To which extent do personality characteristics and preferences explain expected mobility premiums of prospective academics? After a brief discussion of the applied estimation specification in Sect. 5.1, Sect. 5.2 provides scenario-specific answers to this question. In Sect. 5.3, I discuss additional sensitivity checks, addressing the reliability of decisions in a hypothetical context and differing levels of labour market readiness.
5.1 Estimation specification
Scenario-specific mobility premiums (
\(\Updelta\)) are investigated by applying ordinary least squares regressions. The estimation equation, which is the empirical equivalent of Eq.
4′, takes the following form:
$$\Delta _{i}=\beta _{0}+\sum \beta _{\mathrm{Soc}}X_{\mathrm{Soc},i}+\beta _{R}I\left(\phi _{R,i}\right)+\beta _{P}I\left(\phi _{P,i}\right)+\sum \beta _{\psi }I\left(\psi _{\mathrm{Big}5,i}\right)+\sum \beta _{S}I\left(\phi _{S,i}\right)+\beta _{A}I\left(\phi _{A,i}\right)+\sum \beta _{\chi }\chi _{i}+\sum \beta _{a}L_{0}+\beta _{w}\ln w_{O,i}+\varepsilon _{i}$$
(5)
The set of socio-demographic variables (
\(X_{\mathrm{Soc}}\)) encompasses in addition to gender, age, and partnership status also English language proficiency (
\(\Uplambda\)). The preferred specification further contains the full set of individual traits and location-specific conditions, as introduced in the theoretical model in Sect. 3.
18 Personality-related variables, such as willingness to take risks (in the career domain,
\(\phi _{R}\)), patience (
\(\phi _{P}\)) and the Big-Five personality traits (
\(\psi _{\mathrm{Big}5}\)) enter the model in categorical form, indicated by the notation
\(I(.)\). The same holds for the adaptability measure (
\(\phi _{A}\)) and the social preference variables (
\(\phi _{S}\)). For each of these, a standardisation of the original scale variable yielded three distinct groups. The first is the reference group comprising the average-type individuals, whose standardised score is within the range of one standard deviation around the mean. The second group contains individuals scoring more than one standard deviation below the mean (labelled ‘low’) and the third includes those with scores more than one standard deviation above the mean (labelled ‘high’). This approach allows detecting heterogeneous effects across groupings. Previous mobility experiences (
\(\chi\)) are controlled for as well. This includes earlier stays abroad and residential mobility during adolescence, as well as the most recent mobility experience, namely educational mobility.
19
The location-specific conditions (\(L_{0}\), comprises both the economic and hedonic dimension) refer to the district a participant explicitly stated to be his current place of residence. Aside from mostly economic variables, i. e. GDP per capita, building land prices as commodity price level proxy (\(p_{O}\)) and the unemployment rate (\(\pi _{UO}\)), they also comprise a measure of urbanisation (population density). Aspects of urban interconnectedness are also integrated, based on variables representing the time it takes to reach the three closest agglomeration centres by either car or train. Further (more hedonic) amenities (\(a_{o}\)) are directly represented by a measure of access to recreational space and the provision of public goods, gauged by the relative number of communal employees.
In addition, all specifications incorporate the logarithm of the expected post-graduation income levels (\(w_{O}\)). This accounts for cases where individuals might just ask for a reimbursement of fixed monetary moving costs, which are not depending on distance. Comparable amounts, however, might correspond to largely varying mobility premiums, depending on the position in the distribution of expected incomes.
5.2 Scenario-specific results
The main results (Table
3) refer to the sample to those 1851 individuals with four non-missing scenario-specific premiums.
