Approaches to tax evasion: a bibliometric and mapping analysis of Web of Science indexed studies
Abstract
The primary objective of this study is to uncover and examine the patterns of scientific collaboration within the domain of tax evasion and tax avoidance spanning the years 1975 to 2022. To analyze the dissemination of knowledge on a worldwide scale, we investigated the interconnections among authors, journals, countries, and institutions. A total of 1456 publications were retrieved from the Web of Science repository. Bibliographic analysis and network visualization were conducted using CiteSpace. The publications analysed in this study consisted of 1,456 articles authored by 2478 scholars affiliated with 1355 institutions. The publications were distributed among 724 distinct journals and originated from 98 countries. The United States of America is found to be the most productive nation, with McGee, R.W. being recognised as the most prolific author. The League of European Research Universities is recognised as the most productive institution, whereas the Journal of Public Economics is identified as the most productive publication. The findings show that authors who exhibit high levels of productivity also tend to demonstrate a strong inclination toward collaboration. Furthermore, the findings reveal that the interest of the scholars in particular topics in this research has evolved over time.
Keyword : tax evasion, tax avoidance, tax fraud, bibliometric analysis, CiteSpace, visual analysis
How to Cite
Barbu, L., Horobeț, A., Belașcu, L., & Ilie, A. G. (2024). Approaches to tax evasion: a bibliometric and mapping analysis of Web of Science indexed studies. Journal of Business Economics and Management, 25(1), 1–20. https://doi.org/10.3846/jbem.2024.20691
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Yitzhaki, S. (1974). Income tax evasion: A theoretical analysis. Journal of Public Economics, 3(2), 201–202. https://doi.org/10.1016/0047-2727(74)90037-1
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Alkausar, B., Soemarsono, P. N., & Pengesti, N. G. (2021). A bibliometric analysis of tax aggressieveness through tax evasion issues in last decade. The International Journal of Applied Business, 5(2), 193–202. https://doi.org/10.20473/tijab.v5.I2.2021.29451
Alm, J. (2012). Measuring, explaining, and controlling tax evasion: Lessons from theory, experiments, and field studies. International Tax and Public Finance, 19(1), 54–77. https://doi.org/10.1007/s10797-011-9171-2
Alstadsæter, A., Johannesen, N., Le Guern Herry, S., & Zucman, G. (2022). Tax evasion and tax avoidance. Journal of Public Economics, 206, Article 104587. https://doi.org/10.1016/j.jpubeco.2021.104587
Andreoni, J., Erard, B., & Feinstein, J. (1998). Tax compliance. Journal of Economic Literature, 36(2), 818–860. https://EconPapers.repec.org/RePEc:aea:jeclit:v:36:y:1998:i:2:p:818-860
Boateng, K., Omane-Antwi, K. B., & Queku, Y. N. (2022). Tax risk assessment, financial constraints and tax compliance: A bibliometric analysis. Cogent Business & Management, 9(1), Article 2150117. https://doi.org/10.1080/23311975.2022.2150117
Bozhenko, В., & Kuzmenko, О. (2021). Linkages between shadow economy and corruption: A bibliometric analysis. Financial and Credit Activity: Problems of Theory and Practice, 4(39), 176–185. https://doi.org/10.18371/fcaptp.v4i39.241306
Buele, I., & Guerra, T. (2021). Bibliometric analysis of scientific production on tax evasion in ScienceDirect, years 2010 to 2019. Journal of Legal, Ethical and Regulatory Issues, 24(2), 1–15. https://www.abacademies.org/articles/Bibliometric-analysis-of-scientific-production-on-tax-1544-0044-24-2-627.pdf
Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377. https://doi.org/10.1002/asi.20317
Chen, C. (2017). Science mapping: A systematic review of the literature. Journal of Data and Information Science, 2(2), 1–40. https://doi.org/10.1515/jdis-2017-0006
Chen, C., Hu, Z., Liu, S., & Tseng, H. (2012). Emerging trends in regenerative medicine: A scientometric analysis in CiteSpace. Expert Opinion on Biological Therapy, 12(5), 593–608. https://doi.org/10.1517/14712598.2012.674507
