DP2015-08

Cro ss-Country Evidence on Determinants of
Fiscal Policy Effectiveness:
The Ro le of Capital Flows and a Country'sInternational Trade and Financial Position

Agata WIERZBOWSKA
Masahiko SHIBAMOTO
Revised March 25, 2015

CrossCross-country evidence on determinants of fiscal policy effectiveness:
The role of capital flows and a country’country’s international trade and financial position
Agata Wierzbowska*, Masahiko Shibamoto†

Abstract
This paper studies the determinants of size differentials between fiscal multipliers incountries around the world, both advanced and developing economies. Besides domestic
conditions and exchange rate regimes, we also introduce variables not before considered
for explaining multiplier size differentials such as capital flows and the openness ofcapital markets. We also disaggregate GDP into its main components in order to
identify the channels through which external and internal factors can influence GDP
after a change in fiscal policy. Our results point to the existence of a new channel
through which fiscal policy effectiveness is affected. Capital flows, especially FDI flows,
play an important role in determining the sizes of fiscal multipliers, and a country’s
external conditions largely explain GDP changes after fiscal expenditure shocks. Our
results also point toward a strong link between a country’s international position and its
real economy.

JEL Classification: E62; F41.
Keywords: Fiscal policy; Government spending multiplier; Fiscal transmission
mechanism; Cross-country heterogeneity; International linkage.

* Corresponding author: Graduate School of Economics, Kobe University, 2-1, Rokkodai, Nada, Kobe,
657-8501, Japan. E-mail: agata.wierz@gmail.com.
† Reseach Institute for Economics and Business Administration, Kobe University, 2-1, Rokkodai, Nada,
Kobe, 657-8501, Japan. E-mail: shibamoto@rieb.kobe-u.ac.jp. Tel/Fax: +81-78-803-7008.

1.

Introduction

The role and effectiveness of fiscal policy have interested both policymakers andeconomists for a long time. The outbreak of the global financial crisis reopened
discussion about the role of fiscal policy. Recent years have seen governments in many
countries using fiscal expenditure to stimulate their economies and fend off bigger falls
in output. This spending has resulted in high deficits and debt levels, compelling
governments to turn to austerity measures.
Studies on the determinants of fiscal policy effectiveness in this context are of
twofold importance. If fiscal policy is carried out effectively, similar results can beachieved with less expenditure of resources. In times of necessary fiscal expansion, thiswould mean lower budget deficits. When a government takes austerity measures,
knowledge of the conditions for fiscal effectiveness helps the government cut fiscal
spending and balance budgets without significant output slowdowns.
In investigating the efficacy of fiscal policy quantitatively, an essential step is toisolate the ‘policy-induced’ changes in fiscal expenditure, the so-called fiscal expenditure
shocks, in actual economies. There is a large literature which proposes the econometricmethodology to identify fiscal expenditure shocks. A good number of studies, such as
Kuttner and Posen (2001), Blanchard and Perotti (2002), Mountford and Uhlig (2009),
and Fatas and Mihov (2001), employ a structural vector autoregressive (VAR) model toestimate the effect of fiscal expenditure shocks on macroeconomic variables. As theyargue, the structural VAR model is one of useful empirical tools to distinguish fiscal2policy shocks from the automatic effects of unexpected movements in activity on fiscal
variables.1
While the literature on fiscal policy expenditure concentrates on the problem ofidentifying fiscal policy shocks, or on the strength of its effects without explaining thedeterminants of its effectiveness or lack thereof, papers concentrating mainly on thedeterminants of the effectiveness of fiscal policy expenditure are rather scarce.2 In aliterature review on the subject, Hemming et al. (2002) presents both a theory andempirical research results on fiscal policy and its effectiveness. They focus on the size offiscal multipliers, the situations when multipliers can become negative, and thecrowding out effect. In a more recent paper, Ilzetzki et al. (2013) analyse thedeterminants of fiscal multipliers of different sizes across countries. They show that
degree of development, exchange rate flexibility, openness to trade, and debt burden are
the main determinants of fiscal multiplier size.
Yet the analysis of fiscal policy can no longer be carried out only from a domestic
perspective. Increases in global financial flows, the development of international
financial markets, and the international expansion of companies change the whole1

An alternative approach bases on the narrative evidence and focuses on the changes in fiscal

policy expenditure at the specific events not related directly with a country’s economic conditions. Thatis, it takes unanticipated changes in fiscal policy as a proxy of the fiscal expenditure shocks. For
instance, Ramey and Shapiro (1998) use narrative evidence on military spending to construct fiscal
shock variables. Ramey (2011a) compares results based on VAR shocks with the results achieved withshocks based on narrative evidence. While both approaches intend to control for economic situation in
the process of fiscal shocks’ identification, the results might differ depending on the approach used,
resulting in the continued discussion in the literature on the appropriate identification methodology.
2

Ramey (2011b), while briefly reviewing both theoretical and empirical literature on fiscal policy

effects, mentions mostly identification methodology and the effects of fiscal policy shocks.

3economic situation of a country, and with it the country’s methods for carrying out
economic policies. Fiscal policy is no exception.
In this paper, basing on the VAR methodology to identify fiscal expenditure shocks,
we first calculate cumulative fiscal multipliers as measure of fiscal policy effectivenessand then compare the results across countries. We consider the theoretical
determinants of fiscal policy effectiveness studied in previous research and mentioned
in textbooks, in order to identify the reasons for divergent fiscal policy effects. The
variables previously used, however, have described mainly domestic economic
conditions and the international stances of countries and do not in themselves seem tosufficiently explain differences in fiscal spending effectiveness across countries. Thus,
we go further and search for new facts that might serve as determinants of fiscal policy
effects.
Noticing the lack of studies trying to link fiscal policy and advancing globalization
effects, we attempt to find the determinants of fiscal policy effectiveness against thisbackground. While controlling for the factors used in the previous research, we
introduce new variables describing a country’s international financial situation andtrade position, factors not yet considered in this context. We also take interest in thepotential channels through which these variables can determine the influence of fiscal
policy on domestic GDP. To check this point we examine how the potential determinantvariables affect fiscal policy impacts on the four main GDP components: consumption,
investment, exports, and imports.
Our main results single out net capital inflows as one of the most important
determinants of fiscal policy effectiveness. When combined with capital accountopenness as a robustly significant variable, this result implies that a country’s capital4

account, and by extension its financial position, play a significant role in determining
fiscal policy effectiveness. FDI flows, however, turn out to be the most meaningful out of
the capital flows analysed, which suggests that sources of divergent government
expenditure effectiveness lie in the real economy, as well. Our results thus point to a
new channel through which fiscal policy effectiveness is affected, connecting a country’s
real economic situation domestically with its international financial position. We alsofind in an analysis of GDP components that export and import fiscal multipliers aremostly affected by the same variables affecting the GDP as a whole. This reinforces our
finding that a country’s external conditions strongly influence the effectiveness of fiscal
policy on GDP.
The rest of this paper is constructed as follows. Section 2 presents the empiricalmethodology we use to find the determinants of fiscal expenditure effectiveness. Section
3 describes our data and some stylized facts about them. Section 4 presents andinterprets our estimation results. Section 5 analyses the determinants of fiscal
multipliers for the main GDP components. Section 6 concludes.

2.

Empirical methodology

This section describes the econometric methodology we use to find determinants of theeffectiveness of fiscal policy. First we use the VAR model to extract cumulative fiscal
multipliers by country. Next we carry out OLS regressions to determine the variables
influencing the sizes of the multipliers.

2.1. VAR specification

5

Our econometrical strategy starts with a simple VAR framework. First, in eachcountry i , we construct the following structural VAR model consisting of two variables:
government spending (, ) and real GDP (, ):Bi ( L) X i ,t = bi ,0 + εi ,t ,(1)where X i ,t = (g i ,t , yi ,t ) , bi ,0 is a constant vector, Bi ( L) = Bi ,0 − Bi ,1 L − ⋯ − Bi , p L
'

p

is a

p th order lag polynomial of a two-by-two coefficient matrix Bi ,m ( m = 0,⋯ , p ), andεi ,t = (εig,t , εiy,t )' is a vector of serially uncorrelated structural disturbances with a mean
zero and a covariance matrix I . The two-variable model is estimated with 4 lags.
The structural model above can be described by the following reduced-form VAR:

Ai ( L) X i ,t = ai ,0 + ui ,t ,(2)where ai ,0 is a constant vector, Ai ( L) = I − Ai ,1L − ⋯ − Ai , p L

pis a p th order lag

polynomial of two-by-two coefficient matrix Ai ,m ( m = 1, ⋯ , p ), and ui ,t = (ui ,t , ui ,t ) is agy'vector of serially uncorrelated reduced-form innovations with a mean zero and acovariance matrix Σui . We use Cholesky decomposition of the reduced-form covariancematrix Σui to identify structural shocks.3
In our VAR model we place government spending before real GDP. We think it stands
to reason that changes in government spending, a component GDP, influence GDP
contemporaneously, and that GDP shocks have only a lagged impact on the spending.

