The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

Saudi Arabia SA: Mean Population Exposure to PM2.5: per Cub m data was reported at 64.060 mg in 2019. This records a decrease from the previous number of 64.480 mg for 2018. Saudi Arabia SA: Mean Population Exposure to PM2.5: per Cub m data is updated yearly, averaging 64.270 mg from Dec 1990 (Median) to 2019, with 14 observations. The data reached an all-time high of 70.390 mg in 2012 and a record low of 55.950 mg in 1995. Saudi Arabia SA: Mean Population Exposure to PM2.5: per Cub m data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Saudi Arabia – Table SA.OECD.GGI: Social: Air Quality and Health: Non OECD Member: Annual.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Trade is a key means to fight poverty and achieve the Millennium Development Goals, specifically by improving developing country access to markets, and supporting a rules based, predictable trading system. In cooperation with other international development partners, the World Bank launched the Transparency in Trade Initiative to provide free and easy access to data on country-specific trade policies.

Saudi Arabia SA: Tariff Rate: Applied: Simple Mean: Primary Products data was reported at 4.580 % in 2015. This records an increase from the previous number of 3.550 % for 2014. Saudi Arabia SA: Tariff Rate: Applied: Simple Mean: Primary Products data is updated yearly, averaging 4.580 % from Dec 1994 (Median) to 2015, with 17 observations. The data reached an all-time high of 13.490 % in 2001 and a record low of 2.770 % in 2008. Saudi Arabia SA: Tariff Rate: Applied: Simple Mean: Primary Products data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank.WDI: Trade Tariffs. Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals).; ; World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Development's Trade Analysis and Information System (TRAINS) database and the World Trade Organization’s (WTO) Integrated Data Base (IDB) and Consolidated Tariff Schedules (CTS) database.; ;

Tariff rate, most favored nation, simple mean, manufactured products (%) in Saudi Arabia was reported at 5.83 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Saudi Arabia - Tariff rate, most favored nation, simple mean, manufactured products - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2025.

Tariff rate, applied, simple mean, manufactured products (%) in Saudi Arabia was reported at 5.84 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Saudi Arabia - Tariff rate, applied, simple mean, manufactured products - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2025.

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

National Coverage.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above. The sample includes only Saudi Arabians and Arab expatriates. The excluded population represents approximately 20% of the total adult population.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in the majority of economies was 1,000 individuals.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

Saudi Arabia SA: Mean Feed in Tariff For Solar PV Electricity Generation data was reported at 0.000 USD/kWh in 2019. This stayed constant from the previous number of 0.000 USD/kWh for 2018. Saudi Arabia SA: Mean Feed in Tariff For Solar PV Electricity Generation data is updated yearly, averaging 0.000 USD/kWh from Dec 2000 (Median) to 2019, with 20 observations. The data reached an all-time high of 0.000 USD/kWh in 2019 and a record low of 0.000 USD/kWh in 2019. Saudi Arabia SA: Mean Feed in Tariff For Solar PV Electricity Generation data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Saudi Arabia – Table SA.OECD.GGI: Environmental: Environmental Policy, Taxes and Transfers: Non OECD Member: Annual.

Saudi Arabia SA: Tariff Rate: Applied: Weighted Mean: Manufactured Products data was reported at 4.740 % in 2015. This records an increase from the previous number of 3.770 % for 2014. Saudi Arabia SA: Tariff Rate: Applied: Weighted Mean: Manufactured Products data is updated yearly, averaging 4.460 % from Dec 1994 (Median) to 2015, with 17 observations. The data reached an all-time high of 11.610 % in 2000 and a record low of 3.550 % in 2009. Saudi Arabia SA: Tariff Rate: Applied: Weighted Mean: Manufactured Products data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank: Trade Tariffs. Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68.; ; World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Development's Trade Analysis and Information System (TRAINS) database and the World Trade Organization’s (WTO) Integrated Data Base (IDB) and Consolidated Tariff Schedules (CTS) database.; ;

