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Talented but lazy. The height-school premium among Cracow’s schoolboys in the interwar period
Economics and Human Biology 34 (2019) 252–256
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Talented but lazy. The height-school premium among Cracow’s schoolboys in the interwar period Bartosz Ogórek Faculty of Humanities, Pedagogical University of Cracow, ul. Podchora˛z_ ych 2, 30-124 Cracow, Poland
A R T I C L E I N F O
A B S T R A C T
Article history: Received 31 July 2018 Received in revised form 8 November 2018 Accepted 18 December 2018 Available online 21 December 2018
This study finds that a positive association between stature and academic performance measured by the grades for various subjects, the height-school premium, was present in a historical sample of 147 school boys attending a gymnasium (public secondary school) in Cracow, Poland, between the wars. This effect persists when controlling for a set of demographic and socio-economic variables, though the strength of the relationship is modest (0.018 higher average grade for Polish, 0.014 for mathematics, 0.016 for art, and 0.013 for the combined subjects with each centimetre of height). The differences found between the magnitude and significance of the height-premium in different school subjects could be a marker of unequal association between stature, and cognitive, social, and physical skills, suggesting at least a partial role of cognitive ability in this relationship. However, the effect visible at the school level is not consistent between different teachers of the same subjects, hence the mechanism behind the height-school premium in the analysed population to a large extent relied on the subjective judgment of the teachers, who could reward social skills but also discriminate against shorter students. © 2018 Elsevier B.V. All rights reserved.
Keywords: Height BMI Educational attainment Height-school premium Poland
1. Introduction The positive effect of stature on various aspects of human life has been demonstrated in numerous studies. Height premiums can be perceived in earnings (Persico et al., 2004; Case and Paxson, 2008; Kim and Han, 2017), mating (Nettle, 2002; Courtiol et al., 2010), educational attainment (Silventoinen et al., 2000; Magnusson et al., 2006; Meyer and Selmer, 1999), and mortality (Waaler, 1984; Barker et al., 1990; Jousilahti et al., 2000; Floud et al., 2011). This paper focuses on one specific type of height premium, namely the positive association between stature and school performance, described as the height-school premium (Cinnirella et al., 2011). While it is acknowledged that this effect is reflected in the higher educational attainment of taller individuals (Silventoinen et al., 2000; Case et al., 2009; Maurer, 2010; Huang et al., 2015; Szklarska et al., 2007), the precise nature of the relation between school success and physical height is still a matter for debate. In the broadest terms, there are three non-exclusive hypotheses which offer explanations for the effect of the stature on the academic performance. The first places the emphasis on the role of cognitive ability as the mediator between height and educational outcomes (Case and Paxson, 2008; Lundborg et al., 2014). One of the main arguments
E-mail address: [email protected] (B. Ogórek). https://doi.org/10.1016/j.ehb.2018.12.004 1570-677X/© 2018 Elsevier B.V. All rights reserved.
here is the fact that a correlation between height and cognitive skills emerges as early as age 3, i.e. before any potential preferential treatment at school, and remains largely unaltered with age (Case and Paxson, 2008). Higher values of both variables can stem from factors including better nutrition during the prenatal period, infancy, and childhood as proper diet may enhance the students’ performance at school (Taras, 2005). Since the school grades are scores of a different nature than objective test results, and are to a significant extent a reflection of teachers’ subjective judgments, some authors argue that the height-school premium is determined solely by non-cognitive (mainly social) skills. This explanation stresses that higher physical stature may indicate better social skills, as taller children build these more extensively than their shorter peers through engagement in various activities during adolescence (Persico et al., 2004). These skills are likely to be rewarded by teachers regardless of students’ academic performance (Cinnirella et al., 2011). The plausibility of this hypothesis is in part due to the fact that the association between stature and students’ grades is not equal across different settings. For example, Gorry (2017) found that preferential treatment of taller students occurs only in larger schools, where there is a significant degree of competition for the attention of teachers and peers. Finally, since stature is always relative, higher grades attained by taller students might be the effect of pure discrimination against shorter children, irrespective of any type of skills
B. Ogórek / Economics and Human Biology 34 (2019) 252–256
associated with height. Gilmour and Skuse (1996) found that shorter children do not differ from their peers in confidence, selfesteem or acceptance in a group, and moreover report similar social support from their teachers. Inconsistencies in the existence and intensity of the cognitive height premium demonstrated in the literature are also visible in the diachronic perspective. As this relationship varies among different birth cohorts (Magnusson et al., 2006), as well as across countries at different development levels (Guven and Lee, 2015), the occurrence of a height-school premium in historical settings is not obvious. Given the arguments presented above, the contribution of this study is as follows. Firstly, it puts the correlation between height and school performance in a historical context. This is the more important that historical data of this type, especially in the longitudinal setting, are rare (Komlos, 2006). Secondly, it will attempt to differentiate between different skills sets and the ways in which they are affected by height differentials. Cognitive, social, and physical skills will be proxied here by the school grades for various subjects (conduct, mathematics, language, art (drawing), and physical education) in a rather speculative manner. In addition a set of controls is used, with BMI as a measure of the current nutritional status, and number of hours of absences from school as a proxy for general and current health status among them, to see whether the height-school premium persists after accounting for potential confounders. Finally, the consistence of the results obtained from the global models is tested within the groups taught by specific teachers to assess the degree of teachers’ subjectivity. The identification of associations has primarily descriptive aims, as it is nearly impossible to establish causal links providing the explanations for the abovementioned mechanisms on the basis of the data used here. The remainder of the paper is divided into three sections: data and methods, results, and conclusion and discussion. 2. Data and methods The data used in this study are the product of the manual linkage of two kinds of archival records: anthropological measurement cards, and class registers. The measurement cards were generated as part of a scientific project led by Julian TalkoHryncewicz at the Jagiellonian University’s Institute of Anthropology from 1908 to 1933 (Jasicki, 1957). A vast set of variables (height and weight among them) as well as basic socio-demographic facts were recorded annually in several Cracow schools by anthropology students. The class registers are preserved in the National Archive in Cracow and contain information on students and their parents, but most notably on their final grades in different subjects. Grades were awarded on a scale of 2–5, where 2 was unsatisfactory (fail) and 5 was very good. The records for one school, Gymnasium [Public Secondary School] No. 8, in Cracow for the school years 1923/24–1930/31, were indexed based on matches in student name and surname, father’s name, and the geographic origin of the family. The resulting dataset forms an unbalanced panel, as individuals are not equally represented in the sample. It comprises 427 observations of 147 individuals, but the number of entries ranges from 1 to 5, with an average of 2.9. Lessons were taught by several different subject teachers. The teachers usually changed from year to year, so they were assigned to a certain phase of education rather than to a specific class (understood as group of students). In the sample, there were 20 different teachers evaluating conduct (class teachers), 7 for Polish, 5 for mathematics, and 4 for both art and physical education. The subjects analysed were to some extent already preselected, on the basis of their cognitive skills and standard of living, as the gymnasium formed one part of the non-compulsory
