1
Height, Globalization, and Anthropometric Inequality in Brazil, Peru, and Argentina
during the 18th – 20th Centuries
Joerg Baten (Univ Tuebingen and CESifo), Ines Pelger (Univ. Munich), and Linda Twrdek
(Univ Tuebingen)
Abstract
This anthropometric study focuses on three important country histories of the 18th to 20th C.
Two countries are included which have not been studied before – Brazil and Peru. Moreover,
we provide a new data set on Argentina for the crucial period of the late 19th and early 20th
century. We study the trend and inequality experiences during globalization and
deglobalization periods. Brazil experienced a substantial progress in nutritional status between
the 1820s and 1880s. Peru stagnated on relatively low level, whereas Argentina stagnated
during its export boom of the late 19th and early 20th century, after starting from a high level.
Latin American anthropometric inequality was quite modest, except in Peru.
Contact adress: Jörg Baten, Dept. Economic History/Economics, Mohlstrasse 36, 72074
Tuebingen, Germany, Tel. +49-7071-2978167, [email protected]
Acknowledgements: We thank Manuel Hanenberg for his data collection efforts in Rio, Güde
Hansen in Lima, and Kerstin Manzel, who provided age-heaping data. We are also grateful to
the ESF GlobalEuroNet initiative for financial support.
2
Height, Globalization, and Anthropometric Inequality in Brazil, Peru, and Argentina during
the 18th – 20th Centuries
Income and Height
Height studies can contribute to our understanding in particular when little is known about the
GDP development of a country, or when purchasing-power-based indicators and
anthropometric welfare measures do not move into the same direction. The latter is clearly the
case for trends in Argentina and Brazil. Maddison (2001) describes Brazil as an economy
which did not grow much between 1820 and 1913, incomes were not far from the lowest
values ever measured in this century in global comparison. In contrast, we find that the
biological standard of living which is proxied by height development actually increased
during the 19th century, from very low levels which were typical for Southern China and India
to levels that were similar to heights in Central Europe at the time (Morgan 2006, Baten and
Hira 2006, Baten 2006). In contrast, Argentina grew famously rich in the 1870-1913 period.
GDP rose from 1300 to 3800 $ (in 1990 Geary-Khamis $, see Table 1), and real wages were
on European levels at the end of this “Golden Age” (Williamson 1995). In contrast, we
confirm Salvatore´s finding that Argentinean heights stagnated mostly during the period
(Salvatore 2004, 2007). Peru was much poorer than Argentina in 1913, almost as poor as
Brazil but heights were not lower than in Argentina in the 1880s – at least if we compare
heights in Lima (with a high share of whites) with average heights in Argentina. Earlier in the
century, heights in Peru varied strongly. Studying Peru and Brazil, we fill important gaps in
Latin American anthropometric history, as studies on this continent are only available for
Mexico, Argentina, and Colombia available until now (Lopez-Alonso and Condey 2003,
Carson 2005, Meisel and Vega 2007; Salvatore 1998, 2004, 2007, Salvatore and Baten 1998,
Bogin and Keep 1998).
3
Inequality and globalization in Peruvian, Argentine, Brazilian economic history
We first discuss the structure of the Latin American societies under study, before analyzing
their height trends in the following section. The social and economic inequality which
characterized the colonial era of Peru was not fundamentally changed after independence in
1821 (Gootenberg, 1990). The Spanish Empire left a two class system which was held up by
legal, social and tax rules. The importance of racial origin still played a vital role and did not
change much, meaning that whites kept their privileged positions. In contrast, the largest part
of the population lived near subsistence level (Contreras, 2004), so the previous literature
argued -- Maddison 2001, for example, reports a very low GDP. The riches of the country
were consumed by the small white population. They were able to protect their social status
and took actively part in the economic development of the country. Slavery in Peru had been
abolished in the 1850s, but with only limited economic and social consequences, as the
number of slaves had been small anyways. The strong stratification of Peruvian society can be
traced back to the influence of the Spanish Empire whose conquerors not only captured most
of the fertile land and introduced slavery, but also had a number of descendants with Indio
female servants, thus created middling groups of mestizos in this society.
In Argentina, a previously existing stratified society strongly changed during the 19th
century, as the population consisted of more and more recent European immigrants. They
arrived in masses after the mid-century, motivated by the poor conditions prevailing in
Europe and attracted by a vast new territory with free land available to be cultivated. The
Argentine census of 1914 reported that one third of the society were immigrants. By then,
Argentina was already well integrated in the world market and gaining its profits of the
globalization process.
Brazil’s economy in the 18th century was characterized by an agrarian, monocultural
structure. 1815 it became a kingdom with equal rights and obtained independence in 1822.