20 Some model parameters display explanatory power across different scenarios, others are rather scenario-specific.
Table 3
Scenario-specific mobility premiums
Gender (female = 1) | −0.7197 | (1.3516) | 2.8991* | (1.4818) | −6.6906* | (3.7114) | −1.1969 | (3.4233) |
Age | −0.0424 | (0.2950) | 0.8025** | (0.3497) | −0.9193 | (0.8142) | 0.2120 | (0.7946) |
Partnership (yes = 1) | 1.7965 | (1.1705) | 1.7942 | (1.3101) | 6.5887** | (3.3320) | 7.9239*** | (3.0381) |
Language skills (English) | High | 0.2028 | (1.9403) | 0.3641 | (2.2884) | −14.2812** | (6.0308) | −8.2240 | (5.4485) |
Medium | −0.9351 | (1.7837) | −1.0436 | (2.0699) | −7.8434 | (5.8127) | −5.4423 | (5.1402) |
Risk attitude (career domain, \(\phi _{R}\)) | Low | 1.0668 | (1.4569) | −0.8305 | (1.5940) | 8.0559** | (4.0247) | 4.5753 | (3.6874) |
High | 1.5480 | (1.7940) | −0.3313 | (2.0657) | 6.4476 | (6.0069) | 0.1243 | (4.6659) |
Patience (\(\phi _{P}\)) | Low | 3.5968** | (1.6727) | 2.8933 | (1.8766) | 8.1635 | (5.0376) | 7.4790 | (4.7347) |
High | −0.8534 | (1.5963) | −2.8192 | (1.8302) | −3.4149 | (4.6005) | −4.8476 | (4.0071) |
Extraversion (\(\psi _{E}\)) | Low | 2.5990 | (1.8788) | −2.6948 | (2.2157) | 4.6261 | (6.1318) | −6.7797 | (5.1057) |
High | −0.1800 | (1.4925) | 1.1296 | (1.7892) | 1.7766 | (4.4112) | 4.1911 | (3.8364) |
Neuroticism (\(\psi _{N}\)) | Low | −1.4558 | (1.8312) | −1.0017 | (2.0034) | 2.5899 | (6.3460) | −1.3012 | (4.8876) |
High | 0.8838 | (1.6923) | 1.4164 | (1.8689) | −4.2229 | (4.2936) | −0.2036 | (4.1454) |
Openness (\(\psi _{O}\)) | Low | −3.3878** | (1.4769) | −1.3327 | (1.7242) | −4.6182 | (4.3390) | 0.4002 | (4.1392) |
High | −2.5564* | (1.5025) | 0.4247 | (1.7016) | −0.7239 | (4.5052) | 0.9603 | (4.0291) |
Conscientiousness (\(\psi _{C}\)) | Low | −0.2856 | (1.8096) | 0.5959 | (2.2331) | −2.1957 | (5.6295) | 1.2447 | (5.2145) |
High | −0.2953 | (1.5997) | −1.5744 | (1.8060) | −3.0004 | (4.3060) | −3.4233 | (3.8873) |
Agreeableness (\(\psi _{A}\)) | Low | −0.1304 | (1.5247) | 0.4803 | (1.7744) | 0.0486 | (4.3776) | 1.4011 | (3.9828) |
High | 3.1860* | (1.6513) | 2.9933* | (1.6798) | 1.4983 | (4.4005) | −1.0977 | (3.8906) |
Adaptability (\(\phi _{A}\)) | Low | 3.2690** | (1.5967) | 3.2048* | (1.8258) | 8.8515** | (4.4889) | 10.3383** | (4.1936) |
High | 1.1230 | (1.7819) | 0.7855 | (1.8689) | −3.0120 | (5.6145) | −2.3565 | (4.0201) |
Importance of proximity (family, \(\phi _{S}\)) | Low | −2.0220 | (1.5136) | −0.6128 | (1.7450) | −5.5772 | (4.6928) | −3.6467 | (3.8005) |
High | 6.8777*** | (2.1252) | −0.0198 | (2.3834) | 19.4975*** | (5.5309) | 12.1282** | (5.4026) |
Importance of proximity (friends, \(\phi _{S}\)) | Low | −4.1752*** | (1.6051) | −2.6304 | (1.8219) | −11.6357** | (4.7672) | −8.7098** | (3.9763) |
High | 6.0000*** | (2.1067) | 6.0702** | (2.4882) | 14.7322** | (6.0359) | 14.8613** | (5.9456) |
Previous mobility experiences (\(\chi\)) |
Residential move (yes = 1) | 0.6710 | (1.3230) | −0.0689 | (1.5600) | 1.0977 | (3.9437) | 1.4013 | (3.5242) |
