IDEAS home Printed from https://ideas.repec.org/p/wbk/wbrwps/8271.html
   My bibliography  Save this paper

Man vs. machine in predicting successful entrepreneurs : evidence from a business plan competition in Nigeria

Author

Listed:
  • Mckenzie,David J.
  • Sansone,Dario
  • Mckenzie,David J.
  • Sansone,Dario

Abstract

This paper compares the relative performance of man and machine in being able to predict outcomes for entrants in a business plan competition in Nigeria. The first human predictions are business plan scores from judges, and the second are simple ad hoc prediction models used by researchers. The paper compares these (out-of-sample) performances with those of three machine learning approaches. The results show that (i) business plan scores from judges are uncorrelated with business survival, employment, sales, or profits three years later; (ii) a few key characteristics of entrepreneurs such as gender, age, ability, and business sector do have some predictive power for future outcomes; (iii) modern machine learning methods do not offer noticeable improvements; (iv) the overall predictive power of all approaches is very low, highlighting the fundamental difficulty of picking winners; and (v) the models do twice as well as random selection in identifying firms in the top tail of performance.

Suggested Citation

  • Mckenzie,David J. & Sansone,Dario & Mckenzie,David J. & Sansone,Dario, 2017. "Man vs. machine in predicting successful entrepreneurs : evidence from a business plan competition in Nigeria," Policy Research Working Paper Series 8271, The World Bank.
  • Handle: RePEc:wbk:wbrwps:8271
    as

    Download full text from publisher

    File URL: http://documents.worldbank.org/curated/en/968231513116778571/pdf/WPS8271.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio E, 2017. "Economic Predictions with Big Data: The Illusion Of Sparsity," CEPR Discussion Papers 12256, C.E.P.R. Discussion Papers.
    2. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
    3. Ramana Nanda, 2016. "Financing high-potential entrepreneurship," IZA World of Labor, Institute for the Study of Labor (IZA), pages 252-252, April.
    4. David McKenzie, 2017. "Identifying and Spurring High-Growth Entrepreneurship: Experimental Evidence from a Business Plan Competition," American Economic Review, American Economic Association, vol. 107(8), pages 2278-2307, August.
    5. Thomas Åstebro & Samir Elhedhli, 2006. "The Effectiveness of Simple Decision Heuristics: Forecasting Commercial Success for Early-Stage Ventures," Management Science, INFORMS, vol. 52(3), pages 395-409, March.
    6. Aaron Chalfin & Oren Danieli & Andrew Hillis & Zubin Jelveh & Michael Luca & Jens Ludwig & Sendhil Mullainathan, 2016. "Productivity and Selection of Human Capital with Machine Learning," American Economic Review, American Economic Association, vol. 106(5), pages 124-127, May.
    7. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011. "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls," Papers 1201.0224, arXiv.org, revised May 2012.
    8. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "High-Dimensional Methods and Inference on Structural and Treatment Effects," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
    9. Skriabikova, Olga J. & Dohmen, Thomas & Kriechel, Ben, 2014. "New evidence on the relationship between risk attitudes and self-employment," Labour Economics, Elsevier, vol. 30(C), pages 176-184.
    10. Hvide, Hans K. & Panos, Georgios A., 2014. "Risk tolerance and entrepreneurship," Journal of Financial Economics, Elsevier, vol. 111(1), pages 200-223.
    11. A. Belloni & D. Chen & V. Chernozhukov & C. Hansen, 2012. "Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain," Econometrica, Econometric Society, vol. 80(6), pages 2369-2429, November.
    12. Celiku,Bledi & Kraay,Aart C., 2017. "Predicting conflict," Policy Research Working Paper Series 8075, The World Bank.
    13. Erin L. Scott & Pian Shu & Roman M. Lubynsky, 2015. "Are “Better” Ideas More Likely to Succeed? An Empirical Analysis of Startup Evaluation," Harvard Business School Working Papers 16-013, Harvard Business School.
    14. Marcel Fafchamps & Christopher Woodruff, 2017. "Identifying Gazelles: Expert Panels vs. Surveys as a Means to Identify Firms with Rapid Growth Potential," World Bank Economic Review, World Bank Group, vol. 31(3), pages 670-686.
    15. Fabiano Schivardi & Claudio Michelacci, 2016. "Are They All Like Bill, Mark, and Steve? The Education Premium for Entrepreneurs," 2016 Meeting Papers 1163, Society for Economic Dynamics.
    16. Zacharakis, Andrew L. & Shepherd, Dean A., 2001. "The nature of information and overconfidence on venture capitalists' decision making," Journal of Business Venturing, Elsevier, vol. 16(4), pages 311-332, July.
    17. Bruhn, Miriam, 2009. "Female-owned firms in Latin America : characteristics, performance, and obstacles to growth," Policy Research Working Paper Series 5122, The World Bank.
    18. repec:aea:jecper:v:31:y:2017:i:2:p:87-106 is not listed on IDEAS
    19. Nick Guenther & Matthias Schonlau, 2016. "Support vector machines," Stata Journal, StataCorp LP, vol. 16(4), pages 917-937, December.
    20. Abhijit V. Banerjee & Esther Duflo, 2014. "(Dis)organization and Success in an Economics MOOC," American Economic Review, American Economic Association, vol. 104(5), pages 514-518, May.
    21. Robert E. Hall & Susan E. Woodward, 2010. "The Burden of the Nondiversifiable Risk of Entrepreneurship," American Economic Review, American Economic Association, vol. 100(3), pages 1163-1194, June.
    22. repec:ags:stataj:117524 is not listed on IDEAS
    23. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2017. "Human Decisions and Machine Predictions," NBER Working Papers 23180, National Bureau of Economic Research, Inc.
    24. Beattie, Graham & Laliberté, Jean-William P. & Oreopoulos, Philip, 2018. "Thrivers and divers: Using non-academic measures to predict college success and failure," Economics of Education Review, Elsevier, vol. 62(C), pages 170-182.
    25. Matthias Schonlau, 2005. "Boosted regression (boosting): An introductory tutorial and a Stata plugin," Stata Journal, StataCorp LP, vol. 5(3), pages 330-354, September.
    26. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    27. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "Supplementary Appendix for "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"," Papers 1305.6099, arXiv.org, revised Jun 2013.
    28. repec:aea:aecrev:v:107:y:2017:i:6:p:1638-55 is not listed on IDEAS
    29. BodenJR., Richard J. & Nucci, Alfred R., 2000. "On the survival prospects of men's and women's new business ventures," Journal of Business Venturing, Elsevier, vol. 15(4), pages 347-362, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Can predicting successful entrepreneurship go beyond “choose smart guys in their 30s”? Comparing machine learning and expert judge predictions
      by David McKenzie in Development Impact on 2018-01-22 12:29:00

    More about this item

    Keywords

    Private Sector Economics; Private Sector Development Law; Marketing; Labor Markets; Gender and Development; Educational Sciences; Educational Populations; Educational Policy and Planning - Textbook; Education for Development (superceded); Education For All;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L26 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Entrepreneurship
    • M13 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - New Firms; Startups
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wbk:wbrwps:8271. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Roula I. Yazigi). General contact details of provider: http://edirc.repec.org/data/dvewbus.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.