4.2 Sensitivity analysis
4.2.4 The height premium at different heights
In this section, we analyze whether the height premium varies by height. For this purpose, we divide the sample into three groups as follows: below 175 centimeters, 175-185 centimeters, and above 175-185 centimeters.22 Table 4.7 gives the results for the total sample and for the sibling sample. For the full sample, it is evident that the height premium is inversely related to height. For all models (Models B through I), the height premium is statistically significant among the shorter individuals, whereas it is virtually non-existent for those above 185 cm.23 Thus, the height premium is largely found among individuals below average height.24
*** Table 4.7 ***
5. Discussion
We find that an additional ten centimeters in height, which is approximately four inches25, is associated with a raw height premium of six percent. This is somewhat lower than the ten percent height premium in wages found by Persico et al. (2004) and Case and Paxson (2008a), but could probably be explained by the institutional setting in Sweden,
22 We have also elaborated by trimming the ends of the height distribution, i.e. ascribing a height of 165 and 195 centimeters to those below and above that height, respectively. Moreover, we ran analyses discarding those with height below or above 165 and 195 centimeters, respectively, resulting in a loss of 2,517 individuals. These restrictions did not change the results of Table 4.1 or 4.2 and are available upon request.
23 Since most of the estimates for the sibling sample are statistically insignificant, these are not commented upon.
24 The height premium may also vary by age. We tested this by interacting age with height, without finding any clear patterns. We also ran regressions discarding younger men, aged 28-32, from the population, which did not change the results. These results are available upon request from the authors.
25 Corresponding to the inter quartile difference in height in the UK, US, and Sweden.
with the existence of “solidarity” wages and a rather narrow wage distribution.26 It could be noted that the height premium in the three countries corresponds to the return to one additional year of schooling in the respective countries – about ten percent in the US and UK (Card, 1999) and six percent in Sweden (using the data explored in this study).
Hence, the earnings height premium for Sweden, though somewhat lower than the wage premiums in US and UK, is still substantial.27
We replicate the findings in Persico et al. (2004) and Case and Paxson (2008a), i.e.
that both cognitive and non-cognitive skills are responsible for explaining part of the height premium. We then introduce a third individual characteristic – physical capacity – that explains an even greater part of the height premium. It has previously been assumed that whereas physical capacity may account for important parts of the height premium in developing countries, where muscular strength and physical capacity are of importance in the production process, it is of limited importance in explaining the height premium in modern western societies. Our results stand in sharp contrast to this assumption. In fact, we find that measures of physical capacity alone accounts for about 80 percent of the height premium. We also find a significant and positive association between physical capacity and earnings, which is independent of height and BMI. A one standard deviation increase in the MWC is associated with an 8 percent increase in earnings, suggesting that physical capacity is indeed something that is valued in the labor market. By also accounting for cognitive and non-cognitive skills in our regressions, we are able to explain the entire height premium in earnings.
Speculating, we propose some potential pathways by which physical capacity may be linked to the height premium. Firstly, and as discussed in Section 2, measures of physical capacity, such as muscle strength, have been found to predict lower total mortality and mortality from cancer and respiratory diseases. Physical capacity and strength therefore
26 Sweden has a much more compressed wage structure than the US. In Sweden, the 90th percentile earns approximately twice as much as the 10th percentile, whereas in the US they earn five times as much.
