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October 2018

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.

Behind Closed Doors: how traditional measures of poverty mask inequality inside the household and a new look at possible solutions

Caren Grown's picture

During the days coming up to, and after October 17, when many stories, numbers, and calls for action will mark the International Day for the Eradication of Poverty, we want to invite you to think for a second on what you imagine a poor household to be like. Is this a husband, wife, and children, or maybe an elderly couple? Are the children girls or boys? And more importantly, do all experience the same deprivations and challenges from the situation they live in?  In a recent blog post and paper, we showed that looking at who lives in poor homes—from gender differences to household composition more broadly—matters  to better understand and tackle poverty.

Globally, female and male poverty rates—defined as the share of women and men who live in poor households—are very similar (12.8 and 12.3 percent, respectively, based on 2013 data). Even in the two regions with the largest number of poor people (and highest poverty rates)—South Asia and Sub-Saharan Africa—gender differences in poverty rates are quite small. This is true for the regions, but also for individual countries, irrespective of their share of poor people. Why is that the case? As Chapter 5 of the 2018 Poverty and Shared Prosperity Report explains, our standard monetary poverty indicator is measured by household, not by individual. So, a person is classified as either poor or nonpoor according to the poverty status of the household in which she or he lives. This approach critically assumes everyone in the household shares equally in household consumption—be they a father, a young child, or a daughter-in-law.  By design, it thus masks differences in individual poverty within a household.

Notwithstanding this shortcoming, when we look a bit deeper the information we have today still shows visible gender differences in poverty rates. Take age, for example. We know that there are more poor children than poor adults, and while we do not find that poverty rates differ much between girls and boys at the early stages of life, stark differences appear between men and women during the peak productive and reproductive years.

Incomes of the poorest are growing in 3 of every 4 economies

Maria Ana Lugo's picture

In much of the world today, the incomes of the poor are growing. The World Bank calls this concept shared prosperity, defined as the average annual growth in income or consumption of the poorest 40 percent (the bottom 40) within each country. So, if shared prosperity in a country is positive, the poor are getting richer.

In addition, the shared prosperity premium is defined as the difference between the annual income or consumption growth rate of the bottom 40 and the annual growth rate of the mean in the economy. A positive premium indicates that the bottom 40 are getting a larger share of overall income in the economy.