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Record W3187112639 · doi:10.1162/asep_a_00832

Shigeyuki Abe Comment on How Did Japan Cope with COVID-19? Big Data and Purchasing Behavior

2021· article· en· W3187112639 on OpenAlex

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aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
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Bibliographic record

VenueAsian Economic Papers · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsPurchasingDownloadIconCitationCoronavirus disease 2019 (COVID-19)AdvertisingIndex (typography)BusinessInternet privacyLibrary scienceWorld Wide WebMarketingComputer scienceMedicine

Abstract

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Shigeyuki Abe, Kyoto University: This paper uses economic purchasing data (the open-source METI Point of Sales, POS, Retail Sales Index) to analyze economic and social behavior during the COVID-19 pandemic period of January 2020 through August 2020. The purchasing data examined covers a wide spectrum of items including face masks, hand sanitizers, gargle medicines, electronic thermometers, button batteries, PCs, webcams, headsets, food, kitchen detergent, cosmetics products, paper products, chlorine-based bleach, and household rubber gloves.The paper succeeds in showing that (a) the Japanese people actively prevented the spread of infection by voluntarily wearing masks, using alcohol-based disinfectants, and gargling; (b) they chose to stay home during the semi-lockdown, leading to an increase in cooking and working from home; and they do not only buy infection prevention essentials during the high infection months of April, July, and August but also during the low infection months of May and June.This paper also using the daily data of prefectures to summarize nicely the geographical distribution of the number of infections and deaths. The infections and deaths are concentrated in the heavily populated metropolitan area (Tokyo, Kanagawa, Chiba, and Saitama) and Kansai region and the spread of infections in the prefectures of these two regions were remarkably similar but other regions show differences in the timing and speed of the spread of infection.I have two comments. First, the authors should have treated the data on some items more cautiously because the quality of the item in January 2020 might not be the same as in August 2020. This change in the quality of the item is in the case of face masks.Non-woven facemask was the most commonly sold type of facemask before the pandemic. When COVID-19 started to spread in Japan, China prohibited the export of non-woven face masks. The outcome was that Japanese face mask manufacturers that had located their production plants in China (such as Unicharm) could not produce and supply them to Japan. The then Prime Minister Shinzo Abe announced plans to distribute two cloth face masks to every household free of charge. The government-provided masks, dubbed “Abenomask”, were widely derided for its poor quality and for their slow delivery to the public. The masks were most unpopular but they could have moderated the rise in mask prices when China stopped the export of face masks. The shortage of face masks and the low quality of Abenomask encouraged a sizable increase in the production of different types of better quality masks. Sharp Corporation, an electric appliance manufacturer, started to produce a non-woven face mask, with many others such as sports manufacturer Mizuno Corporation and apparel giant Uniqlo following suit.If the composition of face mask types are the same before and after COVID-19, a year-on-year comparison of sales growth rates is permissible, but this is not the case. There are vast differences in terms of types, quality, unit price, and durability. One can get a sense of the boom in the number of different types of face masks in Japan by visiting the AEON online store, where 400 different types of face masks are sold at 380 yen to 2,700 yen.1Figure 1 below shows the price trend of Japan-made face masks. As the average price fell from 800 yen in late May 2020 to about 70 yen in early November 2020, and given the wide variety of prices at points in time, the authors’ use of POS data alone cannot reveal accurately how many face masks are obtained by consumers. Figure 1.Price trend of face masks made in JapanSource:Zaiko Sokuho data.My second comment is on the authors’ conclusion that “continuing to prevent the pandemic by wearing masks, washing and sanitizing hands, and gargling, in addition to spending more time at home to maintain a safe distance, will be effective in reducing the spread of the infection”. I want to point out that this conclusion is not the consensus opinion of Japan-based observers.Rochelle Kopp (2020) examined more than 200 variables in a sample of 46 countries and concluded that the feature that received the strongest statistically significant support was that countries in the Asia-Pacific region are more likely to have lower COVID-19 death levels. Kopp's finding of Asia-Pacific exceptionalism is confirmed by Table 1 below which shows the low incidences of infections and deaths in East Asian countries. Table 1.Infection, death, PCR test per 1 million for selected countriesRanksCountryDeaths/1M POPCases/1M POPTests/1M POP1USA79538,512550,4603Brazil79528,561102,7404France75432,828306,5136Spain92234,363468,6987UK81222,454607,3508Italy83523,695339,8789Argentina81830,30581,36710Colombia69424,560119,04111Mexico7878,10520,98012Peru1,07528,672149,16113Germany17411,284318,68121Indonesia581,82819,58923Netherlands52228,561227,05027Philippines743,81850,16030Canada3048,913288,87433Switzerland48634,604296,33934Portugal39026,000423,99935Austria27227,732321,46936Nepal467,57656,92937Sweden63320,574287,83139Ecuador74510,47735,09243Bolivia76012,27330,16047Japan161,04825,92368China360111,16371Myanmar321,47619,01783Singapore59,911720,42184Malaysia101,74278,19493S. Korea1061157,449136Hong Kong14758531,153151Thailand15613,995167Vietnam01313,712179Taiwan0264,512Note:Johns Hopkins University Data retrieved on 24 November 2020. East Asian countries are shown in bold.Shinya Yamanaka, a stem cell researcher who won the Nobel Prize for Medicine in 2012, has hypothesized that there is an unknown “Factor X” that accounts for Japan's low death rate from COVID-19. The identity of Yamanaka's Factor X is “historical immunity” according to Tatsuhiko Kodama who “believes Japan may have had COVID before. Not COVID-19, but something similar that could have left behind historical immunity”; Wingfield-Hayes (2020). Or more generally, the “people in Asia-Pacific countries (including Asian countries as well as Russia, Australia and New Zealand) have some existing background immunity due to more exposure to other coronaviruses that circulate more here” Kopp (2020).In addition, four Japanese psychologists -- Kazuya Nakayachi, Taku Ozaki, Yukihide Shibata, and Ryosuke Yokoi (2020) -- examined six possible psychological reasons behind the Japanese public's practice of wearing face masks. Three of these were about minimizing the risk of infection while three involved other driving psychological forces. The results of their nationwide survey “revealed that people conformed to societal norms in wearing masks and felt relief from anxiety when wearing masks… . [And the overall conclusion of these four psychologists was that] “risk reduction expectations did not affect mask usage.”In light of the above viewpoints held by some leading influential Japan-based analysts, the conclusion by Konishi, Saito, Ishikawa, Kanai, and Igei based on data that ended in August 2020 must be recognized as a bold one.

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Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.870
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.095
GPT teacher head0.279
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it