Tracking Economic Activity in Response to the COVID-19 using nighttime Lights


Paper by Mark Roberts: “Over the last decade, nighttime lights – artificial lighting at night that is associated with human activity and can be detected by satellite sensors – have become a proxy for monitoring economic activity. To examine how the COVID-19 crisis has affected economic activity in Morocco, we calculated monthly lights estimates for both the country overall and at a sub-national level. By examining the intensity of Morocco’s lights in comparison with the quarterly GDP data at the national level, we are also able to confirm that nighttime lights are able to track movements in real economic activity for Morocco….(More)”.

Foundations of complexity economics


Article by W. Brian Arthur: “Conventional, neoclassical economics assumes perfectly rational agents (firms, consumers, investors) who face well-defined problems and arrive at optimal behaviour consistent with — in equilibrium with — the overall outcome caused by this behaviour. This rational, equilibrium system produces an elegant economics, but is restrictive and often unrealistic. Complexity economics relaxes these assumptions. It assumes that agents differ, that they have imperfect information about other agents and must, therefore, try to make sense of the situation they face. Agents explore, react and constantly change their actions and strategies in response to the outcome they mutually create. The resulting outcome may not be in equilibrium and may display patterns and emergent phenomena not visible to equilibrium analysis. The economy becomes something not given and existing but constantly forming from a developing set of actions, strategies and beliefs — something not mechanistic, static, timeless and perfect but organic, always creating itself, alive and full of messy vitality….(More)”.

Unlocking Responsible Access to Data to Increase Equity and Economic Mobility


Report by the Markle Foundation and the Bill and Melinda Gates Foundation (BMGF): “Economic mobility remains elusive for far too many Americans and has been declining for several decades. A person born in 1980 is 50% less likely to earn more than their parents than a person born in 1950 is. While all children who grow up in low-opportunity neighborhoods face mobility challenges, racial, ethnic, and gender disparities add even more complexity. In 99% of neighborhoods in America, Black boys earn less, and are more likely to fall into poverty, than white boys, even when they grow up on the same block, attend the same schools, and have the same family income. In 2016, a Pew Research study found that the median wealth of white households was ten times the median wealth of Black households and eight times that of Hispanic households. The COVID-19 pandemic has further exacerbated existing disparities, as communities of color suffer higher exposure and death rates, along with greater job loss and increased food and housing insecurity.

Reversing this overall decline to address the persistent racial, ethnic, and gender gaps in economic mobility is one of the great challenges of our time. Some progress has been made in identifying the causes and potential solutions to declining mobility, yet policymakers, researchers, and the public still lack access to critical data necessary to understand which policies, programs, interventions, and investments are most effective at creating opportunity for students and workers, particularly those struggling with intergenerational poverty. Data collected across all levels of governments, nonprofit organizations, and private sector companies can help answer foundational policy and research questions on what drives economic mobility. There are promising efforts underway to improve government data infrastructure and processes at both the federal and state levels, but critical data often remains siloed, and legitimate concerns about privacy and civil liberties can make data difficult to share. Often, data on vulnerable populations most in need of services is of poor quality or is not collected at all.

To tackle this challenge, the Bill and Melinda Gates Foundation (BMGF) and the Markle Foundation (Markle) spent much of 2020 working with a diverse range of experts to identify strategic opportunities to accelerate progress towards unlocking data to improve policymaking, answer foundational research questions, and ensure that individuals can easily and responsibly access the information they need to make informed decisions in a rapidly changing environment….(More)”.

European Data Economy: Between Competition and Regulation


Report by René Arnold, Christian Hildebrandt, and Serpil Taş: “Data and its economic impact permeates all sectors of the economy. The data economy is not a new sector, but more like a challenge for all firms to compete and innovate as part of a new wave of economic value creation.

With data playing an increasingly important role across all sectors of the economy, the results of this report point European policymakers to promote the development and adoption of unified reference architectures. These architectures constitute a technology-neutral and cross-sectoral approach that will enable companies small and large to compete and to innovate—unlocking the economic potential of data capture in an increasingly digitized world.

Data access appears to be less of a hindrance to a thriving data economy due to the net increase in capabilities in data capture, elevation, and analysis. What does prove difficult for firms is discovering existing datasets and establishing their suitability for achieving their economic objectives. Reference architectures can facilitate this process as they provide a framework to locate potential providers of relevant datasets and carry sufficient additional information (metadata) about datasets to enable firms to understand whether a particular dataset, or parts of it, fits their purpose.

Whether third-party data access is suitable to solve a specific business task in the first place ought to be a decision at the discretion of the economic actors involved. As our report underscores, data captured in one context with a specific purpose may not be fit for another context or another purpose. Consequently, a firm has to evaluate case-by-case whether first-party data capture, third-party data access, or a mixed approach is the best solution. This evaluation will naturally depend on whether there is any other firm capturing data suitable for the task that is willing to negotiate conditions for third-party access to this data. Unified data architectures may also lower the barriers for a firm capturing suitable data to engage in negotiations, since its adoption will lower the costs of making the data ready for a successful exchange. Such architectures may further integrate licensing provisions ensuring that data, once exchanged, is not used beyond the agreed purpose. It can also bring in functions that improve the discoverability of potential data providers….(More)”.

