Einführung

Hoffmann, Walther G.

doi:10.1007/978-3-642-86088-1_1

Cited by 9 publications

(23 citation statements)

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“…We obtain evidence for an earlier transition from agriculture to industry than historical national accounting suggests (Hoffmann 1965). Germany is regarded as a late industrializer in the classical contribution by Gerschenkron (1962).…”

Section: Introductionmentioning

confidence: 61%

“…Spree (1977) also provides a larger data set, consisting of 265 time series for the period 1840-1880. It is an extremely rich data set, comparable in scale maybe only to Hoffmann (1965). About 50 of the 265 series rely on Hoffmann (1965), 23 on Spiethoff (1955), 22 on Jacobs and Richter (1935), 20 on Borries (1970), 18 on Kirchhain (1971), and 11 on Fremdling (1975).…”

Section: Model Setup and Estimationmentioning

confidence: 99%

“…It is an extremely rich data set, comparable in scale maybe only to Hoffmann (1965). About 50 of the 265 series rely on Hoffmann (1965), 23 on Spiethoff (1955), 22 on Jacobs and Richter (1935), 20 on Borries (1970), 18 on Kirchhain (1971), and 11 on Fremdling (1975). Additionally, we draw on a large amount of scattered quantitative and qualitative literature, and thus obtain a dataset that covers a large proportion of activity in the German economy.…”

Section: Model Setup and Estimationmentioning

confidence: 99%

“…The literature also generally agrees on the subsequent period of depression. Fischer (1985, p. 392), using Hoffmann's (1965) "Output" NNP estimate, calls it a "severe recession", while Borchardt (1976, p. 265) emphasizes its unusual duration and its "unparalleled" increase in unemployment ( Figure 11).…”

Section: The 1840smentioning

confidence: 99%

“…That is why the timing of industrialization is an unresolved matter in the German case. Germany is regarded to be an industrial later comer, based on reconstructed national accounts (Hoffmann 1965, Gerschenkron 1962. Disaggregate time series evidence has been argued to suggest an earlier transition (Spree 1977).…”

Section: Introductionmentioning

confidence: 99%

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Tracking down the business cycle: A dynamic factor model for Germany 1820–1913

Sarferaz¹,

Uebele²

2009

Explorations in Economic History

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractWe use a Bayesian dynamic factor model to measure Germany's pre World War I economic activity. The procedure makes better use of existing time series data than historical national accounting. To investigate industrialization we propose to look at comovement between sectors. We find that Germany's industrial sector developed earlier than stated in the literature, since after the 1860s agricultural time series do not comove with the business cycle anymore. Also, the bulk of comovement between 1820 and 1913 can be traced back to five out of 18 series representing industrial production, investment and demand for industrial inputs. Our factor is impressingly confirmed by a stock price index, leading the factor by 1-2 years. We also find evidence for early market integration in the 1820s and 1830s. Our business cycle dating aims to resolve the debate on German business cycle history. Given the often unsatisfactory quality of national accounting data for the 19th century we show the advantage of dynamic factor models in making efficient use of rare historical time series.

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Section: Introductionmentioning

confidence: 61%

Section: Model Setup and Estimationmentioning

confidence: 99%

Section: Model Setup and Estimationmentioning

confidence: 99%

Section: The 1840smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Tracking down the business cycle: A dynamic factor model for Germany 1820–1913

Sarferaz¹,

Uebele²

2009

Explorations in Economic History

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The Power of Big Data: Historical Time Series on German Education

Diebolt

Franzmann²,

Hippe³

et al. 2017

J. Dem. Econ.

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Numerous primary investigators collected and processed long-termed time series on German educational statistics in the context of their studies. As a result, there are a multitude of quantitative empirical studies. On the one hand, there is the project group on German Educational Statistics.1Its projects were targeted at describing and analyzing the long-term structural changes of the German educational system on a broad empirical and statistical basis. On the other hand, there are comprehensive data compilations of individual research projects, focusing on a wide variety of special educational research topics. The online database “histat” provides central digital access to these datasets on German educational history. Currently, it offers more than 120,000 long-term time series on the German educational system for a period of 200 years. The striking size of the database shows its key importance for researchers in the field of education. Thus, this paper aims to provide useful insights into the background of the database, the special characteristics of the data compilations and their analytical potential. Additionally, examples are given of how the data have already been used by researchers.

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The Yankees of Europe? A New View on Technology and Productivity in German Manufacturing in the Early Twentieth Century

Timmer¹,

Veenstra²,

Woltjer³

2016

J. Econ. Hist.

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Labor productivity in German manufacturing lagged persistently behind the United States in the early twentieth century. Traditionally, this is attributed to dichotomous technology paths across the Atlantic. However, various industry case studies suggest rapid diffusion of U.S. technologies in Germany. We develop a novel decomposition framework based on Data Envelopment Analysis (DEA) to reconcile these findings. We conclude that by 1936 inefficient assimilation of modern production techniques—and not the use of different techniques—accounted for most of the U.S./German labor-productivity gap. Our findings call for a reappraisal of the drivers behind cross-country differences in manufacturing performance.

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Einführung

Cited by 9 publications

References 0 publications

Tracking down the business cycle: A dynamic factor model for Germany 1820–1913

Tracking down the business cycle: A dynamic factor model for Germany 1820–1913

The Power of Big Data: Historical Time Series on German Education

The Yankees of Europe? A New View on Technology and Productivity in German Manufacturing in the Early Twentieth Century

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