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secondary school level within the education system of the Second Polish Republic, and fees were charged for attending (Wagner, 2013). The fee for children of civil servants was reduced by half, and in the case of gifted, but poor students, full exemption was possible (Mauersberg, 1991). Students also had to pass an entrance examination. Gymnasium education lasted for eight years and ended with a matriculation examination (matura) which granted the students access to tertiary education. It is worth noting, however, that this phenomenon should not weigh heavily on the sample representativeness. The fees were as low as 30 zlotys per semester in the years 1925–27 to 55 zlotys in the latter period1. Many of the students in the sample paid only half fees or were even fully exempted due to their parents’ economic situation or occupation as civil servants. As a result, more than 30% of the schoolboys in the study had a guardian in the lowest group of professional skills (mainly labourers, unskilled manual workers, domestic and institutional service etc.) and almost 45% fell into the middle income bracket, which was dominated by civil servants and intellectual workers. As the main focus of the paper is height-related differences in the school performance between individual students, the between-effects model was adopted. The panels are formed by the successive measurements (one for each school year), and the group was defined at the level of the student, hence the measurements are nested within individuals. The model estimates the mean difference in the outcome variable based on the mean difference in the explanatory variables, which means it uses only cross-sectional information, ignoring the within group variation (Rabe-Hesketh and Skrondal, 2008). The argument for this choice might be strengthened by the fact, that the height variable produces nonconsistent effects as confirmed by the results of the Hausman test. While positive changes in height between individuals yields a positive effect for school performance, the growth of individuals over time shows a negative effect. This might be due to the level of difficulty of the education rising in parallel with the teaching course. Another reason for use of the between-effect estimator is the non-linearity of cognitive abilities and height, which has been confirmed in some studies (Teasdale et al., 1991; Heineck, 2009), and the exaggeration of the height divergences between the social strata during the teen years due to the variation in the timing of the growth spurt (Komlos et al., 1992; Steckel, 1995). 3. Results Tables 1 and 2 present the descriptive statistics of the variables used in the analysis. The mean grades obtained in the various subjects differ substantially, which could reflect either students’ specific predispositions or differences in evaluation style between teachers. Interestingly, the correlations between the scores from selected subjects vary from a reasonably high 0.578 (mathematics and Polish) to a low of 0.088 (Polish and physical education). This fact partially confirms the notion that grades directly reflect either different skills sets or teachers’ differing preferences. For the purposes of this study, the grades from Polish, mathematics, and art will be treated as a proxy for cognitive skills. The grading of conduct, i.e. classroom behaviour corresponding to binding cultural and social norms, and reflecting the quality of relationships with teachers and peers, will be used as a marker of social skills (Wentzel, 1991,1993; Welsh et al., 2001). The grade from
1 The average monthly wage for a bricklayer in Cracow in that period ranged from 260 to 312 zlotys, while a bricklayer’s assistant earned on average 102–146 zlotys per month. The average monthly salary in the printing industry was 458 zlotys for qualified workers and 115 zlotys for the female semi-skilled labour force (Derengowski, 1928).
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B. Ogórek / Economics and Human Biology 34 (2019) 252–256
Table 1 Summary of quantitative variables. Variable
Mean
SD
Min
Max
n
Grades: conduct Polish mathematics art physical education
4.26 3.48 3.07 3.87 4.13
0.70 0.78 0.73 0.76 0.80
2 2 2 3 2
5 5 5 5 5
427 427 427 427 427
Height (cm) at age in full years: 11 138.19 12 140.40 13 145.18 14 152.05 15 158.87 16 163.49 17 168.42 18 170.57 19 168.98 20 171.40 21 174.23 22 178.90
6.78 7.45 7.54 8.92 7.65 8.22 7.89 5.79 4.99 5.90 3.46 –
119.00 121.40 125.70 130.00 142.60 145.00 145.50 158.50 160.00 160.60 171.80 178.90
153.00 154.90 169.50 170.30 177.30 177.90 185.00 184.00 176.00 179.10 178.20 178.90
36 59 65 71 61 46 44 25 8 8 3 1
Birth year BMI Hours of absence
2.18 2.08 61.45
1909 13.42 0.00
1919 25.57 458.00
427 427 427
1913.73 18.59 59.71
Table 2 Summary of qualitative variables. Variable
Frequency
Percentage
Guardian’s profession low middle high
131 192 104
30.68 44.96 24.36
Guardian’s place of residence Cracow Cracow region more distant
349 45 33
81.73 10.54 7.73
physical education can be seen as a measure of overall fitness (Wittberg et al., 2009). Obviously, this delimitation is far from being exclusive and robust, since each grade may incorporate a reasonable amount of subjectivity or discrimination. The sample contains a large spread of heights, from 119 cm noted at age 11 to as much as 185 at age 17. The data captures the adolescent growth spurt, which among the students of the school took place on average between the ages of 13 and 15 (height increments equal to 6.87 and 6.82 respectively). The BMI is relatively high in the younger age groups, exceeding that for Austrian cadets at the end of the 19th century. For older age groups the Cracow schoolboys have a lower BMI, but it is still
higher than that observed among the West Point cadets (Komlos, 2006). On the contrary, the frequent and long absences of the students from school (an average of almost 60 lessons missed per year) points to the potentially suboptimal epidemiological situation in the sample. Since the variable ‘hours of absence’ shows significant skewness it was logarithmized for the regression analysis. The stature, occurrence of the peak height velocity, and BMI prove that the population examined enjoyed a relatively high standard of living, which could either reflect the actual robustness of Cracow’s children or confirm to a certain extent the expected selection in terms of social class and opulence. Despite that fact, the socio-economic structure of the sample is far from homogenous. The incidence of group one of the variable ‘guardian’s profession’ (‘low’), which comprises children of fathers with low-level professional qualifications (mainly caretakers, janitors and beadles, industry workers, day labourers, printers, minor officials, and simple craftsmen) is only slightly higher than that of group three (‘high’), which comprises elite categories: freelancers, engineers, officers, top-level management staff of offices and enterprises, restauranteurs, and property owners. The sample is dominated by group two (‘middle’), which was composed of pupils whose parents were officials, railwaymen (including engine drivers), farmers, teachers, tradesmen and master craftsmen. Despite that fact, each of the socio-economic groups is fairly represented in the study. Surprisingly, such distribution does not diverge strongly from the actual social and occupational stratification in Cracow during that time. According to the 1931 population census in the district where the analysed school was located, the lowest qualified category (labourers and domestic/homeworkers) constituted 48.1% of the inhabitants (compared to 30.68% in the study). Hence the underrepresentation of the lowest social stratum is not substantial. Table 3 presents the results of the basic models capturing the association between stature and school performance. The relation is controlled for the exact age at measurement and the potential cohort effect captured by the year of birth, as the cohort spans more than 10 years (1909–1919). Because of the character of the data and the model chosen, the results presented in the table should be read as follows: What is the expected difference in the mean grades of two students who differ by one centimetre in height while being identical in respect of all other covariates? Focusing on the response of each subject grade to height, it is clear that despite being modestly positive, the association is not always statistically significant. However for Polish, mathematics, and art, and for the average of all the grades used in the study, the relationship under investigation does occur significantly. A difference of one centimetre in height between students results in a 0.016–0.018 higher grade, which seems a very small increment, but it is worth recalling the strong heterogeneity of the students’ stature, especially at ages 12–17 (Table 1). It is worth noting that
Table 3 Regression of school grades on height, age and birth year. Conduct
Height (cm) Age at measurement Birth year Constant F-test Between R-sq. Number of groups (individuals) Number of observations
Polish
Maths
Art
coef.
