Discussion Paper No. 118
Cyclical Reaction of Fiscal Policy and its
Relationship with the Current Account Balance
Lamia Bazzaoui, Jun Nagayasu
and Ronald MacDonald

January, 2021

Data Science and Service Research
Discussion Paper

Center for Data Science and Service Research

Graduate School of Economic and Management
Tohoku University27-1 Kawauchi, Aobaku

Sendai 980-8576, JAPANCyclical Reaction of Fiscal Policy and its
Relationship with the Current Account Balance
Lamia Bazzaoui†

Jun Nagayasu‡

Ronald MacDonald§

Abstract
Previous empirical studies aiming to verify the relationship between the current account (CA) and government expenditures have produced mixed results across regions
and countries. In this study, we investigate whether cyclicality affects this relationship,based on a sample of 51 countries and using quarterly and annual data from 2002Q1
to 2018Q4. We use a structural panel vector autoregression model (Pedroni, 2013) toanalyze the relationship between the CA and aggregate and disaggregate government
expenditures for different groups of countries. Our findings indicate that a negative
impact on the CA due to aggregate government spending is only visible in countercyclical economies, suggesting the importance of cyclicality in explaining the dynamics
of the present value model. However, cyclicality is not sufficient for explaining the link
between disaggregate fiscal policy and the CA, due to substantial heterogeneity. A timeseries approach shows that subsidies play a significant role in the CA of Austria, Croatia,
Spain, and Bolivia and that property income is a major CA determinant in countries withlarge external debts. Conversely, the largest components of public spending (compensation of employees, intermediate consumption, and social benefits) play a minor role.
JEL classification: E62, F32, F44
Keywords: cyclicality, fiscal policy, current account, government spending
Declarations of interest: none.
The paper is not under consideration for publication elsewhere.
The research did not receive any specific grant from funding agencies in the public,commercial, or not-for-profit sectors.
†

Tohoku University, Graduate School of Economics & Management,27-1 Kawauchi, Aoba-ku, Sendai,
Miyagi 980-8576 JAPAN. E-mail address: lamia@bazzaoui.com. Tel.: +81 70 2835 7308; Fax: +81 22 795
6270.
‡
Corresponding author. Tohoku University, Graduate School of Economics & Management, 27-1
Kawauchi, Aoba-ku, Sendai, Miyagi 980-8576 JAPAN. Email: jun.nagayasu.d8@tohoku.ac.jp. Tel.: +81 22
795 6265. Fax: +81 22 795 6270.
§
University of Glasgow, Adam Smith Business School, Adam Smith Building, Glasgow G12 8RT, United
Kingdom. Email: Ronald.MacDonald@glasgow.ac.uk. Tel.: +44 141 330 4618. Fax: +44 141 330 4940.

1

1

Introduction

Among the issues related to the decreasing fiscal space in many economies, the expected
effect on the current account (CA) balance is one of the most ambiguous and difficult to understand in macroeconomic analysis . On the one hand, the theoretical literature suggests that
fiscal deficits are accompanied by CA deficits if the relationship between private savings andinvestment is constant. On the other hand, large budget deficits reduce the ability to borrow
in international markets and, thereby, running CA deficits to ensure consumption-smoothing
during economic downturns is less feasible. Understanding the relationship between fiscal
policy and the CA is important since many countries have suffered from both fiscal and CA
deficits simultaneously, and have highly prioritized improving these deficits by formulating
appropriate economic policies.
The main focus of this study is the interaction between government spending and the CA
in 51 countries. Using the intertemporal model of the CA as a theoretical basis, our empiricalanalysis includes a correlation coefficient analysis, the Pedroni (2013) structural panel vector
autoregression (VAR) model, and a time-series Bayesian VAR analysis. Although previous
studies have attempted to uncover the relationship between the CA and fiscal policy, themain novelty of our approach lies in two features: (1) the use of quarterly disaggregate
fiscal data instead of aggregate fiscal variables, while accounting for fiscal cyclicality, and(2) the adoption of a fully structural VAR that decomposes impulse responses into common
and idiosyncratic components, while accounting for the underlying heterogeneity within our
sample.
Our findings indicate that the ability of fiscal policy to affect the CA depends on its relationship with the business cycle, as the expected negative impact of aggregate government
spending only appears in countercyclical economies. However, accounting for cyclicality is
not sufficient for explaining the dynamics between disaggregate fiscal data and the CA. This
results from a substantial heterogeneity within our different subsamples, reflected in quartile
impulse responses and the decomposition into idiosyncratic and common shocks. We de2

rive the main trends by country from a time-series Bayesian VAR approach, based on two
different prior specifications (an independent normal-Wishart and a Litterman-Minnesota approach), using variance decomposition and orthogonalized impulse response functions. We
find that subsidies play a significant role in the CAs of Austria, Croatia, Spain, and Bolivia.
Property income is a major determinant of the CA in countries with high levels of external
debt, such as Italy, Spain, and Armenia. Although compensation of employees, intermediate consumption, and social benefits are the largest components of public spending in mostcountries in our sample, they do not strongly contribute to the determination of the CA.
The remainder of the paper is organized as follows: In the next section, a literature review
on the relationship between the CA and fiscal policy is provided. In Section 3, we describe
the dataset and methodology. The main stylized facts and a preliminary correlation analysis
are provided in Sections 4 and 5, respectively . Section 6 presents the main findings of theempirical study based on the Structural panel and time-series VAR analyses. Finally, the lastsection summarizes the main points of the paper.

2

Literature review

There are numerous literatures and theories on CA determination. Traditionally, it has been
assumed that the CA moves in the same direction as the fiscal balance (as in the twin deficits
hypothesis1 or the Mundell-Fleming framework2 ). However, the link between the CA andboth taxes and government expenditures is not considered to be equivalently strong. The
Ricardian Equivalence Hypothesis (Barro, 1974) implies that there is no relationship between a CA deficit and Taxes because tax changes have no impact on private consumption.3
Formally, the relationship between the CA and the fiscal balance is clear based on the identity S p −I + F B = CA, derived from national income identities, where S p represents private savings, I national
investment, and FB is the fiscal balance, with F B = T − G = S g (which is government savings). G isgovernment expenditures on goods and services and T is tax revenues.
2This model shows that a budget deficit leads to a CA deficit through an increase in interest rates, in addition
to other transmission channels that depend on the exchange rate regime and the nature of capital mobility (thesechannels include the exchange rate, money supply, and private investment).
3
A cut in taxes increases current wealth but this increase is used in extra savings as individuals will expect
future tax increases. On the other hand, private consumption affects the CA, as shown by Eq. 2.
1

3

Conversely, the relationship between the CA and government expenditures is assumed to bestrong and negative, as implied by the intertemporal model of the CA, based on the following
expression:
CAt = (rt − r̃t )At + (Yt − Ỹt ) − (Gt − G̃t ) − (It − I˜t )
"
#


1
+ 1−
r̃t At + Ỹt − G̃t − I˜t ,
˜ σ
(β/R)

(1)

where At is the economy’s stock of net foreign claims at the end of period (t − 1), Yt
is the net domestic product, Gt is government consumption, and It is net investment. Theg σ is the weighted
letters with a tilde represent the permanent level of the variables and (β/R)
g σ ≡average ratio of the(s − t) period’s subjective and market discount factors (β/R)!σ
P∞
β s−t/
s=t Rt,s
Rt,sP∞, where the market discount rate for consumption at time s is Rt,s =
Rt,s
s=t

1

Qs

v=t+1 (1+rv ). This model, provided by Obstfeld and Rogoff (1995), constitutes the prevalent

theoretical framework for studying the dynamics of the CA. It is derived from two elements:
the national income identity and the permanent income hypothesis. According to the latter, the permanent level of consumption is determined by the permanent levels of the net
domestic product, investment, and government expenditures.4
Empirically, the link between fiscal policy and the CA has been analyzed in different
strands of the literature. The first strand focuses on the relationship between fiscal and CA
balances (see Appendix I). The lack of consensus in this literature results from underlying
structural forces in the sample countries that may lead to different correlations and also to
the different methodologies used (Litsios and Pilbeam, 2017). The second strand of theliterature attempts to uncover the main determinants of the CA from a set of variables (e.g.,chosen from the literature on the determinants of savings and investment), which includes the4

The last term of the equation reflects consumption tilting due to differences between world interest rates
and the domestic rate of time preference (1 − β) /β. When the home country is, on average, more impatientσgthan the rest of the world, (β\R)< 1 as β is lower than future world interest rates, inducing a tendency
toward CA deficits, increasing foreign debt and reducing consumption. If, on the other hand, the rest of theworld is more impatient, consumption’s time path will have an upward tilt. The tilting effect is stronger whenσ (expressing intertemporal substitution in consumption) increases (Obstfeld and Rogoff, 1995).

