.76 0.58 0.42 0.66 0.30 0.50 59 85 26 Education index Pupil-teacher ratio index Pupil-teacher ratio index rescaled Publications index (per 1,000 people) Harmonized test score index Quality-adjusted education index Education index rank Quality-adjusted education index rank Quality-adjusted education index –education index 20 North Macedonia 0.70 0.36 0.64 0.71 0.40 0.50 84 86 China 0.66 0.39 0.61 0.76 0.50 0.50 109 87 -22 Thailand 0.68 0.40 0.60 0.68 0.45 0.50 97 88 -9 Brazil 0.69 0.47 0.53 0.73 0.40 0.49 90 89 -1 Jordan 0.67 0.43 0.57 0.72 0.46 0.49 105 90 -15 Colombia 0.68 0.51 0.49 0.65 0.42 0.49 95 91 -4 Saint Lucia 0.67 0.36 0.64 0.42 0.42 0.48 103 92 -11 Qatar 0.66 0.29 0.71 0.81 0.45 0.48 110 93 -17 Bolivia (Plurinational State of) 0.69 0.42 0.58 0.32 0.37 0.48 90 94 State of Palestine 0.68 0.52 0.48 0.58 0.40 0.48 98 95 -3 Saint Kitts and Nevis 0.67 0.35 0.65 0.79 0.39 0.47 102 96 -6 Venezuela (Bolivarian Republic of) 0.70 0.43 0.33 0.47 88 97 Turkmenistan 0.65 0.01 0.41 0.47 111 98 -13 Belize 0.69 0.45 0.55 0.44 0.33 0.47 92 99 Antigua and Barbuda 0.67 0.30 0.70 0.54 0.38 0.47 106 100 -6 Saint Vincent and the Grenadines 0.69 0.35 0.65 0.35 0.32 0.46 94 101 Tajikistan 0.68 0.49 0.51 0.29 0.32 0.46 96 102 Jamaica 0.69 0.53 0.47 0.54 0.31 0.46 93 103 10 Country Education index Pupil-teacher ratio index Pupil-teacher ratio index rescaled Publications index (per 1,000 people) Harmonized test score index Quality-adjusted education index Education index rank Quality-adjusted education index rank Quality-adjusted education index –education index 21 Marshall Islands 0.71 0.50 0.27 0.46 83 104 21 Suriname 0.68 0.32 0.68 0.46 0.33 0.46 100 105 Botswana 0.68 0.50 0.50 0.63 0.32 0.45 101 106 Samoa 0.71 0.59 0.41 0.54 0.25 0.45 79 107 28 Panama 0.70 0.49 0.51 0.50 0.27 0.45 89 108 19 Indonesia 0.65 0.40 0.60 0.61 0.34 0.44 112 109 -3 Dominica 0.63 0.34 0.66 0.67 0.37 0.44 116 110 -6 Tunisia 0.66 0.40 0.60 0.79 0.30 0.44 108 111 Algeria 0.67 0.52 0.48 0.63 0.26 0.43 103 112 South Africa 0.72 0.61 0.39 0.70 0.15 0.43 76 113 37 Eswatini 0.56 0.56 0.44 0.46 0.50 0.42 134 114 -20 Paraguay 0.64 0.52 0.48 0.37 0.30 0.42 115 115 Philippines 0.68 0.59 0.41 0.42 0.22 0.42 99 116 17 Kiribati 0.59 0.54 0.46 0.26 0.40 0.42 122 117 -5 Kuwait 0.64 0.19 0.81 0.71 0.30 0.42 114 118 Kenya 0.53 0.68 0.32 0.44 0.55 0.42 140 119 -21 El Salvador 0.56 0.55 0.45 0.29 0.48 0.42 137 120 -17 Namibia 0.58 0.53 0.47 0.55 0.38 0.41 124 121 -3 Lebanon 0.60 0.30 0.70 0.72 0.32 0.40 120 122 Zimbabwe 0.59 0.65 0.35 0.44 0.34 0.40 123 123 Libya 0.61 0.44 0.27 0.40 119 124 Dominican Republic 0.66 0.44 0.56 0.24 0.16 0.39 107 125 18 Nicaragua 0.57 0.59 0.41 0.29 0.33 0.39 127 126 -1 Country Education index Pupil-teacher ratio index Pupil-teacher ratio index rescaled Publications index (per 1,000 people) Harmonized test score index Quality-adjusted education index Education index rank Quality-adjusted education index rank Quality-adjusted education index –education index 22 Sao Tome and Principe 0.57 0.62 0.38 0.21 0.34 0.38 129 127 -2 Cabo Verde 0.56 0.47 0.53 0.39 0.34 0.38 130 128 -2 India 0.56 0.62 0.38 0.61 0.35 0.38 136 129 -7 Micronesia (Federated States of) 0.58 0.45 0.55 0.49 0.28 0.38 125 130 Cambodia 0.49 0.70 0.30 0.32 0.54 0.38 154 131 -23 Egypt 0.62 0.51 0.49 0.64 0.20 0.38 118 132 14 Morocco 0.57 0.55 0.45 0.65 0.29 0.37 128 133 Maldives 