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Achieving good budgetary governance: What have we learned from PEFA in the past decade?

Lewis Hawke's picture

The national budget is the primary document through which governments present their plans and the resources, including taxes, they intend to collect to fund them.

Many countries present both national and sectoral strategies that identify policy priorities to be funded through the budget. For example, the health sector could include details of policies to provide vaccination on a range of diseases and details of citizens' access to specific healthcare services.

A government's inability to implement the national budget as planned could be a sign of lack of capacity to forecast revenues and expenditures adequately or an inability to properly cost financial impact of government policies, or quite commonly a mixture of all of these issues.

However, in many cases the reason why governments are unable to execute budgets as planned could be, at least partially due to exogenous factors, such as natural disasters, armed conflicts, or increased level of migration flows.

The Sustainable Development Goals—target 16.6—recognize that providing a sound basis for development requires that government budgets are comprehensive, transparent, and realistic.

This is measured through the Public Expenditure and Financial Accountability (PEFA)[1] indicator that assesses the difference between planned and actual budget expenditure in countries across the world.

Since 2005, 147 countries and 178 subnational governments have carried out a PEFA assessment, with national spending more likely to be on target than subnational spending.

Introducing Data360R — data to the power of R

Reg Onglao's picture
 

Last January 2017, the World Bank launched TCdata360 (tcdata360.worldbank.org/), a new open data platform that features more than 2,000 trade and competitiveness indicators from 40+ data sources inside and outside the World Bank Group. Users of the website can compare countries, download raw data, create and share data visualizations on social media, get country snapshots and thematic reports, read data stories, connect through an application programming interface (API), and more.

What do household surveys and project monitoring have in common?

Michael Wild's picture

For verification purposes Survey Solution's picture question allows to document the installation and their new owner.
The implementation and the monitoring of large infrastructure projects is always a challenge. This challenge is even more pronounced, when the beneficiaries are located at the grassroots level. In the case of the Myanmar national electrification project (NEP), the challenge was the implementation and monitoring of around 145,000 households, community centers and schools, which did not have proper access to electricity and are being newly equipped with solar panels under the first contract. The basic information to be collected and monitored include who receives which type of solar PV systems, when, and by which supplier, and whether the users have been satisfactory with the quality of the equipment and installation, etc. The project is expected to eventually benefit 1.2 million households and more than 10,000 villages over 6 years with new electricity services.

Survey Solutions is already well known for its capacity to deal with large scale household surveys with highly complex questionnaires. One of the main strengths of Survey Solutions is its flexibility in designing a questionnaire. Users can easily create complex survey questionnaires through the browser based interface without the use of any complex syntax. For most of the standard survey questionnaires, the provided basic functions are sufficient.

However, it also offers the possibility to modify the questionnaire beyond the basic capabilities, by using the C# programming language. This allows the users to create questionnaires for very specific, non-standard tasks.

#LACfeaturegraph blog contest winner: In Latin America, education is not closing the income gap

Joaquín Muñoz's picture
Also available in: Español | Portuguese

Editor’s Note: In May, the LAC Team for Statistical Development launched the #LACfeaturegraph blog contest, where participants were asked to use poverty, inequality or other welfare data from the LAC Equity Lab to come up with an original analysis and integrate it with a data visualization. We received numerous blog submissions and after carefully reading each blog, we have picked the winner. Here is the winning entry from Joaquín Muñoz from Chile.

Education has long been considered fundamental in paving a country’s road to development. It is an International Human Right, one of the eight Millennium Development Goals and seventeen Sustainable Development Goals, and a critical player in reducing poverty. Thus, government officials and development partners have renewed efforts to ensure access to primary and secondary education worldwide.

In Latin America and the Caribbean, a region that faces stark levels of inequality, educational programs have been designed and funded with the aim of guaranteeing equal opportunities to school access. For instance, while in 1990 primary school enrollment in the region was about 89.9 percent, by 2010 it had increased to 94.2 percent. In the same period, literacy rates progressed as well, increasing from 87.5 percent to 92.6 percent (The World Bank, 2017). Even though the difficulty of achieving universal access to education is daunting, the numbers show that the region is on the right track.