4fiscal balance. For instance, Chinn and Prasad (2003) attempted to study the medium-term
determinants of the CA for a large sample of economies over the period 1971–1995. They
confirmed a positive relation between the CA, fiscal balances, and net foreign assets. Finally,
the third strand of the literature uses the intertemporal model for the CA as a theoreticalbackground. In that case, the permanent level of consumption can be expressed as
C̃t = rBt + r(1 + r)−1∞X

(1 + r)−i Et {Yt+i − It+i − Gt+i } ,i=0where Yt , It , Gt and Bt denote the output, investment, government spending, and net domestic ownership of foreign assets, respectively. The non-stochastic world real interest rate r isassumed to be positive. Using the total income identity leads to the following expression for
the CA (see Nason and Rogers, 2006).5
i
∞ 
X
1
CAt = −
Et ∆N Ot+i ,
1+r
i=1

(2)

where the net output N Ot is given by N Ot = Yt −It −Gt and ∆N Ot+i = N Ot+i −N Ot+i−1 .
The commonly used approach to verify this present value model (PVM) is the methodology
of Campbell (1987) and Campbell and Shiller (1987), which rests on the assumption that the
CA and the first difference of net output are unrestricted bivariate VAR processes.
Some authors succeeded in verifying this intertemporal model through empirical data(Campa and Gavilan, 2011; Hoffmann, 2013). However, more frequently, empirical studies
based on the PVM model led to the rejection of the model (Ghosh, 1995; Milbourne andOtto, 1992; Otto, 1992; Sheffrin and Woo, 1990). Usually, the modeled CA exhibits lessvolatility than the actual data. To solve this issue, Nason and Rogers (2006) attempted toverify whether the fit of the model could be improved if “the usual suspects”6 causing the5
The national income identity can be expressed
as
−1 P∞
−i
Yt = It + Gt + N Xt +rBt + r(1 + r)
(1+ r) Et {Yt+i − It+i − Gt+i }, where the net exports
i=0
N Xt are the difference between the CA and income from net foreign assets: N Xt = CAt − rBt .
6
Namely, non-separable preferences, fiscal policy, real interest rate shocks, external imperfect international
capital mobility, and internalized risk premium

5empirical rejection of the PVM were taken into account. They concluded that the failure ofbasic PVM in explaining CA variation resulted from the absence of exogenous shocks on theworld real interest rate.
The intertemporal model (Eq. 1) shows that the interaction between the CA and government expenditures involves other key variables, namely the de-trended net domestic product,
national investment, and the interest rate. Consequently, the expected negative relationshipbetween government spending and the CA may be altered by the diverging effects of theseother variables. In the case of net domestic product, if we assume that the CA is positively
correlated with the business cycle, then the link between the CA and government expenditures would be negative only if fiscal policy is assumed to be countercyclical. According toKaminsky et al. (2004), the CA would be procyclical in the standard model, since borrowing
from abroad should be countercyclical to ensure consumption-smoothing.7 They provided
the following explanations to a counter-cyclical CA: a procyclical investment that dominates the savings effect, distortions in consumption induced by temporary policies leading
to countercyclical savings (since consumption increases in prosperous times), and residents’dissaving as capital inflows increase in prosperous times.
On the other hand, expectations regarding fiscal cyclicality in the literature vary based on
the theoretical framework. The traditional Keynesian view is based on the idea that public
expenditures should move in a countercyclical fashion and act as a catalyst for aggregate
demand in times of recession. In contrast, the neoclassical framework precludes any countercyclical role for fiscal policy and often considers that government expenditures follow an
exogenously given process (see Lucas and Stokey, 1983).8
Empirical studies on fiscal cyclicality have led to mixed results. The most common findings indicate that policy tends to be less countercyclical than what the theory suggests. Morespecifically, several empirical studies have found that as opposed to industrial economies,
7

Changes in the CA can be explained by the capital account if the impact of international reserves is ignored
Lane (2003) noted that, in the neoclassical framework, government consumption would be expected tobe countercyclical if public and private consumption were substitutes in utility, and procyclical if they werecomplements.
86

fiscal policy in developing countries is procyclical (Gavin and Perotti, 1997; Talvi and Vegh,
2005; Braun, 2001; Lane, 2003; Thornton, 2008). The evidence for OECD countries ismixed, but most studies reported acyclical or slightly countercyclical fiscal policy (Lane,
2003; Wyplosz, 2002).
In this study, we investigate the relationship between government spending and the CA
for a sample of countries while accounting for fiscal cyclicality. Fiscal cyclicality is measured using government spending in domestic currency in line with Kaminsky et al. (2004).
They argued that the concept of fiscal policy cyclicality should be defined based on policy
instruments, that is, government consumption and tax rates,9 as opposed to endogenously
determined outcomes (the fiscal balance or tax revenues). Further, they demonstrated how
the use of any variable expressed as a percentage of gross domestic product (GDP), or the
fiscal balance or tax revenues could be misleading.

3

Data and Methodology

We use data in domestic currencies for a sample of 51 high- and middle-income countries.
We describe the data in Appendix II. The data are used on a per capita basis, in real terms or
deflated through a GDP deflator. Variables are de-trended using the Hodrick-Prescott (HP)filter. After a general examination of the HP-de-trended data for the period 1995Q1–2019Q2,
from which we draw the main stylized facts, we introduce disaggregate fiscal data into theanalysis.
Disaggregate data of government expenditures are from the Government Finance Statistics database of Eurostat and the IMF. The Eurostat database is based on the ESA 2010accounting standards. Government expenditures are defined as the sum of 12 ESA categories10 (see the definitions provided in Appendix III). Data are available for the 28 European Union (EU) countries, starting from 2002. The values match those of the Government
910Data on tax rates are more difficult to obtain.
ESA 2010 Manual, p. 274.

7

Finance Statistics of the IMF (except that the latter excludes the categories of tax expenses
and transfers). We deflate the series using a price deflator calculated from nominal and real
government consumption expenditures and divide by population size. For non-EU countries,
we use data extracted from the IMF Government Finance Statistics database. Our subsample consists of 23 non-EU countries (i.e., 6 countries are excluded from our initial sample).
Additional adjustments to the data are reported in Appendix II.
Since the period of data availability is not the same for all countries, the sample period is
set to 2002Q1–2018Q4. In a first step, we use a correlation analysis to study the relationshipbetween cyclical components of disaggregate fiscal data and both the CA and GDP. Then,based on Eq. 1, we estimate the following linear expression for the CA:







g it = β1 N F Ait − Ng
F Ait +β2 Git − G̃it +β3 Iit − I˜it +β4 (rit − r̃it )+εit ,CAit −CA(3)
where N F Ait represents the net financial assets of country i, Git is government consumption, Iit is net investment, and rit is the short-term interest rate. The variables are expressed
as a percentage of GDP. We apply Pedroni’s (2013) structural heterogeneous panel VAR approach using Goes’s (2016) algorithm to study the impact of changes in government spendingon the CA, for groups based on fiscal cyclicality measures (terciles). The unique feature ofthis approach is that it decomposes the different responses into responses to idiosyncratic
and common shocks. In a subsequent step, we complete our study by a time-series analysis
based on a Bayesian VAR approach (more details are provided in Appendix IV).
We briefly summarize Pedroni’s (2013) approach. Consider a panel composed of i =1, . . . , N individual members, each of which consists of an M ×1 vector of observed endogenous variables yit . The data are assumed to be observed over T time periods (t = 1, ..., T )for each member and used after de-meaning, where the M × 1 vector of de-meaned data is
zit = yit − ȳi . Structural composite white noise shocks it may be cross-sectionally dependent as expressed by the relation it = Λi ¯t + ˜it , where ¯t and ˜it represent common white8

noise shocks shared by all members and member-specific idiosyncratic white noise shocks,
respectively, and Λi is an M × M diagonal matrix with the loading coefficients. The two
types of shocks are assumed to be orthogonal to each other. The moving average representaP ition of the model is as follows: Ri (L) ∆zit = µit , where Ri (L) = I − Pj=1
Rij Lj , with Pi
the lag truncation value, which can differ from one cross section to the other. The associatedstructural form model is ∆zit = Ai (L) it or Bi (L) ∆zit = it , where Bi (L) = Ai (L)−1 .
Short-run restrictions can be imposed on the Bi (0) matrix. In the special case of recursive
restrictions, this is equivalent to the Cholesky orthogonalization.
In our data analysis, the identification strategy is based on a scenario of an exogenous
fiscal policy and an endogenous CA balance. Thereby, in the Cholesky ordering, government
expenditures are placed first and the CA last. The ordering for the disaggregate variables isbased on Granger causality tests. The first step of the methodology is to estimate the reducedform VAR through ordinary least squares (OLS) . Initially, the model is estimated separately
for each cross section. Then, to capture the common dynamics, the M × 1 vector of common
P ttime effects ∆z̄t = Nt−1 N
i=1 ∆zit is calculated and the corresponding reduced-form VAR
model R̄ (L) ∆z̄t = µ̄t is estimated. Then, the appropriate identifying restrictions are used
to obtain the structural shock estimates it = Bi (L) Ri (L)−1 µit and ¯t = B̄ (L) R̄(L)−1 µ̄t .
Moreover, to obtain the elements of the loadings matrix Λi , N ×M OLS regressions of it on¯t are run, based on the relation it = Λi ¯t + ˜it . At this stage, we report the median impulse
responses for our subsamples along with bootstrap confidence intervals from 100 repetitions.
We then report the quartile impulse responses and analyze the decomposition of responses between those to common and those to idiosyncratic shocks. The composite impulse response functions calculated from the individual structural VAR estimation can be
decomposed into common and idiosyncratic shocks as follows: First, a re-scaling of the responses to idiosyncratic shocks is required, based on the following argument: The variances
for the structural shocks can be expressed as E [it 0it ] = E (Λi ¯t + ˜it ) (Λi ¯t + ˜it )0 =Ωi, = Λi Ωi,¯ Λ0i + Ωi,˜ . By setting Ωi,¯ = Ωi, = I, we obtain Ωi,˜ = I − Λi Λi 0 . This9

implies that responses to common shocks for unity-sized shocks would correspond to responses to idiosyncratic shocks for shocks of size 1 − Λ(m, m)2 , where m = 1, ..., M .
To perform the re-scaling, we can rewrite the expression for composite structural shocks
as it = Λi ¯t + (I − Λi Λ0i )1/2 ˜∗it . Finally, this re-scaled form can be used to decompose

