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Mismatch on the Dutch Labour Market in the Great Recession


We study if labour market mismatch has increased in the Netherlands during the Great Recession. First, we estimate a so-called “steady-state” Beveridge curve based on labour market flows. An outward shift of this curve implies decreasing matching efficiency. Second, we construct a mismatch index which enables us to calculate the contribution of sector mismatch to the increase in unemployment. Our analyses show little support for the hypothesis that mismatch is currently a significant problem for the Dutch labour market. At the aggregate level, the Beveridge curve has not shifted outwards. In addition, at most one-ninth of the Dutch unemployment rise can be attributed to sector mismatch, which is comparable to the contribution during the previous downturn.

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  1. 1.

    Mismatch can occur for several reasons. First of all, technological shocks can cause occupations or sectors to disappear, whilst creating new occupations and sectors that require different skills (Bauer and Bender 2004; Goldin and Katz 2008). Mismatch often becomes a problem in the aftermath of a recession, as employers use economic downturns as an opportunity to restructure their firm due to relatively low opportunity costs (see Davis and Haltiwanger 1990; Katz 2010). Secondly, geographical mismatch becomes a problem when the unemployed are not situated in regions where employment is growing. This type of mismatch can occur when business activities are relocated across regions. It can be exacerbated by a housing market crash, which hampers labour market mobility. Thirdly, mismatch can be the result of labour market institutions, such as generous unemployment benefits or low entry conditions for disability schemes. These institutions negatively affect the readiness of the unemployed to search for jobs (Rothstein 2011; Nickell et al. 2003).

  2. 2.

    See Appendices A.1 and A.2 in Şahin et al. (2014) and Appendix A in Marthin (2012) for a detailed solution of the problem. In contrast to Şahin et al. (2014), we use the unemployment level rather than the unemployment rate.

  3. 3.

    In contrast to Şahin et al. (2014), we use the unemployment level rather than the unemployment rate.

  4. 4.

    Detrending the flows data, by using, for example, a HP-filter (see, e.g. Barnichon 2012), would violate our steady-state condition, since we would have to subtract two different values on both sides of the equation, e.g.: \((f_t -d_f (t))-(s_t -d_s (t))\ne f_t -s_t =0\) if \(d_f (t)\ne d_s (t)\), where \(d_f (t)\) and \(d_s (t)\) are the trends from \(f_t \) and \(s_t\), respectively. Therefore, we do not detrend the data.

  5. 5.

    The largest difference with the ILO-definition is the hours threshold. In the Statistics Netherlands definition, unemployment is defined as all persons unemployed or working less than 12 h a week, who are willing and searching for work for at least 12 h a week. The ILO-definition in contrast employs a 1-hour threshold.

  6. 6.

    In our data on unemployment benefit recipients, we have excluded people that took part in the so-called “deeltijd-WW”. The deeltijd-WW was a temporary short-time working scheme between April 2009 and July 2011. The aim of the scheme was the prevention of layoffs during the Great Recession, by providing employers with a wage cost subsidy if they reduced the working hours of their employees. However, people participating in short-time working schemes are still registered as employees and often are not actively searching for another job. Therefore, we find it more accurate to exclude this specific group from our proxy for unemployment.

  7. 7.

    Benefit duration in the Netherlands depends on employment duration, which differs between sectors. So, allocating more unemployed workers to sectors with a high exit rate might not be optimal, because the high rate might reflect a higher fraction of unemployed running out of benefits. This risk, however, is mitigated by the fact that the job-finding and exit rate in all sectors show a strong and positive correlation.

  8. 8.

    The start of the Great Recession is identified based on data regarding quarterly GDP volume mutations. Although the third quarter already shows a slight decline of 0.1 %, the crisis intensified significantly in the fourth quarter of 2008 (\(-\)1.0 %).

  9. 9.

    In Erken et al. (2015), we also study mismatch within sectors (i.e. intrasectoral mismatch), by examining Beveridge curves shifts of each of the 28 individual industries. We conclude that only two out of the 28 sectors show indications of decreasing matching efficiency during the Great Recession. In this paper we abstract from this analysis and solely focus on mismatch between sectors (i.e. sector mismatch) using the mismatch index.

  10. 10.

    In order to assess the influence of our initial unemployment condition, we have also estimated counterfactual unemployment using a lower and higher initial unemployment condition (the dashed and dotted blue lines in Fig. 6). For the lower unemployment condition we use 85 % of the real unemployment rate. For the higher unemployment scenario we used a 15 % higher unemployment condition. It is clear that the impact of the initial condition peters out when moving forward in time; the influence is negligible after 2004.

  11. 11.

