Welcome to iPEHD – the ifo Prussian Economic History Database

Data, Publications, FAQs

• Raw data

  • Before using the data contained in the iPEHD database, it is necessary for the user to understand the structure in which the data are presented. Therefore, please make sure to read the merging data section carefully before accessing the raw data. Apart from describing the data structure, that section presents a procedure to combine data from different census years.

    iPEHD consists of county-level information gathered from different censuses. The data are currently presented in 76 separate data files, organized by content area, specific topic, and census year. Each data file in iPEHD contains a unique county (Kreis) identifier, the county name, the abbreviated district (Regierungsbezirk) name (rb), and a set of variables of census data.

    iPEHD stores its data in comma-separated values (csv) format, which is easily accessible from any statistical software. For example, to open the csv data files in Stata, just type: 
    > insheet using="xxxxxx.csv" <

    To give an example of a data file, the following table shows a brief extract of a few variables for the first few counties (by alphabet) from the data file "ipehd_1819_indu_fac.csv", which contains data on the number of factories in a county in 1819. E.g., the variable "fac1819_brick" documents the total number of brick manufactories in a county in 1819, and the variable "mill1819_water" the total number of water mills.

    Extract from an example data file

    kreiskey1800	county	rb	fac1819_brick	fac1819_lime	fac1819_glass	mill1819_water
    277	Achen	AAC	5	10	2	26
    33	Adelnau	POS	11	6	0	26
    254	Adenau	KOB	0	1	0	71
    196	Ahaus	MUN	11	15	0	20
    255	Ahrweiler	KOB	0	0	0	51
    2	Allenstein	KON	5	0	1	31
    219	Altena	ARN	3	13	0	41
    257	Altenkirchen	KOB	1	0	0	41
    10	Angerburg	GUM	4	26	0	5
    53	Angermünde	POT	13	2	0	28
    32	Anklam	STE	3	0	0	2
    209	Arnsberg	ARN	12	4	0	26
    67	Arnswalde	FRA	7	3	3	29
    160	Aschersleben	MAG	8	5	0	57
    55	(Nieder-)Barnim	POT	8	0	1	30
    54	(Ober-)Barnim	POT	18	0	0	36
    190	Beckum	MUN	8	3	0	22

    Note: Extract from iPEHD data file “ipehd_1819_indu_fac.csv”.

     

    The iPEHD data are categorized into the following eight content areas (also accessible through the sidebar in the upper right corner). A zip-file containing all iPEHD data files together can be accessed here:

    Complete iPEHD Database (ZIP, 1392 KB)

    Education

    This area contains, among others, such data as the number of students, teachers, and schools by school type, literacy, and school finance.

    Occupation

    This area contains, among others, data on the labor force in agriculture, in factories, in manufacturing, in crafts, and in services.

    Wages and Income Tax

    This area contains data on daily wages of day laborers, on teacher income, and on income taxes.

    Industry

    This area contains data on a huge number of different factories, technologies, and transportation.

    Agriculture

    This area contains, among others, such data as livestock, crop yields, soil composition, and the distribution of land.

    Population

    This area contains data on the population by age, by gender, and by marital status, on birth and deaths, and on population with disabilities.

    Religion

    This area contains denomination-specific data on population, literacy, education, occupation, and number of churches.

    Miscellaneous

    This area contains data on the surface area, buildings, municipalities, and residential areas for each county.

    Apart from these eight content areas, the

    Merger File

    provides information on merger variables necessary to combine data from different census years; see the merging data section for details.

• Merging Data

  • iPEHD consists of county-level information gathered from different censuses. The data are currently presented in 76 separate data files, organized by content area, specific topic, and census year. Each data file in iPEHD contains a unique county (Kreis) identifier, the county name, the abbreviated district (Regierungsbezirk) name (rb), and a set of variables of census data. The categorized data can be accessed from the raw data section.