1
the impulse responses such that Ai (L) it = Ai (L) Λi ¯t + (I − Λi Λ0i ) 2 ˜∗it , leading to
the decomposition Ai (L) = Āi (L) + Ãi (L) where Āi (L) = Ai (L) Λi and Ãi (L) =1

Ai (L) (I − Λi Λ0i ) 2 = Ai (L) − Āi (L). The sample distribution of estimated responses
can be used to describe the properties of the sample (with the median, and the 1st and 3rdquartiles used as confidence intervals) or to create fitted values for member-specific impulse
responses.

4

Stylized facts

This section presents an overview of the main stylized facts discovered through a generalexamination of the collected data. We split our sample in different ways, comparing OECD
with non-OECD countries, as well as different regional and income groups .
Stylized fact 1: The correlation between the CA and aggregate government consumption expenditure is weak and differs across countries and time periods.
The correlation coefficients between the CA and general government expenditures (Table 1) are small in most cases. By region, the correlation coefficients between the CA
and general government expenditures is negative or close to zero in almost all cases(except in North America—specifically, in the United States).
Stylized fact 2: Generally, the fiscal policy of OECD countries is either countercyclical or acyclical. Conversely, in developing countries, it is procyclical in most cases.
In line with the results of previous empirical studies, we find that the fiscal policy inOECD countries is, in most cases, either acyclical or countercyclical whereas in developing economies, and particularly Latin American countries, it is procyclical (Table
10

1).
Stylized fact 3: The CA tends to behave acyclically or countercyclically.
As opposed to our expectations, for most groups in our sample, the CA does not appear
to be procyclical during the studied period. Countries with the most procyclical CA
include Croatia (0.77), Canada (0.56), Norway, Sweden, and Singapore (correlation
close to 0.29) . According to the permanent income hypothesis, there will be a deficit
in the CA if consumers expect a future increase in income. Therefore, we can consider
that the data behave in line with the theory only if we suppose that a positive evolutionof GDP in the short run leads to positive expectations about future income.

5

Correlation analysis of disaggregate fiscal data

In this section, we examine the correlation of disaggregate government expenditures datawith the CA and GDP. Using disaggregate fiscal data, we first calculate the share of eachcomponent in the overall government expenditures (Table 2). We note that the most significant components are “Compensation of employees,” “Social benefits,” and “Intermediate
consumption of goods and services,” with a total share of 75% in all expenditures. We
also note that the shares for “Compensation for employees” and, especially, “Intermediate
consumption of goods and services” are relatively larger for non-OECD/ middle-income
economies compared to OECD/high-income economies. The opposite is true for social benefits. Examining the fiscal cyclicality measures for these categories (Tables 3 and 4), wenote that the procyclicality of non-OECD/middle-income is more notable in “Compensationof employees” and “Intermediate consumption.” Overall, the relationship between the cyclical components of the CA and disaggregate expenditures is weak in both income and OECD
groups .

11

6

The CA model: A heterogeneous panel VAR analysis

6.1

Structural VAR analysis: Median impulse responses

We use Pedroni’s (2013) structural panel VAR approach to analyze the relationship between
the CA and disaggregate government expenditures in different groups of countries. Thisapproach controls for country fixed effects and allows for full heterogeneity of dynamics
across countries, as opposed to the standard panel VAR approaches based on average estimates. The estimation is performed after the decomposition of shocks into idiosyncratic
and common components. Our identification strategy is based on a scenario of exogenous
fiscal policy and an endogenous CA. Thereby, in the ordering of the variables, government
expenditures are placed first and the CA last.
The median composite response of the CA to one-unit composite shocks in other variables is shown in Figure 1, with confidence intervals based on 100 bootstrap repetitions. This
preliminary result shows that the relationship between the CA and total government expenditures is not substantially significant. On the other hand, the CA responds positively andsignificantly to a change of a relatively strong magnitude in net foreign assets and negatively
to a change in gross fixed capital formation.
To check whether fiscal cyclicality affects the relationship between the CA and fiscal
policy, we divide the sample into terciles based on the measure of fiscal cyclicality, that is,the correlation between cyclical components of GDP and government expenditures (as in
Table 1). The aim is to separate the sample into countercyclical, acyclical, and procyclicalcountries. We obtain the following groups:
Group 1: countercyclical countries, corresponding to the 1st tercile group in terms of
measures of fiscal cyclicality (correlation with GDP < -0.09)
Group 2: countries in the 2nd tercile group, with a fiscal cyclicality measure between-0.09 and 0.05

12Group 3: group of procyclical countries (3rd tercile group) with a fiscal cyclicality
measure > 0.05
The median composite impulse response function for Group 1 (Figure 2) shows a negative and significant response of the CA to government expenditures in the first period, followed by a positive response in the second period. The opposite is observed for Group 2,
where a positive response to government expenditures in the first period is followed by anegative response in the second period. For Group 3, the resulting response is only weakly
significant. These results clearly indicate that the ability of fiscal policy to affect the CA depends on its interaction with the business cycle. Provided that the CA is positively affectedby the business cycle, then a negative relationship between government spending and the CA
would only be visible in countercyclical economies. In other cases, the relationship would
be less predictable, as confirmed by the impulse responses. Finally, in the procyclical group,
we note that the relationship between the CA and net foreign assets is the most robust one
whereas the negative response to gross fixed capital formation is not significant .11

6.2

Quartile impulse response functions

Next, we analyze the properties of the individual composite responses’ distribution by plotting their median, average, and the 1st and 3rd quartiles as confidence intervals. Since thisapproach shows the response of most of the sample, it is more informative than the medianwith bootstrap confidence intervals or the traditional averaging methods used for panels.
For countercyclical economies (Figure 3), we observe a negative response to total government spending in the first period.12 However, in the second period, the response becomes
positive in 9 out of 17 countries. Although the impact of government expenditures on shocks
to other variables does explain part of this sign change, the main reason for it appears to bea direct lagged positive effect from total government spending to the CA. We then replace
11

Responses to net foreign assets, gross fixed capital formation and the interest rate are not reported in theremaining part of the study because they are similar to those in Figures 1 and 2.
12
Except for 2 countries: Chile and Latvia.

13total government expenditures in the model by disaggregate fiscal data.13
Responses to disaggregate government spending (Figure 4a) are much less significant
due to larger standard errors. Such a heterogeneous response would not have been visible
if we had relied solely on the average or median responses. In acyclical economies, theresponse to total government spending is positive in most of the sample in the first period
and becomes negative immediately after that (Figure 3). None of the government spending
components induces a homogeneous response in this group, except for a positive contemporaneous response to social benefits (Figure 4b). In procyclical economies, responses tototal government spending are not very significant as the median lies close to zero (Figure3). The CA responds negatively to social benefits in the first period but all other responses
are disparate (Figure 4c).

6.3

Decomposition into idiosyncratic and common shocks

The heterogeneity noted in the quartile impulse response functions is confirmed by the decomposition of composite shocks into idiosyncratic and common shocks. Figure 5 reveals
that most composite responses are characterized as idiosyncratic rather than common shocks.
Further, responses to common shocks are opposite in direction to responses to idiosyncratic
shocks in some cases. This is because the groups contain countries that do not respond in asimilar way to global shocks.14 For instance, the 2007 financial crisis led to a deterioration of
the fundamentals of some countries (a negative shock, especially in 2008Q4), but countries
that have been able to weather the crisis do not exhibit a significant change in the variables(the changes in the error terms are small ). Consequently, in the latter group of countries,
common shocks and composite shocks are negatively related. Another possible cause of thisnegative correlation could be an opposite feedback effect from variables affected by the crisis
13

As it is difficult to order disaggregate expenditures based on economic logic, we use Granger causality tests
as a reference. The following ordering of the variables is obtained: property income, subsidies, compensationof employees, intermediate consumption, social benefits, other expenditures, interest rate, net foreign assets,gross fixed capital formation, and the CA.
14
While decomposing individual composite shocks, the term of the loading matrix corresponding to common
shocks is negative, implying a negative correlation between individual and common shocks.