Chen, D., Liu, Z., Luo, Z., Webber, M., & Chen, J. (2016). Bibliometric and visualized analysis of emergy research. Ecological Engineering, 90, 285–293. https://doi.org/10.1016/j.ecoleng.2016.01.026
Clotfelter, C. T. (1983). Tax evasion and tax rates: An analysis of individual returns. The Review of Economics and Statistics, 65(3), 363–373. https://doi.org/10.2307/1924181
Desai, M. A., & Dharmapala, D. (2006). Corporate tax avoidance and high-powered incentives. Journal of Financial Economics, 79(1), 145–179. https://doi.org/10.1016/j.jfineco.2005.02.002
Dyreng, S. D., Hanlon, M., & Maydew, E. L. (2008). Long‐run corporate tax avoidance. The Accounting Review, 83(1), 61–82. https://doi.org/10.2308/accr.2008.83.1.61
Dyreng S. D., Hanlon, M., & Maydew, E. L. (2010). The effects of executives on corporate tax avoidance. The Accounting Review, 85(4), 1163–1189. https://doi.org/10.2308/accr.2010.85.4.1163
Fahimnia, B., Sarkis, J., & Davarzani, H. (2015). Green supply chain management: A review and bibliometric analysis. International Journal of Production Economics, 162, 101–114. https://doi.org/10.1016/j.ijpe.2015.01.003
Hanlon, M., & Heitzman, S. (2010). A review of tax research. Journal of Accounting and Economics, 50(2–3), 127–178. https://doi.org/10.1016/j.jacceco.2010.09.002
Kireenko, A., & Nevzorova, E. (2019). Mapping of academic research on shadow economy and tax evasion. Vestnik Tomskogo gosudarstvennogo universiteta: Economy, 46, 46–59. https://doi.org/10.17223/19988648/46/4
Liu, D., Zhang, J., & Liu, S. (2014). Visualization analysis of research hotspots based on CiteSpace II: Taking medical devices as an example. Medical Devices: Evidence and Research, 2014(7), 357–361. https://doi.org/10.2147/MDER.S69685
Nevzorova, E., Bobek, S., Kireenko, A., & Sklyarov, R. (2016). Tax evasion: The discourse among government, business and science community based on bibliometric analysis. Journal of Tax Reform, 2(3), 227–244. https://doi.org/10.15826/jtr.2016.2.3.026
Nevzorova, E., Kireenko, A., & Sklyarov, R. (2017). Bibliometric analysis of the literature on tax evasion in Russia and foreign countries. Journal of Tax Reform, 3(2), 115–130. https://doi.org/10.15826/jtr.2017.3.2.035
Pickhardt, M., & Prinz, A. (2014). Behavioral dynamics of tax evasion – A survey. Journal of Economic Psychology, 40, 1–19. https://doi.org/10.1016/j.joep.2013.08.006
Rainone, E. (2023). Tax evasion policies and the demand for cash. Journal of Macroeconomics, 76, Article 103520. https://doi.org/10.1016/j.jmacro.2023.103520
Sacco, P. L, Arenas A., & De Domenico, M. (2023). The political economy of big data leaks: Uncovering the skeleton of tax evasion. Chaos, Solitons & Fractals, 168, Article 113182. https://doi.org/10.1016/j.chaos.2023.113182
Slemrod, J. (1985). An empirical test for tax evasion. The Review of Economics and Statistics, 67(2), 232–238. https://doi.org/10.2307/1924722
Wang, Y., Zheng, Q., Ruan, J., Gao, Y., Chen, Y., Li, X., & Dong, B. (2020, December). T-EGAT: A temporal edge enhanced graph attention network for tax evasion detection. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 1410–1415). IEEE. Atlanta, GA, USA. https://doi.org/10.1109/BigData50022.2020.9378157
Yi, H., & Xi, Z. (2008). Trends of DDT research during the period of 1991 to 2005. Scientometrics, 75, 111–122. https://doi.org/10.1007/s11192-007-1828-3
Yitzhaki, S. (1974). Income tax evasion: A theoretical analysis. Journal of Public Economics, 3(2), 201–202. https://doi.org/10.1016/0047-2727(74)90037-1
Zhang, L., Ling, J., & Lin, M. (2022). Artificial intelligence in renewable energy: A comprehensive bibliometric analysis. Energy Reports, 8, 14072–14088. https://doi.org/10.1016/j.egyr.2022.10.347
Zhang, L., Ling, J., & Lin, M. (2023). Carbon neutrality: A comprehensive bibliometric analysis. Environmental Science and Pollution Research, 30, 45498–45514. https://doi.org/10.1007/s11356-023-25797-w
Zhu, X., Yan, Z., Ruan, J., Zheng, Q., & Dong, B. (2018, August). IRTED-TL: An inter-region tax evasion detection method based on transfer learning. In 2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (pp. 1224–1235). IEEE. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00169