We find that taller people enjoy a higher quality of life at older ages in China. Implications are drawn for investment in health human capital in infancy and adolescence.China, Height, Well-being

Asadul Islam

Paul Raschky

Russell Smyth

English

Research Papers in Economics

 
 
Department of Economics 
Issn 1441-5429 
Discussion paper 35/10 
 
 
Height, Health Human Capital and Quality of Life Among Older Chinese 
  
Asadul Islam, Paul Raschky
1 and Russell Smyth 
 
 
 
 
Abstract 
We find that taller people enjoy a higher quality of life at older ages in China. Implications 
are drawn for investment in health human capital in infancy and adolescence. 
Keywords: China, Height, Well-being 
JEL Codes: I1, J14, O1 
 
 
 
 
 
                                                            
1 Corresponding author: Department of Economics, Monash University, 900 Dandenong Rd., Caulfield 
VIC 3145, Australia; email: paul.raschky@buseco.monash.edu.au; Tel. +61 3 99034515; Fax. +61 3 9903 1128 
 
© 2010 Asadul Islam, Paul Raschky and Russell Smyth 
All rights reserved. No part of this paper may be reproduced in any form, or stored in a retrieval system, without the prior 
written permission of the author. 
 1.  Introduction 
Several studies have documented the existence of a wage-height premium (see eg. Schultz, 
2002). Case and Paxson (2008) found that the advantages of being taller follow people into 
old age and that for a sample of people aged 50 and above in the United States, taller people 
have better health along a variety of dimensions. There are several reasons why height might 
be associated with better health at old age (Case & Paxson, 2008). Adult height is a proxy for 
a healthy environment in childhood, which affects cognitive development. Poor health and 
nutrition in childhood are associated with problems such as diabetes, hypertension and 
vascular disease, which persist into old age and make it more difficult to perform daily tasks 
(Brunner, 2005). Childhood environment and cognitive development affect educational 
achievement and occupational choice. Similarly, the choice of occupation and workplace can 
then have adverse effects on health and well-being later in life.
2 We re-examine Case and 
Paxson’s (2008) findings in the context of China. Because China has a rapidly aging 
population, the relationship between health and nutrition in childhood and height, health and 
quality of life in old age has particular policy relevance for that country.  
 
2.  Data and methodology 
We use pilot data from the China Health and Retirement Longitudinal Survey (CHARLS). 
CHARLS contains data from 1,563 randomly selected households collected from two 
provinces, Zhejiang and Gansu, between July and September 2008. CHARLS sampled one 
person aged 45 and over, plus their spouse if one exists, in each household with an age-
eligible person. Hence, overall CHARLS contains information on 2,951 individuals aged 45 
and older (see Zhao et al., 2009). Our measure of health and well-being or quality of life is 
activities of daily living (ADL), which is a composite index of the level of difficulty that the 
                                                            
2 Blue collar workers are at higher risk of exposure to neurotoxicants in the workplace, which have been shown 
to cause a deterioration in health and ability to perform daily activities later in life (Schwartz et al., 2000). respondent has in performing a number of fairly normal and routine day-to-day activities or 
tasks. Specifically, we considered the degree of difficulty experienced by the respondent in 
performing the following 20 tasks: dressing, bathing, eating, getting in or out of bed, walking 
100 metres, walking one kilometre, sitting for two hours, getting up from a chair, climbing 
several flights of stairs, stopping, kneeling or crouching, lifting 10 jin (equivalent to a heavy 
bag of groceries), extending one’s arm, pushing or pulling large objects, urinating, doing 
household chores, preparing hot meals, shopping for groceries, managing money, making 
phone calls and taking medicine. Respondents’ answers were coded: 1= ‘I do not have any 
difficulty in performing the task’; 2= ‘I can perform the task, but only with difficulty’, 3= ‘I 
cannot perform the task’. Our measure of ADL is the sum of responses on all 20 tasks. 
Responses were normalized so that the maximum value of the composite ADL variable is 1 
and the minimum value is zero. The mean value is 0.078 with a standard deviation of 0.129.  
 
