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Using data from 15 European Union economies, we quantify the real effects of supply-side frictions due to the financial disintegration of European countries since the 2008 financial crisis. We develop a multi-country general equilibrium model with heterogeneous countries and destination-specific financial frictions. Financial institutions allocate capital endogenously across countries, determining the cost of capital to firms and the wealth of nations. The cost of financial disintegration is reduced access to capital for firms which results in lower output. Financial disintegration leads to a 0.54% fall in output in Europe since the crisis. We also estimate benefits of further financial integration.
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The Real Eects of Financial (Dis)integration:
A Multi-Country Equilibrium Analysis of Europe
Indraneel Chakraborty1Rong Hai2Hans A. Holter3Serhiy Stepanchuk4
October 24, 2016
Prepared for the Carnegie-Rochester-NYU Conference Series on Public Policy, April 2016
Abstract
Using data from 15 European Union economies, we quantify the real eects of supply-side fric-
tions due to the financial disintegration of European countries since the 2008 financial crisis. We
develop a multi-country general equilibrium model with heterogeneous countries and destination-
specific financial frictions. Financial institutions allocate capital endogenously across countries,
determining the cost of capital to firms and the wealth of nations. The cost of financial disintegra-
tion is reduced access to capital for firms which results in lower output. Financial disintegration
leads to a 0.54% fall in output in Europe since the crisis. We also estimate benefits of further
financial integration.
Keywords: Financial Integration, Global Financial Crisis, Europe, Financial Institutions, Cross-
border Financing.
JEL: E44, E58, F36, F45, F62, G15, G21.
We thank Mark Aguiar, George Alessandria (Editor), Mark Bils, Raphael Boleslavsky, Vidhi Chhaochharia,
Harold Cole, Stefania Garetto (Discussant), Linda Goldberg, Itay Goldstein, Marvin Goodfriend, Urban Jermann,
Narayana Kocherlakota, George Korniotis, Alok Kumar, Leonid Kogan, Salvador Ortigueira, Fabrizio Perri, Tarun
Ramadorai, Dennis Reinhardt, Linda Tesar (Editor), Harald Uhlig, and participants at the Carnegie-Rochester-NYU
Conference on Public Policy and University of Oslo for helpful comments and suggestions. Sarah Khalaf provided
excellent research assistance.
1University of Miami, i.chakraborty@miami.edu. Corresponding author, contact information:
i.chakraborty@miami.edu, Phone: (312) 208-1283, Postal Address: University of Miami School of Business
Administration, 5250 University Drive, 512-A Jenkins Bldg., Coral Gables, FL 33124.
2University of Miami, rhai@bus.miami.edu
3University of Oslo, hans.holter@econ.uio.no
4University of Southampton, s.stepanchuk@soton.ac.uk
1. Introduction
Global financial integration and in particular a more integrated Europe have been important
policy goals, until the 2008 financial crisis. During the crisis, policymakers attempted to reduce fi-
nancial contagion by “ringfencing” risks within national regulatory boundaries. While this helped
limit financial contagion, it also created a segmentation of credit markets. After the financial crisis
and the sovereign debt crisis, the general policy consensus has been that “financial interconnec-
tions can be too destabilizing” (European Commission,2015). Recent academic literature has also
addressed the costs of financial integration (See Bolton and Jeanne,2011;Farhi and Tirole,2014;
Uhlig,2014, among others). In sum, financial integration in Europe seems to have unraveled since
the financial crisis and the sovereign debt crisis.
This paper seeks to quantify the costs of financial disintegration to the European economy.
Such analysis allows a better understanding of the trade-oinvolved between systemic risk reduc-
tion and costs on the economy due to reduced bank lending. We focus on the change in barriers to
capital flows in the European Union since the Great Recession. These international capital flows
could be through equity or debt and could be done by financial institutions such as banks or mutual
funds, or by individuals.
