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New country classifications by income level: 2017-2018

World Bank Data Team's picture
Also available in: Español | 中文 | Français | العربية

Updated country income classifications for the World Bank’s 2018 fiscal year are available here.

The World Bank assigns the world's economies into four income groups — high, upper-middle, lower-middle, and low. We base this assignment on GNI per capita calculated using the Atlas method. The units for this measure and for the thresholds is current US Dollars.

At the Bank, these classifications are used to aggregate data for groups of similar countries. The income-category of a country is not one of the factors used that influence lending decisions.

Each year on July 1st, we update the classifications. They change for two reasons:

1. In each country, factors such as income growth, inflation, exchange rates, and population change, influence GNI per capita.

2. To keep the dollar thresholds which separate the classifications fixed in real terms, we adjust them for inflation.

The data for the first adjustment come from estimates of 2016 GNI per capita which are now available. This year, the thresholds have moved down slightly because of low price inflation and the strengthening of the US dollar. Click here for information about how the World Bank classifies countries.

Updated Thresholds

New thresholds are determined at the start of the Bank’s fiscal year in July and remain fixed for 12 months regardless of subsequent revisions to estimates. As of July 1 2017, the new thresholds for classification by income are:

Threshold GNI/Capita (current US$)
Low-income < 1,005
Lower-middle income 1,006 - 3,955
Upper-middle income 3,956 - 12,235
High-income > 12,235

Changes in Classification

The following countries have new income groups:

Country Old group New group
Angola Upper-middle Lower-middle
Croatia High-income Upper-middle
Georgia Upper-middle Lower-middle
Jordan Upper-middle Lower-middle
Nauru High-income Upper-middle
Palau Upper-middle High-income
Samoa Lower-middle Upper-middle
Tonga Lower-middle Upper-middle

The country and lending groups page provides a complete list of economies classified by income, region, and lending status and links to previous years’ classifications. The classification tables include all World Bank members, plus all other economies with populations of more than 30,000. The term country, used interchangeably with economy, does not imply political independence but refers to any territory for which authorities report separate social or economic statistics.

Tables showing 2016 GNI, GNI per capita, GDP, GDP PPP, and Population data are also available as part of the World Bank's Open Data Catalog. Note that these are preliminary estimates and may be revised. For more information, please contact us at data@worldbank.org.

Q2 2017 Update of World Development Indicators Available

World Bank Data Team's picture

The World Development Indicators database has been updated. This is a regular quarterly update to over 600 indicators and includes both new indicators and updates to existing indicators.

2016 data for population, GDP and GNI-related indicators have been released for countries and aggregates. Other data that have been updated include: balance of payments series, monetary indicators, military expenditure, and merchandise trade. The classifications of countries by income, and aggregations by income group reflect new fiscal year 2018 income classifications.

New Public Private Partnership series have been introduced in this release. The percentage of people with an account (SDG 8.10.2 from the Findex) is also available and disaggregated by sex, income, and education level.

Purchasing Power Parities have been updated for OECD and Eurostat countries to reflect their latest release. Purchasing Power Parities and related indicators in PPP terms for Cuba (expenditures, income, etc.) have been removed.

Data can be accessed via various means including:

- The World Bank’s multi-lingual, mobile-friendly data site, http://data.worldbank.org  
- The DataBank query tool: http://databank.worldbank.org 
- Bulk download in XLS and CSV formats and directly from the API

Chart: Globally, The Number of People Without Access to Electricity is Falling

Tariq Khokhar's picture

Electrification has expanded in all regions and in both urban and rural areas. South Asia has driven global declines, with just 28 percent of rural dwellers lacking electricity in 2014. In most regions, electrification has outpaced population growth. An exception is Sub-­Saharan Africa: 134 million more people in rural areas lacked access in 2014 than in 1994. Read more in the 2017 Atlas of Sustainable Development Goals and in a new feature on "Solar Powers India's Clean Energy Revolution"

 

Chart: Globally, Over 1 Billion People Lack Access to Electricity

Tariq Khokhar's picture
Also available in: العربية

In 2014, around 15 percent of the world’s population, or 1.1 billion had no access to electricity. Nearly half were in rural areas of Sub-Saharan Africa and around a third were rural dwellers in South Asia. Just four countries - India, Nigeria, Ethiopia and Bangladesh are home to about half of all people who lack access to electricity. Read more in the 2017 Atlas of Sustainable Development Goals and in a new feature on "Solar Powers India's Clean Energy Revolution"

 

Discontinuing the DataFinder Mobile Apps

World Bank Data Team's picture

Following the successful release of the new mobile-first data.worldbank.org we’re discontinuing the “DataFinder” series of apps for iOS and Android Devices.

