Height, Health and Cognitive Function at Older Ages: Cross ... · 3/30/2011 · with increasing height. Their findings suggest that efforts to improve prenatal and early life conditions

Height, Health and Cognitive Function at Older Ages: Cross-

National Evidence from Europe

Cahit Guven

Deakin University

Wang-Sheng Lee

RMIT University

March 23, 2011

Preliminary. Please do not cite or quote.

Abstract

Previous research has found that height is correlated with cognitive functioning at older ages.

It therefore makes sense to ask a related question: are people from countries where the

average person is relatively tall smarter? Using data from the Survey of Health, Ageing, and

Retirement in Europe (SHARE), we find empirical evidence that this is the case, even after

controlling for self-reported childhood health, self-reported childhood abilities, parental

characteristics and education. We find that people from countries with relatively tall people,

such as Denmark and the Netherlands, have on average superior cognitive abilities compared

to people from countries with relatively shorter people, such as Italy and Spain.

Keywords: Height, Self-reported health, Cognitive function

JEL Classification: I10

This paper uses data from SHARELIFE release 1, as of November 24th 2010 or SHARE release 2.3.1, as of July

29th 2010. The SHARE data collection has been primarily funded by the European Commission through the 5th

framework programme (project QLK6-CT-2001- 00360 in the thematic programme Quality of Life), through

the 6th framework programme (projects SHARE-I3, RII-CT- 2006-062193, COMPARE, CIT5-CT-2005-

028857, and SHARELIFE, CIT4-CT-2006-028812) and through the 7th framework programme (SHARE-

PREP, 211909 and SHARE-LEAP, 227822). Additional funding from the U.S. National Institute on Aging

(U01 AG09740-13S2, P01 AG005842, P01 AG08291, P30 AG12815, Y1-AG-4553-01 and OGHA 04-064,

IAG BSR06-11, R21 AG025169) as well as from various national sources is gratefully acknowledged (see

www.share-project.org/t3/share/index.php for a full list of funding institutions).

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1. Introduction

Previous research on the influence of early-life conditions on cognitive development

suggests that socioeconomic conditions in childhood and early life experiences have

important influences on cognitive development and abilities in childhood and adolescence, as

well as in young and middle adulthood. For example, children from poor backgrounds show

worse verbal and achievement outcomes in the first 5 years of life (Duncan et al. 1994). Low

socioeconomic status in childhood has also been associated with cognitive function in middle

age, net of years of education completed (Kaplan et al., 2001). It is only in recent years that

researchers adopting a life course approach have begun to trace the origin of cognitive

functioning in old age to early life conditions.1 For example, using longitudinal data in

conjunction with retrospectively collected childhood data, Everson-Rose et al. (2003), Wilson

et al. (2005) and Zhang et al. (2008) all find that higher socioeconomic status during

childhood are weakly associated with a higher absolute level of cognitive function in old age.

More recently, van den Berg et al. (2010) use a unique Dutch longitudinal dataset to examine

the role of early life socio-economic circumstances in protecting individuals from cognitive

decline in the face of adverse events later in life. They show that the cognitive abilities of

those who suffer from strokes later in life are more heavily affected if individuals were born

in adverse socioeconomic conditions.

In the absence of reliable data from early childhood, several recent studies use adult

height as a marker of childhood circumstances.2 For example, Abbot et al. (1998) examine

the relationship of height to later life cognitive function in a sample of elderly Japanese-

American men and find the prevalence of poor cognitive performance declined consistently

with increasing height. Their findings suggest that efforts to improve prenatal and early life

conditions to maximize growth in childhood and adolescence could diminish or delay the

expression of cognitive impairments that occur later in life. Case and Paxon (2008a) further

suggest that height could be an indicator of higher cognitive potential in the sense that people

who do not reach their full genetic height potential do not reach their full genetic cognitive

potential either. They provide evidence that taller individuals are more likely to earn more,

not because of their heights per se, but because of the cognitive skills with which height is

correlated. Using data from the U.S. Health and Retirement Study (HRS), Case and Paxson

1 See Factor-Litvak and Susser (2004) for a recent overview.

2There is a large literature in economic history that uses height as a key measure of physical welfare and the

standard of living. See, for example, the surveys by Steckel (1995, 2009). Aside from genetics, it has been

established that height is influenced by childhood nutrition and disease (e.g., Fogel, 1993; Peck and Lundberg,

1995). Hence, height is commonly seen as a useful marker of overall childhood conditions.

2

(2008b) document a strong association between self-reported height and cognitive function in

later life. Similarly, Maurer (2010) complements the evidence presented in Case and Paxson

(2008b) by examining the later-life cognition of seniors in Latin America and the Caribbean

using data from the Survey on Health, Well-being and Aging in Latin America and the

Caribbean, 2000 (SABE). He finds that that height displays a strong positive association with

later life cognition, which seems somewhat larger for women than for men. These studies

build on the earlier work of psychologists who have previously noted that height appears to

be positively correlated with intelligence (e.g., Humphreys et al, 1985; Jensen and Sinha,

1993; Johnson, 1991; Tanner, 1969).

If within country studies such as those by Case and Paxson (2008b) and Maurer

(2010) find a significant association between height and cognitive function in later life, it is

natural to also ask whether such a correlation exists for individuals across countries. Do

people from countries where the average native person is relatively tall have superior

cognitive abilities compared to people from countries where the average native person is

relatively shorter? It is well known that the average heights differ across nationalities

considerably. Do the taller Austrians and Danes, for example, have higher cognitive abilities

than the shorter Italians and Spaniards?

In this paper, using data from the Survey of Health, Ageing, and Retirement in Europe

(SHARE), we examine the relationship between height and cognitive function in a sample of

older Europeans. Our objectives are twofold. First, we add to the current literature by

extending the work of Case and Paxon (2008b) and Maurer (2010) by examining the

relationship between height and later life cognition in 15 additional European countries not

previously analyzed. Second, we extend the within-country analysis to a cross-country

analysis to determine if countries with relatively taller people have higher levels of cognitive

function than countries with relatively shorter people.

2. Background

2.1 Cross-Country Variation in Height

Garcia and Quintana-Domeque (2007) document the evolution of adult heights in

Europe in the period 1950-1980. They find that average height in the Northern European

countries (Austria, Belgium, Denmark, Finland, Ireland, and Sweden) is higher than in the

Southern ones (Greece, Italy, Portugal, and Spain) for both males and females. Hatton and

Bray (2010) extend the database constructed by Garcia and Quintana-Domeque (2007) by

going through a variety of historical records to include the average heights of men by birth

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cohorts from 1856–60 to 1976–80. Eveleth and Tanner (1976, 1990) produced a world-wide

overview of variations in growth among children aged 2 to 16 years. They used data from

studies undertaken from the 1950s to the 1980s that were based on nationally representative

samples or on samples from large cities within the countries studied. Identical growth curves

were observed for European countries. However, at age 16, they also found that children in

northern European countries were on average taller than children in southern European

countries. Similarly, when De Groot et al. (1991) compared the height of elderly subjects

born between 1913 and 1918 in 19 cities across Europe, they found that these subjects were

tallest in northern European populations.3 In this paper, we exploit this variation in height

across European countries and explore in more detail whether adult height is useful as a

marker of cognitive ability.

