Discussion Paper No. 75
Financial Flows, Global Interest Rates,
and Political Integration

Jun Nagayasu

March , 2018

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Financial Flows, Global Interest Rates, and Political Integration
Jun Nagayasu

Abstract

This paper empirically analyzes international financial flows between 2000 and 2016. Calculating a proxy for global interest rates using a factor model, we confirm that there areglobal elements in national real interest rates, particularly those of advanced countries. Consequently, interest rate differentials are insignificantly associated with financial flows in thesecountries. Instead, exchange rates and political integration have been more consistently influencing investors’ decisions to form financial portfolios than interest rates.
Keywords: financial flows, global interest rate, uncovered interest rate parity
JEL classification: F2, F3



Tohoku University, Graduate School of Economics and Management, Address: 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. I would like to thank for useful comments Rui Chen, Charilaos Mertzanis, Alexis Stenfors, Richard Wernerand other participants in the meetings of Cross Country Perspectives in Finance Conference in Chengdue (China)
and Chiang Mai (Thailand). I also thank for research assistance Gabriel Cordoba.

1Introduction

Since many countries face persistent and excessive imbalances in external flows, numerous studies
have been conducted over the recent decades. Gourinchas and Rey (2010) reviewed transmission
channels, such as trade channel and valuation changes, of external imbalances, and Lane andMilesi-Ferretti (2007) showed the increasing importance of equity investment in country portfolios. According to Bremus and Fratzscher (2015), the recent hike in global liquidity resulted fromexpansionary monetary policies of advanced countries, and the independence of the supervisory
authorities affected cross-border bank credit flows.
Here, rather than focusing directly on trade and valuation channels, we study whether traditional economic and non-economic factors influence international financial flows. The financial
account measures the cross-border acquisition and disposal of financial assets and liabilities, andconsists of direct investments, portfolio investments, financial derivatives, other investments, andreserve assets. Global interest rates have been of interest because many advanced countries haveadopted expansionary monetary policies at an unprecedented scale during this period, and reduced the model’s dimension. Previously absent from economic analyses, we consider political
integration of countries as a determinant.

2

Theoretical framework

This study’s analytical framework is based on the interest rate parity condition (IPC), an important theory in international finance research analyzed by numerous researchers (Engel 2015);
it links nominal exchange rates with interest rates. The uncovered IPC (UIPC) type suggests anequilibrium condition in international financial markets under which the expected returns fromdomestic and foreign investments are the same. The other type, covered IPC, replaces expectedexchange rates with forward rates, so investors can minimize exchange rate risks. Our analysis
is based on the UIPC because data on effective forward exchange rates are not readily available.
Based on the UIPC, the difference in investment returns (DR) between domestic and foreigncountry strategies can be expressed as:

DR : set+1 − st − (it − i∗t )(1)

The nominal effective exchange rate (st ) measures a domestic currency against a single unit offoreign currencies at time t, and is expressed in natural logarithmic form; and nominal interest
rates are shown as it . The expected value is denoted with the superscript (e), and the asterisk
2(∗) indicates a foreign variable. Under the UIPC, Eq. (1) yields a value of 0. This indicates thatinvestors are indifferent to the location of investment; therefore, no movements occur in financial
accounts. Financial movements occur when DR 6= 0. When DR > 0, there are contemporaneous
outflows of net financial assets; when DR < 0, there are financial inflows. Therefore, this study
focuses on adjustment periods when the market is not in equilibrium, and does not hinge strictlyon the UIPC. Alternatively, using the definition of real exchange rates (qt ≡ st − pt + p∗t where
p is a price), Eq. (1) can be restated as:e∆qt+1− ((it − ∆pet+1 ) − (i∗t − ∆pet+1 ))

or

e
∆qt+1
− (Rt − Rt∗ )(2)

where nominal interest rates consist of real interest rates (Rt ) and expected inflation following
the Fisher equation (it − ∆pet+1 = Rt ). While the Fisher equation assumption is required toderive Eq. (2), it is mathematically equivalent to Eq. (1). However, unlike Eq. (1), we canlessen a potential nonlinearity in Eq. (2) caused by zero-bound nominal interest rates, which
many advanced countries have experienced recently. Furthermore, assuming that ∆qt becomes
e ,1 we study whether financial flows can be investigated using the following panel
proxy for ∆qt+1

data specification:

