IDEAS home Printed from https://ideas.repec.org/a/eee/deveco/v141y2019ics0304387818305601.html
   My bibliography  Save this article

Predicting entrepreneurial success is hard: Evidence from a business plan competition in Nigeria

Author

Listed:
  • McKenzie, David
  • Sansone, Dario

Abstract

We compare the absolute and relative performance of three approaches to predicting outcomes for entrants in a business plan competition in Nigeria: Business plan scores from judges, simple ad-hoc prediction models used by researchers, and machine learning approaches. We find 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 competition winners.

Suggested Citation

  • McKenzie, David & Sansone, Dario, 2019. "Predicting entrepreneurial success is hard: Evidence from a business plan competition in Nigeria," Journal of Development Economics, Elsevier, vol. 141(C).
    • Handle: RePEc:eee:deveco:v:141:y:2019:i:c:s0304387818305601
    DOI: 10.1016/j.jdeveco.2019.07.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304387818305601
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jdeveco.2019.07.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. William R. Kerr & Josh Lerner & Antoinette Schoar, 2014. "The Consequences of Entrepreneurial Finance: Evidence from Angel Financings," The Review of Financial Studies, Society for Financial Studies, vol. 27(1), pages 20-55, January.
    2. Michelacci, Claudio & Schivardi, Fabiano, 2020. "Are they all like Bill, Mark, and Steve? The education premium for entrepreneurs," Labour Economics, Elsevier, vol. 67(C).
    3. 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.
    4. Ramana Nanda, 2016. "Financing high-potential entrepreneurship," IZA World of Labor, Institute of Labor Economics (IZA), pages 252-252, April.
    5. 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.
    6. 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.
    7. 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.
    8. Dario Sansone, 2019. "Beyond Early Warning Indicators: High School Dropout and Machine Learning," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(2), pages 456-485, April.
    9. David G. Blanchflower & Phillip B. Levine & David J. Zimmerman, 2003. "Discrimination in the Small-Business Credit Market," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 930-943, November.
    10. 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.
    11. Hvide, Hans K. & Panos, Georgios A., 2014. "Risk tolerance and entrepreneurship," Journal of Financial Economics, Elsevier, vol. 111(1), pages 200-223.
    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," The World Bank Economic Review, World Bank, vol. 31(3), pages 670-686.
    15. 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.
    16. Bruhn, Miriam, 2009. "Female-owned firms in Latin America : characteristics, performance, and obstacles to growth," Policy Research Working Paper Series 5122, The World Bank.
    17. Haltiwanger, John & Hurst, Erik & Miranda, Javier & Schoar, Antoinette (ed.), 2017. "Measuring Entrepreneurial Businesses," National Bureau of Economic Research Books, University of Chicago Press, number 9780226454078, December.
    18. David McKenzie, 2018. "Can Business Owners Form Accurate Counterfactuals? Eliciting Treatment and Control Beliefs About Their Outcomes in the Alternative Treatment Status," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 714-722, October.
    19. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
    20. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    21. Nick Guenther & Matthias Schonlau, 2016. "Support vector machines," Stata Journal, StataCorp LP, vol. 16(4), pages 917-937, December.
    22. 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.
    23. 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.
    24. Suresh de Mel & David McKenzie & Christopher Woodruff, 2010. "Who are the Microenterprise Owners? Evidence from Sri Lanka on Tokman versus De Soto," NBER Chapters, in: International Differences in Entrepreneurship, pages 63-87, National Bureau of Economic Research, Inc.
    25. 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.
    26. Matthias Schonlau, 2005. "Boosted regression (boosting): An introductory tutorial and a Stata plugin," Stata Journal, StataCorp LP, vol. 5(3), pages 330-354, September.
    27. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    28. John Haltiwanger & Erik Hurst & Javier Miranda & Antoinette Schoar, 2017. "Measuring Entrepreneurial Businesses: Current Knowledge and Challenges," NBER Books, National Bureau of Economic Research, Inc, number halt14-1, March.
    29. Sharon Belenzon & Aaron K. Chatterji & Brendan Daley, 2017. "Eponymous Entrepreneurs," American Economic Review, American Economic Association, vol. 107(6), pages 1638-1655, June.