2.2 Possible Explanations for Cross-Country Variation in Cognitive Ability

Technological progress of nations, a key ingredient of a country‟s economic success

and the wealth and well-being of its citizens, has been shown to be related to average national

levels of intelligence (e.g. Gelade, 2008). It is therefore of great interest how cross-country

differences in cognitive abilities arise. Several hypotheses have been put forth in attempts to

explain the variation in the global distribution of cognitive ability. These include exposure to

education and other cognitively challenging environments such as non-agricultural labor,

differing levels on inbreeding across countries, the effects of temperature and climate, as well

as the effects of variation in the intensity of infectious diseases.

Using data on intelligence quotient (IQ) scores for 81 nations and focusing on

bivariate correlations, Barber (2005) finds that average national IQ to be correlated with

enrolment in secondary school (r = 0.72), illiteracy (r = -0.71), agricultural labour (r = -0.70)

and gross national product (r = 0.54). He also proposed that health and nutrition may affect

intelligence, and found that average national IQ correlated negatively with rates of low birth

weight (r = -0.48) and with infant mortality (r = -0.34).

3 Although there is a large genetic component to heights within populations, the contribution of genetics to

variation in mean heights across populations is much smaller. For example, Beard and Blaser (2002) argue that

the marked increase in heights observed throughout the developed world during the twentieth century occurred

too rapidly to be due to selection and genetic variation. Environmental factors are likely to be important. For

example, Cavelaars et al. (2000) found high and significant correlations between average height and infant

mortality for each of the 5-year birth cohorts born between 1920 and 1970 (their Pearson correlation coefficients

averaged 0.8 for men and 0.6 for women), suggesting that infant mortality and height may indeed have common

determinants.

4

Sadat (2008) and Woodley (2009) explore the hypothesis that inbreeding and the

associated reduced phenotypic quality is a cause of the variation in cognitive ability across

the world. Jensen (1983) finds that the effect of inbreeding on the intelligence of the offspring

of first cousins amounts to about 5 IQ points. Consanguineous marriages (i.e., a marriage

between first or second cousins) account for a significant percentage of marriages in some

countries. Although stigmatized in the West, such marriages are common in many Middle

Eastern countries such as Saudi Arabia (39.7%) and Qatar (44.5%), as well as African

countries such as Sudan (50.1%) and Nigeria (51.2%).4 In support of this hypothesis, Sadat

(2008) and Woodley (2009) found significant cross-national correlations in the range of 0.6

to 0.8 between average IQ and measures of inbreeding.

Lynn (1991) and Rushton (1995) proposed that temperature and climate provide

important Darwinian selective pressures for intelligence, with cold climates selecting for

higher intelligence, because low temperatures provide more fitness-related problems for

humans that must be solved through cognitively demanding means, and through more

complex social organization. Some empirical support for this hypothesis was reported in

Templer and Arikawa (2006) who found that persons in colder climates tend to have higher

IQ scores.

Finally, Eppig et al. (2010) have recently provided empirical evidence using a sample

of over 100 countries that average national intelligence correlates significantly and negatively

with rates of infectious disease. This is possible because parasitic infection may intermittently

cause the redirection of energy away from brain development during the crucial years of

childhood development.

2.3 Do Cross-Country Differences in Height have any Economic Significance?

In this paper, we explore yet another hypothesis to explain cross-country variation in

cognitive abilities – the role of height. This is a natural extension of the significant links

found by Case and Paxon (2008b) and Maurer (2010) between height and cognitive function

in later life using within-country studies.

Somewhat related to this hypothesis are papers by Angus Deaton and his co-authors

exploring the significance of cross-country differences in height. Deaton (2007) analyses the

link between adult height, disease and national income using data on 43 countries from the

Demographic and Health Surveys. With the exception of Africa, he finds there is a general

4 These figures are taken from Appendix A in Woodley (2009).

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interregional correspondence between height and national income. Over time, as real incomes

have grown, heights have grown too.

In a related paper, Bozzoli, Deaton and Quintana-Domeque (2009) focus their

analysis on adult height and childhood disease in the US, England and ten European countries

where more detailed household level data are available. They find that both within and

between these countries, there is a close relationship between income per capita and height.

Their findings also suggest that the direction of causality does not appear to run from income

to height, as they find that the disease environment in infancy is the most important

determinant of adult height, not the level of income per head. Indirectly, the role of height is

also possibly related to the Eppig et al. (2010) hypothesis involving the role of infectious

diseases in influencing cognitive functioning, as children who get sick when they are very

young might suffer some physical developmental consequences.5

2.4 Possible Links between Height and Cognitive Ability

To date, the precise mechanisms underlying the relationship between height and IQ

are still not well understood. Case and Paxon‟s (2008b) study highlights the crucial role of

education as a potential pathway linking height and cognitive function in later life. They find

that there is a statistically significant positive association between cognitive function and self-

reported height, which declines considerably once they control for education. They also

highlight a positive association between education and height. Taken together, these findings

suggest that early-life conditions have an effect on later-life cognition results partly due to

higher levels of schooling among children with higher socioeconomic status, which may, in

turn, protect cognitive function at older ages.

Other mechanisms that produce a positive correlation between height and intelligence

have been suggested. It is possible that an unmeasured factor simultaneously affects cognitive

ability and height. For example, Lynn (1990) emphasizes the role of nutrition, arguing that

the most straightforward explanation of the positive association between height and

intelligence is that both are functions of nutrition. He argues that improvements in nutrition

have led to parallel increases in height, head circumference, brain size, and to improved

neurological development and functioning of the brain. On the other hand, biological factors

could be important. Prokosch, Yeo and Miller (2005) suggest that it occurs via an alternative

5 They could, however, be very distinct hypotheses. As we report in Section 4, to the extent that self-reported

information on childhood diseases is reliable, we find that childhood diseases have little effects on reducing the

statistical significance of height in our cognitive function regressions we estimate in Table 7.

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mechanism – people with greater genetic quality and developmental health may

simultaneously have higher intelligence and greater stature. Berger (2001) highlights the

existence of insulin-like growth factors that affect body growth while also influencing areas

of the brain in which cognition occurs.

Twin studies have been used to identify the roles played by a common environment

and shared genes. Sundet et al. (2005), using conscription data of Norwegian twins, conclude

that the environment plays a large role and is responsible for 65 percent of the height-

intelligence correlation, with genes responsible for 35 percent of the observed correlation.

Beauchamp et al. (2010), using a sample of Swedish twins, find results that are very similar

to those reported by Sundet et al. (2005). Silventoinen et al. (2006), however, found in

several samples of Dutch twins that the association between height and intelligence is

primarily genetic in origin.

The role played by nutrition in linking height and cognitive functioning has also been

supported by twin studies. Black et al. (2007) find that, on average, the twin born at the

higher birth weight is significantly taller in adulthood and scores significantly higher on IQ

tests. Similarly, using data from the Minnesota Twin Registry, Behrman and Rosenzweig

(2004) find fetal growth (birth weight divided by gestation) to be significantly associated

with height and years of completed schooling in adulthood.

