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|z 9781498302944
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|a 1018-5941
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|a BD-DhAAL
|c BD-DhAAL
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|a Hu, Yingyao.
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|a Illuminating Economic Growth /
|c Yingyao Hu, Jiaxiong Yao.
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|a Washington, D.C. :
|b International Monetary Fund,
|c 2019.
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|a 1 online resource (57 pages)
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|a IMF Working Papers
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|a <strong>Off-Campus Access:</strong> No User ID or Password Required
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|a <strong>On-Campus Access:</strong> No User ID or Password Required
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|a Electronic access restricted to authorized BRAC University faculty, staff and students
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|a 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.
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|a Mode of access: Internet
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|a United States
|2 imf
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|a Yao, Jiaxiong.
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|a IMF Working Papers; Working Paper ;
|v No. 2019/077
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| 856 |
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|z Full text available on IMF
|u http://elibrary.imf.org/view/journals/001/2019/077/001.2019.issue-077-en.xml
|z IMF e-Library
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