Ictd government revenue dataset

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Information about Ictd government revenue dataset
Economy & Finance

Published on September 25, 2014

Author: ICTDTax

Source: slideshare.net

Description

Presentation by Wilson Prichard (U. Toronto and Research Director, ICTD)

Introduction
Over the last decade, the issue of taxation has benefited from a growing interest from researchers and policy makers, especially regarding its impact on key development components such as economic growth, governance and poverty reduction. However, for a long period, the low quality of available data significantly hindered the quality of the research on the topic and the robustness of the results. This led to the proliferation of small-scale datasets and impeded proper comparison and replication of studies. It is with the aim of coping with this observation that the ICTD developed the GRD. The GRD has been built by compiling and harmonizing numerous existing datasets from various sources, yielding a homogeneous set of data covering a large range of countries over a long period. In addition to expanding the coverage and the quality of the data, the GRD also includes a clear separation between resource and non-resource government revenue, allowing for precise analyses of non-resource tax collection. Although the GRD still suffers from imperfection - particularly because of the successive merging of databases realized from different collection methods - the fact remains that it represents a significant enhancement, which will enable for deeper and more accurate research and improve our understanding of taxation and its effect on economies.

Why this dataset is needed: The limitation of existing data.
The ICTD GRD is based on the understanding that the quality of international revenue data is not only poor, but also insufficient to sustain analysis, thereby leading to misleading or insufficiently robust findings on tax and development.

The ICTD’s goal therefore was to create a single composite dataset that is more complete and more accurate than alternatives, in which one could look up for every country year, any available source of data and compare them, thereby getting the best available source for that country year. Existing international sources (IMF GFS – Pre and post 1990, OECD, CEPALstat, OECDLatAm, OECD AEO, World Bank, Keen and Mansour) all suffer from substantial limitations – reflected in researchers relying increasingly on composite and ad hoc datasets, which are subject to errors, lack transparency and difficulties of comparability. This also makes them hard to replicate, and with huge scope for errors.

Indeed, most of the existing databases exhibit missing data stemming from incomplete range of revenue categories, and failure at consistently distinguishing natural resource wealth. Moreover, non-tax revenues are often not included in databases, thus giving an incomplete picture of government finances.

Finally, in many countries, GDP may be vastly under-estimated, leading to sizable overestimation of key variables as shares of GDP, hence a need to rebase. Equally, irregular rebasing exercises can lead to major breaks in time series

The ICTD Government Revenue Dataset Wilson Prichard Interna1onal Center for Tax and Development

Overview • The ICTD GRD responds to major limits of exis1ng sources for conduc1ng cross-­‐country tax research, with major improvements in data coverage and accuracy by combining data from mul1ple sources – including a more consistent approach to natural resource revenues • This is a cri1cal complement to work at interna1onal organiza1on to improve data over the long-­‐term, as it offers a much improved founda1on for immediate research. • However, it is a very par1al solu1on: There are inescapable limita1ons, which reflect the limits of any available sources, and the imperfec1ons of merging data from mul1ple sources • There is a need for coopera1on and consensus to maintain the dataset as a resource for researchers while new efforts at the IMF, OECD and elsewhere begin to bear fruit.

Outline 1. Mo1va1on 2. Construc1on of the Dataset 3. Limita1ons 4. Lessons and Next Steps

Motivation • Weaknesses of exis,ng data raise serious concerns about the robustness of tax and development research, and reduces value of data for broader descrip1ve and compara1ve exercises • Exis1ng interna1onal sources all suffer from substan1al limita1ons – reflected in researchers relying increasingly on composite and ad hoc datasets • However, ad hoc datasets subject to errors, lack of transparency and difficul1es of comparability

Weaknesses of Existing Sources • Missing data in sources with full country coverage • Limited coverage and comparability of regional sources • Non-­‐tax revenue o>en not included, thus giving incomplete picture of government finances • Failure to consistently dis,nguish natural resource revenues in most exis1ng databases • Incomplete range of revenue categories in many researcher databases • Simple errors, most notably in researcher databases – and oYen driven by merging of sources • Problems with inconsistencies in many GDP series

Potential for Complementarity 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% Ghana: Total tax as % GDP 1990 1993 1996 1999 2002 2005 2008 Year K & M GFS IMF CR WB WDI AEO

Construction of the ICTD GRD 1. A Standard Revenue Classifica1on 2. Compiling Available Interna1onal Sources 3. Compiling and Adding Ar1cle IV data 4. Dealing with Natural Resources 5. Addi1onal Issues 6. A Common GDP Series 7. Manual Data Cleaning

Construction of the ICTD GRD: A Standard Revenue Classification • Tax and Non-­‐tax • Natural Resources • Social Contribu1ons !! Total! Gov’t! Revenue! Total!Gov’t! Revenue! Excluding! Grants! Grants! Tax! Revenue! Non7Tax! Revenue!! ! Social! Contrib utions! Non7 Resource! Direct!Taxes! Indirect! Taxes! Non_Resource! Taxes!on! Incomes,!Profits! and!Capital! Gains! Property!Taxes! Taxes!on! Individuals! Non_Resource! Taxes!on! Corporations! Taxes!on!Goods! and!Services! Taxes!on! International! Trade! Other!Taxes! Sales! Taxes/VAT! Excises! Imports! Exports! Non7 Resource! Tax! Revenue! Resource! Tax! Revenue! Resource! Non7Tax! ! Non7 Resource! Non7Tax! Revenue! Resource! Direct!Taxes! Resource!Taxes! on!Incomes,! Profits!and! Capital!Gains! Resource!Taxes! on!Corporations!