3

We also extend the model to a four-variable version by adding the ratio of the current accountbalance to the GDP (CAt) and real effective exchange rate (REERt), then estimating the model with
two lags for each variable.

6Based on the described VAR specification, we retrieve impulse-response functions forgovernment spending shocks. Then, using real GDP and government spending
responses to a government spending shock, we follow Mountford and Uhlig (2009) andcalculate cumulative multipliers at time H in country i , defined as:H−h∑(1 + r ) yˆ

CM i ( H ) = hH=0

i

i,h

,

∑ (1 + r ) gˆ
h =0

where

,

(3)

−h

i

i ,h

is the estimated response of real GDP to a government spending shock at

period h = 1, ⋯ , H

in country i ,gˆ i , h is the estimated response of the fiscal

expenditure to a government spending shock at period h = 1, ⋯ , H in country i . As adiscount factor ri , we use the sample period median of each country’s bond yield.4

2.2. Cross-country regressions
In the next step we move on to explaining the sizes of the estimated calculated
multipliers of country i , CM i ( H ) (8th period cumulative multiplier in our benchmark
case, H = 8 , for example). To do so we estimate a linear regression model of the form:

CM i ( H ) = β 0 + β1Z1i + ⋯ + β k Z ki + ei ; i = 1,⋯ , N ,where

Z1i ,⋯ , Z ki are candidate(4)

k cross-country variables representing thecharacteristics of country i which can explain the differences of the size of the fiscal
multiplier among countries, and4is the error term.

The results change little, however, if no discounting takes place.

7

We estimate coefficients

β 0 , β1 ,⋯ , β k using Ordinary Least Squares (OLS)

regression with White’s (1980) heteroskedasticity-consistent standard errors.

3.

Data and stylized facts

VAR models are estimated with data of quarterly frequency. All the data are seasonallyadjusted, and all are expressed in logarithms except for the ratio of the current accountbalance to GDP. For model estimations we use datasets for 44 countries, both advanced
and developing economies. The data period varies from country to country, with samples
starting between the years 1980 and 2000 and ending in 2013. A country list withindividual sample periods is provided in the appendix.
Based on the VAR models, we calculate cumulative fiscal multipliers as explained
above and choose 8th period multipliers as dependent variable for our benchmark
cross-country regressions. Then we use 4th period multipliers for a robustness check.
Table 1 presents summary statistics for the 8th and 4th period cumulative multipliers
calculated with a 2-variable VAR model.
The analysis of our estimated cumulative multipliers tells us that fiscal policy effects
do not reach their peak shortly after the shock but tend to have a more prolonged andrising impact on the economy for at least two to three years. The average cumulative
multiplier is higher in the 4th period, one year after the fiscal shock, than in the 8th
period, two years after the shock, but this is very likely to be due to the very low
minimum values in the 8th period. We can see that the maximum value and minimum
value are both higher (in absolute terms) in the 8th period. The median value andstandard deviation are also higher two years after the shock. Thus, fiscal policy steps

8should be planned and taken not from a short-term, but rather a middle-term,
perspective.
We intend to explore the determinants of fiscal policy effectiveness by looking for
explanations for fiscal multiplier sizes using a wide range of macroeconomic andfinancial variables. We follow Ilzetzki et al. (2013) and include in our regression proxies
for a country’s degree of development, exchange rate flexibility, openness to trade, andgovernment debt. We also introduce variables controlling for international finance,
trade balance, and euro adoption.
The development level is proxied by a dummy variable taking the value 1 if a country
is considered an advanced economy by the IMF. The exchange rate regime is also adummy variable, based on the de facto classification of Ilzetzki et al. (2009) taking 1 for
inflexible exchange rate regimes. 5 Openness to trade is measured by the ratio ofsummed exports and imports to GDP. Government debt and trade balance are also
formed as ratios to GDP. International finance is characterised with two variables: theratio of net capital inflows to GDP and the Chinn and Ito index (KAOPEN) measuring
capital account openness. We also introduce a dummy taking the value 1 for countries
that became members of the euro area in the year 1999 (plus Greece, which joined in2001). Detailed descriptions of the data and its sources are provided in the appendix.
We need data that can be used in cross-country analysis. Yet most of the variables wehave are time series. To adjust we take sample period averages of the time series to use
in the regressions. Later, we also use the 2000 year values for the robustness check. In
the case of dummy variables, dummy values as of the year 2000 are included in the5

As fixed exchange rate regime countries we include countries with no separate legal tender, preannounced pegs, currency board arrangements, crawling pegs, or de facto or pre-announced bands orcrawling bands with margins no larger than ±2%.

9regression, with robustness checks for different periods carried out later. The year 2000
seems to be a good point for taking the values of the variables that cannot be averaged,
as it does not include the most recent changes and is available even for the shortest
samples.
Table 2 contains the correlations of our cross-country explanatory variables

Z1i ,⋯ , Z ki . Few of our explanatory variables show high correlations with each other.
The biggest exception is the developed country dummy, which shows high correlations
with most of the other variables. Yet the high correlations of the dummy with many
variables demonstrate the characteristics of the advanced economies quite well andthus pose no problems for the regressions. Specifically, we observe that developed
countries have more open capital accounts and lower net inflows than developing
economies. The latter characteristic may be readily explainable, as the developed
countries with high gross capital inflows are also major capital exporters. The highcorrelation coefficient with the monetary union dummy derives from the fact that all
euro area members are classified as advanced economies. We can also see thatdeveloped countries are usually highly indebted relative to their GDPs. The high debt
levels may derive from the typically higher credibility of these countries and theirability to borrow more money on the market at lower cost. We can observe a similar
relation for the monetary union countries. The high correlation coefficient between the
MU dummy and exchange rate dummy can be explained by our treatment of euro area
countries as countries with fixed exchange rate regimes.
Meanwhile, we find no high correlations between KAOPEN and net inflows, no highcorrelations between KAOPEN and the other variables, and no high correlations
between net inflows and the other variables. One noteworthy finding is the inverse
10

correlation between capital account openness and net inflows. This tells us that lower
capital account openness is associated with higher net capital inflows, and vice versa.
This may point at the fact that capital account restrictions are not always symmetricaland might often restrict capital outflows more than capital inflows. We also see in ourcorrelation matrix, reassuringly, that capital account openness and trade openness arealmost wholly uncorrelated. The introduction of the KAOPEN index into the regression
clearly brings in new information not included in the trade openness variable.

4.

Empirical results

This section presents empirical results based on our econometrical framework. We begin
by presenting benchmark model results. Then we introduce more detailed analyses forcapital flows.

4.1. Benchmark model results
In our benchmark model we regress the 8th period cumulative multipliers with thefollowing variables: developed country dummy, exchange rate regime dummy, monetary
union dummy, capital account openness (KAOPEN), net capital inflows to GDP,government debt to GDP, trade to GDP, and trade balance to GDP. Columns (1) to (10) of
Table 3 present the estimated coefficients. 6
The first points to note are the high values of R2 and the adjusted R2 coefficients inthe benchmark specifications. Values over and just under 50% show that the chosen

6

In the robustness section of the appendix we carry out the analysis for 4th period cumulative

multipliers.

11

explanatory variables fit well and that the model we use explains much of the variationof the dependent variables.
The estimation results imply that capital account openness and net capital inflows
are the most important determinants of fiscal policy effectiveness. The coefficient of net
capital inflows is high and statistically significant both in the case of full regression andin the case of regression with capital inflows as the only regressor beside the constantterm. Note, also, that the very high R2 coefficient for the latter regression implies thatnet capital inflows account for much of the fiscal multiplier difference across countries.
The sign of the coefficient on the net inflows variable implies that higher net capital
inflows diminish the effects of fiscal expansion. That is, the effectiveness of the fiscal
policy dwindles as the capital entering a country increases relative to outflow. This
stands to reason, because foreign capital looks for the most productive investment
opportunities. As a consequence, large amounts of foreign capital entering a country
force government spending to either concentrate on less productive projects or to crowdout private capital. Either way, the fiscal policy becomes much less effective. Note thatthe interpretation we provide here is only general and preliminary. The net inflows
variable represents the wide range of divergent capital inflows and outflows. It would be
impossible to state exactly what influences the fiscal policy expenditure effectiveness, orhow, based on this one result alone. This limitation as well as the high value of thecoefficient, compels us carry out a more detailed analysis of the capital flows later.
We also find that the effects of fiscal policy weaken in countries as their capital
accounts open further. We have confirmed this result in both kinds of regressions
carried out. The effect of higher capital account openness in pushing down fiscal
multipliers is consistent with the theory. The IS-LM framework holds that a complete
12crowding out takes place in a state of perfect capital mobility, rendering fiscal policy
ineffective. According to the above explanation, that is certainly true when an open
capital account leads to higher net capital inflows.
We also find evidence that fiscal policy is more effective in developed countries thanin developing ones. This pattern holds in regression (9). It follows the findings ofIlzetzki et al. (2013), but stands in opposition to some other research identifying higher
fiscal multipliers in developing countries. This result is difficult to interpret for the
moment, but we will return to it after further analysis.
Our data also provide evidence that a country’s trade balance influences theeffectiveness of its fiscal policy expenditure. The coefficient is positive, which implies
that a higher trade surplus supports the effectiveness of fiscal policy expenditures. We
will leave our interpretation for this result for later, just as we do for our earlier resulton the developed country dummy.
Meanwhile, we find no evidence supporting the significance of other variables
studied by Ilzetzki et al. (2013) or other empirical research on the subject. All of themfall short of statistical significance, even when included in our regressions as sole
explanatory variables. Most important, we find no evidence that an exchange rate
regime influences fiscal multipliers, a notion often asserted in the literature on thedeterminants of fiscal policy effectiveness. Also, the government debt to GDP ratio
leaves the fiscal multiplier size unaffected, both for benchmark regression andindividual regression for each variable. In the cases of trade openness, our results
directly oppose those of Ilzetzki et al. (2013). We find that fiscal multipliers are higher
in economies more open to trade.

