IDEAS home Printed from https://ideas.repec.org/p/rza/ersawp/18.html

The Fiscal Theory of Income Distribution in Action: South African Low-income vs. High-Income Earners Response to Fiscal Policy Shocks

Author

Listed:
  • Lindokuhle Talent Zungu

    (University of Zululand)

Abstract

This study seeks to test the fiscal theory of income distribution in action in South Africa using low- and high-income earners, covering the period 1979–2022, using the Bayesian Vector Autoregression (BVAR) model with hierarchical priors. The BVAR technique accommodates a wide range of shortcomings in the data, such as the cross-section, without running out of degrees of freedom, and handles dense parameterization by imposing structure on model coefficients and optimizing the degree of formativeness. This study examines the impact of fiscal policy on income distribution among low-income and high-income earners. It was found that unexpected increases in government expenditure decreased income inequality, while all forms of taxation contributed to it. However, income inequality responds positively to an unexpected 1% increase in government expenditure on high-income earners. Taxation plays a significant role in reducing income inequality with a negative response. This study also explores how income inequality responds to changes in government expenditure and total government revenue. The lagged response suggests that expectations and market dynamics play a crucial role in reducing income inequality regardless of income level. This study suggests that South Africa should adopt a balanced tax policy that combines regressive and progressive measures. This would ensure an equitable distribution of burdens across income levels, with revenue from progressive taxes used to fund social welfare programs, such as education, healthcare, and affordable housing. This approach could reduce the wealth gap, promote social mobility, and create a more just society, making it an effective solution for income inequality in the country.

Suggested Citation

  • Lindokuhle Talent Zungu, 2025. "The Fiscal Theory of Income Distribution in Action: South African Low-income vs. High-Income Earners Response to Fiscal Policy Shocks," ERSA Working Paper Series 18, Economic Research Southern Africa.
  • Handle: RePEc:rza:ersawp:18
    as

    Download full text from publisher

    File URL: https://ersawps.org/index.php/working-paper-series/article/view/18/115
    File Function: First version, 2025
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Michael W. McCracken & Serena Ng, 2016. "FRED-MD: A Monthly Database for Macroeconomic Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
    2. Majid Sameti & Leila Rafie, 2010. "Interaction of Income Distribution, Taxes and Economic Growth (The Case of Iran and Some Selected East Asian Countries)," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 15(1), pages 67-81, winter.
    3. Dotti, Valerio, 2020. "Income inequality, size of government, and tax progressivity: A positive theory," European Economic Review, Elsevier, vol. 121(C).
    4. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
    5. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, June.
    6. Eliphas Ndou, 2024. "Inflation-income inequality nexus in South Africa: the role of inflationary environment," Journal of Applied Economics, Taylor & Francis Journals, vol. 27(1), pages 2316968-231, December.
    7. Gary M. Koop, 2013. "Forecasting with Medium and Large Bayesian VARS," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 177-203, March.
    8. Brown Smith, 2024. "Fiscal Policy Effects on Income Inequality in the United States," American Journal of Economics, AJPO Journals Limited, vol. 8(1), pages 1-12.
    9. Léleng Kebalo & Stéphane Zouri, 2024. "Income inequality in developing countries: fiscal policy’s role amid uncertainty," Journal of Applied Economics, Taylor & Francis Journals, vol. 27(1), pages 2316969-231, December.
    10. Arshad Ali Bhatti & Zakia Batool & Hasnain A. Naqvi, 2015. "Fiscal Policy and Its Role in Reducing Income Inequality- A CGE Analysis for Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 54(4), pages 843-864.
    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. repec:rza:ersawp:898 is not listed on IDEAS
    2. repec:rza:wpaper:898 is not listed on IDEAS
    3. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87, September.
    4. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    5. Chan, Joshua C.C., 2021. "Minnesota-type adaptive hierarchical priors for large Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1212-1226.
    6. Michael W. McCracken & Joseph T. McGillicuddy, 2019. "An empirical investigation of direct and iterated multistep conditional forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 181-204, March.
    7. Brave, Scott A. & Butters, R. Andrew & Justiniano, Alejandro, 2019. "Forecasting economic activity with mixed frequency BVARs," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1692-1707.
    8. Cubadda, Gianluca & Guardabascio, Barbara, 2019. "Representation, estimation and forecasting of the multivariate index-augmented autoregressive model," International Journal of Forecasting, Elsevier, vol. 35(1), pages 67-79.
    9. Zhang, Bo & Nguyen, Bao H. & Sun, Chuanwang, 2024. "Forecasting oil prices: Can large BVARs help?," Energy Economics, Elsevier, vol. 137(C).
    10. Reif Magnus, 2021. "Macroeconomic uncertainty and forecasting macroeconomic aggregates," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(2), pages 1-20, April.
    11. Shovon Sengupta & Sunny Kumar Singh & Tanujit Chakraborty, 2025. "Macroeconomic Forecasting for the G7 countries under Uncertainty Shocks," Papers 2510.23347, arXiv.org.
    12. Jan Pruser, 2024. "A large non-Gaussian structural VAR with application to Monetary Policy," Papers 2412.17598, arXiv.org.
    13. Koop, Gary & Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Bayesian compressed vector autoregressions," Journal of Econometrics, Elsevier, vol. 210(1), pages 135-154.
    14. Thabang NDLOVU & Nozibusiso Mavuso NDLOVU, 2024. "The Dynamic Linkages among Gold Prices, Stock Prices, the Exchange Rate and Interest Rate in South Africa," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 8(1), pages 35-56.
    15. Tuna Can Güleç & Elif Erer & Selim Duramaz, 2026. "Cryptocurrencies as shock transmitters: dynamic connectedness, hedging strategies, and portfolio management across financial markets for higher-order moments," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 12(1), pages 1-58, December.
    16. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2019. "Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors," Journal of Econometrics, Elsevier, vol. 212(1), pages 137-154.
    17. Niko Hauzenberger, 2020. "Flexible Mixture Priors for Large Time-varying Parameter Models," Papers 2006.10088, arXiv.org, revised Nov 2020.
    18. Xiaolei Wang & Tomasz Wo'zniak, 2025. "Bayesian Analyses of Structural Vector Autoregressions with Sign, Zero, and Narrative Restrictions Using the R Package bsvarSIGNs," Papers 2501.16711, arXiv.org.
    19. Niko Hauzenberger & Florian Huber & Luca Onorante, 2021. "Combining shrinkage and sparsity in conjugate vector autoregressive models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(3), pages 304-327, April.
    20. Joshua C. C. Chan & Eric Eisenstat & Chenghan Hou & Gary Koop, 2020. "Composite likelihood methods for large Bayesian VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 692-711, September.
    21. Florian Huber & Gary Koop & Massimiliano Marcellino & Tobias Scheckel, 2024. "Bayesian modelling of VAR precision matrices using stochastic block networks," Papers 2407.16349, arXiv.org.
    22. Cross, Jamie L. & Hou, Chenghan & Poon, Aubrey, 2020. "Macroeconomic forecasting with large Bayesian VARs: Global-local priors and the illusion of sparsity," International Journal of Forecasting, Elsevier, vol. 36(3), pages 899-915.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • E62 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Fiscal Policy; Modern Monetary Theory
    • H30 - Public Economics - - Fiscal Policies and Behavior of Economic Agents - - - General
    • H20 - Public Economics - - Taxation, Subsidies, and Revenue - - - General

    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:rza:ersawp:18. 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: Maggi Sigg (email available below). General contact details of provider: https://ersawps.org/index.php/working-paper-series/ .

    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.