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January 2019

The World Bank’s role in SDG monitoring

Umar Serajuddin's picture

In 2015, leaders of 193 countries formed an ambitious plan to guide global development action for the next 15 years by agreeing on a set of Sustainable Development Goals (SDGs). Four years after their launch, the World Bank’s expertise in development data and its large repository of development indicators has played an important role in helping track progress made towards the achievement of the SDGs.

How does SDG monitoring work and how is the World Bank involved?

To monitor the 17 goals and 169 associated targets, a framework of 230+ indicators was developed by the Inter-agency and Expert Group on SDG Indicators (IAEG-SDGs), a group of UN Member States with international agencies as observers. Different international agencies were assigned as “custodians” of the SDG targets. In this capacity, the custodian agencies work with national statistical offices to develop methodologies for indicators to measure progress on the SDGs. The agencies also work with countries to compile data for SDG indicators, which they submit to the UN Statistics Global SDG database.

The World Bank participates in IAEG-SDGs as an observer and is a custodian or co-custodian (with other agencies) for 20 indicators, and is involved in the development and monitoring of an additional 22 indicators. Altogether, the World Bank is formally engaged with the monitoring of 42 of the 230+ indicators. The indicators cover a wider range of topics in which the World Bank has expertise, including poverty and inequality, social protection, gender equality, financial access, remittances, health, energy, infrastructure, and so on.

What’s an ambitious but realistic target for human capital progress?

Zelalem Yilma Debebe's picture

Globally, 56 percent of children live in countries with Human Capital Index (HCI) scores below 0.5. As these countries gear up to improve their human capital outcomes, it is vital to set a target that is ambitious enough to prompt action and realistic enough to be achieved. One way to get at this is to examine the historical rate of progress that countries demonstrated to be possible.

Using time-series data between 2000 and 2017, we estimated countries' progress in the health components of HCI (fraction of children not stunted, child survival and adult survival) using a non-linear regression model. [1] Our measure of progress is the fraction of gap to the frontier that is eliminated every year- the frontier being 100 percent child and adult survival, and no stunting.,[2]

We address the following two questions:

  1. What is the typical progress in the health components of HCI observed globally?

7 data innovation projects win funding to tackle local challenges

World Bank Data Team's picture

How can data be used to improve disease outbreak warning, urban planning, air quality, or agricultural production? Seven winning projects, which will receive support from the third round of funding for collaborative data innovation projects, do just that and more.

Following the success of the first round of funding in 2017 and the second round of funding in 2018 the World Bank’s Development Data Group and the Global Partnership for Sustainable Development Data launched the Collaborative Data Innovations for Sustainable Development Fund’s third round in June 2018.

This round called for ideas that had an established proof of concept that benefited local decision-making. We were looking for projects that fostered synergies, and collaborations that took advantage of the relative strengths and responsibilities of official and non-official actors in the data ecosystem.

Quality of Open Source Software: how many eyes are enough?

Michael M. Lokshin's picture

In 2004, my colleague Zurab Sajaia and I submitted a maximum likelihood routine to the Stata SSC archive. The program was quickly propelled by the Stata user community to the top 10 most downloaded Stata files; it is still in use now. While experimenting with similar algorithms to develop test procedures (five years after the program’s release), we uncovered an error in the routine. Hundreds, if not thousands, of econometricians had used our program and looked at our code, but no one raised any concerns.

Open Source Software (OSS) is quickly gaining popularity in the corporate world as a practical alternative to costly proprietary software. 78% of companies are now using OSS extensively and open source components are found in more than half of all proprietary software. The rationale is simple: OSS lowers development costs, decreases time to market, increases developer productivity, and accelerates innovation.

Make it convenient, make it credible

Haishan Fu's picture

We’re living in a time of disruptive technologies evolving at an exponential pace. Today, you can enjoy an Impossible Burger (meat industry disrupted) delivered by Caviar (food delivery disrupted) to your AirBnB (hotel industry disrupted) while you’re on FaceTime (telecommunication industry disrupted) urging your teenager to get back to lessons on Khan Academy (education industry disrupted). And all the while, you’re leaving a trail of digital data points.

So rather than trying to predict what the future will bring, I want to focus on the principles we should use to shape it. What do we want the future to look like? In the World Bank’s lobby, there’s a giant inscription that reads “Our Dream is a World Free of Poverty”. I think the key to bringing about that world is getting quality data into the hands of people who can use it to make the world better. To me, this means two things: making data convenient and making data credible.

The curse of the Fire-Horse: How superstition impacted fertility rates in Japan

Emi Suzuki's picture
Data source: Statistics Bureau of Japan

In 1966, Japan experienced a sudden drop in its fertility rate—for just that year. During the 1960s, the fertility rate was about 2.0 to 2.1 children per woman, but in 1966 it dropped dramatically to 1.6 children per woman (Chart 2). The number of births in 1966 was much lower than in surrounding years, as can also be seen in Japan’s population pyramid, where there’s a big dent for people born in 1966 (the highlighted bars). This isn’t an error in the data, it’s real.

Competition and the rise of the machines: Should the AI industry be regulated?

