Links and Do-Files for Research Internship and Thesis
Please find in this list useful step by step tutorials, video tutorials and brief discussions about new and interesting topics for research. This is important for Bachelor and Master students interested in the research internship or thesis work at the chair.
How to research Economic History
In this Ilias course you can find helpful videos, STATA do-files, QGIS introduction tutorials, and data sets regarding research in economic history.
Path Ilias course: Repository > Summer semester 2022 > 6 Faculty of Economics and Social Sciences > Economics > How to research Economic History
Ilias Password: researchEH!
Consulting offer to catch up on pandemic-related learning gaps
If you are interested in catching up on specific deficits in empirical research, especially in the field of economic history, but also in other empirical economic fields, please write an e-mail to wisoge@uni-tuebingen.de (please indicate "Lotsendienst" as subject) so that we can make an appointment.
Our "pilots", Sabrina Litzenberger, Jasmin Seebacher, Maren Stoppel, and Felix Schulz will be happy to explain our services working with applied empirical analysis tools.
This is especially useful if you want to run an independent regression analysis or if you want to work computationally to investigate economic relationships, e.g. in bachelor theses or research internships.
Beratungsangebot zur Aufholung pandemiebedingter Lernrückstände
Falls Sie Interesse haben spezielle Informationsdefizite zur empirischen Forschung, insbesondere im Bereich der Wirtschaftsgeschichte, aber auch in anderen empirischen wirtschaftswissenschaftlichen Bereichen, aufzuarbeiten, schreiben Sie bitte eine E-Mail an wisoge@uni-tuebingen.de (als Betreff bitte "Lotsendienst" angeben) damit wir einen Termin vereinbaren können.
Unsere "Lotsen", Sabrina Litzenberger, Jasmin Seebacher, Maren Stoppel und Felix Schulz erläutern Ihnen gerne unser Angebot über angewandte empirische Analyse-Tools.
Dies ist besonders nützlich, wenn Sie eine selbstständige Regressionsanalyse durchführen oder auch computerkartografisch arbeiten möchten, um wirtschaftswissenschaftliche Zusammenhänge zu untersuchen, z.B. in Bachelorarbeiten oder Forschungspraktika.
ABCC-Index
How to calculate the ABCC index - Video Tutorial
How to calculate the ABCC index - aggregated data - Stata Do File
How to calculate the ABCC index - aggragated data - Stata Sample Data Set
How to calculate the ABCC index - individual data - Stata Do File
Resources for learning Stata
For Stata beginners we recommend these tutorials. You can also find answers to advanced questions here.
General hints
How to create residual scattergram using Stata
How to do a figure with two axes using Excel
How to combine Clio-Infra files
How to do a spatial regression - Presentation (with audio)
How to do a spatial regression - Stata Do File
Thematic maps
How to do thematic maps using Stata
How to do thematic maps using Stata - Template
Here is a do-file that assigns world regions to countries (in two-letter ISO format)
Abbreviations of job titles
When entering individual level data with occupations, it is often faster to use abbreviations and replace them later with the original text, if this is for datasets that have 100 or more occupational cells.
Research Internship
On this internet page we explain how human capital and health can be calculated for almost any country and region in the world over the past two centuries or more. To measure human capital, we are using the age-heaping based numeracy indicator that is quite easily calculated. A few things to keep in mind when using this approach will be explained in the following. Moreover, we explain how health and the inequality of health and income can be calculated from datasets on height, which can also be constructed with very little effort.
We will in addition show you where sources about both ages and height, which are the raw product for the numeracy component of human capital and health inequality, respectively, can be found on the internet so that you can write interesting studies about this topic.
The background of this research is explained in the global economic development book “A History of the Global Economy” (Baten 2016). The reference can be found below. This background book can be bought at a low price. The e-book is available at 17 € and the original paperback is available for 25 €.
References
Baten, J. (Ed.) (2016). A History of the Global Economy: 1500 to the Present. Cambridge: Cambridge University Press.
Further downloads can be found on the internet page of the institute.
Human Capital and Numeracy
Why are some countries rich and continue to grow while others don’t? New Growth Economics highlights human capital as an important explanation for the economic development of countries. The research team at the University of Tübingen was the first to examine this relationship in the long-run for countries worldwide by estimating numeracy skills.
Data on ages allows to estimate the numerical skills and hence a part of human capital for early periods for which few other data exists. This technique has a lot of potential for most parts of the world during the 19th century and for many developing countries until today. The UNESCO study recently included this age-heaping based numeracy approach into the global education monetary report, indicating the relevance of the indicator for contemporary studies.
How can numeracy be measured?
A proxy for numeracy, the ABCC-Index, can be easily computed by relying on ages in census data. This method considers the share of individuals who are able to state their precise age on an annual basis, in contrast to those who report an age rounded to a multiple of five (stating, for example, ‘I am about 35’ when they might be 34 in reality). Crayen and Baten (2010) showed that this proxy reflects human capital well, since it is closely related with other measures for human capital, such as literacy or schooling.
Usually we have census data available, which are collected for one year. These data are divided into age groups (e.g. 23-32, 33-42, ...) in order to assess the educational environment during the first ten years of life, i.e. early childhood and early adolescence, which are more relevant for very basic numeracy formation.
How to calculate the ABCC index in Stata is explained in a video and do-file that you can find above.
Which data can be used?
Computations of numeracy are mostly based on census data, but in principle any source that contains age data can be used to obtain information on numeracy.
A suggested data source is the familysearch.org website, which contains data for a large number of countries. Example data sets are: Argentina National Census 1895, Mexico National Census 1930. A further suggestion is the “Census Mosaic”, especially the data on Rumania, Denmark and France.
For an overview and explanation of typical numeracy values in different world regions and eras, see the book “A History of the Global Economy” (Baten 2016). Further estimates of numeracy obtained in the DFG project "Numeracy in Africa and the Middle East" are collected in a Data Hub.
Interactive teaching: Example for a research-internship
Heights and Biological Standard of Living
What was the standard of living of people 2000 years ago? Adult stature reflects childhood health and nutrition and thereby provides evidence for living standards. Hence, in recent decades, anthropometric research has created the well-being indicator "human stature," which facilitates the measurement of health development by social and regional groups, as well as by gender.
This proxy of the biological standard of living is often, though not always, related to GDP per capita or real wages. Furthermore, it has important implications for labor productivity, demographics, health systems, and less developed countries.
How can biological living standards be measured by heights?
The body height of an individual is largely determined by its genes. In order to be able to use heights as a proxy for the biological standard of living one computes the average values of heights over e.g. a social group for a given period in time. This is because, in contrast to individual heights, current evidence suggests that differences in heights of populations are not caused by genetic differences but are due to environmental influences such as health or nutrition.
Which data can be used?
In convict, conscription or school records, for example, data on heights can be found for rather early periods. Adult height observations are grouped by year of birth because early childhood health and nutrition largely influence adult stature. The heights of children and adolescent reflect the recent biological standard of living and therefore should be grouped by the year of observation.
Very early evidence on heights can be obtained of skeletons from archaeological excavations.
A suggested data source for heights on familysearch.org are, for example, the Dutch Army Records, the US Army Records or the Albanian Census.
The International Economic History Association has taken the initiative to set up a network of scholars working with data on heights and establish a moderated list of datafiles of historical heights. They are collected in this Data Hub.
Example
For an example, look at the book "A History of the Global Economy" (Baten 2016). There you will find an analysis of African heights in chapter 8.