p-value
coef.
p-value
coef.
p-value
0.008 0.144 0.107 203.328 4.45 *** 0.085
0.282 0.021 0.008 0.009
0.018 0.011 0.052 98.948 2.29 * 0.046
0.024 0.867 0.232 0.240
0.016 0.005 0.085 161.790 2.98 ** 0.059
0.036 0.939 0.041 0.043
Note: significance levels *** 0.01 ** 0.05 * 0.1.
coef.
0.016 0.106 0.077 148.002 6.65 *** 0.122 147 427
P.E.
Average
p-value
coef.
p-value
coef.
p-value
0.030 0.083 0.048 0.049
0.009 0.051 0.060 113.025 1.72 0.035
0.188 0.401 0.122 0.131
0.014 0.059 0.076 145.018 5.59 *** 0.105
0.012 0.189 0.009 0.010
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Table 4 Regression of school grades on height, age, birth year and control variables. Conduct
Height (cm) Age at measurement Birth year Guardian’s profession: middle high Guardian’s place of residence: Cracow region more distant BMI Log hours of absence Constant F-test Between R-sq. Number of groups (individuals) Number of observations
Polish
Maths
Art
P.E.
Average
coef.
p-value
coef.
p-value
coef.
p-value
coef.
p-value
coef.
p-value
coef.
p-value
0.009 0.150 0.106
0.248 0.021 0.010
0.018 0.004 0.055
0.044 0.960 0.214
0.014 0.027 0.094
0.083 0.688 0.028
0.015 0.149 0.095
0.047 0.018 0.017
0.011 0.084 0.070
0.165 0.173 0.071
0.013 0.082 0.084
0.019 0.075 0.004
0.073 0.271
0.563 0.071
0.157 0.310
0.261 0.060
0.193 0.281
0.146 0.074
0.171 0.135
0.165 0.351
0.275 0.363
0.025 0.012
0.174 0.272
0.058 0.012
0.092 0.331 0.030 0.012 200.980 2.38 * 0.135
0.634 0.070 0.404 0.501 0.011
0.082 0.337 0.012 0.003 104.885 1.53 0.092
0.700 0.093 0.760 0.895 0.222
0.107 0.139 0.003 0.007 178.780 1.52 0.091
0.596 0.465 0.933 0.719 0.030
0.338 0.268 0.019 0.021 181.625 3.31 *** 0.179 147 427
0.073 0.131 0.583 0.229 0.017
0.311 0.082 0.033 0.023 132.265 2.04 * 0.118
0.093 0.639 0.054 0.493 0.077
0.149 0.092 0.016 0.003 159.707 2.87 ** 0.159
0.283 0.484 0.527 0.815 0.005
Note: significance levels *** 0.01 ** 0.05 * 0.1.
Table 5 Regression of school grades on height, age, birth year and guardian’s profession, by teacher. Teacher A
Height (cm) Age at measurement Birth year Guardian’s profession: middle high Constant F-test Between R-sq. Number of groups (individuals) Number of observations
Teacher B
Teacher A
Teacher B
Teacher A
Teacher B
coef.
p-value
coef.
p-value
coef.
p-value
coef.
p-value
coef.
p-value
coef.
p-value
0.032 0.196 0.049
0.034 0.059 0.540
0.006 0.166 0.034
0.528 0.033 0.548
0.012 0.027 0.153
0.264 0.729 0.003
0.018 0.099 0.021
0.132 0.185 0.753
0.013 0.088 0.092
0.164 0.276 0.124
0.009 0.151 0.109
0.344 0.039 0.014
0.038 0.308 96.905 1.99 * 0.153 61 101
0.887 0.322 0.539
0.269 0.429 63.693 2.54 ** 0.161 72 146
0.108 0.028 0.561
0.072 0.354 292.381 3.30 *** 0.202 71 123
0.692 0.128 0.003
0.241 0.046 36.846 1.08 0.088 62 124
0.223 0.834 0.779
0.288 0.384 178.845 1.71 0.103 80 184
0.100 0.073 0.121
0.047 0.293 209.771 3.39 ** 0.151 101 179
0.772 0.122 0.014
Note: significance levels *** 0.01 ** 0.05 * 0.1. The teachers with the highest numbers of individuals and observations were chosen for comparison, in order to provide sufficient sample size.
