©The Pakistan Development Review

51:4 Part II (Winter 2012) pp. 51:4, 365–380

Analysis of Revenue Potential and Revenue

Effort in Developing Asian Countries
ATTIYA Y. JAVID and UMAIMA ARIF*

1. INTRODUCTION
Countries around the world are increasingly recognising that the effective revenue
system is the most important factor for economic development. Factors effecting revenue
potential measured as the revenue to GDP has been one of the most important issues that
concerns to policy-makers from last three decades. Many developing countries face
difficulties in generating sufficient revenues for public expenditure. In some countries
budget deficits and the unproductive use of public expenditures have narrow the vital
investments in both human resources and basic infrastructure that are necessary for
providing base for sustainable economic growth and development. Too much dependence
on foreign financing may cause problems of debt sustainability; therefore developing
countries will need to depend substantially on domestic revenue generation.
There is a large body of literature on tax revenue potential in developing countries
[Bahl (1971); Tanzi (1987); Leuthold (1991); and Stotsky and Mariam (1997); Gupta
(2007)]. However, there is few studies that examine institutional and governance quality
as a factor influencing tax collection and tax revenue potential. According to Tanzi and
Davoodi (1997) and Gupta (2007) these factors are responsible for low tax collection in
developing countries by allowing citizens inappropriate tax exemptions and enabling tax
evasion due to bad tax administration. Therefore, a precondition for ensuring adequate
revenue collection is a legitimate and responsive institutions following the rule of law
with control on corruption and having high quality bureaucracy to administer. Studies
also confirm that a strong political will to reform is required for successful reform
process [Bird (2004)]. Alm, et al. (2003) suggest that tax records of countries are
reflection of their political or societal institutions.
The present study analyses the idea of taxable potential and tax effort by extending
to measure (fiscal) revenue potential and (fiscal) revenue effort. Total fiscal revenue is
sum of both tax and non-tax revenue collection consisting of cash receipts from taxes,
social contributions, and non-tax sources such as fines, fees, rent, and income from
property or sales.

Attiya Y. Javid <attiyajavid@pide.org.pk> is Professor of Economics at Pakistan Institute of

Development Economics, Islamabad. Umaima Arif <umiama.arif@gmail.com> is Lecturer, Economics
Department, Quaid-i-Azam University, Islamabad.
Authors’ Note: Any remaining errors and omissions are the authors’ sole responsibility.
51:4, 366 Javid and Arif

The main aim of the present study is to empirically investigate the sources of
resource mobilisation for developing Asian countries for the period 1984 to 2010. The
sample of the countries include: Pakistan, Bangladesh, India and Sri Lanka, Indonesia,
Malaysia, Thailand, China, Philippines, Singapore, China, Singapore and Vietnam as
these countries have common characteristics of large and persistent as well as instable
budget deficit. More specifically, the study look at the main determinants of revenues of
the central government, and analyse the extent to which factors such as the structure of
the economy, macroeconomic policy and institutions and the level of development
explain their variation. The study assesses the revenue effort of the sample countries that
is defined as index of the ratio of the actual revenue collection to GDP and the predicted
revenue capacity.
The resources available for fiscal policy are inadequate for South Asian countries
in particular and developing countries in general and this will make difficult to meet all
public expenditures and government can focus on specific expenditures due to political
pressure [Jha (2009)]. India has shown an upward trend in revenue to expenditure ratio
overtime whereas Pakistan, Bangladesh and Sri Lanka have recorded a decline in this
ratio. So public deficit in South Asian countries remains high for Pakistan and Sri Lanka
and countries face considerable resource constraints on financing of the deficit that result
from their expenditure in excess of revenues. India has shown good revenue performance
among South Asian countries but has shown no progress in its performance between
2005 and 2008. Bangladesh’s score enhanced after 2006 but remained still thereafter. The
most disappointing performance has been by Pakistan among the South Asian countries,
however Sri Lanka’s performance was comparable to India’s in 2005 and 2006 but then
has again worsen. China has registered an increase in their revenue to GDP ratio from 5
percent in 1990 to 11 percent in 2011 whereas Singapore, Malaysia, Indonesia,
Philippines and Thailand show a decline in this ratio during this period. Although China
shows a rising trend compared to other countries but even then there is no significant
difference in revenue to GDP ratio of China and the rest of the countries in ASEAN
region. In General falling tax/GDP ratios in these countries leads to structurally
unshakable fiscal deficits and necessitates investigating the main factors that may explain
the variation in resource mobilisation of developing Asian countries. Furthermore, quality
of institution that creates economic stability and a move towards democratic regimes is
also essential for the increasing the revenue collection capacity developing Asian region.
This paper undertakes panel data analysis to estimate revenue potential for a
sample of developing Asian countries during 1984-2010 following the empirical
methodology suggested by Bird, Vazquez, and Torgler (2004) and Gupta (2007). The
estimation results are used as benchmarks to compare revenue potential and revenue
effort in Asian countries. Revenue potential is defined as the estimated revenue to GDP
with the regression, considering a country’s specific macroeconomic, demographic, and
institutional features. Revenue effort is an index of the actual revenue GDP and the
predicted revenue potential.
The study adds to existing empirical literature by comparing fiscal capacity and
fiscal effort among the developing countries of Asian region over longer period of time
from 1984 to 2010 and for almost three decades separately: 1984 to 1990, 1991 to 2000
and 2001 to 2010. Second besides the traditional supply side determinants like GDP per
 Analysis of Revenue Potential and Revenue Effort 51:4, 367