Kanazawa and Reyniers (2009) propose a somewhat more lengthy explanation

comprising of three separate mechanisms and involving genetic evolution over time. The first

mechanism involves the assortative mating of tall men and beautiful women. As height is

desirable in men and physical attractiveness is desirable in women, there should be

assortative mating between tall men and beautiful women (and short men and less beautiful

women). Since both height and physical attractiveness are heritable, this will create a

correlation among their children between height and physical attractiveness, where tall people

(both men and women) are more beautiful than short people. The second mechanism involves

the assortative mating of intelligent men and beautiful women. As intelligent men tend to

attain higher status, at least in the evolutionarily novel environment in recent history, and

high status is desirable in men, and because physical attractiveness is desirable in women,

there should be assortative mating between intelligent (and thus high-status) men and

beautiful women. Since both intelligence and physical attractiveness are heritable, this will

create a correlation among their children between intelligence and physical attractiveness,

where more attractive people are more intelligent than less attractive people. Finally, the

correlation between height and physical attractiveness (produced by the first mechanism

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above) and correlation between intelligence and physical attractiveness (produced by the

second mechanism above) will create a second-order correlation between height and

intelligence.

3. Data

In this study, we use data on health and cognitive functioning from Waves 1 and 2 of

SHARE (Release 2.3.1 of July 29, 2010). The first wave was fielded in 12 countries in

2004/2005: Austria, Belgium, Denmark, France, Greece, Germany, Italy, the Netherlands,

Sweden, Switzerland, Spain and – one year later – Israel. In all countries, probability samples

of nationally representative samples of the community-based population aged 50 and older

were drawn. The 31,000 interviews conducted in that period correspond to a weighted

average household response rate of 61 percent, ranging from 39 percent in Belgium and

Switzerland to 79 percent in France (a thorough description of methodological issues is

contained in Börsch-Supan and Jürges, 2005). The second wave in 2006/2007 opened the

longitudinal dimension, but also collected baseline data from three further countries: the

Czech Republic, Poland and – after some delay – Ireland.

We also use data from the third wave of data collection for SHARE. This data, which

was in the field from October 2008 to May 2009, focuses on people's life histories and is

otherwise commonly referred to as SHARELIFE. Waves 1 and 2 of SHARE provide little

information about what happened earlier in the lives of survey respondents. SHARELIFE

gathered more detailed information on important areas of our respondents‟ lives, ranging

from partners and children over housing and work history to detailed questions on health and

health care. It therefore complements the SHARE panel data by providing life history

information and enhancing our ability to understand how early life experiences and events

throughout life influenced the circumstances of the survey respondents. SHARELIFE data are

available for all the countries in SHARE with the exception of Ireland and Israel.

SHARE is designed to be cross-nationally comparable and is harmonized with the

U.S. Health and Retirement Study (HRS) and the English Longitudinal Study of Ageing

(ELSA). International comparability is achieved by ex-ante harmonization of the survey

instrument and all fieldwork procedures. The common questionnaire and interview mode, the

effort devoted to translation of the questionnaire into the national languages of each country,

and the standardization of fieldwork procedures and interviewing protocols are the most

important design tools adopted to ensure cross-country comparability (Börsch-Supan and

Jürges, 2005).

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In SHARE, cognitive ability is measured using simple tests of orientation in time,

memory (registration and recall of a list of ten words), verbal fluency (a test of executive

function) and numeracy (arithmetical calculations). Participants are also asked to rate

subjectively their reading and writing skills. These tests are administered to all respondents

and are carried out after the first four modules (Cover Screen, Demographics and Networks,

Physical Health, and Behavioral Risks) of the questionnaire. The tests are comparable with

similar tests implemented in the HRS and ELSA, and follow a protocol aimed at minimizing

the potential influences of the interviewer and the interview process.6

The test of orientation in time consists of four questions about the interview date (day,

month, year) and day of the week. Unfortunately, this test shows very little variability across

respondents, with a majority of respondents answering all four questions correctly.

Nevertheless, we include it in this paper for comparability purposes with Case and Paxon

(2008b).

The test of memory consists of verbal registration and recall of a list of 10 words

(butter, arm, letter, queen, ticket, grass, corner, stone, book and stick). The respondent hears

the complete list only once and the test is carried out two times, immediately after the words

are read out (immediate recall) and at the end of the cognitive function module (delayed

recall). The raw total scores of both tests correspond to the number of words that the

respondent recalls.

The test of verbal fluency consists of counting how many distinct elements from a

particular category the respondent can name in a specific time interval. The specific category

used in SHARE is members of the animal kingdom (real or mythical, except repetitions or

proper nouns) and the time interval is one minute for all respondents.

The test of numeracy consists of a few questions involving simple arithmetical

calculations based on real life situations. Respondents who correctly answer the first question

are asked a more difficult one, while those who make a mistake are asked an easier one. The

last question is about compound interest, testing basic financial literacy. The resulting raw

total score ranges from 0 to 4. Finally, respondents are also asked to rate their reading and

writing skills on a five point scale: Excellent = 1, Very good = 2, Good = 3, Fair = 4, Poor =

5.

6 An important drawback of SHARE is that the exact same tests were administered to all respondents of the

same household and to the same individual over time. Repeated exposure to the same tests may induce learning

effects which are likely to improve the cognitive scores of some respondents.

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Our height measure in SHARE is based on self-reported height (“How tall are you?”).

Although many studies have observed a very high correlation between measured height and

self-reported height (e.g., Palta, Prineas, Berman et al., 1982; Steward, 1982; Steward et al.,

1987; Bostrom and Diderichsen, 1997), these studies also found that using self-reported

height leads to a slight overestimation of the average height of the study population.

Moreover, some studies showed that this overestimation was larger among men, among older

age groups, and among lower socio-economic groups (Palta et al., 1982; Steward et al., 1987;

Bostrom and Diderichsen, 1997). However, where the focus is on cross-national comparisons

the main results will only be biased when this over- or underestimation also varies between

countries. Supporting the use of our data is that the large variations in average height we

observed between northern and southern European countries were also reported in studies in

which height was measured (Eveleth and Tanner 1976, 1990; de Groot et al. 1991). Further

reducing the need for a concern that the use of self-reported height in SHARE leads to bias is

that when we regressed measures of cognitive function on two available height measures in

ELSA (both self-reported height and nurse measured height), we found very similar results.

The measure of health we use in our analysis in SHARE is also based on self-reports.

Respondents are asked to self-assess their health on a five-point scale in SHARE. This survey

question has been widely used in health surveys and is meant to reflect overall health status.

In Wave 1, SHARE used the US version of this self-reported health scale (excellent, very

good, good, fair and poor) for some respondents, and also included the European scale (very

good, good, fair, bad and very bad) for other respondents. Respondents were given either the

US or the European scale randomly but not asked to answer both. In Wave 2, SHARE only

used the US scale for all respondents. From an analysis of the distribution of self-reported

health from both scales in Wave 1, it appears that responses are partly based on the specific

words in the response options. Differences in the mean answer (on a scale of 1 to 5) were

different for each country, suggesting that there is not an easy mapping between the scales.