height and basic controls account for between 3.5% (physical education) and 12.2% (art) of the variance in average grades between individuals (Table 4). Controlling for the socio-economic status of the student’s guardian (the father in 85% of cases, the mother in 11%, and another person in the remaining 4%), co-residence with the guardian, BMI, and hours of absence does not alter the relationship substantially. Again, the grades for Polish, mathematics, and art, as well as the average, bear a significant positive effect of minor strength, which could be a sign of a cognitive skills premium. In the case of conduct, Polish, mathematics and physical education, SES of guardian and place of residence seem to be better predictors of school performance, but they do not reduce the association of grades with height. Not surprisingly, a higher social position implies better average grades, and living away from the guardian, usually meaning in lodgings, a dormitory, or with more distant relatives, is related to poorer academic achievement. BMI as a control for current nutritional status, as well as hours of absence proxying current health status proves insignificant in each case. The explanatory power of the models rises with the implementation of additional controls, up to a variation of 18% between students’ grades in the case of art classes. Unexpectedly, the height-school premium is absent in the case of both physical education and conduct, both of which subjects could be perceived as requiring least cognitive skills, and being more related to social skills and overall fitness. The results suggested by the global models pointing to the role of cognitive skill in the height-school premium are tested against
the subjectivity of the teachers’ evaluations. If the mechanism described above is the only one in operation and it is universal, the positive association between height and school performance should be roughly constant across various teachers. Apparently this is not the case, as we can see from Table 5. The height-school premium is present only among students evaluated by certain teachers. These are teacher A for Polish and teachers A for maths and art (although here the effect proves insignificant). Conversely, among the students taught by teacher B in maths, the effect is reversed (though again the effect is insignificant). Some of the teachers (teacher B in Polish, teacher A in art) seem rather to reward other factors associated with the socio-economic status of the student. 4. Conclusion and discussion The results of the analysis points to several findings and conclusions. First of all, there is a discernible height-school premium in not only contemporary but also historical populations, which were usually influenced to a larger extent by high mortality and social inequalities. This could potentially indicate the robustness of the relation between stature and academic performance. The argument is strengthened by the persistence of the association after the inclusion of the set of controls. The various kinds of height premiums should thus be further researched in their historical settings in order to broaden our knowledge of
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B. Ogórek / Economics and Human Biology 34 (2019) 252–256
mechanisms universal to Homo Sapiens, as well as our understanding of societies in the past. This study proves the usefulness of archival records for inquiries of this type. The differences in the effect depending on school subject is compatible with the results of studies on contemporary populations. Case and Paxson (2008) reported that levels of cognitive test scores in childhood vary depending on the character of the test. In contrast to this study, they found that tests of mathematical skills were most responsive to height differences. Language scores, both verbal and non-verbal, proved to be slightly less conditional on height, while activities involving drawing and visual-motor coordination were the least sensitive to the height premium. The associations described in this paper are most visible in the case of language, followed by drawing, with mathematics in last place, but the differences are negligible. Moreover, the lack of consistency of the effect across different teachers indicates that the heightschool premium was heavily dependent on the subjectivity of the teachers’ assessment. While this finding does not fully dismiss the operation of the cognitive skills differentials, it does suggest a major role of the evaluator in the observed premium, as he/she could to varying degrees reward stature, social status, or noncognitive skills. This study is not free from major caveats, most of which tend to apply to the quantitative historical research. The lack of information as to the quality and selection methods of the preserved sources, and small sample size due to the timeconsuming nature of archival research and data processing, might be the main ones. Further effort should certainly be invested in building larger databases, which could produce more robust results. In addition, a considerable proportion of the correlation found here may be attributable to unobserved individual characteristics. The extension of the database towards census records, which would contribute information on sibship sizes and parents’ vital status as well as a more detailed description of the socio-economic status, could provide the solution to some of the problems. Funding This research was supported by the Faculty of Humanities of Pedagogical University of Cracow (subsidy for statutory research). Declarations of interest None. Acknowledgements ski and anonyI am grateful to John Komlos, Michał Kopczyn mous reviewers for their helpful comments, and also to Henryk Gła˛b for allowing me to use the archive of Department of Anthropology at Jagiellonian University. References Barker, D.J., Osmond, C., Golding, J., 1990. Height and mortality in the counties of England and Wales. Ann. Hum. Biol. 17 (1), 1–6. Case, A., Paxson, C., 2008. Stature and status: height, ability, and labor market outcomes. J. Polit. Econ. 116 (3), 499–532.
