Abstract

This variable contains anthropometric information which can be used as a proxy for the inequality of human health and welfare in 119 countries spanning the period 1810-1989, although for most countries only a few birth decades are documented. The data set was created to provide a first attempt to measure inequality, it should be mentioned as a caveat that many individual countries cannot be measured without a substantial amount of measurement error. The tendencies of anthropometric inequality by world region (in which country-specific measurement error tends to average out) is probably informative

Author(s)

Baten, Joerg, University of Tuebingen.; Blum, Mathias, Technical University Munich (formerly University of Tuebingen)

Production date

2013-12-16

Variable(s)

Gini coefficient of male height equivalent in cm

Keywords

Height, Anthropometrics, Living Standard

Time period

1810 -1980. All data refer to the birth decadal average (1810 means 1810-19 etc)

Geographical coverage

Worldwide

Methodologies used for data collection and processing

Reconstruction of height ginis by birth decade using a variety of different sources. See also the text below. The height gini is a transformation of the coefficient of height inequality, based on Moradi and Baten’s formula: g htgini=-33.5 + 20.5*cv

Period of collection

See references

Data collectors

Joerg Baten, Mathias Blum, and many colleagues from around the world


As good as possible, but counter-checking and improvement welcome. Interpretations on individual country level should be done with careful checking

General references

As almost none of the original height variation estimates entered the

data set unchanged, the general reference which should be cited is

Jörg Baten and Matthias Blum, “Anthropometric within-country

Inequality and the Estimation of Skill Premia with Anthropometric

Indicators”, Review of Economics -- Jahrbuch fuer

Wirtschaftswissenschaften 62-2 (2011), pp.107-138.

But as the underlying height data comes from a variety of sources, here

is the complete list:

(we first reported authors and year; then author with first name;

finally title)

Apart from those individual titles, are results are based on the

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The titles cited above were used for the following countries and year:

Country From To (or only birth decade) Source (see above)

