Presentation by Denis Mukama and Fabrizio Santoro at the 7th annual ICTD meeting in Kigali on February 5th 2019.

1. An analysis of discrepancies in
taxpayers’ VAT declarations in
Rwanda
6 February 2018
Giulia Mascagni, Denis Mukama, Fabrizio Santoro

2. Contents of this presentation
1. Research Background
2. VAT Data Used
3. Findings from Cleaning
4. Internal discrepancies
5. External discrepancies
2

3. Research Background
3

4. Research Background
• VAT represents over a third of tax revenue in Rwanda
• Much enforcement effort on VAT, including audits and EBM
• BUT: VAT evasion and misreporting remain widespread
– Limited use of EBM
– Fake receipts
– Refund fraud
• Policy change in VAT input reporting in Jan 2017
• Large amount of detailed data on VAT is available, including
transaction level data, but is it used to its full potential?
• Can we say something about the impact of the policy change on VAT
misreporting and evasion?
4

5. Research Background
1. Are taxpayers reporting discordant transaction-level information
despite the presence of the paper trail?
– Analyse discrepancies within taxpayers from different sources and
between sellers’ and buyers’ reports for the same transaction
– Extent of discrepancies: how many transactions are discordant?
– Depth of discrepancies: how large are they?
– Analyse discrepancies by taxpayer size, sector, location
2. Is there any change on these discrepancies after the policy change?
– Compare discrepancies before and after policy change
– Two quarters before vs two quarters after the change
5

6. Research Background
6

7. VAT Data Used
7

8. VAT Data Used
1. Transaction-level data for all taxpayers July 2016-June 2017
– For buyers: Local Purchases Annex - 9,788 TPs
– For sellers:
• Sales Annex - 12,295 TPs
• EBM data – 11,425 TPs
2. Firm-level declarations data July 2016-June 2017 – 18,336 TPs
– Monthly declarations, about a third
– Quarterly declarations, about two thirds
3. CIT/PIT firm-level declarations FY 2016
– Merge with VAT data to get other information (sector, size, income tax
type, location, etc.)
8

9. Findings from Cleaning
9

10. Findings from Cleaning
1. Firm-level declarations at the Quarter level
o 44% have zero VAT sales and zero VAT purchases
o 35% are nilfilers across all quarters – always all zeros in every field
2. Sales Annex
o 93% have not a valid buyer TIN (final consumers?)
o 5% are nilfilers across all quarters
3. EBM
o 86% have not a valid buyer TIN (final consumers?)
o 2% are nilfilers across all quarters
4. Local Purchases Annex
o Invalid seller TIN: improvement from 1.6% in June 2016 to 0% one year later. This
rate sets to 0% in December 2016 (policy kicked in)
o 2% are nilfilers across all quarters
10

11. Internal Discrepancies
11

12. Internal Discrepancies
1. Match sales/purchases annex with VAT declaration:
o No discrepancy: 98.2% (sales) and 99.9% (purchases) report same
VAT as in declaration
2. Match EBM data with VAT declaration
o 43% have a discrepancy in at least one quarter or 9,449 TINs
o 25% have declaration<EBM while 18% decl>EBM
o Extent of discrepancies
o By sector: Services 43%, Manuf. 42% and Agriculture 29%
o By CIT/PIT: no difference
o By location: no big difference
o By size: 61% in large firms vs 45% in small ones
Note: match at the quarter level; error margin of FRW 5K

13. Discussion of results – Internal Discrepancy
Key finding: 25% of EBM sales have VAT > VAT declared
Why would taxpayers report inconsistent results to RRA?
a) Evasion and taxpayer beliefs on RRA’s capacity
 Taxpayers may not know that RRA has all the necessary data to uncover
discrepancies OR might believe that RRA does not have the capacity to do so
Taxpayers would therefore issue EMB receipts but not declare since tax is
based on declarations
b) Mistakes and quality of taxpayer record-keeping
 We control for an extra zero inputted in the EBM but results do not change
 Taxpayers may be unaware of making a mistake or not knowing how to fix it
o Only 1% of EBM data are “refund” due to mistakes
 Linked to poor recordkeeping – receipts may get lost or deteriorated
13