Case, A., Paxson, C., Islam, M., 2009. Making sense of the labor market height premium: evidence from the British Household Panel Survey. Econ. Lett. 102 (3), 174–176. Cinnirella, F., Piopiunik, M., Winter, J., 2011. Why does height matter for educational attainment? Evidence from German children. Econ. Hum. Biol. 9 (4), 407–418. Courtiol, A., Raymond, M., Godelle, B., Ferdy, J.B., 2010. Mate choice and human stature: homogamy as a unified framework for understanding mating preferences. Evolution 64 (8), 2189–2203. Derengowski, J., 1928. Warunki pracy. Statystyka pracy 7 (2), 137–168. Floud, R., Fogel, R.W., Harris, B., Hong, S.C., 2011. The Changing Body: Health, Nutrition, and Human Development in the Western World Since 1700. Cambridge University Press. Gilmour, J., Skuse, D., 1996. Short stature–the role of intelligence in psychosocial adjustment. Arch. Dis. Child. 75 (1), 25–31. Gorry, D., 2017. The influence of height on academic outcomes. Econ. Educ. Rev. 56, 1–8. Guven, C., Lee, W.S., 2015. Height, aging and cognitive abilities across Europe. Econ. Hum. Biol. 16, 16–29. Heineck, G., 2009. Too tall to be smart? The relationship between height and cognitive abilities. Econ. Lett. 105 (1), 78–80. Huang, Y., van Poppel, F., Lumey, L.H., 2015. Differences in height by education among 371,105 Dutch military conscripts. Econ. Hum. Biol. 17, 202–207. Jasicki, B., 1957. Osrodek Krakowski W Latach 1908–1956: Sto Lat Antropologii stw. Wyd. Nauk.. Polskiej, 1856–1956. Pan Jousilahti, P., Tuomilehto, J., Vartiainen, E., Eriksson, J., Puska, P., 2000. Relation of adult height to cause-specific and total mortality: a prospective follow-up study of 31, 199 middle-aged men and women in Finland. Am. J. Epidemiol. 151 (11), 1112–1120. Kim, T.H., Han, E., 2017. Height premium for job performance. Econ. Hum. Biol. 26, 13–20. Komlos, J., 2006. The height increments and BMI values of elite Central European children and youth in the second half of the 19th century. Ann. Hum. Biol. 33 (3), 309–318. Komlos, J., Tanner, J.M., Davies, P.S.W., Cole, T., 1992. The growth of boys in the Stuttgart Carlschule, 1771–93. Ann. Hum. Biol. 19 (2), 139–152. Lundborg, P., Nystedt, P., Rooth, D.O., 2014. Height and earnings: the role of cognitive and noncognitive skills. J. Hum. Resour. 49 (1), 141–166. Magnusson, P.K., Rasmussen, F., Gyllensten, U.B., 2006. Height at age 18 years is a strong predictor of attained education later in life: cohort study of over 950 000 Swedish men. Int. J. Epidemiol. 35 (3), 658–663. Mauersberg, S., 1991. Społeczne uwarunkowania doste˛pu do szkoły w drugiej ski, K. (Ed.), Oswiata, szkolnictwo i wychowanie w latach rzeczypospolitej. In: Poznan II Rzeczypospolitej. Uniwersytetu Marii Curie-Skłodowskiej, Wydaw, pp. 31–42. Maurer, J., 2010. Height, education and later-life cognition in Latin America and the Caribbean. Econ. Hum. Biol. 8 (2), 168–176. Meyer, H.E., Selmer, R., 1999. Income, educational level and body height. Ann. Hum. Biol. 26 (3), 219–227. Nettle, D., 2002. Height and reproductive success in a cohort of British men. Hum. Nat. 13 (4), 473–491. Persico, N., Postlewaite, A., Silverman, D., 2004. The effect of adolescent experience on labor market outcomes: the case of height. J. Polit. Econ. 112 (5), 1019–1053. Rabe-Hesketh, S., Skrondal, A., 2008. Multilevel and Longitudinal Modeling Using Stata. STATA Press. Silventoinen, K., Kaprio, J., Lahelma, E., 2000. Genetic and environmental contributions to the association between body height and educational attainment: a study of adult Finnish twins. Behav. Genet. 30 (6), 477–485. Steckel, R.H., 1995. Stature and the standard of living. J. Econ. Lit. 33 (4), 1903–1940. Szklarska, A., Kozieł, S., Bielicki, T., Malina, R.M., 2007. Influence of height on attained level of education in males at 19 years of age. J. Biosoc. Sci. 39 (4), 575–582. Taras, H., 2005. Nutrition and student performance at school. J. Sch. Health 75 (6), 199–213. Teasdale, T.W., Owen, D.R., Sørensen, T.I.A., 1991. Intelligence and educational level in adult males at the extremes of stature. Hum. Biol. 19–30. Waaler, H.T., 1984. Height weight and mortality the Norwegian experience. Acta Med. Scand. 215 (S679), 1–56. Wagner, B., 2013. Szkoła Publiczna W Polsce W XX Wieku. Zarys Dziejów Społecznych. Uniwersytet Warszawski. Welsh, M., Parke, R.D., Widaman, K., O’Neil, R., 2001. Linkages between children’s social and academic competence: a longitudinal analysis. J. Sch. Psychol. 39 (6), 463–482. Wentzel, K.R., 1991. Relations between social competence and academic achievement in early adolescence. Child Dev. 62 (5), 1066–1078. Wentzel, K.R., 1993. Does being good make the grade? Social behavior and academic competence in middle school. J. Educ. Psychol. 85 (2), 357. Wittberg, R.A., Northrup, K.L., Cottrel, L., 2009. Children’s physical fitness and academic performance. Am. J. Health Educ. 40 (1), 30–36.
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Talented but lazy. The height-school premium among Cracow’s schoolboys in the interwar period Bartosz Ogórek Faculty of Humanities, Pedagogical University of Cracow, ul. Podchora˛z_ ych 2, 30-124 Cracow, Poland
A R T I C L E I N F O
A B S T R A C T
Article history: Received 31 July 2018 Received in revised form 8 November 2018 Accepted 18 December 2018 Available online 21 December 2018
This study finds that a positive association between stature and academic performance measured by the grades for various subjects, the height-school premium, was present in a historical sample of 147 school boys attending a gymnasium (public secondary school) in Cracow, Poland, between the wars. This effect persists when controlling for a set of demographic and socio-economic variables, though the strength of the relationship is modest (0.018 higher average grade for Polish, 0.014 for mathematics, 0.016 for art, and 0.013 for the combined subjects with each centimetre of height). The differences found between the magnitude and significance of the height-premium in different school subjects could be a marker of unequal association between stature, and cognitive, social, and physical skills, suggesting at least a partial role of cognitive ability in this relationship. However, the effect visible at the school level is not consistent between different teachers of the same subjects, hence the mechanism behind the height-school premium in the analysed population to a large extent relied on the subjective judgment of the teachers, who could reward social skills but also discriminate against shorter students. © 2018 Elsevier B.V. All rights reserved.
Keywords: Height BMI Educational attainment Height-school premium Poland
1. Introduction The positive effect of stature on various aspects of human life has been demonstrated in numerous studies. Height premiums can be perceived in earnings (Persico et al., 2004; Case and Paxson, 2008; Kim and Han, 2017), mating (Nettle, 2002; Courtiol et al., 2010), educational attainment (Silventoinen et al., 2000; Magnusson et al., 2006; Meyer and Selmer, 1999), and mortality (Waaler, 1984; Barker et al., 1990; Jousilahti et al., 2000; Floud et al., 2011). This paper focuses on one specific type of height premium, namely the positive association between stature and school performance, described as the height-school premium (Cinnirella et al., 2011). While it is acknowledged that this effect is reflected in the higher educational attainment of taller individuals (Silventoinen et al., 2000; Case et al., 2009; Maurer, 2010; Huang et al., 2015; Szklarska et al., 2007), the precise nature of the relation between school success and physical height is still a matter for debate. In the broadest terms, there are three non-exclusive hypotheses which offer explanations for the effect of the stature on the academic performance. The first places the emphasis on the role of cognitive ability as the mediator between height and educational outcomes (Case and Paxson, 2008; Lundborg et al., 2014). One of the main arguments
E-mail address: [email protected] (B. Ogórek). https://doi.org/10.1016/j.ehb.2018.12.004 1570-677X/© 2018 Elsevier B.V. All rights reserved.
