IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i13p2180-d845437.html
   My bibliography  Save this article

Who Is the Most Effective Country in Anti-Corruption? From the Perspective of Open Government Data and Gross Domestic Product

Author

Listed:
  • Po-Yuan Shih

    (Department of Finance, National Yunlin University of Science and Technology, Douliu 64002, Taiwan)

  • Cheng-Ping Cheng

    (Department of Finance, National Yunlin University of Science and Technology, Douliu 64002, Taiwan)

  • Dong-Her Shih

    (Department of Information Management, National Yunlin University of Science and Technology, Douliu 64002, Taiwan)

  • Ting-Wei Wu

    (Department of Information Management, National Yunlin University of Science and Technology, Douliu 64002, Taiwan)

  • David C. Yen

    (Jesse H. Jones School of Business, Texas Southern University, 3100 Cleburne Street, Houston, TX 77004, USA)

Abstract

Corruption represents the misuse of public power by government departments for personal gain, hindering a country’s economic growth. Corruption cannot be eliminated by implementing the national democratic system, and mature democratic countries also exist with varying degrees of corruption. Corruption affects people’s trust in the public sector and the country’s economic development. Open government data can help people understand the governance performance of the government to reduce corruption in the public sector. Citizens can use open government data to generate innovative applications and economic value. This study uses a two-stage data envelopment analysis method to assess the anti-corruption efficiency of 21 countries from 2013 to 2017 through open government data, the corruption perception index, and GDP data. Then, the efficiency analyzed is introduced into the BCG (Boston Consulting Group) matrix to observe the distribution of these 21 countries. Analyzing the results showed that Uruguay and Costa Rica in Central and South America are the two most influential countries in fighting corruption. Turkey is at the bottom in the evaluation of anti-corruption efficiency. In addition, discussions of the included countries for their possible improvement in anti-corruption are also provided by using the association rule’s analysis. The study results will provide a reference for governments to effectively carry out anti-corruption work in the future.

