Paper by Luna Yue Huang, Solomon M. Hsiang & Marco Gonzalez-Navarro: “The rigorous evaluation of anti-poverty programs is key to the fight against global poverty. Traditional approaches rely heavily on repeated in-person field surveys to measure program effects. However, this is costly, time-consuming, and often logistically challenging. Here we provide the first evidence that we can conduct such program evaluations based solely on high-resolution satellite imagery and deep learning methods. Our application estimates changes in household welfare in a recent anti-poverty program in rural Kenya. Leveraging a large literature documenting a reliable relationship between housing quality and household wealth, we infer changes in household wealth based on satellite-derived changes in housing quality and obtain consistent results with the traditional field-survey based approach. Our approach generates inexpensive and timely insights on program effectiveness in international development programs…(More)”.
Essay by Andrew Sheng: “Modern science arose by breaking down complex problems into their parts. As Alvin Toffler, an American writer and futurist, wrote in his 1984 foreword to the chemist Ilya Prigogine’s classic book “Order out of Chaos”: “One of the most highly developed skills in contemporary Western civilization is dissection: the split-up of problems into their smallest possible components. We are good at it. So good, we often forget to put the pieces back together again.”
Specialization produces efficiency in production and output. But one unfortunate result is that silos produce a partial perspective from specialist knowledge; very few take a system-wide view on how the parts are related to the whole. When the parts do not fit or work together, the system may break down. As behavioral economist Daniel Kahnemann put it: “We can be blind to the obvious, and we are also blind to our blindness.”
Silos make group collective action more difficult; nation-states, tribes, communities and groups have different ways of knowing and different repositories of knowledge. A new collective mental map is needed, one that moves away from classical Newtonian science, with its linear and mechanical worldview, toward a systems-view of life. The ecologists Fritjof Capra and Pier Luigi Luisi argue that “the major problems of our time — energy, the environment, climate change, food security, financial security — cannot be understood in isolation. They are systemic problems, which means that they are all interconnected and interdependent.”
“Siloed thinking created many of our problems with inequality, injustice and planetary damage.”
A complex, non-linear, systemic view of life sees the whole as a constant interaction between the small and the large: diverse parts that are cooperating and competing at the same time. This organic view of life coincides with the ancient perspective found in numerous cultures — including Chinese, Indian, native Australian and Amerindian — that man and nature are one.
In short, modern Western science has begun to return to the pre-Enlightenment worldview that saw man, God and Earth in somewhat mystic entanglement. The late Chinese scientist Qian Xuesen argued the world was made up of “open giant complex systems” operating within larger open giant complex systems. Human beings themselves are open giant complex systems — every brain has billions of neurons connected to each other through trillions of pathways — continually exchanging and processing information with other humans and the environment. Life is much more complex, dynamic and uncertain than we once assumed.
To describe this dynamic, complex and uncertain systemic whole, we need to evolve trans-disciplinary thinking that integrates the natural, social, biological sciences and arts by transcending disciplinary boundaries. Qian concluded that the only way to describe such systemic complexity and uncertainty is to integrate quantitative with qualitative narratives, exactly what the Nobel Laureate Robert Shiller advocates for in “Narrative Economics.”…(More)”.
Paper by Scott R. Baker & Lorenz Kueng: “The growth of the availability and use of detailed household financial transaction microdata has dramatically expanded the ability of researchers to understand both household decision-making as well as aggregate fluctuations across a wide range of fields. This class of transaction data is derived from a myriad of sources including financial institutions, FinTech apps, and payment intermediaries. We review how these detailed data have been utilized in finance and economics research and the benefits they enable beyond more traditional measures of income, spending, and wealth. We discuss the future potential for this flexible class of data in firm-focused research, real-time policy analysis, and macro statistics….(More)”.
Essay by Susan Ariel Aaronson: “Whether produced domestically or internationally, disinformation is a “wicked” problem that has global impacts. Although trade agreements contain measures that address cross-border disinformation, domestically created disinformation remains out of their reach. This paper looks at how policy makers can use trade agreements to mitigate disinformation and spam while implementing financial and trade sanctions against entities and countries that engage in disseminating cross-border disinformation. Developed and developing countries will need to work together to solve this global problem….(More)”.
About: “Over the past 50 years, researchers have made great strides in analyzing public policy. With better data and improved research methods, we know more than ever about the impacts of government spending.
But despite these advances, it remains surprisingly challenging to answer basic questions about which policies have been most effective.
The difficulty arises because methods for evaluating policy effectiveness are not standardized. This makes it challenging, if not impossible, to compare and contrast across different policies.
Policy Impacts seeks to promote a unified approach for policy evaluation. We seek to promote the Marginal Value of Public Funds, a standardized metric for policy evaluation. We have created the Policy Impacts library, a collaborative effort to track the returns to a wide range of government policies…(More).
Report by the Institute of Community Studies: “We are delighted to unveil a landmark research report, Why don’t they ask us? The role of communities in levelling up. The new report reveals that current approaches to regeneration and economic transformation are not working for the majority of local communities and their economies.
Its key findings are that:
- Interventions have consistently failed to address the most deprived communities, contributing to a 0% average change in the relative spatial deprivation of the most deprived local authorities areas;
- The majority of ‘macro funds’ and economic interventions over the last two decades have not involved communities in a meaningful nor sustainable way;
- The focus of interventions to build local economic resilience typically concentrate on a relatively small number of approaches, which risks missing crucial dimensions of local need, opportunity and agency, and reinforcing gaps between the national and the hyper-local;
- Interventions have tended to concentrate on ‘between-place’ spatial disparities in economic growth at the expense of ‘within-place’ inequalities that exist inside local authority boundaries, which is where the economic strength or weakness of a place is most keenly felt by communities.
