Going Beyond GDP to Nowcast Well-Being Using Retail Market Data

  • Riccardo GuidottiEmail author
  • Michele Coscia
  • Dino Pedreschi
  • Diego Pennacchioli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9564)


One of the most used measures of the economic health of a nation is the Gross Domestic Product (GDP): the market value of all officially recognized final goods and services produced within a country in a given period of time. GDP, prosperity and well-being of the citizens of a country have been shown to be highly correlated. However, GDP is an imperfect measure in many respects. GDP usually takes a lot of time to be estimated and arguably the well-being of the people is not quantifiable simply by the market value of the products available to them. In this paper we use a quantification of the average sophistication of satisfied needs of a population as an alternative to GDP. We show that this quantification can be calculated more easily than GDP and it is a very promising predictor of the GDP value, anticipating its estimation by six months. The measure is arguably a more multifaceted evaluation of the well-being of the population, as it tells us more about how people are satisfying their needs. Our study is based on a large dataset of retail micro transactions happening across the Italian territory.


Gross Domestic Product Bipartite Network Sophistication Measure Revealed Comparative Advantage Seasonal Adjustment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We gratefully thank Luigi Vetturini for the preliminary analysis that made this paper possible. We thank the supermarket company Coop and Walter Fabbri for sharing the data with us and allowing us to analyse and to publish the results. This work is partially supported by the European Community’s H2020 Program under the funding scheme FETPROACT-1-2014: 641191 CIMPLEX, and INFRAIA-1-2014-2015: 654024 SoBigData.


  1. 1.
    Costanza, R., Kubiszewski, I., Giovannini, E., Lovins, H., McGlade, J., Pickett, K.E., Ragnarsdóttir, K.V., Roberts, D., De Vogli, R., Wilkinson, R.: Time to leave gdp behind. Nat. Comment 505, 283–285 (2014)CrossRefGoogle Scholar
  2. 2.
    Wilson, N., Mason, K., Tobias, M., Peacey, M., Huang, Q., Baker, M.: Interpreting google flu trends data for pandemic h1n1 influenza: the new zealand experience. Euro surveillance: bulletin européen sur les maladies transmissibles = European communicable disease bulletin 14(44), 429–433 (2008)Google Scholar
  3. 3.
    Choi, H., Varian, H.: Predicting the present with google trends. Econ. Rec. 88(s1), 2–9 (2012)CrossRefGoogle Scholar
  4. 4.
    Toole, J.L., Lin, Y.R., Muehlegger, E., Shoag, D., Gonzalez, M.C., Lazer, D.: Tracking employment shocks using mobile phone data. arXiv preprint arXiv:1505.06791 (2015)
  5. 5.
    Llorente, A., Cebrian, M., Moro, E., et al.: Social media fingerprints of unemployment. arXiv preprint arXiv:1411.3140 (2014)
  6. 6.
    Lazer, D., Kennedy, R., King, G., Vespignani, A.: The parable of google flu: traps in big data analysis. Science 343, 1203–1205 (2014)CrossRefGoogle Scholar
  7. 7.
    Giannone, D., Reichlin, L., Small, D.: Nowcasting: the real-time informational content of macroeconomic data. J. Monetary Econ. 55(4), 665–676 (2008)CrossRefGoogle Scholar
  8. 8.
    Foroni, C., Marcellino, M.: A comparison of mixed frequency approaches for nowcasting euro area macroeconomic aggregates. Int. J. Forecast. 30(3), 554–568 (2014)CrossRefGoogle Scholar
  9. 9.
    Navicke, J., Rastrigina, O., Sutherland, H.: Nowcasting indicators of poverty risk in the european union: a microsimulation approach. Soc. Indic. Res. 119(1), 101–119 (2014)CrossRefGoogle Scholar
  10. 10.
    Leventi, C., Navicke, J., Rastrigina, O., Sutherland, H.: Nowcasting the income distribution in europe (2014)Google Scholar
  11. 11.
    Hausmann, R., Hidalgo, C., Bustos, S., Coscia, M., Chung, S., Jimenez, J., Simoes, A., Yildirim, M.: The Atlas of Economic Complexity. Puritan Press, Boston (2011)Google Scholar
  12. 12.
    Caldarelli, G., Cristelli, M., Gabrielli, A., Pietronero, L., Scala, A., Tacchella, A.: A network analysis of countries export flows: firm grounds for the building blocks of the economy. PLoS ONE 7(10), e47278 (2012)CrossRefGoogle Scholar
  13. 13.
    Chawla, S.: Feature selection, association rules network and theory building. J. Mach. Learn. Res. Proc. Track 10, 14–21 (2010)Google Scholar
  14. 14.
    Pennacchioli, D., Coscia, M., Rinzivillo, S., Giannotti, F., Pedreschi, D.: The retail market as a complex system. EPJ. Data Sci. 3(1), 1–27 (2014)CrossRefGoogle Scholar
  15. 15.
    Pennacchioli, D., Coscia, M., Rinzivillo, S., Pedreschi, D., Giannotti, F.: Explaining the product range effect in purchase data. In: 2013 IEEE International Conference on Big Data, pp. 648–656. IEEE (2013)Google Scholar
  16. 16.
    Guidotti, R., Coscia, M., Pedreschi, D., Pennacchioli, D.: Behavioral entropy and profitability in retail. In: DSAA (2015)Google Scholar
  17. 17.
    Galbraith, J.W., Tkacz, G.: Nowcasting gdp with electronic payments data (2015)Google Scholar
  18. 18.
    Lawn, P.A.: A theoretical foundation to support the index of sustainable economic welfare (isew), genuine progress indicator (gpi), and other related indexes. Ecol. Econ. 44(1), 105–118 (2003)CrossRefGoogle Scholar
  19. 19.
    Helbing, D., Balietti, S.: How to create an innovation accelerator. Eur. Phys. J. Spec. Top. 195(1), 101–136 (2011)CrossRefGoogle Scholar
  20. 20.
    Lawn, P.A.: An assessment of the valuation methods used to calculate the index of sustainable economic welfare (isew), genuine progress indicator (gpi), and sustainable net benefit index (snbi). Environ. Dev. Sustain. 7(2), 185–208 (2005)CrossRefGoogle Scholar
  21. 21.
    Geng, L., Hamilton, H.J.: Interestingness measures for data mining: a survey. ACM Comput. Surv. 38(3), 1–32 (2006)CrossRefGoogle Scholar
  22. 22.
    Balassa, B.: Trade liberalization and ‘revealed’ comparative advantage. Manchester Sch. 33, 99–123 (1965)CrossRefGoogle Scholar
  23. 23.
    Cristelli, M., Gabrielli, A., Tacchella, A., Caldarelli, G., Pietronero, L.: Measuring the intangibles: a metrics for the economic complexity of countries and products. PloS One 8(8), e70726 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Guidotti, R.: Mobility Ranking-Human Mobility Analysis Using Ranking Measures. University of Pisa, Pisa (2013)Google Scholar
  25. 25.
    Monsell, B.C.: Update on the development of x-13arima-seats. In: Proceedings of the Joint Statistical Meetings: American Statistical Association (2009)Google Scholar
  26. 26.
    Holm, S.: A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Riccardo Guidotti
    • 1
    • 2
    Email author
  • Michele Coscia
    • 3
  • Dino Pedreschi
    • 2
  • Diego Pennacchioli
    • 1
  1. 1.KDDLab ISTI CNRPisaItaly
  2. 2.KDDLab, CS DepartmentUniversity of PisaPisaItaly
  3. 3.CID - HKSCambridgeUSA

Personalised recommendations