capita, international trade, agricultural value added debt as a fraction of GDP the impact
of quality of institution and policy variable on a country’s revenue capacity are analysed.
The corruption index, the law and order and bureaucratic quality scores are used for this
purpose. The indexes are obtained from the International Country Risk Guide (ICRG).
The study is organised as follows. Section 2 discusses the theoretical and empirical
literature in this area. Methodological framework, data/sample and estimation technique
are presented in Section 3. The empirical results of regression analysis to estimate fiscal
potential and index of fiscal effort analysis is presented in Section 4 and the last section
concludes the study.

2. LITERATURE REVIEW
Regression Analysis focused on possible determinants of taxes are used in the
literature to estimate taxable capacity and the tax effort of countries. Taxable capacity is
defined as predicted tax-to-GDP ratio calculated by the estimated coefficients of a
regression specification that takes into account the country specific characteristics [Gupta
(2007); Bird, et al. (2007), Le, Moreno- Dodson, and Rojchaichaninthorn (2008)]. Tax
effort is defined as index of the ratio of the share of the actual collection to GDP and the
predicted taxable capacity. A high tax effort points to a situation when a tax effort index
is above 1, entailing that the country optimally uses its tax base to augment tax revenues
[Stotsky, et al. (1997)]. Likewise, a low tax effort means that tax effort index is below 1,
implying that the country may have potential to increase tax revenues.
Several studies show that variables such as per capita GDP, the sector wise
composition of output, the degree of trade and financial openness, the ratio of foreign aid
to GDP, the ratio of overall debt to GDP, a measure for the informal economy, and
institutional factors such as the degree of political stability and corruption plays an
important role in determining revenue performance of any economy [Gupta (2007); Bird,
et al. (2008) and Le, et al. (2008)]. Lotz and Morss (1967) find that per capita income
and trade share are important determinants of the tax share. Chelliah (1971) relates the
tax share to explanatory variables such as mining share; non-mineral export ratio and
agriculture share and obtain similar results. In a related study covering developing
countries, Tanzi (1992) finds that half of the variation in the tax ratio is explained by per
capita income, import share, agriculture share and foreign debt share.
The effect of trade liberalisation is considered as important determinant that occurs
primarily through reduction in tariffs, then one expects losses in tariff revenue, however
revenue may increase provided trade liberalisation occurs through tariffication of quotas,
eliminations of exemptions, reduction in tariff peaks and improvement in customs
procedure [Keen and Simone (2004)]. Several studies find that there is a positive
relationship between trade openness and the size of the government [Gupta (2007); Bird,
et al. (2007) and Le, et al. (2008)]. Rodrik (1998) also conclude that as societies seem to
demand (and receive) an expanded role for the government in providing social insurance
in more open economies are subject to external risks.
The degree of external indebtedness of a country is also examined as factor that
affects revenue performance of an economy [Gupta (2007)]. For generating necessary
foreign exchange to service the debt, a country may choose to reduce imports that lead to
lower import tax otherwise the country may choose to increase import tariffs or other
51:4, 368 Javid and Arif

taxes to generate a primary budget surplus for debt servicing. The composition of aid has
an important effect on revenue performance, for example, concessional loans are
associated with higher domestic revenue mobilisation, while grants have the opposite
affect [Gupta, et al. (2004)].
Recently, some studies have explored the importance of institutional factors in
determining revenue performance. For example, Bird, Martinez-Vasquez, and Torgler
(2004) find that factors such as corruption, rule of law, entry regulations play key roles.
Several regional studies have looked into quality of institution and governance as
determinants of resource mobilisation. Leuthold (1991) uses panel data to find a positive
impact from trade share and Stotsky and Mariam (1997) find that both agriculture and
mining share are negatively related to the tax ratio, while export share and per capita
income have a positive effect.
Ghura (1998) concludes that the tax ratio rises with income and degree of
openness, and with the share of agriculture in GDP. He also finds that other factors like
corruption, structural reforms and human capital development affect the tax ratio. Most
studies find that per capita GDP and degree of openness is positively related to revenue
performance, but a higher agriculture share lowers it. Studies such as Tanzi (1992) and
Eltony (2002) found that foreign debt is positively related to resource mobilisation.
The present study provides comparison of fiscal capacity and fiscal effort among
the developing countries of Asian region. This study checks the robustness of quality of
institutions and macroeconomic policy variables in determining the fiscal performance of
countries in this region over a long period of time 1984 to 2010 that is further divided
into three sub samples 1984 to 1990, 1991 to 2000 and 2001 to 2010.