Afghanistan

1930 Debets 1970

Afghanistan

1880 Joyce 1926

Albania 1880 1900 Coon 1974

Algeria 1910 1930 Chamla 1972

Angola 1810 1820 Eltis 1982; Eltis 2009

Angola

1940 Santos David 1972

Angola 1890 1930 Stolz 2008

Argentina 1870 1910 Baten Pelger Twrdek 2010

Argentina 1850 1920 Severino Lopez 1948

Armenia

1900 Jarcho 1935

Armenia

1890 Seltzer 1936

Armenia 1850 1860 Stegl Baten 2009; Stegl Baten 2008

Australia

1960 Austr. Bureau of Statistics 1998

Australia 1850 1870 Powys 1901

Australia 1880 1920 Whitwell de Souza Nicholas 1997; Nicholas 2009

Austria 1820 1840 Komlos 1985

Austria 1960 1970 Weber Seidler Wilfing Hauser 1995

Bangladesh 1850 1880 Brennan MacDonald Shlomowitz 1997: Brennan

MacDonald Shlomowitz 2006

Bangladesh 1950 1980 Demographic and Health Survey

Belgium

1960 Eurobarometer 1996

Belgium

1940 Twiesselmann 1969

Benin 1950 1980 Demographic and Health Survey

Benin 1810 1820 Eltis 1982; Eltis 2009

Benin

1910 Trevor 1947

Bolivia (Plurinational State of) 1930 1970 Godoy Leonard Reyes-García

Goodman McDade Huanca Tanner Vadez 2006

Bolivia (Plurinational State of) 1980 Hossain Lestrel Ohtsuki 2005

Botswana

1910 Wells 1952

Brazil 1810 1880 Baten Pelger Twrdek 2010

Brazil 1950 1960 Demographic and Health Survey

Brazil

1970 Kac Ventura Santos 1997

Brazil 1890 1920 Monasterio Signorini 2008

Bulgaria

1880 Drontschilow 1914

Burkina Faso 1940 1980 Demographic and Health Survey

Burkina Faso 1930 Huizinga 1968a

Burkina Faso 1920 Seric 2007

Burundi

1920 Seric 2007

Cameroon 1950 1980 Demographic and Health Survey

Cameroon

1810 Eltis 1982; Eltis 2009

Cameroon

1930 Huizinga 1968b

Cameroon

1900 Malcolm 1925

Cameroon 1890 1910 Seric 2007

Canada 1930 1950 Bailey Carter Mirwald 1982

Canada 1820 1840 Costa Fogel 1998; Fogel Engerman Trussell 1982

Canada 1960 1970 Cranfield Inwood 2007

Canada 1850 1860 Dick 1995

Central African Republic 1940 1970 Demographic and Health Survey

Chad 1950 1980 Demographic and Health Survey

Chad

1870 Gaillard Poutrin 1914

Chad

1940 Hiernaux 1972

Chad

1930 Seric 2007

Chile

1950 Valenzuela Avendaño 1979

China 1890 1920 Morgan 2004

China 1810 1880 Morgan 2006

Colombia 1940 1980 Demographic and Health Survey

Colombia 1870 1930 Meisel Vega 2007

Comoros 1950 1970 Demographic and Health Survey

Congo

1950 Austin 1979

Congo

1810 Eltis 1982; Eltis 2009

Congo

1940 Hiernaux 1964

Congo

1920 Hiernaux 1965

Congo

1930 Seric 2007

Congo

1910 Van De Ginste 1946

Cote d'Ivoire 1940 1970 Demographic and Health Survey

Croatia

1960 Jantz Kimmerle Baraybar 2008

Croatia

1930 Skaric-Juric Ginsburg Kobyliansky Malkin Smolej Narancic Rudan 2003

Cuba

1940 Laska-Mierzejewska 1970

Cyprus 1860 1890 Buxton 1920

Czech Republic 1900 1920 Fetter Hajnis 1962

Czech Republic 1850 1880 Hodinova 2007

Czech Republic 1810 1840 Komlos 1985

Czech Republic 1930 1950 Webb Kuh Pajak Kubinova Malyutina Bobak 2008

Czechoslovakia (until 1993) 1960 Jantz Kimmerle Baraybar 2008

Czechoslovakia (until 1993) 1870 1890 Lebzelter 1923

Democratic Republic of the Congo 1920 Hiernaux 1972

Democratic Republic of the Congo 1930 Seric 2007

Democratic Republic of the Congo 1910 Van De Ginste 1946

Denmark

1960 Eurobarometer 1996

Denmark 1920 1940 Hasle Boldsen Jesper 1991

Denmark

1890 Mackeprang 1923

Dominican Republic 1940 1970 Demographic and Health Survey

Egypt 1930 1980 Demographic and Health Survey

Egypt

1880 Stegl Baten 2009; Stegl Baten 2008

Egypt

1900 Strouhal 1980

Eritrea

1880 Seric 2007

Estonia

1910 Aul 1964

Estonia 1890 1960 Aul 1997

Estonia

