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Satellitendaten zur Schätzung von Regionaleinkommen – Das Beispiel Deutschland

Author

Listed:
  • Christian Leßmann
  • André Seidel
  • Arne Steinkraus

Abstract

Eine neue Möglichkeit zur Schätzung fehlender regionaler Einkommensdaten bieten Satellitenbilder der Erde bei Nacht. Die grundlegende Idee ist, dass wirtschaftliche Aktivitäten, die in den Abendstunden statt finden, Licht benötigen bzw. emittieren. Aus der Lichtemission bei Nacht kann ein Rückschluss auf wirtschaftliche Größen gezogen werden. Ziel dieses Beitrags ist, die in der Literatur verwendeten Daten vorzu stellen sowie die Möglichkeiten und Grenzen der Nutzung von Satellitendaten zur Schätzung von Einkommen zu diskutieren. Im Beitrag werden die Satellitendaten vorgestellt sowie damit verbundene Messprobleme diskutiert. Anhand des Beispiels Deutschlands wird untersucht, inwieweit sich die Lichtemissionsdaten für regionalökonomische Analysen eignen. Es wurde kein besonders großer und zudem wenig robuster Zusammenhang zwischen Lichtemissionen und regionalem Bruttoinlandsprodukt gefunden. Für weniger entwickelte Staaten können die Daten jedoch ein wertvoller Indikator des nationalen oder regionalen Einkommens sein und damit helfen, die sehr lückenhaften Regionalstatistiken zu vervollständigen.

Suggested Citation

  • Christian Leßmann & André Seidel & Arne Steinkraus, 2015. "Satellitendaten zur Schätzung von Regionaleinkommen – Das Beispiel Deutschland," ifo Dresden berichtet, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 22(06), pages 35-42, December.
    • Handle: RePEc:ces:ifodre:v:22:y:2015:i:06:p:35-42
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    File URL: https://www.ifo.de/DocDL/ifodb-2015-06-Lessmann-Satellitendaten.pdf
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    References listed on IDEAS

    as
    1. Nicola Gennaioli & Rafael La Porta & Florencio Lopez-de-Silanes & Andrei Shleifer, 2013. "Human Capital and Regional Development," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 128(1), pages 105-164.
    2. Frank Bickenbach & Eckhardt Bode & Peter Nunnenkamp & Mareike Söder, 2016. "Night lights and regional GDP," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 152(2), pages 425-447, May.
    3. William Nordhaus & Xi Chen, 2015. "A sharper image? Estimates of the precision of nighttime lights as a proxy for economic statistics," Journal of Economic Geography, Oxford University Press, vol. 15(1), pages 217-246.
    4. 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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    Cited by:

    1. Bluhm, Richard & Krause, Melanie, 2022. "Top lights: Bright cities and their contribution to economic development," Journal of Development Economics, Elsevier, vol. 157(C).
    2. Carsten Juergens & Fabian M. Meyer-Heß & Marcus Goebel & Torsten Schmidt, 2021. "Remote Sensing for Short-Term Economic Forecasts," Sustainability, MDPI, vol. 13(17), pages 1-23, August.
    3. Christian Leßmann, 2021. "60 Jahre Mauerbau: Leuchtende Landschaften," ifo Dresden berichtet, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 28(04), pages 29-31, August.

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    More about this item

    Keywords

    Satellitenkommunikation; Licht; Messung; Nachtarbeit; Einkommen; Bruttoinlandsprodukt; Region; Schätzung; Regionalstatistik; Deutschland;
    All these keywords.

    JEL classification:

    • C89 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other
    • L96 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Telecommunications
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General

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