14

on government expenditures in some countries.
Figure 6 provides an example based on the decomposition of the median composite response to property income in the fiscal cyclicality group 1.15 In this case, the reason common
and composite shocks are negatively correlated is that the average property income for thecountries in the group received a negative shock in 2008Q4, but at the individual level, many
countries were either not affected or received this fiscal shock on a different date in 2008 or
2009.16

7

A time-series analysis of the CA model

The heterogeneity of responses to disaggregate government spending shocks suggests theabsence of a strong and robust relationship between a particular component and the CA. As
it is difficult to find a homogeneous response of the CA to disaggregate fiscal data among
the different fiscal cyclicality groups, we run a time-series analysis for each country. We use
a Bayesian VAR model (see Appendix IV) based on an independent normal-Wishart priorwith Gibbs sampling to derive the orthogonalized impulse response functions and report themain responses with 95% confidence bands as well as a variance decomposition of the CA
by country (Table 5). The choice of this method is justified by the need to account for theuncertainties related to the determination of model and parameter values. As a robustness
check, we also estimate the same model with a different prior specification based on the
Litterman-Minnesota approach (Table 6). In a few countries (e.g., Hong Kong and Singa15

In Figure 6, we separate group 1 into two subgroups: subgroup (a), with countries for which responses tocommon shocks and those to composite shocks have opposite signs; and subgroup (b), in which both responses
have the same direction. We find that, at the time of the crisis, the interest rate is the main driving factor behindproperty income shocks in almost all countries. In most countries in the group, this variable was negatively
affected in 2018Q4, as a result of governments’ intervention at the time. However, while in countries ofsubgroup (a) the resulting structural shock to property income is positive due to a negative effect from interest
rates, in subgroup (b), the resulting structural shock is negative.
16
We express the structural shock to the variable s at time t as st = βs −1 µt , where µt is a vector of reducedform shocks at t and βs a vector of contemporaneous effects on s from the Cholesky factor. The main elementin µt in the example is the interest rate (in 2018Q4) with a significant negative shock. However, in subgroup(a), the corresponding factor in βs −1 is also negative, leading to an overall positive structural shock, while insubgroup (b), this term is positive15

pore), the two approaches yield substantially different outcomes but for most of the sample,
the results are consistent.
The variance decomposition (values in the 8th quarter) clearly indicates that the relationship between disaggregate government spending and the CA differs among countries. We
note that the share of subsidies is notably higher in two countercyclical economies: Austria
and Croatia. This could be the result of export subsidies. In the particular case of Croatia, thegovernment still plays a significant role in the economy, which results in overall high government expenditures (over 40% of the GDP in total). Interestingly, for this economy, theimpulse response functions show that subsidies have a negative impact on the CA. Subsidies
are also significant in the case of Bolivia. In this economy, hydrocarbons (especially naturalgas) account for approximately half of the total exports and are managed by state-owned enterprises. Although the natural gas sector was privatized in the 1990s, it was re-nationalized
in 2006.
We note the importance of property income (comprising payable income such as interests and dividends) in high-income countries characterized by high net borrowing fromabroad, such as Italy, Spain, and Portugal, in which more than 30% of public debt is heldby non-resident investors. We note that in Spain, subsidies are also relatively significant in
explaining the CA variation. Among middle-income economies, the contribution of property income to the CA variation is also notable in Armenia and Indonesia. Armenia exhibitsa CA deficit with an exports structure that relies essentially on minerals and precious andnon-precious metals, and top imports that include oil and natural gas. The country’s deficit
is financed by external borrowing and net foreign direct investment. Most of its external debt
was obtained through multi-country credit programs but there is also a significant share of
non-resident investments in government bonds. It is also worth mentioning that the country is known to have a large diaspora spread globally, resulting in the large and important
impact of remittances on the Armenian economy. The impulse response functions show apositive response of the CA to a shock in property income for all these countries , with wide16

confidence bands.
The three largest components of government expenditures (compensation of employees,
intermediate consumption, and social benefits), although representing a higher percentage
of total spending, play a less significant role in the determination of the CA. Exceptionally,
the share of government compensation of employees in the cases of Estonia and Slovenia ishigher than that of all other variables. We also notice that the overall value of compensationof employees in the Estonian CA balance has significantly increased after 2004, which may
be an indication that joining the EU had an impact on the non-resident labor in the country.
The impulse response functions show a positive response of the CA in both Estonia andSlovenia.
Unsurprisingly, net foreign assets strongly affect the CA of Luxembourg, Belgium, andthe United Kingdom. We can see that this component exhibits, on average, less weightin middle-income economies, except in Turkey, which has emerged as a significant capital
investor abroad in recent decades. Gross fixed capital formation generally plays a small rolein CA determination, with the exception of Ireland, the Netherlands, and, to a much lesser
extent, Lithuania, Belarus and Croatia. Finally, in both estimated models, the highest levelof CA persistence is noted in the cases of France and Colombia.

8

Concluding remarks

Using various statistical methods, we have investigated the relationship between the CA andgovernment expenditures. Our findings confirm previously reported difficulties in obtaining
strong empirical evidence in favor of the PVM for a panel of advanced and developing countries. Further, our findings explain why previous studies often showed mixed results about
this relationship.
First, to explain the underlying heterogeneity in our sample, we account for fiscal cyclicality. As reported in previous empirical studies, the cyclicality analysis shows that fiscal
policy tends to be procyclical in middle-income economies and acyclical in high-income
17economies. This procyclicality most notably emerges in two categories of public spending:“Compensation of employees” and “Intermediate consumption.” Our findings confirm thatcyclicality does affect the relationship between aggregate government expenditures and the
CA since the expected negative impact of a fiscal shock appears only in the countercyclical group. Still, cyclicality is not sufficient for explaining the link between disaggregate
fiscal policy and the CA, due to substantial heterogeneity, even within groups with similar
cyclicality measures.
Second, we use a time-series approach and uncover the main public expenditures contributors to CA determination through a variance decomposition analysis. We find that subsidies
play a significant role in Austria, Croatia, Spain, and Bolivia. In contrast, property income
is the most significant contributor in countries with high levels of external debt such as Italy,
Spain, and Armenia. However, the main components of aggregate public spending (compensation of employees, intermediate consumption, and social benefits) do not strongly affect
the CA.

18

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23

TABLESTABLE 1
Correlation coefficient between detrended current account and fiscal variables andcyclicality of the current account and government expenditures by group of countries
AllOECD
non-OECD
Income groups
High Income
Middle-Income
Regions
East Asia
Eastern Europe
Latin America
North America
Pacific
South-East Asia
Southern Africa
West and Central Asia
Western Europe

Correlation of the Current Account with
Government Expenditures Fiscal Balance
-0.07
0.00
-0.04
-0.03
-0.11
0.04

Cyclicality Measures
Current Account Government Expenditures
-0.13
0.08
-0.09
0.04
-0.18
0.14

-0.06
-0.10

-0.01
0.02

-0.12
-0.14

0.07
0.12

0.00
-0.16
-0.01
0.24
-0.02
-0.18
-0.16
-0.01
-0.06

-0.05
0.09
-0.02
0.01
-0.09
0.05
-0.10
0.08
-0.06

-0.13
-0.22
-0.18
-0.05
-0.23
-0.13
-0.19
-0.08
-0.04

0.01
0.09
0.20
-0.27
0.19
0.14
0.28
0.08
0.05

Notes: All variables are in real terms per capita, detrended using the Hodrick Prescott filter. The values in the table represent averagecorrelation coefficients over groups (calculated on a country by country basis). Cyclicality measures correspond to the correlation
coefficients with GDP.

TABLE 2
Breakdown of government expenses by category
Government Expenses categories
Compensation of employees
Intermediate consumption
Interest expenses
Subsidies
Social benefits
Other expenses

All subsample
27%
19%
6%
4%
34%
10%

Income group
High Income Middle income
27%
29%
17%
23%
5%
8%
4%
5%
38%
25%
9%
10%

OECD group
non-OECD OECD
30%
26%
24%
15%
6%
7%
5%
4%
27%
40%
10%
9%

Notes: The share of each component is calculated based on the average values per country (based on variables in real terms per
capita). The obtained shares per country are then averaged over groups of countries. For EU countries, data for government expenses
are extracted from the Government Finance Statistics database of Eurostat (based on ESA 2010 standards). For non EU countries,
data are extracted from the Government Finance Statistics database of International Financial Statistics (IMF). Highlighted values
correspond to shares above 10%.

24

TABLE 3
Cyclicality measures, correlation of government expenses categories with the current
account
Government expenses

Income group
OECD group
High Income Middle income non-OECD OECD
Correlation with GDP
0.17
0.02
0.51
0.38
0.03
0.15
0.03
0.41
0.34
0.02
0.12
0.05
0.27
0.15
0.09
0.02
-0.08
0.27
0.19
-0.09
0.00
-0.16
0.34
0.18
-0.13
-0.01
-0.07
0.12
0.03
-0.04
Correlation with the Current Account
0.04
0.05
0.02
0.04
0.05
-0.03
-0.01
-0.09
-0.06
-0.01
0.03
0.03
0.04
0.02
0.03
0.00
0.02
-0.05
-0.10
0.07
0.07
0.08
0.05
0.07
0.06
-0.05
-0.02
-0.10
-0.08
-0.02
All

Compensation of employees
Intermediate consumption
Interest expenses
Subsidies
Social Benefits
Other expenses
Compensation of employees
Intermediate consumption
Interest expenses
Subsidies
Social Benefits
Other expenses

Notes: the values in the table represent average correlation coefficients (calculated on a country by country basis). The underlying
data correspond to cyclical components of the variables per capita, in real terms.

