The Behavior of Fiscal Policy: Cyclicality and
Discretionary Fiscal Decisions ∗
Jaejoon Woo
January 2005
Kellstadt Graduate School of Business
DePaul University
1 East Jackson Boulevard
Chicago, IL 60604
Abstract
This paper studies the discretionary fiscal decisions and the cyclical behavior of fiscal policy in 96
developed and developing countries. We emphasize the role of social polarization in understanding
pro-cyclical fiscal stances and excessive uses of discretionary fiscal policy that are often observed in a
number of countries. We present a simple model of fiscal policy in which heterogeneous policymakers
strategically behave in making fiscal spending decisions and social polarization of preferences play
a crucial role in the evolution of fiscal volatility and pro-cyclicality. Social polarization is one of the
oldest ideas found in the political economy literature. Yet there are no existing empirical studies
on the role of social polarization in explaining fiscal discretion decisions or fiscal policy behavior
such as cyclicality of fiscal policy. We present evidence that social polarization as measured by
income or educational inequality is consistently positively associated with pro-cyclicality of fiscal
policy and aggressiveness in using discretionary policy. Also, we explore the relationship between
the fiscal cyclical behavior and the magnitude of discretionary shocks. After addressing the endogeneity issue, we find that the size of fiscal policy shocks (as a measure of aggressiveness of use of
discretionary policy) is largely explained by the fiscal behavior which itself is heavily influenced by
social polarization.
JEL Classification: H61, H62, E62, E63
Key Words: Fiscal Discretionary Decisions, Pro-cyclicality of Fiscal Policy, Fiscal Volatility,
Social Polarization
∗
Preliminary. Prepared for a fiscal policy workshop organized by Steinar Holden, Editor of
Scandinavian Journal of Economics, to be held at University of Oslo, Norway, January 2005. I thank
seminar participants at University of Wisconsin-Milwaukee and at Southern Economic Association
Meeting in 2004 for their useful discussions on the earlier version. Tel:(312) 362-5585, Fax:(312)3625452, Email:jwoo1@depaul.edu, homepage: http://fac.comtech.depaul.edu/jwoo
1
1
Introduction
Recently, the merits of using a discretionary fiscal policy to smooth out fluctuations
in output have been increasingly questioned. Most common arguments against using
a discretionary ‘counter-cyclical’ fiscal policy include much weaker fiscal multiplier
effects in practice than suggested in standard Keynesian models (Perotti, 2002), nonKeynesian effects that tax reductions or spending increases at the time of unsustainable budget deficits may actually depress economic activity (Giavazzi and Pagano,
1990; Alesina et al., 2002), and the combinations of fiscal policy lags.1 Strengthening
the case against discretionary fiscal policy further, Fatas and Mihov (2003) present
evidence that aggressive use of discretionary fiscal policy generates undesirable output
volatility and leads to lower growth.
Moreover, there is even evidence of fiscal pro-cyclicality–being expansionary in
good times (booms) and contractionary in bad times (recessions)–in a number of
countries (especially in Latin American and other developing world), which sharply
contrasts with the conventional wisdom that fiscal policy should be ‘counter-cyclical’
(Kaminsky, et al., 2004; Lane 2003; Gavin and Perotti, 1997 among others). This procyclical fiscal policy is believed to have aggravated macroeconomic instability. Yet,
we do not understand well why these countries adopt such a destabilizing pro-cyclical
fiscal policy in the first place, and to what extent such a pro-cyclical fiscal policy
behavior is responsible for macroeconomic volatility that empirical growth literature
identifies as a factor harmful to growth. These are the central questions that we
address in our paper.
In our paper, we study the political and institutional determinants of cyclical
behavior of fiscal policy and discretionary fiscal decisions. In particular, we emphasize
the role of social polarization of preferences in understanding the pro-cyclicality of
fiscal policy and the magnitude of discretionary fiscal policy shocks by developing
a simple model of fiscal policy in which social polarization plays a central role, and
1
In five OECD countries, Perotti (2002) reports that positive government spending multipliers
larger than 1 tend to be the exception. In a study on the US economy, Alan Auerbach (2002)
concluded that there is little evidence that the effects of discretionary fiscal policy have provided a
significant contribution to economic stabilization, if they have worked in the right direction at all.
See Feldstein (2002) also for related discussions.
2
by providing supporting evidence in cross-country regressions for the period of 19602001. Intuitively, a high degree of social polarization of preferences may make it
hard for policymakers to agree on ideal government policies, and hence may cause a
coordination failure among the policymakers (who possible represent heterogeneous
socio-economic groups). In the presence of polarization of social preferences over
public choices, heterogeneous policymakers may have greater incentives to insist on
their preferred policies and may end up choosing individually rational but collectively
inefficient policies for the whole economy, especially when institutional restraints on
policymakers are lacking. Such incentives to put forward their preferred agenda may
become particularly strong during good times when rising government revenues or
newly available resources make their agenda seem more feasible, which produces procyclical fiscal policies. At the same time, discretionary policy actions taken in such a
manner are most likely to yield volatile fiscal outcomes over time.
We formalize this intuition in a simple fiscal game model in which heterogeneous
policymakers strategically behave in determining spending on different types of fiscal
program, and derive the key equations that positively link the degree of social polarization (of preferences) to the degree of pro-cyclicality of fiscal spending and to the
associated fiscal volatility across time periods. Then, we discuss how institutionalized
constraints and other political factors would change the theoretical outcomes.2
Social polarization is one of the oldest ideas found in the political economy literature. And income inequality has long been mentioned as an important source of
social polarization, which may lead to populist fiscal policies and poor macroeconomic
performance. This has been well documented in studies on Latin America and subSaharan Africa.3 Yet there are very few (or no) empirical studies on the role of social
polarization (or income inequality) in explaining fiscal discretionary decisions or fiscal
2
In a related paper, Woo (forthcoming) studies the role of social polarization of preferences
in collective decision-making process and in the development of macroeconomic problems such as
large fiscal deficits, fiscal volatilities and poor growth. In a dynamic model of fiscal policy in an
endogenous growth setting, we show that the size of fiscal deficits, the magnitude of fiscal volatility
and the reduction of growth rate (during a transition path) are positive functions of the degree of
preference polarization.
3
Rodrik (1996), Kauffman and Stallings (1991), and Sachs (1989) among others.
3
policy behavior such as fiscal pro-cyclicality.4 In this paper, we make a contribution
theoretically and empirically by filling this void in the literature.5 To our best knowledge, we present the first econometric evidence that the degree of social polarization
as measured by inequality in income or education is consistently positively associated with the pro-cyclicality of fiscal policy and the size of discretionary fiscal policy
shocks (imposed for reasons other than business cycle management)–see Figures 1A
and 2A for scatter plots of cyclicality of fiscal spending and aggressiveness in using
discretionary fiscal spending against income inequality, respectively.6 Moreover, this
relationship is particularly strong in the absence of institutional constraints and vice
versa.
According to the conventional wisdom, fiscal sustainability issue may be one of
the determinants of whether fiscal stance is pro-cyclical (OECD, 2003). For example,
as the dynamics of debt accumulation becomes, or is perceived to be, unsustainable,
fiscal consolidation may become necessary regardless of the economy’s position in the
business cycle. In other words, high debt may reduce the scope for counter-cyclical
response. Related to this, Gavin and Perotti (1997) suggest that cut-off from the
international capital market may also lead to pro-cyclical fiscal stance in developing
countries, while documenting pro-cyclicality of fiscal policy in Latin America. However, we fail to find any significant evidence in support of these arguments. Our findings suggest that pro-cyclical fiscal stances are direct outcomes of conscious choices
4
Woo (2003) presents a strong evidence that social polarization, as measured by income inequality,
is robustly and positively associated with fiscal deficits in a comprehensive empirical investigation in
a panel data on consolidated public sector deficits for 57 countries in the period of 1970-1990. Hausmann and Gavin (1996) find a positive correlation between income inequality and macroeconomic
instability in the cross-country reression for Latin America.
5
Similarly, there are very few theories that explain why unequal income distributions can lead to
fiscal problems such as fiscal volatility, procyclicality or fiscal deficits. Meltzer and Richard (1981)
and Alesina and Rodrik (1994) suggest that there may be a tendency of the majority to vote for
large redistributive spending in a democratic country with an unequal income distribution. Since
these models assume the case of balanced budget, however, they are silent about fiscal deficits, not
to mention fiscal procyclicality and volatility.
6
The econometric result also holds when we instrument the income inequality measure with
indicators of educational achievements in 1960. The existing empirical evidence suggests strong
correlation among income inequality, educational inequality and educational attainments. See De
Gregorio and Lee (2002) for such evidence.
4
of such policies, rather than simply forced by necessary fiscal restructuring after the
build-up of debt or by external financing conditions.7
Finally, we provide alternative yet distinct explanation for the common finding of
positive correlation between income inequality and macroeconomic volatility. Several
explanations have been put forward to account for the positive correlation. Alesina
and Perotti (1996) aruge that high income inequality causes political and institutional instabilility which results in macroeconomic instability. Aghion et al. (1999)
postulate that unequal access to investment opportunities across individuals due to
income inequality can cause persistent boom-bust credit cycles in the presence of imperfect capital markets. In contrast, we put forward the fiscal instability channel that
links inequality (as a measure of polarization) to discretionary fiscal shocks. We find
that much of discretionary fiscal policy shocks is explained by the way fiscal policy
responds to economic conditions (i.e. fiscal cyclical behavior), which itself is determined by social polarization along with institutionalized constraints (see Figure 3). In
other words, countries that exhibit bigger changes in fiscal spending during boom or
recessions are also more likely to exercise fiscal discretionary policy more aggressively
irrespective of the aggregate demand management principles over the business cycle.
Thus, we posit the following chain through which social polarization and institutional
settings affect the fiscal behavior which in turn determines the magnitude of fiscal
policy shocks.
Social polarization, and checks and balances in policy-making process
⇓
Fiscal policy behavior
⇓
Magnitude of discretionary fiscal policy shocks
⇓
7
On the other hand, the average size of fiscal deficits is significantly positively associated with
the magnitude of discretionary fiscal shocks.