ADL was first regressed on a respondent’s height, measured in centimetres, and control 
variables using OLS. However, adult height is an endogenous variable as it depends on 
environmental factors in infancy and adolescence, suggesting that OLS estimates will be 
biased. The most important environmental factors influencing height are the quality of the 
uterine environment and, in infancy (aged 1-2) and in the adolescent growth spurt, nutritional 
status and exposure to disease (Beard and Blaser, 2002). Epidemiological studies suggest the 
adolescent growth spurt in China is 10-11 for girls and 12-13 for boys.(Ji et al., 1995; Leung 
et al., 1996). We take advantage of the fact that the Cultural Revolution (CR) represented an 
exogenous shock. Adverse effects on agricultural production meant that calorie intake was at 
a bare subsistence level. Despite the existence of ‘barefoot doctors’ in rural areas, the 
political campaigns and disruption to urban institutions, generated an increase in epidemic 
disease and malnutrition in many areas. Morgan (2007) found that individuals born at the height of the CR (1966-1968) were much shorter as adults than those born before or after. 
Hence, the instrumental variables (IVs) we use for height are whether the respondent was 
aged 0-2 in the CR, 10-11 in the CR (if female) or 12-13 in the CR (if male). We also have 
IVs for if the person was aged 0-2 and born in rural China in the CR and if the respondent 
was born in a rural area at any time. The quality of prenatal care, access to healthcare and 
nutritional intake of pregnant mothers and children is lower in rural China. Table 1 contains 
the list of variables, together with their definitions and descriptive statistics. 
[Table 1 about here] 
 
3.  Results 
Table 2 reports OLS results. We find that, on average, taller respondents have less difficulty 
performing everyday activities. (Results are qualitatively similar if estimates are derived 
using Tobit, rather than OLS).  The results for the IV estimates are presented in Table 3. The 
first stage estimates and the validity of the IVs, reported at the bottom of Table 3, suggest that 
these IVs are appropriate instruments for height. The second stage estimates, reported at the 
top of Table 3, indicate that taller people have less difficulties performing everyday activities 
at older ages. These results are consistent with the findings for men and women over the age 
of 50 in the United States, reported in Case and Paxson (2008) and suggest that early life 
experiences, as proxied by adult height, have consequences for well-being at older ages. 
[Table 2 about here] 
[Table 3 about here] 
 
 
 4.  Conclusion 
The results reported here suggest that height is associated with better health and quality of 
life in terms of performing a range of routine daily activities in old age. Because height in 
adulthood is determined by environmental factors aged 0-2 and in the adolescent growth 
spurt, the results reported here have implications for investment in health human capital in 
the womb, infancy and adolescence. Specifically, the findings suggest that investing in health 
and nutrition in these critical times can improve the health and quality of life of people in old 
age, which is potentially a very important result given China’s aging population.  
 
References 
Beard, A.S. and Blaser, M.J. (2002) The ecology of height: the effect of microbial 
transmission on human height. Perspectives in Biology and Medicine, 45(4), pp.475-499. 
 
Brunner, E.J. (2005) Social and biological determinants of cognitive aging. Neurobiology of 
Aging, 26(1), pp.17-20. 
 
Case, A. and Paxson, C. (2008) Height, health and cognitive function at older ages. American 
Economic Review 98 (2), pp. 463–467. 
 
Ji, C.Y., Ohsawa, S. and Kasai, N. (1995) Secular changes in the stature, weight and age at 
maximum growth increments of urban Chinese girls from the 1950s to 1985. American 
Journal of Human Biology, 7(4), pp. 473-484. 
 
Leung, S.S.F., Lau, J.T.F., Xu, Y.Y., Tse, L.Y. et al.  (1996) Secular changes in standing 
height, sitting height and sexual maturation of Chinese – the Hong Kong growth study. 
Annals of Human Biology, 23(4), pp. 297-306. 
 Morgan, S. (2007) Stature and famine in China: The welfare of the survivors of the Great 
Leap Forward famine. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1083059 
 
Schultz, T.P. (2002) Wage gains associated with height as a form of health human capital. 
American Economic Review, 92(2), pp. 349-353. 
 
Schwartz, B.S., Stewart, W.F., Bolla, K.I., Simon D. et al. (2000) Past adult lead exposure is 
associated with longitudinal decline in cognitive function. Neurology, 55(8), pp.1144-1150. 
 
Zhao, Y., Strauss, J., Park, A. and Sun, Y. (2009) ‘China Health and Retirement Longitudinal 
Study, Pilot User’s Guide, National School of Development, Peking University. 
 
