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Heights and living standards in industrializing Germany: The case of Wurttemberg. (Volumes I and II)

Twarog, Sophia Nora, Ph.D.

The Ohio State University, 1993

Copyright ©1998 by Twarog, Sophia Nora. All rights reserved.

UMI 300 N. Zeeb Rd. Ann Arbor, Ml 48106 HEIGHTS AND LIVING STANDARDS IN INDUSTRIALIZING GERMANY: THE CASE OF WURTTEMBERG

Volume I

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

the Degree Doctor of Philosophy in the Graduate School

of The Ohio State University

By

Sophia Twarog

* * * *

The Ohio State University

1993

Committee Members Advisor: Dr. Richard Steckel Dr. Lars Sandberg Dr. Edward Ray Richard Steckel Copyright by Sophia Twarog TO MY PARENTS Dr. Leon and Dr. Katherine Twarog ACKNOWLEDGEMENTS

There have been so many people who have contributed to this project. I wish to thank them all very much.

First, my parents, Drs. Leon and Katherine Twarog, whose support services throughout the dissertation process have been far beyond the normal call of parental duty. They provided me with a large, beautiful, and peaceful place in the country in which to write. My father is spotted all over campus picking up typing, xeroxing, and mailing things to me at "Dissertation Headquarters." They have provided emotional support, excellent ideas, and an occasional kick in the pants — all of which helped move the process along. I am proud to become the third Dr. Twarog in the family.

I wish to extend my deepest thanks to my advisor, Dr. Richard Steckel. 1116

German word for advisor is Doktorvater, or "Dissertation Dad." In many ways he has been father-like in his involved guidance at every stage of this three year project.

He was very generous with his time and attention, and supportive in dozens of ways.

He challenged me to stretch my abilities and do my best, but did not lose sight of the person behind the paper. I could not imagine a better advisor. My thanks also go to my future husband, Alberto Klaas. He has provided me

with many good ideas along the way, as well as the reference that led to my fourth

chapter. He has given me much inspiration across the miles and with something

wonderful to look forward to when the dissertation is done.

This study would not have been possible without the data found in the Stuttgart

Military Archive. I wish to thank the colleagues there for their assistance.

I also wish to thank the Statistisches Landesamt of Baden-Wurttemberg for

access to their large historical collection. Particularly, I wish to thank Herr Peter

Eichfuss, who spent many hours helping me find the information needed.

Many people have been very generous with their advice on this project. This

is particularly true of my Committee members, Edward Ray and Lars Sandberg.

John Komlos has assisted me in many ways over the past three years. Bill Boal and

Stephen Cosslett provided valuable statistical advice. Many colleagues at Academic

Computing Systems of The Ohio State University, particularly Don Gibb, Joe

Damico, and Aaron Supowit, and Fred Ruland were very helpful in getting my

computer programs to run. To all these people I extend a heartfelt thanks.

I wish to thank all my assistants, who entered data and typed for me.

Foremost among them is Mark Nielsen whose help was indespensible in generating

the multitude of graphs in this dissertation. Also my typist, Barry Zvolenski did an outstanding job. I am also grateful for the financial support which enabled this project. I wish to thank The Ohio State University Graduate School for the multi-year fellowship.

The scholarship from the Graduate School and the University of Bonn made data collection in Germany possible.

I wish to thank The Graduate Studies Committee of the Department of

Economics at The Ohio State University for the Dice Fellowship.

Last but not least, I wish to thank my family and friends, for their emotional and tactical support along the way.

v VITA

November 29, 1964 Bom - Columbus, Ohio

1987 ...... B.A., Economics and Preprofessional Studies, University of Notre Dame, Notre Dame, Indiana

1989 ...... M.A., Economics, The Ohio State University, Columbus, Ohio

PUBLICATIONS

Alberto Klaas and Sophia Twarog, "Deregulations in the Financial Sector in Indonesia, 1983-1990," in Growth Determinants in East and Southeast Asian Economics. Koppers, Dingens, and Klaas, eds. Berlin: Duncker & Humblot Verlag, 1991.

FIELDS OF STUDY

Major Field: Economics

Studies in Economic History and Trade and Development TABLE OF CONTENTS

ACKNOWLEDGEMENTS...... ii

VITA...... vi

CHAPTER PAGE

I INTRODUCTION...... 1

II REVIEW OF THE HEIGHT LITERATURE...... 8

2.1 The Growth Process and Factors Influencing Stature...... 8 2.2 The Use of Heights in the Historical Literature...... 17 2.3 The Statement of the Problem...... 26

III GERMAN ECONOMIC HISTORY, 1833-1914 ...... 31

3.1 Introduction ...... 31 3.2 1851-1870 ...... 34 3.3 Factors Accompanying Industrialization ...... 37 3.4 1871-1914, Introduction ...... 39 3.5 Industrial Growth, 1871-1914 ...... 42 3.6 Changes in Living Standards During Industrialization ...... 48 3.7 Regional Variations in German Industrialization. . . . 54

IV WURTTEMBERG ECONOMIC DEVELOPMENT. ... 63

4.1 Industrialization in the Kingdom of Wurttemberg ...... 63 1 Introduction ...... 67 2 First Half of the Nineteenth Century...... 68 3 Third Quarter of the Nineteenth Century...... 71 4 Industrial Expansion after 1875 ...... 75 4.2 Population Development and Workforce Potential. . . . 86 vii CHAPTER PAGE

V THE DATA...... 101

1. Introduction ...... 101 2. Draft Laws in Germany, 1871-1914 ...... 102 3. Primary Data Source ...... 104 4. Sampling Procedure ...... 105

VI METHODOLOGY...... 110

6.1 Introduction ...... 110 6.2 Statistical Methods ...... I ll 1. Quantile Bend Estimation (QBE) ...... I ll 2. Reduced Sample Maximum Likelihood Estimation (RSMLE) ...... 113 3. Komlos and Kim ...... 116 6.3 My Analytical Procedures ...... 117 1. QBE...... 118 2. RSMLE...... 122 3. Komlos and Kim ...... 126

VII RESULTS...... 128

7.1 Overall Height Trends, 1852-1893 ...... 128 1. Unadjusted Averages and QBE Estimates ...... 128 Total Data ...... 129 Draftees/One Year Volunteers ...... 131 Infantry ...... 133 Artillery and Calvary ...... 134 2. RSMLE Average Height Estimates ...... 135 3. Komlos and Kim Estimates ...... 137 7.2 Occupational Results ...... 138 1. Unadjusted Average Heights by Occupational Category ...... 138 2. RSMLE Estimates of Average Height for Seven Occupational Groups ...... 139 3. Average Height by Three Occupational Categories ...... 144 4. Stature and Social Mobility ...... 150

viii CHAPTER PAGE

VII 7.3 Regional Results ...... 153 7.4 Community Size Results ...... 157 7.5 Combinations of Occupational, Regional and Community Size Dummy Variables ...... 162 7.6 Summary ...... 164

VIII DISCUSSION...... 165

8.1 Main Findings ...... 165 8.2 Height Trends in Historical Context ...... 167 8.3 Comparison with Other Indicators of Living Standards ...... 174 Estimates of Real Gross Wages and Real Per Capita Income in Germany ...... 174 Emigration from Germany and Wurttemberg ...... 175 Life Expectancies ...... 176 Infant Mortality Rates ...... 178 Overall Mortality Rates ...... 180 8.4 Regional Patterns ...... 180

IX CONCLUSIONS AND DIRECTIONS FOR FUTURE RESEARCH...... 183

9.1 Summary and Conclusions ...... 183 9.2 Directions for Future Research ...... 188

ix CHAPTER I

Introduction

Industrialization has fascinated economists for years. The rise in industrial

output and real per capita incomes has been researched enthusiastically. Most

economists would likely agree that in the long run, industrialization generally

benefitted the populations of the countries in which it took place. There has been

some controversy, however, concerning industrialization’s impact on the well-being of

those living through it. It is likely that industrialization’s effects varied across regions

and socio-economic groups.

There is evidence that industrialization was often accompanied by social

distress for the working classes, particularly in the earlier phases. Factory workers’ quarters were often cramped, poorly heated, and damp. Workdays were long, usually

10-14 hours. The workplace was often hazardous to health; infectious disease spreads rapidly in such crowded conditions. Medical care, as well as a family’s diet was limited to the wage income earned. For those who left the farm for the city, it is not clear that their standard of living improved.

Optimist versus Pessimist debates for many countries have arisen. In England, and the debate centers approximately on the period 1770-1815.

1 The optimists point to large rises in real per capita income and industrial output. The

pessimists speak of rising food prices after 1750 and especially in the 1790s. Since

lower income workers spend a relatively large portion of their income on food, these

prices increases hit them especially hard. Contemporary accounts, such as those of

Charles Dickens, Engels, Marx, and Macauly, paint pictures of dire poverty and

distress among the lower classes. On the other hand, the skilled workers and the

upper classes were reaping more positive benefits. Thus, income inequality rose.

Similar optimist-pessimist debates exist for the United States, Sweden, and the

Netherlands.

Germany is another country where rapid industrialization led to rising real per

capital income, but also caused social distress along the way. The years 1871-1914

are referred to as the Hochindustrializierung (High Industrialization) period. Between

1871 and 1900, real per capital income rose at the impressive annual rate of 1.46%.

However, the years 1873-1895 are also referred to as the Great Depression, despite

these rising incomes. Rolf Dumke (1988) claims income inequality rose in the 1871-

1914 period.

The optimist-pessimist debate in Germany centers around the 1870s and 1880s, i.e., the beginning of the high industrialization phase. Pessimists point to signs of social distress and increasing numbers of poor needing alms. The distress in the

1880s was great enough to induce Bismarck to lay down the pinnings of a safety net for those "falling through the cracks" during the rapid transition in the economy. By the end of the decade, Germany became the first large industrialized country to have compulsory health and accident insurance, as well as disability and old age pensions.

After the 1890s, it is generally agreed that the living standards of German workers were improving.

The optimist-pessimist debates raise questions about the limitations of real per capital income as a measure of living standards. First, it tells us nothing about the distribution of income. If income is becoming more unequally distributed, the income of some groups might be falling even if real per capita incomes are rising. Second, real per capital income estimates are only as reliable as the data used to construct them. Cost of living indices are always arbitrary, and even historical output and income data is seldom without flaws. Third, real per capital income does not capture important aspects of the physical quality of life, e.g., health, changes in working and living conditions, pollution, and changes in life expectancy. Surely, most of us would regard positive changes in the above categories as improvements in welfare.

In the last two decades, economic historians have discovered a new tool for examining changes in welfare in the past: average population heights. This variable will be discussed at length in Chapter II. The following merely summarizes. Average population height has advantages as an indicator of living standards, especially physical well-being. It is a measure of net nutrition:

Caloric Intake Caloric claims due to Calories left body maintenance, over for illness, and growth physical activity

It is highly correlated with socio-economic status: higher income generally means better diet and access to health care, as well as less physically intensive work.

Comparisons between countries have shown the correlation between average height and per capital income to be extremely high (.8 to .9) (Steckel, 1982). Moreover, heights are sensitive to income distribution: ceteris paribus, at a given per capita income, average heights decline as income inequality rises. The measure also captures aspects of the physical quality of life. Heights are positively correlated with good health and negatively correlated with mortality rates in all age groups (Floud et.al, 1990). It is indeed arguable that stature more accurately reflects changes in the physical well-being of a population than real per capita income.

The following chapter discusses the height variable in more depth. It also recounts the variety of ways in which average height has been used in the economic history literature.

Most relevant to my research are explorations of height trends during industrialization. Average height trends can shed new light on old optimist-pessimist debates. In studies of England and the United States, average heights fell during industrialization, particularly in the early phases (Floud et.al, 1990, and Steckel,

1990). Thus, the height data tends to support the pessimist view. 5

This study examines height trends in Germany during industrialization. A

major goal of the study is to determine whether Germany fits the pattern of falling

average heights during industrialization observed in England and the United States. If

this hypothesis is true, this adds support to the argument that, in general, rapid

industrialization is initially associated with some hardship, usually for the working

class. Countries today striving for rapid industrialization at all costs may want to take

note.

My sample consists of information on 14,800 soldiers serving in the army of

the Kingdom of Wurttemberg between 1871 and 1913. Information collected includes

height, occupation of the soldier and often of the father, birthdate and place,

residence and incidences of illness while serving.

Wurttemberg is a region in southwest Germany, that is today part of the prosperous state of Baden-Wurttemberg. Despite its current position as a leading industrial region, Wurttemberg’s industrial development has been relatively neglected in the literature. In fact, what I have written in Chapter IV in this paper is the most I have seen in English on this subject.

Industrialization in Wurttemberg was perhaps not as dramatic as in the Ruhr, where coal deposits led heavy industrial establishments and spurred massive migration and urbanization flows. In coal-less Wurttemberg, the focus was on light industry.

The industrial structure is relatively decentralized and establishments were smaller.

Yet in terms of percent of population employed in the commercial sector,

Wurttemberg tends to be close to the average for the German Empire during 6

industrialization. It is thus arguable that Wurttemberg is more representative of the

average German industrial experience than the Rhein-Ruhr area, about which much

ado has been made.

In this study, height data is analyzed to test the hypothesis that heights fell

during early industrialization in Germany. I also estimate the height trends of

occupational and regional subgroups in Wurttemberg, and explore the relationship

between height and its historical covariates. Specific hypotheses and contributions to

the literature will be presented in Section 2.3: Statement of the Problem. It is first

necessary to review the literature on heights in order to ground the hypotheses.

This dissertation is set up as follows: Chapter II is a review of the height

literature. Section 2.1 discussedsthe human growth process and the relationship

between nutrition, disease, socio-economic status and stature. Section 2.2 describes

the ways in which heights have been used in the economic history literature, with

emphasis on height trends during industrialization. Section 2.3 contains specific

hypotheses to be tested and the contributions of this study to the literature.

Chapter III paints the German historical background between 1833 and 1914.

The first five sections present the supply-oriented account of German industrialization.

Section 3.6 examines trends in some alternative measures of health and welfare for

Germany. Section 3.7 takes issue with treating 19th Century Germany as a homogeneous unit. It explores the large regional differences during German industrialization, and places Wurttemberg in the German context. Chapter IV describes the unique industrialization process in Wurttemberg in

the 19th century. The effects of natural resource endowment and agricultural

structures on the pattern of industrial development are explored.

Chapter V, the data, describes the primary data source, draft laws in

Germany, and sampling procedure.

Chapter VI, methodology, discusses the statistical methods used. Due to

minimum height requirements et al., the height distributions of soldiers tend to be

eroded at the lower tail. Three methods of dealing with this situation are Quantile

Bend Estimation (QBE), Reduced Sample Maximum Likelihood Estimation (RSMLE),

and a method proposed by Komlos and Kim (1990). These methods and the ways in

which I applied them to my data are described in this chapter.

Chapter VII presents the statistical results. First, overall height trends are

described, followed by height trends of draftees, one year volunteers, and infantry.

Section 7.2 presents occupational results. Section 7.3 presents regional results.

Section 7.4 presents community size results. Section 7.5 describes regression results

using combinations of occupational, regional, and community size dummy variables.

Chapter VIII, the discussion chapter, highlights the major results of the

statistical estimation, and the fate of the hypotheses stated in Chapter II. An attempt is made to explain the height trends observed in Wurttemberg, and to put the results in perspective.

Chapter IX presents conclusions and directions for future research. CHAPTER II

A Review of the Height Literature

2.1 The Growth Process and Factors Influencing Stature

To use height as a measure of health, one must first have an understanding of

the human growth process, and factors which influence attained stature. The Nature

versus Nurture debate arises here. Are differences in height due to genetic or

environmental factors? What are the effects of malnutrition and infection? What are

the relationships between heights and other indicators of welfare such as

socio-economic class and per capita income? This section addresses these questions.

First, let us take a closer look at the height variable. As discussed earlier, height is a measure of an individual’s history of net nutrition. It reflects both the diet, as well as maintenance and claims made on the body such as work intensity and disease. Figure 2.1 shows the relationship between stature, its proximate determinants, its socio-economic determinants, and its functional consequences.

Heights are directly determined by the proximate determinants, such as diet, disease, and work intensity. Many of these proximate determinants are functions of socio­ economic determinants, such as income, public health, sanitation, and disease environment (Steckel, 1990).

8 Height may in turn have functional consequences. For example, height was

found to be positively correlated (generally, =0.3) with scores on tests of intelligence

or ability. (Tanner, 1978, p. 149) The upwardly socially mobile tend to be taller than

those in the social class left behind. (Tanner, 1978, p. 148) Moreover, "a number of

recent studies have shown that, after controlling for other variables, height has an

independent effect on life expectation even in middle age and after." (Floud, et.al.,

1990, p.266) The results of a study on 1.8 million Norwegian men and women, ages

20 to 90, show a clear negative relationship between stature and mortality for all age

groups (except sometimes for the very tallest individuals.) This information is

summarized in Figure 2.2. Figure 2.3 shows the relative mortality rates by height for

Norwegian men aged 49-59. Figure 2.4 shows a similar relationship between stature

and rejection rates for chronic conditions among men examined for the Union Army.

(Floud et.al., 1990, pp.266-269) In Vienna, I saw a similar pattern in the rejection

rates for the Austro-Hungarian army during the second half of the nineteenth century and the beginning of the twentieth century. The rejection rate decreased steadily as height increased, until the range above 180 cm was reached, whereupon it rose again.

In speaking of functional consequences of height, one must be careful about correlations and causation. It could be that positive proximate and socioeconomic effects that generate greater stature also cause increases in intellectual ability and self confidence, and better health. Study in this area is still in progress. 10

The Growth Process

Figure 2.5 shows the height attained curves for a typical boy and girl (in

Britain, 1965). (Tanner, 1978, p. 13) The figure shows that up to adolescence, girls

and boys are about the same height. Girls’ adolescent growth spurt generally occurs

two years earlier than that of boys. Thus shortly after age eleven, she surpasses the

boy in height, and remains taller until about age fourteen. At that point, the boy’s

growth spurt is well under way and hers is slowing down.

Figure 2.6 depicts typical individual growth velocity curves (Tanner, p. 14).

For boys and girls, the highest velocity in heights occurs in infancy. This velocity

slows down after the age of four or five, and falls (or in some cases remains constant)

until the adolescent growth spurt occurs. The peak adolescent velocity is about

one-half of the growth velocity during infancy. For females, this typically begins (in

Britain) at age 10.5, and reaches peak velocity at age twelve. Boys reach their peak two years later. The magnitude of boy’s adolescent growth spurt is slightly larger than the girl’s. The fact that men are generally taller than women reflects this, as well as the two additional years of growth men have before their spurt occurs

(Tanner, 1978; Steckel, 1990, p. 14).

Genetic Versus Environmental Factors

Stature reflects a complex interaction between genetic and environmental factors. An individual is bom with a genetically potential height. If all the factors 11

necessary for growth are present, he or she can attain this biological maximum.

However in an environment of poverty and disease, the genetic maximum might not

be achieved.

Genetics play a larger role when dealing with individual cases of

heights. But if one looks instead at the average height of a population, many of these

genetic differences cancel each other out. Richard Steckel writes:

"Although genes are important determinants of individual height,

studies of genetically similar and dissimilar populations under various

environmental conditions suggest that differences in average height

across most populations are largely attributable to environmental

factors." (Steckel, 1990, p. 15)

Figures 2.7 and 2.8 display the mean heights of boys and girls of

the three major racial groups: European, African, and Asian. The growth

curves are fairly similar. However, some Asiatics are consistently shorter than their

European or African-descended counterparts. This should be taken into account in

studies across countries. (Tanner, p. 138) However, racial differences do not play a role in this study, since the population of Wurttemberg in the late 19th and early

20th centuries was fairly homogeneous in terms of race/ethnicity.

Effects of Malnutrition and Disease

Nutrition and diet provide the caloric energy needed for various claims upon the body, including maintenance, physical activity, disease, and growth. For example, a 1 year old needs 330 kilojoules per kilogram of weight per 12

day for bodily maintenance; 80 kj/kg/day for normal physical activity; and 20

kj/kg/day for growth. If the caloric intake falls below 330 kj, growth will stop and

physical activity decrease. Illness also makes a claim upon the caloric intake. It too

can result in no caloric residual left for growth (Tanner, p. 129).

Malnutrition can therefore delay growth. Figure 2.9 shows the

effect of malnutrition during the First and Second World Wars on the heights

of schoolchildren in Stuttgart, Germany. As long as the episode of malnutrition is not

too long nor too severe, recovery is possible. Much depends on conditions following

the episode. If good nutrition and a healthy environment prevail, then the child can

experience a period of catch up growth, and might be able to achieve normal height.

The growth period often continues into the early twenties. Richard Steckel (1986)

documents a remarkable catch up phase for American slaves, due to much

improved diet at age ten, when the children were big enough to work in the

fields.

However, catch up growth requires more calories than normal growth.

If the caloric intake is not high enough, full catch-up growth may not occur.

It seems clear that "prolonged periods of undemutrition may have lasting effects on adult size." (Evelyth and Tanner, 1990, p. 195)

The effects of disease on growth are similar to malnutrition in that disease can cause a slow down in growth, followed by catch-up growth when conditions improve. Yet chronic undemutrition and disease exposure can lead to stunted growth. There is in fact a synergistic effect between nutrition and disease. 13

Poorly fed children are more susceptible to disease. Also, diseases can cause an

increase in malabsorption of nutrients into the body. Thus, even if an individual’s

dietary intake seems sufficient, the nutrients actually being absorbed into the body

may be less than sufficient for the claims made. It seems that declining incidence of

malabsorption in Germany could have contributed to increases in stature. (Wurm,

1982) I plan to explore this more in future research.

The effects of malnutrition and disease on height are greatest during

the periods of most intense growth activity: infancy and adolescence. "In

many populations the period when the child is most at risk from the combination of

malnutrition and infection is from birth to 5 years." (Evelyth and Tanner, p. 194) The

effects during adolescence do not seem to be quite as large, but more study needs to

be done in this area. Most effects of nutrition on adult height seem to happen before

puberty (Evelyth and Tanner, p. 196).

Effect of Urbanization

It appears that the effects of urban living on average health status

have changed over time. In the Middle Ages, cities were often net killers of people. Overcrowding and poor sanitation led to rapid spread of infectious

diseases. (Sandberg, lecture, AU89) Often, rural populations were taller than the

urban populations. Today, "children in urban areas are usually larger and have a

more rapid tempo of growth than children in the surrounding countryside. This apparently results from the provision of a regular supply of goods, of health and sanitation services, and educational, recreational and welfare facilities. Areas of high 14

population density which lack such characteristics, as for instance the shanty slums of

South America and Africa, do not show the same effect on growth as the orderly

towns of the industrialized countries." (Tanner, p. 144)

When did this turn-around occur in Europe, particularly in Germany?

What are the effects of different size categories of cities and towns? For example, is

the health status better in small cities or in very large cities? These issues will be

among those addressed in this study.

Heights and Socio-Economic Levels

Study after study has shown that the children of upper and middle

socio-economic classes tend to be taller than the children of the lower classes. For example, consider Figure 2.10 (Evelyth & Tanner, p. 199) showing the differences in average heights between relatively well-off and poorly-off groups in 11 different countries. Social class differences were smallest in the Scandinavian countries, perhaps indicating a more equal distribution of income than in the other countries, where the differences are much larger.

The larger stature associated with higher socio-economic status (SES) can be explained as follows. Diet is largely a function of income. At lower socio-economic levels, diet is particularly sensitive to increases in income, since their income elasticity for food is quite high. Increases in income can also mean an ability to purchase more health care, thus shortening and/or preventing episodes of illness. Income and SES are positively correlated. 15

Work intensity, another claim on the diet, is negatively correlated with SES.

These factors interact to produce the observed trend (Komlos, 1990, p.609, Steckel,

1990).

The relationship between average population height and per capita income has

been examined more closely by Richard Steckel. Using data published in Evelyth and

Tanner (1976), he calculated the correlation coefficient between the log of per capita

income and average population heights. Table 2.1 shows his results. The correlation

coefficient was quite high, ranging from 0.84 to 0.90. (Steckel, 1982)

According to Steckel: "While it will be argued that income is a potent

determinant of stature that operates via diet, disease, and work intensity, one must

recognize that other factors may be involved. The disease load is a function of

personal hygiene, public health measures, and the disease environment while

technology and methods of labor organization influence work intensity. In addition,

cultural values such as the pattern of food distribution within the family, methods of

preparation, and tastes and preferences for foods may also be relevant. " (Steckel,

1990, p. 16)

Additionally, the distribution of income among members of a population will

have an effect upon average population heights. For a given per capita income,

increasing inequality of income distribution will decrease the average population

height. Rising inequality tended to be associated with early industrialization in many countries, possibly accounting for much of the decline in average population heights often observed at that time. 16

Heights as a Measure of Welfare

We have seen that average population heights are highly correlated with per

capita income, but sensitive to the distribution of that income. Stature is affected

negatively by episodes of malnutrition and disease, and positvely by many public

health measures. It has a negative correlation with morbidity and mortality rates, and

a positive correlation with life expectancy. It fits in with the basic needs approach to

living standards, and meets the criteria of Morris Morris’ International Standard of

the Physical Quality of Life. (Morris, 1979 and Steckel, 1990, p.2If) It is a net

indicator, reflecting many components of welfare and living standards, more broadly

defined.

Since height is influenced by so many factors, one encounters an identification

problem when trying to interpret average height trends. One cannot claim with

complete certainty that, for example, an observed height decline was due to

epidemics, lower real incomes, the negative effects or urbanization or increasing

income inequality.

The identification problem extends to timing as well. The net nutritional status

of a child has the most impact on growth during the years of highest growth velocity,

the first five years and, to a lesser extent, adolescence. Thus generally height trends

are interpreted in light of circumstances at infancy and during the first five years of

life. But catch-up growth can disguise periods of deprivation, if a difficult period is

followed by better conditions. Moreover, heights tell us nothing about the quality of life after growth has stopped. 17

Thus, average population height does have its limitations as an indicator of welfare. But most welfare measures do. Real per capita income estimates, for example, are based on cost of living indices which are intrinsically arbitrary. On the whole, the advantages of height as a health and welfare measure strongly outweight its limitations.

J.M.Tanner sums it up well: "The study of growth emerges also as a powerful tool for monitoring the health and nutrition of populations, especially in ecological and economic circumstances that are suboptimal. It is equally powerful for studying the effect of political organization upon the relative welfare of the various social, cultural and ethnic groups which make up a modem state. Thus the study of growth has a very direct bearing upon human welfare." (Tanner, p.219)

2.2 The Use of Heights in the Historical Literature

Economic historians often have to contend with scanty data when examining trends in the past. Estimates of gross national product, costs of living, and real wages and per capita income are based on estimates of estimates. A cost of living index is highly sensitive to changes in the weights of different goods and upon the quality of data used in its computation. Often health information has been included in historical studies. Infant mortality rates and life expectations have been used as alternative measures of human welfare.

In the 1970s, average population height made its debut in the economic history literature. The first studies were on stature and growth curves of American slaves.

Measuring the welfare of slaves in terms of wages or per capita income is obviously 18 quite difficult. One of the most striking results of this research was the incredibly small children age 1-10 experienced significant catch up growth after their nutrition improved at working age. Tables 2.2 and 2.3 show this catch up growth for male and female slaves (Steckel, 1986).

Studies of slaves have been predominantly made on those living in the

United States. Richard Steckel has published quite a bit in this area. B. W.

Higman (1984) used stature as a health indicator in his book on the slave populations of the British Caribbean, 1807-1834. He looked at average heights of slaves in Trinidad, 1813, St. Lucia, 1815, Berbice, 1819, and Cuba, 1855-59.

African-born and Creole slaves in Cuba were the shortest. Of the African-born, there was considerable variation in heights across ethnic/regional groups. David Eltis

(1982) explored this phenomenon in greater depth for the period 1819-1839. He divided his data into zones and ports of embarkation. To give an idea of the variance, the average stature of north Congo area males was 157.2 centimeters, and that of the Bight of Benin area was 166.3 centimeters. The differences seem to be due to both ethnic and environmental factors.

In the past decade, many economic historians have started using stature as a proxy for health and well-being in their studies of the past. Most of this research has occurred in the United States and Great Britain. Thus, these two countries are among the best-studied at this point. 19

Fogel et al. (1983) wrote a fairly comprehensive article on changes in heights in America and Britain. The article provides a good overview of some of the most relevant issues related to stature, and draws upon thirteen data sources covering the period 1750 to 1937. It discusses statistical and selectivity issues, and uses RSMLE and QBE estimation methods (discussed in section IV) to estimate average population heights. The article also addresses the influence of height on social and economic behavior. For instance, it was found that taller slaves had a higher market value than shorter slaves, suggesting that the taller ones had higher productivity. This article is useful recommendable as an introduction to the use of heights as a measure of the average nutritional status of a country’s citizens.

Floud, Wachter, and Gregory (1985) use evidence on army recruits for

1870 to 1914 to discuss the physical state of the British working class. It was found that the average stature of this group was below the twentieth centile of modem British male standards, and substantially below the mean for all

British males at that time. The authors discovered a small upward trend in the average heights of the working class population, indicating some improvement in their health status during this period. An article on the period of industrialization and a book covering a longer period of time in Britain are discussed below.

Numerous studies have been undertaken on American populations. Richard

Steckel (1990) has compiled many of the major findings on stature and living standards in the United States. There are significant differences in stature across regions, occupations, and the native- and foreign-born. Those bom in the densely- 20

populated Northeast are shorter than those bom in the west or south. The rural-born

are taller than the urban-born as late as WWII. Native-born Americans tended to be

taller than foreign-born. Indeed, Americans appear to have achieved modem height

standards at an earlier date than most European countries.

Records from the period 1755-1763 in colonial America were examined by

Theodore Steegman and P.A. Hasely (1988). The relative tallness of American-born

men compared to European immigrants was already apparent at this early time.

(171.6 cm vs. 167.4 cm) Those living in rural areas were on average two centimeters

taller than those living in urban areas amongst the American native-born. The authors

also noticed a thermal effect, with average stature declining in the coldest and hottest

climatic extremes. Those in fairly temperate regions were tallest. This could,

however, be related to regional differences other than temperature, such as extent of

urbanization, food supply available, and public health measures.

Robert Margo and Richard Steckel (1983) analyzed the stature of native-born

Whites during the Antebellum period in the United States. Using regression analysis on data drawn from the Civil War muster rolls, they examine regional, occupational, and urban-rural differences, finding considerable variation across these categories.

They also found that short-distance migrants were not significantly taller than non-migrants, but some of the long- distance westward migrants were taller than the populations they left in the East.

Lars Sandberg and Richard Steckel (1980 and 1987) have looked at the

Swedish case. They examined height trends of Swedish males bom between 1720 and 1870. In their discussion, they compare the height trends with historical

circumstances. The height data was particularly revealing for the 1830-1870 period,

where an optimist-pessimist debate prevails. Their evidence tends to support the

pessimists’ view. Heights were considerably lower (166.8) in the 1850s than the

1820s-1840s (169.4-169.8). They compared this measure with trends in infant

mortality, which increased substantially during the 1850s and 1860s. Thus the above

two measures of health point in a similar direction.

The height studies that are most relevant to this paper are those discussing

height trends during industrialization. As mentioned in the Introduction, a decline in

average heights often accompanies industrialization, particularly in its earlier phases.

Three cases have been examined thus far: Austria-Hungary, Great Britain, and the

United States.

In this book Nutrition and Economic Development in the Eighteenth-Centurv

Habsburg Monarchy. John Komlos (1989) uses anthropometric data (gathered mainly

from military sources) to shed light on the beginning of industrialization in the

Habsburg Monarchy during the eighteenth century. Increasing heights in the 1730s and 1740s were related to good harvests in these years. The improved nutritional status led to accelerated population growth and eventually "to a dramatic decline in the marginal production of labor in agriculture below what it was in industrial endeavors. This amounted to the beginning of a Malthusian subsistence crisis"

(Komlos, 1989, p. 171), similar to those in the 12th, 13th, and 16th centuries.

Average heights declined during the 1750s -- 1780s. 22

Komlos argues that the government responded to the dire situation by clearing

away the obstacles to labor mobility and expansion of the industrial sector. Since the

marginal product of labor was higher in industry than in agriculture, "great economic

gains were obtained" (Komlos, 1989, p. 171). The increased industrial output was

traded for nutrients from less developed/more agricultural countries, which lifted the

Malthusian ceiling. Population growth continued without a major deterioration of

nutritional status. Average population heights remained steady from the 1790s to the

1830s.

Komlos uses the method developed in Komlos and Kim (1990) to derive

estimates of height trends. It will be discussed, along with the more widely-used

QBE and RSMLE methods, in Chapter VI, Methodology.

There have been several studies of height trends during industrialization in

Great Britain. Roderick Floud, Kenneth Wachter, and Annabel Gregory (1990) joined efforts to write Heights. Health and History: Nutritional Status in the United

Kingdom. 1750-19809. Using a sample of 108,171 soldiers in the volunteer British army and marines, they chart the stature trends for the United Kingdom’s working class. The methods used are the QBE and the RSMLE, using as truncation points at or near the official minimum height standard for a given recruit’s military branch and year. Given the correlation between height and passing military physical exams, it seems to me that erosion of the lower tail of the height distribution could extend 23

beyond the minimum height standard. I suggest limit testing for stability of estimates

to choose the proper truncation point used with the RSMLE. This will be discussed

further in the methodology chapter.

I also have some reservations concerning their sampling procedure. They

made lists of all the available description books and drew a random sample of books.

Starting with the first book drawn, information was recorded on all those who joined in a given decade; this process continued with other books until approximately 5,000 were collected for each decade. What worries me about this method is the differing height standards among military units. Random draws can often yield odd results. I suggest checking the military branch mix in each period to see if, for example, a rise in heights could be partially attributable to larger part of the sample being drawn that period from taller military units.

Figure 2.11 (Floud et al., 1990) shows their smoothed estimates of average heights for 18 year old military recruits bom between 1750 and 1916. This figure shows rising average heights for those bom from 1750 through the early 1840s.

Thereafter, heights decline, rising again in the 1870s cohorts through 1916. They also did estimates on older age groups with similar results except the dip was not as deep and the nadir was generally for those bom in the early 1850s. These results suggest that the third quarter of the 19th century was difficult for the British working population, while during early industrialization, living standards were improving.

There have, however, been two studies indicating that heights were falling during the last quarter of the 18th century. Nicholas and Steckel (1990) looked at 24

height trends during early British industrialization 1770-1815. Figure 2.12 shows

height trends by birth cohorts, divided into rural and urban groups. Both groups peak

in the early 1780s and decline thereafter, especially the urban dwellers.

Komlos (1993) criticized Floud, Wachter, and Gregory’s use of the RSMLE

and QBE, claiming the height distributions used do not resemble truncated or lower-

tail-eroded normal distributions enough for the programs to function properly. He

does have a point; the pooling of Army and Royal marine results did result in some

non-normal distributions. Komlos instead suggests truncating all distributions at a

given point and looking at the trend of the simple mean of the reduced sample. He

constructs an index of height trends based on this Komlos and Kim (1990). Figure

2.13 (Komlos, 1993, p. 137) shows his height trend estimates for England, Scotland, and Ireland. Like Nicolas and Steckel (1990) he finds heights declining during the last quarter of the 18th century, particularly for those bom in England and Scotland.

Komlos suggests that a Malthusian crisis was beginning in late 18th century Britain, similar to his findings for the Austro-Hungarian empire.

Floud, Wachter, and Gregory (1993) defended their work and attacked

Komlos’ methodology. A valid point which they make is that comparisons of the simple mean of the reduced sample make sense only if the standard deviation of the height distribution is constant across groups. They object to the idea of an impending

Malthusian crisis.

Thus we see there is controversy over when exactly the dip in heights took place during industrialization in the United Kingdom. Floud, Wachter and Gregory 25

say heights fell in the 1840s and 1850s, and was largely due to the urbanization which

accompanied later industrialization. Heights were rising during early

industrialization. Using the same data source but different methods, Komlos

disagrees with that last statement, finding a fall in heights during the late 1700s.

Nicholas and Steckel (1990) using an independent data source support Komlos’

findings. Hopefully more research will be done on this period of British history so

this issue can be resolved.

The third country where height trends during industrialization have been

studied is the United States. Fogel (1986) found a substantial decline in average

heights during industrialization in the United States in the second half of the 19th century. The top panel of Figure 2.14 shows this decline. The bottom panel demonstrates that the decline in stature was accompanied by a fall in life expectancy at age 10.

This study focuses on heights, health, and industrialization during

German industrialization. Komlos (1990) looked at the very early phases of industrialization, the second half of the 18th century. His broad conclusion was that the upper classes were improving their nutritional and health situations, whereas many lower-class groups throughout Europe were experiencing falling living standards during proto-industrialization. He says this evidence supports the Kuznets’ inverted-U hypothesis that inequality initially grew and then fell with the onset of modem economic growth. I believe, however, that industrialization and modem economic growth really began in 26

Wurttemberg in the second half of the 19th century. Dumke (1988) found rising

income inequality in Germany between 1850 and 1913, the main period of German

industrialization.

Helmut Wurm has done considerable study on German heights since

before the Middle Ages. While he has never statistically analyzed height

data, he has an extensive collection of secondary sources from multitudes of

sources and periods. Figure 2.15 illustrates his estimation of height trends, from 600

to the present. The top line is sort of an upper bound, and the bottom line is a lower bound. Thus the distance between the two lines at any given year approximates the range in heights (due to different socio- economic and regional factors). There does seem to be a dip in the first half of the 19th century, with generally rising heights thereafter.

In my study of the Kingdom of Wurttemberg, I hope to gain insights between the relation between heights, health, and industrialization with indepth analysis. The main goal is to see whether Germany fits the pattern of falling average heights during industrialization. Occupational and regional differences will also be explored. In the next section hypotheses and contributions to the literature will be discussed.

2.3 The Statement of the Problem

The information collected on 14,800 soldiers in the Wurttemberg army includes birth date and place of residence, occupation of the soldier, (often) occupation of the soldier’s father, height (in centimeters) and incidences of illnesses 27 while serving. The data allows description of overall height trends in Wurttemberg, as well as in its occupational and regional subgroups.

It also enables exploration of a number of hypotheses concerning stature trends and their covariates.

The occupational information was coded into seven categories: 1) upper white collar, 2) lower white collar, 3) skilled, 4) semi-skilled, 5) unskilled workers as well as 6) businessmen and those employed in 7) agriculture. For the purpose of some time trend analyses, the white collar workers and businessmen are grouped together as the upper class; the skilled, semi-skilled, and unskilled workers comprise the working class.

The occupational data allows the testing of several hypotheses:

1) Average heights of the working class declined in Wurttemberg during

the 1870s and 1880s -- the onset of rapid German industrialization.

2) Members of the upper class are taller than the working class.

3) The "rich/poor" height differential increases during the early years of

industrialization.

4) The upwardly socially mobile are taller than their immobile or

downwardly mobile counterparts.

My study of economic history of Wurttemberg revealed substantial economic differences across regions within the Kingdom. Thus I hypothesize that there are significant regional differences in average height. For the statistical analysis, the four

Kreise (administrative districts) of Wurttemberg are used. Height differences in the 28

64 Oberamte (counties) will be presented graphically. Factors which might help

explain the observed regional height patterns, such as climate, wages, railway

connections, migration flows, public health measures, and industrial development, are

explored but not tested in a statistical sense in this study.

The third major area of analysis will be the rural/urban question. In studies of

height trends in other industrialized countries it has been observed that in past

centuries, rural residents were taller than urban residents. At some point, generally

in the early 20th century, this situation was reversed. In industrialized countries

today, urban residents are taller than rural residents. If Germany also experienced

such a turn-around in the early 20th century, then the urban dwellers in my sample

(birthyears 1852-1893) should be shorter than the rural residents. Thus, I hypothesize that those living in communities of more than 2,000 residents (in 1895) are shorter than rural dwellers.

The rural/urban division is in some ways too simplistic. The urban group includes a wide range of community sizes. It is reasonable to assert that a city of

5,000 residents is different from one with 150,000 residents. Therefore, I have grouped the communities into six size categories according to the population statistics of 1895. The categories are:

1) less than 2,000 residents

2) 2,000 - 4,999 residents

3) 5,000 - 9,999 residents

4) 10,000 - 19,999 residents 29

5) 20,000 - 49,000 residents

6) more than 50,000 residents

The urban//rural hypothesis could be extended as: there is a negative relationship between stature and size of community.

Contributions to the Literature

This study:

1) Brings new evidence into the Optimist-Pessimist debate for Germany.

2) Illuminates the experiences of different occupational groups during

German industrialization. To the best of my knowledge, no one has

previously attempted to quantify this issue.

3) Verifies whether Germany fits the pattern observed elsewhere of a dip

in heights during industrialization.

4) Gives non-German speakers an introduction to industrial development

in Wurttemberg.

5) Evaluates the QBE, RSMLE, and K&K methods.

6) Offers suggestions on sampling and estimation procedures.

7) Increases understanding of the height variable and its covariates.

Information on fathers’ occupations is rare. My results on the

relationship between height and social mobility are particularly

valuable.

8) Sheds some light on factors contributing to regional differences in

heights. 30

9) Explores the effects of community size on stature.

The next two chapters paint the historical backdrop for this study. Chapter III describes German industrialization and regional variations in economic development.

Chapter IV examines industrialization in the Kingdom of Wurttemberg. CHAPTER III

German Economic History, 1833-1914

3.1 Introduction

Germany’s industrialization has been called "the most remarkable

economic achievement of the nineteenth century" (Dillard, p.308). The most rapid

period of industrialization occurred between 1871 and 1914. But a good deal was

accomplished prior to this, particularly after 1850. Per capita real income (in 1913

prices) rose from 333 marks in 1852, to 424 marks in 1871, to 647 marks in 1900,

and to 730 marks in 1910. The annual rate of growth of real per capita was 1.27% in

1852-1871, 1.46% in 1871-1900, and 1.21% in 1900-1910. These figures were calculated from Mitchell (1975). Huge increases in industrial output were accompanied by rising real wages throughout most of the period. In 1800, Germany was a predominantly agrarian society, with 75-80% of the population working in agriculture. (IPB, p. 13) On the eve of the first World War, it was the industrial giant on the European continent.

When people speak about industrialization period in Germany, they are often referring to 1871-1914. This period is called Hochindustrializierung or

("High Industrialization") in the the German literature. Its beginning coincides with the unification of numerous germanic states into the Deutsches Reich (German

Empire). Yet considerable progress was made prior to 1871. In the mid 1830s, two

31 32 important events ushered in what has been referred to as "the dawn of the industrial era", 1834-1851. (Henderson, 1975) The first was the establishment of the Zoll

Verein (German Customs Union) on January 1, 1834. The second, on December 7,

1835, was the opening of the first railway line in the German territory.

Speaking of Germany prior to 1871 is difficult. Most writers use the territory of the Zollverein, and that is the convention followed in this paper unless otherwise noted. Following the Napoleanic Wars, Germany was split into 39 states of varying sizes. The two rivalrous big powers were Prussia and Austria.

There were several medium-sized states (Wurttemberg, Bavaria, Baden, Hanover, and

Saxony), numerous smaller kingdoms, and four free cities (Henderson, 1975). Each of these sovereign states had their own rules, tariffs, currency, etc. Trade over long distances was hindered by the necessity of paying tolls to cross each territory. Today the Rhine river is dotted with old castles, whose cannons once threatened those who dared to not pay the toll.

The Zoll Verein linked 18 states with combined population of nearly 23.5 million into a customs union. Prussia’s rival Austria was not included. Map 3.1 shows the area encompassed by the Zoll Verein. All states agreed to adopt Prussia’s tariff regime, which had been reformed in a liberal direction in 1818. In general, raw materials were duty-free, manufactured goods carried a 10% import and consumption tax, while colonial goods and wine had tariffs of about 20%. (Henderson, 1975, p.29). By reducing artificial trade barriers, the formation of the Zoll Verein stimulated interregional trade. 33

Trade was also stimulated by the development of the German railway system.

The first step was the opening of the four-mile line between Nuernberg and Fuerth in

Bavaria. After several years, a few other lines were built in Prussia and Saxony.

Eventually momentum built up, and the railway network spread across Germany.

Some were private enterprises, while others were operated and/or owned by one of

the states. Table 3.1 shows the rapid expansion of German railways. Twenty years

after their introduction, there were 14,690 kilometers of railroads in operation. In

1915, this figure was 62,410 km.

Railways are not only important in stimulating trade and opening new

markets. They also create increased demand for coal, iron, and steel, promoting

expansion in those industries. Moreover, since Germany’s coal was mostly in remote

frontier areas, the railways made it possible to transport the coal to manufacturing

sites on a larger scale. Coal output rose from 2.1 million metric tons in 1834 to 5.7

million metric tons in 1851, plus 2.1 million metric tons of lignite1 or brown coal.

See Table 3.2. By 1851, Germany had surpassed France in coal output, although it

remained far behind Great Britain.

’Lignite is an intermediate between bituminous coal and peat. In terms of thermal value, nine tons of lignite equal approximately two tons of coal (Henderson, p. 139). See Bowden, p. 509 for a more detailed discussion of the heat values of the various German coals. Before 1850, most ironworks still relied on charcoal rather than coal. This was due to an abundance of trees. There were very few coke furnaces. From 1835 to 1850, production of pig iron in Germany increased at an average annual rate of

2.5%, from 144,000 to 215,000 tons. See Table 3.3, (Bowden, p.472).

3.2 1851-1870

The period 1851 to 1870 saw considerable growth in the coal and iron industries. In 1851, there was a reform of Prussia’s Mining Law, where the state gave up its control over coal extraction, sales, wages, and financing. Since most of

Germany’s coal fields are located in Prussia, this caused a boom in the coal industry, particularly 1851-1857. As Table 3.2 shows, coal output increased from 5.7 million tons in 1851 to nearly 26.8 million tons in 1869. Output of lignite rose from 2.1 to

7.6 million metric tons. Figure 3.1 illustrates the rise in coal (including lignite) production from 1810-1870. The coalminers did not share in the good fortune of their bosses. The deregulation gave mineowners considerable power over wages, which they kept low, and hours, usually 12-hour shifts (Henderson, p. 133).

In 1850, only 10.8% of all pig iron produced in Germay was smelted with coke. But Germany’s timber supplies were dwindling, causing increased use of blast furnaces in the 1850’s. Figure 3.1 shows the effect of the new process on pig iron production. The average annual rate of increase was 14% during 1850-51, and continued at 8.5% until 1870 (Bowden, p.472). Table 3.3 displays tons of pig iron produced in Germany in selected years between 1825 and 1870. 35

Two new processes for steel manufacturing which were discovered in the

1850’s, were introduced in Germany in the late 1860's. These were the Bessemer

process and the Siemens-Martin process. (See Henderson, p. 142 for discussion.) Yet

these two processes were incompatible with phosphoric iron ores, which comprised

much of Germany’s iron resources. Germany had to import non- phosphoric ores.

Hoffman’s index of German steel production (1913 = 100) shows a rise from 1.2 in

1850 to 6.0 in 1870 (Hoffman, pp. 352-53), with 126,000 metric tons produced in

1870. There was an increase in the scale of iron- and steelworks during this period

(Henderson, p. 142).

The steam engine was first introduced in a German spinning mill in 1822.

However, the dissemination of this process was slow. Most occurred after 1840.

Table 3.4 (Bowden, p.468) shows the increased use of steam engines between 1846

and 1861. The total number of steam engines rose from 1,416 to 10,113.

While textiles are overshadowed by the coal and metallugical industries in the

story of German industrialization, they did play a role. In the mid- 19th century, the

textile industry was second only to agriculture in terms of units of production, employees, and exports (Henderson, p. 143). The textile industry responded slowly to new power machinery, particularly wool and linen industries, which were the least-suited to the machines developed in this period. Strong resistance to factory work by peasant weavers in the linen industry caused a general decline in that industry. Some mechanization took place in the wool industry, which grew steadily 36

but slowly between 1850 and 1870. However, Germany’s exports of woolen goods

grew considerably, to become the number one textile export. Silk production also

rose, but played a relatively minor role.

The biggest strides towards large-scale capitalistic production of textiles were

made in the cotton industry. Considerable expansion occurred in the 1850’s, leading

to cotton being the largest textile industry in 1861. But domestic output was still not

large enough to supply the home market. Germany continued to import cotton and

yam from England to supply the weaving factories. The American Civil War with its

blockade on cotton exports from the southern states caused a large jump in the price

of cotton. Cotton mills in Europe were hit hard. This explains the stagnation in the amount of yam consumed and in cotton exports in Germany in the 1860’s. The distress in the southern German states of Bavaria and Wurttemberg was not as severe.

They were already using imports of raw cotton from India, and simply increased their supplies from this source. Due to the collapse of inefficient firms and the introduction of machinery bought at cut-rate prices from English firms, the cotton famine actually increased the overall efficiency of the German cotton industry

(Henderson, pp. 144-145).

The growth of the major industries described above was augmented by a banking revolution in the 1850’s. Many banks were reorganized as joint-stock companies. Although Prussia refused to give charters to new joint-stock banks in the

1850’s, numerous new credit banks were established in other parts of Germany at this time. Also, several cooperative credit banks were set up (Henderson, pp. 123-129). 37

Table 3.5 shows the increase in savings accounts for nine big German states

from 1830-1915. The figures under the heading ’a’ represent the number of savings

accounts per 100 residents of each state. Those under column ’b’ represent per capita

savings in marks. The last column depicts the rise in total savings (in millions of

marks) in the area of the Deutsche Reich from 1840-1915. The level of savings in

1910 was over 100 times the level in 1850 (although inflation was not taken into

account in deriving these figures).

German banks were quite involved in industry. They financed many industrial

projects, and developed close long-standing relationships with their industrial clients.

Indeed there was a great deal of overlap between the boards of directors of the banks

and of the industries they served (Sandberg, 1989).

The boom years of 1851-1857 were slowed down by the financial crisis which

struck in November, 1857. Bank failures in England caused panic in Hamburg,

leading to the failure of 150 firms in this port city. Failures in other parts of

Germany followed. This was followed by a slow recovery, and a more cautious approach in the 1860’s. Some of the inefficient firms had shut down, and larger,

stronger companies were forming (Henderson, pp. 120-1).

3.3 Factors Accompanying Industrialization

Two factors which usually accompany industrialization-population growth and urbanization- were present in Germany. Table 3.6 shows the increase in 38

population from 1816 to 1915. The natural rate of increase was even greater than the

figures shown due to high emigration rates. The overseas emigration from Germany

by decades is shown in Table 3.7.

Urbanization was also well under way in the second half of the 19th

century. Table 3.8 shows the changes in the percentage of the population living in

different sized communities for selected years between 1852 and 1961. Notice, the

percentage of those living in communities of less than 2,000 fell from 67.3% in 1852

to 40.0% in 1910. Notice also the large rise in those living in cities of over 100,000

residents, from 2.6% of the population in 1852 to 21.3% in 1910. Table 3.9 shows

the rise of the great cities in Germany from 1875 to 1910. The first three columns

indicate each city’s population in the years 1875, 1890, and 1910. The last column

gives the rate of increase between 1875 and 1910. The average increase for the 22

cities examined (those cities with a population over over 200,000 in 1910) was

202.7%.

Urbanization is closely connected with the shift in economic importance from agriculture to industry and commerce. As mentioned above, in 1800, 75-80% of the labor force worked in agriculture. In 1871, this percentage had fallen to 47.3%

(Bowden, p.504), in 1882 to 42.3%, in 1907 to 34.0%, and in 1925 to 30.5% of the active labor force. See Table 3.10. At the same time, the percentage of the labor force involved in industry and crafts, as well as commerce and communications was growing steadily. From Table 3.11 one notices the rise in industrial and craft workers from 24.3% of the labor force in 1849/58, to 30.7% in 1885/89, and further 39 to 35.1 in 1910/13. Those involved in trade, banking and insurance rose from 5% of the labor force in 1849/58 to 11% in 1910/13. The percentage involved in transportation rose from 1.1% to 3.6%. The percentage of miners went up (0.9%-

2.8%), while that of domestic servants decreased (9.3% to 5.2%). Also during this time period, the labor force participation rate rose from 43.9% to 46%. For a more year by year analysis of the above, see Table 3.12.

An agricultural revolution accompanied the industrial revolution in Germany.

Although the percentage of the population involved in agriculture decreased, agricultural output generally increased (See Mitchell, 1975). The freeing of the serfs occurred in Prussia in the first half of the 19th century. The second half of the 19th century saw improvements in mechanical farm equipment, the introduction of crop rotation, and later the successful application of chemical fertilizers, from the growing

German chemical industry. Productivity in agriculture increased. For example in

1800, mowing 100 square meters of wheat with a sickle took one hour. With a reaper/binder in 1900, it took two minutes (IPB p. 11).

3.4 1871-1914 Introduction

The unification of Germany into the Deutsches Reich occurred in 1871. It followed on the heels of a brief civil war between Prussia and Austria, allied with several of the southern German states, and the Fran co-Prussian war. In both cases,

Prussia and her allies emerged as victors. Prussia had pushed Austria out of the contest for economic dominance in Germany, and the conflicts of the short civil war were apparently forgotten as the German states united against an outside enemy, 40

France. Under the constitution of the Reich, the central authority had power over tariffs, excise taxes, international relations, currency, and weights and measures. The states had control over direct taxes, transportation, and technical education. The

Reich’s grounding was followed by the Currency Law of 1873, which replaced the multitude of state currencies with the mark, which was based on the gold standard.

Also in 1873, the Reich’s Railway Office was set up. 1875 saw the establishment of a national central bank, the Reichsbank (Henderson, pp. 159-60).

The years 1872-73 were marked by high inflation, in part due to indemnity payments from France that were made two years ahead of schedule. There was a boom in new companies, and a very optimistic spirit. There was a rush to buy land, houses, and shares of companies in the hopes of a quick profit due to rising prices.

Many of the new and expanding companies were very shaky, sure to benefit no one but the company promoters. "Before long, the boom in Germany and

Austria-Hungary degenerated into a wild orgy of speculation" (Henderson, p. 164)

{co. law 1870, Henderson p. 159}.

The crash came in Austria on May 8, 1873, when shares plummetted on the

Vienna stock exchange. Within a few days, the financial crisis spread to Germany.

There were bankruptcies everywhere, and many scandals became unearthed.

Industrial output, prices, sales, and wages all fell, while unemployment rose. Not until 1876 did Germany again reach the level of industrial output it had had in 1872

(Henderson, pp. 169-70). 41

The period 1873-1896 has been called the great depression. But there is

controversy over this. Falling prices and profits hurt the the capital owners, but real

wages might have gone up, because prices fell faster than wages. Industrial output

and the value of foreign trade increased during this period. Several firms expanded in

this period, often becoming linked together in cartels. The six great German banks

assumed the dominant position many of them still enjoy today. The same can be said

of Siemens, Krupp, and General Electric and others-companies which rose to the

forefront at this time. It is interesting to note that cartels were not discouraged as in

the United Steates, where the Sherman Act of 1890 prohibited monopoly and unfair

trade practices. Indeed, cartels were encouraged. Banks would even pressure on

their industrial clients to form cartels with the other big firms in their industry when

competition was getting too cut-throat (Henderson, pp. 176-185).

Some contemporary writers describe the new prosperity of the workers in

glowing terms. For instance, Hans Rosenberg claims that the after 1880, the standard

of living of the working class as a whole inproved; it was the factory owners whose

profit margins were shrinking despite large increases in output who were "depressed".

Also hard hit by the depression were the independent craftsmen and the small farmers

and traders, whose way of life was being slowly phased out by the new system.

(Rosenberg, p.54) Others paint a picture of dire need and increased misery.

For the period 1897 to 1914, most sources claim that the living standards of the workers improved during this period. Data on some measures of living standards will be presented later in this chapter. 42

3.5 Industrial Growth, 1871-1914

Between 1873 and 1914, German national income rose from 15,195 million marks to 49,501 million marks. Per capita national income grew at 21.6% per decade, at a time when England’s was growing at a rate of 12.5% per decade.

Germany’s share of global manufactured goods rose from 13% in 1870 to 16% in

1900. The established industries, especially coal and metallurgy, continued their ascent. Additionally, new industries were quite successful, particularly the chemical, electrical, and shipbuilding industries (Henderson, pp. 178-206).

These years saw an increase in the scale of operations. In 1875, the independent master craftsman was the norm. Of all industrial workers, 64.3 percent of them worked in establishments of less than five wage-eamers. See Table

3.13. In 1895, the percentage of those employed in strictly industrial establishments of less than six workers was only 31.7 percent, while those employed in establishments of 20+ workers comprised 51.7% of all industrial wage earners

(Bowden, p.502).

The production of coal continued to rise during this period. Table 3.14 shows coal production of the U.S., Great Britain, Germany, France, and Belgium, from

1864 to 1935. One sees that by 1875, Germany’s coal production had surpassed that of France, and by 1915 was nearly abreast of Great Britain. According to Bowden,

"coal production [in Germany] was increasing at a rate of 5.5 per cent per year in the years 1865 to 1875; the rate declined to 3.9 per cent in the period 1875 to 1895, 43

rising thereafter to a rate of 4.5 percent per year. It will be remembered that the

rates of increase from 1830 to 1865 were very high, though the quantities involved

were small" (Bowden, pp.510-11).

The left panel of Figure 3.2 graphically depicts the increases in German,

French, and British coal production, 1865-1930.

The right panel of Figure 3.2 shows a similar strong rise in German pig iron

and steel production. These industries received a big boost from the annexation of

Lorraine in 1871. Lorraine contains the largest iron ore deposits in Europe, although

they are largely composed of phosphoric ore, which could not be used with many of

the industrial processes of 1871. These ores were able to be fully utilized when the

Gilchrist-Thomas process (the basic process) was developed in England in 1878.

Germany bought the rights to this process in 1881 (Dillard, p.311). Its dissemination

was gradual, which is why the output figures do not show a big leap in the 1880’s.

But output of iron ore increased from 6.7 million tons in 1887 to more than 28

million tons in 1887 (Henderson, p.236). Germany’s steel output doubled every

decade between 1880 and the beginning of WWI (Dillard, p.311).

In textiles, the use of power machinery and the scale of operation increased.

Cotton spinning was fairly mechanized by mid-19th century, with modem factories set up in many cities. The shift to power weaving occurred mainly between 1875 and

1895. The annexation of Alsace in 1871 gave a big boost to Germany’s cotton industry output. "In 1868 there were in Alsace 2,131,000 cotton spindles, 48,536 looms, and 100 calicao-printing machines. In Germany, at that time, there were 44 about 3,000,000 cotton spindles and 37,000 looms....[Alsatian] output in cotton yam and cloth was somewhat greater than the output of all the German states." (Bowden, pp.498-9) Exports of cotton textiles and yams rose to a value of 500 million marks in 1913 (Henderson, p.238).

The woolen industry also grew and modernized. Many worsted and combing spinning mills were set up and operated as joint-stock companies. There was a shift in the source of raw wool used from domestic to imported by 1900. The value of

Germany’s wool exports was 361 million marks in 1913 (Henderson, p .238). The silk industry was modernized by the rapid growth in powerloom weaving Between

1890 and 1909, the number of powerlooms nearly doubled, while the number of handlooms fell by more than 85 percent. The value of silk exports rose from 16 million marks in 1887 to more than 200 million marks in 1913. (Henderson, pp.

238-9) The annexation of Alsace in 1871 also boosted these two industries. Cotton, silk, and woolen textiles were produced with degree of mechanization with power unmatched in Europe at that time (Bowden, p.499).

One of the major new industries to rise in this period was the chemical industry. The spectacular rise of this industry reflects Germany’s success in applying science to industry. Dudley Dillard claims "that the Germans were the best-schooled people in the nineteenth century." (Dillard, p.308) Cumpulsory education had been established in Prussia under Frederick the Great (more than a century earlier than

England, France, or the United States). Education was under the control of the State, and German universities and technical schools were subsidized. "Universal education 45

for the common people provided a massive flow of literate and alert workers and the

universities and advanced technical schools supplied the leadership for applying

science to industry. Its large number of educated people, especially scientifically

trained people, proved to be a resource of incalculable value to Germany." (Dillard,

p.308) This highly-educated workforce enhanced Germany’s progress in all her

industries. For one thing, Germans were skilled borrowers of technology from other

countries. They even had an institute (the Gewerbe Institut) which specialized in

importing English machinery and assisting its being put into production in Germany.

This, combined with her relatively late start, enabled Germany to build large factories

with the most up-to-date technology from the start. The dislocation caused by

modernization was generally not as painful as in Great Britain (Dillard, p.413).

Nowhere is this link between science and industry more clear than in the

chemical industry. The government actively promoted the study of chemistry by

pouring funds into chemistry research and training. In 1872, there were, for

example, more students studying chemistry at the University of Munich than in all

universities in England. There was a close relationship between the universities and

the chemical industry. Many of the innovations in synthetics were made by chemistry professors, and quickly produced by the industry (Dillard, p.309). Most large chemical firms also had on-site research labs (Henderson, p. 186).

The boom in the chemical industry began after the Franco-Prussian war.

Between 1870 and 1874, 42 chemical companies formed. By 1896, there were 108.

Yet from the start, the chemical industry was dominated by a few big firms, such as 46

Frederich Bayer & Company. Germany’s mineral resources supplied these companies

with their raw materials: rock salt, potassium, iron pyrites for sulfuric acid, coal tars

for synthetic dyes, etc.. In the 1870s, Germany took over leadership in the

production of synthetic dyes from Great Britain. Many of these first dyes were based

on coal tar. By 1913, Germany was supplying more than 75% of the world synthetic

dyes market. A plethora of innovations occurred during High Industrialization. New

drugs were produced, including the first of the sulfa drugs in 1908. German chemists

developed literally thousands of products from coal. There were a variety of spin-off

industries, such as chemical fertilizers and pharmaceuticals. The fertilizers greatly aided productivity in agriculture. Chemicals were and still are a major export for

Germany (Dillard, pp. 308-310 & Henderson, pp. 186-189).

Science and industry worked together closely in the electrical industry as well.

By electrical industry, I mean the manufacture of electrical products, not the direct generation and sale of electrical power. This industry made rapid progress in the last quarter of the nineteenth century. Siemens and the German General Electric

Company were the two big players in the field, in part due to control of patents on electrical manufactured goods. Among other things, these companies produced light bulbs, telegraphs, telephones, numerous electrical machines with motors. In 1891, the first electrical tram was opened in Germany; in 1907, there were 3,719 kilometers of tramway in the country. The electric companies also laid power and telegraph 47

lines over short and long distances. In 1913, the value of Germany’s electrical exports was 220 million marks. Germany accounted for about one half of global trade in electrical products (Henderson, pp. 190-198; Dillard, p.312).

Germany’s trade in general was on the rise in this period. Table 3.15 shows the overall increase in exports and imports for selected years between 1872 and 1913.

Notice, the total nominal value of exports plus imports rose from just under 6 billion marks in 1872 nearly 21 billion marks in 1913. Also, the proportion of finished industrial products in total exports rose from 38% in 1873 to 63% in 1913 (Stolper, pp.52-53).

The increase in German output and trade spurred the development of shipbuilding and the German merchant marine. Major growth in shipbuilding occurred in the 1880s. Due to their relatively late start in this industry, the German shipbuilders were able to focus at once on iron and steel steamships, instead of making the difficult transition from sailing to steam vessels. Between 1892 and 1907, Germany’s share of world shipbuilding increased from 7.3% to 13.8%.