5
Macroeconomic volatility
Indeed, once we control for the fiscal cyclical behavior through an instrumental variable method for the endogeneity/simultaneity concern, social polarization and institutionalized checks and balances become insignificant, while the fiscal behavior
indicator remains statistically significant in the regression of fiscal shocks.
There are studies that have previously examined issues of fiscal pro-cyclicality
and discretionary fiscal policy from different points of view and mostly in the OECD
country sample or a sample of Latin American countries (especially for the fiscal
pro-cyclicality issue). Arreaza et al. (1999) and Lane (2003) investigate the cyclical
behavior of fiscal policy in the OECD country sample, both using regression-based
estimates of cyclicality of fiscal variables. Arreaza et al. (1999) find that government
consumption is weakly pro-cyclical and fiscal surpluses are pro-cyclical, but mainly
focus on the consumption smoothing via fiscal policy rather than the cyclicality itself. Lane (2003) attempts to explain the cross-country variations in cyclicality of
various fiscal components across OECD countries, and an interesting result from the
political economy perspectives is that a measure of political constraints (POLCON
from Henisz, 2000) tends to enter the regression with statistical significance (but often
with wrong sign depending on types of spending). For a sample of Latin American
countries, Stein et al. (1999) also show that political economy (particularly a measure
of political competition as measured by the number of representatives per district)
is useful in explaining the cross-country variation in the cyclicality of government
consumption. By contrast, we consider a larger pool of socio-economic, political and
institutional determinants of fiscal cyclicality of general government expenditures in
a large sample of 96 countries for the period of 1960-2001, while exploiting both
time-series and cross-country dimension of the data.
In our empirical investigation of the size of discretionary fiscal policy shocks, we
focus on volatility of changes in government expenditures that do not reflect current macroeconomic conditions, which we interpret as the aggressiveness of use of
fiscal discretionary policy. Fatas and Mihov (2003) use this type of measure of discretionary policy shocks to show that governments that use fiscal policy aggressively
6
induce macroeconomic instability, which lowers economic growth. Also, they find
that lack of political constraints tends to be positively associated with aggressive use
of fiscal policy. However, their main purpose of this regression is to establish a good
instrument for fiscal policy shocks to be used in the regression of growth. So, they do
not explore a full range of economic and socio-political determinants of the magnitude
of discretionary policy shocks, which is a part of goal in our paper. In addition to
presenting evidence in support of our social polarization hypothesis, we also explore
how the fiscal behavior and the aggressiveness of use of fiscal policy are related to
each other.
The plan of the paper is as follows. Section 2 presents a simple theory of fiscal
volatility and pro-cyclicality, and discusses related literatures. Section 3 discusses the
data and regression results for fiscal cyclicality and fiscal discretionary decisions. Our
conclusions are offered in Section 4. Appendix and data appendix follow.
2
A Simple Theory of Fiscal Volatility and Procyclicality
2.1
The Basic Model
To study the theoretical relationship among pro-cyclicality, volatility of fiscal outcomes, and social polarization, we consider a simple two-period model of fiscal policy.8
There are two policymakers, i = 1, 2, who jointly constitute the fiscal authority of the
economy. The fiscal policy consists of government spending for two different types
of public goods {g1t , g2t }2t=1 and taxes {Tt }2t=1 . They access to the tax revenue–in
other words, they face the same government budget constraint.9 To keep the model as
8
For a fuller dynamic model, one can refer to Woo (forthcoming) in which we present a differential
game of fiscal policy embedded in an endogenous growth framework.
9
Our fiscal mechanism is related to the literature of fiscal politics and particularly to the common
pool problem literature. The related papers are Weingast et al. (1981), Tornell and Lane (1999),
Hallerberg and von Hagen (1999), and Velasco (1999). However, only the work of Tornell and Lane
(1998) is related to the issue of fiscal volatility, whereas the others are only concerned with budget
deficits or overspending. They show that interest groups’ total appropriation of the economy capital
stock rises more than proportionally to the winfall gains to the capital, and link severity of the
common pool problem with the number of participants in the collective decision-making process.
7
simple as possible, we consider a fixed tax rate τ and a fixed total output Y for both
periods, so that tax revenue T in each period is equal to τ Y . However, our result
does not rely on any particular level of tax revenue, as it becomes clear later. Policymaker 1 decides how much she wants to spend for provision of public good, g1t and
policymaker 2 decides on g2t . Yet they may not agree on the ideal public good composition and hence may differ in their preferences for the two public goods. Specifically,
policymaker i chooses {git }2t=1 for any possible {gjt }2t=1 policymaker j(6= i) chooses to
maximizes her own objective function subject to the government budget constraint
as follows:
Max J i =
2
X
t=1
subject to
β t−1 [αi v(git ) + (1 − αi )v(gjt )], i, j = 1, 2, and j 6= i.
bt − bt−1 = rbt−1 + g1t + g2t − T,
(1)
(2)
where 0 < β < 1 is the subject discount rate of the policymakers; b is the government
debt; and v(·) is a concave function satisfying the Inada condition. The difference
in policymakers’ preferences for the public goods is reflected by αi . We assume that
0 ≤ αi ≤ 1, for i=1, 2 and α2 ≤ 12 ≤ α1 . This implies that policymaker 1 is assumed
to derive utility from g1 at least as much as from g2 . Similarly, policymaker 2 likes g2
at least as much as g1 . We define θ = α1 −α2 and interpret it as the degree of difference
in their preferences for two public goods. We can think of θ as a degree of preference
polarization. We note that 0 ≤ θ ≤ 1. While θ = 1 implies the complete disagreement
on the composition of two public goods between two groups, θ = 0 implies the total
agreement in their preferences. We will see how the degree of polarization θ affects
the cyclicality of fiscal spending policy and volatility of fiscal outcomes.
Thus, the related empirical studies focus on the number of decision makers such as the cabinet
size (see, for example, Lane, 2003 and Kontopoulos and Perotti, 1999), although the theoretical
relationship between the number of groups and the common pool problem is fragile because it
depends crucially on the assumptions about the utility functional shape.
By contrast, we introduce a new dimension of preference polarization into a two-player common
pool game, which produces a sharply different prediction that the common pool problem will be
more likely to occur and be more severe in societies with higher degrees of polarization. For more
details, see Woo (forthcoming).
8
2.2
Pro-cyclicality and Volatility of Fiscal Outcomes
Now we solve for this dynamic game. For simplicity, assume that in the second
period, each policymaker gets an equal share of remaining government resources (after
government debt is paid off), and let b0 = 0. Policymaker 1 maximizes her objective
function by choosing g11 and g12 , taking policymaker 2’s actions g21 and g22 as given,
Max {α1 v(g11 ) + (1 − α1 )v(g21 )} + β{α1 v(g12 ) + (1 − α1 )v(g22 )}
{g11 ,g12 }
(3)
subject to
(i) b1 = g11 + g21 − T,
(4)
T − (1 + r)b1
.
(5)
2
We solve this game by backwards induction. In the subgame consisting of the
second period, each policymaker gets an equal share of remaining government resources (after government debt is paid off), which is represented by the constraint
(ii) above. Thus, we can just concentrate on the policymakers’ spending decision in
the first period. Any Nash equilibrium of the reduced first-period game that takes
the constraint (ii) into consideration will be a subgame-perfect equilibrium of this
two-period game. We can rewrite the above optimization problem by using the above
budget constraints as follows:
(ii) g12 = g22 =
1
Max {α1 v(g11 ) + (1 − α2 )v(g21 )} + βv( [(2 + r)T − (1 + r)(g11 + g21 )])}.
{g1 }
2
(6)
The first—order condition (assuming an interior solution) is
α1 v0 (g11 ) −
1+r 0
βv (g12 ) = 0.
2
(7)
Similarly, the first order condition for policymaker 2’s optimization problem is given
by (1 − α2 )v 0 (g21 ) − 1+r
βv0 (g22 ) = 0. In the case of an iso-elastic utility functional
2
1
form, v(g) = ln g, and β = 1+r
, the first order conditions become
2α1 =
g11
g21
, and 2(1 − α2 ) =
g12
g22
9
(8)
Using the first order conditions along with the budget constraints, one can easily
show that the subgame-perfect equilibrium level of spending for the two public goods
in the first period is
?
g11
=
α1 (2 + r)
(1 − α2 )(2 + r)
?
T, and g21
T
=
[1 + (1 + r)(1 + θ)]
[1 + (1 + r)(1 + θ)]
(9)
The total government spending in equilibrium is therefore
?
?
G1 = g11
+ g21
=
(1 + θ)(2 + r)
T.
[1 + (1 + r)(1 + θ)]
(10)
Now, we establish that a country with a higher degree of polarization will exhibit
greater fiscal pro-cyclicality and experience greater fluctuations in its fiscal outcomes.
A more polarized society would suffer from a greater fluctuation in its government
spending in response to a shock to tax revenue of the same magnitude. Hence,
dG1
dG1
(1 + θ)(2 + r)
=
= l(θ, r) ≥ 1 (with equality when θ = 0). (11)
=
dT
[1 + (1 + r)(1 + θ)]
d(τ Y )
The amount of fiscal spending change in response to a shock to tax revenue rises
with the degree of polarization (i.e., ∂l(·)/∂θ > 0). An increase in income and hence
tax revenue can be translated into a more than proportional increase in spending
through the polarization effect if the degree of polarization is positive.10 Moreover,
the higher the polarization, the bigger the increase in G for a given increase in income.
But this leads to a sharper reduction in subsequent spending because the increase in
tax revenue is dissipated more quickly. This result can explain the pro-cyclicality of
fiscal outcomes observed predominantly in Latin American countries, which has been
documented by Gavin and Perotti (1997). For instance, during a boom (recession),
the fiscal spending rises (falls) more than tax revenue does, tending to lead to deficits
(surpluses) over this period.11
10
In fact, this result corresponds to a permant tax revenue change. It is straightforward to extend
the model into a tempory tax revenue change. See the appendix.
