Tạp chí đã xuất bản
2004
ISSN
ISSN 2615-9813
ISSN (số cũ) 1859-3682

SỐ 185 | Tháng 8/2021

Chính phủ điện tử, văn hóa quốc gia và tham nhũng: Bằng chứng từ tiếp cận Bayes

Nguyễn Văn Điệp, Phạm Xuân Thu, Nguyễn Hoàng Thi

Tóm tắt:

Mục tiêu của bài viết này nhằm phân tích ảnh hưởng của chính phủ điện tử (CPĐT) đến tham nhũng trong bối cảnh khác biệt văn hóa giữa các quốc gia. Phương pháp hồi quy tuyến tính theo trường phái Bayes được dùng để xử lý bộ dữ liệu bảng của 66 quốc gia trong giai đoạn 2003–2018. Kết quả nghiên cứu cho thấy ảnh hưởng mạnh mẽ và tích cực của CPĐT đến việc kiểm soát tham nhũng của các quốc gia. Bên cạnh đó, mức độ ảnh hưởng của CPĐT đến việc kiểm soát tham nhũng tùy thuộc vào bối cảnh văn hóa quốc gia. Cụ thể, việc phát triển và tham gia CPĐT sẽ giúp kiểm soát tham nhũng hiệu quả hơn ở các quốc gia có nền văn hóa mà mức độ khoảng cách quyền lực, chủ nghĩa cá nhân, nam quyền và né tránh rủi ro cao. Ngoài ra, kết quả nghiên cứu cũng cho thấy GDP bình quân đầu người, ổn định chính trị và chính phủ hiệu quả có ảnh hưởng tích cực đến kiểm soát tham nhũng. Kết quả này hàm ý rằng, bên cạnh yếu tố văn hóa thì các yếu tố kinh tế và chính trị cũng góp phần làm giảm thiểu tham nhũng ở cấp độ quốc gia.

 

Tài liệu tham khảo:

