## htnea2021005

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---

### Purpose and mutual objectives
- The note outlines the interest of Revenue Administrations (RAs) and National Statistical Offices (NSOs) in the quality of data at their disposal, and how collaboration between these organizations can contribute to improving data quality.
- Mutual benefits:
  - Improve coverage of registers and update register information.
  - Improve classification of economic activity.
  - Enable joint analysis to address data shortcomings.
  - For RAs: improved taxpayer compliance and revenue mobilization.
  - For NSOs: administrative data can enable expanded coverage of the economy and reduce timeframes required for publishing economic time series and national accounts.
  - Spillover benefits to whole government: enhanced policy formulation, planning, and service delivery capability.

### Data uses and complementarities
- Administrative data applications for NSOs (verbatim list):
  - Compilation and updating of a statistical business register (SBR); administrative records in general, and tax records in particular, are recommended sources for an SBR (see, for example, ADB 2014).
  - Using administrative records as the primary source for compiling statistics (for example, trade statistics from customs administration records, unit prices from import records, national accounts statistics from value-added tax records and income tax returns).
  - Blending administrative data with survey data to produce statistics, given that there is better coverage of small businesses through administrative sources than is generally possible through sample surveys.
  - Employing value-added tax (VAT) and income tax records in compiling national accounts statistics.
  - Benchmarking sample survey results against administrative data aggregates.
- Administrative and external data applications for RAs (verbatim list):
  - Pre-filling tax returns using third-party data.
  - Compliance risk analysis and risk ranking.
  - Validating the completeness and accuracy of information provided by taxpayers on their economic activity.
  - Modeling and forecasting tax revenue.
  - Monitoring noncompliance through quantifying the tax gap by tax type in a top-down approach, which relies on independent economic and social data.
  - Formulating compliance-enhancing strategies, by drawing on information about taxpayer attitudes and behavioral patterns.

### Parallels in data management and registers
- Both RAs and NSOs:
  - Collect, process, validate, and maintain registers.
  - Rely on respondent confidentiality.
  - May use registers as starting points (Taxpayer Registers and Statistical Business Registers).
  - Produce outputs that are inputs for the other (revenue statistics needed for National Accounts; economic statistics needed for revenue estimates and forecasts).
- TADAT desired outcome (verbatim):
  - “All businesses, individuals, and other entities that are required to register are included in a taxpayer registration database. Information held in the database is complete, accurate, and up-to-date.” (TADAT 2019)

### Confidentiality and data-sharing safeguards (layers listed verbatim)
- Legislation is generally in place to protect the confidentiality of both taxpayers and survey respondents. Legislation may be amended to specify what data can be shared and the purpose for which it can be shared.
- RAs and NSOs often use a memorandum of understanding (MOU) or a service-level agreement (SLA) to set out measures to maintain integrity and confidentiality of shared data; these should be reviewed regularly.
- Staff of both RAs and NSOs often must sign an oath of secrecy; staff on partnership projects may sign the partner organization’s oath as well.
- Microdata can be anonymized by removing identifiers; rare characteristics can be suppressed or mapped to ranges; outlier records may be excluded or aggregated.
- Access to microdata can be provided within secure data facilities (data laboratories) that are physically secure, access controlled, not connected to external networks, and with output devices disabled; analysis may be checked by an administrator before release. South Africa and the United Kingdom are cited as using secure data facilities to provide research access to tax data.

### Guidelines for effective RA–NSO collaboration (Steps)
- Step 1: Identify Potential Areas of Cooperation
  - Key questions: data weaknesses; relative strengths in data collection and management; mutual data needs for disaggregation; achieving a common industry classification.
- Step 2: Review Existing Legislation
  - Study laws governing tax administration and official statistics to determine if amendments are needed to support mutually beneficial data flows.
- Step 3: Formalize Relations with an MOU or SLA
  - An MOU or SLA can formalize responsibilities and data flows within the legal framework; SLAs may be amended more regularly as needs evolve.
- Step 4: Share Nonconfidential Information
  - Share information on concepts, definitions, quality assurance processes, and anticipated changes in revenue administration processes or statistical outputs that affect joint analysis.
- Step 5: Jointly Undertake a Concrete Project with a Well-Defined Scope
  - Begin with projects that yield insights for both organizations and institutionalize working arrangements while addressing confidentiality concerns.

