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Dominica’s path to resilient recovery after Hurricane Maria

Keren Charles's picture


Located in the warm waters of the Eastern Caribbean, Dominica is no stranger to tropical storms and hurricanes.  Yet Hurricane Maria, which battered Dominica last September, was unlike anything the island nation had ever seen. Packing winds of over 160 miles per hour, the Category 5 hurricane claimed the lives of 30 people and caused total damages and losses exceeding US$1.3 billion.

Socio-Emotional Skills Wanted! – New Big Data Evidence from India

Saori Imaizumi's picture


We all hear about the importance of “socio-emotional skills” when looking for a job. Employers are said to be looking for individuals who are hardworking, meet deadlines, are reliable, creative, collaborative … the list goes on depending on the occupation. In recent years, it seems, these skills have become equally important as technical skills. But do employers really care about these soft skills when hiring? If so, what type of personality do they favor?

New country classifications by income level: 2018-2019

World Bank Data Team's picture

Updated country income classifications for the World Bank’s 2019 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 2017 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 2018, the new thresholds for classification by income are:

Threshold GNI/Capita (current US$)
Low-income < 995
Lower-middle income 996 - 3,895
Upper-middle income 3,896 - 12,055
High-income > 12,055

Changes in Classification

The following countries have new income groups:

Country Old group New group
Argentina Upper-middle High-income
Armenia Lower-middle Upper-middle
Croatia Upper-middle High-income
Guatemala Lower-middle Upper-middle
Jordan Lower-middle Upper-middle
Panama Upper-middle High-income
Syrian Arab Rep. Lower-middle Low-income
Tajikistan Lower-middle Low-income
Yemen Rep. Lower-middle Low-income

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 2017 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 [email protected]

Q2 2018 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 1,600 indicators and includes both new indicators and updates to existing indicators. 

Data for population and national accounts, including GDP and GNI-related indicators, have been released for countries and aggregates.

The methodology for presenting value added for the services sector has been revised, and financial intermediary services indirectly measured (FISIM) are presented separately. Historically, FISIM was used in the calculation of the “Services, etc” indicator. Starting with July 2018 update of the WDI, FISIM is presented as a separate series, where available. In addition, the “Final consumption expenditure, etc” and “Household consumption expenditure, etc” data included any existing statistical discrepancy between GDP according to production methodology and GDP according to expenditure methodology. Starting with this update, these two series will no longer be published. Instead, indicators for final consumption expenditure and household consumption expenditure are now available. Users can find the statistical discrepancy listed as a separate indicator. You can access the latest list of indicator additions, deletions, descriptions and code changes here. The methodology for calculating value added shares has also been updated.  
 
Other data that have been updated include FDI, tariffs, monetary and prices indicators, balance of payments, trade, health, military expenditure, air traffic, CPIA ratings, and fisheries. Purchasing Power Parities (PPP) have been updated for OECD and Eurostat countries to show the latest release. The country classification hierarchies and group aggregate data reflect the new fiscal year 2019 income classifications. Historical data have been revised as necessary.

Data can be accessed via various means including:

- The World Bank’s main multi-lingual and mobile-friendly data website, http://data.worldbank.org 
- The DataBank query tool: http://databank.worldbank.org which includes archived versions of WDI
Bulk download in XLS and CSV formats and directly from the API
 

For thriving cities, people vs. nature is a false choice

Joel Paque's picture
Brooklyn Bridge Park, New York
Brooklyn Bridge Park, New York. Photo credit: © Kevin Arnold
Municipal leaders face hundreds of difficult choices every day. With so many needs and worthy programs, how does one choose where to invest limited funding? In the face of pressing human needs, cities too often decide that funding for environmental programs will have to wait.

But pitting people against nature in this way offers a false choice.

Five ways Nigeria can realize mobile technology's potential for the unbanked

Leora Klapper's picture

Although it’s Africa's largest economy, Nigeria is missing out on the region’s most exciting financial innovation: mobile money.
 
Twenty-one percent of adults in Sub-Saharan Africa have a mobile money account, nearly double the share from 2014, according to the latest Global Findex report.
 
By contrast, Nigeria lags behind: just 6% of adults have a mobile money account, a number virtually unchanged from 2014.

The challenges of bringing development to the remote areas of Colombia

Erwin de Nys's picture


In 2017-18 we visited the Meta department in Colombia on multiple occasions. Located right where Colombia’s Llanos Orientales (Eastern Plains) disappear south into the vastness of the Amazon rainforest, this area of the size of Belgium, the Netherlands, and Luxembourg combined is a magical spot in the world’s second most biodiverse country.
 
Meta is not a poor region - it boasts some of the nation’s largest oil reserves. Highly fertile soil and multiple thermal floors have created a boom in agribusiness in recent years, while its geographic proximity to Colombia’s capital has more recently led to a thriving tourism industry.
 
Despite having made significant progress on many fronts, this region still faces critical challenges. On our last visit, we had the opportunity to chat for hours with several small-scale farmers from south-western Meta – a sub-region where economic development has been seriously damaged by the cultivation of coca leaf, the raw material used to produce cocaine.
 

What makes a good case for allocating more of the national budget to the health sector?

Maxwell Bruku Dapaah's picture

Making the case for increasing the national budget allocation to the health sector is critical if more domestic resources are to be garnered for financing universal health coverage. Yet, there are competing priorities for more allocation for other sectors. While political will remains pivotal to decisions on national priorities, against limited resources, fiscal managers- such as ministries of finance or treasury- have a challenging job translating national priorities into budget allocations for sectors.

Weekly links June 29: cash does more good things, interpreting IHS, technical assistance to banks increased credit, and more...

David McKenzie's picture

Data analytics for transport planning: five lessons from the field

Tatiana Peralta Quiros's picture
Photo: Justin De La Ornellas/Flickr
When we think about what transport will look like in the future, one of the key things we know is that it will be filled and underpinned by data.

We constantly hear about the unlimited opportunities coming from the use of data. However, a looming question is yet to be answered: How do we sustainably go from data to planning? The goal of governments should not be to amass the largest amount of data, but rather “to turn data into information, and information into insight.” Those insights will help drive better planning and policy making.

Last year, as part of the Word Bank’s longstanding engagement on urban transport in Argentina, we started working with the Ministry of Transport’s Planning Department to tap the potential of data analytics for transport planning. The goal was to create a set of tools that could be deployed to collect and use data for improved transport planning.

In that context, we lead the development of a tool that derives origin-destination matrices from public transport smartcards, giving us new insight into the mobility patterns of Buenos Aires residents. The project also supported the creation of a smartphone application that collects high-resolution mobility data and can be used for citizen engagement through dynamic mobility surveys. This has helped to update the transport model in Buenos Aires city metropolitan area (AMBA).

Here are some of the lessons we learnt from that experience.

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