Daily mobility patterns of small business owners and homeworkers in post-industrial cities

https://doi.org/10.1016/j.compenvurbsys.2020.101564Get rights and content

Highlights

  • Primary dataset from 702 participants tracked using a bespoke GPS smartphone app.

  • Small business owners/self employed show high daily mobility compared to employees.

  • Working from home is not associated with lower overall daily mobility, and tends to be associated with higher mobility in the case of small-business owners/self employed.

  • Women have lower levels of daily mobility than men (less trips, shorter travel distances and duration) but did not stay closer to home on average.

  • Home-based employees and home-based business owners did not, on average, stay closer to home relative to premise-based employees.

Abstract

The rise of small businesses, self-employment, and homeworking are transforming traditional industrial ways of working. Our research fills a noticeable gap in the literature by using portable devices (i.e., smartphones) to capture individual mobility data on an understudied population group – small business owners (owner managers and self-employed with up to 49 employees) and whether they work from home in comparison with employees who work at their employer's premises or partly or mainly from home. We recorded week-long individual GPS data on 702 participants and derived a set of measures of daily mobility (number of trips, trip duration, trip distance, and maximum distance from home). Each measure is modelled against a range of individual and neighbourhood-level covariates. Our findings contrast with existing studies that suggest homeworking or self-employment may be associated with lower levels of daily mobility or with compensatory effects between work and non-work travel. Overall, our study points to higher levels of daily mobility of owners of small businesses and the self-employed in cities as they travel longer distances. Further, some homeworkers have on aggregate longer daily trip distances than ‘traditional’ premise-based employees. Most striking, female home-based business owners fall into this group. If homeworking is here to stay after the COVID-19 pandemic, we may see both increases and/or decreases of daily mobility depending on worker types and gender.

Introduction

Key fixed geographical locations of people's daily activities associated with work-related activities are undergoing radical changes (even before the onset of COVID-19 travel restrictions), at least partially due to the proliferation of information and communication technologies (ICTs) and new modes of work (Choo & Mokhtarian, 2007; Kwan, 2007). ICTs have blurred the boundaries between home and work as they allow people to work partially or exclusively in their own home. More recently, we are seeing widespread shift to remote and home-based working as a result of COVID-19 (Reuschke & Felstead, 2020). Telecommuting of employees, defined as working from or at home, has received much attention in the transportation literature even before the COVID-19 pandemic. Primary focus has been understanding residential choices and changes in total travel distance and time through the reduction of commuting trips (Helminen & Ristimäki, 2007; Ory & Mokhtarian, 2006; Zhu, 2013) and the timing of the commute (Lachapelle, Tanguay, & Neumark-Gaudet, 2018). However, much less attention has been given to homeworking by workers who are self-employed or run their own businesses (rather than employees).

In many post-industrial economies, self-employment and small business ownership have increased substantially in its share of the overall workforce as a result of parallel processes shaping the nature of work: structural changes in the economy (e.g., changing supply chains and, outsourcing), and the rise of the gig economy (Wood, Lehdonvirta, & Graham, 2018), new technologies that enable small businesses to access distant markets and compete with larger firms (Clark & Douglas, 2011), and a changing workforce that places more emphasis on non-monetary values of work such as work-life balance (Baumberg & Meager, 2015; Burchell, Sehnbruch, Piasna, & Agloni, 2014). For women especially, self-employment and small business ownership appears to offer opportunities to combine work and family (Craig, Powell, & Cortis, 2012; Walker, Wang, & Redmond, 2008; Wellington, 2006).