The government supported this industry by investing in docks and sometimes granting subsidies. The German Merchant Marine also expanded rapidly, its total tonnage surpassing that of the United States in 1884, and France in 1889, although it was never as large as England’s (Henderson, pp. 198-203). 48

3.6 Changes in Living Standards During Industrialization

In the literature, much of the discussion on workers’ standards of living has

focussed on the 1870’s and 1880’s. At a time when industry was growing rapidly,

(albeit with some difficulties) there were accounts that all was not well for the

working class.

Henderson describes the situation as follows: "Social distress had followed in

the wake of the rapid industrialization. Although the living standards of certain

workers had gradually improved, particularly during the short boom of the early

1870s, the poor-law authorities, the churches, and various charitable organizations

were fighting an uphill battle against the effects of low wages, long hours,

unemployment, sickness, and bad housing conditions." (Henderson, pp.229-230) We

will return to this period in a later section which discusses the welfare of the lower

socio-economic groups during industrialization (Henderson, p. 176).

The distress of the workers prompted Bismarck to make Germany the first big

industrialized country to lay down the foundations of a welfare state. Britain did not

follow Germany’s lead until 25 years later. Several important pieces of social

legislation were passed in the 1880s. The Health Insurance Law of 1883 decreed

cumpolsory health insurance for factory workers, miners, and some other

occupations. In later years, the groups covered were extended to include farm

workers, apprentices, and daily laborers. Between 1885 and 1910, the percentage of the population covered increased from 10% to 21.5%. The costs were distributed between employers and employees, and entitled those covered to free medical care 49 and payments during illness (Henderson, pp.229- 31). The Accident Insurance Law of

1884 covered those in the manufacturing, shipping, construction, agriculture, and forestry sectors. The full cost was borne by the employers in each sector. In 1889,

13 million workers were covered; this rose to nearly 24 million in 1909. In the event of fatal accidents, payments were made to the wife and children of the deceased

(Henderson, pp.231-2).

Old Age and Disability Pensions came into effect in 1889, and covered groups similar to those covered by the first two acts. The cost was split between employer and employee, and the Reich government added a small subsidy. Old age pensions began at the age of 70. Those claiming invalidity pensions nearly tripled between

1895 and 1903. But when the government increased inspection of claims, the numbers started to decline.

In 1911, all three areas of legislation were combined under the Insurance

Consolidation Law. The major gap that these laws did not fill, however, was unemployment insurance. This came later, along with compulsory health insurance for all workers (Klaas, 1991).

The question then arises: did this social legislation improve the health and well-being of the working class? To answer this and other questions, let us turn to data on some common measures of health and welfare. We will examine per capita income, wage income estimates, average work week in industry, infant mortality rates, life expectations, and Hoffman’s index of net nutrition. 50

Per Capita Income

Figure 3.3 is composed of four figures. On each, the dotted line represents

per capita income, an index where 1913 = 100. (It is the same in all four figures.)

We see a rise in the early 1870s, followed by an uneven stagnation during the late

1870s early 1880s. From the early 1880s, it rises fairly steadily, except for a small

dip around 1890 and another in the first years of the new century. Thereafter it rises

again.

Real Wage Income

The solid lines in the four graphs depict four estimates of the development of real wage income from 1871 to 1913, with 1913 as the base year of the index. The estimates are, in a clockwise direction, by a) Kuczynski, b) Bry, c) Orsagh, and d)

Desai. Over this period, real wages were on the rise. The progress was not always smooth, however. There are periods in which they fell, generally the late 1870s/early

1880s, the late 1880s, and sometimes 1900 and 1910. For estimates by Kuczynski,

Desai, and Orsagh, of changes in the cost of living, see Figure 3.4.

Length of Workweek/Workday in Industry

Table 3.16 shows changes in the average workday in industry from 1800 to

1914. Starting in the second half of the nineteenth century, there is a general decline.

This trend is also evident in Table 3.17 of the average workweek in industry,

1830-1914. Table 3.18 looks at the changes in some individual industries: textiles, metal production, metal-working, chemical, printing, construction, and wood. 51

Infant Mortality Rates

Continuous data on infant mortality rates in all of Germany in the nineteenth

century are rather hard to find, and may or may not still exist. Table 3.19 gives the

infant mortality rates in Prussia from 1816 to 1900. Columns 1 and 4 are for male

babies, columns 2 and 5 are for females, and the total is presented in columns 3 and

6. The overall trend seems to be a decline in the total infant mortality rates until

about 1850, followed by a rise and then stagnation. Table 3.20 shows infant

mortality rates for the Deutsches Reich, 1901-1938, and for West Germany, 1949 to

1975. The total rate falls from 20.67% in 1901 to 16.35% in 1914. The fall in

infant mortality rates continued in later years. Notice that in both tables, the rate for

males is higher than the rate for females in every year. Apparently female babies

have a somewhat stronger constitution than male babies.

Infant mortality rates varied by region and occupational class of the father.

Table 3.21 gives mid-nineteenth century estimates of the infant mortality rates in

seven German regions/states. They range from 12.3% in Oldenburg in 1855/64 to

35.4% in Wurttemberg in 1858/66. Notice that infant mortality actually rose in

Wurttemberg, from 34.8% in 1846/56. Wurttemberg’s infant mortality rate was

exceptionally high compared to the other regions. We will look at this subject more

closely in Chapter VIII.

The infant mortality rate of illegitimate children was higher than that of legitimate children. Table 3.22 shows that this difference was increasing (at least) in

Prussia and Bavaria in the 19th century. 52

Table 3.23 gives infant mortality rates in 1877/79 and 1912/13 in Prussia for

the following occupational classes: self-employed, public official/bureaucrat, white

collar worker, skilled worker, unskilled worker, servants (domestic servants and

farmhands). The infant mortality rate of all the classes decreased between 1877/79

and 1912/13. The rates of the white collar workers and the public officials were cut

in half. In percentage terms, the gains in infant health were the least among the

unskilled workers and servants.

Life Expectations

Figure 3.6 shows the development of life expectancy in the 19th and early

20th centuries. The data for 1780-1869 are only for males, age 30 and 60, in the

Schwalmer Region. After that, they are for the Reich, and then for West Germany,

for ages zero, 30, and 60. In this part of the diagram, the sc lid line represents the life expectancy of males, and the dotted line represents that of females. Females had higher life expectancy at all three ages studied and for all years in 1870-1976.

Table 3.24 gives the life expectancy at ages zero, 30, and 60, for males(M) and females(W), for selected periods between 1871 and 1976. Prior to 1936, the figures are for the Reich; after 1946, the they are for West Germany. Between

1871/90 and 1974/76, life expectancy at birth nearly doubled. The largest gains were after 1881/90. 53

Hoffman’s "Index of Net Nutrition"

Hoffman constructed an index reflecting net nutrition for the period

1850-1959, using aggregate German data. His method was as follows: First he compiled aggregate data on consumption of 26 categories of food and drink. He then figured out the caloric value of that food. Thus he calculated the total calories consumed in Germany each year.

He then figured out how many calories the population needed, using information on the labor force and number of births. Table 3.25 shows his figures on the normal caloric needs for males and females in various age groups. He further estimates the additional demands placed on the diet due to work and childbirth. For example, he estimates and additional 130 calories needed per hour of work in factories, mining, agriculture, crafts, and transport; 500 calories per day in other occupations; 600 calories for a day as a housewife; and 150,000 calories per birth.

Table 3.26 shows the results of these two calculations for five year periods between 1850 and 1950. Total calories consumed (measured in billion kilocalories) appear in column 1. How many calories the population should be consuming (in billion kcal) appear in column 2. The ratio of calories consumed over calories needed appears in column 3. We see a low of 75% of caloric needs being met in the

1850/54 period. The ratio rises, reaching 100 in the 1875/79 period. In 1880/84, the ratio falls again to 95%. Afterwards, it never again goes below 100%. It rises steadily through 1904, declines slightly until 1954, whereupon it rises again. 54

This is an interesting index to be sure. However, it does not show any

regional or occupational differences in net nutrition-differences which undoubtedly

exist. A ratio of 100% does not mean that every person had enough to eat. It is

likely that this ratio must rise to well over 100% before claims could be made that

most of the working class was adequately meeting its caloric needs. Unfortunately, I

know of no index of the equality of food distribution. The closest measure would

probably be variability in heights -- a measure of net nutrition. Income inequality is

also a proxy. It was rising during industrialization (Dumke, 1988).

3.7 Regional Variations in German Industrialization

The description of German economic development in sections 3.1 to 3.5 above

encapsulates the standard story of German industrialization presented in the literature.

Yet there is an important aspect missing from this story: regional variations.

The regional differences in Germany were very large. Indeed, it makes it

difficult to talk about Germany as a whole. Using national averages often obscures and distorts the true picture of development.

The description of regional differentiation during German industrialization has been largely neglected in the German economic history literature. With a few exceptions, (to be described below), the literature strongly emphasizes development in heavy industry. Light industry has not received as much attention. Moreover, the focus is always on Prussia — especially the Ruhr, Upper Silesia, the Saar area, and 55

sometimes Saxony. Other areas are given considerably less attention, if indeed any.

The story presented for Germany is often an interpolation of the development of

heavily industrial areas in Prussia.

Yet regional variations in Germany economic development were substantial and in fact, intensified during industrialization (Tipton, 1976). It is arguable that regional differences were larger in Germany than in many of its neighbors. The

reasons for this are explored below.

The period of rapid German industrialization (1871-1914) coincided with the time of the German Empire. This loose confederation adhered to common laws in the areas of trade, currency, and military duty. But states still retained a great deal of authority in other areas. It is not surprising that the considerable differences in state industrial policy combined with differences in natural resources endowment led to large regional variations in the economic development of Germany. Different cultural attitudes also played a role. For example, the entrepreneurial Rheinlanders came up with many ingenious ways to get around restrictive Prussian laws.

Resource endowment affected the type of industry as well as the degree and timing of mechanization in the German states. Areas with large coal deposits such as the Ruhr developed heavy industry and large mechanized factories. Areas without coal tended to develop lighter industry. Also, since transport costs of coal were high, they often relied on water power longer, especially if there were plentiful streams.

This usually delayed mechanization. 56

Agricultural structures varied across regions. In Eastern Prussia, for example,

large landowners held most of the land as well as the political power. The area’s

grain exports were quite profitable until prices starting falling due to international

competition beginning in the mid 1870s. But the landowners anti-industrial stance

thwarted rapid industrial growth. Per capita incomes began to decline and floods of

workers migrated to the rising industrial centers in the Ruhr and elsewhere. Other

regions had a preponderance of small farmers. In Wurttemberg, for example, the

bonds of these farmers to their land delayed large scale industrialization, and

ultimately led to a decentralized industrial structure still apparent today. In the

earliest phases, cottage industry played a very important role.

The best attempt thus far at a comprehensive, comparative, description of

regional differences in German industrialization is by Frank Tipton (1976). Tipton

goes into a fair amount of depth in describing the economic development of East

Prussia, Saxony, the Ruhr, Berlin. His treatment of the Southern German states, is

more limited. There is certainly room for an even more indepth comparative piece in the literature.

It is not the purpose of this paper to describe in detail the economic development of all the regions of Germany. For readers interested in that, I suggest

Tipton’s book as a good starting point.

I would, however, like to share Tipton’s findings on regional specialization during industrialization. Map 3.2 shows the 32 regions he used in his analysis.

Employment was divided into three major groups: agriculture, industry (which 57

includes traditional manufacturing methods) and services (including trade, domestic

service, and professional). Census data is available for some regions in 1861 and for

all regions in 1882, 1895, and 1907.

Tipton derived indices of specialization for each of the 32 regions, as well as a

General Index of Specialization (GIS) and an index of sectoral concentration for each

sector. His method is as follows:

"General index of specialization: each region’s total employment was

multiplied by the average share of employment in each sector. These

hypothetical figures, representing regional employment in each sector.

These hypothetical figures, representing regional employment in each

sector if the region did not deviate from the average, were subtracted

from actual regional employment in each sector. The absolute sum of

the differences in all regions was expressed as a percentage of total

employment.

Sectoral concentration: the absolute differences between actual

employment in the sector in each region and employment equal to the

average share were summed and expressed as a percentage of total

employment in the sector" (Tipton, 1976).

For the regions available in 1861, the GIS was 24.8% in 1861, and rose to

29.2 in 1882. Sectoral concentration rose in agriculture, fell slightly in industry, and maintained its low level in services (Tipton, p. 47). The corresponding results for all regions in 1882, 1895, and 1907 are shown in Table 3.27. The GIS rises steadily, as 58 does the sectoral concentration agriculture. That in industry falls, indicating increasing shares of industry across Germany, not necessarily a stagnation of established industrialization centers. Keep in mind that these figures do not take into account the substantial specialization within sectors. Were the categories sliced finer, all indices would be higher.

Table 3.28 shows the index of specialization2 in the 32 regions, 1882, 1895, and 1907. According to Tipton, anything over 30% can be considered "high"

(Tipton, p. 10). Berlin, Saxony, the Rurh, the Hanse cities, and the agricultural provinces in East Prussia are the most specialized. The industrial and trade centers tended to lose their "differentness" over the period as the German proportion of industrial workers increased. The noted agricultural regions diverged from the national average as national shares in agriculture dropped. The mixed regions had varying experiences. Wurttemberg’s specialization index rose from 14.1 in 1882 to in

1895 and to 18.6 in 1907 (Note: Tipton’s Wurttemberg region also includes

Hohenzollem, a small adjacent state.) Wurttemberg had a small service sector (3-4 points below the national average), smaller industrial sector (3.5 points below national average) and larger agricultural sector (7-10 points above the national average). The trends in Wurttemberg reflected those at the national level: increasing employment in

2Method: "Each region’s total employment was multiplied by the average share of employment in each sector. These hypothetical figures, representing regional employment in each sector if the region did not deviate from the average, wre subtracted from actual regional employment in each sector. The absolute sum of the differences for all three sectors was expressed as a percentage of total employment in the region" (Tipton, p. 86 ). 59

industry (up ten percentage points and falling employment in agriculture (down nearly

12 percentage points). Table 3.29 presents the data Tipton used on Wurttemberg-

Hohenzollem. This can be compared with Table 3.30 which shows the distribution of

employment in German for 1882, 1895, and 1907.

Tipton gives a great deal of attention to the location of railways and the

regional pattern of growth. He writes:

"The struggle within and between regions often centered on the location

of railway lines, for the simple reason that these lines were crucial to

the location of industrial expansion. The advantages conferred on some

districts by connection with the railway system were matched by the

disadvantages of regions not so favored. This was the major cause of

the dichotomy between backward and advanced districts in the later

nineteenth century. "Self-feeding" growth at the favored centers was

purchased at the expense of "emptying" and progressive stagnation for

what rapidly became the agricultural hinterland. This was particularly

true in Prussia, where the government built local lines where existing

traffic was greatest and returns on its railway investment would

therefore be highest" (Tipton, p. 147).

Areas well connected to rail systems (such as Berlin and the Ruhr) were not limited by local demand. Expansion of railways thus stimulated specialization, and made gains from economies of scale possible. But more isolated areas could not expand as rapidly and often lost many workers to migration. 60

In Wurttemberg, political conflicts with neighboring states and concerns about

railway profitability delayed strong rail connections with other states until the 1870s

and 1880s. When the connections were made, the infusion of cheap grain imports

drove agricultural prices down, making agricultural production less attractive. This

increased the willingness of the population to work in factories, thus stimulating

industrial development. Within Wurttemberg, migration flows were strongly in the

direction of the middle Neckar area, where early railroad construction has centered.

In many German states’ railway policies, the focus was on building railways

where they were sure to make a profit, especially densely populated, expanding regions. The positive externalities of railways — i.e., their economic stimulation effects — were often unheeded.

Hubert Kiesewetter (1986) also studied regional differences in German industrialization. He uses the Berufszahlung (Employment Census) of 1871 to analyze these differences in 72 regions at the time of the founding of the German Empire.

The average percentage of the German labor force in the secondary sector was

32.8%. Yet there was considerable variability. 41 regions had higher percentages; including Wurttemberg with 39.86%. Within the four Kreis of Wurttemberg, Jagst

Kreis had the lowest percentage of its labor force in the secondary sector (33.75%).

Next came the Donau Kreis (39.22%) and the Neckar Kreis (40.87%). The

Schwarzwald Kreis (44.90%) actually ranked tenth among the 77 regions in the share of labor in the secondary sector. The percentage in the tertiary sector was 9.0% for the German Empire and 9.08% in Wurttemberg. 61

For easier interpretation, Kiesewetter decided to divide the area of Germany

into regions of comparable size (between approximately 5,000 and 60,000 km2). This

reduces the areas of study from 77 to 28. Table 3.31 presents the surface area,

proportion of labor in the secondary sector, and the population density of each region.

The regions are ordered in descending shares of the secondary sector (Column 4).

The rank in population density is in Column 5. The Kingdom of Saxony and the

Prussian Province of Rheinland are first and second in both rankings. In the

secondary sector’s share of the labor force, they are followed by the Province of

Westfalen and the Kingdom of Wurttemberg. In population density, the next highest figures are for Grossherzogtum (Grand Duchy of) Hessen and Elsass-Lothringen.

Kiesewetter states that population density can be used as an indicator of the degree of industrialization. He uses the population density rankings to divide the regional industrialization process into three categories3. The first type includes regions such as Saxony and the Rheinland. These areas industrialized relatively early, with a focus on heavy industry, such as coal, iron, and capital equipment, often accompanied by textiles. The second group includes Wurttemberg, Baden, and

Hessen, which did not have large deposits of coal and iron ore. This delayed rapid industrialization until, through factor substitution and state and private efforts, breakthroughs occurred in the late 19th century. The third group is further subdivided into two types: In the de-industrializing areas -- such as the Erzgebirge,

’I was a bit surprised that the share of employment in the secondary sector was seemingly ignored in devising these categories. 62

the Eifel, and the Harz — found their traditional ironworks losing ground to the huge

factories in the Ruhr et.al. Their mountainous locations proved disadvantageous.

The second subgroup consists of the agricultural regions such as Posen, East Prussia,

and lower Bavaria. Industrialization was very slow here. But there were

improvements in agricultural techniques such as the use of artificial fertilizer and

machines. This prevented their being swallowed up in the Malthusian Trap

(Kiesewetter, 1986, p. 59-60).

The above discussion has stressed the differences in economic development

between regions in Germany. The differences are so large that rarely is the story of

economic development in a given region accurately portrayed by the story at the

macro level. This is certainly true of the Kingdom of Wurttemberg.

The next section describes the process of industrialization in Wurttemberg in

the second half of the 19th century. As indicated by its relatively low specialization

index, Wurttemberg’s employment structure did not deviate greatly from the national average. Thus a case could be made that Wurttemberg’s economic experience in the late 19th century is representative of the German experience. In some ways this is true. But Wurttemberg’s natural resource endowment, agricultural structure, population development, industrial policy, and railway expansion led to its own unique process of industrialization. Explaining this process is the subject of the following section. CHAPTER IV

Wurttemberg Economic Development

This chapter is divided into three sections. Section one tracks the course of

industrialization in Wurttemberg from its first stirring in the 1820s through the early

20th century, with a focus on the second half of the 19th century. Section two

discusses the Kingdom’s population development, agricultural structure, workforce

potential, and the recruitment of labor and industry. Section three pulls together

information on indicators of health and nutrition in Wurttemberg in the second half of

the 19th century.

4.1 Industrialization in the Kingdom of Wurttemberg

Probably the best source of information on industrialization prior to the

founding of the German Empire are the Occupation and Commercial Sector surveys

taken in 1846 and 1861 in Wurttemberg. Thereafter, the results of the German empire censuses are available for the following years: 1875, 1882, 1895, 1907,

1925, 1933, and 1939. The results of these surveys have been gathered and well presented in a work by Klaus Megerle(1982): Wurttemberg Im

Industrialisierungsprozess Deutschlands. This book is the most indepth and comprehensive source of information on the economic development of Wurttemberg that I have found. It has provided me with most of my understanding of industrialization in Wurttemberg and is the foundation of this chapter.

63 64

Before beginning our story, I would to raise a note of caution concerning interpretation of the census results. One should always keep the business cycle in mind. There were differences between the business cycles of the German Empire and those of Wurttemberg. This complicates the comparison somewhat.

In Germany the business cycle developments could be described as follows:

1845 and 1846 were good years. In 1847 there was a crisis, followed by depression lasting until 1852. The years 1853 to 1855 saw an upswing which ended with the world economic crisis of 1856-57. The depression lasted until 1861. The German

Empire census in 1875 took place in the cyclical depression of 1873 to 1878. The census in 1882 took place at the end of the weak upswing following the depression.

In 1895, after the "Great Depression" had ended, began a stronger rise in the business cycle. 1907 was a transition year between the cyclical upswing since 1903 and the shortage of capital and the crisis that followed. 1925 was at the beginning of an upswing. 1933 was the beginning of recovery from the world crisis. In 1939 we were in Nazi times, with the large build-up of the military industry.

The business cycle in Wurttemberg, especially during the early years, differed from that of the German empire on average. Wurttemberg had an agricultural crisis in 1846-47. There was a very bad economic situation until 1854-55. Thereafter, there was a large upturn due to better harvests and increase in foreign demand, and government investment in railroads. This upswing was slowed by the world crisis, but not stopped. There was an expansionary phase until the 1870s, possibly the 1880s

(Megerle, pgs. 78-80). 65

The main indicator to be used in this section in the Gewcrbesatz, which is

employment in commercial enterprises as a percentage of population. Information on

commercial sector employment is readily available for all German regions. Thus it

allows comparison of Wurttemberg and the other German regions.

This measure tells us something about how labor was used, with an increase in

this percentage indicating, or accompanying, industrialization. Yet it cannot

completely capture the process. For example, changes in capital might increase the

productivity of labor, but that would not be captured in the Gewerbesatz. In

interregional comparisons, the importance of the commercial sector would be

underestimated in more capital-intensive areas. It also neglects the impact of

changing technology, which increases labor force productivity. Thus output could

rise while employment remains constant.

The advantages of using the sectoral employment structure, however,

outweight the disadvantages. First it is simple to understand. Second, the

distribution of the labor force likely had a large impact on life styles and political

policies. Third, alternative measures of interregional differences, such as real per

capita income differentials, rely on cost of living indices. These indices are

intrinsically arbitrary and only as good as the data used to construct them. Reliable

data sources on incomes output and prices that are comparable across regions are not

readily available. Fourth, the employment census of the German Empire do provide consistent data on sectoral employment structure. It is the most reliable source of

regional economic information available. 66

Thus commercial sector employment is the best indicator for interregional comparison, given data limitations. It should, however, be used along with other measures of industrialization. These could include average size of establishment, mechanization, and number of steam engines. Used together with auxiliary measures, commercial sector employment captures many regional economic differences.

Figure 4.1 (Megerle, p. 152), is a summary of the story to come. It shows the changes in the Gewerbesatz (the percentage of the population employed in the commercial sector) between 1832 and 1939 for the German empire (the Customs

Union before 1871), as well as Saxony, the Rhine Provence, Bavaria, and of course,

Wurttemberg. After 1882, the trend in all regions was strongly upward until the

1930s depression. Between 1846 and 1882, however, trends differ across regions.

This often reflects differing business cycles. For example, the low point of the

German average was in 1861, reflecting the Depression mentioned above.

Wurttemberg, on the other hand, was in economic upswing from around 1855 into the

1870s. Between 1875 and 1882 Wurttemberg’s Gewerbesatz actually fell during a period of stagnation. At the same time, the German empire was entering her Hoch

Industrialisierung (High Industrialization) phase. 67

Development in the First Half of the 19th Century

Introduction

In the 1820s and 1830s there were some stirring in an industrial direction.

Some authors believed that this marked the beginning of industrialization in

Wurttemberg, but this is probably too early. Gustav Schmoller estimates that in

1822, those employed in commercial enterprises made up 7.39% of the population,

and 22.07% of male adults. In 1835 these figures were 12.49% and 37.73%

respectively. So there was an increase during this period. In 1831, there are 257

factories, of which 104 were founded after 1820, and 71 since 1826. This is an

indication of the growth of the commercial sector (Megerle, p. 83). Some factories

were appearing, however, especially paper mills, linen, wool and cotton spinneries,

which were often mechanized, but application of technology was not generally used elsewhere. Even in the factories, traditional techniques were often used. They might be better called manufactories.1

In 1828 there was still little foreign trade. Over half of all imports and exports were agricultural and forestry products. Finished products accounted for only a quarter of exports and one-fifth of imports. After 1830, there was some increase in trade.

'Komlos (1989) 68

The Customs Union (founded 1834) had a positive influence. It led to bigger

markets and thus to increased trade. Bavaria became a main market for

Wurttemberg’s goods, especially factory goods. Also important exports markets were

Switzerland, Baden, and other German states.

A notable piece of legislation was the Gewerbe ordinance of 1828. This freed

factories from many guild restrictions and regulations — e.g., how many apprentices

they should take, choice of tools and machines, with whom they could compete, etc.

It was a middle step towards freeing up producing enterprises.

The government had no promotion plan for Wurttemberg’s industry before the

mid 1830s. After the 1830s they did develop a formula concerning which industrial firms to promote. The winners were new branches or those bringing technical progress to existing domestic industrial branches (Megerle, p. 91-92). But

Wurttemberg’s politicians were still more concerned with the agricultural sector, and therefore were not truly industry-promoting.

The First Half of the 19th Century

While one cannot claim that industrialization began in the 1830s, there was progress in this period. The Employment Survey of 1832 indicates that commercial sector employment in Wurttemberg equalled 12.14% of the population and 36.3% of male adults. These figures overestimate commercial employment, since those for whom this was their secondary occupation were counted. As will be discussed in the 69

next section, there were a great many small landowners in Wurttemberg who

supplemented their income by working at a trade part time. This was especially true

for weavers and cloth makers, but also in many other enterprises.

Table 4.1 presents the results of the 1832 survey of commercial enterprises.

Traditional craftsmen ( Handwerker) and their helpers account for 75.6% of

commercial employment. Factory owners and workers account for only 3.7%.

In the manufacturing sector, small enterprises dominated. In 1832, 45.68% of

all 324 factories and manufactories surveyed had less than 10 workers. Only 22

establishments had over 100 employees (Megerle, p. 96).

The largest industrial branch was textiles. The 128 factories employed approximately 60% of all manufacturing workers. The next largest branches were alcohol and tobacco (7% of manufacturing employees), metal finishing (6.5%), paper mills (6.4%), straw weaving (5.5%), and iron works (4.5%) (Megerle, p. 97).

The connection between cottage industry and the central factory was strong across industrial branches, with an estimated 40% of all manufacturing employees working outside the factory. In textiles, this percentage was even higher — approximately 50% (Megerle, p. 97). This situation had advantages for both the workers and the factory owners. Small farmers could supplement their agricultural livelihood with part time work in the commercial sector. The combined occupations provided a sort of safety net. For entrepreneurs, this form offered: (1) the 70

transferral of the investment costs of machines and equipment, (2) the ability to vary

the number of workers, and (3) the ability to respond quickly to business cycle

fluctuations (Megerle, p. 98).

Map 4.1 (Megerle, p. 100), shows the regional distribution of employment in

factories, manufacturing, and mining in Wurttemberg in 1832. At first glance,

industry seems to be somewhat decentralized. Yet even this early, the concentration

tendency in the middle Neckar area is evident. 22% of all manufacturing employees

worked in the Oberamts (counties) of Stuttgart, Esslingen, Cannstatt, and

Ludwigsburg (Megerle, p. 101). A secondary concentration appears in Heidenheim

and Aalen, where many state-owned iron works were located as well as textile mills

in Heidenheim. Smaller industrial concentrations are clustered in the Schwarzwald

Kreis (District) in southwest Wurttemberg and across central Wurttemberg.

Comparing the results of the 1832 and the 1835-36 surveys of employment and

the commercial enterprises, one sees a 30-33 % increase in the number of

manufacturing workers. Machine building appeared as a new branch, and there was an increase in metal finishing, paper, musical instrument, and leather factories

(Megerle, p. 103). Light industry dominates.

Still factory workers made up only 5% of all commercial sector workers. One cannot speak of an industrial take-off. Most mechanization was in 12 large cotton

mills with 33,000 spindles and 1,200-1,500 employees (Megerle, p. 105).

It is difficult to accurately compare Wurttemberg’s employment statistics with those of other German states due to differing procedures. Thus, the following is 71

more of an approximation. The percentage of population employed in Handwerk,

manufacturing, and factories was approximately 8.9% in Prussia (1834), 10.4% in the

Rheinland province (1834), 11.7% in the Westfalen province (1834), and 11.54% in

Wurttemberg (1832). Wurttemberg’s percentage is higher than the Prussian average,

and between that of the Rheinland and Westfalen (Megerle, p. 106).

Industrialization Breakthrough: The Third Quarter of the 19th Century

The information for these years comes from the surveys of commercial

enterprises in 1852, 1861, and 1875. Table 4.2 (Megerle, p. 167-168) shows the growth in population and commercial sector employment. The percentage of the male population working at commercial enterprises rose from 37.73% in 1835-36 to

40.53% in 1852. During the late 1840s and early 1850s bad harvests led to an increased willingness to work in the commercial sector. Industrial growth accelerated in the boom years of the late 1850s, bringing this figure up to 45.81% in 1861.

Between 1861 and 1875, there was a smaller increase to 47.78% (Megerle, p. 108).

Table 4.3 compares changes in population and in commercial sector employment in the three periods: 1835-36--1852, 1852—1861, and 1861—1875. The strongest growth in the commercial sector was between 1852 and 1861. While population actually fell at an annual rate of .08%, the number of workers in commercial sectors increased 2.01% per year.

Relative to other German states, Wurttemberg had a higher than average

Gewerbesatz (percent of population employed in commercial enterprises) in 1852 and

1861, surpassed only by the Kingdom of Saxony. Between 1861 and 1875, however, 72

Wurttemberg’s Gewerbesatz stagnated while in other states it was strongly on the rise.

In 1875, it was just slightly above the German average (Megerle, p. 108-109).

In the third quarter of the 19th century, Wurttemberg had extensive small

industry but lagged behind in big industrial development. In 1875, over 75% of the

commercial sector worked in establishments of less than six workers. This small

establishment characteristic of Wurttemberg’s industry is apparent even today,

although the average size of establishment has risen over time.

Tracking industrial development between 1832, 1852, 1861, and 1875 is

difficult since the survey format used in 1875 was different from previous years. In

the earlier surveys, distinction was made between factories, traditional craftsmen, and

trade. According to this method, there were 39,775 factory workers in 1861. They

comprised 14.8% of commercial sector employees and 2.3% of this population.

In the survey of 1875, however, the distinction was made between big and

small enterprises, where big is defined as six or more workers. By this reckoning,

there were 146.321 small enterprises with 217,419 workers and 2,381 big establishments with 70,629 workers. Those working in big enterprises made up

24.52% of commercial sector employees and 3.75% of the population. There were about 17,000 establishments listed as factories in 1861, but that are listed as small enterprises in 1875. 73

Information on employment in factories (before 1875) and large establishments

(1875) is presented in Table 4.4. Table 4.5 shows the preponderance of small

enterprises in Wurttemberg compared to eight other regions in Germany. Only

Bavaria displayed this tendency more strongly.

Mechanization was limited in Wurttemberg at this time. Table 4.6 shows the

number and the power of steam engines in Wurttemberg and in some other German

areas in 1846-52 (1852 is Wurttemberg), 1861 and 1875. Wurttemberg has few

steam engines, in per capita terms the lowest. In 1852 Wurttemberg had 25 steam

machines. In 1861 it had 253, rising to 780 in 1875. In contrast the Rhein province

had over 6,000 in 1875.

The reason for this is Wurttemberg’s natural resource endowment. It did not

have coal itself, and thus imported coal from the Saarland. Transportation costs of

coal was very high, equalling 150% of the value of the coal. On the other hand,

Wurttemberg had a generous supply of rivers and streams. Thus it stuck to water power. In 1861 Wurttemberg derived 13 times more power from water than from steam. In 1875, even in the big enterprises, water was the most important power source. For small enterprises, water was still adequate.

The major exception to this was the textile industry. Between 1852 and 1875, the number of textile workers did not increase much, but the number of spindles and mechanical looms increased significantly. The number of mechanical looms rose from 764 in 1852 to 2,488 in 1861, and in just the big enterprises in 1875, to 6,359

(Megerle, p. 115-118). 74

Table 4.7 shows the number of workers in the factories/large enterprises by

industrial branch in Wurttemberg for the years 1852, 1861, and 1875. Especially in

1852, the textile industry dominates, although the figures for 1852 are somewhat

inflated because many home weavers, which work for factories were counted in 1852

but not in the later surveys. In 1875 two other industries have significant shares

including the food, drink, and tobacco industry, and machines and instruments

industry. (Please recall the difficulties in comparing 1852 and 1861 results to those

of 1875.) In 1875, the machine and instrument industry actually surpasses textiles in

terms of total number of employees. This is unusual when one recalls the lack of coal in Wurttemberg. The chemical industry tended to stay behind and salt mining

fell during this period because it was reaching its natural limits. Wurttemberg did not have many iron foundries, but there were large numbers of silver and goldsmiths.

We see from the above that the industrial structure of Wurttemberg tends to be in the work intensive finishing industries. They seem to be going into an area in which the large share of skilled workers, the Handwerker, will be used to their best advantage.

In other words, they were taking advantage of their comparative advantage.

Map 4.2 shows the regional distribution of factory workers in Wurttemberg in

1861. In comparison with the map for 1832, the basic story is the same, although there were some changes. The growing agglomeration in the middle Neckar area is clear. Stuttgart city has the largest number of factory workers by far. Additionally, several counties in the Schwarzwald Kreis - Reutlingen, Rottweil, Balingen,

Ravensburg, and Urach — had increased their industrial production a great deal. The 75

concentration of industry in these areas is very surprising; with the exception of

Ravensburg and Reutlingen, the other areas mentioned have no railway connections at

all. Rottweil had just gotten its connection two years earlier, in 1859. This suggests

that fast and cheap transportation for goods and workers was not a major priority at

this time for the small enterprises so characteristic of Wurttemberg. Large textile

mills tended to locate on the water, particularly on the Neckar or its tributaries. The

spinneries in Heidenheim were located on the Brenz River. The industrial branch which found it necessary to locate along railway lines was the metal finishing and

machine building branch. Thus the most important production areas were Heilbronn,

Cannstatt, Stuttgart, Goeppingen, and especially Esslingen. There were also a few factories in Ludwigsburg, Biberach, Ravensburg, and Tettnang, all of which were located on the north-south railway line, completed in 1850 (Megerle, p. 122-123).

The industrial structure of Wurttemberg retained its decentralized, yet centralized character. Just as in 1832 there were some part of the kingdom that were not at all touched by industrialization, including the northeastern part of the kingdom, the area to the southwest of Stuttgart, and most of the southeastern part. Stuttgart is by far the main industrial center, with Esslingen placing a strong second.

Industrial Expansion after 1875

Industrialization in Wurttemberg in the late 1850s was as strong as most other regions in Germany. But in the 1860s and the 1870s, judging from the employment statistics there was stagnation in the process, indicating some of the obstacles

Wurttemberg faced in industrialization. In the third quarter of the 19th century 76

Wurttemberg stayed behind in large industrial development. After 1882, and

especially after 1895, however, there was rapid expansion of the commercial sector.

This expansionary trend continued into the 20th century, with a few short term

downturns. The breakthrough in the 1880s was largely due to improved railway and

shipping connections with the rest of Germany, and thus the world. The competition

from foreign agricultural products drove down the domestic prices in Wurttemberg.

Thus agriculture was no longer as attractive and small farmers were more willing to

leave the land to work in factories. This opened the door for rapid expansion.

Insight into the industrialization process can be gained from the occupational

surveys of the German empire in 1882, 1895, 1907, 1925, 1933, and 1939. In the 64

year period between 1875 and 1939, there was a remarkable expansion and the

demand for labor in the commercial sector. The number of workers in this area rose over 280%. As a percentage of total population, commercial sector employment rose

from 15.31% in 1875 to 38.38% in 1939.

Table 4.8 summarizes the survey results on population development in the commercial sector in 1875, 1882, 1895, 1907, 1925, 1933, and 1939. Between 1875 and 1882 it appears that commercial employment as a percentage of the population fell from 15.13% to 14.79% (although there was an increase of over 7,000 commercial sector employees). The figures are somewhat misleading, however, for those who worked both in the commercial and agricultural sectors in 1875 tended to 77

be counted as commercial sector employees, whereas in 1882 they were often counted

as agricultural employees. Yet the stagnation is apparent. The number of firms

actually fell.

After 1882 the commercial sector in Wurttemberg had reached a phase of

strong, steady, expansion. Between 1882 and 1895, the number of workers in the

commercial sector rose by over 93,000, which was greater than the increase in

population. This is an annual increase rate of 2.42%. Between 1895 and 1907

growth accelerated. Commercial sector employment rose by nearly 130,000, which is

an annual increase rate of 2.78%. Through the first quarter of the 20th century, the

expansion continued, very slightly dampened. Commercial sector employment rose

by over 240,000 workers, at an annual increase rate of 2.62%. The percentage of population employed in the commercial sector rose from 14.79% in 1882 to 18.66%

in 1895, further to 22.08% in 1907, and 29.53% in 1925. The depression in the

1930s was evident in the census data for all regions, including Wurttemberg. The number of commercial sector employees decreased by over 37,000. But in 1939 there was a strong rebound. Commercial sector employment increased by over 387,000.

In part this was due to the military build-up under the Nazis.

Table 4.9 shows commercial sector employment as the percentage of the population of several German regions, in the years 1875 to 1939. Comparing

Wurttemberg’s figures with those of the other regions, its relative stagnation is clear.

The German average was increasing between 1875 and 1882, whereas in

Wurttemberg it was falling. After 1882, however, Wurttemberg begins to close the 78

gap. In 1895 and 1907 its Gewerbesatz is less than 1.1 percentage points under the

German average. In 1925 and beyond Wurttemberg’s share of population in the

commercial sector exceeded the German average in ever increasing amounts. In 1939

Wurttemberg had 38.38% and the German average was 33.39%. It surpassed the

other regions, sometimes earlier. In 1895 it passed Prussia. In 1907 it passed

Hessen. In 1939 it was second only to Saxony. One notices in the table that

Wurttemberg seemed to be more resilient in the world economic crisis in the 1930s.

Gewerbesatz dropped by only 4 points in Wurttemberg, whereas in Saxony it dropped

by 14 points. The results of this table were presented in Figure 4.1 in the

introduction of this section. The reader may want to refer to that diagram again.

Table 4.10 shows the increase in the percentage of the population employed in

the commercial sector in the German regions presented in Table 4.9. The increase in

Wurttemberg between 1875 and 1939 was 23.07 points, and between 1882 and 1939 was 23.66 points. This was well above the increases in the other examined regions and in the German Empire. It seems industrialization had its booming phase a little bit later in Wurttemberg than elsewhere in Germany, but once it took off, it made up for lost time.

The industrial structure in Wurttemberg changed substantially between 1875 and 1939. Table 4.11 shows number of employees in enterprises with six or more workers as a proportion of all commercial sector employees and of population.

Between 1875 and 1882 a number of commercial sector employees in large enterprises, as a percentage of total population rose only slightly, from 3.75% to 4.07%. Between 1882 and 1895 it more than doubled, reaching 8.74%. The rise

continued strongly through 1939, with a downturn in 1933. In 1939, 27.53% of the

population and 71.72% of commercial sector employees worked in enterprises with

over five employees. Note there is some difficulty comparing the first two censuses

with the last five. In the former, the only actual workers were counted. In the latter,

the managers and owners of the enterprises were also included. If the 1882 criteria

were used for the year 1895, one would count 172,913 commercial sector employees,

employed in large enterprises, which equals 44.05% of commercial sector

employment and 8.31% of the population (Megerle, p. 130).

Table 4.12, shows the percentage of commercial sector employees working in

enterprises with six or more workers in several German areas for the survey years

between 1875 and 1939. While the Wurttemberg figure rises during this time, even

in 1939, it is two points below the German average. So Wurttemberg’s industry was

no longer dominated by small firms. Yet it remained below the national average.

This finding is reinforced by the information in Table 4.13 which shows the average

number of employees in commercial enterprises in several German regions for the

survey years 1875 to 1939. Wurttemberg’s average business size increased from 1.94

in 1875 to 2.05 in 1882, to 2.81 in 1895, to 3.83 in 1907, to 4.93 in 1925, falling to

4.2 in the Depression, and bouncing back and rising to 5.84 in 1939. In all survey years the average business size in Wurttemberg is smaller than the German average.

Only Bavaria and in some years Hessen have lower average business sizes. 80

The relatively small scale of Wurttemberg’s commercial enterprises, its lack of

coal, and the industrial concentration in high end finishing and processing branches,

influenced the pattern of mechanization. Table 4.14 shows the number of mechanized

enterprises and the power output in Wurttemberg in the survey years between 1875

and 1939. The number of mechanized firms rose throughout the period, but

especially after 1895 mechanized firms made up 0.87% of all firms in 1875. This

rose to 26.14% in 1939. However, the power output per mechanized firm rose

somewhat but remained fairly constant, ranging from 14 to 25.3 PS. This reflects the

usefulness of smaller engines and the predominance of fine processing industries of

Wurttemberg. Due to a lack of coal, Wurttemberg relied on water power for a large

part of its energy needs until the end of the 19th century. In 1875 nearly 54% of

power was derived from water, as compared to 45.6% from steam engines. The

share had nearly equalized by 1895, with a slightly larger share now in steam engines. In the survey of 1907, however, electricity and electrical motors are making their advent. There is a substantial substitution of electrical power for water power ~ nearly 20% between 1895 and 1907. In the following years, electrical power continued to replace other power sources. In 1925 it accounted for 71.94% of power supply and in 1939, 81.9%.

Table 4.15 shows mechanized firms and engine power in several German regions in 1875, 1907, and 1939. The percentage of firms in Wurttemberg which were motorized rose from 0.87% in 1875 to 7.9% in 1907, and to 26.14% in 1939.

The German average was 0.88%, 6.73%, and 21.64% respectively. Thus, 81

Wurttemberg’s percentage of mechanized firms was approximately equal to the

German average in 1875, exceeded it slightly in 1907, and in 1939, exceeded not only

the German average but that of every other region. On the other hand, in terms of

power output, Wurttemberg lagged far behind many other areas, especially Prussia

and its provinces of Westafallen and the Rhineland. The power output per

mechanized firm was the lowest in Wurttemberg in 1907 and in 1939. As mentioned

before this reflects the fine finishing and small scale characteristics of Wurttemberg’s

industry. Wurttemberg used relatively more electrical power than other states

(Megerle, p. 137).

The focus in Wurttemberg was on finishing and processing industries. This

was partly due to the lack of raw materials, as well as the unfavorable transportation

possibilities. Primary industries such as mining, and iron and steel works employed

only 1.23% of all commercial sector employees in 1939. Stones, earth, and the

chemical industry were also not very large. Only the wood industry grew and especially the fine finishing of wood were significant (Megerle, p. 137).

Table 4.16 shows the number of employees in the most important industrial branches and their proportion of all commercial sector employees in Wurttemberg between 1875 and 1939. The focus on finishing and processing industries is clear.

Another thing one notices in this table was the strong relative decrease in the production of consumer goods and the rise of investment good production. For example, in 1875 43.56% of all commercial sector employees were including textiles, food, tobacco, and drink industries. By 1939, this proportion had fallen to 22.33%. 82

Another characteristic of Wurttemberg’s industrial structure is its

diversification in production. No one branch ever has more than 20% of all

commercial sector employment in any of the seven survey years. The diversification

of production increased throughout the period. In 1875, the clothing industry was the

largest industrial branch, employing 17.41% of all commercial sector workers. In

1939, the largest group — machines, steel, and transportation building - only had a

10.96% share. Six other industrial branches had between six and ten percent of

commercial employment. The diversified production base probably enhanced

Wurttemberg’s resiliency during difficult economic times.

Compared to the German average, Wurttemberg had a larger percent of its

population employed in metal finishing, machine steel and transportation machine

building, electrotechnical goods, optics, and fine mechanics, textiles, wood and

carving industry, the production of instruments and toys, as well as the clothing

industry in 1939. In this year Wurttemberg had 4.18% of the population of the

German empire, yet it employed 6.51% of all machine transport building workers,

6.1% of the electrotechnical industry, 14.25% of all optics and fine machinery workers, 12.17% of music instrument and toy makers. In the 64 year period from

1895 to 1939, Wurttemberg went from a relatively undeveloped area to one of the strongest industrial areas in Germany (Megerle, pp. 141-2).

Both the centralized and decentralized character of the spatial structure of

Wurttemberg’s industry continued in the last quarter of the 19th century and beyond.

On the one hand, there was a strong expansion in the industrial centers, particularly 83

in Stuttgart and its surroundings. In 1895 35,447 people worked in 1,708 big

enterprises in Stuttgart. That accounts for 23.5% of all large enterprises in

Wurttemberg and 19.49% of all workers in Wurttemberg employed in large

enterprises. (Recall large enterprises are those with six or more employees.) If you

include the counties of Stuttgart, Canstatt, Esslingen, and Ludwigsburg, the

percentages rise to 34.7% of all large enterprises and 32.69% of all workers in large

enterprises in Wurttemberg. The agglomeration in this area is evident in Map 4.3,

which shows the regional distribution of employees in large enterprises in the year

1895. Yet not all industry is located in the Stuttgart area. A considerable amount

developed to the east of Stuttgart, particularly in Goeppingen, Geislingen,

Heidenheim, and Ulm. In the southwest comer of the kingdom, in the Schwarzweld

Kreis, another secondary industrial agglomeration is apparent, as well as directly to

the north and south of Stuttgart. There are smaller industrial developments scattered throughout the rest of the kingdom. So industry is relatively decentralized, but with concentration in several regions. With the exception of Aalen and Heidenheim, and perhaps Hall, the Jagst Kreis was relatively undeveloped as was the southern Donau

Kreis with the exception of Ravensburg. Most counties in the Neckar Kreis (except directly east of Stuttgart) had a fair amount of industry. In the Schwarzwald Kreis the southern counties and the northeastern counties had industrial developments, but the northwestern counties remained behind (Megerle, p. 145-147).

Map 4.4 shows the regional distribution of workers in the commercial sector of Wurttemberg in 1925. In this map we have a distinction between industrial 84 employees, shown by solid circles, and employees in other parts of the commercial sector, shown by the dotted circles. The regional distribution of commercial sector employment has not changed dramatically from the 1895 scenario. Stuttgart and the middle Neckar area have the largest share of industrial and other commercial sector workers. Yet there is development to the west of Stuttgart, as well as directly to its north and south, and a secondary agglomeration in the southwest comer of the kingdom — all as in 1895 (Megerle, p. 147-149).

In the city of Stuttgart alone, 172,844 people were employed in the commercial sector, including 107,152 people in industry, manufacturing, and crafts.

If one looks at the great Stuttgart area (the middle Neckar), which includes Esslingen, and Ludwigsburg, as well as Boeblingen, Leonberg, and Waiblingen, we find that

32.7% of all industrial workers and over 35% of all commercial sector employees worked in these areas. The secondary industrial centers to the north, south, and east of Stuttgart, as well as the southwest comer of the kingdom employed another third of all commercial sector workers. Tne Oberamts Ulm and Ravensburg had slightly under 4% of commercial sector employment. The remaining quarter of commercial sector employees were scattered throughout the rest of Wurttemberg (Megerle, pp.

147-149). The decentralized and yet decentralized character of Wurttemberg’s spatial structure is maintained.

Railway Development

No discussion of industrialization in the 19th century would be complete without a look at railway development. Table 4.17 displays information on railway 85 development in 27 German regions, between 1839 and 1914. Prussia with 64.5% of the surface area of the German Empire and 61.9% of the 1910 German population, had 61% of all German railways. Wurttemberg, with 3.6% of the German Empire surface area, had 3.75% of its population ar.d 3.25% of its rail lines in 1910

(Calculated from Kiesewetter [1989, pp 124-125] and Table 4.17). So Wurttemberg had relatively less railways per capita in 1910 compared to Prussia, as well as the

German average.

Most railway expansion in Wurttemberg took place between 1850 and 1880.

1187 kilometers of lines were laid. Only 561 kilometers of track were laid between

1880 and 1914. The latter were generally secondary lines with smaller, slower trains

(.Festschrift).

As Tipton (1976) suggests, there is a connection between the location of railways and industry. One can compare Maps 4.1-4.4 showing the spatial structure of Wurttemberg’s industry with Map 4.5, showing the railways in Baden-

Wurttemberg in 1972, and when various segments were completed.

The earliest part of Wurttemberg’s railway lines were laid in the 1840s. By

1850, the North-South line ran from Heilbronn in northern Wurttemberg, through

Ludwigsburg, Stuttgart, Cannstatt, and Esslingen, (completed by 1848), and further through Ulm, Biberach, and Ravensburg, ending in Friedrichshafen and the Boden

Sea. Comparing with Map 4.2, we see factories located all along the line from

Heilbronn to Ulm. In the agricultural Donau Kreis, the line did not seem to stimulate industrial production, except possibly in Ravensburg. By 1861, Reutlingen and 86

Nuertingen to the south of Stuttgart and Gmund and Aalen to the east were also

connected. All these areas together account for the majority of industrial development

in 1861.

There are, however, a couple of striking exceptions. The secondary industrial

agglomeration in Obemdorf, Rottweil, and Balingen in the southwest comer of the

kingdom had no railway connections until the late 1860s or 1870s. The same holds

true for Urach, southwest of Stuttgart. Apparently, land transportation of goods and

workers was not as important in the industrial development in these areas.

In the late 1860s and beyond the rail network was extended throughout the

Kingdom. The concentration was in the middle Neckar area, around Stuttgart. Large

parts of the Jagst, Donau, and Schwarzwald Kreis, however, remained isolated.

These areas often retained their agricultural character into the 20th century.

4.2 Population Development and Workforce Potential

There is often too little attention given to the interdependence of population,

labor force potential, and the use of the labor force in studies of industrialization, particularly regional studies. These factors are, however, necessary to understand the specific pattern of development seen in Wurttemberg.

Let us first consider population density. For Wurttemberg as a whole the population density rose from 72.3 residents per square kilometer in 1816, to 80.5 in

1834, and further to 90.7 in 1849. Map 4.6 (page 199) shows the population density in the individual Oberamts in 1834. The Neckar Kreis had a particularly high population density, followed by the Schwarzwald Kreis, whereas the population 87 densities in the Donau and the Jagst Kreise were fairly low. Only Saxony and Hessen had higher population densities among all the German states. The high population density in the Neckar and Schwarzwald Kreise would be a favorable condition for the recruiting of labor for factories that were appearing. The density was especially high in the middle Neckar area, composed of Cannstatt, Ludwigsburg, Esslingen, and

Waiblingen. Their population densities were 212.1, 186.5, 181.8, and 175.1 residents per square kilometer respectively. For the city of Stuttgart this figure was

1,277.4 residents per square kilometer. In contrast, many Oberamts in the Donau

Kreis had less than 45 residents per square kilometer (Megerle, p. 198-200).

The population density pattern reflects regional differences in inheritance laws and agriculture structure between the areas. The Neckar Kreis is part of the old

Wurttembergish Realteilungsgebiet, where property was divided among heirs. With a strong splintering of goods, we see high population densities. In the eastern part of

Wurttemberg, Anerbenrecht prevailed. Property was passed to a single heir. This led to larger agricultural enterprises and relatively low population density.

In light of the high population density in Wurttemberg, the rate of population growth was surprisingly low. Table 4.18 (page 201) shows population growth and the average yearly increase in population in Wurttemberg between 1816 and 1900. The annual rate of increase never exceeds one percent, and in fact between 1850 and 1856 there was even a loss of population. Compared to the other German states at this time, population growth was very low. In general the southern states had a lower population growth than the northern German states. Yet even in relation to its 88

neighbors, such as Bavaria, Wurttemberg had a lower population growth rate. This

was not due to a low birth rate. In fact, the raw birth rate was higher than in the

Rhineland. But the birth surplus rates were very low compared to other areas. This could be due to the exceptionally high rate of stillbirths and child mortality in

Wurttemberg.2 Thus the natural population increase was not very high, but it was further decreased by substantial out migration.

Table 4.19 (page 202) shows the loss of population of Wurttemberg due to out migration in individual phases between the years 1816 and 1900. By far the largest out migration was during the agricultural and transition crisis of 1847-1855, where the yearly rate was 18,000 per year. The second highest emigration rates occurred in the

1880-1884 period. Table 4.20 (page 203) shows the net migration for several

German areas in yearly averages for individual phases ranging from 1817-25 to 1857-

65. It is clear that Wurttemberg consistently had the highest net migration loss of the areas shown here. This was largely due to overpopulation in Wurttemberg.

Wurttemberg apparently had enough population for industrialization. Yet factories found it difficult to recruit and hold on to workers, particularly skilled and qualified workers. There are many contemporaneous accounts of such complaints by factory owners. These claims are surprising, however, because the trades were very strong in Wurttemberg. In 1832, 9.2% of the population in Wurttemberg were

Handwerkers (traditional). These skilled workers were rather evenly distributed across the land. The Donau Kreis had 9.5%, Neckar Kreis had 9.3%, Schwarzwald

2These topics are discussed in Section 8.2. 89

Kreis 9.2%, and Jagst Kreis 8.2%. In the city of Stuttgart, they accounted for

18.9% of the population. If we compare the percentage of Handwerkers in

Wurttemberg to that in Prussia, there were only 6 out of 64 counties in Wurttemberg

where the percentage of Handwerkers was lower than in the Prussian average

(Megerle, p. 204-207).

Yet the factory owners complained about the lack of reliable and cheap labor.

Their biggest problem was worker flux. The factory owners even tried to form a

union so that they would not steal each others workers. The wages of factory

workers were considerably higher than those of the tradesmen/helpers or apprentices.

It was hard to convince the workers to come and work full time in the factories for

reasons which will be discussed later. Thus, factory workers were paid more in the

beginning of industrialization. It was estimated that in 1841 the relationship between

the ratio of wages to the value of the industrial product for Wurttemberg was 33 to

100, whereas in Saxony it was somewhere between 11 to 116 to 100. Hans Loreth estimates the following relationship between average wages in Wurttemberg and average wages in the German empire: From the 1820s to the 1850s wages in

Wurttemberg were higher than the German average; they converged in 1855; between

1860 and 1885 the Wurttemberg wage was again higher than the German average; around 1885 the situation reverses and the wages are lower in Wurttemberg. That situation persisted until 1913, although the differentials decreased over that period.

But it seems clear, that at least in the early phase of industrialization in Wurttemberg, labor was more expensive than in most other German areas (Megerle, p. 211). 90

Studies comparing workforce potential and workplaces available in

Wurttemberg from the 1820s to the 1860s, show that there was not a labor shortage

per se. The number of workplaces per 100 potential workers was 100 in 1821, 93.7

in 1849, and 105.5 in 1864 (Megerle, p. 213). So there was not exactly a labor

shortage, but there were regional bottlenecks. It seems that often the workforce just

did not want to work full time in factories. The next section helps explain why.

Agricultural Structure in Industrialization

It was difficult to recruit labor in Wurttemberg for industrialization because of

the agricultural structure. Wurttemberg was the land of small farmers, resembling

France in some ways, more than other parts of Germany. Seventy-nine percent of all

landowners had less than 3.15 hectars of arable land in 1857. This was especially

true in western Wurttemberg in the Realteilungs area. In Waiblingen and Cannstatt,

94.1% were small landowners, and in the city of Stuttgart, 98.5%. In all but two

Oberamts, it was over 50%. The average holding was 8.9 morgans where ten morgans equal 3.5 hectars (Megerle, p. 215).

Map 4.7 shows the percentage of small farmers — those with 3.5 hectars or less in the various Oberamts in 1857. It is obvious that the largest percentage of those small farmers was in the Neckar Kreis. In western Wurttemberg the average holding was less than 4 morgans; in Stuggart it was only 2.12 morgans (.67 hectars)

(Megerle, p. 24). Map 4.8 shows the average size of agricultural enterprise by

Oberamt in the year 1857. 91

The farms were too small to subsist from the farms alone. A farming family

needs 10 hectars to feed itself and in the wine-growing areas, it needs 5 hectars more.

Thus, second jobs were necessary for the family to survive. In 1852 approximately

46% of these small farmers found it necessary to take a second job. This resulted in

a very strong link between agriculture and Gewerbe (commercial enterprises). It is

very difficult to separate agricultural and industrial workers, since many were both.

The borders were fluid. Moreover, the small holdings made recruiting for

industrialization harder. With the farm, the worker can accumulate for himself.

Therefore, they held onto the land and there were not enough wage workers for the

factories. A land-less labor class was just not there. In 1852, the statistics show that

only 39% of all industrial workers were only industrial workers (Megerle, 1982).

Working a small plot of land in addition to working in commercial enterprises

provided a safety net for the workers and their families. The farms protected workers

from unemployment and they could shift their labor according to which sector the

returns were the greatest. The people had two jobs, neither of which alone would be enough to support the family, but together they acted as a safety net. Many factory

workers left in the summer to work their own fields, thus contributing to the worker flux factory owners complained about.

The preponderance of small farmers with limited education and materials meant that an agricultural revolution via innovation, was basically impossible in

Wurttemberg. That the farmers were loathe to leave their land holdings meant a 92

certain decentralization of industry in the land. Often very small communities were

highly industrialized. This is a characteristic which is still observable today. One

sees small factories everywhere.

Cottage industry workers were used a great deal, especially when production

was not too mechanized. This production form made the joining of agriculture and

industry possible, but it definitely slowed down mechanization and thus

industrialization in Wurttemberg. In 1832, over 40% of the "factory workers"

actually worked outside of the factory building (Megerle, p. 222). The ties of small

farmers to the land delayed industrialization. Mechanization stagnated and social

mobility was hindered.

There is an important connection between an immobile labor force, especially in the lower classes and industrial development. Just as in many other parts of

Germany, there was not much intermigration or urbanization in Wurttemberg before

1850. Stuttgart itself reached 50,000 inhabitants only in 1852.

Nevertheless, as Table 4.21 shows, cities with populations over 5,000 grew faster than smaller communities. The population in cities over 5,000 strong rose

32.1% between 1834 and 1852 — over three times the Wurttemberg average.

According to a study by Paul Sick (1853), between 1842 and 1852, there was a good deal of emigration from both cities and rural areas. An estimated 35,848 rural residents and 8,808 urban residents left Wurttemberg. The internal migration of close to 15,000 was in the direction of the cities. The migration balance of the cities was

6,106 (1.59% of the 1842 population of the cities), whereas the rural areas lost 93

50,532 (3.8% of the 1842 rural population). Emigration rates were higher in densely- populated western Wurttemberg, especially from the rural areas. Only 1.1% of

Wurttemberg’s rural population migrated to the cities; 2.7% left the kingdom

(Megerle, pp. 224-226). Many headed for North America.

The potential pool of migrants consisted primarily of the offspring of small craftsmen and farmers, as well as the non-inheriting offspring of landowners in eastern Wurttemberg. The market from small craftsmen was basically saturated.

Many land holdings in western Wurttemberg were too small to be viably splintered even more. It seems that the material offering of factory work in the early phase of industrialization was too small to entice the migrants. By migrating out of

Wurttemberg, they had the possibility of opening their own commercial enterprise, or working their own piece of land in eastern Europe or overseas. After completing their military duty, young men were free to go. Many did. Those who did not, tended to lack either the courage or the financial means for emigration (Megerle, pp.

226-7).

Thus recruitment of the labor force for city-base industry was very difficult in

Wurttemberg. Mobile workers preferred to emigrate. The farmers were tied to their land and thus could not devote themselves entirely to factory work. They also did not have to sell themselves at any price. Wages in industry had to be higher than those in agriculture. If the wages fell in the commercial sector, people simply devoted more time to agriculture. Thus the industrial wage floor was determined by the outlook in agriculture. Even though factory salaries were higher, they were not high enough to 94

lure large numbers of workers away from the security of their own piece of land.

The proletariate-like small craftsmen and farmers stayed on the land. Their use on a

large scale of industrial production was not possible at this time.

The Development of Workforce Potential and Industrial Structure Since the Middle of the Nineteenth Century

The situation described above changed fundamentally for a short time in the

middle of the 19th century. There was an agricultural crisis, which began in 1846-47

and lasted in Wurttemberg till 1852-53. Due to the crisis, many small farmers and

craftsmen lost their main or second source of income. It threatened their very existence. The crisis began with failed crops and grains and then got worse due to the potato blight. The blight affected 14% of the 1848 crop, 16% in 1849, 36% in

1850, 44% in 1851, 11% in 1852, and 5% in 1853 (Megerle, pp. 229-230).

To give one an idea of the extent of this failed crop, consider the following: in a normal year such as 1857-58, per capita there were 97 liters of grain and 416.5 liters of potatoes harvested. In 1847-48 there were only 67.6 liters of grain and

110.8 liters of potatoes. In 1852-53 these figures fell to 45 liters of grain and 7.1 liters of potatoes. As one may surmise the price for those products rose considerably.

The price on grains on average, rose 50% between the years 1845 and 1847

(Megerle, p. 230).

The large land owners held their income fairly constant, because the prices of their products were so high. It was the craftsmen who were farmers on the side, who were hit very hard. They devoted more time to handicraft, away from the failed agriculture area. This caused the supply of craft items to increase, which in turn 95

caused the price of these products to go down. Adding to the troubles of the small

craftsmen was increased competition with local factories for the local markets. The

demand in local markets were dramatically reduced via the agricultural crisis, so the

competition was tough. The small craftsmen still used the old ways whereas many of

the factories were mechanized. The incomes of the small craftsmen were driven

down to the bare minimum. In the beginning of the 1850s, the craftsmen transition

crisis reached its highpoint. There was a large increase in the number of poor

receiving alms and many communities were bankrupt (Megerle, p. 231).

There was a flood of out migration: between 1847 and 1855 nearly 10 percent

of the population left Wurttemberg. Due to this emigration, population actually fell

4,3% in that period. The biggest population loss was in the densely populated

western regions. In the east - where farms were bigger -- less people left. Between

1850 and 1855 the Neckar Kreis lost 7.2% of its population; Schwarzwald Kreis lost

10.47% of its population; the Jagst Kreis lost 7.02%; the Donau Kreis lost 5.2% of its population. During these five years 7.69% of Wurttemberg’s population left on average (Megerle, pp. 231-232).

There were a great number of bankruptcies at this time. Bankruptcies rose from 1,062 in 1840 to 2,300 in 1847, and further to 7,582 in 1852. The highpoint was 8,813 in 1854, falling to 4,773 in 1856 (Megerle, p. 232). Table 4.22 shows the number of bankruptcies and occupational categories in Wurttemberg between 1840 and 1847. Nearly half of all bankruptcies were in the commercial sector, in other 96

words the small craftsmen. The second largest group was the small farmers. Most of

the bankruptcies -- especially for the small craftsmen — occurred in the Neckar and

Schwarzwald regions.

