Impacts of Online Labour Markets on Rural Employment

Fabian Braesemann, Otto Kässi, Vili Lehdonvirta

Oxford Internet Institute - University of Oxford, 1 St Giles - OX1 3JS Oxford - United Kingdom

Urbanization is one of the megatrends of the 21st century. People flock to urban centres thanks to the availability of jobs and other opportunities. Short distances in cities allow ideas and knowledge to move easily from one person and workplace to another. But this geographic concentration brings problems, too: congestion in urban centres, and deprivation in rural areas. The Internet has fuelled hopes to alleviate this divide, by allowing information to flow freely across long distances. Yet actual consequences are mixed: while connecting digitally to colleagues via telecommuting and virtual teamwork is reality, getting hired in the first place remains much easier in cities than rural areas.

This is because online job boards replaced newspaper advertisements, but the hiring processes retained many traditional parts, such as face-to-face interviews. Now, online labour platforms are reimagining the entire hiring and work process. By enabling workers and employers to find each other, conclude contracts, and deliver work without being physically in the same location, online labour markets may finally deliver on the Internet’s promise to bring new earnings opportunities to remote areas. This study empirically evaluates this hypothesis.

Theory

Cities can support more specialized jobs than rural areas (1,2,3). The larger its’ size, the more fine-grained the potential division of labour. Productivity gains from learning drives further specialisation of workers. Consequently, cities feature greater occupational diversity than rural areas, with skilled specialists commanding higher wages than undifferentiated generalists, pulling more people to urban areas. In rural areas, instead, where employers are sparse, narrow specialists will find it difficult to secure enough work and will likely have to accept more generalist and less well remunerated tasks.

If online labour platforms alleviate geographic constraints on job search, then this should particularly benefit people with specialized skills in rural areas, as the platforms connect skilled workers to specialized demand beyond their local area. Urban specialists already enjoy ample local demand for their skills, and platforms offer just an additional channel to market their human capital. This urban-rural divide should not affect generalists in the same way, as job opportunities for them are less spatially clustered.

Materials and Methods

The study analyses six months’ transaction data from a leading online freelancing platform. For more than 300,000 transactions, the dataset lists the job category, the wage and both the employers’ and contractors’ location on a country-city-zip code level. This allows to map the data with very high geographic granularity.

The scope is restricted to projects with either the employer or the contractor positioned in the United States. This allows us to compare the online labour market with official employment statistics, published by the U.S. Census Bureau on the county and zip code level (4). As we matched the different categories of online jobs to the official classification used by the U.S. Bu- reau of Labor Statistics, we could relate the distribution of online supply and demand to local employment, disaggregated by occupational groups (5).

A B

C

Online projects in C & M (per 1,000 'offline' C&M jobs) 33.8 14.9 8.9 5.5 4.0 2.9 2.1 1.4 0.8 0.2 0

− − − − − − − − − − 1,250.0 33.8 14.9 8.9 5.5 4.0 2.9 2.1 1.4 0.8

Relative number of 'Computer and Mathematical' online projects

Fig. 1. (A) The number of online projects completed by freelances in ’Computer and Mathematics’ (C&M) per 1,000 C&M jobs in the official labour market per U.S. county. (B) Relative number of C&M online projects in New York City. (C) Relationship between the share of urban population and the relative number of C & M jobs (logarithmic scale).

Preliminary Results and Outlook

The distribution of the online labour market participation in the high-skill occupation ’Com- puter and Mathematics’ varies considerably between rural areas in the Midwest and urbanised coastal regions, as shown in Fig.1A (6). Spatial clustering of online activity persists also within urban areas (Fig.1B). Most of the local online labour supply of Computer and Mathematical projects in New York City comes from the central business district in Manhattan, while the resi- dential areas, The Bronx and Queens, participate less. A closer look on the relative supply of the online projects reveals a negative relationship to the share of urban population on the county level (Fig. 1C). Freelancers from rural areas tend to make more active use of online labour markets.

These results suggest evidence for the hypothesis that online labour platforms affect the urban- rural divide, as they bring income opportunities to specialists in remote areas. To better com- prehend this process, we will model the local determinants of occupation-specific online labour supply on the county level and quantify the spatial distribution within agglomerations.

References and Notes

1. Sveikauskas, L. The productivity of cities. The Quarterly Journal of Economics 393–413 (1975). 2. Quigley, J. M. Urban diversity and economic growth. The Journal of Economic Perspectives 12, 127–138 (1998).

3. Bettencourt, L. M., Samaniego, H. & Youn, H. Professional diversity and the productivity of cities. Scientific Reports 4 (2014).

4. In summary, the data considered consists of 184,359 transactions: 172,710 projects with U. S. employers, 34,198 with U. S. freelancers, including an overlap of 22,549 projects with both U. S. buyer and contractor.

5. The Standardized Occupation Coding for Computer-assisted Epidemiological Research tool developed by the U.S. Department of Health and Human Services (https://soccer.nci.nih.gov/soccer/) can be used to match free-text information describing job tasks to official occupations.

6. The figure is normalised by the number of jobs of that occupational group in the official labour market