Following Meenakshi et al. (2008), one possible way around this problem is to construct a

binary measure of self-reported health that makes the European and US scale responses as

comparable as possible: Those who report excellent, very good or good health on the

American scale are considered to be in “good” health, whereas those who report to be in fair

or poor health are classified as being in “bad” health. Using the European scale, those in very

good or good health are classified as being in “good” health while those reporting fair, bad or

very bad health are considered to be in “bad” health.

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We use two measures of well-being. First, there is a question asking: “Have you ever

experienced depression in your life?” We code the binary response whereby „1‟ indicates that

they have ever been depressed, and „0‟ indicates that they‟ve never experienced depression

before. Second, levels of depression among participants in the SHARE survey were also

measured using the Euro-D 12-item scale, as this has been validated in earlier cross-European

studies of depression prevalence (Prince et al. 1999a, Prince et al. 1999b). Respondents were

asked questions with reference to presence of feelings of depression, pessimism, wishing

death, guilt, irritability, tearfulness, fatigue, sleeping troubles, loss of interest, loss of appetite,

reduction in concentration, and loss of enjoyment over the last month. In order to get a

measure of subjective well-being, we „reverse coded‟ the answers of respondents with 1 =

„absence of feeling' or 0 = „presence of feeling'. The sum can thus range between 0 and 12,

with a higher sum indicative of lower degrees of depression, and lower values corresponding

to more depression.

4. Results for Individual European Countries

In this section, we focus on replicating the country studies of Case and Paxson

(2008b) and Maurer (2010) who report significant associations between height and cognitive

function in later life. It is interesting to see if their results hold for many of the developed

European countries. Table 1 provides descriptive statistics of height, the seven cognitive

function variables we use in our analysis, as well as the three health variables and key control

variables.

[Table 1 about here]

Using pooled data, the estimated results in which these seven cognitive measures are

regressed on respondents‟ heights (measured in centimetres), age, race, gender country

dummies and survey wave are reported in Table 2. Focusing on the coefficient for height, for

all 15 European countries as a whole, it can be seen that it is highly statistically significant in

columns one to seven, suggesting that taller persons perform better on average in the

cognitive tests as compared to their shorter counterparts. For example, a person that is 10 cm

taller is expected to score 0.16 more points on the immediate recall test.


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We next turn to examining the country specific results not reported in Table 2, which

is essentially a replication of the analysis by Case and Paxon (2008b) using the HRS for each

of the 15 European countries in the SHARE data set. We can see that the coefficient on

height is statistically significant in the majority of cases (Table 3). The main exceptions are

for Israel, where in six out of the seven cognitive measures, the height coefficient is not

significant, and for Ireland, where in four of the seven cognitive measures, the height

coefficient is not significant. The height coefficient is generally not significant for the test on

orientation in time (column 3) because of little variation in the test score, with most people

scoring close to full marks in the test (mean of 3.81 out of 4).


Table 4 examines the determinants of height, education and occupational choice. The

estimates presented here are based on 13 countries as both Ireland and Israel have no

childhood data collected via SHARELIFE available. The first column presents the result of a

regression of height on childhood correlates of height – father‟s occupation at age 14 (as a

proxy for childhood socioeconomic status) and self-reported childhood health status.

Respondents whose fathers were in white collar occupations when they were 14 years old are

on average 1.2 centimetres taller than those whose fathers were not, whereas respondents

who reported having “good” childhood health are likely to be 0.13 centimetres taller on

average. Looking at the determinants of years of schooling (column 2 of Table 4) and white

collar occupation (columns 3-4 of Table 4), our findings echo those reported in Case and

Paxon (2008b) – even controlling for father‟s occupation and childhood health, height

remains a significant determinant of education and occupation.


Table 5 presents the results of regressions similar to those in Table 2, with the

difference that extra control variables for education, father‟s occupation at age 14, own

occupation and childhood health have been added. Strikingly, despite the inclusion of

education as a control variable, a factor which largely diminished the role of height in similar

regressions estimated in Case and Paxon (2008b), the height coefficient is still statistically

significantly correlated with our seven measures of cognitive functioning and subjective

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health. This suggests that in the SHARE data, height does not only operate via education in

affecting cognitive outcomes.


As a marker of childhood conditions, the residual effects of childhood health may

continue to be reflected and included in the coefficient on height in regressions on adult

outcomes if information on childhood circumstance is not accurately captured. Hence, in

addition to using self-reported childhood health status in Table 4, we consider additional

childhood history variables available in the SHARE data set. These relate to the home

environment in which the child was brought up as well as self-reported childhood abilities at

age 10. In column 1 of Table 6, the results of the height regression show that the signs on the

childhood history variables are as expected. For example, having parents that drank heavily

or having parents with mental problems led to poorer parenting and as a result poorer

childhood growth. As an indicator of household wealth in childhood, it can be seen that larger

dwellings are associated with better childhood growth, likely through the better provisions of

childhood resources. On the other hand, competition for such limited resources with other

family members is likely to lead to reduced growth, as reflected in the significant negative

coefficient in the variable „dwelling with more than 4 people‟.

Self-reported abilities in mathematics and language are significantly positively

associated with years of schooling and having a white collar occupation. Including these

childhood history variables have little effect, however, on reducing the significance of the

height coefficient in the regressions using education and occupation as the dependent variable

(columns 2-4 of Table 6).


In Table 7, we add the childhood history variables to the regressions on cognitive

function and health estimated in Table 5. Interestingly, comparing the coefficients for height

in Tables 5 and 7, we can see that the inclusion of more detailed childhood history variables

hardly has any effects on the size of the height coefficient for all seven cognitive outcomes.


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Our results for 15 additional European countries provide more empirical support for

the link between height and cognitive functioning. However, it is less clear what channels

height operates through to affect cognitive functioning in later ages.

Our evidence suggests that education is not the main pathway that height affects

cognitive functioning, as including years of schooling did not affect the significance of the

height coefficient. We also tried including information on the highest degree earned, but this

made no substantive difference. It is likely that height possibly captures some other aspects of

early childhood health experiences that we are unable to measure, or alternatively some other

factors associated with cognitive functioning that we have not included in our models.

The adverse early-life conditions that affect cognitive functioning that have been

studied are mostly nutritional (e.g., Lynn, 1989; Kretchmer et al. 1996). However, exposure

to high levels of stress or illness during the critical childhood years – a much less researched

area – could also have important effects. In an attempt to test whether childhood health

shocks might have affected both height and cognitive development, we also tried including

detailed information regarding childhood health conditions in SHARE. These include:

whether or not missed school due to health problems, infectious diseases, broken

bones/fractures, asthma, other allergies, other respiratory problems, chronic ear problems,

severe headaches and migraines, epilepsy/seizures/fits, emotional/psychological problems,

appendicitis, diabetes/high blood sugar, heart trouble, leukaemia/lymphoma,

cancer/malignant tumor, diseases of the blood, diseases of digestive system, and upper

respiratory organs diseases. Despite restricting the first occurrences of these health conditions

to ages 0 to 5, we found that these variables did not reduce the statistical significance for

height.

