Today’s post is by Johannes Jütting, Manager of the Partnership in Statistics for Development in the 21st Century (PARIS21), which promotes the better use and production of statistics in developing countries. PARIS21’s new report, A Road Map for a Country-led Data Revolution, sets out a detailed programme to ensure developing countries can monitor the Sustainable Development Goals and benefit from technological and other innovations in data collection and dissemination.
Tradition tells us that more than 3,000 years ago, Moses went to the top of Mount Sinai and came back down with 10 commandments. When the world’s presidents and prime ministers go to the top of the Sustainable Development Goals (SDGs) mountain in New York late this summer they will come down with not 10 commandments but 169. Too many?
Some people certainly think so. “Stupid development goals,” The Economist said recently. It argued that the 17 SDGs and roughly 169 targets should “honour Moses and be pruned to ten goals”. Others disagree. In a report for the Overseas Development Institute, May Miller-Dawkins, warned of the dangers of letting practicality “blunt ambition”. She backed SDGs with “high ambition”.
The debate over the “right” number of goals and targets is interesting, important even. But it misses a key point: No matter how many goals and targets are finally agreed, if we can’t measure their real impact on people’s lives, on our societies and on the environment, then they risk becoming irrelevant.
Unfortunately, we already know that many developing countries have problems compiling even basic social and economic statistics, never mind the complex web of data that will be needed to monitor the SDGs. A few examples: In 2013, about 35% of all live births were not officially registered worldwide, rising to two-thirds in developing countries. In Africa, just seven countries have data on their total number of landholders and women landholders, and none have data from before 2004. Last but not least, fast-changing economies and associated measurement challenges mean we are not sure today if we have worldwide a billion people living in extreme poverty, half a billion or more than a billion.
Why does this matter? Without adequate data, we cannot identify the problems that planning and policymaking need to address. We also cannot judge if governments and others are meeting their commitments. As a report from the Centre for Global Development notes, “Data […] serve as a ‘currency’ for accountability among and within governments, citizens, and civil society at large, and they can be used to hold development agencies accountable.”
So data matters. Despite this, blank spaces persist in the statistics of many developing countries. And they persist even at a time when the world is experiencing a “data revolution” – a rising deluge of data matched by ever-increasing demand for data.
Despite the challenges, we are optimistic that all countries, including the poorer ones, can make quick, dramatic progress in meeting their data challenges. Firstly, there is not only a growing awareness of the problems countries are facing but also a growing willingness to do something about it. Statistical offices in almost 40 developing countries have signed up to our Data Declaration, in which they state that “the time is now to bring the data revolution to everyone, everywhere”.
Second, new technologies are already helping to revolutionise the world of data. PARIS21’s Innovations Inventory has compiled hundreds of ways in which technology is making it easier and less costly to collect data and providing new sources of data, like “big data”. Examples abound, from NGO to private sector initiatives. As part of its Data for Development (D4D) challenge, Orange Senegal opened up its mobile-phone call-log data for researchers to generate insights into health, transportation, demographics, income inequality, and more. Another truly “Big Idea” comes from Restless Development, a youth-led development agency that equips young people with knowledge, skills, and platforms necessary to effectively interpret data in order to mobilise citizens to take action.
Third, we are optimistic because we want to build on what is already there – existing national statistical systems. Clearly, many are far from ready to join the data revolution; a colleague recalls visiting one national statistical office that couldn’t pay its power bill and had to negotiate with a neighbour to string an extension cord from his home to the office. That may be an extreme example, but other challenges – including technology gaps, shortages of trained staff, weak data dissemination and poor relations with users – are all too common. Nevertheless, national statistical agencies are the only entities with the expertise and legal frameworks to play the lead role in collecting, processing and disseminating data. It is on them that the data revolution for development for sustainable development must be built.
Of course, our Road Map for a Country-led Data Revolution is only a start. Much else needs to happen. This includes designing pilot projects, finding better ways to integrate new sources of data in existing national systems and – unsurprisingly – finding extra funding. But here again we are optimistic. We don’t accept that the cost of monitoring the SDGs will be “crippling”. With our colleagues in the UN Sustainable Development Solutions Network, we have calculated that additional donor funding of $200 million a year, matched by a similar rise in domestic funding, would enable the 77 IDA countries (“The World Bank’s Fund for the Poorest”) to successfully monitor their progress the SDGs – yes, even the proposed 17 goals and 169 targets!
