IDEAS home Printed from https://ideas.repec.org/a/rjr/romjef/vy2013i4p161-170.html
   My bibliography  Save this article

Corruption and Income Inequality in Asian Countries: Bootstrap Panel Granger Causality Test

Author

Listed:
  • Chiung-Ju Huang

    (Department of Public Finance, Feng Chia University, Taiwan)

Abstract

The purpose of this study is to investigate the causal relationship between corruption and income inequality experienced in ten Asian economies over the period 1995 to 2010. This study utilizes the bootstrap panel Granger causality approach, which allows both cross-sectional dependence and heterogeneity across countries, and is based on seemingly unrelated regressions (SUR) systems and Wald tests with country-specific bootstrap critical values. The empirical results show that there is a unidirectional causality from corruption to income inequality in China and the Philippines. Meanwhile, a one-way causal relationship running from income inequality to corruption exists in Indonesia, Japan, Korea, and Thailand.

Suggested Citation

  • Chiung-Ju Huang, 2013. "Corruption and Income Inequality in Asian Countries: Bootstrap Panel Granger Causality Test," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 161-170, December.
    • Handle: RePEc:rjr:romjef:v::y:2013:i:4:p:161-170
    as

    Download full text from publisher

    File URL: http://www.ipe.ro/rjef/rjef4_13/rjef4_2013p161-170.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nelson C. Mark & Masao Ogaki & Donggyu Sul, 2005. "Dynamic Seemingly Unrelated Cointegrating Regressions," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 797-820.
    2. Oguzhan C. Dincer & Burak Gunalp, 2012. "Corruption And Income Inequality In The United States," Contemporary Economic Policy, Western Economic Association International, vol. 30(2), pages 283-292, April.
    3. Phillips, Peter C B, 1995. "Fully Modified Least Squares and Vector Autoregression," Econometrica, Econometric Society, vol. 63(5), pages 1023-1078, September.
    4. Dobson, Stephen & Ramlogan-Dobson, Carlyn, 2010. "Is there a trade-off between income inequality and corruption? Evidence from Latin America," Economics Letters, Elsevier, vol. 107(2), pages 102-104, May.
    5. T. S. Breusch & A. R. Pagan, 1980. "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 239-253.
    6. M. Hashem Pesaran & Aman Ullah & Takashi Yamagata, 2008. "A bias-adjusted LM test of error cross-section independence," Econometrics Journal, Royal Economic Society, vol. 11(1), pages 105-127, March.
    7. M. Hashem Pesaran, 2021. "General diagnostic tests for cross-sectional dependence in panels," Empirical Economics, Springer, vol. 60(1), pages 13-50, January.
    8. Hashem Pesaran, M. & Yamagata, Takashi, 2008. "Testing slope homogeneity in large panels," Journal of Econometrics, Elsevier, vol. 142(1), pages 50-93, January.
    9. Hongyi Li & Lixin Colin Xu & Heng‐fu Zou, 2000. "Corruption, Income Distribution, and Growth," Economics and Politics, Wiley Blackwell, vol. 12(2), pages 155-182, July.
    10. Hindriks, Jean & Keen, Michael & Muthoo, Abhinay, 1999. "Corruption, extortion and evasion," Journal of Public Economics, Elsevier, vol. 74(3), pages 395-430, December.
    11. repec:exe:wpaper:98/09 is not listed on IDEAS
    12. Enrico Colombatto, 2003. "Why is Corruption Tolerated?," The Review of Austrian Economics, Springer;Society for the Development of Austrian Economics, vol. 16(4), pages 363-379, December.
    13. Mr. Vito Tanzi & Mr. Hamid R Davoodi, 1997. "Corruption, Public Investment, and Growth," IMF Working Papers 1997/139, International Monetary Fund.
    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. Sulemana, Iddisah & Kpienbaareh, Daniel, 2018. "An empirical examination of the relationship between income inequality and corruption in Africa," Economic Analysis and Policy, Elsevier, vol. 60(C), pages 27-42.
    2. Sher Khan, 2022. "Investigating the Effect of Income Inequality on Corruption: New Evidence from 23 Emerging Countries," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 13(3), pages 2100-2126, September.
    3. Asgari, Heshmatolah & Moridian, Ali, 2023. "Investigating the Role of Human Capital and Shadow Economy in the Impact of Natural Resource Rent on Income Inequality with Regime Change (in Persian)," The Journal of Planning and Budgeting (٠صلنامه برنامه ریزی و بودجه), Institute for Management and Planning studies, vol. 28(4), pages 75-110, December.
    4. Alvarado, Rafael & Tillaguango, Brayan & López-Sánchez, Michelle & Ponce, Pablo & Işık, Cem, 2021. "Heterogeneous impact of natural resources on income inequality: The role of the shadow economy and human capital index," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 690-704.

