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The Gig Economy and Income Inequality
- 1. The Gig Economy andIncome Inequality Presented by: Johannes M. Bauer Department of Media and Information IBEI–cetic.br/nic.br/cbi.br Summer School Barcelona, July 3, 2018
- 2. The Gig Economy andIncome Inequality Johannes M. Bauer Department of Media and Information IBEI–cetic.br/nic.br/cbi.br Summer School Barcelona, July 3, 2018
- 3. Main points • Thegig economy is shaped by technological, economic, and political forces • It promises tremendous opportunities to create new jobs and innovative work arrangements • At the same time, it risks undermining aspects of social goals tied to traditional employment • Emerging technologies (autonomous vehicles, Internet of Things, AI) will further change the nature of work and the gig economy
- 4. Plan for today •Emergence and structure of the gig economy • Basic economics of the gig economy • The digital economy and income inequality
- 5. Emergence and structureof the gig economy
- 6. Changing modes ofproduction and work • Gig, sharing, and freelance economy refer to new forms of production based on economic transactions among decentralized agents enabled by new forms of digital intermediation (e.g. Uber, Airbnb) • Related to other types of transactions enabled in the digital economy • Gift economy (direct or indirect reciprocity, e.g. Couchsurfing) (Sundararajan 2016) • Commons-based peer production (e.g. Wikipedia, Linux) (Benkler 2016) • Crowd capitalism (e.g. AngelList for VC funding) and large-scale outsourcing • Recently, NextGen Work is used to describe a new way of working that helps people earn more, upskill, and achieve One Life that blends work and home (ManpowerGroup, 2017) • Include part-time, contingent, contract, temporary, freelance, permalance, independent contractor, on-demand online, and online platform working
- 7. Technological, economic, politicalforces • Enabling technologies and services • Significant performance gains in ICTs, increasing broadband connectivity and ubiquitous computing • Building of digitally mediated trust among participants in a transaction (recommender systems, provider and customer ratings, algorithms) • Flexible and easy-to-use (mobile) payment systems • Emerging technologies (AI, IoT, robotics) will cause additional change • Economic transformation from pipeline to platform markets • Platform businesses generate value by enabling transactions among other market participants (e.g. Airbnb links owners of property with people seeking accommodation) • Flexibilization of labor markets, weakening of labor relations and traditional worker protections
- 8. Labor and capitalplatforms Source: Farrrell & Greig, 2016 “Sharing” of an asset “Sharing” of labor and skills
- 9. Sharing economy • Manyexamples of sharing throughout the history of humankind • 1970s highlighted “collaborative consumption” (Felson & Spaeth, 1978; Botsman & Rogers, 2010) • Vibrant range of activities have emerged, emphasizing • Underused assets (e.g. apartments, cars, computing power) • Monetization by sharing with others (users pay for fractional use) • New market organization in which digital intermediaries (platforms) facilitate efficient peer-to-peer exchanges (e.g. Uber, Lyft, Airbnb, BlaBlaCar, Luxe) • Transactions benefit from direct and indirect network effects: the value of the platform grows as the number of participants increases
- 10. Gig (on demand)economy • Provision of services such as • Assembling furniture (e.g. TaskRabbit) • Buying groceries (e.g. Shipt) • Wrapping presents (e.g. Gift Six) • Simple household repairs (e.g. Handy) • Laundry (e.g. Cleanly) • Filling out surveys or solve other “human intelligence tasks” (e.g. Amazon Mechanical Turk) • Data entry, clerical • Translation services • Creative and multimedia • Initially low-skill activities but increasingly services that require higher skills • Intensely competitive, with a high (but poorly documented) failure rate of start-ups Source: The Economist, January 3, 2015.
