Journal of Technology Innovation (기술혁신연구)

Korea Society for Innovation Managemetn & Economics (기술경영경제학회)
권호 표지
DOI QR코드

DOI QR Code

A Dynamic Panel Analysis of the Determinants of Adoption of Industrial Robots

동적 패널모형을 이용한 산업용 로봇 도입의 결정요인 분석

  • 정진화 (서울대학교 농경제사회학부, 농업생명과학연구원) ;
  • 임동근 (서울대학교 농경제사회학부)
  • Published : 2018.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we analyze the determinants of the adoption of industrial robots using the data from 42 countries, and thereby examine the factors underlying the rapid expansion of industrial robots in Korea. To this end, the industrial robot data for the years 2001-2016 were drawn from the World Robotics dataset of the International Federation of Robotics (IFR). The explanatory variables included labor market environment variables and innovation capacity variables extracted from the dataset of the relevant international organizations. For data analysis, the Arellano-Bond dynamic panel analysis was performed to control for the endogeneity problem of some explanatory variables. The empirical results confirmed the exceptionally rapid expansion of industrial robots in Korea as compared to other countries, even when considering the national income level, employment cost, and innovation capacity. This phenomenon could be attributed to both the demand-side and supply-side factors. For one thing, changes in the labor market environment, such as an increase in employment costs, have led to an increase of the corporate demand for industrial robots. For another, the supply-side factors, such as an increase in the capital intensity and innovation capacity of companies, have also contributed to the widespread adoption of industrial robots.

본 연구는 세계 42개국의 자료를 사용하여 산업용 로봇 도입의 결정요인을 분석하고, 한국에서 산업용 로봇이 빠르게 확산되고 있는 원인을 진단하였다. 산업용 로봇 변수는 국제로봇협회(IFR)의 2001년-2016년 "World Robotics: Industrial Robots" 자료를 사용하였다. 설명변수는 노동시장환경 변수와 혁신역량 변수를 포함하며, 관련 변수들은 해당 국제기관들의 자료에서 추출하였다. 실증분석에는 일부 설명변수의 내생성을 통제하기 위해 Arellano-Bond 동적 패널분석을 사용하였다. 분석결과, 한국은 소득수준이나 고용비용 및 혁신역량 등을 고려하더라도 다른 국가들에 비해 산업용 로봇 도입이 매우 빠르게 확대되어 온 것을 확인할 수 있었다. 이는 수요 측면과 공급 측면 모두에서 그 원인을 찾을 수 있다. 즉, 고용비용 증가 등의 노동시장환경 변화가 산업용 로봇 도입에 대한 기업 수요를 견인하였으며, 경제 전반의 자본집약도 증가와 기업의 혁신역량 증대와 같은 공급 측면 요인 또한 산업용 로봇의 도입을 촉진시켰다.