3. METHODOLOGY AND DATA

The present study analyses revenue performance by estimating revenue potential
and calculating revenue effort index for developing Asian countries over the period of
1984 to 2010.1 The empirical methodology applied by Bird, Vazquez, and Torgler (2004)
and Gupta (2007) is adopted to examine the potential revenue capacity of developing
Asian countries. Revenue (fiscal) potential is the predicted revenue to GDP ratio
estimated from the regression based on the country specific characteristics and revenue
(fiscal) effort is ratio between the actual collection to GDP and predicted revenue
capacity [Bird, et al. (2004) and Le, et al. (2008)].2 The empirical specification of the

1
Revenue potential refers to the predicted revenue to GDP ratio that can be estimated with the
regression, taking into account a country’s specific macroeconomic, demographic, and institutional features.
While lacking solid theoretical foundations, actual tax to GDP (likewise revenue to GDP) is one of the most
commonly used measures for cross country comparison of tax (fiscal) effort. The advantages of this measure are
that it is easy to obtain and gives quick overview of revenue performance across countries. The problem is that,
the measurement of the potential revenue capacity is based on, a priori, set of explanatory variables that
determine the potential capacity of a country to collect revenue, but it does not reflect either the demand for
higher public expenditures or the political willingness to collect revenue as pointed out by Bird (1978) and
Toye (1978).
2
A high fiscal effort is the case when effort index is above 1, indicating that the country well utilises its
revenue base to increase revenues and a low fiscal effort is the case when effort index is below 1 implying that
the country may have relatively substantial potential to raise revenues [Stotsky, et al. (1997); Bird, et al. (2004)
and Le, et al. (2008)]. This index allows to compare country’s revenue effort vis-à-vis that of its peers [Tanzi
and Zee (2000)].
 Analysis of Revenue Potential and Revenue Effort 51:4, 369

model that measures the revenue potential by estimating the determinants of revenue is
express as:
Revenue/GDP = F (Economic, Demographic, Institutional, Policy)
More specifically the basic specification of the model takes the following form:3

R e vnue / GDPit  i  1GDPCit  2Tradeit  3 Debtit Popgit  Insit  Infit  it (1)

R e vnue / GDPit    1GDPCit  2Tradeit  3 Debtit Popgit  Insit  Infit  vi  it (2)

Where revenue to GDP ratio for the country i for the period t which is function of
economic variables, demographic, institutional/governance quality and policy variables,
The vector of economic variables measures the structural characteristics of countries and
it includes GDP per capita, trade to GDP, external debt to GDP in the basic specification.
The share of agriculture to GDP and share of manufacture to GDP4 are also examined as
the determinants of revenue potential of the Asian countries. The population growth is
taken as demographic variables. The vector of institution includes the variables that
capture institutions and quality of governance such as control of corruption, high
bureaucracy quality and law and order scores. 5 The inflation rate is used as macro-
economic policy variable which effects the investment and income level of the
country.
Income level, measured as GDP per capita, is used as a proxy for the level of a
country’s development, and it is expected to be positively related with the government’s
ability to collect revenues and the citizens’ ability to pay revenue. Thus, it is expected
that GDP per capita to have a positive and significant impact on fiscal revenue [Bahl
(1971); Fox, et al. (2005); Piancastelli (2001); Gupta (2007); Bird, et al. (2004) and Le,
et al. (2008)]. Trade tax revenue being a major source of tax revenue in developing
countries [Rodrik (1998); Piancastelli (2001); Norregaard and Khan (2007); Gupta
(2007); Aizenman and JinJarak (2009)] lowers the overall tax-to-GDP ratio in post trade
liberalisation era under the Uruguay Round of World Trade Organisation. The effect of
trade liberalisation may be ambiguous due to two opposite effects on taxes. On the one
hand, it may have a negative impact on taxes and fiscal revenue as higher trade openness
is expected to lower taxes collected on imports and export. On the other hand, given that
higher trade openness leads to higher economic growth rates, open economies grow
faster; and as a result, more taxes can be collected with increasing this tax base. It is
expected that the second effect outweigh in case of Asian countries and trade openness
has a positive impact on taxes and total fiscal revenue. Further, Gupta (2007) documents
that if this liberalisation is undertaken through reduction in tariffs then it is expected that