Suggested Citation

  • Po-Yuan Shih & Cheng-Ping Cheng & Dong-Her Shih & Ting-Wei Wu & David C. Yen, 2022. "Who Is the Most Effective Country in Anti-Corruption? From the Perspective of Open Government Data and Gross Domestic Product," Mathematics, MDPI, vol. 10(13), pages 1-20, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2180-:d:845437
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/13/2180/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/13/2180/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Leviäkangas, Pekka & Molarius, Riitta, 2020. "Open government data policy and value added - Evidence on transport safety agency case," Technology in Society, Elsevier, vol. 63(C).
    2. Ren Qing-dao-er-ji & Rui Pang & Yue Chang, 2020. "An Improved HotSpot Algorithm and Its Application to Sandstorm Data in Inner Mongolia," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, April.
    3. Jakob Svensson, 2005. "Eight Questions about Corruption," Journal of Economic Perspectives, American Economic Association, vol. 19(3), pages 19-42, Summer.
    4. Denisova-Schmidt, Elena & Prytula, Yaroslav, 2018. "Business corruption in Ukraine: A way to get things done?," Business Horizons, Elsevier, vol. 61(6), pages 867-879.
    5. Yi-Chieh Chen & Lin-Huan Hu & Wan Chen Lu & Jei-Zheng Wu & Jiun-Jen Yang, 2021. "Multiple Criteria Decision-Making for Developing an International Game Participation Strategy: A Novel Application of the Data Envelopment Analysis (DEA) Two-Stage Efficiency Process," Mathematics, MDPI, vol. 9(14), pages 1-16, July.
    6. 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.
    7. Kao, Chiang & Hwang, Shiuh-Nan, 2008. "Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan," European Journal of Operational Research, Elsevier, vol. 185(1), pages 418-429, February.
    8. Tran, Quoc Trung, 2020. "Corruption and corporate cash holdings: international evidence," Journal of Multinational Financial Management, Elsevier, vol. 54(C).
    9. Brown, David S. & Touchton, Michael & Whitford, Andrew, 2011. "Political Polarization as a Constraint on Corruption: A Cross-national Comparison," World Development, Elsevier, vol. 39(9), pages 1516-1529, September.
    10. Coffman, Chad D. & Anderson, Brian S., 2018. "Under the table: Exploring the type and communication of corruption on opportunity pursuit," Journal of Business Venturing Insights, Elsevier, vol. 10(C), pages 1-1.
    11. Cummins, Mark & Gillanders, Robert, 2020. "Greasing the Turbines? Corruption and access to electricity in Africa," Energy Policy, Elsevier, vol. 137(C).
    12. Melki, Mickael & Pickering, Andrew, 2020. "Polarization and corruption in America," European Economic Review, Elsevier, vol. 124(C).
    13. Adrian Ioana & Vasile Mirea & Cezar Balescu, 2009. "Analysis of Service Quality Management in the Materials Industry using the BCG Matrix Method," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 11(26), pages 270-276, June.
    14. Lawrence M. Seiford & Joe Zhu, 1999. "Profitability and Marketability of the Top 55 U.S. Commercial Banks," Management Science, INFORMS, vol. 45(9), pages 1270-1288, September.
    15. Barbara Ubaldi, 2013. "Open Government Data: Towards Empirical Analysis of Open Government Data Initiatives," OECD Working Papers on Public Governance 22, OECD Publishing.
    16. Li, Yongjun & Lei, Xiyang & Dai, Qianzhi & Liang, Liang, 2015. "Performance evaluation of participating nations at the 2012 London Summer Olympics by a two-stage data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 243(3), pages 964-973.
    17. Chen, Kun & Song, Yao-yao & Pan, Jiao-feng & Yang, Guo-liang, 2020. "Measuring destocking performance of the Chinese real estate industry: A DEA-Malmquist approach," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    18. Fath, Sean & Kay, Aaron C., 2018. "“If hierarchical, then corrupt”: Exploring people’s tendency to associate hierarchy with corruption in organizations," Organizational Behavior and Human Decision Processes, Elsevier, vol. 149(C), pages 145-164.
    19. Sonia Aviles-Sacoto & Wade D Cook & Raha Imanirad & Joe Zhu, 2015. "Two-stage network DEA: when intermediate measures can be treated as outputs from the second stage," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(11), pages 1868-1877, November.
    20. Malanski, Leonardo Köppe & Póvoa, Angela Cristiane Santos, 2021. "Economic growth and corruption in emerging markets: Does economic freedom matter?," International Economics, Elsevier, vol. 166(C), pages 58-70.
    21. Fukuyama, Hirofumi & Matousek, Roman, 2017. "Modelling bank performance: A network DEA approach," European Journal of Operational Research, Elsevier, vol. 259(2), pages 721-732.
    22. Dincer, Oguzhan C. & Fredriksson, Per G., 2018. "Corruption and environmental regulatory policy in the United States: Does trust matter?," Resource and Energy Economics, Elsevier, vol. 54(C), pages 212-225.
    23. Charnes, A. & Cooper, W. W. & Rhodes, E., 1979. "Measuring the efficiency of decision-making units," European Journal of Operational Research, Elsevier, vol. 3(4), pages 339-338, July.
    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. Jin, Baoling & Han, Ying & Kou, Po, 2023. "Dynamically evaluating the comprehensive efficiency of technological innovation and low-carbon economy in China's industrial sectors," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).

