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• W4387670691 abstract "ABSTRACTThe novel coronavirus disease 2019 (COVID-19) outbreak has caused tremendous turmoil worldwide and is yet to be contained. Therefore, mitigating the number of people infected by COVID-19 remains a fundamental policy goal for several countries. This study aims to analyse the dynamic relationship between mobility and the rate of change in the number of new infections (NNIs) in Japan. Another goal is to evaluate the effects of various policies, such as mobility control and vaccination, as well as the impact of climate factors on the NNIs. The analysis reveals a strong positive relationship between the growth rate of the NNIs and mobility. Our results also indicate that the government-imposed measure of regulating mobility shows a weaker effect on preventing the spread of infection, as the emergency declaration is repeatedly invoked. By contrast, the recent increase in the infection rate seems to reduce the spread of infection by inducing voluntary restraint, and this phenomenon has become even stronger in the recent period. Regarding the effect of vaccination, the results demonstrate little effect on attenuating the spread of infection. However, it has significantly weakened the mobility – spread relationship, suggesting that it may help in the implementation of economic revitalization policies.KEYWORDS: COVID-19vaccinationimpulse response analysisSIR modelmobility control policyJEL CLASSIFICATION: C23H10I18 AcknowledgmentsThis paper is part of the results of a research project by the second author at the Research Institute of Economy, Trade and Industry (RIETI). We would like to thank Shiba Suzuki, Vu Tuan Khai, Toshiaki Shoji, Hibiki Ichiue, Kaiji Motegi, Taisuke Nakata, Yoshihiko Kadoya, Peter Evan, participants at the 16th International Symposium on Econometric Theory and Applications (SETA2022), 2022 Japanese Economic Association Spring Meeting, and seminar participants at Seikei University, and Keio University for many useful comments on the original draft of this paper. This research was partially supported by a grant-in-aid from Zengin Foundation for Studies on Economics and Finance (2021). All erros are our own.Disclosure statementNo potential conflict of interest was reported by the author(s).Supplementary materialSupplemental data for this article can be accessed online at https://doi.org/10.1080/00036846.2023.2269630Notes1 The declaration of an SOE by the Japanese government is not legally binding and carries no penalties, such as fines or arrests. However, since many people complied with the Japanese government’s verbal ‘request’ to refrain from going out, Watanabe and Yabu (Citation2021) considered it a ‘voluntary lockdown’.2 The spatial unit used in our analysis is prefectures, which are equivalent to states in the US.3 For the application of the SIR model to economic analysis, please refer to Avery et al. (Citation2020). The estimation and identification of models related to the SIR model are discussed in Arias et al. (Citation2021) and Korolev (Citation2021).4 In this study, the number of test-positive cases is synonymous to the number of infected cases.5 EquationEquation (1)(1) ΔIt+1=βtStN−γIt=(βtst−γ)It(1) can be regarded as a variant of the controlled Galton-Watson process, which is a state-dependent random walk process developed to analyse population growth processes. The important theoretical studies include Küster (Citation1985), Keller et al. (Citation1987), and Klebaner (Citation1989), among others. For its application to the macroeconomic time series, see Granger et al. (Citation1997).6 Wilson (Citation2021) set the removal rate at 10 days considering the Centers for Disease Control and Prevention study. However, footnote 11 of Wilson (Citation2021) also reported that a model assuming a seven-day infectious period yielded very similar empirical results.7 According to the classification by the Japan Meteorological Agency (JMA), a hot summer day is defined as a day on which the temperature goes above 30 degree Celsius (°C). In comparison, an ice day is defined as a day on which the temperature stays below 0°C. Similar to Wilson (Citation2021), we also estimate a model in which the maximum weekly temperature replaces the number of hot summer and ice days, but the coefficient of temperature is not significant.8 Following Fujii and Nakata (Citation2021), we use the efficacy rate of Pfizer vaccines after the second dose that is reported in the UK’s SPI-M-O Summary on 31 March 2021. To assess robustness, we also estimate the models assuming 0.95 efficacy rate, as used in Wilson (Citation2021), and obtain qualitatively similar outcomes.9 As for the mobility and meteorological variables, we include the lagged variables up to three lags. However, the Akaike information criterion and the Schwarz Bayesian information criterion mostly select the zero lag. Therefore, we include only the contemporaneous variables.10 These are Hokkaido, Miyagi, Ibaraki, Tochigi, Saitama, Chiba, Tokyo, Kanagawa, Gifu, Shizuoka, Aichi, Mie, Shiga, Kyoto, Osaka, Hyogo, Nara, Hiroshima, Fukuoka, and Okinawa.11 Note that Google Maps, which is one of the most important sources of the google mobility data, dominates the other map applications in Japan with more than 80% share. As a result, the Google Mobility Reports can effectively represent the majority of mobility trends in Japan. Moreover, our mobility data is derived from the relative change from the base date of the Google Mobility data, not the level of mobility itself. Therefore, we anticipate that any potential limitations in representativeness would have minimal impact on our analysis.12 The analysis period is shortened by four weeks, from the week of 2 August 2020 to the week of 14 November 2021.13 Table A1 in the Online Supplement reports the descriptive statistics of the regression variables.14 See Figure A3 in the Online Supplement.15 For the estimates of the weekly time fixed effects, see Section C of Online Supplement.Additional informationFundingThe work was supported by the Zengin Foundation For Studies On Economics And Finance [2104]." @default.
• W4387670691 created "2023-10-17" @default.
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• W4387670691 date "2023-10-15" @default.
• W4387670691 modified "2023-10-17" @default.
• W4387670691 title "Exploring the dynamic relationship between mobility and the spread of COVID-19, and the role of vaccines" @default.
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• W4387670691 doi "https://doi.org/10.1080/00036846.2023.2269630" @default.
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