3
The fixed effect panel regression specification is given in Equation (1) and random effect specification
on Equation (2) respectively.
4
The sector-wise composition of GDP also affects revenue collection capacity because in some sectors
of the economy it is easy to impose tax than others. For example, the agriculture sector is considered as difficult
to tax, especially if there are a large number of subsistence farmers. On the other hand, manufacturing sector
consisting of a few large firms can generate large tax. These components of GDP are added one by one to avoid
multicolinearity.
5
Due to high correlation between institutional variables one variable is included in the specification at a
time.
51:4, 370 Javid and Arif

tariff revenue will be reduced. On the other hand, Keen and Simone (2004) argue revenue
may increase if trade liberalisation takes place through tariffication of quotas,
eliminations of exemptions, reduction in tariff peaks and improvement in customs
procedure. Rodrik (1998) also comes to conclusion that there is a positive association
between trade openness and the government consumption, as people demand (and
receive) increasing amount of public goods in more open economies subject to external
risks.
The revenue potential is effected by the debt of a country as to generate the
necessary foreign exchange to service the debt, a country may choose to reduce imports
and import taxes will be lower. Alternatively, the policy may be to increase import tariffs
or other taxes in order to register budget surplus to service the debt [Gupta (2007)].
Therefore, it is expected that level of indebtedness of the country is positively associated
with revenue potential of the country.
The recent empirical literature finds non-traditional variables like institutional and
governance quality as important determinants of revenue potential for developing
countries. The institutional and governance factors impact revenue collection potential by
influencing tax evasion, inappropriate revenue exemptions, and weak revenue collection
administration [Tanzi and Davoodi (1997)]. Bird, et al. (2004) argue that any successful
tax reform should be rooted in a strong political will to reform, and Alm and Martinez-
Vazquez (2004) document that a country’s tax record is reflection of its political or
societal institutions. Bird, Martinez-Vazquez, and Torgler (2004) conclude that rule of
law and control of corruption is necessary prerequisite for a more satisfactory revenue
effort. For example poor law and order conditions in the economy induce people to avoid
the tax and non-tax payments. If corruption is high in an economy, large part of business
community would prefer to work underground by paying bribes to avoiding high revenue
payments. If societies have feelings that their interests are well represented at government
level and they are satisfied with quality and quantity of public goods like health,
education etc., there would be willingness to pay revenues. To evaluate the impact of
these institutional variables on revenue performance three governance indicators
computed by International Country Risk Data Guide are included; corruption index,
bureaucracy quality and law and order scores. It is expected that control of corruption,
high quality of bureaucracy and strong law and order enforcement are positively
associated with the revenue potential of developing Asian countries.
The inflation is policy variable that is included to measure the quality of a
country’s macroeconomic policies. It allows capturing direct effect of inflation on
revenue collection through its impact on consumption and investment, and subsequently
on their related tax categories. It is expected that inflation has negative effect on revenue
collection capacity.

3.1. Data and Sample

The study used annual data on economic, political and institutional variables,
from 1984 to 2010. The source of economic data is International Financial Statistics
and World Development Indicators. Institutional variables are obtained from
International Country Risk Data Guide (ICRG). Economic variables revealing
structural distinctiveness of the countries include real GDP per capita, agriculture value
 Analysis of Revenue Potential and Revenue Effort 51:4, 371

addition to GDP, trade to GDP, debt to GDP. The GDP per capita is expected to have
positive impact on revenue collection capacity of a country with level of income and
citizens also demand more public goods and services. On the other hand large
agriculture sector is difficult to tax because of large share of subsistence and politically
infeasibility, and reduction in need of public goods and services which are urban based.
It is relative easier to tax foreign trade compared to domestic activities as goods enter
and leave the country at specific places. Therefore, it is expected that trade openness
has a positive impact on revenue collection. Inflation is measured as percentage change
in consumer price index and it is expected that inflation has negative impact on revenue
collection capacity of the country.
The demographic variables include population growth and as the rate of population
growth increases, the revenue collection system finds difficult to capture new revenue
payers especially when revenue collection administration capacity is weak. Therefore, the
population growth rate is expected to be negatively related to the revenue potential of a
country. Inflation measures the quality of a country’s macroeconomic policies. The
quality of fiscal and monetary policies in terms of revenue is measured by Inflation rate
as high level of inflation would reduce the revenue to GDP ratio due to negatively
effecting consumption and investing capacity and thus decreasing tax revenue generated
from these categories.
The quality of institutions captures various aspects of the governance of the public
sector, such as control of corruption, rule of law; high bureaucracy quality and these
factors are expected to be positively associated with revenue collection capacity of a
country. A higher value of institutional indicates a higher quality of institutions. The
corruption index measures the extent of corruption by assigning a numerical value to a
country. The index ranges from 1 to 6, where a higher number means lower corruption.
Similarly the law and order index also ranges from 1 to 6. The bureaucracy quality index
is an alternative institutional indicator of governance and it ranges from 1 to 4. Following
Tanzi and Davoodi (1997) in this analysis institutional variables are used after rescaling
the original ICRG corruption index, law and order index and bureaucracy quality
indicator to a range of –6 (least corrupt or best bureaucratic quality and best law and
order conditions) and –1 (most corrupt or worst bureaucratic quality and law and order
conditions).