### Elements to include in a Memorandum of Understanding (selected)
- Make explicit the legal basis for RA-NSO collaboration.
- Possible elements (selected):
  - Objectives of the collaboration and intended outcomes (for example, joint maintenance of registers).
  - Constitution of a Technical Working Group (TWG) and a Management Committee.
  - Data and information to be shared, such as:
    - Provision of tax administration microdata by the RA to the NSO.
    - Provision of validated industry classifications by the NSO to the RA.
    - Provision of social, demographic, and economic statistics by the NSO to the RA for forecasting and tax gap estimation.
    - Sharing of methods, concepts, and definitions used in compiling official statistics.
    - Sharing of information by the RA on administrative processes, changes in revenue administration and tax policy (for example, changes in the VAT registration threshold), and estimates of levels of fraud that could impact statistics compiled using tax records.
    - Sharing of information on quality assurance and validation methods, and on systems and processes used to collect and manage data and produce reports.
  - Commitment to meet defined quality standards and provide feedback on data quality.
  - Specifications for maintenance of data integrity and confidentiality.
  - Specification of data to be exchanged: what data, how frequently, and secure formats/media.

### Possible initial joint projects (selected examples)
- 1. Improving register coverage and accuracy for a sector of national interest (for example, the construction industry).
- 2. Harmonizing the economic classifications of large businesses/taxpayers used by the RA and NSO.
- 3. Training of RA staff by the NSO on ISIC and the national standard economic activity classification.
- 4. Conducting a survey of taxpayers on perceptions of the RA and compliance attitudes (NSOs may assist to provide independent data collection and processing).
- 5. Estimating the tax gap for an indirect tax using official economic statistics to develop deeper understanding of data and highlight data quality challenges.
- 6. Comparing income distributions from NSO surveys and income tax records and examining reasons for differences.
- 7. Publishing aggregate statistics on revenue collection.
- 8. Making available anonymized revenue administration records in a secure environment for statistical and research purposes.
- Supporting statistic: “Sixty-one percent of RAs undertake taxpayer surveys, and of these, half outsource surveys (ISORA 2016).”

### Box 1 highlights — Maintaining the Confidentiality of Taxpayer Information
- Key overlaps between Taxpayer Registers and SBRs:
  - Many countries use taxpayer registers together with tax returns to construct and update SBRs.
  - The coverage and accuracy of taxpayer registers impacts the SBR.
  - Joint RA-NSO maintenance of registers can be explored to optimize resources and eliminate duplication of maintenance effort.
  - Cooperation with suppliers of data used in constructing SBRs is needed for SBR maintenance.
  - Where registers are less developed, joint register design can be considered (for example, Gambia Revenue Authority and Gambian Bureau of Statistics agreed joint responsibility for design and maintenance rules).
- Elements required in Taxpayer Registers and SBRs (from Table 1) — verbatim fields listed for each register:
  - Taxpayer Registers (From the TADAT Field Guide): Name; Address and contact details; Date of birth/incorporation; Nature of business activity and/or economic/industry sector, classified according to government or other recognized coding system; Taxpayer segment; Filing and payment obligations; Identity of associated entities and related parties.
  - SBRs (From Guidelines for Building Statistical Registers in Africa): Identification and contact data; Demographic data, for example, date when enterprise started operations, date when incorporated; Descriptive data, for example, activity status, legal form, institutional sector, economic activity; Linkages, for example, enterprises and enterprise groups to which they belong, establishments belonging to an enterprise.

