Early life environment and adult height: The case of Chile

Accepted Manuscript Title: Early Life Environment and Adult Height: The Case of Chile Authors: Florencia Borrescio-Higa, Carlos Guillermo Bozzoli, Federico Droller PII: DOI: Reference:

S1570-677X(18)30158-8 https://doi.org/10.1016/j.ehb.2018.11.003 EHB 748

To appear in:

Economics and Human Biology

Received date: Revised date: Accepted date:

12 June 2018 21 November 2018 23 November 2018

Please cite this article as: Borrescio-Higa F, Bozzoli CG, Droller F, Early Life Environment and Adult Height: The Case of Chile, Economics and Human Biology (2018), https://doi.org/10.1016/j.ehb.2018.11.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Early Life Environment and Adult Height: The Case of Chile

November 2018 * ** ***

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Florencia Borrescio-Higa*, Carlos Guillermo Bozzoli**, Federico Droller***

Universidad Adolfo Ibañez, Chile Fundación Bunge y Born and Universidad Torcuato Di Tella, Argentina Universidad de Santiago de Chile, Chile

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We analyze the relation of early-life disease environment and adult height in Chile, between 1960 and 1989 The decline in Infant Mortality Rate explains almost all of the increase in height GDP per capita does not appear to explain gains in adult heights This pattern persists after controlling for internal migration and urbanization rates Public Health policies are strongly related to this long-term effect on adult height

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Highlights

Abstract

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In this paper, we analyze the relationship between adult height and early-life disease environment, proxied by the infant mortality rate (IMR) in the first year of life, using cohort-region level data for

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Chile for 1960-1989. IMRs show a remarkable reduction of 100 points per thousand over this thirtyyear period, declining from 119.4 to 21.0 per thousand. We also document a 0.96 cm increase in

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height per decade. We find that the drop in IMRs observed among our cohorts explains almost all of the long-term trend in rising adult heights, and that GDP per capita does not appear to have any predictive power in this context. Results are robust in a variety of specifications, which include area and cohort dummies, an adjustment for internal migration, and urbanization rates. Our results point to the long-term effect of a public health policy.

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JEL Codes: I15, O54

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Keywords: Adult Height, Infant Mortality, Income, Developing Country

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Introduction

It has long been acknowledged that human height is a key measure of health and living standards (Fogel, 1994; Baten, 1999; Steckel, 1995). Yet while the determinants of adult height across

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populations and over time, and the effects of conditions during infancy on the subsequent stature individuals attain as adults, have been documented for developed countries, evidence for developing countries remains scarce (Steckel, 2009; Galofré-Vilà, 2018). In this paper, we contribute to the literature on modern adult height determinants in developing countries by analyzing cohorts born between 1960 and 1989 for all 13 regions in Chile (as defined in 1990), comprising a total of 3,644 individuals. Our study is based on cohort data, measured adult height, the burden of disease in the first

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year of life (proxied by the infant mortality rate, henceforth IMR), and indicators of economic wellbeing during the first year of life (proxied by per capita gross domestic product, henceforth per capita

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GDP). Our study covers a time during which the country experienced both stagnation and economic

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growth. This thirty-year period also saw the implementation of public health programs that diminished

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malnutrition and drastically reduced infant mortality rates, particularly compared to other countries in South America. More specifically, between 1960 and 1989 Chile’s per-capita GDP grew from $3,810

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to $5,950, while its infant mortality rate declined from 119.4 deaths per 1,000 live births for the cohort 1960 to 21 deaths per 1,000 live births in that of 1989.1 No other country in the Americas

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experienced such a sharp decline in infant mortality rates as those observed over this period. Adult height is not only a direct measure of long-run health or biological well-being, but is

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linked to living standards, life span, income, levels of education, earnings, and productivity (Baten and Komlos, 1998; Case and Paxson, 2008; Deaton, 2007; Deaton and Arora, 2009; Schultz and

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Strauss, 2008; Steckel, 2009). Determinants of adult stature include genetic factors, and, more importantly, net nutrition (Steckel, 2009; Perkins et al., 2016). Cumulative net nutrition is, in turn, determined by food availability and by the incidence of diseases during the growth period, which

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affects the absorption of nutrients or diverts them from their normal use in infant development (as proxied by gains in height and weight). In this study, we use IMRs as a measure of the disease environment in the first year of life in a given region, a factor that accounts for the difference between gross and net nutrition. In addition, per

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Chile’s current GDP per capita is $15,000 (in 2010 constant-US$, as of January 2018) while the IMR is 7.2 deaths per 1,000 live births. 3

capita GDP can be interpreted as a proxy for food availability or income, as in Quintana-Domeque et al. (2011), or as an indicator for the general production capacity of a region, as in Baten and Blum (2014). We build upon a large body of research measuring the negative effect of the disease environment in early life on adult height. Using data from Spain, Quintana-Domeque et al. (2011)

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find, for example, that a reduction in the IMR of 30 deaths per 1,000 live births explains 70% of the gains in height for the period 1961-1980, a time of demographic transition and economic development that followed the Civil War and difficult post-war living conditions. Bozzoli et al. (2009) similarly document a strong link between post-neonatal mortality and average adult height in a number of European countries and in the United States.

Evidence on modern adult height in developing countries is scarcer (see Baten and Blum (2014)

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for first estimates). Deaton (2007) finds no consistent relationship across and within countries between adult height and childhood mortality or living conditions for a set of 43 developing countries,

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and advances the idea of selection versus debilitation. On the one hand, a high-disease environment

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increases the survival cutoff (i.e., higher mortality rates), and their model predicts an increment in the

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average adult height of the population. On the other hand, for those who do survive, there is a reduction in average adult height, an effect that works in the opposite direction to selection.

