The relationship between height and leadership: Evidence from across Europe

Journal Pre-proof About the Relation between Height and Leadership: Evidence from Europe Felix Bittmann

PII:

S1570-677X(19)30076-0

DOI:

https://doi.org/10.1016/j.ehb.2019.100829

Reference:

EHB 100829

To appear in:

Economics and Human Biology

Received Date:

20 March 2019

Revised Date:

31 October 2019

Accepted Date:

14 November 2019

Please cite this article as: Bittmann F, About the Relation between Height and Leadership: Evidence from Europe, Economics and Human Biology (2019), doi: https://doi.org/10.1016/j.ehb.2019.100829

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About the Relation between Height and Leadership: Evidence from Europe

Felix Bittmann ([email protected]) – Corresponding Author 0000-0003-0802-5854 (ORCID) Otto-Friedrich-Universität Bamberg Feldkirchenstrasse 21 96045 Bamberg, Germany Tel. 0049-(0)-951-8631442

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Highlights  The study investigate the association between height and leadership in Europe.  Height is positively associated with leadership for women.  There is no effect for men when controlling for education and occupational position.  For women, absolute and relative height are about equally strong.

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Abstract: To better explicate the well-researched finding that taller individuals have higher wages on average, potential mechanisms should be studied in detail. The present analysis investigates the relationship between height and the probability of being in a leadership position in the workplace using multinational European Social Survey data from 19 countries. Studying full-time, employed individuals between 20 and 55 years of age reveals considerable country differences which is beneficial for the estimated multilevel models as variation is increased. The results indicate a statistically significant effect whereby women are 0.15 percentage points more likely to be in a leadership position for each additional centimetre of absolute height when controlling for education and occupational position whereas there is no effect for men. In order to study the relevance of absolute vs relative height, which is the difference to the local peergroup, regional data is utilized. The main findings are that there is no effect of relative height for men but a statistically significant effect for women. For them, absolute and relative effects are about equally strong.

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Keywords: Height, social comparison, leadership, European Social Survey.

JEL: C21, D6, I10.

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Word Count: 6997.

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1 Introduction Physical height is one of the most prominent and outstanding characteristics that is obvious in any form of direct social interaction. Consequently, the effects of height on a large set of outcomes have been well studied in the social sciences. Just to name a few, being tall is associated with higher educational attainment (Magnusson, Rasmussen, and Gyllensten 2006), higher intelligence (Sundet et al. 2005), a lower risk of suicide (Magnusson et al. 2005), reporting higher levels of happiness (Carrieri and De Paola 2012; Deaton and Arora 2009) and higher wages (Kim and Han 2017; Case and Paxson 2008; Cinnirella, Piopiunik, and Winter 2011; Heineck 2005; Herpin 2005; Hübler 2009; Lundborg, Nystedt, and Rooth 2009; Persico, Postlewaite, and Silverman 2004; Böckerman 2017). As the number of cited sources underlines, especially this last aspect has been studied in detail,

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drawing data from a large number of different cultures. However, the mechanisms that might elucidate this phenomenon are less understood. Why do tall people earn more on average? The explanations why tall people enjoy these positive outcomes are quite controversial and disentangling the mechanisms that are accountable for these effects is a major challenge. Scientists are usually not able,

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at least in observational studies, to monitor all factors that affect the studied outcome (wages) which might introduce spurious correlations. For example, many studies show a quite strong effect of height on wages which is significantly reduced after other factors, like intelligence, cognitive ability or

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educational attainment, are introduced into the models. One central chain of reasoning is that tall people display these positive outcomes as they are more often in leading positions at the workspace

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and have responsibility for subordinates (Lindqvist 2012). This appears to be a highly relevant starting point for further research. However, there are more open questions, for example, the effects of gender. Many studies only include men in their samples (often due to data restrictions), therefore, the effect

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for women is less researched. Do they also profit from being tall or are the effects quite different? These questions deserve more attention, especially when equal rights at the workplace are still an

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issue many countries have to deal with. Lastly, the question arises whether absolute height or relative height is more significant when examining the effects of being tall. This aspect is crucial as it also

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refers to the entanglement of a set of confounders: is height important because it is a good predictor of health and intelligence (absolute value) or is it important for selecting the individuals who are taller than their peers and therefore perceived as more dominant (relative value)? Previous studies indicate that this relative measure can be of great relevance and deserves more attention from researchers (Stulp et al. 2013). In summary, the paper has three main research questions: •

How does height affect the probability of being in a leading position in the workplace?

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Is the effect of height similar for men and women or are interactions present? 2

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Is absolute or relative height a better predictor of being in a leadership position?

