IMF Working Papers

Illuminating Economic Growth

By Yingyao Hu, Jiaxiong Yao

April 9, 2019

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Yingyao Hu, and Jiaxiong Yao. Illuminating Economic Growth, (USA: International Monetary Fund, 2019) accessed November 8, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

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 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.

Subject: Economic sectors, GDP measurement, Income, Informal economy, National accounts, Personal income

Keywords: Economic activity, GDP measurement, GDP per capita, Global, Income, Informal economy, Light-predicted GDP, Low income, Measurement error, Nighttime light, Nighttime lights, Real GDP measure, Real gross domestic product, WP

Publication Details

  • Pages:

    57

  • Volume:

    ---

  • DOI:

    ---

  • Issue:

    ---

  • Series:

    Working Paper No. 2019/077

  • Stock No:

    WPIEA2019077

  • ISBN:

    9781498302944

  • ISSN:

    1018-5941

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