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Based on data from Brazil, for the period 1950-1980, De Oliveira and Quintana-Domeque (2014) find that infant mortality rates do not correlate with average adult height, while per capita GDP

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does. Coffey (2015) documents a negative relationship between height and measures of early life mortality in India for cohorts born between 1970 and 1983. The paper also shows that aggregate

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income in early childhood predicts adult height, particularly for men. These two cases for nondeveloped countries suggest that context may matter. More recently, Schneider and Ogasawara (2018)

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find that IMR in early life did not have a strong influence on the growth pattern of children in interwar Japan. Moreover, it remains unclear as to whether per capita GDP or IMRs are the best predictors for subsequent adult height.

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We are not aware of any studies on modern adult height and childhood mortality for Chile.

Llorca-Jaña et al. (2018) work with a sample of soldiers, showing the evolution of adult height in eighteenth-century Chile, and describing how the latter relates to per capita GDP and real wages. In a different paper, Llorca-Jaña et al. (forth.) show changes in adult height for a sample of soldiers in the nineteenth century. Núñez and Pérez (2015) instead study the height of boys in Chile during the nineteenth and twentieth centuries and document differences in their height across socioeconomic 4

groups. They find divergences of up to 11 centimeters across categories of boys up until 1950, but no statistically significant differences in heights after that year. They argue that the reduction in height inequalities across socioeconomic groups can be explained by the introduction of health and social policies that improved well-being, nutrition, and living standards. Baten et al. (2009) analyze adult

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heights for Brazil, Argentina, and Peru for the nineteenth and the early twentieth centuries, comparing welfare trends. Challú and Silva-Castañeda (2016) document the evolution of female heights in twelve Latin American countries, including Chile, during the second half of the 20th century.

Between 1960 and 1990 IMRs decreased in Chile, as well as in other countries in Latin America. However, the dramatic decline in Chile had no precedent in the region; certainly no other

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country managed to reduce its IMRs by 82% over this period of time. In fact, in 1960 Chile had one of the highest IMRs in the region, yet by 1990 it had the lowest (see Internet Appendix Table 1). It was

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only in 1985, towards the end of this phase of declining IMRs, that a period of strong Chilean

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economic growth began, primarily affecting the material conditions of the youngest cohorts in our

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sample. The transition was thus demographic (urbanization) and epidemiologic in nature, not necessarily a period marked by salient improvements in economic well-being (that could, in turn, have

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had an impact on nutrition during childhood). In addition, our analysis covers both an early era of democratically elected governments that promoted and expanded the welfare-state, as well as a time

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of military rule, during which social spending was drastically reduced and inequality increased (McGuire, 2010; Salvatore et al., 2010). That said, public health policies responding to high rates of

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malnutrition and mortality, particularly among infants and children, were never abandoned (Goldsmith, 2017; Zarate, 2008). Health programs included, but were not limited to, the distribution

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of supplementary food (mostly in the form of milk powder, given to infants, and pregnant and breastfeeding women), regular health checks, and vaccinations. Urbanization and access to drinking water and sewage also contributed to creating a more sanitary environment: in 1960 only 40% of the

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population had access to drinking water and even less (35%) had an adequate sewage system, whereas by 1985, 91% of the population had access to drinking water and 83% had some sewage system (Monckeberg et al., 1987).

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We find that the strong reduction in IMRs in Chile explains almost all of the increment in adultheight over this period, while per capita GDP does not appear to have any predictive power. The most conservative specification indicates that a drop of 100 points in the IMR is related to an increase of 2.2 centimeters in height, almost all of the rise in height observed over the period of study. These

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results are robust in a variety of specifications, which include geographical areas and cohort fixed effects, an adjustment for internal migration, and the inclusion of urbanization rates. Our estimates are based on cohort-region data as well as individual level data. Moreover, in some specifications we also analyze differences between men and women.

The rest of the paper is organized as follows. Section 2 describes the data. Section 3 presents the evolution of the main variables under analysis, the results, and robustness checks. In Section 4, we

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explore the role played by public health policies in the decline of infant mortality rates in Chile, and

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Section 5 concludes.

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Data

Data on heights come from the National Health Surveys (ENS, Encuesta Nacional de Salud de

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Chile), conducted in 2003 and 2009 by the Chilean Ministry of Health. ENS surveys are representative at the national and regional levels, with a well-defined sampling design based on the

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2002 National Census. The samples include a total of 3,644 observations, of which 1,234 observations are from the 2003 ENS 2003 and 2,410 observations from the 2009 ENS. Individuals in the sample

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were visited by trained specialists who, in addition to conducting the survey, took blood samples and body measurements. It is important to emphasize that heights were not self-reported, but measured by

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an expert so as to assure consistency in the survey data (self-reported heights have been shown to be upwardly biased (Perkins et al., 2016)). Using these National Health Surveys, we construct six birth

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cohorts (1960-1964, 1965-1969, 1970-1974, 1975-1979, 1980-1984 and 1985-1989) for all 13 regions of Chile.

We begin by computing the average height of adults for each cohort across the regions. Adults are defined as individuals aged 20 to 50, as in (Quintana-Domeque et al., 2012). We exclude individuals older than 50 so as to prevent mortality selection and the effects of height loss among the elderly. Moreover, full adult height is commonly attained around age 20 in developing countries, a 6

delayed pattern compared to richer countries (Deaton, 2008). After pooling the data from both of the ENS surveys, we compute the mean heights of men and women separately (at the cohort-region level), and then take the simple average between the aforementioned averages at the cohort-region level (Quintana-Domeque et al., 2011). Appendix Table 1 presents descriptive statistics of gender

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specific heights by decade and region. The annual IMR for each region is computed using vital statistics records from the Office of Statistics and Health Information (DEIS - Ministerio de Salud de Chile). Regional per capita GDP (in constant prices) from 1960 to 1990 comes from the Ministry of Social Development (Diaz-Vernon, 2004).2 Finally, the rate of urbanization is computed using the 1940, 1960, 1970, 1982, and 1992 National Censuses (Instituto Nacional de Estadisticas). Based on CORFO (1950), we define 6 geographical areas: Big North (regions I, II); Small North (regions III,

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IV); Central Area (regions V, VI, VII); Metropolitan Area (region XIII); South (regions VIII, IX, X) and Austral Area (regions XI, XII).3 All variables are at the regional level, and we consider 13 regions

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to have consistent units over time. Internet Appendix Table 2 presents descriptive statistics of all the

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variables.