11
Countries that enjoyed the euphoria of resource booms in the past often ran fiscal deficits and
current account deficits, contrary to the prediction of the neoclassical theory that (temporary)
resource boom should lead to the fiscal surplus and current account surplus due to consumption
smoothing. Tornell and Lane (1998) show how windfall gains can result in a deterioration of the
current account balance. On the other hand, Talvi and Vegh (2000) argue that procyclical fiscal
policy can be optimal if there is greater political pressure for higher government spending with rising
output levels.
10
The intuition is as follows: Given that two polarized policymakers equally share
the remaining government resources, whatever resources one does not exploit today
may or may not be left, depending on the other’s behavior. Hence, each has an incentive to overexploit the common resource today. More important, such an incentive
to overexploit the common tax revenue rises with the magnitude of disagreements
as measured by the degree of preference polarization θ. As either | α1 − 12 | or
¯
¯
¯
¯
?
?
¯(1 − α2 ) − 12 ¯ becomes larger, the optimal g11
or g21
becomes bigger, causing a larger
government spending in the first period. It follows that the bigger θ = α1 − α2 becomes, the larger the fiscal spending. The same dynamic negative externality operates
in generating the pro-cyclical spending and counter-cyclical fiscal balance in response
to a shock to tax revenue (or equivalently to output). However, note that since each
still cares about spending tomorrow, the optimal level of spending on a preferred item
in period two is not driven to zero. (Recall that v(·) satisfies the Inada condition.)
Income inequality has long been mentioned to be important sources of social
polarization or social conflicts (for example, Sachs, 1989; Kauffman and Stallings,
1991). In a society with more unequal income distribution and hence possibly greater
social polarization, struggles over government spending would be more likely to be
acute, leading to a large fiscal spending. In other words, people in the economy
have much more divergent preferences for the composition of government spending
when there is a sharp conflict of interests among the sectors. These divisions lead
each representative policymaker to spend more for her favorite sector and to exert
negative externality on the other, contributing to bigger overall spending.12
On the other hand, social polarization (say, due to income inequality) is often
thought to be associated with socio-political instability as well (see Drazen, 2000 for
example). In a society with high income inequality, there might be stronger incentives for different groups to organize and engage in activities outside the market and
outside the normal channels of political representation to appropriate some of the
12
Many studies suggest that polarization of prefernces about public policy is also highly related
to ethnic fragmentations in a society (see Easterly and Levine, 1997; Alesina et al., 1999). Empirically, we can address this problem by considering both income inequality and ethnic fragmentation
measures. However, the ethnic division measure of Easterly and Levine (1997), which is obtained
from Taylor and Hudson (1972), turned out not to be statistically significant, once we control for
income inequality.
11
resources of the other groups. High levels of socio-political unrest may not only make
the downfall of the present government more likely but may dramatically shorten the
horizons of politicians. With a shortened expected tenure in office, the government
would be more likely to engage in short-term policies at the expense of macroeconomic
stability. Yet we show that polarization and political uncertainty play distinct roles
in generating volatile fiscal outcomes and pro-cyclical spending policy. Interestingly,
social polarization and political uncertainty are shown to compound to produce even
worse fiscal outcomes. One way to incorporate the political uncertainty is to consider
the discount factor. Let us suppose that the policymakers now face a constant positive probability of being removed from the office. This amounts to lowering β (i.e.,
1
discounting the future more heavily so that β ≤ 1+r
).
Then, the subgame-perfect equilibrium level of spending for the two public goods
1
under the assumption of β ≤ 1+r
in the first period becomes
?
=
g11
α1 (2 + r)
(1 − α2 )(2 + r)
?
T, and g21
T
=
(1 + r)[β + (1 + θ)]
(1 + r)[β + (1 + θ)]
(12)
The total government spending in equilibrium is now
?
?
G1 = g11
+ g21
=
(1 + θ)(2 + r)
T.
(1 + r)[β + (1 + θ)]
(13)
Thus, the absolute size of spending change resulting from a shock to tax revenue
in period 1 is bigger when the policymakers are less patient due to their tenure
uncertainty, as one can below.
dG1
dG1
(1 + θ)(2 + r)
=
= h(θ, β, r) ≥ 1,
=
dT
(1 + r)[β + (1 + θ)]
d(τ Y )
(14)
1
where the equality holds when θ = 0 and β = 1+r
. That is, ∂h(·)/∂β < 0. Fig.
4 shows the magnitude of fiscal spending fluctuations in the presence of polarization,
compared to the stable spending path under no polarization. It is evident that the
higher the polarization, the greater the fluctuations in spending over time. This is
also true when there is a shock to tax revenue (see the dotted lines labeled G1 and G2
after shock). Finally, the volatility of fiscal discretionary spending over time (relative
to the fiscal spending path under no polarization) is
12
V ar(G) =
θ2 (1 + (1 + r)2 )
T 2,
[1 + (1 + r)(1 + θ)]
(15)
where ∂V ar(G)/∂θ > 0.13 We summarize these results in the following proposition.
Proposition 1 (i) The higher the polarization, the more procyclical and the more
volatile the fiscal spending. (ii) The less patient the policymakers, the more procyclical and the more volatile the fiscal spending. (iii) When there is polarization (or
policymakers relatively heavily discount the future events), the fluctuations in fiscal
outcomes are always greater than the social optimum.
This model yields a sharp empirical prediction that fiscal spending tends to be volatile
and pro-cyclical in countries with highly polarized societies. In the next section, we
test the main implications of this proposition using a large cross-country data.
3
Econometric Evidence
We begin by quantifying the empirical relationship between cyclicality, magnitude of
fiscal policy shocks, and a range of economic variables. We then broaden our scope
by examining socio-political and institutional variables. By doing this, we can be
more confident that our results shown later do not merely capture residual effects of
other economic variables. Based on an annual panel data for 96 countries over the
period of 1960-2001, we exploit both time-series for each country and cross-country
variations. Our main data are from World Development Indicators CD-rom 2003 and
Penn-World Table 6.1. Refer to the appendix for more details about sources of our
data.
3.1
Cyclicality of Fiscal Policy
In our paper, we define the cyclicality of fiscal policy in terms of government spending–
that is, a policy instrument rather than fiscal outcomes such as primary balance, tax
13
This is the dispersion of fiscal spending in the presence of polarization relative to that under no
polarization which is the case of social planner’s solution.
13
revenue and fiscal variables as a percent of GDP that are endogenous variables and
whose cyclical behaviors are ambiguous. Tax rates could be an alternative indicator
of cyclicality of fiscal policy, but there is no systematic data on tax rates available
for a large number of countries. To obtain a measure of cyclicality of fiscal policy, we
estimate the following regression for each country i:
∆ log Git = αi + βi ∆ log RGDPit + γt t + εit ,
(16)
where i and t denote the country and the year (1960-2001), and ε is an error term. We
correct for first-order auto-correlation in the residuals by using a standard two-step
Prais-Winsten procedure. The term, Git is the real general government spending, and
RGDPit is the real GDP and αi is a constant term. A time trend t is also added. We
interpret the coefficient βi as the response of government spending to an idiosyncratic
(percent) change in RGDP: it measures the elasticity of government spending with
respect to real output growth. This is our preferred measure of cyclicality of fiscal
policy in country i. A positive value of βi indicates pro-cyclicality of fiscal policy,
whereas a negative value implies counter-cyclical behavior. A value greater than one
implies that general government spending rises (falls) more than proportionally in
response to a positive (negative) shock to output.
It is necessary to note that there is no consensus on how to measure the cyclicality
of fiscal policy. As an alternative to our regression-based measure of cyclicality, some
previous studies have used the correlation between government spending and output,
after filtered by the Hodrick-Prescott method (Talvi and Vegh, 2000, and Kaminsky et
al., 2004). As Forbes and Rigobon (2002) points, however, the unadjusted correlation
coefficient can be misleading if samples have different levels of volatility. Thus, we
prefer to use the regression to obtain the cyclicality measure. Lane (2003), Arreaza
et al. (1999), and Sorensen et al. (2001) also adopted the regression-based measures
to study the cyclical behaviors of fiscal policy in their OECD country samples.
Table 1 shows the summary statistics on the estimated β for various groups of
countries. High-income developed countries such as the OECD country group tend
to exhibit much lower pro-cyclicality than developing countries, whereas there is a
substantial variation in the cyclicality across countries within each group. Among
the developing countries, the Latin America and the Caribbean show greater pro14
cyclicality than other regions, its average of the estimated β being greater than 1!
To study the link between pro-cyclicality of fiscal policy and social polarization,
we now explore the cross-country variation in our data. Figure 1A shows the simple
b and Gini coefficient, the
scatter plot between our measure of fiscal pro-cyclicality (β)
standard measure of income inequality, which is the average of all the Gini coefficients
in the 1970s available in Deininger and Squire (1996) data.14 A positive correlation
between those two indicators is quite striking.
Table 2 shows the results of cross-country regression on our measure of fiscal prob Regressions in Table 2 confirm the visual impression of
cyclicality, the estimated β.
Figures 1A and 1B. The log of initial real GDP per capita in 1960, INLRGDPCH,
is introduced in order to control for potential effects of economic backwardness on
fiscal policy. For example, poor countries may have relatively inefficient tax and
spending systems and may therefore be more prone to poor fiscal outcomes such as
pro-cyclical spending or frequent uses of discretionary policy. Alternatively, INLRGDPCH may capture some socio-political effects on the fiscal outcomes if social
conflicts are greater in poor countries. Because heteroskedasticity may be more important in a cross-country sample, the reported standard errors of the coefficients are
based on White’s (1980) heteroskedasticity-consistent covariance matrix, which reduces the sensitivity of inference and hypothesis test using OLS estimator to general
form of heteroskedasticity.
Government size, the general government expenditures as a percent of GDP (AG14
The income inequality data during the 1960s are missing for many developing countries and
of poorer quality. To maintain a reasonably large sample, we decided to use the average of all
the available Gini coefficients in the 1970s. As a robustness check on our results reported in this
paper, we use an indicator of educational inequality in 1960 from Barro and Lee (2000), which is
found to be consistently correlated with income inequality (De Gregorio and Lee, 2002). Also, we
instrument income inequality indictor with educational attainment measures, which are also found
to be consistently correlated with income inequality.