    County-level Structure of the Data

    Starting after the Congress of Vienna in 1815, Prussia reformed its administrative structure and introduced the county level. At the time, the dimension of a county was meant to follow borders of previously existing administrative units. The maximum distance to the administrative center was meant to be two to three Prussian Miles (roughly 15 to 23 km or 9 to 14 miles), such that every inhabitant could travel forth and back within a day. The population size was meant to range between 20,000 in sparsely populated areas and 36,000 in densely populated areas.

    Throughout the 19th century, various administrative reforms reshaped the county structure of Prussia. As the population grew over time, it became necessary to divide existing administrative units in order to reduce administrative efforts. Most of these changes were partitions of one county into two or more counties.

    Thus, it is usually possible to reconstruct earlier administrative units by aggregating data from later years to the former structure. A drawback of this procedure is that the researcher loses part of the variation provided by having more observations. Still, the procedure appears necessary in order to have intertemporal comparability of the units of observation. The alternative would be to assign the same early data to two or more subsequently parted units, introducing measurement error if observed data were not uniformly distributed in the original area.

    A peculiarity of the Prussian county system is the city county. Starting with the introduction of the county level in 1815, the so-called Immediatstädte (immediate towns) became a county themselves. As urbanization advanced, an increasing number of cities were detached from their original county and became a county of their own. Thus, the database often contains a Landkreis(rural county) and a Stadtkreis (city county) with similar names. For example, there are six pairs of Landkreis/Stadtkreis information among the 335 county observations in the 1849 classification and 20 pairs among the 458 county observations in the 1874 classification.

    The external links section provides links to websites that contain lists of all Prussian counties, as well as more insight into the reforms.

    County Identifiers

    All data in iPEHD reflect the administrative conditions in place at the date of publication of the census. Since censuses often ordered the counties in different ways, identifiers were assigned reflecting the order of each census. Thus, each county in each census has been assigned a continuous number which is unique within a census but not across censuses. The identifiers are named kreiskeyYYYY, where YYYY represents the four-digit year (see below for additional peculiarities of the 1816-21 data).

    The year in the identifier denotes the administrative structure of Prussia, which is not necessarily the same as the census year. In some cases, different identifiers (e.g., kreiskey1871 and kreiskey1874) even had to be assigned to data from the same census year (1871) because the Royal Prussian Statistical Office used different aggregations in different publications of data from the same census.

    Intertemporal Comparisons

    If you are interested in intertemporal comparisons and the construction of panel datasets using iPEHD, please make sure to get familiar with the structure of the data first.

    One of the biggest challenges of using iPEHD data from different years is to make sure that the dimension of the units of observation is comparable over time. Our county identifiers, together with the merge-county file, provide a service that facilitates such linkage. If you want to use it, please closely follow the nine steps mentioned below. Our suggestion is that, in order to analyze the data using a comparable set of observations, you will need to collapse the data to the earliest set of counties in the data.

    However, we have to point out that at the end of the day, the best way to structure and use the data will be specific to every single research project. To find the best possible solution to this task is a crucial part of any research project and thus lies in the responsibility of every individual researcher.

    If you want to conduct intertemporal comparisons, our suggestion is to follow the following nine-step procedure. For this, you will have to download the following file:

    Merge-county file (CSV, 130 KB)

    If your goal is to construct only cross-sections, then follow the procedure only until step 3.

    1. Choose datasets from the same census year.

    2. Merge all datasets from the same census year using the identifier (e.g. kreiskey1882).

    3. Save the cross-section.

    4. Use the merge-county file provided on this page.

    5. Drop all duplicate and missing observations from the merge-county file according to the identifier in the cross-section (e.g. kreiskey1882): See examples below.

    6. Merge the merge-county file with the cross-section using the identifier (e.g. kreiskey1882).

    7. Aggregate (sum/mean) all variables in the cross-section to the aggregation level of the earliest census in your analysis using the identifier of the earliest census in your analysis (crucial step!).