4
Brazil held a special economic alliance with Great Britain since 1810, due to the protection
Britain provided during the relocation of the Portuguese Court to Brazil in 1808 which was
driven by the Portuguese fear of Napoleonic Troops. This special alliance meant the opening
of the Brazilian harbours and a low-flat preference custom for British imports (which did not
hold vice versa for Brazilian trade to Britain). Compared to the other Latin American
countries, Brazil’s independence had unique features: unlike its neighbors it stayed a
monarchy after independence. Brazil underwent a comparatively peaceful transition to
independence and in spite of repeated attempts of secession the entity of Brazil remained as
one state. Moreover, it developed from a state to a nation (Bernecker et al. 2000, p.139).
Slavery as an institution survived the decline of other slavery systems by decades. Brazil was
the last country to abolish slavery in 1888, under British pressure, although already in 1850
new imports of slaves were prohibited. The consequence was a lack of workers in the
prospering coffee plantations in the South. After a big slave exodus from the stagnating sugar
plantations in the Northeast to the South, European immigrants served as substitutes for
slaves, hired by coffee barons, they came mainly after the second half of the 19th century. The
slow transition to industrialization (mostly in the 20th century) and the surprisingly late
economic boom starting in the late 19th century are caused mainly by domestic developments,
rather than the special relationship with Britain, as the older literature had argued.
Detailed studies on income inequality have been generated for some Latin American countries
(Bertola 2005). Bertola argues that there was increasing inequality during the first
globalisation boom in Uruguay, Argentina, and other new world countries.
What was the export structure of our countries? Peru has been a worldwide
acknowledged supplier of silver until the end of the colonial era, but during the early 19th
century investments and profits were declining for various reasons (Contreras, 2004). At the
same time the country started off with a new product which was going to refill the empty
public treasury. The product was called guano and was to be found mainly on the coastal area
5
of the country. The demand for guano held on long until the 1860s, with ever rising prices.
Guano drove up incomes in the domestic economy, especially in Lima, where most of the
profits were obtained (Gootenberg, 1990).
In contrast, Argentina became famous for its rapidly growing agricultural exports,
mainly beef and wheat. During the first decades of the 20th century, Argentina had one of the
highest per capita incomes in the world and a remarkable export growth (Díaz Alejandro,
1970). Rural output and exports expanded with the railroad network which connected the vast
territory to the main harbour in Buenos Aires, while the immigration of labor and capital was
being reinforced not only by the government but was also considered a lucrative bargain for
everyone abroad. We would have expected that the biological standard of living was high and
rising in Argentina during the export boom years at the end of the 19th century. We actually
find below that it was high, but not rising. Moreover, after examining the density of cattle per
capita in the named provinces that there existed a proximity advantage for heights. People
living in provinces with high density of cattle were taller than others originating from
provinces with less live stock. However, some of the provinces had already started to trade
these proximity-food quality advantages away for more income, such as La Pampa and Rio
Negro, whereas Chubut-Santa Cruz still enjoyed those advantages.
After three decades of frenetic guano prosperity, Peru was entering recession in the
mid-1870s which corresponded chronologically with an adverse international trade situation.
The un-diversified export structure of Peru strongly depended on the market dynamics of
Great Britain and France (Gootenberg, 1989). The Argentine economy in contrast kept
prospering even after the turn of the century because of its continuous expansion of cultivated
area and foreign investments and entered a crisis not before the 1930s.
Brazilian sugar, until 1815 leading in the world market, stagnated due to the
augmenting competition with other Latin American sugar producing countries and the surging
of sugar beet in Europe. Coffee soon overtook sugar as the most important export staple. Until
6
1830, Brazil had relatively low export revenues (4 million British Pounds on yearly average).
In the course of the 19th century coffee replaced sugar as the most important export good.
After 1830 the coffee and sugar exports into the US increased, the United States were
detected as a new expanding market. By the mid of the century Brazil already had a balance
of trade surplus and in 1850 there was as much export volume shipped to the U.S., as to Great
Britain. Between 1820 and 1860 Brazilian export prices rose by 22%, import prices fell by the
same amount; this resulted in an upgrade of the terms of trade at about 70% for Brazil (Leff
1982, 81 f.).
