Comparative Analysis of The Impact of Big Data on Corruption in Selected Developing and Developed Countries


  •   Adewale Joel Adebisi

  •   Cherif Guermat


Big data analytics adoption is gaining momentum amongst both policy makers and business leaders. Indeed many sectors in the economy and society have been adopting recent information technology innovations to deal with issues that were previously impossible to tackle. One of these issues is corruption. This paper considers the impact of big data on corruption in developed and developing countries. Specifically, we investigate the effect of internet usage on corruption prevention and early detection, and examine the impact of investment in data-driven technology on corruption prevention and early detection. Finally, we evaluate the impact of mobile data subscription on total corruption. We use secondary data covering 1995 to 2020 for three low FinTech developing countries and three mature FinTech developed countries. Random effect regression models are employed to estimate and test for the impact of big data on corruption in developing and developed countries. We find that internet usage and fixed telephone subscription have a significant negative impact on corruption in developing countries. Investment in technology, mobile phone users subscription and gross domestic product also have a significant negative impact on corruption in developing countries. However, inflation has no significant effect on corruption in developing countries. In contrast, we find no significant impact of big data on corruption within developed countries. Big data adoption, therefore, seems to hinder corruption in developing countries, but not in developed countries.

Keywords: Big data, corruption, internet subscription, phone subscription, technology


Aluko A, Bagheri M. The impact of money laundering on economic and financial stability and on political development in developing countries: The case of Nigeria. Journal of Money Laundering Control. 2012.

Olken BA, Pande R. Corruption in developing countries. Annu. Rev. Econ. 2012; 4(1): 479-509.

Mauro P, Driscoll DD. Why worry about corruption?. Washington, DC: International Monetary Fund; 1997.

Petheram A, Pasquarelli W, Stirling R. The Next Generation of Anti-Corruption Tools: Big Data, Open Data & Artificial Intelligence. 2019.

Jiangnan ZH, Huang H, Zhang D. Big Tigers, Big Data: Learning Social Reactions to China’s Anticorruption Campaign Through Online Feedback: Learning Social Reactions to China's Anticorruption Campaign through Online Feedback. Public Administration Review. 2019; 79(4): 500-13.

Segal T. Big Data [Internet]. Investopedia. 2021.

Giacalone M, Scippacercola S. Big data: Issues and an Overview in Some Strategic Sectors. Journal of Applied Quantitative Methods. 2016; 11(3).

Agarwal R, Dhar V. Big data, data science, and analytics: The opportunity and challenge for IS research. Information Systems Research. 2014; 25(3): 443-8.

Ranjan J, Foropon C. Big data analytics in building the competitive intelligence of organizations. International Journal of Information Management. 2021; 56: 102231.

Daniel B. Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology. 2015; 46(5): 904-20.

Azuh DE, Joshua S. The Use Of Data In Policy-Making In Nigeria's Educational Sector: Implications For National Development. 2016.

Desouza KC, Jacob B. Big data in the public sector: Lessons for practitioners and scholars. Administration & Society. 2017; 49(7): 1043-64.

Rogge N, Agasisti T, De Witte K. Big data and the measurement of public organizations’ performance and efficiency: The state-of-the-art. Public Policy and Administration. 2017; 32(4): 263-81.

Lavertu S. We all need help: “Big data” and the mismeasure of public administration. Public Administration Review. 2016; 76(6): 864-72.

Kim GH, Trimi S, Chung JH. Big-data applications in the government sector. Communications of the ACM. Association for Computing Machinery. 2014; 57 (3): 78–85.

Lannoo K, Parlour R. Anti-Money Laundering in the EU: Time to get serious. Centre for European Policy Studies; 2021.

Soudijn MR, de Been WH. Law enforcement and money laundering: Big data is coming. Criminal Defiance in Europe and Beyond. 2020: 399-426.

