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Chinese Online Unemployment-Related Searches and Macroeconomic Indicators

Author

Listed:
  • Zhi Su

    (International School of Economics and Management, Capital University of Economics and Business, Beijing 100070, China)

Abstract

Official monthly unemployment data is unavailable in China, while intense public interest in unemployment requires timely and accurate information. Using data on web queries from lead search engines in China, Baidu and Google, I build two indices measuring intensity of online unemployment-related searches. The unemployment-related search indices identify a structural break in the time series between October and November 2008, which corresponds to a turning point indicated by some macroeconomic indicators. The unemployment- related search indices are proven to have significant correlation with Purchasing Managers¡¯ Employment Indices and a set of macroeconomic indicators that are closely related to changes in unemployment in China. The results of Granger causality analysis show that the unemployment-related search indices can improve predictions of the macroeconomic indicators. It suggests that unemploy- ment-related searches can potentially provide valuable, timely, and low-cost information for macroeconomic monitoring.

Suggested Citation

  • Zhi Su, 2014. "Chinese Online Unemployment-Related Searches and Macroeconomic Indicators," Frontiers of Economics in China-Selected Publications from Chinese Universities, Higher Education Press, vol. 9(4), pages 573-605, December.
    • Handle: RePEc:fec:journl:v:9:y:2014:i:4:p:573-605
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    File URL: http://journal.hep.com.cn/fec/EN/10.3868/s060-003-014-0027-3
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    Citations

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    Cited by:

    1. Martin Obschonka & Mingjie Zhou & Yixin Zhou & Jianxin Zhang & Rainer K. Silbereisen, 2019. "“Confucian” traits, entrepreneurial personality, and entrepreneurship in China: a regional analysis," Small Business Economics, Springer, vol. 53(4), pages 961-979, December.
    2. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 2-12, April.
    3. Simionescu, Mihaela & Zimmermann, Klaus F., 2017. "Big Data and Unemployment Analysis," GLO Discussion Paper Series 81, Global Labor Organization (GLO).
    4. Krzysztof DRACHAL, 2020. "Forecasting the Inflation Rate in Poland and U.S. Using Dynamic Model Averaging (DMA) and Google Queries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 18-34, July.
    5. Simionescu, Mihaela & Cifuentes-Faura, Javier, 2022. "Can unemployment forecasts based on Google Trends help government design better policies? An investigation based on Spain and Portugal," Journal of Policy Modeling, Elsevier, vol. 44(1), pages 1-21.
    6. Mihaela, Simionescu, 2020. "Improving unemployment rate forecasts at regional level in Romania using Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 155(C).

    More about this item

    Keywords

    macroeconomic monitoring; Baidu Index; Google Trends; unemploy- ment-related search indices;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

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