2 Theory and hypotheses

Previous studies clearly indicate that taller people are more likely to be selected for leading or managerial positions (Egolf and Corder 1991; Judge and Cable 2004; Lindqvist 2012; Stogdill 1948). In the USA, a consistent finding is that presidents, senators and CEOs are taller than the average man (Etcoff 2000). To build adequate theoretical arguments that allow the derivation of testable hypotheses, one should recall that height is of relevance for humans due to two main factors: firstly, height is a useful property that has direct benefits and secondly, height is a proxy for other relevant

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but less visible properties. The first aspect refers to the argument that taller people are stronger on average, as they can carry heavier objects or are stronger, which might be relevant for conflicts or fighting (Carrier 2011). Experiments indicate that taller individuals are more likely to win a dyadic confrontation (Stulp et al. 2015) and are less likely to react to dominance signals of other men

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(Watkins et al. 2010). It is known that height is associated with dominant and assertive personalities (Melamed 1992; Young and French 1996; Zebrowitz 2013). Consequently, it can be argued that taller men are more likely to emerge as leaders due to their high dominance status in the social hierarchy.

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Experiments also indicate that females prefer tall men as potential partners due to their strength and abilities (Salska et al. 2008; Yancey and Emerson 2016). Another line of argumentation refers to one

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school of thought in the literature about cognition, which argues that conceptual thinking is partly based on bodily morphology and action (Schubert 2005). To give a simple example, words like “up” or “large” are usually associated with dominance and power; while the contrary is true for the opposite

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words, like “down” or “small” (Giessner and Schubert 2007). The second line of reasoning does not refer to the actual benefits of being tall but acknowledges that tallness is correlated with other useful

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abilities. For example, individuals who were malnourished as children rarely grow tall due to the consequences of the shortage of calories and vitamins. Consequently, being tall is a proper indicator that someone is healthy and well nourished (Mueller and Mazur 2001; Silventoinen, Lahelma, and

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Rahkonen 1999). Additionally, many studies report the correlation between height and intelligence (Beauchamp et al. 2011; Generation Scotland et al. 2014). As intelligence is undoubtedly of great relevance even today, taller people might more often become leaders as others perceive them as more able and intelligent (Blaker et al. 2013; Hensley 1993). Height then might be a positive factor for leadership as other individuals pick them more often due to their physical abilities, health and perceived cognitive skills. Based on these assumptions, one can formulate hypothesis one: the taller an individual, the larger the probability that this individual will be in a leadership position.

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Regarding the second research question, it seems important to underline that height is a prominent feature of sexual dimorphism in humans, which means that gender differences are present in all races and men are usually taller than women (Gray and Wolfe 1980). The sources of these differences might be attributable to different social roles in ancient times, as men were hunters and their height was a factor of importance, while women in their social roles or as gatherers had fewer benefits of height.1 Consequently, tall men were probably the most successful ones in hunting or fighting due to their strength, while height was not an outstanding factor to recognize the most able women. However, as long as health and intelligence are still correlated with height in women, it might still be of relevance. While one study reports that the effect of height on leadership is stronger for men than for women (Blaker et al. 2013:22), a meta-study does not find significant gender differences since both men and

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women profit equally from height (Judge and Cable 2004). In summary, there is only weak evidence that supports gender differences for the effect of height. Consequently, one would expect that height has about the same effects for men and women (Hypothesis 2).

Finally, the question arises whether height is more important as an absolute factor, that is, is every

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centimetre more of height is of importance, or rather a relative one, meaning that the absolute size is unimportant as long as one is taller than one’s peers. On the one hand, height seems important to

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signal health and intelligence, which would favour absolute height. On the other hand, humans as social animals pay a great deal of attention to the comparison with others and rankings and hierarchies

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evolve in almost all cultures, which would favour relative height as more important. One study, which investigates the role of presidential candidates finds that relative height, that is the height in comparison to the opponent, is significantly correlated to votes received (Stulp et al. 2013:163). This

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finding is also valid outside the US, as politically leaders are usually taller than the average height in a country (Murray and Schmitz 2011). Another study, that does not look at leadership but happiness, finds that happiness depends on both absolute and relative height, which at least gives some hint that

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both measurements might be relevant (Carrieri and De Paola 2012:296). Heineck (2005:478-481), who investigates the relation between height and income in Germany, reports that absolute height is

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a predictor of wages for both men and women, while adding the relative measurements to the model only has a significant effect for males. However, there are no relevant studies that actually compare the effect of absolute and relative height with regard to leadership to test which measurement provides the stronger effects. Consequently, due to the lack of a clear theoretical foundation or previous results, it seems best to avoid formulating a directed hypothesis and leave this to the empirical outcomes.

