Abstract
What mechanisms govern the mobility of employees who voluntarily switch employers for better opportunities, given a temporary contract (TC)? We attempt to answer this question by exploring this issue in Southern and Central European countries. We use cross-sectional data from the European Union Statistics on Income and Living Conditions survey for the 2005–2016 period. We estimate a flexible simultaneous equation model for binary responses by assuming the presence of an age-varying association between voluntary mobility and having a TC. After accounting for several socio-demographic and economic variables, we find a nonlinear decreasing relation between age and the outcomes, while we detect heterogeneous nonlinear patterns in the association between voluntary mobility and having a TC across countries. These insights can support policy-makers aiming to promote initiatives that facilitate the professional mobility of employees given a TC for an efficient allocation of human capital in the production system.
Acknowledgements
We thank the two anonymous reviewers for their useful suggestions, which helped improve the clarity and quality of the article. The opinions expressed herein are those of the authors and do not reflect those of the institutions of affiliation.
Compliance with Ethical Standards
Funding: This research has not received funding.
Conflict of Interest: The authors declare that they have no conflict of interest.
Appendix
Kendall’s
| Country | Kendall’s | Confidence intervals |
|---|---|---|
| France | 0.0812 | (-0.096,0.247) |
| Italy | 0.0346 | (-0.167,0.257) |
| Spain | 0.0411 | (-0.153,0.208) |
| Austria | 0.0205 | (-0.163,0.268) |
| Germany | 0.0944 | (-0.160,0.364) |
| Czech Republic | 0.0333 | (-0.111,0.222) |
| Poland | -0.1680 | (-0.337,0.031) |
The Akaike information criterion obtained after estimation of the system of two binary equations without including a third equation to model the copula association parameter as a function of employee age. We have estimated 15 models for each country (3 combinations of link functions
| Southern European countries | Central European countries | ||||||
|---|---|---|---|---|---|---|---|
| Link function and copula | France | Italy | Spain | Austria | Germany | Czech Republic | Poland |
| Eq.1:Probit; Eq.2:Probit | |||||||
| Gaussian | 49,330 | 85,338 | 77,263 | 25,417 | 55,914 | 54,956 | 99,236 |
| Clayton | 49,311 | 85,318 | 77,275 | 25,411 | 55,901 | 54,941 | 99,218 |
| Gumbel | 49,337 | 85,348 | 77,273 | 25,420 | 55,925 | 54,965 | 99,254 |
| Joe | 49,366 | 85,430 | 77,383 | 25,442 | 56,011 | 55,028 | 99,415 |
| Frank | 49,353 | 85,394 | 77,316 | 25,436 | 55,973 | 55,010 | 99,306 |
| Eq.1:Probit; Eq.2:Cloglog | |||||||
| Gaussian | 49,349 | 85,340 | 77,256 | 25,428 | 55,947 | 54,957 | 99,243 |
| Clayton | 49,331 | 85,319 | 77,270 | 25,422 | 55,934 | 54,943 | 99,225 |
| Gumbel | 49,356 | 85,350 | 77,265 | 25,431 | 55,958 | 54,966 | 99,262 |
| Joe | 49,386 | 85,432 | 77,373 | 25,452 | 56,045 | 55,029 | 99,424 |
| Frank | 49,372 | 85,396 | 77,307 | 25,446 | 56,007 | 55,011 | 99,314 |
| Eq.1:Cloglog; Eq.2:Cloglog | |||||||
| Gaussian | 49,487 | 85,658 | 77,836 | 25,378 | 55,850 | 54,983 | 99,603 |
| Clayton | 49,468 | 85,636 | 77,847 | 25,372 | 55,837 | 54,968 | 99,579 |
| Gumbel | 49,493 | 85,669 | 77,845 | 25,381 | 55,861 | 54,992 | 99,622 |
| Joe | 49,522 | 85,754 | 77,954 | 25,403 | 55,952 | 55,055 | 99,787 |
| Frank | 49,508 | 85,713 | 77,884 | 25,396 | 55,907 | 55,036 | 99,672 |
| Sample period | 2005– | 2005– | 2005– | 2005– | 2007– | 2006– | 2006– |
| 2016 | 2015 | 2016 | 2016 | 2016 | 2016 | 2016 | |
The Akaike information criterion obtained after estimation of the system of two binary equations and including a third equation to model the copula association parameter as a function of employee age (see Section 3). We have estimated 15 models for each country (3 combinations of link functions
| Southern European countries | Central European countries | ||||||
|---|---|---|---|---|---|---|---|
| Link function and copula | France | Italy | Spain | Austria | Germany | Czech Republic | Poland |
| Eq.1:Probit; Eq.2:Probit | |||||||
| Gaussian | 49,290 | 85,340 | 77,245 | 25,404 | 55,871 | 54,937 | 99,189 |
| Clayton | 49,297 | 85,311 | 77,253 | 25,407 | 55,882 | 54,931 | 99,191 |
| Gumbel | 49,299 | 85,349 | 77,249 | 25,410 | 55,876 | 54,941 | 99,193 |
| Joe | 49,309 | 85,421 | 77,314 | 25,418 | 55,909 | 54,966 | 99,252 |
| Frank | 49,296 | 85,390 | 77,275 | 25,409 | 55,892 | 54,961 | 99,219 |
| Eq.1:Probit; Eq.2:Cloglog | |||||||
| Gaussian | 49,309 | 85,660 | 77,238 | 25,415 | 55,903 | 54,939 | 99,198 |
| Clayton | 49,316 | 85,631 | 77,248 | 25,418 | 55,914 | 54,933 | 99,199 |
| Gumbel | 49,318 | 85,669 | 77,241 | 25,420 | 55,908 | 54,942 | 99,203 |
| Joe | 49,328 | 85,744 | 77,305 | 25,429 | 55,942 | 54,967 | 99,262 |
| Frank | 49,315 | 85,709 | 77,267 | 25,420 | 55,924 | 54,962 | 99,229 |
| Eq.1:Cloglog; Eq.2:Cloglog | |||||||
| Gaussian | 49,445 | 85,660 | 77,817 | 25,365 | 55,809 | 54,964 | 99,557 |
| Clayton | 49,451 | 85,631 | 77,828 | 25,367 | 55,820 | 54,959 | 99,554 |
| Gumbel | 49,454 | 85,669 | 77,820 | 25,370 | 55,814 | 54,968 | 99,561 |
| Joe | 49,463 | 85,744 | 77,882 | 25,380 | 55,848 | 54,995 | 99,620 |
| Frank | 49,450 | 85,709 | 77,842 | 25,370 | 55,829 | 54,987 | 99,586 |
| Sample period | 2005– | 2005– | 2005– | 2005– | 2007– | 2006– | 2006– |
| 2016 | 2015 | 2016 | 2016 | 2016 | 2016 | 2016 | |
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