### Classification of economic activity and operational notes
- The same industry classification should be used by the RA and NSO to allow sectoral comparisons between tax revenue aggregates and official statistics (for example, alignment to the International Standard Industrial Classification (ISIC)).
- NSOs often “inherit” taxpayer classifications from the RA but may amend them; providing feedback to the RA on changes is important.
- NSOs can assist RAs by:
  - Providing training in industry classification to RA staff.
  - Providing a “coder” to facilitate standard economic classification code assignments from descriptions supplied by taxpayers.
- Continuous change in economic agents necessitates collaboration to update industry classification; engagements by RA or NSO that reveal inaccuracies or changes should be used to update records.

### Analysis of tax administration records and official statistical data — practical considerations
- Three highlighted uses of data sharing:
  - The use of tax administration records in compiling official economic statistics by the NSO.
  - The use of official statistics in tax gap analysis by RAs.
  - The probing of weaknesses in the coverage of registers and consequent data collection.
- Practical considerations and challenges:
  - Tax administration records may validate, supplement, or replace sample surveys; in some countries use of administrative data is required (examples: Nordic countries and the Netherlands).
  - Revenue administration data (income tax, VAT, employers’ withholding tax) can cover more businesses—particularly small businesses—than feasible survey samples.
  - Tax data were not captured for producing economic statistics and may not match standard statistical concepts and definitions; NSOs need to understand RA data collation and processes in detail.
  - Tax gap analysis requires independent economic data sources; aggregates from the NSO (national accounts) are among the most important for estimating potential tax and quantifying tax gaps.
  - Both NSOs and RAs face incomplete coverage of economic agents in their registers; taxpayer records may cover smaller registered businesses better than NSO sample surveys, while RAs may lack information on unregistered businesses.
  - Understanding the non-observed economy is relevant to tax compliance strategies and tax policy.

### Annex — Typical differences between administrative data and survey data (selected)
- Entities contained in registers may differ:
  - Taxpayer registers record legal and natural persons required to register for tax; large complex businesses may have multiple registrations for the same tax type.
  - SBRs may record distinct establishments where components differ in activity and location.
- Industry classification differences can produce disparate sectoral aggregates because RAs may record all activity of a large business as one type while SBRs break it down.
- No exact correspondence typically exists between National Accounts aggregates and any particular tax base; adjustments are necessary when estimating tax gaps (example reference: Ueda (2018)).
- Trends in tax revenue aggregates do not necessarily mirror trends in measures of economic activity because of tax policy changes and taxpayer behavior (including compliance).
- Reference periods and accounting bases may differ:
  - There may be differences in reference periods for statistical collections and accounting periods for tax; adjustments may be necessary for meaningful comparisons.
  - Timing differences: the System of National Accounts requires accrual recording, while most RAs record on a cash basis.

### Key statistics and survey findings (exact)
- Fifty-six and 58 percent, respectively, of tax administrations indicated that they had formal programs to improve register quality in 2015 and 2016, respectively (ISORA 2016).
- This figure rose to 73 percent in 2016 and 2017 (ISORA 2018).
- “Sixty-one percent of RAs undertake taxpayer surveys, and of these, half outsource surveys (ISORA 2016).”

*International Monetary Fund | htnea2021005 - references (PDF)*

### references.

### References.

### Publication identifiers and ordering
- ISBN: 9781513577708 (paper)
- Classification: LCC QA76.9.D3 G38 2021
- Subjects (LCSH): Database management—Quality control. | Institutional cooperation—Handbooks, manuals, etc. | Revenue management. | Tax administration and procedure.
- Publication orders may be placed online, by fax, or through the mail:
  - International Monetary Fund, Publication Services
  - PO Box 92780, Washington, DC 20090, U.S.A.
  - Tel.: (202) 623-7430 Fax: (202) 623-7201
  - Email: publications@imf.org
  - www.imf bookstore.org
- Page/issue markers present in the source: "iiiInternational Monetary Fund | April 2021" and "vInternational Monetary Fund | April 2021" and "1International Monetary Fund | April 2021" indicating the document dating and pagination.