We also tried other measures of income inequality: GINIHI and AGINIHI from Deininger and
Squire (1996). The indicator GINIHI is high-quality data of Gini coefficients measured as close to
1970 as possible. The indicator AGINIHI is the decade average of all high-quality data of Gini
coefficients. The results are again similar to those reported in the paper. The main advantage of
using AGINI over the other income inequality measures is a larger number of observations available.
Therefore, we report regression results, primarily using AGINI to maintain the largest number of
observations possible. However, it should be noted that these indicators are highly correlated, and
that income inequality measured by Gini coefficients is very persistent over time.
15
EXP) (averaged over the period of 1960-2001), is included to control for the stabilizing
effects of government size on GDP volatility: larger governments stabilize output (see
Gali, 1994; Fatas and Mihov, 2001 for evidence in the OECD country sample). One
can associate the size of government with the strength of automatic stabilizer. That
is, bigger governments tend to be more counter-cyclical, with the expected coefficient
being of minus (—) sign in our regression. Trade openness (TRADE) is also included
in our base specification, which is the sum of exports and imports as a percentage of
GDP, averaged over the period of 1960-2001. In his widely cited paper, Rodrik (1998)
argues that given that the government attempts to facilitate consumption smoothing
by conducting a counter-cyclical policy, more open economies tend to have larger governments because trade openness exposes a country to external shocks. Then, trade
openness is expected to enter the regression with the minus (—) sign. The coefficient
of AGEXP is significant at the 5% level and of the correct sign (—). The estimate
suggests that a 10 percentage point increase in AGEXP is associated with an increase
in counter-cyclicality of fiscal policy of around 0.04. Columns (4) show that TRADE
has the expected sign (—), but is not significant.
It is remarkable that AGINI enters the regression with positive coefficients that
are highly significant at the 1% level–except for column (5) where it is significant
at 5%–as our theory predicts. Greater pro-cyclicality of fiscal spending is observed
in countries with high degrees of social polarization as measured by the income
inequality indicator. The regression results confirm the visual association between
pro-cyclicality and Gini coefficient in Figure 1. According to the estimated coefficient,
a 10 point increase in Gini coefficient is associated with an additional 0.02 increase in
fiscal pro-cyclicality. This result is also broadly consistent with the view that social
polarization is an important determining factor of fiscal policy decision process and
it tends to be associated with fiscal instability or lack of fiscal discipline such as large
fiscal deficits and volatility (Woo, 2003).
According to the conventional wisdom, fiscal sustainability issue is one of the
determinants of whether fiscal stance is pro-cyclical (OECD, 2003). For example, as
the dynamics of debt accumulation becomes, or is perceived to be, unsustainable,
fiscal consolidation may become necessary regardless of the economy’s position in the
business cycle. In other words, high debt may reduce the scope for counter-cyclical
16
response. Related to this, Gavin and Perotti (1997) suggest that cuts off from the
international capital market may also lead to pro-cyclical fiscal stance in developing
countries, while documenting pro-cyclicality of fiscal policy in Latin America. To
test these arguments, we include two measures as additional explanatory variables:
FSURP and CAB. The indicator FSURP is the government fiscal balance (exclusive of
grants) as a percent of GDP, averaged over the period of 1970-98. Thus, the expected
sign of the coefficient of FSURP is minus (—). The current account balance is a
good indicator of external financing constraints, and persistent large current account
deficits are often associated with external debt crises and possible losses of (or limited)
access to international capital markets. Thus, the indicator CAB is expected to enter
the regression with a negative coefficient. Regressions (5), (6), (9), and (10) show the
outcomes. The coefficients of these indicators are all insignificant at the conventional
levels, and only the coefficients of FSURP are of the correct sign (—).
Regressions (7)—(10) use the educational inequality (EDINEQ) as an alternative
measure of social polarization. The educational inequality is the dispersion of educational attainment in the population in 1960, and is calculated as the standard deviation of schooling based on Barro and Lee (2000). The literature emphasizes education
as one of the major factors affecting the degree of income inequality. Typically, the
human capital model of income distribution including the work of Schultz, Becker
and Mincer implies that the distribution of earnings (or income) is determined by the
level and the distribution of schooling across the population, and predicts positive
association between educational inequality and income inequality. De Gregorio and
Lee (2002) confirm this positive association in a panel data for about 100 countries
over the period of 1960—1990, using the same income inequality data, Deininger and
Squire (1996). They also report that higher educational attainment of the population
is negatively associated with income inequality, which we utilize in the IV (instrumental variable) method. Columns (7)—(10) show that the coefficients of EDINEQ
are all significant at the 1—5 % levels, and of the correct sign (+). The results remain
much the same for other explanatory variables. Indicator EDINEQ has a couple of
advantages over AGINI. First, it has more data points available than AGINI. Secondly, one can view the educational distribution in 1960 as pre-determined, so using
EDINEQ clearly avoids the reverse causality, if any.
17
Before we examine additional determinants of fiscal cyclicality, we try to address
the potential endogeneity of income inequality, government size, and trade using the
instrumental-variable (IV) estimation method for our benchmark regression specification. By using some exogenous components of these variables largely determined
by characteristics of countries (that are unrelated to the unobserved error), we investigate whether income inequality is a significant determinant of fiscal cyclicality. Our
main instrument for AGINI is average years of schooling of the total population in
1960 and percentage of population with primary schooling completed in 1960, which
are obtained from Barro and Lee (2000); for AGEXP the general government expenditures as a percent of GDP in 1960; and for TRADE Frankel and Romer (1999)’s
gravity-model-predicted-trade-share that is widely used as an instrument for trade in
estimating the impact of trade on growth in the literature.
As the IV method, we employ two-step feasible efficient GMM method, which
produces a consistent and efficient estimator in the presence of heteroskedasticity
that is more likely in a cross-country study. The conventional IV coefficient estimates
are still consistent, whereas its estimates of the standard errors are inconsistent. The
latter can be partially addressed by using heteroskedasticity-consistent Huber-White
standard errors, yet this conventional IV estimator is still inefficient when there is
heteroskedasticity.
Columns (11) and (12) present the IV regression results. They are largely consistent with OLS regression results, and suggest that greater income inequality indeed
leads to greater tendency of pro-cyclical fiscal spending policy, rather than the other
way around. The instrumental variable must satisfy two requirements: it must be
correlated with the included endogenous variable(s), and orthogonal to the error process. Over-identification test (Hansen J-test statistic) is employed to test the validity
of instrument(s) that the instrument(s) is orthogonal to the error process. The null
hypothesis is that the instrument is orthogonal to the error. A rejection of the null
hypothesis implies that the instrument(s) are not satisfying the orthogonality conditions required for its employment. Hansen J-statistic is consistent in the presence of
general form of heteroskedasticity. As one can see, they are all accepted, indicating
that these IVs satisfy the orthogonality condition. The last rows show F-test statistics
from the first-stage regressions, a test of joint significance of the excluded IVs. The
18
first-stage regressions are reduced form regressions of the endogenous variable on the
full set of instruments, so that the relevant test statistics relate to the explanatory
power of the excluded instruments in these regressions. The F-test results indicate
that our instruments are significantly correlated with the endogenous variable.
In Table 4, we consider additional explanatory variables of cyclicality of fiscal
policy. As our theory suggests, the political uncertainty may lead to pro-cyclical
behavior of fiscal spending by shortening current policymakers’ expected tenure in
office and providing incentives to engage in a short-term myopic policies. However,
political uncertainty is a multidimensional phenomenon that cannot be captured by a
single variable. To capture this multidimensional political uncertainty, we construct
a composite index by applying the principal components analysis to four variables,
COUPS (coups d’etat), REVOLS (revolutions), GOVCRIS (government crises) and
ASSASSIN (political assassinations), which are obtained from Banks (2003):
PINSTAB = 0.0897*GOVCRIS + 0.4727*REVOLS + 0.3327*COUPS
+ 0.1392*ASSASSIN.15
Columns (1) and (4) show that the coefficients of PINSTAB are of the expected
sign (+), and significant at 10% and 1%, respectively. The estimate implies that
one standard deviation increase in PINSTAB raises fiscal pro-cyclicality by 0.17—
0.23.16 One potential issue in interpreting our regression results is the interaction
between the indicator of political instability and the indicator of social polarization
(income inequality). Alesina and Perotti (1996) find evidence that income inequality
increases political instability. In a society with high income inequality, there might
be stronger incentives for different groups to organize and engage in activities outside
the market and outside the normal channels of political representation to appropriate
15
The principal components analysis is a statistical technique that helps us to reduce the number
of variables in an analysis by describing linear combinations of the variables that contain most of
the information (that is, linear combinations with the greatest variance). All the variables that are
included in POLINSTAB are standardized so that they have a mean of zero and standard deviation
of one at the outset.
16
We also tried each of the four variables individually. They are positively associated with the
fiscal cyclicality, but their coefficients tend to be statistically insignificant. All of their coefficients
have the expected sign (+), but only the coefficient of REVOLS are significant at the 5% level (not
reported).
19
some of the resources of the other groups. If so, some of the variation in fiscal
pro-cyclicality captured by political instability measures merely reflects the effects of
income inequality. Interestingly, however, the size of the coefficients of AGINI remain
the same and also stay statistically significant at 1-5%, even after we include the
political instability indicator, which implies this may not be the case.
Next, we consider measures of institutional quality for a couple of reasons. First,
high-quality institutions can make a difference for public finance: a more efficient
tax-collection system and better monitoring on disbursement should strengthen the
fiscal position of the government and the effectiveness of fiscal policy as an aggregate
demand management tool (such as counter-cyclical fiscal spending policy). Second,
when institutions of conflict management are well-established and work well enough
to suppress conflicts of interest among different groups, the social polarization effect
we found earlier may be less important in determining the fiscal outcomes. Third,
institutional quality indicators such as the efficiency of public sector, the degree of
corruption, and the rule of law can be a good measure of the quality of budgetary
institutions governing fiscal policy decisions. This is because government institutions
tend to be shaped by common factors such as economic, political, cultural, and historical circumstances and change only slowly (La Porta et al., 1999). As measures
of institutional quality, we use two indicators, ICRGE and POLCON. The indicator ICRGE, which has received a lot of attention in the growth literature, is based
on underlying numerical evaluations regarding the rule of law, bureaucratic quality,
corruption, expropriation risk, and government repudiation of contracts. It ranges
between 0 and 10, with high values representing better institutions. The indicator
POLCON, which is obtained from Henisz (2000), captures the extent to which the
executives face political constraints in implementing his or her policy. It is based
on the number of institutionally embedded veto players among various branches of
government. Persson et al. (1997) show that separation of powers with appropriate
checks and balances can lead to significant improvement in equilibrium outcomes by
reducing the rents extracted by politicians. Thus, one can argue that better checks
and balances and greater power dispersion may lead to more sound fiscal policy by
20
reducing the harmful polarization effects on fiscal behavior.17
Columns (2), (3), (5) and (6) show that the coefficients of ICRGE and POLCON are of the expected sign (—) and significant at the 1-5%, except for POLCON
in (5). They remain largely significant for inclusion of other explanatory variables,
although the coefficient of POLCON loses statistical significance if we use EDINEQ.
