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Evaluation of technology clubs by clustering: A cautionary note

Author

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  • Andres, Antonio Rodriguez
  • Otero, Abraham
  • Amavilah, Voxi Heinrich
Abstract
Applications of machine learning techniques to economic problems are increasing. These are powerful techniques with great potential to extract insights from economic data. However, care must be taken to apply them correctly, or the wrong conclusions may be drawn. In the technology clubs literature, after applying a clustering algorithm, some authors train a supervised machine learning technique, such as a decision tree or a neural network, to predict the label of the clusters. Then, they use some performance metric (typically, accuracy) of that prediction as a measure of the quality of the clustering configuration they have found. This is an error with potential negative implications for policy, because obtaining a high accuracy in such a prediction does not mean that the clustering configuration found is correct. This paper explains in detail why this modus operandi is not sound from theoretical point of view and uses computer simulations to demonstrate it. We caution policy and indicate the direction for future investigations.

Suggested Citation

  • Andres, Antonio Rodriguez & Otero, Abraham & Amavilah, Voxi Heinrich, 2021. "Evaluation of technology clubs by clustering: A cautionary note," MPRA Paper 109138, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:109138
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    1. Durlauf, Steven N & Johnson, Paul A, 1995. "Multiple Regimes and Cross-Country Growth Behaviour," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(4), pages 365-384, Oct.-Dec..
    2. Ellis Scharfenaker, Markus P.A. Schneider, 2019. "Labor Market Segmentation and the Distribution of Income: New Evidence from Internal Census Bureau Data," Working Paper Series, Department of Economics, University of Utah 2019_08, University of Utah, Department of Economics.
    3. Patrick Bajari & Christina Dalton & Han Hong & Ahmed Khwaja, 2014. "Moral hazard, adverse selection, and health expenditures: A semiparametric analysis," RAND Journal of Economics, RAND Corporation, vol. 45(4), pages 747-763, December.
    4. Saba, Charles Shaaba & David, Oladipo Olalekan, 2020. "Convergence patterns in global ICT: Fresh insights from a club clustering algorithm," Telecommunications Policy, Elsevier, vol. 44(10).
    5. Castellacci, Fulvio, 2008. "Technology clubs, technology gaps and growth trajectories," Structural Change and Economic Dynamics, Elsevier, vol. 19(4), pages 301-314, December.
    6. Ashesh Rambachan & Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan, 2020. "An Economic Perspective on Algorithmic Fairness," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 91-95, May.
    7. Susan C. Athey & Kevin A. Bryan & Joshua S. Gans, 2020. "The Allocation of Decision Authority to Human and Artificial Intelligence," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 80-84, May.
    8. Castellacci, Fulvio & Archibugi, Daniele, 2008. "The technology clubs: The distribution of knowledge across nations," Research Policy, Elsevier, vol. 37(10), pages 1659-1673, December.
    9. Steffen Q. Mueller, 2020. "Pre- and within-season attendance forecasting in Major League Baseball: a random forest approach," Applied Economics, Taylor & Francis Journals, vol. 52(41), pages 4512-4528, September.
    10. Janet Currie & Henrik Kleven & Esmée Zwiers, 2020. "Technology and Big Data Are Changing Economics: Mining Text to Track Methods," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 42-48, May.
    11. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    12. Jessica Clement, 2020. "Social protection clusters in sub‐Saharan Africa," International Journal of Social Welfare, John Wiley & Sons, vol. 29(1), pages 20-28, January.
    13. Bo Cowgill & Megan T. Stevenson, 2020. "Algorithmic Social Engineering," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 96-100, May.
    14. Fagerberg, Jan & Srholec, Martin & Knell, Mark, 2007. "The Competitiveness of Nations: Why Some Countries Prosper While Others Fall Behind," World Development, Elsevier, vol. 35(10), pages 1595-1620, October.
    15. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    16. Ricardo Fraiman & Badih Ghattas & Marcela Svarc, 2013. "Interpretable clustering using unsupervised binary trees," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(2), pages 125-145, June.
    17. Yan Liu & Tian Xie, 2019. "Machine learning versus econometrics: prediction of box office," Applied Economics Letters, Taylor & Francis Journals, vol. 26(2), pages 124-130, January.
    18. Fulvio Castellacci, 2011. "Closing the Technology Gap?," Review of Development Economics, Wiley Blackwell, vol. 15(1), pages 180-197, February.
    19. Nalan Baştürk & Richard Paap & Dick van Dijk, 2012. "Structural differences in economic growth: an endogenous clustering approach," Applied Economics, Taylor & Francis Journals, vol. 44(1), pages 119-134, January.
    20. Murray Wolfson & Zagros Madjd-Sadjadi & Patrick James, 2004. "Identifying National Types: A Cluster Analysis of Politics, Economics, and Conflict," Journal of Peace Research, Peace Research Institute Oslo, vol. 41(5), pages 607-623, September.
    21. Ahlquist, John S. & Breunig, Christian, 2012. "Model-based Clustering and Typologies in the Social Sciences," Political Analysis, Cambridge University Press, vol. 20(1), pages 92-112, January.
    22. Aaron Kreiner & John Duca, 2020. "Can machine learning on economic data better forecast the unemployment rate?," Applied Economics Letters, Taylor & Francis Journals, vol. 27(17), pages 1434-1437, October.
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    Cited by:

    1. Voxi Heinrich Amavilah & Antonio Rodríguez Andrés, 2024. "Knowledge Economy and the Economic Performance of African Countries: A Seemingly Unrelated and Recursive Approach," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 110-143, March.
    2. Voxi Heinrich Amavilah & Antonio Rodríguez Andrés, 2024. "Knowledge Economy and the Economic Performance of African Countries: A Seemingly Unrelated and Recursive Approach," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 110-143, March.

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    More about this item

    Keywords

    Machine learning; clustering; technological change; technology clubs; knowledge economy; cross-country;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy
    • O57 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Comparative Studies of Countries
    • P41 - Political Economy and Comparative Economic Systems - - Other Economic Systems - - - Planning, Coordination, and Reform

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