TABLE 4
Correlation of disaggregate government expenditures with the current account (by region)
Compensation Intermediate

Interestof employees

consumption

expenses

East Asia

0.04

0.11

-0.03

0.07

0.003

-0.12

Eastern Europe

0.08

-0.18

0.15

-0.05

0.21

-0.12

Latin America

0.04

0.10

0.03

-0.01

0.07

-0.04

North America

0.06

0.09

0.15

-0.04

-0.01

0.24

Pacific

-0.02

-0.13

-0.28

0.07

-0.09

-0.04

South-East Asia

0.07

0.06

-0.17

-0.26

-0.02

-0.03

Southern Africa

0.24

-0.09

-0.12

-0.17

-0.17

-0.35

West and Central Asia

0.02

-0.02

0.00

0.04

0.11

0.08

Western Europe

0.03

0.01

0.01

0.08

0.02

-0.01

Total

0.05

-0.03

0.03

0.00

0.07

-0.05

Subsidies

SocialOther

Benefits expenses

Notes: the values in the table represent average correlation coefficients (calculated on a country by country basis). The underlying
data correspond to cyclical components of the variables per capita, in real terms.

25

TABLE 5
Variance decomposition of the CA by country after 8 quarters (Independent
Normal-Wishart prior)a. Group 1 (countercyclical)
Countriespi

subAustria
Belgium
CanadaChile
Croatia
Denmark
FinlandFranceGermanyJapan
LatviaLuxembourg
Slovakia
South Korea
SwedenSwitzerland
United States
AverageStandard Deviation

0.03
0.04
0.08
0.07
0.05
0.07
0.14
0.04
0.09
0.15
0.05
0.07
0.08
0.15
0.05
0.06
0.08
0.08
0.04

0.33
0.06
0.16
0.05
0.52
0.08
0.07
0.06
0.14
0.39
0.15
0.07
0.07
0.09
0.05
0.10
0.51
0.17
0.16

Countriespi

sub

Czech Republic
Estonia
GreeceHong Kong
Italy
Lithuania
Netherlands
New ZealandPortugal
Singapore
Slovenia
Spain
United Kingdom
AverageStandard Deviation

0.07
0.08
0.17
0.33
0.36
0.11
0.06
0.09
0.25
0.45
0.11
0.16
0.06
0.18
0.13

0.13
0.14
0.10
0.14
0.04
0.10
0.06
0.10
0.10

comp
ic
sb
High-Income
0.14 0.09 0.02
0.07 0.04 0.05
0.12 0.08 0.09
0.06 0.10 0.05
0.06 0.02 0.03
0.07 0.10 0.08
0.07 0.10 0.03
0.11 0.08 0.05
0.08 0.11 0.06
0.06 0.07 0.02
0.05 0.08 0.04
0.05 0.06 0.03
0.08 0.16 0.05
0.05 0.06 0.08
0.06 0.07 0.02
0.07 0.08 0.06
0.02 0.04 0.05
0.07 0.08 0.05
0.03 0.03 0.02

oth

rate

nfa

gfcf

ca

0.04
0.06
0.08
0.03
0.03
0.03
0.09
0.03
0.02
0.03
0.04
0.03
0.04
0.05
0.04
0.04
0.13
0.05
0.03

0.03
0.05
0.05
0.23
0.02
0.10
0.05
0.03
0.03
0.02
0.14
0.04
0.02
0.16
0.10
0.07
0.03
0.07
0.06

0.15
0.45
0.06
0.09
0.04
0.24
0.17
0.04
0.17
0.01
0.10
0.39
0.05
0.09
0.36
0.23
0.04
0.16
0.13

0.06
0.03
0.03
0.03
0.13
0.08
0.02
0.05
0.03
0.03
0.04
0.03
0.03
0.02
0.02
0.03
0.02
0.04
0.03

0.11
0.15
0.26
0.30
0.10
0.15
0.26
0.51
0.26
0.23
0.29
0.22
0.43
0.26
0.22
0.26
0.08
0.24
0.11

oth

rate

nfa

gfcf

ca

0.06
0.08
0.04
0.05
0.07
0.03
0.06
0.05
0.05
0.03
0.02
0.04
0.08
0.05
0.02

0.04
0.01
0.04
0.05
0.04
0.02
0.05
0.20
0.10
0.05
0.04
0.11
0.04
0.06
0.05

0.29
0.07
0.03
0.04
0.07
0.11
0.22
0.24
0.07
0.09
0.16
0.04
0.30
0.13
0.09

0.02
0.08
0.06
0.02
0.03
0.20
0.31
0.04
0.05
0.02
0.07
0.14
0.02
0.08
0.08

0.11
0.19
0.37
0.30
0.18
0.25
0.11
0.16
0.20
0.23
0.27
0.08
0.20
0.20
0.08

b. Group 2 (acyclical)

0.04
0.26
0.05
0.10
0.06

Bulgaria
0.13 0.19
Colombia
0.11 0.08
Indonesia
0.19 0.04
Romania
0.09 0.14
Thailand
0.17 0.06
Average
0.14 0.10
Standard Deviation 0.04 0.06

comp
ic
sb
High-Income
0.14 0.12 0.03
0.26 0.05 0.03
0.07 0.03 0.08
0.02 0.03 0.03
0.11 0.06 0.04
0.05 0.07 0.05
0.05 0.04 0.05
0.06 0.05 0.03
0.05 0.05 0.09
0.06 0.05 0.03
0.21 0.04 0.03
0.11 0.03 0.04
0.03 0.04 0.17
0.09 0.05 0.05
0.07 0.02 0.04
Middle-Income
0.11 0.13 0.11
0.04 0.04 0.03
0.07 0.06 0.04
0.09 0.07 0.17
0.06 0.02 0.09
0.07 0.06 0.09
0.02 0.04 0.05

26

0.03 0.10 0.04 0.01 0.15
0.03 0.04 0.07 0.06 0.49
0.06 0.05 0.16 0.05 0.29
0.03 0.01 0.02 0.10 0.28
0.02 0.03 0.12 0.06 0.36
0.03 0.05 0.08 0.06 0.31
0.01 0.03 0.05 0.03 0.11

c. Group 3 (procyclical)
Countriespi

subAustralia
0.03 0.12
Hungary
0.05 0.04
Iceland0.10 0.20
Ireland0.04 0.04
Norway0.10 0.06
Average
0.06 0.09
Standard Deviation 0.03 0.06
Armenia0.22 0.07
Belarus0.03 0.05
Bolivia0.05 0.14
Brazil0.14 0.06
Georgia
0.19 0.04
Kazakhstan
0.13 0.10
Mexico0.09 0.09
Moldova
0.10 0.06
Peru
0.05
South Africa
0.06 0.05
Turkey0.10 0.05
Average
0.10 0.07
Standard Deviation 0.06 0.03

comp
ic
sb
High-Income
0.08 0.05 0.19
0.04 0.04 0.03
0.04 0.07 0.05
0.03 0.04 0.04
0.07 0.08 0.03
0.05 0.05 0.07
0.02 0.01 0.06
Middle-Income
0.07 0.07 0.03
0.09 0.04 0.10
0.06 0.03 0.04
0.05 0.04 0.04
0.06 0.09 0.04
0.05 0.04 0.07
0.05 0.08 0.04
0.19 0.03 0.03
0.04 0.11 0.05
0.04 0.13 0.08
0.06 0.09 0.06
0.07 0.07 0.05
0.04 0.03 0.02

oth

rate

nfa

gfcf

ca

0.03
0.03
0.10
0.03
0.04
0.04
0.03

0.14
0.02
0.07
0.04
0.05
0.06
0.04

0.07
0.03
0.18
0.03
0.16
0.09
0.06

0.02
0.03
0.12
0.65
0.05
0.18
0.24

0.28
0.69
0.08
0.06
0.38
0.30
0.23

0.03 0.06 0.19 0.10
0.08 0.04 0.13 0.24
0.04 0.04 0.04 0.07
0.05 0.20 0.09 0.17
0.03 0.17 0.03 0.05
0.04 0.13 0.04 0.02
0.05 0.06 0.25 0.02
0.04 0.05 0.17 0.02
0.05 0.06 0.10 0.16
0.09 0.04 0.10 0.06
0.09 0.05 0.15 0.11
0.05 0.08 0.12 0.09
0.02 0.06 0.07 0.07

0.17
0.19
0.49
0.15
0.31
0.38
0.27
0.32
0.38
0.35
0.23
0.30
0.10

Notes: ca= current account balance, pi= property income , sub= subsidies, comp= compensation of employees, ic= intermediate
consumption, sb= social benefits, oth= other expenditures, rate= interest rate, nfa= net foreign assets, gfcf= gross fixed capitalformation. Data in domestic currency divided by GDP. Group 1 includes countries of the 1st tercile in terms of measures of fiscal
cyclicality defined as the correlation between cyclical components of GDP and government expenditures (corresponding to a fiscal
cyclicality ¡ -0.09). Group 2 is the group of countries of the 2nd tercile in terms of measures of fiscal cyclicality (fiscal cyclicality
measure between -0.09 and 0.05). Group 3 is the group of countries of the 3rd tercile in terms of measures of fiscal cyclicality (fiscal
cyclicality measure above 0.05). Values above 0.2 are highlighted. Cholesky ordering: pi, sub, comp, ic, sb, oth, rate, nfa, gfcf, ca