Michael M. Lokshin's picture

A multinational conglomerate uses artificial intelligence (AI) algorithms to gather intelligence about the news you peruse, social media activity, and shopping preferences. They choose the ads you passively consume on your newsfeed and throughout your social media accounts, your internet searches, and even the music you hear, creating an incrementally increasingly customized version of reality specifically for you. Your days are subtly influenced by marketers, behavioral scientists, and mathematicians armed with cloud supercomputers. All of this is done in the name of maximizing profit to influence what you’re thinking, buying, and whom you will be electing…

Sound familiar? Apocalyptic prognoses of the impact of AI on the future of human civilization have long been en vogue, but seem to be increasingly frequent topics of popular discussion. Elon Musk, Bill Gates, Stephen Hawking, Vint Cerf, Raymond Weil, together with a host of other commentators and—of course—all the Matrix and Terminator films, have expressed a spectrum of concerns about the world-ending implications of AI. They run the gamut from the convincingly possible (widespread unemployment[1]) to the increasingly plausible (varying degrees of mind control) to the outright cinematic (rampaging robots). François Chollet‏, the creator of a deep neural net platform, sees the potential for “mass population control via message targeting and propaganda bot armies.” Calls for study, restraint, and/or regulation typically follow these remonstrations.

Monitoring the SDGs with purchasing power parities

Edie Purdie's picture

The ICP blog series explores ideas and issues under the International Comparison Program umbrella – including innovations in price and data collection, discussions on purpose and methodology, as well the use of purchasing power parities in the growing world of development data. Authors from across the globe, whether ICP practitioners or researchers making use of ICP data, are encouraged to submit relevant blogs for consideration to [email protected].

It has been over three years since countries adopted the UN’s 2030 Agenda for Sustainable Development and its 17 Sustainable Development Goals. From the outset, a number of targets were identified to help pinpoint the desired outcomes within these broad areas – 169 in total. Monitoring progress towards each of these targets relies on data originating in countries, and which are often collected in partnership with regional and international organizations. The World Bank’s Atlas of Sustainable Development Goals used such data to visualize trends and comparisons across the globe, drawing on data from World Development Indicators and many other sources.

Purchasing Power Parity (PPP) data, from the International Comparison Program, play an important role in this monitoring: by eliminating the effect of price level differences between countries they allow us to measure living standards and other economic trends in real, comparable terms.  PPPs are utilized in a number of the official SDG indicators, but also in other associated indicators, which help us to explore the underlying issues and impacts of the goals and targets more deeply.  The four charts presented here exemplify the crucial insights PPPs help provide in SDG monitoring and analysis.

Goal 1 seeks to eradicate poverty in all its forms by 2030. Extreme poverty is measured using the international poverty line of $1.90 a day using 2011 PPPs. The use of PPPs ensures that the poverty line represents the same standard of living in every county. Higher poverty lines used by the World Bank better measure poverty in lower-middle and upper-middle income countries.  Using these poverty lines, we can visualize the shifts in population living at various standards of living.

How well can you plan your survey: the analysis of 2,000 surveys in 143 countries

Michael M. Lokshin's picture

Our interviewers are still in the field, we need more time to complete the survey, could you extend our server for two more months? We receive such requests every day. Why do so many of our users fail to estimate the timing of their fieldwork?

Survey Solutions is a free platform for data collection developed by the World Bank and used by hundreds of agencies and firms in 143 countries. Many users of the Survey Solutions host data on free cloud servers provided by the World Bank. A user requests a server by filling in a form where he indicates the duration of the planned survey, the number of cases to be collected, and provides other relevant information. We impose no restrictions on how long a user can use the servers. Any survey end date is accepted. Over the last six years we have accumulated data on more than 2,000 surveys. We use information about surveys that collected 50 or more cases for this analysis.

How well can people conducting surveys follow the survey schedule?

Half of the world’s poor live in just 5 countries

Roy Katayama's picture
Also available in: Español | Français | العربية

Of the world’s 736 million extreme poor in 2015, 368 million—half of the total—lived in just 5 countries. The 5 countries with the highest number of extreme poor are (in descending order): India, Nigeria, Democratic Republic of Congo, Ethiopia, and Bangladesh. They also happen to be the most populous countries of South Asia and Sub-Saharan Africa, the two regions that together account for 85 percent (629 million) of the world’s poor. Therefore, to make significant continued progress towards the global target of reducing extreme poverty (those living on less than $1.90 a day) to less than 3 percent by 2030, large reductions in poverty in these five countries will be crucial.

Innovations in satellite measurements for development

Ran Goldblatt's picture

Bottom line

Combinatorial innovation is driving innovation in satellite-based economic measurements at unprecedented resolution, frequency and scale. Increasing availability of satellite data and rapid advancements in machine learning methods are enabling a better understanding into the fundamental forces shaping economic development.

Why satellite data innovations matter

The desire of human beings to “think spatially” to understand how people and objects are organized in space has not changed much since Eratosthenes—the Greek astronomer best known as the “father of Geography”—first used the term “Geographika” around 250 BC. Centuries later, our understanding of economic geography is being propelled forward by new data and new capabilities to rapidly process, analyze and convert these vast data flows into meaningful and near real-time information.