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One should not forget that height is also a cost-factor for humans as taller individuals burn more calories and, therefore, have a higher calorie requirement, which might be of disadvantage in times of famine.

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The current study is designed to contribute to the literature concerning the following aspects: firstly, testing and replicating previous studies, which were often done using only the male population of one specific country with a much broader sample (more countries and both genders), which should improve external validity. Secondly, testing for interactions between gender and height and thirdly, attempting to answer the question of whether absolute or relative height is of greater relevance.

3 Data and empirical model All findings are based on the European Social Survey (ESS) round 7 (2014, edition 2.2) which provides a rich set of relevant variables (The ESS Data Archive 2018). This survey is conducted every

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two years in a varying number of European countries. Round seven includes 21 countries with a total of 40,185 participants who are surveyed face-to-face. Note that both Hungary and Estonia are not included in any of the following analyses since information is missing on relevant variables (education and household income). The average response rate is reported to be 70.0% while there are large country differences. Height is self-reported in centimetres. To operationalize leadership, the

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following binary variable was constructed: respondents were classified as “leaders” if they were responsible for at least six subordinates at their workplace, reported a high degree of autonomy when

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organising the daily work (level of eight or larger on a scale from zero to ten) and worked in a company with at least ten employees. The cut-off points are the respective medians to reflect that

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leaders should be above average. Since this definition appears to some extent arbitrary, because other cut-off values could have been chosen, the resulting proportions were compared to statistics from the official European statistical agency (Eurostat). To do this, the share of persons in “managerial

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positions”, the EU definition, was compared to the computed proportions using the own definition of leadership. This official definition is widely used, for example, to compute the gender pay gap or

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access to leadership positions for women (Bourgeais 2017). The mean over the absolute differences for all available countries in Q2/2014 is 0.038, the standard

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deviation 0.028. This should signal that the chosen definition of leadership is quite similar to the results of official statistics and reflects the relevant aspects of “leadership”. Regarding the third hypothesis, a variable was constructed to measure relative height. This is made possible as there are 250 regions differentiated in the ESS, which map to the NUTS nomenclature. For example, for Germany, the regional indicators map to the 16 German federal states, while this is individual for each country and documented in detail in the ESS files. Within each region, the average height is calculated for both genders. The relative height is the difference between height of respondent and regional mean, calculated as a continuous variable in centimetres. Therefore, the personal reference group is constructed using region and gender. Other variables might also be relevant, for example, 5

age group or educational attainment (Carrieri and De Paola 2012:294; Lång and Nystedt 2018). However, as this would in some cases lead to very small reference groups, which might be problematic in the following analyses, it was decided to use these variables as controls instead (which is then not a perfect form of stratification but a (linear) approximation). To take into account the possibility that the height of the reference group may be correlated with the reference group’s economic conditions, leading to a spurious correlation, average household income per region is introduced as a control variable. Secondly, a large number of control variables were selected to account for spurious correlations. These variables are parental educational attainment, which can be seen as an indicator of social origin, the continent of birth of both parents, age (and age squared to account for the possible nonlinear

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influence of age), self-rated health, average income per region and gender. As explained above, the sex of a person might be of central interest and assumed effects are possibly quite different, therefore, all analyses are estimated separately by gender. This procedure allows for the flexible estimation of effects without introducing a large number of interactions. Finally, two relevant controls are

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introduced separately in a nested design: educational attainment of the respondent, which serves as a rough indicator of cognitive abilities, and the occupation (coded using the ISCO08 scheme with 48

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distinct categories). The level of education was recoded from ISCED with eight distinct categories into three categories for a more convenient interpretation (lower secondary or below as “low”, upper

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secondary to vocational as “medium”, any tertiary degree as “high”). As it is well known that height and intelligence are related, controlling for these two variables might be highly relevant in order to estimate the effects of height that are not transmitted by these. Formulated differently: by introducing

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these two well-known mediators between height and leadership, the pathway of their influence is blocked (Case and Paxson 2008; Spanhel 2010). Finally, the sample is restricted for all analyses. Firstly, following the idea of Heineck (2005), only adults in a certain age range (20 to 55) are retained

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as height can only be viewed as constant for people in this range. Secondly, only respondents who are employed full-time are kept for analyses. Thirdly, only complete cases are used in analyses

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(listwise deletion).

As data from many different countries are available, it seems crucial to account for the hierarchical data structure (respondents nested within countries). Consequently, country fixed-effects are included using country-dummies in every model presented. If one would ignore this aspect, standard errors and, consequently, p-values might be highly biased.