### Disclaimer
- DISCLAIMER: Fiscal Affairs Department (FAD) How to Notes offer practical advice from IMF staff members to policymakers on important fiscal issues. The views expressed in FAD How to Notes are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

### Contents overview (selected headings and pagination cues)
- Acronyms ........................................................................................................................................ v
- 1 Introduction .............................................................................................................................. 1
- 2 Common Ground for RA–NSO Collaboration ................................................................... 2
- 3 Key Collaboration Areas ...................................................................................................... 3
  - Maintaining High-Quality Registers ............................................................................. 4
  - Classification of Economic Activity ............................................................................. 5
  - Analysis of Tax Administration Records and Official Statistical Data ........................ 6
- 4 Guidelines for Effective Collaboration between RAs and NSOs .................................. 6
  - Identify Potential Areas of Cooperation .................................................................. 6
  - Review Existing Legislation to Identify Amendments Required to Optimize RA-NSO Engagement ................................................................................................................................ 7
  - Formalize Working Relations with an MOU or an SLA ............................................. 7
  - Share Relevant Information that Is Not Confidential ................................................ 8
  - Jointly Undertake a Concrete Project with a Well-defined Scope ............................ 8
- Annex 1 Examples of Differences between Administrative Data and Survey Data ...... 10
- References ................................................................................................................................. 11
- CONTENTS (document footer/header indicator)

### Acronyms (as listed)
- ISIC  International Standard Industrial Classification of all Economic Activities
- ISORA    International Survey on Revenue Administration
- MOU      Memorandum of Understanding
- NSO  National Statistical Office
- RA   Revenue Administration
- SBR  Statistical Business Register
- SLA  Service Level Agreement
- TADAT   Tax Administration Diagnostic Assessment Tool
- TWG  Technical Working Group
- VAT  Value-Added Tax

### Key excerpts and substantive points drawn from the referenced material
- Purpose:
  - The note outlines the interest of Revenue Administrations (RAs) and National Statistical Offices (NSOs) in the quality of data at their disposal, and how collaboration between these organizations can contribute to improving data quality.
- Mutual benefits and objectives:
  - Collaboration can improve coverage of registers, update register information, improve classification of economic activity, and enable joint analysis to address data shortcomings.
  - For RAs: improved taxpayer compliance and revenue mobilization.
  - For NSOs: administrative data can enable expanded coverage of the economy and reduce timeframes required for publishing economic time series and national accounts.
  - Spillover benefits to whole government: enhanced policy formulation, planning, and service delivery capability.
- Data usage by NSOs (administrative records applications listed verbatim):
  - Compilation and updating of a statistical business register (SBR); administrative records in general, and tax records in particular, are recommended sources for an SBR (see, for example, ADB 2014).
  - Using administrative records as the primary source for compiling statistics (for example, trade statistics from customs administration records, unit prices from import records, national accounts statistics from value-added tax records and income tax returns).
  - Blending administrative data with survey data to produce statistics, given that there is better coverage of small businesses through administrative sources than is generally possible through sample surveys.
  - Employing value-added tax (VAT) and income tax records in compiling national accounts statistics.
  - Benchmarking sample survey results against administrative data aggregates.
- Data usage by RAs (external data applications listed verbatim):
  - Pre-filling tax returns using third-party data.
  - Compliance risk analysis and risk ranking.
  - Validating the completeness and accuracy of information provided by taxpayers on their economic activity.
  - Modeling and forecasting tax revenue.
  - Monitoring noncompliance through quantifying the tax gap by tax type in a top-down approach, which relies on independent economic and social data.
  - Formulating compliance-enhancing strategies, by drawing on information about taxpayer attitudes and behavioral patterns.
- Parallels in data management and data flows:
  - Both RAs and NSOs collect, process, validate, and maintain registers; both rely on respondent confidentiality; both may use registers as starting points (Taxpayer Registers and Statistical Business Registers); and outputs of one are inputs for the other (revenue statistics needed for National Accounts; economic statistics needed for revenue estimates and forecasts).
- Confidentiality and data-sharing safeguards (layers listed verbatim):
  - Legislation is generally in place to protect the confidentiality of both taxpayers and survey respondents. Legislation may be amended to specify what data can be shared and the purpose for which it can be shared.
  - RAs and NSOs often use a memorandum of understanding (MOU) or a service-level agreement (SLA) to set out measures to maintain integrity and confidentiality of shared data; these should be reviewed regularly.
  - Staff of both RAs and NSOs often must sign an oath of secrecy; staff on partnership projects may sign the partner organization’s oath as well.
  - Microdata can be anonymized by removing identifiers; rare characteristics can be suppressed or mapped to ranges; outlier records may be excluded or aggregated.
  - Access to microdata can be provided within secure data facilities (data laboratories) that are physically secure, access controlled, not connected to external networks, and with output devices disabled; analysis may be checked by an administrator before release. South Africa and the United Kingdom are cited as using secure data facilities to provide research access to tax data.
- TADAT and register-quality indicators:
  - TADAT desired outcome of taxpayer registration quoted verbatim: “All businesses, individuals, and other entities that are required to register are included in a taxpayer registration database. Information held in the database is complete, accurate, and up-to-date.” (TADAT 2019)
- ISORA survey results (exact percentages preserved):
  - Fifty-six and 58 percent, respectively, of tax administrations indicated that they had formal programs to improve register quality in 2015 and 2016, respectively (ISORA 2016). This figure rose to 73 percent in 2016 and 2017 (ISORA 2018).