Keywords

References

  1. 김민영.조민지.임업(2017), "자동화 기술의 발전이 지역노동시장 중간일자리 감소에 미치는 영향-잠재성장모형의 적용", 국토연구, 제93권, pp. 25-41.
  2. 김희수(2018), "4차 산업혁명 시대의 신성장 동력과 포용적 성장", 한국경제포럼, 제11권 제2호, pp. 59-92.
  3. 문창호.김시연(2016), "기술혁신지향성의 선행요인과 기술혁신성과에 미치는 영향: 국내 IT 중소기업 사례", 기술혁신연구, 제24권 제1호, pp. 49-84. https://doi.org/10.14383/SIME.2016.24.1.49
  4. 배미경(2008), "한국 로봇산업의 부문별 성장잠재력 추정", 한국경제연구, 제21권, pp. 5-32.
  5. 오호영(2018), "제4차 산업혁명과 한국경제의 일자리 충격", 한국경제포럼, 제11권 제2호, pp. 93-115.
  6. 이병훈(2018), "4차 산업혁명과 노사관계-노사갈등 이슈와 서구 노조들의 대응전략을 중심으로", 한국사회정책, 제25권 제2호, pp. 429-446.
  7. 이주관.정진화(2014), "특허생산과 기술성과: 기업 혁신전략의 역할", 기술혁신연구, 제22권 제1호, pp. 149-175. https://doi.org/10.14383/SIME.2014.22.1.006
  8. 임동근.정진화(2017), "제품혁신이 기업의 수익 및 재무안정성에 미치는 영향", 기술혁신연구, 제25권 제4호, pp. 239-261. https://doi.org/10.14383/SIME.2017.25.4.239
  9. 임재헌.신진교(2012), "중소기업의 기술혁신 관리요소에 관한 실증연구", 기술혁신연구, 제20권 제2호, pp. 75-107.
  10. 하태정.문선웅(2013), "정부연구개발투자의 제조업 고용창출효과에 관한 실증분석", 기술혁신연구, 제21권 제1호, pp. 1-26.
  11. 한국과학기술기획평가원(2016), 2016년도 연구개발활동조사보고서, 한국과학기술기획평가원.
  12. 허재준(2017), "4차 산업혁명이 일자리에 미치는 변화와 대응", 노동리뷰, 2017년 3월호.
  13. Acemoglu, D. and P. Restrepo (2017), "The Race Between Machine and Man: Implications of Technology for Growth, Factor Shares and Employment", NBER Working Paper, No. 22252.
  14. Arellano, M. and S. Bond (1991), "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations", Review of Economic Studies, Vol. 58, pp. 277-297. https://doi.org/10.2307/2297968
  15. Arntz, M., T. Gregory and U. Zierahn (2016), "The Risk of Automation for Jobs in OECD Countries", OECD Social, Employment and Migration Working Paper, No. 189.
  16. Bassanini, A. and E. Ernst (2002), "Labour Market Regulation, Industrial Relations and Technological Regimes: A Tale of Comparative Advantage", Industrial and Corporate Change, Vol. 11, No. 3, pp. 391-426. https://doi.org/10.1093/icc/11.3.391
  17. Carbonero, F., E. Ernst and E. Weber (2018), "Robots Worldwide: The Impact of Automation on Employment and Trade", ILO Research Department Working Paper, No. 36.
  18. Castellacci, F. and J. M. Natera (2013), "The Dynamics of National Innovation Systems: A Panel Cointegration Analysis of the Coevolution between Innovative Capability and Absorptive Capacity", Research Policy, Vol. 42, No. 3, pp. 579-594. https://doi.org/10.1016/j.respol.2012.10.006
  19. Chen, H. H., A. H. Lee and Y. Tong (2006), "Analysis of New Product Mix Selection at TFT-LCD Technological Conglomerate Network under Uncertainty", Technovation, Vol. 26, No. 11, pp. 1210-1221. https://doi.org/10.1016/j.technovation.2005.08.013
  20. Cho, J. and J. Kim (2018), "Identifying Factors Reinforcing Robotization: Interactive Forces of Employment, Working Hour and Wage", Sustainability, Vol. 10, No. 2, p. 490. https://doi.org/10.3390/su10020490
  21. Fabrizio, K. R. (2009), "Absorptive Capacity and the Search for Innovation", Research Policy, Vol. 38, No. 2, pp. 255-267. https://doi.org/10.1016/j.respol.2008.10.023
  22. Falk, M. (2006), "What Drives Business Research and Development (R&D) Intensity across Organisation for Economic Co-operation and Development (OECD) Countries?", Applied Economics, Vol. 38, No. 5, pp. 533-547. https://doi.org/10.1080/00036840500391187
  23. Frey, C. B. and M. A. Osborne (2017), "The Future of Employment: How Susceptible are Jobs to Computerisation?", Technological Forecasting & Social Change, Vol. 114, pp. 254-280. https://doi.org/10.1016/j.techfore.2016.08.019
  24. Gilson, R. J. (2010), "Locating Innovation: The Endogeneity of Technology, Organizational Structure, and Financial Contracting", Columbia Law Review, Vol. 110, No. 3, pp. 885-917.
  25. Ginarte, J. C. and W. G. Park (1997), "Determinants of Patent Rights: A Cross-national Study", Research Policy, Vol. 26, No. 3, pp. 283-301. https://doi.org/10.1016/S0048-7333(97)00022-X
  26. Gomez, J. and P. Vargas (2009), "The Effect of Financial Constraints, Absorptive Capacity and Complementarities on the Adoption of Multiple Process Technologies", Research Policy, Vol. 38, No. 1, pp. 106-119. https://doi.org/10.1016/j.respol.2008.10.013
  27. Golda, G., A. Kampa and I. Paprocka (2018), "Analysis of Human Operators and Industrial Robots Performance and Reliability", Management and Production Engineering Review, Vol. 9, No. 1, pp. 24-33.
  28. Graetz, G. and G. Michaels (2017), "Is Modern Technology Responsible for Jobless Recoveries?", American Economic Review: Papers & Proceedings, Vol. 107, No. 5, pp. 168-173. https://doi.org/10.1257/aer.p20171100