The mobile apps have been popular since the launch of the Bank’s Open Data Initiative but have seen declining use since our switch to a mobile-first website.

As of July 1, the following apps will no longer be available:

  • World Bank DataFinder
  • World Bank Climate Change DataFinder
  • World Bank Gender DataFinder
  • World Bank HealthStats DataFinder
  • World Bank Jobs DataFinder
  • World Bank LAC Poverty DataFinder
  • World Bank Poverty DataFinder
  • ICP DataFinder

Apps already installed on devices will continue to function but will no longer receive any updates.

You can continue to access all the data presented in these apps via data.worldbank.org and through the databases listed in our data catalog.

New Partnership for Capacity Development in Household Surveys for Welfare Analysis

Vini Vaid's picture

In low- and middle-income countries, household surveys are often the primary source of socio-economic data used by decision makers to make informed decisions and monitor national development plans and the SDGs. However, household surveys continue to suffer from low quality and limited cross-country comparability, and many countries lack the necessary resources and know-how to develop and maintain sustainable household survey systems.
 
The World Bank’s Center for Development Data (C4D2) in Rome and the Bank of Italy— with financial support by the Italian Agency for Development Cooperation and commitments from other Italian and African institutions—have launched a new initiative to address these issues.

The Partnership for Capacity Development in Household Surveys for Welfare Analysis aims to improve the quality and sustainability of national surveys by strengthening capacity in regional training centers in the collection, analysis, and use of household surveys and other microdata, as well as in the integration of household surveys with other data sources.
 
On Monday, nine partners signed an MoU describing the intent of the Partnership, at the Bank of Italy in Rome. The signatories included Haishan Fu (Director, Development Data Group, World Bank), Valeria Sannucci (Deputy Governor, Bank of Italy), Pietro Sebastiani (Director General for Cooperation and Development, Ministry of Foreign Affairs and International Cooperation of the Italian Republic), Laura Frigenti (Director, Italian Agency for Development Cooperation), Giorgio Alleva (President, Italian National Institute of Statistics), Stefano Vella (Research Manager, Italian National Institute of Health), Oliver Chinganya (Director, African Centre for Statistics of the UN Economic Commission for Africa), Frank Mkumbo (Rector, Eastern Africa Statistical Training Center), and Hugues Kouadio (Director, École Nationale Supérieure de Statistique et d’Économie Appliquée).
 
The Partnership will offer a biannual Training Week on household surveys and thematic workshops on specialized topics to be held in Italy in training facilities made available by the Bank of Italy, as well as regular short courses and seminars held at regional statistical training facilities to maximize outreach and impact. The first of a series of Training-of-Trainers (ToT) courses will be held in Fall 2017.
 
For more information, please contact: c4d2@worldbank.org.

If you know what stakeholders really think, can you engage more effectively?

Svetlana Markova's picture

The World Bank Group surveys its stakeholders from country governments, development organizations, civil society, private sector, academia, and media in all client countries across the globe. Building a dialogue with national governments and non-state partners based of the data received directly from them is an effective way to engage stakeholders in discussions in any development area at any possible level.

Let's take the education sector as an example to see how Country Survey data might influence the engagement that the Bank Group has on this highly prioritized area of work.

When Country Surveys ask what respondents identify as the greatest development priority in their country, overall, education is perceived as a top priority (31%, N=263) in India.1 However, in a large country, stakeholder opinions across geographic locations may differ, and the Country Survey data can be 'sliced and diced' to provide insight into stakeholders' opinions based on their geography, gender, level of collaboration with the Bank Group, etc. In India the data analyzed at the state level shows significant differences in stakeholder perceptions of the importance of education. The survey results can be used as a basis for further in-depth analyses of client's needs in education in different states and, therefore, lead to more targeted engagement on the ground. In the case of the India Country Survey, the Ns at the geographical level may be too small to reach specific conclusions, but this example illustrates the possibility for targeted analysis.