1970 Lintsi Kaarma 1997

Ethiopia (from 1993) 1940 1970 Demographic and Health Survey

Ethiopia (from 1993) 1920 Schreider 1950

Finland

1930 Kajanoja 1971

Finland

1880 Kajava 1927

Finland

1900 Nickul 1927

Finland 1940 1960 Silvertoinen Lahelma Lundberg Rahkonen 2001

France 1930 1940 Chamla 1964

France 1810 1920 Heyberger 2007

France

1970 Olivier 1991

France

1950 Olivier Devine 1985

France

1960 Pineau 1993

Gabon

1920 Chabeuf 1959

Gabon 1950 1980 Demographic and Health Survey

Georgia

1900 Field 1953

Georgia

1890 Weninger 1951

Germany 1810 1840 Baten 1999; Baten 2006

Germany 1850 1880 Baten 2006

Germany

1890 Baten Fertig 2010

Germany

1910 Breitinger 1934

Germany 1930 1970 Jäger Zellner Kromeyer-Hauschild Lüdde Eisele

Hebebrand 2001

Ghana 1830 1840 Austin Baten van Leeuwen 2007

Ghana 1870 1920 Austin Moradi Baten 2007

Ghana 1940 1980 Demographic and Health Survey

Ghana

1810 Eltis 1982; Eltis 2009

Greece

1880 Hasluck Morant 1929

Greece

1930 Hertzberg 1963

Greece 1950 1960 Manolis Neroutsos Zafeiratos 1995

Greece

1870 Roberts 1954

Guatemala 1940 1970 Demographic and Health Survey

Guatemala 1890 1910 Faulhaber 1970

Guinea

1910 De Lestrange 1950

Guinea 1950 1970 Demographic and Health Survey

Guinea

1890 Seric 2007

Guinea-Bissau 1920 1950 Stolz 2008

Guyana

1900 Gillin 1967

Haiti 1940 1980 Demographic and Health Survey

Hungary 1950 1970 Gyenis Joubert 2004

Hungary 1810 1840 Komlos 1985

Hungary

1860 Weisbach 1889

India

1950 Basu 1985

India 1840 1890 Brennan MacDonald Shlomowitz 1997: Brennan MacDonald

Shlomowitz 2006

India

1970 Demographic and Health Survey

India 1910 1940 Guntupalli Baten 2006

India

1960 Sirajuddin 1993

Indonesia (until 1999) 1840 1910 Baten Stegl Van Der Eng 2009

Iran (Islamic Republic of) 1960 1980 Janghorbani Amini Willett Gouya

Delavari Alikhani Mahdavi 2007

Iran (Islamic Republic of) 1870 1910 Stegl Baten 2009; Stegl Baten 2008

Iraq 1880 1900 Stegl Baten 2009; Stegl Baten 2008

Ireland 1820 1840 Costa Fogel 1998; Fogel Engerman Trussell 1982

Ireland

1960 Eurobarometer 1996

Ireland

1810 Mokyr O Grada 1994

Ireland 1900 1910 Relethforth Less 1981

Ireland

1870 Whitwell de Souza Nicholas 1997; Nicholas 2009

Israel

1930 Benoist 1971

Italy 1810 1820 A'Hearn 2003; A'Hearn 2006

Italy 1880 1890 Danubio Amicone Vargiu 2005

Italy

1930 Hertzberg 1963

Italy

1960 Sanna Floris Cosseddu 1993

Italy 1940 1950 Susanne 1979

Jamaica 1910 1930 Ashcroft Ling Lovell Miall 1966

Jamaica

1890 Davenport Steggerda 1929

Japan

1980 Hossain Lestrel Ohtsuki 2005

Japan 1930 1960 Kimura 1984

Japan 1880 1890 Matsumura 1925

Japan

1920 Watanabe Kondo Matsunaga 1975

Japan

1900 Yoshida 1926

Kazakhstan

1910 Debets 1970

Kazakhstan 1940 1970 Demographic and Health Survey

Kazakhstan

1960 Facchhini 1998

Kazakhstan

1890 Field 1948

Kazakhstan

1930 Miklashevskaia 1973

Kenya 1940 1980 Demographic and Health Survey

Kenya

1910 Field 1952

Kenya 1880 1930 Seric 2007

Kyrgyzstan 1940 1970 Demographic and Health Survey

Kyrgyzstan

1890 Jarcho 1935

Kyrgyzstan

1880 Joyce 1926

Kyrgyzstan 1900 1930 Miklashevskaia 1973

Latvia 1880 1900 Backmann 1924

Lebanon

1900 Seltzer 1936

Lebanon

1890 Shanklin 1938

Madagascar

1920 Chabeuf 1969

Madagascar 1950 1970 Demographic and Health Survey

Malawi 1950 1980 Demographic and Health Survey

Malawi 1920 1940 Laing 1964

Malawi

1870 Maynard Turner 1914

Malawi

1930 Nurse 1969

Malaysia

1860 Martin 1905

Mali 1970 1980 Demographic and Health Survey

Mali

1930 Huizinga Birnie-Teller 1966

Mali 1940 1960 Prazuck Fisch Pichard Sidibe 1901