Data
We have military data on a representative sample men in Argentina who were measured in
1927, and we included those from age 17 to 52, i.e. birth cohorts 1875 to 1910, 6953 in total
(Table 2). The sample was drawn from a general register of the whole male population
preserved in the “servicio histórica del militar” in Buenos Aires, from which a series of pages
was chosen randomly. Among the youngest, there might be some growth potential left for the
last cohort, although height was at a relatively high level (which means normally that growth
is finished early). Regionally, we collected Argentineans from all large regions of the country,
and included also the Capital Federal, which is slightly oversampled relative to its population
weight (Figure 1). While there might have been some survivor bias in all three samples, but
the Argentinean one in particular, other studies on the subject indicated that it was mild or
negligible (Moradi and Baten 2005; Guntupalli and Baten 2006).
In contrast, for both Peru and Brazil we rely on prison samples, which normally tend
to be socially biased towards the lower strata, although the height bias of prison samples is
typically not as large as the occupational bias. In Bavaria, for example, where both types of
height samples were available, military conscripts from all social strata were not taller than
prisoners (Baten 1999, Baten and Murray 2000). In the National Archive of Lima, we
7
collected all height data which were accessible to us, and the same applies to the State
Archive in Rio de Janeiro.1 Brazilian prisoners were measured in 1861-1903, Peruvians in
1866-1909. Our Brazilian sample is larger (6799 cases, Table 2) than the one from Peru (1445
cases), and more geographically varied: The Peru dataset refers mostly to Lima, plus some
immigrants, as the prison under study was only responsible for Lima and its nearer
surroundings. The birth decades from 1870s to 1910s are well-covered for Argentina, the ones
for 1810-1880s for Brazil, and 1820-1880s for Peru, although for the latter, sample sizes are
much smaller. (Table 2).
How representative are our data? One way to assess the representativeness is to check
the education level of occupation in the sample and for census population born in the same
birth decade and region. The other possibility is to compare age-heaping levels of sample and
census population (Crayen and Baten 2007). Our sample from Lima has a similar age-heaping
level as the census reported for the late period of the sample (Figure 2).2 For Argentina, we
should not have large problems with representativeness, as the whole population was recorded
in those sources.
A similar ranking also evolves if we compare the average Armstrong values for the
capitals (Figure 3). Armstrong has suggested to classify all occupations in no occupation (0),
unskilled (1), semi-skilled (2), skilled (3), (non-manual intermediate or) semi-professionals
(4) and professionals (5). Comparing the average Armstrong values for the three capitals we
have in our sample, we obtain the ranking expected by GDP levels (Table 1): Argentina does
better than Peru, followed by Brazil. This corresponds with the earliest schooling information
(Crayen and Baten 2007).
For Brazil, we have information on the migrant status and hence can control for it. But
the Argetine military census did not report country of birth. Could there have been some
1
Lima – Archivo General de la Nación. Archival source “penitenciaria central”, the main prison in Lima, Libros
de Entradas y Salida de Reos, Nr. 3.20.3.3.1.1.4 to 26). Rio - #.
2
Census data refers to those asked in 1940 who were born in the 1870s and 1880s, i.e. aged in their 50s and 60s
at the time of the census. See Crayen and Baten (2007) on potential ageing effects.
8
regional bias, that migrants from taller countries migrated to some regions in particular and
therefore caused heights to be larger there? Figure 4 indeed indicates that there might have
been this kind of relationship. However, we did the same exercise for the share of migrants
coming from countries with below-average heights (Portugal, Spain, Italy etc.). Again, there
is a positive relationship with heights. We conclude that migrants simply went to regions
where living conditions were good, and there was probably no systematic bias introduced
from migrants coming from different countries.
Trends of height in Argentina, Brazil, and Peru
We performed a series of regressions in order to estimate the time trend of heights by birth
cohorts in our three countries (see Baten 2000 on the fact that the overwhelming share of
height variation is caused in the first years of life). The advantage – compared with simple
averages – is that we can control for regional, age, and occupational composition One could
imagine, that, for example, richer strata might be overrepresented among the first cohorts
(typically older individuals), and hence it is better to control for composition effects. The
number of cases in our regressions does not perfectly correspond with the one in Table 2 and
3, as some information on the explanatory variables was missing.
The first regression refers to Peru (or rather, Lima), using the full data set, and taking
the white adult non-migrant population born in the 1840s as the reference category. Compared
with the 1840s, all other birth decades had taller heights, especially the last ones. Migrants
were about one centimeter taller, with a p-value of 0.15. The occupational groups are not
significant, perhaps due to the 1840s which had a particularly strong impact on the more
educated strata in Lima (see below). Another real possibility is that occupational group
correlated highly with skin color: the Indios often had the Armstrong 0 and 1 occupations,
sometimes 2 for farmers. Hence only the skin color coefficients might be significant, taking
up some of the occupational variation. Indios were 6 cm shorter than whites, mestizos 3 cm,
9
and Asian born (mostly Chinese) 4.2 cm. The blacks were rather taller than whites in Peru,
what we will discuss below in greater detail. In model (2), we also checked whether the
occupational groups obtained more significance if we considered only those born after the
1840s, but the difference to model (1) is quite modest.
