Global Working Paper Series
At the Duke University Center for International and Global Studies (DUCIGS), we are actively engaged in publishing new research. The Duke Global Working Paper Series provides a space for scholars from across the disciplines to explore international topics. DUCIGS welcomes submissions from Duke experts and affiliated scholars.
Papers in this series are published to the Social Science Research Network as part of the Duke Global Working Paper Series. This series is edited by Giovanni Zanalda.
For the style guide and submission form visit: https://duke.qualtrics.com/jfe/form/SV_6zFTllGEGelzUZT
For more information, email Rohini Thakkar (rt54@duke.edu).
This paper is one of three country studies of successful anti-poverty measures during upper middle-income levels, the other two being Japan and the Republic of Korea. Though the US did not advance an explicit anti-poverty agenda until the 1960s, assisting the economically distressed was a key priority of the New Deal. Average education, life expectancy and earnings all increased during 1920-1960. Poverty fell by two-thirds to around 22 percent as the mean income rose and income inequality fell beginning in the 1940s. Economic gaps among Black Americans, women, the South, and rural areas converged, though these gaps persist to this day. Migration, urbanization, and the structural shift away from agricultural jobs transformed the economy. These, along with factors such as strong collective bargaining and access to education, helped keep low incomes rising amidst overall growth. New Deal policies that impacted market incomes (labor laws, farm subsidies, education) fueled poverty reduction more than transfers (direct relief, work relief, social insurance). Though welfare programs helped lower the poverty gap and were important policy innovations, the payment levels were too low to bring people out of poverty—defined in a manner appropriate for a country on the cusp of high income—until well after 1960.
This paper is one of three country studies of successful anti-poverty measures during upper middle-income levels, the other two being South Korea and the United States. Japan’s welfare-through-growth strategy appears to have worked through much of its development process, especially during its upper-middle income phase. But with suddenly slowing growth, higher unemployment and increased poverty among sections of Japanese society after the economy reached high income levels, the Japanese government was slow to adapt the country’s unemployment protections, labor market regulations, and social security in general. The paper describes what the Japan’s welfare-through-growth strategy entailed, why it worked through its upper-middle income growth phase and why it collapsed after the start of Japan’s high-income era. For upper middle-income countries like China, Japan represents an especially useful case study on the changes in policies and programs to reduce poverty and alleviate other social stresses that will inevitably become important in the decades ahead.
This paper is one of three country studies of successful anti-poverty measures during upper-middle-income levels, the other two being Japan and the United States. South Korea may well be the most successful case of economic development in recorded history. Within two generations, it was transformed from a post-conflict country to a post-industrial society that has low poverty rates, high per capita income levels, and close to the highest educational attainment, health outcomes and living standards in the world. The paper attributes South Korea’s success in reducing poverty to a sequenced combination of measures aimed at agricultural productivity and rural livelihoods, export-oriented industrialization and urbanization, and early and sustained investments in human capital. It provides details that may be useful for formulating anti-poverty strategies middle-income countries that are on the cusp of high-income.
India is a lower-middle-income country (LMIC) with 21% of its population living below the international poverty line. Yet, its government health expenditure in 2016 was only 1.17% of its ross domestic product (GDP), a share that is even lower than the average for low-income countries. India also faces a shift in disease burden, with non-communicable diseases (NCDs) emerging as top causes of mortality while infectious diseases and maternal, neonatal, and nutritional health remain areas of concern. To address these challenges and improve healthcare access and affordability for poor and vulnerable populations, India launched Pradhan Mantri Jan Arogya Yojana (PM-JAY) in 2018 as a successor to the Rashtriya Swasthya Bima Yojana (RSBY) scheme. To further inform policy development, we synthesized the early experiences of PM-JAY by conducting a narrative review, focusing on the three dimensions of universal health coverage (UHC): population coverage, service coverage, and financial risk protection.
As more countries move from low- to middle-income status, they are perceived as increasingly capable of financing their own health systems. Some donors have begun to transition their support out of such middle-income countries (MICs) to redirect their funds to countries with greater needs. However, this transition may leave a funding gap for MICs that could be difficult to fill when external resources decline. If not carefully managed, such financial shifts could lead to the loss of health gains that occurred while receiving substantial external financial support. Understanding levels of donor dependency (i.e., whether or not a country is likely to have capacity to fill a funding gap caused by donor transition) and donor concentration (i.e., when only a few donors make up the majority of aid) can illuminate areas of potential vulnerability for transition. In this study, we analyzed Kenya’s health system for donor dependency and donor concentration.
Although the proportion of the world’s population living in poverty has declined substantially over the last two decades, the absolute number of people that live in poverty or vulnerable conditions has remained high. Nearly 70% of the poor now live in countries classified as middle-income.3 We conducted a document review and comparative analysis of six of the largest global health donors to better understand the extent to which they incorporate subnational poverty into their allocation decisions and programming. The donors we studied were Gavi, the Vaccine Alliance (Gavi); the Global Fund to Fight AIDS, Tuberculosis and Malaria (the Global Fund); the President’s Emergency Plan for AIDS Relief (PEPFAR); the United States Agency for International Development (USAID)—specifically, its Global Health Bureau; the World Bank’s International Development Association (IDA); and the Government of Japan. We found that most donor high-level strategy documentation allude to the relationship between poverty and health by, for example, noting the financial burden of specific diseases targeted or the disproportionate disease burdens that may fall on the poorest people. However, only two of these donors, Gavi and IDA, incorporate any subnational poverty indicators or broader subnational poverty focus that could be tracked and monitored over time. Gavi and IDA also integrate household level wealth or health expenditure data in their routine monitoring processes, though there is limited information about how much this integration influences how these two donors target aid toward the poorest communities. For the other four donors—Global Fund, PEPFAR, USAID, and Japan—subnational poverty is either not addressed or else is invoked in the context of other social or demographic factors that make certain groups of people vulnerable to disease (e.g., sex workers’ vulnerability to HIV).