Saudi Arabia Consumer Price Index (CPI): TT: Operation Private Transport Means data was reported at 101.500 1999=100 in 2011. This records an increase from the previous number of 97.550 1999=100 for 2010. Saudi Arabia Consumer Price Index (CPI): TT: Operation Private Transport Means data is updated yearly, averaging 97.875 1999=100 from Dec 2002 (Median) to 2011, with 10 observations. The data reached an all-time high of 101.500 1999=100 in 2011 and a record low of 91.000 1999=100 in 2007. Saudi Arabia Consumer Price Index (CPI): TT: Operation Private Transport Means data remains active status in CEIC and is reported by General Authority for Statistics. The data is categorized under Global Database’s Saudi Arabia – Table SA.I007: Consumer Price Index: 1999=100: Annual.

Saudi Arabia SA: PM2.5 Air Pollution: Mean Annual Exposure: Micrograms per Cubic Meter data was reported at 187.871 mcg/Cub m in 2016. This records an increase from the previous number of 182.921 mcg/Cub m for 2015. Saudi Arabia SA: PM2.5 Air Pollution: Mean Annual Exposure: Micrograms per Cubic Meter data is updated yearly, averaging 125.961 mcg/Cub m from Dec 1990 (Median) to 2016, with 11 observations. The data reached an all-time high of 187.871 mcg/Cub m in 2016 and a record low of 83.903 mcg/Cub m in 1995. Saudi Arabia SA: PM2.5 Air Pollution: Mean Annual Exposure: Micrograms per Cubic Meter data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank: Environment: Pollution. Population-weighted exposure to ambient PM2.5 pollution is defined as the average level of exposure of a nation's population to concentrations of suspended particles measuring less than 2.5 microns in aerodynamic diameter, which are capable of penetrating deep into the respiratory tract and causing severe health damage. Exposure is calculated by weighting mean annual concentrations of PM2.5 by population in both urban and rural areas.; ; Brauer, M. et al. 2016, for the Global Burden of Disease Study 2016.; Weighted Average;

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National Coverage. Sample includes only Saudis, Arab expatriates, and non-Arabs who were able to participate in the survey in Arabic or English.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

As in the first edition, the indicators in the 2014 Global Findex are drawn from survey data covering almost 150,000 people in more than 140 economies-representing more than 97 percent of the world's population. The survey was carried out over the 2014 calendar year by Gallup, Inc. as part of its Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 140 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. The set of indicators will be collected again in 2017.

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or is the customary methodology. In most economies the fieldwork is completed in two to four weeks. In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size in Saudi Arabia was 1,018 individuals.

Mode of data collection

Other [oth]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on cash withdrawals, saving using an informal savings club or person outside the family, domestic remittances, school fees, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

The currency chart for Saudi Arabia shows historical data for the Saudi Riyals per USD exchange rate. These are monthly averages and not end-of-month currency values. An increase means depreciation against the USD as one can exchange more Saudi Riyals per USD. Depreciation implies that goods from...

Saudi Arabia Mean Feed-in Tariff: Geothermal data was reported at 0.000 USD in 2019. This stayed constant from the previous number of 0.000 USD for 2018. Saudi Arabia Mean Feed-in Tariff: Geothermal data is updated yearly, averaging 0.000 USD from Dec 2000 (Median) to 2019, with 20 observations. The data reached an all-time high of 0.000 USD in 2019 and a record low of 0.000 USD in 2019. Saudi Arabia Mean Feed-in Tariff: Geothermal data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Saudi Arabia – Table SA.OECD.ESG: Environmental: Renewable Energy Feed-in Tariffs: by Sources: Non OECD Member: Annual.

