Annex 1. Examples of Differences between Administrative Data and Survey Data
This note emphasizes that users of administrative data and survey data need to understand differences between data from different sources. Some examples of typical differences that may be encountered in working with tax administration data and official statistics are as follows:
The entities contained in registers may differ. Taxpayer registers will contain records of legal and natural persons that are required to register for tax. In the case of large, complex businesses, it may be possible to have more than one registration for the same tax type (for example, where different payrolls are run within the business, each may be associated with a registration for pay-as-you-earn). SBRs may record distinct establishments, where components of an enterprise differ in economic activity and physical locations. As the economic activity of each entity in a taxpayer register or SBR should be recorded, this can, for example, lead to differences in sectoral aggregates.
Unless there is close collaboration between the RA and NSO on industry classification, there are many reasons why disparate sectoral aggregates may not be associated with the same set of businesses. One of the reasons for this follows from the point above: the economic activity of each entity in a taxpayer register or SBR should be recorded. Thus, an RA may be recording all the economic activity associated with a large business as one type of activity, while this may be broken down into several economic activities in the SBR.
Typically there is no exact correspondence between economic aggregates available from the compilation of National Accounts and any particular tax base. For this reason, adjustments are made to economic statistical aggregates to estimate tax gaps when applying RA-GAP methodologies (see, for example, Ueda (2018) where the relationship between the tax base for corporate income tax and gross operating surplus is set out).
Trends in tax revenue aggregates do not mirror trends in measures of economic activity. In addition to the point made previously, tax policy changes and taxpayer behavior (including compliance) also affect tax revenue aggregates over time.
There may be differences in the reference periods for statistical collections and the accounting periods for tax. Adjustments may be necessary to make meaningful comparisons of tax administration and economic statistics aggregates over time.
There may be timing differences in the recording of taxable transactions and economic activity in national accounts. The System of National Accounts requires that transactions be recorded on an accrual basis. Most RAs record transactions on a cash basis.
References
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
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
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
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
National Treasury and SARS. 2020. “2020 Tax Statistics.” National Treasury and SARS, Pretoria, South Africa. https://www.sars.gov.za/About/SATaxSystem/Pages/Tax-Statistics.aspx
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
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
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
TADAT. 2019. “Tax Administration Diagnostic Assessment Tool Field Guide.” https://www.tadat.org/assets/files/TADAT%20Field%20Guide%202019%20-%20English.pdf
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, 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
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
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
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
This paper was reviewed by Michael Keen and benefitted from comments by Katherine Baer, Joseph Crowley, Claudia Dziobek, Andrew Masters, Andrew Okello, Lisbeth Rivas, and Mick Thackray.
See IMF Technical Manuals and Notes TMN/17/04 (Hutton, 2017), TMN/17/05 (Thackray, ,2017) and TNMEA2018002 (Ueda, 2018) for detail on estimating the tax gap for VAT, Excise and CIT respectively.
See IMF Working Paper “Using Administrative Data to Enhance Policymaking in Developing Countries: Tax Data and the National Accounts” (Rivas 2018).
An example of how VAT fraud impacted trade and balance of payment statistics in the United Kingdom is discussed in the report “VAT Missing Trader Intra-Community Fraud: the Effects on Trade Statistics” (OECD 2004).
The application of revenue administration data to the evaluation and formulation of tax policy has been covered in another how-to-note (Grote 2017).
Rivas (2018) covers country examples showing the ways in which tax data have been used in compiling national accounts.
For simplicity, the term “taxpayers” will be used to refer to businesses, individuals, households, and other organizations in exercising either their obligations as taxpayers or as respondents to official statistical surveys.
TADAT describes the desired outcome of taxpayer registration as: “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)
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).
See, for example, http://www.wiesbaden2018.bfs.admin.ch/ agenda-papers/ where country reports to the Wiesbaden Group identify inaccuracies in address information and economic classification in taxpayer registers as challenges.
Cooperation with data providers is necessary to understand the concepts, ensure the continued supply of data, and enable linkages to be made between various sources (United Nations 2020).
See, for example, the discussion on compliance risk management in TADAT (2019).
See, for example, the country reports to the Wiesbaden Group on Business Registers http://www.wiesbaden2018.bfs.admin.ch/agenda-papers/.
For a more detailed discussion, see Keen (2015).
TADAT (2019) requires a measure of “the extent of initiatives to detect businesses and individuals who are required to register but fail to do so.”
All tax administrations indicated that they had powers to gather information in ISORA 2016 (Crandall 2019).
Sixty-one percent of RAs undertake taxpayer surveys, and of these, half outsource surveys (ISORA 2016).
Many RAs publish statistics on revenue collection (for example, Rwanda [RRA 2020], South Africa [National Treasury 2020], Uganda [URA, 2020]). The publication of tax statistics can serve to improve public understanding of revenue collections, offer transparency on tax incidence, and promote accountability.