Long-term correlations between the development of rail transport and the economic growth of the German Reich (1872-1913)

Abstract

Purpose: This study is part of the trend of researching new economic history using econometric analysis (the new economic history paradigm), still not very popular in Europe and the world (outside the USA and UK). The main purpose of the article was to use the Granger cointegration test to confirm the long-term correlations between the level of economic growth and the development of the German Reich railways. Design/Methodology/Approach: In the field of theoretical analysis, a review of international literature on the study of the interdependence of economic growth and the development of transport, including rail transport, was carried out. The empirical analysis was based on available statistical data for the period of 1872–1913. Econometric methods were used, including: stationary test using ADF and KPSS tests, Engle-Granger cointegration test, as well as the analysis of the impulse response function. Findings: The results of the research received confirm that there was a long-term correlation between the level of economic growth in Germany (expressed as Net National Product (NNP) and the level of rail freight symbolizing the development of railways. Practical Implications: The Granger causality test allows the elimination of economic variables that are not in a causal relationship, which in turn leads to a better explanation of the studied economic phenomenon. A special case in VAR auto-regression models when the analysed time series are integrated in the first degree I(1). Originality/Value: Considering the importance of transport for the economy, it is particularly important to examine whether the development of transport had an impact on the level of economic growth, and whether economic growth led to the development of the transport industry, and perhaps this relationship was two-way. The obtained results are the foundation for the construction of vector-autoregressive models (VAR) and the study of long-term relationships.