Bridging the Technological Divide

Page 161

5. The spread of digital platforms has raised concerns about the changing nature of employment status, ushering in a more flexible workforce and eroding traditional employer-employee relationships. Blurring lines between formal and casual employment, characterized by independent or temporary work arrangements (such as on-call workers, contract workers, or freelancers) could be problematic. Workers face the risk of insecure working environments. Beyond occupations in transportation such as drivers or delivery, these platforms are affecting other occupations ranging from arts and design, media, and communication to other services. The growth of digital platforms has expanded the share of workers performing on-demand tasks—the so-called gig economy. With its allure of flexibility and compensation (Hall and Krueger 2018), the gig economy has grown exponentially and helped workers buffer against income and expense shocks (Farrell and Greig 2016). The incidence of alternative work arrangements has been rising in the United States, with the share of all workers growing from 10.7 p ­ ercent in 2005 to as high as 15.8 p ­ ercent in late 2015 (Katz and Krueger 2019). 6. Enlyft is a private digital platform providing a database on B2B technology based on machine learning. 7. The BPS is an initiative led by the World Bank Group to collect and harmonize firm-level data to understand the impact of COVID-19 on the private sector in developing countries. ApedoAmah et al. (2020) summarize the first round of data collection. The severity of the effect of the COVID-19 shock on businesses has been well documented across countries and data sources. See Adams-Prassl et al. (2020); Bartik et al. (2020); Dai, Hu, and Zhang (2020); Fairlie (2020a, 2020b); and Humphries, Neilson, and Ulyssea (2020). 8. The average drop in sales in the first four weeks following the peak of the shock is between 60 ­percent and 75 ­percent. In the next four months, the drop in sales narrowed to 47 ­percent in week 8, 47 ­percent in week 12, and 43 ­percent after week 16. Although nearly 90 ­percent of businesses were open 10 weeks after the peak of the outbreak, the negative impact on sales still loomed large. 9. This section is based on Avalos et al. (forthcoming), a background paper for this volume. 10. This section is based on Comin et al. (2022), a background paper for this volume. 11. One standard deviation in pre-pandemic technology sophistication is associated with an increase of 10 ­percentage points in the likelihood of starting or increasing the use of digital technologies. Firms whose technology sophistication index is one standard deviation (0.62 ­percentage points) higher than the average (1.78) tend to rely on specialized software to perform business administration or production planning and sourcing; online chat or internet to interact with customers; debit/credit card and online payment; and computers for quality control. Firms whose technology index of 2.78 is 1 ­percentage point higher than the average tend to be very close to the frontier in performing tasks such as business administration and planning (for example, use ERP systems) and use basic to more sophisticated digital technologies in all other GBFs.

References Adams-Prassl, A., T. Boneva, M. Golin, and C. Rauh. 2020. “Inequality in the Impact of the Coronavirus Shock: Evidence from Real Time Surveys.” Journal of Public Economics 189 (September): 104245. Adhvaryu, A., N. Kala, and A. Nyshadham. 2020. “The Light and the Heat: Productivity Co-benefits of Energy-Saving Technology.” Review of Economics and Statistics 102 (4): 779–92. Anderson, M., and J. Magruder. 2012. “Learning from the Crowd: Regression Discontinuity Estimates of the Effects of an Online Review Database.” Economic Journal 122 (563): 957–89. Apedo-Amah, M. C., B. Avdiu, M. Cruz, X. Cirera, E. Davies, A. Grover, L. Iacovone, U. Kilinc, D. Medvedev, F. O. Maduko, S. Poupakis, J. Torres, and T. T. Tran. 2020. “Unmasking the Impact of COVID-19 on Business: Firm-Level Evidence from around the World.” Policy Research Working Paper 9434, World Bank, Washington, DC.

Digital Technologies and Resilience to Shocks 135


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A.1 Number of Establishments Surveyed, by Strata

4min
pages 236-237

7.5 The Difference between Vouchers and Grants

8min
pages 219-222

Notes

5min
pages 224-225

Corporation (KOTEC

2min
page 217

References

7min
pages 226-229

7.3 Agriculture Extension: The Case of Embrapa

6min
pages 214-216

Instruments to Support Technology Upgrading at the Firm Level

2min
page 209

Adoption of Technology

6min
pages 211-213

7.1 Digital Platforms Are Prone to Market Concentration and Dominance

9min
pages 198-201

References

6min
pages 192-194

6.1 Specific Barriers to the Use of Digital Platforms

2min
page 176

Surrounded by Digital Infrastructure

0
page 174

Factual Evidence on Drivers of and Obstacles to Technology Adoption

4min
pages 172-173

References

8min
pages 161-166

Notes

2min
page 160

Technology and Resilience

2min
page 146

Digital Technologies

2min
page 138

Introduction

1min
page 137

References

4min
pages 134-136

4.10 Technology Sophistication Contributes to Wage Inequality within Firms

1min
page 132

Introduction

1min
page 121

References

2min
pages 117-120

Functions Manually

1min
page 100

Technology Differences across and within Sectors

2min
page 96

Introduction

1min
page 95

References

3min
pages 93-94

Summing Up

2min
page 91

Notes

2min
page 92

Other Technology Facts

2min
page 86

Business Functions Varies across Firm Size

1min
page 83

Introduction

1min
page 73

Using the FAT Data to Understand Some of the Limitations of Standard Measures of Technology

4min
pages 63-64

References

4min
pages 70-72

Measuring Adoption and Use of Technology by Firms

2min
page 48

References

3min
pages 42-46

Opening the Black Box: The Firm-level Adoption of Technology (FAT) Survey

4min
pages 50-51

Introduction

1min
page 47

Notes

2min
page 41

Technology (FAT) Survey

1min
page 52
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