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Quantitative analyses of long run human capital development [Elektronische Ressource] : age heaping as an indicator for numeracy / vorgelegt von Dorothee Crayen

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IQUANTITATIVE ANALYSES OF LONG-RUN HUMAN CAPITAL DEVELOPMENT: AGE HEAPING AS AN INDICATOR FOR NUMERACY Inaugural-Dissertation zur Erlangung des Doktorgrades der Wirtschaftswissenschaftlichen Fakultät der Eberhard-Karls-Universität Tübingen vorgelegt von Dorothee Crayen Marburg a.d. Lahn 2008 I Dekan: Professor Dr. Kerstin Pull Erstkorrektor: Professor Dr. Jörg Baten Zweitkorrektor: Professor Dr. Laszlo Goerke Tag der mündlichen Prüfung: 10. März 2009 IIACKNOWLEDGEMENTS First of all, I would like to thank my advisor Joerg Baten. His dedication to research and to the mentoring of PhD students is unique, and he gave me invaluable guidance. He also made it possible for me to gain teaching experience, for which I am deeply grateful. Moreover, his encouragement to present my research to an international audience on various summer schools, workshops and conferences gave me important professional feedback and experience. I am particularly indebted to Laszlo Goerke. He has not only been one of the best teachers I have ever encountered, his unprejudiced approach to research is exemplary and made a deep impression on me. I am glad he was willing to act as my second adviser and I am thankful for his valuable comments to my research.
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I
QUANTITATIVE ANALYSES OF LONG-RUN HUMAN
CAPITAL DEVELOPMENT:
AGE HEAPING AS AN INDICATOR FOR NUMERACY






Inaugural-Dissertation
zur Erlangung des Doktorgrades
der Wirtschaftswissenschaftlichen Fakultät
der Eberhard-Karls-Universität Tübingen



vorgelegt von



Dorothee Crayen
Marburg a.d. Lahn





2008
I








































Dekan: Professor Dr. Kerstin Pull
Erstkorrektor: Professor Dr. Jörg Baten
Zweitkorrektor: Professor Dr. Laszlo Goerke
Tag der mündlichen Prüfung: 10. März 2009
II
ACKNOWLEDGEMENTS

First of all, I would like to thank my advisor Joerg Baten. His dedication to research and
to the mentoring of PhD students is unique, and he gave me invaluable guidance. He also
made it possible for me to gain teaching experience, for which I am deeply grateful.
Moreover, his encouragement to present my research to an international audience on
various summer schools, workshops and conferences gave me important professional
feedback and experience. I am particularly indebted to Laszlo Goerke. He has not only
been one of the best teachers I have ever encountered, his unprejudiced approach to
research is exemplary and made a deep impression on me. I am glad he was willing to act
as my second adviser and I am thankful for his valuable comments to my research. I am
greatly appreciative to Josef Molsberger for taking the chair of my disputation and for
giving me valuable feedback.
I recognize, and am grateful for, the financial support of the German Research
Foundation (Deutsche Forschungsgemeinschaft, DFG). Being part of the DFG Research
Training Group at the Faculty of Economics and Business Administration at Tuebingen
University broadened my knowledge and stimulated my research by creating an
important platform for the presentation and discussion of research. I am also owing
gratitude to the Marie Curie Research Training Network ‘Unifying the European
Experience: Historical Lessons of Pan-European Development’ that granted me a pre-
doctoral fellowship at the Universitat Pompeu Fabra in Barcelona. My paper with Joerg
Baten and Hans-Joachim Voth originated during this research stay. Other parts of my
dissertation have gained a lot from summer schools and workshops organized and funded
by Globaleuronet, by the European Science Foundation, the Marie Curie Research
Training Network, and by the Centre for Economic Policy Research.
Several other people have played a decisive role in my understanding of economic
history, in the process of knowledge accumulation and in my ways of working. By virtue
of his objectivity, diligence and kindness Brian A’Hearn acted as a role model. I enjoyed
every discussion with him and his comments were always insightful. I learned a lot from
Hans-Joachim Voth, who shared his ideas and experience and gave me inestimable
advice. He has got a admirable intuition for asking the right questions and his creativity
inspired my work. I would like to express my gratitude to all the participants of
III
conferences, workshops and summer schools who contributed to my dissertation by their
critical questions, their advice and by their ideas that supplied new input for my work.
Special thanks go to Jan Luiten van Zanden, Robert Allen, Gregory Clark, David Mitch,
Tim Leuning and Albert Carreras. The discussions with Peter Koudijs, Karine van der
Beek and Kerstin Enflo were also invaluable to me. I thank Friedrich Breyer, Juergen
Meckl and Manfred Stadler for being supportive from the beginning on, both personally
and professionally. Their perpetual confidence in my person often amazed me and it
means a lot to me.
I am grateful to my peers and colleagues of the Tuebingen research group for
many conversations about my work, and help with the constant challenges I faced in
completing this project. In particular, I wish to thank Normann Mueller, Kirsten
Labuske, Mojgan Stegl, Kerstin Manzel, Dominic Behle, Aravinda Meera Guntupalli,
Alexander Moradi, Nikola Köpke, Margaryta Korolenko, Sonja Rabus, Eva Rosenstock,
Daniel Schwekendiek, Linda Twrdek, Luis Huergo and Dominik Ohly.
Mein ganz besonderer Dank gebührt meiner Familie, deren bedingungslose und
immerwährende Unterstützung mir den Weg in die Forschung und Wissenschaft geebnet
und mir die Kraft gegeben hat, meine Arbeiten auch über anstrengende Phasen hinweg
weiter zu entwickeln und zum Abschluss zu bringen. Knut, ich kann Dir nicht genug
danken für Dein Vertrauen, und den mentalen wie praktischen Beistand. Deine Ruhe und
Geduld geben mir die nötige Sicherheit und Bodenhaftung, ohne die ich sonst nie solche
Leistung bringen könnte. Ich danken meinen Eltern von Herzen, dass sie mir immer
größte Freiheit gelassen, und mich stets bereitwillig und nach allen Kräften unterstützt
haben. Missen möchte ich auch nicht die Unterstützung meiner Geschwister sowie von
Bianca, Christoph, Regine, Horst, Iris, Paul, Ilka, Malte, Lilly, Kathrin, Nina, Tini und
Tine.

