Transcript The power to tax in Africa: VAT, LTU, and SARA

La mesure de la performance dans les administrations fiscales et
douanières des pays en développement
FERDI, ICTD, CERDI
Jeudi et vendredi 12 - 13 juin 2014, Clermont-Ferrand, France.
The power to tax in Africa:
VAT, LTU, and SARA
Christian Ebeke, Mario Mansour
and Gregoire Rota Graziosi
International Monetary Fund
CERDI and FERDI.
This Working Paper should not be reported as representing the views of the IMF.
The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those
of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to
elicit comments and to further debate.
Introduction
• The power to tax is a core function of the state.
– Preponderant role of taxation in the relationship between rulers and
citizens (Ardant, 1971, Friedman, 1974, and Tilly, 1990).
– State building => governments’ ability to raise taxes (Brautigan et al.,
2008; and Besley and Persson, 2011).
• Three major reforms in tax policy and tax administration in SubSaharan Africa (SSA):
– Large Taxpayer Units (LTUs): a rationalization of the tax administration
through the segmentation of taxpayers according to their turnover;
– Value Added Tax (VAT): a partial delegation of revenue collection to
the private sector (self-enforcing mechanism though the credit-invoice
method);
– and Semi-Autonomous Revenue Authorities (SARAs): a strategic
delegation of tax collection to an agent.
• A common denominator of these reforms: increase revenue
through better enforcement of tax laws.
Introduction
• Our analysis: estimating the effectiveness of these three tax
reforms on domestic revenue mobilization.
• Our database: tax revenue from non-renewable natural resources
to GDP ratio for 41 SSA countries over 1980-2010 (Mansour, 2014).
• Our empirical analysis: a wide range of complementing empirical
approaches to address the issue of self-selection associated with
the decision to adopt each reform.
–
–
–
–
Fixed-effects estimates
Dynamic panel estimates with fixed-effects.
the propensity score matching,
and the synthetic control method.
• Our results:
– VAT and SARAs improve significantly tax revenue mobilization;
– the impact of LTU is debatable.
LTU: strengths and weaknesses
– Initially in Latin America in the late 70s,
– Establishing a self-contained office within the tax administration, acting as single
clearance window for large taxpayers for the main domestic taxes: income taxes,
VAT…
• Strengths:
– Segmentation => the specialization of the personnel of the tax administration,
– Centralization of core functions for all taxes,
– An opportunity to induce other efficiency-improving reforms such as selfassessment, adoption of unique taxpayer identification numbers, electronic filling,
and new computerized information system (Baer, 2002; and McCarten, 2005)
• Weaknesses:
– Concentration of the tax administration efforts and most competent human
resources to a small number of large taxpayers => the emergence of a dual
economy
– Risk of unbalanced resource allocation between tax collection from existing well
known taxpayers, and extending the tax base to include tax evaders (Terkper,
2003).
– Strategic behavior from taxpayers, who may break-up their activities in order to
remain below the LTU threshold= > a missing middle.
VAT: strengths and weaknesses
• Introduced in France (1954): a tax on final consumption.
• Two properties:
– Neutral on production decisions.
– Self-enforcing: collected by firms at the various stages of production
and distribution through the invoice-credit mechanism, which
provides an incentive to report purchases (input) in order to claim a
credit against VAT on sales (output).
• Strengths:
– An incentive to formalize economic activity since the informal sector would be
considered as final consumer and would support all the VAT paid on its inputs;
– Devolution of a part of the tax collection effort to the private sector,.
• Weaknesses
– Poorly implemented in Africa:
• High VAT rates, which created pressure for exemptions
• Inadequate registration threshold
– Exposed to lobby groups for tax expenditure every year.
SARA: strengths and weaknesses
– A drastic reform: delegating tax collection to an autonomous agency
(Kydland and Prescott, 1994)
• Strengths:
– Autonomy (which may differ significantly across countries) = a signal
to a more credible audit policy without any political interference
(Taliercio, 2004).
– Signal effect reinforced by the greater flexibility of SARA to manage its
human resources than standard public sector agencies (Fjeldstadt and
Moore, 2009 and Moore, 2014).
– Merger of tax and customs administrations into a single entity.
• Weaknesses
– Credibility of the commitment non sufficient (< an independent
Central Bank).
– A threat on the consistency of policymaking due to the separation
between tax collection and tax policy (Fjeldstad and Moore, 2009).
– The mobility of tax auditors from SARA to the private sector =>
conflicts of interests, revolving doors.
Data and stylized facts (Average tax
revenue to GDP in SSA over 1980-2010)
20%
18%
16%
14%
12%
1980
1990
2000
2010
Year
Non natural resource tax revenue / GDP
Tax revenue / GDP
Fitted values
Fitted values
The adoption of LTU, VAT and SARA in
SSA (1980-2010)
40
35
30
25
VAT
20
LTU
15
SARA
10
5
0
1980
1985
1990
1995
2000
2005
2010
Reform sequencing
0
No reform
SARA
LTU
10
VAT
LTU and SARA same year
LTU and VAT same year
20
30
40
0
1
Source: Authors.
