IDEAS home Printed from https://ideas.repec.org/a/sae/entthe/v2y1977i2p4-15.html
   My bibliography  Save this article

Business Failures, Managerial Competence, and Macroeconomic Variables

Author

Listed:
  • William Dipietro
  • Bansi Sawhney

Abstract

Identifying the causes of business failures is crucial for effective policy making, and is especially important for small businesses which account for the largest component of firm failures in the U.S. As an example, if we find that managerial skills are important in reducing firm failures, resources can be directed toward management training and education programs. Policies designed to reduce failures would not only reduce the hardships on the individuals affected directly by such failures but would also aid in the smooth functioning of the economy. There are two important factors which jointly determine the failure rate of businesses in the economy. The first is internal — the effectiveness of management, and the second is external — the general economic environment. Overall the purpose of this paper is to use the traditional economic model of the firm to discuss the importance of each of these factors in determining business failures and to use time-series data to assess the relative weight of each factor in determining failures over time. Specifically the objectives of this paper are: first, to economically define managerial competency; second, to test the hypothesis that managerial efficiency has increased over time; and third, to assess the effect of selected macroeconomic variables on firm failures.

Suggested Citation

  • William Dipietro & Bansi Sawhney, 1977. "Business Failures, Managerial Competence, and Macroeconomic Variables," Entrepreneurship Theory and Practice, , vol. 2(2), pages 4-15, October.
    • Handle: RePEc:sae:entthe:v:2:y:1977:i:2:p:4-15
    DOI: 10.1177/104225877700200202
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/104225877700200202
    Download Restriction: no
    File URL: https://libkey.io/10.1177/104225877700200202?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
    ---><---

    References listed on IDEAS

    as
    1. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    2. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    3. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    4. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. James J. Chrisman & John Leslie, 1989. "Strategic, Administrative, and Operating Problems: The Impact of Outsiders on Small Firm Performance," Entrepreneurship Theory and Practice, , vol. 13(3), pages 37-52, April.

    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. Antonio Davila & George Foster & Xiaobin He & Carlos Shimizu, 2015. "The rise and fall of startups: Creation and destruction of revenue and jobs by young companies," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 6-35, February.
    2. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    3. Pavol Durana & Lucia Michalkova & Andrej Privara & Josef Marousek & Milos Tumpach, 2021. "Does the life cycle affect earnings management and bankruptcy?," Oeconomia Copernicana, Institute of Economic Research, vol. 12(2), pages 425-461, June.
    4. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    5. Jie Sun & Jie Li & Hamido Fujita & Wenguo Ai, 2023. "Multiclass financial distress prediction based on one‐versus‐one decomposition integrated with improved decision‐directed acyclic graph," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1167-1186, August.
    6. Guido Max Mantovani & Gregory Gadzinski, 2022. "How to Rate the Financial Performance of Private Companies? A Tailored Integrated Rating Methodology Applied to North-Eastern Italian Districts," JRFM, MDPI, vol. 15(11), pages 1-18, October.
    7. Enrico Supino & Nicola Piras, 2022. "Le performance dei modelli di credit scoring in contesti di forte instabilit? macroeconomica: il ruolo delle Reti Neurali Artificiali," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2022(2), pages 41-61.
    8. Adriana Csikosova & Maria Janoskova & Katarina Culkova, 2020. "Application of Discriminant Analysis for Avoiding the Risk of Quarry Operation Failure," JRFM, MDPI, vol. 13(10), pages 1-14, September.
    9. Haoming Wang & Xiangdong Liu, 2021. "Undersampling bankruptcy prediction: Taiwan bankruptcy data," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-17, July.
    10. Trueck, Stefan & Rachev, Svetlozar T., 2008. "Rating Based Modeling of Credit Risk," Elsevier Monographs, Elsevier, edition 1, number 9780123736833.
    11. E. Fedorova A. & M. Chukhlantseva A. & D. Chekrizov V. & ЕЛЕНА Федорова АНАТОЛЬЕВНА & МАРИЯ Чухланцева АЛЕКСАНДРОВНА & ДМИТРИЙ Чекризов ВАСИЛЬЕВИЧ, 2017. "Нормативные значения коэффициентов финансовой устойчивости: особенности видов экономической деятельности // Normative Values of Financial Stability Ratios: Industry-Specific Features," Управленческие науки // Management Science, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 7(2), pages 44-55.
    12. Eling, Martin & Jia, Ruo, 2018. "Business failure, efficiency, and volatility: Evidence from the European insurance industry," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 58-76.
    13. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    14. Ahsan Habib & Mabel D' Costa & Hedy Jiaying Huang & Md. Borhan Uddin Bhuiyan & Li Sun, 2020. "Determinants and consequences of financial distress: review of the empirical literature," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(S1), pages 1023-1075, April.
    15. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
    16. Youssef Zizi & Mohamed Oudgou & Abdeslam El Moudden, 2020. "Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach," Risks, MDPI, vol. 8(4), pages 1-21, October.
    17. Shoukat Ali & Ramiz ur Rehman & Wang Yuan & Muhammad Ishfaq Ahmad & Rizwan Ali, 2022. "Does foreign institutional ownership mediate the nexus between board diversity and the risk of financial distress? A case of an emerging economy of China," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 12(3), pages 553-581, September.
    18. Juraini Zainol Abidin & Nur Adiana Hiau Abdullah & Karren Lee-Hwei Khaw, 2020. "Predicting SMEs Failure: Logistic Regression vs Artificial Neural Network Models," Capital Markets Review, Malaysian Finance Association, vol. 28(2), pages 29-41.
    19. Hamid Waqas & Rohani Md-Rus, 2018. "Predicting financial distress: Applicability of O-score model for Pakistani firms," Business and Economic Horizons (BEH), Prague Development Center, vol. 14(2), pages 389-401, April.
    20. Edith Navarrete Marneou & Edgar Sansores Guerrero, 2011. "Quintano Roo Mexico Micro, Small And Medium Sized Business Failure: An Multi Variable Analysis, El Fracaso De Las Micro, Pequenas Y Medianas Empresas En Quintana Roo, Mexico: Un Analisis Multivariante," Revista Internacional Administracion & Finanzas, The Institute for Business and Finance Research, vol. 4(3), pages 21-33.

    More about this item

    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:sae:entthe:v:2:y:1977:i:2:p:4-15. 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: SAGE Publications (email available below). General contact details of provider: .

    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.