Labour Market Adjustments to Financing Conditions under Sectoral Rigidities in the Euro Area

An Erratum to this article was published on 16 July 2018

An Erratum to this article was published on 16 July 2018

This article has been updated

Abstract

This paper analyses empirically the response of labour market indicators to changing financing conditions in a panel of 15 euro area countries from 1999Q1 to 2015Q4. Using a local projections approach, we estimate impulse responses of three margins of sectoral labour market adjustment – employment, hours worked and real wages. Consistent with recent results in the literature, we find contractionary financing shocks to depress all three indicators of the labour market. Furthermore, responses are asymmetric depending on the sign of the shock, different in magnitude depending on the sectoral composition, and sensitive to labour market institutions such as employment protection legislation and union density. Finally, labour market institutions seem to mainly affect the relative strength of the adjustment margins and not so much the overall response of the wage bill.

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Change history

  • 16 July 2018

    The original version of this article unfortunately contained mistakes.

  • 16 July 2018

    The original version of this article unfortunately contained mistakes.

Notes

  1. 1.

    Based on a sample of sectoral data for OECD countries, Boeri et al. (2013) find that “the shift of the Okun’s law intercept for financial crises is almost one half of that occurring during ordinary recessions”. Hence, output growth required to avoid unemployment from rising needs to be more than one and a half as strong in following financial crisis compared to non-financial recessions.

  2. 2.

    See for instance Christiano et al. (2011), Mumtaz and Zanetti (2016), Zanetti (2015), Ben-Mohamed and Salès (2015).

  3. 3.

    Boeri and Jimeno (2015) note that in the post-crisis period “in Germany adjustment along the intensive margin reduced the response of unemployment to the output fall, in Spain it is the decline in labour hoarding […] together with a slight increase in participation and an initial increase in hours worked per employee that explains the rise in the unemployment rate.”

  4. 4.

    For some of the most recent examples see for instance IMF (2016).

  5. 5.

    Austria, Belgium, Cyprus, Spain, Finland, France, Greece, Ireland, Italy, Luxembourg, Malta, Netherlands, Portugal, Slovenia, Slovakia.

  6. 6.

    See also IMF ( 2016) for a similar approach.

  7. 7.

    We use this measure to strip credit growth from demand driven increases, which would also directly affect employment. Results using the difference in loan growth rates are quantitatively similar.

  8. 8.

    We define programme periods as follows: Greece since 2010, Ireland 2011 to 2013, Portugal 2011 to 2014, Cyprus 2013 to 2016, Spain 2012 to 2013.

  9. 9.

    To some extent this is a result of interpolations due to data availability, which limits the variability across time, leaves however the overall trend between the beginning and end of sample observations unchanged.

  10. 10.

    In Table 2 in the Appendix, we provide a full set of regression results that correspond to baseline impulse responses shown in Figure 4 at forecast horizons h=0,4,8 Table 2 illustrates that GVA growth is an important additional driver of employment and hours worked while the change in the GVA deflator significantly determines employment growth and real wages in the short run. Figure 11 in the Appendix provides results for the whole sample including programme period observations. Differences lie in the lack of convergence of employment back to the initial level as a result of a longer lasting rebalancing process during the economic adjustment programme. Responses of hours worked turn insignificant while wage dynamics for the full sample also lack the convergence pattern.

  11. 11.

    To do so, we further augment equation (2) by including interaction terms between the indicator for the sign of the shock, the shock itself and the labour market regulation indicator. Given this triple interaction, results should be interpreted with care.

  12. 12.

    The response is not driven by a specific sector or country.

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Correspondence to Sebastian Weber.

Additional information

This research was partly undertaken while all three authors were at the ECB. The views expressed arethose of the authors and do not necessarily reflect those of the ECB, EIB, IMF or NIESR

Appendix

Appendix

Table 1 Descriptive statistics
Table 2 Baseline regression results
Table 3 Baseline regression results (loan shock)
Fig. 11
figure11

Baseline compared to full sample including programme period observations. Note: Baseline specification blue (dark) line; alternative specification orange (light) line. Dashed lines 90% confidence interval calculated with Driscoll-Kraay standard errors. Spread shock

Fig. 12
figure12

Baseline compared to sample excluding public sector. Note: Baseline specification blue (dark) line; alternative specification orange (light) line. Dashed lines 90% confidence interval calculated with Driscoll-Kraay standard errors. Spread shock

Fig. 13
figure13

Loan shock measure. Note: Quarterly change in percentage points, excluding programme period observations. Solid line depicts median across countries. Dashed lines depict interquartile range across countries. Loan shocks are measured using the residual from a regression of real annual loan growth on its quarterly lag and GDP growth, multiplied by −1

Fig. 14
figure14

Baseline results (loan shock). Note: Dashed lines 90% confidence interval calculated with Driscoll-Kraay standard errors

Fig. 15
figure15

Tightening and easing loan shocks. Note: Tightening (positive) loan shock blue (dark) line; easing (negative) loan shock orange (light) line. Dashed lines 90% confidence interval calculated with Driscoll-Kraay standard errors

Fig. 16
figure16

Results for high and low EPL (loan shock). Note: Low rigidity blue (dark) line (20th percentile of sample EPL); high rigidity orange (light) line (80th percentile of sample EPL). Dashed lines 90% confidence interval calculated with Driscoll-Kraay standard errors

Fig. 17
figure17

Results for high and low union density (loan shock). Note: Low rigidity blue (dark) line (20th percentile of sample union density); high rigidity orange (light) line (80th percentile of sample union density). Dashed lines 90% confidence interval calculated with Driscoll-Kraay standard errors

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Hantzsche, A., Savsek, S. & Weber, S. Labour Market Adjustments to Financing Conditions under Sectoral Rigidities in the Euro Area. Open Econ Rev 29, 769–794 (2018). https://doi.org/10.1007/s11079-018-9485-0

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Keywords

  • Labour market institutions
  • Employment
  • Wages
  • Sectoral adjustment
  • Financing conditions
  • Local projections method

JEL Classification

  • E24
  • E44
  • J31
  • L51