3.2. Estimation Technique

The panel data estimation techniques fixed effect and random effect models and
dynamic panel data model are used. The econometric issues related with these techniques
are the presence of country specific fixed effects and endogeneity. To deal with these
issues, Arellono and Bond (1991) introduced the Generalised Method of Moments after
first differencing the equation. Latter, Blundell and Bond (1998) suggest efficiency can
be increased by adding the original equation in the level to the system, if the first
difference of the explanatory variables is uncorrelated with original effects. Lagged
dependent and exogenous variables can be used as instrument variables. Multicollinearity
is another problem which arises when two or more explanatory variables appeared to be
highly correlated with each other and to resolve this problem the highly correlated
explanatory variables are used in separate specifications.
51:4, 372 Javid and Arif

4. EMPIRICAL RESULTS
The analysis begins with basic specification of the revenue model 1 and
determinants include the log of per capita GDP, trade to GDP, debt in GDP, population
growth and control of corruption and inflation. Generalised method of Moments of
Blundell and Bond (1998) is used as estimation technique that allows to deal with country
specific effects and any edogeneity that may be due to the correlation of the country
specific effects and dependent variable. The result of Hausman test indicates that fixed
effects specifications best describes the data in almost all specifications. Latter in model 2
and 3 bureaucracy quality and law and order score are included one by one. Then GDP
per capita is replaced by agriculture value added to GDP in model 4, 5 and 6. The results
of fixed effect models 1 to 6 are presented in Table 1.

Table 1
Determinants of Revenue Potential in Developing Asian Countries: 1984 2010
Mod 1 Mod 2 Mod 3 Mod 4 Mod 5 Mod 6
Constant 0.12* 0.19* 0.14* 0.14* 0.16* 0.18*
(5.40) (7.22) (6.35) (3.41) (4.6) (5.8)
GDP per Capita 0.08* 0.07* 0.07*
(3.92) (3.5) (1.95)
Agriculture Value Addition to GDP –0.05* –0.04* –0.02*
(–3.3) (–2.04) (–1.6)
Trade/GDP 0.05* 0.06* 0.04* 0.04 0.03 0.08
(2.26) (3.10) (3.85) (0.7) (0.09) (0.56)
Debt/GDP 0.48* 0.35* 0.20* 0.39* 0.41* 0.42*
(4.14) (9.90) (9.90) (7.3) (7.8) (7.7)
Population Growth –0.04* –0.03* –0.05* –0.03* –0.02* –0.03*
(–2.32) (–5.44) (–2.98) (–4.4) (–4.2) (–3.89)
Inflation –0.02** –0.01** –0.01** –0.01 –0.01 0.01
(–1.94) (–1.89) (–1.85) (–0.44) (–0.57) (–0.66)
Control of Corruption 0.01* 0.02*
(3.98) (3.9)
High Quality Bureaucracy 0.01* 0.01*
(2.79) (2.1)
Best Law and Order Conditions 0.008* 0.009*
(2.95) (1.8)
Sargan Test (p-value) (0.18) (0.11) (0.13) (0.21) (0.17) (0.10)
Hausman Test (p-value) (0.27) (0.21) (0.11) (0.58) (0.31) (0.44)
R2 0.71 0.65 0.65 0.69 0.64 0.65
Note: *Indicates significance at 1 percent, ** at 5 percent and *** at 10 percent level. The Hausman Test
supports fixed effect model. The GMM is estimation technique and lag exogenous are used as
instruments and Sargan J-Test for overidentying restrictions confirms that the error term is uncorrelated
with the instruments. The Hausman test supports that error terms are uncorrelated with explanatory
variables so fixed effect model is better choice.

The per capita GDP has significantly positive impact in basic specification of
revenue potential model 1 suggesting that the capacity to collect and pay revenue
increases with the level of development of sample countries. This result is consistent with
earlier studies [Chelliah (1971); Bahl (1971); Fox, et al. (2005); Gupta (2007)]. The trade
 Analysis of Revenue Potential and Revenue Effort 51:4, 373

openness is positive and significant determinant of revenue to GDP because trade-related