Now, we propose a working hypothesis that social polarization is important, yet its
effect on fiscal cyclicality might be more pronounced or suppressed, depending on the
institutional environment. We stress the importance of the interplay between social
polarization and poor institutions in understanding fiscal pro-cyclicality and volatility by constructing composite indices of social polarization that take institutional
constraints into account. Here we consider our main indicators of social polarization,
AGINI and EDINEQ; two institutional variables, ICRGE and POLCON.
SOCPOL1=AGINI×(1- POLCON),
SOCPOL2=AGINI×(10-ICRGE),
SOCPOL3=EDINEQ×(1- POLCON), and
SOCPOL4=EDINEQ×(10-ICRGE).
The idea behind these indices is that the effect on fiscal cyclicality of social polarization is stronger in the absence of checks and balances in policy-making process
or in poor institutional settings. These indices are expected to be associated with
stronger pro-cyclicality of fiscal policy, which is supported by regressions (7)—(10) of
Table 4. They are all significant at the 1—5% level and are associated with greater procyclicality, even after controlling for all the important and significant variables. These
results confirm our main argument that social polarization is significantly positively
associated with pro-cyclicality of fiscal spending.
3.2
Magnitude of Discretionary Fiscal Policy Shocks
17
However, it may not be that unambiguous whether a larger number of veto powers should be
associated with better fiscal policy. Tsebelis (1995) argues that regime instability is associated with
a larger number of veto players that lack ideological cohesion. According to this hypothesis, one can
expect more veto players to be associated with fiscal instability and larger fiscal deficits (see Woo,
2003).
21
Now we turn to the issue of fiscal volatility and aggressiveness of fiscal discretionary
policy. Our theoretical model suggests that the absolute size in fiscal spending change
in response to a shock to output rises with the degree of polarization and also increases
with the degree of political uncertainty (reflected in a lower value of β). This implies that the fiscal spending path would be smoother at times of shocks to output
in the absence of polarization and political uncertainty. Furthermore, this leads to
a sharper reduction in subsequent spending because the increase in tax revenue is
dissipated more quickly, producing larger fiscal spending fluctuations over time.18 In
implementing an econometric exercise to test the hypothesis that fiscal volatility (or
aggressiveness of fiscal discretionary policy) will be greater in a country with highly
polarized society, we have to find an appropriate measure of fiscal volatility or aggressiveness in discretionary fiscal spending. One easiest way to find a fiscal volatility
measure is to obtain the standard deviation of fiscal spending (or its annual growth
rate) over the sample period. However, it would not allow us to differentiate discretionary fiscal spending from that used for smoothing out business cycles. Following
the literature, we use the term discretionary fiscal policy to refer to changes in fiscal
policy that is implemented for reasons other than current macroeconomic condition
such as booms or recessions. And we try to obtain a measure of fiscal volatility
that reflects aggressiveness in using discretionary fiscal spending which is not used
for smoothing out the output fluctuations over the business cycle. That is, such a
measure would reflect the cyclically-adjusted fiscal policy stance.
We adopt a regression-based measure of discretionary fiscal policy. Specifically,
we estimate the following regression equation for each country in the periods of 196018
In other words, greater polarization means larger fiscal deficits today (due to overspending) and
more drastic spending cuts tomorrow. In practice, it does not have to be government spending
that must adjust. The government can use taxes or seigniorage to pay the debt. No matter which
instruments are used, however, today’s larger deficits still mean more drastic adjustments in fiscal
policies in the future, making fiscal outcomes volatile over time. In most hyperinflationary episodes,
large budget deficits were often an initial cause. On the other hand, it is a commonly shared view
that developing countries in general have much narrower tax bases relative to those of industrial
countries. Also, the collection of tax revenue in developing countries is often hindered by limited
administrative capacity and political constraints (Agenor and Montiel, 1999). This implies that
developing countries might find it harder to raise taxes in order to cut budget deficits than cutting
expenditures.
22
2001:
∆ log Git = αi + βi ∆ log RGDPit + δi ∆ log Git−1 + ζi Xit + γt + εit ,
(17)
where Xit is a vector of other explanatory variables such as inflation and inflation
squared. Our country-specific measure of discretionary policy is the standard deviation of the residuals (s.d. of εit ) from the above regression, which is denoted by σ²i . In
other words, we measure the aggressiveness of discretionary policy by the magnitude
of discretionary spending shock volatility that is not accounted for by macroeconomic
variables such as real GDP, inflation and etc..19
Table 2 presents the summary statistics on the estimated fiscal discretionary policy
shock σε for groups of countries. As with the pro-cyclicality, developed countries such
as the OECD country group exhibit much a smaller magnitude of their fiscal policy
shocks, which is only one-third of the counterpart of developing countries. While there
are a substantial variation in the magnitude of fiscal policy shocks across countries
within each group, sub-Saharan African and Latin American countries exhibit greatest
fiscal discretionary policy shocks.20 Before we explore the cross-country variation
in our data by running regressions, we first look at scatter plots of our measure of
fiscal shock (σε ) against social polarization indicators, Gini coefficient and educational
inequality, which are shown in Figures 2A and 2B, respectively. A positive correlation
between the two measures is quite evident from the scatter plots.
Table 5 reports cross-country regression results for fiscal discretionary policy
shocks. Column (1) confirms the positive correlation between income inequality and
the size of fiscal policy shock shown in Figure 2A. Successively, we include additional
explanatory variables starting with initial income per capita, the government size, and
trade openness in columns (2) through (4). The coefficients of AGEXP (government
size) are not statistically significant, albeit of expected sign (—). So are the coefficients
19
To adress the endogeneity problem in estimating the regression involving the contemporary
output growth, we employ the IV (instrumental variable) method and use two laggs of output
growth and oil price index as excluded instruments for output growth. Also, heteroskedasticity and
autocorrelation-consistent covariance matrix is employed. The regression specification is similar to
Fatas and Mihov (2003).
20
Even if one looks at the simple standard deviations of fiscal spending (or its growth rates), the
big picture remains the same.
23
of trade openness. The initial economic development as measured by initial income
per capita, INRGDPCH, enters the regressions with highly significant negativelysigned coefficients. Relatively richer countries in 1960 tend to have experienced much
smaller fiscal policy shocks, controlling for other variables.
Column (5) adds growth rate of terms of trade multiplied by trade openness (EXT)
as a proxy for external shocks to the economy. External shocks can be a source of fiscal
instability, especially in many developing countries. Changes in export and import
prices can affect the public sector balance either through the profits of exporting
public enterprises or through import tariffs and taxes on exports. The growth of
terms of trade is expected to be associated with smaller budget deficits and hence
relatively smaller necessity of fiscal adjustments, and to have a greater impact in
economies that are more open to trade. The coefficient of EXT has the expected sign
(—), but is not significant.
As with the case of fiscal cyclicality, excessive fiscal deficits may be caused by rapid
increases in fiscal spending, and the resulting debt accumulation may eventually make
fiscal adjustments necessary regardless of the economy’s position in the business cycle.
Thus, large fiscal deficits may be associated with great volatility of fiscal spending.
Similarly, large current account deficits may be associated with greater fiscal spending
fluctuations. Hence the expected sign of the coefficients of both FSURP and CAB
is minus (—). The results of regression using these variables are reported in columns
(6), (7), (11) and (12). In contrast to the case of fiscal cyclicality, the coefficients of
FSURP are significant at 1-5%, and of the expected sign (—). CAB also enters the
regression with the expected negative-signed but statistically weak coefficients.
Again, the coefficients of income inequality, AGINI, are all significant at the 1-5%
and its impact on the size of fiscal policy shocks is substantial. Using the regression
(6), an increase in income inequality by 10 point increase in Gini coefficient is associated with an increase in the size of fiscal shock by 20%!21 For instance, if Peru had
a lower income inequality as much as 10 Gini point, it would have enjoyed smaller
fiscal policy shocks that Singapore has exhibited over the sample period. The Gini
21
Since we use the log of fiscal policy shocks (lnσ² ), rather than σ² , as the dependent variable in
our regressions, the following holds: %∆σ² = (100∗coefficient of AGINI)∗∆AGINI.
24
coefficients in Peru and Singapore were 54.08 and 42.13, whereas the magnitude of
fiscal shocks was 0.093 and 0.064 each. Even if we substitute EDINEQ for AGINI,
the regression results remain much the same. The coefficients of EDINEQ are all
statistically significant at various levels.
Columns (13) and (14) apply the IV method to a parsimonious regression. IV
regressions confirm the OLS results, indicating that an increase in income inequality
leads to greater fiscal policy shocks, rather than the other way around. We have
used the same IVs as in IV regressions for fiscal cyclicality, and they satisfy the two
conditions for an appropriate instrumental variable as indicated by over-identification
J-statistic and F-test on joint significance of excluded instruments.
