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Encouraging digital tax tools as a response to Covid: evidence from Eswatini

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Abstract

Many tax authorities changed the mode of interacting with taxpayers from physical to online as a response to the Covid-19 pandemic. We study the effect of the e-tax-filing in Eswatini, using a difference-in-difference and propensity score methods that exploit the limited take-up of e-tax filing. We present three sets of results. First, larger and more IT-sophisticated firms are more likely to adopt e-Tax. Second, after adoption, e-Tax has mixed results on filing behavior and reporting accuracy. Third, companies remit less tax after adoption e-tax-filing.

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Notes

  1. E-filing refers to the online filing and submission of tax returns, usually on online platforms managed by the revenue authority.

  2. E-payment refers to the payment of taxes through digital financial services, such as (1) electronic funds transfer (EFT)-based instruments—direct credit and debit transfers that go directly from one account to another; (2) card-based payment instruments—credit-, debit-, scratch- and charge-card payments that typically involve a physical plastic card, and are initiated, authorised, authenticated, cleared and settled electronically, (3) electronic money (e-money)-based instruments—online money with payment instructions initiated via the internet, mobile money and prepaid cards.

  3. Yilmaz and Coolidge (2013) suggest that the impact is greater when adoption is mandated, as in Nepal and South Africa at the time of their study.

  4. For some preliminary evidence on e-registration, see Knebelmann (2019), Kamara et al. (2020) and Okunogbe (2021). See Carrillo et al (2017), Mittal and Mahajan (2017), Brockmeyer and Hernandez (2019), Shah (2020), Chalendard et al. (2020) for more on the effectiveness of third-party data reporting in LMICs.

  5. PIT has a maximum marginal rate of 33% and exemptions for income below SZL41,000 (US$2848).

  6. Taxpayers can also email or phone the ERS outside of e-Tax, but most taxpayers prefer using the system (interview with ERS staff, 4 November 2021).

  7. This is in line with evidence from papers like Eilu (2018) and Mascagni et al. (2021b).

  8. From our data, it is unclear whether the e-Tax registration is initiated by the taxpayer themselves or by a tax official on their behalf, say during the online submission of a paper-based return. We acknowledge this as a limitation in our study.

  9. In the 2020 ATAF ICT survey, 36% of RAs reported poor internet penetration and power connectivity with a 10–40% monthly power availability (ATAF 2021).

  10. The GTMI is a composite index based on 48 key indicators on 198 economies based on 4 Indexes: the Core Government Systems Index (CGSI), with 15 indicators; the Public Service Delivery Index (PSDI), with six composite indicators; the Citizen Engagement Index (CEI), with 12 indicators; and the GovTech Enablers Index (GTEI), with 15 indicators.
The GTMI is the simple average of the four components measuring the maturity of GovTech focus areas, which are computed as the normalised weighted averages of relevant indicator scores.

  11. According to the index, Eswatini is closer to African countries such as Ethiopia (0.33), Sierra Leone (0.37) and Zimbabwe (0.38), but lags behind with respect to more IT-advanced governments as Rwanda (0.53) and Uganda (0.62).

  12. All datasets were shared on 20 May 2021.

  13. The taxpayer registry dataset comprises 76,771 observations in total, from which we drop suspended taxpayers and those entities not registered for income tax.

  14. The e-tax registration dataset comprises 31,334 observations corresponding to 20,576 unique registrations—all those taxpayers who registered with e-Tax any time from 2013 (when the service was launched) to May 2021.

  15. Results do not change if we employ a simple OLS regression model or a Tobit model. The tables are omitted for the sake of brevity and are available upon request.

  16. Early adopters are much more likely to be companies than individuals, large in size and operating in the tertiary sector. In addition, those who register early are significantly more likely to submit frequent returns, and more likely to provide an email address or a phone number in their return.

  17. The variables PAYE, district Hhohho, and Urban were dropped by the lasso logit model, for negligeable effect on predicting e-Tax registration.

  18. The lasso2 coefficients are a result of minimising the residual sum of squares, where the command first runs a full coefficient path for a list of lambda (the tuning parameter chosen by cross-validation), then runs the model selected by EBIC (a type of information criterion). The coefficient of lasso represents the predictive power of each variable, proportional with the magnitude of the coefficient. Post-lasso OLS coefficients are a result of running an OLS regression using the selected predictors.

  19. A similar approach is taken in Mascagni et al. (2021), studying the impact of the adoption of sales registration machines in Ethiopia.

  20. Among 13,605 observations, 13,578 observations are retained and 27 observations are out of the common support.

  21. The propensity score is created by running a logistic model using the treatment (e-Tax registration) as an outcome and the set of cofounders as explanatory variables.

  22. The variables that we used for the match are type of IT, registration year, sector, urban location, district, missing email address, log cost of sales, log total assets, log land property.

  23. We selected 0.06 as the bandwidth in the kernel matching algorithm, as common practice in the literature.

  24. The log odds ratio is the probability of success divided by the probability of failure.

  25. When considering the probability to file an arrear tax return, the impact of the mandate is actually negative and significant, implying that adopters are also less likely to file for previous tax years. We omit the table for the sake of brevity.

  26. For instance, nil-filing of companies is unaffected, in a context where previous research showed how this is often the preferred choice of companies. These differences could be explained by the fact that companies might be better equipped to put in place tax minimisation schemes, and are probably less concerned of being on the tax agency’s radar.

  27. We allow for a negligible margin of error of about US$5.

  28. We build this outcome by comparing income tax returns and payment data, and cross-checking the tax liability declared in the return form with the tax actually paid in the payment dataset.