Leveraging Open Source as a Public Institution — New analysis reveals significant returns on investment in open source technologies

Vivien Deparday's picture

Examples abound of leading tech companies that have adopted open source strategy and contribute actively to open source tools and communities. Google, for example, has been a long contributor to open source with projects – such as its popular mobile operating system, Android – and recently launched a directory of the numerous projects. Amazon Web Services (AWS) is another major advocate, running most of its cloud services using open source software, and is adopting an open source strategy to better contribute back to the wider community. But can, and should, public institutions embrace an open source philosophy?

In fact, organizations of all types are increasingly taking advantage of the many benefits open source can bring in terms of cost-effectiveness, better code, lower barriers of entry, flexibility, and continual innovation. Clearly, these many benefits not only address the many misconceptions and stereotypes about open source software, but are also energizing new players to actively participate in the open source movement. Organizations like the National Geospatial-Intelligence Agency (NGA) have been systematically adopting and leveraging open sources best practices for their geospatial technology, and even the U.S. Federal Government has also adopted a far-reaching open source policy to spur innovation and foster civic engagement.

So, how can the World Bank – an institution that purchases and develops a significant amount of software – also participate and contribute to these communities? How can we make sure that, in the era of the ‘knowledge Bank’, digital and re-usable public goods (including open source software, data, and research) are available beyond single projects or reports?

Adding to existing MDG drinking water data for the SDG world

Libbet Loughnan's picture
Also available in: العربية | Français | Español

This blog is part of a series accompanying the Atlas of Sustainable Development Goals (SDG) 2017In response to frequent questions from those trying to gain familiarity with the monitoring method of SDG target 6.1, we use this blog to elaborate on the overview presented in the Atlas.

Here we are looking just at the new water indicator: 'The percentage of the population using safely managed drinking water services', defined as an MDG-style improved drinking water source, which is:

  • located on premises
  • available when needed, and
  • compliant with fecal (zero E.coli in 100mL sample of the household's source of drinking water) and priority chemical standards

These changes reflect evolving global consensus on what can best be monitored to support development. They are designed to denote opportunities: representing the full water cycle and fecal-oral chain, quantifying issues that were less visible through MDG-lenses, and informing action to meet domestic targets as well as the World Bank Group Twin Goals and the SDGs. That is, so long as the data is collected.

Until household surveys integrate the additional measurements, data constraints mean that only limited insights are yet possible on how the shift to the SDG framework will play out in various countries. As outlined in a recent blog, an initiative led by the World Bank's Water and Poverty Equity Global Practices - called the Water Supply, Sanitation, and Hygiene (WASH) Poverty Diagnostic - is supporting rollout of the new SDG measurements. The Diagnostics have helped not just highlight evidence gaps but also successfully developed partnerships collecting critical SDG measurements in Ethiopia, Tajikistan, Nigeria, DRC, and West Bank and Gaza, as well as Ecuador.

The Diagnostic has also been engaging with countries to help relate their historical data to the new framework. As with the data production, this is mutually informed by the WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation (JMP), helping ease uptake of the results in official SDG monitoring.

There are straightforward elements to this: MDG-style "improved" drinking water, the "on premises" component of the MDG-period "piped water on premises", contribute some of the building blocks of SDG classification "safely managed".

Many countries also have some data on whether a drinking water source was within 30 minutes roundtrip versus farther afield. Although not part of the binary SDG indicator, this will routinely be used to distinguish "basic" from worse drinking water. Imagine that your daily life relied on water fetched from over 30 mins away!

"Available when needed" and "compliant with fecal and priority chemical standard" are new to the global monitoring framework.

Chart: Global Growth Forecast to Reach 2.7 Percent in 2017

Tariq Khokhar's picture
Also available in: Français | العربية | 中文 | Español

The World Bank forecasts that global economic growth will strengthen to 2.7 percent in 2017 as a pickup in manufacturing and trade, rising market confidence, and stabilizing commodity prices allow growth to resume in commodity-exporting emerging market and developing economies. Growth in advanced economies is expected to accelerate to 1.9 percent in 2017, and growth in emerging market and developing economies as will rise to 4.1 percent this year from 3.5 percent in 2016. Read more and download Global Economic Prospects.

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