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Results

Table 1 shows the evolution of height (in centimeters) and IMR by birth cohort – region cells. Over

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time, all regions show a strong and steady decline in infant mortality. For the oldest cohort (those born between 1960 and 1964), mortality rates in vulnerable regions rose as high as 164.8 per 1,000 live

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births, as in the case of region IX (Araucanía). The nearby regions VIII and X (Bio-Bio and Los Lagos) show similarly high rates, above 150 per 1,000 births for the oldest cohort. In contrast, the

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populous region XIII, home to Santiago, the capital city of Chile, had lower infant mortality rates for

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the same cohort of about 88 per 1,000 live births.

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See also Banco Central de Chile (2000), Duncan and Fuentes (2005), and Vial and Bonacic (1994).

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CORFO (1950) similarly divides Chile in six geographical areas. Working from these divisions, we instead combine two smaller areas in the South and separate the Metropolitan Region from the Central area, given that the former includes the large capital city of Santiago. 7

Simple means across regions (by cohort) reveal a height of 162 cm for the 1960-64 cohort, gradually rising to 164.4 cm for the 1985-89 cohort, a total increase of about 2.4 cm, or slightly under 1 cm per decade. To put this into perspective, using mostly self-reported data from Europe and the USA, Bozzoli et al. (2009) find that between 1950 and 1980, the poorer countries of their sample (i.e.,

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Greece, Italy, Portugal, and Spain) experienced a marked gain in stature of about 1.7 cm/decade. This is a higher rate compared to actually measured adult statures in Brazil over the same period, which showed an increment of 0.9 cm/decade.

Meanwhile, infant mortality rates in Chile (simple means across regions) decreased, from the oldest to the youngest cohort, from 119.4 to just 21.0 per 1,000: a remarkable reduction of almost 100 points per thousand in infant mortality. Steady declines in IMRs occurred across all regions and do

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not show signs of leveling off. Figure 1 in the Appendix presents the geographical variation in IMRs

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in 1960.

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What role did this dramatic reduction in infant mortality play in the rise in statures? Was the

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decline in the burden of disease during the first year of life the salient factor behind the greater subsequent heights of these newborns as adults? As mentioned above, there are other factors at work

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— rural-urban migration, changes in long-term economic and public policy — that may also explain secular changes in adult height. We consider all these possibilities in the analysis that follows.

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We begin by plotting the bivariate relation patterns between our main variables: mean heights (cm), IMRs, and the logarithm of per capita GDP (hereafter log(pcGDP)). Each dot in the scatter plots

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is a cohort-region observation (there are 78 observations). Figure 1 shows a negative relation between mean height and IMR, as well as a negative relation between IMR and log(pcGDP), while there is no

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discernible correlation pattern between height and log(pcGDP). The figure also presents pairwise correlations between these variables: IMR shows a strong linear relationship with both log(pcGDP) and height. Log(pcGDP) is also correlated with height but the relationship is not as strong. Below, we

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analyze these correlations more extensively.

INSERT FIG 1 HERE

Our main results are displayed in Table 2 below. All regressions are weighted by the number of observations in each cell. Standard errors are robust to heteroscedasticity and clustered at the 8

geographical area level. Column 1 shows estimates of a simple OLS regression explaining height by IMR without controlling for geographical area or cohort fixed effects. The coefficient indicates that a drop of 100 points in the IMR is related to an increase of 2.86 cm in height. In column 2 we add cohort fixed effects (not jointly significant), while column 3 adds geographical area fixed effects,

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which are jointly significant. Column 4 adds both geographical area and cohort fixed effects. Note that across specifications the coefficient attached to IMR remains fairly stable. Column 5 adds (log) per capita GDP as an additional control variable, but log(pcGDP) is not individually significant nor does it affect the magnitude or the significance of the coefficient attached to IMR. Regressions in columns 1 to 5 have been estimated using the 6 cohorts in all 13 regions, although not all cohortregion cells have the same number of individuals, some cells have less than 30 individuals upon

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which we average. As a robustness check, in columns 6, 7 and 8 we exclude from our analysis all cells with less than 30 observations. Note that the sample size reduces from 78 to 65 observations (mostly

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coming from a reduction in region-cohort cells of the youngest cohort). Column 6 controls for cohort

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fixed effects, column 7 controls for geographical area fixed effects and column 8 controls for both

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fixed effects; log(pcGDP) is included in all three regressions. Results show that the coefficient on IMR remains stable and significant in all but the last column, where it is less precisely estimated.

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In sum, the most conservative specification, with both cohort and area fixed effects in column 5, implies that the drop in IMRs observed across cohorts explains almost all of the change in height in

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our setting.

We also model a non-linear relation between IMR and adult height as an exercise to explore

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selection vs. scarring effects. According to Bozzoli et al. (2009) the disease environment (proxied by IMR) could operate in opposite directions on the height of adults who survived infancy. On the one

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hand, a poor disease environment could have a scarring effect on newborns. This effect would induce a negative relationship between IMR at birth and the height these individuals attain as adults. On the other hand, some newborns may die, who are less likely to achieve normal adult height. As they are

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consequently “selected out,” there is an increase of the average height of those who did survive, thus creating a positive link between IMR and adult height, an effect known as selection. We do not, however, find evidence of a non-linear relation between height and IMR. In our dataset, decreasing

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IMRs are correlated with taller cohorts, suggesting that the scarring effect is prevailing over selection.4

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INSERT TABLE 2 HERE

One potential issue with our data is that while individuals report their region of current residence in the ENS surveys, their region of birth of the individual is not included. Our results could therefore be biased if the average height of those born and currently residing in the same region differs from the average height of those born in one region but currently residing in another. In order to assess this possibility, we explore the role of interregional migration patterns. Using census data

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from 2002, we compute in Table 3 the proportion of individuals in each region born in that same region. In several northern and southern regions of Chile, particularly regions I, II, and XII (Tarapacá,

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Antofagasta, and Magallanes, respectively), a relatively low proportion of the population is native to

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the region. Others, such as regions X (Los Lagos) and VIII (Bio-Bio), instead display relatively high

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proportions of the population being native to the region.

