Wage Theft and Technology in the Home Care Context

Author:

Ming Joy1ORCID,Gong Dana1ORCID,Ngai Chit Sum Eunice1ORCID,Sterling Madeline2ORCID,Vashistha Aditya1ORCID,Dell Nicola3ORCID

Affiliation:

1. Cornell University, Ithaca, NY, USA

2. Weill Cornell Medicine, New York, NY, USA

3. Cornell Tech, New York, NY, USA

Abstract

Home care workers (HCWs) are professionals who provide care to older adults and people with disabilities at home. However, HCWs are vulnerable and especially susceptible to wage theft, or not being paid their legally-entitled wages in full by their employers. Prior work has examined other low-wage work settings to show how technology is designed and deployed has the potential to both cause and address wage theft. We extend this work by examining the relationship between technology and wage theft in the home care context. We collaborated closely with a local grassroots organization to conduct interviews with workers and labor, legal, and payroll experts. We uncovered how the complex, volatile, and diverse nature of home care complicates the errors in time-tracking systems. Through design provocations and focus groups with workers and experts, we also investigated the potential of technology as a part of broader efforts to curb wage theft through educating and empowering isolated HCWs. While we found that approachable design could reduce errors in existing systems, make employer processes more transparent, and help workers exchange knowledge to build collective power, we also discuss concerns around burden, privacy, and accountability when designing technologies for HCWs and other low-wage workers.

Funder

National Science Foundation

Publisher

Association for Computing Machinery (ACM)

Reference109 articles.

1. 2015. Federal minimum wage & overtime protections for home care workers. https://s27147.pcdn.co/wp-content/uploads/NELP-Fact-Sheet-Companionship-Rules-Reform.pdf.

2. 2015. Independent Living Centers. http://www.acces.nysed.gov/vr/independent-living-centers. Accessed: 2022--12--13.

3. 2019. Free Time Card Calculator. https://www.redcort.com/free-timecard-calculator. Accessed: 2022--12--13.

4. 2022. Electronic Visit Verification (EVV) Resource Library. https://www.health.ny.gov/health_care/medicaid/redesign/evv/faqs.htm. Accessed: 2022--12--13.

5. 2022. Harvest: Time Tracking Software With Invoicing. https://www.getharvest.com/. Accessed: 2022--12--13.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3