    Results are available on request.

  12. 12.

    We calculated an index that only takes differences in labour markets tightness into account , i.e. we dropped the term \(\left( {\frac{\phi _i}{\overline{\phi _t}}}\right) \) in Eq. (9). The level of this index is substantially lower than the index in the main text.

  13. 13.

    Şahin et al. (2014) also show that the bulk of the unemployed find work in the sector in which they were previously active.

  14. 14.

    Elsby et al. (2016) emphasize that joining markets (i.e. sub-sectors) lowers the likelihood of mismatch, as some markets (i.e. sub-sectors) with unemployed workers will be joined with vacant jobs.

  15. 15.

    Since both Lazear and Spletzer (2012) and Shibata (2013) look at a lower amount of sectors than our study, we have to block bootstrap our simple mismatch index to compare our findings to theirs. Our block bootstrapped estimate is 0.24 when we look at the same number of sectors used by Shibata (2013) and this is close to his estimate of 0.23. However, we find a much lower level of occupational mismatch (0.12) than Lazear and Spletzer, who find a level of about 0.4.


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Corresponding author

Correspondence to Hugo Erken.

Additional information

The views in this article are those of the authors and do not necessarily reflect the policy or position of the CPB or Rabobank. We would like to thank Rob Vlek and Marcia Kantoor from UWV, as well as Statistics Netherlands for customized data deliverance. In addition, we thank two anonymous reviewers, Jan van Ours, Pieter Gautier, Bas ter Weel, Daniel van Vuuren, Adam Elbourne, Marloes de Graaf-Zijl, Pieter van Winden, Mark Mink and Rob Luginbuhl for useful comments on earlier versions of this paper. This paper was presented at the Netherlands Economist Day 2014, the Ministry of Economic Affairs and the Ministry of Social Affairs and Employment.


Appendix 1: Recovery Path from the Great Recession

The deviation from the steady-state curve is given by \(\Delta v_t =\left| {v_t -v^{*}} \right| \). Deviations from the steady-state curve during the last recovery phase (2004Q2–2008Q3) will provide the fundamentals for our recovery path. Since the peak in unemployment differs between each crisis, we scale the observed unemployment rates linearly to range from the equilibrium unemployment \(u_{eq} \) (the lowest observed unemployment rate) to the expected return point \(\widehat{u}_r\), which we define by the unemployment prediction where \(\widehat{u}_t <\widehat{u}_{t-1} \) and \(\widehat{u}_{t-1} > \widehat{u}_{t-2} \), instead. Next, the following curve is fitted:

$$\begin{aligned} \Delta v_t =\frac{a\cdot u^{2}+b\cdot u+c}{u}=a\cdot u+b+\frac{c}{u} \end{aligned}$$
Table 3 Linear panel estimation results for pre-crisis data (2000–2008)
Table 4 Linear panel estimation results for crisis data (2009–2014)

This curve has two constraints. First of all, the deviation of \(v_t\) at the (forecasted) return point \(\widehat{u}_r \) is constrained at 0. Furthermore the deviation of \(v_t \) at the steady-state unemployment \(u_{eq} \) is constrained at 0. Now we minimise the Sum of Squared Errors with respect to the parameters a, b and c, given these constraints. This optimisation problem is given by:

$$\begin{aligned}&\min _{a,b,c} \sum _{t=1}^T \left( {v_t -a\cdot u+b+\frac{c}{u}} \right) ^{2} \nonumber \\&\hbox {subject to}\quad \begin{array}{ll} {v_r -a\cdot \widehat{u}_r +b+\frac{c}{\widehat{u}_r}=0} \\ {v_{eq} -a\cdot u_{eq} +b+\frac{c}{u_{eq}}=0} \\ \end{array} \end{aligned}$$

and solved using a GRG2 method. The corresponding Beveridge curve vacancy rate \((\widehat{v}_t)\) is calculated and the expected deviation \(\widehat{\Delta v_t}\) is added. We have also estimated a curve using pre- and crisis observations without scaling. The results from this analysis only differ marginally from the path illustrated in Fig. 4.

Appendix 2: Linear Panel Estimation Results

As a sensitivity analysis, we have estimated the regression specified by Eq. (8) using only pre-crisis data and using only post-crisis data.

See Appendix Tables 3 and 4.

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Erken, H., van Loon, E. & Verbeek, W. Mismatch on the Dutch Labour Market in the Great Recession. De Economist 163, 435–459 (2015).

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  • Beveridge curve
  • Mismatch
  • Labour market
  • Unemployment
  • Vacancies
  • Great Recession

JEL Classification

  • E20
  • E24
  • J63
  • J69