The small craftsmen and farmers were not very well off before the agricultural

crisis. The agricultural crisis was followed by the craftsmen ( Gewerbliche ) transition

crisis. This double blow led to a total threat to their system and thus to massive

emigration. Even those who remained behind were mobilized. The alternative

income and agriculture was no longer there because of the failed crops. There was an

increase in willingness to work in the factories and to shift the labor to its more

productive area. The population of the eight cities, which at the time had larger industrial settlements (Stuttgart, Heilbronn, Esslingen, Cannstatt, Gmund,

Ravensburg, Heidenheim, Aalen) rose 10.1% between 1844 and 1855. In contrast the population in other areas in Wurttemberg fell by 5.1% (Megerle, p. 233).

These developments changed the power position between the craftsmen and the factory owners. In the early 1850s factory wages in Wurttemberg fell. The gap between higher Wurttemberg wages and average German wages decreased and eventually disappeared. Worker productivity per mark spent on wages was able to reach the label common elsewhere in Germany. This caused an increase in expected profits, which combined with increasing foreign demand, led to an increase in investment in industry (Megerle, p. 234).

After the mid 1850s, however, there were better harvests. The better prospects in agriculture led to a decrease in the supply of labor for factories, and thus 97 to an increase in factory wages. The situation for small craftsmen stabilized, due to the better harvests as well as less competition for local markets since factories were increasing their exports. So this crisis sort of subsided and the situation was similar to before the crisis, with craftsmen having their little plot of land as insurance. In some ways, the migration benefitted the rural dwellers who remained, since the average landholding increased during the 1850s and 1860s due to migration. Once again the industrial reserve army was not completely available (Megerle, pp. 234-

235).

The mobility of the population did not, however, completely disappear with the improved economic circumstances. Urbanization was taking place. Between 1855 and 1875, communities of population 20,000+ (in 1895) experienced an 82.1% increase in residents. Those with 10 to 20 thousand gained 40.8%. Those with 5 to

10 thousand gained 31.1%. Those under 5,000 gained only 5.2%. The total population increase in this time was 12.7%, although the natural population growth was 20.6%, indicating migration outflows. The population of Stuttgart more than doubled during this period. While there was a migration surplus into the cities, one must keep in mind, however, that the percentage of population in cities of 5,000+ residents in Wurttemberg in 1871 was only 16.2%. Compared to the average in the

German empire — 23.7% — this is fairly low (Megerle, pp. 235-236).

In the following period the population development did not change very much.

This is surprising, especially considering that since the 1880s the wages in the commercial sector were higher than the wages in agriculture and this difference was 98

increasing. Since the 1880s the importance of a second job in agriculture fell. Until

the 1870s Wurttemberg’s supply of agriculture came totally from its own harvest.

However, the railway connection to the foreign railway network brought Wurttemberg

in contact with the rest of the world. Cheaper foreign agricultural products were

brought into Wurttemberg on a large scale. With the exception of the Franco-German

War, since 1873, the prices of grains fell slowly but continually. From 29.47 marks

per 100 kg. in 1873, prices fell to 17.24 marks per dz in 1892/94 (Megerle, p. 237).

In the last quarter of the 19th century the price of agricultural products fell

while wages were rising. The prices for manufactured goods fell slightly. The small

craftsmen with secondary occupations in agriculture were hit doubly hard by this price

development, since the prices of their products were decreasing and the labor costs

were rising. Factory work became more attractive and there seemed to have been a

pick up in commercial economic activity, especially after 1882.

Some urbanization and migration did take place, but still not on a massive

scale. Figure 4.2 graphically depicts the urbanization trend between 1871 and 1910.

The dotted line represents the total population of Wurttemberg and each of the bars

represent residents of six classes of community size. In absolute terms, the numbers living in communities of less than 2,000 residents stagnated or declined; in percentage terms there was a significant decline. In 1871 over 41 % lived in communities of less than 1,000; by 1910 this had fallen to 28.7%. Meanwhile, the percentage of those living in cities of 20,000 residents or more (including Stuttgart) rose from 6.5% of the population in 1871 to 21.2% in 1910. Figure 4.3 shows the rise in the population 99 of communities of 2,000 plus residents between 1834 and 1910. The population of

Stuttgart more than tripled (KSL, 1910 census). Even so, compared to Rheinland,

Westfalen, or Berlin, Wurttemberg’s internal migration was fairly small.

The population density in Wurttemberg increased especially in the areas, which even 50 years earlier were already densely populated. The highest densities were in the middle Neckar area, which not surprisingly had the largest industrial conglomeration in Wurttemberg.

Despite this concentration tendency, Wurttemberg’s industry remained fairly decentralized. In 1895, only 52.6% of the commercial enterprises with more than five workers were located in the 12 cities with populations over 10,000 (Megerle, p.

239). The relatively immobile population forced industry to follow its workers into the countryside. Often the smallest communities had the largest factories.

Many industrial workers in the cities lived in the country. They were still connected to agriculture, and cottage industry was well developed. In 1900, 13% of all those working in the commercial sector were commuters (Megerle, p. 240). The commuters and the cottage industry were strongest in the Neckar and Schwarzwald regions. This confirms the strong relationship between agriculture and commercial employment in densely populated western Wurttemberg.

The decentralized industry was due primarily to the need for labor. Yet it was made easier in Wurttemberg due to: (1) lots of processing/finishing industries, which meant that raw material deposits and markets were not as important because the ratio of transportation costs to the product price was fairly low, (2) the extension of the 100 transport network, insured transportation of raw materials, half-finished goods, and workers to and from the farthest village, and (3) the connection between factory and agriculture work meant that factory wages could fall and be balanced partially by a second job in agriculture (Megerle, pp. 240-241).

Industrialization in Wurttemberg - particularly the recruitment of an industrial labor force — was strongly influenced by the agricultural structure of the land, the coupling of commercial with agricultural occupations, and the property rights of the rural population. These factors combined with the relatively unhindered immigration possibilities, limited the rise of a landless, propertiless proletariat in Wurttemberg.

While this may have slowed industrialization in the early phases, later this was favorable for industrial development. Wurttemberg’s wage workers were in general, better off, than in other German states, due to the insurance the worker had from the small piece of land. Wurttemberg’s workers were never really a hapless, landless proletariat in the Marxist sense. CHAPTER V

The Data

Introduction

One of the best sources of information on historical heights is military data.

Records on individual soldiers almost always includes heights, often accompanied by information on birthdate, birthplace, occupation, and occupation of the father. While data on volunteer armies is useful, there arises selectivity questions, i.e., what types of men are inclined to volunteer. It may be difficult to ascertain the relationship between the sample and the underlying population. On the other hand, in cases where there was a universal draft law under which all young men of a certain age were examined, the situation is somewhat simplified. Here, the ideal data source would be the recruitment lists, i.e., information on all those mustered up and examined. When these are not available, one must turn to the list of troops, i.e., those who were examined and taken by the army.

Those actually drafted had to have a certain level of physical fitness, and often had to meet a minimum height standard. While examining records in the military archive in Vienna, I noticed positive relationship between height and passing the fitness exam, (at least up to the tallest individuals, where the acceptance rate declined

101 somewhat). If this is part of a universal trend, one would expect an erosion of the lower tail of the sample height distributions, probably truncated at the minimum height. Ways of dealing with this problem will be discussed later.

Draft Laws in German, 1871-1914

When Germany was united in 1871, a universal draft law was put into effect.

Article 57 of the German Reich Constitution states that every German was obligated to serve in the military. Duty began following the twentieth birthday and lasted until the 27th birthday. The first three years are active service, and the last four years are spent in the reserves. In peacetime, the size of the military force was to be one percent of the population, calculated by state (Verfassung des Deutschen Reiches vom

16. April 1871 Abschnitt XI. Reichskriegswesen). A soldier generally served in the state in which he has his legal residence at the time of drafting. He can choose to serve in another state, but is then bound by this choice (Gesetz betreffend die

Verpflichtung zum Kriegsdienst, vom 9.Nov. 1869).

All twenty year olds had to show up at the local recruiting depot to be measured. There they were either declared fit, unconditionally unfit (due to severe illness or handicaps), or conditionally unfit (either too short or suffering from an illness in which recovery is likely). Those who fell into the latter category would be

102 103 examined again the following year. If they were still categorized as conditionally unfit, they could be called back a third year. But in the third year, a final decision had to be made regarding their fitness for military duty.

It was recognized that seven years of military service was a disadvantage to those trying to develop a career. There was another option open for those who could financially afford it. Educated young people who could feed, clothe, equip, and take care of themselves could volunteer for one year of active duty, after which they would be freed to the reserves. Often they could then become reserve officers.

The minimum height standard for service with weapons was set at 157.5 cm in

1871 and lowered to 157 cm in 1875. This was the height standard for the infantry.

However, those who were shorter than 1 m 62 cm could only be taken if they were of an especially strong physical constitution, and if there were not enough healthy candidates who were taller than 162 cm to meet the year’s quota of recruits.

Some categories of soldiers had special height requirements. For example, the members of the light calvary had to be between 162 and 172 cm tall, and those of the heavy calvary, between 167 and 178 cm tall, and those of the heavy calvary, between

167 and 178 cm. There was no minimum height for service without weapons (e.g., tailors, cooks, pharmacists) (Dienst-Vorschriften der Koeniglich Preussischen Armee, dritte Auflage, 1874).

In 1893, the minimum height standard for service with weapons was lowered to 154 cm. The absence of a minimum height standard for service without weapons continued ("Aenderung der Deutschen Wehrordnung vom 22.Nov., 188", in K.

Wuert. Kriegsministerium Verordnungsblatt No. 72. ll.Dez. 1893, section 31). 104

Primary Data Source

Although the universal draft law implied that the height of nearly every twenty

year old German male was recorded, the vast majority of this information was

destroyed during World War II. For example, almost all the records of those called

up and measured were lost. There may be copies of local recruitment lists located in

small town archives, but these are somewhat limited in their applications. For the

period of the German Reich, the only continuous data source that I am aware of is the

Friedenstammrollen —peacetime troop lists for the army in Wuerttemberg, Germany.

This information is only available for those who were taller than the minimum

standard and passed the physical exam. The recruitment lists for this period were

unfortunately destroyed.3

Until 1871, Wurttemberg was an independent kingdom. After it became part

of the German Reich, it still retained a fair amount of autonomy. However, due to

the military conventions of 21-25 November, 1870, the military regulations of the

German Reich held in Wurttemberg, too. Most soldiers drafted in Wurttemberg

served in Wurttemberg.

The data is located in the Military Archive, a branch of the Hauptstaatsarchiv in Stuttgart, the present day capital of the state of Baden-Wurttemberg, and formerly the capital of the Kingdom of Wurttemberg. Records are available on both the active troops and the reserves. To avoid repeats, I collected data only on the active troops.

3They are available for the earlier part of the 19th Century. Professor John Komlos (Ludwigs-Maximillian Universitat in Munich) has collected a sample. I look forward to the comparisons possible when his data is analyzed. 105

These records are called the Fricdenstammrolle since it was a time of peace. Records

are available for the period 1871-1914. However, the records for 1871, 1872, and

1914 are few and far between. Therefore, my data set includes primarily

observations from 1873-1913. This implies birth cohorts of 1851-1895, although few

observations for birth years 1851 and 1852 since the majority of troops enlisted in

1873 were bom in 1853.

The active troops were divided into several categories. In 1873, the active

troops numbered 12,688. The infantry was by far the largest group with 8.645

enlisted men (68.24% of the total). With 2,088 soldiers (16.48%), the calvary was

the second largest group. The artillery made up 12.13%, which includes 100

members of the train (those who drove the wagons). There were also 240 pioneers,

which were technical troops who built bridges and the like,and 158 in the

Armeezweige, mostly medical workers(Militaeratats 1870/73, Militaer Archiv band

E271C #205).

Sampling Procedure

Most of my sample was drawn from the Infantry. There are several reasons for this: it was the largest branch, it was most representative of the population since no special skills were required (as in the Pioneers) and although there was a minimum height standard, there was no maximum height (as in the case of the Calvary).

From 1873 to 1894, there were eight infantry regiments in the Wurttemberg army. From 1895 to 1913, there were ten. Unfortunately, all records for one of the 106

original eight regiments have been lost, leaving 7 and 9 regiments respectively. Most

infantry recruits served in the regiment nearest their residence and/or that of their

parents.

Infantry regiments were generally divided into 12 companies, each with 40-60

soldiers.Examination of the data reveals a strange height pattern across companies

within a given regiment. The average height in the first company is the greatest; it

declines through companies 2-4. It rises again in the 5th company and declines again

until the 9th company, where it rises a final time and declines through company 12.

This pattern could be due to the fact that each regiment had three commanders, each of which were in charge of four companies. It seems that each commander arranged his troops according to height.

This pattern necessitates sampling across companies as well as across regiments. I recorded observations for some regiments in the even-numbered years, and others in the odd. Thus all companies of all infantry regiments were sampled every two year period. The basic infantry sample size and composition is as follows:

Period 1873-1894 (7 Regiments)

8/CO : 2 1-yr. volunteers, 6 regular recruits

168 vol. + 504 reg. = 672 total / 2 year period

Total = 7,392

Period 1895-1913 (9 Regiments)

6/CO : 1 volunteer, 5 regular recruits

108 vol. + 540 reg. = 648 total / 2 year period 107

Total = 6,156

Planned Total for 41 year period = 13,548

The actual total was 12,516 since some of the record books were missing. If a

book for a given company in a given year is missing, I took the records from the

same company in an adjacent year. If the adjacent years’ books are also missing, I

left this space blank. Also, slightly more records were recorded for the years 1874,

1875, 1912, and 1913 (8 and 10 per company). These years comprised the initial

sample used for my proposal.

The Artillery and Calvary did not exhibit noticeable height patterns across companies. Therefore I drew records from one company for every year. I took 24 observations per year for the Artillery and 32 per year for the Calvary - half from the light Calvary (Dragonier) and half from the heavy Calvary (Ulanen). This process should yield 984 Artillery and 1,312 Calvary observations. 2,296 total. 2,283 observations were collected. Records for one Light Calvary regiment were missing for some years. I substituted from the other branch where possible; this upsets the regional balance of the Calvary.

The following information was collected for each soldier:

1. An identification code, consisting of regiment, company, soldier

number, and measurement year.

2. Birth date and place (town and Oberamt) (Wurttemberg was subdivided

into 64 Oberamte or counties). 108

3. Father’s occupation, codified (available for approximately 60% of

soldiers).

4. Soldier’s occupation, codified

5. Whether soldier is a one-year volunteer or regular recruit

6. Height, recorded to nearest half centimeter.

For approximately half of the soldiers, information on the soldier’s residence prior to

service entry and on his parents’ residence was recorded. For approximately twenty

percent, information on incidents of illness and injury during service and the length of

service were recorded.

The occupational code used consists of main general categories:

1. Upper white collar workers

2. Lower white collar workers

3. Skilled workers

4. Semi-skilled workers

5. Unskilled workers

6. Businessmen

7. Agriculture

Further subdivisions were made on the basis of occupational strength requirements and other characteristics. These subdivisions are listed in Tables 7.2.1 and 7.2.2, which will be discussed in the results chapter.

One difficulty with the occupational data is that the job descriptions in each category could be changing over time. For example, if a soldier’s listed occupation 109

was weaver, there is often no way of knowing whether he worked at home using

traditional methods, or in a factory with mechanized looms. Another dificulty is that

a large percentage of Wurttemberg’s male population worked in the commercial

sector, but farmed a small plot of land on the side. I believe most of these

individuals listed their commercial sector occupation as their main occupation, but

there is no way of checking this.

Before moving on to methodological considerations, it is necessary to check

the composition of the sample. Tables 5.1 and 5.2 show the composition of the

reduced sample used for the RSMLE and Komlos and Kim methods by soldier’s

occupation and region of birth in seven six-year birth cohorts.

In Table 5.1, one sees that upper class workers are somewhat overrepresented.

The percentage employed in agriculture is somewhat underestimated, since many

skilled, semi-skilled, and unskilled workers had a secondary occupation in agriculture.

The shifts in occupational composition are, however, not unreasonable. The percentage employed in agriculture is generally falling as would be expected.

In Table 5.2, the regional composition and its shifts reflect the population and migration trends in Wurttemberg. The Neckar Kreis with its net migration surplus is increasing its share of the sample. The Jagst Kreis with its large net migration losses is losing its share of the sample. CHAPTER VI

Methodology

6.1 Introduction

The main purpose of this study is to estimate and explain the height trends of the population of Wurttemberg and of its occupational and regional subgroups during industrialization in the second half of the 19th century. It is generally accepted that human heights tend to be normally distributed. The height distribution of soldiers, however, tend to be truncated and/or eroded at the lower tail. This is due to minimum height standards, as well as a negative correlation between stature and the rejection rate based on weakness or illness.

If minimum height laws were enforced and rejection rates of draftees in different height classes were similar, then the height distribution would resemble a truncated normal distribution. Otherwise, one would observe erosion of the lower tail.

Using regular regression techniques is not appropriate when truncation or erosion is present. OLS and MLE estimates of the means will be too large, the estimates of the variance will be too low, and the estimators of the parameters will be biased toward zero (Green, 1990).

110 Ill

This erosion of the lower tail is present in my data. See Figures 6.1 - 6.6.

Figure 6.1 - 6.3 show the height distribution of Infantry soldiers bom beween 1860

and 1864, with separate charts for one-year volunteers and regular recruits. Figures

6.4 shows the Artillery and Calvary. Figure 6.5 shows the total sample. Figure 6.6

shows the total sample for birth years 1885-90.

This chapter discusses three methods of dealing with a truncation or erosion

problem. The Quantile Bend Estimation (QBE) procedure involves estimating and

correcting for the amount of erosion in the observed sample. The Reduced Sample

Maximum Likelihood Estimation (RSMLE) converts an erosion problem into a clean

truncation by discarding observations in the contaminated lower tail. Standard

maximum likelihood techniques are then used to estimate the mean of a truncated

normal distribution. Komlos and Kim (1990) also truncate the sample above the extent of erosion. They use the simple mean of the reduced sample to investigate height trends. Each of these methods will be discussed in section 6.2. How these

methods were applied in my study is the subject of section 6.3.

6.2 Statistical Methods

Quantile Bend Estimation (QBE)

The QBE was developed by Trussel and Wachter (Trussel and Wachter, 1984;

Wachter, 1981; Trussel and Wachter, 1982). Its estimates of the mean and standard deviation are based on estimates of shortfall in the distribution. The amount of shortfall is the percentage of the height observations that are missing in the sample distribution due to erosion in the lower tail. The extent of shortfall refers to the 112

range of the sample height distribution contaminated by shortfall. Since human

heights are normally distributed, the sample distribution above the extent of shortfall

should conform to the upper ranges of a normal distribution.

The QBE is based on a graphic comparison of the quantiles (i.e., centiles,

deciles, etc.) of the sample distribution and of a standard normal distribution. When

the graph of these points sharing the same centile is straight, this indicates that the

sample conforms to a normal distribution in the height range where this occurs. If,

however, there is lower-tail erosion, the quantile plot bends.

The QBE procedure first estimates the amount of shortfall by finding that

value which produces the straightest quantile plot above the extent of shortfall. For a given shortfall guess, those missing are put back as shadow observations into the

sample, increasing the sample size. The centiles of the sample distribution are then calculated based on this augmented sample, and plotted against the standard normal.

A robust regression line is fitted to the quantile plot above the extent of shortfall.

The best shortfall guess minimizes the index of bending — the weighted mean squared residual of the last iteration of the robust regression (Wachter and Trussel, 1982).

Once the best guess of shortfall has been found, we can then calculate the centiles for the uncontaminated part of the distribution, including the 50th centile — the mean.

The slope of the robust regression line yields the standard deviation estimate.

Figure 6.7 provides a graphical example of the QBE method. It is Trussel and

Wachter’s application of the QBE to the heights of the British Royal Marines over age

21, 1750-59 (Wachter, 1981). A histogram of the sample distribution is on the left. 113

The quantile plot is on the right. Based on a shortfall estimate of .24 (i.e., 24% of the original sample was missing), the average height is estimated to be 65 inches -- the intercept of the quantile plot. The standard deviation is 2.5 inches -- the absolute value of slope of the quantile plot.

Reduced Sample Maximum Likelihood (RSMLE)

The RSMLE is based on standard maximum likelihood methods applied to truncated normal distributions. Suppose height (hj is normally distributed with mean and standard deviation. The likelihood function of this distribution truncated from below at point a is:

(1) l = - T 7 [ ------^------] i - * ( « zH ) o

where i = the number of observations

= standard normal p.d.f.

# = standard normal c.d.f. 114

If data are treated as discrete, the likelihood function can be adjusted. For

example, suppose heights are grouped into one centimeter cells. The joint likelihood

function would then be:

(2) - a J z R ) L=n[------—; o

where j = 1 - J

J = total number of height cells

nj = the number of individuals with height = j centimeters in the

sample

(Trussel and Wachter, 1984).

The log-likelihood function is then maximized using numerical techniques.

In applying the RSMLE to height distributions with eroded lower tails, a critical decision is choosing the point of truncation. There is a large trade-off involved. If the truncation point is too low and not above the extent of shortfall,

RSMLE estimates will be biased. The mean will be over-estimated and the standard deviation will be underestimated. On the other hand, as the truncation point is raised, progressively more information is being discarded. The standard errors of estimates increase steadily. Thus the precision of the estimates falls, and confidence intervals become broader, decreasing the power of the hypothesis testing. In theory, for truncation points above the extent of shortfall, the estimates of underlying parameters 115

should cease to change. In practice, since the uncontaminated part of the sample

distribution rarely conforms 100% to a normal distribution, such stability is not

always obvious.

One needs to develop a discard rule. These can be based on apriori

information such as minimum height standards or involves setting an upper limit or

acceptable standard errors. (Floud, et al., 1990), for example, set this limit at 0.5

cm.) Discard rules have been somewhat arbitrary. There is a need to develop a

method of estimating the proper discard point, based on the sample itself. While that

is beyond the present scope of this study, it is worthy of future research.

The RSMLE method can be used for truncated regression aimed at estimating

the covariates of heights. We assume that

(3) p-p'x.

Where x1 is a vector of covariates such as occupation or region in the case of heights

The regression is of the form:

(4) h ti=^'x^ei

The log likelihood function becomes:

(5) lnL=-— (In (27t) + lno2) (y^-p^) 2-Y' In [1-® ( a - A * * ) ] z 20 o

(Greene, 1990, p. 721). 116

If one wishes not to treat standard deviation as a constant but rather as determined by a vector Zi of covariates,

(6) = can also be substituted into the likelihood function (See Trussel and

Wachter, 1984).

If upper tail erosion is also present in a sample distribution, the likelihood function can be adapted accordingly. Of course, this involves discarding taller as well as shorter observations, thus increasing standard errors of estimates.

Komlos and Kim

Komlos and Kim (1990) developed an alternative method of estimating height trends. Assuming a constant standard deviation and a given truncation point, the mean of the truncated normal distribution is an increasing function of the mean of the underlying distribution. Thus, changes in the mean of the reduced sample move in the same directions as changes in the mean of the underlying population. This can be used to compare with height trends estimated via QBE or RSMLE.

To get back at the average population height and its standard deviation, they employ RSMLE & QBE estimates for a base year. 117

Statistical Methods

If the height distribution is a sharply truncated, normal distribution, the

RSMLE - based on standard likelihood maximization procedures — would generally

be the best choice. If, however, the lower tail is eroded, using the RSMLE will

involve throwing away often substantial amounts of information (i.e., all heights in

the lower eroded tail). The Quantile Bend Estimator is an alternative using all

information. Its drawback is its volatility, especially when sample sizes are small or

distributions have odd shapes. The Komlos and Kim method is less sensitive to

strange distributions, but also throws away a lot of information (like the RSMLE).

While it is difficult to go from the mean of the reduced sample back to the average population height, this method captures trend directions. It hinges, however, upon the assumption of a constant standard deviation, an assumption which does not always reflect reality.

6.3 My Analytical Procedures

In my study, I used the QBE and the RSMLE methods to estimate the average height of Wurttemberg’s adult male population bom between 1852 and 1893 and its regional and occupational subgroups. RSMLE truncated regression was performed, regressing height on occupational, regional, community size and temporal dummy variables. The Komlos and Kim method was used to check height trends of the population. An overview of my data analysis follows.

As mentioned in Chapter Five, all infantry regiments were sampled every two years. Thus, in slicing data into smaller temporal segments, periods with an even 118

number of years should be used. Since the periods are even-numbered, there is no

midbirth year per se. The midpoint of the period falls between two years. In graphs

and tables in this paper, the term "midbirth year" refers to January 1st of the second

middle year. For example, the period 1852-57 would be centered on January 1,

1855.

In the QBE analysis, I used two-, four-, six-, and even twelve year moving

averages. For the RSMLE, I divided the 42 year period of study into seven phases of

six years each. These choices were largely driven by sample size. In the case of the

RSMLE, I also wanted a phase to start in 1882, when a major shift towards the

commercial sector began (as discussed in Chapter Four).

Quantile Bend Estimation

The Quantile Bend Estimation of the mean and standard deviation of heights

was done on the infantry and the total data set using two- and six- year moving averages of birth cohorts. The infantry and total data were separated into draftees and one year volunteers -- an approximate rich/poor division for reasons explained in

Section 5.2. The draftees’ stature could be estimated with two year birth cohorts, although a smoother effect was achieved using larger periods. The smaller number of one year volunteers necessitated at least six birth years in a period.

I also ran QBE estimates with artillery and calvary soldiers counted twice and infantry soldiers counted once. Based on what information I could find in the military archive, my sampling rate of artillery and calvary soldiers is approximately half that of the infantry. Thus, doubling their weight should bring sample proportions closer 119

to army proportions. The limited number of years for which I located information on

sizes of army branches as well as the sometimes quirky small sample distributions or

the artillery and calvary give me some reservations about this weighting scheme. For

occupational and regional analysis, the observations from all three army branches

were counted equally.

The seven main occupational groups described in Chapter V were condensed

into three broad categories. The upper class is composed of upper and lower white

collar workers as well as businessmen (Categories 1,2, and 6). The working class

consists of skilled, semi-skilled, and unskilled workers (Categories 3,4, and 5).

Those employed in agriculture (Category 7) comprise the final broad occupational

category. Again, this grouping was largely driven by sample size requirements.

While there were many farmers and skilled workers, the other five of the original

seven occupational groups were not large enough to support reliable series of QBE height estimates.

The kingdom of Wurttemberg was divided into four administrative districts called Kreise: The Neckar Kreis, the Schwarzwald Kreis, the Donau Kreis, and the

Jagst Kreis. QBE estimates were made for each of the four regions, using four-year and six-year moving averages. Using these four regions eases comparison with other sources of information on Wurttemberg, particularly the reports of the Kingdom’s

Statistical Bureau. 120

RSMLE estimation was done using the Truncated Regression command in the

LIMDEP statistical package.4 Six year periods were used. The 42 year span of birth

years is thus subdivided into seven phases: 1852-57 through 1888-93.

Choosing the Discard Point

As mentioned earlier, a critical part of applying the RSMLE method to eroded height data is choosing the truncation point. In this study, the choice of 163 centimeters is based on a priori informatino on minimum height standards and on examination of the stability of average height estimates using different truncation points.

As mentioned in the data chapter, the highest minimum height standard was

157.5 cm; but those accepted who were under 162 centimeters had to be exceptionally strong and healthy. Thus it is reasonable to expect lower tail erosion through 162 centimeters at least in the earlier phases. The truncation point should be set at 162 centimeters or higher so as not to be biased.

Limit-testing was then performed on each of the seven phases. For each phase, the average height was estimated using seven truncation points, ranging from

162 cm to 165 cm, in half-centimeter intervals. Table 6.1 presents RSMLE limit testing results for the seven phases.

The trade-off involved in choosing a discard point for RSMLE is can be clearly seen in Table 6.1. As the truncation point rises and approaches or even passes

4This program treats heights as continuous. The soldiers are measured to the nearest half centimeter (1/5 inch). While there is some heaping on even centimeters, it is not enough to affect the results significantly. 121

the underlying population mean, standard errors of estimates increase quickly. In

Phase 1, for example, the standard error rises from 0.7 cm at a 162 cm discard

point to 1.279 cm at 165 cm. The standard errors in Phase 1 are higher than those in

the following phases because the height estimates are very near the truncation point.

When the estimated mean is actually below the truncation point, the RSMLE program

is working with less than half of a full distribution; the standard errors rise

dramatically.

On the other hand, raising the truncation point protects against bias in the

estimates. Once the truncation point is above the range contaminated by shortfall,

average height estimates tend to stabilize. However, when the truncation point is

above the estimated mean, the estimates become much more unreliable. Thus, if

possible, the truncation point needs to be above the contaminated ranges so as not to

be upwardly biased, but below the true population mean.

In view of the above considerations, a truncation point of 163 centimeters is

used throughout this study, both for RSMLE and Komlos and Kim’s method. This

choice was based on the apriori information that the lower tail was probably

contaminated by shortfall through 162 cm and on inspection of Table 6.1. The

estimates using a limit of 162 centimeters are greater than those using higher

truncation points in several phases. For example in Phase 1, the average height

etimate drops by over one centimeter when the limit is raised from 162 to 163 centimeters. By 163 centimeters, however, the estimates have stabilized in most phases. In some cases, such as Phase 4, the estimated mean falls at higher truncation 122

points. This occurs when the estimated mean is at or below the truncation point.

This causes the estimates to be somewhat unreliable and the standard errors too large.

I do not believe that the declining average height estimates imply continued

contamination in that range.

The slight heaping on even centimeters is evident. The half centimeter

truncation points often yield slightly higher mean height estimates than their even-

centimeter neighbors. I consider truncating on an even-centimeter to be more

reliable.

Thus, 163 centimeters will be used throughout this paper as the truncation

point. While it is true that some phases (e.g., phases 2 and 3) could support a lower

truncation point, I prefer to be consistent. Given all the considerations described

above, I believe 163 centimeters to be the best choice.

The Models

The models estimated by RSMLE basically consist of truncated regressions

(limit= 163 cm) of height on various sets of dummy variables. These include birth

period, soldier’s occupation, father’s occupation, region of birth, and size of

community of birth.

First, heights are regressed on each set of dummies separately. These results are used to describe height trends over time of the total population and its ocupational and regional subgroups. Then, the interaction between the categorical variables is explored by regressing height on combinations of dummy variables. With a foot tall stack of truncated regression results, a certain amount of selectivity is called for. In 123 this paper, only a fraction of these permutations of combinations will be presented.

Others are available upon request from the author.

Description of the Dummy Variables

1. Temporal

Using the total data set height is regressed on six dummy variables. The results are used to derive average height estimates for the seven six-year phases.

Individual estimates are also made for each phase separately, which allows for changing standard deviation.

Each six year phase was subdivided into three two-year periods. Regression was run by phase using two period dummies. The excluded class was the first two years of each phase. These results allow RSMLE height estimates in 21 two-year periods.

2. Occupational

Occupations are divided into the seven main categories as described in Chapter

V: upper white collar workers, lower white collar workers, businessmen, skilled, semi-skilled, and unskilled workers, and those employed in agriculture. For purposes of comparison with QBE results, these seven categories are condensed into three: the upper class (white collar workers and businessmen) the lower class (skilled, semi-, and unskilled workers), and agriculture. 124

First, truncated regression was run on each phase using the seven occupational categories. Height was regressed on six dummies for the soldiers occupation, then on six for the father’s occupation, and Finally on all twelve dummies. In each case the excluded class was agriculture.

Then, the process was repeated using the three broader occupational categories. Dummies were used for the upper class and the working class. The excluded class was again agriculture.

Regional

The four administrative districts Kreise — Neckar Kreis, Schwarzwald Kreis,

Donau Kreis, and Jagst Kreis — are the regions used. Regressions of height on three regional dummies are performed for each six-year phase. The excluded class is the

Donau Kreis.

Community Size

Communities were grouped into six categories based on population in 1895.

These categories are:

1) less than 2,000 residents

2) 2,000 - 4,999 residents

3) 5,000 - 9,999 residents

4) 10,000 - 19,999 residents

5) 20,000 - 49,999 residents

6) 50,000+ residents (Stuttgart) 125

Regression of heights on five community size dummies were run for each phase. The excluded class is by far the largest: communities with less than 2,000 residents in

1895. I also experimented with using population in 1895 as a continuous variable.

Age

While exact birth dates are known, I do not know the exact date upon which the height of the recruits was recorded. I do know the year in which a soldier started his service. Thus, in this study, age is simply the measurement year minus the birth year. The large majority of soldiers in the sample are twenty years old.

Age was not included in the majority of the regression models. Regressions of height on age find the coefficient to be negative. In other words, 20 year olds were taller than 22 year olds. This is due to the recruitment process. If a soldier was found to be too short, or unfit, when he first showed up at the recruiting depot at age

20, he was called back the following year. The process could be repeated the following year. In the third year, a final decision was made. Thus the tallest and healthiest soldiers were taken at age 20. The shorter and less healthy were taken at later ages, if at all. The sample data cannot indicate whether growth has stopped by age 20. It is likely, though, that it had not. Thus results probably underestimate the final adult heights somewhat. 126

Models

In summary, a model using all these variables together would have the

following form:

(7) hL^acrY phase} * E - V h ..REGION' E

Where: Dummies for

Phase, - Phase6 six year birth periods

soldier’s occupation

SOC, - SOC6 or seven categories or SOC, - SOC2 three categories

Father’s occupation FOC, - FOC6 seven categories FOC, - FOC2 three categories

REGION, - REGIONj Four regions

CS, - CS5 Six community size categories

Komlos and Kim

Trends of the simple mean of the reduced sample (heights greater than or equal to 163 cm) were examined for the total data, as well as its four regional and three occupational subgroups. Six-year moving averages were used, except for father’s occupation where 12-year cohorts were examined. SMRS trends of draftees and one-year volunteers were also examined. 127

Summary

Height trends are estimated for the overall male population of Wurttemberg as well as its occupational and regional subgroups. Four methods of analysis will be employed in this endeavor:

1) unadjusted average height

2) Quantile Bend Estimation (QBE)

3) Reduced Sample Maximum Likelihood (RSMLE)

4) Komlos and Kim: Simple Mean of the Reduced Sample (SMRS)

The results presented in the next chapter are organized by research question:

Overall height trends, occupational differences, regional differences, and community size differences. The results of different estimation techniques in each research area will be compared and contrasted. CHAPTER VII

Results

7.1 Overall Height Trends, 1852-1893

The results in this section are organized by method used: QBE, RSMLE, and

Komlos and Kim. Section 7.1.1 presents QBE average height estimates and compare

them with unadjusted average height trends. First height trends in the total sample

are estimated (birth cohorts of 1852-1893). Then the data is divided into one year

volunteers (generally upper class) and regular draftees. Height trends in the infantry

from which the bulk of the total sample was drawn were then estimated separately,

along with its one year volunteer and draftee subdivisions. The smaller sample from

the artillery and calvary is then discussed.

In section 7.1.2, RSMLE average height estimates for six and two year birth

periods (1852-1893) are presented.

In section 7.1.3, trends in the simple mean of the reduced sample are examined.

7.1.1 Unadjusted Averages and QBE Estimates

128 129

7.1.1.1 Total Data

Let us first take a look at the height trends of the raw data. Figure 7.1 shows

a four year moving average' of unadjusted average height between 1851 and 1892.

Note this overestimates the true population height since shortfall is not taken into

account. Nevertheless, it provides some point of comparison for the estimates to

come.

What one immediately notices is the extremely low stature in the early 1850s,

the unadjusted average is 2.5 centimeters below the stature of the late 1850s. Starting

in the 1860s and getting deeper in the 1870s there is a fall in average height. In the

late 1870s there is a fairly sharp rise through the mid 1880s. Then average height

makes another dip in the late 1880s and rises thereafter. The changes in heights are not very large after the 1860s. However, differences would tend to be underestimated, since shortfall and changing height standards are not being taken into account.

The quantile bend estimation technique was applied to this data. I ran the

QBE program on two year, four year, and six year moving averages of birth cohorts.

Table 7.1 shows the QBE output for two year birth cohorts, between 1852/53 and

1892/93. The table shows the QBE estimate of average height and the standard deviation for each two year period, as well as the shortfall, i.e., the portion of the sample missing due to erosion in the lower tail. The unadjusted average height is also

'The method used for Figure 7.1 is different from most graphs. The unadjusted average height was calculated for each birth year. The height plotted is the average of the four averages. JYR is the First of the four years. 130

given as a reference. Figure 7.2 compares the QBE estimates of height trends with

the unadjusted average height of the two year birth cohorts.6 The QBE estimates are

often far below the unadjusted average, especially in the 1852-1853 period where it is over Five centimeters lower. The QBE estimates seem somewhat volatile, with large differences between adjacent years. Most of this volatility is driven by different shortfall estimates. Figure 7.3 shows the QBE program’s shortfall estimates over time. The larger shortfall estimates also drive the differences in standard deviation estimates as shown in Figure 7.4.

If we increase to a four year moving average, the QBE estimator becomes less volatile. This can be seen in Figure 7.5. As Figures 7.6 and 7.7 show, the shortfall and standard deviation estimates are also somewhat less erratic.

Table 7.2 shows QBE results for four-year birth cohorts. The sample size ranges from nearly 1,200 up to over 1,600 for every four year period. The smaller sample sizes with the two year periods — generally between five and eight hundred -- do not seem to be quite large enough for the QBE program to perform optimally.

The trend towards stability continues if we use six-year moving averages.

QBE average height, shortfall, and standard deviation estimates using six-year birth cohorts are presented in Figure 7.8, 7.9, and 7.10. Table 7.3 shows QBE results for the six-year birth cohorts.

6On the x-axis of the time trend charts, mid-birth year refers to January 1 of the year right after the midpoint. For example, if the period is 1852 through 1855, the midbirth year refers to January 1 , 1854. 131

Comparing Figure 7.5 and 7.8, the general height patterns are similar. There

is a rise through the 1850s, followed by a small decline and another rise. Then a

much steeper fall begins in the late 1860s, early 1870s, and is at its lowest through

the mid 1870s. This is followed by a rise through the early 1880s and a second dip

in average heights in the late 1880s, followed by rise again.

In the rest of this chapter, the tables of QBE results (such as Tables 7.1 and

7.2), will not be included. The information from these tables will be presented

graphically in this chapter. Those tables are available from the author upon request.

7.1.1.2 Total Data, Draftees/One-Year Volunteers

Since there was a universal draft law in Germany at this time, all young men

age 20 and above could be drafted. However, those who were able to pass a written

exam and who had the financial resources to feed, clothe, and equip themselves could

volunteer for one year of active duty, instead of the usual two or three. Thereafter

they would go into the reserves. The division of the sample into regular draftees and

one year volunteers, is therefore, a rough upper class/lower class distinction.

Figure 7.11 shows QBE average height estimates for the draftees using two

year birth cohorts. The low stature in the early 1850s, as well as the dips in height in

the early to mid 1870s, and late 1880s are evident. There is also a quick dip in the

early to mid 1860s.

Figure 7.12 graphs QBE average height estimates using six-year moving averages. The larger samples smooth out the graph considerably. 132

The three low stature periods seen in the two year QBE results become more

pronounced. The short stature of the early 1850s (less than 164 cm) is matched by

the mid 1870s nadir of the first dip in heights. The downward trend begins in the late

1850s/early 1860s and accelerates in the late 1860s/early 1870s. The late 1870s saw

rising heights, followed by a second downturn in the 1880s. After the 1882-87 period, stature rose rapidly.

The unadjusted average height trend tells the same story, but dampened.

Heights rose briefly after the agricultural crisis of the early 1850s was over, but what

followed was a long period of gradual decline until the late 1870s. A second dip in heights occurred in the mid 1880s. Thereafter, heights rose.

Figure 7.12, showing draftees in six-year moving averages is very similar to that as 7.8 showing the total data in six-year moving averages. This is not surprising since draftees make up the bulk of the sample.

Figure 7.13 shows the QBE average heights of one year volunteers in six-year birth cohorts. They are considerably taller than the draftees, and their height trend was different. Until the late 1870s, average heights were generally increasing, although with some jaggedness in the trend. Starting in the early 1880s however, the trend seems to be downward, suggesting the welfare of the upper classes was falling at this time. We will check this with other occupational results in section 7.2. The sample sizes used in the six year moving averages of one year volunteers, tend to be 133 fairly small, between 300 and 400 observations per period. This is especially true before the 1857-62 period. Thus the earlier estimates are volatile and might be somewhat unreliable.

Since the RSMLE and Komlos and Kim methods both involve truncating all distributions at 163 centimeters, I decided to test the QBE program’s consistency by running it on the reduced samples (heights at 163 cm). Figure 7.14 shows the QBE height estimates thus obtained for one-year volunteers, draftees, and the total data set.

I was pleased by the similarity to the previous results. For the lower class, the double dip in heights in the 1870s and again in the 1880s is still evident, and of similar magnitude. The upper class is improving their net nutritional status until the late 1870s/early 1880s, when a decline began.

The rich/poor height differential (proxied here by the volunteer/draftee height differential) is at its smallest during the 1850s and 1860s. Starting with the 1865-70 cohort, the gap begins to grow. The height differentials in the 1870s and 1880s are approximately twice the earlier level. This could support Dumke’s (1988) claim of increasing income inequality in Germany in the 1871-1914 period.

Section 7.1.1.3 Infantry

The bulk of the total sample comes from the infantry, with slightly over 2,000 observations from the artillery and the calvary. There are some questions as to the proper mix between infantry and the other branches. Different height standards applied, making combination of observations from different branches somewhat tricky. Let us examine the height trends in the infantry. Figure 7.15 shows 134

unadjusted average heights of the infantry, in a four year moving average. The trend

resembles that seen for the total data in Figure 7.1, except that the double-dip in

heights seems a bit more pronounced. The methods used are identical.

Figure 7.16 shows QBE average height estimates for the infantry in a two year

moving average. Figure 7.17 shows infantry draftees, using two year birth cohorts.

The two diagrams generally resemble one another, both showing the double dip.

Figure 7.18 shows average height estimates for infantry volunteers, using a six year

moving average. The pattern is similar to that seen in Figure 7.13.

Section 7.1.1.4. Artillery and Calvary

Figure 7.19 shows the unadjusted average heights in four-year moving

average, for the observations for the artillery and calvary. The trend is different from

that seen for the infantry in Figure 7.15 although the methods are the same. There is a large fall in the unadjusted average heights, starting in the late 1850s, and reaching its low point in the early 1870s. Thereafter there is a rise through the early 1880s, and a fall again, in the late 1880s.

One thing I noticed about the artillery and calvary observations, is the very small number of one year volunteers, particularly in that middle period, where the very large dip in heights occurs. In fact, in the 1860s and 1870s, I do not think there were any one year volunteers. Whereas, in the early and mid 1850s there were a few. The sample size from these branches is considerably smaller than that from the 135

infantry, and therefore the height estimates are not quite as reliable as the infantry

estimates. Also, members of the calvary tended to be from rural areas, since

familiarity with horses was desirable.

The question of weighting was discussed in the previous chapter. I sampled

the calvary and artillery at approximately one-half the rate at which I sampled the

infantry. Therefore, doubling the weight of artillery and calvary observations should

bring the overall height estimates closer to the true proportions in the military. This

was tried. The results are presented in Figure 7.20. The surprising difference is how

much the shortfalls estimates decrease and thus the average height estimates increase

in the early 1850s, compared to Figure 7.8, which shows height trends for the total

unweighted sample. The estimate for 1852/57 increased by over two centimeters.

After the late 1850s however, the pattern is similar, except for a couple of estimates

in the 1860s and 1870s. The double dip trend in heights is still evident. The dip in

the late 1880s looks deeper.

Section 7.1.2 RSMLE, Average Height Estimates, Total Data

The RSMLE was run on 7 six-year phases, 1852-57, 1888-93. A clean

truncation point of 163 centimeters was chosen for reasons explained in the previous

chapter. Table 7.4 shows the estimated average height in each of the seven phases,

along with the standard error of the average height and standard deviation estimates.

This is compared with the quantile bend estimations of average height for the same

six year time periods. 136

Figure 7.21 compares the QBE and RSMLE average height estimates for the 7

six-year phases. Both estimators agree that heights were very low in the 1852-57

period, and rise in the period thereafter. From Phase II to Phase III the QBE shows

heights falling, whereas the RSMLE shows heights rising slightly. Between Phase III

and Phase IV (1864-69 and 1870-75), both estimators show a fall in heights of about

one centimeter. In the following phase, both show a rise in heights between one-half

and one centimeter. Thereafter the pictures diverge. The RSMLE shows a steady

rise in average heights, the QBE sees a second fall, or a second dip, in the period.

Thereafter, in the final period the QBE estimate is about a centimeter higher than the

RSMLE estimate.

In summary, the QBE and RSMLE results agree that times were very hard in

Phase I, 1852-57, and there was a decline in stature that reached its low point in the

1870-75 period. However, the RSMLE, using six-year periods, picks up a single dip

in heights; whereas, the QBE displays a double dip in heights, the second in the late

1880s. For both estimators heights were on the rise in the final phase, 1888-93.

« RSMLE estimates of average heights were then calculated for the three two-

year periods within each phase. This was done by running RSMLE truncated

regression for each phase, using two two-year dummies. The baseline was always the

first two years in each phase. Table 7.5 shows the RSMLE average height estimates

for two year birth cohorts, between 1852-53 and 1892-93. The relatively high truncation point of the RSMLE at 163 centimeters leads to fairly large standard errors of parameter estimates. The rise in heights from 160.56 cm in 1852/53 to 164.67 cm 137 in 1856/57 is significant at the one percent level, as is the increase in heights in the

1880-81 period. The fall in heights in 1884-85 compared to the previous period was significant at the 5% level.

Figure 7.22 graphically displays the average height estimates calculated in

Table 7.5. One sees a very large rise in heights in the 1850s. There is then a fall in heights through most of the 1870s, although with some rise in the middle. Heights then rise only to have a second major downturn, starting in the mid 1880s.

Thereafter heights generally rose.

If we compare this with Figure 7.8 showing the QBE average height estimates, using six year moving average, a general similarity is evident. Both are showing stature rising in the 1850s; both show falling heights in the 1870s, followed by a rise through the early 1880s, falling again in the mid 1880s, and rising in the late 1880s, early 1890s.

In both graphs the biggest dip is in the 1870s. It could be that birth cohorts from these years experienced a "double wammy." Not only were times bad, when they were infants, but about 15 years later when they were entering their second growth spurt, times were bad again. That could explain why their heights tend to be lower than heights of those bom during the second dip in stature.

Section 7.1.3 Komlos and Kim Estimates

All heights below 163 centimeters were discarded. The simple mean of the reduced sample was calculated for six-year moving averages of one-year volunteers, 138

draftees, and all soldiers. Please recall that the number thus produced is not an

estimate of average height. The method can, however, shed light on height trends.

Figure 7.23 shows the trends of the SMRS of volunteers, draftees, and the

total data. The trends for draftees and total resemble the QBE and RSMLE results,

except the changes are much less dramatic. The one-year volunteers are generally

improving their nutritional status, especially in the late 1870s and early 1880s.

7.2 Occupational Results

The results presented in this section are based on the fathers’ occupation

(available for approximately half of all observations) and the soldiers’ occupation

(available for nearly all 14,799 observations). Occupations were coded into seven

main categories, and often further subdivided according to characteristics such as

strength needed to perform the job.

7.2.1 Unadjusted Average Heights by Occupational Category

Table 7.6 lists the seven main occupational categories and their subdivisions

for the father’s occupation. The table lists the unadjusted average heights for each group for birth years 1852-73, 1874-95, as well as the entire 1852-1895 period. As

would be expected, the upper-white collar workers had the tallest sons, with the lower-white collar workers a strong second. These groups generally showed considerable increases in height in the early/late periods comparison. White collar professionals gained over 3 centimeters. The average heights continue to decline as we move from skilled to semi-skilled to unskilled workers. Notice that the Masters were taller than those who did not reach that status in the three subgroups comprising 139 skilled workers. The businessmen are a bit shorter than the lower white collar workers, but taller than skilled workers. In agriculture, soldiers whose fathers were large landowners were taller than those whose fathers were small farmers, although this difference decreases somewhat over time. The winegrowers’ sons were over two centimeters shorter than the small farmers, possibly due to the stunting effects of alcohol drinking during pregnancy. The shortest of all were the illegitimate children.

Table 7.7 shows average height by the soldiers’ occupational category. The pattern strongly resembles that seen in Table 7.6. The tallest group is the university students, followed by professionals, lower-white collar workers, and businessmen.

Skilled, semi-, and unskilled workers tend to be about 4 centimeters shorter. There are not major differences between these three groups, but the helpers of skilled workers are generally shorter than the skilled workers per se. In agriculture the large landowners are taller than the small farmers and winegrowers. The fishermen, foresters, and shepherds are also quite tall. The agricultural laborers are nearly the shortest group.

7.2.2 RSMLE Estimates of Average Height for Seven Occupational Groups

The height trends of the seven main occupational groups was estimated using

RSMLE methods. For each of the seven six-year phases, height was regressed on six occupational dummies, with agricultural workers as the excluded class.

Soldier’s Occupation, Seven Categories

Table 7.8 presents the results of RSMLE truncated regression of height on soldier’s occupation (7 categories) for seven six-year phases 1852/57 — 1888-93. 140

Table 7.9 presents the log-likelihood of the model based on the null hypothesis, where all occupational dummies are restricted to zero. Using Likelihood ratio tests (with six degrees of freedom), soldier’s occupation was significant at the .005% level in all seven phases. Thus, the hypothesis of significant height differences between occupational groups is supported.

In viewing Table 7.8 one sees that upper white collar workers and businessmen were taller than the baseline agricultural category in all seven phases.

These results are significant at the .005% level. Lower white collar workers were significantly taller than agricultural workers in all but the second phase, using a 10% significance level. The small sample size of the lower white collar workers yields larger standard errors of the estimate coefficients, reducing t-statistics and significance levels. Nevertheless, these results support the hypothesis that the upper classes were taller.

The skilled and semi-skilled workers tended to be shorter than those in the agricultural sector. Skilled workers were shorter than the baseline in all but the

1852/57 and 1882/87 periods, using a 1% significance level. Semiskilled workers were significantly shorter (10% level) than the baseline in five of seven phases. The small samples of unskilled workers make their estimated coefficients somewhat unreliable. Their height differs significantly from the baseline in only two phases. 141

The estimated average height was calculated by adding the estimated coefficients to the constant term (the average height of those in agriculture). Table

7.10 displays the average height estimates thus obtained for the seven occupation groups in the seven phases. This information is displayed graphically in Figure 7.27.

In Figure 7.24, the upper white collar workers are always the tallest group, with heights rising from 169.7 cm in the early 1850s, birth cohorts to over 172.5 cm in the 1880s, and early 1890s. The lower white collar workers and the businessmen are the next tallest groups, followed by those in agriculture. The unskilled workers were often taller than the skilled and semi-skilled workers. This could be partially due to occupational self-selection, i.e., those who are big and strong might choose a more manual type of labor where their strength would be an asset. Also, some of the daily laborers in this category could have been working in agriculture.

Father’s Occupation, Seven Categories

The father’s occupation has a more direct impact on the health and nutrition of the soldiers during the critical first five years. The subsample for which I have the father’s occupation is somewhat different than the overall sample. It seems to have more farmers and rural dwellers, especially from the Jagst region (where people were shorter).

Figure 7.25 shows the average height in the seven phases for the subsample for which fathers’ occupations were known. When compared with the RSMLE estimates of the trend of the whole sample (Figure 7.20) there are noticeable differences. Heights are much lower in the 1852-57 phase, rising thereafter. In 142

1870-75 and 1876-81, stature declines. In 1882-87 and 1888-93 it rises slightly. In

the total sample, the rise begins in the 1876-81 phase. Table 7.11 displays the

information used in deriving Figure 7.25

RSMLE truncated regression of heights on father’s occupation (7) was

performed on each of the seven phases. Table 7.12 presents the results. Agriculture

is the excluded class.

Log-likelihood tests (6 d.f.) were made for the joint significance of father’s

occupation for each phase. (Null hypothesis models’ log-likelihoods are in Table

7.11.) In all seven phases, significance levels of .005 were obtained. Thus, the

occupation of the father is a significant factor explaining heights.

The standard errors of the individual coefficients are larger than in Table 7.8

showing soldier’s occupational results — particularly in the first five phases, where the

reduced sample sizes range from 741 to 977 observations. The sons of skilled,

semiskilled, and unskilled workers are generally not significantly shorter or taller than

the agricultural baseline. The rich/poor differential, however, is still clear. The sons of upper white collar workers were significantly taller than the baseline in all phases at the 5% level, and in six at the 1% level. Estimated coefficients range from 2.86 to

7.22 cm. The coefficients rise rapidly between phase 2 and 7. Lower white collar workers’ sons were taller than the baseline in five phases (p = .10). Estimated coefficients range from 1.74 to 5.47 centimeters. The sons of business men were not significantly taller than the baseline until the last three phases. 143

Table 7.13 and Figure 7.26 display average height by fathers' occupations in

each of the seven phases. When viewing this figure, please keep in mind the

relatively small samples of each occupational category. Before discarding all

observations below 163 cm, there are many farmers and skilled workers (over 2,000

of each), but none of the other five categories has more than 800 observations.

Businessmen (6), lower white collar workers (2), and unskilled workers (5) all had

less than 500 observations each. After the distribution was truncated, the sample

sizes decreased even more. Thus a certain amount of caution should be used in

interpreting these results.

The basic pattern is similar to that observed for the soldiers’ occupations. The white collar workers tend to be the tallest. They and the businessmen were not hit as hard by the agricultural crisis of the early 1850s. The sons of businessmen were getting taller between 1852-57 and 1876-81. Their average height rose over 7 centimeters in this time, suggesting that business was good. The height trend of farmers’ sons is similar to the overall height trend of the subsample seen in Figure

7.25. The sons of skilled workers were generally taller than those of semi- and unskilled workers. All three groups saw a considerable rise in stature in 1858-63.

Sons of skilled workers were nearly 2 centimeters taller in 1864-69 whereas the height of the sons of unskilled and semiskilled workers fell. The average heights of the least skilled two groups are very close in all phases. Their stature rises in 1870-

75 and falls in the two phases that follow. Between 1876-81 and 1888-93, skilled workers and farmers’ sons show constant or slightly increasing stature. 144

7.2.3 Average Height by Three Occupational Categories, Soldier’s Occupation

7.2.3.1 QBE Estimates and Unadjusted Height Trends

In order to examine occupational stature trends over time using the QBE, it is

necessary to use broader occupational categories. The upper- and lower-white collar

workers are combined with businessmen to form the upper class category. Skilled,

semiskilled, and unskilled workers make up the working class. Agricultural workers

remain in the agriculture category.

Six year moving averages were used in examining the soldiers’ occupational categories. There are over 900 members of the working class in each of the 37 six- year birth cohorts, and at least 500 in the agricultural sector. The upper class has the smallest samples. The first three periods have 200-300 observations; thereafter * sample sizes are between 318 and 462.

Figure 7.27 shows the unadjusted average height by soldier’s occupational category. Even in the raw data where differentials tend to be underestimated, the relative tallness of the upper class is clear. Upper class heights always exceed working class heights by at least three centimeters. In the birth cohorts of the 1860s and 1870s, the differential increases, reaching over five and a half centimeters in

1876-81. Thereafter the tendency is towards convergence, with working class heights rising and upper class heights falling slightly.

Those working in agriculture are generally 2-4 centimeters shorter than the upper class, but about one centimeter taller than the working class - until the 1880s 145 when working class and agricultural heights begin to converge. The general pattern for the working class average height is of a shallow dip in heights. Heights are falling slightly during the 1860s and early 1870s, rising during the late 1870s and beyond with a slight decline in the mid 1880s. Agricultural workers follow a similar pattern, although the height rise in the late 1870s is larger, and the decline in the mid

1880s is deeper. Agricultural prices were plummeting in Wurttemberg in the 1880s.

Figure 7.28 shows the QBE estimates using six year moving averages for each occupational category. The QBE estimates resemble the unadjusted average height estimates in that the upper class is considerably taller than the farmers who are slightly taller than the working class. The story for the working class and the farmers is similar to before, with the exception of much shorter stature during the early 1850s for both groups, and a more pronounced dip in heights in the 1870s for the working class. The upper class has a stature decline during the late 1850s and 1860s, followed by rising heights in the 1870s and mid 1880s; thereafter heights fall again.

There seems to be convergence in stature in the late 1850s and early 1860s, followed by divergence through the 1870s. In the 1876-81 birth cohort, the difference between the upper class and working class estimated heights reaches its maximum of 8.36 centimeters. Thereafter, the tendency is towards somewhat uneven conversion. For 1888-93, the estimated differential is only 2.97 centimeters. 146

Komlos and Kim Estimates of Height Trends, Three Occupational Categories

The samples were truncated at 163 centimeters, and the simple mean of the reduced sample was calculated for the upper, working, and agricultural groups. Six- year moving averages were used. Figure 7.29 displays the trends of the average height of the reduced sample for the three groups.

The height rank of the three categories remains the same as QBE results: The upper class is considerably taller than those in agriculture, who are in turn taller than members of the working class. The SMRS of the upper class falls in the late 1850s and early 1860s, rises briefly in the mid 1860s, rises briefly in the mid 1860s, and falls again in the late 1860s and early 1870s. Beginning with the 1870-75 birth cohort, there is then a marked rise in heights peaking in the 1877-82 cohort. This is followed by a period of stagnation or slight decline.

The SMRS of those in agriculture rises in the 1850s, declines slightly in the

1860s, rises during the early 1870s, peaking in 1877-82, then falls over a centimeter by 1884-89. In the last decade, the SMRS rises again.

The SMRS of the working class is fairly steady with a shallow downward trend through the 1860s. A more noticable dip occurs in mid 1870s, followed by a marked rise in the SMRS for birth cohorts 1874-79 through 1879-84. A shallow dip in the mid to late 1880s birth cohorts is followed by stable and then slightly increasing SMRS.

The occupational height trends in Figure 7.29 can be compared with Figure

7.28, showing the corresponding QBE trends. There are definite similarities, 147

although changes in the SMRS are much smaller, as is to be expected. The QBE

picks up the double dip in working class heights more clearly than the SMRS. Also,

the QBE shows average heights of the upper class falling considerably after the early

1880s. The SMRS picks up a shallow dip in upper class height trends.

Note, the QBE estimates incorporate changing standard deviation estimates.

For the SMRS of different periods to be completely comparable, one must assume a

constant standard deviaiton for each occupational category. So, for example, if

standard deviations of the height distribution of the working class were rising during

the late 1850s and 1860s, the fairly constant SMRS results could be masking a

downward trend in true population heights.

RSMLE - Three Occupational Categories

Truncated regression of height on the soldier’s occupational categories (3) was

performed on each of the seven phases. As before, those employed in agriculture

were the excluded class. The regression results are shown in Table 7.14.

Likelihood Ratio Testing again indicates that soldiers occupational category does significantly contribute to observed heights in all phases (p .005). In all phases, the upper class was three to six centimeters taller than the agricultural baseline. With t-statistics ranging from 5.469 to 9.485, this finding is significant at the .01% level. 148

Working class heights were lower than the agricultural sector heights, usually

be 1-2 centimeters. This difference is significant at the 10% level for all seven

phases, and at the 1 % level for all but the first and sixth phase. These two phases

ocrresponded with bad times in agriculture, especially the 1852-57 period.

Using these regression results, I calculated the RSMLE average height

estimates of the three occupational categories in the seven six-year phases. Table

7.15 displays these RSMLE estimates along with the corresponding QBE estimates.

Also presented are the simple means of the reduced sample, as suggested by Komlos

and Kim (1990). The latter is not meant to be estimates of average heights; they are

strongly upwardly biased. Rather, they are indicative of trends. But I was surprised

by the extreme similarity of the results of the two last methods.

Figure 7.30 shows the RSMLE estimates of average heights by soldiers’

occupational category in the seven six-year phases. Figure 7.31 shows the QBE

estimates of the same. Figure 7.32 shows the height trends of the simple mean

reduced samples.

Figure 7.240 shows all three estimates of upper class heights. The RSMLE and QBE pictures are quite different. The RSMLE estimates, however, do run fairly parallel to the SMRS trends. That, plus the volatility of QBE estimates based on small sample size, makes me believe the RSMLE results more. Thus upper class heights rose through the 1876-81 period (with perhaps a small decline in 1870-75).

Thereafter, they remained fairly constant. 149

Figure 7.241 shows the three estimates of stature trends of those working in

agriculture. Here the RSMLE and QBE estimated trends match quite well. The net

nutrition of farmers bom in the early 1850s was quite poor; heights were less than

164 centimeters. In 1858-63, stature increased, followed by falling heights, reaching

the nadir in the 1870-75 period. 1876-81 were better years, reflected by increased

stature. But heights fell again in 1882-87 before rising in the final period.

Figure 7.242 compares the three estimates of working class height trends. The

match between the QBE and RSMLE estimates is fairly good, but not quite as good

as for agriculture workers. Both QBE and RSMLE show a dip in the height of the

working class. With the QBE, the decline begins in the late 1860s and continues

throughout the 1870s. Thereafter heights rise. With the RSMLE, the decline in

stature is only apparent in the 1870-75 period. Thereafter, heights increase, until the

very last phase where they decline by 0.04 centimeters (compared to 1882-87).

7.2.3.2 Father’s Occupation, Three Categories

The results obtained using father’s occupational category (3) do not differ radically from those using the soldier’s occupational category. For the QBE and

Komlos and Kim methods, 12-year moving averages were used. Figure 7.243 shows unadjusted average height trends. Figure 7.244 presents the QBE results. Figure

7.245 presents the trends in the simple mean of the reduced sample (SMRS).

RSMLE estimates were done using truncated regression for each six-year phase. The regression results are presented in Table 7.16. Again, likelihood ratio testing (2 d.f.) indicates joint significance of the coefficients at the 0.5% level for all 150 phases. The upper class is significantly taller than the agricultural baseline in all phases (p = .01 for all phases except the second). The sons of the working class were significantly shorter than the agricultural baseline only in the second and seventh phase. Their coefficients, however, were always negative.

7.2.4 Stature and Social Mobility

It has been observed in other studies that there is a positive correlation between stature and upward social mobility (Tannner, 1978, p. 148). In this study, the approximately 7,700 observations with complete father’s and soldier’s occupational information were used to explore this relationship. Both unadjusted averages and RSMLE techniques were employed. The results support the hypothesis that the upwardly socially mobile are taller than the socially immobile (i.e., sons in the same occupational class as their fathers), who are in turn taller than the downwardly socially mobile.

Table 7.17 shows the unadjusted average height by father’s and soldier’s occupational category. Although the differentials are underestimated, the positive relationship between upward social mobility and stature is clear. The tallest subgroup consists of fathers and soldiers who are both upper white collar workers (172.68 cm.). The downwardly socially mobile are shorter. For example, when the father is an upper white collar worker and the son is a lower white collar worker, the average height falls to 170.13 cm., and further to 168.46 if the son is a skilled worker. As 151

another example, when both father and son are skilled workers, the average height is

166.49 cm. If the son moves up to lower or upper white collar status, the average

height increases to 170.53 cm. and 171.58 cm. respectively.