134.2. Gross capital flows analysis
In studying the results of our benchmark specifications, we observe that the ratio of net
capital inflows to GDP is one of the most important factors influencing the size of fiscal
multipliers. Earlier we mentioned that the net capital inflows variable is very generaland that a detailed interpretation based solely on that variable would be difficult toprovide. Thus, the first question we encounter is whether gross capital flows can better
explain the level of fiscal policy effectiveness. That is, we want to check whether thelevels of capital flowing out of or into a country are important for the fiscal multipliers,or whether the difference between the outflows and inflows of capital plays a big role.
We check this by carrying out regressions similar to before, substituting net capital
inflows with gross capital inflows and gross capital outflows.
Before presenting regression results, we report correlation coefficients for the two
new explanatory variables presented in Table 4. We first notice a very high correlation
between gross capital inflows and gross capital outflows. This tells us that countries
with high capital inflows are also countries that have high capital outflows. Meanwhile,
both of the new explanatory variables correlate weakly with the developed country
dummy. These weak correlations may be attributable to the tendency of developing
countries to have higher gross flows as a ratio of GDP, a tendency partly explainable bytheir lower GDPs. The higher correlation with outflows versus inflows in the developed
country dummy shows that these countries often export more capital than they import.
Gross capital inflows and gross capital outflows both have low positive correlations
with capital account openness. This finding shows that higher openness stimulates
capital flows in both ways, albeit not to a considerable degree. The correlation
coefficient is higher for capital outflows, which means that the abolishment of14

restrictions stimulates gross outflows more than inflows. This pattern may be connectedto the point we made earlier, namely, the tendency of capital account restrictions to bestricter for capital outflows than inflows. Therefore, the opening of the capital accountwill naturally signify a higher increase in outflows than in inflows.
Here we check the importance of gross capital flows for fiscal policy effectiveness bycarrying out three types of regression. First we introduce gross inflows and grossoutflows, separately, to the regressions with other explanatory variables. Then we use
both variables at the same time in our specification.
Table 5 presents the results. When using only gross inflows or gross outflows, none ofthese variables are significant for the size of the fiscal multipliers. Fiscal policy
effectiveness cannot be explained by looking at just one of these variables. And if wesubstitute net capital inflows with just one of the gross values, we also observe big falls
in the R-squared and adjusted R-squared statistics.
The situation changes completely when we introduce both gross inflows and outflows
to the model. We observe that both variables are significant, with high coefficients of
opposite signs. The negative sign of the coefficient on gross inflows informs us that the
inflow of capital causes a decline in fiscal multipliers. Gross capital outflows, meanwhile,
stimulate the effectiveness of fiscal policy. These results are consistent with ourinterpretation of the results for net capital inflows. When foreign capital enters acountry, it chooses the most productive opportunities, leaving less space for government
spending. The capital outflow frees that space, allowing fiscal stimulation to exert more
effects. One has to be careful with these results, however, given the very highcorrelation coefficient between gross capital inflows and gross capital outflows.

15

We can judge, in summary, that the important variables to consider when forecasting
the effects of fiscal policy are not limited to gross inflows or gross outflows, but include
net capital inflows as well. What matters is not only the gross inflow or gross outflow ofcapital in a country, but also the difference between the inflow and outflow.

4.3.

Detailed analysis of capital flows

The above estimation results show that just gross capital inflows or gross capitaloutflows are insufficient to explain differences in fiscal multiplier sizes. We therefore
carry out an even more detailed analysis dividing capital inflows and outflows according
to the IMF classification into FDI, portfolio, and other flows. We carry out regressions ofour benchmark specification with each variable substituting net capital inflows. We also
introduce net inflows for each category.
Before moving to the regression results we will turn to the correlations of thedetailed capital flow variables. As Table 6 shows, few of the capital flow variables showhigh correlations with the other explanatory variables. The biggest exception is the highnegative correlation between net FDI flows and the developed country dummy. This,
like the previous analysis, points to the basic characteristics connected to a country’s
development level and implies that net FDI flows are lower in advanced economies andhigher in developing ones.
We can observe high correlations between outflows and inflows of the same category.
The correlation is highest for other capital flows, followed closely by portfolio flows. The
correlation between outflows and inflows is the lowest among the FDI flows.
Because we carry out regressions that include all categories of inflows and outflows
at the same time, we also check correlations for all types of inflows and outflows with16

each other. In doing so, we find high correlation coefficients between portfolio outflows
and other outflows. The values are less pronounced for the rest of the categories, so we
assume there are no problems with carrying out this kind of regression.
Tables 7-10 present the results of OLS regressions. On the whole, the results imply
that fiscal policy effectiveness is influenced not by capital inflows, but capital outflows.
None of the inflow variables considered are statistically significant. We achievesignificant results, on the other hand, for FDI outflows, as well as for all outflow
variables when they are included in the model at the same time.
The results also show that the FDI flows are the most important determinant offiscal policy effectiveness. FDI outflows and net FDI variables are both statisticallysignificant, and the coefficient is the highest among the types of outflows considered.
The signs imply that higher FDI outflows increase the effectiveness of fiscal policy
expenditure while higher net FDI inflows diminish it.
Portfolio flows, on the other hand, cannot be considered determinants of fiscal
multiplier sizes. Portfolio outflows, inflows, and net values all fall short of statisticalsignificance. We only find a significant coefficient when outflows of all types are put into
one regression. The negative sign implies that higher capital outflows increase fiscal
policy effectiveness. Yet none of the coefficients in the individual regressions are
significant, so we conclude that there is no direct connection between portfolio
investment and fiscal expenditure effectiveness.
We find evidence, however, that other capital flows are important. Net other flows
are statistically significant with a high negative coefficient. This implies that higher net
inflows of other kinds of capital lead to decreases in fiscal multipliers. The variable“other outflows” is also significant when put into regression together with the outflows
17

of the other two types. A positive coefficient sign means that fiscal spending is more
effective when there is a higher outflow of other kinds of capital. The interpretation ofthese results, however, is convoluted. The “others” category consists of flows of different
types such as bank lending or official flows, and the type of flow that actually influences
fiscal policy effectiveness is hard to determine within the wide range of possibilities.
We can also see that the trade balance variable is quite robust in our specification.
This finding confirms our previous observation that fiscal policy tends to be more
effective in countries with trade surpluses. A country has a trade surplus when it earns
more from its exports than it spends on its imports. Hence, companies within such a
country have excess capital. The national income identity implies that countries withcurrent account surpluses finance the current account deficits of their trade partners bylending to them. Capital therefore flows out from the country, and we assume (based onother results) that the outflow raises fiscal policy effectiveness.
On the whole, our results point at a completely new channel through which
effectiveness of fiscal policy is affected. The significance of the FDI outflow and tradebalance variables lets us conclude that fiscal policy is more effective in countries withinternationally strong companies and the capacity to work out trade surpluses andinvest those surpluses abroad. This is so because companies that generate high export
revenues and companies that invest abroad are very likely to have high competitive
skills. These companies have high and rising productivity, which both influences thedomestic economies of their countries and improves the effectiveness of country’s fiscal
policies. And growth is not constrained by the level of domestic demand, as the limit
rises through the import of demand from abroad. The potential demand of the economy
becomes higher, so there is also more space for fiscal policy to create additional demand18without reaching excess demand levels. The effectiveness of fiscal spending therefore
increases. While few studies paid attention to the role of fiscal policy on a country’s
international financial and trade situation, it seems natural to conclude that FDI
outflow and trade balances, which determine fiscal multipliers most out of the types offlow considered, are important given that governments stimulate their economies
mainly by making long-term investments in the real sector.
This analysis also helps us explain the positive significant coefficient of the developed
country dummy in some specifications. The positive sign implies that fiscal policy ismore effective in developed countries than in developing ones. Our new channel
presents an explanation to this result. We have stated that the fiscal policy is more
effective in countries that have high FDI outflows and trade surpluses, and by extension,
globally oriented companies with sufficient strength to generate such flows. These
conditions are more likely to be fulfilled by advanced economies than by developing
countries. Therefore, fiscal policy will also be more effective in the former group, as ourestimation results show.