14. External Discrepancy
14

15. External Discrepancies
Large number of “missing partners” in a valid transaction:
• 64% of sales in sales’ annex with a valid buyer TIN do not have a
corresponding purchase in the purchases’ annex
– About 30K buyers not claiming in the year
• Very similar results when we look at sales with valid buyer TIN in EBM data
• On the other hand, 65% of purchases in purchases’ annex do not have a
corresponding sale in the sales’ annex
– About 9K missing sellers
– Fully expected: buyers may be claiming inexistent sales or VAT evasion by sellers
– Indeed, it decreases after the policy change (see next slide)
More consistency of purchases with EBM data – buyers have incentive to
ask for receipts + EBM data more reliable:
• Only 25% of purchases in purchases’ annex with a valid seller TIN do not
have a corresponding EBM sale (8,600 sellers)

16. External Discrepancies – Policy change
16
Discrepancy Quarter 1 Quarter 2 Quarter 3 Quarter 4 Total
Sales Annex >
Purchases Annex 28% 29.8% 32.5%*** 32.6% 31%
Sales Annex <
Purchases Annex 21.1% 19.8% 14.9%*** 15.6% 17.5%
EBM sales data >
Purchases Annex
28.8% 30.4% 35%*** 34.2% 32.2%
EBM sales data <
Purchases Annex
17.3% 16.3% 10%*** 12.2% 13.7%
o Restrict to pairs that actually report the same transactions
o Drastic decrease in cases where buyer’s report is larger than seller’s (rows 2-4)
o Increase in seller over-reporting (rows 1-3) which may be due to high % of
rejected claims

17. Key finding: 2/3 of sales/EBM reports are not claimed by buyers + 11%
are claimed but at a lower VAT! – Why?
i. Buyers who cannot claim – either they are exempted or not VAT registered
o Exempted sectors 78% vs non-exempted sectors 63%
o We look at VAT-reg. only – still 52% are missing buyer records
ii. Buyers may postpone the refund claim to offset future VAT due
i. We look at discrepancy at they year level – still 59% missing buyer records
iii. Buyers do not know how to claim – fail to claim
iv. General distrust towards the refund system
v. Non-compliance: by “appearing small” both on inputs and sales, they can avoid
suspicion by not claiming inputs they would have – two checks:
i. 5% of buyers not-claiming are in refund position + a quarter has VAT inputs = VAT
output
ii. For buyers underclaiming the VAT inputs is 63% VAT ouput vs 46% of the broader
population – underclaimers might go in refund position if they claim more
17
Discussion of results – External Discrepancy

18. Revenue gains and
Recommendations
18

19. Internal Discrepancy with EBM data
o Revenue gains from closing the gap?
– Only cases in which EBM VAT > VAT declared
– Strong assumption: EBM data always report the correct amount
– RWF 38.2 billion revenues
o Recommendations:
– Clean the EBM dataset, avoid mistakes:
o taxpayer education + online tools to explain how EBM works
o adopt a system that can detect large mistakes
– Automatic verification procedure: taxpayers cannot file a sales annex for a
larger amount than the declaration
– Systematic cross-checks with EBM data (after mistakes are minimized)
– Research:
o Field experiment with different nudging SMS
o Test different hypothesis: enforcement, provide information, etc.

20. External Discrepancy
o Revenue gains from closing the gap?
– Focus only on gap “seller VAT < buyer VAT” – it means a gain
• Closing the gap “seller VAT > buyer VAT” means a revenue loss
– Heavy underlying assumptions on buyers VAT
– RWF 103 and 39 billion for gap between buyer VAT and, respectively,
sales annexes or EBM data
o Recommendations:
– Systematic cross-checks of cases of buyer over-reporting/seller under-
reporting – but need adequate capacity and staff
– Add random cases of discrepancy, on top of the (sensible) criterion of
investigating only large ones
– Research: what determines the large misreporting?
o Field experiment on random pairs of buyers/sellers with discrepancy
o Test different hypothesis: enforcement, provide information, etc.
o Include short phone survey to gain more information