Exchange participation (yes = 1) | −2.6014** | (1.2690) | −1.4469 | (1.4204) | −7.7866** | (3.4505) | −3.7100 | (3.1709) |
Stay abroad (yes = 1) | −4.7414*** | (1.5033) | −4.1557*** | (1.6069) | −16.9764*** | (3.8522) | −17.1766*** | (3.2677) |
Educational mobility (km) | −0.0239*** | (0.0058) | −0.0254*** | (0.0060) | −0.0535*** | (0.0169) | −0.0454*** | (0.0140) |
Local conditions at origin (\(L_{O}\)) |
GDP (per capita) | −0.4408** | (0.1916) | −0.4078** | (0.2025) | −1.4459** | (0.5720) | −1.2819** | (0.5165) |
Building land prices | 0.0868*** | (0.0278) | 0.0237 | (0.0314) | 0.1760** | (0.0833) | 0.0795 | (0.0784) |
Accessibility (train) | −0.0881* | (0.0508) | −0.0824 | (0.0570) | −0.2835** | (0.1406) | −0.3259** | (0.1314) |
Accessibility (car) | −0.0444 | (0.0903) | 0.0524 | (0.1056) | 0.1183 | (0.2784) | 0.0923 | (0.2594) |
Pop. density | −0.0032* | (0.0018) | −0.0022 | (0.0021) | −0.0020 | (0.0063) | 0.0001 | (0.0058) |
Recreational area (per capita) | 0.0216 | (0.0220) | −0.0181 | (0.0275) | 0.1261* | (0.0711) | 0.1105 | (0.0717) |
Public services | −0.0199 | (0.0410) | 0.0630 | (0.0473) | −0.0331 | (0.1287) | 0.0637 | (0.1141) |
Unemployment rate (\(\pi _{UO}\)) | −0.4863 | (0.5302) | −0.4212 | (0.6319) | −2.9644* | (1.7863) | −3.0359* | (1.7150) |
Relative income control (\(\ln w_{0}\)) | −6.1957*** | (1.3197) | −10.0112*** | (1.7196) | −13.6083*** | (3.5053) | −19.0974*** | (3.2757) |
Observations | 1851 | 1851 | 1851 | 1851 |
df (model) | 38 | 38 | 38 | 38 |
F-statistic | 7.75 | 3.64 | 7.17 | 6.41 |
Prob > F | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
R-squared | 0.1280 | 0.0834 | 0.1093 | 0.1080 |
Adjusted R‑squared | 0.1097 | 0.0642 | 0.0906 | 0.0893 |
Social preferences (
\(\phi _{S}\)), e. g. importance of proximity to social reference persons, have to be heavily compensated for, especially in the scenario with an existing job alternative: if an individual has a distinctive affinity to familiar reference persons, the observed internal mobility premium is 13 percentage points higher.
21 Notably, proximity to family loses all explanatory power and it is only proximity to peers which retains its predictive power in the internal unemployment scenario. The network of friends has a higher value, e. g. a peer network can provide information on job openings. Overall, this lends strong support in favour of the psychic costs hypothesis—if existing social ties are especially relevant, people expect to be compensated more copiously for the discomfort of moving and being apart from familiar reference persons. The coefficients’ relative size is in line with findings of Dahl and Sorenson (
2010), who documented technical workers’ high valuation of proximity to their parents or former classmates.
22 This further suggests that factors of high relevance in a real-world context can also be uncovered in an analysis of expected ex ante premiums.