4 The effect of height on being in a leadership position The first and second research questions investigate the relation between absolute height and the probability to be in a leadership position. As the dependent variable is binary (leadership yes or no), 6

binary logistic regressions are estimated. All analyses are performed using Stata 15 (command logit).2 However, before advanced models are estimated, it is useful to have a brief look at the central descriptive statistics. Firstly, there is quite a large variation of height between countries and average male height ranges from 172cm (Portugal) to 180cm (the Netherlands). The results are depicted in Figure 1. More variables that are relevant are summarized for each country in Table 1. There is also quite a broad range of variation with respect to leadership positions: while for the total sample the probability is about 12.6% for men and 5.0% for women, there are values from 5.5% (Czech Republic) to 21.4% (Netherlands, both values for men). This variation should be beneficial for all analyses. In the next step, the relationship between average height and the probability of being in a leadership position is inspected separately for each country. To do so, a very simple regression is fit

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with height and gender (and their interactions) being the only explanatory variables. Only employed respondents aged 20 to 55 are included. Doing so allows us to check whether only some countries “drive” the results or whether there is a positive association in general for the entire sample. Results, which are purely descriptive, are shown in Figure 2. Although there are some countries with a

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negative association (for example Sweden or Poland for men), the overall majority displays a positive association between height and the probability of being in a leadership position. However, as there are no controls considered at all in this computation, results could be spurious and should only be a

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first impression of the data.

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Complete do-files are available upon request.

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Source: ESS7, own calculations. Height is measured in centimetres.

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>> Table 1 about here <<

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Figure 2: Relation between height and leadership for each country

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Source: ESS7, own calculations. The solid line represents the relation for men, the dashed line the relation for women.

As explained above, three nested models are calculated separately for men and women. The following

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model of probability of being in a leadership position (L) is estimated: 𝐿𝑜𝑔𝑖𝑡(𝐿) = 𝛽0 + 𝛽𝐻 + 𝛾𝐶 1 + 𝛿𝐶 2 + 𝜀

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where β0 is the intercept, H is the height of the individual in centimetres, C1 is a vector of the first set of control variables including age, age squared, continent of birth of the parents, educational attainment of the parents, self-rated health, average income per region and all country dummies. C2

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is a second vector that includes educational attainment of the respondent and current occupational position in the labour market (ISCO08). Results are displayed in Table 2.

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>> Table 2 about here <<

The first models, without any controls, find a significant positive influence of height on the probability of being in a leadership position. After introducing educational attainment and occupational position, these effects vanish for men, while they remain significant for women. The conclusion is that there are differences by gender and for women, height is a relevant factor, even when controlling for education and position in the labour market. As a robustness check, higher-order 9

terms for height were introduced (polynomial regression) to check whether the model fits improve. As this is not the case, one can conclude that the relation between height and leadership is indeed linear.

5 Absolute and relative height Concerning the third research question, it seems crucial to test whether the absolute or the relative height, that is the difference to the peer group, is more relevant for leadership positions. As outlined above, a variable was constructed to assess the relative influence. The models are built as above with being in a leadership position as the (binary) dependent variable. However, there is a minor difference: peer groups that included less than 16 persons were not included in the analysis, as the expected variance of height appeared to be too large, which might bias the results. Variation of regional height

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was assessed using scatterplots to ensure that there is, also within a country, enough variation and there is no strong separation by country. Results are presented in Table 3. The results are similar to the absolute height: while for men, the coefficient of relative height is positive in the first two models, it loses its significance when controlling for education and position. For women, however, the effect

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stays significant even in the saturated model.

Finally, to compare whether the effects are stronger for absolute or relative height, Average Marginal

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Effects (AMEs) can be used since they are comparable across models (Mood 2010). Results are reported in Table 4, which lists AMEs computed for the saturated models including all controls. For

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men, the AMEs are not statistically different from zero, which also becomes obvious as the 95% confidence intervals include zero. For women the results are quite different: firstly, both AMEs for absolute and relative height are statistically different from zero and the effect is stronger for relative

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height, while absolute difference between the two results is small. The interpretation is as follows: for women, an increase in one centimetre more with respect to the average regional height results in

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the likelihood of being in a leadership position increasing by 0.17 percentage points. For absolute

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height, this increase is only around 0.148 percentage points.

Robustness checks

To test the overall robustness of the results, it should be kept in mind that there is no general definition of “leadership” available and the operationalization could easily influence results. To account for this issue, a second definition of “leadership” was employed and calculations were repeated. To do so, the definition from Eurostat (official European statistical agency) for “managerial position” was used.

