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Does labor market history matter?

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Abstract

This paper finds that labor market history plays an important role in the Danish labor market both by directly affecting the transitions between labor market states and indirectly through the wage. When comparing the relative importance of different types of state dependence, it is found that occurrence dependence from non-employment states seems to have the strongest effect on the employment rate, while employment history is the main driver of state dependence in the wage. Predictions based on the estimated model reveal potential negative long-term effects from external employment shocks and potential positive benefits from employment programs for long-term unemployed.

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Notes

  1. Flinn and Heckman (1983) argue for a differentiation between unemployment and out of the labor force.

  2. This benefit level is from 2003, which is the last year observed in the data.

    If a person moves from an education to unemployment, the compensation is up to 82 % and the maximum amount in 2003 was 617 DKK.

  3. The use of a hazard function as a flexible estimator of the wage distribution originates from Donald et al (2000).

  4. See Honoré (1993) and Horny and Picchio (2010) for identification in a single-spell framework relying on the MPH assumption and see Abbring and Van den Berg (2003), Horny and Picchio (2010) and Picchio (2012) for identification relying on time-varying exogenous variables or multiple observations per individual.

  5. See Rosholm (2001), D’Addio and Rosholm (2002a) and D’Addio and Rosholm (2002b) for methods to account for the initial condition problem.

  6. Alternative one might consider following the procedures of Gaure (2012).

  7. In order to test this initial condition assumption, the empirical model is also estimated with another version of the initial condition. In this version, the sample is selected such that in each year all individuals at the age of 16 are included in the sample and followed until the end of the sampling period. The assumption in this version is that there exists no relevant labor market history before the age of 16. This assumption is stronger than the one used in the paper. However, the data sample in this version will, by construction, include a lot of students, which will make the information that can be drawn from transitions in and out of the state outside the labor market harder to interpret. The sample also includes a lot of employment spells ex ante primary education that are not necessarily a relevant part of the individual employment histories.

    However, the primary empirical results based on the two different data samples are the same. Hence, the initial condition used in the paper does not, in this sense, appear problematic.

  8. Including the baseline hazards and unobserved effects, this gives a parameter space of 428 parameters where the coefficients are to be estimated.

  9. The empirical model was also estimated with dummy variables for each of the five regions in Denmark, using the metropolitan region as reference. Since the four province regions seemed to react very similarly, the parameter space was simplified to include only one variable describing the meteropolitan area around Copenhagen.

  10. See the Fig. 10 in Appendix 1 for an illustration.

  11. Given the functional form of the hazard function, the effect in percent can be found by \((\)exp\((\beta )-1)*100\), where \(\beta \) is the estimate from Tables 3 or 4.

  12. These include state dependence effects between the non-employment states.

  13. The model was also estimated on a regional level and for different educational groups with similar results.

  14. Except for the case of unemployed women, where employment experience has a negative effect on the transition rate into employment.

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Acknowledgments

I express my thanks for useful comments on this paper and earlier drafts to the following people: Michael Svarer, Rune Vejlin, Gustaf Bruze, Tue Gørgens, Bernd Fitzenberger, and two anonymous referees. I have also benefited from discussions with participants in seminars at WISE, DGPE, and Aarhus University Brownbag Workshops. Any remaining errors are my own

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Authors and Affiliations

  1. Department of Economics and Business, Aarhus University, Fuglesangs Allé 4, 8210 , Aarhus V, Denmark

    Rune V. Lesner

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  1. Rune V. Lesner
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Correspondence to Rune V. Lesner.

Appendices

Appendices

Appendix 1: Labor market history and the explanatory variables used to describe it

See Fig. 10.

Fig. 10
figure 10

An illustration of the explanatory variables used to describe the labor market history

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Appendix 2: Estimation results for men and women

See Tables 7, 8 and 9.

Table 7 Estimation results for men (Std. Err.)
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Table 8 Estimation results for women (Std. Err.)
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Table 9 Estimation results from wage hazards (Std. Err.)
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Appendix 3: Distribution of the unobservables

See Table 10

Table 10 Distribution of the unobservables
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Appendix 4: Baseline transition rates

See Fig. 11.

Fig. 11
figure 11

Baseline transition intensities for transitions between the labor market states: employment, unemployment and outside the labor market

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Appendix 5: Simulation algorithm

Once the parameter estimates from the hazard functions have been determined, the event history and the wages implied by these parameter estimates can be simulated using the following algorithm.

  1. 1.

    Draw a vector of parameter estimates assuming normality around the estimates given the estimated variance-covariance matrix.

  2. 2.

    Generate \(1000\) individuals by drawing \(1000\) times from the empirical distribution of initial observable characteristics, initial states and from the estimated distribution of unobservable characteristics.

  3. 3.

    When the state is employment, the wage is predicted based on observable and unobserved characteristics.

  4. 4.

    The predicted transition hazard functions are computed.

  5. 5.

    Each year after entry, the year, age, unemployment rate and in turn the wage and transition rates are updated.

  6. 6.

    Every time a spell is ended, the lagged duration and occurrence dependence are updated.

  7. 7.

    The simulation is stopped for a predicted event history when the year reaches the time of right censoring in the data.

  8. 8.

    The steps 1 to 7 are repeated \(200\) times in order to construct Monte Carlo confidence intervals.

Appendix 6: Employment shock

See Fig. 12.

Fig. 12
figure 12

The predicted effect on the employment rate for the entire sampled population of external employment shocks affecting 10 %, 25 % or 50 % of the employed population two years after labor market entry

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Appendix 7: Policy for long-term unemployed

See Fig. 13.

Fig. 13
figure 13

The predicted effect of employment programs for long-term unemployed of duration 3 or 6 months on the employment rate of the entire sampled population. The baseline case of no program is used as point of reference

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Lesner, R.V. Does labor market history matter?. Empir Econ 48, 1327–1364 (2015). https://doi.org/10.1007/s00181-014-0826-6

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  • DOI: https://doi.org/10.1007/s00181-014-0826-6

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