    8. Repeat steps 1 to 7 for datasets from other census years.

    9. Merge the resulting cross-sections using the identifier of the earliest census in your analysis.

    Example from the Merger File

    In the example below, you will find that the eight illustrative counties observed in 1901 were established from six counties in 1874 and five counties in 1849. Between 1849 and 1874, the 'Elbing Landkreis' had been divided into 'Elbing Stadtkreis' and 'Elbing Landkreis'. Between 1874 and 1901, the 'Danzig Landkreis' had been divided into 'Danzig Niederung', 'Danzig Höhe', and 'Dirschau'.

    Kreiskey 1901	County1901	Kreiskey 1874	County1874	Kreiskey 1849	County1849
    38	ELBING STADTKREIS	38	ELBING STADTKREIS	37	ELBING LANDKREIS
    39	ELBING LANDKREIS	39	ELBING LANDKREIS	37	ELBING LANDKREIS
    40	MARIENBURG IN PREUSSEN	40	MARIENBURG IN PREUSSEN	38	MARIENBURG IN PREUSSEN
    41	DANZIG STADTKREIS	41	DANZIG STADTKREIS	39	DANZIG STADTKREIS
    42	DANZIG NIEDERUNG	42	DANZIG LANDKREIS	40	DANZIG LANDKREIS
    43	DANZIG HOHE	42	DANZIG LANDKREIS	40	DANZIG LANDKREIS
    44	DIRSCHAU	42	DANZIG LANDKREIS	40	DANZIG LANDKREIS
    45	PREUSSISCH STARGARD	43	PREUSSISCH STARGARD	41	PREUSSISCH STARGARD

    Note: Extract from the iPEHD merge file “ipehd_merge_county.csv”.

    In order to have a comparable set of observations when performing intertemporal comparisons between 1901 and 1849, you will have to aggregate the observations of 'Danzig Niederung', 'Danzig Höhe', and 'Dirschau' to match 'Danzig Landkreis'. Thus, you should always aggregate the data to the aggregation level of the earliest census year in your analysis (step 7).

    However, if you would like to perform intertemporal comparisons between e.g. 1874 and 1849, you will need to drop the duplicate entries of 'Danzig Landkreis' from the merger file first (step 5). In addition, you will need to drop entries from the merger file that have missing observations on the county identifier in the respective year. Such missing observations exist because some territories were annexed by Prussia only after the respective census year.

    As one example of how to merge datasets from 1874 and 1849, the following Stata code exemplifies the nine steps of the suggested procedure: Download

    Peculiarity of the Data from 1816 to 1821

    By 1816, Prussia had just started her administrative reform that established the county level. In some parts of the country, the reforms had not been finalized even in 1821. Thus, the data from the censuses in 1816 until after 1821 sometimes reflect old administrative units.

    Unfortunately, due to the reform, these old units were subsequently aggregated and then newly divided in order to establish the new counties. This makes it impossible to accurately match the data of (some of) the administrative units from the early censuses to (some) counties in subsequent censuses. We thus coded the kreiskey1800 which aggregates the data to a higher level. You need to use kreiskey1800 to link the 1816-1821 data to later periods.

    However, iPEHD also provides a unique identifier that allows merging data from the same census for these cross-sections. These identifiers are named ‘id1816’ and ‘id1819’. In order to merge data from 1816 to other data from 1816, please use id1816. In order to merge data from 1819 or 1821 to other data from 1819 or 1821, please use id1819. In order to merge data from 1816, 1819, or 1821 to data from subsequent censuses, please follow the steps below:

    1. Choose datasets from 1816, 1819, or 1821.

    2. Merge all datasets from the same census using the identifier (idYYYY).

    3. Aggregate (sum/mean) all cross sections using the identifier 'kreiskey1800'.

    4. Merge the cross section with aggregated data from subsequent censuses using the identifier 'kreiskey1800'.

• Codebooks

  • The codebooks provide additional information for each variable contained in iPEHD. There is one codebook for each year, so that you will find explanations for each variable in the codebook for the corresponding year. A summary codebook that combines all years is also provided; this summary codebook allows a content search of the whole iPEHD.