Men and women who are self-employed or run their own businesses may work from commercial premises (e.g. shops, offices, art studios); but many work from home (Mason, Carter, & Tagg, 2011) or various other places (Liegl, 2014) such as co-working spaces (Clifton, Füzi, & Loudon, 2019). Thus, the diversity in both worker types and work locations has become profoundly higher in contemporary post-industrial economies than was the case in industrial economies – even before the COVID-19 pandemic that resulted in a surge in homeworking in mature economies. The COVID-19 pandemic has underlined that an increased level of workers who are less bound to employer's premises will have a profound impact on urban travel and city systems (Tranos, Reggiani, & Nijkamp, 2013). Those who mainly work from home may travel less or may spend more time in their neighbourhood or in proximate areas, when compared with premise-based workers (Saxena & Mokhtarian, 1997; Zenkteler, Darchen, Mateo-Babiano, & Baffour, 2019). While current research in homeworking has attracted a lot of attention, to date very little research has focused on the daily travel patterns of self-employed workers and those running a small business, that are workers who are not working for an employer. With few exceptions (Mokhtarian & Henderson, 1998; Shin, 2019), transport studies have rarely disaggregated workers by their employment status and whether they run their own business and have thus paid little attention to the transformations being observed in in the workplace.

The overall objective of this paper is to use detailed GPS tracking data to study the daily mobility of under-researched socio-economic groups. Our first research question is whether the daily mobility of individuals in cities is significantly influenced by whether workers are employees or business owners (including as self-employed). Our second research question addresses how individuals who work partly or mainly from home differ (or not) in their daily mobility from those who do not work from home. We further break down these travel patterns by gender and ask in our third research question, whether the daily mobility of homeworkers and business owners/the self-employed is shaped by gender differences. With this approach, we reveal, for the first time, the complexity of homeworking, employment status or small business ownership and gender in cities. With an interest in small business ownership (an established category in business research defined as businesses with 0–49 employees including sole proprietors and owner managers), we specifically investigate the extent to which small business owners are associated with daily mobility patterns that diverge from the daily mobility of ‘traditional’ workers (i.e. employees with separate employer's premises), whether homeworking is producing new daily mobility patterns in cities, and whether this is shaped by differences between men's and women's travel. Specifically, we ask whether small business ownership reproduces established gender differences in daily mobility.

To capture daily mobility patterns of small business owners and contrast them with those of employees, we collected a detailed primary GPS dataset from a survey of workers in two cities in England (United Kingdom). Data were collected pre-COVID-19 but our findings have rather increased in relevance since more and more people started to work from home during the global COVID-19 pandemic, and it is predicted that homeworking is here to stay (Felstead and Reuschke, 2020). GPS tracking data are particularly well-suited to capture highly detailed records of individual daily mobility activity (places visited, travel routes, and timing) without incurring recall error associated with daily travel diaries (Stopher & Shen, 2011). The GPS data we collected cover several days of a standard working week of the surveyed workers thus it contains sufficient variability in daily activity patterns (Kang & Scott, 2010). The GPS data are augmented by an extensive individual questionnaire allowing us to investigate daily mobility patterns related to personal, work and location factors of each individual in our study. We derive four measures of overall daily travel (number of trips, trip duration, trip distance, maximum distance from home) and model each measure against a range of individual and neighbourhood-level covariates believed to be associated with individual-level daily mobility. Findings demonstrate that differences in daily mobility patterns of business ownership/self-employment become apparent most strongly in the intersection with gender.

Section snippets

Self-employment, small business ownership and individual mobility

Economic geographers have highlighted the importance of local social ties and knowledge spill-overs for entrepreneurship and developing a business (Andersson & Larsson, 2016). It is assumed that entrepreneurs and small business owners are strongly embedded in place through their networks (Hanson, 2005). However, there is little research on the daily mobility of small business owners.

Few transport studies exist that have investigated the commutes of the self-employed (rather than small business

Participant recruitment and study groups

We study daily mobility of workers in two cities in England (United Kingdom): Brighton & Hove and Leeds, chosen based on their geographical attributes and their employment characteristics drawn from the 2011 Census of Population data. Brighton & Hove was selected as a medium-sized city (2018 population; 290,400) in the economically strong South East. Brighton & Hove has high proportions of self-employed workers (13.4%; 2018 data) and homeworkers (11%; 2011 data), directly relating to our

Daily number of trips

Daily number of trips showed very little variation between study group types (Fig. 2). After controlling for individual and neighbourhood-level covariates (Table 2; R2GLMM = 0.436), we found no significant differences in the daily number of trips taken between the different worker type groups. However, when more closely analysed by gender, we found that among men, business owners/the self-employed with the home as their base made significantly more trips (about 20% more; exp(β) = 1.213) than