In Table 6, we show regression results with additional explanatory variables such
as political uncertainty measures, institutional quality, and composite indicators of
social polarization that takes the institutional quality into consideration, SOCPOL1—
SOCPOL4. Those results lend strong support to our main argument that social
polarization as measured by inequality of income or of education is consistently positively significantly associated with greater fiscal policy shocks, particularly in the
absence of good quality institutions or well-established checks and balances in public
decision-making process. It is also noteworthy that fiscal surplus enters the regression of fiscal policy shocks with significantly negative coefficients. As the conventional
wisdom goes, it suggests that fiscal prudence as measured by long-term budget surpluses helps the government not to feed unnecessary discretionary fiscal shocks into
the economy, presumably by refraining from overspending during good times and
avoiding fiscal consolidation that would require substantial fiscal spending cuts. This
would result in a low magnitude of fiscal policy shocks, making long-term budget
surpluses negatively associated with overall small fiscal policy shocks.
3.3
Magnitude of Discretionary Fiscal Policy Shocks and Fiscal Behavior
Up to this point, we have separately looked at the issues of cyclical behavior of fiscal
policy and the size of fiscal policy shocks (as a measure of aggressiveness in using
discretionary fiscal policy), and have shown that social polarization as measured by
income inequality or inequality in educational distribution is consistently associated
25
with them both. Now we explore how much of cross-country variation in the magnitude of discretionary policy shocks is explained by the way fiscal policy responds
to economic conditions (i.e. fiscal cyclical behavior). We posit the following chain
through which social polarization and institutional settings affect the fiscal behavior
which in turn determines the magnitude of fiscal policy shocks.
Social Polarization, and Checks and Balances in policy-making process
⇓
Fiscal Policy Behavior
⇓
Magnitude of Discretionary Fiscal Policy Shocks
⇓
Macroeconomic volatility
In other words, countries that exhibit bigger changes in fiscal spending during
boom or recessions (or in response to windfall gains such as commodity booms) are
also more likely to exercise fiscal discretionary policy more aggressively irrespective
of the aggregate demand management principles over the business cycle (see Figure
3).
b enters the regression of
Columns (1) and (2) in Table 7 show that the beta (β)
fiscal policy shock (log of σε ) with statistically significant positive coefficients. That
is, the fiscal behavior in response to business cycles or other economic conditions is
positively associated with the magnitude of fiscal policy shocks. As we already saw,
however, both measures are significantly associated with inequality measures and indicators of institutionalized checks and balances. Thus, we would have to worry about
the simultaneity and endogeneity problem in estimating the relationship between the
b and the fiscal policy shock (log of σ ). We apply the two-step feasible
beta (β)
ε
GMM method to instrument the beta, AGEXP, and TRADE by indicators of educational attainment in 1960, initial general government expenditure in 1960, Frankel
26
and Romer (1999)’s gravity-model-predicted-trade-share (as shown in the table, they
satisfy the requirements for appropriate instruments). We also include FSURP since
it is consistently significant in the regression of the fiscal policy shock, whereas it
is not in the regression of the beta. Since we do not have a good instrument for
FSURP, we treat it as exogenous. Even if we do not include FSURP, the results are
b
much the same as one can see in Column (3). Again, the coefficients of the beta (β)
are all significant at various levels and of the correct sign (+). See Columns (3)—(6).
Importantly, once we control for the beta, all the coefficients of inequality indicators
(AGINI or EDINEQ) and of POLCON become insignificant. This implies that social
polarization and institutionalized checks and balances influence the magnitude of discretionary fiscal policy shocks by determining the way government reacts to business
cycles and other economic conditions, which itself is significantly positively associated
with the size of fiscal policy shocks.
4
Concluding remarks
We have examined the determinants of fiscal cyclicality and fiscal policy shocks for a
large sample of countries over the period of 1960-2001. As our simple theory suggests,
social polarization of preferences seems to lie in depth behind the fiscal problems
such as highly pro-cyclical fiscal policy and excessive fiscal policy shocks, which tend
to make economies unstable and lower economic growth. Income inequality and
educational inequality as proxies for social polarization are consistently positively
significantly associated with both the degree of fiscal pro-cyclicality and the size of
fiscal discretionary policy shocks.
Interestingly, after addressing the endogeneity issue, we find that the size of fiscal
policy shocks (as a measure of aggressiveness of use of discretionary policy) is largely
explained by the fiscal behavior which itself is heavily influenced by social polarization.
This finding suggests that the fiscal policy shocks are simply the outcomes of fiscal
behavior, and hence addressing the pro-cyclicality problem of fiscal behavior would
also address the issue of how to reduce the volatility of fiscal shocks.
In this regard, it would be more important to limit the scope for pro-cyclical fiscal
responses in reaction to business cycles or other events such as windfall gains due to
27
commodity booms. Our findings suggest that institutionalized checks and balances
in public decision-making process and the government institutions of good quality in
general matter for the fiscal behavior and its outcomes. In particular, countries with
highly polarized societies (or greater political instability) may improve upon fiscal
policy decisions and their operations by imposing more stringent constraints on fiscal
policymakers. Thus, building institutional constraints may be a practical solution to
achieving fiscal discipline and fiscal soundness, yet our findings strongly suggest that
tackling social polarization directly would be conducive to fiscal prudence. Indeed, a
recent literature on social cohesion/trust also emphasizes beneficial effects of social
cohesion to the economy. For example, tackling social polarization directly may take
different forms such as redistribution (including land reforms), provision of public
education, and building effective institutions of conflict management. However, there
still remain a few important questions such as what the most effective way to overcome social polarization and achieve social cohesion is, what the determinants and
effects of redistribution are, and the relationship between redistribution and economic
development. These will be interesting research topics that we intend to visit in the
near future.
5
Appendix
A. Temporary Change in Tax Revenue.
In this appendix, we consider a temporary change in tax revenue. Without loss
of generality, let us assume that there is one-time positive shock to the output in the
first period, and then the output returns to the natural level, Y in the second period.
So Y1 = Y + ξ and Y2 = Y . The total government spending in equilibrium is now
?
?
G1 = g11
+ g21
=
(1 + θ)[(1 + r)T1 + T2 ]
.
(1 + r)[β + (1 + θ)]
(A1)
Thus, the absolute size of spending change resulting from a shock to tax revenue
is
+ −
dG1
dG1
(1 + θ)
=
(A2)
=
= k( θ , β) > 0.
dT1
d(τ ξ)
[β + (1 + θ)]
The magnitude of fiscal spending increase in response to a positive shock to the
output rises with the degree of polarization θ and falls with the discount factor β.
28
B. Fiscal Spending Path under Social Planner’s Solution
∗
∗
A social planner is assumed to choose g1t
and g2t
to maximize the weighted average
of the two policymakers’ utility functions. The social planner’s problem is to then
∗
∗
maximize the following objective function W, with respect to g1t
and g2t
, subject to
the government budget:
b log g11 + (1 − α)
b log g21 } + β{α
b log g12 + (1 − α)
b log g22 },
W = [α
S
(B1)
N
b = α +α
. The social planner’s optimization problem can be computed in a
where α
2
way similar to each policymaker’s maximization problem.
1
b log g11 +(1− α)
b log g21 }+β{log[ (2+r)T −(1+r)(g11 +g21 )]}. (B2)
Max W = [α
{g11 , g21 }
2
The social planner’s solution is
∗
=
g11
b
b
(2 + r)α
(2 + r)(1 − α)
∗
T and g21
T.
=
(1 + β)(1 + r)
(1 + β)(1 + r)
(B3)
The equilibrium total government spending under social planner’s solution is now
G∗1 social planner =
(2 + r)
T.
(1 + r)(1 + β)
(B4)
Thus, the absolute size of spending change resulting from a shock to tax revenue is
dG1
dG1
(2 + r)
,
=
=
dT social planner d(τ Y )
(1 + r)(1 + β)
(B5)
1
where it becomes 1 if β = 1+r
. One can easily show that the absolute size of fiscal
spending change in response to a shock to output of the same size would always be
smaller under social planner’s solution than under the non-cooperative solution of
the polarized policymakers eq.(14), except when α1 = α2 = 12 (i.e., no polarization,
θ = 0). That is, only when there is no polarization does the non-cooperative solution
coincide with that of the social planner.
b ≤ α1 (similarly, (1 − α)
b ≤ (1 − α2 )), it
Note that since α1 ≥ α2 and hence α
is straightforward to see that G∗1 social planner ≤ G?1 non-cooperative solution . Therefore, the
social planner’s optimal spending level (in the first period) is always lower than the
non-cooperative solution of the polarized policymakers, again except when θ = 0.
Similarly, the social planner’s optimal size of budget deficit is lower than the noncooperative solution.
29
6
Data Appendix
to be added.
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35
Table 1
Fiscal Cyclicality in 1960-2001: Estimated βˆ
OECD Countries
Developing Countries
East Asian Countries
Latin American Countries
Sub-Saharan African Countries
Entire sample (96 countries)
Mean
0.173
0.789
0.459
1.035
0.666
0.633
s.t.d.
0.362
0.597
0.672
0.648
0.609
0.607
minimum
-0.411
-1.225
-0.144
-0.325
-1.225
-1.225
maximum
0.901
1.970
1.578
1.970
1.805
1.970
Note: the country group classification follows that of World Bank.
Table 2
Magnitude of Discretionary Fiscal Policy Shocks in 1960-2001:
Estimated σε (in log)
OECD Countries
Developing Countries
East Asian Countries
Latin American Countries
Sub-Saharan African Countries
Entire Sample (96 countries)
Mean
0.030
0.124
0.067
0.118
0.148
0.101
s.t.d.
0.013
0.073
0.038
0.084
0.072
0.075
minimum
0.015
0.03
0.019
0.05
0.03
0.015
Note: the country group classification follows that of World Bank.