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INSERT TABLE 3 HERE

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Following Bosch et al. (2009) we test whether selectivity in migration might affect the consistency of our estimators. We use their framework to define the proportion of individuals born in a cohort-

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region that stays in that region as 𝜆𝑖,𝑡 and construct three variables for each cohort-region:

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((1 − 𝜆𝑖,𝑡 )/𝜆𝑖,𝑡 ) the ratio of those who migrated with respect to those who stayed in their region of birth,

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̃ = (1−𝜆𝑖,𝑡) ∙ 𝐼𝑀𝑅, the migration adjusted IMR, and IMR 𝜆 𝑖,𝑡

1−𝜆𝑖,𝑡 ̃ log(𝑝𝑐𝐺𝐷𝑃 ) ∙ log(𝑝𝑐𝐺𝐷𝑃𝑖,𝑡 ) , the migration adjusted GDP. 𝑖,𝑡 ) = ( 𝜆 𝑖,𝑡

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Results available upon request. It is possible that each opposite effect dominates over a specific range of IMR, for example, at “low” IMR levels scarring effect prevails, while at “high” IMR levels selection effects predominates (Bozzoli et al., 2009). For the case of Chile, it remains unclear whether prior to 1960, when IMR was higher than 150 deaths per 1,000 live births, the selection or scarring effect predominated. 10

Letting subindex i=1,..I denote regions and subindex t=1,…T denote cohorts, we calculate ℎ̅𝑖,𝑡 /𝜆𝑖,𝑡 as the adjusted height, and regress the latter on the usual variables and include ( 𝜆𝑖,𝑡

𝜆𝑖,𝑡

1−𝜆𝑖,𝑡

), (

𝜆𝑖,𝑡

) 𝐼𝑀𝑅 and

) log(𝑝𝑐𝐺𝐷𝑃𝑖,𝑡 ) as additional controls. When added to the standard regressions, either

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1−𝜆𝑖,𝑡

(

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individually or jointly, these two variables pick-up the migration selectivity linked to differences in IMR and GDP and thus serve as a test of selection (under the null hypothesis of no migration ̃ ̃ and log(pcGDP) selectivity). A non-significant F test (or t-test) performed on IMR can be interpreted as an indication that migration is not correlated with either IMR or log(pcGDP).

Table 4 shows the different specifications used to test for selectivity (of a specific type) in

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̃ and different combinations of fixed effects by area and cohort. In these ), IMR

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1−𝜆

IMR, (

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migration patterns that could affect the consistency of the estimators. In columns 1 to 4, we display

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̃ suggests no potential selectivity (as explained above and specifications, the non-significance of IMR

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more extensively in Bosch et al., 2009) while the coefficient of IMR remains stable, significant, and at a similar level compared to those displayed in Table 2. In column 4, adding both controls for cohort and area fixed effects does not affect the significance of IMR, and again there is no evidence of

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selectivity. That said, the cohort effects are not jointly significant, such that results from column 3 are

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̃ , including cohort and area fixed preferred. Columns 5 to 8 include log(pcGDP) and log(pcGDP) effects in different combinations. As before, there no evidence of migration selectivity, which can be

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̃ ; the null hypothesis that the coefficients ̃ and log(pcGDP) tested as a joint significance F-test on IMR attached to both variables are 0 is not rejected. Note that including fixed effects for both cohort and

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area in column 8, though not jointly significant, decreases the precision of the estimation of the IMR

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coefficient. In sum, we do not find evidence of selective migration of the type tested.

INSERT TABLE 4 HERE

While an important part of Chile’s demographic history over the second half of the twentieth century was shaped by changes in sanitary conditions (which can in part be captured by disease burden at birth, approximated in our study by IMRs), another important factor was migration from rural to urban regions. Castaneda (1996), for example, describes a gradual rise in urbanization rates 11

(accompanied by better sanitation and provision of health services). We consider this possibility, using the available series on urbanization attributed to each cohort-region of individuals, and test whether this variable could compete with IMRs in explaining the increase in adult height. Table 5 is similar to Table 2, but controls for urbanization rates instead of GDP. The pairwise correlation

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between height and urbanization is 0.502 (statistically significant at the 5% level). While urbanization is significant in columns 1 and 2, it is not once we include area fixed effects (columns 3, 4, 5 and 6). In column 5 we also include (log) per capita GDP, and the coefficient on IMR remains stable and significant. Finally, in column 6 we exclude IMR from the regression, and still find that neither urbanization nor log(pcGDP) is statistically significant. Thus, urbanization rates do not seem to either play a role or cast any doubt on the importance of IMR as a predictor of height. The fact that

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urbanization rates, albeit positive, are not statistically significant could be related to the process of urbanization over time. A variance decomposition of urbanization within and across regions shows

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that most of the variance is explained by the latter, while within regions there is little variance over

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time. Indeed, once we include area fixed effects, the relatively small variance in urbanization rates

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within regions does not explain the increase in heights.

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INSERT TABLE 5 HERE

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Overall, these specifications further support our results. The health environment in Chile proxied by infant mortality rates, improved considerably over time, and our estimates show the significant effect

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this amelioration had on the population, as seen in rising adult statures. To further support our results, we further analyze the relationship between the disease

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environment and adult heights using individual-level data, allowing to treat male and female heights separately. We start by replicating the main analysis where each individual’s height is now regressed against a dummy variable identifying females, the region’s IMR, (log) per capita GDP, and

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urbanization rate at the region level, a dummy variable identifying the survey’s year and geographical area and cohort fixed effects. The results are presented in Table 6. Column 1 includes cohort fixed effects, but not area fixed effects; column 2 includes geographical area fixed effects and not cohort fixed effects; and column 3 includes both geographical area and cohort fixed effects. The coefficient on IMR moves between -0.034 and -0.022, almost identical to the results found on previous tables, and is always statistically significant. Interestingly, the female indicator variable is negative and 12

significant, implying that females are on average 13 cm shorter than males, as has been documented in other studies (Eveleth and Tanner, 1990). Neither per capita GDP nor urbanization has a statistically significant coefficient. In sum,

significant effect of IMR on heights.