Mexico 1840 1890 Carson 2007

Mexico

1980 Hossain Lestrel Ohtsuki 2005

Mexico 1950 1970 Rubalcava Teruel 2006

Morocco

1880 Coon 1931

Morocco 1950 1980 Demographic and Health Survey

Mozambique 1950 1980 Demographic and Health Survey

Mozambique 1810 Eltis 1982; Eltis 2009

Mozambique 1870 Maynard Turner 1914

Mozambique 1910 Seric 2007

Myanmar

1880 Risley 1906

Myanmar 1850 1870 Risley 1906; Waddell 1900

Namibia 1940 1970 Demographic and Health Survey

Namibia

1930 Seric 2007

Namibia

1880 Wells 1952

Nepal

1930 Bhasin 1974

Netherlands 1960 Eurobarometer 1996

Netherlands 1940 Van Wieringen 1972

Nicaragua 1940 1980 Demographic and Health Survey

Niger 1940 1970 Demographic and Health Survey

Niger

1910 Lobsiger-Dellenbach 1951

Nigeria 1810 1820 Eltis 1982; Eltis 2009

Nigeria

1890 Talbot Mulhall 1962

Norway

1900 Bryn Schreiner 1929; Waaler Schiotz 1932

Norway 1820 1840 Costa Fogel 1998; Fogel Engerman Trussell 1982

Norway 1870 1940 Udjus 1964

Pakistan 1850 1890 Brennan MacDonald Shlomowitz 2006

Pakistan

1900 Mazumdar 1976

Pakistan 1930 1940 Singh 1972

Pakistan

1870 Waddell 1900; Brennan McDonald Shlomowitz 2006

Papua New Guinea 1860 Chalmers 1898; Hagen 1898; Schellong 1891

Papua New Guinea 1930 Littlewood 1972

Papua New Guinea 1880 Lorentz 1923

People's Republic of Korea 1950 1980 Pak 2004

People's Republic of Korea 1880 Shirokogoroff 1968

Peru 1940 1980 Demographic and Health Survey

Peru

1920 Frisancho 1976

Peru 1820 1880 Manzel Twrdek 2010

Philippines

1860 Bean Planta 1911; Schebesta 1952; Bean 1909a; Bean 1909b; Bean

1909c

Philippines 1870 1890 Bean Planta 1911; Schebesta 1952; Bean 1909a; Bean

1909b; Bean 1909c; Wastl 1957

Philippines 1900 1910 Schebesta 1952; Wastl 1957

Poland

1980 Hossain Lestrel Ohtsuki 2005

Poland 1840 1970 Kopczynski 2009

Poland

1930 Webb Kuh Pajak Kubinova Malyutina Bobak 2008

Portugal

1980 Hossain Lestrel Ohtsuki 2005

Portugal

1960 Padez 2007

Portugal

1970 Padez Johnston 1999

Portugal 1810 1910 Stolz 2008

Puerto Rico 1940 1970

Puerto Rico 1890 1910 Godoy Goodman Levins Caram Seyfried 2007

Puerto Rico

1980 Hossain Lestrel Ohtsuki 2005

Puerto Rico 1920 1930 Thieme 1959

Republic of Korea 1950 1980 Korean Research Institute of Standard and

Science 2004

Republic of Korea 1940 Sang-Do 1978

Russian Federation 1890 1900 Jarcho 1935

Russian Federation 1920 1950 Mironov 2006

Russian Federation 1910 Oshanin 1964

Rwanda

1870 Czekanowski 1910

Rwanda 1950 1980 Demographic and Health Survey

Rwanda

1910 Oschinsky 1954

Rwanda

1920 Seric 2007

Senegal 1940 1970 Demographic and Health Survey

Senegal 1810 1820 Eltis 1982; Eltis 2009

Senegal 1910 1920 Seric 2007

Slovakia 1920 1940 Fetter Hajnis 1962

Slovenia 1920 1970 Stefancic Tomazo-Ravnik 1998

Slovenia

1860 Weisbach 1903

Somalia

1880 Puccioni 1931; Radlauer 1915

Somalia

1890 Puccioni 1931; Radlauer 1915; Bertram 1932

Somalia 1910 1920 Seric 2007

South Africa 1890 1910 Crayen 2006

South Africa 1920 1940 Laing 1964

Spain 1840 1880 Martínez-Carrion 1994

Spain

1960 Mesa Fuster Sanchez-Andres Marrodan 1993

Sudan (until 2011) 1910 Field 1952

Sudan (until 2011) 1920 Roberts 1954

Sudan (until 2011) 1930 Sukkar 1976

Sweden 1840 1900 Hultkrantz 1927

Sweden

1910 Lundman 1939

Sweden

1930 Oezer 2008

Sweden

1870 Pearl 1905

Sweden 1810 1850 Sandberg Steckel 1987

Sweden 1940 1960 Silvertoinen Lahelma Lundberg Rahkonen 2001

Switzerland 1870 1910 Brennecke 1935

Switzerland 1820 1830 Costa Fogel 1998; Fogel Engerman Trussell 1982

Syrian Arab Republic 1890 Sabatini 1936

Syrian Arab Republic 1900 Seltzer 1936