Since 2016, the global edition of the Sustainable Development Report (SDR) has provided the most up-to-date data to track and rank the performance of all UN member states on the SDGs. This year’s edition was written by a group of independent experts at the SDG Transformation Center, an initiative of the SDSN. It focuses on the UN Summit of the Future, with an opening chapter endorsed by 100+ global scientists and practitioners. The report also includes two thematic chapters, related to SDG 17 (Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development) and SDG 2 (End hunger, achieve food security and improved nutrition and promote sustainable agriculture).This year’s SDR highlights five key findings:On average, globally, only 16% of the SDG targets are on track to be achieved by 2030, with the remaining 84% demonstrating limited or a reversal of progress. At the global level, SDG progress has been stagnant since 2020, with SDG 2 (Zero Hunger), SDG11 (Sustainable Cities and Communities), SDG14 (Life Below Water), SDG15 (Life on Land) and SDG16 (Peace, Justice, and Strong Institutions) particularly off-track. Globally, the five SDG targets on which the highest proportion of countries show a reversal of progress since 2015 include: obesity rate (under SDG 2), press freedom (under SDG 16), the red list index (under SDG 15), sustainable nitrogen management (under SDG 2), and – due in a large part to the COVID-19 pandemic and other factors that may vary across countries – life expectancy at birth (under SDG 3). Goals and targets related to basic access to infrastructure and services, including SDG9 (Industry, Innovation, and Infrastructure), show slightly more positive trends, although progress remains too slow and uneven across countries.The pace of SDG progress varies significantly across country groups. Nordic countries continue to lead on SDG achievement, with BRICS demonstrating strong progress and poor and vulnerable nations lagging far behind. Similar to past years, European countries – notably Nordic countries – top the 2024 SDG Index. Finland ranks number 1 on the SDG Index, followed by Sweden (#2), Denmark (#3), Germany (#4), and France (#5). Yet, even these countries face significant challenges in achieving several SDGs. Average SDG progress in BRICS (Brazil, the Russian Federation, India, China, and South Africa) and BRICS+ (Egypt, Ethiopia, Iran, Saudi Arabia, and the United Arab Emirates) since 2015 has been faster than the world average. In addition, East and South Asia has emerged as the region that has made the most SDG progress since 2015. By contrast, the gap between the world average SDG Index and the performance of the poorest and most vulnerable countries, including Small Island Developing States (SIDS), has widened since 2015.Sustainable development remains a long-term investment challenge. Reforming the Global Financial Architecture is more urgent than ever. The world requires many essential public goods that far transcend the nation-state. Low-income countries (LICs) and lower-middle-income countries (LMICs) urgently need to gain access to affordable long-term capital so that they can invest at scale to achieve their sustainable development objectives. Mobilizing the necessary levels of finance will require new institutions, new forms of global financing — including global taxation —, and new priorities for global financing, such as investing in quality education for all. The report presents five complementary strategies to reform the Global Financial Architecture.Global challenges require global cooperation. Barbados ranks the highest in its commitment to UN-based multilateralism; the United States ranks last. As with the challenge of SDGs, strengthening multilateralism requires metrics and monitoring. The report’s new Index of countries’ support to UN-based multilateralism (UN-Mi) ranks countries based on their engagement with the UN system including treaty ratification, votes at the UN General Assembly, membership in UN organizations, participation in conflicts and militarization, use of unilateral sanctions and financial contributions to the UN. The five countries most committed to UN-based multilateralism are: Barbados (#1), Antigua and Barbuda (#2), Uruguay (#3), Mauritius (#4), and the Maldives (#5). By contrast, the United States (#193), Somalia (#192), South Sudan (#191), Israel (#190), and the Democratic Republic of Korea (#189) rank the lowest on the UN-Mi.SDG targets related to food and land systems are particularly off-track. The SDR presents new FABLE pathways to support sustainable food and land systems. Globally, 600 million people will still suffer from hunger by 2030, obesity is increasing globally, and greenhouse gas emissions from Agriculture, Forestry, and Other Land Use (AFOLU) represent almost a quarter of annual global GHG emissions. The new FABLE pathways brought together more than 80 local researchers across 22 countries