In contrast, Argentina had almost no movement of average height – almost all time
coefficients are insignificant (reference category was birth decade 1900s). The difference
between unskilled and semiskilled was particularly large in Argentina. In the latter group, the
farmers were included, but they had an extra coefficient on top of that of 0.4 cm. The
professionals in Argentina were also much taller, on average 3.4 centimer taller than unskilled
workers. For Argentina, we do not have skin color or ethnic characteristics, hence all social
differences are concentrated in those occupational coefficients. Moreover, the number of
cases is quite high for Argentina and Brazil, hence we probably have less noise in those
estimates, compared to Peru. Brazil had quite modest occupational differences, except for the
professionals, which were 3 cm taller than the unskilled. The farmers were actually shorter,
but we have only 64 observations on farmers aged 20-60, which indicates that there might
have been special selectivity caused by the legal system (such as farmers normally not being
incarcerated in Rio, only a very special subgroup was). Hence we will not interpret this result.
While we have skin color information, skin color did not play a large role in the Brazilian
regressions, as we will also see below. We also controlled for age composition, as we
included the 19-22 and 51-60 year-olds in the Brazil regression. The results were as expected,
except for the 51-60-year-olds, which were actually taller than the adults. This is probably a
composition effect, as they were mostly born in the first decades. We should probably
augment the height estimates for the 1810s and 1820s by some
enefited s. Even so, the
time coefficients show a distinct upward trend for Brazil. Brazilians born in the 1880s were
5.3 cm taller than those born in the period before the 1810s, which is the constant here. The
R-squares are generally low, as it is quite usual in individual height regressions – we know
10
that we cannot capture individual genetic height variation, which forms a large part of the
unexplained part. (As soon as heights are averaged, say, by regions, and the genetic
component averages out, R-squares increase dramatically, see Baten 1999.)
From the time coefficients and the constants, we are able to graph a time trend, which
refers to the reference category of unskilled workers or unknown occupation of white (Peru)
and mixed (Brazil) population (Figure 6). Next we adjust for occupational group, using
sample weights, and by skin color, using population weights, to obtain population averages
for each birth cohort and country. These adjusted anthropometric series are considerably
lower for Peru, as the new series reflects the large population weight of Indios and Mestizos,
who were underpresented in the Lima sample (Figure 6a). As a result, we can state that
Argentinean heights were quite impressive at the end of the 19th and during the the early 20th
century, but they did not increase much, as we might have expected given the GDP boom.
There was also a strong increase of Brazilian heights from very low initial levels, which might
be adjusted upwards by perhaps 0.8 cm for the first birth cohort, as we discussed above. But
even then, Brazilians born in the 1810s were shorter than 163 cm. It followed a continuous
increase until at least the 1880s, to a respectable value close to 168 cm. In contrast, heights in
Peru basically stagnated on a low level, and our trend estimates for Peru would even be
slightly less optimistic, if we would have taken into account the growing inequality between
whites and Indios. Morevor, Peruvian heights were quite volatile in the first decades. This
might perhaps be caused by small sample size, but the 1840s decline could also reflect real
developments: Europe experienced its “hungry forties”, and the usual concomitants of famine,
infectious disease, might have been imported to Peru as well.
Was this 1840s decline also observable in other Latin American big cities? We can
disaggregate the Brazilian data set to study the development in Rio de Janeiro (Figure 7). We
do not observe a strong decline during the 1840s, although until the 1870s, height in Rio
remained on quite modest levels.
11
Brazil had a strong immigration especially in the latter part of the century, mostly
from Portugal. Could it be the case that the strong height increase was brought about by
migration of taller individuals? The evidence suggests, that in general migrants from Portugal
tended to be shorter than native Brazilians, at least before the 1860s (Figure 8). Only
thereafter, we observe a small height advantage of Portuguese migrants. This might have
contributed to the height increase, but only to a very small extent. Both Brazilian born and
Portuguese born migrants experienced a strong height increase during the last birth decades.
