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Poverty lies beyond the unemployed

Isis Gaddis's picture

Globally, poverty by employment status is highest among unpaid workers (22 percent), followed by self-employment, and those out of the labor force (both 12 percent). Not surprisingly, income-earning capacity (proxied by employment status) is strongly associated with poverty and gender. When disaggregated by sex, there are roughly equal numbers of men and women among the poor who are unemployed. There are more men than women among the self-employed poor. However, women make up most of the poor who are unpaid workers or out of the labor force. To learn more, read the recently released Poverty and Shared Prosperity report 2018, “Piecing Together the Poverty Puzzle.”

International Debt Statistics 2019: External debt stocks at end-2017 stood at over $7 trillion

Evis Rucaj's picture
The 2019 edition of International Debt Statistics (IDS) has just been published.
International Debt Statistics 2019 presents statistics and analysis on the external debt and financial flows (debt and equity) for the world's economies for 2015. This publication provides more than 200 time series indicators from 1970 to 2017 for most reporting countries. To access the report and related products you can:

 
This year's edition is released just 10 months after the 2017 reference period, making comprehensive debt statistics available faster than ever before. It presents comprehensive stock and flow data for individual countries and for regional and analytical groupings. 

In addition to the data published in multiple formats online, IDS includes a concise analysis of the global debt landscape, which will be expanded on in a series of Debt Bulletin over the next year.

More than money: Counting poverty in multiple forms

Dhiraj Sharma's picture

Consider two households that have the same level of consumption (or income) per person but they differ in the following ways. All the children in the first household go to school, while the children in the second household work to support the family. The first household obtains drinking water from a tap connected to the public distribution network, whereas the second household fetches water from a nearby stream. At night, the first home is illuminated with electricity, whereas the second home is dark. A lay person would easily recognize which of these two families is better off. Yet, traditional measures of household well-being would put the two households on par because conventionally, household well-being has been measured using consumption (or income).

Global Findex 2017 microdata available for download

Leora Klapper's picture
We're thrilled to release the 2017 Global Findex microdata, featuring individual survey responses from roughly 150,000 adults globally. Get it here, along with documentation including a variable list, questionnaire, and information on sampling procedures and data weighting.
 
Downloading the data is easy. At the microdata library, you'll see a screen that looks like this:
 

 

The Goods, the Bad, and the Ugly: Data and the food system

Julian Lampietti's picture
Photo Credit: Goodluz/Shutterstock.com

The business of agriculture and food is driven by data, making it the treasure trove of today’s agri-food system. Whether it’s today’s soil moisture, tomorrow’s weather forecast, or the price of rice in Riyadh, every bit of data can improve the efficiency with which the world’s 570 million farmers put food into the mouths of its soon-to-be eight billion consumers. Digital technologies are facilitating the flow of data through the food system, shrinking information asymmetries and fashioning new markets along the way. How can we ensure these new markets are appropriately contested, and the treasure does not end up in the hands of a couple of gunslingers? Is there a public sector’s role in generating and disseminating data that on the one hand encourages innovation and competition and on the other reduces opportunities for market capture? One place to look may be at the crossroads of internet and public goods.

We all remember from econ class that public goods can’t be efficiently allocated by markets because they are non-rival and non-excludable. There are precious few examples of true public goods – national defense, clean air, and lighthouses come to mind. That is, at least until Coase’s in “The Lighthouse in Economics” argued that lighthouses are excludable because it was possible to temporarily turn-off the lighthouse when a ship sailed by that didn’t pay their port fees.

Checklist: 10 guiding principles for effective use of risk data

Simone Balog-Way's picture
Local city officials and university students in Can Tho, Vietnam
collaborate and learn about innovative mapping technology
Photo credit: Robert Banick/GFDRR

Effective decision-making in disaster risk management requires good risk data. That’s why at the Global Facility for Disaster Reduction and Recovery (GFDRR)’s Open Data for Resilience Initiative (OpenDRI), our work focuses on improving processes surrounding the dissemination, creation, and communication of risk data—from using drones to map flood vulnerability in Niger to building a geospatial data sharing platform in Bangladesh.

And while much more progress is needed to improve the quality and availability of risk data, the good news is that governments, international agencies, and scientific institutions are increasingly making their data open and available to planners, civil contingency managers, and responders. Combined with advances in technology, the movement for open data is generating an unprecedented volume of risk data. OpenDRI’s Open Data for Resilience Index monitors this trend by tracking the existence, availability, and openness of data on disaster risk and resilience worldwide.