taxes are easier to impose [Gupta (2007)]. The result indicates that debt has a positive
effect on revenue potential; Gupta (2007) finds that debt is negatively related with
revenue performance. The population growth rate is negatively related to the revenue
potential. Bird, et al. (2004) also finds the inverse relationship between population
growth and resource mobilisation suggesting that as the rate of population growth
increases, the tax system may lag behind in its ability to capture new taxpayers. Inflation
has negative and significant impact on the revenue capacity of the sample countries. The
negative relationship of inflation confirms inflation detrimental impact on revenue
collection potential of countries and is consistent with the results reported by Agbeyegbe,
Stotsky, and Mariam (2004). The high inflation rate reduce the purchasing power and
investing ability of consumers and therefore negatively impact the revenue collection
from these heads.
The impact of institutional quality on revenue collection is positive and significant
as expected as they are added one by one in model 1, 2 and 3 (Table 1). This is consistent
with the findings of Tanzi and Davoodi (2000) and Bird, Martinez-Vazquez, and Torgler
(2004) and Gupta (2007). These results support that quality of institution and governance
increase the revenue capacity and this is a direct channel for the impact of institutions on
revenue collection. There is indirect impact that institutions have through shadow
economic activity.
In model 4, 5 and 6 (Table 2) agriculture value added to GDP is replaced by GDP
per capita. The agriculture to GDP has negative and significant relationship with revenue
potential of sample countries. The presence of large agriculture sector is considered
administratively and politically difficult taxing agriculture and government rather wants
to either provide tax exemptions or subsidies. This also reduces the demand for
government services, since many public sector activities are urban based [Tanzi (1992);
Gupta (2007)]. Most of the variables have expected relationship with revenue potential of
Asian countries; however the effect of inflation and trade openness turns out to be
insignificant. The trade openness has less role in revenue generation in countries which
are more agriculture may be due to fact that have a negative impact on taxes and fiscal
revenue as higher trade openness is expected to lower taxes collected on imports and
export that offsets the positive effect because of fact that higher trade openness leads to
higher economic growth rates [Combes and Saadi-Sadeq (2006)].6
Table 2 reports the results from the dynamic panel models. The results of models 7,
8 and 9 confirm that lagged revenue to GDP is a strong and significant predictor of current
revenue potential. Gupta (2007) also finds random walk type of result in cross country
analysis in this regard. The result indicates that per capita GDP, debt to GDP are significant
predictor of revenue potential. However, the impact of per capita GDP is substantially
smaller in the dynamic specification. The impact of both agriculture value added to GDP
and population growth are negative as expected in model 10, 11 and 12 but impacts are
marginally smaller in the dynamic specification. The indebtness of country has positive
significant effect on revenue potential in all six models. The trade openness and inflation
are no more significant determinants of revenue potential of sample countries.
6
Appendix Tables A2 and A3 reports the results of models 1 to 12 for sub-periods: 1984 to 1990, 1991
to 2000 and 2001 to 2010. The results of regression analysis are almost the same for the sub-periods as well.
51:4, 374 Javid and Arif

Table 2
Determinants of Revenue Potential/Capacity in Developing Asian Countries:
Dynamic Panel Model
Mod 7 Mod 8 Mod 9 Mod 10 Mod 11 Mod 12
Constant 0.05 0.06 0.05 0.05 0.05 0.04
(3.6) (3.76) (3.49) (3.5) (3.1) (2.05)
Lag Revenue/GDP 0.72 0.74 0.72 0.68 0.69 0.68
(17.7) (17.01) (17.4) (11.6) (12.9) (12.8)
GDP per Capita 0.03 0.04 0.03
(1.84) (1.89) (1.85)
Agriculture Value Addition to GDP –0.03** –0.02** –0.01**
(–1.88) (–1.90) (–1.92)
Trade to GDP 0.005 0.006 0.004 0.01 –0.005 0.008
(1.13) (1.38) (1.03) (0.20) (–0.08) (0.12)
Debt/GDP 0.07* 0.07* 0.07* 0.07* 0.07* 0.08*
(5.37) (5.36) (5.29) (3.47) (3.3) (3.6)
Population Growth –0.02* –0.02** –0.02* –0.03** –0.03* –0.03*
(–2.33) (–1.93) (–2.34) (–1.85) (–1.97) (–1.94)
Inflation –0.05 –0.06 –0.03 –0.01 –0.01 0.01
(–0.76) (–0.83) (–0.55) (–0.32) (–0.84) (–0.68)
Control of Corruption 0.01* 0.01*
(2.31) (2.44)
High quality Bureaucracy 0.02** 0.007*
(1.95) (2.23)
Best Law and Order Conditions 0.02* 0.02*
(2.33) (2.05)
Sargan Test (p-value) (0.15) (0.27) (0.13) (0.21) (0.20) (0.22)
Hausman Test (p-value) (0.42) (0.35) (0.44) (0.15) (0.14) (0.19)
R2 0.75 0.72 0.71 0.73 0.70 0.70
Note: *Indicates significance at 1 percent, ** at 5 percent and *** at 10 percent level. The Hausman Test
supports fixed effect model. The GMM is estimation technique and lag exogenous are used as
instruments and Sargan J-Test for overidentying restrictions confirms that the error term is uncorrelated
with the instruments. The Hausman test supports that error terms are uncorrelated with explanatory
variables so fixed effect model is better choice.

This above panel regression provide a simple empirical analysis of the predicted
values of the revenue to GDP obtained through Equation 1 that measure the revenue
potential of Asian countries. The ratio of the actual to predicted revenue is calculated to
measure the level of revenue effort of sample Asian countries [Bird, et al. (2004) and
Gupta (2007)].

