Online Appendix: The Impact of COVID-19 on Japanese Firms: Mobility and Resilience via Remote Work
Summary
An online appendix for The Impact of COVID-19 on Japanese Firms: Mobility and Resilience via Remote Work, by Manabu Nose, Daiji Kawaguchi and Sagiri Kitao, dated April 16, 2022. The appendix gives more details about the data, including the heterogeneity of work-from-home adoption across prefectures and industries and the characteristics of firms that adopted work from home before COVID-19. It includes Table 1 comparing firm age, number of employees, sales, profit margin and credit score by remote work adoption in 2019, with the source given as the TSR-CREPE survey and TSR credit file. A robustness analysis section examines the effects of pre-COVID-19 flexibility, controlling for a Business Continuity Plan indicator.
Summary drafted by a model from the document's text below and checked by script against that text before publication. It is a navigation aid, not a reading of what the document proves. Where AI is used
- Issuer
- Source documents
- Document type
- The Impact Of Covid 19 On Japanese Firms Mobility And Resilience Via Remote Work
- Case
- The Impact Of Covid 19 On Japanese Firms Mobility And Resilience Via Remote Work
Cite this document
“Online Appendix: The Impact of COVID-19 on Japanese Firms: Mobility and Resilience via Remote Work.”
Original at doi.org. Archived at Pandemic Darlings, https://pandemicdarlings.com/source-documents/the-impact-of-covid-19-on-japanese-firms-mobility-and-resilience-via-remote-work/
, SHA-256 9587840a40bcaa0b0730773410f6665cfcf607fe3e29f11a05feda777d4c13ed.Full text
Online Appendix for
“The Impact of COVID-19 on Japanese Firms:
Mobility and Resilience via Remote Work”
Manabu Nose∗
Daiji Kawaguchi†
Sagiri Kitao‡
April 16, 2022
1 More Details about the Data
1.1 Heterogeneity in Remote Work Adoptions
WfH firms are spread across prefectures and industries, as shown in Figures 1 and 2,
which display the shares of firms in each prefecture and industry-size that adopt the WfH
arrangements prior to the COVID-19. There are five prefectures (Yamanashi, Wakayama,
Tottori, Kagawa and Nagasaki), where there is no firm that adopted the WfH arrangement
and the share of firms adopting WfH is in the range between 3% to 20%. In the size-
industry dimension, all industries have both firms that do and do not adopt WfH, but
there are industries where no small firms adopted WfH.
We also computed the fraction of firms adopting the WfH for all industry-size-prefecture
groups. Figure 3 shows the distribution of firms by the WfH fraction of the group that
they belong to. The share of firms whose industry-size-prefecture group has no WfH firm
is about 30%, and 50% of firms have the share above 9%.
∗
The International Monetary Fund and The University of Tokyo, manabu.nose@gmail.com
†
The University of Tokyo, Research Institute of Economy, Trade and Industry, and IZA,
kawaguchi@e.u-tokyo.ac.jp
‡
Research Institute of Economy, Trade and Industry and The University of Tokyo, sa-
giri.kitao@gmail.com
1
Figure 1: Fraction of Firms Adopting WfH (By Prefecture)
Source: TSR-CREPE survey.
Figure 2: Fraction of Firms Adopting WfH (By Industry and Size)
Source: TSR-CREPE survey.
2
Figure 3: Fraction of Firms Adopting WfH (By Industry-Size-Prefecture)
Source: TSR-CREPE survey.
1.2 Characteristics of Firms Adopting WfH
Table 1 summarizes the characteristics of the firms before COVID-19 by the adoption of
WfH. Firms that adopted WfH prior to the pandemic are larger, in terms of number of
employees and total sales, and relatively younger, while the differences in profit margins
and credit scores were marginal and insignificant.
