Economic Analysis based on the Mobile Phone GPS Data and Monitoring Consumer Behavior During the COVID-19 Pandemic

  • Yoshiyuki Suimon University of Tokyo
Keywords: Mobile Phone GPS Data, Big Data, Economic Activity, Consumer Spending, COVID-19

Abstract

In order to understand what is happening in the underlying economy, it is useful to gain a picture of what people are doing. When people go out, they may do so in order to engage in some kind of economic activity. In this research, we measured the level of economic activity by gaining a macro picture of people's movements. Specifically, we used the location data (GPS data) of mobile phones owned by the customers of major Japanese mobile carrier au to measure changes in the movements of people in key urban areas, and to show the relationship between these changes and macroeconomic variables. Our results found a notable correlation between the number of visitors to city areas and GDP consumer spending and spending-related statistics. Furthermore, we found that the people's movements especially have a strong correlation with the consumption data of services. In addition, we found that there is an inverse correlation between online spending and the people's movements. In Japan, the spread of COVID-19 has had a marked impact on people's activity in 2020. This negative correlation indicates that a change in people's behavior while COVID-19 continues to spread, in the form of staying at home more, led to an increase in online shopping.

References

Suimon, Y. and Yanai, M.: Using business data to nowcast consumption, JCB Con-sumption NOW Report (2019) https://www.jcbconsumptionnow.com/info/news-54

Choi, H., Varian, H.: Predicting the present with Google trends, Economic Record, Vol.88, No. s1, pp.2-9 (2012)

Scott, S.L., Varian, H.: Bayesian variable selection for nowcasting economic time series, NBER Working Paper, No.19567 (2013)

Goel, S., Hofman, J.M., Lahaie, S., Pennock, D.M. and Watts, D.J.: Predicting con-sumer behavior with web search, Proc Natl Acad Sci USA, Vol.107, No.41, pp.17486-17490 (2010)

Preis, T., Moat, H.S., and Stanley, H.E.: Quantifying trading behavior in financial markets using Google trends, Scientific Reports, Vol.3, No.1684 (2013)

Preis, T., Moat, H.S., Stanley, H.E., and Bishop, S.R.: Quantifying the advantage of looking forward, Scientific Reports, Vol.2, No.350 (2012)

Curme, C., Preis, T., Stanley, H.E., Moat, H.S.: Quantifying the semantics of search behaviour before stock market moves, Proc Natl Acad Sci USA, Vol.111, No.32, pp.11600-11605 (2014)

Antenucci. D., Cafarella, M., Levenstein, M., Ré, C., Shapiro, M.D.: Using social media to measure labor market flows, NBER Working Paper, No. 20010 (2014)

Llorente, A., Garcia-Herranz, M., Cebrian, M. and Moro, E.: Social media fingerprints of unemployment, PLOS ONE, Vol.10.5, No. e0128692 (2015)

Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market, Journal of Computational Science, Vol.2, No.1, pp.1-8 (2011)

Suimon, Y and Fukuma, T.: Manufacturing Industrial Production Activity Analysis using Night-time light Image of Satellite, The Japanese Society for Artificial Intelligence, SIG-KBS, No.117, pp.25-27 (2019)

Chen, X. and Nordhaus, W.D.: Using luminosity data as a proxy for economic statistics, Proc Natl Acad Sci USA, Vol.108, No.21, pp.8589-8594 (2011)

Henderson, J.V., Storeygard, A. and Weil, D.N.: Measuring economic growth from outer space, American Economic Review, Vol.102, No.2, pp.994-1028 (2012)

Michalopoulos, S. and Papaioannou, E.: Pre-colonial ethnic institutions and contem-porary African development, Econometrica, Vol.81, No.1, pp.113-152 (2013)

Suimon, Y., Izumi, K., Shimada, T., Sakaji, H. and Matsushima, H., Estimating Manufacturing Activity via Machine Learning Analysis of High-frequency Electricity Demand Patterns, International Conference on Business Management of Technology (2020)

Toole, J.L., Lin, Y.R., Muehlegger, E., Shoag, D., González, M.C. and Lazer, D.: Tracking employment shocks using mobile phone data, Journal of The Royal Society Interface, Vol.12, No.107, pp.20150185 (2015)

Almaatouq, A., Prieto-Castrillo, F. and Pentland, A.: Mobile communication signatures of unemployment. , International conference on social informatics. pp 407-418 (2016)

Blumenstock, J., Cadamuro, G., On, R.: Predicting poverty and wealth from mobile phone metadata, Science , Vol.350, No,6264, pp.1073-1076 (2015)

Suimon, Y. and Yanai, M.: Analysis of economic activity using mobile phone GPS data and estimating impact of COVID-19, International Conference on Decision Science, Theory and Management (2020)

Dong, L., Chen, S., Cheng, Y., Wu, Z., Li, C., and Wu, H.: Measuring Economic Activity in China with Mobile Big Data, EPJ Data Science, Vol.6, No.29, pp.1-17 (2017)

Anderson, L. R. and Mellor, J. M.: Predicting health behaviors with an experimental measure of risk preference, Journal of Health Economics, Vol.27, No.5, pp.1260‒1274 (2008)

Barsky, R. B., Juster, F. T., Kimball, M. S., and Shapiro, M. D.: Preference parameters and behavioral heterogeneity: An experimental approach in the health and retirement study, The Quarterly Journal of Economics, Vol.112, No.2, pp.537‒579 (1997)

Guiso, L. and Paiella, M.: The Role of Risk Aversion in Predicting Individual Behaviors, CEPR Discussion Paper, No.4591 (2004)

Hersch, J. and Viscusi, W. K.: Smoking and other risky behaviors, Journal of Drug Issues, Vol.28, No.3, pp.645‒661 (1998)

Bavel, J. J. V., Baicker, K., Boggio, P. S., et al.: Using social and behavioral science to support COVID-19 pandemic response, Nature Human Behavior, Vol.4, pp460–471 (2020)

Published
2021-12-06
Section
Technical Papers (Business Management of Technology)