Construction of the ICTD GRD: Compiling Alternative Sources • IMF GFS (pre and post-­‐1990) • OECD • CEPALSTAT • OECD LatAm • OECD AEO • World Bank • Keen and Mansour

Construction of the ICTD GRD; Article IV Data • Ar1cle IV data oYen available where other sources missing – though is less rigorously reviewed, so should be used when it matches surrounding sources • Requires careful categoriza1on, as revenue categories vary across countries and over 1me 30% 25% 20% 15% Albania: tax/GDP ratio (%) Ratio (%) Year 10% 5% 0% 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Art. IV (GG) GFS (GG) GFS (CG) GFS (CG+SS) Michigan Ross WTD WB

Construction of the ICTD GRD: Natural Resources • Interna1onal sources are inconsistent in classifying resource revenue between taxes and non-­‐tax revenue • Non-­‐resource Angola 1996 tax revenue is the analy1cally interes1ng category, which requires excluding natural resource component of tax • Some1mes possible using OECD, most oYen rely on IMF Ar1cle IV Total Revenue Total Tax Taxes on Income Total Non-­‐ Tax Rev Resource Revenue Non-­‐ Resource Non-­‐Tax Pre-­‐ Adjustment 48.9% 48.6% 32% 0.3% -­‐ 0.3% Post-­‐ Adjustment 48.9% 4.8% 0.9% 44.1% 43.8% 0.3%

Construction of the ICTD GRD: Other Issues • Consistent approach to social contribu,ons: Varia1on across sources in the inclusion of social contribu1ons can lead to incompa1bility. We report all figures inclusive and exclusive of social. • Dealing with federal states: Focusing exclusively on central government can vastly understate tax collec1on in federal states. We adopt general government data where it is significantly different from central data, and consistent over 1me. • Direct and Indirect Taxes: Owing to differences across sources in sub-­‐categories of taxa1on, we calculate direct and indirect taxes for all country-­‐years.

Construction of the ICTD GRD: Common GDP Series • There are simple differences across sources in GDP figures, making transparency and consistency about GDP figures as important as the tax data • Growing recogni1on that underes1ma1on of GDP can lead to vast overes1ma1on of key variables as shares of GDP • Equally, irregular rebasing exercises can lead to major breaks in 1me series data unless applied retroac1ve to earlier periods – which is frequently not the case

Construction of the ICTD GRD: Common GDP Series Ghana: Total tax as % source-specific GDP 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% 1980 1984 1988 1992 1996 2000 2004 2008 Year K & M GFS IMF CR WB WDI Ghana: Total tax as % common GDP series 16.0% 14.0% 12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 Year K & M GFS IMF CR WB WDI

Construction of the ICTD GRD: Manual Data Cleaning • Data merging would, ideally, be automated, filling gaps in a “baseline” source with pre-­‐determined “second best” data • However, inconsistencies across sources – some reflec1ng differing methods, others reflec1ng simple data discrepancies – imply that automated processes result in incompa1ble and inconsistent 1me series • As such, it is necessary to manually clean the data to ensure consistency within countries between data sources

Developing Country Data Coverage Data coverage is drama1cally more complete than for other sources, including the most widely used composite dataset, from the IMF FAD. ICTD GRD IMF FAD IMF Art IV IMF GFS WDI Total Revenue 2317 1913 1484 1391 1060 Total Tax 2348 1976 1895 1396 1060 Taxes on Income, Profits and Capital Gains 1900 1909 1341 1395 1060 Taxes on Goods and Services 1952 1856 1092 1395 1060

Continued Limitations 1. S,ll significant missing data 2. Challenges in dealing with resource revenues 1. Some1mes data is not available, so countries excluded from analysis 2. OYen impossible to dis1nguish resource revenue from other non-­‐tax revenue 3. Defini1onal issues in deciding what classifies as resource revenue 3. Varia,on across sources o>en inexplicable, data inherently imperfect – and merging choices inevitably subjec1ve

Lessons and Next Steps 1. Dealing with resource revenues is cri1cal, needs to be integrated with interna1onal databases and requires a common framework 2. Ajen1on to GDP figures equallly cri1cal, and any dataset should separately provide LCU figures, % of GDP figures and clearly documented GDP series 3. Any dataset should deal with both tax and non-­‐tax revenues in order to be analy1cally useful for research, while also adop1ng a consistent approach to social contribu1ons 4. There remain opportuni1es for much improved interna1onal coopera1on, as there is currently major overlap and duplica1on – some1mes even within organiza1ons – and new ini1a1ves have tended to address some, but not all, of the challenges noted here. 5. Merging data from mul1ple sources for research is fraught with risks – and is extremely 1me intensive – thus placing a premium on establishing a single accepted source, transparency and providing resources for long-­‐term maintenance of the dataset

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