  
 
 
Table 1: Description of variables 
 
 
Variable Mean 
(Std. Dev.) 
Description  
ADL difficulty  0.135 
(0.195) 
Composite index of the level of difficulty respondent 
has with performing 20 activities of daily living. 
Height 158.38 
(8.46) 
Respondent’s height in cm 
Age   58.434 
(9.989) 
Respondent’s age in years  
Female   0.520  Dummy =1 if respondent is female  
  (0.499)  0 otherwise  
Gansu   0.461  Dummy =1 if respondent lives in Gansu  
  (0.499)  0 respondent lives in Zhejiang  
Rural   0.833  Dummy =1 if respondent lives in rural area  
  (0.373)  0 respondent lives in urban area  
Child health  0.673  Dummy =1 if respondent reported "excellent"   
  (0.469)  or "very good" health during childhood,  
    0 otherwise  
Education 3.247 
(3.528) 
Highest year of education of respondent  
Cultural Revolution 0-2 years  0.039 
(0.194) 
Dummy =1 if respondent was between born or between and 2 years old 
during Cultural Revolution, 0 otherwise 
Cultural Revolution 10-11 years 
female 
0.200 
(0.400) 
Dummy =1 if respondent was between 10 and 11 years  old during 
Cultural revolution and female, 0 otherwise 
Cultural Revolution 12-13 years 
male 
0.183 
(0.387) 
Dummy =1 if respondent was between 12 and 13 years  old during 
Cultural revolution and male, 0 otherwise 
Born rural  0.064  Dummy =1 if respondent was born in a rural area, 
 (0.245)  0  otherwise 
  
Table 2: OLS estimates of determinants of ADL difficulty 
 
Height -0.002*** -0.002** 
 (0.001)  (0.001) 
Age 0.006*** 0.006***
 (0.001)  (0.000) 
Female   0.050*** 0.051***
 (0.012)  (0.011) 
Gansu   0.147***
   (0.008) 
Rural   0.033***
   (0.009) 
Child health    -0.025***
   (0.008) 
Education   -0.004***
   (0.001) 
Constant   0.099   -0.066  
 (0.124)  (0.113) 
N   1946   1864  
Notes: Robust standard errors (in parentheses). ***, **, * indicate significance at the 1, 5 and 10%-level. 
 
 
 
  
 
 
  
Table 3: IV estimates of determinants of ADL difficulty 
 
Second stage estimates 
Height -0.013*   
 (0.007)   
Age 0.004*** 
 (0.001)   
Female   -0.074  
 (0.074)   
Gansu 0.149*** 
 (0.009)   
Rural 0.013   
 (0.016)   
Child health   -0.023**  
 (0.009)   
Education -0.002   
 (0.002)   
Constant   1.972*  
 (1.193)   
First stage estimates 
Cultural Revolution   -1.947** 
0-2 years   (0.825) 
Cultural Revolution   -0.862* 
10-11 years, female   (0.503) 
Cultural Revolution   -1.036* 
12-13 years, male   (0.551) 
Cultural Revolution   -1.022 
0-2 years * Born rural   (2.003)** 
Born rural  -1.438 
 (0.679) 
Angrist-Pischke Test
a 0.003 
Kleibergen-Paap statistic
a 0.007  
Hansen Test
a 0.544 
N   1864  
Notes: Robust standard errors (in parentheses). 
ap-values.   ***, **, * indicate significance at the 1, 5 and 10%-
level.  
 
 

Height, health and cognitive function at older ages.

Past adult lead exposure is associated with longitudinal decline in cognitive function.

Secular changes in standing height, sitting height and sexual maturation of Chinese – the Hong Kong growth study.

Secular changes in the stature, weight and age at maximum growth increments of urban Chinese girls from the 1950s to

Social and biological determinants of cognitive aging.

Stature and famine in China: The welfare of the survivors of the Great Leap Forward famine. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1083059

The ecology of height: the effect of microbial transmission on human height.

Height, Health Human Capital and Quality of Life Among Older Chinese

http://www.buseco.monash.edu.au/eco/research/papers/2010/3510heightislamraschkysmyth.pdf

Height, Health Human Capital and Quality of Life Among Older Chinese

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