N Fit = a + b∆qit + cdifit + dzit + uit

(3)

∗: a
where subscript i represents countries. N F is the net financial flow and difit = Rit − Rit

positive (negative) N F implies net lending (borrowing). Consistent with implications from theDR, the expected sign of the parameters is positive for b and negative for c, implying that a
depreciation of home exchange rates and/or an increase in foreign interest rates will result in net
financial outflows. It contains the residuals (u) that may represent expectation errors and risk
premium (z) to generalize the UIPC. Eq. (3) is parsimonious, but captures the effect of carrytrades that are designed to make profits by borrowing money in low-interest rate countries, andinvesting it in high-interest rate countries, during stable exchange rate periods (Bhansali 2007).
Carry trades have been used to explain recent movements of some major currencies, such as
Australian dollar, Japanese yen, and Swiss franc.
1

We make this assumption because expected effective exchange rate data are not available. However, theeoverall results from other expectations formations (e.g., ∆qt+1=∆qt+1 ) remain similar to the ones presented inthis paper. Similar remarks apply to calculation of real interest rates.

3

3

Statistical method

A problem in global country analysis is the identification and creation of foreign variables. For
example, while we can identify national interest rates individually, a proxy for foreign interest
rates equivalent to global interest rate(s) is unobservable. In the absence of global interest
rates, the model becomes complicated because all foreign variables enter into a specificationindividually, known as the curse of dimensionality. Thus, we estimate global interest rates usinga factor model, which allows us to decompose interest rates into common and country-specificcomponents.
Consequently, the initial task is to identify the number of common factors. There has been
progress in identifying and estimating factor models (e.g., Bai and Ng (2002) for static factor
models and Stock and Watson (2005) for dynamic factor models). We use the static factor model
because it maps onto the dynamic factor model (Bai and Ng 2007). For countries (i = 1, . . . , N )and time (t = 1, . . . , T ), the static factor model is:

Xit = Λ0i Ft + eit(4)0

The standardized data (X) is decomposed into the common component (Λi Ft ) and idiosyn0

cratic component (eit ). Λi is a vector of factor loadings and Ft is a vector of common factors. To
find the appropriate number of common factors, Bai and Ng (2002) proposed the information
criteria (IC) that consists of two components:

IC(k) = ln(V (k, F )) + kg(N, T )(5)

The first component, ln(V (k, F )), is the average residual variance for k common factors:
P PT
02
V (k, F ) = (N T )−1 N
i=1
t=1 (Xit − Λi Ft ) . The second component, kg(N, T ), is a penalty
function to avoid over-fitting of the model. Several ICs can be constructed by different forms of
the penalty function. Like the standard IC in time series analysis, the smallest IC corresponds tothe model that fits the data most. It indicates the appropriate number of common components(r, 0 ≤ r ≤ k). Here, the following two types of IC (IC1 and IC2) are used.
IC1 = ln(V (·)) + k((N + T )/N T )ln(N T /(N + T ))IC2 = ln(V (·)) + k((N + T )/N T )ln(min{N, T })One potential problem associated with factor models is that, while theoretically correct, it

4becomes difficult to interpret each common factor (Ft = [ft1 , ft2 , ft3 , ..., ftr ]) in an economicallymeaningful way when r increases. In order to circumvent this problem, this study regards Λ0i Ft ,which is driven by global interest rates (ft ), as one variable. Thus, heterogeneous responses ofcountries to global factors are also included in our global variables.