INSERT TABLE 6 HERE

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individual level results, as well as aggregate results at the cohort-region level, show a statistically

We also use this individual-level data to test for sexual stature dimorphism (Moradi and Guntupalli, 2009). More specifically, we evaluate whether there are differences in the role of the

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burden of disease and mother-child health policies (proxied by IMR), and log(pcGDP), on the adult stature of males and females. To test whether the sensitivity to these variables differs by gender, we

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perform regressions using an indicator that the person is female (female), an interaction between

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female and IMR, and an interaction between female and log(pcGDP), as well as area indicators and

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cohort indicators. This framework allows the coefficients for log(pcGDP) and IMR to differ between females and males. Columns 6 and 7 of Table 7 show the coefficients of these regressions. Regardless

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of the column, the sensitivity of women to IMR, proxied by the coefficient on the interaction between female and IMR, is amplified. The coefficient of the interaction is not statistically significant,

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although the total sensitivity measured as the sum of the coefficients on IMR and on the interaction between female and IMR is more negative.5 In column 7, the coefficient on the interaction between

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female and log(pcGDP) is also not significant. We therefore do not find evidence that female stature is relatively more sensitive to environmental changes, burden of disease, and mother-child health

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policies.

Disease Environment and Infant Mortality Rates in Chile

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For males the coefficient is -0.022 (so that a drop of 100 points per 1,000 live births in IMR results on a height gain of about 2.2 cm.), while for females the sensitivity is -0.026 (so that the gain in height after a drop of 100 points in IMR results of a gain of 2.6 cm), although this difference is not statistically significant. 13

In the previous section, our regression analysis showed that (log) per capita GDP is neither stable nor statistically significant across all specifications. In other words, we do not find evidence for per capita GDP as a potential explanation for the increase in adult height, once other controls are included in the models. While it is common in the literature to relate adult height increases to GDP or

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wage increments over time (Case and Paxson, 2008; Galofré-Vilà, 2018; Schick and Steckel, 2015), this does not appear to be the case in Chile, at least not for the period under analysis. The “Chilean growth miracle,” a period of sustained and rapid economic growth, did not in fact start until around 1985, and thus only overlaps with the last cohorts in our sample.

Our regression results do, however, show that IMR is a statistically significant variable that explains the increase in adult height over time. If we look as far back as 1920, the IMR reached an

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astonishing 250 infant deaths per 1,000 live births. Over the thirty years from 1920 to 1950, the IMR

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declined to around 120. By 1960, we observe further change in the IMR, which then continued to

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considerably decline in the decades that followed (Raczynski and Oyarzo, 1981). Indeed, from 1960 onwards, IMRs decreased every decade without showing any sign of slowing (although there is, of

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course, a natural lower limit to infant mortality).

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A vast literature describes the social awareness of doctors and health practitioners in Chile relative to the importance of tackling malnutrition and mortality in the population, and documents

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health programs implemented by the Chilean government (see Cárcamo et al., 2014; Goldsmith, 2017 and Raczynski and Oyarzo, 1981, among others). Indeed, the reduction in IMRs reflects improvement

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in the disease environment. Even in periods of economic recession, health policies targeting pregnant women and infants were not abandoned and provided a safety net for low income families.

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Over the course of the twentieth century the Chilean government pursued several programs

aimed at reducing the IMR and malnutrition in the population (Brieba, 2018; Goldsmith, 2017;

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Monckeberg et al., 1987). One such measure, called “Milk Programs,” saw government distribution of milk to infants and pregnant women, which started as targeted interventions within a few communities. These programs gained popularity and later became a central part of the government’s public health agenda (Goldsmith, 2017; Raczynski and Oyarzo, 1981). In 1952, the National Health Service (Servicio Nacional de Salud - SNS) was created, and shortly thereafter, in 1954, the National Program for Complementary Nutrition (Programa Nacional de Alimentación Complementaria – PNAC) was launched. PNAC provided children, and pregnant and breastfeeding women with milk, in 14

an effort to supplement dietary intake and prevent malnutrition. This program continued the mission of previous Milk Programs, but targeted the whole population. Table 7 shows the amount of milk distributed by the program, as well as the number of live births and the birth rate for each year. The amount of milk (and related food supplements) distributed by the program rose over time. During this

1,000 individuals.

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INSERT TABLE 7 HERE

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period, we can observe a demographic transition, with the birth rate declining to around 23 births per

The amount of milk distribution increased significantly during this period (see Internet

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Appendix Figure 1), while live births simultaneously stabilized around 270,000 births per year. The

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PNAC not only offered better nutrition, but also provided access to medical care for mothers and

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children. Milk was dispensed to recipients mostly through SNS health centers, and health checks by an expert were mandatory for all beneficiaries (Raczynski and Oyarzo, 1981). In this respect, the

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program also contributed to expanding public health to a population with minimal or no access to a

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health practitioner.

Health policies were implemented during a time of democratically elected governments and formed part of a broad welfare-state agenda. Although health care expenditure was significantly

EP

reduced during military rule (1973-1990), targeted maternal and infant health care and nutrition policies were continued, and contributed to the ongoing decline of infant mortality (McGuire, 2010).

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In fact, the sharpest reductions in IMRs occurred in the 1970’s, precisely during the military regime and a period of economic stagnation, which saw increases in poverty and income inequality

A

(McGuire, 2010; Salvatore et al., 2010). In short, the Chilean government actively pursued public health policies aimed at lowering mortality and malnutrition and their efforts were rewarded.