    The codebooks list the variable name (“variable name”), the name of the data file where it can be found (“ipehd datasets”), an English label (“label”), and the original label in German language (“original label”). The German language label is similar to the table headings found in the original sources. The English label leads with the year and is a shortened (direct) translation of the German label; in cases where a translation is not feasible, the original German term was adopted. On top of each set of variables, the codebooks also indicate the source of this set of variables (“source”).

    In any case, it is always recommendable to access the original sources for detailed information. They often give helpful insights regarding the exact attributes of the variables. You can also find additional explanations by reading the publications that first used the specific iPEHD data.

    1816-1901 Codebook Summary (XLSX, 122 KB)

    1816 Codebook (XLSX, 13 KB)

    1816-1821 Codebook (XLSX, 11 KB)

    1819 Codebook (XLSX, 11 KB)

    1821 Codebook (XLSX, 10 KB)

    1829 Codebook (XLSX, 12 KB)

    1849 Codebook (XLSX, 62 KB)

    1858 Codebook (XLSX, 10 KB)

    1862 Codebook (XLSX, 10 KB)

    1864 Codebook (XLSX, 13 KB)

    1866 Codebook (XLSX, 10 KB)

    1871 Codebook (XLSX, 53 KB)

    1878 Codebook (XLSX, 10 KB)

    1882 Codebook (XLSX, 26 KB)

    1886 Codebook (XLSX, 29 KB)

    1892 Codebook (XLSX, 10 KB)

    1896 Codebook (XLSX, 10 KB)

    1901 Codebook (XLSX, 10 KB)

• Sources

  • The iPEHD data have been digitized from different sources originally published by the Royal Prussian Statistical Bureau or its employees. This page provides a list of all the volumes used as sources for iPEHD. By now, complete scans of many of these volumes can be found at Google Books.

    1816-21

    Mützell, Alexander A. (1821-25). Neues Topographisch-statistisch-geographisches Wörterbuch des Preussischen Staats, Vol. 1-6. Halle: Karl August Kümmel.

    1829

    Preussisches Statistisches Landesamt (1829). Beiträge zur Statistik der Königlichen Preussischen Rheinlande, aus amtlichen Nachrichten zusammengestellt. Aachen: J.A. Mayer.

    1849

    Statistisches Bureau zu Berlin (1851-55). Tabellen und amtliche Nachrichten über den Preussischen Staat für das Jahr 1849, Vol. 1-6b. Berlin: Statistisches Bureau zu Berlin.

    1858

    Meitzen, August (1868). Der Boden und die landwirthschaftlichen Verhältnisse des Preussischen Staates, Vol. 1-4. Berlin: Verlag von Paul Parey.

    1862

    Königlich Preussisches Statistisches Bureau (1863). Die Eisen-, Stein- und Wasserstrassen des preussischen Staates im Jahre 1862, in Zeitschrift des Königlich Preussischen Statistischen Bureaus, Vol. 3, 206–214. Berlin: Verlag der Königlichen Geheimen Ober-Hofbuchdruckerei.

    1864

    Königliches Statistisches Bureau in Berlin (1867). Die Ergebnisse der Volkszählung und Volksbeschreibung, der Gebäude und Viehzählung, nach den Aufnahmen vom 3. December 1864, resp. Anfang 1865 und die Statistik der Bewegung der Bevölkerung in den Jahren 1862, 1863 und 1864. Preussische Statistik Vol. 10. Berlin: Verlag von Ernst Kuehn.

    1866

    Meitzen, August (1868). Der Boden und die landwirthschaftlichen Verhältnisse des Preussischen Staates, Vol. 1-4. Berlin: Verlag von Paul Parey.

    1871

    Königliches Statistisches Bureau (1873-74). Die Gemeinden und Gutsbezirke des Preussischen Staates und ihre Bevölkerung: Nach den Urmaterialien der allgemeinen Volkszählung vom 1.December 1871, Vol. 1-11. Berlin: Verlag des Königlichen Statistischen Bureaus

    Königliches Statistisches Bureau in Berlin (1875). Die Ergebnisse der Volkszählung und Volksbeschreibung im Preussischen Staate vom 1. December 1871. Preussische Statistik Vol. 30. Berlin: Verlag des Königlichen Statistischen Bureaus.