The relationship was further explored using RSMLE truncated regression. The

reduced sample (height > = 163 cm.) contains 7,176 observations with completed

occupational information. Table 7.18 displays RSMLE truncated regression results of

height on a) father’s occupation, b) soldier’s occupation, and c) both father’s and

soldier’s occupation. In all three models, the excluded class is those working in

agriculture.

If there were no relationship between stature and social mobility, adding

soldier’s occupation into the model with father’s occupation would not increase the model’s explanatory power. However, log-likelihood testing with six degrees of freedom strongly indicate that adding soldier’s occupation increases the models explanatory power, with a significance level of .005%.

To specifically test whether the upwardly mobile are taller and the downwardly mobile are shorter, than those soldiers remaining in the same occupational category as their father, a different approach was employed. The occupational categories upper class (upper and lower white collar workers and businessmen), working class (skilled, semi-skilled, and unskilled workers) and agriculture were used. Only those observations where the father and the soldier were either upper or working class were 152

kept. If one or both was employed in agriculture, the observation was discarded.

This was done for clarity’s sake. For example, it is not obvious when the father is a

farmer and the soldier is a tailor, whether the son is socially mobile or not.

Two RSMLE truncated regressions were then run. The first uses observations

where the father is upper class. Height is regressed on a constant term and a dummy

variable which takes the value of one if the soldier is working class. The dummy

coefficient thus measures the effect of downward mobility.

In the second regression, only those observations where the father is working

class were used. Height was regressed on a constant term and a dummy variable

which takes the value of one when the soldier is upwardly socially mobile, i.e., upper

class. The dummy coefficient thus measures the effect of upward mobility. In both

regressions, the excluded class is the socially immobile.

Table 7.19 displays the results of these two regressions. In the first, the

coefficient for the downward mobility dummy is -4.89 cm and its t-statistic is -5.839.

In the second, the coefficient for the upward mobility dummy is 4.05 and its t-statistic

is 6.736. These results strongly support the hypotheses that the upwardly mobile are

taller and the downwardly mobile are shorter than those remaining in the same

occupational class as their fathers. Both these findings are significant at the .0001 %

level.

Table 7.20 shows the RSMLE average height by father and soldier’s occupational category. These were obtained by using the constant term for the immobile excluded class and adding the mobility coefficient to the constant term for 153

the socially mobile. The mobility effect is so strong that those soldiers bom into the

working class but who made it into the upper class are over three centimeters taller

than those bom into the upper class but are themselves working class.

7.3 Regional Results

The kingdom of Wurttemberg was divided into four administrative units call

Kreise: the Neckar Kreis in the Northwest, the Schwarzwald Kreis in the Southwest,

the Jagst Kreis in the Northeast, and the Donau Kreis in the Southeast. Stature trends

in these four regions are estimated by QBE and RSMLE methods.

Wurttemberg was further subdivided into 64 Oberamte or counties. Sample

sizes are too small to do reliable time series estimates of height by county. I did,

however, calculate the unadjusted average height by Oberamt for two periods:

birthyears 1852-73 and 1874-1893. This information on both periods is displayed in

Table 7.21.

Map 7.1 graphically depicts differences in the unadjusted average height by

Oberamt in the early period (birthyears 1852-73). Map 7.2 does the same for the late period (birth years 1874-93). Comparing the two maps, average height is higher in the large majority of the Oberamts in the later period. Especially in the later period, the taller areas are concentrated in the Neckar and Schwarzwald Kreis. Those in the eastern parts of the kingdom were generally shorter.

Height Trends in the Four Kreise

Six-year periods were used in analyzing stature trends by region. This brought the number of observations in each regional subsample to over 400. 154

Unadjusted Average Height

Figure 7.39 shows unadjusted average height trends by region. The differentials are underestimated, but even so, regional differences in height levels and trends are clear. Those bom in the Neckar Kreis are always the tallest; those bom in the Jagst Kreis are generally the shortest, particularly in the late 1850s and 1860s.

QBE

Figure 7.40 shows the QBE average height by region estimates. The estimates seem somewhat volatile, often changing by two centimeters between periods. Perhaps a larger sample size is needed. The Neckar still ranks among the tallest, and the

Jagst among the shortest. But the volatility of the graphs makes interpretation a bit difficult.

Komlos and Kim

Figure 7.41 shows trends in the simple mean of the reduced sample of each region. The trends resemble those seen in unadjusted average heights. Those bom in the Neckar Kreis are generally tallest, and those bom in the Jagst Kreis the shortest.

The double dip in stature in the late 1860s, 1870s and 1880s is most evident in the

Neckar and Donau regions. The Jagst and Schwarzwald Kreis had a dip in stature earlier, in the late 1850s, early 1860s. There is a regional height convergence in the late 1860s and early 1870s, followed by divergence. RSMLE

Truncated regression of heights on three regional dummies was performed for

each of the seven phases. Those bom in the Donau Kreis comprise the excluded

class. The results are presented in Table 7.22.

The joint significance of the regional variables in each phase was tested usinng

a Chi-squared distribution with three degrees of freedom. The Likelihood Ratio tests

did not reject the null hypotheses for phase 1 (birth years 1852-57) and phase 4 (birth

years 1870-75). For the other five phases, the null was rejected with a significance

level of 5% in phase 5 (1876-81) and 1/2% or 1% in phases 2,3,6, and 7.

Thus, regional height differentials were most pronounced in the birth cohorts

of 1858-1869 and 1882-1893. The agricultural crisis of the early 1850s seems to

have hit net nutrition across the Kingdom, leaving average heights in all regions

between 162.8 and 163.5 centimeters in 1852-57. The birthyears 1870-75 also

indicate regional convergence. These findings are consistent with the graphs

presented earlier in this chapter.

The regional differences do not, however, seem to have as much explanatory power as occupational differences. As can be seen in Table 7.23, only six out of the

21 estimated dummy coefficients are significant at the 5% level or higher.

Those bom in the Jagst Kreis are significantly shorter than the excluded Donau class in birth years 1858-1869 (Phases 2 and 3) and 1888-93. Those bom in the 156

Schwarzwald Kreis are significantly shorter in phase 3 (1864-69), and taller (at p =

.10) in phase 6 (1882-87). The Neckar Kreis always has positive coefficients; they

are significant at the 5% level in the last three phases (birth years 1876-93).

Table 7.23 displays RSMLE estimates of average height by region in seven

six-year phases, 1852-57 to 1888-93. The method of derivation was similar to that

used to derive heights by occupation (i.e., adding the estimated coefficients of the

regional dummies to the constant term). In this case, the Donau Kreis was the

baseline category.

Figure 7.42 graphically displays the RSMLE estimates of regional height

trends. Soldiers bom in the Neckar Kreis were tallest in all screening phases; those

bom in the Jagst Kreis were generally among the shortest. All four regions had their

shortest statues in the earliest phase, rising considerably in 1858-63. Thereafter, all

but the Schwarzwald Kreis experienced slight height gains through 1864-69. But in

1870-75, heights dropped considerably across the board, rising again in 1876-81. In

the last two phases, net nutrition in the Neckar and Donau Kreis is improving; that of

the Jagst Kreis is declining, then stagnating; that of the Schwarzwald Kreis rises

strongly to less than one half centimeter below the Neckar Kreis in 1882-87, and declines thereafter. 157

7.4 Community Size Results

The effects of community size on heights were investigated using RSMLE

truncated regression for each phase. In my data, exact birth town was recorded if the

population of the town was 1,850 or more in 1895. Otherwise, the word "rural" was

recorded.

First regressions of height on a continuous population (community size in

1895) variable. For those soldiers bom in communities with less than 1,850, the

population variable was assigned a value of 925 (half of 1,850).

The RSMLE truncated regression results of height on population in the 7

phases are presented in Table 7.24. Population’s estimated coefficient is positive and

significant at the 1 % level in all seven phases. The absolute size of population’s

coefficient is considerably smaller in the last two phases (birth years 1882-1893) than

in the first five. Partially, this is due to a rising constant term.

The second mode of analysis uses six categories of community size:

less than 2,000 residents (rural)

2,000 - 4,999 residents (CS2000)

5,000 - 9,999 residents (CS5000)

10,000 - 19,999 residents (CS10000)

20,000 - 49,000 residents (CS20000)

50,000+ residents (Stuttgart) (CS50000)

Height differences between these six categories of community size were explored by phase using unadjusted averages and RSMLE truncated regression results. 158

Table 7.25 and Figure 7.43 the unadjusted average height by community size

category for each of the seven phases. As community size increases, unadjusted

average heights generally rise in all seven phases. Those bom in communities with

less than 2,000 are the shortest in all but the first phase. Those bom in Stuttgart are

the tallest through the first four phases. But average heights in Stuttgart decline in the

last three phases, while the other categories generally maintained or increased stature.

Those bom in cities with 20,000 - 49,999 residents (Cannstatt, Esslingen, Heilbronn,

and Ulm) became the tallest group in the final two phases.

For all categories, unadjusted average height falls in Phase 4 (birth years

1870-75). In the following phase, stature increased in all categories except Stuttgart.

Possibly the negative effects or urbanization began to increase as Stuttgart’s

population grew. The largest height gains were for those bom in communities with

5,000 - 49,000 residents.

Table 7.26 shows the truncated regression results of height on community size dummies for the seven phases. The excluded class is those bom in communities with less than 2,000 residents.

The positive relationship between stature and community size seen using unadjusted averages is supported by the truncated regression results. Of 35 "urban" community size coefficients (across 7 phases), 32 are positive. The coefficient for

Stuttgart (CS50000) is significant at the 5 % level or above in all phases, and at the 159

1% level in all but Phase 6. In the last three phases (birth years 1876-1893), those

bom in communities of 10,000 - 49,999 residents (in 1895) are significantly taller

than the rural baseline (p = .005).

The RSMLE estimates of average height by birth community size in the seven

phases were obtained by adding the estimated dummy coefficients to the rural constant

term. The results are presented in Table 7.27 and Figure 7.44.

Urban-Rural Differences

In the third mode of analysis, birth communities are divided into rural and

urban groups, where urban includes communities with 2,000 or more residents in

1895. The height trends and urban-rural differences were examined using unadjusted

averages, trends in the average height of the reduced sample, as well as RSMLE and

QBE average height estimates.

Figure 7.45 shows unadjusted average height trends in urban and rural birth

towns. The urban-born are clearly taller than the rural-born in all six-year birth cohorts. The (underestimated) urban-rural height differential is smallest for birth cohorts of the 1850s and early 1860s. The gap widens for those bom in the 1860s and 1870s, reaching its maximum in the late 1870s. Thereafter the differential narrows, approximately to the level in the 1860s.

The trend in both groups is irregularly downward through the 1870-75 cohort.

This is the nadir for the urban-born. Thereafter, stature of the urban-born rises considerably through the 1878-83 birth cohort, followed by a sharp decline, reaching its nadir in 1881-86. Thereafter, heights haltingly recover, reaching the levels of the 160

late 1870s by 1888-93. On the other hand, the stature of the rural-born continues its

gradual decline through 1873-78. Thereafter heights rise rapidly through the 1880-85

birth cohort. A brief dip for mid-1880s birth cohorts is followed by height gains for

the late 1880s and early 1890s cohorts. In many respects, the height trends of the

rural-born reflect those of the urban-born with a bit of a time lag.

Figure 7.46 depicts trends in the average height of the reduced sample (height

t. 163 cm.) for the urban and rural categories. The story does not differ substantially

from the one told before. Stature trends are positive for both groups in the early

1850s. The urban bom are taller. The double dip in the late 1860s/early 1870s and

in the early 1880s are apparent. Rural heights stagnate or decline through the 1870s,

rise in the early 1880s, fall slightly in the mid 1880s, and recover thereafter.

RSMLE

To obtain RSMLE average height estimates for the urban and rural bom, truncated regression of height on a constant term and an urban dummy was performed for the seven phases. The rural-born comprise the excluded class.

Table 7.28 summarizes these regression results. The coefficient or the urban dummy is significant in all seven phases. The standard errors in Phase 1 (Birth years

1852-57) are quite high since the truncation point is high relative to the underlying population mean. Thus, the significance level is 10%. In the second phase, the level is 1%. In the last five phases, the level is .001%. From 1852/57 through 1876/81, the estimated urban height advantage increases from 1.38 to 3.18 centimeters. In the 161

1882-87 birth cohort, the gap falls to 1.75 centimeters, reflecting rising rural and

falling urban heights. The gap widens to 2.29 centimeters in the final cohort, as

urban heights rise again and rural heights stagnate.

RSMLE average height estimates for the urban-born were derived by adding the urban dummy coefficient to the constant term (rural average height). The

RSMLE average height estimates by phase are shown in Table 7.29, and graphically displayed in Figure 7.47.

The RSMLE trend agreed fairly well with the unadjusted average and the average of the reduced sample trends. The urban-born are taller in all seven phases, with the gap widening through 1876-81. Thereafter it narrows somewhat. The major difference is that the RSMLE method shows rural heights rising for the 1864-69 birth cohort. The unadjusted and reduced sample trends show heights falling.

QBE

Quantile Bend Estimation of urban and rural heights was performed using six- year moving averages. The trends are graphically displayed in Figure 7.48. While the trends in the last twenty birth years resemble those produced by the other methods, the QBE results for the first twenty years differ significantly. The QBE method shows the urban-born to generally be taller than the rural-born until the 1868-

73 birth cohort.

To check the robustness of this result, the sample was truncated at 163 centimeters and the QBE program run using the reduced sample. The results are 162

displayed in Figure 7.49. The stability of the QBE estimates is considerable. The

major difference is the estimated height of urban dwellers is lower in all birth cohorts

through 1865-70.

The QBE rural height estimates are considerably higher than RSMLE estimates

in the 1860s. The reverse is true for urban heights. As Table 7.30 shows, QBE

estimates are driven by shortfall estimates. For the birth cohorts of 1855/59 through

1867-72, the shortfall for the rural-born is six percent or less. For the urban-born,

the shortfall in this period ranges from 6 to 34 percent. Between 1859/64 and

1864/69 it is always over 21 percent.

It is not completely clear what drives these different results. The individual birth cohort height distributions need to be examined for all subcategories to see if some odd distributions are distorting the QBE results. Also, the relatively small sample size of the urban-born in the earlier phases could be affecting the estimates.

For now, the similarity between the unadjusted, reduced sample, and RSMLE trends provides the strongest argument. It seems likely that the urban-born were taller than the rural-born throughout the second half of the nineteenth century, and not just after 1868/73. However, this subject is not resolved with 100% clarity.

7.5 Combinations of Occupational Regional, and Community Size Dummy Variables

In the course of the last two months, dozens of alternative specifications were estimated. To present all these results in this dissertation is unfeasible. In this 163

section, regression results on three revealing models will be presented: Model A

regresses height on community size and regional dummies. The excluded class was bom in the Donau Kreis in a community with less than 2,000 residents.

Model B regresses height on soldier’s occupational category (7) and community size. The excluded class is employed in agriculture and were bom in communities of less than 2,000 residents.

Model C regresses height on soldier’s occupational category, community size, and region. The excluded class is employed in agriculture and bom in the Donau

Kreis in a community of less than 2,000 residents.

Table 7.31 presents the RSMLE truncated regression results for the three models in the seven phases. In making comparisons between models, keep in mind that the excluded class is different in each model. For example, in the Donau Kreis, land holdings ere larger. Comparing models A and C, adding the characteristic farmer to the excluded class raises the estimated constant term by 0.5 to

2 centimeters.

The soldiers’ occupational category was used in models B and C. The upper white collar workers, lower white collar workers, and businessmen were significantly taller than the agricultural baseline in both models and in all phases at the 5 % level or better. Thus, the previous finding that the upper classes were taller was strongly supported. Skilled workers tended to be shorter than agriculture workers. This result was significant at the 5% level or better in both models in phases 2,3,4, and 7, and in model C in phase 5. In phases 1 and 6, the height difference was not significant. 164

Regional results lose some of their robustness when combined with other

factors. As before, no regional coefficients were significant in phases 1 and 4.

Those bom in the Jagst Kreis were significantly shorter than the Donau (et al)

baseline in phases 2 and 3 (p=.05). The Neckar Kreis and Schwarzwald Kreis also

had significant negative coefficients (p=.10) in phase 3. Those bom in the

Schwarzwald Kreis emerge as significantly taller than the baseline in phase 5, 6, and

7. Those bom in the Neckar Kreis are significantly taller only in phase 6, birth years

1882-87.

The community size results also lose some of their robustness when combined

with other independent variables. Not many individual coefficients are significant. In

the first five phases, those bom in Stuttgart (CS50000) are significantly taller than rural dwellers. In phase 6, however, the positive coefficient is too small to be significant. In phase 7, it is negative but insignificant. In the last three phases, the positive coefficients of other urban categories become significant, particularly those bom in cities with 20,000 - 49,999 residents.

7.6 Summary

In this chapter Unadjusted Average Heights, Quantile Bend Estimation,

Reduced Sample Maximum, Likelihood Estimation, and the Komlos and Kim methods were used. Overall height trends, trends by military branch and status, and trends within occupational, regional, and community size subgroup were presented.

In the next chapter, the height results will be interpreted within the historical context of Wurttemberg. CHAPTER VIII

Discussion

Introduction

This chapter begins with an overview of the main findings of this study. The

fate of the hypotheses concerning overall height trends as well as occupational,

regional, and community size height trends and differentials. An attempt is then

made to interpret the results in light of the historical context. Height trends are

compared with other indicators of health and well-being such as wages, infant

mortality, overall mortality and life expectancy.

8.1 Main Findings

The question which triggered this entire study was this: Does Germany fit the

pattern of falling lower class heights during industrialization observed in England and

the United States? The results support this hypothesis. In fact, there seems to be a double dip in stature of the lower class during industrialization in Germany. This is perhaps most clearly seen in Figure 7.12 showing QBE average heights of draftees.

(Recall, those who had the necessary education and financial means usually chose to be one-year volunteers.) The first dip begins in the 1860s and reaches its peak in the

1872-77 birth cohort, having fallen about three centimeters in ten years.

165 166

Stature fully recovers by 1878-1883, only to fall about two centimeters by 1882-1887.

Thereafter the trend is strongly positive.

The second group of hypotheses address occupational differences in height

levels and trends. Dummy variables representing 3 and 7categories of father’s and

soldier’s occupation were found to be jointly significant at the 1 % level in all seven

periods of analysis. The upper class is significantly taller than the working class and

those employed in agriculture in all phases. That richer people are taller agrees with

dozens of other studies (See Eveelyth and Tanner [1992] for examples.) Moreover,

the rich/poor height differential increases during early industrializatin in

Wurttemberg. This agrees with Dumke’s assertion that income inequality rose in

Germany 1871-1913. Towards the end of the 19th century, however, the trend is towards convergence.

The third hypothesis that my results strongly support is that the upwardly

mobile are taller than their immobile or downwardly mobile counteiparts. This finding supports Tanner’s (1978) claim of the same.

The hypothesis of regional differences in average height (using four regions) was supported in five of the seven phases. The hard times of the early 1850s and

1870s wre apparently felt throughout the Kingdom, and regional differences were too small to be significant. Particularly later in the overall period, those bom in the

Neckar Kreis emerge as significantly taller than the other regions. Jagst Kreis generally produced the shortest soldiers. 167

Height trends as well as levels differ among regions. The Schwarzwald and

Jagst Kreis experienced a dip in heights in the 1860s and were recovering as the

Neckar and Donau Kreis heights were falling in the 1870s.

The last main research area dealt with height differentials by community size.

The hypothesis that urban dwellers were shorter than rural residents in the second half

of the 19th century in Germany was not supported by that data. Those bom in

communities of less than 2,000 residents were shorter than those in larger

communities. When height was regressed on the population of the community of

birth (treated as a continuous variable), the coefficient was highly significant in all

seven phases.

8.2 Height Trends in Historical Context

In this section, the height trends described in Chapter VII will be discussed in light of the historical information presented in Chapters III and IV. Information on other health and welfare indicators such as mortality rates and life expectancies in

Wurttemberg will be compared with observed height trends.

In the late 1840s and early 1850s, there was a severe agricultural crisis in

Wurttemberg. Farmers and craftsmen/part-time farmers lost their main or second source of income. Craftsmen who were farmers on the side, shifted their labor away from the failed agricultural sector into handicraft. The supply of handiwork products increased as did competition from factories for local markets. This combined with decreased local demand due to the agricultural crisis, pushed the price of manufactured products down. The traditional small craftsmen competing with 168 mechanized factories saw their income driven to subsistence levels. In the early

1850s, the craftsmen transition crisis reached its highpoint.

Between 1840 and 1854, the number of bankruptcies increased 80% from

1,062 to 8,813 (Megerle, p. 232). As Table 4.22 shows, nearly half the bankruptcies were small craftsmen. The second largest group hit were the small farmers.

The hard times of the early 1850s are reflected in average heights. Most graphs in this study begin with the 1852-57 birth cohort. Since this period includes the post-agricultural crisis years of 1856 and 1857, the average height underestimates the fall in heights during the late 1840s and early 1850s.

Nevertheless, the poor net nutrition of the times is clear. The RSMLE estimates of average heights of the farmers and of the working class are 163.75 and

162.49 centimeters shorter than in any other phase. The upper class is over five centimeters taller than the other two groups.

After 1854-55, harvests improved greatly. This is reflected in the rapid rise in stature in the total data, and particularly in the agricultural and working class. The

RSMLE average height estimates for the farmers and the working class bom in 1858-

63 are 166.57 and 164.81 centimeters respectively - gains of two to three centimeters. QBE comparisons indicate the stature of these two groups rising by 3 to

4.5 centimeters. 169

The late 1850s were boom years for Wurttemberg’s commercial sector. The

expansionary phase continued into the 1870s. Between 1852 and 1861 the percentage

of adult males employed in the commercial sector rose from 40.53% to 45.81 %.

Between 1861 and 1875 the increase was smaller, to 47.78%.

The boom in the late 1850s was due to the increased willingness to work in the

factories. The increased supply of labor caused wages in Wurttemberg to fall

somewhat. In the 1820s through the 1850s the wage in Wurttemberg had been higher

than the German average; by 1855 it was approximately equal. Once the situation in

the agricultural sector improved wages began to rise, and in the 1860 to 1885 period

the Wurttemberg wages again were above the German average.

So after the mid 1850s, due to the better harvest, factory wages in

Wurttemberg increased. Also factories started to export more, due to the beginnings of railway construction. This meant less local competition for the independent craftsmen. Their situation stabilized somewhat.

During the early 1850s there was a massive outflow of migrants seeking to get away from the bad situation. Between 1850 and 1855, 7.7% of Wurttemberg’s population emigrated. For those who stayed behind this was somewhat advantageous, since the average size of land holdings increased. This may have also contributed to the rising and then stable height pattern observed for the working class in those employed in the agricultural sector in the late 1850s, and the 1860s.

In Chapter III, unification of Germany into the Deuches Reich (German

Empire) occurred in 1871. It followed on the heels of the Franco-Prussian War. 170

Indemnity payments from France contributed to high inflation in the years 1872 and

1873. There was a speculation boom and many shaky investments were made. A

very optimistic spirit prevailed.

The crash came in 1873, with shares plummeting first in Austria, and then in

Germany. There were many bankruptcies. Industrial output prices, sales, and wages

fell, while unemployment rose. Not until 1876 did Germany again reach the level of

industrial output it had in 1872 (Henderson, p. 159-170).

As can be seen in Figure 7.12, showing six-year moving averages of total

draftees’ heights over time, the crash in the mid 1870s corresponds with the nadir of

a large dip in heights. The downward trend in draftees’ heights actually began in the

1860s but accelerated rapidly once the year 1873 and 1874 become part of the moving

averages. According to QBE estimates, between the birth cohorts of 1864-69 and

1872-77, average lower class heights fell by nearly three centimeters; thereafter

heights rise through the birth cohort of 1879-84. The unadjusted average heights also

shows falling heights in this period, although not as markedly as the QBE estimates.

In viewing the QBE estimates of average height by soldier occupational category presented in Figure 7.28, it is clear that the working class had experienced the largest height declines in the early and mid 1870s. The farmers also had a dip in heights in the mid 1870s as seen in Figure 7.242. Using the RSMLE method confirms the pattern of falling heights, reaching its low point in the 1870-75 birth cohort. The simple mean of reduced sample shows a very slight downward bending 171

period, but as explained earlier the standard deviations were increasing at the time of

the crash. The true population height could be falling, but the simple mean of the

reduced sample would not pick that up.

As can be seen in Figure 7.241, the QBE and RSMLE agree that the welfare

of farmers fell in this period and partially recovered in the late 1870s.

As Figure 7.24 indicates the results on the upper class are mixed. The

RSMLE shows a fallen average height, whereas the QBE and SMRS (Simple Mean of

the Reduced Sample) indicate height is rising in the period. The three indicators also do not agree on what happens in the final three phases. The RSMLE and Komlos and

Kim method show relatively stable stature, whereas the QBE shows the stature of the upper class falling. Here it would be useful to have some sort of independent data source to confirm which pattern is the true one. Without such a data source, however, I would place my bets on the RSMLE since parallels the SMRS trends fairly closely and since the small sample sizes of upper class workers cause QBE estimates to be somewhat unreliable. However, one cannot say that the issue of nutritional status of the upper class during industrialization in Wurttemberg has been completely and clearly answered.

In the 1870s and early 1880s industrial growth was not particularly rapid in

Wurttemberg. While its percentage of population employed in the commercial sector had been higher than the German average until then, in 1882 it fell below the German average. But after 1882 it began to rise significantly, catching up with the average. 172

Until the 1870s Wurttemberg's supply of agricultural products came primarily

from domestic farmers. However, in the 1870s and 1880s, improved railway

connections with the rest of the world brought cheap imported grain to the Kingdom.

The price of grain started to fall in the 1870s and in the 1880s. This eroded the

importance of the secondary occupation in agriculture. It is likely that this helped

spur a more rapid industrialization seen after 1882, since agriculture was no longer as

attractive an option.

In the last quarter of the 19th century the price of agriculture fell in

Wurttemberg were wages were constant or possibly rising. The price of

manufactured goods fell. The small craftsmen and the small farmers were both hit by this development. Those hit hardest were the ones employed in both sectors, since the price of their products were falling and their labor costs were not. The farmers were certainly hit by this development as can be seen by the large dip in heights in the 1882-87 period.

The 1880s were rather difficult. There were stories of increased number of the poor needing alms, and much social distress. However, there is little quantitative information on the welfare of the people of Wurttemberg. The height results for the total data show, heights are falling in the mid 1880s, and then they recover.

Thereafter the heights of the working class and of farmers, generally are improving or constant. This second dip in heights of the lower classes can be seen most clearly in 173

the height trends of the draftees. Recall the draftees consist primarily of craftsmen,

semi and unskilled workers and small farmers. It is showing a big fall in heights at

that time.

Throughout the 1870s and 1880s the small craftsmen were eventually being

phased out. The amount of competition depended upon export opportunities of local

factories and sometimes things were better than other times. Their welfare was not

rapidly improving in this period. The height data indicates that the transition crisis

exacted its toll, although some workers were probably improving their welfare during

this time.

The rich/poor differential tended to be smallest in the 1850s and 1860s, but in the late 1860s it begins to grow, suggesting rising income and equality in

Wurttemberg in the second half of the 19th century.

According to Megerle, in 1885 to 1913 wages in Wurttemberg had fallen below the German average (Megerly, 1982). This is probably in part due to the low price of agricultural goods, which implied lower opportunity cost of working in industry. The increase in labor supply for factories caused factory wages to fall.

This in turn spurred the growth of the commercial sector seen after 1882. Wages in industry still had to be higher than those in agriculture to entice the workers, otherwise potential migrants to cities often preferred just to migrate out of the country. In fact as we will see there was a large emigration in the mid 1880s, corresponding with the time of falling heights. 174

8.3 Comparison with Other Indicators of Living Standards

Estimates of Real Gross Wages and Real Per Capita Income in Germany

In Chapter III, the development of real gross wages, real per capita income, and cost of living estimates from 1871 to 1913 were discussed. The information is presented graphically in Figures 3.6 and 3.7. The graphs begin with 1871, therefore it is difficult to know whether the low per capita income in 1871 was the nadir of a fall in per capita income for that period. Nevertheless we can compare these results with the second half of this study’s timeframe. Figure 3.7 shows three cost of living estimates. Prices rose rapidly in the early 1870s and then fell during the late 1870s, rising again in the early 1880s, falling some in the late 1880s, rising slightly in the

1890s, and falling in 1895, the end of our survey period. Thereafter prices rose. In

Figure 3.3, the development of real gross wages, three of which are based on cost of living indices in Figure 3.4 are shown, along with real per capita income. All four estimates of real wages fall somewhat in the early 1880s. This is especially pronounced in Bry’s estimate, showing a large fall in the early 1880s. Some of the other one’s, however, did not show such a dramatic decline. Real per capita income declined somewhat in the late 1870s, early 1880s, but generally the trend is on the rise, with a few bumps along the way.

A second indicator used in Chapter III was Hoffman’s Indicator of Net

Nutrition. Table 3.26 showed changes in this index, which is the ratio of calories actually consumed over the calories needed in the whole German population. The method used was discussed in the text. In 1850/54 only 75% of the population’s 175

caloric needs were being met. This rose to 80% in 1855/59 and was between 89 and

90% between 1860 and 1874. So there was a 15 year period of stagnation. It rose to

100% in 1875/79, a time at which average heights were rising, and fell down to 95%

in 1880/84, a time when heights were falling in Wurttemberg. Starting in the

1885/89 period the index begins to rise, reaching 124% at the turn of the century.

Thereafter it falls somewhat and levels off.

The low Hoffman index, corresponds to the low average heights estimates for

the early 1850s. The increases in the late 1850s are also similar. Thereafter the

Hoffman index remains fairly constant, whereas the height data is showing a fall in

the mid 1870s. That could be reconciled if the distribution of food became more

inequal at that time. (I discussed this topic in Section 3.6.) The rise in the late 1870s

and the fall in the early 1880s also correspond quite well to the height data, so overall

Hoffman’s Index of Net Nutrition agrees with the story told by average height trends.

Emigration from Germany and Wurttemberg

The pattern of emigration rates of time in Wurttemberg reflects that in

Germany. Table 3.7 shows the overseas emigration from Germany as a whole by

decade. The most emigration took place in the 1880s, followed closely by the decade of the 1850s. The 1860s and 1870s were also fairly high.

Table 4.19 shows a loss of population due to emigration from Wurttemberg in

12 phases between 1816 and 1900. It shows the total in the per year emigration loss.

The period with the highest rates was 1847-55, the time of the agricultural crisis and the transition crisis, beginnings of industrial growth. Wurttemberg lost 18,000 people 176 per year. The rates remain fairly high, with the exception of 1874-79 where only

1,420 left per year. In 1880-84 the annual rate of emigration jumped dramatically to

9,540, falling to just below 6,000 in the following period, 1885-93. While the time periods used in the two tables are not identical, the similarity in emigration patterns over time is clear.

The two periods of highest emigration, the 1847-55, and 1880-84, both correspond to low average heights in this study. The emigration rate in the early

1870s - 4,970 per year — are substantial, but not exceptionally so. Emigration rates are falling between 1885 and 1900, corresponding to general height increases in my sample.

Life Expectancies

Data on life expectancies in Germany and Wurttemberg is available for the last quarter of the nineteenth century. Table 8.1 displays life expectancies at several ages in three periods (the 1870s, 1890s, and 1901-1910) for males and females. For the

1870s, Wurttemberg’s data is based on 1876-80 and Germany’s on 1871-80.

In the first two periods, life expectancies at birth in Wurttemberg were lower than the German average. However, at age 1 and older, Wurttemberg’s life expectancies for males in all three periods tended to be greater than the German average. In large part this is due to the exceptionally high infant mortality rates in

Wurttemberg. Those who survived the first year apparently had hardier constitutions.

It appears that the mortality rates of children aged five and under were also relatively high, especially in the 1876-80 period. This is deduced from the fact that Ml

the life expectancies did not change much between ages 2 and 6. For example, a

female child aged two had a life expectancy of 52.2 years, whereas a female child

aged six had a life expectancy of 52.0 years in the 1876-80 period. By the 1901/1910

period, this was no longer the case, suggesting young childhood was no longer as

dangerous.

Females bom in Wurttemberg tended to live longer than males. Their life

expectancy at birth was two to three years longer than that of the males in all three

periods. The differential continues at later ages, although decreasing. Even after age

1, however, the average female life expectancy in Wurttemberg was slightly lower

than the German female average. For the males, the reverse is true.

The trend toward increasing life expectancies in both Germany and

Wurttemberg is clear, it is strongest at the youngest ages. For example, life

expectancy at birth for males in Wurttemberg increased from 34.3 years in 1876-80 to

39.74 years in 1891/1900 and further to 45.5 years in 1901/1910. In general, the gains in the last period were larger on an annual basis than those in the second period.

The gaps in the life expectancy information make a comparison with the observed height trends difficult. One does not know, for example, about the 1880s, or the second quarter of the century. That health had improved in the 1890s as compared to the 1870s is, however, supported by both the height and life expectations data. 178

Infant Mortality Rates

In the 1840s and 1850s, Wurttemberg had one of the highest (if not indeed the

highest) infant mortality rates in western Europe. 34.78 percent of all infants died in

their first year in the 1946-56 period. Other German areas (Bavaria, Prussia, etc.)

had rates under 30%. Sweden, Denmark, Belgium, England, and France all had rates

under 20% (WJB, 1874).

Infant deaths accounted for 42.18% of all deaths (excluding stillbirths) in

Wurttemberg. This is a very high proportion. In France, Norway, Belgium,

Denmark, Prussia, et al., the proportion of infant deaths was much smaller - between

17 and 24 percent.

The exceptionally high infant mortality rates in Wurttemberg are, however, not

completely due to poor living standards. Rather, it seems to be largely attributable to cultural mores. Women in Wurttemberg, especially those in the Donau Kreis, tended

not to breast feed their babies. Instead they fed them a mixture of flour and fat. The children did not receive the immunological protection of breast milk and thus were

more susceptible to disease. Also, families often had a rather fatalistic attitude toward the survival of an infant. Apparently, they preferred that if it were not going to live anyway, that it die in infancy rather than when it was older and more cherished. 179

The highest infant mortality rates were in the Donau Kreis. For example, in

1862, the three other Kreis had infant mortality rates between 31 and 33 percent.

The Donau Kreis had a 42% rate (MCB, 1867). The vast majority of those infants

died without medical attention — again reflecting the fatalistic attitudes.

Landholdings were larger in the Donau Kreis than in other areas. Incomes

were relatively higher. But breast feeding was simply not the custom there. Thus the

well-off Donau Kreis had much higher infant mortality rates than, for example, small

farmers/craftsmen in the Schwarzwald Kreis.

That the high infant mortality rates in Wurttemberg were caused largely by breast feeding practices makes interpretation of trends over time somewhat difficult.

If the rates are falling, it could be due to improved income and nutrition. It could also be due to changing cultural customs and attitudes.

Table 8.2 and figure 8.1 show infant mortality rates in Wurttemberg, 1812-

1897 (where available). The rate in 1812-22 (32.06%) was lower than in 1846-56

(34.78%) which was lower than in 1858-66 (35.4%). Thus the earliest phases of industrialization were accompanied by rising infant mortality rates. The rate is still high (32.7%) in 1871-76. Between 1877 and 1886, it is between 27.3 and 30.3%.

Thereafter, it never rises above 27%. The 1884-93 average is 26.16%. The 1896-97 average is 22.82%. 180

Overall Mortality Rates

Overall mortality rates in Wurttemberg are shown in Table 8.3. Rates in the

early 1860s are higher than those at the end of the 1850s. I could not find

information on the 1866-71 period. Between 1872 and 1880, the mortality rate

remains over 3%. In 1875, it jumps to 4.34%. After 1880, mortality rates begin to

decline. The 1894-97 average is 2.4%. Much of this decline in overall mortality

rates is due to decreasing infant mortality rates. Figure 8.2 graphs the trends in total

mortality in the 19th century.

8.4 Regional Patterns

As Maps 7.1 and 7.2 show, there was considerable regional variation in unadjusted average heights. It is likely that the differentials seen in these maps underestimate the true differentials, since shortfall was probably highest in those counties where people were shortest. Those bom in the western part of the Kingdom, particularly in the Neckar Kreis tend to be taller than those bom in the eastern part.

These maps can be compared with Map 8.,1 showing the regional distribution of wages in Wurttemberg in 1898. A general similarity can be seen, with high wage areas generally enjoying taller stature. However, there are exceptions to this rule.

Map 4.5 shows the railway development in Wurttemberg between 1840 and

1934. The first major railway line was the North-South Line, completed in 1850. It begins in the northwest at Heilbronn, travels south to Stuttgart, then southeast through

Esslingen, Goeppingen, Geislingen, and Ulm, then south through Biberach, and

Ravensburg, ending in Friedrichshafen (in Tettrang) on the Boden Sea. If one 181

compares this route with Map 7.1 (Birth years 1852-73), the positive connection is

clear. Almost without exception, the tallest counties are located on this railway line.

On the other hand, in large parts of eastern Wurttemberg there was scarcely

any railway development before the 1870s. Many regions in the Jagst and Donau

Kreise remained unconnected to the railway network through the turn of the century

and beyond. These areas typically had shorter average heights, both in the early and

late periods.

It seems that the positive effects of railways (or phenomena associated with

railways), i.e., regular supply of nutrients and opening up export markets, outweighed

the negative effects, such as increase in communicable diseases.

There is another major factor which seems to have contributed to regional

height differences: breast feeding practices. It seems that the higher the percentage

of infants breast-fed, the lower the infant mortality rates, and the taller the people.

This relationship was examined for all of Germany in the first decade of the

twentieth century by Michael Haines. Map 8.2 shows the percentage of infants ever

breast fed around 1910 in different parts of Germany. This can be compared with

Map 8.3 showing 1910 infant mortality rates and Map 8.4 showing average heights of

military recruits in 1906. It is strikingly clear that those areas with the largest percentage of breast-fed infants had the lowest infant mortality rates and the tallest

soldiers. This is the case for the northwestern German provinces. In southern 182

Germany, the reverse is true. The relationship between breast feeding practices and stature is consistent with findings that show stature is sensitive to environmental conditions in early childhood.

This pattern is also observable within the Kingdom of Wurttemberg. The

Donau Kreis had larger than average land holdings and were generally wealthier than, for example, the small farmers in the Schwarzwald Kreis. Yet as discussed in Seciton

8.3, infant mortality rates were much higher and generally stature lower in the Donau

Kreis. This is largely attributable to the very low levels of breast feeding in the

Donau Kreis. CHAPTER IX

Conclusions and Directions for Future Research

9.1 Summary and Conclusions

This dissertation has added another piece to the puzzle concerning

industrialization’s impact on the welfare of those living through it. Optimist-Pessimist

debates exist for most industrialized countries. The optimists point to rising real per

capita income. The pessimists generally point out that this measure fails to capture

changes in the distribution of income and in the physical quality of life. The pessimists believe that industrialization was initially accompanied by social distress,

particularly for the lower and middle classes.

In recent years, a powerful new measure of health and living standards has

been used to shed new light on this debate. When height trends were examined in

Great Britain and the United States, falling average heights were discovered during industrialization. Thus, the height evidence supports the pessimists’ view of declining living standards during some phases of industrialization.

The question then arises: did declining living standards always decline during industrialization, or is the dip in stature in Great Britain and the United States due to characteristics unique to those countries? In other words, can we generalize from these two observations? It is necessary to examine other cases.

183 184

The main objective of this dissertation is to determine whether height trends in

Germany fit the pattern of a dip during industrialization. The Optimist-Pessimist

debate in Germany centers on the 1870’s and 1880’s, the beginning of the high

industrialization phase. Thus, it was hypothesized that a dip in average heights would be found in that period.

To examine this hypothesis, information was collected on 14,800 soldiers bom in Wurttemberg, Germany between 1852 and 1893. It was discovered that there was in fact a double dip in heights — one in the early 1870’s and one in the mid 1880’s.

Stature was also very low in the early 1850’s. These results were presented and discussed in Chapters VII and VIII. Let us now review the highlights.

In the late 1840’s and early 1850’s there was massive crop failure in

Wurttemberg and spread of a potato blight. Since Wurttemberg’s markets were fairly isolated, the impact on the net nutrition was very strong. The RSMLE estimates of average height of the male population bom in the 1852-57 period is 163.12 centimeters - lower than in any other six-year period. Particularly short were those bom in the years 1852-1853. The RSMLE estimates of their average height was

160.56 centimeters. This is below the second centile of the modem British study.

The agricultural crisis hit farmers and independent craftsmen especially hard.

With their main or secondary source of income in agriculture gone, many shifted their labor into manufactured goods. This caused the prices of these goods to fall, driving incomes to subsistence levels. There were many bankruptcies, particularly among small farmers and traditional craftsmen. 185

The failed agricultural sector temporarily loosened the strong ties to the land.

Willingness to work in factories increased. In the 1850’s, there was a spurt of

industrial development. The new competition for a shrunken local market furthered

traditional craftsmen’s woes. The Transition Crisis reached its high point in the early

1850’s.

The upper class was considerably taller than those employed in agriculture,

who were slightly taller than the working class. This pattern continues throughout the

42-year period. The occupational height differentials, however, do change.

In the late 1850’s, better harvests returned. Also, Wurttemberg’s factories began to export more, reducing pressure on traditional craftsmen. Wages began to rise. Average heights rose rapidly.

The 1860’s saw modest increases in the percentage of the population employed in the commercial sector. Average heights remained steady or rising through the mid

1860’s. In the late 1860’s, a gradual decline begins. This accelerates in the early

1870’s, reaching its nadir of approximately 165.18 cm in birth cohorts of 1870-75.

Both the RSMLE and QBE results indicate average height declined by approximately one centimeter between the birth cohorts of 1864-69 and 1870-75.

What caused this height decline? The years 1871-73 were characterized by high inflation. There was a speculation boom. The crash came in 1873/75 in 186

Wurttemberg. Again there were many bankruptcies and reports of social distress. The

mortality rate jumped by over 30% between 1874 and 1875. The working and middle

classes experienced the largest height declines, falling 1.3-1.4 centimeters from 1864-

69 to 1870-75.

The late 1870’s, however, seem to have been a time of recovery. Average

heights were rising in the late 1870’s and early 1880’s, the time of high inflation and

the financial crisis had passed. Wages were steady or rising, while prices began to fall

slightly.

In the 1870’s and 1880’s, Wurttemberg’s railway connection to the outside world were improved. The influx of cheap imported grains drove agricultural prices in Wurttemberg down dramatically, especially in the 1880’s. Farmers in particular were hit hard by this development. RSMLE estimates of average population heights declined by 1.4 centimeters between 1882/83 and 1884/85.

With their small plots of land and relatively traditional methods of farming, the farmers couldn’t compete with the cheap imports. The strong ties to the land were weakened. There was an increased willingness to work in factories, which spurred considerable growth in the commercial sector. There was also a massive outflow of emigrants. Many went to the United states. The emigration had positive externalities on those who stayed behind, since average size of landholdings increased.

Recovery of average heights began in the birth cohorts of the late 1880’s and early 1890’s. This coincided with falling emigration and mortality rates. This roughly corresponds to the period of rising stature in the United States after its large height 187 dip. There could be a strong link between this rise and improved public health measures. Also, improved transportation systems enabled a constant supply of nutrients (at least in those areas well-connected) and links to export markets.

The occupational differences were strongly significant throughout the period.

Their explanatory power is greater than that of regional and community size differences. The upper class was taller than the farmers who were in turn slightly taller than the working/middle class.

One of the most interesting occupational results was the clear positive correlation between stature and upward social mobility. The 7,400 observations for which both father’s and soldier’s occupation were available were analyzed. Given the father’s occupation, the soldiers who are upwardly socially mobile are taller than those in the same class as their father. The socially immobile are in turn taller than the downwardly socially mobile.

In conclusion, this study has revealed that Germany fits the pattern of falling average heights during industrialization. The pattern however is also different from that observed in Great Britain and the United States. First, there is a double dip in heights, one in the early 1870’s and another in the mid-1880’s. Heights were also quite low in the early 1850’s— the time of severe agricultural crisis and early industrialization. Second, the height declines were briefer and recovery faster.

The evidence thus supports the Pessimists view that industrialization is generally accompanied by periods of social distress and declining net nutrition. Height 188

trends indicate this is true in Germany, Great Britain, and the United States. The

developing countries of today, which are determined to industrialize as quickly as

possible, might wish to take note.

9.2 Directions for Future Research

This study analyzed trends using four measures: unadjusted average heights

(biased), the Quantile Bend Estimator, the Reduced Sample Maximum Likelihood

Estimator, and the Simple Mean of the Reduced Sample. When these methods yield

similar results, one can feel fairly confident in drawing conclusions from them. But when they differ, one is left with the dilemma of trying to figure out why they are different, and which one to believe.

The QBE and RSMLE methods rely on the assumption that the upper ranges of the sample conform well to a normal distribution. Doing this by visual inspection of the graphs might rule out some obviously non-normal cases, but the process is subjective.

There is a need to develop a formal statistical test of whether the truncated sample distrubution resembles a truncated normal distribution enough to justify using the QBE and RSMLE methods.

A study such of this is by its nature, open-ended. There are many areas still left to explore. 189

One area worthy of further investigation is the relationship between community

size and stature. My results show urban dwellers generally being taller than rural

dwellers throughout most of the period. This was not always the case in the history

of Europe and the reasons need to be explored.

A plausible explanation could be that public health measures in Wurttemberg

were better and/or earlier than in other regions. The timing and extent of public

health measures need to be explored further. The Kingdom had a health police force

(Gesundheitspolizei). This police force patroled local communities, examining the drinking water among other things. If for example, there was a slaughter house near a well and it was contaminating the water, the slaughter house was closed or the well would be moved. There were many reports of this. Unfortunately I cannot find any sort of a summary. All I have been able to find until now are descriptions of individual acts committed by this health police. I will continue the search.

The other question I was not able to answer, despite significant efforts, while I was in Stuttgart, Germany, was when the sewage systems were built in Stuttgart and the other cities. There may be more creative ways of finding out this information and

I intend to pursue them.

Immunizations did take place in Wurttemberg. I wish to find out when exactly that started. I recall having seen reports of immunizations but did not photocopy all of that information, just a few examples. If I return to Stuttgart in the future, I will 190

once again, visit the Statistical Bureau of Baden-Wurttemberg to learn more about the

extent and the beginnings of immunization programs. Certainly by the 1880s,

however, they were in place.

Another area I wish to explore is the relationship between stature and

migration. There is some information available on internal migration in

Wurttemberg, although I have not yet had a chance to explore all of it. It would be interesting to see if the average heights in the areas that people were leaving were less than those in areas where people were on net arriving. At least on the Kreis/District level, this seems to be true. The jagst Kreis generally had the highest net internal migration loss as well as the shortest soldiers. The Neckar Kreis, on the other hand, generally had the largest internal migration surplus and the tallest soldiers.

Another interesting area is the relationship between railways and net nutrition.

It is plausible that railways were associated with an increased supply of goods, as well as opening doors to export markets. Regional height patterns indicate that railways had a positive effect on net nutrition and stature. To do a study of this I believe very detailed information would be needed on when exactly which towns were hooked up to the rail system. This information may be obtainable. In the future I hope to find out.

In this dissertation I have analyzed only a part of the information that was collected on the soldiers of Wurttemberg. This data needs to be edited — approximately a 150 hour ordeal, maybe more. But the possibilities are certainly rich. For approximately half the observations there is information on the parent’s 191

residence and the soldier’s residence at the time of enlistment, including both birth

towns and birth counties. There is also information on incidences of illnesses that the

soldiers experienced while serving. This all needs to be codified, but could be quite

interesting in examining the relationship between stature and various diseases. It

appears that malabsorption was a big problem in the 19th century. This would mean

that even if enough calories were being consumed, not all of them were being

absorbed. Thus, this malady would strongly effect the net nutrition of the individual.

I believe the malabsorption rates in Wurttemberg were falling towards the end of the

19th century. This could have contributed to the general height rises seen in that period.

In summary, average heights were very low for those bom during the agricultural and transition crises of the early 1850s. Thereafter, stature recovered through the birth cohorts of the early 1860s. In the late 1860s began a gradual decline which accelerated in the early 1870s, reaching its nadir approximately in the birth cohorts of 1870-75. Rapid recovery followed for those bom in the late 1870s and early 1880s. There is then a second brief dip in the mid 1880s, followed again by fairly rapid recovery.

These findings support the hypothesis that Germany fits the pattern observed elsewhere of falling average heights during industrialization. Yet the height trends in

Germany differ in important respects from those observed elsewhere. There is in fact 192

a double dip in heights during the first two decades, Germany's High Industrialization

Phase — one in the early 1870s and one in the mid 1880s. The periods of decline are,

however, relatively short and recovery fairly rapid.

Those bom during the agricultural and transitional crisis of the early 1850s

were, however, the shortest in the entire period. This suggests that in relatively

insular regions, such as Wurttemberg in the 1850s, where almost all food consumed

was locally produced, extensive crop failure dealt a severe blow to net nutrition.

Thus, the agricultural crisis can be at least as harsh as the crises associated with industrialization. If this is true, then it challenges the Marxist contention that industrialization and negative business cycle effects resulted in greater hardship for the working class than they had experienced in earlier times.

The low stature of the early 1850s in Wurttemberg corresponds to a dramatic fall in stature in Sweden, beginning in the late 1840s and reaching its low point in the early 1850s (Sandberg and Steckel, 1986). Sandberg and Steckel cite rising income inequality and increasing percentages of children bom into lower class families as explanations for the decline.

Stature in Great Britain and the United States was also declining in the early

1850s, although the downward trend began in the second quarter of the nineteenth century and lasted until the 1850s/1860s in Great Britain and the 1880s in the United

States. Thereafter heights were on the rise in both countries (Floud et.al, 1990 and

Steckel, 1990). After the mid 1880s, heights were on the rise in Wurttemberg as well. 193

Studies of the United Staters and Great Britain cite the negative impact of

urbanization as a main contributory factor in the stature declines. In the mid 19th

century in Great Britain and in the United States, the urban-born were shorter than the

rural-born. This rural height advantage continued in the United States at least into the

first quarter of the twentieth century (Steckel, 1990, and Floud et., 1990). As the

percentage bom in relatively unhealthy urban environments increased, average

population heights fell.

Does urbanization also drive the height declines observed in Wurttemberg?

That is unlikely. In Wurttemberg the urban-born are taller than the rural-born

throughout the second half of the nineteenth century.

Those bom in Stuttgart are the tallest group in the birth cohorts of the 1850s,

1860s, and 1870s. This is in sharp contrast to those bom in London and Stockholm, who were considerably shorter than their rural and small city compatriots.

Heights in Stuttgart are, however, on the decline since the late 1860s. Perhaps the negative effects or urbanization were beginning to be felt as the city’s population increased dramatically. Possbly higher rents eroded real income in Stuttgart.

Meanwhile, cities with 20,000 - 49,999 residents were experiencing height gains in the last phases. Their average height surpassed that of Stuttgart in the 1882-87 birth cohort.

Why did the urban-born have a height advantage in Wurttemberg? There are several possible explanations. The first is that public health measures wree implemented fairly early in Wurttemberg. Based on the information collected thus 194

far, this seems probable. There was a substantial government employee contingent

working on public health issues including a "health police" which checked drinking water quality and conditions in the schools, among other things. It is likely that government health officials had more imapct in the urban areas than in the rural.

One problem of which the government was acutely aware of since at lest the

1860s was the exceptionally high infant mortality rates in Wurttemberg, largely caused by the lack of breast feeding. If the government launched a publicity campaign, encouraging breast feeding, it would likely affect urban dwellers first.

Thus higher percentages of infants being breast fed in cities could have contributed to the urban height advantage.

Other possible explanations include 1) better access to medical attention, 2) higher real wages, 3) a more regular supply of nutrients, particularly for urban areas with railway connections, and 4) better educational opportunities leading to higher productivity and earnings.

Another difference between the results of this and other studies is that the dips in height are briefer than in Great Britain and the United States. This could be partially due to Wurttemberg’s high emigraion rates acting as a safety valve. When the situation got too bad, a lot of people left. This generally had a positive effect on those who stayed behind, since, for example, the average land holding size increased.

Three other facctors might also explain the brevity of the height dips in

Wurttemberg. First, many in the Kingdom worked in both the agricultural and commercial sectors. If prospects were dim in one sector, they could shift more labor 195 into the other sector. The two occupations provided a sort of safety net for

Wurttemberg’s workers. The second feature is the relatively diversified production, making the Kingdom’s economy more resilient to shocks. Third, in Wurttemberg, the average commercial enterprise was quite small. This may have reduced the risk of communicable disease spread in the work place.

The rapid recovery of stature in the birth cohorts of the late 1880s and early

1890s could have been partially caused by the public health revolution which affected medical practice all over Europe starting in the 1880s. Also, in the mid 1880s, the

German Empire Legislature passed significant social legislation, providing health insurance, as well as old-age and disability pensions.

These are only a few of the many directions left to explore. Many questions remain concerning the relationship of stature to factors such as social mobility, population density, community size, breast feeding practices, labor productivity, etc.

There are also many historical situations where height evidence could shed new light.

Finally, there is a need for more height studies in the modem world, particularly in lesser-developed nations. Average heights are a powerful tool for monitoring the health of populations. Height data could be used, for example, to measure the effectiveness of social programs, such as nutritional supplements or health education. Our increasing knowledge about stature could have many practical applications in today’s world.

HEIGHTS AND LIVING STANDARDS IN INDUSTRIALIZING GERMANY: THE CASE OF WURTTEMBERG

Volume 11

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

the Degree Doctor of Philosophy in the Graduate School

of The Ohio State University

By

Sophia Twarog

****

The Ohio State University

1993

Committee Members Advisor: Dr. Richard Steckel Dr. Lars Sandberg Dr. Edward Ray Dr. Richard Steckel TABLE OF CONTENTS Volume Two

List of Figures ...... xii

List of Tables ...... xix

List of Maps ...... xxvii

CHAPTER PAGE

X FIGURES, TABLES, AND MAPS...... 196

REFERENCES...... 383 LIST OF FIGURES

Page

Figure 2.1: Relationships Involving Stature...... 196

Figure 2.2: The Association in Modern Norway Between Body Height and Mortality by Sex and Age...... 197

Figure 2.3: Relative Mortality Rates Among Norwegian Men Aged 40-59, Between 1963 and 1979 ...... 198

Figure 2.4: Relative Rejection Rates for Chronic Conditions in a Sample of 4245 Men Aged 23-49 Examined for the Union Army . . 199

Figure 2.5: Typical Individual Height-Attained Curves for Boys and Girls (Supine Length to the Age of 2)...... 200

Figure 2.6: Typical Individual Growth Velocity Curves for Boys and Girls. These Curves Represent the Velocity of the Typical Boy and Girl at Any Given Instant...... 201

Figure 2.7: Mean Heights of European, Asian, and African- American Boys...... 202

Figure 2.8: Mean Heights of European, Asian and African-American Girls...... 203

Figure 2.9: Effects of Malnutrition on Growth in Height, Stuttgart School Children...... 204

Figure 2.10: Comparison of Heights of Poor and Well-off Boys in Eleven Countries...... 205

xii Page

Figure 2.11: Average Height of 18-ycar-old Military Recruits Serving in the British Army and Royal Marines, Birthyears 1750-1916...... 209

Figure 2.12: Average Height Profile (5-Year Moving Average) for British Men Born Between 1770 and 1815...... 210

Figure 2.13: Estimated Trend in the Height of Soldiers in the Army, 1740-1859 (Average of All Ages)(100 = the English National Average, 1740-49)...... 211

Figure 2.14: Average Height of American Native-Born White Males by Year of Birth and the Trend in Their Life Expectancy at Age 10.... 212

Figure 2.15: Hypothetical Approximate Changes in Average German Heights, 600-prescnt ...... 212

Figure 3.1: Production of Coal and Pig Iron in Germany and France, 1810-1870...... 215

Figure 3.2: Production of Coal, Pig Iron, and Steel in Germany, Great Britain, and France...... 224

Figure 3.3: Development of Real Gross Wages (1913 = 100), 4 Estimates, and Real Per Capita Income (dotted lin e )...... 225

Figure 3.4: Development of Cost of Living Indices in Germany 1871-1913, (1913 = 100), Estimates by a) Kuczynski b) Desai and c) Orsagh...... 226

Figure 3.5: Development of Life Expectations at Ages 0, 30, and 60 years in Schwalmer Region (1780/1809-1840/69), German Empire and the Republic of German (1871/80-1974/76) ...... 232

Figure 4.1: Commercial Sector Employment as a Percentage of Population in Several German Regions, 1832-1939...... 241

Figure 4.2: Population in Six Community Size Categories in Wurttemberg, 1871, 1880, 1890, 1900, and 1910...... 269

xiii Page

Figure 4.3: Population Development in Urban and Rural Communities in Wurttemberg, 1834-1910...... 270

Figure 6.1: Height Distribution, Infantry, Birthyears 1860-64...... 272

Figure 6.2: Height Distribution, Infantry Draftees, Birthyears 1860-64 ...... 272

Figure 6.3: Height Distribution, Infantry One-Year Volunteers, Birthyears 1860-64 ...... 272

Figure 6.4: Height Distribution, Artillery and Cavalry, Birthyears1860-64 ...... 273

Figure 6.5: Height Distribution, Total Data, Birthyears 1860-64 ...... 273

Figure 6.6: Height Distribution, Total Data, Birthyears 1885-89 ...... 274

Figure 6.7: Histogram and Quantile Plot, British Royal Marines Over Age 21, 1750-59 ...... 275

Figure 7.1: Unadjusted Average Height, Four Year Moving Average, Birthyears 1851-1892, Total Data ...... 278

Figure 7.2: Unadjusted and QBE Average Height Estimates, Total Data, 2-Year Moving Average, Birthyears 1852/1853-1892/1893...... 279

Figure 7.3: QBE Shortfall Estimates, Total Data, 2-Year Moving Average, Birthyears 1852/53-1892/93...... 281

Figure 7.4: QBE Standard Deviation Estimates, Total Data, 2-Year Moving Average, Birthyears 1852/53-1892/93...... 282

Figure 7.5: QBE and Unadjusted Average Height, Total Data, 4-Year Moving Average, Birthyears 1852/55-1890/93...... 283

Figure 7.6: QBE Shortfall Estimates, Total Data, 4-Year Moving Average, Birthyears 1852/55-1890/93...... 284

Figure 7.7: QBE Standard Deviation Estimates, Total Data, 4-Year Moving Average, Birthyears 1852/55-1890/93...... 285

xiv Page

Figure 7.8: QBE and Unadjusted Average Height Estimates, Total Data, 6-Year Moving Average, Birthyears 1852/57-1888/93 ...... 287

Figure 7.9: QBE Shortfall Estimates, Total Data.6-Year Moving Average, Birthyears 1852/57-1888/93...... 288

Figure 7.10: QBE Standard Deviation Estimates, Total Data, 6-Year Moving Average, Birthyears 1852/57-1888/93...... 289

Figure 7.11: QBE and Unadjusted Average Heights, Draftees, 2-Year Moving Average, Birthyears 1852/57-1888/93 ...... 290

Figure 7.12: QBE and Unadjusted Average Heights, Draftees, 6-Year Moving Average, Birthyears 1852/57-1888/93...... 292

Figure 7.13: QBE and Unadjusted Average Heights, One-Year Volunteers, 6-Year Moving Average, Birthyears 1852/57-1888/93 ...... 293

Figure 7.14: QBE Average Height Estimates using Reduced Sample, Draftees, One-Year Volunteers, and Total Soldiers, 6-Year Moving Average, Birthyears 1852/57-1888/93...... 294

Figure 7.15: Unadjusted Average Heights, 4-Year Moving Average, Infantry, Birthyears 1852/57-1888/93...... 295

Figure 7.16: QBE and Unadjusted Average Heights, Infantry, 2-Year Moving Average, Birthyears 1852/57-1888/93...... 296

Figure 7.17: QBE and Unadjusted Average Heights, Infantry Draftees, 2-Year Moving Average, Birthyears 1852/57-1888/93 ...... 297

Figure 7.18: QBE and Unadjusted Average Heights, Infantry One- Year Volunteers, 6-Year Moving Average, Birthyears 1852/57-1888/93 . . 298

Figure 7.19: Unadjusted Average Heights, 4-Year Moving Average, Artillery and Calvary, Birthyears 1852/57-1888/93...... 299

Figure 7.20: QBE and Unadjusted Average Heights, Double Artillery and Calvary, 6-Year Moving Average, Birthyears 1852/57-1888/93...... 300

xv Page

Figure 7.21: RSMLE and QBE Average Height Estimates, Total Data, 7 Phases, Birthyears 1852/57-1888/93 ...... 302

Figure 7.22: RSMLE Average Height Estimates, Total Data, 2- Year Birth Cohorts, Birthyears 1852/57-1888/93 ...... 304

Figure 7.23: Average Height of Reduced Sample, Draftees, One-Year Volunteers, and Total Data. 6-Year Moving Average, Birthyears 1852/57-1888/93 ...... 305

Figure 7.24: RSMLE Average Height Estimates by Soldier’s Occupational Category(7), 7 Phases, Birthyears 1852/57-1888/93 ...... 314

Figure 7.25: RSMLE Average Height Estimates for Observations with Father’s Occupation, 7 Phases, Birthyears 1852/57-1888/93 ...... 316

Figure 7.26: RSMLE Average Height Estimates by Father’s Occupation(7), 7 Phases, Birthyears 1852/57-1888/93 ...... 319

Figure 7.27: Unadjusted Average Height by Soldier’s Occupation(3), 6-Year Moving Average, Birthyears 1852/57-1888/93 . . . 321

Figure 7.28: QBE Average Height Estimates by Soldier’s Occupation(3), 6-Year Moving Average, Birthyears 1852/57-1888/93 . . . 322

Figure 7.29: Average Height of Reduced Samples by Soldier’s Occupation(3), 6-Year Moving Average, Birthyears 1852/57-1888/93 . . . 323

Figure 7.30: RSMLE Estimates of Average Height by Soldier’s Occupation(3), 7 Phases, Birthyears 1852/57-1888/93 ...... 327

Figure 7.31 :QBE Estimates of Average Height by Soldier’s Occupation(3), 7 Phases, Birthyears 1852/57-1888/93 ...... 328

Figure 7.32: Average Height of Reduced Sample, by Soldier’s Occupation(3), 7 Phases, Birthyears 1852/57-1888/93 ...... 329

Figure 7.33: Upper Class, Average Height, RSMLE, QBE, and Komlos Estimates, 7 Phases, Birthyears 1852/57-1888/93 ...... 330

xv i Page

Figure 7.34: Agriculture Average Height, RSMLE, QBE, and Komlos Estimates, 7 Phases, Birthyears 1852/57-1888/93 ...... 331

Figure 7.35: Working Class, Average Height. RSMLE, QBE, and Komlos Estimates, 7 Phases, Birthyears 1852/57-1888/93 ...... 332

Figure 7.36: Unadjusted Average Height by Father’s Occupation(3), 12-Year Moving Average, Birthyears 1852/57-1888/93 . . . 333

Figure 7.37: QBE Average Height Estimates by Father’s Occupation(3), 12-Year Moving Average, Birthyears 1852/57-1888/93 . . . 334