Conclusion

In this paper, we assess early life predictors of adult height using data on cohorts born in Chile between 1960 and 1989. During this period, IMRs decreased dramatically, a drop unparalleled in any 15

other country in the region. Our estimation indicates that a 100-point decline in the IMR is related to an increase of 2.2 cm in height, or almost all of the increase in stature observed over our period of study. This effect is smaller than that found in other work, as the drop in IMRs is significantly stronger in our observation window. Indeed, if the magnitude of the effect was similar to those found

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in other studies, it would imply an unreasonable predicted increase in height. 6

High rates of sustained economic growth only began towards the end of our period of study, and could explain why the drastic reduction in IMRs (a proxy for change in the disease environment at birth) explains almost all of the secular increment in height. Per capita GDP does not, however, appear to have any predictive power, although if newer health surveys were incorporated, including the cohorts born during the so-called “Chilean economic growth miracle,” GDP might gain predictive

U

power. Urbanization rates at birth are similarly not a significant predictor of subsequent height. If the coefficient attached to IMR remains stable for future cohorts, and considering the zero-bound in

N

IMRs, gains in height are likely to become less pronounced, unless other drivers become more

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important in the current century.

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Our results point to the central role of the disease environment during the first year of life: drastic reductions in IMRs across cohorts predict gains in adult height. Moreover, this increase in statures

D

reflects the positive long-term effects of public health policies. Our study consequently has important implications in terms of orienting sanitary policies towards newborns, and of future returns in living

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standards (income, education, earnings, and productivity) and longer lifespans.

Contributors: all authors were responsible for the study design, data collection, literature review, data

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analysis, and drafted the manuscript. All authors have approved the final article.

Funding: FBH is supported by grant 11160513 of Proyecto FONDECYT Iniciacion from CONICYT in Chile.

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FD is supported by grant 11170498 of Proyecto FONDECYT Iniciacion from CONICYT in Chile. The content is solely the responsibility of the authors and does not necessarily represent the official views or policies of CONICYT. The funders did not play a role in the design and conduct of the study, in the collection, analysis, and interpretation of the data, and in the preparation, review, or approval of the manuscript. 6

For instance, the IMR parameters in Quintana-Domeque et al. (2011) together with the drop in IMRs in Chile would predict an increase of 10 centimeters in height, almost impossible to attain in 3 decades. Notice, however, that the IMR coefficients reported in Quintana-Domeque et al. (2011) are based on reported height, rather than measured height as in our case. 16

Declarations of interest: none

Acknowledgments: We thank the editor, Joerg Baten, and two anonymous reviewers for their helpful

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comments on previous versions of this article.

17

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Universidad Alberto Hurtado, Santiago de Chile.

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Figures and Tables

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Figure 1: Pairwise Correlation

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Source: DEIS, ENS 2003 and 2009, and Diaz-Vernon (2004). See text for more details.

22

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Region I II III IV V VI VII VIII

1965

IMR 21.9 28.8 26.9 33.5 23.9 26.4 30 34.9

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IMR 71.1 102.4 97.7 104 75.1 101.2 113.2 131.7 145 141.5 124.7 63.6 67.3 103

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Height 164.2 161.1 163.2 161.5 162.9 161.4 160.9 161.3 161.3 161 161.1 162.5 163 162

PT

IMR 83.5 117.1 117 128.4 94.5 120.7 140.6 153 164.8 157.3 123 65 87.8 119.4

CC E

I II III IV V VI VII VIII IX X XI XII XIII Average

1960

ED

Region

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Table 1: Mean Heights and Infant Mortality

1980

1970

Height 163.3 166.3 163.7 162.8 163.4 161.9 162.6 162.4 160.8 159.5 161.5 162.4 164.9 162.7

IMR 57.9 63.6 74.2 79.6 55.7 74.9 90.1 102.8 126.7 116 101.5 47.2 51.1 80.1

Height 162.3 164.2 162.3 162.8 163 162.8 161.7 164.3 163.5 159.5 164.1 166.2 164.1 163.1

IMR 41.4 49 54.8 59.6 43.5 55 57 69.8 84.6 70.5 82.5 35.1 33.6 56.6

1975 Height 166.3 163.9 164.9 165.6 163.3 164.1 163.5 164.8 164.6 160.6 162.5 165.1 166.1 164.3

IMR 48.7 63.4 65.3 71.1 51.9 66.2 75.5 85.9

Average Height 164.1 164 163.7 163.8 163.8 162.4 163.1 163.6

1985 Height 164.8 162.5 164.7 166.2 164.5 162.9 165.2 164

IMR 16.1 19.2 21.1 21.7 18.8 18.7 22.4 23.4

Height 163.8 165.9 163.6 164.1 165.7 161 165.1 164.7 23

N U SC RI PT

A

CC E

PT

ED

M

A

IX 42.9 163.1 30 166.4 X 36.8 163.4 24.5 164 XI 35.3 162.8 28.1 165 XII 21.3 163.5 12.9 162.9 XIII 18.6 166.3 15.9 165.3 Average 29.3 164.2 21 164.4 Source: DEIS, ENS 2003 and 2009. See text for details on source data.

24

99 91.1 82.5 40.8 45.7 68.2

163.3 161.3 162.8 163.8 165 163.4

(3)

(4)

(5)

(6) nobs>30

(7) nobs>30

(8) nobs>30

-0.0262*** (0.00175)

-0.0277*** (0.00277)

166.2*** (0.646)

164.9*** (0.126)

164.9*** (0.491)

-0.0224** (0.00687) 0.419 (0.543) 158.6*** (8.089)

-0.0295** (0.0101) 0.420 (0.513) 159.8*** (8.017)

-0.0255*** (0.00345) 0.574 (0.455) 157.0*** (6.446)

-0.0197 (0.0108) 0.807 (0.666) 153.0*** (10.22)

78 0.501 no no 0.495

78 0.537 yes no 0.498

78 0.564 no yes 0.527

78 0.592 yes yes 0.524

78 0.595 yes yes 0.521

65 0.577 yes no 0.525

65 0.603 no yes 0.554

65 0.638 yes yes 0.555

-

2.28 0.19 -

22.30 0.00

1.96 0.24 2.9e+05 0.00

1.90 0.25 6715.65 0.00

11.02 0.01 -

14.12 0.01

14.46 0.00 4.8e+05 0.00

-0.0286*** (0.00282)

log per capita GDP

PT

CC E

Observations R-squared Cohort FE Area FE Adj. R-squared

165.5*** (0.367)

ED

Constant

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F-test: Cohort FE Prob > F: Cohort FE F-test: Area FE Prob > F: Area FE

-0.0344*** (0.00490)

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IMR

(2)

A

(1) Dependent Variable: Adult height in cm

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Table 2: Main Results (OLS)

Note: OLS regression for adult height in cm, independent variables are IMR, log per capita GDP and cohort and area fixed effects. Cluster-robust standard errors at the area-level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Source: DEIS, ENS 2003 and 2009, and Diaz-Vernon (2004). See text for details on source data.