27

TABLE 6
Variance decomposition of the CA by country after 8 quarters (Litterman-Minnesota prior)a. Group 1 (countercyclical)
Countriespi

subAustria
Belgium
CanadaChile
Croatia
Denmark
FinlandFranceGermanyJapan
LatviaLuxembourg
Slovakia
South Korea
SwedenSwitzerland
United States
AverageStandard Deviation

0.01
0.02
0.08
0.02
0.02
0.05
0.05
0.00
0.04
0.03
0.00
0.05
0.03
0.03
0.01
0.01
0.01
0.03
0.02

0.18
0.04
0.02
0.01
0.23
0.09
0.07
0.01
0.10
0.06
0.08
0.02
0.01
0.05
0.01
0.04
0.02
0.06
0.06

Countriespi

sub

Czech Republic
Estonia
GreeceHong Kong
Italy
Lithuania
Netherlands
New ZealandPortugal
Singapore
Slovenia
Spain
United Kingdom
AverageStandard Deviation

0.08
0.00
0.07
0.00
0.27
0.04
0.02
0.10
0.09
0.01
0.02
0.24
0.00
0.07
0.09

0.08
0.09
0.05
0.00
0.02
0.05
0.05
0.02
0.05
0.00
0.01
0.15
0.00
0.04
0.04

comp
ic
sb
High-Income
0.12 0.02 0.04
0.03 0.03 0.03
0.02 0.01 0.02
0.05 0.02 0.06
0.12 0.04 0.01
0.06 0.03 0.07
0.03 0.02 0.01
0.01 0.04 0.00
0.05 0.08 0.03
0.02 0.01 0.06
0.07 0.01 0.01
0.01 0.01 0.02
0.09 0.11 0.05
0.03 0.01 0.01
0.02 0.08 0.00
0.02 0.01 0.01
0.00 0.01 0.10
0.04 0.03 0.03
0.04 0.03 0.03

oth

rate

nfa

gfcf

ca

0.05
0.04
0.00
0.00
0.04
0.03
0.04
0.00
0.00
0.03
0.02
0.01
0.01
0.01
0.03
0.00
0.18
0.03
0.04

0.00
0.03
0.03
0.06
0.02
0.03
0.00
0.00
0.01
0.00
0.13
0.01
0.00
0.07
0.01
0.00
0.05
0.03
0.03

0.19
0.44
0.02
0.02
0.02
0.23
0.20
0.02
0.07
0.01
0.18
0.49
0.07
0.08
0.28
0.25
0.09
0.16
0.14

0.16
0.03
0.00
0.06
0.17
0.08
0.01
0.01
0.02
0.04
0.02
0.03
0.01
0.03
0.01
0.01
0.11
0.05
0.05

0.23
0.32
0.78
0.69
0.33
0.33
0.58
0.90
0.61
0.74
0.49
0.36
0.61
0.70
0.56
0.65
0.43
0.55
0.18

b. Group 2 (acyclical)

Bulgaria
0.09 0.12
Colombia
0.01 0.01
Indonesia
0.15 0.02
Romania0.02 0.04
Thailand0.04 0.04
Average
0.06 0.05
Standard Deviation 0.05 0.04

comp
ic
sb
High-Income
0.11 0.06 0.01
0.16 0.01 0.07
0.14 0.01 0.00
0.02 0.00 0.00
0.07 0.02 0.08
0.02 0.05 0.01
0.03 0.01 0.03
0.01 0.03 0.00
0.12 0.01 0.14
0.01 0.02 0.00
0.22 0.04 0.02
0.12 0.02 0.06
0.04 0.02 0.05
0.08 0.02 0.04
0.06 0.02 0.04
Middle-Income
0.04 0.10 0.11
0.00 0.01 0.01
0.05 0.00 0.01
0.07 0.05 0.05
0.00 0.00 0.04
0.03 0.03 0.04
0.03 0.04 0.04

28

oth

rate

nfa

gfcf

ca

0.02
0.04
0.00
0.02
0.02
0.00
0.02
0.03
0.02
0.00
0.00
0.00
0.01
0.01
0.01

0.00
0.01
0.01
0.02
0.02
0.02
0.02
0.08
0.02
0.01
0.00
0.06
0.02
0.02
0.02

0.32
0.13
0.00
0.02
0.10
0.09
0.20
0.29
0.04
0.09
0.18
0.00
0.34
0.14
0.11

0.03
0.01
0.02
0.01
0.01
0.18
0.35
0.08
0.12
0.04
0.02
0.08
0.00
0.07
0.09

0.29
0.47
0.69
0.90
0.39
0.54
0.28
0.35
0.38
0.81
0.49
0.26
0.51
0.49
0.20

0.01 0.01 0.01 0.01 0.48
0.00 0.00 0.05 0.03 0.87
0.01 0.04 0.05 0.00 0.66
0.01 0.00 0.06 0.05 0.66
0.00 0.00 0.14 0.05 0.68
0.01 0.01 0.06 0.03 0.67
0.00 0.02 0.04 0.02 0.12

c. Group 3 (procyclical)
Countriespi

subAustralia
0.00 0.02
Hungary
0.15 0.00
Iceland0.13 0.05
Ireland0.01 0.00
Norway0.05 0.01
Average0.07 0.02
Standard Deviation 0.06 0.02
Armenia
0.19 0.02
Belarus0.00 0.07
Bolivia0.03 0.13
Brazil0.07 0.01
Georgia0.03 0.01
Kazakhstan
0.08 0.05
Mexico0.01 0.03
Moldova
0.06 0.02
Peru
0.04 0.00
South Africa
0.00 0.00
Turkey0.07 0.03
Average
0.05 0.03
Standard Deviation 0.05 0.04

comp
ic
sb
High-Income
0.07 0.01 0.13
0.08 0.04 0.01
0.01 0.08 0.04
0.01 0.01 0.02
0.03 0.01 0.00
0.04 0.03 0.04
0.03 0.03 0.05
Middle-Income
0.04 0.00 0.01
0.05 0.00 0.03
0.09 0.00 0.02
0.00 0.01 0.02
0.02 0.08 0.01
0.01 0.01 0.01
0.02 0.01 0.00
0.13 0.01 0.01
0.03 0.01 0.01
0.01 0.01 0.02
0.01 0.08 0.02
0.04 0.02 0.01
0.04 0.03 0.01

oth

rate

nfa

gfcf

ca

0.04
0.02
0.11
0.00
0.04
0.04
0.04

0.04
0.00
0.07
0.00
0.01
0.02
0.02

0.05
0.02
0.15
0.02
0.25
0.10
0.09

0.00
0.06
0.10
0.72
0.01
0.18
0.27

0.64
0.61
0.27
0.20
0.59
0.46
0.19

0.00
0.01
0.00
0.04
0.00
0.01
0.01
0.00
0.02
0.03
0.04
0.02
0.01

0.04
0.00
0.00
0.09
0.11
0.07
0.01
0.01
0.01
0.03
0.08
0.04
0.04

0.09
0.16
0.00
0.13
0.02
0.00
0.15
0.05
0.09
0.04
0.23
0.09
0.07

0.27
0.18
0.00
0.10
0.01
0.00
0.04
0.02
0.10
0.01
0.14
0.08
0.08

0.33
0.49
0.73
0.52
0.71
0.76
0.74
0.69
0.70
0.82
0.31
0.62
0.17

Notes: ca= current account balance, pi= property income , sub= subsidies, comp= compensation of employees, ic= intermediate
consumption, sb= social benefits, oth= other expenditures, rate= interest rate, nfa= net foreign assets, gfcf= gross fixed capitalformation. Data in domestic currency divided by GDP. Group 1 is the sample’s 1st tercile in terms of measures of fiscal cyclicality(¡ -0.09). Group 2 is the 2nd tercile (between -0.09 and 0.05). Group 3 is the 3rd tercile (above 0.05). Values greater than 0.2 arehighlighted. Cholesky ordering: pi, sub, comp, ic, sb, oth, rate, nfa, gfcf, ca

29

Figures
FIGURE 1: Median response of the current account to one unit composite shocks for thewhole sample with bootstrap confidence intervals based on 100 repetitions (aggregate fiscal
data)

FIGURE 2: Median response of the current account to one unit composite shocks by fiscal
cyclicality groups with bootstrap confidence intervals based on 100 repetitions (aggregate
fiscal data)30

Notes: Group 1 is the group of countries of the 1st tercile in terms of measures of fiscal cyclicality defined as the correlation between
cyclical components of GDP and government expenditures (corresponding to a fiscal cyclicality < -0.09). Group 2 is the group ofcountries of the 2nd tercile in terms of measures of fiscal cyclicality (fiscal cyclicality measure between -0.09 and 0.05). Group 3 is thegroup of countries of the 3rd tercile in terms of measures of fiscal cyclicality (fiscal cyclicality measure above 0.05).