Inequality of height
Disaggregating by social status we can gain a first glance on anthropometric inequality in
those three important Latin American economies, although the Peruvian sample might be a bit
small for disaggregation. We concentrate only on adult males (age 19-60) within non-extreme
height intervals (<125 cm >200 cm excluded). We distinguish lower class (Armstrong
category 0,1) from middle class (2,3) and higher strata (4,5). Compared with European
standards, height inequality in Argentina was quite low (Figure A). Except for Peru, numbers
of cases are mostly sufficient (Table 3). The middle and higher groups were relatively close
together, in the range of typically 168 to 169 cm, quite an impressive stature by European
standards where heights only slowly started to increase. What does it mean that the skilled
and professionals (including their respective semi-groups) are close, whereas the unskilled are
much shorter? Either craftsmen and farmer skills are rewarded relatively highly, compared
with professional skills. Or, as another possible interpretation, craftsmen and farmers in
particular enjoyed the well-known proximity to protein advantages, whereas professionals
lived more often in the federal capital, remote from protein production. In spite of their
income premium, professionals might not have had a much better nutritional status than
farmers and craftsmen. The proximity effects were clearly present as can be seen from the
relatively tall stature of the unskilled, compared with European and especially Southern
12
European standards, from which most Argentineans originated. The Spanish, Italian, and
Portuguese heights in the 19th century were in the range of 161-165 cm in the 19th century
(Baten 2006).
In Argentina, the social differences were relatively stable over time. There was almost
no trend, except perhaps for a very modest increase of the anthropometric values of skilled
occupations. With the growing export economy, the farmers in particular might have
enefited, but given the high initial height level, the upward trend was modest (Figure A1).
As the place of birth is not given, we do not know whether a possible upward trend in
Argentina was counterbalanced with growing immigration of shorter individuals from Europe.
Switching to Brazil, we have to be even more careful in the analysis of social
differences, as this sample comes from a prison, and especially those professionals which
were incarcerated could be a biased sample of the Brazilian professionals, and their sample
size is small (Figure B). However, they also had a clear height advantage over the other social
groups, in particular before the 1870s. In the 1870s and 1880s, the lower and middle classes
enefited clearly from improving nutrition and health conditions in Brazil. The already
modest inequality of height by occupational group was further compressed during this period,
if we can rely on this prison sample. Future counter-checking of this result is clearly needed.
Even more caution is needed when interpreting social differences based on the
Peruvian prison dataset, as the number of observations is small (Figure C, Table 3). We have
already excluded most birth cohorts for which the N was too small. We can confirm based on
this disaggregated view that the 1840s in Peru were a difficult period, not only for the lower
classes, but also for the skilled and professionals. The professionals were much shorter than
those born in the 1850s and 1880s for which we have enough cases. This might actually
indicate that the 1840s crisis was caused by a catastrophic disease environment, as disease
affects different social strata in a more egalitarian way than income effects (Guntupalli and
Baten 2005, Komlos 1996). The lower classes might have
enefited in Peru in the 1880s, as
13
they did in Brazil, but this is only supported by a single point of observation. In general, if we
can trust the limited data on Peru, social differences were vast. Disregarding the exceptional
1840s, professionals were up to 3-5 cm taller than the unskilled groups. This large difference
is well in the range of European height inequalities, and quite untypical for Latin American
frontier economies. It might have been caused by the fact that the Peruvian society was more
stratified than the immigrant society of Argentina, and the slave holding and later immigrant
society of Brazil. In Peru, the main social difference might have been reinforced by the ethnic
difference between those of European and those of Indio origin, a difference which was
carefully preserved by European governments who distributed basic educational infrastructure
mainly to European descendents, and kept land inequality between the ethnic groups. Height
inequality between whites and Indios was actually growing during the 19th century (Figure D).
At the end, whites were not less than eight centimeters taller than Indios. This difference had
been initially much smaller. Interestingly, this difference does not apply to black slaves or
former slaves. Fogel (1974/1995) and Steckel (1986) have argued that slave owners actually
provided quite good nutrition to slaves, after they survived childhood mortality hazards.
Especially in a situation when importing slaves became more and more difficult and
expensive, slave-owners gave often nutritious diets to their slaves, including, for example,
offals and other varieties of cheap protein. The famous protein-rich Brazilian feijoada meal
came out of this tradition, and in fact, the black population kept those eating habits more or
less voluntarily some time after slavery was finally abolished. In Peru, the blacks were not
shorter, but rather taller than whites (although their number was small), and in Brazil, they
were taller in the 1820s, 1840s and 1850s (Figure E). Could it be that black slaves in Africa
were systematically selected by height, as only tall slaves meant good profits for slavehunters? Eltis (1982) provided a number of arguments against strong height selectivity in
slave-hunts, and we provide another one here: The relatively favorable height values of black
14
Brazilians can probably not be explained with genetic reasoning, as the blacks born in Africa
were much shorter than the blacks born in Brazil, in the feijoada environment.