  29. Hall, B. H. (1989), "The Impact of Corporate Restructuring on Industrial Research and Development", NBER Working Paper, No. 3216.
  30. Hollon, C. J. and G. N. Rogol (1985), "How Robotization Affects People", Business Horizons, Vol. 28, No. 3, pp. 74-80. https://doi.org/10.1016/0007-6813(85)90012-6
  31. International Federation of Robotics (2017), World Robotics 2017: Industrial Robots, International Federation of Robotics: Frankfurt, Germany.
  32. Kanwar, S. and R. Evenson (2003), "Does Intellectual Property Protection Spur Technological Change?", Oxford Economic Papers, Vol. 55, pp. 235-264. https://doi.org/10.1093/oep/55.2.235
  33. Korinek, A. and J. E. Stiglitz (2017), "Artificial Intelligence, Worker-Replacing Technological Progress and Income Distribution", NBER Working Paper, No. 24174.
  34. Leontief, W. (1983), "Technological Advance, Economic Growth, and the Distribution of Income", Population and Development Review, Vol. 9, No. 3, pp. 403-410. https://doi.org/10.2307/1973315
  35. Lloyd-Ellis, H. (1999), "Endogenous Technological Change and Wage Inequality", American Economic Review, Vol. 89, No. 1, pp. 47-77. https://doi.org/10.1257/aer.89.1.47
  36. McKinsey Global Institute (2017), Job Lost, Jobs Gained: Workforce Transitions in a Time of Automation, McKinsey & Company: New York, USA.
  37. Misztal, A., M. Butlewski, A. Jasiak and S. Janik (2015), "The Human Role in a Progressive Trend of Foundry Automation", Metalurgija, Vol. 54, No. 2, pp. 429-432.
  38. Morgantini, M. (1984), "Man Confronted by the Third Technological Generation", Design Issues, Vol. 1, No. 2, pp. 21-25. https://doi.org/10.2307/1511496
  39. OECD (2018), Main Sciences & Technology Indicator 2018, OECD Stats: 2018 MSTI Database.
  40. Romer, P. M. (1990), "Endogenous Technological Change", Journal of Political Economy, Vol. 98, No. 5, pp. S71-S102. https://doi.org/10.1086/261725
  41. Roodman, D. (2009), "How to Do Xtabond2: An Introduction to Difference and System GMM in Stata", Stata Journal, Vol. 9, No. 1, pp. 86-136. https://doi.org/10.1177/1536867X0900900106
  42. Rumberger, R. W. (1984), "High Technology and Job Loss", Technology in Society, Vol. 6, No. 4, pp. 263-284. https://doi.org/10.1016/0160-791X(84)90022-8
  43. Song, M., H. Bij and M. Weggeman (2006), "Factors for Improving the Level of Knowledge Generation in New Product Development", R&D Management, Vol. 36, No. 2, pp. 173-187. https://doi.org/10.1111/j.1467-9310.2006.00424.x
  44. Sumer, B. (2018), "Impact of Industry 4.0 on Occupations and Employment in Turkey", European Scientific Journal April 2018 Edition, Vol. 14, No. 10. pp. 1-17.
  45. Sun, Y. and D. Du (2010), "Determinants of Industrial Innovation in China: Evidence from its Recent Economic Census", Technovation, Vol. 30, No. 9-10, pp. 540-550. https://doi.org/10.1016/j.technovation.2010.05.003
  46. Torii, Y. (1989), "Robotization in Korea: Trend and Implications for Industrial Development", Technological Forecasting and Social Change, Vol. 35, pp. 179-190. https://doi.org/10.1016/0040-1625(89)90054-1
  47. Vamos, T. (2014), "The Human Role in the Age of Information", AI & Society: Knowledge, Culture and Communication, Vol. 29, No. 2, pp. 277-282. https://doi.org/10.1007/s00146-013-0481-z
  48. Wang, D., D. Sutherland, L. Ning, Y. Wang and X. Pan (2018), "Exploring the Influence of Political Connections and Managerial Overconfidence on R&D Intensity in China's Large-scale Private Sector Firms", Technovation, Vol. 69, pp. 40-53. https://doi.org/10.1016/j.technovation.2017.10.007
  49. World Bank Group (2018), World Development Indicators Database 2018, World Bank Publications.
  50. World Economic Forum (2016), The Future of Jobs: Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution, World Economic Forum: Geneva, Switzerland.
  51. Yi, Z., J. Hong, W. C. Hsu and C. Wang (2017), "The Role of State Ownership and Institutions in the Innovation Performace of Emerging Market Enterprises: Evidence from China", Technovation, Vol. 62-63, pp. 4-13. https://doi.org/10.1016/j.technovation.2017.04.002
  52. Zhai, Y. M., W. Q. Sun, S. B. Tsai, Z. Wang, Y. Zhao and Q. Chen (2018), "An Empirical Study on Entrepreneurial Orientation, Absorptive Capacity, and SMEs' Innovation Performance: A Sustainable Perspective", Sustainability, Vol. 10, No. 2, p. 314. https://doi.org/10.3390/su10020314