- 11. Freelance economy • Servicesor products, produced by independent contractors, that require a higher level of skill • Wide range of services, new intermediaries • Software development, programming (e.g. Upwork) • Accounting services (e.g. Gigcountant) • Legal work (e.g. Axiom) • Consulting (e.g. Eden McCallum) • Medical services (e.g. Medicast, in 2016 bought by a large U.S. health service provider, Providence St. Joseph Health) • Individuals freelance to have greater flexibility, work from home, build a business, exert more control over creative projects
- 12. Adoption across many sectors •Collaborative Economy Honeycomb developed by Jeremiah Owyang, Crowd Companies Council • Research on 460 start-ups, 260 of which were included • Details and method at http://www.web- strategist.com/blog/2016/03/10/honey comb-3-0-the-collaborative-economy- market-expansion-sxsw/
- 13. NextGen Work andthe vision of One Life • One Life: flexible integration of work and home in NextGen Work • Widely embraced among the young • India, Mexico: 99% • U.S., Spain, Australia, Italy, UK: 90-94% • Sweden: 85-89% • France: 80-84% • Germany, Netherlands: 75-79% • Japan 70-74% ManpowerGroup, 2017, p. 7
- 14. The U.S. gigeconomy (narrowly interpreted) Self-employment rate (% employed workers) Self-employed Employed Paid U.S. workforce 2018 U.S. Bureau of Labor Statistics data • In 2017, 5.8 million workers held contingent jobs (~3.8% of all workers) • In 2017, 10.6 million workers had alternative work arrangements (6.9% of total employment) • On-call (1.7%) • Temporary help agency workers (0.9%) • Contract agency workers (0.6%) Source: BLS (2018)~21% of workforce, slight decline
- 15. The U.S. gigeconomy (broadly interpreted) Reasons for participation Gig work by educational attainment Source: Federal Reserve System, 2018 31% of workforce, slight increase
- 16. Global freelancing: OnlineLabor Index (OLI) • Project by the Oxford Internet Institute led by Vili Lehdonwirta, focusing on freelancing, crowdwork) • Suggests 26% growth in projects between July 2016 and July 2017. • Largest employer country is the U.S., largest labor supplier country India. • http://ilabour.oii.ox.ac.uk/the -online-gig-economy-grew- 26-over-the-past-year/ for more details Fast growth in projects
- 17. Common characteristics, prosand cons • Decentral provision of services • Coordinated by new digital intermediaries • Online mechanisms to build trust (ratings, reputation systems) • Online payment systems • High flexibility and variability • New organization of work or new form of outsourcing?
- 18. Basic economics ofthe gig economy
- 19. Frameworks • Digital intermediationand coordination • Reduced transaction and coordination cost shift the boundaries between (hierarchical) firms and (decentralized) markets (Coase, 1937), expanding market transactions • Emergence of platform markets • In platform markets, value is generated by facilitating transactions between participants that would otherwise not have been able to engage in a business relationship (Parker et al., 2016) • Digital capitalism as a new mode of production • Significant wealth accumulation and inequality (Piketty 2014) • Wealth creation and extraction (Mazzucato, 2013, 2018)
- 20. Digital intermediation andtransaction cost Price (P) Quantity (Q) S S–TC Supply Supply after reduction transaction costs (cost of negotiating, enforcing contracts) Reduction in transaction costs Demand (consumer willingness to pay) D P1 P2 Q2 Q1
- 21. Creation of newmarkets Price (P) Quantity (Q) S0 S0–TC Supply (S0). In this situation transaction and other costs are prohibitively high. The willingness to pay of consumers is below the level needed to recover costs Supply based on significantly reduced transaction costs (S0-TC). Now a market price P exists that allows recovering costs of supply and quantity Q is transacted Significant reduction in transaction costs Demand (consumer willingness to pay) D P Q
- 22. Platform business models 22 Cross-sidenetwork effectsSame-side network effects Platform 1 Side 1 Side 2 Side n Platform 2 • Same-sided network effects imply that the value of the platform increases with the growth of participants on one market side • Cross-side network effects imply that the value of the platform increases with the number of complementary participants • Combined with reduced transaction costs this creates a virtuous cycle of growth
- 23. La Ruche QuiDit Oui! (“The Beehive that Says Yes”) platform business model
- 24. Uber platform businessmodel Source: Henten & Windekilde, 2016. 70-80% to drivers 20-30% to Uber Pays 100% to Uber