However, the figure below shows that even though there has been a significant increase in the total years of education between 2004 and 2014 among the region’s population, the top 60 percent and the bottom 40 percent have experienced unequal income gains. While both groups experienced an increase in years spent in school, the data suggest that the top 60 percent, which was already wealthier and longer-schooled, saw a greater increase in their median daily per capita income than the bottom 40 percent. This finding is consistent with other evidence that suggests that income returns to schooling differ across the wage distribution (Harmon, Oosterbeek and Walker, 2000).

Source: Author's graph using LAC Equity Lab tabulations of SEDLAC (CEDLAS and the World Bank).

Latest from the LSMS: New data from Tanzania and Nigeria, dynamics of wellbeing in Ethiopia & using non-standard units in data collection

Vini Vaid's picture

Message from Gero Carletto (Manager, LSMS)

It has been a busy few months for the LSMS team! Together with several Italian and African institutions, we recently launched the Partnership for Capacity Development in Household Surveys for Welfare Analysis. The initiative cements a long-term collaboration to train trainers from regional training institutions in Sub-Saharan Africa to harmonize survey data and promote the adoption of best practices in household surveys across the region (see below for more details). In addition, we have contributed to several international conferences and meetings, such as the Annual Bank Conference on Africa (featured below), where we witnessed the creative use of the data we helped collect and disseminate. Finally, LSMS was part of a documentary on the Public Broadcasting Service (PBS) called The Crowd & The Cloud. The fourth episode featured our very own Talip Kilic and the Uganda Bureau of Statistics, working hand in hand to produce household and farm-level panel data, which have been game changers in informing government policymaking and investment decisions, as well as in advancing the methodological frontier. We look forward to many more exciting quarters as we continue to work with our partners to improve the household survey landscape!

Using Non-Standard Units in Data Collection: The Latest in the LSMS Guidebook Series

Vini Vaid's picture
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Food consumption and agricultural production are two critical components for monitoring poverty and household well-being in low- and middle-income countries. Accurate measurement of both provides a better contextual understanding and contributes to more effective policy design.

At present, there is no standard methodology for collecting food quantities in national surveys. Often, respondents are required to estimate quantities in standard units (usually metric units), requiring respondents to convert into kilograms, for example, when many respondents are more comfortable reporting their food consumption and production using familiar “local” or “non-standard” units. But how many tomatoes are in one kilogram? How much does a local small tin or basket of maize flour weight? This conversion process is often an uncommon or abstract task for respondents and this added difficulty can introduce measurement error. Allowing respondents to report quantities directly in NSUs places less of a burden on respondents and may ultimately lead to better quality data by improving the accuracy of information provided.

This new Guidebook provides guidance for effectively including non-standard units (NSUs) into data-collection activities — from establishing the list of allowable NSUs to properly collecting conversion factors for the NSUs, with advice on how to incorporate all the components into data collection. An NSU-focused market survey is a critical part of preparing the conversion factors required for effectively using NSU data in analytical work. As such, the bulk of this Guidebook focuses on implementing the market survey and on calculating conversion factors to ensure the highest-quality data when using NSUs.

The Guidebook is the result of collaboration between the World Bank's Living Standards Measurement Study (LSMS) team, the Central Statistical Agency of Ethiopia, the National Bureau of Statistics in Nigeria, the National Statistics Office of Malawi, and the Uganda Bureau of Statistics.

For practical advice on household survey design, visit the LSMS Guidebooks page: http://go.worldbank.org/0ZOAP159L0

Supporting data for development: applications open for a new innovation fund

Haishan Fu's picture
Also available in: العربية | Français | 中文 | Español
Image credit: The Crowd and The Cloud


I’m pleased to announce that applications are now open for the second round of a new data innovation fund which was announced last month at the UN’s High Level Political Forum.

