The ICTD Government Revenue Dataset Presentation made at the ICTD Annual Centre Meeting Arusha, 2014

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

2. 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

3. 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

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

5. 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

6. Construction of the ICTD GRD:
A Standard Revenue Classification
• Tax
and
Non-­‐tax
• Natural
Resources
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7. 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

8. 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
0%
5%
10%
15%
20%
25%
30%
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Ratio(%)
Year
Albania: tax/GDP ratio (%)
Art. IV (GG) GFS (GG) GFS (CG) GFS (CG+SS) Michigan Ross WTD WB

9. 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%

10. 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

11. Construction of the ICTD GRD:
Common GDP Series
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
1980 1984 1988 1992 1996 2000 2004 2008
Year
Ghana: Total tax as % source-specific GDP
K & M
GFS
IMF CR
WB WDI
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
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

12. 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

13. 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

14. Using the Data:
The Case of Aid and Taxation

15. 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

16. 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)

17. 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

18. 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.

19. 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

20. 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!

21. 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_loans
2
-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)*