36
maximum
0.063
0.440
0.130
0.440
0.416
0.440
Table 3. Cross-country Regression of Fiscal Cyclicality in 1960-2001
Dependent variable: procyclicality βˆ
Variables
(1)
OLS
(2)
OLS
(3)
OLS
(4)
OLS
(5)
OLS
(6)
OLS
(7)
OLS
(8)
OLS
(9)
OLS
(10)
OLS
Constant
-0.321
(0.294)
0.022*
(0.007)
-0.419
(1.147)
0.022*
(0.007)
-0.554
(1.051)
0.021*
(0.007)
-0.461
(1.056)
0.021*
(0.007)
-1.101
(1.235)
0.018**
(0.008)
-0.441
(1.10)
0.022*
(0.007)
-0.200
(0.803)
0.049
(0.848)
-0.623
(0.959)
0.11
(0.927)
0.025*
(0.008)
0.112
(.099)
-0.039**
(.016)
0.024*
(0.009)
0.101
(0.102)
-0.037**
(0.016)
-0.001
(0.001)
0.019**
(0.009)
0.195
(0.119)
-0.066*
(0.024)
0.005
(0.003)
-0.003
(0.023)
0.023**
(0.009)
0.081
(0.113)
-0.034**
(0.017)
-0.001
(0.001)
AGINI
EDINEQ
0.01
(0.12)
INLRGDPCH
AGEXP
0.107
(0.125)
-0.042**
(.020)
TRADE
0.102
(0.126)
-0.040**
(0.02)
-0.001
(1.056)
FSURP
0.199
(0.146)
-0.067**
(0.027)
0.005
(0.004)
-0.015
(0.025)
0.097
(0.138)
-0.039***
(0.022)
-0.001
(0.001)
0.002
(0.018)
CAB
Over-identification
J-statistics
F-test (p-value) on
joint significance of
excluded instruments
Adj. R2
No. of Obs.
0.09
68
0.07
68
0.17
68
0.16
68
0.21
61
0.15
68
(11)
IV
-3.276***
(1.70)
0.051*
(.018)
-4.065**
(2.02)
0.055*
(.020)
0.379**
(.151)
-0.093**
(.038)
0.412**
(.165)
-0.095**
(.039)
0.006
(.005)
0.890
Accept
0.000
0.000
0.853
Accept
0.000
0.000
0.000
62
0.007
(0.011)
0.14
85
0.14
85
0.15
74
0.12
84
62
Note: White heteroskedasticity-consistent standard errors are reported in parentheses. See data appendix for definitions and sources.
Levels of significance are indicated by asterisks: * 1 percent, ** 5 percent, *** 10 percent.
For the two-step feasible efficient GMM estimation, the instrumental variables for AGINI are ays1960 primcomp1960; for AGEXP
ingexp in 1960; for Trade Frankel and Romer (1999)’s gravity-model-predicted-trade-share.
37
(12)
IV
Table 4
Cross-country Regression of Fiscal Cyclicality in 1960-2001 with Additional Explanatory Variables
Dependent variable: Cyclicality βˆ
Variables
INLRGDPCH
AGINI
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
0.145
(.126)
0.021*
(.008)
0.31**
(.128)
0.02**
(.007)
0.364**
(.140)
0.014***
(.007)
0.19***
(.105)
0.289*
(.108)
0.373*
(.131)
0.266**
(.121)
0.304**
(.129)
0.244**
(0.099)
0.276**
(0.117)
0.024*
(.008)
-0.027***
(.015)
0.0006
(.002)
0.296*
(.104)
0.019**
(.009)
-0.025
(.017)
-0.0001
(.002)
0.272*
(.101)
-0.566
(.361)
0.021**
(.009)
-0.024
(.016)
-.00004
(.001)
0.137
(.116)
-0.032
(.023)
-0.0004
(.002)
0.180
(.119)
-0.032
(.020)
-0.00004
(.001)
0.039
(.132)
-0.027***
(0.016)
0.0001
(0.002)
0.289*
(0.101)
-0.028
(0.017)
0.0002
(0.002)
0.215**
(0.105)
EDINEQ
AGEXP
TRADE
POLINSTAB
POLCON
ICRGE
-0.031
(.02)
0.0003
(.002)
0.212***
(.121)
-0.029
(.023)
-0.0006
(.002)
0.176
(.115)
-0.822**
(.356)
-0.029
(.018)
0.0000
(.001)
0.015
(.128)
-0.172*
(.054)
-0.136**
(.054)
0.02**
(.006)
SOCPOL1
0.004*
(.001)
SOCPOL2
0.023*
(0.007)
SOCPOL3
0.004*
(0.001)
0.29
77
SOCPOL4
0.21
0.31
0.37
0.22
0.27
0.31
0.29
0.35
0.27
Adj. R2
67
65
64
84
82
77
65
64
82
No. of Obs.
Note: All the regressions include an intercept. White heteroskedasticity-consistent standard errors are reported in parentheses. Levels of
significance are indicated by asterisks: * 1 percent, ** 5 percent, *** 10 percent. See data appendix for definitions and sources.
38
Table 5
Cross-country Regression of Fiscal Discretionary Policy Shock in 1960-2001
Dependent variable: log of σε
Variables
(1)
OLS
(2)
OLS
(3)
OLS
(4)
OLS
(5)
OLS
(6)
OLS
(7)
OLS
(8)
OLS
(9)
OLS
(10)
OLS
(11)
OLS
(12)
OLS
(13)
IV
(14)
IV
Constant
-4.513*
(0.405)
0.042*
(0.009)
-0.448
(0.86)
0.023*
(0.01)
-0.523
(0.84)
0.023*
(0.01)
-0.511
(0.85)
0.023*
(0.01)
-0.691
(0.851)
0.023*
(0.008)
-1.701
(1.046)
0.020**
(0.008)
-0.678
(0.908)
0.021**
(0.009)
0.866
(0.657)
0.959
(0.659)
0.694
(0.679)
-0.36
(0.914)
-0.015
(0.787)
-2.857
(2.036)
0.051**
(0.023)
-3.129
(2.063)
0.052**
(0.023)
0.015**
(0.007)
-0.443*
(0.089)
-0.02
(0.016)
0.014**
(0.007)
-0.45*
(0.088)
-0.018
(0.016)
-0.001
(0.001)
0.015**
(0.006)
-0.415*
(0.096)
-0.025
(0.017)
0.013***
(0.007)
-0.293**
(0.119)
-0.042***
(0.022)
0.003
(0.004)
0.014**
(0.006)
-0.327*
(0.101)
-0.029***
(0.017)
-0.0003
(0.001)
-0.218
(0.15)
-0.021
(0.028)
-0.209
(0.155)
-0.020
(0.028)
0.002
(0.003)
AGINI
EDINEQ
INLRGDPCH
AGEXP
TRADE
EXT
FSURP
-0.406*
(0.09)
-0.351*
(0.10)
-0.024
(0.02)
-0.352*
(0.10)
-0.023
(0.02)
-0.0002
(0.001)
-0.328*
(0.109)
-0.028
(0.023)
-0.206
(0.135)
-0.05**
(0.025)
0.005
(0.004)
-0.314**
(0.129)
-0.03
(0.026)
-0.00002
(0.001)
-0.064
(0.096)
-0.08
(0.072)
-0.051**
(0.025)
-0.06*
(0.021)
-0.014
(0.026)
CAB
-0.035***
(0.019)
0.493
0.605
OverAccept Accept
identification
J-statistics
0.000
0.000
F-test (p-value) on
0.000
0.000
joint significance
0.000
of excluded
instruments
0.24
0.40
0.42
0.41
0.42
0.45
0.40
0.44
0.43
0.44
0.44
0.46
Adj. R2
68
68
68
68
67
61
68
85
85
84
74
84
62
62
No. of Obs.
Note: White heteroskedasticity-consistent standard errors are reported in parentheses. Levels of significance are indicated by asterisks: * 1 percent,
** 5 percent, *** 10 percent. For the two-step feasible efficient GMM estimation, the instrumental variables are the same as in Table 3.
39
Table 6
Cross-country Regression of Fiscal Discretionary Policy Shock in 1960-2001 with Additional Explanatory Variables
Dependent variable: log of σε
Variables
INLRGDPCH
AGINI
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
-0.19
(0.131)
0.021*
(0.008)
-0.024
(0.157)
0.017**
(0.007)
0.021
(0.137)
0.012***
(0.007)
-0.081
(0.16)
-0.016
(0.137)
-0.074
(0.146)
-0.041
(0.128)
-0.181
(0.135)
-0.095
(0.144)
0.007
(0.007)
-0.029
(0.023)
0.003
(0.004)
-0.046**
(0.018)
0.121
(0.116)
-1.066**
(0.423)
0.013
(0.01)
-0.018
(0.018)
0.001
(0.003)
-0.035**
(0.017)
-0.086
(0.123)
-0.039
(0.028)
0.006***
(0.003)
-0.039***
(0.022)
0.162
(0.162)
-0.024
(0.023)
0.002
(0.003)
-0.038***
(0.02)
0.001
(0.162)
-0.033
(0.022)
0.003
(0.004)
-0.051*
(0.019)
0.166
(0.125)
-0.034
(0.023)
0.004
(0.004)
-0.049*
(0.018)
0.061
(0.129)
EDINEQ
AGEXP
TRADE
FSURP
POLINSTAB
POLCON
ICRGE
-0.034
(0.023)
0.006***
(0.003)
-0.049**
(0.022)
0.217
(0.16)
-0.032
(0.027)
0.005***
(0.003)
-0.038***
(0.02)
0.154
(0.155)
-1.007**
(0.414)
-0.017
(0.019)
0.002
(0.003)
-0.03
(0.019)
-0.045
(0.145)
-0.221*
(0.055)
-0.212*
(0.046)
0.021*
(0.007)
SOCPOL1
0.004*
(0.001)
SOCPOL2
0.021*
(0.007)
SOCPOL3
0.005*
(0.002)
0.55
67
SOCPOL4
0.47
0.52
0.59
0.52
0.59
0.51
0.57
0.51
Adj. R2
61
59
58
72
67
59
58
72
No. of Obs.
Note: All the regressions include an intercept. White heteroskedasticity-consistent standard errors are reported in parentheses. Levels of
significance are indicated by asterisks: * 1 percent, ** 5 percent, *** 10 percent. See data appendix for definitions and sources.