  1. Andersen, T. B. (2009). E-Government as an anti-corruption strategy. Information Economics and Policy, 21(3), 201-210.
  2. Basyal, D. K., Poudyal, N., & Seo, J. W. (2018). Does E-government reduce corruption? Evidence from a heterogeneous panel data model. Transforming Government: People, Process and Policy, 12(2), 134-154.
  3. Berrell, M. (2021). National Culture and the Social Relations of Anywhere Working. In Anywhere Working and the Future of Work (pp. 23-59). Hershey, Pennsylvania: IGI Global.
  4. Brooks, S. P., & Gelman, A. (1998). General Methods for Monitoring Convergence of Iterative Simulations. Journal of Computational and Graphical Statistics, 7(4), 434-455.
  5. Elbahnasawy, N. G. (2014). E-Government, Internet Adoption, and Corruption: An Empirical Investigation. World Development, 57, 114-126.
  6. Ha, C. N. (2020). Posterior Summary of Bayes Error Using Monte-Carlo Sampling and Its Application in Credit Scoring, Asian Journal of Economics and Banking, 4(2),117-126.
  7. Hofstede, G. (1984). Culture's Consequences: International Differences in Work-Related Values. Beverly Hills: SAGE Publications.
  8. House, R. J., Hanges, P. J., Javidan, M., Dorfman, P. W., & Gupta, V. (2004). Culture, leadership, and organizations: The GLOBE study of 62 societies. Beverly Hills: Sage publications.
  9. Jaeger, P. T. (2006). Assessing Section 508 compliance on federal e-government Web sites: A multi-method, user-centered evaluation of accessibility for persons with disabilities. Government Information Quarterly, 23(2), 169-190.
  10. Jain, A. K. (2001). Corruption: A Review. Journal of Economic Surveys, 15(1), 71-121.
  11. Klitgaard, R. (1988). Controlling Corruption. Berkeley: University of California Press.
  12. Krishnan, S., Teo, T. S. H., & Lim, V. K. G. (2013). Examining the relationships among e-government maturity, corruption, economic prosperity and environmental degradation: A cross-country analysis. Information & Management, 50(8), 638-649.
  13. Kruschke, J. K., Aguinis, H., & Joo, H. (2014). The time has come: Bayesian methods for data analysis in the organizational sciences: Erratum. Organizational Research Methods, 17(1), 107-107.
  14. Linh, N. T. X. (2020). Social Existence Determines Consciousness: How the Economy Matters for Cultural Changes? A Study of Selected Asian Countries. Asian Journal of Economics and Banking, 4(1), 127-146.
  15. Lio, M. C., Liu, M. C., & Ou, Y. P. (2011). Can the internet reduce corruption? A cross-country study based on dynamic panel data models. Government Information Quarterly, 28(1), 47-53.
  16. Manoharan, A. (2013). A Three Dimensional Assessment of U.S. County e-Government. State and Local Government Review, 45(3), 153-162.
  17. Nam, T. (2018). Examining the anti-corruption effect of e-government and the moderating effect of national culture: A cross-country study. Government Information Quarterly, 35(2), 273-282.
  18. Nguyen, D. V., & Duong, M. T. H. (2021). Shadow Economy, Corruption and Economic Growth: An Analysis of BRICS Countries. The Journal of Asian Finance, Economics and Business, 8(4), 665-672.
  19. Nguyen, V. D., & Duong, T. H. M. (2022). Corruption, Shadow Economy, FDI, and Tax Revenue in BRICS: A Bayesian Approach. Montenegrin Journal of Economics, 18(2), 55-64.
  20. Oanh, T. T. K., Diep, N. V., Truyen, P. T., & Chau, N. X. B. (2022). The impact of public expenditure on economic growth of provinces and cities in the Southern Key Economic Zone of Vietnam: Bayesian approach. In N. Ngoc Thach, D. T. Ha, N. D. Trung, & V. Kreinovich (Eds.), Prediction and Causality in Econometrics and Related Topics (pp. 328-344). Cham: Springer International Publishing.
  21. Park, C. H., & Kim, K. (2019). E-government as an anti-corruption tool: panel data analysis across countries. International Review of Administrative Sciences, 86(4), 691-707.
  22. Roberts, G. O., & Rosenthal, J. S. (2001). Optimal scaling for various Metropolis-Hastings algorithms. Statistical Science, 16(4), 351-367.
  23. Rose-Ackerman, S. (1996). The Political Economy of Corruption: Causes and Consequences. Viewpoint, 74, 1-4.
  24. Seligson, M. A. (2002). The Impact of Corruption on Regime Legitimacy: A Comparative Study of Four Latin American Countries. The Journal of Politics, 64(2), 408-433.
  25. Thach, N. N. (2021). How Values Influence Economic Progress? Evidence from South and Southeast Asian Countries. In N. Ngoc Thach, V. Kreinovich, & N. D. Trung (Eds.), Data Science for Financial Econometrics (pp. 207-221). Cham: Springer International Publishing.
  26. Thach, N. N., Anh, L. H., & An, P. T. H. (2019). The effects of public expenditure on economic growth in Asia countries: A Bayesian model averaging approach. Asian Journal of Economics and Banking, 3(1), 126-149.
  27. Trafimow, D., Amrhein, V., Areshenkoff, C. N., Barrera-Causil, C. J., Beh, E. J., Bilgiç, Y. K., ... Cepeda-Freyre, H. A. (2018). Manipulating the alpha level cannot cure significance testing. Frontiers in Psychology, 9, 699.
  28. Villoria, M., Van Ryzin, G. G., & Lavena, C. F. (2013). Social and Political Consequences of Administrative Corruption: A Study of Public Perceptions in Spain. Public Administration Review, 73(1), 85-94.
  29. Vladik, K., Olga, K., Nguyen, N. T., & Nguyen, D. T. (2019). Use of Symmetries in Economics: An Overview. Asian Journal of Economics and Banking, 3(1), 20-39.
  30. Wasserstein, R. L., Schirm, A. L., & Lazar, N. A. (2019). Moving to a World Beyond “p < 0.05”. The American Statistician, 73(sup1), 1-19.
  31. Zhao, H., Ahn, M. J., & Manoharan, A. P. (2017). E-government, corruption reduction and culture: a study based on panel data of 57 countries. Paper presented at the Proceedings of the 18th Annual International Conference on Digital Government Research, Staten Island, NY, USA.


E-Government, National Culture and Corruption: Evidence from Bayesian Approach

Abstract:

This paper aims to analyze the influence of e-government on corruption in the context of cultural differences between countries. The Bayesian linear regression method is used to process panel datasets of 66 countries from 2003 to 2018. The results show a strong and positive influence of e-government on the control of corruption. In addition, the extent to which e-government influences corruption control depends on the national cultural context. In particular, the development and participation of e-government will help control corruption more effectively in countries with cultures where the degree of power distance, individualism, masculinity, and uncertainty avoidance high. Also, the research results show that GDP per capita, political stability, and government effectiveness have a positive effect on controlling corruption. This result implies that, besides cultural factors, economic and political factors also reduce corruption at the national level.