Many countries are now transitioning away from donor aid for health as they move from low- to middle-income status and see improved health outcomes. To promote better planning and preparedness for transition, many transition readiness assessment tools (TRAs) have been developed in recent years. The goal of this study was to identify and review existing TRAs to better understand the current landscape of how such tools are being used and the potential gaps among the currently available tools. There are several key limitations among existing tools. There are also many areas of overlap between tools, as well as clear gaps among the current tools available. For example, limited consideration has been given to emerging challenges for transitioning countries, such as demographic and disease transitions (e.g., aging populations and a shift in the burden of disease from infections to non-communicable diseases). Many critical health interventions, including vaccines and maternal and child health services, are ignored by current TRAs. Donors are the financial and technical “drivers” of all the TRAs, and so these tools are not being shaped by transitioning countries themselves. Therefore, it is difficult to determine whether or not the TRAs as designed will address the most critical needs of transitioning countries. Additionally, the role that in-country stakeholders are expected to play in the assessment process is not clearly defined and the methodologies of TRAs are not publicly available, thereby potentially limiting their usefulness to users.
This paper provides a summary survey of the policy approaches to artificial intelligence-based technologies in China, the European Union, and the United States. China has the most aggressive approach, launching major initiatives since 2015 such as ‘Made in China 2025’, the Internet Plus Plan, the New Generation Artificial Intelligence Development Plan, and the Artificial Intelligence Standardization White Paper. In 2018, the EU finalized both the European AI Strategy and ‘Made in Europe’ or the Coordinated Plan on the Development and Use of Artificial Intelligence. Despite a traditional reticence to adopt national strategies and perhaps pushed by growing concerns about China, the US Government announced the American AI Initiative and a National AI R&D Strategic Plan in 2019. The AI approaches in these three economies reflect their relative strengths—state control in China, citizen voice in Europe, and business practices in America. Unencumbered by privacy concerns, China’s strategy is geared to exploit the abundance of domestic data and to develop AI talent through central schemes and massive injections of money. The European Union’s regulations and spending priorities are guided by the objective of building citizen trust in AI-based technologies by safeguarding privacy and ameliorating disruptions in national labor markets. The mainstay of the US approach is to strengthen the linkages between business and AI-related research, and find ways to fund basic R&D. Despite efforts to indigenize AI innovations, all three economies face challenges. China’s AI strategy continues to rely disproportionately on just three tech giants: Baidu, Tencent and Alibaba, which have investments in more than a 100 AI-involved companies. Europe’s AI resources are unbalanced geographically—a quarter of Europe’s AI talent is in the UK and another quarter in Germany and France—and Brexit poses a serious risk. More than half of the AI talent in the US is foreign born, so immigration policies will inevitably be a central component of a national AI strategy.
First, this paper discusses how AI-based technologies that are powering the “Fourth Industrial Revolution” are similar in their speed, effects and prerequisites to general purpose technologies (GPTs) that came before and how they are different. AI-based technologies are similar to the steam engine, electric power, and information technology in that (a) they change the nature of work but do not reduce it in aggregate; (b) they increase productivity when accompanied by innovations in business process and investment in human capital; and (c) they leave little room for leapfrogging because profitable adoption of new techniques requires having adapted to previous rounds. AI is different from earlier GPTs mainly in that the speed of its spread is likely to be faster. Second, it provides an economic classification of these technologies based on the channels through which they reduce costs, distinguishing between transactional, informational, and operational technologies. This distinction is relevant because they differ in their structural, social and spatial effects. Third, the paper provides a framework that brings together the economic consequences of AI-based technologies and common policy goals: competitiveness, cohesion and convergence. Finally, it illustrates how this framework can be applied to the European Union. EU member states do least well in transactional platforms where scale matters a lot, they do somewhat better in informational technologies where both market size and regulations matter, and some of them are global leaders in the development and use of AI-based operational technologies such as robots. As with earlier waves of technical change, creation and adoption of AI-based technologies will require good education and well-designed R&D incentives, regulatory capacity, and robust tax-and-transfer systems.
African utilities face the complex and interrelated challenges of being capital constrained, having a relatively low demand base of customers with lower abilities to pay, high levels of system inefficiency, and very large unserved and underserved populations in their service territory. Traditional business-as-usual methods for electrification have fallen short, and utilities in sub-Saharan Africa are critically under-equipped for the universal access challenge. Advances in digitized and decentralized technology offer new delivery models for reliable and affordable access that African utilities could leverage to solve these dual challenges of financial sustainability and universal access. Indeed, though the 'future grid' conversation predominantly focuses on advanced energy markets, the value proposition of such advances is arguably stronger in African markets. From the literature, we find that the inertia of the entrenched regulatory frameworks governing competition, tariffs, and investment priorities are a major hurdle keeping African utility incentives at odds with technology trends and propose reforms. Using case studies, we identify key regulatory reforms needed to transform the sector, and best practice models for novel public-private sector engagement to unlock opportunity.