디지털융복합연구 (Journal of Digital Convergence)

  • 제19권5호
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  • Pages.1-13
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  • 2021
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  • 2713-6434(pISSN)
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  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
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플랫폼 노동시장의 구직기간 단축 결정요인: 웹크롤링과 생존모형을 이용한 분석

Determinants of Shortening Job-hunting Period in Platform Labor Market: Analysis by using Web Crawling and Survival Model

  • 투고 : 2020.10.21
  • 심사 : 2021.05.20
  • 발행 : 2021.05.28
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초록

본 연구의 목적은 플랫폼 노동시장에서 신규 구직자의 임금수준이 첫 업무획득기간에 어떠한 영향을 주는지 분석하는 것이다. 최근 플랫폼 노동시장은 실업률 증가를 해결하기 위한 대안의 하나로 주목받고 있다. 플랫폼 노동시장에서 양질의 일자리를 창출하기 위해서는 고용주와 고용인 간의 신뢰형성이 중요하다. 기존 연구에서는 이전 고용주의 피드백이 고용주와 고용인간의 정보 비대칭 문제해결을 위해 중요하다고 하였다. 다만, 첫 번째 업무를 획득하지 못한 신규 구직자의 경우 이전 고용주에 의한 피드백이 존재하지 않는다. 이에 본 연구는 플랫폼에서는 임금이 고용주가 아닌 구직자들에 의해 스스로 제시된다는 점에 착안하여 신규 구직자의 낮은 임금이 구직기간 단축에 영향을 줄 수 있는지 확인하고자 한다. 이를 위해 Freelancer.com에서 발췌한 3,704명의 구직자 정보를 사용한다. 생존 분석 결과에 따르면, 플랫폼 노동시장에서 신규 구직자의 낮은 임금은 구직기간 단축에 유의한 영향을 주는 것으로 나타났다.

The purpose of this research is to analyze how the wage level of new job seekers in the platform labor market affects the period on getting the first job. Recently, the platform gets attention as one of alternatives to solve the increase of unemployment rate. It is important to create quality jobs that we build up a trust between employers and employees in the platform. Previous studies showed that feedback from previous employers is important for solving the information asymmetry problem between those people. However, there is no feedback for new job seekers who have not get the first job. Therefore, we focus on the fact that wages are presented by job seekers rather than employers in the platform, and we will figure out that the low wages of new job seekers may affect the shortening of job-hunting period. For this reason, we use 3,704 job seekers of Freelancer.com. Survival analysis shows that low wages for new job seekers have a significant impact on shortening job-hunting period.