_International Monetary Fund | htnea2021005 - references (PDF)_

### Box 1. Maintaining the Confidentiality of Taxpayer Information

### Box 1. Maintaining the Confidentiality of Taxpayer Information

### Key overlaps between Taxpayer Registers and Statistical Business Registers (SBRs)
- Many countries use taxpayer registers together with tax returns to construct and update SBRs.
- The coverage and accuracy of taxpayer registers impacts the SBR.
- Joint RA-NSO maintenance of registers can be explored to optimize resources and eliminate duplication of maintenance effort.
- Cooperation with suppliers of data used in constructing SBRs is needed for SBR maintenance.
- Where registers are less developed, joint register design can be considered (for example, Gambia Revenue Authority and Gambian Bureau of Statistics agreed joint responsibility for design and maintenance rules).

### Elements required in Taxpayer Registers and SBRs (from Table 1)
- Taxpayer Registers (From the TADAT Field Guide)
  - Name
  - Address and contact details
  - Date of birth/incorporation
  - Nature of business activity and/or economic/industry sector, classified according to government or other recognized coding system
  - Taxpayer segment
  - Filing and payment obligations
  - Identity of associated entities and related parties
- SBRs (From Guidelines for Building Statistical Registers in Africa)
  - Identification and contact data
  - Demographic data, for example, date when enterprise started operations, date when incorporated
  - Descriptive data, for example, activity status, legal form, institutional sector, economic activity
  - Linkages, for example, enterprises and enterprise groups to which they belong, establishments belonging to an enterprise

### Classification of Economic Activity
- Accurate industry classification is needed by RAs and NSOs because:
  - Tax provisions can depend on economic activity.
  - Compliance improvement plans may be industry-specific.
- The same industry classification should be used by the RA and NSO to allow sectoral comparisons between tax revenue aggregates and official statistics (for example, alignment to the International Standard Industrial Classification (ISIC)).
- Ideally RAs and NSOs should use the same classification code for the same business entity; differences, particularly for large taxpayers, inhibit meaningful sectoral analysis.
- NSOs often “inherit” taxpayer classifications from the RA but may amend them; providing feedback to the RA on changes is important.
- NSOs can assist RAs by:
  - Providing training in industry classification to RA staff.
  - Providing a “coder” to facilitate standard economic classification code assignments from descriptions supplied by taxpayers.
- Continuous change in economic agents necessitates collaboration to update industry classification; engagements by RA or NSO that reveal inaccuracies or changes should be used to update records.