Syrian Arab Republic 1880 Shanklin 1938

Taiwan

1940 Morgan Liu 2007

Tajikistan 1880 1910 Abdushelishvili 1968

Tajikistan

1870 Oshanin 1964

Thailand

1900 Andrews 1943

The former Yugoslav Republic of Macedonia 1910 1930 Miszkiewicz 1961

Togo 1950 1970 Demographic and Health Survey

Turkey 1950 1970 Demographic and Health Survey

Turkey

1880 Hausschildt Wagenseil 1931

Turkey

1930 Hertzberg 1963

Turkey

1980 Oezer 2008

Turkey 1860 1870 Stegl Baten 2009; Stegl Baten 2008

Turkmenistan 1910 Oshanin 1964

Turkmenistan 1890 Oshanin 1964b

Uganda 1950 1980 Demographic and Health Survey

Uganda

1910 Oschinsky 1954

Ukraine 1820 1840 Komlos 1985

Ukraine 1870 1880 Pöch 1926

United Kingdom of Great Britain and Northern Ireland 1900 1910 Clements

Pickett 1957

United Kingdom of Great Britain and Northern Ireland 1820 1840 Costa

Fogel 1998; Fogel Engerman Trussell 1982

United Kingdom of Great Britain and Northern Ireland 1960 Eurobarometer

1996

United Kingdom of Great Britain and Northern Ireland 1830 Floud Wachter

Gregory 1990; Floud Wachter Gregory 1991

United Kingdom of Great Britain and Northern Ireland 1890 1920 Kemsley

1951

United Kingdom of Great Britain and Northern Ireland 1810 Mokyr O Grada

1994

United Kingdom of Great Britain and Northern Ireland 1850 1950 Rosenbaum

1988

United Republic of Tanzania 1940 1970 Demographic and Health Survey

United Republic of Tanzania 1930 Jürgens 1967

United Republic of Tanzania 1910 Oschinsky 1954

United Republic of Tanzania 1920 Seric 2007

United Republic of Tanzania 1900 Trevor 1947

United States of America 1940 1960 BRFSS Annual Survey Data 1995

United States of America 1810 1840 Costa Fogel 1998; Fogel Engerman

Trussell 1982

United States of America 1850 1860 Komlos 2006

United States of America 1870 Murray 1997

United States of America 1880 Steckel Haurin 1994

United States of America 1920 1930 U.S. Department of Health 1976

United States of America 1890 1910 Wu 1994

Uzbekistan 1940 1970 Demographic and Health Survey

Uzbekistan

1900 Jarcho 1935

Uzbekistan

1890 Oshanin 1964

Uzbekistan

1910 Oshanin 1964a

Viet Nam

1920 Chabeuf 1967

Yemen

1900 Demographic and Health Survey

Zambia 1940 1980 Demographic and Health Survey

Zimbabwe 1900 1970 Demographic and Health Survey

This is an excerpt of the paper by Baten and Blum (2012), which was

published in a modified way in the following way (please cite this):

Jörg Baten and Matthias Blum

“Anthropometric within-country Inequality and the Estimation of Skill

Premia with Anthropometric Indicators”, Review of Economics --

Jahrbuch fuer Wirtschaftswissenschaften 62-2 (2011), pp.107-138.

Recently, a method to measure inequality has been proposed that is based

on anthropometric indicators. Baten (1999, 2000) argued that the

coefficient of variation of human stature is correlated with overall

inequality in a society, and that it can be used as indicator,

especially where income inequality measures are lacking. This

correlation has been confirmed in further analyses, for example by

Pradhan et al. (2003), Moradi and Baten (2005), Sunder (2003),

Guntupalli and Baten (2006), Baten (2000a), Blum (2009), Baten et al.

(2009). The idea behind is that heights reflect nutritional conditions

during early childhood and youth. As wealthier people have better access

to food and shelter and less illnesses, they tend to be taller than the

poorer part of the population. Hence, the variation of height of a

certain cohort may be indicative of income distribution during the

decade of their birth.