Previous mobility experiences (\(\chi\)), supposed to strengthen adjustment capabilities (\(\gamma\)) in the model, are indeed associated with lower expected mobility premiums. Participants who spent time abroad expected a 4.2 to 4.7 percentage point smaller internal mobility premium. Those who displayed higher levels of educational mobility, e. g. by choosing a study location 100 kilometres beyond the closest alternative, feature a 2.4 to 2.5 percentage points diminished ex ante mobility premium. Across specifications, residential mobility during adolescence does not exhibit any explanatory power—the impact of mobility experiences in the distant past seem to fade out over time. In the domain of adjustment capabilities one can observe some differences too, especially between the scenario assuming existing job alternative and the scenario assuming unemployment. Only in the first one, individuals with short-term cross-border mobility experience (exchange participation) reduce the expected mobility premium. This effect can be observed for both the internal and the cross-border scenario.
Across all four scenarios, adaptability to new circumstances, a measure hypothesised to impact on adjustment capability and related to the concept of hedonic adaptation (Frederick and Loewenstein
1999; Graham and Oswald
2010) proves to be a predictor of inflated mobility premiums. In both internal migration scenarios, individuals rating themselves as least adaptable to new circumstances expect on average mobility premiums that are 3.2 percentage points above those of the reference group, consisting of respondents of medium adaptability. This number varies between 8.9 and 10.3 percentage points in the cross-border scenarios.
With respect to personality traits in a narrower sense, the evidence is mixed. The Big-Five personality trait agreeableness, for instance, displays a similar level of explanatory power for internal mobility premiums: Most agreeable individuals ask for an additional premium of ca. 3 percentage points.
23 If these individuals expect episodes of labour mobility to be prompted by a future employer, they might expect a compensation for showing such distinct form of commitment to the requirements of the job. And indeed, there is evidence that agreeableness and job performance are positively correlated (Mount et al.
1998), respectively agreeable individuals evince also higher levels of job involvement (Liao and Lee
2009). Openness to experience is the second Big-Five trait which displays significant effects in the internal migration scenario, assuming an existing job alternative. Both groups, those scoring highest and those scoring lowest in this trait feature lower mobility premiums, however, only the first is in line with the hypotheses presented in Sect. 3.3. Risk attitude does not explain any variation in case of internal mobility premiums. Least patient individuals, however, ask for a 3.6 percentage point mobility premium in the try-your-luck scenario—their focus on the present may lead to an overemphasis of contemporary monetary compensation relative to the creation of long-term perspectives.
24
Cross-border mobility premiums are not only larger in absolute terms, but feature a higher elasticity with respect to personality and preference parameters (Table
3): significant coefficients in the cross-border specifications are typically two or three times the size of the corresponding coefficient in the internal migration scenarios.
Important factors are once again previous mobility experiences and adaptability, both fostering adjustment capabilities. Beyond that, English language proficiency (
\(\Uplambda\)) is also significantly related to cross-border mobility premiums in the alternative job scenario: highest levels of language proficiency (native-speakers and those speaking fluently in all situations), are paralleled by reduced mobility premiums by more than 14 percentage points. Contrasting these results with the OLS model comparison in Table O.2 (online appendix) provides an explanation why English skills display no significance in the cross-border unemployment scenario: in the specification without previous mobility experiences, English proficiency is highly significant too. This suggests that language proficiency and previous mobility are interrelated and act jointly as facilitators to future cross-border mobility.
25
Social preferences prove to be robust predictors of cross-border mobility premiums, accordingly to the modelling approach of psychic costs. People who value their existing social ties strive to maintain them: Individuals with highest preference for being close to family and friends expect a 27 (given unemployment) to 34 (given job alternative) percentage point cross-border mobility premium. Those who are in a relationship feature in contrast to the internal scenarios now a markedly positive premium (6.6 to 7.9 percentage points). Whilst internal work migration over, by all likelihood, a shorter distance would in principle allow a weekend relationship, this would probably change when a cross-border move is considered. Perceived psychic costs in such a cross-border scenario would be substantial. Hence, to tip the scale in favour of inducing geographically mobile behaviour requires a larger weight, corresponding to a higher mobility premium in both scenarios.
Moving to another country might be considered as a relatively radical change, especially in the case of try-your-luck migration with a job alternative back home. This can be seen in the alternative employment scenario: in order to consider a labour market in another country, least risk prone individuals expect on average a cross-border mobility premium of 8.1 percentage points.