We don’t yet know if that will turn out to be the final number of SDG “commandments”. But here’s something we do know – developed and developing countries are on the cusp of a huge and dramatic change in how they collect and disseminate. True, unlike Moses, we don’t live in a time of miracles. But with the aid of a clear road map, strong political will and “miraculous” technologies, we really are much closer to the promised land of better data than we realise.
Informing a Data Revolution – PARIS21
Watch the launch of A Road Map for a Country-led Data Revolution at the Cartagena Data Festival on Monday 20 April from 1700 hours UTC (noon in Cartagena, 1pm in New York, 6pm in London, 7pm in Paris, 2am in Tokyo).
Not much good has come from the Ebola crisis, save this: It has raised awareness of the fact that we already have a weapon in our hands that could help fight such epidemics – our mobile phones.
There’s already evidence to show that the idea can work. Following the earthquake and cholera outbreak in Haiti in 2010, for example, “call-data records” from mobile phones were used to track people’s movements, so allowing experts to “infer, with empirical data and in real-time, where people are, and how many, and where they are probably headed,” according to The Economist. That’s vital information in health crises, where epidemiologists need to know if people are moving into or out of highly infected areas.
The technique has been also been used to follow people’s movements in the wake of natural catastrophes, for example after the 2011 earthquake in Japan. And there’s growing interest in seeing how it could be used to track survivors of extreme weather events, such as Typhoon Haiyan in the Philippines, especially as climate change threatens to raise the frequency of such disasters.
But there’s a problem. Even if such tracking methods don’t involve eavesdropping on callers’ conversations, they do involve a breach of their privacy. And in the case of the Ebola outbreak, that seems to have been a major obstacle in preventing mobile operators from releasing their phone records.
There’s also the problem that for everyone involved – mobile operators, government regulators and researchers – this is still somewhat uncharted territory. There’s a general recognition that call-data records have potential to ease suffering during epidemics and after calamities but, as The Economist again notes, “the data are unlikely to be released without stronger leadership that brings together operators, regulators and researchers”.
Still, even if the Ebola crisis has highlighted what remains to be done, it’s impressive to see the ways in which mobiles are already being used to collect data in developing countries. Perhaps that shouldn’t be a surprise. After all, according to the International Telecommunications Union, mobile-phone penetration now approaches 90% in developing countries (and 69% in Africa). This doesn’t mean that nine out of ten people have handsets. But even setting aside all those people and businesses with second or third phones, it’s clear that unprecedented numbers of people now have a device in their pocket that’s not just a phone but also a powerful little computer.
That’s potentially important for developing countries, many of which lack the infrastructure and personnel to compile adequate statistics. As the World Bank’s Shanta Devarajan has noted, widely cited poverty data for Africa for 2005 relies on robust statistics from just 39 of the continent’s countries, with only 11 able to supply comparable data for the same year.
These data holes make it difficult to measure progress and to identify priorities for development. In response, there have been growing calls for a “data revolution”, which would require action on a range of fronts, including greater investment in government statistical offices in developing countries and making better use of “Big Data” and innovative technologies, like mobile phones.
Encouragingly, there are signs that some of this is already happening. For example, an SMS-based survey in Tunisia investigated remittances, an area where hard facts are notoriously scarce and where estimates of how money migrants are sending back home are just that – estimates. It found that more than a quarter of remittances are sent back by hand, more than the total sent via Western Union. Insights like that could help to provide more accurate data on what is an important source of income in many developing countries.
Mobile phones are also being used to “crowd source” data on price changes, which, as Gillian Jones reports, can be used to “compile near real-time consumer price inflation data”. Local residents take photos of price tags in shops and markets and send them to a central data store. There, they are analysed to provide data on price changes as well as scarcities. Field agents are paid a few cents for each photo they send, but that can add up to an income of as much as $25 a month. And how are they paid? Over their phones, of course.
Global Call for Innovations: The Partnership in Statistics for Development in the 21st Century (PARIS21) has launched a global call for innovations to highlight organizational approaches and new technologies to help realise the data revolution. It is seeking case studies in crowd sourcing; data management; monitoring and reporting; open data; real-time data; remote sensing; research standards; visualization; skills development; and technical infrastructure.