    1878

    Herrfurth, Ludwig and Conrad Studt (1880). Finanzstatistik der Kreise des preussischen Staates für das Jahr 1877/78. Zeitschrift des Preussischen Statistischen Landesamtes, Ergänzungshefte, Vol. 7. Berlin: Verlag des Königlichen Statistischen Bureaus

    1882

    Königliches Statistisches Bureau in Berlin (1884/85). Die Ergebnisse der Berufsstatistik vom 5. Juni 1882 im preussischen Staat. Preussische Statistik Vol. 76 a-c. Berlin: Verlag des Königlichen Statistischen Bureaus.

    1886

    Königliches Statistisches Bureau in Berlin (1887). Die Ergebnisse der Ermittelung des Ernteertrags im preussischen Staate für das Jahr 1886. Preussische Statistik Vol. 92. Berlin: Verlag des Königlichen Statistischen Bureaus.

    Königliches Statistisches Bureau in Berlin (1889). Das gesammte Volksschulwesen im preußischen Staate im Jahre 1886. Preussische Statistik Vol. 101. Berlin: Verlag des Königlichen Statistischen Bureaus.

    1892

    Neuhaus, Georg (1904). Die ortsüblichen Tagelöhne gewöhnlicher Tagearbeiter in Preußen 1892 und 1901, in Zeitschrift des Königlich Preussischen Statistischen Bureaus, Vol. 44, 310–346. Berlin: Verlag des Königlichen Statistischen Bureaus.

    1896

    Königliches Statistisches Bureau in Berlin (1897). Die Ergebnisse der Ermittelung des Ernteertrags im preussischen Staate für das Jahr 1896. Preussische Statistik Vol. 147. Berlin: Verlag des Königlichen Statistischen Bureaus.

    1901

    Neuhaus, Georg (1904). Die ortsüblichen Tagelöhne gewöhnlicher Tagearbeiter in Preußen 1892 und 1901, in Zeitschrift des Königlich Preussischen Statistischen Bureaus, Vol. 44, 310–346. Berlin: Verlag des Königlichen Statistischen Bureaus.

• Publications

  • A lot of research in economic history has used data from the iPEHD by now. These publications are listed below, ordered by the year in which the first working-paper version of the paper has been published. The list also provides brief descriptions of and links to the papers; for papers published in academic journals, please always refer to the final published versions, which often provide substantial additional robustness analyses than the initial working-paper versions. For those papers already published in academic journals, we also provide ready-made datasets and codes to replicate the tables published in the papers using Stata.

    Recent Projects Using Prussian Data

    Becker, Sascha O., and Luigi Pascali (2019). Religion, Division of Labor, and Conflict: Anti-semitism in Germany over 600 Years. American Economic Review 109 (5): 1764-1804.

    Bauernschuster, Stefan, Anastasia Driva, and Erik Hornung (2019). Bismarck's Health Insurance and the Mortality Decline. Journal of the European Economic Association, forthcoming.

    Hornung, Erik (2019). Diasporas, Diversity, and Economic Activity: Evidence from 18th-century Berlin. Explorations in Economic History 73: 101261.

    Cinnirella, Francesco, and Ruth Schueler (2018). Nation Building: The Role of Central Spending in Education. Explorations in Economic History 67 (1): 18-39.

    Cinnirella, Francesco, and Jochen Streb (2017). The Role of Human Capital and Innovation in Economic Development: Evidence from Post-Malthusian Prussia. Journal of Economic Growth 22 (2): 193-227.

    Ashraf, Quamrul, Francesco Cinnirella, Oded Galor, Boris Gershman, and Erik Hornung (2017). Capital-Skill Complementarity and the Emergence of Labor Emancipation. CESifo Working Paper 6423.