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This operationalization employed ISCO08 codes (2-digit codes 11, 12, 13, 14)3 (International Labour Office and International Labour Organization 2012:87). Since these codes are available in the ESS, the information can be included as a robustness check. To stay within the scope of this paper, only final results are displayed at this point. Note that the full model including all controls was used to compute the results, however, controlling for occupation was no longer possible since this variable was already used in the definition of leadership and would be, therefore, a perfect predictor of leadership. Table 5 lists results from the robustness analyses, results should be comparable to Table 4. >> PLACE TABLE 5 HERE <<

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Overall, the results are very similar between the two definitions of leadership, with one exception: using the Eurostat definition of leadership, there is a statistically significant effect left in the full models for men, albeit a weak one. This finding is illuminating with respect to the explanation of the effects of height and the suggested mediators. While in the first models occupations were used as a control variable (no effect left for men), this was not possible in the robustness check (significant

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effect for men present). One explanation is as follows: taller men are selected more often into leadership and managerial occupations and profit from their height, however, when controlling for

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occupation, the effect vanishes, and when comparing managers to other managers, height is not beneficial any longer. This interpretation is in line with findings from previous studies (Case and

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Paxson 2008). The robustness check indicates that most results are stable and some differences can be explained by the occupational position in the labour market. As a second robustness check, it was tested whether results change when some countries are omitted from the analyses. To do so, the

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regression models with all covariates were repeated, each time leaving out one country. By doing so it can be tested whether one country is accountable for the majority of the effect. These tests are estimated for the complete models (M3, M6, N3, and N6). In all cases, the results do not change

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significantly, meaning that the size of the coefficients is similar and significant results always stay significant (p < 0.05), while the insignificant results always remain insignificant. Therefore, one can

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conclude that no single country is responsible for the findings but that they are rather robust.

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These codes represent Chief Executives, Senior Officials and Legislators (11), Administrative and Commercial Managers (12), Production and Specialized Services Managers (13) and Hospitality, Retail and Other Services Managers (14).

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6 Discussion The main goal of the analyses was to investigate the relation between height and the probability of being in a leadership position. Using multilevel data from 19 European countries made this possible due to a large number of observations and a high variation between and within countries. Regarding the first two research questions, the first models (M1 M2, M4, and M5) show that height does have a positive significant effect on the probability of being in a leadership position for both men and women. However, these results change when controlling for educational degree and occupational position in the labour market, two well-known mediators between height and leadership. For men, the effects vanish and are no longer significant. For women however, they become weaker but remain significant. This may mean that absolute height does not have an independent effect on leadership for

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men when educational degree and position in the labour market are taken into account. In other words, when two average men with the same degrees and in the same position are compared, there is no advantage to the taller man. For women, this result is different. Even after blocking the mediators education and occupational position, height is of relevance and every centimetre more in height

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results in a higher probability of being a leader. Consequently, hypothesis one is only partially accepted (only for women) and hypothesis two is rejected since only women profit from height in

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contrast to theoretical expectations, which highlights the present interaction. Regarding the third research question, it was tested whether absolute or relative height has the larger

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influence on leadership. To compare models and genders, AMEs were calculated for the saturated models including all controls as they allow for the comparison from a statistical point of view. For men, AMEs reflect the result of both absolute and relative coefficients and are not statistically

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different from zero. For women, the picture is, again, different and both absolute and relative AMEs are significantly different from zero. While the AME for relative height is slightly larger than the AME for absolute height, calculated confidence intervals for both point estimates clearly overlap.

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Therefore, the effect of absolute and relative height is about equally strong. The interpretation is that, neither for men nor for women, there is a difference between the importance of absolute and relative

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height, while only women profit from additional height. It does not make a difference whether the absolute height or the relative height, that is the comparison to the local peer group, is taken into account.

The results suggest that, for men, the effects of leadership are mediated through educational titles, which can be seen as a rough proxy for cognitive ability, and the position in the labour market. If these effects are controlled for, there is no significant effect of height remaining. This is the case for both absolute and relative measurements of height. As the robustness checks indicate, this might be especially due to the effect of occupation in the labour market and it could be the case that taller men 12

are selected into managerial occupations, but within these occupational categories, additional height is no longer beneficial. Overall, the robustness checks strengthen the general findings. At this point it is not evident why the findings are quite different for women as for them there are further pathways between height and leadership. No variable included in the study can explain away the effect of height for women. However, since the study includes many different European countries and the effects are quite robust in the nested design, one could argue that these are substantive effects. Future analyses should focus on this finding and expand it. Furthermore, it seems desirable to shed more light on the role of absolute and relative height since there is only very little evidence in the existing literature and further work seems necessary to answer this question satisfactorily. In addition, shedding more light on macro variables, like welfare state regimes or political systems, appears like a highly relevant

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starting point for future research endeavors. How are these macro factors related to the effects of height and are interactions present? Using this question as the basis for a new research project might be highly rewarding and should be addressed in the future.