4

Data

Quarterly data on the balance of payments (BoP) from 2000Q1 to 2016Q2 are collected from the
BoP statistics of the International Monetary Fund (IMF). These data are compiled according to
the Balance of Payments and International Investment Position Manual 6th edition. Increases
in assets and liabilities are recorded with a positive sign. The net financial account balanceis calculated as changes in assets minus changes in liabilities. As such, net lendingthat is,the country supplies funds to the rest of the worldis recorded with a positive sign, and net
borrowing, with a negative sign.
Data on effective exchange rates, interest rates, and prices are gathered from the IMF’s
International Financial Statistics (IFS). Prices are proxied by the consumer price index (CPI)and most nominal interest rates by market rates (see Table 1).2 Following the Fischer equation,
real interest rates are obtained by subtracting expected inflation from nominal interest rates.
We calculate expected inflation (∆pet+1 ) as ∆pt . The countries under investigation are selected
purely due to data availability; they include both advanced and developing countries from avariety of geographical locations.
Table 2 provides summary statistics of key data. First, both financial and current accounts
are negative on average, implying that both financial inflows and trade deficits have often coexisted. Second, the BoP is dominated by financial and current accounts, and the capital accountconstitutes only a small fraction of the total external balance. The average value of the financial,
capital, and current balances implies that errors and omissions are significant in the BoP statistics. Third, the correlation coefficients show that the BoP components barely correlate withreal exchange rate depreciation and real interest rates. In contrast, a high correlation between
current and financial accounts is reported in the table. These are the reasons why we focus on
financial, rather than capital accounts.
2

In studies on the IPC, the London Interbank Offered Rates have often been used as benchmark (risk free)interest rates, but are not available for many countries analyzed in this study.

5

5

Empirical results

First, we calculate global interest rates for 51 countries using the factor model. Table 3 reports
the number of common factors in real interest rates based on the ICs with k = 10. The
appropriate number of common factors vary according to ICs, but we find at least one global
rates in all models. The presence of global interest rates is consistent with previous studies using
different statistical methodologies (Gagnon and Unferth 1995, Ratti and Vespignani 2015). Ratti
and Vespignani (2015) also showed that a global interest rate is determined by changes in global
monetary aggregates, oil prices, global output, and global prices. The difference between the
average common and idiosyncratic components has diminished since the 2008 Lehman Shock(Figure 1).
Next, we study the determinants of financial flows based on Eq. (3) using instrument variables, such as a proxy for financial market openness (Chinn and Ito 2006) and the net current
account balance. They are expected to be correlated with explanatory variables. This specification is estimated by fixed and random effects models in the panel data context the former
model is based on the two-stage least square (2SLS) method, while the latter on the error component 2SLS random-effects estimator (Baltagi 2008). Furthermore, the residuals in Eq. (3) aredecomposed into two elements: uit = µi + vit .
As a proxy for risk premiums, we use the VIX, known as a fear index. It is calculatedby the Chicago Board Options Exchange from implied volatility of options prices in the US
equity market. Similar statistics are obtained for Europe (VSTOXX) and Japan (VXJ) fromthe Datastream and Osaka University, respectively. Here, we create two kinds of crisis-related
data one is the VIX (vix), and the other is the average of three fear indexes (crisis).
The results from IV estimation methods are reported in Table 4. We find that there is astrong tendency for countries to have experienced capital outflow at times of financial crises.
This is confirmed by a positive relationship between NF and vix (crisis) because the increase infear indexes implies higher uncertainty in financial markets. This result may be partly because
sizable global financial flows are conducted by large advanced economies, such as the US, Japan,
and Germany. Furthermore, in line with theoretical predictions, interest rate spreads have often
influenced NF negatively with some statistical significance (the 10 percent level). These results
remain generally unchanged regardless of choice of crisis variables.
To check for robustness, we initially study whether our conclusion is sensitive to groups ofcountries (advanced and developing countries). Here, the definition of advanced countries is
consistent with the IMF (http://www.imf.org/en/data), and non-advanced countries are named6developing countries. The results, from the random effects model supported in the previous
analysis, show that the interest rate differentials are more important determinants of NF indeveloping countries than in advanced countries (Table 5). While parameters of interest rate
spreads are statistically significant and negative for developing countries, they are insignificant
for advanced countries. Thus, global interest rates have moved more in tandem with interest
rates of advanced countries, and more representatives of interest rates of these countries. Thisfinding is consistent with the fact that country-specific factors, such as transaction costs, tax,
uncertainty and constraints in borrowing and lending often discussed as reasons for a failure of
IPC, are more prominent in developing countries.
We also analyze the role of global economic and political integration in the NF determination. We thus add their proxies, known as the KOF index of globalization (Dreher 2006),
available through 2014. This comprehensive economic openness index is constructed by utilizing the trade-GDP ratio, foreign direct investment, portfolio investment, and income payments
to foreign countries, as well as the restriction level of hidden import barriers, tariff rates, andcapital account restrictions. The political globalization index is compiled by using the number of embassies, international treaties, and international organizations in countries, as well as
personnel in the UN Security Council missions. According to these indexes, many European
countries, such as the Netherlands, are ranked as highly globalized countries.
Using again the IV method, we confirm our previous conclusion regarding the relationshipbetween NF, interest rates, and exchange rates (Table 6). Furthermore, it shows that globalizedcountries, in terms of economics, tend to have experienced financial outflows, but this effect isstatistically insignificant. In contrast, political globalization influences NF significantly. Indeed,a strong political tie with other countries seems to have helped induce financial inflows. By
the definition of this index, political globalization refers to high political transparency to therest of the world. Therefore, high political integration seemingly functions to reduce country
risk factors, and makes the country more attractive to foreign investors. Our result that noneconomic factors, such as institutional quality (i.e., transparency), are important determinantsof NF in addition to economic factors may be well-known to practitioners, but has not been
investigated because of lack of indicators to represent institutional transparency. Our finding
supplements Bremus and Fratzscher’s (2015) study, which discussed how other non-economic
factors, such as the independence of the regulators, affect credit flows.