### Analysis of Tax Administration Records and Official Statistical Data
- Data sharing between NSOs and RAs contributes to the effectiveness of each organization. Three highlighted uses:
  - The use of tax administration records in compiling official economic statistics by the NSO.
  - The use of official statistics in tax gap analysis by RAs.
  - The probing of weaknesses in the coverage of registers and consequent data collection.
- Practical considerations and challenges:
  - Tax administration records may validate, supplement, or replace sample surveys; in some countries use of administrative data is required (examples: Nordic countries and the Netherlands).
  - Revenue administration data (income tax, VAT, employers’ withholding tax) can cover more businesses—particularly small businesses—than feasible survey samples.
  - Tax data were not captured for producing economic statistics and may not match standard statistical concepts and definitions; NSOs need to understand RA data collation and processes in detail.
  - Tax gap analysis requires independent economic data sources; aggregates from the NSO (national accounts) are among the most important for estimating potential tax and quantifying tax gaps.
  - Both NSOs and RAs face incomplete coverage of economic agents in their registers; taxpayer records may cover smaller registered businesses better than NSO sample surveys, while RAs may lack information on unregistered businesses.
  - Understanding the non-observed economy is relevant to tax compliance strategies and tax policy (see Box 2 in source).
- Ongoing dialogue is needed to reconcile differences in definitions and concepts used by RAs and NSOs; technical differences are listed in the Annex.

### Guidelines for Effective RA-NSO Collaboration (Steps)
- Step 1: Identify Potential Areas of Cooperation
  - Key questions to discuss include data weaknesses, relative strengths in data collection and management, mutual data needs for disaggregation, and achieving a common industry classification.
- Step 2: Review Existing Legislation
  - Study laws governing tax administration and official statistics to determine if amendments are needed to support mutually beneficial data flows.
- Step 3: Formalize Relations with an MOU or SLA
  - An MOU or SLA can formalize responsibilities and data flows within the legal framework; SLAs may be amended more regularly as needs evolve.
- Step 4: Share Nonconfidential Information
  - Share information on concepts, definitions, quality assurance processes, and anticipated changes in revenue administration processes or statistical outputs that affect joint analysis.
- Step 5: Jointly Undertake a Concrete Project with a Well-Defined Scope
  - Begin with projects that yield insights for both organizations and institutionalize working arrangements while addressing confidentiality concerns.

### Elements to include in a Memorandum of Understanding (Box 3)
- Make explicit the legal basis for RA-NSO collaboration.
- Possible elements:
  - Objectives of the collaboration and intended outcomes (for example, joint maintenance of registers).
  - Constitution of a Technical Working Group (TWG) and a Management Committee.
  - Data and information to be shared, such as:
    - Provision of tax administration microdata by the RA to the NSO.
    - Provision of validated industry classifications by the NSO to the RA.
    - Provision of social, demographic, and economic statistics by the NSO to the RA for forecasting and tax gap estimation.
    - Sharing of methods, concepts, and definitions used in compiling official statistics.
    - Sharing of information by the RA on administrative processes, changes in revenue administration and tax policy (for example, changes in the VAT registration threshold), and estimates of levels of fraud that could impact statistics compiled using tax records.
    - Sharing of information on quality assurance and validation methods, and on systems and processes used to collect and manage data and produce reports.
  - Commitment to meet defined quality standards and provide feedback on data quality.
  - Specifications for maintenance of data integrity and confidentiality.
  - Specification of data to be exchanged: what data, how frequently, and secure formats/media.