Summary and discussion

Using a longitudinal primary GPS survey augmented by a questionnaire-based survey, we tested for differences in daily mobility of small business owners (including the self-employed) versus employees further disaggregated by whether they work primarily in the home or from separate premises and by gender. In the case of small business owners, we further differentiated between those who run their business from home or use their home as the base for the business but the activities are performed

Acknowledgements

This study was funded by the European Research Council, the Starting Grant WORKANDHOME (ERC- 2014-STG 639403). The authors would like to acknowledge the thoughtful and constructive feedback of the 5 reviewers and the associate editor, whose comments greatly improved the presentation of the manuscript.

References (90)

  • Z. Patterson et al.

    Itinerum: The open smartphone travel survey platform

    SoftwareX

    (2019)
  • P. Rietveld

    Telework and the transition to lower energy use in transport: On the relevance of rebound effects

    Environmental Innovation and Societal Transitions

    (2011)
  • E. Sandow

    Commuting behaviour in sparsely populated areas: Evidence from northern Sweden

    Journal of Transport Geography

    (2008)
  • J. Scheiner

    Social inequalities in travel behaviour: Trip distances in the context of residential self-selection and lifestyles

    Journal of Transport Geography

    (2010)
  • S. Schönfelder et al.

    Activity spaces: Measures of social exclusion?

    Transport Policy

    (2003)
  • T. Schwanen et al.

    How fixed is fixed? Gendered rigidity of space–time constraints and geographies of everyday activities

    Geoforum

    (2008)
  • E.J. Shin

    Self-employment and travel behavior: A case study of workers in Central Puget sound

    Transport Policy

    (2019)
  • S. Spaccapietra et al.

    A conceptual view on trajectories

    Data & Knowledge Engineering

    (2008)
  • S. Tilley et al.

    The gender turnaround: Young women now travelling more than young men

    Journal of Transport Geography

    (2016)
  • E. Tranos et al.

    Accessibility of cities in the digital economy

    Cities

    (2013)
  • A.J. Wellington

    Self-employment: The new solution for balancing family and career?

    Labour Economics

    (2006)
  • A. Ahmed et al.

    Seventy minutes plus or minus 10—A review of travel time budget studies

    Transport Reviews

    (2014)
  • M. Andersson et al.

    Local entrepreneurship clusters in cities

    Journal of Economic Geography

    (2016)
  • P.N. Balepur et al.

    Transportation impacts of center-based telecommuting: Interim findings from the neighborhood Telecenters project

    Transportation

    (1998)
  • K. Barton

    MuMIn: Multi-model inference (R package version 1.43.6; CRAN)

    (2019)
  • D. Bates et al.

    Fitting linear mixed-effects models using lme4

    Journal of Statistical Software

    (2015)
  • B. Baumberg et al.

    Job quality and the self-employed

  • R. Becker et al.

    Human mobility characterization from cellular network data

    Communications of the ACM

    (2013)
  • F. Bomhof et al.

    Systematic analysis of rebound effects for “Greening by ICT” initiatives (SSRN scholarly paper ID 1659725)

    (2009)
  • B. Burchell et al.

    The quality of employment and decent work: Definitions, methodologies, and ongoing debates

    Cambridge Journal of Economics

    (2014)
  • D. Clark et al.

    Information and communication technology adoption and diffusion in micro-enterprises: The case of techno-savvy home-based businesses

    International Journal of Entrepreneurship and Small Business

    (2011)
  • N. Clifton et al.

    Coworking in the digital economy: Context, motivations, and outcomes

    Futures

    (2019)
  • L. Craig et al.

    Self-employment, work-family time and the gender division of labour

    Work, Employment and Society

    (2012)
  • R. Crane

    Is there a quiet revolution in Women’s travel? Revisiting the gender gap in commuting

    Journal of the American Planning Association

    (2007)
  • J. Fanning Madden

    Why women work closer to home

    Urban Studies

    (1981)
  • A. Felstead et al.

    Homeworking in the UK: Before and during the 2020 lockdown

    (2020)
  • J. Fox et al.

    An R companion to applied regression

    (2019)
  • A. Gelman et al.

