The latest revelations regarding covert data sharing practices by large tech companies demand governments finally take action to curb the unwanted exploitation of user data. To date, attention has been focused on privacy regulation; governments would be well served to look at tax policy, too. Digital taxes would better align taxation rights with value creation in the digital economy. They might also serve to communicate the growing frustration with abusive data management practices by the biggest offenders.
The EU’s new General Data Protection Regulation (GDPR) recently went into effect. You have probably received emails regarding your data resident on email servers and applications. And while the media focus has also remained on data concerns with Facebook and other personal data, the impact of the GDPR on developing countries has received little attention. Their exports of data-based services rely on the free flow of data across borders. Strengthened regulation can make international data transfers more difficult. And traditional trade rules and regulatory cooperation cannot resolve this conflict.
Online pundits, hurried journalists and policymakers love precision. They crave numbers. Preferably exact numbers; ranges suggest uncertainty and make them anxious. As a result, they will love the World Poverty Clock (WPC), a new website that claims to track progress towards ending global poverty in real time (see also this blog and Financial Times article). The website tells you that 632,470,507 people are currently living in extreme poverty - or were, on December 6 at 10:00am… Even more amazingly, the site claims to forecast poverty at any point in the future until 2030, the deadline for the UN’s Sustainable Development Goals. By scrolling along the elegant timeline on the bottom of the WPC screen you will learn, for example, that in 2028, 459,309,506 people will be living in extreme poverty!
The primary motivation for predicting data in economics, health sciences, and other disciplines has been to deal with various forms of missing data problems. However, one could also make a case for adopting prediction methods to obtain more cost-efficient estimates of welfare indicators when it is expensive to observe the outcome of interest (in comparison with its predictors). For example, consider the estimation of poverty and malnutrition rates. The conventional estimators in this case require household- and individual-level data on expenditures and health outcomes. Collecting this data is generally costly. It is not uncommon that in developing countries, where poverty and poor health outcomes are most pressing, statistical agencies do not have the budget that is needed to collect these data frequently. As a result, official estimates of poverty and malnutrition are often outdated: For example, across the 26 low-income countries in Sub-Saharan Africa over the period between 1993 and 2012, the national poverty rate and prevalence of stunting for children under five are on average reported only once every five years and once every ten years in the World Development Indicators.
Economic shocks can be painful and destructive, especially in fragile countries that can get trapped into a cycle of conflict and violence. Effective policy responses must be implemented quickly and based on evidence. This requires reliable and timely data, which are usually unavailable in such countries. This was particularly true for South Sudan, a country that has faced multiple shocks since its independence in 2011. Recognizing the need for such data in this fragile country to assess economic shocks, the team developed a real-time dashboard to track daily exchange rates and weekly market prices (click here for instructions how to use it).
The limited availability of data on poverty and inequality poses major challenges to the monitoring of the World Bank Group’s twin goals – ending extreme poverty and boosting shared prosperity. According to a recently completed study, for nearly one hundred countries at most two poverty estimates are available over the past decade.Worse still, for around half of them there was either one or no poverty estimate available.* Increasing the frequency of data on poverty is critical to effectively monitoring the Bank’s twin goals.
Against this background, the science of “Big Data” is often looked to as providing a potential solution. A famous example of this science is “Google Flu Trends (GFT)”, which uses search outcomes of Google to predict flu outbreaks. This technology has proven extremely quick to produce predictions and is also very cost-effective. The rapidly increasing volumes of raw data and the accompanying improvement of computer science have enabled us to fill other kinds of data gaps in ways that we could not even have dreamt of in the past.
The data and processes needed to measure global poverty and gauge improvements in the prosperity of the bottom 40% of people in each country present complex challenges and provoke considerable debate amongst poverty experts.
From the comparability of household surveys and their use in policy design to the utility of purchasing power parity data as a unifying standard for measuring the poor, the devil in global poverty analysis is in the details. It’s also vital to understand the World Bank’s recently adopted twin goals in a broader context, to see how they fit into a broader array of monitorable indicators that each come with their own specific features and insights. We must also listen to client governments and outside partners when they prefer to go beyond income to look at multidimensional social welfare functions.
My experiences with field work thus far have been nothing if not adventurous. I seem to attract broken glass – a rock the size of a small coconut crashing through my 3rd floor window in Zanzibar, for instance, or the windows of my taxi being broken with baseball bats by an armed mob in Mali. Just the other day, my boss and I came within inches of dying in a fiery plane crash – we were on our way back to the main island of Zanzibar from Pemba island in a tiny 12-seater Soviet-era plane, and were just about to land in a strong crosswind when the engine on my side failed. We managed to land, somehow, and taxied to a stop right there on the runway to wait for a vehicle (ironically, it ended up being an ambulance) to take us to the terminal.