https://orcid.org/0000-0003-3333-7198
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ABSTRACT
In this paper we investigate whether destruction due to natural disasters induces industries to increase their regional production efficiency using the case of pre-war Japan, a setting of frequent disasters and technological upgrading. To this end we compile a regional sectoral dataset of natural disaster destruction and production for machinery and textiles. We then employ a stochastic frontier analysis (SFA) approach to estimate the role of disaster events on changes in production efficiency. Our results show that earthquakes led to increases in efficiency for both machinery and textiles, although they were substantially greater for textiles due to the recovery persisting longer. Overall earthquakes contributed 6.8% of efficiency gains in textiles and 3.1% in machinery. However, allowing events to compound in their impact showed that such gains were dampened when there were damaging earthquakes in subsequent years. In contrast, for climate-related natural disaster events there is only weak, if any, evidence that these played a significant role in determining productive efficiency.
ACKNOWLEDGEMENTS
The early version of this paper was published as a discussion paper (Mohan et al., Citation2020). We also thank an editor and two anonymous referees for helpful and excellent comments.
DISCLOSURE STATEMENT
No potential conflict of interest was reported by the authors.
Notes
1. The uncertainty regarding such a negative impact is whether this will only manifest itself over the short term, that is, whether economies will recover quickly back to their equilibrium growth trajectory or if recovery will only be gradual.
2. From a theoretical modeling perspective, the predictions in this regard rest in part crucially on the choice of economic growth model. Models based on neoclassical growth theory are generally only able to predict a negative impact because they assume rather than try to model technical change; in contrast, endogenous growth models can facilitate higher technology growth after natural disasters.
3. Pelli and Tschopp (Citation2017) found that countries switch to exporting industries in which they have a comparative advantage after natural disasters.
4. One exception is Okazaki et al. (Citation2019), who studied the upgrading of machine horsepower after an earthquake.
5. According to the Census of Manufacture, shares of output in all manufacturing sectors is 42% for textiles (the largest) and 16% for the machine sector (the second largest) as of 1920.
6. SFA was previously employed to study the technical inefficiency of Japanese regional industries by Otsuka et al. (Citation2010) and Otsuka and Goto (Citation2015).
7. A similar approach was taken for Caribbean countries with regard to hurricanes and their impact on country technical efficiency by Mohan et al. (Citation2019), who found that there was a short efficiency boost for these islands. Arguably, this approach is more suitable to the context here, since we are explicitly comparing efficiency across regions for the same sector within the same country where it is much more likely that technologies and inputs are similar. In contrast, comparisons at the national level, as in Mohan et al. (Citation2019), must inherently assume that feasible countries can achieve the same technological frontiers regardless of differences in resources and sectoral structures.
8. Abe et al. (Citation2017) show that the share of manufacturing was less than 30% in 1900, but this had risen to 44% by 1925.
9. Using Japan’s Census of Manufacture data, Minami (Citation1976) illustrated the evolution of motive power and emphasized how this evolution contributed to industrialization.
10. After the First World War, the indigenous industries stagnated for a time, which led to a ‘dual structure’ of large productive firms and small unproductive firms (Nakamura, Citation1971).
11. See Appendix B in the supplemental data online.
12. Unfortunately, information on the number of factories with machines and the amount of horsepower is unavailable for a few prefectures in 1922 due to technical reasons at the statistical office. The Great Kanto Earthquake in September 1923 destroyed the statistical office building and the government lost the data for 1922 in the process of compiling the data.
13. After the big fire, the Order of Fire Defence (‘Bouka Rei’) was legislated in Tokyo City in 1881. In the 22 main streets in central area of Tokyo City, all buildings were requested to replace the architecture by brick or stone until a due date. In the central Tokyo wards, such as Nihonbashi, Kyobashi, Kanda and Kojimachi Wards, buildings were requested to use fireproof materials (e.g., steel and copper) for their roofs, doors and windows.
14. Tokyo Nichinichi newspaper (2 October 1917).
15. Tokyo Asahi newspaper (2 October 1917).
16. Tokyo Asahi newspaper (2 October 1917).
17. Tokyo Asahi newspaper (2 October 1917).
18. Tokyo Asahi newspaper (6 April 1918).
19. The Law of Factories came into force in 1916. It prohibited child labour and long working hours of young female workers. However, many workers in textile sectors continued to work for long hours.
20. Tokyo Nichi Nichi newspaper (25 September 1923).
21. A similar approach was taken by Otsuka et al. (Citation2010) who examined whether agglomeration economies, market access and public fiscal transfers affected Japanese regional industries. They found that while agglomeration economies and greater market access increased efficiency, public fiscal transfers had a negative effect.
22. This is due to the law of diminishing marginal returns.
23. The 1920s were characterized as the period of ‘engine revolution’ and many manufacturing factories introduced machines with electric motors, involving high-horsepower engines (Minami, Citation1976). Abe et al. (Citation2017) found that the machinery sector saw a higher percentage of factories with machines as well as the use of machines with electric motors compared with the textile sector as of 1919.
24. The raw correlation between mean regional inefficiency across the two sectors is 0.57.
25. Including further lags would have substantially reduced our sample.