키워드

참고문헌

  1. S. K. Rhee. (2010). The Definition of Two-sided Market and Its Conditions. International Telecommunications Policy Review, 17(4), 73-105.
  2. T. Eisenmann, G. Parker & M. W. Van Alstyne. (2006). Strategies for two-sided markets. Harvard business review, 84(10), 92.
  3. C. Kim & J. Jeon. (2011). Development of New Mobile Application Service Concept : Application Store DB Analysis. The Journal of Internet Electronic Commerce Research, 11(2), 257-272.
  4. C. Park, H. Kim & J. Han. (2011). Business Strategy for Scenarios in Mobile Application Market : Focusing on the Responding Strategies of Mobile Operators. The Journal of Internet Electronic Commerce Research, 11(2), 75-107. https://doi.org/10.1007/s10660-010-9066-9
  5. O. Kassi & V. Lehdonvirta. (2018). Online labour index: Measuring the online gig economy for policy and research. Technological forecasting and social change, 137, 241-248. https://doi.org/10.1016/j.techfore.2018.07.056
  6. S. C. Kuek, C. Paradi-Guilford, T. Fayomi, S. Imaizumi, P. Ipeirotis, P. Pina & M. Singh. (2015). The Global Opportunity in Online Outsourcing. Washington, DC. : ©World Bank.
  7. B. C. Greenwald. (1986). Adverse selection in the labour market. The Review of Economic Studies, 53(3), 325-347. https://doi.org/10.2307/2297632
  8. U.S. Department of Labor. (2020). History of Federal Minimum Wage Rates Under the Fair Labor Standards Act, 1938 - 2009.
  9. J. Horton, W. R. Kerr & C. Stanton. (2017). Digital labor markets and global talent flows. In: G. H. Hanson, W. R. Kerr & S. Turner. (eds) High-skilled migration to the United States and its economic consequences. Chicago: University of Chicago Press.
  10. C. Krieger-Boden & A. Sorgner. (2018). Labor market opportunities for women in the digital age. Economics Discussion Papers, No 2018-18, Kiel Institute for the World Economy.
  11. M. Mamertino & T. Sinclair. (2019). Migration and Online Job Search: A Gravity Model Approach. Economics Letters, 181, 5-53.
  12. A. Benson, A. Sojourner & A. Umyarov. (2020). Can reputation discipline the gig economy? Experimental evidence from an online labor market. Management Science, 66(5), 1802-1825. https://doi.org/10.1287/mnsc.2019.3303
  13. Z. Li, Y. Hong & Z. Zhang. (2018, January). An empirical analysis of the impacts of the sharing economy platforms on the US labor market. Proceedings of the 51st Hawaii International Conference on System Sciences. (pp.1-9).
  14. J. Lim. (2017). The Government's Supporting Strategies to the Productive Prosumer Economy for the Successful Transition to the Fourth Industrial Revolution Era: Human Resource Development Perspectives for Solving Job problems. Informatization Policy, 24(2), 87-104. https://doi.org/10.22693/NIAIP.2017.24.2.087
  15. G. Lazaroiu & D. Rommer. (2017). Digital technologies, labor markets, and economic reputation. Ekonomicko-manazerske spektrum, 11(2), 13-21.
  16. A. Pallais. (2014). Inefficient hiring in entry-level labor markets. American Economic Review, 104(11), 3565-99. https://doi.org/10.1257/aer.104.11.3565
  17. F. Bogliacino, V. Cirillo, C. Codagnone & D. Guarascio. (2020). Quantity and Quality of Work in the Platform Economy. In: Zimmermann K. (eds) Handbook of Labor, Human Resources and Population Economics. Springer, Cham.
  18. E. Brynjolfsson & A. McAfee. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York : W.W. Norton & Company.
  19. C. Anderson. (2006). The long tail: Why the future of business is selling less of more. London : Hachette Books.
  20. E. Brynjolfsson, Y. J. Hu & M. D. Smith. (2006). From niches to riches: Anatomy of the long tail. Sloan Management Review, 47(4), 67-71.
  21. D. H. Autor. (2001). Wiring the labor market. The Journal of Economic Perspectives, 15(1), 25-40. https://doi.org/10.1257/jep.15.1.25
  22. H. Ko, D. H. Park & H. B. Jeong. (2019). Deployment of Artificial Intelligence Technology and Law and Economics of Employment Discrimination. Korean Journal of Law and Economics, 16(1), 39-70. https://doi.org/10.46758/kjle.2019.04.16.1.39
  23. H. S. Farber & R. Gibbons. (1996). Learning and wage dynamics. The Quarterly Journal of Economics, 111(4), 1007-1047. https://doi.org/10.2307/2946706
  24. J. G. Altonji & C. R Pierret. (2001). Employer learning and statistical discrimination. The quarterly journal of economics, 116(1), 313-350. https://doi.org/10.1162/003355301556329
  25. F. Lange. (2007). The speed of employer learning. Journal of Labor Economics, 25(1), 1-35. https://doi.org/10.1086/508730
  26. C. O. Bae & J. W. Kim. (2016, May). The Study for Performance Analysis of Ability-based Employment based on NCS. Proceedings of the Korean Academic Association of Business Administration 2016 Summer Conference. (pp. 31-40). Seoul : Korean Academic Association of Business Administration.
  27. S. Tadelis. (2016). Reputation and feedback systems in online platform markets. Annual Review of Economics, 8, 321-340. https://doi.org/10.1146/annurev-economics-080315-015325
  28. C. T. Stanton & C. Thomas. (2016). Landing the first job: The value of intermediaries in online hiring. The Review of Economic Studies, 83(2), 810-854. https://doi.org/10.1093/restud/rdv042
  29. S. Suri, D. G. Goldstein & W. A. Mason. (2011, August). Honesty in an online labor market. Proceedings of Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence. Menlo Park : The AAAI Press.
  30. J. Horton, N. Leonard & J. Golden. (2015). Reputation Inflation: Evidence from an Online Labor Market. Working paper NYU.
  31. K. S. Choi. (2016). Effects of the Gap between Reservation and Market Wage on the Period to Get First Job. Quarterly Journal of Labor Policy, 16(2), 33-63. https://doi.org/10.22914/JLP.2016.16.2.002
  32. W. H. Cho. (1995). Youth Unemployment Problem and The Analysis of Unemployment Probability in Korea. Korean Journal of Labor Economics, 18(1), 107-128.
  33. B. Kaufman & J. Hotchkiss. (2006). The economics of labor markets. New York: Thomson/South Western.
  34. D. T. Mortensen. (1986). Job search and labor market analysis. In: O. Ashenfelter & R. Layard. (eds) Handbook of Labor Economics Volume 2. North Holland: Elsevier.
  35. C. Codagnone & B. Martens. (2016). Scoping the sharing economy: Origins, definitions, impact and regulatory issues. Institute for Prospective Technological Studies Digital Economy Working Paper 2016/01, JRC100369.
  36. S. Banfi & B. Villena-Roldan. (2019). Do high-wage jobs attract more applicants? Directed search evidence from the online labor market. Journal of Labor Economics, 37(3), 715-746. https://doi.org/10.1086/702627
  37. M. Belot, P. Kircher & P. Muller. (2019). Providing advice to jobseekers at low cost: An experimental study on online advice. The review of economic studies, 86(4), 1411-1447. https://doi.org/10.1093/restud/rdy059
  38. J. Faberman & M. Kudlyak. (2016). What does online job search tell us about the labor market?. Economic perspectives, 40(1), 1-15.
  39. M. Kokkodis, P. Papadimitriou & P. G. Ipeirotis. (2015, February). Hiring behavior models for online labor markets. Proceedings of the Eighth ACM International Conference on Web Search and Data Mining. (pp. 223-232). New York : Association for Computing Machinery.
  40. B. Kim. (2016). Worker Characteristics and Search Duration: Duration Model. The Journal of Women and Economics, 13(1), 93-123.
  41. S. M. Kim, (2011). Business Failure Prediction Using Survival Analysis and Survival Time Analysis. Journal of SME Finance, Summer, 78-107.
  42. S. Szczypinski. (April 19, 2019). What Is the Average Annual Income for a Freelancer in the U.S.? The balance small business.
  43. G. Heinze & M. Schemper. (2002). A solution to the problem of separation in logistic regression. Statistics in medicine, 21(16), 2409-241 https://doi.org/10.1002/sim.1047