31

FIGURE 3: Quartile impulse responses of the current account to a one-unit composite shock
to aggregate government expenditures (by fiscal cyclicality group)

FIGURE 4: Quartile impulse responses of the current account to a one-unit composite shock
to disaggregate government expenditures (by fiscal cyclicality group)a. Group 1 (countercyclical)

Notes: CA= current account balance, PI= property income, SUB= subsidies, COMP= Compensation of employees, IC= intermediate
consumption, SB= social benefits, OTH= Other expenditures. Data in domestic currency divided by GDP. Group 1 contains countries of
the 1st tercile in terms of fiscal cyclicality (measure < -0.09). Group 2 is the second tercile (acyclical economies with measure between-0.09 and 0.05) and Group 3 is the third tercile (procyclical economies with measure >0.05)

32

b. Group 2 (acyclical)

c. Group 3 (procyclical)

33

FIGURE 5: Decomposition of the median composite response of the current account to a
one-unit composite shock to disaggregate government expenditures between common andidiosyncratic responses (by fiscal cyclicality group)a. Group 1 (countercyclical)

b. Group 2 (acyclical)

34

c. Group 3 (procyclical)

Notes: CA= current account balance, PI= property income, SUB= subsidies, COMP= Compensation of employees, IC= intermediate
consumption, SB= social benefits, OTH= Other expenditures. Data in domestic currency divided by GDP. Group 1 is the group ofcountries of the 1st tercile in terms of measures of fiscal cyclicality defined as the correlation between cyclical components of GDP andgovernment expenditures (< -0.09). Group 2 is the group of countries of the 2nd tercile (between -0.09 and 0.05). Group 3 is the the 3rd
tercile (fiscal cyclicality measure above 0.05).

FIGURE 6: Decomposition of the median composite response of the current account to a one
unit composite shock to property income expenditures between common and idiosyncratic
responses (by subgroups of group 1)

Notes: CA= ratio of current account balance/GDP, PI= property income/GDP. Group 1 contains countries of the 1st tercile in terms offiscal cyclicality (value < -0.09). Subgroup (b) includes the countries of Croatia, Denmark, Finland, Luxembourg and Switzerland andsubgroup (a) the remaining 11 countercyclical economies

35

36

Saudi Arabia

TurkeyAlkaswani (2000)

Altintas & Taban (2011)

Hutchison & Pigott (1984)

Holmes (2011)

Hoffmann (2013)

Glick & Rogoff (1995)

Ghosh & Ostry (1995)

Engel & Wang (2011)Enders and Lee (1990)

Darrat (1988)

Chinn & Prasad (2003)

Chihi, Normandin (2013)

Campa & Gavilan (2011)

VAR model, causality tests
OLS

1960-1984

Methodology
VAR (multivariate extension),
Granger causality tests, impulse response functions
Cointegration analysis,
error correction models, granger causality tests
ARDL, cointegration analysis,
Granger causality

1960-1993 (unbalanced)

1974-2010

1970-1999

1979-1985 (monthly)

Period1$ increase in fiscal deficit leads 0.30$ decline in CA surplus

CAB causes FB, bi-directional causality for Malaysia

Twin deficits co-moving together in the long-run

CAB causes FB

Conclusion
Twin deficits are connected through the transmission mechanisms
of interest rates and exchange rates

Previous empirical studies on the twin deficits hypothesis (part 1)

Estimation based on an intertemporal model for the CA,
Model accepted for 6 countries + Derivation of expectations
VAR, Granger causality tests, volatility,
10 euro area countries
1977Q1-2005Q4 (unbalanced)of future income and relative prices underlying the CAB
decomposition, tests for structural breaks
24 developing countries
1960-2005 (unbalanced)
VAR models, covariance analysis
Positive co-movement between the external and budget deficits
18 industrialized
CAB positively correlated with FB
1971-1995
Cross-section and panel regression techniques+ 71 developing economies
and initial shocks of net foreign assets
Granger multivariate causality tests
USA
Quarterly data from 1960 to 1984
Bi-directional causality+ Akaike error criterion
USA
Quarterly data from 1947Q3 to 1987Q1 VAR
Temporary increase in budget deficit worsens CA deficit
Volatility, correlation analysis, simulations based on a
Volatility of imports and exports 3x higher than GDP, imports and
RBC two-sector model including
exports are pro-cyclical, and positively correlated with each other,
25 OECD countries
1973Q1 to 2006Q3
durables and nondurables
net exports are counter-cyclical
5 industrialized
quarterly unbalanced data
Hypothesis of full consumption smoothing accepted
VAR (consumption-smoothing approach)+ 45 developing countries
from 1960 to 1991
for 2/3 of developing economies
Regression analysis using different models for
Positive response of investment to shocks, negative
investment and the current account and global/
G7 countries
1961-1990
response of the CAB only in country-specific shocks
country-specific productivity shocks
Model explains more than 70% of the CA variation,
VAR GMM , structural shocks, and FEVD, based
importance of the consumption titling and smoothing adjustment
on an estimated present-value model with
China
1982-2007
channel based on prices expectations, factors related to China’s
non-tradable goods
domestic financial development are key in understanding global imbalances
US
1960Q1-2008Q4
Markov-switching regimes regression analysis
Long-run positive relationship between FB and CAB
Heuristic, nontechnical analysis based on
Causality from FB to CAB throughUSA
1973-1983
a theoretical framework linking FB to CAB
exchange rates (affected by interest rates)

5 southeast-Asian
Anoruo & Ramchander (1998)
developing economies
USA, Canada, Japan,
Bernheim (1988)
Mexico, UK, Germany

USA

Countries

Abell (1990)

Study

Appendix I

Appendices37

USA, Canada

USA + Canada

India

Pakistan

Miller & Russek (1989)

Normandin (1999)

Otto (1992)

Ratha (2012)

Saeed, Khan (2012)

Absence of co-integration, short-run causality
from budget deficit to trade deficit

ARDL bounds cointegration test
Deterministic + stochastic techniques for separating
the secular components from the cyclical componentsof the twin deficits, + cointegration analysis
VAR, causality tests, GMM
estimations,unit root tests, cointegration tests

1948-1993 (annual data)

GreeceAustralia

Iran

USA

Vamvoukas (1999)

Winner (1993)

Zamanzadeh, Mehrara (2011)

Zietz & Pemberton (1990)

Quarterly data, 1972-1987

1959-2007

1948-1989

1972 to 2011

1971-2009

1972 to 2008

Causality from FB to CAB in short and long-run

Cointegration analysis, VECM and causality tests
Cointegration analysis, error correction models,Granger causality tests

Twin deficits hypothesis accepted, REH rejected

Cointegration analysis,
Vector error correction model (VECM)
2SLS for a structural simultaneous equation
framework including three different
transmission channels

Budget deficit does not explain the 1980s CA deficit.
Transmission channels from FB to CAB are more likely to berevenues and consumption than interest and exchange rates

Rejection of the twin deficits hypothesis,
REH holds for the Australian case

OLS regression

Linear and asymmetriccointegration tests, allowing for structural breaks

Cointegration analysis, Granger causality

the twin deficit hypothesis is accepted by rejecting REH,
CAB granger causes FB
No linear cointegration, existence of asymmetric long-run
effects from FB to CAB
Bi-directional causality between FB and CAB,
long-run effect of FB on CAB

Twin deficit hypothesis in the short-run, REH in the long-run

Rejection of the consumption-smoothing hypothesis

Twin deficits hypothesis confirmed + taking into accountthe stochastic properties of the budget deficit is crucial.

CAB contains unit root after joining Euro zone +presence of statistical association between FB and CAB

Quarterly data, USA 1950Q1 to 1988Q4
VAR estimation based on a life-cycle modeland Canada 1950Ql to 1987Q4.
A bounds- testing approach to
1998-2009 (quarterly and monthly)
cointegration and error-correction modelling

Quarterly data 1950 Q1-1992 Q3

Quarterly, 1946Q1-1987Q3

Tufail, Anwar, Raza & Abbas (2014) Pakistan

Portugal, Ireland,
Italy, Greece, Spain

USA

Litsios & Pilbeam (2017)

Trachanas, Katrakilidis (2013)

Greece, Portugal, Spain

Leachman & Francis (2002)
1980Q2- 2015 Q2

Co-integration and multicointegration

Quarterly data
1974 Q1-1992 Q2

USA

Lane (2003)

Causality from FB to CAB, no co-integration

OLS+Weighted LS

1960–1998

22 OECD countries

Kumhof & Laxton (2013)

Conclusion
Validation of the twin deficits hypothesis, bi-directional causality

Methodology

Higher fiscal deficits lead to a short-run and long-run
deterioration of the current account balance.
Permanently higher fiscal deficits increase interest rates.
Countries with volatile output and dispersed
political power are the most likely to run pro-cyclicalfiscal policies

PeriodQuarterly data from
1972Q1–1987Q4

Previous empirical studies on the twin deficits hypothesis (part 2)
Simulation of an increase in fiscal deficits ina derived open economy business cycle modelwith finite-horizon households

8 countries

Countries
VAR

Kearney and Monadjemi (1990)

Study

Appendix II: Data description
Data have been extracted from the following sources, in millions of domestic currency, usedin real terms and divided by the population (population data from the IFS)
Variable
Gross Domestic
Product

