Journal of Business Cycle Research

, Volume 12, Issue 1, pp 81–117 | Cite as

Forecasting Employment in Europe: Are Survey Results Helpful?

  • Robert LehmannEmail author
  • Antje Weyh
Research Paper


In this paper we evaluate the forecasting performance of employment expectations for employment growth in 15 European states. Our data cover the period from the first quarter 1998 to the fourth quarter 2014. With in-sample analyses and pseudo out-of-sample exercises, we find that for most of the European states considered, the survey-based indicator model outperforms common benchmark models. It is therefore a powerful tool for generating more accurate employment forecasts. We observe the best results for one quarter ahead predictions that are primarily the aim of the survey question. However, employment expectations also work well for longer forecast horizons in some countries.


Employment forecasting European business survey Employment expectations Granger causality 

JEL Classification

E27 J00 J49 



Our special thanks goes to two anonymous referees, Alfred Garloff and Marcel Thum for their careful reading of our manuscript. We are grateful to Wolfgang Nagl, Michael Weber and seminar participants at the Technische Universität Dresden for helpful comments and suggestions; and to Lisa Giani Contini for editing this text. We also thank the participants at the meeting of the Regional Research Network from the Institute for Employment Research in November 2013. Comments from the 1st CGDE Doctoral Workshop are also gratefully acknowledged.


  1. Abberger, K. (2007a). Forecasting quarter-on-quarter changes of German GDP with monthly business tendency survey results. Ifo Working Paper No. 40.Google Scholar
  2. Abberger, K. (2007b). Qualitative business surveys and the assessment of employment—A case study for Germany. International Journal of Forecasting, 23(2), 249–258.CrossRefGoogle Scholar
  3. Ang, A., Bekaert, G., & Wei, M. (2007). Do macro variables, asset markets, or surveys forecast inflation better? Journal of Monetary Economics, 54(4), 1163–1212.CrossRefGoogle Scholar
  4. Chatfield, C. (1995). Model uncertainty, data mining and statistical inference. Journal of the Royal Statistical Society, Series A, 158(3), 419–466.CrossRefGoogle Scholar
  5. Clark, T. E., & West, K. D. (2007). Approximately normal tests for equal predicitve accuracy in nested models. Journal of Econometrics, 138(1), 291–311.CrossRefGoogle Scholar
  6. Claveria, O., Pons, E., & Ramos, R. (2007). Business and consumer expectations and macroeconomic forecasts. International Journal of Forecasting, 23(1), 47–69.CrossRefGoogle Scholar
  7. Croux, C., Dekimpe, M. G., & Lemmens, A. (2005). On the predictive content of production surveys: A pan-European study. International Journal of Forecasting, 21(2), 363–375.CrossRefGoogle Scholar
  8. Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics, 13(3), 253–263.Google Scholar
  9. dos Santos, R. H. (2003). The use of qualitative data for short term analysis. Banco de Portugal Economic Bulletin, 9(3), 101–118.Google Scholar
  10. EBRD (2013). Transition report 2013. European Bank for Reconstruction and Development, ISBN 9781898802402.Google Scholar
  11. European Commission (2007). The Joint Harmonised EU Programme of Business and Consumer Surveys—User Guide. Directorate General for Economic and Financial Affairs.
  12. Eurostat (2008). NACE Rev. 2—Statistical classification of economic activities in the European Community. Eurostat Methodologies and Working Papers, ISSN 1977-0375.Google Scholar
  13. Fritsche, U., & Stephan, S. (2002). Leading indicators of German business cycles. Journal of Economics and Statistics, 222(3), 289–315.Google Scholar
  14. Gayer, C. (2005). Forecast evaluation of European commission survey indicators. Journal of Business Cycle Measurement and Analysis, 2005(2), 157–183.CrossRefGoogle Scholar
  15. Giacomini, R., & Rossi, B. (2010). Forecast comparisons in unstable environments. Journal of Applied Econometrics, 25(4), 595–620.CrossRefGoogle Scholar
  16. Graff, M., Mannino, M., & Siegenthaler, M. (2012). A real time evaluation of employment forecasts in Switzerland. KOF Working Papers No. 320.Google Scholar
  17. Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438.CrossRefGoogle Scholar
  18. Hanssens, M., & Vanden Abeele, P. M. (1987). A time-series study of the formation and predictive performance of EEC production survey expectations. Journal of Business & Economic Statistics, 5(4), 507–519.Google Scholar
  19. Hansson, J., Jansson, P., & Löf, M. (2005). Business survey data: Do they help in forecasting GDP growth? International Journal of Forecasting, 21(2), 377–389.CrossRefGoogle Scholar
  20. Hartle, D. (1958). Predictions derived from the employment forecast survey. The Canadian Journal of Economics and Political Science, 24(3), 373–390.CrossRefGoogle Scholar
  21. Hutter, C., & Weber, E. (2015). Constructing a new leading indicator for unemployment from a survey among German employment agencies. Applied Economics, 47(3), 3540–3558.CrossRefGoogle Scholar
  22. Lehmann, R. (2015). Survey-based indicators vs. hard data: What improves export forecasts in Europe? Ifo Working Paper No. 196.Google Scholar
  23. Lütkepohl, H. (2005). New introduction to multiple time series analysis. Berlin: Springer.CrossRefGoogle Scholar
  24. Martinsen, K., Ravazzolo, F., & Wulfsberg, F. (2014). Forecasting macroeconomic variables using disaggregate survey data. International Journal of Forecasting, 30(1), 65–77.CrossRefGoogle Scholar
  25. Ng, S., & Perron, P. (2001). Lag length selection and the construction of unit root tests with good size and power. Econometrica, 69(6), 1519–1554.CrossRefGoogle Scholar
  26. Österholm, P. (2010). Improving unemployment rate forecasts using survey data. Finnish Economic Papers, 23(1), 16–26.Google Scholar
  27. Robinzonov, N., & Wohlrabe, K. (2010). Freedom of choice in macroeconomic forecasting. CESifo Economic Studies, 56(2), 192–220.CrossRefGoogle Scholar
  28. Rossi, B., & Sekhposyan, T. (2011). Understanding models’ forecasting performance. Journal of Econometrics, 164(1), 158–172.CrossRefGoogle Scholar
  29. Seiler, C. (2014). On the robustness of balance statistics with respect to nonresponse. OECD Journal: Journal of Business Cycle Measurement and Analysis, 2014(2), 45–62.Google Scholar
  30. Siliverstovs, B. (2013). Do business tendency surveys help in forecasting employment? A real-time evidence for Switzerland. OECD Journal: Journal of Business Cycle Measurement and Analysis, 2013(2), 1–20.Google Scholar
  31. Weber, E. & Zika, G. (2015). Labour market forecasting: is disaggregation useful? Applied Economics, 48(23), 2183–2198.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Ifo InstituteIfo Center for Business Cycle Analysis and SurveysMunichGermany
  2. 2.Institute for Employment ResearchRegional IAB Office SaxonyChemnitzGermany

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