### Possible initial joint projects (selected examples)
- 1. Improving register coverage and accuracy for a sector of national interest (for example, the construction industry).
- 2. Harmonizing the economic classifications of large businesses/taxpayers used by the RA and NSO.
- 3. Training of RA staff by the NSO on ISIC and the national standard economic activity classification.
- 4. Conducting a survey of taxpayers on perceptions of the RA and compliance attitudes (NSOs may assist to provide independent data collection and processing).
- 5. Estimating the tax gap for an indirect tax using official economic statistics to develop deeper understanding of data and highlight data quality challenges.
- 6. Comparing income distributions from NSO surveys and income tax records and examining reasons for differences.
- 7. Publishing aggregate statistics on revenue collection.
- 8. Making available anonymized revenue administration records in a secure environment for statistical and research purposes.
- Supporting statistic: “Sixty-one percent of RAs undertake taxpayer surveys, and of these, half outsource surveys (ISORA 2016).”

### Annex — Typical differences between administrative data and survey data
- Entities contained in registers may differ:
  - Taxpayer registers record legal and natural persons required to register for tax; large complex businesses may have multiple registrations for the same tax type.
  - SBRs may record distinct establishments where components differ in activity and location.
- Industry classification differences can produce disparate sectoral aggregates because RAs may record all activity of a large business as one type while SBRs break it down.
- No exact correspondence typically exists between National Accounts aggregates and any particular tax base; adjustments are necessary when estimating tax gaps (example reference: Ueda (2018)).
- Trends in tax revenue aggregates do not necessarily mirror trends in measures of economic activity because of tax policy changes and taxpayer behavior (including compliance).
- Reference periods and accounting bases may differ:
  - There may be differences in reference periods for statistical collections and accounting periods for tax; adjustments may be necessary for meaningful comparisons.
  - Timing differences: the System of National Accounts requires accrual recording, while most RAs record on a cash basis.

*International Monetary Fund | April 2021*

### References

### References

### Multilateral and international organization publications
- African Development Bank. 2014. “Guidelines for Building Statistical Business Registers in Africa.” African Development Bank. https:// www .afdb .org/ fileadmin/ uploads/ afdb/ Documents/ Project -and -Operations/ Guidelines _for _Building _Statistical _Business _Registers _in _Africa .pdf
- OECD, ILO, IMF, CIS, and others. 2002. “Measuring the Non-Observed Economy: A Handbook.” Organisation for Economic Co-operation and Development, Paris. https:// doi .org/ 10 .1787/ 9789264175358 -en
- OECD. 2004. “VAT Missing Trader Intra-Community Fraud: The Effects on Trade Statistics.” Organisation for Economic Co-operation and Development, Paris. http:// www .oecd .org/ sdd/ its/ 31458752 .pdf
- United Nations. 2020. “United Nations Guidelines on Statistical Business Registers.” United Nations, New York. https:// unstats .un .org/ unsd/ business -stat/ SBR/ Documents/ UN _Guidelines _on _SBR .pdf
- UN Statistics Division. 2014. “Report on global status of statistical business register programmes.” https:// unstats .un .org/ unsd/ economic _stat/ Economic _Census/ globalSbrAssessmentReport .pdf
- UN. 2009. “System of National Accounts 2008.” United Nations, New York. https:// www .imf .org/ en/ Publications/ Books/ Issues/ 2016/ 12/ 31/ System -of -National -Accounts -2008 -23239