- Where funds and interventions have had higher levels of community involvement, these have typically been disconnected from the structures where decisions are taken, undermining their aim of building community power into local economic solutions…(More)”.
Position paper by Robert D. Atkinson: “With the rise of China and other robust economic competitors, the United States needs a more coherent national advanced technology strategy.1 Effectively crafting and implementing such a strategy requires the right kind of economic data. In part because of years of budget cuts to federal economic data agencies, coupled with a long-standing disregard of the need for sectoral and firm-level economic data to inform an industrial strategy, the federal government is severely lacking in the kinds of data needed.
Notwithstanding the hundreds of millions of dollars spent every year and the thousands of economists working for the federal government, the exact nature of the challenges to U.S. capabilities with regard to the competitiveness of America’s traded sectors is only weakly understood. At least since after the Great Depression, the federal government has never felt the need to develop strategic economic intelligence in order to fully understand the competitive position of its traded sectors or to help support overall economic productivity.2 Rather, most of the focus goes to understanding the ups and downs of the business cycle….
If the U.S. government is going to develop more effective policies to spur competitiveness, growth, and opportunity it will need to support better data collection. It should be able to understand the U.S. competitive position vis-à-vis other nations on key technologies and industries, as well as key strengths and weaknesses and where specific policies are needed.
Better data can also identify weaknesses in U.S. competitiveness that policy can address. For example, in the 1980s, studies conducted as part of the Census of Manufactures (studies that have long been discontinued) found many smaller firms lagging behind badly in costs and quality for reasons including inefficient work organization and obsolete machinery and equipment. End-product manufacturers bought parts and components from many of these smaller enterprises at prices higher than those paid by foreign-based firms with more efficient suppliers, contributing to the cost and quality disadvantages of U.S.-based manufacturers. Legislators heeded the findings in crafting what is now called the Manufacturing Extension Partnership, a program that, if too small in scale to have a significant impact on U.S. manufacturing overall, continues to provide meaningful assistance to thousands of companies each year.5
Moreover, as the federal government institutes more technology and industry policies and programs—as exemplified in the Senate U.S. Innovation and Competition Act—better data will be important to evaluate their effectiveness.
Finally, data are a key 21st century infrastructure. In a decentralized economy, good outcomes are possible only if organizations make good decisions—and that requires data, which, because of its public goods nature, is a quintessential role of government….(More)”.
About: “Metroverse is an urban economy navigator built at the Growth Lab at Harvard University. It is based on over a decade of research on how economies grow and diversify and offers a detailed look into the specialization patterns of cities.
As a dynamic resource, the tool is continually evolving with new data and features to help answer questions such as:
- What is the economic composition of my city?
- How does my city compare to cities around the globe?
- Which cities look most like mine?
- What are the technological capabilities that underpin my city’s current economy?
- Which growth and diversification paths does that suggest for the future?
As city leaders, job seekers, investors and researchers grapple with 21st century urbanization challenges, the answer to these questions are fundamental to understanding the potential of a city.
Metroverse delivers new insights on these questions by placing a city’s technological capabilities and knowhow at the heart of its growth prospects, where the range and nature of existing capabilities strongly influences how future diversification unfolds. Metroverse makes visible what a city is good at today to help understand what it can become tomorrow…(More)”.
Report by Pew Research Center: “Artificial intelligence systems “understand” and shape a lot of what happens in people’s lives. AI applications “speak” to people and answer questions when the name of a digital voice assistant is called out. They run the chatbots that handle customer-service issues people have with companies. They help diagnose cancer and other medical conditions. They scour the use of credit cards for signs of fraud, and they determine who could be a credit risk.
They help people drive from point A to point B and update traffic information to shorten travel times. They are the operating system of driverless vehicles. They sift applications to make recommendations about job candidates. They determine the material that is offered up in people’s newsfeeds and video choices.
They recognize people’s faces, translate languages and suggest how to complete people’s sentences or search queries. They can “read” people’s emotions. They beat them at sophisticated games. They write news stories, paint in the style of Vincent Van Gogh and create music that sounds quite like the Beatles and Bach.
As this unfolds, a number of experts and advocates around the world have become worried about the long-term impact and implications of AI applications. They have concerns about how advances in AI will affect what it means to be human, to be productive and to exercise free will. Dozens of convenings and study groups have issued papers proposing what the tenets of ethical AI design should be, and government working teams have tried to address these issues. In light of this, Pew Research Center and Elon University’s Imagining the Internet Center asked experts where they thought efforts aimed at creating ethical artificial intelligence would stand in the year 2030….(More)”
Paper by Daniel P. Gross & Bhaven N. Sampat: “Innovation policy can be a crucial component of governments’ responses to crises. Because speed is a paramount objective, crisis innovation may also require different policy tools than innovation policy in non-crisis times, raising distinct questions and tradeoffs. In this paper, we survey the U.S. policy response to two crises where innovation was crucial to a resolution: World War II and the COVID-19 pandemic. After providing an overview of the main elements of each of these efforts, we discuss how they compare, and to what degree their differences reflect the nature of the central innovation policy problems and the maturity of the U.S. innovation system. We then explore four key tradeoffs for crisis innovation policy—top-down vs. bottom-up priority setting, concentrated vs. distributed funding, patent policy, and managing disruptions to the innovation system—and provide a logic for policy choices. Finally, we describe the longer-run impacts of the World War II effort and use these lessons to speculate on the potential long-run effects of the COVID-19 crisis on innovation policy and the innovation system….(More)”.