Figure 7.38: Average Height of Reduced Sample by Father’s Occupation, 12-Year Moving Average, Birthyears 1852/57-1888/93...... 335

Figure 7.39: Unadjusted Average Heights by Region, 6-Year Moving Average, Birthyears 1852/57-1888/93...... 345

Figure 7.40: QBE Average Height Estimates by Region, 6-Year Moving Average, Birthyears 1852/57-1888/93...... 346

. Figure 7.41: Average Height of Reduced Samples, 6-Year Moving Average, Birthyears 1852/57-1888/93...... 347

Figure 7.42: RSMLE Estimates of Average Height by Region, 7 Phases, Birthyears 1852/57-1888/93...... 350

Figure 7.43 Unadjusted Average Height in Community Size Categories, 7 Phases, Birthyears 1852/57-1888/93 ...... 353

Figure 7.44: RSMLE Average Height Estimates by Birth Community Size (in 1895), 7 Phases, Birthyears 1852/57-1888/93 ...... 356

Figure 7.45: Unadjusted Average Height Estimates, Rural (It 2,000) and Urban Communities, 6-Year Moving Average, 1882/57-1888/93 ...... 358

Figure 7.46: Average Height of Reduced Samples (ht> or = 162 cm), 6-Year Moving Average, 1882/57-1888/93 ...... 359

xvii Page

Figure 7.47: RSMLE Estimates of Average Height for Soldiers Bom in Rural (LT 2,000 residents) and Urban Communities, 7 Phases, 1852/57 to 1888/93 ...... 362

Figure 7.48: QBE Average Height in Rural (It 2,000) and Urban Communities, 6-Year Moving Average, Birthyears 1852/57-1888/93 ...... 363

Figure 7.49: QBE Average Height Estimates Using Reduced Sample, Rural (It 2,000) and Urban Communities, 6-Year Moving Average, 1882/57-1888/93 ...... 364

Figure 8.1: Infant Mortality Rates in Wurttemberg, 1812-1897 (available years) ...... 377

Figure 8.2: Mortality Rates in Wurttemberg 1812-1897 (available years)...... 378

xviii LIST OF TABLES

Page

Table 2.1: Correlations Between Average Height and the Log of Per Capita Income...... 206

Table 2.2: Estimated Heights of Male Slaves Compared with Modern Standards...... 207

Table 2.3: Estimated Heights of Female Slaves Compared with Modem Standards...... 208

Table 3.1: German Railroads (in km), 1835-1915...... 214

Table 3.2: Output of Coal (in Thousands of Metric Tons)...... 214

Table 3.3: The Production of Pig Iron in Germany, 1825-1870...... 215

Table 3.4: Steam Engines Installed in the German Customs Union, 1846 and 1861 ...... 216

Table 3.5: Number of Bank Accounts and Total Deposits per 100 Residents in Several German Regions, 1830-1915...... 216

Table 3.6: Population on Reich Territory, 1816-1915...... 217

Table 3.7: Overseas Emigration from Germany...... 217

Table 3.8: Percentage of Population in Community-Size Classes, 1852-1961...... 218

Table 3.9: Growth of the Big German Cities, 1875-1910...... 218

xix Page

Table 3.10: Employment Structure in Germany, 1882. 1907, 1925, and 1933...... ”...... 219

Table 3.11: Employment Structure in Germany Economic220 Sector, 1849-1959...... 220

Table 3.12: Employment Structure in Germany by Economic Sector, 1846-1959 (1000’s)...... 221

Table 3.13: Industrial Population of Germany, 1875...... 223

Table 3.14: Coal Production of the World, in Millions of Metric Tons, 1864-1935...... 223

Table 3.15: German Foreign Trade, 1872-1913...... 224

Table 3.16: Development of Average Workday in German Industry, 1800-1914...... 227

Table 3.17: Development of Average Workweek in German Industry, 1830-1914...... 227

Table 3.18: Development of Average Work Week in Different Branches of German Industry...... 228

Table 3.19: Development of Infant Mortality Rate in Prussia, 1816-1900...... 229

Table 3.20: Development of Infant Mortality Rate in German Empire, 1901-1938, and Federal Republic of Germany, 1949-1975...... 230

Table 3.21: Infant Mortality in Several German Regions, mid-1800’s...... 231

Table 3.22: The Development of the Difference Between Infant Mortality Rates of Illegitimate and Legitimate Children in Prussia, 1816/20 to 1886/90, and in Bavaria 1840/41 to 1889/95...... 231

Table 3.23: Infant Mortality Rale by Occupation of Father in Germany in 1877/79 and 1912/13...... 232

xx Page

Table 3.24: Life Expectancy, Ages 0, 30 and 60, Males and Females in German Empire...... 233

Table 3.25: Normal Caloric Needs by Age and Sex per Person per Day...... 234

Table 3.26: Hoffman’s Index of Net Nutrition, 1850 - 1959...... 234

Table 3.27: Sectoral Concentration and General Index of Specialization in Germany, 1882, 1895, and 1907...... 236

Table 3.28: Specialization in Regions of Germany, 1882, 1895, and 1907...... 237

Table 3.29: Employment Structure in Wurttemberg and Hohenzollem, 1861, 1875, 1882, 1895, and 1907...... 238

Table 3.30: Distribution of Employment in Germany, 1882, 1895, and 1907...... 239

Table 3.31: Degree of Industrialization in 28 German States and Regions, 1871...... 240

Table 4.1: Commercial Sector Employment in Wurttemberg in 1832...... 242

Table 4.2: Population and Commercial Sector Employment 1835/36, 1852, 1861, 1875 ...... 244

Table 4.3: Changes in Population and Employment in Commercial Enterprises...... 245

Table 4.4: Employment in Factories/Large Enterprises in Wurttemberg 1832, 1852, 1861, 1875 246

Table 4.5: Employment in Small and Large Enterprises in Several German Regions in 1875...... 247

Table 4.6: Number and Power Output of Steam Used In Commercial Enterprises in Several German Regions, 1846(1852), 1861, 1875...... 248

xxi Page

Table 4.7: Employment in Factories/Large Enterprises by Industrial Branch in Wurttemberg, 1852, 1861, and 1875...... 249

Table 4.8: Population and Commercial Sector Employment 1875, 1882, 1895, 1907, 1933, and 1939...... 251

Table 4.9: Commercial Sector Employment as a Percentage of the Population (Gewerbesatz) in SeveralGerman Regions, 1875-1939...... 252

Table 4.10: Increase in the Percentage of the Population Employed in the Commercial Sector in Several German Regions, 1875-1939 and 1882-1939...... 252

Table 4.11: Employment in Enterprises With Six or More Workers in Wurttemberg, 1875-1939...... 253

Table 4.12: Percentage of Commercial Sector Employees Working in Enterprises with Six or More Workers in Several German Regions, 1875-1939...... 254

Table 4.13: Average Number of Employees in Commercial Enterprises in Several German Regions, 1875-1939...... 255

Table 4.14: Mechanized Enterprises and Engine Output in Wurttemberg 1875-1939...... 256

Table 4.15: Mechanized Enterprises and Engine Output in Several German Areas, 1875, 1907, and 1939...... 257

Table 4.16: Employment by Industrial Branch and Its Share of Total Commercial Sector Employment in Wurttemberg, 1875-1939...... 258

Table 4.17: Regional Development of Railroads in Germany 1839-1914 (in km)...... 261

Table 4.18: Population and Average Annual Population Growth Rate in Wurttemberg, 1816-1900...... 264

Table 4.19: Loss of Population Due to Emigration from Wurttemberg, 12 Phases, 1816-1900...... 264

xxii Page

Table 4.20: Average Annual Migration Balance for Several German Areas in Seven Phases, 1817/25 to 1857/65 ...... 265

Table 4.21: Population Development in Wurttemberg According to Community Size Category, 1834 and 1852...... 265

Table 4.22: Bankruptcies According to Occupation and Region (Kreis) in Wurttemberg, 1840-1847...... 266

Table 5.1: Composition of Reduced Sample (Ht> or = 163 cm) by Soldiers Occupational Category in 7 Phases, Birthyears 1852/57-1888/93 ...... 271

Table 5.2: Composition of Reduced Sample (Ht> or = 163 cm) by Region in 7 Phases, Birthyears 1852/57-1888/93 271

Table 6.1: Limit Testing RSMLE Average Height and Standard Deviation Estimates, by Phase...... 276

Table 7.1: QBE Results and Unadjusted Average Heights, 2- Year Moving Average ...... 280

Table 7.2: QBE Results, Total Data, 4-Year Moving Average, Birthyears 1852-1855 ...... 286

Table 7.3: QBE Results, Total Data, 6-Year Moving Average, Birthyears 1852/57-1888/93 ...... 291

Table 7.4: RSMLE and QBE Average Height Estimates, 7 Phases, Birthyears 1852/57-1888/93 ...... 301

Table 7.5: RSMLE and Average Height Estimates, Total Data, 2- Year Birth Cohorts, Birthyears 1852/57-1888/93 ...... 303

Table 7.6: Unadjusted Average Height by Father’s Occupational Category, Early and Late Periods...... 306

Table 7.7: Unadjusted Average Height by Soldier’s Occupational Category, Early and Late Periods...... 308

xxiii Page

Table 7.8: RSMLE Truncated Regression of Height on Soldier's OccupationalDummies, 7 Phases,Birthyears 1852/57-1888/93 . 310

Table 7.9: RSMLE Truncated Regression of Height on Constant Term, Observations with complete information on Birth Region and Soldier's Occupation ...... 312

Table 7.10: RSMLE Average Height Estimates by Soldier’s Occupational Category(7), 7 Phases, Birthyears1852/57-1888/93 ...... 313

Table 7.11: RSMLE Average Height Estimates for Observations with Father’s Occupation, 7 Phases, Birthyears 1852/57- 1888/93 ...... 315

Table 7.12: RSMLE Truncated Regression on Father’s Occupational Dummies, 7 Phases, Birthyears 1852/57- 1888/93 ...... 317

Table 7.13: RSMLE Average Height Estimates by Father’s Occupation(7), 7 Phases, Birthyears 1852/57-1888/93 ...... 320

Table 7.14: RSMLE Truncated Regression of Height on Soldier’s Occupation(3), 7 Phases, Birthyears 1852/57-1888/93 ...... 324

Table 7.15: Comparison of RSMLE, QBE and Komlos Estimates of Average Heights by Soldier’s Occupational Category(3), 7 Phases, Birthyears 1852/57-1888/93 ...... 326

Table 7.16: RSMLE Truncated Regression: Height on Father’s Occupation(3), 7 Phases, Birthyears 1852/57-1888/93 ...... 336

Table 7.17: Unadjusted Average Height by Father/Soldier Occupational Categories...... 338

Table 7.18: RSMLE Truncated Regression Results of Height on Father’s, Soldier’s, and Both Occupations, Birthyears 1852-1893 ...... 339

Table 7.19: RSMLE Truncated Regression Results using Dummies For Upward and Downward Mobility, Birthyears 1852-1893 ...... 340

xxiv Page

Table 7.20: RSMLE Average Height Estimates by Father’s and Soldier's Occupation, Birthyears 1852-1893 ...... 340

Table 7.21: Unadjusted Average Height by Oberamt, Early and Late Periods ...... 341

Table 7.22: RSMLE Truncated Regression of Heights on Regional Dummies, 7 Phases, Birthyears 1852/57-1888/93 ...... 348

Table 7.23: RSMLE Estimates of Average Height by Region, 7 Phases, Birthyears 1852/57-1888/93 ...... 349

Table 7.24: RSMLE Truncated Regression of Height on Population(1895) of Birthtown, 7 Phases, Birthyears 1852/57-1888/93 . . . 351

Table 7.25: Unadjusted Average Height in 6 Community Size Categories by Phase, 1852/57-1888/93 ...... 352

Table 7.26: RSMLE Truncated Regression of Height Community Size Dummies, 7 Phases, Birthyears 1852/57-1888/93 ...... 354

Table 7.27: RSMLE Average Height Estimates by Birth Community Size (in 1895), 7-Phases, Birthyears 1852/57-1888/93 ...... 357

Table 7.28: RSMLE Truncated Regression Results of Height on Urban Birthtown (2,000+ Residents) Dummy, 7 Phases, Birthyears 1852/57-1888/93 ...... 360

Table 7.29: RSMLE Estimates of Average Height for Soldier’s Bom in Rural (Lt 2,000 Residents) and Urban Communities, 7 Phases, Birthyears 1852/57-1888/93 ...... 361

Table 7.30: QBE Results by Rural and Urban Birthtown, 6-Year Moving Average, Birthyears 1852/57-1888/93...... 365

Table 7.31: RSMLE Truncated Regression Results, Three Models Using Combinations of Soldier’s Occupation, Community Size, and Region, by Phase 1852/57-1888/93 ...... 367

XXV Page

Table 8.1: Life Expectancy for Males and Females in Wurttemberg and Germany (y ea rs)...... 374

Table 8.2: Infant Mortality Rates in Wurttemberg 1812-1897 (available years) . . 375

Table 8.3: Mortality Rates in Wurttemberg, 1812-1897 (available years)...... 376

xxvi LIST OF MAPS

Page

Map 3.1: The Zollverein (Customs Union) and the Tax Union, 1834 ...... 213

Map 3.2: German Regional D ivisions...... 235

Map 4.1: Regional Distribution of Employment in Factories, Manufacturing, and Mining in Wurttemberg, 1832 ...... 243

Map 4.2: Regional Distribution of Employment in Factories in Wurttemberg, 1861...... 250

Map 4.3: Regional Distribution of Workers in the Commercial Sector in Wurttemberg, 1925...... 259

Map 4.4: Regional Distribution of Employers in Enterprises with 6+ Employees, 1895...... 260

Map 4.5: Development of Railway Network in Wurttemberg ...... 262

Map 4.6: Population Density in the 64 Oberamts of Wurttemberg,1834 ...... 263

Map 4.7: Percentage of Small Farmers (less than 3.5 hectares) in the 64 Oberamts of Wurttemberg, 1857...... 267

Map 4.8: Average Size of Agriculture Enterprise in 64 Oberamts of Wurttemberg, 1857...... 268

Map 7.1: Regional Distribution of Unadjusted Average Heights (by Oberamt), Early Period (Birthyears = 1 8 5 2 -7 3...... ) 343

xxvii Page

Map 7.2: Regional Distribution of Unadjusted Average Heights (by Oberamt), Late Period (Birthyears 1874-93) ...... 344

Map 8.1: Regional Distribution of Average Daily Wages for Male Workers, Age 16+, in Wurttemberg. 1898...... 379

Map 8.2: Percentage of Infants Ever Breastfed in Several German Regions around 1 9 1 0...... 380

Map 8.3: Infant Mortality Rates (out of 1,000 Births) in Several German Regions in 1910...... 381

Map 8.4: Heights of Military Recruits (cm.) in Several German Regions in 1906...... 382

xxviii CHAPTER X

Figures, Tables, and Maps

Socioeconomic Proximate Functional Stature Determinants Determinants Consequences

' Income • Diet Mortality ' Public Health • Disease Age ■ Personal Hygiene • Work Intensity Gender 1 Disease Environment • Maintenance Sources Disease ■ Technology • Genetic Morbidity ' Labor Organization Mental ’ Cultural Values Military Personality Manilests Oath Takers Students Passports Convicts Pottos Registration of Free Negroes National Guard Firemen Voter Registration Contraband Slava Property Runaway Slave Ads Surveys Skeletal Remains Runaway Indentured Servant Ads

Figure 2.1: Relationships Involving Stature. Source: Steckel (1992)

196 iue .: h Ascain n oen owy Between Norway Modern in Association The 2.2: Figure 18) n lu e a. (1990) al. et Floud in (1984) Body Height and Mortality by Sex and Age. Source: Waaler Waaler Source: Age. and Sex by Mortality and Height Body

Percent 0 0 1 0 J 40 - 0 6 8 150 - - 7 180 170 Z ~ 80-84 - ■V . 85-69 85-69 , 9 7 - 5 7 ,- 190 - -,6 0 -6 4 i 4 -6 0 --,6 - - 50-54 | 50-54 - - Body height (cm) 20-24 / - 29 9 -2 5 .-2 ' 55-59 1 55-59 ' ■ 70-74 I 75-79 I 75-79 ■70-74 •35-39 -30-34 -30-34 45-49 j i 55-59 08 ••• |80-84 85-69 ' 06 . 60-64 56 • 65-69 .. 70-74 140 04 , 40-44 45-49 -54• 0 5 03 . 30-34 -- 9 -3 5 3 20-24 '■ 25-29% 5 160 150 Females \ 170 180 197 iue .: eaie otlt Rts mn Nrein Men Norwegian Among Rates Mortality Relative 2.3: Figure gd 05, ewe 16 ad 99 Suc: alr18) in Waaler(1984) 269 p. Source: (1990), 1979. al et and Floud 1963 Between 40-59, Aged

Relative mortality 0.« 2 1 2 1 . . . . 0 5 0 5 - - - - 140

150 160 Body height (cm) height Body 170 180 190 200 198 iue .: eaie eeto Rts o hoi Conditions Chronic for Rates Rejection Relative 2.4: Figure ry Suc: lu e a (90, . 269. p. (1990), al et Floud Source: Army. in a Sample of 4245 Men Aged 23-49 Examined for the Union Union the for Examined 23-49 Aged Men 4245 of Sample a in

Relative rejection rate 0 2 2 3 1 1 ...... 5 0 5 0 0 5 ------

i 1 ~i 59

61 -----

63 1 -----

Height (inches) Height 65 icltr iess diseases Circulatory 1 -----

67 1 ----- nunlhris — • hernias Inguinal

69 1 -----

All causes causes All 71 a et o— teeth Bad 1 -----

73 1 -----

75 1 -----

• 77 ------1 ......

------... • o 199 200

I90r

18oi

! Roy» 1701- GM* 160K

I50F

UOr

Z 100

901-

601

SO

A ge, years

Figure 2.5: Typical Individual Height-Attained Curves for Boys and Girls (Supine Length to the Age of 2). Source: Tanner, Whitehouse, and Takaishi (1966) in Tanner (1978), p. 13. Figure 2.6: Typical Individual Growth Velocity Curves for Boys and Girls. Girls. and Boys for Curves Velocity Growth Individual Typical 2.6: Figure

Height gain, cm/yr These Curves Represent the Velocity of the Typical Boy and and Boy Typical the of Velocity the Represent Curves These Girl at Any Given Instant. Source: Modified from Tanner, Tanner, from Modified Source: Instant. Given Any at Girl hthue adTkih (96 i Tne 17) p 13. p. (1978), Tanner in (1966) Takaishi and Whitehouse, 2 1 14- 13- ■ 4 - Age. yean Age. CirU* 201 202

180 r

•EivoMinlCondonl •— —» African onginlWMfMgtofl.DC) AaiancfHowgAong.6 ISwaM oil)

Age. years tiii 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Figure 2.7: Mean Heights of European, Asian, and African- American Boys. Source: Evelyth and Tanner (1976) in Tanner (1978), p. 138. 203

180

170

160

150 /•'

130

1201- _•

110

100 EuropMnlLondont 90 African ongmfWaafMnQlon. OCI AawttcfMongacnq. 7*«8waii*off) 80 Age. years

Figure 2.8: Mean Heights of European, Asian and African-American Girls. Source: Evelyth and Tanner (1976) in Tanner (1978), p. 13 204

HEIGHT BOYS CM. WARS HEIGHT GIRLS CM. WAR I 170 u o r

SUL 14-17 160

ISO ISO -JSUl h-m n-o 140 140

ISO ISO

120 120 7-» 7-1

1910 1920 1940 1930 1910 1920 1930 1940 19 SO

Figure 2.9: Effects of Malnutrition on Growth in Height, Stuttgart School Children. Source: Tanner (1962) in Evelyth and Tanner(1976 205

Poorly-otl 130 128 Well-off 126 124 II IP 122 ,|H " N 120 I I ll 1 18 PIP 116 PIP I I 14 PIP PIP 112 PIP PIP 110 PIP PIP 108 PIP 106 PIP 104 PIP PIP 102 PIP 100 m i iifl Oslo USA UK 1960s Hong Kong JamaicaII Guatemala Sweden UK !9H0s Japan Nigeria ll India Costa Rica P Figure 2.10: Comparison of Heights of Poor and Well-off BoysP in Eleven Countries. Source: Evelyth and Tanner (1976).It Ps i 206

Table 2.1: Correlations Between Average Height and the Log of Per Capita Income.

Group Corre la 1 1 on Number of Countries

Boys Aged 12a 0.90 16

Girls Aged 12a 0.89 15

Adult Men^ 0.84 16

Adult Women*3 0.90 17

Source: Calculated from data in Eveleth and Tanner (1976), Summers et a l.

(1980), and World Bank (1980). The results are reproduced from Steckel

(1983).

a. The countries represented for boys and girls are Czechoslovakia, Egypt,

German Democratic Republic, Ghana, India, Japan, Lebanon, Netherlands, Nev

Zealand, Republic of Korea, Soviet Union, Taiwan, United States, and

Uruguay; the boys also include Mozambique. The United States has two

height studies. 7 b. The countries represented for adults are Bulgaria, Czechoslovakia, India,

Indonesia, Netherlands, Paraguay, Soviet Union, Taiwan, and the United

States. The adult men sample also includes Denmark and Zaire, and the

adult women sample also includes France, New Zealand, Republic of Korea,

and Ireland. India and Zaire have multiple height studies.

Source: Steckel, 1982. 207

Table 2.2: Estimated Heights of Male Slaves Compared with Modern Standards.

Standard Standard Estimated Col. (4| Deviation Deviations t entile Slave Modem Minus of Moslem Melon- .if Age Height Velocity’ Standard1' Col. (2) Sianslard' MiyJcrn Moslem a s 33.70 2. *5 41.34 s.64 1 2."l 0 2 5.3 3*.42 2.62 43 90 3 4* 2 07 2 6< II 1 6.3 40.93 2.41 46.30 5.37 2.20 2.41 • o 7 7.3 43 26 2.24 4* 3* 5.32 2.30 2.31 1.0 *.3 4S 42 2.10 30.73 5.33 2.3* 2.24 1 3 9 3 47.47 2.00 32.*7 5 40 2 47 2.19 1 t 10.3 49.43 1.96 54.*4 5.39 2 Ml 2 07 19 11.3 51.42 1.99 56.97 5.35 2 *0 l.'W 2.4 12.3 33.44 2.0* 59.17 5.73 3 112 1 '*t 2.9 M.3 33.39 2.21 61.73 6.14 3.30 1.56 3 1 14.3 57 83 2.31 64.37 6.72 3 33 2.01 s x 13.3 60.13 2.26 66.97 6.*2 3 02 2.26 12 16.5 62.29 1.97 6*..3I 6.02 2.6* 2.25 12 17.3 64 04 1.31 6*.70 4 66 2.62 1.7* 3 * 1*3 63.30 1.02 6N.7R 3 4* 2.62 1 31 9 2 19.5 66.11 0.63 6R.7K 2.67 2.62 1 02 .15 » 2 0 J 66.39 0.36 6*.7* 2.19 2.62 0.K4 20.(1 21.3 66.*6 0.20 6*,7* 1.92 2.62 0 73 23.3 Adult 67.17 6*.7* 1.61 2.62 0.61 27.1

* Value of (he first derivative of the Preece-Baines function at exact nee shown. This is an "instantaneous" measure of velocity. k From i. M. Tanner. R. H. Whilehouse. and M. Takaishi. "Standards from Birth to Maiiiriiv for Height. Weight. Height Velocity, and Weight Velocity: Dritixh Children. Part II.” A n ln rc s o f Distale in Childhood. 41 (Dec. 1966). pp. 6I3-J5. * Augmented to compensate for aggregation of exact ages according to M J.R . I lealv'. " Pic hffcct of Age-Grouping on the Distribution of a Measurement Affected hy Growth." Am rnm nJournal o f Physical Aaihropotogr. 20 (Mar. 1962). pp. 49-JO. This adjustment is particularly important for ages at which growth is rapid. Source: Steckel (1986). Table 2.3: Estimated Heights of Female Slaves Compared with Modem Standards.

Sliimliiid St.nml.iiil F.*timated f'ol. (41 Deviation Deviation* i entile Slave Modern Minn* nf Modem Relmv nf AfC lleitht Velocity1 Standard* Col. 12) Standard* Modern Modern 4.5 '5.00 2.77 40 57 4.97 1.94 2.56 il * < < i* 51 2.51 41 41 4 00 2.07 2.57 n q 6 5 40 95 2.29 41 51 4 90 2.20 2.25 l.i 7 J 4VI2 2.11 4* II 4 99 2 *o 2.17 I * * .< 45 16 1.9* *o.5l 5 15 2.40 2. 1* 1 6 9 5 47 12 1 95 *2.56 5.44 2.50 2.15 1 * 10 5 49 06 1.99 54.92 *56 2.6* 2.19 1 4 II.J 51.15 2.16 57.52 6.59 1 04 2.10 1 5 12.3 55.59 2.5* 60 04 6 6* 2.91 2.27 1 2 IV5 5 5. *4 2.16 62.17 6 55 2.6* 2.59 it 5 14 * *5 1* 2.16 61.41 5 25 2 4* 2.14 1 6 I ' 5 60 04 1.55 61.7R 1 74 2.55 1.57 < 5 16.5 61.24 11.90 61.56 2.62 2.36 1 II 1 1 4 17 5 61 91 0 46 63.56 1.9* 2.56 II Ml It 11 1*5 62.24 0 22 65 *6 1 62 2.56 0 69 1 5 19 5 62.59 n .m 61.56 1 47 2.56 0 6* 6 5 20 5 62.46 o o« 61 56 1 40 2.56 0 *9 7 5 21.3 62.49 0 0 2 61 56 1 57 2.56 0.5* 5 1 Adidl 62.51 61 56 1.35 2.56 0.57 5 4

Source: Steckel (1986). iue .1 Aeae egto 1-erod iiay Recruits Military 18-year-old of Height Average 2.11: Figure Average height (in) 64- 60-1 62- 6 6 8 6 78- - - Serving in the British Army and Royal Marines, Birthyears Birthyears Marines, Royal and Army British the in Serving 7011. ore Fod t l (1990) al. et Floud Source: 1750-1916. erie 10 1900 1800 Recruited o n . i o n L o o o o o o o ao ao Birthdate 1850 160 cm165 cm 155 cm 170 cm 175 cm 209 INCHES

66.5 ■mr*L /

66

65.5

- URBAN 64.5 + RURAL

1770 1775 1780 1785 1790 1795 1800 1805 YEARS

Figure 2.12: Average Height Profile (5-Year Moving Average) for British Men Bom Between 1770 and 1815. Source: Nicholas and Steckel (1990). iue .3 Etmtd rn i te egt fSlir i the in Soldiers of Height the in Trend Estimated 2.13: Figure Army, 1740-1859 (Average of All Ages)(100 = the English English the = Ages)(100 All of (Average 1740-1859 Army, ainl vrg, 709. ore Kmo (1993). Komlos Source: 1740-9). Average, National

Index 100 101 too 101 98 99 98 1720 1780 1740 1720 1760 1780 Year Year 1800 1800 801840 1820 8014 18601760 1840 1820 I8601740 Scotland Ireland 211 212

Haight m cm Haight m »ncnag 17« trend In im ig t llnal halgnt 69 (S-yaar Oirtn cohort!) 174 173 InterpoUllon oaiad on 66 Ohio National Guard

t70 166

1710 1730 17S0 1770 1790 1610 1630 1650 1670 1690 1910 1930 Tear ol Otrth

90 trend In a 10 ( M e m inotHnt el S-rear oertoda)

Iron* ragiatratton data SO wolgMaa nnchrdea lorelen aem) A average el nor mem and «out hem e°,-

30

Figure 2.14. Average Height of American Native-Born White Males by Year of Birth and the Trend in Their Life Expectancy at Age 10. Source: Fogel (1986).

cm

180

175

170

165

160

aoe rat aoa mb iqoo m o d ttno uno teao m

Figure 2.15. Hypothetical Approximate Changes in Average German Heights, 600-present. Source: Wurm (1982), p. 26. 213 Map 3.1: The Zollverein (Customs Union) and the Tax Union, 1834.

W

TAX U N IO N

100 200

Source: Henderson (1975), p. 12. Tabic 3.1: German Railroads (in km). 1835-1915.

Mileage in Operation Increase per decade 1835 6 1845 2.300 2,300 1855 8,290 5,990 1865 14,600 6,400 1875 27,930 13,240 1885 37,650 9,720 1895 46,560 8,910 1905 56,980 10,420 1915 62,410 5,430

Source Stopler, p. 71.

Table 3.2: Output of Coal (in Thousands of Metric Tons).

Germany Aorvevui*ons uied throuQhoui tr>•* tto>t HC BC HC Hire Coil BC Brown Ccal

1815 1843 3,600 900 1816 1844 3.800 1,000 1817 1.300 4.400 1818 1,300 1845 1,200 4.600 1819 1.200 1846 1,300 1847 4.800 1,500 1820 1.300 1848 4,400 1,700 1821 1.400 1849 4.600 1.800 1822 1.500 1850 5.100 1.800 1823 1.500 1851 5.700 2.100 1824 1.600 1852 6,500 2.400 1825 1.600 1853 7,000 2.500 1826 1.600 1854 8,400 2,600 1827 1,700 1855 9 9 0 0 2,900 1828 1.700 1856 10.600 3.200 1829 1,700 1657 11.200 3,800 1830 1.800 1858 12.200 4.000 1831 1,700 1859 11.400 4.300 1832 1.900 1860 12.348 4.383 1833 2.1 0 0 1861 14.133 4.622 1834 2,100 1862 15.576 5,084 1835 2 .100 • 863 16.907 5.460 1836 2,300 1864 19.409 6.204 1837 2,700 500 1365 21,795 6.758 1838 2,900 600 I ” 1866 21.630 1839 3,000 700 6.533 1867 23 806 6 995 1840 3,200 700 1868 25.705 7.1 74 1841 3,400 600 1869 26.774 7.570 1842 3,800 1.000

Source: Mitchell (1975). 215

Tabic 3.3: The Production of Pig Iron tn Germany

Year Tons Year Tons 1825 40,000 1850 215,000 1830 46,000 1855 420.000 1835 144,000 1860 529,000 1840 183,000 1865 988,000 1845 185.000 1870 1,390,000

Source: Bowden (1937)

of Tom

Coal Production 40.000 Pig Iron Production 1000 30,000 750

500 im 450 Franoi 400 •000 350 300 10,000 250 4000 200 Franc*. 1000

ISO

1000

Cl. 100

1000 2000

TOO 1500 40

Figure 3.1: Production of Coal and Pig Iron in Germany and France, 1810-1870. Source: Bowden (1937)! 216

Tabic 3.4: Steam Engines Installed in the German Customs Union. 1846 and 1861.

[ 1846 ] [ 1861 1 Number Horse­ Per Cent Number Horse­ Per Cent power of Total power of Total H . P . H.P. Mines 340 10,664 40.4 2,059 72,350 38.0 Metallurgy 143 4,164 15.8 763 18,634 10.0 Machine building 139 1,687 6.4 618 6,584 3.5 Textile* 261 4,744 18.2 1,394 30,638 16.0 Agriculture 164 2,050 7.7 1,693 21,183 11.5 Miscellaneous 369 3,045 11.5 3,586 35,260 21.0 1,416 26,354 1 0 0 .0 10,113 184,649 1 0 0 0

Source: Bowden (1937).

Table 3.5: Number of Bank Accounts and Total Deposits per 100 Residents in Several German Regions, 1830-1915.

Jahr Baden 1 Thiiringen Hamburg/ Sachsen Hessen PreuBen Wurttem­ Mecklen- Bay cm D eut­ Bremen berg burg- sches Schwerin Reich4 a; b> a: b> b ‘ j’ b* b 1 a; b> a; b* a* b* a* b> 1850 8 26 C.l O.3 1840 '3 43 5 5 2 5 1 64,2 1850 22 94 S / 3 10 2 3 *6).8 i 860 6 43 35 212 '3 43 6 18 3 8 387.6 1870 7 }S 43 101 18 46 8 3i 6 20 33 6 10 9*5.4 1880 11 86 48 158 3' " J 11 68 11 58 46 6 ■7 261),8 1890 •7 .48 37 4i5 46 167 ■7 119 '9 110 17 67 66 10 33 5 *37.3 1900 aaj a«7 44 281 56 220 22 181 45 167 43 110 21 8) •3 54 8 8) 8,6 1910 3* 180 J* 35° J6 3?4 66 359 43 281 34 470 3' 4I> 22 109 '5 88 16780,6 19 1S J* 46 a 61 4aj 73 44} 74 4C6 49 34i 3* 3'4 37 48j 22 1 16 18 109 ao ) 8o ,7

Source: Kiesewetter (1989), p. 302. 217

Table 3.6: Population on Reich Territory, 1816-1915.

Increase Population by decades x S16 : 4,833,00c) 1 8 2 5 2 8 , 1 1 3 ,0 0 0 1 3 .2 % 1635 30,802,000 9.6 1 8 4 5 3 4 ,2 9 0 ,0 0 0 1 1 .3 « 8 5 5 3 6 , 1 3 8 ,0 0 0 5 .4 1 8 6 5 3 9 ,5 4 8 ,0 0 0 9.4 1 8 7 5 4 2 ,5 1 8 ,0 0 0 7 .5 1 8 8 5 4 6 ,7 0 7 ,0 0 0 9 .9 1 8 9 5 5 2 ,0 0 1 ,0 0 0 1 1 .3 1 9 0 5 6 0 ,3 1 4 ,0 0 0 1 6 .0 2 9 * 5 6 7 ,8 8 3 ,0 0 0 1 2 .5

Source: Stolper (1940).

Table 3.7: Overseas Emigration from Germany.

Overseas Emigration from Germany

1 8 2 1 - 3 0 ...... 8 ,5 0 0 1 8 3 1 - 4 0 ...... 1 6 7 ,7 0 0 1 8 4 1 - 5 0 ...... 4 6 9 ,3 0 0 1 8 5 1 - 6 0 ...... 1,0 7 5 ,0 0 0 1 8 6 1 - 7 0 ...... 8 3 2 ,7 0 0 1 8 7 1 - 8 0 ...... 6 : 6 ,0 0 0 1 8 8 1 - 9 0 ...... 1, 3 4 2 ,4 0 0 1 8 9 1 - 1 9 0 0 ...... 5 2 9 ,9 0 0 1 9 0 1 - 1 0 ...... 2 7 9 ,6 0 0 1 9 1 1 - 2 0 ...... 9 1 ,0 0 0 1 9 2 1 - 3 0 ...... 5 6 7 ,3 0 0

Total ...... 5 ,9 8 9 ,4 0 0

Source: Stolper (1940). 218 Table 3.8: Percentage ot Population in Communitv-Size Classes, 1852-1961.

Jkhr 1851 | 1871 1880 1800 1000 1010 I 1025 1038 1080 1050 1051

grdOankUiM 1 1 2 3 4 5 a ! 7 8 0 10 11 bis unter 2000 67,3 83,9 59,2 53,0 46,2 40,0 , 35,8 32,9 30,1 28,9 23,2 2000 bia 6000 13,1 12,4 11,9 19,1 11,6 11.3 : 10.8 10,6 10,7 13,6 12,4 6000 bia 10000 8,2 8,3 8,6 19,1 7,1 7,6 1 6,9 7.1 7.5 8.9 9.1 10000 bia 20000 4,8 4,9 5.4 6.0 6,3 6.5 l 6,2 6,0 6,4 7.1 7,4 20000 bia 60000 3,6 3.6 4,8 6,9 7,3 7,9 1 8,0 7.7 8,4 8,8 10,0 60000 bia 100000 2.6 4,1 3,3 2,9 4,5 5.4 1 6.7 5,3 5,3 5.4 8,7 100000 u. dariiber 2,8 4,8 8,8 12,1 17,1 21,3 | 26,8 30,4 31,6 27,3 31,1

Source: Hoffman (1965), p. 178.

Table 3.9. Growth of the Big German Cities, 1875-1910.

City 1875 1890 1910 Growth rate (In %)

Berlin 966.859 1,587,794 2,071,257 114.3 Bremen 102.532 125,684 217,437 112.1 Breslau 239,050 335,186 512,105 114.2 Charlottenburg 25,847 76,859 305,978 1,083.8 Chemnitz 78,209 138,954 287,807 268.0 Dortmund 57,742 89,663 214,226 271.0 Dresden 197.295 276.522 548,308 177.9 Duesseldorf 80,695 144,642 358,728 344.6 Duisbure 37,380 59,285 229,438 513.8 Essen-Ruhr 54,790 78,706 294,653 437.8 Frankfurt a. M. 103,136 179,985 414,576 302.0 Hamburg 264,675 323,923 931,035 251.8 Hannover 106,677 163.593 302,375 183.5 Kiel 37,246 69,172 211,627 468.2 Koeln 135,371 281.681 516,527 281.6 Koenigsberg 122,636 161.666 245,994 100.6 Leipzig 127.387 295.025 589,850 363.0 M agdeburg 87,925 202,234 279,629 218.0 Muechen 193,024 349,024 596,467 209.0 Nuernberg 91.018 142.590 333,142 266.0 Stettin 80,972 116,228 236,113 191.6 Stuttgart 107,273 139,817 286,218 166.8 22 cities 3,297,739 5,338,233 9,983,490 202.7

Source: Kiesewetter (1989), p. 135. Table 3.10: Employment Structure in Germany, 1882, 1907, 1925, and 1933.

Number of Gainfully Employed

1882 1907 1925 1933 (three ciphers omitted) Agriculture and forestry . . . 7 ,1 3 5 8 ,5 5 7 9<763 9>343 Industry and crafts. 5 ,9 8 8 9 ,9 8 1 13*479 13,053 Commerce and communica­ tions 1,420 3,441 5 ,1 8 5 5,939 Public and private services .. 9 8 4 1,712 2 ,1 8 8 2,699 Domestic service 1,358 1,465 1 ,3 9 4 '* 29 6

Total 16,885 2 5 ,1 5 6 3 2 ,0 0 9 32,296

Percentage of Total Agriculture and forestry...... 42.3 34.0 30.5 28.9 Industry and cra fts...... 35-5 39-7 42.1 4 0 4 Commerce and communications 8 4 13-7 16.2 1 8 4 5.8 6.8 6.8 8 4 8.0 5.8 4-4 3-9 100.0 100.0 100.0 100.0

Source: Stolper (1940), p. 41. Table 3.11: Employment Structure in Germany by Economic Sector, 1849-1959.

Periods Agriculture Mining Industry and Transportation Trade Domestic Other Total Labor Force Crafts Services Workers Participation Rate (in %) 1,000 % 1 2 3 4 5 6 7 8 9 1849/55 54.6 0.9 24.3 1.1 5.0 9.3 4.8 15,126 43.9 1861/71 50.9 1.3 26.3 7.2 8.8 5.5 16,450 43.0 1878/79 49.1 1.4 27.7 2.0 6.6 7.7 5.5 19,416 43.7 1880/84 48.2 1.6 28.2 2.1 6.9 7.5 5.5 19,992 43.7 1885/89 45.5 1.6 30.7 2.3 7.2 7.1 5.6 21,302 44.7 1890/94 42.6 1.8 32.4 2.6 7.9 6.7 6.0 22,651 45.1 1895/99 40.0 2.0 33.7 2.8 8.7 6.4 6.4 24,277 45.3 1900/04 38.0 2.4 34.4 3.1 9.7 5.8 6.6 26,043 45.1 1905/09 35.8 2.6 35.1 3.5 10.6 5.6 6.8 28,047 45.2 1910/13 35.1 2.8 35.1 3.6 11.0 5.2 7.2 30,243 46.0 1925 31.5 2.4 37.7 4.7 12.5 4.4 6.8 31,033 49 7 1933 33.9 1.6 31.0 4.9 14.6 4.0 9.9 26,687 40.9 1939 27.4 1.9 38.9 5.2 12.4 3.8 10.2 39,680 1950/54 21.6 2.8 40.7 5.7 14.6 2.8 11.8 21,541 45.0 1955/59 16.5 2.6 44.2 5.6 16.9 2.5 11.7 24,521 48.6

Source: Hoffman (1965), p.35. Table 3.12: Employment Structure in Germany by Economic Sector, 1846-1926 (1,000's).

Year Agriculture Mining Industry and Transportatio Trade Bank Servants Misc. Defense Total Crafts n 1 2 3 4 5 6 7 8 9 1846 98 3,305 132 728 1,400 398 259 1849 8,206 95 3,396 147 720 1,405 411 341 14,813 1852 8,293 114 3,649 158 757 1,361 432 264 15,028 1855 8,195 157 3,709 168 773 1,442 447 307 15,198 1858 8,235 183 3,938 204 783 1,403 457 289 15,492 1861 8,253 174 4,187 230 823 1,411 473 416 15,967 1867 8,333 225 4,155 271 879 1,422 508 378 16,171 1871 8,541 255 4,762 1359 1,491 530 399 17,337 1875 9,230 286 [5,153 349 1,116 1,490 589 430 18.643 1876 9,250 285 6,327 1,150 430 18,891 1877 9,382 273 5,346 431 19,108 1878 9,518 275 5,300 378 1,262 1,484 1628 432 19,277 1879 9,568 278 5,468 384 1,301 1,489 634 432 19,554 1880 9,565 297 5,504 395 1,319 1,492 632 434 19,638 1881 9,609 309 5,492 406 1,350 1,476 642 461 19,745 1882 9,665 323 5,580 420 1,377 1,487 645 461 19,958 1883 9,711 337 5,691 437 1,402 1,498 651 461 20,188 1884 9,698 343 5,887 451 1,430 1,508 652 461 20,430 1885 9,700 345 6,005 461 1,457 1,488 659 462 20,577 1886 9,740 340 6,307 477 1,487 1,486 672 464 20,973 1887 9,720 340 6,489 486 1,526 1,502 695 507 21,265 1888 9,645 352 6,763 612 1,579 1,525 723 507 21,606 1889 9,638 371 7,123 537 1,625 1,539 749 508 22,090 Table 3.12: Cont'd.

1890 9,565 398 7,337 569 1,684 1,532 778 509 22,372 1891 9,551 418 7,343 590 1,733 1,519 802 520 22,485 1892 9,543 425 7,375 596 1,784 1,512 823 530 22,588 1893 9,656 423 7,311 600 1,840 1,529 848 531 22,738 1894 9,765 420 7,361 607 1,892 1,541 870 605 23,070 1895 9,788 432 7,524 620 1,970 1,571 894 606 23,405 1896 9,778 446 7,909 633 2,036 1,567 915 607 23,891 1897 9,728 472 8,211 667 2,101 1,566 936 609 24,200 1898 9,720 499 8,506 709 2,172 1,544 959 610 24,719 1899 9,709 527 8,741 741 2,248 1,520 984 612 25,082 1900 9,754 575 8,950 783 2,340 1,508 1,009 620 25,548 1901 9,825 617 8,784 794 2,423 1,502 1,037 635 25,617 1902 9,947 613 8,777 803 2,516 1,503 1,071 639 25,869 1903 9,987 631 9,026 824 2,627 1,509 1,103 642 26,349 1904 9,999 656 9,208 857 2,728 1,518 1,133 645 26,834 1905 9,926 665 9,572 901 2,806 1,541 1,159 651 27,221 1906 9,888 692 9,880 957 2,897 1,568 1,194 658 27,734 1907 9,897 [739 10,070 1,012 2,970 1,581 1,233 664 28,166 1908 10,006 792 9,857 1,032 3,042 1,583 1,278 67G 28,350 1909 10,350 809 9,873 1,037 3,127 1,571 1,320 675 28,762 1910 10,542 816 10,184 1,048 3,216 1,570 1,364 680 29,420 1911 10,627 827 10,550 1,077 3,292 1,569 1,404 688 30,034 1912 10,663 840 10,818 1,116 3,371 1,567 1,453 721 30,549 1913 10,701 863 10,857 1,174 3,474 1,542 1,493 864 30,968 1924 759 10,845 1925 9,778 743 11,708 1,472 3,864 1,357 1,969 142 31,033 1926 9,680 690 10,496 1,467 3,945 1,339 2,092 143 29,852

Source: Hoffman (1965), pp 204-205. 222 Table 3.13: Industrial Population of Germany, 1875.

Number of Wage Total Number of Wage Percent of Total Earners per Earners Establishments Less than 5 4,159,000 64.3 5 to 10 189,000 2.9 11 to 50 891,000 13.8 Over 50 1,231,000 19.0 Total 6,470,000 100.0

Source: Bowden (1937), p.501.

Table 3.14: Coal Production of the World, in Millions of Metric Tons, 1864-1935.

United Great Germany France Belgium Total Slates Britain (indudes lignite) 173.7 1864 22.8 94.3 26.1 11.2 11.1 1865 24.7 99.7 28.3 11.8 11.8 182.0 1870 29.9 112.2 34.8 13.3 13.6 217.8 1875 48.2 135.4 48.5 16.9 1S.0 285J 1880 66.8 149.3 59.1 19.3 16.8 339.3 1885 102.1 161.9 73.6 19.5 17.4 412.8 1890 141.6 184.5 89.2 26.0 20.3 513.1 562.3 1895 171.7 194.3 103.8 28.2 20.4 1900 243.4 228.7 149.5 33.4 23.4 765.1 1905 351.1 239.8 173.6 36.0 21.8 928.0 1910 445.8 264.5 221.9 38.5 23.1 1,143.7 1915 531.6 283.5 259.1 19.9 15.6 1,270.0 1920 598.0 233.2 252.3 25.3 22.3 1,317.0 1925 527.7 247.0 272.3 48.0 23.0 1,372.0 1930 487.0 247.7 301.7 54.9 27.4 1,413.0 1933 342.3 210.3 247.1 47.9 25.2 1,154.0 1935 381.2 226.5 292.0 47.1 26.4 1,327.0

Source: Bowden (1937), p. 511. 224

Million* Million* of Ten* of Ton*

Coal Production Pig Iron Production 300 IS O -30

200 10.0 ■ 20

cr. v; B ritain ^ j 100, * 5.0 ■ 1 0 “ “• 4.5 to

/ Franc*

Steel Production

1.0

Gr. B ritain IMS *70 '60 '?0 IS00 ’10 *20 *30

Figure 3.2: Production of Coal, Pig Iron, and Steel in Germany, Great Britain, and France. Source: Bowden (1937)

Table 3.15: German Foreign Trade, 1872-1913.

(Without re-export; in marks) Exports Imports 1 8 7 2 ...... 2 ,4 9 2 ,0 0 0 ,0 0 0 3 ,4 6 5 ,0 0 0 ,0 0 0 1 8 8 0 ...... 2 ,9 7 7 ,0 0 0 ,0 0 0 2 ,8 4 4 ,0 0 0 ,0 0 0 1 8 9 0 ...... 3 .4 1 0 ,0 0 0 ,0 0 0 4 , 2 7 3 ,0 0 0 , 0 0 0 1 9 0 0 ...... 4 ,7 5 3 ,0 0 0 ,0 0 0 6 ,0 4 3 ,0 0 0 ,0 0 0 1 9 1 0 ...... 7 ,4 7 5 ,0 0 0 ,0 0 0 8 ,9 3 4 ,0 0 0 , 0 0 0 1 9 1 3 ...... 1 0 ,0 9 7 ,0 0 0 ,0 0 0 1 0 ,7 7 0 ,0 0 0 ,0 0 0

Source: Stolper (1940), p. 52. Figure 3.3: Development of Real Gross Wages (1913 = 100), 4 Estimates, and and Estimates, 4 100), = (1913 Wages Gross Real of Development 3.3: Figure noch D tsat noch K u c zy n sk i too 100 50 to p.101 el e aia noe dte ln) Suc: ign (1982), Wiegand Source: line). (dotted Income Capita Per Real o m 1190 1900 mo Johr Jahr ID •c o u too to 60 50 90 t oo 70 to o m o m o m 19 00 1900 Jahr Jahr 225 226

Indi'i t.'V/J • .07)

>00

95

90

J?

to

75

70

45 ^C/l/

M90 t«9 5 «C0 J9C5

ii M O*«o* OracgA

Figure 3.4: Development of Cost of Living Indices in Germany (1871-1913 (1913 = 100), Estimates by a) Kuczynski b) Desai and c) Orsagh. Source: Wiegand (1982). p. 92 227 Table 3.16: Development of Average Workday in German Industry, 1800-1914.

Period Average Workday (Hours) around 1800 10-12 around 1820 11-14 around 1830-1860 14-16 1861-1870 12-14 1871-1880 12 1881-1890 11 1911-1995 10.5-11 1996-1900 10.5 1901-1905 10-10.5 1906-1910 10-10.5 1911-1914 10

Source: Wiegand (1982), p. 45.

Table 3.17: Development of Average Workweek in German Industry, 1830-1914.

Period Average Hours per Week around 1830-1860 80-85 1861-1870 75 1871-1880 72 1881-1890 66 1891-1895 63-65 1896-1900 61-63 1901-1905 59-61 1906-1910 58-60 1911-1914 54-60

Source: Wiegand (1982), p. 45. Table 3.18: Development ol Average Work Week in Different Branches of German Industry.

Average Hours Worked per Week Period Textiles Metal Metal W ork Chemicals Printing Construction Wood Extraction around 1825 ca.75 around 1850 ca. 90 1860-1870 ca. 81 1870-1875 69 1871-1880 ca. 72 1881-1890 ca. 69 1880-1905 60 before 1885 66 1885-1890 63 before 1890 66 60 1891-1895 65 58.5 1891-1900 63 60 1893 61.5 1896-1900 61.5-64.5 57 1897 59-3 1901-1905 59.5-62.5 54 60 1902 58.3 1906 57.0 1906-1910 57.5-60.5 53 1901-1914 60 56 1911-1914 52 1911-1915 55-60 1913/14 54-60 54-57 1914 58 1916-1918 50

Source: Wiegand (1982), p. 47 228 229

Table 3.19: Development of Infant Mortality Rate in Prussia, 1816-1900

Year Male Female Total Year Male Female Total 1816 18.77 15.89 17.37 1856 19.52 16.81 18.19 1817 18.79 15.98 17.42 1857 21.24 18.42 19.36 1818 17.44 14.89 16.20 1858 22.76 19.63 21.23 1819 18.72 16.00 17.39 1859 22.11 19.31 20.75 1820 17.20 14.53 15.90 1860 20.01 17.16 18.62 1821 16.96 14.30 15.66 1861 22.83 19.83 21.37 1822 18.73 16.03 17.52 1862 21.20 18.47 19.87 1823 18.18 15.56 16.91 1863 22.48 19.51 21.03 1824 18.01 15.22 16.65 1864 21.46 18.30 19.91 1825 18.25 15.42 16.87 1865 23.50 20.54 22.11 1826 19.79 16.87 19.36 1866 22.89 19.63 21.27 1827 19.07 16.99 18.65 1875 22.95 19.63 21.34 1828 19.17 16.29 17.71 1876 22.20 18.81 20.55 1829 19.17 16.45 17.85 1877 21.50 18.42 20.00 1830 19.44 16.54 18.02 1878 22.03 18.81 20.45 1831 19.71 17.10 18.44 1879 21.07 17.97 19.56 1832 19.34 16.78 18.09 1880 23.21 20.13 21.71 1833 19.83 17.03 18.47 1881 21.36 18.42 19.93 1834 21.33 18.40 19.90 1882 22.35 19.13 20.78 1835 18.86 16.14 17.53 1883 22.68 19.47 21.12 1836 18.06 15.34 16.73 1884 22.83 19.72 21.32 1837 20.42 17.26 18.88 1885 21.88 18.84 20.40 1838 19.17 16.48 17.85 1886 24.07 20.90 22.53 1839 20.39 17.53 18.99 1887 21.39 18.40 19.94 1840 19.35 16.38 17.90 1888 21.30 18.14 19.82 1841 19.66 16.92 18.32 1889 22.19 19.19 20.73 1842 20.07 17.26 18.70 1890 22.53 19.36 20.99 1843 21.00 18.22 19.64 1891 21.67 18.39 20.07 1844 18.01 15.44 16.76 1892 22.81 19.34 21.13 1845 19.28 16.42 17.89 1893 22.16 19.04 20.64 1846 20.38 18.14 19.54 1894 19.57 21.03 18.04 1847 21.40 18.46 19.97 1895 22.85 19.38 21.16 1848 20.91 18.20 19.63 1896 20.64 17.56 19.74 1849 18.16 15.76 16.98 1897 22.02 18.85 20.48 1850 19.85 16.94 18.43 1898 20.79 17.77 19.32 1851 19.03 16.38 17.74 1899 22.00 18.74 20.41 1852 22.00 19.24 20.50 1900 22.90 19.50 21.25 1853 20.52 17.59 19.09 1854 21.59 18.78 20.22 1855 20.63 18.04 19.37

Source: Wiegand (1982), p.396 230

Table 3.20: Development of Infant Mortality Rate in the German Empire, 1901-1938, and Federal Republic of Germany, 1949-1975.

Year Male Female Total Year Male Female Total 1901 22.27 18.98 20.67 1935 7.65 6.00 6.85 1902 19.90 16.63 18.31 1936 7.39 5.78 6.61 1903 22.02 18.68 20.39 1937 6.43 1904 21.19 17.99 18.74 1938 6.72 5.17 5.97 1905 22.16 18.80 20.53 1949 6.52 5.22 5.89 1906 20.10 16.86 18.52 1950 6.19 4.88 5.56 1907 19.05 15.96 17.55 1951 5.93 4.67 5.32 1908 19.36 16.18 17.81 1952 5.36 4.26 4.83 1909 18.39 15.43 16.95 1953 5.16 4.11 4.65 1910 17.56 14.72 16.18 1954 4.77 3.81 4.31 1911 20.69 17.65 19.21 1955 4.61 3.71 4.18 1912 16.03 13.36 14.73 1956 4.28 3.42 3.86 1913 16.38 13.68 15.07 1957 4.08 3.39 3.64 1914 17.70 14.91 16.35 1958 4.00 3.17 3.60 1919 13.15 10.91 12.07 1959 3.79 3.03 3.42 1920 14.38 11.75 13.11 1960 3.76 2.96 3.37 1921 14.92 11.78 13.38 1961 3.54 2.77 3.17 1922 14.24 11.60 12.97 1962 3.27 2.56 3.92 1923 14.40 11.87 13.18 1963 3.00 2.37 2.70 1924 11.90 9.74 10.86 1964 2.81 2.22 2.52 1925 11.58 9.38 10.52 1965 2.66 2.09 2.38 1926 11.18 9.07 10.16 1966 2.66 2.04 2.36 1927 10.71 8.62 9.70 1967 2.57 1.98 2.28 1928 9.90 7.88 8.92 1968 2.57 1.96 2.27 1929 10.67 8.54 9.64 1969 2.63 2.03 2.34 1930 9.35 7.51 8.45 1970 2.69 2.01 2.36 1931 9.23 7.30 8.29 1971 2.62 2.02 2.33 1932 8.70 7.07 7.91 1972 2.55 1.96 2.26 1933 8.49 6.76 7.65 1973 2.61 1.95 2.29 1934 7.34 6.76 6.88 1974 2.39 1.81 2.11 1975 2.22 1.71 2.97

Source: Wiegand (1982), p.397 Table 3.21: Infant Mortality in Several German Regions. mid-1800’s.

State Period Infant Mortality Rate Oldenburg 1855/64 12.3 Schleswig-Holstein and 1855/59 12.4 Lauenburg Prussia 1859/64 20.4 Sachsen 1859/65 26.3 Baden 1856/63 26.3 Bayern 1827/69 30.7 W urttem berg 1846/56 34.8 W urttem berg 1858/66 35.4

Source: Georg von Mayr (1870) in Wiegand (1982).

Table 3.22: The Development of the Difference Between Infant Mortality Rates of Illegitimate and Legitimate Children in Prussia, 1816/20 to 1886/90, and in Bavaria 1840/41 to 1889/95.

Period Prussia Prussia Prussia Period Bavaria Male Male+Female Female 1816/20 8.9 9.8 1835/36-40/41 5.4 1821/30 8.5 9.4 1841/42-47/48 5.7 1831/40 10.2 11.4 1848/49-54/55 6.1 1841/50 11.9 13.0 1855/56-61/62 6.7 1851/60 13.9 15.3 1862/63-68/69 7.7 1861/66 14.4 15.4 1869/78 9.0 1875/82 16.06 1879/88 9.6 1886/90 16.55 1889/95 9.1

Source: Wiegand (1982), p.349 Table 3.23: Infant Mortality Rate by Occupation of Father in Germany in 1877/79 and 1912/13.

Father's Occupation Period Period 1877/79 1912/13 Setf-Em ployed 18.2 12.3 Public Official 17.5 8.3 White Collar Workers 18.6 9.3 Skilled Worker 18.9 13.1 Unskilled Worker 20.6 17.4 Servants 29.6 22.5 Total 20.1 14.8

Source: Reinhard Spree (1979) in Wiegand (1982), p.349

Years •0 -T-

70 — Male — Female AgeO *0

SO Age 30

40

JO

20 Age 60

10

''''III T I I 1 ■■■! t I I I I I 1000 10 20 JO 40 SO 40 70 40 90 1900 10 20 30 40 SO 60 70

Figure 3.5: Development of Life Expectations at Ages 0, 30, and 60 years in Schwalmer Region (1780/1809-1840/69), German Empire and the Republic of German (1871/80-1974/76). Source: Wiegand (1982), p.340. 233

Table 3.24: Life Expectancy, Ages 0, 30, 60, Males and Females in German Empire, 1871/80 to 1974/76

Life Expectancy at A rc Period 0 .0 60 M F M FMF 1 2 3 4 5 6 1871/80 35.58 38.45 31.41 33.07 12.11 12.71 1881/90 37.17 40.25 32.11 34.21 12.43 13.14 1891/1900 40.56 43.97 33.46 35.62 12.82 13.60 1901/10 44.82 48.33 34.55 36.94 13.14 14.17 1910/11 47.41 50.68 35.29 37.30 13.18 14.17 1924/26 55.97 58.82 38.56 39.76 14.60 15.51 1932/34 59.86 62.81 39.47 41.05 15.11 16.07 1946/47 57.72 63.44 39.20 42.72 15.18 16.99 1949/51 64.56 68.46 41.32 43.89 16.20 17.46 1958/59 66.75 71.88 41.39 45.30 15.74 18.27 1959/60 66.69 71.94 41.21 45.27 15.53 18.22 1960/62 66.86 72.39 41.14 45.53 15.49 18.48 1962/63 67.10 72.77 41.02 45.64 15.33 18.55 1963/64 67.32 73.13 41.10 45.84 15.40 18.75 1964/66 67.58 73.48 41.17 46.03 15.46 18.92 1966/68 67.55 73.58 41.04 46.04 15.29 18.88 1968/70 67.24 73.44 40.75 45.90 15.02 18.77 1970/72 67.41 73.83 41.00 46.30 15.31 19.12 1972/74 67.87 74.36 41.24 46.70 15.52 19.46 1974/76 68.30 74.81 41.36 46.95 15.64 19.66

Source: Wiegand (1982), p.395. 234

Table 3.25: Normal Caloric Needs by Age and Sex Per Person Per Day

Age in Years Male Female under 6 1,000 950 6 to 13 1,730 1,600 14 to 19 2,800 2,400 20 to 44 2,400 2,200 45 to 64 2,300 2,100 65 and over 2,150 2,000

Source: Hoffman (1965), p. 658

Table 3.26: Hoffman's Index of Net Nutrition, 1850-1950.

Year Actual in Billions Needed in Billions Actual/Needed Kcal Kcal % 1850/54 27,730 36,750 75 1855/59 30,240 37,650 80 1860/64 34,980 39,340 89 1865/69 36,710 40,860 90 1870/74 39,140 43,400 90 1875/79 46,530 46,440 100 1880/84 46,130 48,530 95 1885/89 52,850 50,610 104 1890/94 57,670 53,250 108 1995/99 67,410 56,860 119 1900/04 74,620 60,820 124 1905/09 79,160 65,140 122 1910/13 81,530 69,100 118 1925/29 74,540 68,860 108 1930/34 76,440 68,650 111 1935/38 80,440 72,430 111 1950/54 56,490 50,800 111 1955/59 61,390 54,080 114

Source: Hoffman (1965), p. 659. 235 Map 3.2: German Regional Divisions.

V 1 ast and West Prussia o. Prussian Saxonv 11. East Prussia R. District of Magdeburg. Anhalt ( West Prussia S. Districts of Merseburg and Erfurt. Thurineian Stales n I’osen T. Districts ol Munster and Minden. Lippe. Waldeck i Pi'mcrama U. Districts of Diisscldorl and Arnsherg (Ruhrl r District ol Oppeln (Upper Silesia i V. District of Aachen ( i Districts ol Hrcslau and l.icgnu/ (Lower Silesia i \v. District of Kdln fCologne) ii Dislricl ol Franklurl X. Districts of Trier and Koblenz i District of Polsdam V. Hcssen-Nnssau. Ohcrhcsscn i Berlin z. Bavaria, excluding Rheinplalz K Mecklenhur e-Schwerin and Meek leu burn- St rclitz •\ A. Wurttemberg. Ilohenzollern 1 s. hlesw le-Holsicm BB. Baden

M 11.mover CC. Hesse, excluding Oherhcsscn V II.mover. Oldenburg. Rr.uinschweie. Schaumburg l.ippc DD. Rheinplalz < ) 1 ubeck. Bremen. Hamburg ( H.mse t dies I I“K. Lorraine 1' Kingdom of Su.\onv FF \lsace

Source: Tipton (1976). Table 3.27: Sectoral Concentration and General Index of Specialization in Germany, 1882, 1895, and 1907.

1882 1895 1907 Genera! index of specialization 23.6 25.2 27.1 Sectoral concentration Agriculture 22.9 28.3 36.2 Industry 28.0 25.5 23.2 Services 18.1 18.2 19.9 S o u r c e : Computed from data in Statistical Appendix.

M e t h o d : General index of specialization: each region's total employment was mul­ tiplied by the average share of employment in each sector. These hypothetical figures, representing regional employment in each sector if the region did not deviate from the average, were subtracted from actual regional employment in each sector. The absolute sum of the differences in all regions was expressed as a percentage of total employment. Sectoral concentration: the absolute differences between actual employment in the sector in each region and employment equal to the average share were summed and expressed as a percentage of total employment in the sector.

Source: Tipton (1976). Table 3.28: Specialization in Regions of Germany, 1882, 1895, and 1907.

1882 1895 1907 East B. East Prussia 34.7 40.8 46.1 C. West Prussia 28.5 33.9 38.6 D. Posen 37.7 43.9 51.4 E. Pomerania 19.4 26.3 32.6 F. Upper Silesia 14.0 11.3 14.4 G. Lower Silesia 2.1 4.2 7.7 H. District of Frankfurt 20.7 22.5 26.3 North and Central I. Potsdam 7.2 14.7 25.9 J. Berlin 97.3 83.9 69.8 K. Mecklenburg 13.7 20.3 28.4 L. Schleswig-Holstein 12.0 14.7 16.3 N. Hanover, etc. 11.6 13.2 15.8 O. Hanse cities 86.2 75.0 62.0 P. Kingdom of Saxony 52.3 49.5 44.6 R. Magdeburg, etc. 7.0 2.5 1.6 S. Merseburg, etc. 11.5 9.8 11.4 West T. Northern Westphalia 14.6 10.3 9.8 U. R uhr 51.3 51.8 50.7 V. Aachen 23.5 16.9 16.8 W. Cologne 21.0 27.0 30.4 X. Trier and Koblenz 15.7 18.2 18.1 Y. Hessen-Nassau, etc. 3.6 5.9 3.4 South Z. Bavaria 25.8 26.3 31.6 AA. Wurttemberg 14.1 17.9 18.6 BB. Baden 16.9 14.7 10.4 CC. Hesse 19.6 18.4 18.6 DD. Rheinpfalz 18.5 10.1 11.2 EE. Lorraine 5.6 14.5 10.0 F F . Alsace 4.1 1.1 4.8

Source: Tipton (1976). 238 Table 3.29: Employment Structure in Wurttemberg and Hohenzollem, 1861, 1875, 1882, 1895, and 1907.