### IMF publications and Working Papers
- Crandall, William, Elizabeth Gavin, and Andrew Masters. 2019. “ISORA 2016 Understanding Revenue Administration.” International Monetary Fund, Washington, DC. https:// www .imf .org/ en/ Publications/ Departmental -Papers -Policy -Papers/ Issues/ 2019/ 03/ 07/ ISORA -2016 -Understanding -Revenue -Administration -46337
- Grote, Martin. 2017. “How to Establish a Tax Policy Unit.” International Monetary Fund, Washington, DC. https:// www .imf .org/ ~/ media/ Files/ Publications/ HowToNotes/ howtonote1707 .ashx
- Hutton, Eric. 2017. “The Revenue Administration—Gap Analysis Program: Model and Methodology for Value-Added Tax Gap Estimation.” International Monetary Fund, Washington, DC. https:// www .imf .org/ ~/ media/ Files/ Publications/ TNM/ 2017/ tnm1704 .ashx
- Keen, Michael, Juan Toro, Katherine Baer, and others. 2015. “Current Challenges in Revenue Mobilization: Improving Tax Compliance.” International Monetary Fund, Washington, DC. https:// www .imf .org/ external/ np/ pp/ eng/ 2015/ 020215a .pdf
- Rivas, Lisbeth, and Joe Crowley. 2018. “Using Administrative Data to Enhance Policymaking in Developing Countries: Tax Data and the National Accounts.” IMF Working Paper No. 18/175, International Monetary Fund, Washington, DC. https:// www .imf .org/ en/ Publications/ WP/ Issues/ 2018/ 08/ 02/ Using -Administrative -Data -to -Enhance -Policymaking -in -Developing -Countries -Tax -Data -and -the -46054
- Thackray, Mick, and Martina Alexova. 2017. “The Revenue Administration—Gap Analysis Program: An Analytical Framework for Excise Gap Estimation.” International Monetary Fund, Washington, DC. https:// www .imf .org/ ~/ media/ Files/ Publications/ TNM/ 2017/ tnm1705 .ashx
- Ueda, Junji. 2018. “Estimating the Corporate Income Tax Gap: The RA-GAP Methodology.” International Monetary Fund, Washington, DC. https:// www .imf .org/ en/ Publications/ TNM/ Issues/ 2018/ 09/ 12/ Estimating -the -Corporate -Income -Tax -Gap -The -RA -GAP -Methodology -45890
- Ueda, Junji, and Mick Thackray. 2014. “South African RA-GAP Study.” International Monetary Fund, Washington, DC. http:// www .taxcom .org .za/ docs/ 20150220 %20IMF %20SA %20VAT %20Compliance %20Gap %20Report .pdf
- ISORA. 2016. “International Survey on Revenue Administration” ISORA, Washington, DC. http:// data .rafit .org
- ISORA. 2018. “International Survey on Revenue Administration.” ISORA, Washington, DC. http:// data .rafit .org
- Hutton, Eric. 2017. “The Revenue Administration—Gap Analysis Program: Model and Methodology for Value-Added Tax Gap Estimation.” International Monetary Fund, Washington, DC.. https:// www .imf .org/ ~/ media/ Files/ Publications/ TNM/ 2017/ tnm1704 .ashx

### National and regional tax authority publications and statistics
- National Treasury and SARS. 2020. “2020 Tax Statistics.” National Treasury and SARS, Pretoria, South Africa. https:// www .sars .gov .za/ About/ SATaxSystem/ Pages/ Ta x -Statistics .aspx
- RRA. 2020. “Tax Statistics in Rwanda FY2018/19.” Rwanda Revenue Authority, Kigali. https:// www .rra .gov .rw/ fileadmin/ user _upload/ tax _statistics _in _rwanda _fy _20182019 .pdf
- URA. 2020. “Annual Revenue Performance Report FY 2019/2020.” Uganda Revenue Authority, Kampala. https:// www .ura .go .ug/ openFile .do ?path =// webupload// upload// download// staticContent// TOPMENU// 9907// 10192 _RPR .pdf

### Tools, guides, and diagnostic frameworks
- TADAT. 2019. “Tax Administration Diagnostic Assessment Tool Field Guide.” https:// www .tadat .org/ assets/ files/ TADAT %20Field %20Guide %202019 %20 -%20English .pdf

*htnea2021005 - References*

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_Source: https://www.imf.org/-/media/files/publications/howtonotes/2021/english/htnea2021005.pdf_