5.

Determinants of GDP component multipliers
multipliers

The results from the previous section show that a country’s international position
largely influences the effectiveness of its fiscal policy. Capital flows, and especially FDI,
play the most important role. This leads us to a question: through what channels canexternal factors influence a country’s GDP? To explore this we now study the main GDP
components, namely, private sector consumption, private sector investment, andexports and imports, and check the determinants of their multipliers after the change ingovernment fiscal expenditure.
19

The procedure is similar to the above. First, using VAR models for each country andeach GDP component separately, we calculate cumulative multipliers. Then we use themultipliers as dependent variables in cross-country regressions in order to determine
factors responsible for their sizes.
Table 11 presents summary statistics of the fiscal multipliers for GDP components.
The figures show that investment has the highest multipliers by far. That is, fiscal
policy influences GDP mainly through its impact on investment. The second mostimportant component through which the policy can influence GDP is imports, and thethird is consumption. Fiscal expenditure is the least effective in influencing a country’s
exports.
Table 12 holds the results of the cross-country regressions with fiscal multipliers for
consumption, investment, exports, and imports as dependent variables.
The results for consumption multipliers imply that the high importance of capital
flows mentioned above does not hold for private consumption. Neither net capital
inflows nor FDI flows are statistically significant. On the other hand, portfolio and otherflows, especially inflows, seem to play a role in determining private sector consumption
after the change in fiscal expenditure. The higher the inflows to the economy are, thelower the multiplier seems to become. More important, the ratio of government debt to
GDP seems to be the most important determinant of the consumption multipliers, eventhough it has little importance for GDP as a whole.
Next, similar to the case with consumption, differences in investment multipliers
after the fiscal expenditure shock also seem to depend less on ratio of net capital inflows
to GDP than GDP itself. Again, portfolio inflows seem to hold the most importantmeaning among the different kinds of capital. Additionally, a country’s trade balance20also determines investment multipliers. A country’s international situation therefore
seems to influence investment more than consumption, and investment is the main
channel through which the current account works on the GDP multipliers. Finally,
similar to consumption, a higher ratio of government debt to GDP decreases also theinvestment fiscal multipliers.
The results of the regressions with export multipliers as dependent variables imply
that the size of the multiplier seems to be highly influenced by all types of capital flows,
both inflows and outflows. The higher the FDI and other flows are, the higher themultiplier for exports become, and the opposite holds for portfolio flows.
Lastly, the results for the determinants of the sizes of the imports multipliers imply
that a country’s exchange rate regime and monetary union (euro area) participation are
the strongest determinants of the extent to which imports increase after fiscal
expenditure shock. The capital flows also extend some meaning for the divergent
multiplier sizes across countries, with FDI – both inflows and outflows – holding thehighest importance.
Summing up, the results imply that external conditions strongly influence thechanges not only in GDP as a whole, but also in some of its components, after fiscal
expenditure shocks. Section 4 implies that a country’s international position, especially
its capital flows and trade balance, is the important determinant of the size of the fiscal
multipliers. In this Section we show that FDI flows not only confer a large influence on
the size of the fiscal multiplier for GDP, but also have the strongest explanatory powerover multipliers for exports and imports.
On the other hand, consumption and investment multipliers seem to depend more ona country’s internal characteristics. Especially, our results imply that in countries with21

high debt burden measured by the government debt to GDP ratio, government spending
shocks have less or negative impact on consumption and investment. This finding
supports the view suggested by the previous literature, e.g., Blanchard (1990),
Sutherland (1997), Perotti (1999), and Ilzetzki et al. (2013).7
Our results in no way refute the role of domestic demand in contributing to GDPchanges. In fact, we can confirm the very high levels of investment multipliers. Rather,
they provide evidence that a country’s external conditions also play a considerable rolein determining the effectiveness of its fiscal policy spending.
One of the channels through which government expenditure can influence exports
and imports is investment in infrastructure. Besides the traditionally studied channelof increased employment, wages, and thus consumption, one can also think of theinfrastructure investment carried out to lower transportation costs, facilitate thetransportation of goods, and thus increase trade. That strategy is implemented mostoften in developing countries, mostly as a means of encouraging foreign investment andthereby increasing production, employment, and trade, and ultimately GDP growth.
Our results on the vital role of external conditions also imply that government
expenditure has more influence on the behaviour of firms than on individuals. This
point is also largely confirmed by the strength of FDI flows and trade balance as

7

There are competing views regarding the efficacy of fiscal policy; an increase in government

expenditure has a positive effect on GDP/private consumption (standard Keynesian effect), no effect(Ricardian equivalence), and a negative effect (non-Keynesian effect). Using a nonlinear fiscal policy
model and a cross-country data of OECD countries, Perotti (1999) finds that the non-Keynesian effectoccurred only when the government debt to GDP ratio was high. Blanchard (1990) and Sutherland(1997) have theoretically demonstrated this nonlinear effect and its dependence on the economic
environment.

22determinants of the fiscal multipliers and by the fact that the investment component
has the highest multiplier values.

6.

Concluding remarks

In this study we present evidence on a new channel through which domestic fiscal policy
effectiveness is affected. Based on economic theory and previous studies, we analyse theimportance of domestic conditions and the importance of the exchange rate regime andtrade openness as proxies for international conditions. We consider these factors
insufficient, however, for analysing the role of the international environment in fiscal
expenditure effectiveness. We therefore introduce, to our analysis of the fiscal multiplierdeterminants a number of variables describing capital market openness and capital
flows, as well. We also disaggregate GDP into its main components in order to identify
the channels through which external and internal factors can influence GDP after afiscal policy changes.
Our results single out a country’s capital flows as one of the most important
determinants of fiscal multiplier size. The effectiveness of fiscal expansion is limited incountries with large net capital inflows and open capital accounts. What’s more, not all
flow categories affect fiscal multipliers equally. FDI flows are by far the most influential
determinants of the effectiveness of fiscal policy, while portfolio flows have relatively
little influence.
Especially high FDI outflows can explain higher fiscal multipliers well. This result
points at the link between a country’s international financial situation and real
economy conditions. While a country’s capital account situation is vital for fiscal policy
effectiveness, the magnitude of FDI flow depends strongly on a country’s macroeconomic
23

condition. To explain this result, we demonstrate that high FDI outflows can take placewhen a country has strong, internationally competitive companies that are able andwilling to invest abroad. This condition can only be fulfilled with a good domestic
macroeconomic situation and growth environment.
The extensive influence of FDI capital flows on the size of export and importmultipliers shows that GDP changes are largely explained by external conditions thatpossibly outweigh changes in domestic demand. Moreover, all of the results provided
suggest that fiscal expenditure influences the behaviour of mainly companies and not
individuals.
The results achieved provide important hints for carrying out fiscal policies. The
existence of internationally competitive companies also helps a domestic economy,raises productiveness, and thereby makes fiscal policy more effective, as well.
A country’s fiscal policy expenditure (fiscal stimulation introduced in order to raiseGDP) should therefore focus on the creation of good development conditions for its
companies to help them achieve higher international competitiveness. This will help thedomestic economy by pushing up productivity and trade surpluses, and further
increasing the effectiveness of fiscal policy.
More effective fiscal expenditure also means that lower spending levels can beachieved without diminished results. Such an outcome would translate into a sounderfiscal stance (lower deficits/debt). In other words, well fitted fiscal stimulation needs
fewer resources, which makes it possible to somehow limit the role of the state. It isenough to limit state intervention to the creation of good development conditions and let
the market do the rest.

24

Our analysis is not free from potential caveats. In this paper we apply VAR
estimation methodology in order to identify ‘policy-induced’ fiscal shocks. As mentioned
in the introduction, the discussion on the appropriate methodology for identification of
fiscal policy shocks is still ongoing and the results might differ depending on the chosen
method. In particular, the fiscal VAR literature points at potential problems for
identifying fiscal shocks, problems mainly to do with the anticipation of policy changes(e.g., Ramey, 2011a).8 Therefore, we cannot rule out some changes in our results ininvestigating the cross-country heterogeneity of the effect of `unanticipated’ fiscal
shocks. The issue of the differentials between results based on different identification
approaches, however, is not the subject of this paper and is left for further study in the
future.

APPENDIXI.

Data definitions and sources
Government

spending

–

sum

of

government

final

consumption

expenditure/government consumption and government gross capital formation/
government investment. Sources: Eurostat, Oxford Economics
Gross Domestic Product – Sources: Eurostat, Oxford Economics, countries’
statistical offices

8

There is usually a lag between a decision on fiscal policy expenditure and its implementation.