Whites were initially very short in Brazil, perhaps a heritage of Portuguese dietary
tradition (although the figure excludes migrants), but the white heights in Brazil increased
most, by five centimeters. Brown heights, i.e. mostly descendent of interracial relationships,
moved initially closer with black heights, and in the later periods closer with white heights.
The black population was the only group which was not growing over the 19th century, and
they fell back in the 1880s, which was not caused by still growing young adults. After the
abolition of slavery in Brazil the obligation of the lords to feed their slaves ceased and a big
part of the blacks lived on the streets and had worse nutrition than before.
Summarizing social and ethnic differences, we find that Peru was probably the most
unequal society, and ethnic differences grew over the 19th century. In Brazil, social
differences were modest in terms of height, and they might have declined when conditions
improved in the 1870s and 1880s, although the black population did not benefit. In Argentina,
differences were modest and relatively stable.
Conclusion
While GDP studies suggest nearly a stagnation on very low levels for Brazil, and a rapid
income growth for Argentina, this does not automatically transfer into similar height
developments. We find that Brazil could in fact reach substantial improvements between the
1820s and 1880s, a period of structural modernisation in many regards. One element of the
modernisation was the gradual move away from a slavery system with substantial slave
imports from Africa, although ironically the black population did benefit least from the
modernisation in Brazil in terms of nutritional status. In contrast, Argentina had rapid GDP
growth between 1870 and 1913, but at the same time a larger share of food which had been
previously consumed within the regions was now exported to foreign countries. As a
15
consequence, the ‘first era of globalization’ did not improve nutrition and the biological
standard of living as much as expected, hence confirming Salvatore’s earlier work on
Argentina. For Peru, the export boom period did not lead to a positive height trend in Lima,
rather heights stagnated on a relatively low level, which previous estimates of real wage
studies (Gootenberg 1990, p. 40). Our trend estimates for Peru would even be slightly less
optimistic, if we would have taken into account the growing inequality between whites and
Indios.
Did globalisation impact on inequality? We find that the change of inequality was
modest in globalisation boom periods such as Argentina 1870-1913, Brazil after the 1870s,
and Peru in the 1860s. Brazilian anthropometric inequality was even compressed by
globalisation. In Argentina, even in the growing export age, lower income groups benefited
from proximity-equality effects, i.e., in the proximity of protein production which cannot be
completely transported to markets of high purchasing power. In such as situation, also the
lower income groups could afford a relatively good diet. In contrast, Peru had probably a
much stronger stratification of society as reflected in height by occupational group and skin
colour. Especially during the export boom of Guano, height inequality between groups
defined by skin colour rose substantially. The white population of Lima grew much taller than
the Indio and Mestizo population, and the higher occupational groups ended up taller than the
unskilled.
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18
Tables
Table 1: GDP per capita in selected Latin American economies (Source: Maddison
2001)
Argentina – Argentine
Brazil – Brésil
Peru – Pérou
Uruguay
Total Latin America
1820
1850
646
686
692
1870
1,311
713
1890
2,152
794
2,181
681
2,147
1900
2,756
678
817
2,219
1,109
1910
3,822
769
975
3,136
Table 2: Number of cases by country, birth decade and occupational category
Argentina
. ta bdec if age<61 & ht>125 & ht<200
bdec |
Freq.
Percent
------------+--------------------------1875 |
668
9.61
1880 |
715
10.28
1885 |
760
10.93
1890 |
845
12.15
1895 |
997
14.34
1900 |
1,045
15.03
1905 |
1,021
14.68
1910 |
902
12.97
------------+--------------------------Total |
6,953
100.00
Brazil:
. ta bdec if female==0 & age>18 & age<61 & height>125 & height<200
bdec |
Freq.
Percent
------------+-------------------------1800 |
28
0.41
1810 |
75
1.10
1820 |
323
4.75
1830 |
705
10.37
1840 |
1,265
18.61
1850 |
1,604
23.59
1860 |
1,740
25.59
1870 |
887
13.05
1880 |
172
2.53
------------+-------------------------Total |
6,799
100.00
PE:
. ta bdec if female==0 & age>18 & age<61 & ht>125 & ht<200
bdec |
Freq.