25

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Table 3: Proportion of Chileans Residing in each Region who were Born in that Region Cohort

1960

1965

1970

I II III IV V VI VII VIII IX X XI XII XIII

0.438 0.557 0.6 0.75 0.714 0.748 0.824 0.848 0.772 0.831 0.569 0.43 0.66

0.461 0.551 0.623 0.754 0.728 0.748 0.831 0.86 0.775 0.83 0.606 0.445 0.677

0.525 0.623 0.651 0.726 0.734 0.759 0.829 0.868 0.774 0.822 0.649 0.495 0.701

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ED

PT

CC E

A

Region

1975

1980

1985

0.566 0.658 0.688 0.741 0.748 0.792 0.85 0.867 0.774 0.828 0.703 0.554 0.719

0.623 0.686 0.726 0.761 0.762 0.804 0.856 0.862 0.783 0.829 0.673 0.528 0.793

0.733 0.802 0.794 0.821 0.821 0.849 0.882 0.901 0.828 0.856 0.787 0.763 0.873

A

Source: INE Census 2002. See text for details on source data.

26

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Table 4: Regressions Adjusted for Migration

(1) (2) (3) Dependent Variable: Adult height in cm (adjusted for migration) 0.0337*** 0.0467**

A

IMR

CC E

Constant

PT

Adjusted log pc GDP

(7)

(8)

-0.0284***

-0.0239**

-0.0278**

-0.0390**

-0.0269**

-0.0137

(0.0149) 188.0*** (15.99) -0.00986 (0.0261) 0.979 (0.743) -1.700 (1.086) 153.0*** (10.56)

(0.00798) 185.8*** (27.66) -0.0250 (0.0250) 0.683 (1.258) -1.380 (1.859) 155.0*** (17.88)

(0.0134) 172.9*** (24.68) -0.0107 (0.0199) 0.836 (1.317) -0.506 (1.629) 150.8*** (19.70)

78 yes no

78 no yes

78 yes yes

(0.00573) 165.5*** (0.532) -0.0203 (0.0180)

(0.00688) 165.3*** (0.633) -0.0104 (0.0187)

165.8*** (0.625)

167.4*** (2.081)

164.8*** (0.191)

163.6*** (1.536)

(0.00833) 200.8*** (21.06) -0.0221 (0.0262) 1.237 (0.919) -2.499 (1.441) 148.0*** (12.81)

78 no no

78 yes no

78 no yes

78 yes yes

78 no no

A

Observations Cohort FE Area FE

(6)

(0.0144) 163.2*** (1.569) -0.00293 (0.0208)

M

Log per capita GDP

(5)

(0.00587) 164.5*** (0.726) -0.0108 (0.0166)

ED

Adjusted IMR

(4)

Note: OLS regressions, Adult height (adjusted for migration) is the dependent variable and IMR, log pc GDP, the ratio, adjusted IMR and adjusted log pc GDP the independent variables. Cluster-robust standard errors at the area-level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Source: DEIS, ENS 2003 and 2009, and Diaz-Vernon (2004). See text for details on source data.

27

-0.0218*** (0.00190) 2.484** (0.648)

Urbanization

ED

log per capita GDP

CC E

Observations R-squared Cohort FE Area FE Adj. R-squared

163.2*** (0.466)

PT

Constant

78 0.544 no no 0.532

(3)

(4)

(5)

(6)

-0.0180** (0.00527) 2.784** (1.061)

-0.0228*** (0.00312) 2.070 (1.662)

-0.0182** (0.00598) 2.209 (1.792)

162.3*** (1.226)

163.3*** (1.263)

162.5*** (1.627)

-0.0200** (0.00653) 3.234 (2.292) -0.492 (0.247) 168.9*** (3.333)

3.863 (2.091) -0.0854 (0.365) 160.6*** (4.235)

78 0.571 yes no 0.528

78 0.577 no yes 0.534

78 0.604 yes yes 0.531

78 0.606 yes yes 0.526

78 0.589 no yes 0.513

M

IMR

(2)

A

(1) Dependent Variable: Adult height in cm

N U SC RI PT

Table 5: OLS Regressions including Urbanization

A

Note: OLS regression for adult height in cm, independent variables are IMR, urbanization, log per capita GDP and cohort and area fixed effects. Cluster-robust standard errors at the area-level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Source: DEIS, INE, ENS 2003 and 2009, and Diaz-Vernon (2004). See text for details on source data.

28

(1) Dependent Variable: Adult height in cm -0.034*** (0.006) -13.012*** (0.251)

female * log pc GDP

PT

Urbanization

CC E

A

(5)

(6)

(7)

-0.027*** (0.003) -13.004*** (0.248)

-0.024*** (0.003) -12.998*** (0.243)

-0.022*** (0.003) -12.996*** (0.243)

-0.022*** (0.003) -12.677*** (0.206) -0.004 (0.002)

-0.022*** (0.002) -12.202 (6.116) -0.005 (0.003) -0.033 (0.441)

1.587 (1.487)

log per capita GDP

Observations R-squared ENS 2009 FE Cohort FE Area FE Adj. R-squared

(4)

ED

female * IMR

Constant

(3)

M

female Indicator

-0.029*** (0.002) -13.004*** (0.245)

A

IMR

(2)

N U SC RI PT

Table 6: Main Results using Individual-level Data

172.411*** (0.909)

171.722*** (0.332)

3,644 0.526 yes yes no 0.525

3,644 0.527 yes no yes 0.526

0.321 0.314 0.333 (0.401) (0.400) (0.483) 171.252*** 166.514*** 169.740*** 166.455*** 166.182*** (0.661) (5.374) (1.012) (5.418) (6.409) 3,644 0.528 yes yes yes 0.526

3,644 0.528 yes yes yes 0.526

3,644 0.528 yes yes yes 0.527

3,644 0.528 yes yes yes 0.526

3,644 0.528 yes yes yes 0.526

Note: OLS regression with individual-level data for adult height in cm, independent variables are IMR, female indicator, urbanization, log per capita GDP and ENS, cohort and area fixed effects. Cluster-robust standard errors at the area-level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Source: DEIS, INE, ENS 2003 and 2009, and Diaz-Vernon (2004). See text for details on source data.