To infer the changes in the barriers to capital flows we develop a multi-country, general equi-
librium model of international capital allocation among heterogeneous countries. In our model,
heterogeneous countries solve an optimal portfolio diversification problem with the objective of
maximizing risk-adjusted returns. We estimate destination-specific haircuts (financial frictions)
that can justify the observed post-crisis capital flows by targeting moments on the share of foreign
investments before and after the financial crisis.5We find that the estimated GDP-weighted finan-
cial frictions in Continental Europe increased by 54.3% between the two time periods 2000-2007
and 2008-2011. We then quantify the impact of these changes on the level and distribution of eco-
nomic activity and welfare. We find that the increase in financial disintegration leads to a 0.54%
drop in GDP. The welfare loss is 0.20 percentage point in terms of consumption equivalence.
5Data on foreign claims outstanding are used for the identification.
1
Trade in the EU has grown significantly for decades. However, the European economy is
divided across political boundaries, with each nation deciding policies for its own benefit. The
right panel of Figure 1(a) shows that business lending in the EU countries, as measured by the
stock of loans reported, has not risen much after the crisis. The left panel of Figure 1(a) shows
that gross incoming cross-border financing, as measured by the stock of loans as a fraction of
output, has been falling steadily since the crisis.6While a portion of the decline in cross-border
financing may be attributable to an increase in sovereign risk and a decline in demand for capital
in Europe, this paper seeks to understand what fraction of this dierence is due to supply side
factors; specifically financial disintegration. Figure 2shows the fraction of incoming cross-border
financing to total financing by domestic and cross-border financial institutions.7The figure shows
that incoming cross-border financing as a fraction of total financing (domestic and cross-border)
has declined.8
Two concerns should be addressed before we move forward. First, did cross-border financing
fall beyond a general decline in domestic financing? Second, to what extent is the fall explained by
a fall in flows to countries with high sovereign risk, such as Greece? Figure 1(b) and Table 1show
the fraction of cross-border financing into a country as a fraction of total financing by domestic
financial institutions (FIs) and cross-border FIs.9The argument is that more financially integrated
countries receive more cross-border financing, and thus a higher fraction suggests more integration.
The left panel of Figure 1(b) shows a longer time period and the six largest European economies.
The right panel focuses on three major economies in continental Europe and shows the decline
in financial integration post-crisis in more detail. Germany, which is the largest capital provider
to the rest of Europe, did not experience a significant drop in cross-border financing, but France
6Data from the U.S. are included for comparison. Data are obtained from Bank for Interna-
tional Settlements (BIS) Statistics Explorer Table C3 (See http://stats.bis.org/statx/srs/table/c3, details in Sec-
tion 2). Data for the right panel are obtained from European Central Bank Statistical DataWarehouse (See
https://sdw.ecb.europa.eu/browse.do?node=8549726) and the Federal Reserve Bank of St. Louis (FRED Economic
Data, See https://fred.stlouisfed.org/series/BUSLOANS/).
7The fractions are calculated using the stock of gross claims outstanding against firms of a specific country in a
year using data from BIS Statistics Explorer Table C3. Details of the calculation are in Section 2.
8Table 1discussed later in Section 2provides detailed information regarding cross-border financing.
9Data for this figure are also obtained from data from BIS Statistics Explorer Table C3.
2
and Spain did. While one may argue that Spain faced sovereign risk and hence lost cross-border
capital (BBB+credit rating from S&P), the right panel shows that the same is true even for France
despite its very high credit quality (AA credit rating from S&P). Thus, cross-border financing fell
beyond a general decline in domestic financing, and this fall is not specific to countries with high
sovereign risk.
The paper seeks to quantify the benefits of financial integration in the run-up to the crisis and
the costs of the disintegration of credit markets since the crisis. The benefit of financial integration
is measured in this paper by an increase in aggregate output and by conducting a welfare analysis in
consumption equivalents. The cost of financial disintegration is also measured in the same manner.
In order to arrive at its conclusions, the paper develops a multi-country general equilibrium
model with heterogeneous countries. Countries dier based on firm productivity, household capi-
tal, labor force characteristics, and destination-specific haircuts. In the model, financial institutions
intermediate household capital flows within the country and across the border to the other 14 coun-
tries. The objective of the capital allocation decision is to maximize risk-adjusted returns through
diversification of assets across the countries. The key friction in this international capital flow deci-
sion is financial, i.e. households from one country obtain a return on the capital invested in another
country based on the level of financial integration between the two countries. As domestic rates of
return on investment are endogenous outcomes per country based on the local demand, and across
our 15 EU country area, the supply of capital, the financial integration constraints create heteroge-
neous costs of capital for firms. The representative firm in each country maximizes profits using a
Cobb-Douglas production function where the firm faces country-specific productivity, labor sup-
ply, the depreciation rate of capital, and output elasticity of capital. Capital and labor rental rates
are determined in equilibrium based on cross-country capital allocation decisions of households in
the 15 countries discussed above.