40
Table 7
Cross-country Regression: Fiscal Discretionary Policy Shock in 1960-2001 and
Fiscal Policy Behavior
Dependent variable: log of σε
Variables
(1)
OLS
(2)
OLS
(3)
IV1
(4)
IV
(5)
IV
(6)
IV
Beta ( βˆ )
0.476*
(0.148)
0.365*
(0.110)
-0.521*
(0.064)
0.993*
(0.342)
-0.526*
(0.117)
0.038
(0.029)
-0.001
(0.002)
0.922*
(0.312)
-0.599*
(0.153)
0.067
(0.048)
-0.008
(0.005)
-0.052**
(0.022)
0.650***
(0.349)
-0.433***
(0.245)
0.068
(0.052)
-0.007
(0.007)
-0.038***
(0.021)
0.014
(0.013)
0.783**
(0.342)
-0.372***
(0.221)
0.047
(0.043)
-0.004
(0.005)
-0.0543*
(0.02)
INLRGDPCH
AGEXP
TRADE
FSURP
AGINI
EDINEQ
POLCON
0.795
Accept
0.000
0.000
0.000
80
Over-identification
J-statistics
F-test (p-value) on joint significance
of excluded instruments
0.119
Accept
0.000
0.000
0.000
69
-0.475
(0.672)
0.527
Accept
0.000
0.000
0.002
54
0.14
0.52
Adj. R2
96
96
No. of Obs.
Note: All the regressions include an intercept. White heteroskedasticity-consistent standard
errors are reported in parentheses. Levels of significance are indicated by asterisks: * 1 percent,
** 5 percent, *** 10 percent.
1. The two-step feasible efficient GMM was used for the IV estimation. Beta ( βˆ ), AGEXP, and
TRADE were instrumented by ays1960, primcomp1960, GEXP in 1960, Frankel and Romer
(1999)’s gravity-model-predicted-trade-share (i.e. excluded instruments) as well as other
explanatory variables.
41
0.005
(0.011)
-0.569
(0.56)
0.19
Accept
0.000
0.000
0.002
68
Figure 1A
Cyclicality of Fiscal Policy and Income Inequality
Argentin
1.88236
Trinidad
Mexico
Sierra L
Barbados
Indonesi
Banglade
beta1
Peru
Costa
Ri
Guatemal
Cote d'I
UruguayPanama
Pakistan
Israel
Hungary
Venezuel
Philippi
Senegal
Chile
Jamaica
SouthBrazil
Af
PortugalFiji
Turkey
Morocco
Switzerl Egypt, AIreland
Seychell
NorwayIndia
Colombia
El Salva
Gabon
Thailand
Nigeria
Netherla
Tunisia
New Zeal
Malaysia
Uganda
Denmark
Spain
Sri Lank
Dominica
Austria
Sweden Hong
Germany
Kon
Greece
Japan
United S
BelgiumSingapor
Italy R
Korea,
France
United K
Australi
Canada
Zambia
Kenya
Ecuador
Finland
Botswana
Malawi
-1.22537
22.17
Gini coefficient
Data Source: Refer to the Data Appendix.
42
65.38
Figure 1B
Cyclicality of Fiscal Policy and Educational Inequality
1.9696
Haiti
Argentin
Trinidad
Bolivia
Mexico
Barbados
Congo, D
beta1
Peru
Costa Ri
Benin
Banglade
Zambia
Guatemal
Cameroon
Algeria
Uruguay
Panama
Ecuador
Kenya Pakistan Central
Iceland Senegal
Israel
Chile
Papua Ne
Jamaica
Niger
Mauritiu
Brazil
South
Af
Portugal
Fiji
Turkey
Ireland
Mali
SwitzerlSeychell Hungary
Egypt, A
Syrian
NorwayThailand El Salva
Colombia
India A
Lesotho
Paraguay
Netherla
Tunisia
New Zeal
Malaysia
Uganda
Denmark
Spain
Sri Lank Dominica
Austria
Sweden
Germany
Hong
Kon
Greece
Congo, R
Zimbabwe
Nicaragu
Japan
United S
Belgium
Italy
France
Australi
Canada
Finland
Singapor
Korea,
R
Botswana
United
K
Honduras
Togo
Malawi
-1.22537
.543906
Sierra L
Indonesi
Venezuel
Philippi
Ghana
edineq1960
Data Source: Refer to the Data Appendix.
43
36.4354
Figure 2A
Magnitude of Discretionary Fiscal Policy Shock and Income Inequality
Argentin
-.82008
Banglade
Dominica
Gabon
Malawi
Nigeria
Senegal
lsigmav1
Uganda
Indonesi
Barbados
Korea,
R
Sierra L
Jamaica
Trinidad
Seychell
Venezuel Brazil
Fiji
ColombiaPeru
Uruguay
Chile
Turkey
Tunisia
Guatemal
Morocco
Malaysia
South Af
Panama
Botswana
Singapor
Greece
Cote d'I
Israel Sri Lank
Egypt, A
Pakistan
Hungary
Italy
Costa
Ri
El Salva
Philippi Mexico
Thailand
Hong Kon
India
New Zeal
Australi Portugal
Ireland
Germany
Finland
Belgium
Norway
Canada Spain
UnitedDenmark
K
S
Netherla United
Sweden
SwitzerlJapan
Austria
Zambia
Ecuador
Kenya
France
-4.19069
22.17
Gini coefficient
Data Source: Refer to the Data Appendix.
44
65.38
Figure 2B
Magnitude of Discretionary Fiscal Policy Shock and Educational Inequality
Argentin
-.82008
Congo, D
Banglade
Zambia
Malawi
Dominica
Senegal
Nicaragu
lsigmav1
Congo, R
Benin
-4.19069
.543906
Haiti
Niger
Mali
Togo
Zimbabwe
Uganda
Sierra L
Indonesi
Jamaica
Ghana Central
Barbados
Ecuador
Trinidad
Korea,
R
Lesotho
Seychell
Venezuel
Fiji
Bolivia
Brazil
Paraguay
Peru
Cameroon
SriColombia
Lank
Israel
Chile Uruguay
Papua
Syrian
ANe
Algeria
Turkey
Guatemal
Tunisia
Egypt, A
Pakistan
South Af Malaysia
Hungary
Panama
Botswana
Singapor
Greece
Honduras
Italy
Iceland
Costa
Ri
Kenya
Mexico El Salva
Philippi
Thailand
Hong Kon
India
New Zeal
Portugal
Australi
Ireland
Germany
Mauritiu
Finland
Belgium
Norway
Spain
Canada
United K
Denmark
United
S Sweden
Netherla
JapanSwitzerl
Austria
France
edineq1960
Data Source: Refer to the Data Appendix.
45
36.4354
Figure 3
Cyclicality of Fiscal Policy and Magnitude of Discretionary Fiscal Policy Shock
-.82008
Congo, D
Malawi
Banglade
Zambia
Rwanda
Niger
DominicaNigeria
Burundi
Mali Senegal
Nicaragu
Guinea-B
T ogo Congo, R
Burkina
ZimbabweUganda
Mauritan
Chad
Gabon
Argentin
Haiti
Sierra L
Indonesi
Central
Jamaica
Ghana
Barbados
Ecuador
T rinidad
Lesotho
Madagasc
Seychell
Venezuel
Fiji
Bolivia
Brazil
Paraguay
Cameroon Peru
Benin Sri Lank
Colombia Israel
Uruguay
Ne
Chile
SyrianT urkey
A Papua
Algeria
T unisia
Egypt,
A PakistanGuatemal
Morocco
Malaysia
South Af
Hungary Panama
Botswana
SingaporGreece
Cote d'I
Honduras
Italy
Iceland Costa Ri
Kenya
El S alva
Philippi Mexico
T hailand
Hong Kon
New Zeal
India
Portugal
Australi
Ireland
Mauritiu
Finland Germany
Belgium
Norway
Spain
Canada
United K United S Denmark
Sweden Netherla
Japan
Switzerl
Austria
lsigmav1
Korea, R
-4.19069
France
-1.22537
beta1
Data Source: Refer to the Data Appendix.
46
1.9696
Figure 4
Social Polarization, Cyclicality of Fiscal Policy and Magnitude of Discretionary
Fiscal Spending Fluctuations over Two Periods:
The case of a permanent positive tax shock
T, G
G1 (after shock)
∆G1 (θ>0) > ∆T
G1 (before shock)
T+∆T =G1+∆G1= G2+∆ G2 (θ=0)
∆T =∆G1 (θ=0)= ∆G2 (θ=0)
T =G1 =G2 (θ=0)
∆G2 (θ>0) < ∆T
Period 1
G2 (after shock)
G2 (before shock)
Period 2
Note: ∆G1 in the presence of polarization is equal to ∆G1 =
Time
(1 + θ )(2 + r )
∆T where
[1 + (1 + r )(1 + θ )]
(1 + θ )(2 + r )
≥ 1 (with equality when θ=0).
[1 + (1 + r )(1 + θ )]
On the other hand, ∆G 2 =
(2 + r )
(2 + r )
∆T where
≤ 1 (with
[1 + (1 + r )(1 + θ )]
[1 + (1 + r )(1 + θ )]
equality when θ=0).
47
Figure 5
Partial Association between Magnitude of Discretionary Fiscal Spending Shocks
and Cyclical Behavior of Fiscal Policy
coef = .36475441, (robust) se = .11009091, t = 3.31
Argentin
2.34754
e( lsigmav1 | X)
Gabon
Congo, D
Venezuel
Nicaragu
Trinidad
Niger
Banglade
Senegal
Zambia
Barbados
Israel
Uruguay
Jamaica
Haiti
Nigeria Seychell CentralRwanda
Chile
Peru
Fiji Ecuador
Mali
South
AfIceland
Hungary
Zimbabwe
Bolivia
Brazil
Italy
New Zeal
Chad
Paraguay
Mauritan
Colombia
Togo
Korea, R
Algeria
Turkey
Greece
Sierra L
Australi
Burundi
Papua Ne Guatemal Indonesi
Burkina
Cameroon
Germany
Madagasc
Ghana
Tunisia
Malaysia
Mexico
Lank
Panama Costa Ri
Congo, Sri
RUganda
Syrian
A
Guinea-B
Singapor
El Salva
Benin
Egypt,
A
Canada Belgium
Finland
Lesotho
Morocco
Switzerl
Norway
United S
Cote d'I
HondurasUnited KHong Kon Denmark Ireland
Sweden Netherla
Portugal
Philippi
Botswana
Spain
Pakistan
Mauritiu
Austria
Dominica
Malawi
France
Japan
Kenya
India
-1.22399
-2.02827
Thailand
e( beta1 | X )
Note: Based on the Regression (1) in Table 7.
48
1.35627