21. Thank you
Your feedback is welcome

22. Appendix

23. Internal Discrepancies – Extent and Depth
Extensive margin Intensive margin
Discrepancy
Extent of
discrepancy
% positive –
VAT decl >
Annex/EBM
% negative -
VAT decl <
Annex/EBM
Average
discrepancy
Average
positive
discrepancy
Average
negative
discrepancy
Declaration
– sales
Annex
1.8% 0.4% 1.4% 42% 13% 50%
Declaration
– Purchase
Annex
0.05% 0.03% 0.02% 58% 28% 86%
Declaration
– EBM 43% 18% 25% 34% 26% 39%
23
- Discrepancy occurs when the gap is larger than the Rwf 5,000 error margin.
- Extent refers to how many observations of the total have a discrepancy
- Average shows the discrepancy size as the share of the larger of the two[ being
compared.
- Note: with 100K RWF margin, 69% have 0 discrepancy and for 18% EBM VAT is higher

24. Internal Discrepancies – Heterogeneity
24
- Discrepancy occurs when the gap is larger than the Rwf 5,000 error margin.
- Extent refers to how many observations of the total have a discrepancy
- Average shows the discrepancy size as the share of the larger of the two[ being compared.
Extensive margin Intensive margin
Discrepancy
Extent:
Agriculture
Extent:
Manuf.
Extent:
Services
Depth:
Agriculture
Depth:
Manuf.
Depth:
Services
Declaration –
sales Annex
1.6% 4% 2% 34% 48% 39%
Declaration –
Purchase
Annex
0% 0% 0.04% - - 42%
Declaration –
EBM
29% 42% 44% 44% 30% 29%

25. 25
- Discrepancy occurs when the gap is larger than the Rwf 5,000 error margin.
- Extent refers to how many observations of the total have a discrepancy
- Average shows the discrepancy size as the share of the larger of the two[ being compared.
Discrepancy CIT PIT Small Large in Kigali
out of
Kigali
Declaration –
sales Annex
2% 1.6% 1.4% 6.8% 2.1% 1.6%
Declaration –
Purchase
Annex
0.05% 0.04% 0.04% 0.09% 0.05% 0.04%
Declaration –
EBM
44.3% 44% 45% 61% 47.5% 44.3%
Internal Discrepancies - Heterogeneity

26. No
correspondi
ng record
Consistent
correspondi
ng record
Lower
correspondi
ng record
Higher
correspondi
ng record
Total
1 EBM data
(Compared to
purchase annex)
66% 18.2%
10.9%
(Depth: 50%)
4.6%
(Depth: 47%)
100%
2 Sales annex
(Compared to
purchase annex)
64% 18.5%
11.2%
(Depth: 50%)
6.3%
(Depth: 52%)
100%
3 Purchase annex
(Compared to EBM
data)
25% 40.3%
10.2%
(Depth: 47%)
24.1%
(Depth: 50%)
100%
4 Purchase annex
(Compared to sales
annex)
65% 18.1%
6.1%
(Depth: 52%)
10.9%
(Depth: 50%)
100%
External Discrepancies
- The average depth shows the discrepancy size as a share of the largest amount of the
two being compared.
- To control for outliers, all discrepancy amounts are capped at the 99th percentile.

27. External Discrepancies – Heterogeneity of sellers
27
Discrepancy
Location Taxpayer Type Sector
In Kigali
Out of
Kigali
CIT PIT Agriculture Manufacturing Services
Sales Annex <
Purchases
Annex
36.3% 35.8% 36.4% 35% 40% 33.5% 36.4%
EBM sales data
< Purchases
Annex 30% 26.7% 30% 29% 29% 31% 30%
- Table shows cases where buyer reports more than the seller (unilateral evasion).
- The opposite scenario was omitted for brevity and both add up to 100%.
- P-values were also tested and all equal to zero meaning that differences within
location, tax type and sectors are highly statistically significant.

28. Buyers under-claiming at the Year level
28
Note: figures at the quarter level in brackets. A discrepancy occurs if the gap is
larger than the error margin of RWF 5,000. The extent of discrepancy refers to how
many transactions out of the total have a discrepancy.
No
corresponding
record
Consistent
correspondin
g record
Lower
correspon
ding
record
Higher
corresponding
record
Total
Sales annex
(Compared to
purchase annex)
59% (64%)
20%
(18.5%)
13.4%
(11.2%)
7.4%
(6.2%)
100%