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Illuminating economic growth

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  • Hu, Yingyao
  • Yao, Jiaxiong

Abstract

This paper seeks to illuminate national accounts GDP growth using satellite-recorded nighttime lights in a measurement error model framework. Using recently developed results in conjunction with reasonable assumptions about the exogeneity of the lights data generating process, we identify and estimate the relationship between nighttime light growth and GDP growth, as well as the nonparametric distribution of errors in both measures. We obtain three key results: (i) the elasticity of nighttime lights to GDP is about 1.3; (ii) national accounts GDP growth measures are less precise for low and middle income countries, and nighttime lights can play a big role in improving such measures; and (iii) our new measure of GDP growth, based on the optimal combination of nighttime lights and national accounts data under our identification assumptions, implies that China and India had considerably lower growth rates than official data suggested between 1993 and 2013. We expect our statistical framework and methodology to have a broad impact on measuring GDP using additional information.

Suggested Citation

  • Hu, Yingyao & Yao, Jiaxiong, 2022. "Illuminating economic growth," Journal of Econometrics, Elsevier, vol. 228(2), pages 359-378.
  • Handle: RePEc:eee:econom:v:228:y:2022:i:2:p:359-378
    DOI: 10.1016/j.jeconom.2021.05.007
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    References listed on IDEAS

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    1. Shuaizhang Feng & Yingyao Hu, 2013. "Misclassification Errors and the Underestimation of the US Unemployment Rate," American Economic Review, American Economic Association, vol. 103(2), pages 1054-1070, April.
    2. Aruoba, S. Borağan & Diebold, Francis X. & Nalewaik, Jeremy & Schorfheide, Frank & Song, Dongho, 2016. "Improving GDP measurement: A measurement-error perspective," Journal of Econometrics, Elsevier, vol. 191(2), pages 384-397.
    3. Joachim Freyberger, 2017. "On Completeness and Consistency in Nonparametric Instrumental Variable Models," Econometrica, Econometric Society, vol. 85, pages 1629-1644, September.
    4. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    5. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
    6. Griliches, Zvi & Hausman, Jerry A., 1986. "Errors in variables in panel data," Journal of Econometrics, Elsevier, vol. 31(1), pages 93-118, February.
    7. Yingyao Hu & Susanne M. Schennach, 2008. "Instrumental Variable Treatment of Nonclassical Measurement Error Models," Econometrica, Econometric Society, vol. 76(1), pages 195-216, January.
    8. Ivan A. Canay & Andres Santos & Azeem M. Shaikh, 2013. "On the Testability of Identification in Some Nonparametric Models With Endogeneity," Econometrica, Econometric Society, vol. 81(6), pages 2535-2559, November.
    9. Raymond Carroll & Xiaohong Chen & Yingyao Hu, 2010. "Identification and estimation of nonlinear models using two samples with nonclassical measurement errors," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(4), pages 379-399.
    10. Gibson, John & Olivia, Susan & Boe-Gibson, Geua & Li, Chao, 2021. "Which night lights data should we use in economics, and where?," Journal of Development Economics, Elsevier, vol. 149(C).
    11. Hu, Yingyao, 2017. "The Econometrics of Unobservables -- Latent Variable and Measurement Error Models and Their Applications in Empirical Industrial Organization and Labor Economics [The Econometrics of Unobservables]," Economics Working Paper Archive 64578, The Johns Hopkins University,Department of Economics, revised 2021.
    12. Dave Donaldson & Adam Storeygard, 2016. "The View from Above: Applications of Satellite Data in Economics," Journal of Economic Perspectives, American Economic Association, vol. 30(4), pages 171-198, Fall.
    13. Maxim Pinkovskiy & Xavier Sala-i-Martin, 2016. "Lights, Camera … Income! Illuminating the National Accounts-Household Surveys Debate," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(2), pages 579-631.
    14. Chen, Xiaohong, 2007. "Large Sample Sieve Estimation of Semi-Nonparametric Models," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 76, Elsevier.
    15. J. Vernon Henderson & Adam Storeygard & David N. Weil, 2012. "Measuring Economic Growth from Outer Space," American Economic Review, American Economic Association, vol. 102(2), pages 994-1028, April.
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    Citations

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    Cited by:

    1. Dang,Hai-Anh H. & Pullinger,John James & Serajuddin,Umar & Stacy,Brian William, 2024. "Reviewing Assessment Tools for Measuring Country Statistical Capacity," Policy Research Working Paper Series 10717, The World Bank.
    2. Shapiro, Daniel & Oh, Chang Hoon & Zhang, Peng, 2023. "Nighttime lights data and their implications for IB research," Journal of International Management, Elsevier, vol. 29(5).
    3. Beyer, Robert C.M. & Jain, Tarun & Sinha, Sonalika, 2023. "Lights out? COVID-19 containment policies and economic activity," Journal of Asian Economics, Elsevier, vol. 85(C).
    4. Liu, Honglin & Liu, Qiao & Liu, Yufei, 2023. "The world price of macro opacity: Through the lens of nighttime satellites," Economics Letters, Elsevier, vol. 228(C).
    5. Zhang, Qi & Hu, Yi & Jiao, Jianbin & Wang, Shouyang, 2023. "Is refined oil price regulation a “shock absorber” for crude oil price shocks?," Energy Policy, Elsevier, vol. 173(C).
    6. Ahmed Shoukry Rashad, 2022. "The Power of Travel Search Data in Forecasting the Tourism Demand in Dubai," Forecasting, MDPI, vol. 4(3), pages 1-11, July.
    7. Francisco Corona & Elio Atenógenes Villaseñor & Jesús López-Pérez & Ranyart R. Suárez, 2023. "Estimating Mexican municipal-level economic activity indicators using nighttime lights," Empirical Economics, Springer, vol. 65(3), pages 1197-1214, September.
    8. Matthieu Charpe, 2023. "Convergence heterogeneity at the local level in sub‐Saharan Africa," Papers in Regional Science, Wiley Blackwell, vol. 102(2), pages 273-305, April.

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    More about this item

    Keywords

    Measurement error; Nighttime lights; GDP growth;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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