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Monitoring short-term labour cost developments in the European Union: which indicators to trust?

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  • Gilles Mourre
  • Michael Thiel

Abstract

This paper reviews the available indicators in the European Union to monitor short-term labour cost developments, i.e. of quarterly frequency, with a special focus on the euro area. It clarifies concepts, provides information on availability and compares the indicators against various statistical criteria, their historical track record and their predictive capacities. The paper mainly focuses on the supply side, particularly considering labour cost developments in terms of risks for price stability. It is found that no single indicator can be considered clearly superior and able to replace the others without loss of information, as each indicator concentrates on a specific dimension of labour costs and is affected by statistical flaws. The assessment of short-term wage developments should reasonably be based on the broadest available set of statistics so as to get a balanced and careful view. The empirical analysis shows that when forecasting core inflation one-step ahead for the euro area as a whole, the labour cost index and the ECFIN wage indicator display the higher predictive accuracy. Moreover, composite labour cost indicators (encompassing at least two indicators) clearly outperform any single wage indicator. Compensation per employee empirically appears the best, albeit weak, leading wage indicator of private consumption.

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Bibliographic Info

Paper provided by Directorate General Economic and Monetary Affairs (DG ECFIN), European Commission in its series European Economy - Economic Papers with number 258.

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Length: 42 pages
Date of creation: Oct 2006
Date of revision:
Handle: RePEc:euf:ecopap:0258

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Keywords: Labour cost indicators; wages and compensations; short-term macroeconomic developments; core inflation; private consumption; leading indicators; Mourre; Thiel;

References

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  1. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  2. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
  3. Moser, Gabriel & Rumler, Fabio & Scharler, Johann, 2007. "Forecasting Austrian inflation," Economic Modelling, Elsevier, vol. 24(3), pages 470-480, May.
  4. Franck Sédillot & Nigel Pain, 2003. "Indicator Models of Real GDP Growth in Selected OECD Countries," OECD Economics Department Working Papers 364, OECD Publishing.
  5. Rünstler, Gerhard & Sédillot, Franck, 2003. "Short-term estimates of euro area real GDP by means of monthly data," Working Paper Series 0276, European Central Bank.
  6. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
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