- 25. Management challenges • Recruitingparticipants to the platform • Producer side (drivers, hosts, tool owners, farmers, freelancers, artists) • Customers (individuals needing transportation, seeking accommodations, needing help) • Establishing trust between participants • Recommender systems covering all participants • Secure payment arrangements • Protection of privacy • Finding a sustainable revenue model • Low barriers to entry in many segments of the sharing economy imply intense competition unless first-mover advantages can be secured • Concerns about social impacts
- 26. Efficiency and innovation •The gig economy has spurred many improvements in the quality and diversity of services offered • Ride-sharing services have put pressure on existing taxi services to improve quality and reduce prices • Platforms like Airbnb have diversified the supply of accommodation and created new networks of personal relations • Better utilization of asset capacity and time increases productivity • Unused housing space, cars, computing power • Flexibilization of work generates benefits for those who face other time constraints • Tweaking efficiency at the margin (eliminating waste) or structural gains? • Overall effects on productivity and growth subject to further examination
- 27. A virtuous cycle •Innovation and network effects reduce the cost of supply S0S1S2 • Innovation and network effects increase willingness to pay (demand) D0D1D2 • The market exhibits long- run declining prices and expanding volumes Price (P) Quantity (Q) D0 D1 D2 S0 S1 S2P0 P2 Q2Q1Q0 Long-run market development
- 28. Critiques of digitalcapitalism • Offers a perspective critical of the digital transformation frame advanced by the tech industry (and many policy-makers) • Piketty and colleagues point to renewed dynamic of capitalist system in which return on capital (r) is higher than growth in the economy (g). With r>g, the importance of wealth will increase and inequality will rise (Piketty & Saez, 2003; Piketty, 2014) • Digital capitalism leads to enormous generation of wealth for firms organizing platform markets, often based on intangible capital (e.g. knowledge, know how) (Srnicek, 2014; Haskel & Westlake, 2017) • Confusion of wealth extraction with wealth generation in modern capitalism (Mazzucato, 2018)
- 29. The digital economyand income inequality
- 30. Employment and incomeeffects • Conflicting employment effects of the digital economy • Creation of tremendous opportunities for new and interesting jobs • Potentially significant loss of jobs (e.g. Osborne & Frey, 2017 find that 60% of U.S. jobs are at risk by 2050; more recent studies find smaller negative effects) • Change in the skill composition of the workforce (e.g. Autor, 2015) • Whether the digital economy reduces or aggravates unemployment and income inequality depends on • Whether work relations can be transformed in positive ways • How well countries transition to and participate in the digital economy • Provision of complementary services (e.g. continuing education, universal basic income support, infrastructure services, R&D support)
- 31. Disappearance of middle-skilljobs in the U.S. “Market share” of these jobs increased “Market share” of these jobs decreased 20 40 60 80 1000 1979-1989 1989-1999 2007-2012 1999-2007 Skill percentile (ranked by 1979 mean log wage) Sources: Autor (2015); McAfee (2018)
- 32. … and inother countries Source: McAfee (2018); Goos, Manning, Salomons (2014)
- 33. Micro and macrodynamics of the gig economy • At the individual (micro) level, increased connectivity typically goes hand in hand with higher income • At an aggregate (macro) level, counteracting forces exist • Winner-takes-all effects (first mover advantages) • Automation of routine jobs and deskilling • International mobility of work, capital, and knowledge • Productivity effects of ICTs • Increased connectivity may reduce or increase income inequality Bauer (2017) Individual level of use Individual income Income inequality Aggregate level Individual level + + + + – Level of use Emergence, Determination Net effect?
- 34. Expected contribution tohousehold income • Empirical study by Garcia- Murillo, MacInnes, Bauer, & Hong (2016) • M-Turk survey in June 2016 • N=420 individuals • Located in the U.S. • 18 years or older • Explored participation in the sharing, gig and freelance economy • Studied key drivers of participation and effects 0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 Sharing Gig Freelance Percentofparticipants 0-25% 25-50% 50-75% 75-100%
- 35. Estimated actual contributionto income Source: Farrell & Greig, 2016
- 36.
