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Taxation, regulation and the information efficiency of the Berlin stock exchange, 1892-1913
No full textWe calculated a daily stock market index for the Berlin Stock Exchange. Data appendix to: Sergey Gelman/Carsten Burhop: Taxation, regulation and the information efficiency of the Berlin stock exchange, 1892–1913, in: European Review of Economic History, 12 (2008)/1, S. 39–66, https://doi.org/10.1017/S1361491608002104
Data Sources on the 19th and Early 20th Century German Capital Market. Challenges and Opportunities: A daily bank performance index 1920-1931
No full textIt is a share index derived from the daily prices of 39 banks listed on the Berlin Stock Exchange. The Excel file contains both the indices presented in the paper and the underlying raw data
Courland Census Microdata, 1803–1804
Full text linkThe database contains information on individuals coded from the 1803-1804 census lists of six towns in Courland. This cross-sectional data documents social, demographic and economic life in the multinational Baltic urban millieu. The original documents are kept at the Herder Institute in Marburg and at the Latvian State Historical Archives in Rīga. The data is provided as a comma separated CSV UTF-8 file along with a readme file. The database consists of 39 variables. First 30 variables adhere to coding standards defined in Siegfried Gruber's "Mosaic data files: Documentation of harmonized variables, Version 1.9 Mosaic 1.9" (https://assets.ipums.org/_files/mosaic/mosaic_data_files_harmonized_variables_version_1.9.pdf). Additional variables are: archive Data type: string. Name of the archives in which the source is held. LVVA for Latvian State Historical Archives. geonameid Data type: integer. Unique code of the place according to the Geonames gazetteer (see http://geonames.org). illness_or Data type: string. Diseases and disabilities of a person if specified in the source. notes Data type: string. Additional notes provided in the source. place_document Data type: string. German name of the place where a person was enumerated. place_origin Data type: string. Place of origin of a person as indicated in the source. ref_no Data type: string. Call number of the source. relig_or Data type: string. "Nationality" in the original wording. sheet_no Data type: string. Sheet number in the archival file in which the record was registered
Determinants of mine closures in a declining industry: The example of West German hard-coal mining after World War Two
No full textData appendix to: Tobias A. Jopp: Determinants of mine closures in a declining industry: The example of West German hard-coal mining after World War Two, in: Economics Letters 226, 2023, 111096. https://doi.org/10.1016/j.econlet.2023.11109
NS-Zwangsarbeiter im Deutschen Reich: Eine Statistik vom 30. September 1944 nach Arbeitsamtsbezirken
No full textDatenanhang zu: Mark Spoerer: NS-Zwangsarbeiter im Deutschen Reich. Eine Statistik vom 30. September 1944 nach Arbeitsamtsbezirken, in: Vierteljahrshefte für Zeitgeschichte 49 (2001), S. 665-684, https://www.ifz-muenchen.de/heftarchiv/2001_4_6_spoerer.pdf
More Than a Feeling: Dataset on media sentiment regarding the Berlin Stock Exchange
No full textData appendix to: Lino Wehrheim/Janos Borst-Graetz/Bernhard Liebl/Manuel Burghardt/Mark Spoerer: More than a Feeling. Introducing an NLP-Based Media Sentiment Index for the Berlin Stock Exchange, 1872–1930, in: Historical Methods: A Journal of Quantitative and Interdisciplinary History 58 (2025), S: 139–159. https://doi.org/10.1080/01615440.2025.2506427. Financial economists and psychologists agree that collective sentiment plays a crucial role in financial markets. We present our newly created data for a daily aspect-based index that captures the sentiment at the Berlin Stock Exchange from 1872 to 1930, a time when Berlin was the key financial market in Germany. This index is based on market reports published every trading day in the Berliner Börsen-Zeitung, which give a verbal description of the sentiment among market participants. Due to daily publication and the long observation period, our corpus consists of about 18,000 market reports. To derive sentiment values, we apply a combination of expert annotation and machine learning. Methdologically, this data covers two novel aspects: First, we focus on both historical and highly domain-specific language, a dual challenge that thus far has rarely been addressed. As there are many similar historical sources, such as reports by chambers of commerce, our solutions will be helpful for future research. Second, we address a characteristic but neglected feature of financial texts that might be relevant also in a broader sentiment analysis context. Particularly, we focus on aspect- and entity-based sentiment analysis. Note: The data was generated in the project „More Than a Feeling: Media Sentiment as a Mirror of Investors’ Expectations at the Berlin Stock Exchange, 1872-1930
Datenbank Konjunkturprognosen für Deutschland
Full text linkDie Datenbank umfasst Prognosedaten zur wirtschaftlichen Entwicklung in der Bundesrepublik aus Prognoseberichten von deutschen Wirtschaftsforschungsinstituten, dem Internationalen Währungsfonds (IWF) und der Organisation für wirtschaftliche Zusammenarbeit und Entwicklung (OECD) aus den Jahren 1960 bis 2019
Local minimum wages in Germany, 1885-1914
Full text linkThis dataset contains the ‘ortsüblichen Tagelohn gewöhnlicher Tagearbeiter’ (customary local daily wage of ordinary day laborers), which was determined by the German administration in consultation with local authorities and companies when calculating sickness benefits after the introduction of nationwide statutory health insurance in 1883. It was therefore not an average wage, but the lowest wage paid, which interestingly also took into account non-monetary wage benefits. As it was collected nationwide according to uniform criteria, it offers, in comparison with price data, the possibility of determining and comparing real wages and the standard of living in Germany at a very detailed local level. The data set presented here is limited to the reproduction of daily wages for 210 cities; hundreds of other smaller municipalities are listed in the sources cited
War, Bond Prices, and Public Opinion: How Did the Amsterdam Bond Market Perceive the Belligerents' War Effort During World War One?
No full textThis dataset contains additional data underlying the monograph "War, Bond Prices, and Public Opinion" (2021). For a discussion of the data, please turn to the study's data section, the corresponding Online Appendix availabe on the homepage of Mohr Siebeck (https://www.mohrsiebeck.com/buch/war-bond-prices-and-public-opinion-9783161595363?), and Jopp, Tobias A.: Contemporaries’ opinion on the Allied and Central Powers’ performance during the First World War: Measuring perceptions with sovereign debt prices, in: European Review of Economic History 20(2), 2016, pp. 242-273. https://doi.org/10.1093/ereh/hew001 Contents (daily frequency): - Prolongatiekoers at Amsterdam Stock Exchange (1/1/1914-31/12/1919); - Selected exchange rates at Amsterdam Stock Exchange (1/7/1914-31/12/1919); - Selected Dutch municipal bonds' prices at Amsterdam Stock Exchange (1/1/1914-31/12/1919); - Selected Dutch companies' stock prices at Amsterdam Stock Exchange (1/1/1914-31/12/1919
China's Overseas Lending: Replication dataset
No full textCompared with China's pre-eminent status in world trade, its role in global finance is poorly understood. This paper studies the size, terms and destination of Chinese official international lending on the basis of a new “consensus” database of 4900 loans and grants to 146 countries, 1949–2017. Using the loan-level lending data we estimate outstanding debt stocks owed to China for more than 100 developing and emerging economies since 2000. As of 2017, China had become the world's largest official creditor, surpassing the World Bank and the IMF. The terms of China's state-driven international loans typically resemble commercial rather than official lending. We also find that 50% of China's official lending to developing countries is not reported in the most widely used official debt statistics. These “hidden” debts have important implications for debt sustainability