5. Cross-Country Differences in Cognitive Function

In this section, we extend the within-country analysis conducted thus far to a cross-

country framework. If tall people within each country demonstrate superior cognitive abilities

relative to shorter people, and this finding appears to be very robust for many different

countries, then it is natural to wonder if people from countries with relatively tall people are

smarter than people from countries with relatively shorter people.

5.1 Cross-Country Results using SHARE

Figure 1 provides average height by country for the both men and women, as well as

separately by gender. It can be seen that the Dutch are the tallest in the sample, with male

14

average heights of about 1.78 metres and female average heights of about 1.66 metres. The

Danes and the Swedes are also relatively tall, with average male heights over 1.77 metres and

average female height over 1.64 metres. On the other hand, people from Spain, Ireland and

Italy are the shortest in our sample. Our results largely correspond with the historical

European height data that is reported in Garcia and Quintana-Domeque (2007) and Hatton

and Bray (2010).

[Figure 1 about here]

Figures 2 to 6 provide country averages of the five cognitive measures in order to

highlight the raw differences across countries. There is considerable cross-country variation

in most measures, such as numeracy (Figure 2), verbal fluency (Figure 4), immediate recall

(Figure 5) and delayed recall (Figure 6). The sole exception is the average date score. But that

lack of variation is easily explained – with most people scoring close to full marks for the

test, there is simply not much room for cross-country variation. A casual glance at the figures

suggests that height could be correlated with cognitive functioning, as countries with the

shortest people – Italy and Spain – also tend to perform the most poorly on all cognitive

measures. On the other hand, the Northern European countries with relatively taller people

tend to score better on each test.

[Figures 2-6 about here]

In order to examine the cross-country relationship between height and each of the

cognitive measures, we first regressed height and each of the cognitive and health outcomes

on a full set of person-level covariates to control for people‟s different characteristics (see the

list of covariates in Table 6 and 7) and country dummies. Table 8 provides the summary

results of the 14x11 = 144 separately estimated regressions, with Spain as the omitted

reference country.

The correlations of the variables in Table 8 are presented in Table 9. We emphasize

that these cross-country correlations are based on the country dummies reflect adjustments

made for differences in individual level characteristics, and are not simply cross-country

correlations of country averages. The latter is the approach most commonly taken when only

aggregate country level data are available (e.g., in the empirical economic growth literature).

15

Our approach of building up country level aggregates from micro-level data helps us to better

adjust for the different characteristics that people from different countries have.



Our findings are striking. We had previously seen that there are strong positive

associations between height and cognitive function in each of the 15 countries (Table 3).

Here, we also find that countries with taller people have higher levels of cognitive function in

our cross-country comparisons. High correlations can be found, in particular, for verbal

fluency, immediate recall, and numeracy. While we report simple Pearson correlation

coefficients in the first row of Table 9, it is arguable that more appropriate correlation

measures rely only on the ordinality of the measures. We therefore also perform both

Kendall‟s and Spearman‟s rank correlation tests. Kendall‟s tau statistic is particularly suitable

for smaller data sets such as ours. Two-sided tests of the null hypothesis of no correlation

between the country dummies suggest that at the five percent level, there are significant

correlations between height and verbal fluency, height and immediate word recall, height and

delayed word recall, height and numeracy, and height and psychological well-being.

Plots of the cross-country relationships between height and the various measures of

cognitive function and health are given in Figure 7.

[Figure 7 about here]

5.2 Cross-Country Correlations of Height and Alternative Cognitive Measures

In this section, we provide estimates of the cross-country correlations of height with

some other commonly used national indicators of cognitive function. In addition to the results

we have already discussed with regards to the SHARE data, these additional results are

suggestive of the potential important information that variation in country-level average

heights contain.

Table 10 provides the correlation of the country dummies for height from column 1 of

Table 8 with several publicly available measures of cognitive function. First, we correlate

16

height with scores from the Programme for International Student Assessment (PISA).7 PISA

scores are generally regarded as informative and reliable indicators of cross-country

comparisons for cognitive ability (Hanushek and Woessman, 2008). In the top two rows of

Table 10, we can see that national height based on the SHARE data is highly correlated with

the performance of 15 year olds in the PISA mathematics score. However, it appears to not be

correlated with the PISA literacy score. The correlation between height and the PISA

mathematics score is significant at the five percent level according to Spearman‟s rank

correlation test and is nearly significant at the ten percent level according to Kendall‟s rank

correlation test.

Next, we also correlate height with various indices of talent and creativity taken from

Florida and Tinagli (2004). We can see in the lower half of Table 10 that national height is

highly correlated with all four measures of talent and creativity. The Pearson correlation

between height and the creativity index is 0.82, suggesting that countries with relatively tall

people are more innovative and creative, which could have long term implications for

economic growth. Hypothesis tests using both Kendall‟s and Spearman‟s rank correlation

tests find that height is significantly correlated with the creativity index, the tolerance index

and the technology index.


Clearly, the height measure we use does not correspond directly with measures for 15

year olds or the labor force as a whole. However, beyond what is given in Garcia and

Quintana-Domeque (2007) and Hatton and Bray (2010), height data by country and age

group does not appear to be readily available. Assuming the rank ordering of countries based

on height remains the same when other age groups are used, the results presented in Table 10

might be viewed as being suggestive of the potential uses that country-level height data might

provide.8

Previous work has focused on identifying important drivers of various national

indicators of policy importance, such as economic development (e.g., Deaton, 2007;

7 PISA (http://www.pisa.oecd.org) is a regular survey of 15-year olds which assesses aspects of their

preparedness for adult life. Four assessments have so far been carried out, in 2000, 2003, 2006 and 2009. 8 In spirit with Zipf‟s law, the rank ordering of countries based on height could provide useful information that

makes the actual collection of height data less important, especially if this rank ordering is stable over time. For

example, when we estimate a regression of log height on log ranking using the SHARE data, we get an R2 of

0.9813. This suggests that the rank ordering of countries based on height can almost predict the value of height

perfectly.

http://www.pisa.oecd.org/

17

Hanushek and Woessman, 2008), economic literacy (e.g., Jappelli, 2010) and creativity (e.g.,

Florida and Tinagli, 2004). The results reported in this section are suggestive of the role that

height might play as a driver of policy related goals and the potential importance of ensuring

that growth is maximized during childhood.

6. Conclusions

Height has been used as a key marker of physical welfare and the standard of living,

as well as a marker of childhood health. Previous research based on national surveys has

found that height is correlated with cognitive function at older ages. Using data for 15

additional European countries, this paper provides further empirical support for the notion

that there exist a significant association between height and cognitive function in later life.

In this paper, we also ask a related and interesting question: are people from countries

where the average person is relatively tall smarter? To our knowledge, this paper is the first

to explore the link between height and cross-country differences in cognitive functioning.

Using data from the Survey of Health, Ageing, and Retirement in Europe, we find empirical

evidence that this is the case, even after controlling for self-reported childhood health, self-

reported childhood ability, parental characteristics and education.