    Cinnirella, Francesco, and Jochen Streb (2017). Religious Tolerance as Engine of Innovation. CESifo Working Paper 6797.

    Cinnirella, Francesco, and Ruth Schueler (2016). The Cost of Decentralization: Linguistic Polarization and the Provision of Education. CESifo Working Paper 5894.

    Hornung, Erik (2015). Railroads and Growth in Prussia. Journal of the European Economic Association 13 (4): 699-736.

    Hornung, Erik (2014). Immigration and the Diffusion of Technology: The Huguenot Diaspora in Prussia. American Economic Review 104 (1): 84-122.

    2014

    Education and Religious Participation: City-Level Evidence from Germany’s Secularization Period 1890-1930

    Using panel data of advanced-school enrollment and Protestant church attendance in German cities between 1890 and 1930, this paper finds that education is negatively related to church attendance in panel models with fixed effects.

    Published version:  Becker, Sascha O., Markus Nagler, and Ludger Woessmann (2017). Education and Religious Participation: City-Level Evidence from Germany’s Secularization Period 1890-1930. Journal of Economic Growth 22 (3): 273-311.
    Data download: Datasets & Stata codes (ZIP)

    2013

    Not the Opium of the People: Income and Secularization in a Panel of Prussian Counties

    Combining income data with data on Protestant church attendance in Prussian counties for six waves from 1886-1911, the paper finds that – in contrast to the negative cross-sectional association – panel analyses do not confirm a significant relationship between income and church attendance.

    Published version:  Becker, Sascha O., and Ludger Woessmann (2013). Not the Opium of the People: Income and Secularization in a Panel of Prussian Counties. American Economic Review: Papers & Proceedings 103 (3): 539-544.
    Data download: Datasets & Stata codes

    2012

    iPEHD - The ifo Prussian Economic History Database

    This paper documents the ifo Prussian Economic History Database (iPEHD), which provides a rich collection of county-level variables for 19th-century Prussia in a digitized and structured way.

    Published version: Becker, Sascha O., Francesco Cinnirella, Erik Hornung, and Ludger Woessmann (2014). iPEHD – The ifo Prussian Economic History Database. Historical Methods 47 (2): 57-66.

    2011

    Landownership Concentration and the Expansion of Education

    Combining data from several censuses that effectively span the entire 19th century (1816, 1849, 1864, 1886, and 1896), as well as data from a 1866 classification of soil composition, this paper finds that landownership concentration, a proxy for the institution of serf labor, has a negative effect on school enrollment which diminishes in the second half of the century.

    Published version: Cinnirella, Francesco and Erik Hornung, Landownership Concentration and the Expansion of Education. Journal of Development Economics 121: 135-152.
     

    Knocking on Heaven’s Door? Protestantism and Suicide

    Using data from 1816-21 and 1869-71, this paper finds a substantial positive effect of Protestantism on suicide.

    Published version:  Becker, Sascha O., and Ludger Woessmann (2018). Social Cohesion, Religious Beliefs, and the Effect of Protestantism on Suicide (with S.O. Becker). Review of Economics and Statistics 100 (3): 377-391.
    Data download: Datasets & Stata codes
     

    Does Women's Education Affect Fertility? Evidence from Pre-Demographic Transition Prussia

    Combining data from three censuses – 1816, 1849, and 1867 – this paper finds a negative residual effect of women's education on fertility, despite controlling for several demand and supply factors.

    Published version:  Becker, Sascha O., Francesco Cinnirella, and Ludger Woessmann (2013). Does Women’s Education Affect Fertility? Evidence from Pre-Demographic Transition Prussia. European Review of Economic History 17 (1): 24-44.
    Data download: Datasets & Stata codes (ZIP, 46 KB)

    2010

    The Effect of Investment in Children's Education on Fertility in 1816 Prussia

    Using data from the 1816 census, this paper finds a significant negative causal effect of education on fertility – evidence for a child quantity-quality trade-off – already several decades before the demographic transition and shows that it is robust to accounting for spatial autocorrelation.