- 37. Harnessing the powerof the gig economy • New forms of digitally mediated work allow innovative models to integrate work and other aspects of life • Individual firms may adopt contracts with benefit options to stabilize platform participation (e.g. Hong 2018) • Measures at the aggregate level (labor markets, social policy, tax policy) • Provision of new opportunities for lifelong education and retraining • Design of social safety nets that are not tied to traditional employment • Minimal income policies (e.g. negative income tax, universal basic income) • Even though societies with higher average income can accommodate higher levels of inequality, the biggest challenge may be overcoming persistent and rising income and wealth disparities
- 38. Recap of mainpoints • The gig economy is shaped by technological, economic, and political forces • It promises tremendous opportunities to create new jobs and innovative work arrangements • At the same time, it risks undermining aspects of social goals tied to traditional employment • Emerging technologies (autonomous vehicles, Internet of Things, AI) will further change the nature of work and the gig economy
- 39. References • Autor, D.H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3-30. • Bauer, J. M. (2017). The Internet and income inequality: Socio-economic challenges in a hyperconnected society. Telecommunications Policy. doi:10.1016/j.telpol.2017.05.009. Published online 16 June 2017. • Benkler, Y. (2016). Peer production and cooperation. In J. M. Bauer & M. Latzer (Eds.), Handbook on the economics of the Internet (pp. 91-119). Cheltenham, UK; Northampton, MA: Edward Elgar. • Botsman, R., & Rogers, R. (2010). What's mine is yours: The rise of collaborative consumption. New York: Harper Business. • BLS. (2018). Contingent and alternative employment arrangements. Washington, DC: United States Department of Labor, Bureau of Labor Statistics. • Coase, R. H. (1937). The nature of the firm. Economica N.S., 4(16), 386-405. • Eubanks, V. (2017). Automating inequality: How high-tech tools profile, police, and punish the poor. New York: St. Martin's Press. • Farrell, D., & Greig, F. (2016). Paychecks, paydays, and the online platform economy. New York: J.P. Morgan Chase & Co. Institute. • Federal Reserve System. (2018). Report on the economic well-being of U.S. households in 2017-2018. Washington, DC, Board of the Governors of the Federal Research System. • Felson, M., & Spaeth, J. L. (1978). Community structure and collaborative consumption: A routine activity approach. American Behavioral Scientist, 21(4), 614-624. • Garcia-Murillo, M., MacInnes, I., Bauer, J. M., & Hong, S. J. (2016). Individual to individual services and the future of work. Paper presented at the 2016 Biennial Conference of the International Telecommunications Society (ITS) Taipei, Taiwan.
- 40. References … • Goos,M., Manning, A. & Salomons, A. (2014). Explaining job polarization: Routine-biased technological change and offshoring. American Economic Review, 104(8): 2509-2526. • Haskel, J., & Westlake, S. (2017). Capitalism without Capital. Princeton, NJ; Oxford, UK: Princeton University Press. • Henten, A., & Windekilde, I. M. (2016). Transaction costs and the sharing economy. Info, 18(1), 1-15. • Hong, S.J. (2018). Effects of flexibility and stability on the decision to work for ride-sharing services. Ph.D. Dissertation, Department of Media and Information, Michigan State University, USA. • McAfee, A. (2018). Technology, jobs and wages. Paper presented at the Summit on Technology and Jobs, Washington, D.C. • McAfee, A., & Brynjolfsson, E. (2017). Machine, platform, crowd: Harnessing our digital future. New York: W. W. Norton & Company. • Manpower Group. (2017). #Gig Responsibility: The Rise of NextGen Work. Milwaukee, WI: Manpower Group. • Mazzucato, M. (2013). The entrepreneurial state: Debunking public vs. private sector myth. London: Anthem Press. • Mazzucato, M. (2018). The value of everything: Making and taking in the global economy: Allen Lane. • Parker, G. G., Van Alstyne, M. W., & Choudary, S. P. (2016). Platform Revolution: How Networked Markets Are Transforming the Economy--And How to Make Them Work for You. New York: W.W. Norton. • Piketty, T. (2014). Capital in the twenty-first century. Cambridge, MA: The Belknap Press of Harvard University Press.
- 41. References … • Srnicek,N. (2017). Platform capitalism. Malden, MA: Polity Press. • Sundararajan, A. (2016). The sharing economy: The end of employment and the rise of crowd-based capitalism. Cambridge, MA: MIT Press.
- 42. Johannes M. Bauer Professorand Chairperson Department of Media and Information Michigan State University, East Lansing, MI 48824, USA [email protected], +1.517.944.4154 http://www.msu.edu/~bauerj 42