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Working 9 to 5? Measuring Hyperlocal Worker Productivity with Public Wifi Network Data
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2020-06-19
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[PDF-2.94 MB]
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Abstract:The accurate estimation of workday length is essential to estimate total labor supply, and has a significant bearing on the assessment of labor productivity and worker well-being. Using probe request data from a 53 access-point, publicly-accessible Wi-Fi network in the Lower Manhattan district of New York City, we develop a method to measure localized worker activity patterns. Our Wi-Fi network data consist of over 10,000,000 probe requests per day, accounting for approximately 9.5 million unique devices over the study period from April 2017 to September 2017. We describe worker activity at various spatial and temporal aggregations in order to define baseline workday patterns and compute the workday length. We find a substantial population with characteristic workday lengths (e.g. 9am-5pm) during the workdays, as well as diurnal activity patterns that are consistent with expected worker behavior. These temporal patterns provide sufficient evidence to reinforce our assumptions about the ability to identify worker populations from Wi-Fi data. Finally, we compute the workday length for each identified worker and aggregate these workday lengths to estimate collective workday patterns to understand the uniformity of worker behavior. We find workday lengths of 7 hours and 40 minutes on average, which shorten substantially on Fridays and days surrounding holidays. We also find considerable seasonal variation in total workday hours supplied in our study area. This dynamic pattern of hours-worked suggests that our methodology is able to accurately assess workday lengths at high spatial resolution and temporal frequency. The ability to quantify hyperlocal worker activity patterns has a broad range of applications, including estimates of localized economic output and changes in labor supply.
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Content Notes:The research reported in this pre-print was funded by the US DOT University Transportation Centers Program.
This content is held in a repository external to US DOT and NTL, but has been made available by the authors with the following caveat on the first page of the pre-print: Draft – Not for Citation Without Permission of the Authors. Citation: Johnson, Nicholas and Bonczak, Bartosz and Gupta, Arpit and Kontokosta, Constantine E., Working 9 to 5? Measuring Hyperlocal Worker Productivity with Public WiFi Network Data (June 19, 2020). NYU Stern School of Business, Available at SSRN: https://ssrn.com/abstract=3631217 or http://dx.doi.org/10.2139/ssrn.3631217
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