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Illuminating Economic Growth

Author: Yingyao Hu; Jiaxiong Yao; IMF e-Library - York University.
Publisher: Washington, D.C. : International Monetary Fund, 2019.
Series: IMF Working Papers; Working Paper, no. 19/77.
Edition/Format:   eBook : Document : International government publication : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print Version:
Hu, Yingyao.
Illuminating Economic Growth.
Washington, D.C. : International Monetary Fund, 2019
Material Type: Document, Government publication, International government publication, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Yingyao Hu; Jiaxiong Yao; IMF e-Library - York University.
ISBN: 1498302947 9781498302944 9781498310789 1498310788
OCLC Number: 1104867669
Description: 1 online resource (57 pages)
Series Title: IMF Working Papers; Working Paper, no. 19/77.
Responsibility: Hu, Yingyao.

Abstract:

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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