Table 1: Characteristics of Firms Adopting WfH in 2019
(1)
Mean (Remote=1) Mean (Remote=0) Difference
Firm age 36.639 44.162 -7.523∗∗∗
Number of employees 632.384 94.005 538.378∗∗∗
Log(sales) 14.195 13.944 0.252∗∗∗
Log(sales per employee) 7.863 7.940 -0.078∗∗∗
Profit margin 0.019 0.023 -0.004
Sales growth 0.052 0.036 0.016∗∗
Credit score 54.328 54.101 0.228∗
Source:TSR-CREPE survey and TSR credit file
Note: All firm characteristics are from latest available financial statements before the
pandemic. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01
3
2 Robustness Analysis
2.1 Effects of Pre-COVID-19 Flexibility
The mitigation effect of WfH entails not only being able to continue the same operations
remotely, but also making the production process more efficient with the help of new
technology. In particular, the mitigation effect of WfH may include the effect of the
digital transformation, which reflects the firms’ flexibility and readiness to adopt new
ways of working that presumably help them to be more adaptive to emergency situations.
Although it is difficult to completely isolate the effect, we estimate the model that controls
for the dummy variable, which indicates the presence of Business Continuity Plan (BCP)
prior to the pandemic. The idea is to look at the potential bias caused by the unobserved
tendency to adapt to emergency situations,
As shown in Table 2, quantitative results are not affected much by the inclusion of the
dummy variable and the potential bias by the firm’s unobserved business adaptability to
emergency appears to be limited.
4
Table 2: Impact by Adoption of Remote Work and BCP Implementation Before COVID-
19
(1) (2) (3) (4) (5) (6)
All Low contact High contact All Low contact High contact
Panel A: Sales Growth
Change in Mobility 0.277∗∗∗ 0.258∗∗∗ 0.316∗∗∗ 0.277∗∗∗ 0.257∗∗∗ 0.314∗∗∗
(0.021) (0.024) (0.030) (0.022) (0.023) (0.033)
Change in Mobility × Remote 2019 -0.153∗∗∗ -0.194∗∗∗ -0.024 -0.152∗∗∗ -0.194∗∗∗ -0.024
(0.041) (0.047) (0.063) (0.041) (0.047) (0.063)
Remote 2019 -0.015 -0.011 -0.023 -0.014 -0.010 -0.022
(0.012) (0.013) (0.020) (0.012) (0.013) (0.019)
BCP 2019 0.007 0.006 0.010
(0.008) (0.011) (0.026)
R2 0.12 0.09 0.17 0.12 0.09 0.17
Panel B: Employment Growth
Change in Mobility 0.012∗∗ 0.012∗ 0.010∗ 0.012∗∗ 0.012∗ 0.010
(0.005) (0.006) (0.006) (0.005) (0.007) (0.006)
Change in Mobility × Remote 2019 0.004 0.008 -0.010 0.004 0.008 -0.010
(0.013) (0.020) (0.020) (0.013) (0.020) (0.020)
Remote 2019 -0.008∗∗ -0.011∗∗∗ -0.001 -0.008∗∗ -0.011∗∗∗ -0.000
(0.004) (0.004) (0.007) (0.004) (0.004) (0.007)
BCP 2019 0.000 -0.002 0.003
(0.002) (0.005) (0.007)
R2 0.13 0.11 0.16 0.13 0.11 0.16
Panel C: Hours Growth
Change in Mobility 0.212∗∗∗ 0.207∗∗∗ 0.221∗∗∗ 0.212∗∗∗ 0.207∗∗∗ 0.218∗∗∗
(0.009) (0.013) (0.011) (0.009) (0.013) (0.012)
Change in Mobility × Remote 2019 -0.073∗∗∗ -0.079∗∗∗ -0.046 -0.073∗∗∗ -0.079∗∗ -0.046
(0.027) (0.030) (0.036) (0.027) (0.030) (0.036)
Remote 2019 -0.004 -0.004 -0.004 -0.003 -0.004 -0.002
(0.007) (0.010) (0.011) (0.007) (0.010) (0.011)
BCP 2019 0.007 0.003 0.017∗∗
(0.006) (0.009) (0.008)
R2 0.13 0.11 0.18 0.13 0.11 0.18
Industry FE 3-digit 3-digit 3-digit 3-digit 3-digit 3-digit
Est Size FE Yes Yes Yes Yes Yes Yes
N 29,056 19,264 9,792 29,056 19,264 9,792
Source: Google Community Mobility Report and TSR-CREPE survey
Note: Standard errors robust against prefecture-level clustering are reported in
parentheses. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01
2.2 Effects of Wholesale and Retail Sectors
There is a concern that the pre-COVID-19 adoption of WfH may be positively correlated
with the adoption of e-commerce and that the mitigation effect of WfH may confound the
difference between traditional firms and online firms. We do not have information about
the share of online business at each firm, but we conjecture that it would be wholesale
and retail sectors that are most likely to engage in e-commerce before the COVID-19. To
assess the importance of this possible hypothesis, we drop the wholesale and retail sectors
5
from the model and report the results of the estimation.
