Abstract
This paper looks at the impact industry instability has on worker separations. Workers leave firms one of two ways: (i) voluntarily by quitting; or (ii) involuntarily through firm layoffs. Using data drawn from the Longitudinal Worker File, a Canadian firm-worker matched employment database, we are able distinguish between voluntary and involuntary separations using information on reasons for separations and assess the impact industry shutdown rates have on worker separation rates, both voluntarily and involuntarily. Once controlling for various factors and potential selection bias, we find that industry shutdown rates have a positive and significant effect on the overall separation, layoff and quit rates of workers. Finally, industry instability has a much larger impact on layoff rates when comparing voluntary and involuntary separations.
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Notes
- 1.
For example, Jacobson et al. (1993), Gottschalk and Moffitt (1994), Gottschalk and Moffitt (2009), Beach et al. (2003) and Morissette and Ostrovsky (2005) study impacts on worker wages; Browning and Lusardi (1996) investigates consumption decisions; Pistaferri (2003) shows altering family savings and labour supply decisions; Guiso et al. (2002) looks at occupational choices; and Fraser (2001) even demonstrates impacts on fertility behaviour.
- 2.
- 3.
All exiting firms shutdown, but not all shutdown are exits. Multiple consecutive years of zero payroll would be needed to identify firm exit. Huynh et al. (2010) investigate firm exit in the context of industry instability.
- 4.
The marginal effect on the industry shutdown rate is an elasticity since the probit models a probability and shutdown rates are probabilities as well. We report results in terms of marginal effects estimated at the means of the continuous explanatory variables and with dummy variables set to zero.
- 5.
\(\frac{\partial \phi (\hat{\theta }^{S}z^{S})} {\partial age} \vert _{Age=\overline{Age}} =\phi (\hat{\theta }^{S}z_{B}^{S})\big(\hat{\gamma }_{1}A + 2\hat{\gamma }_{2}\overline{Age} + 3\hat{\gamma }_{3}\overline{Age}^{2} + 4\hat{\gamma }_{4}\overline{Age}^{3}\big)\), where z B S is a set of benchmark characteristics described in the previous footnote and Ď• is a standard normal density function.
- 6.
The baseline probability of a worker separation is 34.4 %, while the baseline probability of a firm layoff is 24.7 %.
- 7.
We thank Michael Veall for this observation.
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Acknowledgements
Huynh and Voia gratefully acknowledges the assistance and hospitality of Statistics Canada Economic and Social Analysis Divisions. The authors “Kim P. Huynh, Yuri Ostrovsky, Robert J. Petrunia, and Marcel C. Voia” are especially indebted to Leonard Landry for his gracious efforts on data management. We thank Michael Veall, Iourii Manovskii, Gueorgui Kambourov, John Stevens, James Townsend and participants of the 2009 Cornell VRDC conference and 2010 Canadian Economics Association for comments and suggestions. The views expressed in them are those of their authors and not necessarily the views of the Bank of Canada or Statistics Canada. All errors and opinions are our own.
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Huynh, K.P., Ostrovsky, Y., Petrunia, R.J., Voia, MC. (2016). Worker Separations and Industry Instability. In: Greene, W., Khalaf, L., Sickles, R., Veall, M., Voia, MC. (eds) Productivity and Efficiency Analysis. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-23228-7_10
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