here is the fact that a correlation between height and cognitive skills emerges as early as age 3, i.e. before any potential preferential treatment at school, and remains largely unaltered with age (Case and Paxson, 2008). Higher values of both variables can stem from factors including better nutrition during the prenatal period, infancy, and childhood as proper diet may enhance the students’ performance at school (Taras, 2005). Since the school grades are scores of a different nature than objective test results, and are to a significant extent a reflection of teachers’ subjective judgments, some authors argue that the height-school premium is determined solely by non-cognitive (mainly social) skills. This explanation stresses that higher physical stature may indicate better social skills, as taller children build these more extensively than their shorter peers through engagement in various activities during adolescence (Persico et al., 2004). These skills are likely to be rewarded by teachers regardless of students’ academic performance (Cinnirella et al., 2011). The plausibility of this hypothesis is in part due to the fact that the association between stature and students’ grades is not equal across different settings. For example, Gorry (2017) found that preferential treatment of taller students occurs only in larger schools, where there is a significant degree of competition for the attention of teachers and peers. Finally, since stature is always relative, higher grades attained by taller students might be the effect of pure discrimination against shorter children, irrespective of any type of skills
B. Ogórek / Economics and Human Biology 34 (2019) 252–256
associated with height. Gilmour and Skuse (1996) found that shorter children do not differ from their peers in confidence, selfesteem or acceptance in a group, and moreover report similar social support from their teachers. Inconsistencies in the existence and intensity of the cognitive height premium demonstrated in the literature are also visible in the diachronic perspective. As this relationship varies among different birth cohorts (Magnusson et al., 2006), as well as across countries at different development levels (Guven and Lee, 2015), the occurrence of a height-school premium in historical settings is not obvious. Given the arguments presented above, the contribution of this study is as follows. Firstly, it puts the correlation between height and school performance in a historical context. This is the more important that historical data of this type, especially in the longitudinal setting, are rare (Komlos, 2006). Secondly, it will attempt to differentiate between different skills sets and the ways in which they are affected by height differentials. Cognitive, social, and physical skills will be proxied here by the school grades for various subjects (conduct, mathematics, language, art (drawing), and physical education) in a rather speculative manner. In addition a set of controls is used, with BMI as a measure of the current nutritional status, and number of hours of absences from school as a proxy for general and current health status among them, to see whether the height-school premium persists after accounting for potential confounders. Finally, the consistence of the results obtained from the global models is tested within the groups taught by specific teachers to assess the degree of teachers’ subjectivity. The identification of associations has primarily descriptive aims, as it is nearly impossible to establish causal links providing the explanations for the abovementioned mechanisms on the basis of the data used here. The remainder of the paper is divided into three sections: data and methods, results, and conclusion and discussion. 2. Data and methods The data used in this study are the product of the manual linkage of two kinds of archival records: anthropological measurement cards, and class registers. The measurement cards were generated as part of a scientific project led by Julian TalkoHryncewicz at the Jagiellonian University’s Institute of Anthropology from 1908 to 1933 (Jasicki, 1957). A vast set of variables (height and weight among them) as well as basic socio-demographic facts were recorded annually in several Cracow schools by anthropology students. The class registers are preserved in the National Archive in Cracow and contain information on students and their parents, but most notably on their final grades in different subjects. Grades were awarded on a scale of 2–5, where 2 was unsatisfactory (fail) and 5 was very good. The records for one school, Gymnasium [Public Secondary School] No. 8, in Cracow for the school years 1923/24–1930/31, were indexed based on matches in student name and surname, father’s name, and the geographic origin of the family. The resulting dataset forms an unbalanced panel, as individuals are not equally represented in the sample. It comprises 427 observations of 147 individuals, but the number of entries ranges from 1 to 5, with an average of 2.9. Lessons were taught by several different subject teachers. The teachers usually changed from year to year, so they were assigned to a certain phase of education rather than to a specific class (understood as group of students). In the sample, there were 20 different teachers evaluating conduct (class teachers), 7 for Polish, 5 for mathematics, and 4 for both art and physical education. The subjects analysed were to some extent already preselected, on the basis of their cognitive skills and standard of living, as the gymnasium formed one part of the non-compulsory
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secondary school level within the education system of the Second Polish Republic, and fees were charged for attending (Wagner, 2013). The fee for children of civil servants was reduced by half, and in the case of gifted, but poor students, full exemption was possible (Mauersberg, 1991). Students also had to pass an entrance examination. Gymnasium education lasted for eight years and ended with a matriculation examination (matura) which granted the students access to tertiary education. It is worth noting, however, that this phenomenon should not weigh heavily on the sample representativeness. The fees were as low as 30 zlotys per semester in the years 1925–27 to 55 zlotys in the latter period1. Many of the students in the sample paid only half fees or were even fully exempted due to their parents’ economic situation or occupation as civil servants. As a result, more than 30% of the schoolboys in the study had a guardian in the lowest group of professional skills (mainly labourers, unskilled manual workers, domestic and institutional service etc.) and almost 45% fell into the middle income bracket, which was dominated by civil servants and intellectual workers. As the main focus of the paper is height-related differences in the school performance between individual students, the between-effects model was adopted. The panels are formed by the successive measurements (one for each school year), and the group was defined at the level of the student, hence the measurements are nested within individuals. The model estimates the mean difference in the outcome variable based on the mean difference in the explanatory variables, which means it uses only cross-sectional information, ignoring the within group variation (Rabe-Hesketh and Skrondal, 2008). The argument for this choice might be strengthened by the fact, that the height variable produces nonconsistent effects as confirmed by the results of the Hausman test. While positive changes in height between individuals yields a positive effect for school performance, the growth of individuals over time shows a negative effect. This might be due to the level of difficulty of the education rising in parallel with the teaching course. Another reason for use of the between-effect estimator is the non-linearity of cognitive abilities and height, which has been confirmed in some studies (Teasdale et al., 1991; Heineck, 2009), and the exaggeration of the height divergences between the social strata during the teen years due to the variation in the timing of the growth spurt (Komlos et al., 1992; Steckel, 1995). 3. Results Tables 1 and 2 present the descriptive statistics of the variables used in the analysis. The mean grades obtained in the various subjects differ substantially, which could reflect either students’ specific predispositions or differences in evaluation style between teachers. Interestingly, the correlations between the scores from selected subjects vary from a reasonably high 0.578 (mathematics and Polish) to a low of 0.088 (Polish and physical education). This fact partially confirms the notion that grades directly reflect either different skills sets or teachers’ differing preferences. For the purposes of this study, the grades from Polish, mathematics, and art will be treated as a proxy for cognitive skills. The grading of conduct, i.e. classroom behaviour corresponding to binding cultural and social norms, and reflecting the quality of relationships with teachers and peers, will be used as a marker of social skills (Wentzel, 1991,1993; Welsh et al., 2001). The grade from
1 The average monthly wage for a bricklayer in Cracow in that period ranged from 260 to 312 zlotys, while a bricklayer’s assistant earned on average 102–146 zlotys per month. The average monthly salary in the printing industry was 458 zlotys for qualified workers and 115 zlotys for the female semi-skilled labour force (Derengowski, 1928).