27 Its magnitude could also be related to the ethnic and immigrant earnings gaps in Sweden. The annual earnings of men born in Sweden with a non-European parental background is sixteen percent lower compared to the earnings of native Swedish men (Nordin and Rooth, in press), which, based on the raw results of this study, is equivalent to the difference in earnings between men that differ by about twenty-seven centimeters in height. The observed Swedish gender earnings gap is 16 percent (Kumlin 2007).
signal good health (Metter et al. 2002; Gale et al. 2007; Wijndaele et al. 2007). Due to the strong correlation between height and physical capacity, employers may therefore use height as a marker of long-term health, which is rewarded in the labour market. Secondly, technological advancements have not completely removed physically demanding jobs in western countries. In line with this, our results suggest that the height premium to a larger extent reflects physical capacity at the lower end of the earnings distribution, where jobs can reasonably be assumed to be more physically demanding and where physical capacity is thus more directly related to the productivity of the individual. Thirdly, insofar physical capacity is associated with habits such as exercise and a balanced diet it may, apart from health, signal certain rewarded personality traits, such as self-control, endurance and patience, etc., i.e. traits that partly coincide with the notion of non-cognitive skills. Finally, physical strength has been related to attractiveness. This would mean that the physical strength that comes with greater height is mainly rewarded because physical strength is perceived as attractive. An interesting route for future research would be to distinguish between these alternative explanations and further explore alternative mechanisms by which physical capacity is linked to the height premium.
References:
Antelius, J. and Björklund, A. (2000). How Reliable are Register Data for Studies of the Return on Schooling? An examination of Swedish Data, Scandinavian Journal of Educational Research, 44(4):341–355.
Behrman, J.R. and Rosenzweig, M.R. (2004). Returns to birthweight, Review of Economics and Statistics, 86(2):586-601.
Black, S.E., Devereux, P.J. and Salvanes, K.G. (2007). From the cradle to the labor market? The effect of birth weight on adult outcomes, Quarterly Journal of Economics, 122(1): 409-439.
Blair, S.N., Kohl, H.W. III, Paffenbarger, R.S. Jr., Clark, D.G., Cooper, G.H., and Gibbons, L.W. (1989). Physical fitness and all-cause mortality: a prospective study of healthy men and women. JAMA 262:2395-2401.
Borghans, L., Duckworth, A., Heckman J., and ter Weel B. (2008). The Economics and Psychology of Personality Traits, Journal of Human Resources, 43(4): 972-1059.
Buckley, J., Holmes, J., and Mapp, G. (1999). Exercise on prescription. Cardiovascular activity for health. Butterworth-Heinemann; 1 edition.
Card, D. (1999). The Return to Education. In Ashenfelter O, Card D (eds) Handbook of Labor Economics, pp. 1801-1863. Elsevier. Amsterdam.
Carroll, J. (1993). Human cognitive abilities. A survey of factor-analytic studies, Cambridge: University Press.
Case, A. and Paxson, C. (2008a). Stature and status: Height, ability, and labor market outcomes, Journal of Political Economy, 116(3):499-532.
Case, A. and Paxson, C. (2008b). Height, Health and Cognitive Function at Older Ages, American Economic Review: Papers and Proceedings, 98 (2): 463-467.
Cawley, J., Heckman, J. and Vytlacil, E. (2001). Three observations on wages and measured cognitive ability, Labour Economics, 8(4):419-442.
Dinda, S., Gangopadhyay, P.K., Chattopadhyay, B.P., Saiyed, H.N., Pal, M. and Bharati, P. (2006). Height, weight and earnings among coalminers in India, Economics and Human Biology, 4(3):342-350.
Ekelund, L-G., Haskell, W.L., Johnson, J.L., Whaley, F.S., Criqui, M.H., and Sheps, D.S.
(1988). Physical fitness as a predictor of cardiovascular mortality in
asymptomatic North American men: the Lipid Research Clinics Mortality Follow-up Study. New England Journal of Medicine, 319:1379-1384.
Everett P. and Sills F. (1952). Relationship of grip strength to stature, somato-type components and anthropometric measurements of hand, Res Q, 23:161–6.
Fink, B., Neave, N. and Seydel, H. (2007). Male facial appearance signals physical strenght to women, American Journal of Human Biology, 19:82-87.
Frieze, I.H., Olson, J.E., and Good, D.C. (1990). Perceived and Actual Discrimination in the Salaries of Male and Female Managers, J. Appl. Soc. Psychology, 20: 46–67.