Turning to location-specific conditions at the origin, the significant proxies for amenity levels display the expected signs across scenarios. The lower the degree of accessibility of agglomeration centres, measured as longer travel time by train, the lower the expected mobility premium. For one, this points towards a fundamental value of being geographically well connected and having access to metropolitan markets or amenities.
26 But then, in conjuncture with an insignificant coefficient for accessibility by car, this result suggests that cars are not the crucial means of transportation for the surveyed cohort. The provision of public services, accounted for as public employees in relation to population does not exhibit a significant association across scenarios. The most hedonic amenity measure (recreational area per capita) is only significant in the cross-border scenario assuming an existing job alternative.
With respect to local economic conditions, the hypothesised dampening effect of higher unemployment rates at origin emerges only in the cross-border scenarios: a one percent increase in the unemployment rate implies individuals lower the expected cross-border mobility premium by around 3 percentage points. GDP per capita and the price level proxy (building land prices) at the district level yield a conspicuous result at first glance. One would have expected that, controlling for unemployment risk, individuals from relatively richer regions would request higher compensations. Albeit, there is a possible explanation for this result: if individuals from high income districts have a more wealthy background, their overall financial position could be more favourable so they might put less weight on potential income gains from migration. Building land prices, on the other hand, show the opposite sign compared to the hypothesis on commodity price levels. This, however, can be reconciled acknowledging that this proxy seems to be primarily a measure for housing prices (thus in the end rents as well): the results are now consistent with the literature on compensating differentials where local amenity levels can be capitalised into housing prices (Graves
1983). People from municipalities where building prices are one standard deviation higher expect on average an additional mobility premium of almost 7 percentage points.
27
5.3 Sensitivity checks
This research’s pivotal point are ex ante mobility premiums, which shine a light on individuals who might expect especially high compensation levels in order to relocate to a spatially distinct labour market. A first sensitivity check is applied to cope with the hypothetical nature of the underlying scenarios, in which these mobility premiums have been elicited. Undoubtedly, a hypothetical willingness to migrate does not always coincide with a subsequent actual migratory decision (Lu
1999).
The theory of planned behaviour (cf. Ajzen
1991) provides further guidance regarding the circumstances such that a hypothetical statement can be interpreted as reliable precursor of actual behaviour: assuming a person has
actual behavioural control over an outcome, stronger
intentions together with more pronounced levels of
perceived behavioural control would result in a higher likelihood that someone actually performs a certain behaviour. Conveying this concept to the migration scenarios at hand, actual behavioural control merely implies that someone was physically able to migrate and had the (financial) resources to do so. Following this idea, sensitivity check (A) integrates factors which are requirements such that planned (or hypothetical) behaviour would converge towards actual behaviour. An implication of the theory of planned behaviour would then be that the measures for perceived behavioural control and migration-related intentions should display explanatory power with respect to expected mobility premiums. If the main findings from the scenario-specific results prove to be robust regarding the inclusion of these important behavioural determinants, the inference drawn in the previous section would gain in validity.
Based on this theoretical ground, two new components are introduced: the first is a measure of perceived behavioural control (
\(\theta _{R}\)), i. e. the perceived probability of succeeding at a given migratory path. This perceived success probability is proxied by individuals’ assessment regarding the riskiness of a specific move to another state or another country. The second component (
\(\theta _{M}\)) captures migration intentions, which are integrated as expected likelihood of moving to another state (or country in Europe) in the first five years after graduation. Past mobility behaviour or habits, additional important precursors of behavioural outcomes (Connor and Armitage
1998), have already been included in the previously discussed specifications.