to assess how 16 targets related to food security, climate mitigation, biodiversity conservation, and water quality could be achieved by 2030 and 2050. The continuation of current trends widens the gap with targets related to climate mitigation, biodiversity, and water quality. Pursuing commitments that have been already taken by countries would improve the situation, but they are still largely insufficient. Significant progress is possible but requires several dramatic changes: 1) avoid overconsumption beyond recommended levels and limit animal-based protein consumption with dietary shifts compatible with cultural preferences; 2) invest to foster productivity, particularly for products and areas with strong demand growth; and 3) implement inclusive, robust, and transparent monitoring systems to halt deforestation. Our sustainable pathway avoids up to 100 million hectares of deforestation by 2030 and 100 Gt CO2 emissions by 2050. Additional measures would be needed to avoid trade-offs with on-farm employment and water pollution due to excessive fertilizer application and ensure that no one is left behind, particularly to end hunger.About the AuthorsProf. Jeffrey SachsDirector, SDSN; Project Director of the SDG IndexJeffrey D. Sachs is a world-renowned professor of economics, leader in sustainable development, senior UN advisor, bestselling author, and syndicated columnist whose monthly newspaper columns appear in more than 100 countries. He is the co-recipient of the 2015 Blue Planet Prize, the leading global prize for environmental leadership, and many other international awards and honors. He has twice been named among Time magazine’s 100 most influential world leaders. He was called by the New York Times, “probably the most important economist in the world,” and by Time magazine, “the world’s best known economist.” A survey by The Economist in 2011 ranked Professor Sachs as amongst the world’s three most influential living economists of the first decade of the 21st century.Professor Sachs serves as the Director of the Center for Sustainable Development at Columbia University. He is University Professor at Columbia University, the university’s highest academic rank. During 2002 to 2016 he served as the Director of the Earth Institute. Sachs is Special Advisor to United Nations Secretary-General António Guterres on the Sustainable Development Goals, and previously advised UN Secretary-General Ban Ki-moon on both the Sustainable Development Goals and Millennium Development Goals and UN Secretary-General Kofi Annan on the Millennium Development Goals.Guillaume LafortuneDirector, SDSN Paris; Scientific Co-Director of the SDG IndexGuillaume Lafortune took up his duties as Director of SDSN Paris in January 2021. He joined SDSN in 2017 to coordinate the production of the Sustainable Development Report and other projects on SDG data and statistics.Previously, he has served as an economist at the Organisation for Economic Co-operation and Development (OECD) working on public governance reforms and statistics. He was one of the lead advisors for the production of the 2015 and 2017 flagship statistical report Government at a Glance. He also contributed to analytical work related to public sector efficiency, open government data and citizens’ satisfaction with public services. Earlier, Guillaume worked as an economist at the Ministry of Economic Development in the Government of Quebec (Canada). Guillaume holds a M.Sc in public administration from the National School of Public Administration (ENAP) in Montreal and a B.Sc in international economics from the University of Montreal.Contact: EmailGrayson FullerManager, SDG Index & Data team, SDSNGrayson Fuller is the manager of the SDG Index and of the team working on SDG data and statistics at SDSN. He is co-author of the Sustainable Development Report, for which he manages the data, coding, and statistical analyses. He also coordinates the production of regional and subnational editions of the SDG Index, in addition to other statistical reports, in collaboration with national governments, NGOs and international organizations such as the WHO, UNDP and the European Commission. Grayson received his Masters degree in Economic Development at Sciences Po Paris. He holds a Bachelors in Romance Languages and Latin American Studies from Harvard University, where he graduated cum laude. Grayson has lived in several Latin American countries and speaks English, Spanish, French, Portuguese and Italian. He enjoys playing the violin, rock-climbing and taking care of his numerous plants in his free time.Contact: EmailAbout the PublishersDublin University PressDublin University Press is Ireland’s oldest printing and publishing house with its origins in Trinity College Dublin in 1734. The mission of Dublin University Press is to benefit society through scholarly communication, education, research and discourse. To further this goal, the Press