Data source
Data in real terms from the IMF’s
International Financial Statistics(IFS) database

Net Foreign Assets

IMF- Balance of Payments andInternational Investment Position
database
Data in nominal terms from Datastream (DS mnemonic = ”countrycode” & CURBALA)

Current accountbalance

General government consumption expenditure

Gross fixed capital formation
Privatesumptioncon-

Short-term interest rates

Data in real terms from Datastream (DS mnemonic: ”countrycode” & XGCSA.D, if absent data
without seasonal adjustment taken
instead, DS mnemonic: ”countrycode” & XGCSA.C)
Data in current prices from
Datastream (”country code” &GFCF.C), or from the IFS
Data in current prices from Datastream (”country code” & CNPER.D), or from the IFS
Short-term interest rates from IFS
(IMF)

38

Adjustment
Gaps completed using nominal
series and GDP deflator calculated from annual series, or using
growth rate of annual real series(in absence of nominal quarterly
data)

Deflated using calculated GDP deflator. For data only available in
US dollars, data for other variables
converted to US dollars basedon PPP (from the World Bank
database)

Converted to real terms using a deflator calculated based on annual
nominal and real series from IFS

Gaps completed based on Shortterm interest rates from Datastream (OECD: ”country code” &OCFISTR, Oxford economics:”country code” & XRCB..R)or the Policy rate (Datastream:”country code” & PRATE.) or the
Money Market rates (IFS)Variable
Fiscal balance

Data source
Data in current prices from Datastream (”country code” & GOVBALA) or from the IFS

Adjustment
Deflated using calculated GDP deflator

Disaggregate fiscal data (European Union)

Government
Finance
Statistics(Eurostat)
http://ec.europa.eu/eurostat/data/database (access from website:
General and regional statistics >>Economy and Finance>>Government Statistics)
Government
Finance
Statistics (IMF), ”Expense” dataset
http://data.imf.org/?sk=
3C005430-5FDC-4A07-947464D64F1FB3DC

Deflator calculated from real
Government consumption expenditures series and nominal
series extracted from IFS (whenunavailable, calculation is madefrom annual data)

Disaggregate fiscal data (non-EU
countries)

Deflator calculated from real
Government consumption expenditures series and nominal series
extracted from IFS, averaged
yearly (when unavailable, calculation is made from annual data)

Additional adjustments
For disaggregate fiscal data of non EU countries which were available on an annual basis, we estimated quarterly data using the tool of temporal disaggregation from JDemetra+ software17 (provided by Eurostat). The
quarterly values were estimated using quarterly GDP as the higher frequency indicator series. The chosen approach was the Chow-Lin method (Chow and Lin,1971), based on an autoregressive model of order 1 for theerror vector. After those adjustments, the resulting dataset is a balanced panel of 51 countries over the period
2002Q1-2018Q4.

Appendix III: Description of government expenses categories
Government expenditure comprises the following ESA categories. These definitions have been taken from the
ESA 2010 manual (Chapters 3 and 4).
• P2 Intermediate consumption: intermediate consumption consists of goods and services consumed as
inputs by a process of production, excluding fixed assets whose consumption is recorded as consumptionof fixed capital. The goods and services are either transformed or used up by the production process.
• P5 Gross capital formation: includes in addition gross fixed capital formation, changes in inventories
and acquisitions less disposals of valuables• D1 Compensation of employees: defined as the total remuneration, in cash or in kind, payable by an
employer to an employee in return for work done by the latter during an accounting period. It includes
wages and salaries in addition to social contributions such as pensions
17

http://ec.europa.eu/eurostat/cros/content/download en (replacing old Ecotrim)39• D29 Other taxes on production, payable: consist of all taxes that enterprises incur as a result ofengaging in production, independent of the quantity or value of the goods and services produced orsold.
• D3 Subsidies, payable: current unrequited payments which general government or the institutions of
the European Union make to resident producers.
• D4 Property income, payable: property income (D.4) accrues when the owners of financial assets andnatural resources put them at the disposal of other institutional units. The income payable for the useof financial assets is called investment income, while that payable for the use of a natural resource iscalled rent. Property income is the sum of investment income and rent (e.g. interests and dividends).
• D5 Current taxes on incomes, wealth, etc.: “current taxes on income, wealth, etc.” (D.5) cover allcompulsory, unrequited payments, in cash or in kind, levied periodically by general government and by
the rest of the world on the income and wealth of institutional units, and some periodic taxes which areassessed neither on that income nor that wealth.
• Social benefits: social benefits include the following:– D62 Social benefits other than social transfers in kind: made up of:– Social security benefits in cash: social security benefits in cash are social insurance benefits
payable in cash to households by social security funds. Reimbursements are excluded and treatedas social transfers in kind (D.632).
– Other social insurance benefits: other social insurance benefits correspond to benefits payable byemployers in the context of other employment related social insurance schemes. Other employmentrelated social insurance benefits are social benefits (in cash or in kind) payable by social insurance
schemes other than social security to contributors to the schemes, their dependents or their survivors.
– Social assistance benefits in cash: social assistance benefits in cash are current transfers payable
to households by government units or NPISHs to meet the same needs as social insurance benefits
but which are not made under a social insurance scheme requiring participation usually by meansof social contributions.
• D632 Social transfers in kind- purchased market production: individual goods and services in theform of reimbursements by social security funds of approved expenditures made by households on
specific goods and services; or provided directly to the beneficiaries by market producers from which
general government purchases the corresponding goods and services.
• D7 Other current transfers: include net non-life insurance premiums, non-life insurance claims,
Current transfers within general government, Current international cooperation, miscellaneous current
transfers and VAT- and GNI-based EU own resources.

40• D8 Adjustment for the change in pension entitlements: the adjustment for the change in pension
entitlements (D.8) represents the adjustment needed to make appear in the savings of households thechange in the pension entitlements on which households have a definite claim. The pension entitlementchange comes from contributions and benefits recorded in the secondary distribution of income account.
• D9 Capital transfers, payable: capital transfers require the acquisition or disposal of an asset, or
assets, by at least one of the parties to the transaction. Whether made in cash or in kind, they result in
a commensurate change in the financial, or non-financial, assets shown in the balance sheets of one orboth parties to the transaction.
• NP Acquisitions less disposals of non-produced assets: non-produced assets consist of assets thathave not been produced within the production boundary, and that may be used in the production of
goods and services. This includes acquisitions of natural resources, contracts/leases/licenses, goodwill
and marketing assets.

Appendix IV: Methodology for estimating individual Bayesian VAR models by country
The time-series Bayesian VAR approach used on the paper is based on independent normal-Wishart priors
with Gibbs sampling, from which we derive orthogonalized impulse response functions and the corresponding
variance decomposition. The independent normal-Wishart priors set for β and Σ (respectively the vector ofparameters and the residual variance-covariance matrix) areβ ∼ N (β0 , V0 )
Σ ∼ IW (S0 , α0 )β0 is a vector of nearly all zeros except the diagonal elements corresponding to coefficients of a variable's
first own lag (hyper-parameter µ1 ) that can be set to a different value, usually 1 for a random walk or lessfor a AR(1) process. In the present case, we set the prior means to zero. For V0 , we use the following
hyperparameters λ1 = 0.1, λ2 = 0.5 , λ3 = 1 and λ4 = 100 , common in the literature. The residual standard
deviations of the variables are calculated based on an unrestricted least square estimate. Conditional posterior
distributions for the dataset y are(β|y, Σ) ∼ N β̄, V̄(Σ|y, β) ∼ IW S̄, ᾱ
where


−1
−1
V̄ = V0 −1 + Σ̂−1 ⊗ (X 0 X) β̄ = V̄ V0 −1 β0 + Σ̂−1 ⊗ X 0 y
S̄ = S0 + E 0 E
ᾱ = α0 + T

41

where S0−1 = λ1 I, V0 −1 = λ2 I, and α0 = λ3 . With the residual matrix E = Y − XB for β = vec (B),y = vec (Y ) and X the regressors matrix. The scale matrix S̄ and the degrees of freedom ᾱ are calculated
based on prior error variance of endogenous variables S0 18 and prior degrees of freedom of the error-term
α0 . We set the scale to be equal to 0.00001 and the prior degrees of freedom to be equal to 10 (number ofendogenous variables). Based on an initial estimate of Σ, a Gibb’s sampler is used to obtain properties of theunconditional posteriors with 10000 iterations and 200 burn-in draws. As a robustness check, we also estimate
the same model using the Litterman-Minnesota prior based on hyper-parameters λ1 = 0.1, λ2 = 0.99 , λ3 = 1
and determination of the residual variance-covariance matrix from univariate autoregressive estimates.

18

Following Karlsson (2012), S0 can be set to be the diagonal variance covariance matrix obtained fromindividual AR regressions.

42

Appendix V: Orthogonalized impulse response functions by country
I. Response of the CA to a shock to Compensation of employees

43

44

II. Response of the CA to a shock to Intermediate Consumption

45

46

III. Response of the CA to a shock to Social Benefits

47

48

IV. Response of the CA to a shock to Subsidies

49

50

V. Response of the CA to a shock to Property Income51

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