1861 1875 1882 1895 1007

(OOO) i% ) lOOO) 1 T ) (000) i'T-) (000) f'c) (000) CT) I Population 1800 2023 2137 2407 2. Employment 976 1083 1193 V Agriculture 5 50 56.3 '54 5 1.2 s 32 44 5 9 Industrv 227 28.4" 343 31.6 458 38.4 8 Service 149 15.3 186 17.2 203 17 0 Distribution of Industrial Employment 6 Mining, smelting 2.5 2.4 0 9 2.4 0.7 1.9 0.4 7. Manutacturing 213 a so 225 84.2 276 82.0 35.3 79.7 S. Construction 27.5 27.6 28.2 10.5 41.2 12.2 60.6 13.7 0 Transportation 12.0 4.5 17.5 s a 27.0 6.1 Distribution of Manufacturing Employment: 10. Metals 33 15.5 40 5 18.2 41.9 18.6 61.4 1 1 2 98.8 28.0 1 1. Clav, glass, sand 11 5.2 9.0 4.0 11.1 4.9 15.6 5 6 19.8 5.6 12. Woods 24 11.3 26.8 12.0 26.3 1 1.7 31.1 11.2 37.5 10.6 13. Textiles 47 22.1 41.5 18.7 34.4 15.3 41.3 14.9 54.3 15.4 14. Clothing 38 17.8 50.1 22.5 57.0 25.4 58.1 21.0 54.6 15.5 15. Food, drink 44 20.7 36.9 16.6 33.5 14.9 41.2 14.9 48.3 1 3.7 16. Other 16 7.4 17.7 8.0 20.6 9.2 27.8 10.1 39.4 11.2 Distribution of Service Employment: 17. Trade, hotels 38.9 26.1 54.4 29.2 75.2 37.0 18. Domestic service 61.5 41.2 69.2 37.2 54.9 27.0 19. Professional 20.4 13.7 25.7 13.8 35.5 17.5 20. Government 10.5 7.0 12.4 6.7 14.9 7.3 21. Military 18.1 12.1 24.6 13.2 22.8 11.2

Source: Tipton (1976) Table 3.30: Distribution ot Employment in Germany, 1882, 1895, and 1907.

1882 1895 1907 (000) (%) (000) (%) (000) (%) 1. Population 45,200 51,800 61,700 2. Employment 21,302 24,047 28,081 3. Agriculture 10,581 49.6 10,241 42.6 9,881 35.2 4. Industry 6,691 31.4 8,714 36.3 12,016 42.7 5. Services 4.030 19.0 5,092 21.1 6,184 22.0 Distribution of Indus­ trial Employment: 6. Mining 431 6.8 536 6.4 861 7.4 7. Manufacturing 4,858 76.6 6,1 vo 73.7 8,173 70.3 8. Construction 614 9.7 1,043 12.5 1,564 13.5 9. Transportation 437 6.9 615 7.4 1,026 8.8 Distribution of Manu­ facturing Employment: 10. Metals 835 17.2 1,221 19.8 2,057 25.1 11. Clay, glass, sand 377 7.8 558 9.0 771 9.4 12. Woods 480 9.9 597 9.7 771 9.4 13. Textiles 913 18.8 992 16.1 1.088 13.3 14. Clothing 1,133 22.9 1,221 19.8 1,304 16.0 15. Food, drink 695 14.3 949 15.4 1,240 15.2 16. Other 425 9.1 632 10.2 942 11.6 Distribution of Ser­ vice Employment: 17. Trade, hotels 1,133 28.1 1,724 33.9 2,452 39.6 18. Domestic service 1,723 42.7 1,771 34.8 1,737 28.1 19. Professional 143 3.6 192 3.8 271 4.4 20. Government 570 14.2 799 15.7 1,060 17.1 21. Military 461 11.4 606 11.9 664 10.7

Source: Tipton (1976). 240

Table 3.31: Degree of Industrialization in 28 German States and Regions, 1871

+ Surface Commercial Population Rank Rank Area Sector Density According According (km 2) Employment1 (Pop/km to (2) to (3) as % of Total Employment 1 2 3 4 5 Kgr. Sachsen 14,992.9 49.5 170.5 1 1 -Rheinland3 27,000.2 41.1 132.8 2 2 -Westfalen3 20,218.6 40.2 87.8 3 10

Kgr. Wurttemberg 19,507 39.9 93.2 4 7

Ghzgt. Baden 15,070.3 38.7 97.0 5 6 -Sachsen 25,267.3 35.2 83.2 6 11 -Brandenburg 39,842.3 35.1 71.9 7 14

Ghzgt. Hessen 7,688.4 33.2 110.9 8 3 -Schlesien3 40,335.1 32.4 91.9 9 8 -Hessen-Nasau3 15,708.0 32.1 89.2 10 9 -Obcrfranken4 7,001.6 31.4 77.3 11 12 -Mittclfranken4 7,559.6 30.8 77.2 12 13 -Pfalz4 5,939.2 29.7 103.6 13 5

Ghzgt. Oldenberg 6,429.1 29.0 49.0 14 24

-Schwaben4 9,496.4 28.4 61.4 15 16 -Schleswig- 19,018.8 26.4 52.4 16 19 Holstein3

Elsass-Lothringen 14,521.8 26.3 106.7 17 4

-Hannover3 38.509.4 25.4 50.9 18 21 -Obcrbayem4 17,052.6 24.4 49.4 19 23 -Obcrpfalz4 9,668.2 23.2 51.5 20 20 -Pommem^ 30,131.4 21.5 47.5 21 25 -Unterfranken4 8,401.4 21.8 69.8 22 15 -Nicderbayem4 10,771.4 19.2 56.1 23 17

Ghzgt. Mecklen- 13,126.9 18.8 42.5 24 26 burg-Schwerin -Prussia3'^ 62,555.7 17.6 50.2 25 22 Posen3 28,991.5 17.2 54.6 26 18

Source: Kiesewetter (1986), p.55 241

40

• | £UI!VCT«;iM U£W. I Deutsches Reich i t B a y e m

Census Year

Figure 4.1: Commercial Sector Employment as a Percentage of Population in Several German Regions, 1832-1939. Source: Megerle (1982), p. 152. 242

Table 4.1 : Commercial Sector Employment in Wurttemberg in 1832

Employment Commercial Sector Employment Surveyed as a % of Occupations Masters/ Helpers Total Population Male Adults Entrepreneurs Craftsmen 113,943 30,981 144,924 9.18 27.47 Factories and 269 6,852 7,121 0.45 1.35 Manufacturing Peddlers 7,892 16 7,908 0.50 1.50 Businessmen 2,666 1,110 3,776 0.24 0.72 Mills 5,342 272 5,614 0.36 1.06 Innkeepers 12,012 764 12,776 0.81 2.42 Beverage 9,263 154 9,417 0.6 1.78 "Factories" (breweries) Total 151,387 40,149 191,536 12.14 36.3

Source: Megerle, p.94 Map 4.1: Regional Distribution of Employment in Factories, Manufacturing, and Mining in Wurttemberg, 1832,

KONj o GER ISU O h r

• HAUL

•Ml OGOF •VAI

)WEL NOG °GMO

SO

q p MTO TBG

r 6 b' iULM BLA MON

EHIo fVffl

SPA ® B 0

SLQ

LEU

TET

i 2 5 - 6 0 > 200 1 400unn Employees in Factories

< 25-90 > 200 J5 ™BOO Employees______Outside Factories

Source: Megerle(l982) 244

Table 4.2 : Population and Commercial Sector Employment in Wurttemberg 1835/36, 1852, 1861, 1875

1 2 3 4 5 Survey Year Population Male. Adult Commercial (3) as % of(l) (3) as % of Population Sector Gewerbebesatz (2) Employment 1835/36 1.571,012 520,097 196,256 12.49 37.73 1852 1,733,263 561,997 227,774 13.14 40.53 1861 1,720,708 586,938 268,890 15.63 45.81 1875 1,881,505 602,905 288,048 15.31 47.78

Source: Megerle pp. 107-108 derived from Gewerbezaehlungen 1835/36, 1862, 1861, 1875 245

Table 4.3 : Changes in Population and Employment in Commercial Enterprises in Wurttemberg

Population Total Increase Annual Population Growth Rate # % % 1835/36-1852 162,251 10.30 9,544 0.61 1852-1861 -12,555 -0.70 -1,395 -0.08 1861-1875 160,797 9.30 11,486 0.67

Employment in Commercial Enterprises Total Increase Annual Growth Rate #%# % 1835/36 - 1852 31,518 16.10 1,854 0.94 1852-1861 41,116 18.10 4,568 2.01 1861-1875 19,156 7.10 1,368 0.51

Source Megerle, p. 108 246

Table 4.4 : Employment in Factories/ Large Enterprises in Wurttemberg, 1832, 1852, 1861, 1875

Business Total Survey Absolute # Increase Avg. Annual Increase 1832 9,430 58 13 1852 32,333 22,903 1,145 1861 39.775 7,442 827 1875 70,629 30,854 2,204

Business Proportion of All Commercial Sector Employees Survey % Increase Avg. Annual Increase 1832 4.923 1852 14.195 9.272 0.464 1861 14.792 0.597 0.066 1875 24.52 (9.728) (0.695)

Business Proportion of Population Survey % Increase Avg. Annual Increase 1832 0.598 1852 1.865 1.267 0.063 1861 2.312 0.447 0.05 1875 3.754 (1.442) (0.103)

Source: Megerle, p. 112 Note: Business surveys before 1875 used factories as a category. The 1875 survey uses small and large enterprises (6+ employees). These are not 100% comparable. 247

Table 4.5: Em ploym ent in Small and Large Enterprises in Several German Regions in 1875.

State Employment in Small Enterprises (1-5 Employees) or Region # of workers % of all commercial % of population sector employment Sachsen 369,459 59.43 13.38 Wurttemberg 217,419 75.48 11.56 Bayem 547,925 77.45 10.91 Ghzm. Hessen 96,235 71.61 10.88 Rheinprovinz 401,721 55.56 10.56 Baden 158,246 66.38 10.50 German Empire 4,159,231 64.28 9.73 Westfalen 170,499 48.37 8.95 Prussia 2,258,363 62.28 8.77

State Employment in Large Enterprises (6+ Employees) or Region # o f workers % of all commercial % of population sector employment Sachsen 262,883 41.57 9.52 Wurttemberg 70,629 24.52 3.75 Bayem 159,526 22.55 3.18 Ghzm. Hessen 38,148 28.39 4.31 Rheinprovinz 321,258 44.44 8.44 Baden 80.163 33.62 5.32 German Empire 2,311,399 35.72 5.41 Westfalen 181,974 51.63 9.55 Prussia 1,367,565 37.72 5.31

Source: Megerle, p. 112 248

Table 4.6 : Number and Power Output of Steam Engines used in Commercial Enterprises in Several German Regions. 1846(1852), 1861, 1875

State or 1846 1861 1875 (Wurttemberg 1852) (Large Enterprises onlv) Province Steam Engines PS Steam Engines PS Steam Engines PS

Rheinprovinz —— 1,885 52,141 6,536 207,815 Westfalen —— 758 25,978 3,869 159,983 Sachsen 227 2,751 912 15,144 2,818 57,287 Brandenburg 1849: 358 -- 1,024 12,903 2,762 42,962 Bayem 79 1,504 456 9,083 1,817 37,593 Hannover —-- 452 6,900 1,283 24,558 Baden 24 361 226 2,954 659 12,353 Wurttemberg 25 211 253 2,841 780 11,150 Pommem —— 166 2,043 567 10,652 Hessen 25 438 213 1,844 492 6,271

Source: Megerle p. 115 249

Table 4.7 : Employment in Factories/Large Enterprises by Industrial Branch in Wurttemberg, 1852, 1861, and 1875

Industrial Employment in Factories/Large Enterprises Branch 1852 1861 1875 # % share tt % share # % share Textiles 18,548 57.74 17.268 31.33 16,188 22.92 Wood and Carvings 2,322 7.18 2,352 4.27 3,532 5.00 Chemicals 2,185 6.76 1,590 2.88 724 1.03 Food, Beverages, and 1,953 6.04 3,858 7.00 8,023 11.36 Tobacco Mining and Foundries 1,895 5.86 2,474 4.49 2,399 3.40 Metal Working 1,692 5.23 3,564 6.47 6,910 9.78 Paper and Leather 1,497 4.63 2,097 3.80 4,275 6.05 Machines and 1,081 3.34 3,027 5.49 9,415 13.33 Instruments Glass, Clay, and Sand 830 2.57 1,696 3.08 2,317 3.28 Clothing 577 1.78 2,234 4.05 4,416 6.25 Printing Trade 461 1,409 1,838 2.60 Heating and Lighting 68 0.21 409 0.74 1,045 1.48 Construction 645 1.17 3,818 5.41

Source: Megerle p.118 250

Map 4.2: Regional Distribution of Employment in Factories in Wurttemberg 1861.

>Ohr

BRHi

BES OQOFl OBAN OELW LBG

)WEL LEOO

CALWi NEM SO \ ESS OOP HOM° HER -KIR

r6 b' RTL

>BAL EHIo

OBID •TUT

WAL

LEU1

i 50-KB Employees J 500 3 1.000 • 2.000 » 4.000

Source: Megerle (1982) 251

Table 4.8 : Population and Commercial Sector Employment in Wurttemberg 1875, 1882, 1895, 1907, 1933, and 1939

Survey Number of Population Growth Commercial Enterprises Year Residents Absolute % Increase Average Annual Number Increase Increase Growth Rate 1875 1,881,505 148,702 1882 1,996,558 115,053 6.11 0.87 143,983 -4,719 1895 2,081,151 84,593 4.24 0.33 176,191 32,208 1907 2,345,300 264,145 12.69 1.06 170,238 -5,953 1925 2,580,235 234,935 10.02 0.56 162,103 -8,135 1933 2,696,324 116,089 4.50 0.56 172,312 10,209 1939 2,896,920 200,596 7.44 1.24 190,540 18,228

Survey Commercial Increase in Commercial Sector Employment Commercial Sector Year Sector Employment as a % Employment Absolute % Increase Average Annual of the Population Increase Growth 1875 288,048 15.31 1882 295,216 7,168 2.49 0.36 14.79 1895 388,257 93,041 31.52 2.42 18.66 1392,532) (97,316) (32.96) (2.54) (18.86) 1907 517,813 129.556 33.37 2.78 22.08 (125,281) (31.92) (2.66 ) 1925 761,894 244,081 47.14 2.26 29.53 (799,918) (282,105) (54.48) (3.03) (31.00) 1933 724,168 -37,726 -4.95 -0.62 26.86 (-75,750) (-9.47) (-1.18) since 1933: 53.54 8.92 387,730 1939 1,111,898 since 1925: 45.94 3.28 38.38 350,004 since 1925: (39.00) (2.78) 311,980

Source: Mergerle p. 126 Table 4.9 : Commercial Sector Employment as a Percentage of the Population (Gewerbebesatz) in Several German Regions, 1875-1939

State or Province 1875 1882 1895 1907 1925 1933 1939 Sachsen 22.91 26.33 30.38 34.25 42.95 28.91 44.76 Wurttemberg 15.31 14.72 18.86 22.08 31.00 26.86 38.38 Rheinprovinz 19.00 20.57 22.97 26.25 32.85 23.28 35.62 Baden 15.82 15.51 20.94 25.42 30.22 23.02 34.95 Westfalen 18.50 18.37 21.24 24.43 31.66 22.14 33.80 Deusches Reich 15.14 16.23 19.64 23.14 30.04 22.35 33.39 Prussia 14.09 15.43 18.45 21.81 29.11 21.29 32.47 Bayem 14.09 13.01 17.25 2038 25.08 20.89 31.57 Hessen 15.20 15.05 19.33 21.56 27.36 21.05 31.24

Source: Megerle, p. 128

Table 4.10: Increase in the Percentage of the Population Employed in the Commercial Sector in Several German Regions, 1875-1939 and 1882-1939

Period Wurttemberg Sachsen Baden Prussia Dt. Reich Bavaria Rheinprov. Hessen Westfalen 1875-1939 23.07 21.85 19.13 18.36 18.25 17.46 16.62 16.04 15.30 1882-1939 23.66 18.43 19.44 17.04 17.16 18.56 15.05 16.19 15.43

Source: Megerle, p.130 252 253

Table 4.11 Employment in Enterprises with Six or More Workers in Wurttemberg, 1875-1939

Survey Commercial Sector Commercial Sector Employment in Enterprises with 6 + Year Employment workers # workers Increase # workers Increase % of total %of Commercial Population sector employment 1875 288,048 70,629 24.25 3.75 1882 295,216 7,168 81,348 10,719 27.56 4.07 1895 392,532 97,316 181,867 (100,519) 46.33 8.74 1907 517,813 125,281 309,515 127,648 59.77 13.20 1925 799,918 282,105 559,015 249,500 69.88 21.67 1933 724,168 -75,750 449,430 -109,585 62.06 16.67 1939 1,111,898 387,730 797,421 347,991 71.72 27.53

Source: Megerle, p. 130 Table 4.12: Percentage of Commercial Sector Employees Working in Enterprises with Six or More Workers in Several German Regions, 1875-1939

Survey Year Westfalen Rhein Province Prussia Sachsen Dt. Reich Baden Wurttemberg Hessen Bavaria 1875 51.63 44.44 37.72 41.57 35.72 33.62 24.52 28.39 22.55 1882 52.59 46.33 40.02 42.56 38.23 36.42 27.56 30.23 24.07 1895 64.24 60.34 54.46 58.58 53.54 55.14 46.33 48.81 43.98 1907 72.90 69.60 64.10 65.1 62.70 65.60 59.77 56.30 53.1 1925 78.67 75.39 72.72 73.52 71.41 73.09 69.88 65.19 64.88 1933 67.77 64.53 61.21 64.69 60.31 61.53 62.06 52.99 52.78 1939 78.24 77.15 74.86 74.35 73.82 73.09 71.72 69.13 68.21

Source: Megerle, p. 131 Table 4.13 : Average Number of Employees in Commercial Enterprises in Several German Regions, 1875-1939

State or Province 1875 1882 1895 1907 1925 1933 1939 Westfalen 2.81 3.33 4.42 5.93 7.64 5.24 7.79 Rheinprovinz 2.41 2.82 3.77 5.13 6.45 4.62 7.46 Prussia 2.17 2.58 3.37 4.40 5.72 4.25 6.69 Deusches Reich 2.21 2.48 3.27 4.19 5.37 4.12 6.35 Baden 2.27 2.43 3.34 4.51 5.53 4.27 6.25 Sachsen 2.65 2.60 3.53 4.18 5.48 4.40 5.89 Wurttemberfi 1.94 2.05 2.81 3.83 4.93 4.20 5.84 Hessen 2.23 2.24 3.00 3.49 4.38 3.53 5.55 Bayem 2.01 2.02 2.72 3.40 4.32 3.48 5.36

Source: Megerle, p. 132 256

Table 4.14: Mechanized Enterprises and Engine Output in Wurttemberg 1875-1939

Survey Mechanized Entcrpnscs Power Output of Engines Average Power Year Used Per % of all Total in PS PS =% Share Mechanized enterprises Enterprise Water Steam Electric Power Power Power

1875 1,297 p. 87 24,423 53.96 45.65 -- 18.83

1882 4,605 2.50 65,000 ? 7 -- ca. 14 1895 6,663 3.78 105,000 ^a. 46.9 tea. 48.7 ca. 1.3 ca. 15.8 1907 12,744 7.49 282,821 24.49 48.52 19.09 22.19 1925 27,774 17.13 577,384 ? 7 71.94 20.79 1939 49,800 26.14 1,257,931 7 7 81.90 25.26

Source: Megerle, p. 135 257

Tabic 4.15: Mechanized Enterprises and Engine Output in Several German Areas, 1875, 1907, and 1939

State or Province Commercial Sector Survey 1875 Mechanized Enterprises Power Output in PS # % of all Commercial Total Per Enterprise Enterprises Westfalen 1,882 1.50 173,646 92.27 Rhein Province 3,304 1.10 220,834 66.84 Prussia 14,642 0.88 680,104 46.45 Deutsches Reich 25,775 0.88 1,055,750 40.96 Sachsen 2,768 1.16 82,131 29.67 Baden 930 0.88 21,820 23.46 Bayem 2,468 0.70 68,431 27.73 Hessen 503 0.83 7,167 14.25 Wurttemberg 1,297 0.87 24,423 18.83

State or Province Commercial Sector Survey 1907 Mechanized Enterprises Power Output in PS # % of all Commercial Total Per Enterprise Enterprises Westfalen 13,700 7.30 1,333,590 97.12 Rhein Province 30,769 7.81 1,963,487 63.82 Prussia 153,877 6.99 6,674,407 43.38 Deutsches Reich 270,975 6.73 10,362,584 38.24 Sachsen 25,222 5.72 930,455 36.89 Baden 10,383 7.50 317,517 30.58 Bayem 32,411 6.70 766,827 23.66 Hessen 5,208 5.43 142,242 27.31 Wurttemberg 12,744 7.49 282,821 22.19

State or Province Commercial Sector Survey 1939 Mechanized Enterprises Power Output in PS # % of all Commercial Total Per Enterprise Enterprises Westfalen 49,149 21.74 5,550,327 112.93 Rhein Province 83,904 22.20 6,525,353 77.71 Prussia 414,664 20.53 24,388,437 58.81 Deutsches Reich 803,954 21.64 37,014,613 46.04 Sachsen 90,588 22.78 2,730,236 30.14 Baden 36,574 26.12 1,063,219 29.07 Bayem 107,938 22.55 2,930,752 27.15 Hessen 20,650 24.96 549,012 26.59 Wurttemberg 49,800 26.14 1,257,931 25.26 Source: Megerle, p. 136 Table 4.16: Employment by Industrial Branch and its Share of Total Commercial Sector Employment in Wurttemberg, 1875-1939

Industrial 1875 1882 1895 1907 1925 1933 1939 Branch % # % % # % # % # % # % Clothing 50,150 17.41 55,389 18.76 56,103 14.45 52,438 10.13 77,134 9.64 65,070 8.99 75,009 6.75 Textiles 39,479 13.71 33,546 11.36 39,808 10.25 25,422 10.12 82,104 10.26 80,316 11.09 100,853 9.07

Food, Beverages, and 35,843 12.44 32,620 11.05 38,007 9.79 47,447 9.16 58,640 7.33 66,014 9.12 72,331 6.51 Tobacco Construction 26,582 9.23 27,328 9.26 40,157 10.34 59,340 11.46 60,814 7.60 59,072 8.16 95,027 8.55 Wood and Carving 26,006 9.03 25,705 8.71 30,174 7.77 36,853 7.12 59,242 7.41 45,806 6.33 56,393 5.07 Metal Working 22,262 7.73 23,231 7.87 31,395 8.09 42,938 8.29 50,130 6.27 38,559 5.32 67,299 6.05 Construction of 17,368 6.03 17,822 6.04 29,204 7.52 55,222 10.66 62,275 8.16 47,803 6.60 121,916 10.96 Machines and Vehicles Electrical Products 64,082 8.01 17,012 2.35 41,444 3.73 Optics, Fine Machines, 22,479 3.10 39,949 3.59 and Instruments Clay, Glass, and Sand 8,575 2.98 10,673 3.62 15,110 3.89 19,431 3.75 17,114 2.14 13,883 1.92 19,569 1.76 Paper and Printing 7,040 2.44 8,880 3.00 12,998 3.34 20,334 3.93 28,915 3.61 24,271 3.35 29,778 2.68 Leather and Linoleum 6,147 2.13 6,571 2.23 7,675 1.98 9,205 1.78 12,137 1.52 10,946 1.51 14,559 1.31 Chemicals and Rubber 3,695 1.28 4,140 1.40 5,643 1.45 8,374 1.62 11,142 1.39 7,999 1.10 10,235 0.92 Mining and Foundries 2,407 0.84 2,070 0.70 2,231 0.57 1,827 0.35 9,659 1.21 5,611 0.77 13,702 1.23

Source: Megerle, p. 138 Map 4.3: Regional Distribution of Employees m Enterprises with 6+ Employees, 1895, Source: Megerle(1982)

oMOH

oGER NSU c-WBG Bnn HALL

• Ml BES

,VA| i ELW UBG iWEL NBG

NEH

HDHJ J h e b j KIR GEI N A G . ) _ FDS

awn

OBO BAL

ROW

•ROL SPA cBIB

SLG WAL

LEU 35000 RAV

rr WAN TET

Employees in Lsrge Enterprises 10000

I 5000 I 2 500 ! 500-1000

Source: Mergerle (1982). 260

Map 4.4: Regional Distribution of Workers in the Commercial Sector in Wurttemberg, 1925, Source: Megerle(1982)

Employee* in Olher Brancke* 6 0 0 0 0

KLiN ■HR

OCRA HALL

IAR IAN q ELW LOG

NBO NEH CALW COP •HER NAI

ifiA L EH © ROW © L A U

|C Q © R O L

IAN Employee* in Indutury

25.000

10000 1000 - 2.000

Source: Megerle (1982) 261

Table 4.17 : Regional Development of Railroads in Germany 1839-1914 (in km)

State or 1839 1850 1870 1880 1890 1900 1910 1914 Region •East Prussia » > 836.2 988.8 1,553.5 2,219.4 2,782.3 2,964.4 -West Prussia » > 847.3 1,331.1 1,617.6 2,231.8 2,376.2 -Brandenburg 26.4 642.9 1,328.7 2,414.2 2,769.1 3,457.7 4,005 4,240.2 -Pommem 109.5 580.1 1,098.7 1,421.4 1,879.7 2,275.3 2,352.4 -Posen 85.1 529.4 1,121 1,717.7 2,069.4 2,716.7 2,845.4 -Schlesien 646.8 1,551.3 2,703.7 3,291.8 3,940.4 4,601.7 4,785.3 -Sachsen 27.3 502 1,112.6 1,895.2 2,334 2,762.6 2,866 3,047.1 -Schleswig- 159.8 559.9 811.6 1,256.9 1,370.1 1,570.1 1,570.8 Holstein -Hannover 359.6 921.1 1,873 2,295.7 2,630.5 3.203.7 3,319.5 -Westfalen 342.2 947.7 1,939.3 2,245.9 2,520.3 3,277.5 3,403.5 -Hessen-Nassau 14.7 352.8 773.1 1,198.2 1,505.3 1,709.5 2,057.9 2,166 -Rheinland 15.8 321.1 1.590.4 2,681.5 3,356.9 3,699.3 4,353.4 4,781.8 -Hohenzollem 12 80.6 90.7 90.7 90.7 90.7 Prussia-Total 84.2 3,549.5 10,821.4 19,653.6 25,170 29.967.2 36,032 37,943.2 Bayem 28 608.8 2,756.4 4,842.7 5.530.1 6.719.8 7,988.7 8,460.6 Sachsen 115.5 436.1 1,040.8 2,041.1 2,237.6 2,447.4 2,659.9 2,678.7 Wurttemberg 250 1,028.2 1,437.2 1,502.6 1,617.3 1,918.5 1,998.2 Baden 302.6 951.4 1,316.6 1,484.3 1,779.9 2,025.3 2,114.1 Hessen 110.6 597.8 781.2 924.5 1180 1471 1,505.6 Mecklenburg 226.2 397.8 533 1,207.2 1,420.5 1,452.7 1,477.5 Oldenburg 159.5 326.5 410.1 561.7 659.5 684.9 Braunschweig 11.9 84 221.5 339.1 440.4 513.7 639.2 647.8 Anhalt 92.1 164.9 238.8 267.9 294.8 293.9 294.1 Thuringcn1 147.3 430.9 828 1,095.7 1,423.8 1,676.4 1,707.3 Lippe 24.7 24.7 53.7 53.7 122.3 133.6 133.8 Elsass- 766 1,143.6 1,342.5 1,642.8 1,826.9 1,837.8 Lothringen Deutsches- 239.6 5,856.4 18,667.2 33,644.8 41,817.7 49,878.4 59,030.9 61,749.4 Reich

Source: Kiesewetter (1989) pp.256-7 262

Map 4.5: Development of Railway Network in Wurttemberg

INTWICKIUNC DIS EISiNtAHNNfTZIS

1•K b Ukwng 263

Map 4.6: Population Density in the 64 Obcramts of Wurttemberg, 1834,

Kingdom and Kreu Borden (Oberamt) (County) Border

kCAA

ual W2

h o h *

et-ao — Kingdom Average 80.5 H ...» 101-t30

i S Ubar 130 Residents per Km^

Source: Megerle(1982) 264

Table 4.18 Population and Average Annual Population Growth Rate in Wurttemberg, 1816-1900

Year Population Average Annual Population Growth Rate 1816 1,410,327 1825 1,505,720 0.75 1834 1,570,169 0.48 1843 1,680,798 0.78 1852 1,733,263 0.35 1861 1,720,708 -0.08 1871 1,818,539 0.57 1880 1,971.118 0.93 1890 2,036,522 0.33 1900 2,169,480 0.65

Source: Megerle, p.201

Table 4.19: Loss of Population Due to Emigration from Wurttemberg, 12 Phases, 1816-1900

Migration Loss Period Total Per Year 1816-18 20,000 6,670 1819-28 16,500 1,650 1829-34 30,500 6,100 1835-46 36,000 3,000 1847-55 162,000 18,000 1856-64 r 45,000 5,000 1865-70 43,000 7,170 1871-73 14,913 4,970 1874-79 8,492 1,420 1880-84 47,707 9,540 1885-93 50,211 5.580 1894-1900 11,824 1,690 1816-1900 486,142 5,720

Source: Megerle, p.202 265

Table 4.20: Average Annual Migration Balance for Several German Areas in Seven Phases, 1817/25 to 1857/65

Migration Balance, Yearly Average , by Phase Phase Northeast Prussian Rhein Westfalen Kingdom of Baden Wurttemberg Germany Saxony Province Saxony 1817/25 -1,620 +2,975 +5,492 +741 +2,673 -1,162 1826/34 +3,844 +228 +4,266 +1,966 + 18,439 -897 -5,049 1835/46 +9,723 +2,631 +5,624 -820 +1,533 -1,924 -3,065 1847/48 -13,880 + 100 -5,420 -4,272 +2,613 -7,855 -9,129 1849/51 +650 -4,384 -4,507 -4,818 +828 -15,382 -15,772 1852/56 -8,617 -5,882 -1,853 -4,332 + 1,502 -14,850 -20,381 1857/65 -5,080 -4,508 +2,871 -1,785 +4,246 -1,580 -5,979

Source: Megerle, p.203

Table 4.21: Population Development in Wurttemberg According to Community Size Category, 1834 and 1852

Community Size Category Population 1834 Population 1852 % Population (1895) # of Residents Increase Over 20,000 79,780 105,422 32.1 10,000 - 20,000 52,338 57,460 9.8 5,000 -10,000 84,501 94,037 11.3 Under 5,000 1,353,577 1,476,344 9.1 For Comparison Wttbg. Actual Population 1,571,012 1,733,263 10.3 Development Wttbg. Natural Population 1,571,012 1,854,504 18.0 Development

Source: Megerle, p.224 266

Table: 4.22 Bankruptcies According to Occupation and Region (Kreis) in Wurttemberg, 1840-1847

Region (Kreis) Commercial Factory Peddlers and Farmers and Small No Tolal sector Owners Innkeepers Landowners Farms Occupation Workers recorded Neckarkreis 1,548 20 236 209 579 413 3,005 Schwarzwald kreis 2,037 7 453 213 507 329 3,646 Jagstkreis 1,233 5 249 153 602 539 2,077 Donaukreis 1,081 9 23 4 104 333 316 2,781 Wurttemberg 5,899 41 1,172 679 2,121 1,597 11,509

Source: Megerle p.232 267

Map 4.7: Percentage of small farmers (less than 3.5 hectares) in the 64 Oberamts of Wurttemberg. 1857

Kingdom and Khes Borders Oberamt (County) Borders

•ELW

WN.

LEU' RAW

•WAN TFT

7 0 -7 9 * Kiagdom Average 79%

8 5 -9 0 *

Source: Megerle(1982) Map 4.8: Average Size of Agricultural Enterprise in 64 Oberamts of Wurttemberg, 1857.

K iafdea aed Kitae B aden O b e a n t (C om m * > B a d e n

I Liber 19 Morgen

0 3 14-19 Morgen

□ 9-14 Morgen — Kiagdom Average 8.9 Morgea

H 7-8.9 M orgen

lAil 5-7 Morgen

I'-l Bit S Morgen (1 M orgen - 0 3 1 5 2 he)

Source: Megerle(1982) 269 1003U

■n u pi rail i i i I M l : 2 <87 674. 1818639. 19 7 1 1 1 8 . 2038682. 2 1 8 9 4 8 0 . 1871. 1880. 189a 1000. 1910.

S K M fK I 20- 100.000 R c u d u u 5- 20.000 R o i d n u 2-5.000 R n i J c o u 1- 2.000 KoiJcnu lca> Iku 1.000 R c a O u u I □

Tol*l PopwlftllOA

Figure 4.2: Population in Six Community Size Categories in Wurttemberg, 1871, 1880, 1890, 1900, and 1910. Source: KSL, 1910 Census in Wurttemberg. 270

100000

1200000

1100000

1000000 II

900000

800000 II

700000

500000

<*00000

300000

200000

100000

Ym, K3HH3 52 61 71 75 80 85 90 95tt0005g1fl Bg05 7300«90 85 80 75 71 61 52

UrtM CeaanRiUe* wtib Run! Connuiua with

o v e r 5.000 { ) over 2.000 rceiJcnu u»4«r 2,000 { ) u n d e r 1.000 readC R t*

Figure 4.3: Population Development in Urban and Rural Communities in Wurttemberg, 1834-1910. Source: KSL. 1910 Census in Wurttemberg. Table 5.1: Composition of Reduced Sample (Ht ^ 163 cm) by Soldier’s Occupational Category in 7 Phases, Birthyears, 1852/57 -1888/93

Byrs Period 1 Byrs Period 2 Byrs Period 3 Byrs Period 4 Byrs Period 5 ByTS Period 6 Byrs Period 7 1852-57 1858-63 1864-69 1870-75 1876-81 1882-87 1888-93 N 1,488 1,613 1,617 1,695 1,722 1,749 2,017 Soldiers Occupation % of R.S. % of R.S. % of R.S. % of R.S. % of R.S. % of R.S. % of R.S. Upper WC 4.64 3.31 7.30 6.90 10.22 12.06 10.61 Lower WC 2.82 4.59 3.77 4.54 4.99 4.80 4.21 Skilled 43.78 39.93 33.46 35.16 39.32 35.74 32.77 Semi-skilled 6.18 5.15 8.91 10.86 10.92 13.49 15.52 Unskilled 1.41 1.18 1.30 2.18 2.85 1.37 2.28 Busimessmen 6.79 7.01 3.91 9.09 6.50 6.23 6.79 Agriculture 34.88 33.83 36.35 31.27 25.20 26.31 27.82 Upper WC+ Lower 14.25 19.90 19.98 20.53 21.72 23.10 21.62 WC+Businessmen Skilled, Semi-skilled, and 50.87 46.25 43.66 48.20 53.08 50.60 50.58 Unskilled Workers Agriculture 34.88 33.83 36.35 31.27 25.20 26.31 27.82

Table 5.2: Composition of Reduced Sample (ht a: 163 cm) by Region in 7 Phases, Birthyears 1852/57 - 1888/93

Byrs Period 1 Byrs Period 2 Byrs Period 3 Byrs Period 4 Byrs Period 5 Byrs Period 6 Byrs Period 7 1852-57 1858-63 1864-69 1870-75 1876-81 1882-87 1888-93 Region % of R.S. % of R.S. % of R.S. % of R.S. % of R.S. % of R.S. % of R.S. Neckar 25.13 25.66 25.79 27.02 27.23 27.33 28.21 Schwarzwald 26.41 25.53 25.42 24.90 28.51 28.30 26.67 Jagst 25.33 23.19 22.51 23.60 21.84 20.07 20.92 Donau 23.13 24.62 25.71 24.48 22.42 24.30 24.20 272

III III 111 111 HI II* I TO 171 171 171 17* III 1*7 1*1 I** ITT MJipaiat

Figure 6.1: Height Distribution, Infantry, Birthyears 1860-64.

HIM •••••

• »••• »«»M• •• • • ••m IMH ••••• a a a a a H ill a a a a a • •••• »MM ••••• a a a a a • •••• ••••• ••••• •••«• a a a a a aaaaa ••••• •••»• ••••• ••••• a a a a a HIM ••••• • •••• llltt • •••• • t i n ••••a aaaaa him

•■••a ••■•• aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa ••••• aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa IMH aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa • •••• aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa • •• • • aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa a a a a a aaaaa aaaaa aaaaa aaaaa aaaaa a a a a a aaaaa aaaaa ••••a aaaaa aaaaa aaaaa aaaaa aaaaa a a a a a a a a a a aaaaa aaaaa ••••a aaaaa aaaia aaaaa ••••• ••••• a a a a a a a a a a aaaaa aaaaa ••••• a a a a a aaaaa aaaaa aaaaa a a a a a a a a a a aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa If*

Figure 6.2: Height Distribution, Infantry Draftees, Birthyears 1860-64

• a i a a a a a a a aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa aaaaa a^aaa ••••• a a a a a ••••a aaaaa • •••a aaaaa aaaaa a a a a a

a a a a a a a a a a a a a a a

I**.I 1*9.* 171.f 171.9 17*.| 1*1.1 t

Figure 6.3: Height Distribution, Infantry One-Year Volunteers, Birthyears 1860-64 273

• »**•

*****

1Tt.lt

Figure 6.4: Height Distribution, Artillery and Cavalry, Birthyears 1860-64

Figure 6.5: Height Distribution, Total Data, Birthyears 1860-64 ITT

Figure 6.6: Height Distribution, Total Data, Birthyears 1885-89 275

Shortfal1

= .24

70 " -

.20 10 0 2.0 1 .0 1 .0 Histogram Proportions Normal Quantiles

Figure 6.7: Histogram and Quantile Plot, British Royal Marines Over Age 21, 1750-59. Source: Fogel et al (1990) Table 6.1: Limit Testing RSMLE Average Height and Standard Deviation Estimates, by Phase

Phase 1. Birth Cohorts 852 - 57 Phase 2 . Birth Cohorts 1858 - 1863 Truncation N Average Height Standard Deviation Truncation N Average Height Standard Deviation joint (standard error, cm) (standard error, cm) joint (standard error, cm) (standard error, cm) ht £ 162 cm 1599 164.18 7.1344 it £ 162 cm 1723 166.19 0.3749 6.4268 0.223 ht £ 162.5 cm 1540 163.91 0.7030 7.2329 0.3329 ht £ 162.5 cm 1665 166.24 0.4057 6.4017 0.2335 ht is 163 cm 1488 163.12 0.8910 7.5118 0.3853 ht z 163 cm 1613 166.05 0.4659 6.4776 0.2537 ht £ 163.5 cm 1416 163.01 0.9946 7.5400 0.4131 ht £163.5 cm 1543 166.27 0.4853 6.3748 0.2597 ht 2; 164 cm 1342 162.98 1.095 7.5384 0.4396 ht £164 cm 1494 165.79 0.5978 6.5633 0.2947 ht £ 164.5 cm 1258 163.65 1.075 7.3068 0.4358 ht £164.5 cm 1413 166.27 0.5932 6.3683 0.2932 ht £ 165 cm 1194 163.18 1.279 7.4478 0.4869 ht £ 165 cm 1364 165.51 0.7718 6.6419 0.3445

Phase 3 . Birth Cohorts 1864 - 1869 Phase 4 . Birth Cohorts 1870 - 1875 Truncation N Average Height Standard Deviation Truncation N Average Height Standard Deviation joint (standard error, cm) (standard error, cm) joint (standard error, cm) (standard error, cm) ht £ 162 cm 1739 166.14 0.3654 6.3133 0.2177 ht £ 162 cm 1812 165.58 0.4329 6.7604 0.2368 ht £ 162.5 cm 1662 166.60 0.3548 6.0935 0.2131 ht £ 162.5 cm 1748 165.59 0.4727 6.7516 0.2562 ht £ 163 cm 1617 166.32 0.4194 6.2150 0.2356 ht £ 163 cm 1695 165.18 0.5664 6.9115 0.2806 ht £163.5 cm 1547 166.55 0.4348 6.1040 0.2404 ht £ 163.5 cm 1628 165.03 0.6413 6.9621 0.2860 ht £164 cm 1499 166.12 0.5348 6.2790 0.2724 it £164 cm 1578 164.01 0.8553 7.3239 0.3146 ht £ 164.5 cm 1436 165.94 0.6161 6.3390 0.2969 ht £164.5 cm 1501 163.81 0.9737 7.3839 0.3156 ht £ 165 cm 1379 165.34 0.7759 6.5499 0.3421 ht £ 165 cm 1499 161.83 1.450 7.9915 0.3520 276 Table 6.1: Limit Testing RSMLE Average Height and Standard Deviation Estimates, by Phase, Cont.

Phase 5 . Birth Cohorts 876-81 Phase 6. Birth Cohorts 1882-87 Truncation N Average Height Standard Deviation Truncation N Average Height Standard Deviation joint (standard error, cm) (standard error, cm) joint (standard error, cm) (standard error, cm) ht £ 162 cm 1826 166.18 0.4069 6.8732 0.2368 ht £ 162 cm 7 166.52 0.3748 6.7624 0.2245 ht £ 162.5 cm 1775 165.97 0.4642 6.9568 0.2562 ht £ 162.5 cm 2114 166.48 0.3787 6.7621 0.2184 ht £ 163 cm 1722 165.68 0.5398 7.0725 0.2806 ht £ 163 cm 2064 166.01 0.4604 6.9622 0.2458 ht a 163.5 cm 1647 165.95 0.5578 6.9569 0.2860 ht £ 163.5 cm 1995 165.77 0.5291 7.0504 0.2673 ht £164 cm 1589 165.63 0.6521 7.0726 0.3146 ht £ 164 cm 1928 165.31 0.6360 7.2220 0.2901 ht £ 164.5 cm 1508 166.07 0.6555 6.8992 0.3156 ht £ 164.5 cm 1839 165.38 0.6877 7.1870 0.3139 ht £ 165 cm 1449 165.65 0.7327 7.0448 0.3529 ht £ 165 cm 1788 163.79 0.9944 7.7245 0.3948

Phase 7 . Birth Cohorts 1888-93 Truncation N Unadjusted Average Height Standard Deviation point AVHT (cm), standard (standard error, cm) (standard error, cm) error ht £ 162 cm 2137 166.61 0.3510 6.8339 0.2108 ht £ 162.5 cm 2061 169.73 166.85 0.3601 6.7160 0.2136 ht £ 163 cm 2017 169.89 166.37 0.4388 6.9243 0.2407 ht £ 163.5 cm 1919 170.24 0.1070 167.00 0.4142 6.6423 0.2320 ht £164 cm 1875 170.40 0.1068 166.33 0.5280 6.9189 ht £ 164.5 cm 1785 170.72 166.72 0.5325 6.7551 0.2699 ht £ 165 cm 1747 170.86 0.1067 16535 0.7688 7.2607 0.3380 277 KM

1 6 0 .S «

A A A

A A 160.0 ♦

A A A A A A A A A A 1 6 7 .S ♦ A A

A A A A A

167.0 «

166.5 •

166.0 ♦

1 6 5 .5 * I 51 57 S3 54 55 56 57 50 50 60 61 62 6 ) 64 65 66 67 60 69 70 71 72 73 74 75 76 77 70 79 00 01 02 03 04 05 06 07 00 09 90 91 92 JTK figure 7.1: Unadjusted Average Height, Four Year Moving Average, 278 Birthyears 1851-1892, Total Data Figure 7.2: Unadjusted and Q BE Average Height Estimates, Total Data, 2-Ycar M oving Average, oving M 2-Ycar Data, Total Estimates, Height Average BE Q and Unadjusted 7.2: Figure

centimeters 168 158 160 162 164 166 170 156 ityas 1852/1853-1892/1893 Birthyears n in m in in in in cm in CO o o co oo oo o O) to 00 O O CO CO co CO to co cm 00 oo m

00

00 BirthCohorts I' 0 0 '- I . " t CM CT) CON CO 0 O 0 CO 00 CO 00 00 (D N N ■ m - - co o r- 0 oo 00 CO CO CT) CO O oo CO oo in

00 Unadjusted Average Average Unadjusted QBE Average Height Average QBE Height Table 7.1: QBE Results and Unad|usted Average Heights. 2- Ycar Moving Average

RTHYEARS | fOBS. | UNADJ. AVHT | QBE AVHT | QBE ST. DEV. ! SHORTFALL 1852-53 432 166.792236 161.1917270 7.7481279 0.4799996 1853-54 690 167.540024 161.4871980 8.3670454 0.4799992 1854-55 764 167.959167 165.5711210 6.7472401 0.2399998 1855-56 674 167.958450 166.5657200 6.2232313 0.1599996 1856-57 590 167.773407 165.2233430 7.1152630 0.2599993 1857-58 647 167.928986 165.0645900 7.1568308 0.2899994 1858-59 649 167.821640 167.5030060 5.6402664 0.0500000 1859-60 678 167.506073 167.5150760 5.3248892 0.0200000 1860-61 685 167.582840 167.3606410 5.7784395 0.0400000 1861-62 656 167.736465 166.4217680 6.5371161 0.1399994 1862-63 665 167.672363 164.8592220 7.6905127 0.2499993 1863-64 683 167.425140 163.1237340 8.0708809 0. 3699992 1864-65 647 167.348816 167.2262120 5.4614697 0.0300000 1865-66 649 167.504517 167.2592620 5.9584484 0.0400000 1866-67 662 167.543137 167.1720580 6.1512365 0.0500000 1867-68 669 167.618454 166.5730130 6.4151106 0.1199996 1868-69 728 167.548584 166.2072140 6.5321684 0.1499994 1869-70 748 167.252075 166.7199710 5.9617729 0.0699999 1870-71 733 167.000336 166.1414640 6.2356911 0.0999997 1871-72 722 167.291199 163.4515380 7.9901581 0.3499990 1872-73 745 167.521805 164.1473390 7.8069324 0.3199996 1873-74 692 167.597717 166.4066010 6.7799110 0.1299995 1874-75 710 167.140656 164.3898160 7.4477024 0.2699992 1875-76 741 166.964737 163.5162200 7.3359528 0.3499994 1876-77 743 167.297943 164.0761410 7.5370016 0.3099993 1877-78 752 167.585770 166.4647830 6.9414186 0.0999997 1878-79 723 168.008987 167.5307460 6.3565874 0.0500000 1879-80 690 168.314301 167.7947850 6.6295834 0.0600000 1880-81 697 168.111725 166.9387660 7.2163105 0.1099997 1881-82 731 167.782486 164.4233090 8.0702677 0.2999998 1882-83 764 168.111252 164.8723300 7.7487183 0.3199992 1883-84 721 167.795410 166.5728000 6.5980978 0.1599993 1884-85 678 167.452423 166.8577880 6.3248234 0.0799999 1885-86 714 167.682419 164.3507840 7.9166937 0.2999998 1886-87 729 167.989700 163.7803190 8.2784014 0.3699992 1887-88 760 168.061172 164.0863800 8.0335951 0.3599997 1888-89 775 168.169662 165.1499480 7.0490269 0.2899994 1889-90 759 168.381409 167.2644960 6.7406254 0.1299995 1890-91 837 168.013138 167.8819120 6.0465250 0.0400000 1891-92 930 168.094620 167.8311310 6.3881121 0.0500000 1892-93 845 168.877502 167.9188690 6.6099176 0.0600000 0 .5

0.45

0.4

0.35

0.3

0.25 ■----- QBE Shortfall

0.2

0.15

0.05

COin O) y— CO in i^- CT) y— COin CD ,— 00 in r- O) y— in in in in CD CDCD co CDr- f'- r^- h- N- CO CO CO 00 CO CT) CNJ tT co COO CNJrf CD00 o CNI co 00 o CNJ Nt CD COo in in in in COCOCO CO CO h- h- i^. r^ 00 CO 00 COCO CT) 00 COCO00 00 CO COCOCO00 co co co co COCO CO CO oo CO T- T—T—T—T— 1— — ^—1— t- 1—,— y— T—T“ ■t— Birth Cohorts

Figure 7.3: QBE Shortfall Estimates, Total Data, 2-Year Moving Average, Birthyears 1852/53-1892/93 Figure 7.4: Q BE Standard Deviation Estimates, Total Data, 2-Year M oving Average, oving M 2-Year Data, Total Estimates, Deviation Standard BE Q 7.4: Figure

Centimeters 7 5 ni/iNO)rOK)Nci)T-oinNOi>-oins(jit-n inmininiDoiBiDfflNNNNNooioocoeooiO) oooocooooococooooooooooocooococococooocooo ininininiDiDioiBiONNNNNioiDoajcooioi N'tlOCOON^lOcboNTftDCOON^IDCOOW ityas 1852/53-1892/93 Birthyears ■ ■ ■ BirthCohorts ------■ —

QBE Standard QBE Deviation 282 iue .: Ead ndutd vrg Hih,Ttl aa 4Ya vn Average, oving M 4-Year Data, Total Height, Average Unadjusted and BE Q 7.5: Figure

centimeters 167 T 169 6 -- 162 - 163 6 --- lH)t i I I I -l-H-H) i I l-H It t II M I H 161 I I t-H -l I I I H I I I I ityas 1852/55-1890/93 Birthyears cii nin in 0C 0C 00 CO 00 CO 00 m ni in in in □ ,— —

CO in OO CD ,— Dto ID up T— co h- —T-~ T— 00 0C 00 CO h- 00 h- 00 - r 00 ID o - r BirthCohorts O(6 CO o CO -h- h- - r yr~ CD —— T— CD CNJ O00 CO CJ> - r CO CO CVJ in T— CO oo CO in CO T— 00 00 CO cn -r— -T— ------i L — ■ — QBE Average Height Average QBE Height Average Unadjusted oo 0 .4

0.35

0.3

0.25

0.2 QBE Shortfall

0.15

0.1

0.05

0 in s a t- m m r- o> t- cn in in in 10 (O ID (O ID N CD

Birth Cohorts

Figure 7.6: QBE Shortfall Estimates, Total Data, 4-Year Moving Average, 284 Birthyears 1852/55-1890/93 Figure 7.7: Q BE Standard Deviation Estimates, Total Data, 4-Year M oving Average, oving M 4-Year Data, Total Estimates, Deviation Standard BE Q 7.7: Figure

Centimeters 2 0 3 4 6 5 7 8 9 1 oi ooboci oco6cj 0( 6 (D (0 c\j4 6 o c (o c(i4 o b o (o 4 w i6 (o 4 w ni ni o( O( oNSNNsa)Dt so> o is o c to )(D a s N N S N io (O iO (O io in in in in ini/)intO(oiD(oiDSNNSNfflajoo(0(00)oi cococooocooooooooococooooococooocococooo ns r Noi- nNoi- nNOir-o O N in o i'-c o N in ii-o o iN u irn sa in ityas 1852/55-1890/93 Birthyears BirthCohorts ------■ —

Deviation Standard QBE 285 286

Tabic 7.2: QBE Results. Total Data. 4-Year Moving Average, Birthvears 1852/55-1890/93

TOTAL OBSERVATIONS USED = 14002

SUB. ! BIRTHYEARS ! JOBS. ! UNADJ. AVHT | QBE AVHT ! QBE ST. DEV.| SHORT 1 1852-55 1196 167.537674 163.3489990 7.4336834 0.3799992 2 1853-56 1364 167.746780 164.3766170 7.1770592 0.3199996 3 1854-57 1354 167.878220 165.8265840 6.7490368 0.2099996 4 1855-58 1321 167.943970 166.7894900 6.2659464 0.1299995 5 1856-59 1239 167.798615 166.2314610 6.4312973 0.1799999 6 1857-60 1325 167.712540 167.4727780 5.7161303 0.0400000 7 1858-61 1335 167.698441 167.4685060 5.6469183 0.0400000 8 1859-62 1335 167.618912 167.2835690 5.7595367 0.0500000 9 1860-63 1351 167.626480 166.5952760 6.5149679 0.1099997 10 1861-64 1340 167.577179 165.7892150 6.8590641 0.1799999 11 1862-65 1312 167.512756 165.6116180 6.8864546 0.1899994 12 1863-66 1332 167.463821 166.5179440 6.5080738 0.0999997 13 1864-67 1309 167.447037 167.2008510 5.8157272 0.0400000 14 1865-68 1318 167.562302 167.2068020 6.0307417 0.0500000 15 1866-69 1390 167.545944 166.78S0800 6.3015594 0.0899998 16 1867-70 1417 167.425003 166.9776310 5.9916801 0.0600000 17 1868-71 1461 167.273483 166.6062010 6.1700268 0.0799999 18 1869-72 1470 167.271286 166.1915440 6.5412149 0.1199999 19 1870-73 1478 167.263184 164.8099670 7.1941700 0.2499993 20 1871-74 1414 167.441208 164.3355100 7.6192322 0.2999994 21 1872-75 1455 167.335815 164.5183870 7.4991484 0.2799995 22 1873-76 1433 167.270401 164.5604710 7.2131243 0.2799995 23 1874-77 1453 167.221085 164.7509000 7.2536383 0.2499993 24 1875-78 1493 167.277542 164.9429780 7.21769S2 0.2299995 25 1876-79 1466 167.648605 166.3751220 6.7647438 0.1299995 26 1877-80 1442 167.934372 167.1055910 6.7916574 0.0799999 27 1878-81 1420 168.059418 167.1250150 6.8504601 0.0899998 28 1879-82 1421 168.040726 166.7478790 7.1094084 0.1299995 29 1880-83 1461 168.111481 167.4713440 6.8273611 0.0699999 30 1881-84 1452 167.788910 165.0598140 7.4720316 0.2799991 31 1882-85 1442 167.801483 164.6612240 7.5251598 0.3199992 32 1883-86 1435 167.739197 166.6932830 6.7236099 0.1199996 33 1884-87 1407 167.730804 164.9315640 7.4807339 0.2799995 34 1885-88 1474 167.877701 164.5389860 7 .82034S9 0.3099993 35 1886-89 1504 168.082443 164.2284240 8.0415812 0.3499990 36 1887-90 1519 168.221191 164.7962800 7.7218847 0.3299995 37 1888-91 1612 168.088394 167.1875460 6.5981941 0.1099997 38 1889-92 1689 168.223495 167.6432650 6.5146341 0.0799999 39 1890-93 1682 168.169724 167.8980710 6.3366604 0.0500000 Figure 7.8: Q BE and Unadjusted A verage Height Estim ates, Total Data, 6-Year M oving Average, oving M 6-Year Data, Total ates, Estim Height verage A Unadjusted and BE Q 7.8: Figure

centimeters 162 164 166 165 168 163 167 169 ityas 1852/57-1888/93 Birthyears in m r-o) BirthCohorts ■ am* O) co ■Q QBE Average Height Average QBE ■Q

Unadjusted Average Average Unadjusted Height 287 0 .3 5

0.3

0.25

0.2 - QBE Shortfall 0.15 -

0.1 -

0.05 --

I I f I I M I I-1 ! I II l-f I I-I I I I II ! I I H -t H I I I I i'- 05 t- CO m n - co co in CT) CO m h- o> ■>- CO in in co co 00 CO CO co r- r- h-I 00I co CO 00 CT)I CD I CM4 co oo o OU co CO 6 CMTT CO CO o CM 4 CO CO in in in in co CO CO CO CO t''. h- r-. co CO CO CO CO oo oo co oo co CO CO 00 oo 00 CO CO 00 co co CO CO CO CO Birth Cohorts

Figure 7.9: QBE Shortfall Estimates, Total Data,6-Year Moving Average, Birthyears 1852/57-1888/93 288 Figure 7.10: Q BE Standard Deviation Estimates, Total Data, 6-Year M oving Average, oving M 6-Year Data, Total Estimates, Deviation Standard BE Q 7.10: Figure

Centimeters 2 0 3 4 6 5 7 8 1 o co co cm 4 up up in in ityas 1852/57-1888/93 Birthyears CT) ( O CO CO t O CO in in CO 0(0 - co in

10 O O CO CD CO CO CO CO ( 0(0 ct >

BirthCohorts co in k O O 0 0 CO CO CO CO CO - coi- m D O O CO CD 0 00 - h CC OCCM O C OC COC O 4 C O O C OCCO OC O C O DC T ) N ON -r ) -C O

QBE Standard Standard QBE Deviation 289 Tabic 7.3: QBE Results, Total Data, 6-Year Moving Average, Birthyears 1852/57-1888/93

BIRTHYEARS : #035. : UNADJ. AVHT ! QBE AVHT ! QBE ST. DEV. ] SHORTFALL 1786 167 . 61S3-C 164 .0963900 .3002176 0.3299991 1352-57 0.2499993 1853-58 2011 io I . 805374 165 .2715610 6 .9163S99 2003 167. 359963 156 .2976440 6.3804626 0.1599996 1854-59 0 .C39S998 1855-60 1995 167 795425 167 .0347930 £.5314041 1925 167. 721390 167 .3729400 5.8392515 0 .C500000 1856-61 0.0699999 1857-62 1992 167. 720093 167 .1571040 5.5760065 2000 167 689774 167 .2065730 5.0466080 0.0600000 1858-63 0.1299995 1859-64 2018 167. 553299 166 .3704070 6.3832855 167 . 536530 166 .9413760 6.1401653 0.0699999 1860-65 1998 0.1299995 1861-66 19e9 167 . 552467 166 .3238070 6.5689669 1974 167. 522919 166 .4037020 6.5070057 0.1199999 1862-67 0.1999993 1863-68 2001 167. 515488 165 .7285310 6.7318029 2037 167. 483307 165 .8225860 6.4027127 0.1999993 1864-69 0.0500000 1865-70 2066 167. 449951 167 .0993190 5.9536419 4.866-71 2123 167. 357529 166 .7391080 6.1547861 0.0699999 2139 167. 379837 166 .3126830 6.4954329 0.1199999 1867-72 0.2499993 " 1868-73 2206 167. 357346 164 .9589540 7.0801439 2162 167. 375778 165 .0713960 7.0976601 0.2399994 1869-74 0.2399994 1870-75 2188 167. 223434 164 .9027710 7.1545753 1871-76 2155 167. 277374 164 .3878480 7.3858852 0.2899994 2198 167. 323013 164 .0037380 7.6364412 0.3199992 1872-77 0.1899994 1873-78 2185 167. 378937 165 .4995570 7.0383081 1874-79 2176 167. 482880 166 .0241700 6.8645840 0.1499997 1875-80 2183 167. 605240 165 .9957120 7.0231400 0.1599996 2163 167. 797836 166 .5803220 6.9039049 0.1199996 1876-81 0.1099997 1877-82 2173 167. 883270 166 .7419590 7.0081606 1878-83 2184 168. 077545 167 .3153990 6.7412596 0.0799999 2142 167. 958145 166 .5050510 6.9950590 0.1599996 1879-84 0.0899998 1880-85 2139 167. 902573 167 .0826870 6.7818241 1881-86 2166 167. 753799 166 .5312040 6.9391022 0.1299995 1882-87 2171 167. 864685 165 .0917360 7.4984951 0.2799995 0.2899994 1883-88 2195 167. 850677 164 .9566350 7.4946356 1884-89 2182 167. 836673 164 .9215700 7.5789070 0.2899994 1885-90 233 L 6 8 048920 165 .0612180 7.6144228 0.2899994 0. '.'99999 1886-91 2341 168. 057663 166 .3931270 7.0294695 1887-92 2449 163 • 173126 166 .0359950 7.1983316 0.12 99995 1888-93 2457 168 169708 157 .5641170 5.5313127 0.0799999 Figure 7.11 Q BE and Unadjusted Average Heights, Draftees, 2-Year M oving Average, oving M 2-Year Draftees, Heights, Average Unadjusted and BE Q 7.11 Figure

centimeters 5 - 154 - 156 5 t l-H 152 :: 158 160 162 168 164 166

ityas 1852/57-1888/93 Birthyears Min CM oCO in co CO ni m in ▼— in

OCO CO in T — iHilllHII I H I l l l i l-H H Ili H-H I HI I I III I I II I I 00 in T CNJ CT) ^— CO ^— CO f CD — OCO CO m T CO CO CD 4 — co O0 Oc 00 co CO 00 CO T“ Birth Cohorts oCO co 6 - r y— h- r^- N. ▼- O' d d ^— - - CO d> o r~ T — CM OCD CO CO 00 O00 CO i — CO in CO nCM cn O05 CO CO OCD CO CO t — t— T— CO QBE Average Height Average QBE Height Average Unadjusted 168 T

166 --

o» 165 - Unadjusted Average Height

QBE Average Height

163 -

162 --

161 H \ H-l H \ I H I-I I I t-H f-H H --f H I H I I I II I NOii-oinNoir-omNaT-ninNOiT-n inintotDioioffiNSSNNeooooomcooiO) W'tlflOOON'ttOtOON'tlOOlOWTfmtO u)inu)in(O0ffl(O(ONNNSscoa)(Oco(o cocoaooooocooococococococococococococo Birth Cohorts

Figure 7.12 QBE and Unadjusted Average Heights, Draftees, 6-Year Moving Average, 292 Birthyears 1852/57-1888/93 17 4 r

» 170 - Unadjusted Average © E 169 Height QBE Average Height o 168

1-4 1 I H n H-i I I I I I II l-H H 4-H I I H II II I I N Ol t- n Ifl N (J) t- co in n oir-ninsoit-n in in co co co co co r>. r- r*- NooncocooooiDi l l ...... N'JCOCOOW'TCCD o CM Tj- CO CO O CM i f CO CO inminincococoiocou / *ti *41 ui w w vw vi/ vi/ i-*»h- ih- iIs- i ^ r^ 00 CO CO 00 CO oocooogocooococooooocooooococococdcoco Birth Cohorts

Figure 7.13: QBE and Unadjusted Average Heights, One -Year Volunteers, 6-Year Moving Average, 293 Birthyears 1852/57-1888/93 iue71 EAvrg egtEtmts sn eue Sml, rfes OeYa Volunteers, One-Year Draftees, Sample, Reduced using Estimates Height verage A BE Q 7.14 Figure

centimeters 164 174 166 170 160 162 168 172 158 n oa Slir, -erMoigAeae Brher 1852/57-188/93 Birthyears Average, oving M 6-Year Soldiers, Total and O O O O CO CO CO CO CO t — nm (j) s BirthCohorts O CT1 N n i CO r O 0 O O CO CO CO 00 CO 05 O CO -r------♦ ■ □ — — —