7

6

Conclusion

Using a factor model, we show that there are indeed global elements in real interest rates. However, the financial flows are determined more significantly and consistently by the exchange rates
and political transparency: the role of interest rate differentials is found to be weak in advanced
countries. This result supports the convergence of real interest rates among these countries, andimplies that carry trades, which provide a popular explanation for financial movements among
selected advanced countries, are rather limited in the global context.

References
Bai, J. and S. Ng (2002). Determining the number of factors in approximate factor models.
Econometrica 70 (1), 191–221.
Bai, J. and S. Ng (2004). A panic attack on unit roots and cointegration. Econometrica 72 (4),1127–1177.
Bai, J. and S. Ng (2007). Determining the number of primitive shocks in factor models. Journalof Business & Economic Statistics 25 (1), 52–60.
Baltagi, B. (2008). Econometric analysis of panel data (4th ed.). John Wiley & Sons.
Bhansali, V. (2007). Volatility and the carry trade. Journal of Fixed Income 17 (3), 72–84.
Bremus, F. and M. Fratzscher (2015). Drivers of structural change in cross-border banking since
the global financial crisis. Journal of International Money and Finance 52 (Supplement C),
32 – 59.
Chinn, M. D. and H. Ito (2006). What matters for financial development? capital controls,
institutions, and interactions. Journal of development economics 81 (1), 163–192.
Dreher, A. (2006). Does globalization affect growth? evidence from a new index of globalization.
Applied Economics 38 (10), 1091–1110.
Engel, C. (2014). Exchange rates and interest parity. Handbook of International Economics 4,
453–522.
Gagnon, J. E. and M. D. Unferth (1995). Is there a world real interest rate?
international Money and Finance 14 (6), 845–855.

Journal ofLane, P. R. and G. M. Milesi-Ferretti (2007). The external wealth of nations mark ii: Revisedand extended estimates of foreign assets and liabilities, 1970–2004. Journal of international
Economics 73 (2), 223–250.
Levich, R. M. (2013). The Evidence and Impact of Financial Globalization, Chapter Evidence
on Financial Globalization and Crises: Interest Rate Parity, pp. 417–27. Elsevier.
Ratti, R. A. and J. L. Vespignani (2015). What drives the global interest rate. Federal Bank of
Dallas Working Paper (241).
Stock, J. H. and M. W. Watson (2005). Implications of dynamic factor models for var analysis.
NBER Working Paper Series, 11467.