Table
5 reports the results of this first robustness check. The main findings of the empirical analysis (Table
3), i. e. the importance of psychic costs and adjustment capability, prove to be robust. Yet, the sensitivity check yields directly interpretable significant coefficients too: individuals who assess a certain move to be hardly risky at all expect a lower premium, albeit not in the internal unemployment scenario. Moreover, the underlying item, directly addressing subjectively perceived riskiness of a specific mobility form, absorbs more variation than the baseline risk variable, referring to individuals’ willingness to take risks in the career domain. Secondly, the less (more) inclined someone is to move within the first five years after graduation to a certain destination the higher (lower) the respective mobility premium: prospective labour market entrants, freshly graduated from university, who had no prior intention to move to another regional labour market, ask for an especially high premium. Given the rich set of controls, this inflated premium is mostly attributable to an extremely pronounced place attachment amongst the future highly-skilled labour force, and hence, it is required to overcome a sort of internal resistance against any form of migration behaviour. Another result is worth mentioning, as coefficients of high levels of English proficiency are now smaller and insignificant. This is not contrary to the claim that English as
lingua franca fosters successful socio-cultural or labour-market integration abroad, for the following reason: better English skills reduce the likelihood of post-migration hardships and transaction costs abroad, thus increase the likelihood of a successful migratory event. When controlling directly for expected riskiness of a move to another country, the related variation is no longer absorbed by the facilitator ‘language skills’, but by the corresponding control variable.
A second sensitivity check (B) addresses aspects of labour market readiness. Low levels of labour market readiness could be associated with a lack of information on how employers value labour and qualifications. This can translate into unrealistic wage expectations, and thus, ex ante mobility premiums which would be either disproportionately scaled up or down.
28 Two groups displaying low degrees of labour market readiness come to mind: respondents who have not yet gained any labour market experience and individuals who recently entered university, hence, have no urgent need to think actively about job search and form salary expectations. The opposite can be expected of those already being enrolled in a masters’ programme, since they are likely to enter the labour market within the next two years. To evaluate whether labour market experience might affect wage-related considerations, and thus the mobility premium, a vocational training variable is added. It is supplemented by a variable containing information on general labour market experience (full-time, part-time or mini-job and none). Those who already gathered full-time working experience, and thereby received a payroll, might have a more realistic knowledge about how the labour market values their skills.
While neither the essential baseline results nor those from sensitivity check (A) change for the internal migration scenarios, labour market readiness is informative with respect to the process of forming wage expectations (Table O.5, online appendix). In the internal scenarios, those who already advanced to their masters’ studies expect across labour market scenarios 8.5 to 10 percentage points lower mobility premiums. Previous work experience, however, does not influence individuals’ expectations considering internal migration scenarios. This finding is reversed for the cross-border scenarios, where those with some work experience (part-time or mini-job) expect a significant positive premium in the try-your-luck scenario. A more in-depth investigation of differential effects of labour market readiness in a split-sample analysis (full-time vs. no full-time work experience sample, Table O.6) reveals a varying degree of importance of previous mobility experiences and social preferences. Stays abroad (or exchange participation) are significant predictors of mobility premiums only for those without full-time work experience. Educational mobility, i. e. the selection of a more distant university is significant for the group with more pronounced labour market readiness. With respect to social preferences, inflated premiums are exclusively observed for those without full-time work experience.
Further sensitivity analyses investigated the degree of co-linearity of independent variables, potentially inflating standard errors. All individual-specific variables were found to have a variance inflation factor (VIF) far below five. Only two variables featured a VIF above the critical threshold of 10 (GDP per capita and building land prices). Table O.1 and Table O.2 (online appendix) document that the inclusion of these two variables does not alter the overall patterns regarding size or significance of the individual-specific variables.
Furthermore, I re-estimated the scenario-specific mobility premiums using quantile regression techniques. The obtained estimates for the three analysed quartiles (
\(q=0.25\),
\(q=0.50\) and
\(q=0.75\)) inform about the estimates’ sensitivity with respect to the distribution of the dependent variables and the impact of outliers (Table O.3 and Table O.4, online appendix).
29 The overall patterns are comparable to the results from the ordinary least squares regressions, although coefficient sizes vary foreseeably across quantiles.
Ultimately, all applied sensitivity analyses document the robustness of the essential findings: expected mobility premiums vary substantially across individuals.
30 This variation is largely due to those personality-related aspects which either foster adjustment capability or heighten psychic costs.