Total Draftees Total Total Soldiers Total B VTo .. of R.S. of AVHT QBE B VTo .. of R.S. of AVHT QBE B AH fRS of R.S. of AVHT QBE 1 -Year Volunteers 1 -Year 294 168.5 ♦

A A A A A 168.0 ♦ A A A A A A A A A A A A A A A A 167.5 ♦A AAAA AA A A A A HA 4 A A A A A A 167.0 ♦

A 166.5 *

A 166.0 ♦

16S.S ♦ A

16S.0 ♦ SI S2 53 S« SS 56 57 58 58 60 61 62 63 64 65 66 67 68 69 70 71 72 7J 74 75 76 77 78 79 80 81 82 83 84 8S 86 87 88 89 90 91 92 JTR

Figure 7.15: Unadjusted Average Heights, 4-Year Moving Average, Infantry, Birthyears 1852/57-1888/93 g 170

168

166

164

162 Unadjusted Average Height

E 160 QBE Average Height

158

156

154

152 n to O) cm in co f"- O CO CD « CM

CO CO COCOCOOOCOCOCOCOCOCOCOCOCOCO Birth Cohorts

Figure 7.16: QBE and Unadjusted Average Heights, Infantry, 2-Year Moving Average, 296 Birihyears 1852/57-1888/93 Figure 7.17: Q BE and Unadjusted Average Heights, Infantry Draftees, 2-year M oving Average, oving M 2-year Draftees, Infantry Heights, Average Unadjusted and BE Q 7.17: Figure

centimeters 156 168 168 152 154 -- -- I I H I t H HI-HI lil I I I I I I I I I I HI I I I I I I I I I I ityas 1852/57-1888/93 Birthyears CO t CSI nm 00 in — r * in CO n00 00 in in ^ — 00 cn nC CO CO in CO m 000 00 CM T“ — T nCO in CO 4 t — Or>- CO Or-CO r^- 00 00 00 ri. CO y~ BirthCohorts o y— T— m t — 00 h- Od in j d d) CO T — o 00 oo 00 00 r-- t - co 000 00 OCO CO , — CD ▼— O) 0O) 00 CO 000 00 00 y — CM 05 yL — , o QBE Average Height Average QBE o ■ Unadjusted Average Unadjusted ■

Height 297 Figure 7.18: Q BE and Unadjusted A verage Heights, Infantry One-Year Volunteers, One-Year Infantry Heights, verage A Unadjusted and BE Q 7.18: Figure

centimeters -- 8 6 1 -- 9 6 1 -erMoigAeae Brher 1852/57-1888/93 Birthyears Average, oving M 6-Year OOCOCOOOOOOOOOOOCOOOOOCDaOCOCOCOOOOOCO o c o i o c o c o c m n i n i n t u T “ T — — — — — T ^ T T 1 T— 1— T— T— ^ T— — T ~ ^ Y— T— T— T— T— T“ T— ▼“ — ID T ~ ▼ CD CD CO CD n i n i . . i u > c in . n i co ■>- o , . _ o c o c , -H H H-H i u i u i v v/ y w l i* r j u r* - n i^* vls vw vy vl/ vu BirthCohorts id co o in co -H H op p o o c p o D Oo dj 4 C CO CO 4 j d COo CD - i 0 1 in n o) r- to t - r ) o n n i 0 O O O CO CO CO CO 00 vu vu ca O) O ) O p o u j UJ UJ j u wu

QBE Average Height Average QBE Height Average Unadjusted 298 MAI | 119.00 * A A

A 151.75 ♦A

A A

1 5 1 .5 0 ♦ A A

151.25 ♦

A A 111.00 »

117.75

157.50 ♦

157.25 ♦

157.00 ♦ 51 52 51 SI 55 55 57 SI 50 50 51 52 51 51 55 55 57 51 51 70 71 71 71 71 7S 75 77 71 70 10 II 12 II II IS 15 17 II 10 90 91 92 JTR

Figure 7.19 Unadjusted Average Heights, 4-Year Moving Average, Artillery and Calvary, Birthyears 1852/57-1888/93 299 169 -r

Unadjusted Average n 166 Height

QBE Unadjusted v 165 Average Height

164 --

163

162 i ii i m m i 44 lfH H I H MU It I I I I I S O) i- O ifl S 01 cn in oiromsoit-n in in to co co to to k r^- saocotocooio) « • i i t i t i • i i i i i « i i i N^ioooow^ioaj 6 CM 'ficaioN'fiooo ininininiotocoeotouiuiu)uittjiuiuiuu;>r>r>i^r>istuisiDisr~ NNSOOtOOlOOO cocococococococococococococooococococo Birth Cohorts

Figure 7.20: QBE and Unadjusted Average Heights, Double Artillery and Calvary, 6-Year Moving Average, Birthyears 1852/57-1888/93 Table 7.4: RSMLE and QBE Average Height Estimates, 7 Phases, Birthyears 1852/57-1888/93

Birth Cohorts 1852-57 1858-63 1864-69 1870-75 1876-81 1882-87 1888-93 N 1488 1613 1617 1695 1722 1749 2017 Average Height RSMLE 163.12 166.05 166.32 165.18 165.68 166.23 166.37 Standard Error 0.891 0.4659 0.4194 0.5664 0.5398 0.4754 0.4388 Standard Deviation 7.5118 6.4776 6.215 6.9115 7.0725 6.8764 6.9243 Standard Error 0.3853 0.2537 0.2356 0.2852 0.2806 0.2588 0.2407 QBE 164.3464 167.4566 166.0726 165.1528 166.8303 165.3417 167.8141 Log Likelihood -4097.6 -4493.3 -4476.8 -4729 -4886.4 -4975.7 -5764.7 168

167

166

Average Height 164 RSMLE QBE 163

162

161

160 r- o CD in in to 00 co c\i oo CD oo in co oo oo ao 00 00 oo oo Birth Cohorts

Figure 7.21: RSMLE and QBE Average Height Estimates, Total Data, 7 Phases, Birthyears 1852/57-1888/93 Tabic 7.5: RSMLE and Average Height Estimates, Total Data, 2-Year Birth Cohorts, Birthyears 1852/53-1888/93

Coefl. S.E. t-stat AVHT Phase 1 1852-53 160.56 1.196 160.56 1854-55 3.3892* ** 0.8749 3.874 163.95 1856-57 4.1063* *• 0.9199 4.464 164.67 Phase 2 1858-59 165.87 0.5771 165.87 1860-61 0.0155 0.569 0.027 165.89 1862-63 0.5237 0.5693 0.092 166.39 Phase 3 1864-65 165.89 0.5441 165.89 1866-67 0.7972 0.5475 1.456 166.69 1868-69 0.4578 0.5363 0.854 166.36 Phase 4 1870-71 164.48 0.7042 164.48 1872-73 1.1975* 0.618 1.938 165.68 1874-75 0.90439 0.6334 1.428 165.38 Phase 5 1876-77 164.86 0.6734 164.86 1878-79 0.8112 0.6171 1.315 165.67 1880-81 1.9174*** 0.6184 3.101 166.78 Phase 6 1882-83 166.85 0.5475 166.85 1884-85 -0.1928** 0.5918 -2.366 165.45 1886-87 -0.06263 0.5613 -1.116 166.22 Phase 7 1888-89 166.24 0.5445 166.24 1890-91 -0.1928 0.5457 -0.353 166.05 1892-93 0.739 0.5369 1.376 166.98 *=significant at 10% level ••^significant at 5% level ••• = significant at 1% level 167

166

165

164

163

162 AVHT

161

160

159

158

157

in in ID ID co co CO CDCD CO CO CDCD

Birth Cohorts

Figure 7.22: RSMLE Average Height Estimates, Total Data, 2-Year Birth Cohorts, Birthyears 1852/57-1888/93 304 174

------■— AVHT of R.S. of Total Draftees

— □ — E 170 -- AVHT of R.S. of Total 1-Year Volunteers

------♦ — AVHT of R.S. of Total Soldiers 168 --

167 -

166 M I I I I M I I I I ■II I I H I I I I I I I I II I I I I I NOii-ninsciT-ninNO) O lO N O) t- O inU)(ujuju_>ujujujujr-r-n-r^r-tuujuuujcuu)ob6c\j4t6ob oooooooooocooooooooooooooococooooooooo Birth Cohorts

Figure 7.23: Average Height of Reduced Sample, Draftees, One-Year Volunteers, and Total Data, 6-Year Moving Average, Birthyears 1852/57-1888/93 306

Table 7.6: Unadjusted Average Height by Father's Occupational Category, Early and Late Periods

Father's EARLY LATE TOTAL Occupational BYR = 1852-1873 BYR = 1874-1895 BYR = 1852-1895 Category N AVHT N AVHT N AVHT 1.UPPER WHITE COLLAR WORKERS IP: Professionals 88 170.84 128 174.06 216 172.75 1PL: Teachers 49 169.53 82 172.24 131 171.23 1PP: Preachers 24 170.83 39 172.62 63 17194 IF: Factory Owners 41 170.67 65 170.26 106 170.42 1G: Owner of Goods 6 167.83 13 170.77 19 169.84 1: Other 8 171.38 22 169.93 30 170.32

2.LOWER WHITE 90 170.1 165 171.19 255 170.80 COLLAR WORKERS eg: lower level officials, techichians

3.SKILLED WORKERS 3N: No strength required 582 166.36 703 166.18 1285 166.26 eg:tailor, engraver, baker • 3NM: Meister 3 169.5 79 168.18 82 168.23 3M: Medium strength required 344 166.58 489 166.49 833 166.53 egxonstruction jobs 3MM: Meister 16 165.94 77 168.0 93 167.64 3S: Strength required 123 167.80 130 168.31 253 168.06 e.g. Miller, Smith 3SM: Meister 2 175.5 5 165.40 7 168.29

4.SEMI-SKJLLED WORKERS 62 166.79 148 165.52 210 165.90 4N: No strength requirement eg: Cook, Barber, Servant 4S: Some strength requirement 88 165.45 163 167.22 251 166.60 eg: Gaurd, Ploliceman 4SM: Butcher 52 166.01 56 166.51 108 167.27 4F: Factory Worker 15 165.0 92 167.39 107 167.06 4FE: Iron factory worker 5 161.5 15 167.07 20 165.68 4SE: Miners - 1 . - 11 167.27 307

Tabic 7.6: Cont'd.

Father's EARLY LATE TOTAL Occupational N AVHT N AVHT N AVHT Category Cont. 5.UNSKILLED WORKERS 224 165.72 271 165.96 495 165.85 (eg: Tageiochncr)

6.BUSINESSMEN 6: Businessmen (Kaufmann) 114 168.67 172 170.86 286 169.98 6H: Small Salesmen (Haendlcr) 12 167.17 9 165.39 21 166.40 6W: Innkeepers (Wirt) 35 167.40 43 167.70 78 167.56

7.AGRICULTURE (ALL) 7L: Large Landowner/Farmer 52 168.91 137 168.82 189 168.84 (Landwirt/Ockonom) 7B: Small farmer (Bauer) 965 167.32 1126 167.83 2091 167.60 7G: Gardener (Gacrtnor) 9 165.44 21 166.31 30 166.05 7F: Fishermen, Forestry 31 167.17 44 167.36 75 167.28 Workers, Hunters 7S: Sheperds 31 168.59 24 166.94 55 167.87 7K:Agricultural laborers 12 167.17 15 166.47 27 166.78 7W: Vinter/Winegrower 71 165.04 121 166.50 192 165.96 (Weingaertner)

8.ILLEGITIMATE CHILDREN 25 162.48 23 164.65 48 163.52 308

Tabic 7.7: Unadjusted Average Height by Soldier's Occupational Category, Early and Late Periods

SOLDIER’S EARLY LATE TOTAL OCCUPATION BYR = 1852- BYR = 1874- BYR = 1852- 1873 1895 1895 N AVHT N AVHT N AVHT 1.UPPER WHITE COLLAR WORKERS IP: Professionals 116 171.69 1914 172.14 310 171.97 IS: University Students 282 171.28 413 173.48 695 172.59

2.LOWER WHITE COLLAR WORKERS 2: 99 170.16 184 170.36 283 170.29 2S: Students, non-university 133 169.29 110 170.97 243 170.05 eg: Postpraktikant

3:SKILLED WORKERS 3N: No strength requirement 1449 166.24 1123 165.99 2572 166.13 3NG: Hclpcr/Apprcnticc -- 71 164.15 73 164.22 3M: Medium Strength 706 166.54 902 167.00 1608 166.80 3MG: Helper/Apprentice -- 101 166.56 102 166.51 3S: Strength 274 166.80 164 166.91 438 166.84 3SG: Helper/ Apprentice -- 6 166.08 7 164.79

4:SEMI-SKILLED WORKERS 4N: No strength requirement 69 165.56 163 165.64 232 165.62 4S: Strength required 112 166.84 166 166.86 278 166.85 4F: Factory workers 153 165.95 424 166.46 577 166.33 4SM: Butchers 142 166.44 129 166.14 271 166.30

5:UNSKILLED WORKERS 102 167.18 169 166.26 271 166.61

6:BUSINESSMEN 6:Businessman 460 170.53 402 170.80 862 170.65 6H: Small Salesman 3 163.67 9 168.83 12 167.54 309

Table 7.7: Cont’d

SOLDIER’S EARLY LATE TOTAL OCCUPATION BYR = 1852- BYR = 1874- BYR = 1852- 1873 1895 1895 NAVHTN AVHT NAVHT 7: AGRICULTURE 7L: Large landowner 63 169.21 52 169.14 115 169.18 7B: Small farmer 1200 167.78 917 167.87 2117 167.82 7G: Gardener 46 166.11 40 164.41 86 165.32 7F: Fisherman, Foresters 33 168.75 31 169.94 64 169.33 7S: Shepherds 76 168.66 15 166.17 91 168.25 7K: Agricultural Laborers 417 165.60 455 166.54 872 166.09 7W: Vinter 137 167.49 83 167.54 220 167.51 310

Table 7.8: RSMLE Truncated Regression ol Height on Soldier’s Occupational Dummies. 7 Phases. Birthyears 1852/57- 1888/93

Thrashold values for tha modalt Lover* 162.9000 Upper**Infinity variable Coafflclant Std. Error t-ratlo Probit»>x Ha an of X std.Dav.of X Constant 163.78 0.8365 195.791 0.00000 SONUP 5.9273 1.225 4.839 0.00000 0.46371E-01 0.21036 SONLOWC 4.5269 1.550 2.920 0.00350 0.28226E-01 0.16567 SONSK -1.0993 0.6808 -1.615 0.10635 0.43280 0.49563 SONSEHI -2.8215 1.423 -1.983 0.04733 0.61828E-01 0.24092 SONUN 0.15778 2.486 0.063 0.94940 0.14113E-01 0.11800 30NBUS 5.1463 1.075 4.786 0.00000 0.67876E-01 0.25162 Sigma 7.0331 0.3321 21.175 0.00000

Thrasbold valuas for tha modali Lower* 162. 9000 Uppar**infinity Varlabla Coafflclant std. Error t-ratlo Probit>>x Maan of X Std.Dav.of X Constant 166.68 0.4826 345.377 0.00000 SONUP 4.5857 0.7284 6.295 0.00000 0.83075E-01 0.27608 SONLOWC ••0.82178E-01 1.049 -0.078 0.93757 0.45877E-01 0.20928 SONSK -1.8233 0.5172 -3.525 0.00042 0.39926 0.48990 S0N3EMX -1.5019 1.058 -1.419 0.15582 0.51457E-01 0.22100 SONUN 0.66285E-01 1.965 0.034 0.97308 0.11779E-01 0.10792 SONBUS 3.0812 0.7966 3.868 0.00011 0.70056E-01 0.25532 Sigma 6.0857 0.2225 27.350 0.00000

Thrashold valuas for tha modalt Lover* 162. 9000 Upper**Infinity Varlabla Coafflclant Std. Error t-ratio Probit«>x Naan of X Std.Dav.of X Constant 166.47 0.4481 371.529 0.00000 SONUP 4.6956 0.7211 6.511 0.00000 0.72975Er01 0.26018 SONLOWC 3.2445 0.9717 3.339 0.00084 0.37724E-01 . C.19059 SONSK -1.3025 0.5020 -2.595 0.00947 0.33457 0.47199 SONSEMX -0.98120 0.7834 -1.252 0.21041 0.89054E-01 0.28491 SONUN -0.64560 1.853 -0.348 0.72760 0.12987E-01 0.11325 SONBUS 3.4153 0.6848 4.987 0.00000 0.89054E-01 0.28491 Sigma 5.8275 0.2062 28.267 0.00000

Thrashold valuas for tha modalt Lover* 162. 9000 Uppar**inflnity Varlabla Coafflclant Std. Error t-ratlo Probit:>x Maan of X Std.Dav.of x Constant 166.08 0.5357 310.022 0.00000 SONUP 5.7634 0.8037 7.171 0.00000 0.69027E-01 0.25357 SONLOWC 4.1119 0.9679 4.248 0.00002 0.45428E-01 0.20830 SONSK -1.9517 0.5687 -3.432 0.00060 0.35162 0.47762 SONSEMX -3.1491 0.8741 -3.603 0.00031 0.10855 0.31117 SONUN 3.2459 1.360 2.386 0.01702 0.21829E-01 0.14617 SONBUS 2.5490 0.7676 3.321 0.00090 0.908S5E-01 0.28749 Sigma 6.2888 0.2331 26.984 0.00000

Thrashold valuas for tha modalt Lovar* 162. 9000 Upper**Infinity Varlabla Coafflclant Std. Error t-ratio Probiti>x Maan of X std.Dav.of x

Constant 166.91 0.5121 325.939 0 .0 0 0 0 0 SONUP 5.8426 0.6935 8.425 0 .0 0 0 0 0 0.10221 0.30301 SONLOWC 2.5964 0.9353 2.776 0.00550 0.49942E-01 0.21789 SONSK -2.4120 0.5612 -4.298 0.00002 0.39315 0.48859 SONSEMX -2.6554 0.82S0 -3.219 0.00129 0.10918 0.31195 SONUN -1.3521 1.367 -0.989 0.32271 0.28455E-01 0.16632 SONBUS 4.2563 0.8171 5.209 0 .0 0 0 0 0 0.65041E-01 0.24667 Sigma 6.2286 0.2151 28.957 0 .0 0 0 0 0 311

Table 7.8, cont’d.

Log-LiXalihood ...... -4875.6 Thrashold valuas for tha aodalt Lovar* 162.9000 Uppar**Infinity Varlabla coafflclant Std. Error t-ratlo Prt>bitt>x Naan of X std.Dav.of X Constant 166.29 0.5052 329.186 0.00000 SONUP 6.5830 0.6480 10.159 0.00000 7.12064 0.32580 SONLOHC 5.2507 0.8837 5.942 7.00000 * 4CC27S-01 0.21389 SONSK -0.64216 0.5368 -1.196 : 2:i£i* 0.35735 0.47936 SONSEKX -1.2723 0.7197 -1.768 C .07710 C.1349' 0.34175 SONUN -3.9787 2.171 -1.832 C.06688 C. 13722E-C1 0.11637 a n n a n a 2.7640 0.8434 3.277 0.00105 0.62321E-01 0.24181 Slgaa 6.1033 0.2025 30.145 0.00000

Log-Likalibood...... -5667.0 Thrashold valuas for tha nodalt Lovar* 162.9000 Uppar*+lnflnlty Varlabla Coafflclant Std. Error t-ratlo Probiti>x Naan of X Std.Dav.of X Constant 166.80 0.4618 361.242 0.00000 SONUP 5.8260 0.6286 9.268 0.00000 0.10610 0.30804 SONKNKT 2.1814 0.9391 2.323 0.02019 0.42142E-01 0.20096 SGNSX -1.4789 0.5170 -2.861 0.00423 0.32771 0.46950 30NSEKX -1.7248 0.6474 -2.664 0.00772 0.15S18 0.36217 90NUN -0.36083 1.351 -0.267 0.78942 0.22B06E-01 0.14932 SONBOS 4.5700 0.7376 6.196 0.00000 0.67923E-01 0.25168 Slgaa 6.2624 0.19S6 32.018 0.00000 Table 7.9: RSMLE Truncated Regression of Height on Constant Term, Observations with complete information on Birth Region and Soldier’s Occupation

Log-Llkallhood...... -4097.6 T b r n h o U valuas for tbs nodali Lovar* 162.9000 Varlabla coafflclant Std. Error t-ratlo Probiti>x Constant 163.12 0.8910 183.079 0.00000 Sigma 7.5118 0.3853 19.497 0.00000

Log-Llkallhood...... -4493.3 Thrashold valuas for tha aodalt Lovar" 162.9000 Varlabla Coafflclant Std. Error t-ratlo Probitt>x Constant 166.05 0.4659 356.430 Sigma 6.4776 0.2537 25.535 0.00000

Log-LlKalihood...... -4476.8 Thrashold valuas for tha modali Lovar* 162.9000 Varlabla Coafflclant Std. Error t-ratlo Probsti>x Constant 166.32 0.4194 396.569 0.00000 sigma 6.2150 0.2356 26.375 0.00000

Log-Likallhood...... -4729.0 Thrashold valuas for tha modali Lovar* 162.9000 Varlabla Coafflclant Std. Error t-ratlo Probiti>* Constant 165.18 0.5664 29ir63o"~oTooOO(T sigma 6.9115 0.2852 24.231 0.00000

Log-Likallhood...... -4886.4 Thrashold valuas for tbs modali Lovar* 162.9000 Varlabla Coafflclant Std. Error t-ratlo Probtti>x Constant 165.68 0.5398 306.929 0.00000 Sigma 7.0725 0.2806 25.203 0.00000

Log-Likallhood...... -4975.7 Thrashold valuas for tha nodali Lovar* 162.9000 Varlabla Coafflclant Std. Error t-ratlo Probtti>x Constant 166.23 0.4756 349.545 0.00000 Sigma 6.8764 0.2588 26.572 0.00000

Log-Likallhood...... -5764.7 Thrashold valuas for tha nodali Lovar* 162.9000 Varlabla Coafflclant Std. Error t-ratlo Probitt>x Constant 166.37 0.4388 379.121 0.00000 sigma 6.9243 0.2407 28.764 0.00000 Table 7.10 RSMLE Average Height Estimates by Soldier's Occupational Category(7), 7 Phases, Birthyears 1852/57-1888/93

Birth Cohorts 1852-57 1858-63 1864-69 1870-75 1876-81 1882-87 1888-93 N 1488 1613 1617 1695 1722 1749 2017 Upper White Collar 169.71 171.27 171.17 171.84 172.75 172.87 172.63 Lower White Collar 168.31 166.6 169.71 170.19 169.51 171.54 168.98 Business 168.93 169.76 169.89 168.63 171.17 169.05 171.37 Baseline Agriculture 163.78 166.68 166.47 166.08 166.91 166.29 166.8 Skilled 162.68 164.86 165.17 164.13 164.5 165.65 165.32 Semi-Skilled 160.96 165.18 165.49 162.93 164.25 165.02 165.08 Unskilled 163.94 166.75 165.82 169.33 165.56 162.31 166.44 Sigma 7.0331 6.0857 5.8275 6.2888 6.2286 6.1033 6.2624 Log Likeihood -4062.2 -4445.7 -4425.7 -4657.7 -4784.7 -4875.6 -566.7 174

172

170 ------■— Upper White Collar

168 — □— Lower White Collar

aw 166 ------♦— e Business ® E 164 ------0--- Baseline Agriculture £ e o 162 ------*— Skilled

160 ------£i— Semi-Skilled

158 ------•— Unskilled

156

154 CO o> in co CO co( co CVI CO CO in in co co co co Birth Cohorts

Figure 7.24 RSMLE Average Height Estimates by Soldier's Occupational Category(7), 7 Phases, Birthyears 1852/57-1888/93 Table 7.11: RSMLE Average Height Estimates for Observations with Father’s Occupation, 7 Phases, Birthyears 1852/57-1888/93

Birth Cohorts 1852-57 1858-63 1864-69 1870-75 1876-81 1882-87 1888-93 N | 977 741 802 780 892 1255 1729 Average Height RSMLI 159.05 165.82 166.89 166.19 165.6 165.93 165.99 Standard Error 2.053 0.7069 0.4765 0.6183 0.7301 0.5859 0.5177 Standard Deviation 8.8318 6.4448 5.7264 6.2321 6.8815 6.8728 7.0883 Standard Error 0.7092 0.3786 0.2872 0.3438 0.3795 0.312 0.2749 Log Likeihood -2,668.70 -2,050.90 -2,198.80 -2,155.30 -2,506.60 -3,548.60 -4,938.40 Figure 7.25: RSMLE Average Height Estimates for Observations with Father's Occupation, Father's with Observations for Estimates Height Average RSMLE 7.25: Figure

centimeters 58 15 0 16 2 16 4 16 4 5 1 6 5 1 166 168 hss ityas 1852/57-1888/93 Birthyears Phases, 7 oo in in CD oo co in Average Height RSMLE Height Average 00 CO Birth Cohorts Birth in OCO CO cii oo h- CO CO CO O) CO I ------■ — Average Height Average RSMLE 317

Table 7.12: RSMLE Truncated Regression on Father s Occupational Dummies. 7 Phases, Birthvears 1852/57- 1888/93

Log-Likallhood...... -2658.9 Threshold values for ths nodelt Lover* 162.9000 Upper**lnfinity Variable Coofflcisnt Std. Error t-ratlo Probiti>x Moan of X Std.Dav.of X Constant 159.56 1.920 83.125 0.00000 UFHC 7.2196 2.128 3.392 0.00069 0.45036E-01 0.20749 LOMC 5.4726 2.778 1.970 0.04887 0.25S89E-01 0.15799 fwrr.T.im -0.90851 1.177 -0.772 0.44023 0.33572 0.47248 SEMI -1.4471 2.661 -0.544 0.58661 0.44012E-01 0.20523 UHSK 0.30314B-01 2.364 0.013 0.98977 0.51177E-01 0.22047 BUS 3.9621 2.548 1.555 0.11991 0.33777E-01 0.18075 Signa 8.5010 0.6471 13.137 0.00000

Log-Likallhood...... -2040.5 Threshold valuas for tha aodali Lovar* 162.9000 Uppare+infinity Varlabla Coafflclant std. Error t-ratlo Probtt:>x Mean of X std.Dav.of X Constant 166.39 0.7410 224.535 0.00000 UFMC 2.8617 1.171 2.443 0.01457 0.76923E-01 0.26665 LOMC 4.3749 1.800 2.431 0.01506 0.25641E-01 0.15817 SKILLED -1.1689 0.7959 -1.469 0.14196 0.31984 0.46673 SEMI -1.5424 1.389 -1.110 0.26689 0.72874E-01 0.26011 OMSK -2.0555 1.679 -1.224 0.22078 0.49933E-01 0.21795 BUS -1.7828 1.584 -1.126 0.26029 0.55331E-01 0.22878 Signa 6.2506 0.3546 17.626 0.00000

Log-Llkallhood...... -2182.4 Thrashold valuas for tha aodalt Lovar* 162.9000 Upper»*infinlty Varlabla Coafflclant Std. Error t-ratlo Probiti>x Maan of X Std.Dav.of X Constant 167.11 0.5375 310.911 0.00000 UFMC 3.2828 0.9233 3.555 0.00038 0.78554E-01 0.26921 LOMC 2.0090 1.443 1.392 0.16389 0.29925E-01 0.17049 s k i l l e d 0.42860E-01 0.6367 0.067 0.94633 0.28928 0.45371 SEMI -1.9549 1.141 -1.714 0.08653 0.73566E-01 0.26123 UHSK -3.6803 1.221 -3.013 0.00259 0.77307E-01 0.26724 BUS 0.82594 1.127 0.733 0.46359 0.57357E-01 0.23267 Signa 5.5177 0.2661 20.734 0.00000

Log-Llkallhood...... -2144.2 Thrashold valuas for tha nodali Lovar* 162.9000 uppar*+infinity Varlabla Coafflclant Std. Error t-ratlo Probitox Moan of X std.Dav.of X Constant 165.94 0.7197 230.572 0.00000 UFMC 4.3864 1.120 3.917 0.00009 0.7179SE-01 0.25831 LOMC 2.8562 1.545 1.849 0.06446 0.35897E-01 0.18615 SKILLED -0.15248 0.7549 -0.202 0.83993 0.31538 0.46497 SBHX -0.88941 1.284 -0.693 0.48834 0.75641E-01 0.26459 UHSK 0.50190 1.224 0.410 0.68181 0.74359E-01 0.26252 BOS 1.9891 1.307 1.S22 0.12792 0.56410E-01 0.23086 Signa 6.0476 0.3227 18.738 0.00000 318

Table 7.12, cont’d.

Log-Likallhood...... -2483.0 Thrashold valuas for tha nodali Lovar- 162.9000 Uppar*+lnfinity Varlabla coafflclant Std. Error t-ratlo Probttox Moan of X std.Dav.of X Constant 165.85 0.7585 218.661 0.00000 UPWC 5.5069 1.162 4.737 0.00000 0.66143E-01 0.24867 LCMC 1.7344 1.768 0.981 0.32645 0.30269E-01 0.17142 SKILLED -0.95571 0.7735 -1.236 0.21664 0.34305 0.47499 SEMI -0.18204 1.173 -0.155 0.87672 0.91928E-01 0.28909 UHSK -1.6237 1.466 -1.107 0.26815 0.61659E-01 0.24067 BUS 5.0236 1.277 3.934 0.00008 0.52691E-01 0.22354 Sigma 6.4787 0.3338 19.412 0.00000

Log-LlXallbood...... -3505.4 Thrashold valuas for tha modalt Lovar* 162.9000 Uppar*+infinlty Varlabla coafflclant Std. Error t-ratlo Probttox Maan of X std.Dav.of X Constant 165.86 0.6166 268.987 0.00000 UPWC 5.7430 0.8327 6.897 0.00000 0.99602E-01 0.29959 LOMC 5.2239 1.140 4.582 0.00000 0.43028B-01 0.20300 SKILLED -0.77690 0.6598 -1.177 0.23902 0.30438 0.46033 SEMI 0.34477 0.9120 0.378 0.70540 0.10359 0.30484 UHSK -2.0640 1.376 -1.500 0.13357 0.48606E-01 0.21513 BUS 2.6482 1.131 2.342 0.01919 0.50996B-01 0.22008 Sigma 6.3773 0.2665 23.931 0.00000

Log-Llkallhood...... -4875.8 Thrashold valuas for tha modalt Lovar* 162.9000 Uppsr— *-lnf inity Varlabla Coafflclant std. Error t-ratlo Probttox Maan of X std.Dav.of X Constant 166.32 0.S17S 321.411 0.00000 UPWC 6.6260 0.7413 8.938 0.00000 0.87334E-01 0.28241 LOMC 3.1575 1.005 3.142 0.00168 0.45691B-01 0.20887 SKILLED -1.0713 0.5704 -1.878 0.06034 0.29786 0.45745 SEMI -1.2641 0.8137 -1.554 0.12030 0.10758 0.30993 UHSK -1.5562 1.122 -1.386 0.16562 0.S1475E-01 0.22103 BUS 2.4928 1.013 2.462 0.01383 0.46B48B-01 0.21137 Sigma 6.5390 0.2322 28.165 0.00000 175

170 ------■— Upper White Collar

----- □— Lower White Collar

165 ------♦ — Skilled

------o ------Semi-Skilled

160 ------*— Unskilled

------£s------Business

------• ------155 Baseline Agriculture

150 n O) m cn coi to CT) 00 oo in ooooin oo oo cooo Birth Cohorts

Figure 7.26: RSMLE Average Height Estimates by Father's Occupation(7), 7 Phases, Birthyears 1852/57-1888/93 Table 7.13: RSMLE Average Height Estimates by Father's Occupation(7), 7 Phases, Birthyears 1852/57-1888/93

Birth Cohorts 1852-57 1858-63 1864-69 1870-75 1876-81 1882-87 1888-93 N 977 741 802 780 892 1255 1729 Upper White Collar 166.78 169.25 170.39 170.33 171.36 171.6 172.95 Lower White Collar 165.03 170.76 169.12 168.8 167.58 171.08 169.48 Skilled 158.65 165.22 167.15 165.79 164.89 165.08 165.25 Semi-Skilled 158.11 164.33 163.43 166.44 164.23 163.8 164.76 Unskilled 159.59 164.33 163.43 166.44 164.23 163.8 164.76 Business 163.52 164.61 167.94 167.93 170.87 168.57 168.81 Baseline Agriculture 159.56 166.39 167.11 165.94 165.85 165.86 166.32 Sigma 8.501 6.2506 5.5177 6.0476 6.4787 6.3773 6.539 Log Likeihood -2658.9 -2040.5 -2182.4 -2144.2 -2483 -3505.4 -4875.8 17 3 t

172 + wr 171

170

169 + Upper Class

1a» 168 ______Working 1 167 « o Agriculture Class 166

165 --

164 --

163

162 I II I I II H I H I I I H-44-H--H-I I -H I H I I II II NOit-oinNCTii-owNffli-oinNoiTn lf)lf)(0(0(D(DlONNSSSfflCOmcOffl010) cvi4(oco6c\i4ibob6c\i4i6c66cvi4(£>(oinini/)lf)©(0(D(OtDNNNNNCOncOCOCO COCOCOCOCOCOCOCOCOCOOOCOOOCOCOCOCOCOCO ▼— T— T— T— , — T— T— 1— 1— T— T— T— 1— 'I— T” T— T— *r~ , — Birth Cohorts

Figure 7.27: Unadjusted Average Height by Soldier's Occupation(3), 6-Year Moving Average, Birthyears 1852/57-1888/93 174

172

170

168 Upper Class J ^ 166 Working Class

Agriculture Class 164

162

160

158

Birth Cohorts

Figure 7.28: QBE Average Height Estimates by Soldier's Occupation(3), 6-Year Moving Average, Birthyears 1852/57-1888/93 173 T

172

171 w ------■— AVHT of R.S. of White Collar & Business 170 - Class

----- o — AVHT of R.S. of 169 -b Skilled, Semiskilled, and Unskilled Workers ° 168 ~ ------* — AVHT of R.S. of 167 - Agriculture Workers

166 --

165 + H-H I l-H H I I t-H I I II I I I I It I H II I I I II f- CD T-oinsoii-ninNoii-oinNaT-n inuj in (OtptOtOtONNNNSISCOOOffilSaiCn u vu v p- i'*' w uj uj w g> u) cocococococooococococooocococococococo N4®co6N4toco6N4iooo6c!i4fflffl inioininiofflfflmmNNNsscocoeofflco oococococococococooococoncocococoooco Birth Cohorts

Figure 7.29: Average Height of Reduced Samples by Soldier’s Occupation(3), 6-Year Moving Average, 323 Birthyears 1852/57-1888/93 Table 7.14: RSMLE Truncated Regression of Height on 324 Soldier’s OccupattonO), 7 Phases. Birthyears 1852/57-1888/93

Lo^-Llkalltaoedi ...... -4043.4 Threabold valuaa for turn aodalt Lower* 162.9000 Upper*«-lnflnlty Variable Coafflclant Std. Error t-ratlo Probitt>x Mean of X std.Dav.of X Cana cant 163.75 0.1417 194.554 0.00000 u r m 5.2991 0.6514 6.224- 0.00000 0.14247 0.34965 W O K O B -1.2616 0.6621 -1.906 0.0S670 0.50874 0.50009 Slgaa 7.0479 0.3337 21.120 0.00000

Log-Llkalihood...... -4454.7 Threabold valuaa for tha aodalt Lover* 162.9000 Upper**inf lnlty Variable Coefficient std. Error t-ratlo Probttt>x Maan of X Std.Dav.of X Coastant 166.57 0.4980 334.470 0.00000 UPfKR 3.1374 0.5737 5.469 0.00000 0.19901 0.39938 WOBtIM B -1.7607 0.5083 -3.464 0.00053 0.46249 0.49875 Slow 6.1635 0.2283 26.996 0.00000

Log-LUcallhood...... -4427.2 Threabold valuaa for the aodalt Lower* 162.9000 Upper**inflnlty Variable Coafflclant Std. Error t-ratlo probttt>x Naan of X std.Dav.of x Conatant 166.45 0.4505 368.480 0.00000 3.8735 0.5288 7.325 0.00000 0.19975 0.39994 -1.2192 0.4692 -2.599 0.00936 0.43661 0.49612 5.8404 0.2071 28.207 0.00000

rwg.r.ifc«HhiMi^ ...... —4671.8 Threabold valuaa for tha aodalt Lower* 162.9000 Upper*+lnf lnlty Variable coafflclant Std. Error t-ratlo Probitox Maan of X Std.Dav.of X Conatant 165.89 0.5613 295.547 0.00000 UFPEX 4.1219 0.6057 . 6.806 0.00000 0.20531 0.40405 VR38KZK -1.9510 0.5439 -3.587 0.00033 0.48201 0.49982 Slgaa 6.4056 0.2421 26.456 0.00000

_ -4790.9 Thraabold valuaa for tha aodalt Lover* 162.9000 Upper**Infinity Variable Coafflclant std. Error t-ratlo Probitox Moan of X std.Dav.of x Conatant 166.83 0.5223 319.419 0.00000 UCTZE 4.7110 0.5849 8.055 0.00000 0.21719 0.41245 tfOKXSO -2.4266 0.5363 -4.525 0.00001 0.53078 0.49920 Slgaa 6.2833 0.2188 28.721 0.00000 325 Table 7.14. cont’d

Log-Likelihood...... -4887.1 Threshold values for the siodeli Lower* 162.9000 Upper*«-infinity Variable Coefficient Std. Error t-ratio Probiti>x Mean of X std.Dev.of X Constant 166.15 0.5223 318.104 0.00000 UPPER 5.4304 0.5725 9.485 0.00000 0.23099 0.42159 WORKING -0.90088 0.5155 -1.748 0.0805S 0.50600 0.50011 Slgaa 6.1933 0.2083 29.737 0.00000

Log-Likelihood...... -5674.6 Threshold values for the modeli Lower* 162.9000 Upper=+infinity Variable Coefficient Std. Error t-ratio Probit:>x Mean of X Std.Dev.of X Constant 166.73 0.4710 353.957 0.00000 UPPER 4.8175 0.5326 9.046 0.00000 0.21616 0.41173 WORKING -1.5156 0.4774 -3.174 0.00150 0.50570 0.50009 Slgaa 6.3170 0.1989 31.759 0.00000 Table 7.15: Comparison of RSMLE QBE and Komlos Estimates of Average Heights by Soldier's Occupational Category (3), 7 Phases, Birthyears 1852/57-1888/93

Birth Cohorts 1852-57 1858-63 1864-69 1870-75 1876-81 1882-87 1888-93 RSMLE Upper Class RSMLE AVHT 169.05 169.71 170.32 170.01 171.54 171.58 171.55 Agriculture RSMLE AVHT 163.75 166.57 166.45 165.89 166.83 166.15 166.73 Working C lass RSMLE AVHT 162.49 164.81 165.23 163.94 164.4 165.25 165.21 QBE Upper Class QBE AVHT 170.95 169.41 168.14 170.02 172.29 172.04 169.38 Agriculture QBE AVHT 163.31 167.78 167.49 167.1 167.77 167.01 168.54 Working C lass QBE AVHT 163.56 166.53 165.41 164.07 163.93 165.49 166.41 KOMLOS Upper Class AVHT of R.S. 171.42 171.25 171.48 171.6 172.61 172.59 172.62 Agriculture AVHT of R.S. 168.84 169.41 169.11 169.26 169.64 169.23 169.61 Working Class AVHT of R.S. 168.37 168.58 168.51 168.41 168.5 168.8 168.88 172

170

168

Upper Class RSMLE 166 AVHT

Agriculture RSMLE 164 AVHT

162 Working Class RSMLE AVHT

160

158

156 ai m co to CO CO o>I co CO in in CO co co co co co co CO Birth Cohorts

Figure 7.30: RSMLE Estimates of Average Height by Soldier's Occupation(3), 7 Phases

Birthyears 1852/57-1888/93 327 174

172

170 ------■— Upper Class QBE wo) 168 AVHT 9 9 --- □— E 166 Agriculture QBE AVHT E 9 ----*— ° 164 Working Class QBE AVHT 162 --

160 --

158 -I 1------1------1------1------1------1------1 f - CO CT) in ,— CO in to to r^- CO 00 cn C\l CO 4 6 to cil 00 in in (O 1^ CO 00 €0 00 00 00 00 CO 00 Y— ,— ,— — Y- ,— ■I— Birth Cohorts

Figure 7.31: QBE Estimates of Average Height by Soldier's Occupation(3), 7 Phases, Birthyears 1852/57-1888/93 1 7 3

172

171 ------■— Upper Class AVHT of a R.S. ® 170 ® — Agriculture AVHT of E R.S. I 169 o ------♦— Working Class AVHT of R.S. 168

167

166 h- co 05 in r- co in CO co i-- CO co 05 t cii CO cii CO ■n CO co 00 co CO CO co CO Birth Cohorts

Figure 7.32: Average Height of Reduced Sample, by Soldier's Occupation(3), 7 Phases, Birthyears 1852/57-1888/93 1 7 3 - ♦

1 7 2

171 Upper Class RSMLE 1 7 0 AVHT

Upper Class QBE 1 6 9 AVHT

16 8 Upper Class AVHT of R.S. 167

1 6 6

165 CO CO O)CO CO in in co oo co oo oo oo oo

Birth Cohorts

Figure 7.33: Upper Class, Average Height, RSMLE, QBE, and Komlos Estimates, 7 Phases, Birthyears 1852/57-1888/93 Ol'L' 168 --

Agriculture RSMLE AVHT 2 166 - Agriculture QBE E 165 -- AVHT

Agriculture AVHT of R.S.

r» CO o> in 1^ CO CO CO f'- CO in I I I CO O)I cvi CO o CO CM CO in in CO N. CO CO CO oo CO CO CO CO CO

Birtn Cohorts

Figure 7.34: Agriculture Average Height, RSMLE, QBE, and Komlos Estimates, 7 Phases, Birthyears 1852/57-1888/93 169 t

168 -

167 --

------■— Working Class RSMLE AVHT

------□— Working Class QBE AVHT

------♦— Working C lass AVHT of R.S.

161 --

160 --

159 - CO O) in T— r^- CO in CO CO r^- oo oo 05 CVI 00 4 6 CO CNJ 00 in in CO r» N. 00 OO 00 00 00 00 00 00 CO ▼— T— 7— T— 7— 7— 7~ Birth Cohorts

Figure 7.35: Working Class, Average Height, RSMLE, QBE, and Komlos Estimates, 7 Phases, Birthyears 1852/57-1888/93 172 T ■■■■■ 171 --

170 ■ ■ 169 + AVHT of Upper Class 2 168 + I P ° C\3 o q O0 q 0 0 0 Q 0 AVHT of Agriculture I '6 7 i o AVHT of Working Clas 166

165 --

164 --

163 -II H-l i -H I I-1 I M 4-1 H II I I I I II I I I I ninsoii-ninN ffir-ninso) r- CO lOfflffl(DNNSNNffl(DOO«)ffl O) O) iiiiiiiiiiiiii CMtlflfflOCM'tBtDONtlDO o c\j l/)10ini/l(D(£)0(D(ONNNNS CO 00 cocococooocococococococococo CO CO

Birth Cohorts

Figure 7.36: Unadjusted Average Height by Father's Occupation(3), 12-Year Moving Average, Birthyears 1852/57-1888/93 333 172 T

_■■■ A 170 -- a ■* ■ . j j v * • v > 168 ------■— QBE of Average Height of Upper co Class ® 166

160

158 I M I II M II I I I H I I l-H I I I I I I I I I I CO ID N O) i — CO lO s oi r- co w n a •r- CO CD CD CD CD r-~ r - N S ID 0 0 CO 0 0 CO O)I cn I N'fCDCOOCMrfCDCO O W Tf co co O CM IDtniniDCDCOCDCDCO r^- r*~ r- CO 00 cooocooooocooooooooooooooooo 00 CO

Birth Cohorts

Figure 7.37: QBE Average Height Estimates by Father's Occupation(3), 12-Year Moving Average, Birthyears 1852/57-1888/93 173 T

"■■ 172

171 ------■— AVHT of R.S. of Upper 0) Class ® 170 E — □— AVHT of R.S. of « Working Class 1 I 169 ------♦— AVHT of R.S. of Agriculture Class 168 --

167 --

166 I I I I I I I l + l I I I l-f I I -( I I l I I I I I I I I I ninNffir-ninNoir-o«NO)--n (OIDIDIOSNSNNIOIOCOOOIOOlCn T“cocooooococococococococococococo T-r*T*T-T-1-l-T“ T-T-T"1-r’ T-T- NTficoOW^ieboW'tlDtOON inininifllDI0(D!0

Figure 7.38: Average Height of Reduced Sample by Father's Occupation, 12-Year Moving Average, Birthyears 1852/57-1888/93 336

Table 7.16: RSMLE Truncated Regression: Height on Father’s Occupation(3), 7 Phases. Birthyears 1852/57-1888/93

Log-Likelihood...... -2659.6 ___ Threshold values for the models Lower* 162.9000 Upper=*infinity Variable Coefficient Std. Error t-ratio Probsts>x Mean of X Std.Dev.of X

Constant 159.49 1.939 82.273 0.00000 FUPPER 5.8281 1.603 3.635 0.00028 0.10440 0.30594 FWORKXNG -0.85095 1.101 -0.773 0.43971 0.43091 0.49546 Sigma 8.5265 0.6517 13.084 0.00000

Log-Likelihood...... -2045.4 Threshold values for the models Lowers 162.9000 Upper=*infinity Variable Coefficient Std. Error t-ratio Prob:tt>x Mean of X Std.Dev.of X Constant 166.25 0.7713 215.536 0.00000 FUPPER 1.7470 0.9432 1.852 0.06400 0.15789 0.36489 FWORKXNG -1.3501 0.7514 -1.797 0.07235 0.44265 0.49704. Signa 6.3449 0.3660 17.336 0.00000

Log-Likelihood...... -2189.8 Threshold values for the models Lowers 162.9000 Upper**infinity Variable Coefficient Std. Error t-ratio Prob:ts>x Mean of X Std.Dev.of X Constant 166.99 0.5587 298.884 0.00000 FUPPER 2.2859 ' 0.7385 3.095 0.00197 0.16584 0.37216 rWORKXNC -0.85847 0.5985 -1.434 0.15150 0.44015 0.49671 Sigma S.6060 0.2750 20.389 0.00000

Log-Likelihood...... -2145.8 Threshold values for the models Lower= 162.9000 Uppers*infinity Variable Coefficient Std. Error t-ratio Probsts>x Mean of X Std.Dev.of X Constant 165.89 0.7285 .227.708 0.00000 FUPPER 3.2875 0.8659 3.797 0.00015 0.16410 0.37061 FWORKXNG -0.16135 0.6909 -0.234 0.81534 0.46538 0.49912 Slgsui 6.0764 0.3259 18.645 0.00000

Log-Likelihood...... -2485.4 Threshold values for the models Lower* 162.9000 Upper**infinity Variable Coefficient Std. Error t-ratio Probstox Mean of X Std.Dev.of X Constant 165.78 0.7716 214.865 0.00000 FUPPER 4.6720 0.9069 5.152 0.00000 0.14910 0.35639 FWORKXNG -0.89519 0.7141 -1.254 0.20996 0.49664 0.50027 Sigma 6.5214 0.3383 19.278 0.00000 337

Table 7.16, cont’d

Log-Likelihood...... -3510.2 Threshold values for the modeli Lower® 162.9000 Upper=*infinity ''ariable Coefficient Std. Error t-ratio Prob:t:>x Mean of X Std.Dev.of X Constant 165.77 0.6300 263.134 0.00000 FUPPER 4.9114 0.7008 7.008 0.00000 0.19363 0.39530 FWORKXNG -0.64584 0.6023 -1.072 0.28358 0.45657 0.49831 Slgaa 6.4313 0.2711 23.724 0.00000

Log-Likelihood...... -4885.1 Threshold values for the model: Lover® 162.9000 Upper=+infinity Variable Coefficient Std. Error t-ratio Prob:t:>x Mean of X Std.Dev.of X Constan t 166.18 0.5355 310.313 0.00000 FUPPER 4.8495 0.6154 7.880 0.00000 0.17987 0.38419 FWORKXNG -1.1892 0.5235 -2.272 0.02310 0.45691 0.49828 Sigaa 6.6284 0.2385 27.792 0.00000 338

Table 7.17: Unadjusted Average Height by Father/Soldier Occupational Categories

1 2 3 4 N% avht N% avht N % avht N % avht 1 286 50.8 172.68 90 16.0 170.13 24 14.3 168.46 2 0.4 167.5 2 73 28.7 172.42 82 32.3 170.53 34 13.4 169.90 8 3.1 167.44 3 44 1.7 171.58 67 2.6 170.41 1735 68.0 166.49 285 11.2 165.99 4 3 0.4 167.5 25 3.5 167.22 298 42.3 165.74 228 32.3 166.82

5 0 0 - 2 0.4 171.00 182 36.8 165.94 91 18.4 166.00 6 58 15.2 170.80 40 10.5 169.88 58 15.2 166.78 25 6.5 165.04 7 40 1.5 171.71 31 1.2 169.95 613 23.1 166.14 208 ^7.8 166.62 illcfi 0 0 - 1 2.1 161.0 20 41.7 163.08 6 12.5 164.83

5 6 7 N % avht N % avht N % avht TOTAL

0 0 - 151 26.8 171.29 10 1.8 168.5 563 1 0.4 164.5 38 15.0 171.26 18 7.1 168.33 254 52 2.0 166.37 83 3.3 168.12 284 11.1 166.05 2550 16 2.3 167.53 15 2.1 170.90 120 17.0 166.29 705 35 7.1 166.06 1 0.2 168.50 184 37.2 165.57 495 5 1.3 168.00 176 46.1 169.72 20 5.2 167.63 382 34 1.3 167.49 27 1.0 170.54 1699 64.1 167.69 2652 1 2.1 160.5 0 0 - 20 41.7 163.85 48 339

Table 7.18: RSMLE Truncated Regression Results of Height on Father’s, Soldier's, and Both Occupations, Birthyears 1852-1893

Log-Likelihood...... -19878. Threshold values for the nodeli Lower* 162.9000 N(0,1) used for significance.levels. Variable Coefficient Std. Error t-ratlo Probtti>x Mean of X Std.Dav.of X

Constant 165.87 0.2765 599.884 0.00000 UPWC 1.7813 0.5422 3.285 0.00102 0.77341E-01 0.26715 LOHC 1.0292 0.6314 1.630 0.10312 0.35674E-01 0.18549 BUS -0.11820 0.5906 -0.200 0.84136 0.49610E-01 0.21715 SKILLED -0.32315 0.3204 -1.008 0.31323 0.31299 0.46374 UKSK -1.1789 0.5376 -2.193 0.02831 0.57414E-01 0.23265 SEMI -0.27676 0.4569 -0.606 0.54472 0.85424E-01 0.27953 30HUP 4.6781 0.S504 8. 50J 0.00000 0.70373E-01 0.25579 SOHLOWC 2.4542 0.5893 4.165 0.00003 0.4417SE-01 0.20550 S0NBUS 2.8S22 0.5481 5.204 0.00000 0.65078E-01 0.24668 SOKSK -1.1194 0.3205 -3.493 0.00048 0.36176 0.48054 SOHUM 0.80460 0.8832 0.911 0.36232 0.16304E-01 0.12665 SONSEKI -0.93464 0.4320 -2.164 0.03049 0.10577 0.30756 Signs 6.4264 0.1170 54.948 0.00000

Log-Likelihood...... -19889. Threshold values for the eodeli Lower* 162.9000 uppers********** N(0,1) used for significance levels. Variable Coefficient Std. Error t-ratlo Frobttt>x Mean of X Std.Dav.of X Constant 165.73 0.2727 607.704 0.00000 30NUP 5.9293 0.4009 14.791 C.00000 0.70373E-01 0.25579 SOMLOMC 3.2273 0.5054 6.386 0.00000 0.44175E-01 0.20550 y e w w 3.5380 0.4288 8.250 0.00000 0.65078E-01 0.24668 SOMBK -1.2862 0.2753 -4.673 0.00000 0.36176 0.48054 0.47869 0.8736 0.548 0.58370 0.16304E-01 0.12665 SOMSEMI -1.1028 0.4075 -2.706 0.00680 0.10577 0.30756 s ig n s 6.4517 0.1179 54.719 0.00000

Log-Likelihood...... -19942. 5(0?*iniJ.2*for.lSm SaS'liviS*^ *162•9000 upper-**.*...... P rob«t.>x Mean of X Std.Dev.of X C o n sta n t 165.52 0.2772 597.015 0.00000 UPWC 5.5804 0.3946 14.141 LONG 3.7624 0.00000 0 .77341E-01 0.26715 pnff 0.5568 6.757 0.00000 0.35674E-01 2.3860 0.5022 0.18549 4.751 0.00000 0.49610E-01 0.21715 SKILLED -0.72864 0.2808 -2.595 fwawr -1.5 8 0 6 0.00947 0.31299 0.46374 0.5448 -2.902 0.00371 0.S7414E-01 SEMI -0.66577 0.4430 0.23265 S lg e a -1.503 0.13285 0.85424E-01 0.27953 6.5564 0.1220 53.751 0.00000 340

Table 7.19: RSMLE Truncated Regression Results using Dummies For Upward and Downward Mobility, Birthyears 1852-1893

Log-Likelihood ...... -7539.9 Threshold valuei for the aodalt Lower* 162.9000 Uppe!**•••••••*• 11(0,1) used for significance levels. Variable Coefficient std. Error t-ratio Probiti>x Mean of x Std.Dav.of X Constant 164.66 0.4268 385.797 0.00000 UPPER 4.0528 0.6016 6.736 0.00000 0.81324X-01 0.27338 S i^ a 6.4189 0.2059 31.179 0.00000

Log-Likelihood ...... -3379.6 Threshold values for the aodeli Lower* 162.9000 uppers********** 11(0,1) used for significance levels. Variable Coefficient std. Error t-ratlo Probiti>x Mean of X Std.Dav.of X Conatant 170.42 0.3340 510.269 0.00000 HOUCXMQ -4.8856 0.8387 -5.839 0.00000 0.13818 0.34524 Signs 6.5627 0.2350 27.931 0.00000

Table 7.20: RSMLE Average Height Estimates by Father's and Soldier's Occupation, Birthyears 1852-1893

Soldier's Occupation Upper Working NofR.S. Father's Occupation Upper 170.42 165.53 1,129 Working 168.71 164.66 2,779 341

Table 7.21: Unadjusted Average Height by Obcramt, Early and Late Periods

BIRTH OBERAMT EARLY PERIOD LATE PERIOD BYR = 1852-1873 BYR = 1874-1893 N AVHT N AVHT Aalen 132 167.398 137 167.376 Backnang 109 166.587 104 167.716 Balingem 132 167.736 145 168.065 Bcsigheim 114 167.248 128 167.617 Biberach 137 168.172 114 168.140 Blaubeurcn 62 167.319 73 167.767 Boeblingen 31 167.532 55 167.964 Brackenhcim 94 167.313 103 168.733 Calw 97 167.711 83 167.072 Cannstatt 56 168.491 121 167.624 Crailsheim 88 166.010 108 166.769 Ehingen 86 167.140 99 166.136 Ellwangen 140 166.759 125 167.032 Esslingen 137 168.262 188 167.434 Freudenstadt 139 167.265 139 168.181 Gaildorf 128 167.305 99 167.606 Geislingen 160 168.276 106 167.255 Gerabronn 134 167.328 107 167.668 Gmuend 122 167.652 157 167.994 Goeppingen 165 167.567 203 167.429 Hall 109 167.069 116 166.664 Heidenheim 161 165.897 170 167.062 Heilbronn 187 167.022 192 169.198 Herrenberg 96 166.813 99 167.702 Horb 101 168.533 71 166.796 Kirchheim 91 167.742 147 167.550 Kuenzelsau 148 167.231 109 167.638 Laupheim 79 167.085 77 167.851 Leonberg 52 167.708 61 169.221 Leutkirch 105 166.938 83 168.386 Ludwigsburg 63 169.651 64 169.656 Marbach 59 166.966 66 167.508 342

Tabic 7.21: Cont'd

Maulbronn 45 167.944 53 167.849 Mergcnthiem 166 167.000 108 168.681 Muensingen 91 167.173 86 167.587 Nagold 111 167.229 92 168.098 Neckarsulm 133 167.729 101 168.226 Neresheim 128 166.958 95 166.489 Ncuenburg 104 166.232 113 167.372 Nuertingcn 103 167.250 156 168.256 Obemdorf 97 167.969 103 167.170 Ochringen 157 166.828 105 167.810 Ravcnsberg 119 167.039 133 167.767 Rcutlingcn 170 167.894 202 168.755 Ricdlingcn 124 166.383 138 167.366 Rottenburg 116 167.754 110 168.305 Rottweit 129 167.495 153 168.059 Saulgau 128 166.863 105 167.102 Schomdorf 97 166.443 117 166.842 Spaichlingen 78 167.372 75 167.180 Sulz 75 167.107 81 168.343 Stuttgart Amt 193 167.912 216 167.479 Stuttgart Stadt 304 179.703 373 169.964 Tettnang 100 168.022 67 168.433 Tuebingen 140 167.443 169 168.873 Tuttlingen 95 167.292 121 167.279 Ulm 207 168.285 193 168.668 Urach 82 167.600 135 167.389 Vaihinger 34 167.265 50 167.950 Waiblinger 49 166.571 58 168.371 Waldsee 116 167.653 99 166.975 Wangen 64 167.559 51 167.892 Weinsberg 120 167.506 98 167.633 Welzheim 99 166.792 75 166.367 Wuerttemberg Total 7300 167.528 7499 167.863 Map 7.1 Regional Distribution of Unadjusted Average Heights (by Oberamt), Early Period (Birthyears = 1852-73)

W*Wil

SC-KWi

LESS THAN 167 CM

167JO-167-499 CM

1675 -167.999 CM

168JO -168599 CM

169 JO CM AND ABOVE Map 7.2 Regional Distribution of Unadjusted Average Heights (by Oberamt), Late Period (Birthyears 1874-93)

19

j 1 ess than 167 cm

] 167.0 - 167.499 cm

] 167.5-167.999 cm

j] 168.0 - 168.99 cm

] 169.0 cm and above 1 6 9 -r

168.5

------■— NECKAR AVHT 167.5 ------□— SCHWARZWALD AVHT

------♦— DONAU AVHT 166.5 ------0 --- JAGST AVHT

165.5

165 t-H -1 I 4 M I I I I I I f \ H M t ) ) l~H I I M I I I I I I NO)i-ninsffli-ninNO)r-ninNO)i-n ininiOtO(0(D(ONNSNNOO(D(OCOCOO)0) cooooococococococooococococococococooo

CNj^-toobocNi^-coobocM^ubebowTttDcbininininiototDtoiONNssNcoootoaa cocooooocococooooococooooooococoaooooo

Birth Cohorts

Figure 7.39: Unadjusted Average Heights by Region, 6-Year MovingA verage, 345 Birthyears 1852/57-1888/93 17 0

168 --

166 ------■— NECKAR QBE AVHT wCO 0> ----- □— SCHWARZWALD QBE E AVHT 0 164 1 0 ------♦— DONAU QBE AVHT o 162 ------o --- JAGST QBE AVHT

160 --

158 I I H I I H I I I I I I II I M I I I I I I t I l-H I I H I I Nffii-ninsoit-oinNoiT-ninNoii-o inifl(D(O(DlO(DNNNNN00(D(O(O(OO)O) COCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCO cvjTtcDobocvj'j-cbobocvj^rtDooocvj'ti-tooo ini/iininiDffiomtoNNNNNoeooocoo cococococococococococococococococococo Birth Cohorts

Figure 7.40: QBE Average Height Estimates by Region, 6-Year Moving Average, Birthyears 1852/57-1888/93 171 T

----- ■---- Neckar AVHT of R.S.

----- □--- SCHWARZWALD AVHT of R.S.

----- ♦--- DONAU AVHT of R.S.

------0--- JAGST AVTH of R.S.