Most fiscal policy changes are therefore believed to be anticipated by the private sector and to
influence the private sector’s behaviour even before the fiscal variables change. As a consequence, ananalysis of the effects of fiscal policy expenditure based on the VAR methodology, i.e., an analysis
focused only on the effects of the innovations of fiscal expenditure, might be inconsistent with the oneof the effect of `unanticipated’ fiscal shocks. However, as Ilzetzki et al. (2013) argue, we believe that
this problem is mitigated especially in developing countries.

25

Current account balance – ratio to GDP. Sources: Oxford Economics, countries’
central banks
Real effective exchange rate – deflated with CPI. Sources: IMF IFS, OECD
Debt to GDP – government debt as ratio of GDP. Sources: IMF, World Economic
Outlook database
Exchange rate dummy – based on the de facto classification by Ilzetzki,
Reinhard and Rogoff (2009); takes a value of 1 for countries with a fixed exchange rate
regime (as defined in Ilzetzki et al. (2013): fixed exchange rate includes no legal tender,
hard pegs, crawling pegs, or de facto or pre-announced bands or crawling bands with
margins no larger than +/- 2%)
Monetary union dummy – takes a value of 1 for countries that had joined theeuro area in 1999 + Greece (countries that joined later, namely, Slovakia, Estonia,
Cyprus, Malta, Latvia, and Lithuania are treated as non-MU countries)
Developed country dummy – takes a value of 1 for developed countries, basedon the IMF list of advanced economies in 2000
Capital account openness – Chinn and Ito index (KAOPEN)
Net capital inflows = [gross inflows (sum of IMF IFS data 78bed, 78bgd, 78bid)]– [gross outflows (sum of IMF IFS data 78bdd, 78bfd, 78bhd)], ratio to GDP. Source:
IMF IFS
Openness of the economy –the sum of exports and imports as a ratio of GDP(exports and imports data taken from the IMF IFS database –in US dollars; GDP fromthe World Economic Outlook database)26

Trade balance – difference between exports and imports, ratio to GDP; (exports
and imports data taken from the IMF IFS database –in US dollars; GDP from the World
Economic Outlook database)
Government bond yield – 10-year government bond yield. Sources: IMF IFS,
OECD, Thomson Reuters

II.

Country list and sample periods

Table A. II.1 lists the countries included in the analysis and the sample starting points.

III.

Robustness checks

III.1. Extended model results
As mentioned above, we use a 2-variable VAR model as our benchmark strategy toachieve cumulative multipliers for use in our cross-sectional regressions. We extend ourVAR model to a 4-variable version, adding current account balance and real effective
exchange rate variables, and calculate cumulative multipliers accordingly. We then use
these multipliers as dependent variables in cross sectional regressions. The main
results and conclusions in this specification are not much changed compared to thebenchmark results.
Net capital inflows are still the main factors explaining the size of the multipliers. In
the detailed analysis of capital flows, FDI flows turn out to be the most important factor
behind fiscal policy effectiveness. Capital account openness seems to play a slightly lessimportant role here, but it cannot be characterized as trivial. More broadly, we achieve
more evidence that trade openness is an essential determinant and that trade balance
plays no role.
27

These observations show, very reassuringly, that fiscal multipliers achieved based on
the simple 2-variable VAR are sufficient to analyse the determinants of fiscal policy
effectiveness.

III.2. Explanatory variable dates
As mentioned before, one of the problems of our research is the choice of a suitable form
for the explanatory variables. Explanatory variables are usually time series data. To
perform regressions checking the determinants of fiscal multipliers, we need totransform these time series into cross-sectional variables. For all of the previous
regressions we use sample average values of our time series data as explanatory
variables. Here, we also carry out the regressions with year 2000 values to verify thatthe results are not influenced by the choice of sample average values. As we can see, theresults do not change significantly. Capital openness and net inflows are still the main
factors explaining fiscal policy effectiveness. We also observe that fiscal policy tends tobe more effective in developed countries.
We also carry out a robustness check for exchange rate dummy variable date. We
calculate our models substituting the year 2000 exchange rate dummy with the dummy
values in years 1995 and 2005. The results also seem robust to these changes, with net
capital inflows, capital account openness, and trade balance becoming the main
variables explaining fiscal spending effectiveness.

III.3. Cumulative multiplier period
The previous results are based on regressions of the 8th period cumulative multiplier(i.e., cumulative reaction 2 years after the shock). We also carry out calculations for the28

4th period cumulative multiplier to verify that the results are left unchanged whenanother period multiplier is used. We observe that many results are robust even whenthe multiplier period changes.
Here too, we find evidence that capital outflows are a main determinant of fiscal
policy effectiveness versus capital inflows. Out of the different kinds of flows, FDI flows,
and especially FDI outflows, play the main role in affecting fiscal policy effectiveness.
We find some evidence supporting the importance of portfolio inflows, portfolio outflows,
and other capital outflows, but these flows still seem less important than FDI flows. We
also achieve robust results for capital account openness, though the role of this variable
seems to be slightly smaller when the 4th period cumulative multiplier is used.
Compared with the results for the 8th period multiplier, we find considerably lessevidence that the trade balance is important as a determinant of fiscal policy
effectiveness and more evidence that trade openness is important.
Some of the differences we observe might be explicable by the fact that some factors
are likely to work better in the shorter period of time, while others work much better
over the longer period. By looking at R2 coefficient, we see that our explanatory
variables explain fiscal multipliers better in the 8th period. Hence, there are probably
other variables influencing fiscal policy effectiveness in the shorter period. As we statein the main text, fiscal policy has more prolonged effects on the economy. We therefore
believe that looking at the 8th period multipliers is more justified.

Acknowledgements
The authors are grateful to Seiichi Fujita, Kenneth N. Kuttner, Shugo Yamamoto, andparticipants of September 2014 Modern Monetary Economics Summer Institute at Kobe
29

University for helpful comments and suggestions. Shibamoto acknowledges thefinancial support from a Grant-in-Aid from the Japanese Ministry of Education.

References
References
1.

Blanchard, O., (1990). Can severe fiscal contractions be expansionary? Tales of

two small European countries: Comment. In Olivier Jean Blanchard and Stanley Fisher
eds. NBER Macroeconomics Annual, Cambridge, Mass. And London: MIT Press,
111-116
2.

Blanchard, O., & Perotti, R. (2002). An empirical characterization of the

dynamic effects of changes in government spending and taxes on output. The Quarterly

Journal of Economics. 117, 4, 1329-1368
3.

Fatas A., & Mihov I. (2001). The effects of fiscal policy on consumption andemployment: theory and evidence, C.E.P.R. Discussion Papers, 2760
4.

Hemming, R., Kell, M., & Mahfouz, S. (2002). Effectiveness of fiscal policy instimulating economic activity - a review of literature. IMF Working Paper, WP/02/208
5.

Ilzetzki, E., Mendoza, E., & Végh, C. (2013). How big (small?) are fiscal

multipliers?. Journal of Monetary Economics. 60, 239-254
6.

Ilzetzki, E., Reinhart C., & Rogoff K. (2010). Exchange Rate Arrangements

Entering the 21st Century: Which Anchor Will Hold? Mimeo. University of Marylandand Harvard University.
7.

Kuttner, K.N., & Posen, A. (2001). The great recession: lessons for

macroeconomic policy for Japan. Brookings Papers on Economic Activity. Economic
Studies Program, The Brookings Institution, vol. 32(2), pages 93-186

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8.

Mountford, A., & Uhlig, H. (2009). What area the effects of fiscal policy shocks?,

Journal of Applied Econometrics, 24, 960-992
9.

Perotti, R. (1999). Fiscal policy in good times and bad. The Quarterly Journal of

Economics. 114, 4, 1399-1436
10.

Ramey, V. (2011a). Identifying government spending shocks: it's all in thetiming. The Quarterly Journal of Economics, 1-50
11.

Ramey, V. (2011b). Can government purchases stimulate the economy?. Journalof Economic Literature, 49:3, 673-685
12.

Ramey, V., & Shapiro, M. (1998). Costly capital reallocation and the effects ofgovernment spending. Carnegie-Rochester Conference Series on Public Policy, 48,
145-19413.

Sutherland, A. (1997). Fiscal crises and aggregate demand: Can High public

debt Reverse the effects of fiscal policy? Journal of Public Economics. 65, 2, 147-162
14.

White,

H.

(1980).