    30. 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.
    31. C. M. Van Praag & J. S. Cramer, 2001. "The Roots of Entrepreneurship and Labour Demand: Individual Ability and Low Risk Aversion," Economica, London School of Economics and Political Science, vol. 68(269), pages 45-62, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. McKenzie, David & Sansone, Dario, 2017. "Man vs. Machine in Predicting Successful Entrepreneurs: Evidence from a Business Plan Competition in Nigeria," CEPR Discussion Papers 12523, C.E.P.R. Discussion Papers.
    2. Filmer,Deon P. & Nahata,Vatsal & Sabarwal,Shwetlena, 2021. "Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness," Policy Research Working Paper Series 9847, The World Bank.
    3. Quignon, Aurelien, 2023. "Crowd-based feedback and early-stage entrepreneurial performance: Evidence from a digital platform," Research Policy, Elsevier, vol. 52(7).
    4. Andini, Monica & Boldrini, Michela & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Paladini, Andrea, 2022. "Machine learning in the service of policy targeting: The case of public credit guarantees," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 434-475.
    5. de Blasio, Guido & D'Ignazio, Alessio & Letta, Marco, 2022. "Gotham city. Predicting ‘corrupted’ municipalities with machine learning," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    6. Monica Andini & Emanuele Ciani & Guido de Blasio & Alessio D'Ignazio & Viola Salvestrini, 2017. "Targeting policy-compliers with machine learning: an application to a tax rebate programme in Italy," Temi di discussione (Economic working papers) 1158, Bank of Italy, Economic Research and International Relations Area.
    7. Andini, Monica & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Salvestrini, Viola, 2018. "Targeting with machine learning: An application to a tax rebate program in Italy," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 86-102.
    8. Guido de Blasio & Alessio D'Ignazio & Marco Letta, 2020. "Predicting Corruption Crimes with Machine Learning. A Study for the Italian Municipalities," Working Papers 16/20, Sapienza University of Rome, DISS.
    9. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
    10. Pekkala Kerr, Sari & Kerr, William, 2020. "Immigrant entrepreneurship in America: Evidence from the survey of business owners 2007 & 2012," Research Policy, Elsevier, vol. 49(3).
    11. Delogu, Marco & Lagravinese, Raffaele & Paolini, Dimitri & Resce, Giuliano, 2024. "Predicting dropout from higher education: Evidence from Italy," Economic Modelling, Elsevier, vol. 130(C).
    12. Lily Davies & Mark Kattenberg & Benedikt Vogt, 2023. "Predicting Firm Exits with Machine Learning: Implications for Selection into COVID-19 Support and Productivity Growth," CPB Discussion Paper 444, CPB Netherlands Bureau for Economic Policy Analysis.
    13. Dario Sansone & Anna Zhu, 2023. "Using Machine Learning to Create an Early Warning System for Welfare Recipients," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(5), pages 959-992, October.
    14. Emma Neuman, 2021. "Performance and job creation among self-employed immigrants and natives in Sweden," Small Business Economics, Springer, vol. 56(1), pages 403-425, January.
    15. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
    16. Andres, Antonio Rodriguez & Otero, Abraham & Amavilah, Voxi Heinrich, 2021. "Using Deep Learning Neural Networks to Predict the Knowledge Economy Index for Developing and Emerging Economies," MPRA Paper 109137, University Library of Munich, Germany.
    17. Alessandra Garbero & Marco Letta, 2022. "Predicting household resilience with machine learning: preliminary cross-country tests," Empirical Economics, Springer, vol. 63(4), pages 2057-2070, October.
    18. Jorge Mejia & Shawn Mankad & Anandasivam Gopal, 2019. "A for Effort? Using the Crowd to Identify Moral Hazard in New York City Restaurant Hygiene Inspections," Information Systems Research, INFORMS, vol. 30(4), pages 1363-1386, December.
    19. 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.
    20. de Blasio, Guido & De Paola, Maria & Poy, Samuele & Scoppa, Vincenzo, 2018. "Risk Aversion and Entrepreneurship: New Evidence Exploiting Exposure to Massive Earthquakes in Italy," IZA Discussion Papers 12057, Institute of Labor Economics (IZA).

    More about this item

    Keywords

    Entrepreneurship; Machine learning; Business plans; Nigeria;
    All these keywords.

    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

    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:eee:deveco:v:141:y:2019:i:c:s0304387818305601. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/devec .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.