ICTs and the urban-rural divide: can online labour platforms bridge the gap?

ABSTRACT

Information and communication technologies have long been predicted to spread economic opportunities to rural areas. However, the actual trend in the 21st century has been the opposite. Knowledge spillovers have fuelled urbanisation and pulled job-seekers into large cities, increasing the gap with rural areas. We argue that new assemblages of technologies and social practices, so-called ‘online labour platforms’, have recently started to counter this trend. By providing effective formal and informal mechanisms of enforcing cooperation, these platforms for project-based remote knowledge work enable users to hire and find work across distance. In analysing data from a leading online labour platform in more than 3000 urban and rural counties in the United States, we find that rural workers made disproportionate use of the online labour market. Rural counties also supplied, on average, higher-skilled online work than urban areas did. However, many of the most remote regions of the country did not participate in the online labour market at all. Our findings highlight the potentials and limitations of such platforms for regional economic development.

CODE AND DATA: www.github.com/Braesemann/Rural

KEYWORDS:

• Information and communication technology
• platform economy
• online labour platforms
• urban-rural divide
• Social data science
• regional development

Acknowledgments

The authors wish to thank the participants of the 'Reshaping Work in the Platform Economy' 2018 conference, whose many helpful comments greatly improved our study, and the two anonymous reviewers whose comments likewise significantly strengthened the manuscript. We moreover wish to express our gratitude to Dr Fabian Stephany for many fruitful conversations during the course of this research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

1 Note that we do not investigate platforms mediating local tasks (such as Uber) or Microwork (such as Amazon Mechanical Turk), but data from an online freelancing platform. Such freelancing platforms coordinate larger projects and tasks that are usually of higher complexity and skill requirements than tasks conducted via Microwork platforms. For more details on the characteristics of the two major forms of online platform work, see Corporaal (2017).

2 In the online setting, reputation via personal recommendations is replaced by the platform reputation system. Therefore, it becomes more independently of persons and places.

3 The relation between urban agglomeration, the concentration of specialised (university) education, and resulting higher wages is an intensively discussed phenomenon in the literature (Adamson et al., 2004; Glaeser et al., 2001; Newbold & Brown, 2015). When comparing the online labour skill level of rural and urban areas, we correct for this disproportionate concentration of human capital in urban areas, as we divide the online labour skill level of a county by the general education level in the county (more details in the next section).

4 Details in (Lehdonvirta et al., 2018).

5 https://www.census.gov/geo/maps-data/data/cbf/cbf_description.html

6 The data is downloaded from https://opendata.fcc.gov/Wireline/Area-Table-June2016/nb5q-gkcn (accessed 2019-06-05).

7 Details in Kässi and Lehdonvirta (2018a).

8 https://www.bls.gov/soc/soc_2010_definitions.pdf

9 https://www.bls.gov/emp/tables/educational-attainment.htm

10 The parameter estimates and standard deviations displayed in the table are calculated from the complete dataset.

11 Moran's I is a correlation-coefficient showing the similarity between neighbouring values (in our case the regression residuals of neighbouring counties). Values close to $+1$ ($-1$) indicate a clustering of similar (dissimilar) values, while values close to zero indicate that spatial autocorrelation is not prevalent. In order to assess its significance, 1000 Monte Carlos simulations were conducted (Good, 2005). In each simulation the residuals are repeatedly randomised over the counties and Moran's I is recalculated. This yields a distribution of simulated Moran's I values, which we compare to the observed value to obtain a p-value estimate.

12 The small white dots in the map represent the zip-code level centroids of online freelancers: most of them are clustered in the country's largest cities (black circles).

Additional information

Funding

This study was supported by a grant from the European Research Council (grant number, 2015-2020) and by a grant from Google (2018).

Notes on contributors

Fabian Braesemann

Fabian Braesemann is Research Fellow and Data Scientist at the Saïd Business School and Research Associate at the Oxford Internet Institute, University of Oxford. His research focuses on data mining and the statistical analysis of large-scale online data to understand market and information dynamics in a digitally connected world [email: [email protected]].

Vili Lehdonvirta

Vili Lehdonvirta is an Associate Professor and Senior Research Fellow at the Oxford Internet Institute, University of Oxford, and a Fellow at the Alan Turing Institute. He is an economic sociologist whose research examines how digital technologies are shaping the organisation of economic activities in society.

Otto Kässi

Otto Kässi is a Research Economist at Etla Economic Research and a Research Associate at the Oxford Internet Institute. His research examines how the developments in automation, communication and artificial intelligence affect firms, workers and economic organisation.