IV
TABLE OF CONTENTS

1 INTRODUCTION..................................................................................................... 1
I Motivation and general comments ........................................................................... 1
II Outline..................................................................................................................... 5
2 QUANTIFYING QUANTITATIVE LITERACY: AGE HEAPING
AND THE HISTORY OF HUMAN CAPITAL.................................................. 11
I. Introduction ........................................................................................................... 12
II. Age Heaping......................................................................................................... 16
III. Measuring age-heaping....................................................................................... 21
IV. Evaluating Indices of Age-Heaping.................................................................... 26
V. Numeracy and literacy in the US census ............................................................. 32
VI. A European age heaping dataset, 1300-1800 ..................................................... 37
VII. Age heaping and literacy in pre-industrial Europe............................................ 44
VIII. New estimates of human capital in the very long run...................................... 46
IX. Conclusion .......................................................................................................... 48
References................................................................................................................. 50
Tables........................................................................................................................ 55
Figures....................................................................................................................... 59
Appendix................................................................................................................... 69
3 NEW EVIDENCE AND NEW METHODS TO MEASURE HUMAN
CAPITAL INEQUALITY BEFORE AND DURING THE
INDUSTRIAL REVOLUTION: FRANCE AND THE U.S. IN THE
TH TH17 TO 19 CENTURIES ................................................................................... 79
I. Introduction ........................................................................................................... 80
II. Views of the literature on inequality and growth................................................. 83
III. A new method to measure human capital inequality.......................................... 88
IV. The Data.............................................................................................................. 97
V. Regional patterns of inequality in France and the U.S. ..................................... 100
VI. Is there an influence of numeracy inequality on welfare growth?.................... 106
V
VII. Conclusion....................................................................................................... 112
References............................................................................................................... 114
Tables...................................................................................................................... 119
Figures..................................................................................................................... 122
Appendix................................................................................................................. 128
4 POOR, HUNGRY AND STUPID: NUMERACY AND THE IMPACT
OF IGH OOD RICES N NDUSTRIALIZING RITAIN, 1780-H F P I I B
1850........................................................................................................................ 134
I Introduction .......................................................................................................... 135
II Nutrition, cognitive ability and occupational outcomes...................................... 139
III Numeracy........................................................................................................... 141
IV Data.................................................................................................................... 143
V Empirical Results ................................................................................................ 148
VI Conclusions........................................................................................................ 156
References............................................................................................................... 159
Tables...................................................................................................................... 165
Figures..................................................................................................................... 170
Appendix................................................................................................................. 174
5 GLOBAL TRENDS IN NUMERACY 1820-1940 AND ITS
IMPLICATIONS FOR LONG-RUN GROWTH................................................ 176
I Introduction .......................................................................................................... 177
II Methodological aspects....................................................................................... 181
III A first glance at country level data .................................................................... 183
IV World region estimates...................................................................................... 185
V Determinants of age heaping............................................................................... 188
VI Implications for empirical growth economics ................................................... 192
VII Conclusion........................................................................................................ 194
References............................................................................................................... 196
Tables...................................................................................................................... 200
Figures..................................................................................................................... 204
VI
Appendix................................................................................................................. 207
6 LIFE EXPECTANCY IN MYSORE STATE DURING THE EARLY
TH20 CENTURY: THE DUAL PURPOSE OF SINGLE YEAR AGE
DISTRIBUTIONS................................................................................................. 213
I. Introduction ......................................................................................................... 214
II. Related Literature............................................................................................... 215
III. Data and Methods ............................................................................................. 220
IV. Possible biases and adjustment procedures ...................................................... 224
V. Results................................................................................................................ 230
VI. Conclusions....................................................................................................... 235
References............................................................................................................... 237
Tables...................................................................................................................... 241
Figures..................................................................................................................... 243
Appendix................................................................................................................. 248
7 SUMMARY AND DIRECTIONS FOR FUTURE RESEARCH....................... 254
VII
SYMBOLS AND ABBREVIATIONS