    Published version:  Becker, Sascha O., Francesco Cinnirella, and Ludger Woessmann (2012). The Effect of Investment in Children’s Education on Fertility in 1816 Prussia. Cliometrica 6 (1): 29-44.
    Data download: Datasets & Stata codes (ZIP, 18 KB)
    
    
    The Effect of Protestantism on Education before the Industrialization: Evidence from 1816 Prussia

    This paper shows that Protestantism led to more schooling already in 1816, before the Industrial Revolution, ruling out that Protestant education just resulted from industrialization.

    Published version:  Becker, Sascha O., and Ludger Woessmann (2010). The Effect of Protestantism on Education before the Industrialization: Evidence from 1816 Prussia. Economics Letters 107 (2): 224-228.
    Data download: Datasets & Stata codes (ZIP, 30 KB)

    2009

    Education and Catch-up in the Industrial Revolution

    This paper combines school-enrollment and factory-employment data from 1816, 1849, and 1882 to show that – in contrast to the state-of-the-art view based on British evidence – basic education is significantly associated with non-textile industrialization in both phases of the Industrial Revolution.

    Published version: Becker, Sascha O., Erik Hornung, and Ludger Woessmann (2010). Education and Catch-up in the Industrial Revolution. American Economic Journal: Macroeconomics 3 (3): 92-126.
    Download: Datasets & Stata codes (ZIP, 73 KB)
    
    The Trade-off between Fertility and Education: Evidence from before the Demographic Transition

    This paper uses data from the 1849 census and other sources to show that a trade-off between child quantity and quality existed already in the 19th century and that causation between fertility and education runs both ways.

    Published version: Becker, Sascha O., Francesco Cinnirella, and Ludger Woessmann (2010). The Trade-off between Fertility and Education: Evidence from before the Demographic Transition. Journal of Economic Growth 15 (3): 177-204.
    Data download: Datasets & Stata codes (ZIP, 32 KB)

    2008

    Luther and the Girls: Religious Denomination and the Female Education Gap in 19th Century Prussia

    Using data from the first Prussian census in 1816, among others, this paper shows that a larger share of Protestants in a county’s population decreased the gender gap in basic education.

    Published version: Becker, Sascha O., and Ludger Woessmann (2008). Luther and the Girls: Religious Denomination and the Female Education Gap in 19th Century Prussia. Scandinavian Journal of Economics 110 (4): 777-805.
    Data download: Datasets & Stata codes (ZIP, 81 KB)

    2007

    Was Weber Wrong? A Human Capital Theory of Protestant Economic History

    This paper uses data from several censuses (Population 1871, Occupation 1882, Education 1886) and additional sources (including the Income Tax Statistics 1877) to show that the higher economic prosperity of Protestant relative to Catholic counties can be accounted for by Protestants' higher literacy (presumably spurred by instruction in reading the Bible), suggesting that explanations based purely on differential work ethics may have limited explanatory power.

    Published version: Becker, Sascha O., and Ludger Woessmann (2009). Was Weber Wrong? A Human Capital Theory of Protestant Economic History. Quarterly Journal of Economics 124 (2): 531-596.
    Data download: Datasets & Stata codes (ZIP, 69 KB)

• External Links

  • iPEHD is certainly not the only project dealing with historical Prussian data at the county level. Other projects provide such services as maps, information on territorial changes, additional data, and other material on Prussian counties. Several of these projects, whose work is highly appreciated and can be viewed as complementary to ours, are listed below.

     

    Galloway Prussia Database 1861 to 1914

    In contrast to the focus of iPEHD on data relevant for economic history, the "Galloway Prussia Database 1861 to 1914" provides digitized Prussian census data for demographic analyses. These data provide a lot of information complementary to iPEHD.

    Galloway Prussia Database 1861 to 1914

     

    IEG Maps

    The maps provided in our thematic maps section were produced with the help of the IEG-Maps project located at the Institute for European History the University of Mainz. Their project homepage provides digital base maps of German and European history, regarding themes such as politics, administration, economics, and transportation.