The aim of this research note is to summarize this strand of literature,

and to compare it with other indicators of inequality, such as skill

premia. We provide estimates of skill premia for a set of countries and

decades for which “height CVs”, as they will be called in the

following, are available. Moreover, we will provide a second set of

estimates based on reasonable interpolations. Skill premia are defined

here as the ratio between a skilled worker in the building trades, and

an unskilled one. This measure is available for a number of countries

since the 19th and early, whereas the amount of other inequality

indicators (such as the gini coefficient of income) is quite limited and

sometimes of low accuracy before the 1970s and 1980s. Clearly, the skill

premium as a measure of inequality does not cover the whole economy, and

the assumption that inequalities between skilled and unskilled building

workers reflects skill premia in other sectors of the economy might not

always hold. Especially subsistence farming, household production and

black market economies might be clearly different. those latter parts of

the economy can be covered quite well with anthropometric measures. This

means that if we find a significant correlation between skill premia,

which is the most abundant income-based inequality indicator, and height

CVs, both measures together will allow to trace inequalities within

countries quite well for the past two centuries.

What are the advantages and disadvantages of anthropometric inequality

measures? Heights offer a good complement to conventional inequality

indicators and constitute perhaps an even better indicator in some

respect. If the distribution of food and medical goods in an economy

becomes more unequal, heights should also become more unequal. Yet while

a correlation with income does exist, this correlation is only partial.

As mentioned above, some important inputs are not traded on markets but

are provided as public goods, such as public health measures or food

supplements for schoolchildren. Public goods lead to modest deviations

between purchasing power-based and height-based inequality measures.

Moreover, income totally neglects transfers within households. This is a

major argument in favor of height-based inequality measures: heights are

an outcome indicator, whereas real income represents an input to human

utility. Deaton (2001) and Pradhan et al. (2003) have argued

convincingly that measures of health inequality are important in their

own right, not only in relation to income. Heights do capture important

biological aspects of the standard of living (Komlos, 1985; Steckel,

1995), irrespective of the fact that some problems regarding the stature

variable may exist.

Anthropometric methods are even more advantageous for studying

developing countries of the 20th and the generally poorer countries of

the 19th century. To date, the development of inequality within LDCs

could not be sufficiently explored because reliable data were lacking.

The well-known Deininger and Squire data set (1996), for example,

provides only eight Gini coefficients of income for Subsahran Africa for

the period before 1980, labeled as “acceptable”. Atkinson and

Brandolini (2001) convincingly pointed to serious flaws in the income

inequality data collected by Deininger and Squire, arising from

insufficient consistency across countries and over time. For those

countries, height inequality measures can provide important additional

insights. We do not claim that height is the only accurate measure of

inequality, but argue that it generates new insights on inequality while

serving as a useful countercheck for other indicators, thereby leading

to more meaningful results overall.

Methodological background of height CVs

The effects of inequality on heights are best understood by comparing

the likely outcomes of a hypothetical situation, in which a population

is exposed to two alternative allocations of resources A and B after

birth:

All individuals receive the same quantity and quality of resources

(nutritional and health inputs). This case refers to a situation of

perfect equality.

Available resources are allocated unequally (but independently of the

genetic height potential of the individuals).

In the case of A, the height distribution should only reflect genetic

factors. Despite perfect equality, we observe a biological variance of

(normally distributed) heights in this case. Yet how does the height

distribution respond to an increase in inequality (B)? The unequal

allocation of nutritional, medical and shelter resources allows some

individuals to gain and grow taller, while others lose and suffer from

decreasing nutritional status. In comparison with the situation of

perfect equality, the individual heights of the rich strata shift

therefore to the right, the poor strata shift to the left. Thus rising

inequality should lead to higher height inequality, although this effect

is weakened by the fact that the genetic height variation accounts for

the largest share of height variation. Even a bimodal height

distribution could result if the resource endowment differed extremely

between groups. In practice, since the biological variance continues to

contribute a large share to the total variance, most height

distributions are normally distributed or very close to normal, but with

a much higher standard deviation than A (but see A’Hearn (2004),

Jacobs, Katzur and Tassenaar (2008) on late teenagers).

The standard deviation is not a satisfactory measure of inequality,

since anthropologists argue that the biological variance increases with

average height (Schmitt and Harrison 1988). The coefficient of variation

(CV) takes this effect into account and is a consistent and robust

estimate of inequality. For a country i and a five-year-age birth decade

t, the CV is defined as:

Thus, the standard deviation ( is expressed as a percentage of the mean

(. Baten (1999, 2000a) compared height differences between social groups

using the CV for early 19th century Bavaria, since an ideal data set was

available for this region and time period, with nearly the entire male

population measured at a homogeneous age and the economic status of all

parents recorded. The measures turned out to be highly correlated.