Fujita, Y., Nakamura, Y., Hiraoka, J., Kobayashi, K., Sakata, K., Nagai, M., and Yanagawa, H. (1995). Physical-strength tests and mortality among visitors to healthpromotion centers in Japan. J Clin Epidemiol, 48:1349-59.
Gale, C.R., Martyn, C.N., Cooper, C., and Sayer, A.A. (2007). Grip strength, body composition, and mortality. Int J Epidemiol, 36:228-35.
Gale, C.R., Martyn, C.N., Kellingray, S., Eastell, R., Cooper, Cyrus. (2001). Intrauterine programming of adult body composition, J Clin Endocrinol Metab, 86:267–72.
Gallup AC, DD White, GG Gallup Jr (2007). Handgrip strength predicts sexual behavior, body morphology, and aggression in male college students. Evolution and Human Behavior, 28:423-429.
Hamermesh, D. and Biddle, J. (1994). Beauty and the labor market, American Economic Review, 84:1174-1194.
Harper, B. (2000). Beauty, Stature and the Labour Market: A Brittish Cohort Study, Oxford Bulletin of Economics and Statistics, 62(1):771-800.
Heckman, J. (2008). Schools, Skills and Synapses, Economic Inquiry, 46(3): 289-324, 2008.
Heckman, J., and Rubinstein, Y. (2001). Importance of Noncognitive Skills: Lessons from the GED Testing Program, American Economic Review, 91(2):145-149.
Heckman, J.J., Stixrud, J. and Urzua, S. (2006). The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior, Journal of Labor
Economics, 24(3):411-482.
Heyward, Vivian H. (2006). Advanced Fitness Assessment and Exercise Prescription.
Human Kinetics Publishers
Hyde, Thomas E. and Marianne S. Gengenbach (2007). Conservative Management of Sports Injuries. 2nd ed; Sudbury, Mass.: Jones & Bartlett, p 845.
Kahn, H.S., Narayan, K.M., Williamson, D.F., and Valdez, R. (2000). Relation of birth weight to lean and fat thigh tissue in young men, Int J Obes Relat Metab Disord, 24:667–72.
Komlos, J. (1990). Height and Social Status in Eighteenth-Century Germany. Journal of Interdisciplinary History (20): 607–621. 1990.
Kumlin, J. (2007). The Sex Wage Gap in Japan and Sweden: The Role of Human Capital, Workplace Sex Composition, and Family Responsibility, European Sociological Review, 23(2):203-221.
Loh, E. (1993). The Economic Effects of Physical Appearance, Social Science Quarterly, 74(2):420-438.
Lundborg, P., Bolin, K., Höjgård, S. and Lindgren, B. (2007). Obesity and Occupation Attainment among the 50+ of Europe, in Bolin, K. and Cawley, J. (Eds.), Advances in Health Economics and Health Services Research, Vol.17, The Economics of Obesity. Elsevier, New York, 219-252
Metter, E.J., Talbot, L.A., Schrager, M., and Conwit. R. (2002). Skeletal muscle strength as a predictor of all-cause mortality in healthy men, J Gerontol A, 57:B359–B365.
Neal, D. and Johnson, W. (1996). The Role of Premarket Factors in Black-White Wage Differences, Journal of Political Economy, 104 (4): 869-895.
Nordin, M. and Rooth, D. (in press). The Employment and Income Gap Between Natives and Second-Generation Immigrants in Sweden, Scandinavian Journal of Economics.
Patton, J.F., Vogel, J.A., Mello, R.P. (1982). Evaluation of a maximal predictive cycle ergometer test of aerobic power. European journal of applied physiology and occupational physiology, 49(1):131-40.
Persico, N., Postlewaite, A, and Silverman, D. (2004). The Effect of Adolescent Experience on Labor Market Outcomes: The Case of Height, Journal of Political Economy, 112(5):1019-1053.
