Industry Structure in Telecommuting and Income Inequality
A Regional Perspective Study
By Houpu Li
Background Information
The COVID-19 pandemic has undoubtedly made remote work a new norm for many industries, significantly altering work patterns and profoundly affecting regional and global economies. While there has been ample research showing that remote work brings new opportunities and advantages to both companies and individuals (such as accessing global talent and increasing employee productivity), it also presents some new risks (like social isolation, income inequality, etc.).
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In the post-pandemic era, even though mandatory home-based work or study requirements have been canceled, studies show that remote work has become a very important work mode. Many employees prefer choosing remote work over returning to the office. Therefore, examining these new risks brought by remote work becomes critically important.
What I Want to Know in This Research Project
In this project, I would like to know the new risk of income inequality with teleworkers brought by working from home(WFH), I think I can divide my introduction into three sections below,
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01. Does the income disparity among remote workers in the United States show significant changes between 2019 and 2021?
02. Does the structure of the regional telework representative industries in the United States change significantly between 2019 and 2021?
03. Is there any correlation between the rate of remote work in a specific industry and the overall income disparity among remote workers? and has this correlation been changed at different geographical units?
Q1：
Does the income disparity among remote workers in the United States show significant changes between 2019 and 2021?
Selected Tracts' diff = Population(Higher than $35000) / Population(lower than $35000)
Answer Q1：
Does the income disparities among remote workers in the United States show significant changes between 2019 and 2021?
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Yes, it is.
The Figure shows us there is a significant change within teleworkers' income disparities by comparing 2021 with 2019, especially in the East(New York), West(San Fransisco Bay Area), and Middle-West(Chicago). Maybe it is because there are more higher income industries clustered in these areas, with a higher adaptability to transfer into WFH, and change the original teleworkers' industry structure in 2021. And cause the income difference increasing within teleworkers.
Q2：
Does the structure of the regional telework representative industries in the United States change significantly between 2019 and 2021?
For example, educational industrial telework rate account 54.7%(301/550) in tract 5,Tompkins County. And the educational industry is the most representive telework industry in tract 5.
Answer Q2：
Does the structure of the regional telework representative industries in the United States change significantly between 2019 and 2021?
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Yes, it is.
In this Figure, each point presents the representative telework industry in each census tract. It is easy to find that the regional representative industries exhibit different distribution patterns across different geographical units, with red dots representing the high-tech industry, blue dots representing the education industry, and yellow dots representing the financial industry, all showing a clustering pattern.
Furthermore, these three industries account for the major remote workers across the U.S.
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red points: high-tech industry
blue points: educational indusrty
yellow points: financial industry
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Meanwhile, there is a significant change of the teleworkers' representative industry structure in 2021 in the U.S., especially in the financial industry/yellow points increase and high tech industry/red points decrease in 2021.
Q3：
Is there any correlation between the rate of remote work in a specific industry and the overall income disparity among remote workers? and has this correlation been changed at different geographical units?
I used the GWR model to predict the correlation between financial industry telework rate and income gap within WFH groups in 2019 and 2021. The function as below,
2019:
Significant Tractss:41390
2021:
SignificantTracts:47889
By comparing the scenario between 2019 and 2021, we can easily find the financial telework industry has a positive correlation with income disparities in most tracts, which means the higher the financial telework rate, the more the income gap. And these correlations are very different across different geographical units.
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Another interesting finding is that compared to 2019, the significant tracts increased from 41390 to 47889, and it seems that tracts with higher coefficients have become more (the darker colors are increasing).
Besides, it seems like a coefficient clustering phenomenon in 2021, which means these tracts surrounding the higher coefficient also appear the similar coefficient.
I used the GWR model to predict the correlation between high-tech industry telework rate and income gap within WFH groups in 2019 and 2021. The function as below,
2019:
Significant Tractss:39025
2021:
SignificantTracts:34781
By comparing the scenario between 2019 and 2021, we can easily find the high-tech telework industry has a positive correlation with income disparities in most tracts, which means the higher the high-tech telework rate, the more the income gap. And these correlations are very different across different geographical units.
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Another interesting finding is that compared to 2019, the significant tracts decreased from 39025 to 34781, but it seems that tracts with higher coefficients have become more (the darker colors are increasing).
Besides, it seems like a coefficient clustering phenomenon in 2021, which means these tracts surrounding the higher coefficient also appear the similar coefficient.
Answer Q3：
Is there any correlation between the rate of remote work in a specific industry and the overall income disparity among remote workers? and has this correlation been changed at different geographical units?
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Yes, it is.
By operating the GWR model, we can easily explore the correlation between the telework rate of selected industries and overall income gaps within WFH groups. we can find both high-tech and financial industries all have a positive relationship with the income gap, and this degree of correlation raises differences across different geographical units(more pronounced in the center of U.S). The higher coefficients seem to become more frequent by comparing with 2019, and another interesting finding is there seems to be a coefficient clustering phenomenon in 2021 for both high-tech and financial industries.
Conclusion
Result Summary
Between 2019 and 2021, there were notable changes in the income disparity and industry structure of remote workers in the United States, especially in areas like New York, the San Francisco Bay Area, and Chicago.
Due to high-income industries being more adaptable to work-from-home models, this may have led to changes in the industry structure of remote workers, potentially increasing the risk of income disparities. To further explore the risks brought by these structural differences, I chose the "high-tech industry" and "financial industry" to examine the extent to which their remote work rates affect income disparities among remote workers and their variability of coefficients in different geographic spaces.
I used the GWR model, and the results clearly show a positive correlation between the remote work rates in the "finance" and "high-tech" industries and the overall income disparity among remote workers. This correlation varies across different geographic units, with seemingly more significant effects in the central United States, and exhibited a more pronounced clustering phenomenon in 2021.
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Why We Should Care About this Topic
Firstly, understanding which industries are more suited to remote work is essentially an exploration of the "resilience" of different industries in the face of sudden risks, such as a global health crisis. The study indicates that some high-income industries demonstrate relatively stronger resilience. Moreover, research shows that 65% of employees do not wish to return to office work, suggesting that remote or hybrid work models have gained significant popularity. However, it is also indicated that while these work models bring advantages to businesses and individuals, they also introduce new risks. For instance, an increase in the remote work rate in certain industries may exacerbate income inequality among remote workers, and the representative remote work industries differ across regions, potentially intensifying economic disparities between different economic levels. Therefore, policymakers and corporate leaders need to pay attention to this trend and take measures to reduce the potential inequalities caused by the widespread adoption of remote work models. This includes promoting equal access to remote work opportunities and supporting low-income industries and regions that may lag in the transition to remote work. Effective human resource and industry allocation strategies are needed to facilitate the movement of talent and knowledge across different geographic spaces and industries, ensuring inclusive and sustainable regional economic growth.