In light of the recent advancement in economic narrative analysis, we develop a computational textual analysis method to study economic history. In this method, we collect narrative data from newspapers to measure economic trends. In particular, the popularity (frequency) of a narrative (keyword) on the newspapers is used as the proxy of the amount of economic activities associated with the narrative term; a high frequency indicates that there is a high volume of economic activities associated with the narrative term and vice versa. Regularized regression algorithms are then applied on the narrative frequency data to identify narrative terms whose associated microeconomic activities have macroeconomic impact. We apply the method to study a classic topic in Chinese economic history research: U.S. Silver Purchase Act and the Chinese price level in 1928–1936. Our results provide new insights into this controversial subject. For example, we find that the economic activity associated with the narrative term silver stock had no impact on the Chinese price level, which is contrary to previous research on the topic by Friedman and Schwartz . Meanwhile, economic activities associated with the narrative terms U.S. silver purchase act and silver export are found to have a negative impact on the Chinese price level. This suggests the concerns at that time about the effects of U.S. Silver Purchase Act on the Chinese economy were not misplaced.
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We obtained the data from the Statistical Monthly ( ).
The ShunBao newspaper database is available in many libraries worldwide as scanned files. The digitization of the database was completed by the Beijing Erudition Digital Technology Research Center ( ), who also made the text data searchable. We accessed the database through the library subscription of Tung Hai University in Taiwan.
This is the default setup of the Shanghai ShunBao ( ) newspaper database.
Here, we use a Wald test to examine the hypothesis.
Please see .
LASSO regression shrinks coefficients to zero, because the two objective terms, the least squared estimation of coefficients and the linear penalty, sometimes interact. The LASSO solution is the first place where the interaction occurs, which corresponds to a zero coefficient.
We made single cv.glmnet run for each model. The \(\lambda \) test sequence is chosen randomly by glmnet. To generate reproducible results, we specified a random number seed at the beginning of each run, so that the \(\lambda \) test sequence is selected deterministically for cross-validation.
The popularity of silver purchase act was at its minimum (zero) over three quarters of the time period. Yet, it is selected as an important feature by the regularized regression algorithms. We surmise that this is because there is a significant correlation between the non-zero “silver purchase act” frequency data and the Shanghai WPI. This correlation outweighs the importance of that of the first 3 quarters of the zero-value data. The same applies to the popularity of “abandonment of the gold standard” data. We will conduct more thorough statistical analysis on this issue in our future work.
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We would like to thank the two anonymous reviewers, whose constructive comments have helped improving this paper. This work is supported by the Ministry of Science and Technology (MOST) Taiwan under the Grant number 106-2410-H-004 -006-MY2.
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Hsu, C., Yu, T. & Chen, SH. Narrative economics using textual analysis of newspaper data: new insights into the U.S. Silver Purchase Act and Chinese price level in 1928–1936. J Comput Soc Sc (2021). https://doi.org/10.1007/s42001-021-00104-0
- Narrative economics
- Textual analysis
- Regularized regression algorithms
- Economic history
- Chinese monetary policy
- U.S. silver purchase act