Tax Effort Analysis

The above analysis has focused on finding the main factors that affect revenue
potential in a sample of developing Asian countries. However, this does not tell whether a
country could not, if it wanted, attain higher revenue potential [Chelliah (1971); Chelliah,
et al. (1975) and Gupta (2007)].
Different countries have different potential to raise revenues that must be taken
into consideration while making cross-country revenue comparisons [Gupta (2007), Bird,
et al. (2004) and Le, et al. (2008)]. The selection of regression results to estimate the
predicted values of revenue ratios are made on the base of their significance and
economic rationale in this analysis [Teera and Hudson (2004)]. Several studies have
 Analysis of Revenue Potential and Revenue Effort 51:4, 375

followed the same approach to measure revenue effort across countries [Gupta (2007);
Bird, et al. (2004)]. The predicted values of the revenue ratio is obtained through model 1
and 4, thus measure the country’s revenue potential, while the ratio of the actual to
predicted revenue is calculated for the level of revenue effort. Thus, a country that lies on
the regression line have a revenue effort index equal to 1, and countries that have actual
revenue effort above predicted revenue performance have a revenue effort index higher
than one, in reverse case revenue effort index is less than 1. The results of revenue effort
are presented in Table 3 for sample countries Malaysia, Indonesia, Thailand, Philippines
and Singapore have exhibited significant revenue performance compared to other
countries, having revenue effort index greater than 1. These countries have probably
largely used their revenue potential. On the other hand, countries like Pakistan,
Bangladesh and Sri Lanka have revenue effort indices well below 1 which suggests that
they have yet to achieve their full revenue potential, as they are constrained by low per
capita GDP, a dominant agriculture sector.

Table 3
Revenue Effort Index for Developing Asian Countries
Model 1: GDP per Capita Model 4: Agriculture Value Added to GDP
1984-90 1991-00 2001-10 1998-10 1984-90 1991-00 2001-10 1984 10
India 0.97 0.94 1.03 0.98 0.96 0.87 1.04 0.98
Pakistan 0.96 0.90 0.85 0.92 1.00 0.93 0.85 0.91
Bangladesh 0.84 0.80 0.82 0.85 0.84 0.80 0.82 0.83
Sri Lanka 1.05 0.89 0.84 0.87 0.88 0.84 0.90 0.89
Malaysia 1.00 0.92 1.02 0.91 1.00 0.85 1.02 0.88
Indonesia 0.85 0.93 1.11 1.07 0.85 0.93 1.04 1.01
Thailand 1.11 1.17 1.01 1.26 1.00 0.92 1.05 1.05
Philippines 0.95 1.03 0.94 1.29 0.89 0.88 0.94 0.99
China 1.12 0.83 1.11 1.01 1.12 0.89 1.11 1.03
Singapore 1.06 1.37 1.31 1.40 1.05 1.14 1.11 1.15
Vietnam 0.84 1.00 0.90 0.87 0.82 0.84 0.99 0.90

5. CONCLUSION
The development of revenue effort index that relates the actual revenues of a
country to its estimated revenue capacity provide an appealing measure that considers
country specific fiscal, demographic, and institutional characteristics. This study analyses
revenue performance across developing Asian countries over the period 1984 to 2010 and
also for the sub periods 1984 to 1990, 1991 to 2000 and 2001 to 2010. The results
indicate that per capita GDP, share of agriculture in GDP and foreign debt are statistically
significant and strong determinants of revenue performance in almost all specifications of
the model. The trade openness and inflation are also having impact on revenue
performance in some specifications. Among the institutional factors, control of corruption
and high bureaucracy quality and improved law and order conditions have a significantly
positive effect on revenue performance in all model specifications. The results confirm
that countries that depend on agriculture value addition tend to have poorer revenue
performance. The analysis highlights that revenue performance depends on level of
development of country, its institutional and governance quality and to macroeconomic
policy and political will for reforms. This analysis can be considered complimentary
51:4, 376 Javid and Arif

providing a broader picture of revenue performance but detailed analysis of a country’s

revenue system that takes account of the country’s overall fiscal policy, public
expenditures needs and the overall level of development in Asian region is needed for
future research. The results imply that architect of revenue reforms must be country
specific that requires broad investigation of the country’s revenue capacity, revenue
performance, and institutional structure.