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Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Declarations of interest: none.

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Tables Table 1: Descriptive statistics by country

CZ DE DK ES FI FR GB IE IL LT NL NO PL PT SE SI

Age (Mean) 49.22 (18.06) 46.94 (18.97) 47.37 (18.83) 46.56 (16.96) 49.90 (18.39) 48.13 (18.94) 48.54 (18.65) 51.31 (19.07) 49.88 (18.74) 52.19 (18.39) 49.39 (18.19) 47.65 (19.63) 49.73 (18.52) 50.74 (18.25) 46.75 (18.68) 47.30 (18.80) 52.90 (19.33) 49.70 (19.90) 49.58 (18.65)

Educ. Father 1.74 (0.59) 1.64 (0.75) 1.79 (0.63) 1.98 (0.50) 2.01 (0.59) 1.81 (0.75) 1.29 (0.79) 1.59 (0.71) 1.59 (0.67) 1.49 (0.78) 1.35 (0.73) 1.69 (0.67) 1.59 (0.69) 1.40 (0.81) 1.83 (0.72) 1.30 (0.75) 1.18 (0.75) 1.64 (0.67) 1.66 (0.63)

Educ. Mother 1.48 (0.58) 1.53 (0.69) 1.55 (0.60) 1.85 (0.49) 1.75 (0.58) 1.68 (0.79) 1.20 (0.53) 1.54 (0.69) 1.44 (0.61) 1.38 (0.64) 1.37 (0.59) 1.63 (0.74) 1.67 (0.70) 1.32 (0.62) 1.73 (0.78) 1.37 (0.61) 1.16 (0.46) 1.63 (0.71) 1.50 (0.61)

Education 1.91 (0.59) 1.95 (0.75) 1.95 (0.63) 2.00 (0.50) 2.12 (0.59) 2.10 (0.75) 1.64 (0.79) 2.01 (0.71) 1.96 (0.67) 1.90 (0.78) 1.80 (0.73) 2.12 (0.67) 2.00 (0.69) 1.91 (0.81) 2.15 (0.72) 1.74 (0.75) 1.51 (0.75) 2.04 (0.67) 1.95 (0.63)

ro of

CH

Leader Eurostat (%) 0.06 (0.24) 0.11 (0.31) 0.11 (0.31) 0.04 (0.21) 0.07 (0.26) 0.08 (0.27) 0.05 (0.22) 0.05 (0.21) 0.03 (0.18) 0.1 (0.3) 0.07 (0.25) 0.11 (0.31) 0.06 (0.23) 0.12 (0.32) 0.11 (0.31) 0.11 (0.32) 0.03 (0.16) 0.06 (0.24) 0.06 (0.24)

-p

BE

Leader (%) 0.06 (0.24) 0.10 (0.30) 0.11 (0.31) 0.04 (0.20) 0.13 (0.33) 0.10 (0.30) 0.08 (0.27) 0.09 (0.28) 0.11 (0.31) 0.09 (0.28) 0.06 (0.24) 0.09 (0.29) 0.05 (0.21) 0.13 (0.34) 0.13 (0.33) 0.05 (0.23) 0.09 (0.28) 0.12 (0.32) 0.10 (0.30)

re

AT

Height [cm] (Mean) 171.34 (8.88) 170.99 (9.08) 171.04 (9.08) 171.56 (9.22) 172.17 (9.57) 173.77 (9.59) 167.83 (9.48) 171.06 (9.44) 168.89 (9.18) 169.24 (10.03) 170.23 (9.41) 167.89 (9.27) 170.41 (9.21) 173.58 (9.21) 173.90 (9.46) 169.25 (9.02) 165.24 (9.22) 172.96 (9.63) 170.82 (8.95)

lP

Country

Jo

ur

na

Source: ESS7, own calculations. Education is measured in three categories from Low (1) to High (3). Standard errors in parentheses. Both definitions of leadership are included.

18

Table 2: Regression results of absolute height on leadership

Continent of Birth (Mother) Europe Africa Asia North America South America Continent of Birth (Father) Europe Africa Asia North America South America Education (Mother) Low Medium High Education (Father) Low Medium High

Fair

Very bad

ur

Average Income per Region

High

Jo

Country Fixed-Effects Occupation Fixed-Effects Constant Observations Pseudo R2 AIC

Ref. -0.358 (0.465) -0.106 (0.428) -1.265 (0.935) 0.081 (0.991)

Ref. -0.114 (0.497) 0.127 (0.463) -1.599 (1.046) 0.697 (1.015)

Ref. 0.193 (0.560) 0.092 (0.542) -0.513 (1.026) -1.583 (1.392)

Ref. 0.187 (0.573) 0.048 (0.564) -0.684 (1.031) -1.882 (1.462)

Ref.