Therefore, high CVs sufficiently reflect social and occupational

differences without relying on classifications. The CV of a totally

equal society is yet unknown and can only be empirically approximated.

For decomposing world health inequality, Pradhan et al. (2003) tried to

standardise height inequality by assuming that the height distributions

in OECD countries reflect the genetic growth potential of individuals

only. However, this would mean that no nutritional and health inequality

exists in OECD countries, which seems highly implausible. In Germany

during the 1990s, for example, height differences between social groups

were as large as two centimeters (Baten and Boehm 2009; Komlos and Kriwy

2003). Even in egalitarian Scandinavia, some height inequality remains

between regions (Sunder 2003).

Moradi and Baten (2005) have estimated the relationship between income

inequality and height CV for 14 African countries and 29 five-year

periods. They controlled for the differences in income definition and

population coverage by including dummy variables. In addition, country

fixed-effects were included ( REF _Ref89485783 h * MERGEFORMAT

Table 2, model 1 and 3) which implies that their analysis focused

mainly on intertemporal effects.

They found that height CV was significant and positively correlated with

the gini coefficients of income (Table 1). An increase in the CV by one

unit corresponded with a rise in the gini coefficient by 13.2 points in

the fixed-effects specification. It is noteworthy that the relationship

between the CV and the gini coefficient is not sensitive to country

fixed-effects in general. In another regression without country fixed

effects (2), they obtained a coefficient between nutritional and income

inequality of 20.9. Both coefficients were very close to Baten and

Fraunholz's (2004) estimate for Latin America, which reported a

significant coefficient of 15.5 based on gini coefficients whose

underlying data are of the highest possible quality. Additional

robustness tests including weighting for sample quality confirmed the

relationship. Moradi and Baten (2005) recommended the following formula

for translating height CVS into income ginis:

(2) Giniit=-33.5+20.5*CVit

Moradi and Baten argued that an excellent case for comparing the

development of both income and height-based inequality measures is

Kenya, for which the estimates by Bigsten (1985) offer a consistent

source with a sufficient number of data points. The development of both

inequality measures is nearly identical, except for the sudden fall of

the gini coefficient in 1955 with which the CV does not correspond. It

is actually not clear which of the two inequality measures describes the

development better, but at least it seems that the CV’s movement is

somewhat smoother and less volatile (the CV might moreover be less

volatile due to some consumption smoothing, as people reduce their

savings in harder times to smooth their consumption). However, both the

strong rise of inequality in Kenya during the early 1950s and the more

gradual rise of the late 1960s are clearly visible in both series.

Similarly, the decline in inequality thereafter is confirmed by both

measures. Summing up, the development of CVs over time serves as a

promising measure of inequality, even more so because in periods and

countries in which other data on inequality are either non-existent or

unreliable.

Actually, most estimates between height CV and income gini have been

performed for the period after 1950s when real incomes started to

increase and a smaller part of that budget was allocated to food and

shelter. This might bias the correlation between height CV and gini

coefficient of income downwards because in many regions a lower portion

of income was spend on food and shelter in the later period. Our main

interest is the period prior to 1950, and especially the poorer

countries. In that period budgets were relatively small, and the

proportion spent on food and shelter high, so height CV and income ginis

should be closer correlated than in the post-1950 period.

In sum, the relationship between gini coefficient of income and height

CV seems quite well-established. Hence we collected all available data

from hundreds of previously published articles (see appendix for a list

of references), and benefited from scholars who provided us with their

original height data sets. We excluded cases with very small numbers of

height measurements, or if only one special group within a country was

included. We took care that late teenage year / early twenties samples,

military truncation, gender, prison selectivity and other factors did

not distort our samples. In order to express height CVs in a more

familiar measure for economists studying inequality issues, we converted

the height CV for each country and birth decade with formula (2) into

income gini equivalents.

Skill Premia and height inequalities

We collected the available evidence of unskilled and skilled building

workers that was compiled by Robert Allen for the 19th century, and the

League of Nations and International Labor Organization Statistics for

the early 20th century. This evidence yielded a panel of 166

observations between the 1800s and 1950s. Most of the time, it has

around 10 observations per decade (mostly from the Allen compilation).