Clean water, cold vaccines, cell phones = a simple way to save lives (OECD Insights blog)
Today’s post is by Angus Deaton, Dwight D. Eisenhower Professor of Economics and International Affairs at the Woodrow Wilson School of Public and International Affairs and the Economics Department at Princeton University.
The happiness revolution is in full swing. Country statistical offices are beginning to collect data on wellbeing, the OECD has prepared a manual on how best to do it, and politicians are promising to focus, not just on economic growth and better material living standards, but on the much wider range of things that are important to people. This broadening of policy is much to be welcomed, as is the wider understanding of the inadequacies of GDP as an exclusive national target. And even if the data on wellbeing have their problems and their critics, a flawed but broader measure, such as wellbeing, can lead to better policy than a flawed but narrow one, such as income.
What are some of the things that we can do with wellbeing measures that cannot be done with standard, income based, policymaking? The concept of utility has been visible everywhere in economic texts and in economic thinking, but invisible in our databases. For goods and services that are bought and sold in markets, the invisibility of utility is not a problem because we can use prices as a guide to how much things matter to people. But that leaves uncovered the vast range of public goods and services—schools, hospitals, parks, airports, even the legal and political systems—that do not have prices. Conventional policy analysis sometimes works with “shadow” prices, the prices that would exist if these things were bought and sold. But these are hard to get right, and the numbers are often controversial. But if we can make utility visible, and measure it, then we can directly calculate how wellbeing responds to a park, or to a faster train line, and we can replace guesswork with measurement. Indeed, the “happiness” literature has estimated thousands of “happiness regressions” that show how self-reported wellbeing is affected by a wide range of circumstances, such as how income stacks up against time spent with friends, what value people place on lower pollution, or on living in a green and pleasant place.
There are many areas where people should make choices for themselves, and governments should either not interfere or should tread very lightly. Providing information is one example of light treading. If happiness regressions tell us that people are more satisfied with their lives when they choose to be clergymen, and not bar owners, or when they live in the mountains rather than in the plains, there is a role for making those facts known, perhaps endorsed by governments who might vet the quality of the research. This is what David Halpern calls “de-shrouding,” telling people how their choices might affect their wellbeing, as judged by the wellbeing of others who have already made those choices. Deshrouding would have no effect if people were fully informed, but people are often not very good at anticipating the long-term consequences of their choices. Happiness research has the potential to allow people to do better.
Even so, the results of happiness regressions must be interpreted with care. That a circumstance is correlated with life satisfaction does not automatically mean that it would be a good policy to promote more of it. Take the example of parents and children, where much of the literature finds that people who live with children are less satisfied with their lives than those who do not. Some of this is likely real, and there is good evidence that children bring a wide range of emotional experiences, including anger, stress, and worry, but also happiness and joy. Yet children do not come to people at random (blind storks?), but (usually) come to people who want them, just as those who are not parents are (usually) those who do not want children. Those who do and do not have children are different in many ways, including their tastes, not least their tastes for being parents. Comparing the wellbeing of parents and non-parents is therefore not a useful guide to young couples who are trying to decide whether or not to have children.
Consider also spatial differences in wellbeing. For example, we may discover that those who live near an airport have lower life satisfaction than those who do not. On a naïve view, people who bought houses under the flight path had no idea how much the noise would affect their lives. On a more sophisticated view, people understood that the noise was bad, but chose to live there because house prices were low, so that the noise actually provided them with an opportunity to live in a larger and more convenient house. In such a case, their low wellbeing reflects, not the noise, but that their incomes are too low to permit them to have a nice house that is also quiet. A policy of limiting noise or restricting landing hours may make such people worse off, because house prices rise, and a choice is no longer available to them. Of course, we can over-rationalize behavior, and people often do make mistakes, so that, in most cases, we have to deal with some mixture of the naïve and sophisticated.
An important question—still unanswered—is whether self-reported wellbeing is all that matters, which would simplify policymaking—or whether it is simply one thing that matters, so that people will trade it off for other good things. To illustrate the why this matters, consider that, in the U.S., once we make adjustment for educational and income differences, African Americans report substantially higher life evaluation than Whites. But African Americans suffer many deprivations beyond income and education; they are more likely to be in jail, to be banned from voting, to live in areas with low-quality schools and hospitals, to be unemployed, and to die young. Does their higher life evaluation mean that these other deprivations can be safely ignored? If not, and we refocus policy entirely towards wellbeing, we are making a mistake, just as when policy focused entirely on income.