APPENDIX

Table A2
Determinants of Revenue Potential in Developing Asian Countries
1984-1990 1991-2000 2001-2010
Mod 1 Mod 2 Mod 3 Mod 1 Mod 2 Mod 3 Mod 1 Mod 2 Mod 3

Constant 0.21* 0.22* 0.23* 0.11* 0.10* 0.10* 0.13* 0.16* 0.06*
(6.39) (7.2) (6.2) (4.11) (4.11) (3.9) (2.34) (2.42) (0.83)

GDP per Capita 0.02* 0.02* 0.02* 0.005* 0.001* 0.001* 0.04* 0.001* 0.01*
(3.8) (5.02) (4.95) (3.12) (2.37) (3.31) (2.04) (2.17) (2.81)

Trade/GDP 0.02* –0.02* 0.03* 0.06* 0.05* 0.05 0.01* 0.02* 0.01*
(2.88) (2.86) (2.02) (7.54) (7.7) (7.7) (2.53) (3.01) (3.12)

Debt/GDP 0.18 0.19 0.188 0.10 0.10 0.10 0.186 0.17 0.17
(4.44) (4.8) (4.23) (4.2) (3.84) (3.89) (4.35) (3.99) (4.26)

Population Growth –0.07 –0.01* 0.005* –0.005* –0.06* –0.006* –0.01* –0.01* –0.01*
(–2.01) (–2.00) (3.15) (–2.12) (–2.4) (–2.5) (–3.2) (–2.60) (–3.17)

Inflation –0.002 –0.001 –0.001 –0.002 –0.002 –0.002 0.001 0.001 0.002
(–1.46) (–1.11) (–0.87) (–2.36) (–2.62) (–2.4) (0.15) (0.74) (1.02)

Control of Corruption 0.012* 0.005 0.01**

(3.25) (2.11) (1.96)

High Quality Bureaucracy 0.013* 0.002* 0.01*

(3.87) (2.64) (2.51)

Law and Order 0.008 0.01* 0.014*

(2.28) (2.78)

Sargan Test (p value) (0.25) (0.18) (0.21) (0.32) (0.23) (0.21) (0.20) (0.31) (0.28)

Hausman Test (p value) (0.43) (0.39) (0.28) (0.13) (0.12) (0.14) (0.09) (0.76) (0.67)

R2 0.73 0.72 0.66 0.70 0.65 0.65 0.72 0.65 0.67

Note: *Indicates significance at 1 percent, ** at 5 percent and *** at 10 percent level. The Hausman Test
supports fixed effect model. The GMM is estimation technique and lag exogenous are used as
instruments.
 Analysis of Revenue Potential and Revenue Effort 51:4, 377

Table A3
Determinants of Revenue Potential in Developing Asian Countries
1984–1990 1991–2000 2001–2010
Mod 1 Mod 2 Mod 3 Mod 1 Mod 2 Mod 3 Mod 1 Mod 2 Mod 3
Constant 0.01 –0.01 –0.03 0.12* 0.12* 0.12* 0.24* 0.29* 0.23*
(0.67) (–0.5) (–0.85) (4.8) (4.3) (3.33) (5.10) (5.35) (3.93)
Agriculture Value –0.02* –0.03* –0.03* –0.02* –0.04* –0.03 –0.05 –0.05 –0.05
Addition to GDP (–3.07) (–3.7) (3.10) (–2.2) (–2.43) (–0.35) (–2.5) (–2.95) (–2.44)
Trade/GDP 0.04* 0.08* 0.06* 0.06* 0.05* 0.05* 0.03** 0.02** 0.02*
(2.4) (5.6) (3.8) (5.3) (5.07) (4.91) (1.9) (1.8) (1.85)
Debt/GDP 0.16* 0.17* 0.15* 0.10* 0.10* 0.10* 0.16* 0.15* 0.16*
(3.9) (4.1) (3.17) (4.7) (4.39) (4.39) (4.2) (3.8) (4.0)
Population Growth –0.01* –0.01* –0.01* –0.01** –0.01* 0.01* –0.01* –0.01* –0.01*
(–2.13) (–2.22) (–2.83) (–1.86) (–1.96) (–2.15) (–2.7) (–2.08) (–2.6)
Inflation –0.02* –0.02* –0.01* –0.02* –0.03* –0.02* –0.01* –0.02* –0.02*
(–1.89) (–1.89) (–2.77) (–2.42) (–2.77) (–2.6) (–2.97) (–2.15) (–2.35)
Control of Corruption 0.01* 0.05* 0.01*
(5.31) (2.14) (2.05)
High Quality Bureaucracy 0.02* 0.03* 0.01* 0.01*
(5.27) (2.70) (2.37) (2.87)
Law and Order 0.01* 0.05*
(3.02) (2.64)
Sargan Test(p value) 0.32) (0.29) (0.31) (0.19) (0.22) (0.21) (0.23) (0.25) (0.35)
Hausman Test (0.18) (0.11) (0.21) (0.16) (0.13) (0.14) (0.09) (0.24) (0.08)
(p value)
R2 0.69 0.68 0.61 0.66 0.65 0.64 0.67 0.66 0.67
Note: *Indicates significance at 1 percent, ** at 5 percent and *** at 10 percent level. The Hausman Test
supports fixed effect model. The GMM is estimation technique and lag exogenous are used as
instruments.

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