A Heteroskedasticity-Consistent

CovarianceMatrixEstimator and a Direct Test for Heteroskedasticity. Econometrica, 48, No. 4, 817-838

31

Table 1. Summary statistics of the 8th and 4th period cumulative multipliers

8th period multiplier

4th period multiplier

Average

.086

.138

Median.224.129

Maximum

.844

.652

Minimum-1.900-.533

Standard deviation

.506

.212

32

Table 2. Correlation coefficients of explanatory variables

Develope Exchange
d country

rate

MU

KAOPEN

dummy

Net

Debt

inflows

Trade to

Trade

GDP

balance-0.200-0.060regimeDevelope
1

0.089

0.552

0.660

-0.5400.471

d country
Exchange
1

0.552

0.186

0.142

0.160

0.249

0.169

rate
regime
MU

1

0.335

-0.1940.412-0.109-0.037dummy

1-0.263

0.240

0.000

-0.1261-0.3670.071-0.038KAOPEN
Net
inflows
Debt to

1

0.053

0.130
GDP
Trade to

1

0.513
GDP
Trade
1
balance33

Table 3. OLS regression coefficients (estimations with constant terms
terms)

Constant term

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

-.025

.087

-.137

.166*

.222***.224***

.037

.080

.294**.350**(-.166)(.815)(-.614)(1.999)(3.679)(3.679)(.372)(1.060)(2.208)(2.639)Developed

.212

.379*

.345

country(1.381)(1.732)(1.651)

-.001

.198

.178

(-.006)(1.348)(1.182)

.412

.009

-.012

(1.381)(.063)(-.082)

-.105

-.051

-.113**(-1.197)(-.765)(-2.145)-4.402***-4.494***-4.446***(-2.747)(-4.988)(-5.037)-.126***-.303***-.286***(-2.107)(-3.366)(-3.225)

.198*

-.017

.005

(1.894)(-.115)(.030)

Exchange rate

Government debt

Openness

Net inflows

KAOPEN

Monetary union

Trade balance.290.218**(.1.573)(2.608)

R2

.046

.000

.051

.015

.271

.091

.030

.036

.581

.594

Adjusted R2

.023

-.024

.029

-.009

.254

.069

.006

.013

.499

.501

Note: tt-statistics are reported in parentheses.
parentheses. ***, **,**, and * indicate statistical significance at the 99%,99%, 95%,
95%, and 90% levels, respectively.

34

Table 4. Chosen correlation coefficients of gross capital flows

Developed country

Gross inflows

Gross outflows

KAOPEN

1

0.043

0.207

0.660

Developed country

1

0.955

0.050

Gross inflows

1

0.134

Gross outflows

1

35

KAOPEN

Table 5. Gross capital flows check

(1)

(2)

(3)

(4)

(5)

(6)

-.028

.043

-.030

.037

.301**.354**(-.166)(.248)(-.165)(.207)(2.310)(2.698)Developed.704**.660**.691**.651**.363.333

country(2.356)(2.234)(2.313)(2.190)(1.689)(1.613)

Exchange rate

.084

.060

.114

.090

.209

.187

(.426)(.300)(.573)(.443)(1.459)(1.273)Government

.155

.129

.158

.134

.011

-.008

debt

(.986)(.818)(1.044)(.892)(.083)(-.057)

Openness

.024

-.045

-.052

-.115

-.083

-.137***(.383)(-.613)(-.766)(-1.215)(-1.260)(-2.840)

-.191

-.215

-4.512***-4.464***(-.965)(-.938)(-5.067)(-5.084)

Constant term

Gross inflows

Gross outflows

KAOPEN

Monetary union

.202

.173

4.682***

4.608***(1.013)(.835)(5.225)(5.202)-.332***-.311***-.329***-.309***-.306***-.289***(-2.911)(-2.737)(-2.880)(-2.704)(-3.420)(-3.282)

-.014

.016

-.084

-.054

-.039

-.014

(-.075)(.084)(-.425)(-.271)(-.252)(-.087)

Trade balance

.256*

.241

.208**(1.747)(1.670)(2.489)

R2

.405

.424

.405

.422

.591

.603

Adjusted R2

.290

.292

.290

.290

.497

.498

Note: tt-statistics are reported in parentheses.
parentheses. ***, **,**, and * indicate statistical significance at the 99%,95%,, and 90% levels, respectively.
95%

36

Table 6. Correlation coefficients for detailed capital flow variables

Trade

FDI

Portfolio

Other

FDI

Portfolio

Otherbalanceinflows

inflows

inflows

outflows

outflows

outflows

0.169

0.223

0.230

0.101

0.262

0.132

0.063

Net FDINet

Net otherportfolio
Exchange rate
-0.024

0.140

0.087
regimeDeveloped-0.060-0.2240.315-0.018

0.264

0.242

0.094

-0.6150.102-0.341country-0.037-0.077

0.436

0.102

0.261

0.303

0.161

-0.4040.190-0.116

MU dummy-0.126-0.0650.144-0.035

0.277

0.104

0.041

-0.3530.058-0.136KAOPEN

0.130

0.042

0.155

0.123

0.305

0.151

0.208

-0.316-0.041-0.209

Debt

0.077

0.132

0.189

0.175

0.394

0.172

0.139

-0.254-0.026

0.187

Government
expenditure
0.512

0.605

0.040

0.483

0.393

0.246

0.497

0.382

-0.5190.263

Trade to GDP-0.0380.311-0.0780.088-0.230-0.139-0.113

0.733

0.132

0.672

Net inflows

1

0.028

0.111

0.380

0.107

0.224

0.332

-0.099-0.3000.305

Trade balance

1-0.045

0.392

0.674

0.098

0.338

0.571

-0.3760.307

FDI inflows

1

0.492

0.179

0.911

0.530

-0.263-0.086

0.189

Portfolio
inflows
1

0.249

0.751

0.964

0.232

-0.783

0.652

Other inflows

1

0.233

0.291

-0.199-0.1840.012

FDI outflows

1

0.783

-0.130-0.487

0.361

Portfolio
outflows
Other
1

0.115

-0.7730.459
outflows

1-0.2970.470

Net FDI1-0.487

Net portfolio

1

37

Net otherTable 7. FDI check

Constant term

Developed country

Exchange rate

Government debt

Openness

FDI inflows

(1)

(2)

(3)

.046

.089

.109

(.270)(.498)(.607).667**.618**.532*(2.282)(2.158)(1.769)

.053

.060

.093

(.273)(.301)(.499)

.107

.077

.112

(.739)(.562)(.750)

-.171

-.176

.054

(-1.126)(.562)(.775)

1.426(.667)

FDI outflows

2.853**(2.258)Net FDI-4.742***(-2.785)

KAOPEN-.304***-.315***-.325***(-2.879)(-2.806)(-2.877)

-.011

-.045

-.075

(-.059)(-.235)(-.399)

.335*

.301*

.049

(1.932)(1.954)(.428)

R

.428

.450

.468

Adjusted R2

.297

.325

.346

Monetary union

Trade balance2

Note: tt-statistics are reported in parentheses.
parentheses. ***, **,**, and * indicate statistical significance at the 99%,95%,95%, and 90% levels, respectively.

38

Table 8. Portfolio check

Constant term

Developed country

Exchange rate

Government debt

Openness

Portfolio inflows

(1)

(2)

(3)

.039

.034

.049

(.222)(.188)(.279).675**.654**.658**(2.282)(2.205)(2.221)

.073

.073

.106

(.363)(.360)(.504)

.123

.133

.128

(.795)(.866)(.839)

-.079

-.081

-.135

(-.993)(-.991)(-1.381)-.336(-1.548)

Portfolio outflows-.052(-.218)Net portfolio-1.325(-1.401)

KAOPEN-.313**-.311**-.309***(-2.713)(-2.699)(-2.728)

.003

-.020

-.026

(.014)(-.099)(-.132)

.257*

.250*

.240

(1.727)(1.703)(1.611)

R

.421

.419

.426

Adjusted R2

.289

.286

.295

Monetary union

Trade balance2

Note: tt-statistics are reported in parentheses.
parentheses. ***, ** and * indicate statistical significance at the 99%,95% and 90% levels, respectively.

39

Table 9. Other flows check

Constant term

Developed country

Exchange rate

Government debt

Openness

Other inflows

(1)

(2)

(3)

.033

.043

.224

(.181)(.247)(1.469).662**.655**.482**(2.216)(2.232)(2.229)

.077

.106

-.057

(.363)(.503)(.356)

.134

.124

.034

(.878)(.837)(.206)

-.088

-.138

-.067

(-1.020)(-1.389)(-.892).044(.123)

Other outflows.541(1.431)

Net other-4.519**(2.185)KAOPEN-.310**-.308***-.279***(-2.720)(-2.729)(-3.067)

-.027

-.068

.057

(-.131)(-.328)(.356).245.233.430**(1.587)(1.589)(2.185)

R

.419

.427

.529

Adjusted R2

.286

.296

.422

Monetary union

Trade balance2

Note: tt-statistics are reported in parentheses.
parentheses. ***, ** and * indicate statistical significance at the 99%,95% and 90% levels, respectively.