Percent
------------+--------------------------1810 |
15
1.04
1820 |
74
5.12
1830 |
205
14.19
1840 |
348
24.08
1850 |
226
15.64
1860 |
131
9.07
1870 |
158
10.93
1880 |
254
17.58
1890 |
34
2.35
------------+--------------------------Total |
1,445
100.00
1913
3,797
811
1,037
3,310
1,481
2001
8,137
5,570
3,630
7,557
5,811
19
Table
AR:
bdec
1875
1875
1875
1880
1880
1880
1885
1885
1885
1890
1890
1890
1895
1895
1895
1900
1900
1900
1905
1905
1905
1910
1910
1910
3: Number of cases by country, birth decade and occupational category
occbr
middle
high
low
middle
high
low
middle
high
low
middle
high
low
middle
high
low
middle
high
low
middle
high
low
middle
high
low
nc
296
120
252
313
127
275
363
140
257
391
141
313
479
176
342
519
120
406
519
92
410
456
65
381
BR:
bdec
1800
1800
1800
1810
1810
1810
1820
1820
1820
1830
1830
1830
1840
1840
1840
1850
1850
1850
1860
1860
1860
1870
1870
1870
1880
1880
1880
occbr
middle
high
low
middle
high
low
middle
high
low
middle
high
low
middle
high
low
middle
high
low
middle
high
low
middle
high
low
middle
high
low
nc
18
6
6
48
7
27
194
18
148
427
35
361
721
59
736
1004
73
998
1144
125
1121
628
79
461
79
7
30
PE:
bdec
1810
1810
1810
1820
1820
1820
1830
1830
1830
1840
occbr
middle
high
low
middle
high
low
middle
high
low
middle
nc
7
4
5
50
7
18
153
35
45
264
20
1840
1840
1850
1850
1850
1860
1860
1860
1870
1870
1870
1880
1880
1880
high
low
middle
high
low
middle
high
low
middle
high
low
middle
high
low
69
106
169
36
62
103
17
16
107
24
27
166
37
48
21
Table 4: Regressions of height in Peru, Peru after the 1840s, Argentina, and Brazil
v1
occgr2
occgr3
occgr4
occgr5
farmer
Peru
0.49
(0.50)
0.22
(0.74)
0.22
(0.80)
0.56
(0.70)
-0.65
(0.41)
mult5
migrant
b1820
b1830
1.00
(0.15)
0.59
(0.56)
2.67***
(0.00)
PE after 1840s
-0.78
(0.36)
-0.40
(0.63)
0.70
(0.48)
1.42
(0.40)
0.31
(0.72)
0.72
(0.33)
0.48
(0.59)
Argentina
1.95***
(0.00)
0.94***
(0.00)
1.73***
(0.00)
3.40***
(0.00)
0.42*
(0.08)
b1840
b1850
b1860
b1870
b1880
2.80***
(0.00)
2.59***
(0.00)
2.24***
(0.00)
2.47***
(0.00)
-0.40
(0.60)
-0.70
(0.38)
-4.17***
(0.00)
-5.58***
(0)
-2.97***
(0.00)
0.02
(0.98)
1.52
(0.19)
-3.80***
(0.00)
-7.28***
(0)
-4.06***
(0.00)
-0.48
(0.63)
1.14
(0.37)
-0.10
(0.88)
b1890
b1910
asia
indio_cholo
mestizo
zambo
black
white
age19
age20
age21
age22
-0.37
(0.19)
-0.41*
(0.060)
-0.16
(0.43)
-0.09
(0.74)
Brazil
0.56***
(0.01)
0.73***
(0.00)
0.61
(0.11)
3.01***
(0.01)
-3.99***
(0.00)
-0.68***
(0.00)
0.19
(0.41)
0.52
(0.54)
1.42*
(0.086)
1.76**
(0.031)
2.11***
(0.0096)
3.22***
(0.00)
4.85***
(7.3e-09)
5.27***
(0.00)
0.22
(0.38)
-0.28
(0.25)
-2.31***
(2.1e-10)
-1.52***
(0.00)
-1.02**
(0.013)
-0.81**
22
age5160
Constant
Observations
Adjusted R-squared
163.74***
1139
0.12
167.54***
614
0.19
166.66***
6951
0.03
(0.021)
0.86*
(0.056)
162.12***
6771
0.03
The Peru regression constant refers to a criminal male born in th1840s of white skin color and age 23-50
The Argentina regression constant refers to a male born in the 1900s and age 27
The Brazil regression constant refers to a criminal male born in the 1810s of brown skin color and age 23-50
AR: . reg ht occgr2 occgr3 occgr4 occgr5 farmer b1* if ht>125 & ht<200 , robust
PE: . reg ht occgr2 occgr3 occgr4 occgr5 farmer migrant b1* asia indio_cholo mestizo zambo black age5* if female==0 & age>22 &
age<51 & ht>125 & ht<200 & bdec>1800 & bdec<1900, robust
BR: . reg height occgr2 occgr3 occgr4 occgr5 farmer mult5 migrant bd1* black white age1* age2* age5* if female==0 & age>18 & age<61
& height>125 & height<200 & bdec>1800 & bdec<1900, robust
Table 5: Overall shares of occupational groups (sample) and skin colors
(population)
AR
occgr
Freq.