    IEG-Maps

     

    MPIDR Project on GIS Maps

    At the Max Planck Institute for Demographic Research (MPIDR) in Rostock, an ongoing research project compiles GIS-libraries with shapefiles of annual cross-sections documenting the changing historical regional administrative boundaries in Germany at the district level since the early 19th century.

    Population History GIS Collection

     

    ZBW Project on Digital Statistics of the German Reich (1873-1883)

    The ZBW - Leibniz Information Center for Economics has an ongoing project on the "Digitisation of the Statistics of the German Reich - Alte Folge (old sequence) - (1873-1883)". The project aims to make the volumes available to be viewed on the internet and, subsequently, also accessible and searchable in spreadsheed formats.

    Digital Imperial Statistics

     

    Documentations of Counties and Territorial Changes

    The following links provide lists of all Prussian counties and insight into territorial reforms (only in German):

    List of the Prussian Counties

    County Reforms

    Territorial Changes of German Municipalities

     

    Source Volumes

    By now, complete scans of many of the original volumes from which the iPEHD data have been digitized, as documented in the source section, can be found at Google Books.

• FAQs

  • In the questions below, we document answers on a few standard problems that users have run into.

     

    1. Where do the data come from?

    The data were originally published in various outlets by the Royal Prussian Statistical Bureau and its staff. We document these under sources. For each specific variable, the codebooks document the specific source from which it stems.

     

    2. How can I get help if I have questions on the underlying data or on how to merge variables from different datasets into one dataset?

    To fully understand the underlying data, there is no other way but to consult the original sources. On how to merge variables, please consult the merging data section. However, at the end of the day, the best way to structure and use the data will be specific to every single research project, and it is the task of the researcher to solve this in the best possible way for the specific research project. This is a crucial part of your research, and you will be responsible for how you do it. The ifo Institute and the persons behind the iPEHD do not have the resources to answer any questions on this.

     

    3. When I try to open the CSV (comma-separated values) files, Excel or some other program puts all the variables from one observation (row) into one cell. What can I do?

    Since CSV files use commas to separate the values, there can be problems with the formatting for some regional settings. If your computer uses the comma for decimal or list separation, the file may open in a difficult-to-read format in Excel. The easiest way to deal with this problem is to change your computers region to an English speaking region.

    On Windows, change your computer's region by going to Start > Control Panel, clicking on Regional and Language Options, and then the Formats tab. Here you'll find the Region menu, where you can specify an English speaking region.

    If you want to stick to your own language, click the Customize button in the Formats tab to open the Customize Regional Options dialog, where you'll find the Decimal and List Separator option. Replace the Decimal symbol with a period, the List separator with a comma, and click OK. After restarting Excel, you should be able to create CSV files that use the comma separator.

    On Mac OS X, change your computer's region by going to Apple Menu > System Preferences, clicking on International, and then the Formats tab. Here you'll find the Region menu, where you can specify an English speaking region.

     

    4. Why is the identifier year sometimes different from the census year?

    The identifier year usually denotes the administrative structure of Prussia – not necessarily the census year. Sometimes data from the same census were published for different purposes at different times, and the administrative boundaries had been changed in-between. The Royal Prussian Statistical Office always used the county structure effective in the year of publication. Thus, data from the same census were published with different aggregations. As a consequence, in some cases different identifiers (e.g., kreiskey1871 and kreiskey1874) had to be assigned to data from the same census year (1871).

     

    5. How should I cite the database?

    When using data from the iPEHD database, please reference it as follows:

    Becker, Sascha, Francesco Cinnirella, Erik Hornung and Ludger Wößmann, "iPEHD - The ifo Prussian Economic History Database", Historical Methods: A Journal of Quantitative and Interdisciplinary History 47 (2), 2014, 57–66, Information, Working paper version available as: CESifo Working Paper 3904 (PDF)