The fund will invest up to $2.5 million in Collaborative Data Innovations for Sustainable Development - ideas to improve the production, management and use of data in poor countries. This year the fund’s thematic areas are “Leave No One Behind” and the environment.

Details on eligibility, criteria and how to apply are here: bit.ly/wb-gpsdd-innovationfund-2017

The initiative is supported by the World Bank’s Trust Fund for Statistical Capacity Building (TFSCB) with financing from the United Kingdom’s Department for International Development (DFID), the Government of Korea and the Department of Foreign Affairs and Trade of Ireland. DFID is the largest contributor to the TFSCB.

Supporting statistics for development

Here in the World Bank’s Development Data group, we’re looking forward to working with the Global Partnership for Sustainable Development Data (GPSDD) again following a successful pilot round of innovation funding last year. But you might be asking - why is the World Bank’s Data team helping to run a data innovation fund?

Global Partnership announces new round of funding for ‘Collaborative Data Innovations for Sustainable Development’

World Bank Data Team's picture
Claire Melamed of the GPSDD & Mahmoud Mohieldin of the World Bank at the High Level Political Forum 2017

Following a successful round of pilot funding for development data innovation projects last year, the Global Partnership for Sustainable Development Data (GPSDD) has announced a second funding round for data for development projects, to open on August 1st 2017.

As part of the ‘Collaborative Data Innovations for Sustainable Development’ funding, which is supported by the World Bank’s Trust Fund for Statistical Capacity Building (TFSCB), GPSDD will seek innovative proposals for data production, dissemination and use.

This year’s call is anchored around two themes: ‘Leave No One Behind’ and the Environment. Once again, the focus is on work supporting low and lower-middle income countries, and on projects that bring together collaborations of different stakeholders to address concrete problems.

The new round of funding was announced by GPSDD’s Executive Director Claire Melamed at a High-Level Political Forum Event ‘Leave No One Behind: Ensuring inclusive SDG progress’ at United Nations HQ in New York. She said:

“There was a fantastic response to ‘Collaborative Data Innovations for Sustainable Development Pilot Funding’ last year, with 400 proposals, from which 10 outstanding ideas were selected. This year we are opening a new round to source innovative projects to protect the environment and ‘Leave No One Behind’.  For the 2017 round we are raising the bar even higher by asking applicants to collaborate from the outset, providing evidence of support from an organisation that is a potential end user. With a wealth of data innovation talent out there, we are excited to see who comes forward.”

The World Bank’s Senior Vice President for the 2030 Development Agenda, United Nations Relations, and Partnerships, Mahmoud Mohieldin, added:

Innovation work doesn't happen in isolation, it requires a network of ideas, individuals and institutions to come together to be more than a sum of their parts. We’ve found this network in the Global Partnership for Sustainable Development Data, and are pleased to be working together to identify and support new ideas to change the way development data are produced, managed and used.”  
 

Application Details and Funding Levels

A New Look at Health, Nutrition & Population Data

Haruna Kashiwase's picture
Also available in: Español | العربية | Français




Data on the size and wellbeing of the world’s populations are among the most widely accessed information on the World Bank’s Data pages.

Today we’re releasing a revamped Health, Nutrition & Population (HNP) Data portal which offers a quick look at over 250 indicators covering topics such as health financing and the health workforce; immunization and the incidence of HIV and AIDS, malaria and tuberculosis, non-communicable diseases and the causes of death; nutrition, clean water and sanitation, and reproductive health; as well as population estimates and population projections.

We encourage you to explore the resources above, here are three stories you can find in the data:

1) In low-income countries, only half of births are attended by skilled health staff.

Delivery assistance provided by doctors, nurses, and trained midwives can save the lives of mothers and children.  While more than 70 percent births are attended by skilled health staff worldwide, this average falls to 51 percent in low-income countries. The poorest women are least likely to deliver babies with assistance from skilled health staff at birth.

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.

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