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Does intersectoral labour mobility pay for academics?

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Abstract

Labour mobility plays a significant role in the diffusion of knowledge and economic growth. In this study, we examined academics’ incentives for mobility among university, private sector and government jobs. Using register data on doctoral degree holders in Finland, we found that moving from academia to the private sector is related to higher subsequent earnings, particularly among young academics and in hard sciences. However, frequent mobility across sectors was related to lower subsequent earnings.

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Notes

  1. The average age of obtaining a Ph.D. degree is relatively high in Finland. For example, the average age of those who obtained their Ph.D. degree in 2000 was 32.4 (Tohtoreiden työllistyminen and sijoittuminen ja tarve 2003). In our sample, the mean age of obtaining a Ph.D. was 35 (median 34), which is in line with the aforementioned statistics.

  2. We do not observe the doctorate holders’ exact titles or positions in the data. However, we are able to control for their career age (time since doctorate degree in 2000) and earnings in 2000, which serve as proxies for the career stage.

  3. The threshold of 48 was chosen because the median age in our baseline sample was 48.

  4. The disciplines of agriculture, social sciences and health were excluded because it was not clear whether these disciplines should be considered “hard” or “soft” sciences.

  5. We used Stata command teffects in the analysis.

  6. We also divided the triple helix mobility into two subgroups: (1) doctorate holders who moved from the university sector to the private sector and then to the government (UPG triple helix) and (2) doctorate holders who changed from the university sector to government and then to the private sector (UGP triple helix). Both triple helix coefficients were statistically insignificant in the baseline model of Table 3. Thus, the order in which the intersectoral mobility occurred does not seem to matter.

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Acknowledgements

We gratefully acknowledge funding from the Strategic Research Council (SRC) at the Academy of Finland for the project “Beyond MALPE-coordination: Integrative envisioning” (Number 303552). We also thank anonymous referees, Merja Kauhanen and Kristian Koerselman for their valuable comments and suggestions.

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Authors and Affiliations

  1. University of Jyväskylä, Jyväskylä, Finland

    Timo Tohmo & Jutta Viinikainen

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  1. Timo Tohmo
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  2. Jutta Viinikainen
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Correspondence to Timo Tohmo.

Appendices

Appendix 1

We estimated a multinomial-logit Inverse Probability Weighting (IPW) model that jointly considers multiple regimes through multinomial logit and constructs suitable counterfactuals through by using probability weights (see, e.g., Takeshima et al. 2016). The explanatory variables in the multinomial logit are the same as those used in the OLS baseline model. Tables 5, 6 and 7 reports the raw and model-adjusted differences in means and the ratios of variances between the treated and untreated groups for each covariate. As Tables 5, 6 and 7 show, the differences in the means before weighting were relatively large. After weighting, the differences in means are typically smaller, and the variance ratios are, in most cases, close to one. The diagnostics indicate that the inverse probability-weighted samples are more comparable across regimes than the unweighted samples. Thus, the diagnostics support the assumption that our model balances covariates. Table 8 presents the estimated average treatment effects on the treated as estimated by multinomial IPW. The negative coefficients are in line with our baseline results in Table 2.

Table 5 The model-adjusted difference in means and ratio of variances between the treated (dual UP) and untreated (stayers) for each covariate
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Table 6 The model-adjusted difference in means and ratio of variances between the treated (dual UG) and untreated (stayers) for each covariate
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Table 7 The model-adjusted difference in means and ratio of variances between the treated (triple helix) and untreated (stayers) for each covariate
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Table 8 Multinomial-logit inverse probability weighting results from earnings regression
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Appendix 2

See Table 9.

Table 9 OLS results from earnings regression
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Tohmo, T., Viinikainen, J. Does intersectoral labour mobility pay for academics?. Scientometrics 113, 83–103 (2017). https://doi.org/10.1007/s11192-017-2477-9

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