In summary, the results of this paper suggest that average height is related to average

measures of cognitive functioning, both within a country and also when comparing across

countries. Furthermore, our results are also suggestive of the link between height and other

national indicators of interest, such as the cognitive functioning of high school students and

the creativity of the adult workforce. It therefore highlights the use of height as a potentially

useful national indicator. At present, height is not a statistic that is routinely collected during

censuses but there appears to be good reasons why there is potential value in doing so. A

shortcoming of our analysis is that our data do not allow us to systematically investigate why

there is a cross-country link between height and cognitive functioning. This would be an

interesting issue for further research.

18

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Table 1: Summary Statistics: Full sample

Variable Mean Std. dev. Min Max

Reading skill 3.66 1.15 1 5Writing skill 3.52 1.20 1 5Date questions score 3.81 0.54 0 4Verbal fluency 18.81 7.51 0 100Immediate recall score 4.89 1.86 0 10Delayed recall score 3.45 2.04 0 10Numeracy test score 0.62 0.24 0 1Depression ever 0.22 0.42 0 1Subjective health 0.26 0.44 0 1Euro-D score 7.82 2.12 0 10Height 167.73 9.12 100.68 220Age 65.01 10.49 50 105Male 0.44 0.49 0 1Foreign born 0.09 0.29 0 1Years of schooling 10.53 4.27 0 25Childhood health 2.06 1.01 1 5Father white collar when 14 0.26 0.44 0 1White collar job 0.52 0.49 0 1Childhood math 2.72 0.89 1 5Childhood language 2.69 0.87 1 5Childhood book 2.06 1.20 1 5

Notes: This table shows the summary statistics of variables for the respondents who were surveyed in the SHAREdata. Means are reported for the continuous variables and proportions are reported for categorical variables withthe standard deviations.

Table 2: Height, Health and Cognitive Functioning in SHARE

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)Reading Writing Date Verbal Immediate Delayed Numeracy Depression Subjective Euro-Dskill skill questions fluency recall recall test ever health measure

score score score score

Height in cm 0.014 0.014 0.002 0.072 0.016 0.017 0.003 -0.001 0.003 0.013(17.34) (16.59) (4.82) (15.41) (14.14) (13.50) (17.85) (3.47) (8.71) (9.17)

Foreign born -0.195 -0.224 -0.034 -2.558 -0.368 -0.289 -0.033 0.021 -0.091 - 0.398(10.06) (11.10) (4.16) (22.84) (13.35) (9.62) (8.79) (3.14) (11.84) (11.53)

Age 0.025 0.027 0.063 0.246 0.109 0.064 0.006 -0.001 -0.008 0.137(4.68) (5.03 ) (18.12) ( 7.76) ( 14.33) (7.67) (5.48) (0.31) (3.96) (13.79)

Age2/100 -0.038 -0.042 -0.055 -0.339 -0.129 -0.099 -0.008 -0.002 -0.002 -0.122(9.49) (10.43) (20.27) (14.45) (22.70) (16.04) (9.36) (1.05) (1.15) (16.37)

Male -0.161 -0.188 -0.034 -0.328 -0.385 -0.501 0.035 -0.101 0.012 0.601(12.10) (13.27) (5.51) (4.19) (20.15) (23.50) (12.92) (22.63) (2.23) (25.33)

Wave dummies YES YES YES YES YES YES YES YES YES YESCountry dummies YES YES YES YES YES YES YES YES YES YESObservations 44649 44647 61939 61711 62029 62048 61255 62439 62879 62388R-squared 0.1774 0.1755 0.0719 0.2563 0.2269 0.2120 0.1246 0.0497 0.1018 0.1074

Notes: t-statistics are reported in parentheses and are in absolute values together with the coefficients which areestimated using OLS. Height is in cm. Standard errors are clustered at the individual level. SHARE waves 1-3are used covering the years 2002-2008 for 15 European countries.

23

Table 3: Coefficient on Height in SHARE: Table 1 for Sub-samples of 15 European Countries



Austria 0.011 0.014 0.003 0.094 0.026 0.020 0.002 -0.002 0.006 0.026(3.10) (3.94) (2.04) (3.46) (4.29) (2.92) (2.79) (1.33) (3.72) (3.96)

Germany 0.016 0.017 0.003 0.101 0.019 0.017 0.004 -0.001 0.003 0.009(6.29) (6.77) (2.64) (6.13) (5.08) (3.93) (8.06) (0.28) (2.31) (1.98)

Sweden 0.016 0.017 -0.001 0.091 0.011 0.008 0.003 -0.001 0.004 0.008(6.75) 6.97 (0.37) (5.22) (2.91) (1.75) (5.88) (0.35) (3.58) (1.76)

Netherlands 0.014 0.015 0.002 0.095 0.013 0.013 0.004 0.001 0.003 0.010(5.46) (5.53) (1.20) (6.76) (3.41) (2.93) (7.48) (0.36) (3.01) (2.22)

Spain 0.020 0.018 0.004 0.054 0.021 0.024 0.003 -0.002 0.004 0.022(5.07) (4.74) (2.36) (3.80) (4.96) (5.58) (4.82) (1.85) (3.25) (3.67)

Italy 0.026 0.025 0.003 0.129 0.031 0.029 0.004 -0.001 0.005 0.021(9.05) (8.40) (1.55) (7.97) (7.83) (6.45) (6.35) (0.11) (4.11) (3.75)

France 0.021 0.025 0.003 0.108 0.017 0.015 0.003 -0.001 0.002 0.012(7.10) (7.90) (2.01) (5.99) (4.11) (3.55) (5.53) (0.42) (1.41) (2.29)

Denmark 0.015 0.018 0.001 0.113 0.016 0.013 0.001 -0.002 0.004 0.012(5.14) (5.46) (0.15) (5.81) (3.46) (2.39) (5.21) (1.34) (2.65) (2.45)

Greece 0.015 0.014 0.001 0.039 0.019 0.019 0.002 -0.002 0.003 0.022(5.22) (3.51) (0.35) (2.94) (4.78) (4.64) (3.68) (3.08) (3.11) (4.87)

Switzerland 0.014 0.016 -0.001 0.062 0.012 0.012 0.002 -0.001 0.002 0.008(3.52) (3.52) (0.03) (2.25) (2.01) (2.09) (2.75) (0.97) (1.73) (1.46)

Belgium 0.012 0.012 0.001 0.079 0.017 0.022 0.003 -0.001 0.001 0.012(4.53) (4.63) (1.09) (5.75) (4.84) (5.34) (5.86) (1.53) (1.24) (2.65)

Israel 0.002 0.005 0.003 0.039 0.008 0.007 0.001 -0.002 -0.001 0.016(0.57) (1.39) (1.44) (2.01) (1.45) (1.47) (0.56) (1.78) (1.43) (2.36)

Czech Republic 0.010 0.011 0.006 0.065 0.021 0.021 0.003 -0.001 0.002 0.017(4.12) (4.25) (3.28) (3.42) (3.70) (4.20) (4.33) (0.17) (1.82) (2.58)

Poland 0.010 0.008 0.001 0.045 0.011 0.019 0.002 -0.002 0.001 0.005(2.68) (2.34) (0.68) (2.57) (2.26) (3.50) (3.09) (1.65) (0.42) (0.72)

Ireland 0.004 0.005 0.002 0.002 0.016 0.014 0.001 0.001 0.002 0.010(1.25) (1.57) (1.30) (0.11) (3.01) (2.26) (1.98) (0.59) (1.52) (2.06)

Notes: t-statistics are reported in parentheses and in absolute values together with the coefficients which areestimated using OLS. Height is in cm. Standard errors are clustered at the individual level. SHARE waves 1-3are used covering the years 2002-2008 for 15 European countries.

