the ictd government revenue dataset
TRANSCRIPT
The ICTD Government Revenue Dataset
Wilson Prichard Interna1onal Center for Tax and Development
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
1. Missing data in sources with full country coverage -‐ limited coverage and comparability of regional sources
2. Non-‐tax revenue o@en not included, thus giving incomplete picture of government finances
3. Failure to consistently dis,nguish natural resource revenues in most exis1ng databases
4. Problems with inconsistencies in many GDP series
Potential for Complementarity
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10.0%
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1990 1993 1996 1999 2002 2005 2008 Year
Ghana: Total tax as % GDP
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. A Common GDP Series
Construction of the ICTD GRD: A Standard Revenue Classification
• Tax and Non-‐tax
• Natural Resources
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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 o`en 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
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10%
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25%
30%
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Rat
io (%
)
Year
Albania: tax/GDP ratio (%)
Art. IV (GG) GFS (GG) GFS (CG) GFS (CG+SS) Michigan Ross WTD WB
Construction of the ICTD GRD: Natural Resources
Angola 1996
• Interna1onal sources are inconsistent in classifying resource revenue between taxes and non-‐tax revenue
• Non-‐resource tax revenue is the analy1cally interes1ng category, which requires excluding natural resource component of tax
• Some1mes possible using OECD, most o`en 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: 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
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5.0%
10.0%
15.0%
20.0%
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1980 1984 1988 1992 1996 2000 2004 2008 Year
Ghana: Total tax as % source-specific GDP
K & M GFS IMF CR WB WDI
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6.0%
8.0%
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12.0%
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1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 Year
Ghana: Total tax as % common GDP series
K & M GFS IMF CR WB WDI
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
Moving Forward 1. While data is not perfect, it is the best available source of data
for cross-‐country research and comparison
2. Challenges in dealing with resource revenues 1. Some1mes data is not available, so countries excluded from analysis 2. O`en impossible to dis1nguish resource revenue from other non-‐
tax revenue 3. Defini1onal issues in deciding what classifies as resource revenue
3. Be careful with GDP figures and cross-‐country comparison 4. Engaging with other exis,ng ini,a,ve:
1. OECD 2. IMF FAD 3. IMF GFS
Using the Data: The Case of Aid and Taxation
Background • Gupta et al. (2004) and Benedek et al. (2014) from the IMF find that: § Aid grants lead to reduced tax revenue § Aid loans have a negligible or posi1ve effect on tax collec1on
• Clear policy implica1ons: § Heavier reliance on loans § More strict condi1onality around revenue collec1on
Hypothesis • The actual rela1onship is more complex:
§ Some1mes nega1ve: There are cases in which aid discourages tax collec1on
§ Some1me posi1ve: There are cases in which aid supports expanded tax collec1on directly or indirectly (TA, condi1onality, expanded spending)
§ Changing across contexts: The impact depends on local context and poli1cal dynamics
§ Changing over 1me: The impact of aid on taxa1on may be different post-‐Cold War, or even more recently, as aid prac1ce has improved
• This view is supported by several recent studies (Clist and Morrissey 2011)
The Problem
• Difficult to resolve disagreement owing to inconsistent, and unavailable, data sources: § Gupta et al. (2003) use an internally constructed fiscal affairs department dataset, which is not publicly available
§ Gupta et al. (2014) use a different internal IMF dataset, which is not publicly available
Our Plan • Agempt to replicate the IMF (Gupta et al.) results using the new ICTD data
• Begin with a pure replica1on, based on their data sources
• Followed by running a wider set of tests using ICTD data.
Results 1. Impossible to replicate using the data
sources they report using
2. Can almost replicate their results using a data set they (eventually) shared with us, but that data set has major errors
3. Impossible to consistently replicate their broad findings using ICTD data, with rela1onship generally negligible
Conclusions 1. Aid DOES NOT have a consistently nega1ve
impact on tax effort, as rela1onship appears to be much more variable across countries
2. Data quality is VERY important, as bad tax data leads to misleading results and policy
3. Use the ICTD GRD!
Core Results Gupta
et al. (2004)
Benedek et al. (2014)
Net aid
Grants Net loans Grants and net loans
net_aid 0.698** (0.3172)
net_aid2 -‐1.014** (0.4829)
net_loans 0.011*** 0.0142 -‐0.635 -‐0.588 (3.05) (0.0176) (0.7744) (0.784)
net_loans2
-0.0001 -‐0.0011 22.948* 18.800
(-0.99) (0.0010) (12.8921) (13.2394) grants -0.016*** -‐0.0151*** 0.685** 0.585*
(-5.00) (0.0010) (0.3309) (0.3401) grants2 0.0004*** 0.0002* -‐1.117** -‐1.012*
(5.07) (0.0001) (0.5613) (0.565)*