Phillips, D.I.W. (1995). Relation of fetal growth to adult muscle mass and glucose tolerance. Diabet Med, 12:686–90.
Richards, M., Hardy, R., Kuh, D. and Wadsworth, M.E.J. (2002). Birthweight, postnatal growth and cognitive function in a national UK birth cohort, International Journal of Epidemiology, 31(2):342-348.
Rooth, D. (in press). Obesity, attractiveness and differential treatment in hiring - a field experiment. Journal of Human Resources.
Sandvik, L., Erikssen, J., Thaulow, E., Erikssen, G., Mundal, R., and Rodahl, K. (1993).
Physical fitness as a predictor of mortality among healthy, middle-aged Norwegian men. N Engl J Med, 328: 533–537
Silventoinen, K., Magnusson, P., Tynelius, P., Kaprio, J., and Finn Rasmussen. (2008).
Heritability of body size and muscle strength in young adulthood: a study of one million Swedish men, Genetic Epidemiology, 32:341-349.
Slattery, M.L., Jacobs, D.R.Jr. (1988). Physical fitness and cardiovascular disease mortality: the US Railroad Study. American Journal of Epidemiology 127:571-580.
Steckel, R.H. (1995). Height and the standard of living, Journal of Economic Literature 33:1903–1940.
Steckel, R.H. (2009). Heights and human welfare: Recent developments and new directions, Explorations in Economic History, 46 (1): 1-23.
Thomas, D., and Strauss, J. (1997). Health and wages: Evidence on men and women in urban Brazil, Journal of Econometrics, 77(1):159-185.
van den Berg, G., Lundborg, P., Nystedt, P., Rooth, DO. (2009). Critical Periods During Childhood and Adolescence: A Study of Adult Height Among Immigrant
Siblings. IZA DP no. 4140.
Wijndaele, K., Duvigneaud, N., Matton, L., Duquet, W., Thomis, M., Beunen, G.,
Lefevre, J., and Philippaerts, R.M. (2007). Muscular strength, aerobic fitness, and metabolic syndrome risk in Flemish adults, Med Sci Sports Exerc 39:233–240.
Wilmore, J.H., and Costill, D.L. (1994). Physiology of Sport and Exercise. Human Kinetics: Champaign.
Figures:
Figure 3.1. Logarithm of annual earnings and height. Total population, 28-38 years old in 2003.
Note: The Height variable has been trimmed at the 1st and 99th percentile, that is, at 165 and 195 centimeters. The graph shows average earnings for each centimeter in height.
Figure 3.2. Logarithm annual earnings and cognitive skills. Total population, 28-38 years old in 2003.
Note: One standard deviation in the cognitive skills variable is approximately 2 units and amounts to about ten percent higher earnings. Cognitive skill is the Enlistment Test Score on cognitive ability. The graph shows average earnings for every integer of the cognitive test score.
Figure 3.3. Non-cognitive skill and earnings.
Note: One standard deviation in the non-cognitive skill variable is approximately 2 units and amounts to about ten percent higher earnings. Non-cognitive skill is the psychological evaluation when enlisting. The graph shows average earnings for every integer of the non-cognitive measure.
Figure 3.4a. Physical capacity (Maximum work capacity) and earnings.
400
Note: One standard deviation in maximum work capacity is 50 units and amounts to about ten percent higher earnings. Maximum work capacity is taken from the Enlistment stationary bike test. The graph shows average earnings for every integer of the bike test score.
Figure 3.4b. Physical capacity (handgrip strength) and earnings.
Note: One standard deviation in handgrip strength is 100 units. The graph shows average earnings for every integer of the handgrip strength score.
Figure 3.5. Cognitive skill and height.
Note: The Height variable has been trimmed at the 1st and 99th percentile. Cognitive skill is the Enlistment Test Score on cognitive ability.
Figure 3.6. Non-cognitive skill and height.