0.57 0.25 0.75 0.38 0.27 0.37 126 134 Guatemala 0.52 0.46 0.54 0.27 0.37 0.36 145 135 -10 Lesotho 0.53 0.62 0.38 0.32 0.33 0.36 141 136 -5 Uganda 0.52 0.71 0.29 0.39 0.34 0.36 143 137 -6 Guyana 0.60 0.52 0.48 0.40 0.16 0.36 121 138 17 Cameroon 0.55 0.72 0.28 0.48 0.28 0.36 138 139 Iraq 0.56 0.66 0.22 0.35 135 140 Congo 0.54 0.74 0.26 0.36 0.25 0.34 139 141 Zambia 0.56 0.70 0.30 0.36 0.21 0.34 133 142 Honduras 0.50 0.56 0.44 0.24 0.35 0.34 150 143 -7 Togo 0.52 0.69 0.31 0.34 0.30 0.34 146 144 -2 Myanmar 0.46 0.52 0.48 0.23 0.44 0.34 161 145 -16 Vanuatu 0.56 0.51 0.49 0.43 0.17 0.34 132 146 14 Bangladesh 0.53 0.59 0.41 0.41 0.24 0.33 142 147 Bhutan 0.50 0.64 0.36 0.56 0.31 0.33 151 148 -3 Nepal 0.52 0.49 0.51 0.46 0.24 0.33 144 149 Country Country Education index Pupil-teacher ratio index Pupil-teacher ratio index rescaled Publications index (per 1,000 people) Harmonized test score index Quality-adjusted education index Education index rank Quality-adjusted education index rank Quality-adjusted education index –education index 23 Timor-Leste 0.51 0.56 0.44 0.34 0.25 0.33 147 150 Comoros 0.48 0.57 0.43 0.22 0.33 0.32 156 151 -5 Benin 0.48 0.68 0.32 0.41 0.30 0.31 157 152 -5 Lao People’s Democratic Republic 0.48 0.49 0.51 0.36 0.24 0.31 155 153 -2 Burundi 0.42 0.71 0.29 0.14 0.44 0.30 168 154 -14 Ghana 0.56 0.56 0.44 0.51 0.03 0.30 130 155 25 Equatorial Guinea 0.47 0.54 0.46 0.14 0.24 0.29 160 156 -4 Madagascar 0.49 0.68 0.32 0.25 0.18 0.29 153 157 Côte d’Ivoire 0.45 0.70 0.30 0.33 0.26 0.29 164 158 -6 Malawi 0.47 0.81 0.19 0.36 0.21 0.29 159 159 United Republic of Tanzania 0.43 0.76 0.24 0.34 0.31 0.29 166 160 -6 Solomon Islands 0.47 0.54 0.46 0.42 0.18 0.29 158 161 Rwanda 0.46 0.82 0.18 0.37 0.21 0.28 162 162 Angola 0.50 0.76 0.24 0.06 0.09 0.28 148 163 15 Papua New Guinea 0.44 0.65 0.35 0.30 0.22 0.27 165 164 -1 Nigeria 0.50 0.66 0.34 0.46 0.03 0.27 148 165 17 Haiti 0.46 0.17 0.13 0.26 163 166 Democratic Republic of the Congo 0.50 0.64 0.36 0.13 0.04 0.26 152 167 15 GuineaBissau 0.41 0.77 0.23 0.31 0.22 0.26 169 168 -1 Country Education index Pupil-teacher ratio index Pupil-teacher ratio index rescaled Publications index (per 1,000 people) Harmonized test score index Quality-adjusted education index Education index rank Quality-adjusted education index rank Quality-adjusted education index –education index 24 Afghanistan 0.41 0.75 0.25 0.19 0.19 0.25 171 169 -2 Mozambique 0.39 0.79 0.21 0.24 0.24 0.25 176 170 -6 Guinea 0.35 0.73 0.27 0.16 0.38 0.25 177 171 -6 Gambia 0.41 0.65 0.35 0.38 0.19 0.25 173 172 -1 Senegal 0.35 0.65 0.35 0.44 0.40 0.24 181 173 -8 Liberia 0.43 0.53 0.47 0.26 0.11 0.24 167 174 Syrian Arab Republic 0.41 0.39 0.13 0.24 169 175 Pakistan 0.40 0.72 0.28 0.55 0.14 0.24 174 176 Mauritania 0.40 0.63 0.37 0.24 0.15 0.23 175 177 Sudan 0.35 0.33 0.28 0.23 180 178 -2 Central African Republic 0.35 0.93 0.07 0.24 0.24 0.22 177 179 Sierra Leone 0.41 0.56 0.44 0.26 0.06 0.22 172 180 Burkina Faso 0.31 0.68 0.32 0.36 0.37 0.22 184 181 -3 Ethiopia 0.34 0.79 0.21 0.40 0.17 0.20 182 182 Yemen 0.35 0.58 0.42 0.25 0.08 0.19 179 183 Djibouti 0.33 0.58 0.42 0.28 0.10 0.18 183 184 South Sudan 0.31 0.75 0.25 0.04 0.13 0.18 185 185 Chad 0.29 0.81 0.19 0.06 0.12 0.16 187 186 -1 Mali 0.29 0.67 0.33 0.24 0.03 0.15 186 187 Eritrea 0.27 0.67 0.33 0.09 0.15 188 188 Niger 0.25 0.65 0.35 0.17 0.02 0.13 189 189 21-00467