As shown in Table 3, the mitigation effect of WfH on sales and hours worked remains
robust and in fact even stronger, when wholesale and retail sectors are excluded from the
regression sample. Retailers in our sample are mainly high-contact firms that benefited
less from WfH, and therefore the confounding effect of e-commerce, if any, appears to be
limited in the sample.
Table 3: Robustness Check: Heterogeneous Impact by Adoption of WfH as of 2019 (drop-
ping wholesale and retail industries)
(1) (2) (3)
All Low contact High contact
Panel A: Sales Growth
Change in Mobility 0.298∗∗∗ 0.243∗∗∗ 0.582∗∗∗
(0.024) (0.025) (0.069)
Change in Mobility × Remote 2019 -0.259∗∗∗ -0.205∗∗∗ -0.488∗∗∗
(0.041) (0.045) (0.122)
Remote 2019 -0.007 -0.008 0.009
(0.015) (0.012) (0.065)
R2 0.14 0.08 0.38
Panel B: Employment Growth
Change in Mobility 0.007 0.008 0.006
(0.006) (0.006) (0.033)
Change in Mobility × Remote 2019 -0.018 -0.025 0.059
(0.018) (0.020) (0.060)
Remote 2019 -0.007∗ -0.004 -0.024
(0.004) (0.004) (0.020)
R2 0.14 0.11 0.22
Panel C: Hours Growth
Change in Mobility 0.226∗∗∗ 0.200∗∗∗ 0.369∗∗∗
(0.009) (0.011) (0.033)
Change in Mobility × Remote 2019 -0.124∗∗∗ -0.106∗∗∗ -0.163∗
(0.024) (0.024) (0.086)
Remote 2019 -0.005 -0.001 -0.034
(0.010) (0.011) (0.025)
R2 0.14 0.11 0.29
Industry FE 3-digit 3-digit 3-digit
Est Size FE Yes Yes Yes
N 20,936 17,824 3,112
Source: Google Community Mobility Report and TSR-CREPE survey
Note: Standard errors robust against prefecture-level clustering are reported in
parentheses. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01
6
3 First-Stage Regressions
Table 4 shows the first-stage regression results for the Bartik IV estimation.
Table 4: Heterogeneous Impact by Adoption of Remote Work between December 2019
and May 2020: First-stage of the Bartik IV Regression
(1) (2)
Change in Mobility x Change in WfH Change in WfH
Change in Mobility × Bartik 0.817∗∗∗ 0.754
(0.260) (0.715)
Bartik -0.134∗∗∗ 0.818∗∗∗
(0.025) (0.088)
N 29056 29056
Kleibergen-Paap F statistic 31.480 31.480
Source: Google Community Mobility Report and TSR-CREPE survey
Note: Standard errors robust against prefecture-level clustering are reported in
parentheses. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01
7
File and source
- File
- the-impact-of-covid-19-on-japanese-firms-mobility-and-resilience-via-remote-work.pdf
- Size
- 173,978 bytes
- SHA-256
- 9587840a40bcaa0b0730773410f6665cfcf607fe3e29f11a05feda777d4c13ed
- Original
- doi.org