Using our model, we internally calibrate financial integration parameters by matching the ob-
served data moments on financial institutions’ cross-border financing for each country with the
corresponding moments generated by the model. We calibrate the model parameters separately
3
for the time period before the financial crisis and the time period after the financial crisis. The
calibrated haircuts are consistent with the change in capital stock and the average foreign invest-
ment share in each country between our two periods of interest, 200-2007, and 2008-2011. We
find a 54.3% increase in estimated financial frictions in Continental Europe since the financial
crisis. These two sets of calibration exercises yield two sets of parameters that govern the de-
gree of financial integration and the real side of the economy before and after the financial crisis.
To test the external validity of our model and calibrated financial frictions, we compare the level
of investment per country produced by the model with the data, the calibrated financial frictions
with those obtained in the literature (Fern´
andez, Klein, Rebucci, Schindler, and Uribe,2015), and
model-generated current account levels in each country with those in the data.
After calibrating the model, we conduct two counter-factual experiments to measure the eects
of changes in financial integration on aggregate output. The first experiment estimates the eect of
supply side frictions due to financial separation since the crisis. In order to quantify the eects of
financial disintegration on output and investment, we compare the pre-financial crisis world with a
counter-factual world where the financial integration level is that of the post-financial crisis world.
The results show that financial disintegration leads to a 0.54 percentage point (pp) drop in output
in continental European countries since the financial crisis. In terms of consumption equivalence,
the aggregate welfare loss for European economies is 0.20 percentage point due to financial dis-
integration. We also evaluate the eects of a fully integrated financial system by comparing the
calibrated model economies with a counterfactual scenario where the countries are fully integrated
financially. In this case, the additional output is 0.37 pp. In terms of consumption equivalence,
welfare gains would be an additional 0.81 pp of consumption compared to that in the pre-crisis
period.
Our paper evaluates the change in barriers to financial integration since the European crisis.
We attribute the increase in home bias in assets to a change in this financial friction which is quite
similar to the works in international trade that evaluate the role of trade frictions in the increase in
home bias in trade in the Great Trade Collapse (See Alessandria, Kaboski, and Midrigan,2010a,b,
4
2013). An important alternative explanation could be that business cycles or asset returns have
become more synchronized since the Great Recession. This has lowered the returns to diversifi-
cation.10 It is noteworthy that despite a steady trend of increasing cross-country asset correlation
over the last two decades, cross-border allocation of capital was rising as well for more than a
decade until the financial crisis (Figure 1(b)). Cross-country asset correlation have continued to
increase since the crisis following the two-decade trend while cross-border allocation of capital
has not. Hence, this alternative explanation does not seem to be able to explain the sharp change
in cross-border capital flow since the crisis.11
Our work relates to the extensive literature in global banking. Peek and Rosengren (2000) have
demonstrated the transmission of shocks from Japan to U.S. through U.S. branches of Japanese
banks. Goldberg (2009) notes that foreign banking institutions play an important part in the global
transmission of shocks due to their size. Cetorelli and Goldberg (2012a,b) discuss the importance
of internal capital markets of global banking conglomerates as a channel for transmission of fi-
nancial shocks during the financial crisis. Seminal research including Cole and Obstfeld (1991);
Backus, Kehoe, and Kydland (1992); Stockman and Tesar (1995) has discussed the benefits from
hedging aggregate shocks through holding foreign investments. Aviat and Coeurdacier (2007);
Niepmann (2013,2015); Fillat, Garetto, and Oldenski (2015) investigate capital allocation de-
cisions of multinational corporations including banks and identify non-diversification motives of
investment. Fillat, Garetto, and Goetz (2016) develop a rich structural model of entry into global
banking with the regulatory framework in the U.S. driving the set of assumptions. Their paper
helps understand the relationship between market access, capital flows, regulation, and entry. Our
paper also focuses on the optimal capital allocation problem of financial institutions to estimate
the eect of financial frictions.