40

Table 10. Inflows and outflows check(1)
Constant term

.052

(1)
Constant term(.304)Developed country.682**(.739)Developed country(2.283)
Exchange rate

.056

.096

Exchange rate-.168

Government debt

1.349

Openness-.257*(-1.885)

FDI outflows(.611)
Portfolio inflows

-.368

.062

Portfolio outflows-.307***

Other outflows.012

KAOPEN-.317***(-2.888)

Monetary union(.060)
Trade balance

.335*

-.066
(-.344)

Trade balance(1.779)2

1.415**(2.166)(-2.882)
Monetary union-1.171**(-2.039)(.142)KAOPEN

2.855**(2.206)(-.904)
Other inflows.011(.088)(-1.109)
FDI inflows.108(.522)(.657)
Openness.652**(2.259)(.273)
Government debt

.125

.299*
(1.821)

2

R

.430

R

.472

Adjusted R2.257

Adjusted R2.312

Note: tt-statistics are reported in parentheses.
parentheses. ***, ** and * indicate statistical significance at the 99%,95% and 90% levels, respectively.

41

Table 11. Summary statistics of fiscal multipliers for GDP components

Consumption multiplier

Investment multiplier

4th period

8th period

4th period

8th period

4th period

8th period

4th period

8th period

Average

0.264

0.362

0.936

0.973

0.119

0.225

0.333

0.442

Median

0.174

0.311

0.614

0.632

0.072

0.146

0.280

0.412

Maximum

2.488

2.524

6.064

6.289

1.949

2.336

2.713

2.278

Minimum-1.163-1.112-1.733-1.862-1.161-1.361-0.920-1.027

Std. dev

0.511

0.572

1.413

1.387

0.581

0.629

0.555

0.546

42

Exports multiplier

Imports multiplierTable 12. Determinants of fiscal multipliers for consumption, investment, exports, and imports.
Consumption multipliers

Constant
Developed country
Exchange rate
Monetary union
KAOPEN
Government debt
Openness
Trade balance
Inflows to GDP

(1)
0.436
(1.730)-0.004(-0.018)
0.143(0.937)0.406(1.416)0.048(0.643)-0.770**(-2.554)0.117(1.144)-0.025(-0.185)0.854(0.670)(2)0.512**(2.537)-0.008(-0.033)
0.202(1.166)0.445(1.380)0.047(0.606)-0.845***(-2.755)
0.061(0.512)-0.064(-0.419)

Portfolio inflows
Other inflows

Portfolio outflows
Other outflows

Adjusted R(5)
1.676***(3.448)-0.219(-0.377)
0.651(0.920)0.929(1.195)-0.031(-0.139)-2.059**(-2.353)
0.053(0.150)-0.430(-1.410)(6)
1.728***(3.017)-0.225(-0.384)
0.695(0.979)0.805(0.995)-0.041(-0.183)-2.148**(-2.123)-0.001(-0.002)-0.496*(-1.791)

Exports multipliers
(7)
0.508
(1.682)-0.236(-0.556)-0.447(-1.639)0.308(0.934)-0.069(-0.589)-0.332(-0.917)0.273(1.389)-0.070(-0.278)
1.205(0.713)1.536(0.430)-2.571**(-2.143)-0.129(-0.142)(8)0.645***(2.891)-0.222(-0.725)-0.352(-1.512)0.227(0.741)-0.030(-0.340)-0.563*(-1.857)-0.269(-1.681)
0.046(0.260)

Imports multipliers(9)0.774***(3.026)-0.310(-0.941)-0.205(-0.794)
0.066(0.212)-0.054(-0.574)-0.646*(-1.907)-0.199(-1.098)-0.087(-0.539)(10)0.758**(2.153)-0.359(-1.080)-0.704***(-3.296)0.824***(3.354)0.047(0.503)-0.169(-0.511)0.082(0.594)0.045(0.282)0.304(0.172)4.343**(2.267)-1.726***(-3.694)
2.869***(8.192)-0.663(-0.386)-0.599(-0.562)0.711(0.664)

FDI outflows

2

Investment multipliers(4)
1.558**(2.075)-0.272(-0.517)0.671(0.912)0.698(0.957)-0.022(-0.112)-1.915**(-2.141)
0.105(0.289)-0.590**(-2.360)
0.759(0.144)-0.224(-0.140)-1.343***(-2.894)
0.797*(1.936)

FDI inflows

R2(3)
0.503**(2.395)-0.037(-0.157)
0.190(1.131)0.407(1.312)0.053(0.660)-0.818**(-2.630)
0.093(0.790)-0.043(-0.309)2.291(0.681)-3.662(-1.400)
2.176(0.677)(11)0.841***(3.426)-0.341(-1.290)-0.769***(-3.367)
0.892***(3.639)0.072(0.819)-0.317(-1.165)-0.222(-1.250)0.350*(1.841)(12)0.887***(3.263)-0.407(-1.441)-0.688***(-2.950)
0.784***(3.174)0.044(0.480)-0.331(-1.130)-0.089(-0.474)
0.119(0.743)4.872**(2.694)-0.549(-1.198)0.140(0.405)2.368*(1.880)-2.654***(-3.759)5.314***(6.572)3.659**(2.575)-0.983(-1.234)
1.057(1.189)

0.229

0.247

0.230

0.264

0.284

0.287

0.246

0.525

0.478

0.252

0.347

0.305

0.053

0.019

-0.004

0.096

0.067

0.070

0.064

0.376

0.314

0.076

0.143

0.087

Note: tt-statistics are reported in parentheses.
parentheses. ***, ** and * indicate statistical significance at the 99%, 95%
95% and 90% levels, respectively

43

Table A.II.1.
A.II.1. Countries and sample starting points

Country

Sample startCountry

Sample start

Australia

1980Q1

Lithuania

1999Q1

Austria

1980Q1

Malaysia

1980Q1

Belgium

1980Q1

Malta

2000Q1

Brazil1989Q1

Mexico

1989Q1

Bulgaria

1999Q1

Netherlands

1980Q4

Canada1980Q1New Zealand

1987Q2

Chile

1989Q1

Norway1980Q1

Cyprus

1995Q1

Philippines

1980Q1

Czech Republic

1999Q1Poland1999Q1

Denmark

1980Q4

Portugal

1980Q4

Estonia

1995Q1

Romania

2000Q1Finland

1980Q1

Russia

1993Q1

France1980Q1

Singapore

1980Q1

Germany

1991Q1

Slovakia

1999Q1

Greece1988Q1

South Africa

1989Q1

Hungary

1999Q1

Spain

1980Q4

Indonesia

1980Q1

Sweden

1980Q1

Ireland

1985Q1

Switzerland

1981Q1

Italy

1981Q4Thailand

1980Q1

Japan

1980Q1

Turkey1987Q2

Korea

1980Q1

United Kingdom

1980Q1

Latvia1999Q1

United States

1980Q144

Table A.III.1. Regression results for the 8th period cumulative multiplier calculated based on a
4-variable VAR model(1)
Constant term

.453***(3.090)Developed country.141(.564)

Exchange rate-.081(.482)

Monetary union.224(1.536)

Kapoen-.184(1.619)Net inflows-3.701***(-4.216)

Government debt-.073(-.451)Openness-.141**(-2.569)

Trade balance.097(1.110)

R2

.373
2

Adjusted R.230

Note: tt-statistics are reported in parentheses.
parentheses. ***, **,**, and * indicate statistical significance at the 99%,95%,95%, and 90% levels, respectively.

45

Table A.III.2. FDI check

Constant term

Developed country

Exchange rate

Government debt

Openness

FDI inflows

(1)

(2)

(3)

.197

.256*

.255

(1.434)(1.828)(1.615)

.408

.353

.292

(1.518)(1.433)(.968)

-.180

-.185

-.151

(-.857)(-.949)(-.807)

.032

-.021

.029

(.195)(-.147)(.177)-.169-.227*.003(-1.162)(-1.945)(.040).877(.392)

FDI outflows

3.425**(2.249)Net FDI-4.103*(-1.893)

KAOPEN-.201*-.210*-.217(-1.808)(-1.816)(-1.868)

.208

.175

.156

(1.198)(1.113)(.931)

.175

.184

-.051

(.911)(1.197)(-.351)

R

.260

.300

.292

Adjusted R2

.090

.140

.130

Monetary union

Trade balance2

Note: tt-statistics are reported in parentheses.
parentheses. ***, **,**, and * indicate statistical significance at the 99%,95%,95%, and 90% levels, respectively.

46

Table A.III.3. Portfolio check

Constant term

Developed country

Exchange rate

Government debt

Openness

Portfolio inflows

(1)

(2)

(3)

.190

.187

.211

(1.344)(1.326)(1.481)

.407

.393

.399

(1.485)(1.448)(1.501)

-.167

-.160

-.124

(-.844)(-.798)(-.622)

.047

.057

.040

(.270)(.328)(.228)

-.115

-.129

-.185*(-1.420)(-1.505)(-1.786)-.041(-.168)

Portfolio outflows.260(1.055)

Net portfolio-1.801(-2.026)

KAOPEN-.205*-.203*-.203*(-1.724)(-1.706)(-1.736)

.203

.179

.197

(1.168)(1.025)(1.181)

.122

.113

.111

(.852)(.801)(.762)

R

.257

.258

.269

Adjusted R2

.087

.089

.101

Monetary union

Trade balance2

Note: tt-statistics are reported in parentheses.
parentheses. ***, **,**, and * indicate statistical significance at the 99%,95%,95%, and 90% levels, respectively.