195 190
185 180
175 170
165 5,50
5,25
5,00
4,75
4,50
Note: The Height variable has been trimmed at the 1st and 99th percentile. Non-cognitive skill is the psychological evaluation when enlisting.
Figure 3.7a. Physical capacity (maximum work capacity) and height
195 190
185 180
175 170
165 340
320
300
280
260
240
Note: The Height variable has been trimmed at the 1st and 99th percentile. One standard deviation in physical capacity is 50 units.
Figure 3.7b. Physical capacity (handgrip strength) and height
Figure 4.1. The height premium along the earnings distribution. Quantile regressions.
.95
Note: The height premium in the graph should be interpreted as the change in earnings associated with a ten centimeters increase in height at different percentiles of the earnings distribution. Model A includes only height and age as regressors, while Model B corresponds to the full model (Model I) in Table 4.1 and Model C corresponds to the model with the physical capacity variables (Model H) in Table 4.1.
Tables:
Table 3.1. Descriptive statistics of the population divided at median height.
Men 28-38 years old, 2003.
!
"
# $ % %
& $ %
# $ ' (
& $ ' (
)
% %
* ' + ,
%
"
& - , . / ' 0
& - , ' .
%% % %
& 0
1 /
% %
2 ( + %0 0
Notes: Standard deviations in parentheses. The variables measuring cognitive skills, non-cognitive skills, and physical capacity are standardized when used in the empirical analysis.
Table 4.1. The height premium disentangled. Men 28-38 years old, 2003. Logarithm of annual earnings. Total population data.
! 3 4 5 ) # 6 7
8 888 % 888 % 888 888 888 888 %888 888 888
"
# $ 888 888 %888 888 888 888 888 888
& $ 888
# $ ' ( 888 888 888 888 888 888 %888
& $ ' ( 888 888 8 8 888 888 888 888
888 888 888 %888 888 %888
"
)
888 888
"
* ' +
%888 %888
"
& - , . / ' 0
& - , ' .
888 888 888
& 0
1 /
888 888 888
9 %
2 ( 0 0 0 0 0 0 0 0 0
Notes: This table reports estimates from the (1) regression model: Log Earnings = a + b*Height + c*X + d*Missing info + e. Model A only includes height and age fixed effects and is estimated using OLS. Model B adds BMI and Model C adds the parental variables. Model D adds cognitive skill, Model E adds non-cognitive skill, while Models F through H add the physical capacity variables. Model I includes all variables.
Table 4.2. The height premium disentangled. Men 28-38 years old, 2003. Logarithm of annual earnings. Siblings data.
! 3 4 5 ) # 6 7
8 888 888 888 888 888 888 888 8
88 8 888 888 %888 888 %888
"
)
888 888
"
* ' +
888 888
"
& - , . / ' 0
& - , ' .
% 888 888 888
& 0
1 /
888 888 888
: ( - 1 (( 2 ; ; ; ; ; ; ; ;
9 %
2 ( %0 %0 %0 %0 %0 %0 %0 %0 %0
Notes: This table reports estimates from the (2) regression model: Log Earnings = a + b*Height + c*X + d*Missing info + f + e. Model A only includes height and age fixed effects and is estimated using OLS for the sibling sample. Model B adds sibling fixed effects and Model C adds BMI. Model D adds cognitive skill, Model E non-cognitive skill, while Models F through H add the physical capacity variables. Model I includes all variables.
Table 4.3. The height premium disentangled. Men 28-38 years old, 2003.
Logarithm of annual earnings. Siblings data.
! 3 4 5 ) # 6
8 888 888 888 88
888 888 %888 88 888
"
)
888 888 888 %888
"
& - , . / ' 0
& - , ' .
% 888
%
% 888
%
888
%
& 0
1 /
888
%
888
%
888
%
) 1 / +
888 %888 % 888
% % % % %
: ( - 1 (( ; ; ; ; ; ; ;
9 % % %
2 ( 0 0 0 0 0 0 0
Notes: This table reports estimates from the regression model (2): Log Earnings = a + b*Height + c*X + d*Missing info + f + e.