2
Order
3
4
Empirical analysis
• Several techniques:
– Fixed-effects estimates and dynamic panel with fixed-effects,
– Propensity score matching estimates,
– Synthetic control method which is a generalization of the differencein-difference technique.
• Why?
– Due to the issue of the self-selection associated with the decisions to
adopt each reform.
– No random assignment of the allocation of LTUs, VAT and SARAs
between countries.
– Potentially large selection-bias associated with the decision to adopt
(and maintain) a tax reform. For example, countries with poor
performance in revenue mobilization are more likely to embark into
ambitious reforms aimed at reversing the trend.
• Expectation:
– A positive impact on revenues of each reform;
– The impact of any combination of these reforms.
Fixed-effects estimates
• The baseline specification:
– A linear relationship between the tax revenue-to-GDP ratio
and each dummy variable representing each of the three
tax reforms. The dummies take the value 1 whenever a
country has in place a VAT, LTU, or a SARA, and 0
otherwise.
– where T and R denote the tax revenue-to-GDP ratio and
the dummy variable identifying the presence of each of
the three tax reforms considered.. X refers to the matrix of
control variable discussed above while ui denotes the
country fixed-effects.
Fixed effects estimation results
Table 1. Fixed effects panel data estimates of the effects of Tax reforms in Africa.
Dependent variable:
Non-hydrocarbon tax revenuesto-GDP
VAT
-1
FE
-2
FE
-3
FE
-4
FE
-5
FE
2.158***
-0.352
0.717**
-0.311
-0.425
-0.323
2.016***
-0.399
1.489***
-0.329
0.204
-0.321
1.509***
-0.415
0.656***
1.015***
-0.246
LTU
0.644**
-0.25
SARA
Fund arrangement dummy,
lagged
Trade openness
Resource rents-to-GDP
Real GDP per capita, log
Agriculture value added-to-GDP
Aid-to-GDP
Constant
Country fixed-effects
Observations
R-squared
Number of countries
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
0.0193**
-0.00923
-2.394***
-0.257
1.378**
-0.553
-0.0398**
0.0179*
-0.00941
-2.399***
-0.258
1.781***
-0.55
-0.0434**
0.0169*
-0.00916
-2.227***
-0.256
1.484***
-0.543
-0.0313
0.0180*
-0.00927
-2.234***
-0.256
1.218**
-0.556
-0.0297
-0.253
-0.00361
-0.00996
-1.968***
-0.266
-0.372
-0.671
-0.0243
-0.02
0.00565
-0.0138
9.108**
-3.894
-0.0202
0.00867
-0.0139
6.996*
-3.902
-0.0199
0.00277
-0.0137
8.136**
-3.84
-0.02
0.000966
-0.0138
9.563**
-3.892
-0.0232
0.0127
-0.0159
18.59***
-4.69
Yes
1,080
0.11
39
Yes
1,080
0.101
39
Yes
1,080
0.127
39
Yes
1,080
0.132
39
Yes
821
0.163
39
Propensity score matching estimates
• A two-step approach
– First: the probability of observing either the VAT, LTU or
SARA in a given country at year t, is estimated conditional
on observable economic conditions and country
characteristics (selection model)
– Second: these probabilities, or propensity scores, are used
to match reform countries to non-reformer countries, and
thereby construct a statistical control group.
• Test the sequencing of the reforms: for each reform,
we investigate whether the initial adoption of a given
tax reform determines the probability of adopting a
subsequent one.
Table 2. Matching estimates of treatment effect on the non-natural resource tax
revenue-to-GDP ratio.
-1
-2
-3
-4
NearestThree
Radius
Kernel
neighbor
nearestmatching
neighbor
matching
Panel A. Treatment effect of VAT adoption on non-hydrocarbon tax
revenues-to-GDP ratio
ATT
No. of treated
No. of controls
Observations
1.855***
-0.629
2.043***
-0.548
1.814***
-0.36
1.813***
-0.36
364
475
839
364
475
839
364
475
839
364
475
839
Panel B. Treatment effect of LTU adoption on non-hydrocarbon tax
revenues-to-GDP ratio
ATT
No. of treated
No. of controls
Observations
0.353
-0.729
0.758
-0.64
0.517
-0.408
0.545
-0.407
268
603
871
268
603
871
268
603
871
268
603
871
Panel C. Treatment effect of SARA adoption on non-hydrocarbon tax
revenues-to-GDP ratio
ATT
No. of treated
No. of controls
Observations
4.529***
-0.807
3.585***
-0.659
2.634***
-0.535
2.805***
-0.531
169
735
904
169
735
904
169
735
904
169
735
904
Notes: ATT: Average treatment ef f ect on the treated. Observations are matched on the
‘common support’ An Epanechnikov kernel is used f or kernel matching. Bootstrapped standard
errors are reported in parentheses. They are based on 100 replications of the data. *** p<0.01,