Percent
0
1
2, incl. farmer
3
4
5
farmer
10
2,629
2,569
770
551
431
1669
0.14
36.77
36.91
11.06
7.92
6.19
23.98
BR
occarm
0
1
2, excl. farmer
3
4
5
farmer
Freq.
Percent
360
4.049
3.112
1.819
433
0.39
69
3.64
40.98
31.49
18.41
4.38
0.39
0.70
PE
occgr
0
1
2, incl. farmer
3
4
5
farmer
Freq.
Percent
13
351
595
501
206
39
178
0.76
20.59
34.90
29.38
12.08
2.29
10.39
Skin color:
Peru:
Mestizo
37
23
White
Asian/afric.
Indio
15
3
45
http://www.country-studies.com/peru/culture,-class,-and-hierarchy-in-society.html
Brazil
White
55
Black
6
Mixed
38
Indio
1
http://www.prcdc.org/summaries/brazil/brazil.html
Figure 1: Provinces in Argentina included in the sample
24
Figure 2: Age-Heaping in Peru: prison sample and census results for Lima
250
200
150
Lima
Prison
100
50
0
1810
1820
1830
1840
1850
1860
1870
Figure 3: Educational status of occupations: average Armstrong-values in three big
cities
5
4
3
capital
rio
lima
2
1
00
19
90
18
80
18
70
18
60
18
50
18
40
18
30
18
20
18
10
18
18
00
0
25
collapse (mean) occgr (count) age if female==0 & age>20 & age<70 & ht>125 &
ht<200 & pr_b==1, by(bdec )
Figure 4: Immigrants from tall countries and height in Argentine’s regions
Figure 5: Immigrants from short countries and height in Argentine’s regions
26
Figure 6: Height development in Argentina, Brazil, and Peru (height level of unskilled
workers or unknown occupations, Armstrong 0/1, whites in Peru, mixed in Brazil)
168
167
166
165
pe
br
ar
164
163
162
161
160
159
1810 1820 1830 1840 1850 1860 1870 1880 1890 1900 1910
Source: Table 4.
27
Figure 6a: Height development in Argentina, Brazil, and Peru (height level adjusted for
occupation and skin color)
169
168
167
166
165
Peru
Brazil
Argentina
164
163
162
161
160
159
1810 1820 1830 1840 1850 1860 1870 1880 1890 1900 1910
source: table 4, calculations in file f:\hb\threecountriesadjusted.xls
28
Figure 7: Height development in Rio de Janeiro
172
170
168
166
164
162
160
158
1810
1820
1830
1840
1850
1860
1870
1880
Figure 8: Inequality by country of birth: Brazil
167
166
165
164
Brazil
Portugal
163
162
161
160
159
1810
1820
1830
1840
1850
1860
1870
1880
. collapse (mean) height (count) nc=age if female==0 & age>18 & age<61 & height>125 & height<200
> & bdec>1800 & bdec<1900 & (co=="br" | co=="pt"), by(co bdec)
29
Figure A: Inequality by occupational status: Argentina
170
169
168
Middle
High
Low
167
166
165
164
1875
1880
1885
1890
1895
1900
1905
Figure A1: Inequality of farmers versus unskilled workers: Argentina
169.5
169
168.5
168
167.5
unskilled
farmer
167
166.5
166
165.5
165
164.5
ht1870
ht1880
ht1890
ht1900
30
Figure B: Inequality by occupational status: Brazil
167
166.5
166
165.5
165
Middle
164.5
High
164
Low
163.5
163
162.5
162
1810 1820 1830 1840 1850 1860 1870 1880
collapse (mean) height (count) nc=age if female==0 & age>18 & age<61 & height>125 & height<200
> & bdec>1800 & bdec<1900 & (co=="br"), by(occbroad bdec)
Figure C: Inequality by occupational status: Peru
167
166
165
164
Middle
High
Low
163
162
161
160
159
158
1820
1830
1840
1850
1860
1870
1880
1890
31
Figure D: Inequality by skin colour: Peru
170
168
166
164
162
white
160
indio
158
156
154
152
1820
1830
1840
1850
1860
1870
1880
Figure E: Inequality by skin colour: Brazil
168
167
166
165
Black
Brown
White
164
163
162
161
160
159
1810
1820
1830
1840
1850
1860
1870
1880
. collapse (mean) height (count) nc=age if female==0 & age>18 & age<61 & height>125 & height<200
> & bdec>1800 & bdec<1900 & co=="br", by(race bdec)
32
Figure F: Regional inequality in Argentina – how much can proximity and trade effects
explain?