One key challenge now is how best to capture, analyze, and communicate this data to inform decision-making. In an effort to provide a framework to guide the use of data in disaster risk management, OpenDRI has developed 10 principles that can be applied throughout a project’s life cycle to help ensure that risk data is used effectively for decision-making. Below, we break down these guiding principles and provide practical examples of how they have been applied.

  1. Put users at the center of project design

Risk information must be grounded in the needs of users at relevant geographic and time scales and provided through accessible and understandable formats. In a successful example of this practice, UNDP Myanmar’s SESAME (Specialized Expert System for Agro-Meteorological Early Warning) drew on local cropping practices to develop location-specific agro-advisories which covered multiple timescales.

From Discovery to Scale: Leveraging big data to improve development outcomes

Michael M. Lokshin's picture

In the last few years, the World Bank has expanded use of big data in more than 150 development projects globally, spanning a wide range of sectors and geographies. Solutions have ranged from using big data to monitor, evaluate, and improve projects—in energy, transport, and agriculture—to poverty diagnostics and understanding how well urban residents are connected to jobs. But, as Haishan Fu, Director of the Development Data Group at the World Bank, has said, “we are just beginning to realize the potential of the data revolution.”

These pilots have taught us that moving from discovery, to incubation, to scale requires a more coordinated and systematic approach. At the World Bank, we found it important to go beyond internal dialogue and assessments. We wanted to listen to and understand the perspectives of our partners in the development and data ecosystems—on current gaps, opportunities, as well as on the role(s) the World Bank should play in order to foster collective action.

Nearly 1 in 2 in the world lives under $5.50 a day

Dean Mitchell Jolliffe's picture
Also available in: Français | Español | العربية

Today, less than 10 percent of the world population lives in extreme poverty. Based on information about basic needs collected from 15 low-income countries, the World Bank defines the extreme poor as those living on less than $1.90 a day. However, because more people in poverty live in middle-income, rather than low-income, countries today, higher poverty lines have been introduced. These lines are $3.20 and $5.50 a day, which are more typical of poverty thresholds for middle-income countries.

Introducing the online guide to the World Development Indicators: A new way to discover data on development

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

The World Development Indicators (WDI) is the World Bank’s premier compilation of international statistics on global development. Drawing from officially recognized sources and including national, regional, and global estimates, the WDI provides access to almost 1,600 indicators for 217 economies, with some time series extending back more than 50 years. The database helps users—analysts, policymakers, academics, and all those curious about the state of the world—to find information related to all aspects of development, both current and historical.

An annual World Development Indicators report was available in print or PDF format until last year. This year, we introduce the World Development Indicators website: a new discovery tool and storytelling platform for our data which takes users behind the scenes with information about data coverage, curation, and methodologies. The goal is to provide a useful, easily accessible guide to the database and make it easy for users to discover what type of indicators are available, how they’re collected, and how they can be visualized to analyze development trends.

So, what can you do on the new World Development Indicators website?

1. Explore available indicators by theme

The indicators in the WDI are organized according to six thematic areas: Poverty and Inequality, People, Environment, Economy, States and Markets, and Global Links. Each thematic page provides an overview of the type of data available, a list of featured indicators, and information about widely used methodologies and current data challenges.

A massive new dataset to help promote health equity and financial protection in health

Adam Wagstaff's picture

Today we’re (re)launching HEFPI—aka the Health Equity and Financial Protection Indicators database. HEFPI sheds light on two major concerns in global health: a concern that the poor do not get left behind in the rush to achieve global health goals; and a concern that health services should be affordable. Neither concern featured in the MDGs; both feature prominently in the SDGs.

The HEFPI database draws on data from over 1,600 household surveys, including the Demographic and Health Survey and the Multiple Indicator Cluster Survey. Most of the 1,600 surveys have been re-analyzed in-house to ensure comparability across surveys and years, since published indicators from different surveys often use different definitions. We have settled on a definition based on recommendations in the relevant literature, and have used that across all surveys and time periods. As a result, the numbers in HEFPI are often different from (and more comparable than) numbers published elsewhere.

The database is, in effect, the fourth in a series. The first was in 2000. That database focused entirely on MDG-era health service and health outcome data—so no financial protection data. It covered just 42 countries, each with one year’s worth of data. The second (in 2007) and third (in 2012) gradually expanded the scope, with the 2012 dataset covering both financial protection and health equity, and getting up to 109 countries, including some high-income countries.

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