Saudi Arabia SA: Tariff Rate: Most Favored Nation: Weighted Mean: Primary Products data was reported at 3.990 % in 2015. This records an increase from the previous number of 3.320 % for 2014. Saudi Arabia SA: Tariff Rate: Most Favored Nation: Weighted Mean: Primary Products data is updated yearly, averaging 3.790 % from Dec 1994 (Median) to 2015, with 17 observations. The data reached an all-time high of 17.070 % in 2000 and a record low of 2.560 % in 2009. Saudi Arabia SA: Tariff Rate: Most Favored Nation: Weighted Mean: Primary Products data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank: Trade Tariffs. Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals).; ; World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Development's Trade Analysis and Information System (TRAINS) database and the World Trade Organization’s (WTO) Integrated Data Base (IDB) and Consolidated Tariff Schedules (CTS) database.; ;

Saudi Arabia SA: Population: as % of Total: Female: Aged 15-64 data was reported at 67.621 % in 2017. This records an increase from the previous number of 67.424 % for 2016. Saudi Arabia SA: Population: as % of Total: Female: Aged 15-64 data is updated yearly, averaging 51.790 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 67.621 % in 2017 and a record low of 49.393 % in 1990. Saudi Arabia SA: Population: as % of Total: Female: Aged 15-64 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank: Population and Urbanization Statistics. Female population between the ages 15 to 64 as a percentage of the total female population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.; ; World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2017 Revision.; Weighted average; Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services.

Saudi Arabia SA: Bound Rate: Simple Mean: All Products data was reported at 10.640 % in 2015. This records a decrease from the previous number of 11.180 % for 2014. Saudi Arabia SA: Bound Rate: Simple Mean: All Products data is updated yearly, averaging 11.180 % from Dec 2005 (Median) to 2015, with 10 observations. The data reached an all-time high of 11.320 % in 2007 and a record low of 10.640 % in 2015. Saudi Arabia SA: Bound Rate: Simple Mean: All Products data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank.WDI: Trade Tariffs. Simple mean bound rate is the unweighted average of all the lines in the tariff schedule in which bound rates have been set. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable.; ; World Bank staff estimates using the World Integrated Trade Solution system, based on data from World Trade Organization.; ;

Saudi Arabia SA: Distance to Frontier Score: 0=Lowest Performance To 100=Frontier data was reported at 62.500 NA in 2017. This records an increase from the previous number of 59.580 NA for 2016. Saudi Arabia SA: Distance to Frontier Score: 0=Lowest Performance To 100=Frontier data is updated yearly, averaging 59.580 NA from Dec 2015 (Median) to 2017, with 3 observations. The data reached an all-time high of 62.500 NA in 2017 and a record low of 58.840 NA in 2015. Saudi Arabia SA: Distance to Frontier Score: 0=Lowest Performance To 100=Frontier data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank.WDI: Business Environment. Distance to frontier score illustrates the distance of an economy to the 'frontier,' which represents the best performance observed on each Doing Business topic across all economies and years included since 2005. An economy's distance to frontier is indicated on a scale from 0 to 100, where 0 represents the lowest performance and 100 the frontier. For example, a score of 75 in 2012 means an economy was 25 percentage points away from the frontier constructed from the best performances across all economies and across time. A score of 80 in 2013 would indicate the economy is improving.; ; World Bank, Doing Business project (http://www.doingbusiness.org/).; Unweighted average; Data are presented for the survey year instead of publication year. Data before 2013 are not comparable with data from 2013 onward due to methodological changes.

Saudi Arabia SA: Population: Female: Aged 15-64 data was reported at 9,561,774.000 Person in 2017. This records an increase from the previous number of 9,384,885.000 Person for 2016. Saudi Arabia SA: Population: Female: Aged 15-64 data is updated yearly, averaging 3,393,713.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 9,561,774.000 Person in 2017 and a record low of 1,074,396.000 Person in 1960. Saudi Arabia SA: Population: Female: Aged 15-64 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank.WDI: Population and Urbanization Statistics. Female population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.; ; World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects: 2017 Revision.; Sum; Relevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services.