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Impact of Corruption on Tax Compliance Behavior in Indonesia

This research examines the impact of perceptions of corruption on intentional non-compliance behavior among personal income taxpayers in Indonesia. The findings provide policy implications for developing economies.

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Impact of Corruption on Tax Compliance Behavior in Indonesia

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  1. Business School Tax & Business Law The impact of perceptions of corruption upon intentional non-compliance behaviour of personal income taxpayers: Arifin Rosid, General Department of Taxation, Indonesia Chris Evans, UNSW Sydney Binh Tran-Nam, UNSW Sydney * Paper prepared for the Conference on Improving Domestic Resource Mobilization and Stemming Illicit Financial Flows Tax Justice for Promoting Equality and Social Justice, Nairobi, 1213 March 2019. Policy implications for developing economies

  2. Overview: • Background • Research design • Findings • Discussion • Conclusion

  3. Problems for conducting business in Indonesia, 2016 Note: Surveyed respondents were required to indicate the five most problematic factors for doing business in their country and to rank them between 1 (most problematic) and 5 (least problematic). Scores correspond to the number of responses weighted according to their rank Source: World Economic Forum (2016)

  4. The Indonesian ‘tax ratio’ is lower than those of most low income countries Tax ratio by per-capita GNI category, 19912006 Source: Adapted from (IMF 2011, pp. 60-62)

  5. Level of perceived corruption correlates with tax ratio Share of tax revenue in GDP and perceived corruption Source: Author’s calculation on 119 countries data of the CPI 2012 data (Transparency International, 2013) and the World Bank data of tax revenue as percentage of GDP (World Bank, 2013).

  6. A strong need to understand operating contexts Influences on tax compliance outcomes Source: OECD (2014, p. 15)

  7. Primary empirical question of the research Do (and how) perceptions of corruption cause intentional non-compliance behaviour in personal income taxpayers?

  8. Conceptual boundaries (1) 5 types of perceived corruption Based on the actors and the sectors involved

  9. Conceptual boundaries (2) ? 2 types of deliberate action The ‘intention-action divide’ phenomenon (World Bank, 2015) Tax compliance is an ex-post definition

  10. Mixed methodology—’qualQUANT’ • In-depth interviews • 9 participant (3 tax agents, 3 taxpayers, 3 tax officers) • Mixed-modes survey • 12 tax offices across 4 regions • 397 respondents (201 employed & 196 self-employed taxpayers)

  11. Theoretical framework

  12. N = 9 First phase—interview results (thematic analysis) Diagrammatic interpretation of qualitative findings A hypothetical model of relationships between high levels of perceived corruption with low levels of compliance behaviour was developed and further investigated in the quantitative phase.

  13. N = 397 Second phase—survey results (structural equation modeling) general corruption (PGC) $ Model A grand corruption (GCO) 3 types of (perceived) corruption were significantly influential Most influential = grand corruption grand tax corruption (GTC) Model B Taxpayers appear to be ‘evasion-minded’ rather than ‘compliance-minded’ Model C Number of models examined = 6

  14. Understanding a spectrum of taxpayers attitude to compliance Source: modified from OECD (2004, p. 41) Operating context

  15. Compliance risk management (CRM) Source: OECD (2010, p. 9). CRM has now a stronger focus on understanding and influencing taxpayer behaviour (Alink & Kommer, 2016)

  16. CRM process is a mean score for perceptions of grand corruption (out of 7) 6.49 Self-employed PITs correctly reported actual income 18% Self-employed PITs underreported between 50%-100% of their actual income 44%

  17. Compliance behaviour analysis What is occurring? Intentional underreporting income is evident among Indonesian PITs. Who is doing it? Self-employed PITs appear to be more non-compliant simply because they have much higher perceived behavioural control over tax underreporting. Why are they doing it? Perceptions of grand corruption, grand tax-corruption and general corruption appear to be responsible for intentional tax underreporting behaviour. Searching for causes

  18. Appendix 1 Designing treatment strategies (analytical hierarchy process) A combined approach to address the adverse impact of perceptions of corruption upon intentional non-compliance behaviour: A tax authority perspective Appendix 2

  19. Conclusion • Environmental scanning is crucial. This research shows that perceptions of certain forms of corruption can have a malign impact upon tax compliance through a variety of forces and relationships. • The extent of high perceived levels of different forms of corruption and its impact upon intentional tax underreporting behaviour in Indonesia are confirmed in both qualitative and quantitative approaches. • Well-informed analysis and a whole-of-government approachare needed to systematically address this endemic issue. • ‘Norm-based intervention’ and ‘segmented-enforcement’ approaches need to be considered in the context of the wider vision of achieving long terms goals, but with caution.

  20. Publications derived from the research: Arifin Rosid, Chris Evans and Binh Tran-Nam (2016): Do Perceptions of Corruption Influence Personal Income Taxpayer Reporting Behaviour? Evidence from Indonesia’, eJournal of Tax Research 14(2): 387425. Thank you Arifin Rosid, Chris Evans and Binh Tran-Nam (2018): Perceptions of Corruption and Tax Non-compliance Behaviour: Policy Implications for Developing Countries, Bulletin of Indonesian Economic Studies 54(1): 136.

  21. Appendix 1 Treatment strategies (1) TAX

  22. Appendix 2 Treatment strategies (2) Segmentation & rotation of special audit projects TAX

  23. Appendix 3 Focused enforcement in detail Segmentation & rotation of special audit projects Special audit projects >10% chance of being audited TAX < 1% chance of being audited Increasing penalties may not have a noticeable effect on compliance, unless the probability of detection is increased significantly (Gneezy & Rustichini, 2000). Uncertainty aversion Probability neglect Availability bias

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