AH Age heaping

CWR Child-Women Ratio

DFG Deutsche Forschungsgemeinschaft

DHS Demographic and Health Surveys

DY Demographic Yearbook

EQ Equation

EU Europe

FE Fixed Effects estimation

IDB International Database

IPUMS Integrated Public Use Microdata Series

IV Instrumental variable estimation

MEN Middle East / North Africa

OLS Ordinary Least Square estimation

RE Random Effects estimation

UN United Nations

US States

W, WI Whipple Index

Ŵ transformed Whipple Index

WP Working paper

WW2, WW II World War 2
1
1 INTRODUCTION

“Unlike literacy, for which signatures, though a poor measure, provide some
baseline, there is no good measure of the level of numeracy.”

Keith Thomas, 1987

I Motivation and general comments
Since the emergence of endogenous growth models in the late 1980s (e.g. Lucas 1988,
Romer 1990) human capital has been recognized as one of the most essential factors
1determining the growth path of an economy. Based on these theoretic models, cross-
country and panel data regressions were conducted to assess the empiric impact of
various measures of schooling on economic development and confirmed the stimulating
role of human capital in the growth process (e.g. Barro and Sala-i-Martin 1995, Barro
2003, Drèze and Sen 1995, Levine and Renelt 1992, Levine and Zervos 1993, World
Bank 1993).
For researchers studying the economic development of nations, the availability of
human capital data is crucial. Fortunately, from the very start of their existence, the
United Nations encouraged and supported their member states to collect data and to
implement registration systems. By these means, the United Nations statistical division
after 1945 was able to provide a broad and standardized data set for most countries. For

1 Following Adam Smith (1776), I use the term human capital to refer broadly to skills, dexterity (physical,
intellectual, psychological, etc) and discernment.

2
instance, the Unesco Statistical Yearbooks and the U.N. Demographic Yearbooks contain
figures on total pupils, enrolment rates and current educational spending. The World
Tables and World Development Reports compiled by the World Bank provide figures on
literacy rates as well as primary and secondary school enrolment ratios. The International
Data Base (IDB) made available by the U.S. Census Bureau contains literacy rates and
other demographic and socio-economic data for 227 countries from 1950 to present. In
addition to the quantitative indicators named above, there are also qualitative measures of
2schooling available, e.g., test scores, dropout rates and average teacher salaries. The
various human capital indicators enable researchers to determine approximations for a
country’s level of human capital and to analyze complex macroeconomic interrelations.
The relevant data is obtained from registration systems, surveys or censuses at regional or
country level. Thanks to the endeavours of the United Nations, there are only a few
countries left nowadays which have not taken at least one census in the post-war period.
By contrast, economists making efforts to examine the impact of human resources
on economic development before the end of World War II often face massive problems.
Methods of collecting data, i.e., registration systems, population censuses or surveys did
neither acquire sufficiently detailed data nor was the recorded data standardized in any
consistent form. Early population counts were conducted primarily for reasons such as
deriving the number of prospective warriors, to identifying the amount of resources
available for military purposes and for estimating tax income. Therefore, little attention
was paid to direct information about a population’s level of education. Typically,
population counts only report the number of inhabitants, possibly subdivided by sex.
More often than not, those counts, undertaken to determine fiscal, labor, and military

2 For a database including qualitative measures of human capital, see for example Barro and Lee (1996).