This paper seeks to illuminate the uncertainty in official GDP per capita measures using auxiliary data. Using satellite-recorded nighttime lights as an additional measurement of true GDP per capita, we provide a statistical framework, in which the error in official GDP per capita may depend on the country’s statistical capacity and the relationship between nighttime lights and true GDP per capita can be nonlinear and vary with geographic location. This paper uses recently developed results for measurement error models to identify and estimate the nonlinear relationship between nighttime lights and true GDP per capita and the nonparametric distribution of errors in official GDP per capita data. We then construct more precise and robust measures of GDP per capita using nighttime lights, official national accounts data, statistical capacity, and geographic locations. We find that GDP per capita measures are less precise for middle and low income countries and nighttime lights can play a bigger role in improving such measures.
This paper presents a novel framework to estimate the elasticity between nighttime lights and quarterly economic activity. The relationship is identified by accounting for varying degrees of measurement errors in nighttime light data across countries. The estimated elasticity is 1.55 for emerging markets and developing economies, ranging from 1.36 to 1.81 across country groups and robust to different model specifications. The paper uses a light-adjusted measure of quarterly economic activity to show that higher levels of development, statistical capacity, and voice and accountability are associated with more precise national accounts data. The elasticity allows quantification of subnational economic impacts. During the COVID-19 pandemic, regions with higher levels of development and population density experienced larger declines in economic activity.
This paper seeks to illuminate the uncertainty in official GDP per capita measures using auxiliary data. Using satellite-recorded nighttime lights as an additional measurement of true GDP per capita, we provide a statistical framework, in which the error in official GDP per capita may depend on the country’s statistical capacity and the relationship between nighttime lights and true GDP per capita can be nonlinear and vary with geographic location. This paper uses recently developed results for measurement error models to identify and estimate the nonlinear relationship between nighttime lights and true GDP per capita and the nonparametric distribution of errors in official GDP per capita data. We then construct more precise and robust measures of GDP per capita using nighttime lights, official national accounts data, statistical capacity, and geographic locations. We find that GDP per capita measures are less precise for middle and low income countries and nighttime lights can play a bigger role in improving such measures.
This paper presents a novel framework to estimate the elasticity between nighttime lights and quarterly economic activity. The relationship is identified by accounting for varying degrees of measurement errors in nighttime light data across countries. The estimated elasticity is 1.55 for emerging markets and developing economies, ranging from 1.36 to 1.81 across country groups and robust to different model specifications. The paper uses a light-adjusted measure of quarterly economic activity to show that higher levels of development, statistical capacity, and voice and accountability are associated with more precise national accounts data. The elasticity allows quantification of subnational economic impacts. During the COVID-19 pandemic, regions with higher levels of development and population density experienced larger declines in economic activity.
China’s breathtaking economic development has been driven by bureaucrats. Even as the country transitioned away from socialist planning toward a market economy, the economic bureaucracy retained a striking degree of influence and control over crafting and implementing policy. Yet bureaucrats are often dismissed as faceless and inconsequential, their role neglected in favor of party leaders’ top-down rule or bottom-up initiatives. Markets with Bureaucratic Characteristics offers a new account of economic policy making in China over the past four decades that reveals how bureaucrats have spurred large-scale transformations from within. Yingyao Wang demonstrates how competition among bureaucrats motivated by careerism has led to the emergence of new policy approaches. Second-tier economic bureaucrats instituted distinctive—and often conflicting—“policy paradigms” aimed at securing their standing and rewriting China’s long-term development plans for their own benefit. Emerging from the middle levels of the bureaucracy, these policy paradigms ultimately reorganized the Chinese economy and reshaped state-market relations. Drawing on fine-grained biographical and interview data, Wang traces how officials coalesced around shared career trajectories, generational experiences, and social networks to create new alliances and rivalries. Shedding new light on the making and trajectory of China’s ambitious economic reforms, this book also provides keen sociological insight into the relations among bureaucracy, states, and markets.
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