24

Table 4: Determinants of Height, Education, and Occupational Choice in SHARE

(1) (2) (3) (4)Height Years of White collar White collarin cm schooling job job

Height in cm 0.054 0.005 0.002(11.34) (7.34) (3.47)

Years of schooling 0.044(41.40)

Father white collar 1.216 2.284 0.305 0.206job when 10 (12.08) (35.62) (35.75) (23.85)Childhood health 0.133 0.253 0.007 0.006

(1.38) (4.43) (0.81) (0.79)Foreign born -1.268 -0.135 -0.037 -0.037

(6.70) (1.10) (2.26) (2.41)Age -0.107 -0.175 0.020 0.029

(2.07) (5.79) (3.88) (5.87)Age2/100 -0.022 0.053 -0.017 -0.020

(0.56) (2.35) (4.88) (5.87)Male 11.402 0.360 -0.140 -0.158

(128.77) (4.80) (13.54) (16.44)Wave dummies YES YES YES YESCountry dummies YES YES YES YES

Notes:t-statistics are reported in parentheses and in absolute values together with the coefficients which areestimated using OLS. Height is in cm. Standard errors are clustered at the individual level. SHARE waves 1-3are used covering the years 2002-2008 for 13 European countries.

Table 5: Childhood and Mid-life Circumstances in SHARE



Height in cm 0.010 0.011 0.002 0.054 0.012 0.013 0.002 -0.001 0.002 0.012(14.08) (13.72) (4.05) (12.03) (10.62) (10.59) (14.64) (2.85) (6.40) (7.59)

White collar 0.376 0.444 0.025 1.365 0.386 0.389 0.054 -0.004 0.076 0.223job (22.95) (25.99) (3.74) (14.86) (16.97) (14.58) (17.41) (0.62) (10.60) (6.27)Father white collar 0.256 0.280 0.005 1.079 0.249 0.249 0.030 0.002 0.040 0.060job when 14 (17.83) (18.52) (1.03) (12.12) (11.91) (10.08) (10.75) (0.29) (6.61) (1.94)Childhood health 0.143 0.156 0.016 0.302 0.070 0.016 0.013 -0.045 0.096 0.340

(10.35) (10.94) (2.87) (3.76) (3.58) (0.72) (4.68) (8.80) (16.12) (13.02)Years of schooling 0.070 0.074 0.007 0.293 0.066 0.068 0.009 -0.001 0.010 0.035

(36.64) (37.18) (10.39) (32.42) (30.48) (28.25) (30.50) (2.31) (18.03) (11.45)Controls YES YES YES YES YES YES YES YES YES YESWave dummies YES YES YES YES YES YES YES YES YES YESCountry dummies YES YES YES YES YES YES YES YES YES YES


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Table 6: Determinants of Height, Education, and Occupational Choice in SHARE with Addi-tional Controls for Childhood History and Parent Characteristics

(1) (2) (3) (4)Height Years of White collar White collarin cm schooling job job

Height in cm 0.036 0.062 0.046(18.23) (15.75) (10.54)

Childhood conditionsMath ability 0.331 0.905 0.083 0.053

(6.63) (33.57) (19.28) (12.49)Language ability 0.299 1.048 0.104 0.066

(5.74) (37.37) (23.25) (14.88)Books 0.536 0.999 0.087 0.047

(12.68) (42.57) (23.58) (12.38)Parents drank heavily -0.549 -0.869 -0.090 -0.043

(3.51) (10.75) (6.76) (3.41)Parents had mental health problems -0.737 0.099 0.021 0.011

(2.51) (0.65) (2.52) (1.47)Father present at home 0.287 0.245 0.025 0.016

(1.82) (2.76) (1.83) (1.24)Mother present at home 0.553 0.321 0.039 0.015

(2.43) (2.47) (1.91) (0.78)Dwelling with no features -0.756 -1.366 -0.162 -0.112

(6.59) (22.62) (16.89) (12.11)Dwelling with > 3 rooms 0.399 0.756 0.055 0.025

(3.96) (13.28) (6.21) (2.99)Dwelling with more than 4 people -0.546 -0.569 -0.061 -0.038

(5.94) (11.14) (7.35) (4.93)Parent’s characteristics

Mother’s age at death 0.009 0.013 0.003 0.001(2.77) (4.72) (4.42) (1.41)

Father’s age at death 0.007 0.008 0.001 0.001(2.11) (2.86) (1.02) (0.49)

Mother’s current health 0.200 0.155 0.012 0.007(2.84) (3.26) (1.62) (0.81)

Controls in Table 4 YES YES YES YESWave dummies YES YES YES YESCountry dummies YES YES YES YES


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Table 7: The Role of Childhood and Mid-life Circumstances in SHARE with Additional Controlsfor Childhood History and Parent Characteristics



Height 0.009 0.010 0.001 0.046 0.010 0.014 0.002 -0.001 0.005 0.009in cm (12.94) (12.47) (3.84) (10.54) (9.58) (7.40) (13.56) (2.81) (7.64) (6.91)ChildhoodconditionsMath 0.150 0.172 0.017 0.870 0.182 0.207 0.041 -0.005 0.022 0.167ability (20.74) (23.04) (5.96) (20.81) (18.10) (17.32) (29.51) (1.75) (7.33) (10.70)Language 0.253 0.287 0.012 0.868 0.217 0.221 0.025 0.009 0.020 0.073ability (34.50) (37.89) (4.19) (19.64) (20.55) (17.71) (17.15) (3.45) (6.37) (4.54)Books 0.114 0.125 0.003 0.841 0.137 0.141 0.017 0.004 0.017 0.050

(19.41) (20.24) (1.45) (23.54) (15.97) (14.18) (14.96) (1.89) (6.64) (3.92)

Parents -0.001 -0.016 -0.013 -0.108 -0.011 -0.011 -0.005 0.019 -0.027 -0.036smoked (0.05) (1.19) (2.50) (1.44) (0.58) (0.49) (2.04) (4.18) (2.68) (3.39)Parents drank -0.091 -0.112 -0.041 -0.419 -0.153 -0.130 -0.024 0.054 -0.113 -0.468heavily (3.85) (4.60) (4.05) (3.15) (4.69) (3.46) (5.60) (6.22) (6.00) (8.84)Parents mental -0.059 -0.080 -0.037 -0.497 -0.024 -0.034 -0.026 0.149 -0.105 -0.589health problems (1.44) (1.84) (2.49) (1.92) (0.39) (0.47) (3.06) (8.27) (3.04) (6.12)