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B. Ogórek / Economics and Human Biology 34 (2019) 252–256
Table 1 Summary of quantitative variables. Variable
Mean
SD
Min
Max
n
Grades: conduct Polish mathematics art physical education
4.26 3.48 3.07 3.87 4.13
0.70 0.78 0.73 0.76 0.80
2 2 2 3 2
5 5 5 5 5
427 427 427 427 427
Height (cm) at age in full years: 11 138.19 12 140.40 13 145.18 14 152.05 15 158.87 16 163.49 17 168.42 18 170.57 19 168.98 20 171.40 21 174.23 22 178.90
6.78 7.45 7.54 8.92 7.65 8.22 7.89 5.79 4.99 5.90 3.46 –
119.00 121.40 125.70 130.00 142.60 145.00 145.50 158.50 160.00 160.60 171.80 178.90
153.00 154.90 169.50 170.30 177.30 177.90 185.00 184.00 176.00 179.10 178.20 178.90
36 59 65 71 61 46 44 25 8 8 3 1
Birth year BMI Hours of absence
2.18 2.08 61.45
1909 13.42 0.00
1919 25.57 458.00
427 427 427
1913.73 18.59 59.71
Table 2 Summary of qualitative variables. Variable
Frequency
Percentage
Guardian’s profession low middle high
131 192 104
30.68 44.96 24.36
Guardian’s place of residence Cracow Cracow region more distant
349 45 33
81.73 10.54 7.73
physical education can be seen as a measure of overall fitness (Wittberg et al., 2009). Obviously, this delimitation is far from being exclusive and robust, since each grade may incorporate a reasonable amount of subjectivity or discrimination. The sample contains a large spread of heights, from 119 cm noted at age 11 to as much as 185 at age 17. The data captures the adolescent growth spurt, which among the students of the school took place on average between the ages of 13 and 15 (height increments equal to 6.87 and 6.82 respectively). The BMI is relatively high in the younger age groups, exceeding that for Austrian cadets at the end of the 19th century. For older age groups the Cracow schoolboys have a lower BMI, but it is still
higher than that observed among the West Point cadets (Komlos, 2006). On the contrary, the frequent and long absences of the students from school (an average of almost 60 lessons missed per year) points to the potentially suboptimal epidemiological situation in the sample. Since the variable ‘hours of absence’ shows significant skewness it was logarithmized for the regression analysis. The stature, occurrence of the peak height velocity, and BMI prove that the population examined enjoyed a relatively high standard of living, which could either reflect the actual robustness of Cracow’s children or confirm to a certain extent the expected selection in terms of social class and opulence. Despite that fact, the socio-economic structure of the sample is far from homogenous. The incidence of group one of the variable ‘guardian’s profession’ (‘low’), which comprises children of fathers with low-level professional qualifications (mainly caretakers, janitors and beadles, industry workers, day labourers, printers, minor officials, and simple craftsmen) is only slightly higher than that of group three (‘high’), which comprises elite categories: freelancers, engineers, officers, top-level management staff of offices and enterprises, restauranteurs, and property owners. The sample is dominated by group two (‘middle’), which was composed of pupils whose parents were officials, railwaymen (including engine drivers), farmers, teachers, tradesmen and master craftsmen. Despite that fact, each of the socio-economic groups is fairly represented in the study. Surprisingly, such distribution does not diverge strongly from the actual social and occupational stratification in Cracow during that time. According to the 1931 population census in the district where the analysed school was located, the lowest qualified category (labourers and domestic/homeworkers) constituted 48.1% of the inhabitants (compared to 30.68% in the study). Hence the underrepresentation of the lowest social stratum is not substantial. Table 3 presents the results of the basic models capturing the association between stature and school performance. The relation is controlled for the exact age at measurement and the potential cohort effect captured by the year of birth, as the cohort spans more than 10 years (1909–1919). Because of the character of the data and the model chosen, the results presented in the table should be read as follows: What is the expected difference in the mean grades of two students who differ by one centimetre in height while being identical in respect of all other covariates? Focusing on the response of each subject grade to height, it is clear that despite being modestly positive, the association is not always statistically significant. However for Polish, mathematics, and art, and for the average of all the grades used in the study, the relationship under investigation does occur significantly. A difference of one centimetre in height between students results in a 0.016–0.018 higher grade, which seems a very small increment, but it is worth recalling the strong heterogeneity of the students’ stature, especially at ages 12–17 (Table 1). It is worth noting that
Table 3 Regression of school grades on height, age and birth year. Conduct
Height (cm) Age at measurement Birth year Constant F-test Between R-sq. Number of groups (individuals) Number of observations
Polish
Maths
Art
coef.
p-value
coef.
p-value
coef.
p-value
0.008 0.144 0.107 203.328 4.45 *** 0.085
0.282 0.021 0.008 0.009
0.018 0.011 0.052 98.948 2.29 * 0.046
0.024 0.867 0.232 0.240
0.016 0.005 0.085 161.790 2.98 ** 0.059
0.036 0.939 0.041 0.043
Note: significance levels *** 0.01 ** 0.05 * 0.1.
coef.
0.016 0.106 0.077 148.002 6.65 *** 0.122 147 427
P.E.
Average
p-value
coef.
p-value
coef.
p-value
0.030 0.083 0.048 0.049
0.009 0.051 0.060 113.025 1.72 0.035
0.188 0.401 0.122 0.131
0.014 0.059 0.076 145.018 5.59 *** 0.105
0.012 0.189 0.009 0.010
B. Ogórek / Economics and Human Biology 34 (2019) 252–256
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Table 4 Regression of school grades on height, age, birth year and control variables. Conduct
Height (cm) Age at measurement Birth year Guardian’s profession: middle high Guardian’s place of residence: Cracow region more distant BMI Log hours of absence Constant F-test Between R-sq. Number of groups (individuals) Number of observations
Polish
Maths
Art
P.E.
Average
coef.
p-value
coef.
p-value
coef.
p-value
coef.
p-value
coef.
p-value
coef.
p-value
0.009 0.150 0.106
0.248 0.021 0.010
0.018 0.004 0.055
0.044 0.960 0.214
0.014 0.027 0.094
0.083 0.688 0.028
0.015 0.149 0.095
0.047 0.018 0.017
0.011 0.084 0.070
0.165 0.173 0.071
0.013 0.082 0.084
0.019 0.075 0.004
0.073 0.271
0.563 0.071
0.157 0.310
0.261 0.060
0.193 0.281
0.146 0.074
0.171 0.135
0.165 0.351
0.275 0.363
0.025 0.012
0.174 0.272
0.058 0.012
0.092 0.331 0.030 0.012 200.980 2.38 * 0.135
0.634 0.070 0.404 0.501 0.011
0.082 0.337 0.012 0.003 104.885 1.53 0.092
0.700 0.093 0.760 0.895 0.222
0.107 0.139 0.003 0.007 178.780 1.52 0.091
0.596 0.465 0.933 0.719 0.030
0.338 0.268 0.019 0.021 181.625 3.31 *** 0.179 147 427
0.073 0.131 0.583 0.229 0.017
0.311 0.082 0.033 0.023 132.265 2.04 * 0.118
0.093 0.639 0.054 0.493 0.077
0.149 0.092 0.016 0.003 159.707 2.87 ** 0.159
0.283 0.484 0.527 0.815 0.005
Note: significance levels *** 0.01 ** 0.05 * 0.1.