    Data analysis using regression and multilevel/hierarchical models

    (2006)
  • J.I. Gimenez-Nadal et al.

    The commuting behavior of workers in the United States: Differences between the employed and the self-employed

    Journal of Transport Geography

    (2018)
  • G. Giuliano

    Information technology, work patterns and intra-metropolitan location: A case study

    Urban Studies

    (1998)
  • P. Gordon et al.

    Gender differences in metropolitan travel behaviour

    Regional Studies

    (1989)
  • S. Hanson

    Perspectives on the geographic stability and mobility of people in cities

    Proceedings of the National Academy of Sciences

    (2005)
  • S. Hanson

    Gender and mobility: New approaches for informing sustainability

    Gender, Place and Culture

    (2010)
  • G. Heinze et al.

    Variable selection – A review and recommendations for the practicing statistician

    Biometrical Journal

    (2018)
  • P.L. Mokhtarian et al.

    Analyzing the travel behavior of home-based workers in the 1991 Caltrans statewide travel survey

    Journal of Transportation and Statistics

    (1998)
  • Cited by (16)

    • Early warning of COVID-19 hotspots using human mobility and web search query data

      2022, Computers, Environment and Urban Systems
      Citation Excerpt :

      Studies on human mobility analysis have used such data to model disease dynamics (Bengtsson et al., 2015; Dodge et al., 2021; Finger et al., 2016; Tizzoni et al., 2014; Wesolowski et al., 2012). During the COVID-19 crisis, various stakeholders have utilized large-scale mobility datasets to evaluate the effects of NPIs in various regions (Bonato et al., 2020; Cintia et al., 2020; Dahlberg et al., 2020; Gao, Rao, Kang, Liang, & Kruse, 2020; Klein et al., 2020; Kraemer et al., 2020; Lai et al., 2020; Long & Reuschke, 2021; Pepe et al., 2020; Santana et al., 2020; Schlosser, Maier, Hinrichs, Zachariae, & Brockmann, 2020; Wellenius et al., 2020; Yabe et al., 2020). The aforementioned studies have shown the effectiveness of using large-scale mobility data to monitor physical co-location of the population (which can be used as a proxy for social contacts) in a fine-grained spatial and temporal scale.

    • Associations between mobility and socio-economic indicators vary across the timeline of the Covid-19 pandemic

      2022, Computers, Environment and Urban Systems
      Citation Excerpt :

      Aggregate measures of mobility are necessary when working with large mobility datasets because aggregation serves as a privacy preserving operation when combined with suitable minimum inclusion criteria (e.g., here we only kept ADA regions with a minimum of 100 devices for subsequent analysis). Individual level studies are often combined with detailed individual level survey information which provide rich datasets for analysis of socio-economic factors associated with mobility at the individual level (Guo, Chai, & Kwan, 2020; Helbich et al., 2016; Kwan, 1999; Long & Reuschke, 2021; Schwanen, Kwan, & Ren, 2008). Here we do not have access to detailed individual level data, and thus focus on aggregate patterns, with a large sample of the population.

    • Advances in portable sensing for urban environments: Understanding cities from a mobility perspective

      2021, Computers, Environment and Urban Systems
      Citation Excerpt :

      Most studies that utilize this data collection paradigm are focused on presenting a proof of concept or discussing methodological issues, and they tend to rely on limited samples. Prominent research fields that have already started to utilize this approach more substantially in an urban context include environmental health and health geography research in which the level of individual exposure to physical and social environmental factors and their impact on people's physical and mental health and wellbeing is measured at high resolutions (Kou et al., 2020; Kwan et al., 2019; Roberts & Helbich, 2021; Zhang, Zhou, et al., 2020); urban and transportation management and planning (Long & Reuschke, 2021; and Millar et al., 2021 in this special issue); and health monitoring including measuring mobility, physical activity, and physiological status (Li et al., 2017). The study of urban subjective experiences and emotions using portable sensors is another field that has emerged in recent years (Birenboim, 2018; Osborne & Jones, 2017; Shoval, Schvimer, & Tamir, 2018).

    View all citing articles on Scopus
    View full text