Models A, B, C and D correspond to Models C, D, G and H in Table 3.2, respectively. Models E through G then add the leadership skill variable.
40
Table 4.4 The raw height premium and its channels. Men 28-38 years old, 2003. Logarithm of annual earnings. Total population. !345) /8"888% 888888888 888 9(-1((2;22; </(-1((22;2; ;((-1((222;; ! 9% 2(00000 Notes: Model A replicates the height premium from Model A in Table 4.1. Models B through E investigate the channels of the height premium by including fixed effects for location, occupation, and education.
Table 4.5. The height premium (*10) and different outcome measures. Siblings. Men 28-38 years old, 2003.
Logarithm of annual earnings.
+ +
& 1 3 888 888 888 888 888
& 1 4 888 888 888 888 888
& 1 5 888 888 888 888 888
& 1 ) 888 888 888 888 888
& 1 8 %888 88 8
& 1 7 888
: ( - 1 (( ; ; ; ; ;
2 ( %0 %0 0 0% 0 %
Notes: Column (i) is the height premium replicated from Table 4.2, while Columns (ii) and (iii) show the height premium for those with top coded earnings and earnings above 100’ SEK, respectively. Columns (iv) and (v) give the height premium when excluding social security benefits and enlistment “fakers”, respectively.
Table 4.6. The height premium (*10) at different percentiles of the earnings distribution.
Men 28-38 years old, 2003.
* ! 3 4
% 888 8
888 %
% 888 888 %888
& 1 888 888 888
% 888 888 888
888 888 888
% 888 %888 888
2 ( 0 0 0
Notes: Model A gives the height premium from a quantile regression of log earnings on height in centimeters and on age. Model B includes the full set of control variables used in the empirical analysis (corresponds to Model A and I in Table 4.1). Model C adds BMI, parental characteristics and the physical capacity variables to Model A.
Table 4.7. The height premium (*10) in different height ranges. Men 28-38 years old, 2003. Logarithm of annual earnings.
# / : /
% % % % % %
& 1 3 888 888
%
88
%
& 1 4 888 888
%
88
%
& 1 5 %888 888
% %
& 1 ) %888 888
%
%
%
& 1 888
%
88
%
& 1 7 %888
% %
: ( - 1 (( 2 2 2 ; ; ;
2 ( 0 0 0 % 0 0 %0 %
Notes: Models B through I correspond to those in Table 4.1.
Appendix:
Table A2. Descriptive statistics of the population. Men 28-38 years old, 2003.
Notes: Standard deviations in parentheses. The variables measuring cognitive skill, non-cognitive skill, and physical capacity are standardized when used in the empirical analysis.
Table A3. Correlations among explanatory variables. Total population. Men 28-38 years old, 2003.
! 3 4 5 ) # 6
! 8 8 8 8 8 %8
3 4 + . 8 8 8 8 8 %8
4 2 + . 8 8 8 8 8 8
5 & - , . / ' 8 8 8 8 8 8
) & 8 8 8 8 8 8
# 3&7 8 8 8 8 8 8
6 ) %8 %8 8 8 8 8
Notes: *) correlation is significant at the 0.01 level
Table A4. The raw height premium and its channels. Men 28-38 years old, 2003.
Logarithm of annual earnings. Siblings data.
! 3 4 5 )
/ 8 " 888 888 %888
%
888 %888
%
9 ( - 1 (( 2 ; 2 2 ;
< / ( - 1 (( 2 2 ; 2 ;
; ( ( - 1
((
2 2 2 ; ;
9
2 ( %0 %0 %0 %0 %0
Notes: Model A replicates the height premium from Model A in Table 4.1. Models B trough E investigate the channels of the height premium where the fixed effects being used are given in rows 3 to 5.