10Cross-country asset correlations have been increasing over time, and an increase in cross-country correlation
should lead to a reduction in diversification benefits. See Lewis (2006); Quinn and Voth (2008); Christoersen,
Errunza, Jacobs, and Langlois (2012). This phenomenon has been ongoing for the last two decades.
11Quinn and Voth (2008) who investigate a century of global market equity correlations also find that that reduc-
tion in barriers has been the driving cause of growing correlations during the last quarter century, though increasing
correlated economic fundamentals also matter.
5
Our paper also contributes to the literature on the impact of financial institutions on the real
economy. Starting with the seminal papers by King and Levine (1993a,b), a literature that in-
cludes Beck, Levine, and Loayza (2000), among others, has shown that financial intermediaries
can promote economic growth. The literature has also established that financial integration and
competition between banks help economic growth as they help with greater entry of small busi-
nesses (See Jayaratne and Strahan,1996;Cetorelli and Strahan,2006;Kerr and Nanda,2009).
Rice and Strahan (2010); Gopalan, Udell, and Yerramilli (2011) suggest a general improvement
in bank credit supply at better rates in the presence of bank competition and financial integration.
Jermann and Quadrini (2012) show that financial shocks contributed significantly to the observed
dynamics of real and financial variables of firms during the recent financial crisis. Our paper shows
that financial frictions in terms of cross-border financing can have a significant eect on the real
economy.
Furthermore, our work is related to the research that considers the impact of international trade
and financial frictions on real allocations. Redding and Sturm (2008) provides evidence for the
importance of market access for economic development. Alessandria et al. (2013) show that trade
wedges can reflect the decisions of importers to change their inventory holdings. Caliendo and
Parro (2015) quantify the trade and welfare eects from tarichanges due to NAFTA. Alessandria,
Choi, Kaboski, and Midrigan (2015) study the question of causation between micro volatility and
business cycles. Our paper relates to this literature as we quantify the real eects of supply side
frictions due to the financial disintegration of European countries.
The rest of the paper is organized as follows. Section 2describes the economic background in
Europe and our datasets. Section 3introduces the model. Section 4discusses the calibration and
the numerical methods used for the model solution. Section 5discusses the numerical results and
counterfactual estimates. Section 6concludes.
6
2. Financial Integration in Europe
This section first provides a background in the evolution of financial integration in Europe. It
then discusses cross-border regulatory policies that may have contributed to financial disintegra-
tion in Europe since the crisis. These policies were implemented to reduce the risk of financial
contagion.
2.1. Financial Integration over Time
Financial integration worldwide and in Europe, in particular, was growing for the last three decades
before the financial crisis. Cross-border financing in Europe had tripled as a fraction of GDP
from the year 2000 to the year 2007, to approximately six trillion Euros or 45% of the GDP (See
Figure 1(a)). To investigate how cross-border financing changed in European countries around the
financial crisis, we utilize data from the Bank for International Settlements (BIS). Details regarding
the data are in Appendix A.
Table 1reports the ratio of stock of cross-border claims against domestic firms as a fraction of
stock of total claims outstanding by all financial institutions (domestic and cross-border) against
the firms in the respective country. The table shows significant variation in cross-border financing
across countries and shows a significant decline worldwide after the crisis.12
2.2. Banking Regulation and Financial Frictions
Achieving an integrated financial capital market is a core ambition of the European Union. How-
ever, during the financial and sovereign debt crises, financial integration allowed capital to move
swiftly across political boundaries in a manner that policy makers found destabilizing. Fears of
“sudden stops” similar to earlier crises in Mexico in 1994, and East Asia in 1997 revived previous
discussions regarding the costs of financial integration (Calvo,1998;Chang and Velasco,2001).
In response to the crisis, central banks and policymakers worldwide first stabilized the financial
12While major economies such as the U.S. can withstand this financial separation more easily due to a very large
domestic capital market – indeed cross-border financing had only risen to 16.5% in the U.S. and has now fallen to
11.9% – this is a larger concern for the European economies where trade and labor markets remain integrated but
financial markets face significant disintegration post-crisis.
