IDEAS home Printed from https://ideas.repec.org/a/rnd/arimbr/v15y2023i3p183-194.html
   My bibliography  Save this article

The Determinants of Household Debt in Malaysia

Author

Listed:
  • Norliza Che Yahya
  • Bushra Mohd Zaki
  • Nik Norain Izzati Nik Azid
  • Nur Fatihah Mohd Ali
  • Nor Amanie Najihah Hussain

Abstract

This study aims to observe the determinants of the factors affecting household debt in Malaysia. This study has listed six independent variables that would affect Malaysia’s household debt: real interest rate, inflation rate, unemployment rate, household consumption expenditure, gross domestic product (GDP), and housing price index (HPI). This research was conducted by using annual data for 30 years (1991-2021); this study also used a quantitative approach to the collection of data using secondary data such as the website of the World Bank, Department of Statistics Malaysia, Eikon Thomson Reuters, and Bank Negara Malaysia. A theoretical model with the hypothesized relationships was tested with the help of the structural equation modelling procedures. Findings showed that the unemployment rate and gross domestic product have a positive and significant relationship with household debt. These determinants are directly affecting household debt in Malaysia, especially in microeconomic factors. This study will contribute to a better understanding that household debt can be influenced by other factors. Further study on the other determinants of household debt may result in varying results.

Suggested Citation

  • Norliza Che Yahya & Bushra Mohd Zaki & Nik Norain Izzati Nik Azid & Nur Fatihah Mohd Ali & Nor Amanie Najihah Hussain, 2023. "The Determinants of Household Debt in Malaysia," Information Management and Business Review, AMH International, vol. 15(3), pages 183-194.
    • Handle: RePEc:rnd:arimbr:v:15:y:2023:i:3:p:183-194
    DOI: 10.22610/imbr.v15i3(I).3528
    as

    Download full text from publisher

    File URL: https://ojs.amhinternational.com/index.php/imbr/article/view/3528/2268
    Download Restriction: no
    File URL: https://ojs.amhinternational.com/index.php/imbr/article/view/3528
    Download Restriction: no
    File URL: https://libkey.io/10.22610/imbr.v15i3(I).3528?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. Suhal Kusairi & Suriyani Muhamad & M Musdholifah & Shu-Chen Chang, 2019. "Labor Market and Household Debt in Asia Pacific Countries: Dynamic Heterogeneous Panel Data Analysis," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 1-15, June.
    2. Khairunnisa Abd Samad & Siti Nurazira Mohd Daud & Nuradli Ridzwan Shah Mohd Dali, 2020. "Determinants of household debt in emerging economies: A macro panel analysis," Cogent Business & Management, Taylor & Francis Journals, vol. 7(1), pages 1831765-183, January.
    3. T Bellotti & J Crook, 2009. "Credit scoring with macroeconomic variables using survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1699-1707, December.
    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. Alexandre, Michel & Antônio Silva Brito, Giovani & Cotrim Martins, Theo, 2017. "Default contagion among credit modalities: evidence from Brazilian data," MPRA Paper 76859, University Library of Munich, Germany.
    2. Ismail Tijjani Idris & Sabri Nayan, 2016. "The Moderating Role of Loan Monitoring on the Relationship between Macroeconomic Variables and Non-performing Loans in Association of Southeast Asian Nations Countries," International Journal of Economics and Financial Issues, Econjournals, vol. 6(2), pages 402-408.
    3. Dirick, Lore & Claeskens, Gerda & Vasnev, Andrey & Baesens, Bart, 2022. "A hierarchical mixture cure model with unobserved heterogeneity for credit risk," Econometrics and Statistics, Elsevier, vol. 22(C), pages 39-55.
    4. Dirick, Lore & Claeskens, Gerda & Baesens, Bart, 2015. "An Akaike information criterion for multiple event mixture cure models," European Journal of Operational Research, Elsevier, vol. 241(2), pages 449-457.
    5. Goldmann, Leonie & Crook, Jonathan & Calabrese, Raffaella, 2024. "A new ordinal mixed-data sampling model with an application to corporate credit rating levels," European Journal of Operational Research, Elsevier, vol. 314(3), pages 1111-1126.
    6. Bellotti, Tony & Crook, Jonathan, 2013. "Forecasting and stress testing credit card default using dynamic models," International Journal of Forecasting, Elsevier, vol. 29(4), pages 563-574.
    7. Ghulam, Yaseen & Derber, Julian, 2018. "Determinants of sovereign defaults," The Quarterly Review of Economics and Finance, Elsevier, vol. 69(C), pages 43-55.
    8. Yufei Xia & Xinyi Guo & Yinguo Li & Lingyun He & Xueyuan Chen, 2022. "Deep learning meets decision trees: An application of a heterogeneous deep forest approach in credit scoring for online consumer lending," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1669-1690, December.
    9. Djeundje, Viani Biatat & Crook, Jonathan, 2018. "Incorporating heterogeneity and macroeconomic variables into multi-state delinquency models for credit cards," European Journal of Operational Research, Elsevier, vol. 271(2), pages 697-709.
    10. Calabrese, Raffaella & Crook, Jonathan, 2020. "Spatial contagion in mortgage defaults: A spatial dynamic survival model with time and space varying coefficients," European Journal of Operational Research, Elsevier, vol. 287(2), pages 749-761.
    11. repec:syb:wpbsba:03/2013 is not listed on IDEAS
    12. Joseph L. Breeden, 2024. "An Age–Period–Cohort Framework for Profit and Profit Volatility Modeling," Mathematics, MDPI, vol. 12(10), pages 1-23, May.
    13. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    14. Rais Ahmad Itoo & A. Selvarasu & José António Filipe, 2015. "Loan Products and Credit Scoring by Commercial Banks (India)," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 5(1), pages 851-851.
    15. Ana Romão & Ricardo Barradas, 2024. "Macroeconomic determinants of households' indebtedness in Portugal: What really matters in the era of financialisation?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 383-401, January.
    16. Medina-Olivares, Victor & Calabrese, Raffaella & Dong, Yizhe & Shi, Baofeng, 2022. "Spatial dependence in microfinance credit default," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1071-1085.
    17. Li, Zhiyong & Li, Aimin & Bellotti, Anthony & Yao, Xiao, 2023. "The profitability of online loans: A competing risks analysis on default and prepayment," European Journal of Operational Research, Elsevier, vol. 306(2), pages 968-985.
    18. Joseph L Breeden & Lyn Thomas, 2016. "Solutions to specification errors in stress testing models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(6), pages 830-840, June.
    19. Ju, Yonghan & Jeon, Song Yi & Sohn, So Young, 2015. "Behavioral technology credit scoring model with time-dependent covariates for stress test," European Journal of Operational Research, Elsevier, vol. 242(3), pages 910-919.
    20. Jiang, Cuiqing & Wang, Zhao & Zhao, Huimin, 2019. "A prediction-driven mixture cure model and its application in credit scoring," European Journal of Operational Research, Elsevier, vol. 277(1), pages 20-31.
    21. Karol Przanowski, 2013. "Banking Retail Consumer Finance Data Generator – Credit Scoring Data Repository," "e-Finanse", University of Information Technology and Management, Institute of Financial Research and Analysis, vol. 9(1), pages 44-59, May.

    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:rnd:arimbr:v:15:y:2023:i:3:p:183-194. 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: Muhammad Tayyab (email available below). General contact details of provider: https://ojs.amhinternational.com/index.php/imbr .

    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.