But in 1950, there are 33 observations, because the International Labor

Organization extended the coverage to a number of poor countries. This

is also the reason why the variation of inequality is slightly higher in

this decade.

When we compare this dataset with overlapping evidence on height ginis,

there is a general correspondence (Figure 1). For example, Spain in the

1850s-1890s, Mexico in the 1950s and the U.S. had quite high

inequalities, whereas inequality was rather low in Denmark and

Switzerland in the 1920s.

Testing this more systematically, we examine in three different

regression specifications whether the relationship between

anthropometric inequality measures and skill premia is significant

(Table 3). The overlap between our database on height ginis and skill

premia yields 50 observations, and each observation reflects on country

and birth decade. given the panel nature, we estimated both fixed and

random effects models in order to study the robustness of the

relatinship (and also simple OLS in column 3). The relationship is

statistically significant and positive. The explanatory power of those

models is reasonably high, although a lot of random fluctuation cannot

be explained.

In order to assess the robustness, we repeated the test with a slightly

larger sample of height ginis, for which we used some conservative

interpolation for Europe, America and Asia (for which the amount of data

allows some interpolation). This brought the number of observations to

125 for the whole period and 101 for the “long” 19th century (until

the 1910s). Adding those values reduces the size of the coefficient

somewhat (which can partly be explained by the fact that the constant is

now estimated to be around 1, whereas it was below 1 in Table 3 and the

Col. 4 of Table 4). But in general, the relationship between

anthropometric inequality and skill premia remains robust. It is also

not affected by the inclusion of decadal or country dummy variables

neither.

Finally, we have also studied the experience of the period until 1910

separately, which might be different from the 1920s to 1950s period, in

which a number of parameters changed in the world economy. However,

again, the estimation results are relatively robust.

Conclusion

In this study, we have reviewed the literature on anthropometric

inequality and developed a model to estimate skill premia based on those

height-based measures. The literature focused so far mainly on the

comparison between income and height ginis of the second half of the

20th century. The correlation with income gini coefficient for African

and Latin countries already demonstrated that this approach is able to

yield plausible results.

In this study, we are building on the fact that direct comparisons with

skill premia have not been done in the previous literate. We found the

relationship between the two inequality indicators to be relatively

strong and robust in different estimation procedures.

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Caribbean

Anguilla[No Data]

Antigua and Barbuda1500 (5)-2013 (21)

Aruba[No Data]

Bahamas1500 (5)-2013 (23)

Barbados1500 (5)-2013 (27)

Bonaire, Sint Eustatius and Saba[No Data]

British Virgin Islands[No Data]

Cayman Islands[No Data]

Cuba1500 (8)-2012 (34)

Curaçao[No Data]

Dominica1500 (5)-2013 (20)

Dominican Republic1500 (6)-2013 (40)

Grenada1500 (5)-2013 (21)

Guadeloupe[No Data]

Haiti1500 (6)-2013 (36)

Jamaica1500 (6)-2013 (38)

Martinique[No Data]

Montserrat[No Data]

In 2010, the Netherlands Organisation for Scientific Research (NWO) awarded a subsidy to the Clio Infra project, of which Jan Luiten van Zanden was the main applicant and which is hosted by the International Institute of Social History (IISH). Clio Infra has set up a number of interconnected databases containing worldwide data on social, economic, and institutional indicators for the past five centuries, with special attention to the past 200 years. These indicators allow research into long-term development of worldwide economic growth and inequality.

Global inequality is one of the key problems of the contemporary world. Some countries have (recently) become wealthy, other countries have remained poor. New theoretical developments in economics - such as new institutional economics, new economic geography, and new growth theory - and the rise of global economic and social history require such processes to be studied on a worldwide scale. Clio Infra provides datasets for the most important indicators. Economic and social historians from around the world have been working together in thematic collaboratories, in order to collect and share their knowledge concerning the relevant indicators of economic performance and its causes. The collected data have been standardized, harmonized, and stored for future use. New indicators to study inequality have been developed. The datasets are accessible through the Clio Infra portal which also offers possibilities for visualization of the data. Clio Infra offers the opportunity to greatly enhance our understanding of the origins, causes and character of the process of global inequality.