Table 5 Regression of school grades on height, age, birth year and guardian’s profession, by teacher. Teacher A
Height (cm) Age at measurement Birth year Guardian’s profession: middle high Constant F-test Between R-sq. Number of groups (individuals) Number of observations
Teacher B
Teacher A
Teacher B
Teacher A
Teacher B
coef.
p-value
coef.
p-value
coef.
p-value
coef.
p-value
coef.
p-value
coef.
p-value
0.032 0.196 0.049
0.034 0.059 0.540
0.006 0.166 0.034
0.528 0.033 0.548
0.012 0.027 0.153
0.264 0.729 0.003
0.018 0.099 0.021
0.132 0.185 0.753
0.013 0.088 0.092
0.164 0.276 0.124
0.009 0.151 0.109
0.344 0.039 0.014
0.038 0.308 96.905 1.99 * 0.153 61 101
0.887 0.322 0.539
0.269 0.429 63.693 2.54 ** 0.161 72 146
0.108 0.028 0.561
0.072 0.354 292.381 3.30 *** 0.202 71 123
0.692 0.128 0.003
0.241 0.046 36.846 1.08 0.088 62 124
0.223 0.834 0.779
0.288 0.384 178.845 1.71 0.103 80 184
0.100 0.073 0.121
0.047 0.293 209.771 3.39 ** 0.151 101 179
0.772 0.122 0.014
Note: significance levels *** 0.01 ** 0.05 * 0.1. The teachers with the highest numbers of individuals and observations were chosen for comparison, in order to provide sufficient sample size.
height and basic controls account for between 3.5% (physical education) and 12.2% (art) of the variance in average grades between individuals (Table 4). Controlling for the socio-economic status of the student’s guardian (the father in 85% of cases, the mother in 11%, and another person in the remaining 4%), co-residence with the guardian, BMI, and hours of absence does not alter the relationship substantially. Again, the grades for Polish, mathematics, and art, as well as the average, bear a significant positive effect of minor strength, which could be a sign of a cognitive skills premium. In the case of conduct, Polish, mathematics and physical education, SES of guardian and place of residence seem to be better predictors of school performance, but they do not reduce the association of grades with height. Not surprisingly, a higher social position implies better average grades, and living away from the guardian, usually meaning in lodgings, a dormitory, or with more distant relatives, is related to poorer academic achievement. BMI as a control for current nutritional status, as well as hours of absence proxying current health status proves insignificant in each case. The explanatory power of the models rises with the implementation of additional controls, up to a variation of 18% between students’ grades in the case of art classes. Unexpectedly, the height-school premium is absent in the case of both physical education and conduct, both of which subjects could be perceived as requiring least cognitive skills, and being more related to social skills and overall fitness. The results suggested by the global models pointing to the role of cognitive skill in the height-school premium are tested against
the subjectivity of the teachers’ evaluations. If the mechanism described above is the only one in operation and it is universal, the positive association between height and school performance should be roughly constant across various teachers. Apparently this is not the case, as we can see from Table 5. The height-school premium is present only among students evaluated by certain teachers. These are teacher A for Polish and teachers A for maths and art (although here the effect proves insignificant). Conversely, among the students taught by teacher B in maths, the effect is reversed (though again the effect is insignificant). Some of the teachers (teacher B in Polish, teacher A in art) seem rather to reward other factors associated with the socio-economic status of the student. 4. Conclusion and discussion The results of the analysis points to several findings and conclusions. First of all, there is a discernible height-school premium in not only contemporary but also historical populations, which were usually influenced to a larger extent by high mortality and social inequalities. This could potentially indicate the robustness of the relation between stature and academic performance. The argument is strengthened by the persistence of the association after the inclusion of the set of controls. The various kinds of height premiums should thus be further researched in their historical settings in order to broaden our knowledge of
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mechanisms universal to Homo Sapiens, as well as our understanding of societies in the past. This study proves the usefulness of archival records for inquiries of this type. The differences in the effect depending on school subject is compatible with the results of studies on contemporary populations. Case and Paxson (2008) reported that levels of cognitive test scores in childhood vary depending on the character of the test. In contrast to this study, they found that tests of mathematical skills were most responsive to height differences. Language scores, both verbal and non-verbal, proved to be slightly less conditional on height, while activities involving drawing and visual-motor coordination were the least sensitive to the height premium. The associations described in this paper are most visible in the case of language, followed by drawing, with mathematics in last place, but the differences are negligible. Moreover, the lack of consistency of the effect across different teachers indicates that the heightschool premium was heavily dependent on the subjectivity of the teachers’ assessment. While this finding does not fully dismiss the operation of the cognitive skills differentials, it does suggest a major role of the evaluator in the observed premium, as he/she could to varying degrees reward stature, social status, or noncognitive skills. This study is not free from major caveats, most of which tend to apply to the quantitative historical research. The lack of information as to the quality and selection methods of the preserved sources, and small sample size due to the timeconsuming nature of archival research and data processing, might be the main ones. Further effort should certainly be invested in building larger databases, which could produce more robust results. In addition, a considerable proportion of the correlation found here may be attributable to unobserved individual characteristics. The extension of the database towards census records, which would contribute information on sibship sizes and parents’ vital status as well as a more detailed description of the socio-economic status, could provide the solution to some of the problems. Funding This research was supported by the Faculty of Humanities of Pedagogical University of Cracow (subsidy for statutory research). Declarations of interest None. Acknowledgements ski and anonyI am grateful to John Komlos, Michał Kopczyn mous reviewers for their helpful comments, and also to Henryk Gła˛b for allowing me to use the archive of Department of Anthropology at Jagiellonian University. References Barker, D.J., Osmond, C., Golding, J., 1990. Height and mortality in the counties of England and Wales. Ann. Hum. Biol. 17 (1), 1–6. Case, A., Paxson, C., 2008. Stature and status: height, ability, and labor market outcomes. J. Polit. Econ. 116 (3), 499–532.
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