    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. Phung, Manh-Trung & Cheng, Cheng-Ping & Guo, Chuanyin & Kao, Chen-Yu, 2020. "Mixed Network DEA with Shared Resources: A Case of Measuring Performance for Banking Industry," Operations Research Perspectives, Elsevier, vol. 7(C).
    2. Xiaohong Liu & Feng Yang & Jie Wu, 2020. "DEA considering technological heterogeneity and intermediate output target setting: the performance analysis of Chinese commercial banks," Annals of Operations Research, Springer, vol. 291(1), pages 605-626, August.
    3. Xiao Shi & Yongjun Li & Ali Emrouznejad & Jianhui Xie & Liang Liang, 2017. "Estimation of potential gains from bank mergers: A novel two-stage cost efficiency DEA model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(9), pages 1045-1055, September.
    4. Fukuyama, Hirofumi & Matousek, Roman & Tzeremes, Nickolaos G., 2020. "A Nerlovian cost inefficiency two-stage DEA model for modeling banks’ production process: Evidence from the Turkish banking system," Omega, Elsevier, vol. 95(C).
    5. Dan Li & Yanfeng Li & Yeming Gong & Jiawei Yang, 2021. "Estimation of bank performance from multiple perspectives: an alternative solution to the deposit dilemma," Journal of Productivity Analysis, Springer, vol. 56(2), pages 151-170, December.
    6. Panagiotis Mitropoulos & Ioannis Mitropoulos, 2020. "Performance evaluation of retail banking services: Is there a trade‐off between production and quality?," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(7), pages 1237-1250, October.
    7. Galagedera, Don U.A. & Watson, John & Premachandra, I.M. & Chen, Yao, 2016. "Modeling leakage in two-stage DEA models: An application to US mutual fund families," Omega, Elsevier, vol. 61(C), pages 62-77.
    8. Suvvari Anandarao & S. Raja Sethu Durai & Phanindra Goyari, 2019. "Efficiency Decomposition in two-stage Data Envelopment Analysis: An application to Life Insurance companies in India," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(2), pages 271-285, June.
    9. Liu, Hui-hui & Yang, Guo-liang & Liu, Xiao-xiao & Song, Yao-yao, 2020. "R&D performance assessment of industrial enterprises in China: A two-stage DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    10. Kao, Chiang & Hwang, Shiuh-Nan, 2011. "Decomposition of technical and scale efficiencies in two-stage production systems," European Journal of Operational Research, Elsevier, vol. 211(3), pages 515-519, June.
    11. Yin, Pengzhen & Sun, Jiasen & Chu, Junfei & Liang, Liang, 2016. "Evaluating the environmental efficiency of a two-stage system with undesired outputs by a DEA approach: An interest preference perspectiveAuthor-Name: Wu, Jie," European Journal of Operational Research, Elsevier, vol. 254(3), pages 1047-1062.
    12. Qingxian An & Fanyong Meng & Sheng Ang & Xiaohong Chen, 2018. "A new approach for fair efficiency decomposition in two-stage structure system," Operational Research, Springer, vol. 18(1), pages 257-272, April.
    13. Volkan Soner Özsoy & Mediha Örkcü & H. Hasan Örkcü, 2021. "A minimax approach for selecting the overall and stage-level most efficient unit in two stage production processes," Annals of Operations Research, Springer, vol. 300(1), pages 137-169, May.
    14. Li, Yongjun & Liu, Jin & Ang, Sheng & Yang, Feng, 2021. "Performance evaluation of two-stage network structures with fixed-sum outputs: An application to the 2018winter Olympic Games," Omega, Elsevier, vol. 102(C).
    15. Fukuyama, Hirofumi & Matousek, Roman, 2018. "Nerlovian revenue inefficiency in a bank production context: Evidence from Shinkin banks," European Journal of Operational Research, Elsevier, vol. 271(1), pages 317-330.
    16. Kourtzidis, Stavros & Matousek, Roman & Tzeremes, Nickolaos G., 2021. "Modelling a multi-period production process: Evidence from the Japanese regional banks," European Journal of Operational Research, Elsevier, vol. 294(1), pages 327-339.
    17. Kao, Chiang, 2009. "Efficiency decomposition in network data envelopment analysis: A relational model," European Journal of Operational Research, Elsevier, vol. 192(3), pages 949-962, February.
    18. Chatzistamoulou, Nikos, 2023. "Is digital transformation the Deus ex Machina towards sustainability transition of the European SMEs?," Ecological Economics, Elsevier, vol. 206(C).
    19. Khezrimotlagh, Dariush & Kaffash, Sepideh & Zhu, Joe, 2022. "U.S. airline mergers’ performance and productivity change," Journal of Air Transport Management, Elsevier, vol. 102(C).
    20. Hadi Ghafoorian & NikIntan Norhan & Mohammed Ndaliman Abubakar & Fazel Mohammadi Nodeh, 2013. "Efficiency Considering Credit Risk in Banking Industry, Using Two-stage DEA," Journal of Social and Development Sciences, AMH International, vol. 4(8), pages 356-360.

    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:gam:jmathe:v:10:y:2022:i:13:p:2180-:d:845437. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.