Mother present 0.287 0.051 0.007 -0.058 0.029 0.071 0.011 -0.032 0.052 0.178at home (0.47) (1.47) (0.49) (0.29) (0.69) (1.34) (1.76) (2.67) (1.94) (2.36)Father present 0.001 0.018 0.017 -0.012 0.019 0.043 0.014 -0.029 0.051 0.154at home (0.3) (0.77) (1.74) (0.09) (0.58) (1.18) (3.22) (3.48) (2.68) (3.06)

Dwelling with no -0.161 -0.170 0.002 -0.630 -0.188 -0.152 -0.015 0.011 -0.031 0.021features (10.04) (10.26) (0.33) (7.47) (8.69) (6.05) (5.03) (1.96) (2.49) (0.59)Dwelling with 0.070 0.075 0.009 0.692 0.113 0.057 0.015 -0.004 0.040 0.114> 3 rooms (5.08) (5.28) (1.58) (8.74) (5.91) (2.57) (5.77) (0.88) (3.68) (3.87)Dwelling with -0.080 -0.082 -0.001 -0.159 -0.079 -0.084 -0.009 0.008 -0.010 -0.060> 4 people (6.21) (6.03) (0.18) (2.07) (4.29) (3.91) (3.56) (1.61) (1.06) (2.15)

Mother’s age 0.002 0.002 0.001 0.011 0.003 0.003 0.001 0.001 0.002 0.004at death (4.04) (4.48) (3.92) (4.28) (5.11) (4.74) (2.21) (0.15) (6.04) (4.33)Father’s age 0.001 0.001 0.001 0.005 0.002 0.001 0.001 -0.001 0.002 0.003at death (0.48) (0.99) (0.54) (2.30) (2.43) (0.20) (3.31) (4.52) (6.02) (3.49)

Mother’s current 0.075 0.081 0.001 0.116 0.051 0.063 0.010 -0.016 0.003 0.197health (8.5) (8.64) (0.53) (2.16) (3.98) (4.25) (5.90) (4.53) (8.54) (10.29)Father’s current 0.049 0.074 0.001 0.143 0.045 0.039 0.001 -0.026 0.003 0.157health (3.81) (5.49) (0.26) (1.80) (2.37) (1.76) (0.31) (4.94) (8.54) (5.28)

Controls in Table 5 YES YES YES YES YES YES YES YES YES YESWave dummies YES YES YES YES YES YES YES YES YES YESCountry dummies YES YES YES YES YES YES YES YES YES YES


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Table 8: Coefficients on Country Dummies in the Height, Cognitive Functioning and HealthRegressions

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)Height Reading Writing Date Verbal Immediate Delayed Numeracy Depression Subjective Euro-D

skill skill questions fluency recall recall test ever health measurescore score score score

Austria 5.8899 1.0787 1.0741 0.2014 7.1517 1.5905 1.1720 0.1654 0.1358 0.3356 0.9353Germany 6.5708 0.7466 0.6990 0.1749 5.6868 1.5976 1.0033 0.1630 -0.0958 0.1024 0.8661Sweden 7.4292 1.4068 1.3788 0.2018 7.9109 1.5187 1.3571 0.1621 -0.0517 0.4269 0.8531Netherlands 8.1212 0.6006 0.5157 0.1344 4.4244 1.3572 1.0862 0.1571 -0.0444 0.2980 0.7974Italy 2.0260 0.3444 0.3028 0.1306 -0.2433 0.5336 0.2646 0.0432 -0.0449 0.0131 0.0469France 3.0406 0.8693 0.7547 0.1431 4.5292 0.7446 0.4641 0.0904 -0.0076 0.1333 0.1263Denmark 7.3879 0.9211 0.8747 0.1183 5.8501 1.4633 1.3197 0.1262 -0.0343 0.4191 0.8721Greece 3.5556 0.5080 0.4713 0.2169 -0.5086 0.9941 0.6415 0.1321 -0.1387 0.3077 0.8219Switzerland 5.0631 1.0420 0.9573 0.1967 5.3617 1.4749 1.1688 0.1762 -0.0831 0.5839 0.9543Belgium 4.4445 0.9062 0.7786 0.1188 4.3370 1.0458 0.6041 0.0861 -0.0295 0.2582 0.4362Israel 4.0857 0.7993 0.8373 0.0581 4.7328 1.0076 0.5612 0.1463 -0.1125 0.2056 0.2430Czech 5.6134 0.7706 0.7307 0.0842 2.7359 0.8619 0.2280 0.1079 0.0951 -0.0685 0.5936Poland 2.8888 0.4665 0.4602 0.1172 0.3462 0.4060 0.0669 0.0496 -0.0336 -0.4911 -0.8795Ireland 1.5199 1.0363 0.8621 0.1408 0.4046 1.2848 1.2528 0.0866 -0.0137 0.5592 0.8139

Notes: Column 1 are the estimated coefficients on country dummies in Table 6 column 1. Columns 2-10 are theestimated coefficients on country dummies in Table 7. For Ireland and Israel, regressions are estimated withoutthe additional controls given in Table 6 that are taken from SHARELIFE.

Table 9: Cross-Country Correlations between Height and Cognitive Functioning and betweenHeight and Health


score score score scoreHeightPearson 0.3670 0.4225 0.1679 0.7538 0.6818 0.5442 0.7231 0.0929 0.2576 0.5396Spearman’s rank 0.3363 0.3890 0.1736 0.7143 0.7011 0.5121 0.6791 -0.0945 0.2571 0.5824Kendall’s rank 0.2747 0.2967 0.1209 0.5385 0.5165 0.4286 0.4725 -0.0110 0.2308 0.4066

Notes: Each row presents the correlation coefficients between column 1 and one of the columns 2-10 in Table 8.

Table 10: Possible National Benefits of National Height

Coefficients of correlation between the country dummies in height and national indicators.

National Indicator Pearson Spearman’s rank Kendall’s rank

Average mean literacy score (PISA 2009) 0.4975 0.4209 0.2949Average mean mathematics score (PISA 2009) 0.4583 0.5165 0.3407Creativity Index 0.8171 0.8545 0.6889Tolerance Index 0.8555 0.903 0.7778Technology Index 0.6911 0.7818 0.5556Talent Index 0.5142 0.503 0.3778

Notes: Each row presents the correlation coefficients between the corresponding national variables and column 1in Table 8. PISA scores are obtained from the OECD database, and the creativity indices are from Florida andTinagli (2004).

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(a) Full sample (b) Men (c) Women

Figure 1: Average Height by Country: 15 European Nations


Figure 2: Average Numeracy Score by Country: 15 European Nations


Figure 3: Average Dates Score by Country: 15 European Nations

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Figure 4: Average Verbal Fluency Score by Country: 15 European Nations


Figure 5: Average Immediate Recall Score by Country: 15 European Nations


Figure 6: Average Delayed Recall Score by Country: 15 European Nations

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(a) Height and Immediate Recall (b) Height and Delayed Recall

(c) Height and Numeracy (d) Height and Verbal Fluency

(e) Height and Date Questions (f) Height and Euro-D Measure of Wellbeing

Figure 7: Positive Correlation between Height and Cognitive Functioning: 15 European Nations

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Height, Health and Cognitive Function at Older Ages: Cross ... · 3/30/2011 · with increasing height. Their findings suggest that efforts to improve prenatal and early life conditions