Economic complexity theory and applications


Paper by César A. Hidalgo: “Economic complexity methods have become popular tools in economic geography, international development and innovation studies. Here, I review economic complexity theory and applications, with a particular focus on two streams of literature: the literature on relatedness, which focuses on the evolution of specialization patterns, and the literature on metrics of economic complexity, which uses dimensionality reduction techniques to create metrics of economic sophistication that are predictive of variations in income, economic growth, emissions and income inequality….(More)”.

Improved targeting for mobile phone surveys: A public-private data collaboration


Blogpost by Kristen Himelein and Lorna McPherson: “Mobile phone surveys have been rapidly deployed by the World Bank to measure the impact of COVID-19 in nearly 100 countries across the world. Previous posts on this blog have discussed the sampling and  implementation challenges associated with these efforts, and coverage errors are an inherent problem to the approach. The survey methodology literature has shown mobile phone survey respondents in the poorest countries are more likely to be male, urban, wealthier, and more highly educated. This bias can stem from phone ownership, as mobile phone surveys are at best representative of mobile phone owners, a group which, particularly in poor countries, may differ from the overall population; or from differential response rates among these owners, with some groups more or less likely to respond to a call from an unknown number. In this post, we share our experiences in trying to improve representativeness and boost sample sizes for the poor in Papua New Guinea (PNG)….(More)”.

Nowcasting Gentrification Using Airbnb Data


Paper by Shomik Jain, Davide Proserpio, Giovanni Quattrone, and Daniele Quercia: “There is a rumbling debate over the impact of gentrification: presumed gentrifiers have been the target of protests and attacks in some cities, while they have been welcome as generators of new jobs and taxes in others. Census data fails to measure neighborhood change in real-time since it is usually updated every ten years. This work shows that Airbnb data can be used to quantify and track neighborhood changes. Specifically, we consider both structured data (e.g. number of listings, number of reviews, listing information) and unstructured data (e.g. user-generated reviews processed with natural language processing and machine learning algorithms) for three major cities, New York City (US), Los Angeles (US), and Greater London (UK). We find that Airbnb data (especially its unstructured part) appears to nowcast neighborhood gentrification, measured as changes in housing affordability and demographics. Overall, our results suggest that user-generated data from online platforms can be used to create socioeconomic indices to complement traditional measures that are less granular, not in real-time, and more costly to obtain….(More)”.

Mission Economy: A Moonshot Guide to Changing Capitalism


Book by Mariana Mazzucato: “Even before the Covid-19 pandemic in 2020, capitalism was stuck. It had no answers to a host of problems, including disease, inequality, the digital divide and, perhaps most blatantly, the environmental crisis. Taking her inspiration from the ‘moonshot’ programmes which successfully co-ordinated public and private sectors on a massive scale, Mariana Mazzucato calls for the same level of boldness and experimentation to be applied to the biggest problems of our time. We must, she argues, rethink the capacities and role of government within the economy and society, and above all recover a sense of public purpose. Mission Economy, whose ideas are already being adopted around the world, offers a way out of our impasse to a more optimistic future….(More)”.

Connected Devices – an Unfair Competition Law Approach to Data Access Rights of Users


Paper by Josef Drexl: “On the European level, promoting the free flow of data and access to data has moved to the forefront of the policy goals concerning the digital economy. A particular aspect of this economy is the advent of connected devices that are increasingly deployed and used in the context of the Internet of Things (IoT). As regards these devices, the Commission has identified the particular problem that the manufacturers may try to remain in control of the data and refuse data access to third parties, thereby impeding the development of innovative business models in secondary data-related markets. To address this issue, this paper discusses potential legislation on data access rights of the users of connected devices. The paper conceives refusals of the device manufacturers to grant access to data vis-à-vis users as a form of unfair trading practice and therefore recommends embedding data access rights of users in the context of the European law against unfair competition. Such access rights would be complementary to other access regimes, including sector-specific data access rights of competitors in secondary markets as well as access rights available under contract and competition law. Against the backdrop of ongoing debates to reform contract and competition law for the purpose of enhancing data access, the paper seeks to draw attention to a so far not explored unfair competition law approach….(More)”.

People understand statistics better than politicians think


Sarah O’Connor at the Financial Times: “In 2015 I took my reporter’s notebook to Liverpool because statistics suggested it was enjoying a jobs boom. The unemployment gap between the northern English city and the national average had shrunk to the smallest in a decade. When I mentioned that fact to people I met, I might as well have said the grass was pink.

“It’s certainly not our experience, I would say I’ve never seen poverty at this level,” was the response from the director of the local Citizens Advice Bureau. A woman who ran a small cake business said: “My cynical side thinks straight away they’ve probably got zero-hours contracts somewhere — [they] are a great way of cooking the books.”

I thought of that trip when I read a newly published study that uses an in-depth survey and focus groups to delve into the British public’s understanding of economics. The headline findings are bleak. Large parts of the public have misperceptions about how economic concepts such as the unemployment rate are measured and they are “sceptical and cynical” about data.

One obvious response would be to blame inadequate education and worry that economic ignorance allows people to be duped by demagogues such as Nigel Farage in the UK and Donald Trump in the US.

Economic literacy classes in schools would certainly be a good idea, especially since most of those surveyed were “deeply interested” in the economy and regretted not understanding the details. But there’s more to this story. The public live and breathe the economy every day. If their first response to a statistic such as the unemployment rate is to say “that doesn’t feel right” (a common response in the focus groups) then perhaps it’s the economists who are missing something….(More)”.