Ref.

Ref.

Ref.

-0.015 (0.425) 0.401 (0.414) 0.888 (0.631) -0.720 (1.127)

-0.010 (0.459) 0.383 (0.446) 1.026 (0.693) -1.221 (1.162)

0.045 (0.563) 0.379 (0.521) -0.004 (1.017) 1.192 (1.041)

0.456 (0.568) 0.425 (0.542) -0.075 (1.023) 1.394 (1.077)

Ref. 0.244* (0.107) 0.511*** (0.153)

Ref. 0.152 (0.115) 0.276 (0.165)

Ref. 0.185 (0.136) 0.185 (0.194)

Ref. 0.074 (0.144) 0.004 (0.204)

Ref. 0.415*** (0.109) 0.261 (0.145)

Ref. 0.235* (0.118) -0.005 (0.158)

Ref. 0.226 (0.138) -0.036 (0.187)

Ref. 0.131 (0.145) -0.256 (0.195)

Ref. -0.312** (0.117) -0.567** (0.176) 0.386 (0.316) 0.489 (0.789) 0.112 (0.102)

Ref. -0.171 (0.123) -0.300 (0.183) 0.853** (0.328) 0.766 (0.806) 0.004 (0.106)

Yes No -12.013*** (1.802) 5522 0.039 2903.1

Ref. 0.244 (0.276) 0.563* (0.287) Yes Yes -7.002** (2.321) 5522 0.133 2683.8

Ref. 0.061 (0.092) -0.309* (0.140) -1.184* (0.524) -0.471 (1.077) 0.208** (0.072)

Ref. 0.196* (0.100) -0.059 (0.151) -1.170* (0.560) -0.552 (1.125) 0.107 (0.078)

Yes No -13.047*** (1.443) 6294 0.062 4349.5

Ref. 0.602** (0.194) 0.774*** (0.207) Yes Yes -7.909*** (1.612) 6294 0.207 3769.2

na

Bad

Education (Resp.) Low Medium

M6 0.024** (0.009) 0.084 (0.056) -0.001 (0.001)

lP

Self-rated health Very good Good

Women M5 0.028*** (0.008) 0.150** (0.054) -0.002* (0.001)

M4 0.029*** (0.008)

ro of

Age*Age

M3 0.005 (0.006) 0.173*** (0.048) -0.002** (0.001)

-p

Age

Men M2 0.016** (0.006) 0.266*** (0.045) -0.003*** (0.001)

re

Absolute Height [cm]

M1 0.015** (0.006)

Yes No -5.339*** (1.042) 6294 0.024 4485.5

Yes No -8.109*** (1.389) 5522 0.022 2915.4

Source: ESS7, own calculations. Reported are logits. Country and occupation dummies are not displayed due to the * space requirements. Standard errors in parentheses. p < 0.05, ** p < 0.01, *** p < 0.001

19

Table 3: Regression results of relative height on leadership

Continent of Birth (Mother) Europe Africa Asia North America South America Continent of Birth (Father) Europe Africa Asia North America South America Education (Mother) Low Medium High Education (Father) Low Medium High Self-rated health Very good Good

Ref. -0.114 (0.498) 0.125 (0.465) -1.596 (1.049) 0.688 (1.013)

Ref. 0.196 (0.559) 0.102 (0.541) -0.523 (1.027) -1.569 (1.395)

Ref 0.191 (0.571) 0.047 (0.563) -0.715 (1.029) -1.852 (1.462)

Ref. -0.018 (0.425) 0.433 (0.418) 0.881 (0.631) -0.694 (1.124)

Ref. -0.013 (0.460) 0.400 (0.448) 1.035 (0.697) -1.188 (1.160)

Ref. 0.056 (0.561) 0.395 (0.520) 0.025 (1.017) 1.176 (1.044)

Ref 0.482 (0.566) 0.450 (0.542) -0.035 (1.019) 1.379 (1.077)

Ref. 0.245* (0.107) 0.512*** (0.153)

Ref. 0.153 (0.115) 0.278 (0.166)

Ref. 0.188 (0.137) 0.212 (0.195)

Ref 0.081 (0.144) 0.036 (0.205)

Ref. 0.416*** (0.110) 0.256 (0.145)

Ref. 0.234* (0.118) -0.011 (0.159)

Ref. 0.230 (0.138) -0.076 (0.189)

Ref 0.136 (0.145) -0.295 (0.197)