8

Table 1: Country coverage and data
CPI
Albania
Armenia, Republic ofAustralia
BelizeBolivia
BrazilBulgaria
CanadaCape Verde
China, P.R.: Hong Kong
Colombia
Costa Rica
Czech Republic
EstoniaEuro Area
Georgia
Guatemala
Hungary
IcelandIndonesia
IsraelJapan
Korea, Republic ofLesotho
Lithuania
Macedonia, FYR
Mauritius
MexicoMoldova
Mongolia
Nepal
New Zealand
Nicaragua
NorwayPanamaPhilippines
PolandRomania
Russian Federation
Singapore
Slovenia
South Africa
SwedenSwitzerland
ThailandTurkeyUkraineUnited Kingdom
United States
Uruguay
Vanuatu

X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X

Market rate

Deposit rate
X

X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X

Global interest
rate analysis
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X

Note: Data are obtained from the IMF. X indicates that data are available.

9

Financial flow
analysis
X
X
X
X
X
X
X

Advancedcountries

X

X
X

X
X
X
X
X
X
X
X
X

X
X
X

X
X
X
X

X
X
X
X

X
X
X

X
X

X
X
X
X
X

X

X
X
X

X
X

X
X
X
X

X
X

Table 2: Summary statistics of key economic variables

Financial account
Capital accountCurrent account
Correlation
coefficient
Financial account
Capital accountCurrent account
Real exchange
rate depreciation
Real interest rate

Financialaccount
1.000-0.018
0.813
0.012
0.027

Mean
-3533.58
103.89-2256.03
Capitalaccount

Std. Dev.
29345.52
1743.1526264.00
Current
account

Min-298080.00-31782.10-231748.00
Real exchange
rate depreciation

1.000-0.040
0.028

1.000
0.017

1.000

0.011

0.014

0.100

Max
282609.30
15361.00
125357.70
Real interest
rate

1.000

Note: Note: The balance of payments statistics are expressed in millions of US dollars.
Real effective exchange rates are based on the CPI and are indexes. Real interest rates
are calculated using the Fisher equation (see the main text). Data are downloaded
from http://data.imf.org.

Table 3: Number of common factors

IC1
IC2
Average no. of common factors
No. of countries

Real Interest Rate
7
1
4
51

Note: Data are standardized, and the maximum
number of common factors (k) are set as 10. Statistics (IC1 and IC2) are based on Bai and Ng (2002).

10

Table 4: Relationships between financial flows, exchange rates, and interest rate differentials
Full sample
r=7
∆q
dif
vix
Constant
σµ
σv
ρ
r=4
∆q
dif
vix
Constant
σµ
σv
ρ
r=1
∆q
dif
vix
Constant
σµ
σv
ρ
r=7
∆q
dif
crisisConstant
σµ
σv
ρ
r=4
∆q
dif
crisisConstant
σµ
σv
ρ
r=1
∆q
dif
crisisConstantσµσv
ρ