  29. Where these online platforms exist, there are reports of non-responsiveness by staff, poor issue resolution, task backlog, and some platforms only open between 8am and 5 pm while tax activities go on 24/7 (ATAF 2021).

  30. Taxpayers who responded to the 2020 ATAF ICT survey said the lack of stability in the tax system was the main barrier to using the RA e-system (ATAF 2021).

  31. For instance, the income tax return seems not to be carefully configured on e-Tax, as there are no validation controls comparing data entered on the balance sheet and actual tax return. Likewise, currently e-Tax cannot pick up a loss brought forward—the taxpayer will incur a larger tax liability as they cannot deduct a previous loss. This reconciliation can be done only manually by ERS staff, implying extra work.

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Acknowledgements

We thank the Eswatini Revenue Service (ERS) for approving the study and providing support throughout its implementation. We are particularly grateful to Edward Groening for helpful comments. We also thank two anonymous reviewers who helped strengthen the study. This paper was supported by the Gates Foundation through the International Centre for Tax and Development DIGITAX programme.

Funding

This study was supported by Bill and Melinda Gates Foundation.ä

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Authors and Affiliations

  1. Institute of Development Studies, Brighton, UK

    Fabrizio Santoro

  2. University of Cambridge, Cambridge, UK

    Razan Amine

  3. Eswatini Revenue Service, Lobamba, Eswatini

    Tanele Magongo

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  1. Fabrizio Santoro
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  2. Razan Amine
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Correspondence to Fabrizio Santoro.

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Appendices

Appendices

1.1 Appendix A: Tables

See Tables 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 and 16.

Table 4 Governance and country indicators
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Table 5 Balance test table between early adopters and adopters after the mandate
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Table 6 Balance test table between control (never adopters) and treatment group (late adopters)
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Table 7 Predictors of e-Tax registration using a lasso logit model
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Table 8 Within-sector PSM-DID estimates of e-Tax on filing behaviour
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Table 9 Within-sector PSM-DID estimates of e-Tax mandate on on-time filing and accuracy
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Table 10 Within-sector PSM-DID estimates of e-Tax mandate on payment behaviour
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Table 11 PSM-DID estimates of e-Tax on filing behaviour—email subsample
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Table 12 PSM-DID estimates of e-Tax mandate on on-time filing and accuracy—email subsample
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Table 13 PSM-DID estimates of e-Tax mandate on tax payment—email subsample
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Table 14 PSM-DID estimates of e-Tax on filing behaviour by size
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Table 15 PSM-DID estimates of e-Tax on filing behaviour by location
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Table 16 PSM-DID estimates of e-Tax on filing behaviour by sector
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1.2 Appendix B: Figures

See Figs. 4, 5, 6, 7, 8, 9, 10, 11, 12 and 13.

Fig. 4
figure 4

E-Tax platform—snapshot 1

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Fig. 5
figure 5

E-Tax platform—snapshot 2

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Fig. 6
figure 6

Timeline of e-Tax implementation

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Fig. 7
figure 7

E-Tax registrations 2013–2021

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Fig. 8
figure 8

E-Tax registrations Jan’20–May’21

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Fig. 9
figure 9

Propensity score distribution by treatment and control groups. Note: The figures plot the distribution of the propensity score, for control group (untreated) and for treatment group, both on support and off support. Units off supports are eventually dropped, and they in a negligible number, thus indicating the high quality of the matching procedure. All data are extracted from ERS administrative data, updated as of May 2021. More details in Sect. 3.1

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Fig. 10
figure 10

Matching balance test. Note: The figures plot the Standardized % bias over the covariates used in the matching, both before and after matching. After matching, the bias is significantly reduced, thus indicating the high quality of the matching procedure. All data are extracted from ERS administrative data, updated as of May 2021. More details in Sect. 3.1

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Fig. 11
figure 11

Matching log odds. Note: The figures plot the distribution of log odds after propensity score matching, before and after matching, thus indicating the high quality of the matching procedure. All data are extracted from ERS administrative data, updated as of May 2021. More details in Sect. 3.1

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Fig. 12
figure 12

Parallel trends for Unmatched Sample. Note: the graphs above describe the average trend of the three different filing outcomes over time, grouped by whether the taxpayer registered for e-Tax after the policy change (Treatment) or did not (Control). The coefficients refer to the interaction between the treatment indicator and year dummies, with the year 2020 as reference point. The outcomes refer to log income, log taxable income and log tax liability as declared by taxpayers in their income tax returns. The regressions are run on the unmatched sample. All data are extracted from ERS administrative data, specifically the income tax return data, updated as of May 2021. More details in Sect. 3.1

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Fig. 13
figure 13

Parallel trends for Unmatched Sample. Note: the graphs above describe the average trend of the two different payment outcomes over time, grouped by whether the taxpayer registered for e-Tax after the policy change (Treatment) or did not (Control). The outcomes refer to the share of taxpayers paying any tax and the log amount of the tax paid. The coefficients refer to the interaction between the treatment indicator and year dummies, with the year 2020 as reference point. The regressions are run on the unmatched sample. All data are extracted from ERS administrative data, specifically the tax payment data, updated as of May 2021. More details in Sect. 3.1

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Santoro, F., Amine, R. & Magongo, T. Encouraging digital tax tools as a response to Covid: evidence from Eswatini. Int Tax Public Finance 31, 95–135 (2024). https://doi.org/10.1007/s10797-023-09810-z

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