Ref. 0.191 (0.100) -0.044 (0.151) -1.174* (0.560) -0.563 (1.125) 0.125 (0.080)

Ref. -0.315** (0.117) -0.551** (0.176) 0.385 (0.317) 0.494 (0.790) 0.137 (0.105)

Ref -0.174 (0.124) -0.281 (0.184) 0.857** (0.329) 0.786 (0.805) 0.019 (0.109)

Yes No -7.387*** (1.189) 5496 0.040 2881.7

Ref 0.227 (0.276) 0.548 (0.287) Yes Yes -3.099 (1.801) 5496 0.134 2664.4

lP

Very bad

ur

Average Income per Region

Jo

Country Fixed-Effects Occupation Fixed-Effects Constant Observations Pseudo R2 AIC

Ref. -0.361 (0.465) -0.127 (0.431) -1.268 (0.935) 0.078 (0.989)

na

Bad

High

N6 0.028** (0.009) 0.083 (0.057) -0.001 (0.001)

Ref. 0.054 (0.092) -0.302* (0.140) -1.192* (0.524) -0.476 (1.077) 0.226** (0.073)

Fair

Education (Resp.) Low Medium

Women N5 0.032*** (0.008) 0.148** (0.054) -0.002* (0.001)

N4 0.031*** (0.008)

ro of

Age*Age

N3 0.006 (0.007) 0.170*** (0.048) -0.002** (0.001)

-p

Age

Men N2 0.016** (0.006) 0.265*** (0.045) -0.003*** (0.001)

re

Relative Height [cm]

N1 0.014* (0.006)

Yes No -2.721*** (0.211) 6270 0.024 4464.1

Ref 0.629** (0.196) 0.810*** (0.209) Yes Yes -7.091*** (1.109) 6270 0.208 3748.1

Yes No -10.294*** (0.984) 6270 0.062 4328.205

Yes No -3.334*** (0.295) 5496 0.023 2894.7

Source: ESS7, own calculations. Reported are logits. Standard errors in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001

20

Table 4: Average marginal effects for absolute and relative height Panel A

Absolute Height Men 1

Women

2

3

4

5

6

Point Estimate (AME)

0.0015004*

0.0015654**

0.000399

0.0019792***

0.0019298**

0.001484**

Standard Error

0.0005792

0.0005818

0.0005504

0.0005586

0.0005653

0.0005432

95% Confidence Interval

0.0003651; 0.0026357

0.0004251; 0.0027057

-0.0006799; 0.0014778

0.0008842; 0.0030741

0.0008218; 0.0030378

0.0004193; 0.0025487

Panel B

Relative Height Men 2 *

3

4

5

0.0004938

0.0021519

0.0021524

0.0017107**

.0005641

0.0005706

0.0005479

0.0014462

0.001628

Standard Error

0.0005835

0.0005859

0.0005554

95% Confidence 0.0003025; Interval 0.0025899 Source: ESS7, own calculations.

0.0004796; 0.0027764

-0.0005947; 0.0015823

re lP na ur Jo 21

***

0.0010463; 0.0010341; 0.0006368; 0.0032574 0.0032707 0.0027846 * ** *** p < 0.05, p < 0.01, p < 0.001

-p

Point Estimate (AME)

***

6

**

ro of

1

Women

Table 5: Average marginal effects for absolute and relative height using a second definition of leadership Panel A

Absolute Height Men 1

Women

2 **

0.00160

3 **

0.001304

4 *

5 0.00133

6 **

0.001196*

Point Estimate (AME)

0.00168

Standard Error

0.000539

0.000541

0.00054

0.00507

0.000509

0.000507

95% Confidence Interval

0.000633; 0.00274

0.000545; 0.00267

0.000244; 0.00236

0.00073; 0.002727

0.000332; 0.00233

0.000204; 0.00219

Panel B

0.00173

**

Relative Height Men

Point Estimate (AME)

0.00149

Standard Error

0.00054

2 **

0.001484 0.00054

3 **

4

0.0011

*

0.00054

0.00159

**

0.000511

5

6

*

0.0011*

0.0013

0.000513

0.00051

ro of

1

Women

Jo

ur

na

lP

re

-p

95% Confidence 0.000433; 0.000412; 0.000120; 0.000595; 0.000246; 0.000124; Interval 0.00256 0.00255 0.00226 0.00260 0.00225 0.00213 Source: ESS7, own calculations. Note that occupations are not used in models 3 and 6 in both panels as they are controlled for implicitly by using a different operationalization for the leadership variable. The definition of leadership is based on ISCO codes (Eurostat definition of leadership). * p < 0.05, ** p < 0.01, *** p < 0.001

22