47

Table A.III.4. Other flows check

Constant term

Developed country

Exchange rate

Government debt

Openness

Other inflows

(1)

(2)

(3)

.190

.202

.325**(1.346)(1.414)(2.087)

.405

.396

.277

(1.519)(1.490)(1.155)

-.169

-.128

-.181

(-.825)(-.629)(-.974)

.048

.035

-.024

(.279)(.211)(-.131)-.113-.183*-.104(-1.237)(-1.702)(-1.499)-.026(-.069)Other outflows.667*(2.005)

Net other-3.224(-1.668)

KAOPEN-.205*-.201*-.183(-1.726)(-1.712)(-1.646)

.203

.145

.258

(1.130)(.832)(1.572)

.123

.103

.251

(.817)(.729)(1.342)

R

.256

.268

.311

Adjusted R2

.087

.100

.153

Monetary union

Trade balance2

Note: tt-statistics are reported in parentheses.
parentheses. ***, **,**, and * indicate statistical significance at the 99%,95%,95%, and 90% levels, respectively.

48

Table A.III.5. Inflows and outflows check(1)
Constant term

.199

(1)
Constant term

(1.457)Developed country.405(1.948)Developed country(1.531)
Exchange rate

-.189

.033

Exchange rate-.164

Government debt

1.004

Openness-0.210*(-1.793)

FDI outflows(.401)
Portfolio inflows

.079

-.139

Portfolio outflows-.201*

Other outflows.215

KAOPEN-.210*(-1.793)

Monetary union(1.152)
Trade balance

.190

.136
(.834)

Trade balance(.834)
2

1.124*(1.784)(-1.809)
Monetary union-.651(-1.207)(-.219)KAOPEN

3.390**(2.178)(.156)
Other inflows-0.063(-0.474)(-1.184)
FDI inflows-.138(-.689)(.197)
Openness.368(1.447)(-.829)Government debt

.282*

.173
(1.078)

2

R

.260

R

.314

Adjusted R2.036

Adjusted R2.106

Note: tt-statistics are reported in parentheses.
parentheses. ***, **,**, and * indicate statistical significance at the 99%,95%,95%, and 90% levels, respectively.

49

Table. A.III.6. Explanatory variables for year 2000 (8th period multiplier)(1)
Constant term-.007(-.037)Developed country.783**(2.557)

Exchange rate-.052(.277)

Monetary union.170(.879)

Kapoen-.313***(-3.125)

Net inflows-2.266***(-3.017)

Government debt

.048(.252)

Openness-.063(-1.098)

Trade balance.216(1.382)

R2.501

Adjusted R2.387

Note: tt-statistics are reported in parentheses.
parentheses. ***, **,**, and * indicate statistical significance at the 99%,95%,95%, and 90% levels, respectively.

50

Table. A.III.7. Different exchange rate dates
dates

Constant term

Developed country

1995 exchange rate

(1)

(2)

.374**.411***(2.638)(3.068)

.300

.261

(1.540)(1.362).036(.302)

2005 exchange rate-.094(-.630)

Monetary union

.128

.236

(1.214)(1.473)-.273***-.263***(-3.118)(-3.124)-4.184***-4.046***(-4.583)(-4.450)

-.012

-.026

(-.084)(-.187)

-.087

-.067

(-1.554)(-.927).240***.257***(2.834)(3.110)

R

.579

.582

Adjusted R2

.483

.487

KapoenNet inflows

Government debt

Openness

Trade balance2

Note: tt-statistics are reported in parentheses.
parentheses. ***, **,**, and * indicate statistical significance at the 99%,95%,95%, and 90% levels, respectively.

51

Table A.III.8. Regression results for 4th period cumulative multiplier calculated based on the
2-variable VAR model.

(1)Constant term

.345***(4.130)Developed country.065(.936)

Exchange rate

.033(.511)

Monetary union.051(.657)

Kapoen-.101***(-3.643)

Net inflows-1.705***(-4.246)

Government debt-.073(-.907)

Openness-.091***(-3.191)

Trade balance.079**(2.446)R2.481

Adjusted R2.362

Note: tt-statistics are reported in parentheses.
parentheses. ***, **,**, and * indicate statistical significance at the 99%,95%,95%, and 90% levels, respectively.

52

Table A.III.9. FDI check

Constant term

Developed country

Exchange rate

Government debt

Openness

FDI inflows

(1)

(2)

(3)

.225***.247***.257***(2.819)(3.019)(3.146).187**.168**.129(2.296)(2.159)(1.571)

-.010

-.014

.001

(-.116)(-.169)(.014)

-.020

-.042

-.027

(-.250)(-.544)(-.365)-.090-.120**-.019(-1.532)(-2.254)(-.598).167(.215)

FDI outflows

1.220*(1.906)Net FDI-2.111**(-2.690)

KAOPEN-.110***-.120***-.117***(-3.460)(-3.534)(-3.588)

.042

.031

.017

(.475)(.363)(.204)

.100

.113*

.002

(1.554)(1.872)(.002)

R

.334

.366

.389

Adjusted R2

.182

.221

.250

Monetary union

Trade balance2

Note: tt-statistics are reported in parentheses.
parentheses. ***, **,**, and * indicate statistical significance at the 99%,95%,95%, and 90% levels, respectively.

53

Table A.III.10. Portfolio check

Constant term

Developed country

Exchange rate

Government debt

Openness

Portfolio inflows

(1)

(2)

(3)

.219***.221***.235***(2.760)(2.824)(2.980).176**.171.184(2.145)(2.10)(2.246)

-.006

.003

.015

(-.078)(.041)(.176)

-.008

-.005

-.022

(-.101)(-.073)(-.279)-.084**-.098**-.116**(-2.145)(-1.505)(-2.224).270**(2.447)

Portfolio outflows.356***(3.670)Net portfolio-0.935(-1.638)

KAOPEN-.109***-.108***-.110***(-3.283)(-3.266)(-3.405)

.019

.011

.038

(.217)(.124)(.443)

.084

.079

.085

(1.548)(1.480)(1.463)

R

.342

.353

.354

Adjusted R2

.192

.205

.205

Monetary union

Trade balance2

Note: tt-statistics are reported in parentheses.
parentheses. ***, **,**, and * indicate statistical significance at the 99%,95%,95%, and 90% levels, respectively.

54

Table A.III.11. Other flows check

Constant term

Developed country

Exchange rate

Government debt

Openness

Other inflows

(1)

(2)

(3)

.222***.233***.278***(2.800)(2.989)(3.240).188**.180**.136*(2.304)(2.251)(1.781)

.001

.021

-.013

(.009)(.247)(-.172)

-.018

-.026

-.045

(-.238)(-.361)(-.562)-.097**-.128**-.075**(-2.196)(-2.492)(-2.316).161(1.355)

Other outflows.482**(2.644)

Net other-1.281(-1.615)

KAOPEN-.110***-.108***-.102***(-3.336)(-3.368)(-3.483)

.026

.000

.063

(.289)(.003)(.748)

.079

.077

.142*

(1.364)(1.328)(1.980)

R

.340

.368

.384

Adjusted R2

.189

.224

.244

Monetary union

Trade balance2

Note: tt-statistics are reported in parentheses.
parentheses. ***, **,**, and * indicate statistical significance at the 99%,95%,95%, and 90% levels, respectively.

55

Table A.III.12
A.III.12.
12. Inflows and outflows check(1)
Constant term

.220**(1)
Constant term

(2.720)Developed country.179**(3.096)Developed country(2.169)Exchange rate

-.005

-.012

Exchange rate-.098

Government debt

.114

Openness-0.165**(-2.382)

FDI outflows(.137)
Portfolio inflows

.214

.075

Portfolio outflows-.108***

Other outflows.018

KAOPEN-.110***(-3.484)

Monetary union(.120)
Trade balance

.087

-.006
(-.071)

Trade balance(1.237)2.494(1.417)(-3.37)
Monetary union-.038(-.139)(.461)KAOPEN

1.172*(1.994)(1.282)Other inflows-.052(-.675)(-1.609)
FDI inflows.015(.170)(-.152)
Openness.164**(2.085)(-.051)Government debt

.256***.099(1.565)

2

R

.344

R

.399

Adjusted R2.145

Adjusted R2.216

Note: tt-statistics are reported in parentheses.
parentheses. ***, **,**, and * indicate statistical significance at the 99%,95%,95%, and 90% levels, respectively.

56