29

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65,245 87,470 138,665 119,496 149,288

Birth Rate (per 1000 indiv.)

1.42 1.36 1.22 1.30 1.42

31.6 27.4 22.8 22.6 22.6

M

1965 1970 1975 1980 1985

Live Births (Millions)

A

Table 7: PNAC and Births Year Milk (Tons.)

A

CC E

PT

ED

Source: Live Births and Birth Rates are from DEIS, Ministry of Health, Chile. Milk includes: milk, milk supplements and from 1984 onwards also supplementary food. Data from Raczynski and Oyarzo (1981).

30

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Appendix

A

CC E

PT

ED

M

A

Appendix Figure 1: IMR by Region, 1960

31

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Appendix Table 1: Height - Descriptive Statistics by Gender, Decade and Region

Female Height

A

Region I II III IV V VI VII VIII IX

Mean 156.1 155.5 155.6 155.6 156.7 154.9 156.2 155.0 154.7

1970-1979

A

Obs. 41 26 43 27 45 49 33 89 35 69 32 35 101 625

M

Stand. Dev. 7.0 6.4 7.8 5.4 6.2 8.5 5.7 6.7 7.4 6.2 6.1 6.8 6.5 6.8

PT

Mean 171.2 169.3 171.9 168.9 169.4 168.5 167.9 168.4 167.1 166.9 167.4 167.7 170.0 168.9

CC E

Region I II III IV V VI VII VIII IX X XI XII XIII Average

1960-1969

ED

Male Height

Mean 170.0 169.6 170.4 171.5 169.6 169.1 168.7 171.8 169.1 167.0 169.5 172.5 171.3 170.3

Stand. Dev. 6.8 5.8 7.4 7.2 6.6 6.0 6.5 5.9 6.1 7.0 6.8 6.9 7.5 6.8

1960-1969 Stand. Dev. 5.7 5.7 6.0 5.2 4.5 5.3 6.3 6.6 6.4

Obs. 48 27 32 28 43 31 27 62 37 41 31 43 98 548

Mean 171.5 170.6 171.3 170.7 172.5 166.7 171.6 171.3 170.4 171.0 171.4 168.7 172.4 171.1

1980-1989 Stand. Dev. 5.6 6.7 7.4 5.5 7.4 3.9 7.7 6.6 4.8 7.2 4.8 5.7 6.2 6.3

Obs. 42 34 25 29 23 12 23 43 21 35 16 29 66 398

Mean 157.0 158.2 156.2 159.6 158.2 157.1 158.6 157.7 158.1

1980-1989 Stand. Dev. 6.5 5.6 6.9 5.7 5.0 4.9 4.9 5.6 7.1

Obs. 59 46 22 30 30 23 41 44 30

1970-1979 Obs. 83 45 43 55 57 50 51 97 41

Mean 158.4 158.0 157.1 157.4 156.8 158.1 156.6 157.0 158.5

Stand. Dev. 7.2 5.2 5.9 6.2 5.9 5.9 5.3 6.4 5.3 32

Obs. 65 53 44 40 61 39 51 83 40

N U SC RI PT 5.5 6.0 5.6 5.4 6.0

A

CC E

PT

ED

M

A

X 152.7 6.4 88 153.8 XI 155.3 5.7 44 157.0 XII 156.4 5.8 41 160.2 XIII 157.9 6.1 146 158.9 Average 155.7 6.1 841 157.5 Source: ENS 2003 and 2009. See text for details on source data.

33

51 37 28 114 706

157.4 157.5 158.0 159.5 158.1

5.0 7.5 6.1 5.5 5.9

56 35 24 86 526

N U SC RI PT

Internet Appendix

Internet Appendix Figure 1: Distribution of Milk (and food-supplements)

Milk (Tons.)

30,000

M

A

20,000

0 1970

1975

1980

1985

1990

A

CC E

PT

1965

ED

10,000

34

N U SC RI PT

Internet Appendix Table 1: Infant mortality rates for selected countries 1960

1970

1980

1990

Argentina Bolivia Brazil Chile Colombia Ecuador Peru Paraguay Uruguay Venezuela, RB

175 128.8 127.7 94.6 120.8 135.6 61.3 57.7 59.6

59.4 144 102.8 67.2 70.4 95.7 103.7 57.8 48.6 48.1

37.5 111.8 77.2 28.2 44.6 67.6 82.8 49.8 35.5 35.4

25.5 85.2 53.4 16 28.9 44.1 56.7 37.1 20.6 24.8

PT

ED

M

A

country

CC E

Source: World Development Indicators, worldbank.org/world-development-indicators.

Internet Appendix Table 2: Descriptive Statistics

A

Variable

Height in cm IMR Female Indicator Age log(pcGDP) Urbanization Rate

Observations

Mean

Stand. Deviation

Minimum

Maximum

3644 3644 3644 3644 3644 3644

162.53 73.87 0.57 34.88 13.91 0.75

9.06 42.57 0.50 8.38 0.47 0.18

140 7.73 0 20 12.93 0.42

203 185.28 1 50 14.79 0.98

Source: DEIS, INE, ENS 2003 and 2009, and Diaz-Vernon (2004). See text for details on source data.

35

36

A ED

PT

CC E A

M

N U SC RI PT