Park CH, Kim K. E-government as an anti-corruption tool: Panel data analysis across countries. International Review of Administrative Sciences. 2020; 86(4): 691-707.

Bukari C. Corruption and firm innovation: a grease or sand in the wheels of commerce? Evidence from lower-middle and upper-middle income economies. Eurasian Business Review. 2021; 11(2): 267-302.

Egunjobi TA. An econometric analysis of the impact of corruption on economic growth in Nigeria. 2013.

Oguine O, Oguine KJ, Bisallah HI. Big Data And Analytics Implementation In Tertiary Institutions To Predict Students Performance In Nigeria. Science Open Preprints. 2021.

Madhuri TS, Babu ER, Uma B, Lakshmi BM. Big-data driven approaches in materials science for real-time detection and prevention of fraud. Materials Today: Proceedings. 2021.

Irving Fisher Committee. The use of big data analytics and artificial intelligence in central banking. IFC Bulletins. 2019.

Quah JS. Combating corruption in six Asian countries: a comparative analysis. Asian Education and Development Studies. 2016.

Khan F. Combating corruption in Pakistan. Asian Education and Development Studies. 2016.

Quah JS. Singapore’s success in combating corruption: lessons for policy makers. Asian Education and Development Studies. 2017.

Hunady J. The effect of the Internet on corruption awareness and corruption incidence in the EU. Information Polity. 2019; 24(1): 75-89.

Lio MC, Liu MC, Ou YP. Can the internet reduce corruption? A cross-country study based on dynamic panel data models. Government Information Quarterly. 2011; 28(1): 47-53.

Starke C, Naab TK, Scherer H. Free to expose corruption: The impact of media freedom, internet access and governmental online service delivery on corruption. International Journal of Communication. 2016; 10: 21.

Carter EB, Carter BL. When autocrats threaten citizens with violence: Evidence from China. British Journal of Political Science. 2022; 52(2): 671-96.

Andersen TB, Bentzen J, Dalgaard CJ, Selaya P. Does the Internet reduce corruption? Evidence from US states and across countries. The World Bank Economic Review. 2011; 25(3): 387-417.

Gregory R. Combating corruption in Vietnam: a commentary. Asian Education and Development Studies. 2016.

Albert AT, Okoli FC. EFCC and the politics of combating corruption in Nigeria (2003-2012). Journal of Financial Crime. 2016.

Muhamad N, Gani NA. A decade of corruption studies in Malaysia. Journal of Financial Crime. 2020.

Azim MI, Sheng K, Barut M. Combating corruption in a microfinance institution. Managerial Auditing Journal. 2017.

Chêne M. Use of mobile phones to detect and deter corruption. U4 Expert Answer. Bergen. 2012.

Shabbir MS, Saleem H, Khan MB. Impact of Internet Adoption and Mobile Phone Penetration on Corruption: Evidence from Selected Asia-Pacific Countries. Global Business Review. 2021; 22(4): 906-20.

Strand C, Hatakka M. Mobile phones as a citizen-controlled anti-corruption tool in East Africa-a literature review. International Conference on Social Implications of Computers in Developing Countries .2017: 753-764.

Sassi S, Ali MS. Corruption in Africa: What role does ICT diffusion play. Telecommunications Policy. 2017; 41(7-8): 662-9.

Sassi S, Ali MS. Corruption in Africa: What role does ICT diffusion play. Telecommunications Policy. 2017; 41(7-8): 662-9.

Bhuvana M, Thirumagal PG, Vasantha S. Big data analytics-a leveraging technology for Indian commercial banks. Indian Journal of Science and Technology. 2016; 9(32): 98643.

Forum WE. Strategic Intelligence | World Economic Forum [Internet]. Strategic Intelligence. 2022.


How to Cite
Adebisi, A., & Guermat, C. (2022). Comparative Analysis of The Impact of Big Data on Corruption in Selected Developing and Developed Countries. European Journal of Information Technologies and Computer Science, 2(3), 1-9.