    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. Huang, Chiung-Ju, 2016. "Is corruption bad for economic growth? Evidence from Asia-Pacific countries," The North American Journal of Economics and Finance, Elsevier, vol. 35(C), pages 247-256.
    2. Yilmaz Bayar & Marius Dan Gavriletea, 2018. "Peace, terrorism and economic growth in Middle East and North African countries," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(5), pages 2373-2392, September.
    3. Puente-Ajovín, Miguel & Sanso-Navarro, Marcos, 2015. "Granger causality between debt and growth: Evidence from OECD countries," International Review of Economics & Finance, Elsevier, vol. 35(C), pages 66-77.
    4. Chu, Hsiao-Ping & Chang, Tsangyao, 2012. "Nuclear energy consumption, oil consumption and economic growth in G-6 countries: Bootstrap panel causality test," Energy Policy, Elsevier, vol. 48(C), pages 762-769.
    5. Yilmaz Bayar & Laura Diaconu (Maxim) & Andrei Maxim, 2020. "Financial Development and CO 2 Emissions in Post-Transition European Union Countries," Sustainability, MDPI, vol. 12(7), pages 1-15, March.
    6. Manuchehr Irandoust, 2019. "Saving and investment causality: implications for financial integration in transition countries of Eastern Europe," International Economics and Economic Policy, Springer, vol. 16(2), pages 397-416, April.
    7. repec:agr:journl:v:3(604):y:2015:i:3(604):p:171-186 is not listed on IDEAS
    8. Yanli LI, Hongfeng PENG & Hongfeng PENG, 2013. "Inflation Persistence in Nine Latin American Countries: Panel SURKSS Test with a Fourier Function," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 132-143, October.
    9. Murshed, Muntasir & Saboori, Behnaz & Madaleno, Mara & Wang, Hong & Doğan, Buhari, 2022. "Exploring the nexuses between nuclear energy, renewable energy, and carbon dioxide emissions: The role of economic complexity in the G7 countries," Renewable Energy, Elsevier, vol. 190(C), pages 664-674.
    10. Chia-I Pan & Tsangyao Chang & Yemane Wolde-Rufael, 2015. "Military Spending and Economic Growth in the Middle East Countries: Bootstrap Panel Causality Test," Defence and Peace Economics, Taylor & Francis Journals, vol. 26(4), pages 443-456, August.
    11. Ming Zhong & Tsangyao Chang & D. P. Tang & Yemane Wolde-Rufael, 2015. "The causal nexus between military spending and unemployment in the G7: a bootstrap panel causality test," Defence and Peace Economics, Taylor & Francis Journals, vol. 26(6), pages 609-622, December.
    12. Irandoust, Manuchehr, 2017. "Metal prices and stock market performance: Is there an empirical link?," Resources Policy, Elsevier, vol. 52(C), pages 389-392.
    13. Mehmet Akif DESTEK, 2018. "Dimensions of globalization and income inequality in transition economies: taking into account cross-sectional dependence," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 9, pages 5-25, December.
    14. Yilmaz Bayar & Marius Dan Gavriletea, 2018. "Foreign Direct Investment Inflows and Financial Development in Central and Eastern European Union Countries: A Panel Cointegration and Causality," IJFS, MDPI, vol. 6(2), pages 1-13, May.
    15. Kassouri, Yacouba & Alola, Andrew Adewale & Savaş, Savaş, 2021. "The dynamics of material consumption in phases of the economic cycle for selected emerging countries," Resources Policy, Elsevier, vol. 70(C).
    16. Irandoust, Manuchehr, 2019. "Wagner on government spending and national income: A new look at an old relationship," Journal of Policy Modeling, Elsevier, vol. 41(4), pages 636-646.
    17. Mutascu, Mihai, 2016. "A bootstrap panel Granger causality analysis of energy consumption and economic growth in the G7 countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 166-171.
    18. Lei-Ju Qiu & Shun-Bin Zhong & Bao-Wen Sun & Yu Song & Xiao-Hua Chen, 2021. "Is internet penetration narrowing the rural–urban income inequality? A cross-regional study of China," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(5), pages 1795-1814, October.
    19. Ozcan, Burcu & Ozturk, Ilhan, 2019. "Renewable energy consumption-economic growth nexus in emerging countries: A bootstrap panel causality test," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 30-37.
    20. Chen, Sen-Sung & Cheng, Shu-Ching & Pan, Guochen & Wu, Tsung-Pao, 2013. "The relationship between globalization and insurance activities: A panel data analysis," Japan and the World Economy, Elsevier, vol. 28(C), pages 151-157.
    21. Yusuf Ekrem AKBAS, 2015. "Financial development and economic growth in emerging market: bootstrap panel causality analysis," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(3(604), A), pages 171-186, Autumn.

    More about this item

    Keywords

    corruption; income inequality; cross-sectional dependence; heterogeneity; panel Granger causality test;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • H80 - Public Economics - - Miscellaneous Issues - - - 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:rjr:romjef:v::y:2013:i:4:p:161-170. 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: Corina Saman (email available below). General contact details of provider: https://edirc.repec.org/data/ipacaro.html .

    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.