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Inference of Factors for Labor Productivity Growth Used Randomized Experiment and Statistical Causality

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  • Ekaterina V. Orlova

    (Department of Economics and Management, Ufa University of Science and Technology, 450000 Ufa, Russia)

Abstract

The study of causal dependencies in economics is fraught with great difficulties, that it is required to consider not only the object structure, but also take into account a huge number of factors acting on the object, about which nothing is either known or difficult to measure. In this paper, we attempt to overcome this problem and apply the theory of statistical causality for labor productivity management. We suggest new technology that provides the inference of causal relations between the special programs implemented in the company’s and employee’s labor productivity. The novelty of the proposed technology is that it is based on a hybrid object model, combines two models: 1—the structural object model about its functioning and development to provide a causal inference and prediction the effect of explicit factors; 2—the model based on observed data to clarify causality and to test it empirically. The technology provides integration of the theory of causal Bayesian networks, methods of randomized controlled experiments and statistical methods, allows under nonlinearity, dynamism, stochasticity and non-stationarity of the initial data, to evaluate the effect of programs on the labor effeciency. The difference between the proposed technology and others is that it ensures determination the synergistic effect of the action of the cause (program) on the effect—labor productivity in condition of hidden factors. The practical significance of the research is the results of its testing the proposed theoretical provisions, methods and technologies on actual data about food service company. The results obtained could contribute to the labor productivity growth over uncertainty of the external and internal factors and provide the companies sustainable development and its profitability growth.

Suggested Citation

  • Ekaterina V. Orlova, 2023. "Inference of Factors for Labor Productivity Growth Used Randomized Experiment and Statistical Causality," Mathematics, MDPI, vol. 11(4), pages 1-22, February.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:863-:d:1061589
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    References listed on IDEAS

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    1. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2021. "Synthetic Difference-in-Differences," American Economic Review, American Economic Association, vol. 111(12), pages 4088-4118, December.
    2. Ekaterina V. Orlova, 2022. "Methodology and Statistical Modeling of Social Capital Influence on Employees’ Individual Innovativeness in a Company," Mathematics, MDPI, vol. 10(11), pages 1-22, May.
    3. Gratton, Lynda & Ghoshal, Sumantra, 2003. "Managing Personal Human Capital:: New Ethos for the 'Volunteer' Employee," European Management Journal, Elsevier, vol. 21(1), pages 1-10, February.
    4. Athey, Susan & Imbens, Guido W., 2022. "Design-based analysis in Difference-In-Differences settings with staggered adoption," Journal of Econometrics, Elsevier, vol. 226(1), pages 62-79.
    5. Slutskin, L., 2017. "Graphical Statistical Methods for Studying Causal Effects. Bayesian Networks," Journal of the New Economic Association, New Economic Association, vol. 36(4), pages 12-30.
    6. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
    7. Ekaterina V. Orlova, 2022. "Design Technology and AI-Based Decision Making Model for Digital Twin Engineering," Future Internet, MDPI, vol. 14(9), pages 1-14, August.
    8. Ekaterina V. Orlova, 2021. "Methodology and Models for Individuals’ Creditworthiness Management Using Digital Footprint Data and Machine Learning Methods," Mathematics, MDPI, vol. 9(15), pages 1-28, August.
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    Cited by:

    1. Ekaterina V. Orlova, 2023. "Dynamic Regimes for Corporate Human Capital Development Used Reinforcement Learning Methods," Mathematics, MDPI, vol. 11(18), pages 1-22, September.

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