Fixed Effects
Coef. Std Err p-value

Random Effects
Coef. Std Err p-value

Hausman Test
p-value

2.842-0.2670.313-12.751
23.25230.036
0.375

0.619
0.182
0.099
2.346

0.000
0.141
0.002
0.000

2.847
-0.2680.313-12.755
103.17830.036
0.922

0.614
0.180
0.098
17.974

0.000
0.137
0.001
0.478

0.999

2.820
-0.306
0.313-12.69923.24929.855
0.377

0.614
0.181
0.099
2.332

0.000
0.092
0.002
0.000

2.825
-0.3070.313-12.704
102.83929.855
0.922

0.609
0.180
0.098
17.914

0.000
0.089
0.001
0.478

0.999

2.845
-0.3010.315-12.757
23.25430.047
0.375

0.619
0.174
0.099
2.348

0.000
0.084
0.002
0.000

2.850
-0.3020.315-12.762103.43930.047
0.922

0.614
0.173
0.099
18.019

0.000
0.081
0.001
0.479

0.999

12.844-1.6251.914-56.345
30.387115.125
0.065

5.006
0.918
0.800
21.296

0.010
0.077
0.017
0.008

12.878-1.6261.919-56.455
102.020115.125
0.440

4.905
0.903
0.786
27.362

0.009
0.072
0.015
0.039

1.000

12.954-1.8481.944-56.900
30.522116.011
0.065

5.081
0.981
0.816
21.638

0.011
0.060
0.017
0.009

12.978-1.8461.947-56.976101.658
116.011
0.434

4.970
0.962
0.801
27.549

0.009
0.055
0.015
0.039

1.000

12.939-1.7581.936-56.757
30.522115.882
0.065

5.058
0.930
0.811
21.519

0.011
0.059
0.017
0.008

12.961-1.7561.939-56.824102.268
115.882
0.438

4.948
0.913
0.795
27.525

0.009
0.054
0.015
0.039

1.000

Note: Statistics based on the IV estimation. Instruments are net current accountbalances and a proxy for financial market openness. The number of common factors
is denoted as r. The vix is the US fear index, and crisis represents the average of fear
index of Japan, Germany and the USA. ρ measures a fraction of variance due to µi . σµand σv are the standard deviation of µi and vit respectively.

11

Table 5: The role of interest rate differentials in advanced and developing
countries
Full sample
r=7
∆q
dif
crisisConstant
σµ
σv
ρ
r=4
∆q
dif
crisisConstant
σµ
σv
ρ
r=1
∆q
dif
crisisConstantσµσvρ

Advanced countries
Coef. Std Err p-value

Developing countries
Coef. Std Err p-value

5.572-2.2040.458-21.440
105.18145.756
0.841

1.497
1.526
0.252
29.749

0.000
0.149
0.069
0.471

2.325
-0.3830.533-15.4641.516
40.102
0.001

0.682
0.144
0.155
4.025

0.001
0.008
0.001
0.000

5.223
-1.0160.457-22.09499.06243.671
0.837

1.399
1.284
0.241
28.062

0.000
0.429
0.058
0.431

2.379
-0.4240.547-15.776
1.635
40.050
0.002

0.692
0.152
0.159
4.107

0.001
0.005
0.001
0.000

5.181
-0.9180.439-21.703103.632
43.419
0.851

1.378
1.064
0.241
29.291

0.000
0.388
0.068
0.459

2.369
-0.4050.547-15.795
1.646
39.889
0.002

0.690
0.147
0.159
4.108

0.001
0.006
0.001
0.000

Note: Statistics based on the random effects model and the IV estimation. Instruments are net current account balances and a proxy for
financial market openness. The number of common factors is denoted
as r.

12

Table 6: Financial flows and globalization
Full sample

Fixed effects
Coef. Std Err p-value

Random effects
Coef. Std Err p-value

Hausman test
p-value

r=7∆q
dif
crisisEconGlobal
PolitGlobal
Constant
σµ
σv
ρ
r= 4
∆q
dif
crisisEconGlob
PolitGlobal
Constant
σµ
σv
ρ

2.486
-0.2500.452
0.230-0.353-5.45622.68327.2870.409

4.370-1.4903.710
1.210-2.110-0.360

0.000
0.137
0.000
0.226
0.035
0.720

2.510
-0.2580.454
0.201-0.351-3.68427.38427.287
0.502

0.558
0.168
0.120
0.174
0.147
14.593

0.000
0.124
0.000
0.247
0.017
0.801

0.036

2.503
-0.2880.457
0.226-0.356-4.99622.67527.4030.406

4.350-1.7003.710
1.190-2.120-0.330

0.000
0.090
0.000
0.236
0.034
0.743

2.530
-0.2960.459
0.198-0.353-3.30927.38227.403
0.500

0.564
0.169
0.121
0.174
0.148
14.619

0.000
0.080
0.000
0.257
0.017
0.821

0.102

r= 1
∆q
dif
crisisEconGlobal
PolitGlobal
Constant
σµ
σv
ρ

2.505
-0.2820.457
0.226-0.360-4.63522.67727.4160.406

4.370-1.7403.720
1.180-2.140-0.300

0.000
0.082
0.000
0.237
0.033
0.761

2.530
-0.2890.458
0.197-0.356-3.01527.36927.416
0.499

0.562
0.162
0.121
0.174
0.148
14.622

0.000
0.073
0.000
0.258
0.016
0.837

0.100

Note: Statistics based on the random effects model and the IV estimation. Instruments are net current account balances, a proxy for financial market openness, andthe globalization index for culture proximity, personal contacts, information flows,
and economic restriction (see Dreher 2006). The number of common factors is denoted as r.

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Figure 1: Cross-country averages of common and idiosyncratic factors in real interest

14