168 --

167.5 -H-+-H t-H-H t H H I II- I M It I H I l-HJ I I H I o CO CO CD CM in CO v— o CO in CO CO CO CO h- h- r-~ 00 00 CO 05 05 00 00 00 00 00 00 00 CO 00 00 00 00 CO ■*~ T— ■*“ T— ▼“ T— y— — y— y— •*“ T—i— CM in 00 T~ N. o CO CD 6) CM in CO in in in CO CO CO r-- 1^ 00 CO 00 CO CO 00 00 00 00 00 00 00 00 T- T— T—,— y~~ T—,— T“ T— ,—00 T—T~00 CO ,— Birth Cohorts

Figure 7.41: Average Height of Reduced Samples, 6-Year Moving Average, Birthyears 1852/57-1888/93 347 348

Tabic 7.22: RSMLE Truncated Regression of Heights on Regional Dummies, 7 Phases. Birthvears 1852/57-1888/93

Threetoold values for tba nodalt Lowar* 162.9000 Upper**infinity Variable coefficient std. Error t-ratio Prob«ti>x Nun of x std.Dev.of X Constant 163.IS 1.062 1S3.618 0.00000 NBC 0.37718 0.9150 0.412 0.68019 0.25134 0.43393 SCB -0.15307 0.9132 -0.168 0.86688 0.26411 0.44101 JAB -0.31061 0.9252 -0.336 0.73708 0.25336 0.43508 Signs 7.5073 0.3848 19.512 0.00000

Constant 166.57 0.5878 283.393 0.00000 NBC 0.48250 0.6295 0.767 0.44337 0.25666 0.43693 SCB -0.88246 0.6434 -1.372 0.17020 0.26534 0.44165 JAB -1.5622 0.6797 -2.298 0.02154 0.23187 0.42215 Sieaa 6.4306 0.2497 25.751 0.00000

Constant 167.07 0.5184 322.314 0.00000 NEC 0.97781E-01 0.5795 0.169 0.86601 0.25788 0.43761 SCB -1.4915 0.6069 -2.458 0.01399 0.25417 0.43553 *760 -1.5746 0.6293 -2.502 0.01235 0.22511 0.41778 Signa 6.1646 0.2316 26.619 0.00000

Constant 165.00 0.7249 227.610 0.00000 NBC 1.0279 0.6960 1.477 0.13971 0.27021 0.44420 SCB -0.21572 0.7256 -0.297 0.76624 0.24897 0.43254 JAB -0.95418E-01 0.7335 -0.130 0.89651 0.23599 0.42474 Signs 6.8920 0.2835 24.314 0.00000

Constant 165.15 0.7345 224.856 0.00000 NBC 1.5005 0.7146 2.100 0.03574 0.27236 0.44530 SCB 0.79487 0.7137 1.114 0.26540 0.28513 0.45161 JAB -0.29168 0.7747 -0.377 0.70654 0.21835 0.4132S Slona 7.0378 0.2776 25.351 0.00000

Constant 165.59 0.6532 253.511 0.00000 NBC 1.5704 0.6566 2.392 0.01676 0.27330 0.44578 SCB 1.2761 0.6538 1.952 0.05096 0.28302 0.45059 JAB -0.49414 0.7361 -0.671 0.50202 0.20069 0.40063 Slgna 6.8256 0.2547 26.802 0.00000

Constant 166.29 0.5845 284.484 0.00000 NBC 1.1534 0.5992 1.925 0.05424 0.28210 0.45013 SCB 0.14568 0.6163 0.236 0.81314 0.26673 0.44236 JAB -1.1361 0.6759 -1.681 0.09279 0.20922 0.40685 Signs 6.8787 0.2373 28.984 0.00000 Table 7.23: RSMLE Estimates of Average Height by Region, 7 Phases, Birthyears 1852/57-1888/93

Birth Cohorts 1852-57 1858-63 1864-69 1870-75 1876-81 1882-87 1888-93 N 1488 1613 1617 1695 1722 1749 2017 NEC 163.53 167.05 167.17 166.03 166.65 167.16 167.44 SCH 163 165.69 165.58 164.78 165.94 166.87 166.44 JAG 162.84 165.01 165.5 164.9 165.86 165.1 165.15 DON 163.15 166.57 167.07 165 165.15 165.59 166.29 Sigma 7.5073 6.4306 6.1646 6.892 7.0378 6.8256 6.8787 Log Likeihood -4097.3 -4487.6 -4470.2 -4726.9 -4882.5 -4969.4 -5758.2 168

167

166

— « 165 NEC SCH 164 JAG

163 DON

162

161

160 CT) ini (O CO CT) CM 0 0 CM CO in CO CO 00 co CO CO Birth Cohorts

Figure 7.42: RSMLE Estimates of Average Height by Region, 7 Phases, Birthyears 1852/57-1888/93 350 351

Tabic 7.24 RSMLE Truncated Regression of Height on Population(1895) of Birthtown. 7 Phases, Birthvears 1852/57- 1888/93

4 hm ii< . ..a...... - 4 0 ( 5 . 8 Threshold values for the uodelt Lomr- 162.9000 Upper**infinity Variable Coefficient std. Error t-ratio Probttt>x Mean of X std.Dev.of X Constant 162.91 0.8919 182.649 0.00000 POP 0.37091E-04 0.9599E-05 3.864 0.00011 7844.6 27903. Slgna 7.4302 0.3778 19.669 0.00000

Log-Likelihood ...... -4470.7 Threshold values for the atodali Lower* 162.9000 Uppor**lnflnlty Variable Coefficient Std. Error t-ratio Probiti>x Kean of X Std.Dev.of X Constant 165.86 0.4628 358.357 0.00000 POP 0.31431E-04 0.6122E-05 5.134 0.00000 10169. 32160. sic 6.3703 0.2450 25.999 0.00000

Log-Likelihood ...... -4438.7 Threshold values for the nodeIt Lover* 162.9000 Upper**infinity Variable Coefficient Std. Error t-ratio Probitox Mean of X Std.Dev.of X Constant 166.12 0.4081 407.047 0.00000 0.32210E-04 0.S053S-05 6.374 0.00000 12347. 36223. Slgna 6.0327 0.2221 27.159 0.00000

Log-Likellbood ...... -4687.7 Threshold values for the uodelt Lower* 162.9000 Upper* »in finity Variable coefficient Std. Error t-ratio Probiti>x Mean of * * std.Dev.of x Constant 164.96 0.5565 296.428 0.00000 POP 0.33032E-04 0.5629B-05 5.869 0.00000 13599. 38207. sic 6.7551 0.2716 24.872 0.00000

L og-L ikelihood...... -4861.6 Threshold values for the uodelt Lower* 162.9000 Upper*«-infin ity Variable coefficient std. Error t-ratio Probiti>x Wean of X Std.Dev.of x Constant 165.47 0.5322 310.930 0.00000 POP 0.31023E-04 0.S755E-05 5.390 0.00000 13194. 37525. Slgna 6.9254 0.2684 25.804 0.00000

> « t1 luwm | -4961.2 Threshold values for the uodelt Lower* 162.9000 Upper**infinlty Variable Coefficient std. Error t-ratio Probiti>x wean of X Std.Dev.of x Constant 166.09 0.4801 345.987 0.00000 POP 0.19014E-04 0.6237E-05 3.049 0.00230 11494. 34370. Signa 6.8264 0.2548 26.796 0.00000

Log-Like 11 hood ...... -5730.8 Threshold values for the uodelt Lower* 162.9000 Upper**infinity Coefficient std. Error t-ratio Probitt>x wean of x Std.Dev.of X Constant 166.18 0.4503 369.090 0.00000 _ °I1S2S! B‘ 04 °*5»13B-05 3.046 0.00232 11477. 34314. Signa 6.9063 0.2398 28.797 0.00000 352

Tabic 7.25 Unadjusted Average Height in 6 Community Size Categories by Phase, 1852/57-1888/93

Community Size Phase 1 Phase 2 Phase 3 Phase 4 (in 1895) BYRS =1852-57 BYRS =1858-63 BYRS =1864-69 BYRS =1870-75 N Mean N Mean N Mean N Mean Less than 2,000 1330 167.38 1414 167.46 1422 167.09 1459 166.87 2,000-4,999 219 167.95 258 167.44 239 167.39 273 166.92 5,000-9,999 78 167.06 96 168.44 108 167.57 135 167.27 10,000-19,999 53 168.19 70 167.86 82 169.12 99 168.50 20,000-49,999 46 168.91 77 168.81 74 168.81 89 168.43 more than 50,000 50 170.63 76 170.94 103 170.76 116 170.68

Community Size Phase 5 Phase 6 Phase 7 (in 1895) BYRS = 1876-81 BYRS = 1882-87 BYRS = 1888-93 N Mean N Mean N Mean Less than 2,000 1431 167.26 1494 167.47 1629 167.19 2,000-4,999 280 167.59 250 167.59 315 168.05 5,000-9,999 135 168.69 143 168.72 161 168.78 10,000-19,999 99 169.65 90 169.47 127 169.55 20,000-49,999 86 170.04 90 170.39 90 170.89 more than 50,000 120 170.17 97 169.64 115 169.47 171

170

------■— more than 50,000 169 ------□— 20,000-49,999 0) ® 168 ------♦ — 10,000-19,999 E ------0— I 167 5,000-9.999 u ------*— 2,000-4,999 166 ------&— Less than 2,000 165

164 I I I I 0 4 CO *T O CO CM CO in in CD CO CO CO CO CO CO 00 CO || O' II CO M 05 || LO || r- l'- in co id r- co 00 T_ CO CO CO CO CO CO CO H O) DC cc cc CE cr DC CO > > > > > - > DC CO CD CO CD CD CO > CO Birth Cohorts

Figure 7.43: Unadjusted Average Height in Community Size Categories, 7 Phases, 353 Birthyears 1852/57-1888/93 Tabic 7.26: RSMLE Truncated Regression of Height Community Size Dummies. 7 Phases. Birthyears 1852/57-1888/93

L o g - Like nnood ...... -4064.9 nmheld nluM for tu b b

Log-fclkeliheod ...... -4499.1 ibrtttoU nluH for tha aodali lo—f 162.9000 Uppan** Infinity Varlabia Coaffielant std. Xrror t-ratlo rn b itm Nona, of X sta.Dov.of X Conatant 169.79 0.4946 342.030 0.00000 C32000 0.49470 0.6997 0.697 0.49600 0.12492 0.33073 CSSOOO 1.6199 1.009 1.610 0.10743 0.4972QB-01 0.21743 C910000 0.399211-01 1.224 0.031 0.97499 0.372909-01 . 0.19992 CS20000 1.9997 1.096 1.900 0.07191 0.410191-01 0.19940 CSSOOOO 4.9791 0.9766 S.094 0.00000 0.429949-01 0.20266 91®aa 6.3994 0.2440 26.096 0.00000

•4437.3 Thrathold n lu u for tha oodoli Lowar* 162.9000 OppaiaHnflnlfy v Vdrlabla cooffleioot std. Error t-ratio fn k itm Mam a t X atd.0or.of X Constant 166.02 0.4309 399.413 0.00000 - CSSOOO 0.39013 0.6647 0.327 0.99940 0.11996 0.3213T cssooo 0.21937 0.9339 0.231 0.91799 0.999669-01 0.22973' C910000 2.1066 0.9909 2.216 0.02672 0.499349-01 0.20941 CS20000 1.6934 1.029 1.642 0.10093 0.403499-01 0.19693 CSSOOOO S.0479 0.9063 6.261 0.00000 0.999669-01 0.22972 Slgna 6.0233 0.2214 27.209 0.00000

Log-Llkallbood ...... -4660.9 ffcraahald aaluas for tho aodalt Lowor* 162.9000 Oppor**Infinity Vdrlablo Coofflolont std. Xrror t-ratlo Vrohitox Naan of X Std.Daw.of X Conatant 164.99 0.9741 297.132 0.00000 C93000 -0.93499 0.7009 -0.669 0.49399 0.12922 0.33107 1.9676 0.9964 1.999 0.04609 0.999419-01 0.2374S C910000 3.7994 1.096 3.999 0.00037 0.496979-01 0.30999 CSSOOOO 1.9461 1.134 1.629 0.10392 0.449109-01 0.20639 CSSOOOO 9.1940 0.9923 5.776 0.00000 0.629009-01 0.24297 S i^ a 6.6966 0.2669 23.130 0.00000 355

Table 7.26, cont’d.

Loe-Llkellhoed ...... -4M».7 . Threshold values for the n o d a l s Lower* 162.9000 Upper**infinity Variable Coaffielant Std. Error t-ratlo Probiti>x Naan of X 3 td.Dev.of X Constant 164.99 0.S591 295.590 0.00000 CS2000 1.0973 0.7342 1.495 0.13502 0.12747 0.33340 CSSOOO 3.3S4S 0.9303 3.606 0.00031 0.629641-01 0.24279 CS10000 4.1973 1.011 4.152 0.00003 0.49476S-01 0.21692 CS20000 4.S764 1.079 4.247 0.00002 0.41909S-01 0.20044 CSSOOOO 4.9297 0.9139 5.293 0.00000 0.605341-01 0.23956 Signs 6.7999 0.2590 26.357 0.00000

Log-Likallhood ...... -49S0.2 Threshold values for tho nodoli Lower* 162.9000 Upper»*lnflnity Variable Coaffielant Std. Error t-ratlo Frobttox Naan of X Std.Daw.of Constant 166.99 0.4972 333.450 0.00000 CS2000 -0.42359 0.7546 -0.561 0.57453 0.11749 0.3220* CSSOOO 1.9497 0.4799 2.216 0.02670 0.697691-01 0.25313 C910000 3.1439 1.041 3.014 0.00267 0.43SS3X-01 0.2041* CS20000 4.0314 0.9950 4.052 0.00006 0.45945X-01 0.20921 CSSOOOO 2.4442 1.001 2.443 0.01456 0.499571-01 0.21771 Slgna 4.7354 0.2476 27.206 0.00000 faAfl tm Lllu IIHaa^ , ... st71 A * Threshold values for the nodeIt Lower*' 162.9000 variable Coefficient std. Error t-ratlo frobiti>x Mean of X 9 td.Dev.of X Constant 166.75 0.474* 349.077 0.00000 C32000 1.3145 0.463S 1.992 0.04760 0.12312 0.32947 CSSOOO 1.4690 0.9461 1.73* 0.0*21* 0.692921-01 0.26401 CS10000 3.0397 0.9950 3.396 0.000** 0.3403SX-01 0.22773 CS20000 4.9590 0.9449 4.93S 0.00000 0.399S0X-01 0.19673 CSSOOOO 2.4696 0.949* 2.600 0.00992 0.490601-01 0.217*9 Slgna 4.8036 0.232S 29.29* 0.00000 1 7 4 T

------■— LT 2000

------□— 2000-4,999

------♦— 5,000-9,999

------o— 10,000-19,999

------*---- 20,000-49,999

------&--- 50,000+ 160 --

158 --

156 -I------1------1------h 18 18 18 18 18 18 18 52- 58- 64- 70- 76- 82- 88- 57 63 69 75 SI 87 93 Birth Cohorts

Figure 7.44: RSMLE Average Height Estimates by Birth Community Size (in 1895), 356 7 Phases, Birthyears 1852/57-1888/93 Table 7.27 RSMLE Average Height Estimates by Birth Community Size (in 1895), 7-Phases, Birthyears 1852/57-1888/93

Community Size 1852-57 1858-63 1864-69 1870-75 1876-81 1882-87 1888-93 LT 2000 162.9 165.75 166.02 164.85 164.98 165.88 165.75 2000-4,999 163.62 166.23 166.37 164.32 166.08 165.46 167.06 5,000-9,999 161.49 167.37 166.23 166.82 168.33 167.83 167.22 10,000-19,999 164.46 165.79 168.13 168.61 169.17 169.02 168.79 20,000-49,999 164.85 167.72 167.7 166.7 169.56 169.91 170.61 50,000+ 168.52 170.73 172.04 170 169.81 168.33 168.22 1 6 9 .5

169

168.5

168 o ------■— « Urban AVHT E 167.5 c ----- □— Rural AVHT ®o 167

166.5

166

165.5 cn r- CO in N O) r CO r^. oo 00 00 00 CO 05 cn

Birth Cohorts

Figure 7.45: Unadjusted Average Height in Rural (It 2,000) and Urban Communities, 358 6-Year Moving Average, 1852/57-1888/93 171.5 T

■ 171 -- \ 170.5 -- / X,.. ■ ■ « 170 + J ' v \ ■* Urban AVHT of R.S. l ,69.5 T> Rural AVHT of R.S. e ° 169

168.5 -

168 -

167.5 NOii-nmsoii-ninN en CO 1/1 N (3) CO U1U)(OIOO(OU)NNNN N- co co co co co oi cn OJ^toeooN^mo O CO 4 CO 00 O CO 4 CO CO inini/iin

Figure 7.46: Average Height of Reduced Samples (ht> or = 162 cm), 6-Year Moving Average, Birthyears 1882/57-1888/93 360

Tabic 7.28: RSMLE Truncated Regression Results of Height on Urban Birthtown (2,000+ Residents) Dummy, 7 Phases, Birthyears 1852/57-1888/93

Phase Birth Cohorts N Log- Rural Urban Significance Urban % of Likelihood Constant CoefT. Level R.S. 1 1852 - 57 1,480 -4,070.9 162.70 1.3821 10% 26.15 2 1858-63 1,609 -4,477.9 165.62 1.6013 1% 29.58 3 1864-69 1,611 -4,450.6 165.85 1.7454 0.001% 31.66 4 1870-75 1,685 -4,696.1 154.58 2.1009 0.001% 33.83 5 1876-81 1,718 -4,855.2 164.83 3.1810 0.001% 34.23 6 1882-87 1,745 -4,959.3 165.76 1.7523 0.001% 32.55 7 1888-93 2,006 -5,722.5 165.68 2.2866 0.001% 33.70 361

Table 7.29: RSMLE Estimates of Average Height for Soldiers Bom in Rural (LT 2,000 residents) and Urban Communities, 7 Phases, Birthyears 1852/57 to 1888/93

Birthcohorts N Urban Rural 1852-1857 1480 164.08 162.7 1858-1863 1609 167.22 165.62 1864-1869 1611 167.6 165.85 1870-1875 1685 166.68 164.58 1876-1881 1718 168.01 164.83 1882-1887 1745 167.51 165.76 1888-1893 2006 167.97 165.68 169

168

167

166

165 Urban

164 Rural

163

162

161

160 1852- 1858- 1864- 1870- 1876- 1882- 1888- 1857 1863 1869 1875 1881 1887 1893 Birth Cohorts

Figure 7.47: RSMLE Estimates of Average Height for Soldiers Bom in Rural (LT 2,000 residents) and Urban Communities, 7 Phases, 1852/57 to 1888/93 170 - r

168 --

* 165 ----- ■— Urban QBE of AVHT

E 164 ------□— Rural QBE of AVHT

160 --

c o in c o in co in in co co co co co oo c o co CD CD CO 00 CO 00 CO CO inminincococococo 00 CO oooooooooocoooooooooco CO oo OO CO

Figure 7.48: QBE Average Height in Rural (It 2,000) and Urban Communities, 6-Year Moving Average, 363 Birthyears 1852/57-1888/93 170

1 6 8

1 6 6

co ------■— Urban QBE AVHT of % R.S. E 1 6 4 1 ------□— Rural QBE AVHT of 0) o R.S.

1 6 2

1 6 0

1 5 8 to in n O) t- co O) i- CO m (0(0 10(0 N. f- oo cn cn

Birth Cohorts

Figure 7.49: QBE Average Height Estimates Using Reduced Sample, Rural (It 2,000) and 364 Urban Communities, 6-Year Moving Average, 1882/57-1888/93 365

Table 7.30: QBE Results by Rural and Urban Birthtown. 6-Year Moving Average, Birthyears 1852/57-1888/93

RURAL BIRTHTOWN (LESS THAN 2000 RESIDENTS) TOTAL OBSERVATIONS USED * 10180 SOB. j BIRTHYEARS | JOBS. j UNADJ. AVHT | QBE AVHT | QBE ST. DEV.|| SHORTFALL 1 1852-57 1330 167.382462 164.6886140 6.8492489 0.2699996 2 1853-58 1483 167.593124 165.6029360 6.5203915 0.2099996 3 1854-59 1471 167.625000 166.5211640 6.0643110 0.1299995 « 1855-60 1443 167.531906 167.2249760 5.5663748 0.0500000 5 1856-61 1363 167.480408 167.1610260 5.6733465 0.0500000 6 1857-62 1394 167.452057 167.0356750 5.6876678 0.0600000 7 1858-63 1414 167.461533 167.0234370 5.8235292 0.0600000 8 1859-64 1421 167.317642 166.9131160 5.7030382 0.0600000 9 1860-65 1395 167.299500 167.0804440 5.6695757 0.0400000 10 1861-66 1395 167.217819 166.7849430 5.8418646 0.0600000 11 1862-67 1400 167.113831 166.8843690 5.6862144 0.0400000 12 1863-68 1398 167.091507 166.7451480 5.8588276 0.0500000 13 1864-69 1422 167.091507 166.8785550 5.6254883 0.0400000 14 1865-70 1437 167.099762 166.7694090 5.8189821 0.0500000 IS 1866-71 1484 166.987350 166.6486210 5.8457823 0.0500000 16 1867-72 1469 167.087677 166.7436220 5.9646931 0.0500000 17 1868-73 1490 167.0S4520 165.2778780 6.7635336 0.1899994 18 1869-74 1462 167.072754 164.1658170 7.1895857 0.2999994 19 1870-75 1460 166.870071 164.7919920 6.9266481 0.2199996 20 1871-76 1427 166.830231 163.7379300 7.2343798 0.3199992 21 1872-77 1430 166.881805 163.4953160 7.4082451 0.3399994 22 1873-78 1420 166.813721 164.5194240 6.8062363 0.2599993 23 1874-79 1435 166.863632 165.8359070 6.2446690 0.1299995 24 1875-80 1435 166.950043 165.6665800 6.4584360 0.1499997 25 1876-81 1431 167.257721 165.6096800 6.6919613 0.1799999 26 1877-82 1448 167.401154 165.0085450 7.2033720 0.2399998 27 1878-83 1481 167.549881 166.2535400 6.7514944 0.1399994 28 1879-84 1488 167.517715 165.3889010 7.1514282 0.2199996 29 1880-8S 1477 167.587082 166.2572940 6.9102354 0.1399994 30 1881-86 1494 167.423523 165.9671780 6.9624300 0.1499994 31 1882-87 1494 167.474060 165.6032410 7.1342497 0.1899994 32 1883-88 1491 167.430069 163.8034060 7.6351652 0.3499994 33 1884-89 1478 167.583389 164.3958890 7.4373083 0.3199992 34 1885-90 1496 167.713394 164.8738250 7.3066502 0.2899994 35 1886-91 1570 167.694580 166.1007540 6.7658682 0.1799995 36 1887-92 1633 167.855774 166.2841490 6.7515545 0.1799999 37 1888-93 1629 167.785141 167.3476100 6.1744156 0.0699999

URBAN BIRTHTOWNi 2,000 + RESIDENTS TOTAL OBSERVATIONS USED = 4539

IB. j BIRTHYEARS j| #OBS. j UNADJ. AVHT jI QBE AVHT | QBE ST. DEV.! SHORTFALL 1 1852-57 446 168.221130 162.5694730 8.4764872 0.4499990 2 1853-58 516 168.356339 163.7495730 8.2155552 0.3799995 3 1854-59 519 168.488068 165.2495880 7.5323524 0.2999994 4 1855-60 544 168.451172 164.9452060 7.5470276 0.3299995 5 1856-61 552 168.315781 167.8189700 6.3560839 0.0600000 6 1857-62 579 168.378983 167.7441860 6.5297890 0.0699999 7 1858-63 577 168.302094 167.5250240 6.6950855 0.0799999 8 1859-64 589 168.163193 165.6732640 7.6396542 0.2199996 9 1860-65 598 168.129807 164.6092530 8.0609035 0.2999994 366

Table 7.30, cont’d

10 1861-66 586 168.394196 165.7049870 7.9614935 0.2299995 11 1862-67 566 168.575851 165.9672850 7.8667727 0.2299995 12 1863-68 595 168.540436 164.5617070 8.2710142 0.3399994 13 1864-69 606 168.404083 166.0939940 7.4228306 0.2199996 14 1865-70 617 168.294556 166.5933530 6.8888693 0.1799999 15 1866-71 625 168.278000 166.8314670 6.9530611 0.1499997 16 1867-72 655 168.077377 166.6689450 6.9290257 0.1499997 17 1868-73 700 168.050797 166.6543730 6.8721733 0.1499997 18 1869-74 684 168.075378 167.1726990 6.7287788 0.0999997 19 1870-75 712 168.007812 167.2825470 6.7313032 0.0799999 20 1871-76 714 168.221191 167.3970180 6.8203163 0.0899998 21 1872-77 753 168.189819 167.4337010 6.9135294 0.0799999 22 1873-78 752 168.484619 167.4429320 7.0983782 0.0899998 23 1874-79 729 168.731644 167.8040770 7.1367893 0.0799999 24 1875-80 734 168.943970 168.1342320 7.1145487 0.0699999 25 1876-81 720 168.938705 168.2682500 7.1104050 0.0600000 26 1877-82 715 168.911880 168.3424840 7.1803579 0.0500000 27 1878-83 693 169.269836 169.0121920 6.7347136 0.0300000 28 1879-84 645 169.037201 168.9376370 6.5502911 0.0300000 29 1880-85 653 168.682236 168.4466550 6.6226120 0.0400000 30 1881-86 664 168.533875 168.2188260 6.6137981 0.0500000 31 1882-87 670 168.757462 167.1093140 7.2263842 0.1799999 32 1883-88 695 168.775528 167.0713200 7.2893896 0.1799995 33 1884-89 695 168.562576 165.9832920 7.7800264 0.2499993 34 1885-90 726 168.761703 167.3298190 7.3587313 0.1499997 35 1886-91 758 168.843658 168.2329100 6.9047012 0.0799999 36 1887-92 799 168.849182 168.4398500 6.8811274 0.0600000 37 1888-93 808 168.946777 168.1335300 7.1377916 0.0899998 Table 7.31 RSMLE Truncated Regression Results. Three Models Using Combinations of Soldier’s Occupation. Community Size, and Region, by Phase 1852/57-1888/93

Log-Likelihood...... -4064.4 Threshold values for the eodeli Lover* 162.9000 Variable Coefficient Std. Error t-ratlo Probttt>x Mean of X Constant 163.11 1.056 154.444 0.00000 CS2000 0.85782 0.9595 0.894 0.37129 0.12770 CSSOOO -1.4091 1.680 -0.839 0.40165 0.432431-01 C310000 1.5124 1.783 0.848 0.39625 0.30405E-01 CS20000 2.2682 1.846 1.229 0.21923 0.28378E-01 CSSOOOO 6.3610 1.675 3.798 0.00015 0.31757E-01 NIC -0.84727 0.9631 -0.880 0.37899 0.24932 SCH -0.26673E-01 0.9109 -0.029 0.97664 0.26419 JAG -0.12539 0.9136 -0.137 0.89084 0.25473 Sigea 7.4132 0.3758 19.729 0.00000

Log-Likelihood...... -4035.5 Threshold values for the aodeli Lover* 162.9000 Variable Coefficient Std. Error t-ratio Probsti>x Mean of X Constant 163.84 0.8368 195.800 0.00000 SOMOP 6.0137 1.286 4.676 0.00000 0.46622E-01 S0HLGMC 4.4630 1.584 2.817 0.00484 0.28378E-01 s o m e -1.0748 0.6913 -1.555 0.11998 0.43243 SOaBEMI -2.6397 1.436 -1.838 0.06603 0.614861-01 0.28628 2.483 0.115 0.90822 0.14189E-01 y p n t 4.9261 1.186 4.153 0.00003 0.66216E-01 C32000 -0.38991 0.9034 -0.432 0.66602 0.12770 CSSOOO -1.6815 1.548 -1.086 0.27752 0.43243E-01 C810000 -1.4212 1.694 -0.839 0.40152 0.30405E-01 CS20000 -0.97497 1.715 -0.568 0.56974 0.28378E-01 CS50000 2.6090 1.455 1.930 0.05360 0.317571-01 Sigea 7.0235 0.3329 21.101 0.00000

IllK^l lhOOOt eeeeeseeeesee *4034 e 4 Threshold values for the nodeIt Lover*’ 162.9000 Variable Coefficient std. Error t-ratlo Probsti>x Mean of X Constant 164.21 0.9668 169.846 0.00000 6.0126 1.287 4.673 0.00000 0.46622E-01 SCHLOWC 4.4372 1.581 2.807 0.00500 0.28378E-01 some -1.1854 0.6992 -1.695 0.09003 0.43243 SOMSDU -2.6304 1.433 -1.836 0.06641 0.61486E-01 0.34922 2.477 0.141 0.88788 0.141891-01 er— stm 4.9280 1.189 4.146 0.00003 0.66216E-01 CS2000 -0.20803 0.9107 -0.228 0.81932 0.12770 CSSOOO -1.6828 1.549 -1.086 0.27736 0.43243B-01 CS10000 1.703 -0.869 0.38460 0.30405E-01 CSSOOOO -0.56435 1.749 -0.323 0.74689 0.283781-01 CSSOOOO 3.6945 1.576 2.345 0.0190S 0.31757E-01 NIC -1.1944 0.8858 -1.348 0.17753 0.24932 SCH -0.667251 0.8365 -0.080 0.93642 0.26419 JA0 -0.16125 0.8361 -0.193 0.84707 0.25473 Sigea 7.0097 0.3314 21.153 0.00000 368

Tabic 7.3], cont'd.

Log-Likelihood...... -4466.6 Threshold values Cor the modeli Lower* 162.9000 Variable Coefficient Std. Error t-ratlo Probiti>x Mean of X Constant 166.50 0.S927 280.945 0.00000 CS2000 0.44S21 0.6952 0.640 0.52190 0.12492 CSSOOO 1.6849 1.007 1.673 0.09432 0.49720E-01 CS10000 0.39416E-01 1.229 0.032 0.97441 0.37290E-01 CS20000 1.4976 1.111 1.348 0.17769 0.41019B-01 CSSOOOO 4.7199 1.069 4.414 0.00001 0.42884E-01 NBC -0.47746 0.660S -0.723 0.46975 0.25606 SCH -0.88189 0.6429 -1.372 0.17016 0.26538 JAO -1.4726 0.6703 -2.197 0.02802 0.23244 Sigma 6.3382 0.2424 26.152 0.00000

Log-Likelihood..... Threshold values for the modeli Lower* 162. 9000 Variable Coefficient Std. Error t-ratio Probiti>x Mean of X Constant 166.71 0.4825 345.522 0.00000 30NUP 4.1572 0.7911 S.25S 0.00000 0.83282E-01 sonouc ■0.33742 1.084 -0.311 0.75559 0.45991E-01 SCHSK -1.8619 0.5243 -3.551 0.00038 0.39901 I50MB8MI -1.S410 1.062 -1.450 0.14697 0.51S85B-01 0.70428E-01 1.957 0.036 0.97129 0.11809E-01 yTHHTfl 2.6223 0.8801 2.979 0.00289 0.70230E-01 C32000 0.683028-01 0.6747 -0.101 0.91936 0.12492 CSSOOO 0.84228 0.9638 0.874 0.38218 0.49720K-01 CS10000 -0.68510 1.168 -0.SB7 0.55749 0.37290E-01 C320Q00 -0.39297E-01 1.074 -0.037 0.97081 0.41019E-01 CSSOOOO 1.9494 1.010 1.930 0.05357 0.428848-01 Sigma 6.0676 0.2213 27.416 0.00000

Log-Likelihood...... Threshold values for the modelt Lower* 162. 9000 Variable Coefficient Std. Error t-ratlo Probstf>x Mean of x Constant 167.28 0.5826 287.123 0.00000 SOMOP 4.1148 0.7892 5.214 0.00000 0.83282E-01 SONLOHC -0.24374 1.082 " -0.225 0.82176 0.459918-01 -1.8561 0.5262 -3.528 0.00042 0.39901 90MSBKZ -1.5432 1.059 -1.4S7 0.14502 0.515858-01 80M0M -0.16599 1.962 -0.085 0.93258 0.118098-01 SONBUS 2.6129 0.8776 2.977 0.00291 0.702308-01 CS2000 -0.94535E-01 0.6754 -0.140 0.88868 0.12492 CSSOOO 0.83314 0.9655 0.863 0.38819 0.497208-01 CS10000 -0.77120 1.175 -0.657 0.51146 0.372908-01 CS20000 -0.39113 1.099 -0.356 0.72202 0.410198-01 cssoooo 1.7591 1.095 1.607 0.10811 0.428848-01 NBC -0.35046 0.6200 -0.565 0.57189 0.25606 SCH -0.50781 0.6029 -0.842 0.39966 0.26538 JM -1.2643 0.6261 -2.019 0.04346 0.23244 Sigma 6.0525 0.2202 27.491 0.00000 Table 7.31, cont’d.

Log-Likelihood...... -4433.3 Threshold values for tha eodelt Lover* 162.9000 Variable Coefficient Std. Error t-ratlo Probiti>k Mean of X Constant 167.02 0.5153 324.150 0.00000 C32000 0.31796 0.6645 0.478 0.63231 0.11856 CS5000 0.45994 0.9373 0.491 0.62363 0.55866E-01 CS10000 2.0503 0.9550 2.147 0.03179 0.459348-01 CS20000 1.4115 1.048 1.347 0.17802 0.403488-01 CS50000 5.3128 0.9198 5.776 0.00000 0.558668-01 MBC -1.2428 0.6238 -1.992 0.04633 0.25636 SCH -1.4314 0.5932 -2.413 0.01582 0.25450 JAG -1.3353 0.6090 -2.192 0.02835 0.22533 Sigea 5.9950 0.2192 27.351 0.00000

Log-Likellhood...... -4402.1 Threshold values for the nodeli Lover* 162.9000 Variable Coefficient Std. Error t-ratio Probit«>x nsan of X Constant 166.58 0.4399 378.671 0.00000 SGKJP 4.2459 0.7751 5.478 0.00000 0.732468-01 sanowc 2.9848 1.012 2.949 0.00319 0.372448-01 aowBK -1.2931 0.5022 -2.575 0.01002 0.33457 SONSXMX -0.93960 0.7774 -1.209 0.22680 0.893858-01 «n— -0.57421 1.828 -0.314 0.75342 0.130358-01 2.9738 0.7625 3.900 0.00010 0.881448-01 CS2000 -0.30432 0.6464 -0.471 0.63780 0.11856 CSS000 -0.76987 0.9094 -0.847 0.39725 0.558668-01 CS10000 0.39622 0.9316 0.425 0.67061 0.459348-01 CS20000 -0.94005 1.031 -0.912 0.36166 0.403488-01 C350000 2.3890 0.8268 2.889 0.00386 0.558668-01 Sigea 5.7656 0.2024 28.489 0.00000

Log-Likelihood...... -4399.0 Threshold values for the eodeli Lover* 162.9000 Variable Coefficient Std. Error t-ratio Probiti>x Mean of X Constant 167.37 0.5171 323.676 0.00000 SONUP 4.1973 0.7725 5.434 0.00000 0.732468-01 S0HL0MC 2.9863 1.008 2.962 0.00306 0.372448-01 some -1.2747 0.5076 -2.511 0.01203 0.33457 S0H88MZ -0.82627 0.7755 -1.065 0.28666 0.893858-01 SOMUN -0.41987 1.822 -0.230 0.81773 0.1303SB-01 WWM 2.9S78 0.7609 3.888 0.00010 0.881448-01 C32000 -0.30478 0.6480 -0.470 0.63810 0.11856 CS5000 -0.64459 0.9137 -0.705 0.48051 0.558668-01 CS10000 0.27706 0.9372 0.296 0.76752 0.459348-01 CS20000 -1.0545 1.053 -1.002 0.31657 0.403488-01 CS50000 2.8063 0.9278 3.025 0.00249 0.558668-01 MBC -1.1816 0.5876 -2.011 0.04435 0.25636 ■SCH -0.96476 0.5621 -1.716 0.08607 0.25450 JAG -1.1515 0.5725 -2.011 0.04430 0.22533 Sigea 5.7458 0.2009 28.593 0.00000 370

Table 7.31, cont’d.

Log-Likelihood...... -4600.7 Threshold values for the aodeli Lover* 162.9000 Variable Coefficient Std. Error t-ratio Frobitox Mean of X Constant 164.94 0.7124 231.517 0.00000 CS2000 -0.50854 0.7882 -0.645 0.51882 0.12522 CS5000 2.0281 0.9940 2.040 0.04131 0.59941E-01 C310000 3.7984 1.061 3.581 0.00034 0.45697E-01 CS20000 1.8610 1.177 1.581 0.11386 0.44S10E-01 CS50000 5.2431 1.033 5.076 0.00000 0.62908E-01 NBC -0.18088 0.7391 -0.245 0.80668 0.26766 SCH -0.31624 0.7054 -0.448 0.65392 0.24985 J AO 0.B8118B-01 0.7098 0.124 0.90120 0.23739 Sigea 6.6950 0.2663 25.137 0.00000

Log-Likelihood...... -4626.5 Threshold values for the aiodelt Lover* 162.9000 Variable Coefficient Std. Error t-ratio Probitox Mean of X Constant 166.19 0.5289 314.213 0.00000 SQMUP 5.3263 0.8720 6.108 0.00000 0.68249E-01 30ML0MC 3.8724 0.9969 3.88S 0.00010 0.45104E-01 9CH8K -1.9431 0.5687 -3.416 0.00063 0.35134 s o u n d -3.0791 0.8706 -3.537 0.00041 0.10801 SOMJM 3.1186 1.353 2.304 0.02120 0.21958E-01 1.8519 0.8691 2.131 0.03311 0.91395E-01 C32000 -1.1223 0.7202 -1.558 0.11914 0.12522 C35000 0.64961 0.9110 0.713 0.47579 0.59941E-01 CS10000 1.1146 1.021 1.091 0.27519 0.4S697E-01 CS20000 -0.11374 1.073 -0.106 0.91555 0.44510E-01 CS50000 1.9484 0.8947 2.178 0.02943 0.62908E-01 Sigea 6.2373 0.2293 27.196 0.00000

Log-Likelihood...... -4626.2 Threshold values for the aodeli Lover* 162.9000 Variable Coefficient Std. Error t-ratio Probiti>x Mean of X Constant 166.37 0.6424 258.988 0.00000 SOHUP 5.3585 0.8739 ^ 6.132 0.00000 0.68249E-01 SGHLONC 3.9227 0.9995 3.924 0.00009 0.45104B-01 -1.9608 0.5719 -3.429 0.00061 0.35134 SOHSEKX -3.0912 0.8719 -3.545 0.00039 0.10801 3.1318 1.355 2.312 0.02079 0.21958E-01 SOHBOS 1.8831 0.8707 2.163 0.03057 0.9139SE-01 C32000 -1.1707 0.7271 -1.610 0.10739 0.12522 CS5000 0.62442 0.9170 0.681 0.49592 0.59941E-01 CS10000 1.0436 1.027 1.016 0.30957 0.45697E-01 CS20000 -0.26496 1.116 -0.237 0.81240 0.44510B-01 CS50000 1.8228 1.020 1.788 0.07381 0.62908E-01 NBC -0.7S458E 0.6644 -0.114 0.90958 0.26766 SCH -0.94366E 0.6363 -0.148 0.88210 0.24985 JAG -0.45022 0.6402 -0.703 0.48189 0.23739 Sigea 6.2351 0.2292 27.207 0.00000 Table 7.31, cont’d.

Log-Likelihood...... -4845.6 Threshold values for the aodelt Lower* 162.9000 Variable Coefficient Std. Error t-ratlo Probit ox Mean of X Constant 164.55 0.7326 224.607 0.00000 C32000 1.1358 0.7427 1.529 0.12619 0.12747 CSSOOO 3.2431 0.9311 3.483 0.00050 0.62864B-01 C310000 4.1224 1.014 4.066 0.00005 0.49476E-01 C320000 4.7660 1.126 4.234 0.00002 0.41909B-01 cssoooo 4.8626 1.041 4.669 0.00000 0.60536E-01 NBC 0.40313 0.7365 0.547 0.58413 0.27066 SCH 0.96154 0.6871 1.399 0.16168 0.28580 JAO 0.22416 0.7429 0.302 0.76286 0.21886 Sigma 6.7907 0.2572 26.398 0.00000

Log-Likelihood...... -4768.8 Threshold values for the modeli Lower* 162.9000 Variable Coefficient Std. Error t-ratlo Probitox Mean of X Constant 166.93 0.508S 328.277 0.00000 SOHUP 5.2240 0.7391 7.068 0.00000 0.10244 S0HL0MC 1.9440 0.9676 2.009 0.04453 0.S00S8B-01 SCHSK -2.5367 0.5606 -4.S25 0.00001 0.39290 SOHSBtl -2.6644 0.8196 -3.251 0.00115 0.10885 efwrai -1.8002 1.390 -1.295 0.19531 0.27939E-01 WWW 3.3428 0.8779 3.808 0.00014 0.65192E-01 C82000 -0.44027 0.6635 -0.664 0.50697 0.12747 CSSOOO 1.2270 0.8404 1.460 0.14431 0.62864E-01 CS10000 1.2092 0.9296 1.301 0.19334 0.49476E-01 CS20000 1.8248 0.9809 1.860 0.06284 0.41909E-01 CSSOOOO 1.8363 0.8372 2.193 0.02827 0.60536E-01 Sigma 6.1784 0.2118 29.177 0.00000

Log-Likelihood...... 4765.8 Threshold values for the modeli Lower* 162.9000 Variable Coefficient std. Error t-ratlo Probitox Mean of X Constant 166.58 0.6495 256.481 0.00000 90M0P 5.1116 0.7356 . 6.949 0.00000 0.10244 SOKLOMC 1.9491 0.9640 2.022 0.04319 0.S0058E-01 s o m e -2.6954 0.5643 -4.776 0.00000 0.39290 soasiMi -2.7735 0.8182 -3.390 0.00070 0.10885 w w n s -2.0568 1.388 -1.482 0.13828 0.27939B-01 soncs 3.2587 0.8755 3.722 0.00020 0.65192E-01 CS2000 -0.37803 0.6673 -0.566 0.57105 0.12747 CSSOOO 1.0894 0.8390 1.298 0.19413 0.62864E-01 CS10000 1.0541 0.9295 1.134 0.25677 0.49476E-01 CS20000 2.0293 1.015 1.999 0.04564 0.419091-01 CSSOOOO 1.8842 0.9403 2.004 0.04509 0.605361-01 NBC 0.42419 0.6371 0.666 0.50551 0.27066 SCH 1.2349 0.5990 2.062 0.03923 0.28580 JIB -0.988011 0.6434 -0.015 0.98775 0.21886 S l ^ a 6.1583 0.2103 29.277 0.00000 Table 7.31, cont’d.

Log-Likelihood...... -4946.3 Threshold values Cor the eodeli Lower- 163.9000 Upper— *-inf ini Variable Coefficient Std. Error t-ratio Probiti>x Mean of X Constant 165.40 0.6503 254.353 0.00000 CS2000 -0.66166 0.7658 -0.864 0.38755 0.11748 CSSOOO 1.8405 0.8808 2.089 0.03666 0.68768E-01 C310000 2.9407 1.045 2.814 0.00489 0.43S53E-01 C320000 3.6803 1.043 3.530 0.00042 0.4S845E-01 CSSOOOO 1.7878 1.098 1.629 0.10336 0.49857E-01 NBC 1.1765 0.6946 1.694 0.09034 0.27278 -SCH 1.2014 0.6456 1.861 0.06277 0.28367 JAO -0.28919 0.7212 -0.401 0.68841 0.20057 Sigea 6.7057 0.2452 27.344 0.00000

Log-Likelihood..... Threshold values for the eodeli Lover- 162 .9000 Upper— ►infini 1 Variable Coefficient Std. Error t-ratlo Probitox Mean of X Constant 166.39 0.5002 332.649 0.00000

Log-Likelihood..... Threshold values for the eodeli Lower- 162. 9000 Variable Coefficient Std. Error t-ratio Probitox Mean of X Constant 166.05 0.5931 279.982 0.00000 SONUF 6.4205 0.6723 9.551 0.00000 0.12092 SOMLONC 5.1677 0.9132 5.659 0.00000 0.48138E-01 some -0.84496 0.5404 -1.564 0.11793 0.35759 SONSEMI -1.4064 0.7175 -1.960 0.04996 0.13410 anfeflf -4.0503 2.132 -1.900 0.05747 0.13754E-01 enmne 2.6891 0.8906 3.020 0.00253 0.61891E-01 C32000 -1.3336 0.6667 -2.000 0.04548 0.11748 CSSOOO 0.51545 0.7768 0.664 0.50699 0.68768E-01 C310000 0.29593 0.9295 0.318 0.75020 0.43553E-01 C320000 0.60252 0.9325 0.646 0.51818 0.4S845E-01 CSSOOOO -1.5308 0.9858 -1.553 0.12048 0.498S7E-01 NBC 1.1166 0.5942 1.879 0.06023 0.27278 SCH 1.3957 0.5578 2.502 0.01235 0.28367 JJU2 0.44615 0.6167 -0.723 0.46939 0.20057 Sigea 6.0272 0.1974 30.S34 0.00000 373

Table 7.31, cont’d.

Log-Likelihood...... -5714.2 Threshold values for the models Lover* 162.9000 Variable Coefficient Std. Error t-ratio Probiti>x Mean of X

Constant 165.86 0.5987 277.040 0 .0 0 0 0 0 C32000 1.0812 0.6717 1.610 0.10748 0.12313 CSSOOO 1.5013 0.8475 1.771 0.07649 0.69292E-01 CS10000 2.9985 0.8974 3.341 0.00083 0.S4835B-01 C320000 4.3378 1.030 4.213 0.00003 0.39880E-01 CSSOOOO 1.8305 1.036 1.767 0.07724 0.49850E-01 NBC 0.55571 0.6347 0.875 0.38131 0.27866 SCH 0.11924 0.6116 0.195 0.84S43 0.26770 JM3 -0.92095 0.6664 -1.382 0.16695 0.21037 Sigma 6.7875 0.2311 29.366 0 .0 0 0 0 0

Log-Likelihood...... 5634.9 Threshold values for 'the aodeli Lower* 162. 9000 Variable Coefficient std. Error t-ratlo Probsti>x Mean of X

Constant 166.79 0.4640 359.445 0 .0 0 0 0 0 SOMOV 5.5333 0.6680 8.284 0 .0 0 0 0 0 0.10668 SOMLQMC 1.9494 0.9783 1.993 0.04629 0.41376E-01 S OUK -1.6265 0.5300 -3.069 0.00215 0.32752 SOMSEHI -1.8439 0.6550 -2.815 0.00487 0.15553 -0.54525 1.379 -0.395 0.69254 0.22433E-01 s g n b o s 4.0095 0.8062 4.973 0 .0 0 0 0 0 0.68295E-01 CS2000 0.27562 0.6067 0.454 0.64960 0.12313 CSSOOO 0.39897 0.7688 0.S19 0.60379 0.69292K-01 CS10000 0.65218 0.8307 0.785 0.43241 0.54835E-01 CS20000 2.0167 0.9204 2.191 0.02844 0.39880E-01 cssoooo - -0.11470 0.8846 -0.130 0.89684 0.49850E-01 Sigma 6.2S52 0.1955 31.991 0 .0 0 0 0 0

Log-Likelihood...... 5628.8 Threshold values for the models Lower* 162. 9000 Upper*+infinit Variable Coefficient Std. Error t-ratio Probitox Mean of X

Constant 166.80 0.5537 301.229 0 .0 0 0 0 0 SOMOP 5.4159 0.6633 8.166 0 .0 0 0 0 0 0.10668 SOMLONC 1.8624 0.9714 1.917 0.05521 0.41376E-01 SONSK -1.8766 0.5327 -3.523 0.00043 0.32752 90M8BM1 -2.1100 0.6560 -3.217 0.00130 0.15553 SOHUM -0.65951 1.369 -0.482 0.63007 0.22433E-01 anmene 4.0027 0.8006 4.999 0 .0 0 0 0 0 0.68295E-01 C32000 0.92141E-01 0.6096 0.151 0.87986 0.12313 CSSOOO 0.39643 0.7663 0.517 0.60493 0.69292E-01 CS10000 0.45822 0.8315 0.551 0.58157 0.54835E-01 CS20000 1.5943 0.9527 1.673 0.09424 0.39880E-01 CS50000 -0.66605 0.9504 -0.701 0.48340 0.49850E-01 NBC 0.69464 0.5613 1.237 0.21592 0.27866 SCH 0.90783 0.5457 1.664 0.09621 0.26770 JAC 0.95253 0.5862 -1.625 0.10418 0.21037 Sigma 6.2207 0.1933 32.177 0 .0 0 0 0 0 374

Table 8.1: Life Expectancy for Males and Females in Wurttemberg and Germany, (years)

Males 1876-80 1871-80 1891/1900 1901/1910 Wurttemburg German Empire Age in Years Wurttemburg German Wurttemburg German 45.15 44.82 Empire Empire At Birth 34.3 35.58 39.74 40.56 56.67 55.12 Arc 1 49.2 ------52.97 51.85 57.53 56.39 Age 2 51.5 54.25 53.67 55.01 54.44 Age 6 51.3 53.15 52.70 34.94 34.55 Age 30 32.8 31.41 33.95 33.46 13.00 13.14 Age 60 12.1 12.11 12.71 12.82

Females 1876-80 1871-80 1891/1900 1900/1910 Age in Years Wurttemburg German Wurttemburg German Wurttemburg German Empire Empire Empire At Birth 36.76 38.45 42.74 43.97 48.08 48.34 Age 1 49.8 ---- 53.87 53.78 57.62 57.20 Age 2 52.2 ---- 55.17 55.59 58.33 58.47 Age 6 52.0 ---- 54.16 54.66 56.04 56.57 Age 30 33.2 33.07 35.01 35.62 36.36 36.94 Age 60 12.2 12.71 12.98 13.60 13.69 14.17

Source: Constructed from Table 3.24 and Das Statistische Handbuch fur das Koenigreich Wurttemburg. 1913. p.55 Table 8.2 Infant Mortality Rates in Wurttembcrg 1812-1897 (available years)

Years Infant Mortality Years Infant Mortality 1858/59 32.8 1883 27.5 1859/60 36.4 1884 29.18 1860/61 31.4 1885 27.61 1861/62 40.8 1886 28.26 1862/63 32.1 1887 23.45 1863/64 35 1888 23.44 1865 40.8 1889 26.51 1858/66 35.4 1890 24.61 1871/76 32.73 1891 25.6 1877 29.9 1892 25.63 1878 29 1893 24.98 1879 30.3 1896 20.77 1880 30.04 1897 24.88 1881 28.42 1884-93 26.16 1882 27.3

Source: Constructed from information in: Das Koenigreich Wuerttemberg ('1863’) M.C.B. (18671 M.B. (1882-84. 1897). W.J.B,,0874.-1884.1894) 376

Table 8.3 Mortality Rates in Wurttemberg,1812-1897 (available years)

Years Mortality Years Mortality 1858/59 2.94 1882 2.857 1859/60 3.06 1883 2.703 1860/61 2.79 1884 2.823 1861/62 3.41 1885 2.798 1862/63 3.17 1886 2.643 1863/64 3.37 1887 2.403 1865 3.52 1888 2.885 1872 3.308 1889 2.68 1873 3.347 1890 2.534 1874 3.292 1891 2.565 1875 4.341 1892 2.654 1876 2.253 1893 3 1877 2.231 1894 2.567 1878 3.078 1895 2.452 1879 3.11 1896 2.219 1880 3.018 1897 2.357 1881 2.902

Source: Constructed from information in: Das Koenigreich Wuerttemberg H8633 M.C.B. (1867). M.B. (1882-84.1897). W.J.B. (1874. 1884. 1894) 4 5

40

35

30

25 Infant Mortality 20

5

10

5

0

CT) co in co m r-~ o t- CO in s oi co in ID ID N N S N CO CO CO CO CO 010) 0) co oo co years

Figure 8.1: Infant Mortality Rates in Wurttemberg, 1812-1897 (available years) 377 4 .5

3.5

2.5 Mortality

0.5

o> t- co m co in N O) ■»- co in r- o> r- co in in CD (O co r-. r- h- f'* 00 oo 00 oo oo 05 05 05 05 oo o cvt CO CO CD CO 00 00 co oo co oo CO CO 00 CO in co co CO 00 00

years

Figure 8.2: Mortality Rates in Wurttemberg 1812-1897 (available years) 378 379

Map 8.1: Regional Distribution of Average Daily Wages for Male Workers, A ge 16+, in W urttemberg, 1898.

1___ 1 a * 150 CZ 2 ■ 160 r m • 170 180 5 2 3 ■ 190 E5H1 . 2.00 210 mini . 220 250 ■ ■ u te-2 5 0

Source: Losch (1898) Map 8.2: Percentage of Infants Ever Breastfed in Several German Regions around 1910.

I

25.0 to 60.0

60.0 to 77.4-

11A to 7 9 .5

79.5 to 90.0

90.0 to 90.0

No Data 380 Source: Haines (1993) Map 8.3: Infant Mortality Rates (out of 1,000 Births) in several German Regions in 1910.

Source: Haines (1993) Map 8.4: Heights of Military Recruits (cm.) in Several German Regions in 1906.

166.39 to 167.26

167.26 to 167.62

167.62 to 168.33

:::::: 168.33 to 168.93

168.93 to 169.78

Source: Haines (1993) 382 REFERENCES

"Aenderung der Deutschen Wehrordnung vom 22. Nov. 1888," in K. Wuert. Kriegsministerium Verordnungsblatt No. 72. ll.Dez. 1893, Section 31.

Boelcke, Willi A. (1989), Sozialgeschichte Baden-Wurttembergs 1800 - 1989. Stuttgart:Verlag W. Kohlhammer .

Boelcke, Willi A. (1987).Handbuch Baden-Wurttemberg: Politik. Wirtschaft. Kultur von der Urgeschichte bis zur Gegenwart. Stuttgart: Verlag W. Kohlhammer.

Bowden, Witt, Karpovich, Michael, and Usher, Abbot Payson, (1937), An Economic History of Europe Since 1750. New York: AMS Press.

DeGrott, Morris H. (1975), Probability and Statistics. Reading, Massachusetts: Addison-Wesley Publishing Compay.

Dienst-Vorschriften der Koeniglichen Preussischen Armee. (1874), dritte Auflage.

Dillard, Dudley, (1967), Economic Development of the North Atlantic Community. Englewood Cliffs, NJ: Prentice-Hall, Inc.

Dumke, Rolf H., (1988), "Income Inequality and Industrialization in Germany," in Paul J. Uselding, ed, Research in Economic History. Greenwich, CT.

Eltis, David, (1982), "Nutritional Trends in Africa and the Americas: Heights of Africans, 1819-1839." Journal of Interdisciplinary History. XII:3 (Winter 1982), 453-475.

Eveleth, Phyllis B. and Tanner, James M., (1976), Worldwide Variations in Human Growth. Cambridge: Cambridge University Press.

Floud, Roderick; Wachter, Kenneth W. and Gregory, Annabel, (1985), The Physical State of the British Working Class. 1870 - 1914: Evidence from Army Recruits. Cambridge, MA: National Bureau of Economic Research, Working Paper Series.

383 384

Floud, Roderick; Wachter, Kenneth and Gregory; Annabel, (1990), Height. Health and History. Cambridge: Cambridge University Press.

Floud, Roderick; Wachter, Kenneth and Gregory, Annabel,(1993),"Measuring historical heights-short cuts or the long way round: a reply to Komlos." in: Economic History Review. XLVI,1(1993), pp 145-154,

Fogel, Robert W.; Engerman, Stanley L.; Floud, Roderick; Friedman, Gerald; Margo, Robert A.; Sokoloff, Kenneth; Steckel, Richard H.; Trussell, T. James; (1983), "Secular Changes in American and British Stature and Nutrition." Journal of Interdisciplinary History. XIV:2, (Autumn, 1983), 445- 481.

Gamer, Kurt, (1938), Die Entwicklung von Koerpergroesse und Koerpergewicht der Stuttgarter Schuljugend in den Jahren 1910-1936. Berlin:Verlag Rudolph Pfau

Gam, Stanley M. (1980), "Human Growth." Annual Review of Anthropology.

Gesetz betreffend die Verpflichtung zum Kriegsdienst. vom 9.Nov. 1869.

Greene, William H. (1990), Econometric Analysis. New York: Macmillan Publishing Company.

Haines, Michael (1993), Personal Correspondance

Henderson, W.O., (1975), The Rise of German Industrial Power. Berkeley: University of California Press.

Higman, B.W., (1984), Slave Populations of the British Caribbean 1807-1834. Baltimore: The John Hopkins University Press.

Hoffmann, Walter G., (1965), Das Wachstum der deutschen Wirtschaft seit der Mitte des 19. Jahrhunderts. Berlin: Springer-Verlag.

Informationen Zur Politischen Bildung 164. Das 19. Jahrhundert: Industrialisierung - Soziale Frage. (1975). Berlin: Bundeszentrale fur politische Bildung.

Kiesewetter, Hubert (1989), Industrielle Revolution in Deutschland: 1815 - 1914. Frankfurt am Main: Suhrkamp Verlag. 385

Kiesewetter, Hurbert, (1986), "Regionale Industrialisierung in Deutschland zur Zeit der Reichsgruendung. Ein vergleichend-quantitativer Versuch." in: Vierteliahrschrift fuer Sozial- und Wirtschaftsgeschichte. 73. Band, Heft 1. Stuttgart: Franz Steiner Verlag.

Kim, Joo Han and Komlos, John, (1990), "Estimating Trends in Historical Heights." Historical Methods. Summer 1990, Volume 23, Number 3.

Klaas, Alberto (1991) Personal correspondence.

Koenigliches Medizinal-Kollegium, (1900), Medizinal-Bericht von Wuerttemberg fuer das Jahr 1897. Stuttgart: W. Kohlhammer.

Koenigliches Medizinal-Kollegium, (1886), Medizinal-Bericht von Wuerttemberg fuer das Jahr 1894. Stuttgart: W. Kohlhammer.

Koenigliches Medizinal-Kollegium, (1884), Medizinal-Bericht von Wuerttemberg fuer die Jahre 1882. 1883. und 1884. Stuttgart: W. Kohlhammer.

Koenigliches Medizinal-Kollegium, (1879), Medizinal-Bericht von Wuerttemberg fuer das Jahr 1876. Stuttgart: W. Kohlhammer.

Koenigliches Statistisches Landesamt, (1910) Statistisches Handbuch fuer das Koenigreich Wurttemberg. Jahrgang 1908 und 1909. Stuttgart: Druck und Kommissionsverlag von W. Kohlhammer.

Koenigliches Statistisches Landesamt, (1908), Mitteilungen des Koeniglichen Statistischen Landesamts. 23 Juli 1908. Stuttgart: Verlag W. Kohlhammer.

Koenigliches Statistisches Landesamt, (1901), Statistisisches Handbuch fuer das Koenigreich Wurttemberg. Jahrgang 1900. Stuttgart: Verlag W. Kohlhammer

Koenigliches Statistisches Landesamt, (1892), Statistisches Jahrbuch fuer das Koenigreich Wurttemberg. Jahrgang 1890 und 1891. Stuttgart: Verlag W. Kohlhammer.

Koenigliches Statistisches Landesamt, (1891), Statistisches Jahrbuch fuer das Koenigreich Wurttemberg. Jahrgang 1889 und 1891. Stuttgart:Verlag W. Kohlhammer.

Koenigliches Statistischen Landesamt, (1886), Das Konigreich Wurttemberg. Eine Beschreibung von Land. Volk und Staat. Stuttgart: Druck und Verlag von W. Kohlhammer. 386

Koenigliches Statistisches Landesamt, (1889), Wurttembergische Jahrbuecher fuer Statistik und Landeskunde. Jahrgang 1887. Stuttgart: Verlag W. Kohlhammer

Koenigliches Statistisches Landesamt, (1910), Wurttembergische Gemeindestatistik. Zweite Ausgabe nach dem Stand vom Jahre 1907. Stuttgart: Druck und Verlag von W. Kohlhammer.

Koenigliches Statistisches Landesamt, (1913), Volkszaehlung 1. Dezember 1910. Stuttgart: Druck und Kommissionsverlag von W. Kohlhammer.

Koenigliches Statistisch-Topographisches Bureau, (1885), Wurttembergische Jahrbuecher fuer Statistik und Landeskunde. Jahrgang 1884. Stuttgart: Verlag W. Kohlhammer.

Koenigliches Statistisch-Topographisches Bureau, (1880), Wurttembergische Jahrbuecher fuer Statistik und Landeskunde. Jahrgang 1880. Stuttgart: Verlag W. Kohlhammer

Koenigliches Statistisch-Topographisches Bureau, (1875), Wuerttembergische Jahrbuecher fuer Statistik und Landeskunde. Jahrgang 1874. Stuttgart: Verlag H. Lindemann

Koenigliches Statistisch-Topographisches Bureau, (1874), Wurttembergische Jahrbuecher fuer Statistik und Landeskunde. Jahrgang 1872. Stuttgart: Verlag H. Lindemann

Koenigliches Statistisch-Topographisches Bureau, (1870), Wurttembergische Jahrbuecher fuer Statistik und Landeskunde. Jahrgang 1869. Stuttgart: Verlag H. Lindemann

Koenigliches Statistisch-Topographisches Bureau, (1863), Das Koenigreich Wurttemberg. Eine Beschreibung von Land. Volk, und Staat. Stuttgart: Verlag Wilhelm Nitzschke.

Komlos, John,(1993),"The secular trend in the biological standard of living in the United Kingdom, 1730-1860 in Economic History Review. XLVI,I(1993),pp 115-144

Komlos, John, (1990), "Height and Social Status in Eighteenth-Century Germany." Journal of Interdisciplinary History XX:4 (Spring 1990). pp. 607- 621. 387

Komlos, John, (1989), Nutrition and Economic Development in the Eighteenth-Centurv Habsburg Monarchy: An Anthropometric History. Princeton, NJ: Priceton University Press.

Losch, Hermann, (1898), Die Arbeitsloehne in Wurttemberg. Stuttgart: Druck von W. Kohlhammer.

Maddala, G.S. (1983). Limited-dependent and Qualitative Variables in Econometrics. Cambridge: Cambridge University Press.

Margo, Robert A. and Steckel, Richard H. (1983), "Heights of Native-Born Whites During the Antebellum Period." Journal of Economic History. Vol. XLIII, No. 1 (March 1983).

Megerle, Kalus, (1982), Wurttemberg im Industrialisierungsprozess Deutschlands. Stuttgart: Verlag Kett-Cotta.

Militaeratlas 1870/73. Military Archive document E271c #205.

Miller, Max and Sauer, Paul, (1971), Wurttembergische Geschichte 1871-1971. Stuttgart: Verlag W. Kohlhammer

Mitchell, B.R., (1975), European Historical Statistics 1750-1970. Great Britain: The Macmillan Press Ltd.

Morris, Morris Davis, (1979), Measuring the Condition of the World’s Poor: The Physical Quality of Life Index. New York: Pergamon Press.

Nicholas, S. and Steckel, R. (1990), "Heights and Health of Workers During the Early Years of British Industrialisation, 1770-1815." Working Papers in Economic History. Canberra, Australia: The Australian National University.

Sandberg, Lars G., (1989), Lectures in European Economic History.

Sandberg, Lars G. and Steckel, Richard H., (1986), "Heights and Economic History: The Swedish Case." Annals of Human Biology. 1987, Vol. 14, No. 2 , 101- 110.

Schneider, Helmut, (1986), Truncated and Censored Samples from Normal Populations. New York: Marcel Dekker, Inc.

Shapiro, S.S. and Wilk, M.B. (1965), "An Analysis of Variance Test for Normality (complete samples)" Biometrika. 52, 591-611. 388

Statistische Notizen ueber Wurttemberg. (1874), Stuttgart: Druck von Verlag Jul. Kleeblatt & Sohn.

Steckel, Richard, (1992),"Stature and living standards in the United States" in: American Economic Growth and Standards of Living Before the Civil War. Robert Gallman and John Wallis, editors, Chicago: National Bureau of Economic Research.

Steckel, Richard H., (1990), "Stature and Living Standards in the United States."

Steckel, Richard H. (1986), "A Peculiar Population: The Nutrition, Health and Mortality of American Slaves from Childhood to Maturity," Journal of Economic History 46, 721-41.

Steckel, Richard H., (1982), "Height and Per Capita Income," Historical Methods 16: 1-7.

Steegman, A. Theodore and Haseley, P.A. (1988), "Stature Variation in the British American Colonies: French and Indian War Records, 1755-1763." American Journal of Physical Anthropology 75:413-421.

Stolper, Gustav, (1940), German Economy 1870-1940. New York: Reynal & Hitchcock.

Tanner, J.M., (1978), Fetus into Man: Physical Growth from Conception to Maturity. Cambridge, MA: Harvard University Press.

Tipton, Frank B., Jr., (1976), Regional Variations in the' Economic Development of Germany During the Nineteenth Century. Middletown, CT: Wesleyan University Press.

Trussell, James and Wachter, Kenneth W., (1982), "Estimating Historical Heights." Journal of the American Statistical Association. June 1982, Volume 77, Number 378.

Trussell, James and Wachter, Kenneth, (1984), "Estimating the Covariates of Historical Heights." Cambridge, MA: National Bureau of Economic Research. NBER Working Paper Series.

Verfassung des Deutschen Reiches vom 16. April 1871. Abschnitt XI Reichskriegswesen. 389

Wachter, Kenneth W., (1981), "Graphical Estimation of Military Heights." Historical Methods. Volume 14, Number 1, Winter 1981.

Wiegand, Erich and Zapf, Wolfgang, (1982), Wandel der Lebensbedingungen in Deutschland: Wohlfahrtsentwicklung seit der Industrialisierung. Frankfurt: Campus Verlag.

Wurm, H. (1982), "Ueber die Schwankungen der durchschnittlichen Koerperhoehe im Verlauf der deutschen Geschichte und die Einfluesse des Eiweisanteiles der Kost." Homo 33(1). pp. 21-42.

Wuerttembergische Aerztlicher Verein, (1867), Medizinisches Correspondenz-Blatt. Band 37. Stuttgart: Verlag Friedrich Schweizerbart.