Re-Visiting the Relationship Between Height

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 Please do not cite or quote.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). 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.

1 (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

2 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.

3 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).

4 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.

5 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

6 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).

7 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.

8 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.

9 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.

[Table 2 about here]

10 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 3 about here]

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 4 about here]

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

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

[Table 5 about here]

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).

[Table 6 about here]

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.

[Table 7 about here]

12 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

13 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).

14 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.

[Table 8 about here] [Table 9 about here]

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

15 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.

[Table 10 about here]

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.

16 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.

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22 Table 1: Summary Statistics: Full sample

Variable Mean Std. dev. Min Max

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

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

Table 2: Height, Health and Cognitive Functioning in SHARE

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 YES Country dummies YES YES YES YES YES YES YES YES YES YES Observations 44649 44647 61939 61711 62029 62048 61255 62439 62879 62388 R-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 are estimated using OLS. Height is in cm. Standard errors are clustered at the individual level. SHARE waves 1-3 are 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 are estimated using OLS. Height is in cm. Standard errors are clustered at the individual level. SHARE waves 1-3 are 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 collar in 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 0.206 job 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 YES Country dummies YES YES YES YES

Notes:t-statistics are reported in parentheses and in absolute values together with the coeﬃcients which are estimated using OLS. Height is in cm. Standard errors are clustered at the individual level. SHARE waves 1-3 are 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 0.223 job (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 0.060 job 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 YES Wave dummies YES YES YES YES YES YES YES YES YES YES Country dummies YES YES YES YES YES YES YES YES YES YES

25 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 collar in cm schooling job job

Height in cm 0.036 0.062 0.046 (18.23) (15.75) (10.54) Childhood conditions Math 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 YES Wave dummies YES YES YES YES Country dummies YES YES YES YES

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

Height 0.009 0.010 0.001 0.046 0.010 0.014 0.002 -0.001 0.005 0.009 in cm (12.94) (12.47) (3.84) (10.54) (9.58) (7.40) (13.56) (2.81) (7.64) (6.91) Childhood conditions Math 0.150 0.172 0.017 0.870 0.182 0.207 0.041 -0.005 0.022 0.167 ability (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 0.073 ability (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 -0.036 smoked (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 -0.468 heavily (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 -0.589 health 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 0.178 at 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 0.154 at 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 0.021 features (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 0.004 at 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 0.003 at 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 0.197 health (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 0.157 health (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 YES Wave dummies YES YES YES YES YES YES YES YES YES YES Country dummies YES YES YES YES YES YES YES YES YES YES

27 Table 8: Coeﬃcients on Country Dummies in the Height, Cognitive Functioning and Health Regressions

(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 measure score score score score

Austria 5.8899 1.0787 1.0741 0.2014 7.1517 1.5905 1.1720 0.1654 0.1358 0.3356 0.9353 Germany 6.5708 0.7466 0.6990 0.1749 5.6868 1.5976 1.0033 0.1630 -0.0958 0.1024 0.8661 Sweden 7.4292 1.4068 1.3788 0.2018 7.9109 1.5187 1.3571 0.1621 -0.0517 0.4269 0.8531 Netherlands 8.1212 0.6006 0.5157 0.1344 4.4244 1.3572 1.0862 0.1571 -0.0444 0.2980 0.7974 Italy 2.0260 0.3444 0.3028 0.1306 -0.2433 0.5336 0.2646 0.0432 -0.0449 0.0131 0.0469 France 3.0406 0.8693 0.7547 0.1431 4.5292 0.7446 0.4641 0.0904 -0.0076 0.1333 0.1263 Denmark 7.3879 0.9211 0.8747 0.1183 5.8501 1.4633 1.3197 0.1262 -0.0343 0.4191 0.8721 Greece 3.5556 0.5080 0.4713 0.2169 -0.5086 0.9941 0.6415 0.1321 -0.1387 0.3077 0.8219 Switzerland 5.0631 1.0420 0.9573 0.1967 5.3617 1.4749 1.1688 0.1762 -0.0831 0.5839 0.9543 Belgium 4.4445 0.9062 0.7786 0.1188 4.3370 1.0458 0.6041 0.0861 -0.0295 0.2582 0.4362 Israel 4.0857 0.7993 0.8373 0.0581 4.7328 1.0076 0.5612 0.1463 -0.1125 0.2056 0.2430 Czech 5.6134 0.7706 0.7307 0.0842 2.7359 0.8619 0.2280 0.1079 0.0951 -0.0685 0.5936 Poland 2.8888 0.4665 0.4602 0.1172 0.3462 0.4060 0.0669 0.0496 -0.0336 -0.4911 -0.8795 Ireland 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 the estimated coeﬃcients on country dummies in Table 7. For Ireland and Israel, regressions are estimated without the additional controls given in Table 6 that are taken from SHARELIFE.

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

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Reading Writing Date Verbal Immediate Delayed Numeracy Depression Subjective Euro-D skill skill questions ﬂuency recall recall test ever health measure score score score score Height Pearson 0.3670 0.4225 0.1679 0.7538 0.6818 0.5442 0.7231 0.0929 0.2576 0.5396 Spearman’s rank 0.3363 0.3890 0.1736 0.7143 0.7011 0.5121 0.6791 -0.0945 0.2571 0.5824 Kendall’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 0.2949 Average mean mathematics score (PISA 2009) 0.4583 0.5165 0.3407 Creativity Index 0.8171 0.8545 0.6889 Tolerance Index 0.8555 0.903 0.7778 Technology Index 0.6911 0.7818 0.5556 Talent Index 0.5142 0.503 0.3778

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

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

Figure 1: Average Height by Country: 15 European Nations

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

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

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

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

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

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

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

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

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

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

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

(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