** p<0.05, * p<0.1.
-1
Nearestneighbor
matching
-2
Three
nearestneighbor
matching
-3
Radius
-4
Kernel
Panel A. Treatment effect of VAT and LTU adoptions
ATT
No. of treated
No. of controls
Observations
-1.912**
-0.831
-1.533**
-0.737
-1.645***
-0.465
-1.616***
-0.469
159
745
904
159
745
904
159
745
904
159
745
904
Panel B. Treatment effect of VAT and SARA adoptions
ATT
No. of treated
No. of controls
Observations
5.702
-3.499
4.608
-3.003
6.410***
-2.243
6.538***
-2.209
19
881
900
19
881
900
19
881
900
19
881
900
Panel C. Treatment effect of LTU and SARA adoptions
ATT
No. of treated
No. of controls
Observations
1.434
-2.2
1.154
-1.914
-0.88
-1.312
-0.934
-1.317
6
898
904
6
898
904
6
898
904
6
898
904
Panel D. Treatment effect of all reform adoptions
ATT
No. of treated
No. of controls
Observations
0.329
-0.927
1.202*
-0.711
2.394***
-0.412
2.403***
-0.41
94
797
891
94
797
891
94
797
891
94
797
891
Notes: ATT: Average treatment ef f ect on the treated. Observations are matched on the ‘common
support’ An Epanechnikov kernel is used f or kernel matching. Bootstrapped standard errors are
reported in parentheses. They are based on 100 replications of the data. *** p<0.01, ** p<0.05, *
p<0.1.
Synthetic control method (SCM)
• Approach developed by Abadie and Gardeazabal (2003)
and extended in Abadie, Diamond, and Hainmueller (2010).
• Question: Does the adoption of the VAT, the LTU or the
SARA in year T lead to higher tax-to-GDP ratio in the years
T+i (i=1,…,10) compared to similar countries that have not
implemented such reforms?
• Advantages of SCM:
– A transparent estimation of the counterfactual outcome
of the treated country —the synthetic control.
– SCM can deal with endogeneity from omitted variable
bias by accounting for the presence of time-varying
unobservable confounders.
11
12
13
14
15
16
Effect of the adoption of LTU
.8
.7
.6
.5
.4
Note: Average taken across countries without missing data.
.3
Counterfactual
10
.2
Actual
5
.1
0
Time
0
-5
Statistical significance (Probability)
-10
.9
1
Lead specific significance levels (P-values)
0
1
2
3
4
5
Time
6
7
8
9
10
12
13
14
15
16
17
Effect of the adoption of VAT
-5
0
time
5
10
Lead Specific Significance Level (P-Values)
.05
.04
.03
.02
.01
Note: Average taken across countries without missing data
0
Counterfactual
Statistical significance (Probability)
Actual
.06
.07
-10
0
1
2
3
4
5
Time horizon
6
7
8
9
10
10
12
14
16
Effect of the adoption of SARA
10
Lead specific significance levels (P-values)
.07
.06
.05
.04
.03
Note: Average taken across countries without missing data.
.02
Counterfactual
.01
Actual
5
.08
0
Time
0
-5
Statistical significance (P-value)
-10
0
1
2
3
Time
4
5
6
7
Conclusion
• Both VAT and SARA have significant positive effects on tax revenue
mobilization.
• The LTU reform is not found to exert any particularly significant effect on
revenues.
– Tax administrations focused on large taxpayers even before LTUs were
implemented;
– LTUs centralized compliance services to large firms, which may have had an
impact on their compliance costs but limited impact on revenues.
– Another factor is the use of information in risk management and audit, areas
where LTUs bring very little.
– The lack of impact of the LTU may be due to missing accompanying measures,
such as better access to information, both internal and external to
government.
• Practical policy implications of our results:
– Fundamental tax reform such as VAT and SARA has a good chance of
mobilizing addition revenues in SSA
– Marginal reforms, such as introduction of LTU, especially if unaccompanied by
other reforms, have little impact (if any) on revenue.
Caveats
• First, the benefits of tax reforms should be weighed against
their costs, which differ across the various reforms
– LTU is certainly the cheaper reform.
– VAT involves a significant compliance cost for the private sector.
– SARAs in SSA countries have been largely supported by donors
and their operational costs are estimated to two percent of tax
revenue in the Eastern African Community.
• Second, our analysis covers non-resource revenue only, but
as we noted earlier, much of the increase in revenue in SSA
since the mid-1990s came from resource revenues.
• Third, a number of SSA countries recently revised their GDP
figures upward, and by a significant margin.
Thank you for your attention
The issue of statistical interferences
• SUTVA:
– All previous estimation methods are based on the
assumption of no interferences between units (countries)
= the stable-unit treatment value assumption (SUTVA).
• Potential positive tax spillovers effects among
neighboring countries => A potentially downward bias.
– The absence of interference among countries = the absence of any tax
competition and more broadly any tax (policies and reforms) spillovers
between countries.
– If one of the studied tax reform is effective in improving revenue mobilization
 increase in the effective tax rate, whatever is the tax base: income or consumption.
 some tax base effect in neighboring countries: the increase in the effective tax rate in one
country after a tax reform may induce an outflow of mobile factors (capital, skilled workers…)
or some cross-border shopping, which means a broadening of the tax base in neighboring
countries
 tax revenue increase in the synthetic group.
 A downward bias.