7
system and since then have focused their attention on ensuring that systemically important finan-
cial institutions are better capitalized and have a clear recovery and resolution plan. In addition,
the European Systemic Risk Board (ESRB) has been entrusted with a macro-prudential oversight
function in Europe. The corresponding institution in the U.S. is the Financial Stability Oversight
Council.
To ensure financial stability, after the financial crisis the European Commission implemented
a series of policy reforms including cross-border banking regulation overhaul: (i) Consultations
on the reorganization and resolution of credit institutions began immediately in December 2007,
which included studying how to ensure transition of assets within a cross-border financial insti-
tution.13 (ii) In addition, Basel II and Basel III capital requirements have been phased in since
the financial crisis.14 (iii) Furthermore, in February 2012, the Liikanen commission was estab-
lished to determine whether, in addition to ongoing regulatory reforms, structural reforms of the
EU financial institutions would strengthen financial stability and improve eciency and consumer
protection, and, if so, to make proposals as appropriate. (iv) Separately, public consultation re-
garding an EU framework for “Cross-Border Crisis Management in the Banking Sector” began in
October 2009. The commission agreed to avoid reliance on taxpayers to stop financial contagion
in the future. The objective of the reform is to create a number of lines of defence against a future
financial crisis that include (a) greater prevention and early intervention, (b) better resolution tools
and cross-border coordination, and (c) resolution funds.15
The resolution fund to finance orderly winding up of financial institutions, a key element of the
resolution mechanism, remains at the national level. The commission recognized the importance
of setting up a single pan-EU fund but pointed to political frictions that make a pan-EU fund or
even further, a global fund, infeasible. Needless to say, if countries are expected to set up their
13See European Commission summary of initiatives on winding-up of credit institutions at http://ec.europa.
eu/finance/bank/windingup/index_en.htm.
14See http://www.bis.org/publ/bcbsca.htm for details on Basel reforms and regulatory framework chronol-
ogy.
15See European Commission Communication at http://europa.eu/rapid/press-release_MEMO-10-214_
en.htm?locale=en.
8
own resolution funds for their institutions, then a moral hazard problem emerges (Holmstrom,
1982). National policymakers’ support for taking cross-border risks declines. This is because the
taxpayers of one country do not wish to insure the financial sector of their country against risks it
takes abroad, as they will be fully internalizing the risks in the case of crisis, but not the rewards in
the case of prosperity, of such cross-border financing.
In sum, a number of policy initiatives have been adopted to reduce the deleterious eects of fu-
ture financial contagion. However, they have also increased cross-border financial frictions (Barth,
Gerard Caprio, and Levine,2013;Fern´
andez et al.,2015;Forbes, Reinhardt, and Wieladek,2016).
Fern´
andez et al. (2015) create a new dataset of capital control restrictions over the last two decades
using the analysis in the Annual Report on Exchange Arrangements and Exchange Restrictions
(AREAER) of International Monetary Fund (IMF).16 Figure E.5 in the Appendix reports the over-
all restrictions index in the 14 European Countries in our sample for which Fern´
andez et al. (2015)
(FRU) report their measure.17 As can be seen, there has been a significant increase in capital con-
trol restriction in recent years. The dataset shows that these restrictions may have started even a
bit earlier that the financial crisis. After the financial crisis, these frictions have stayed in place.
Forbes et al. (2016) argue that increases in microprudential capital requirements tend to reduce
international bank lending and some types of unconventional monetary policy can amplify this ef-
fect. The authors show in particular that the UK’s Funding for Lending Scheme (FLS) significantly
amplified the eects of increased capital requirements and reduced external lending.
The financial crisis provided the counterpoint to the benefits of financial integration that drove
policy before the crisis: the possibility of financial contagion leading to a “doom loop” (See Farhi
and Tirole,2014, among others). This paper does not focus on the costs to the economy due to
possible systemic risk but focuses on the costs to the economy from lesser financial integration.
Answering the question of whether such costs are justified by the potential gains from avoiding
16The AREAER reports the presence of rules and regulations for international transactions by 10 asset categories.
The authors use the narrative description in the AREAER to determine whether or not there are restrictions on inter-
national transactions, with 1 representing the presence of a restriction and 0 representing no restriction.
17Luxembourg is not in the dataset of Fern´
andez et al. (2015).
9
future contagion, is beyond the scope of this paper. The question, however, is an important trade-
othat policymakers must consider (See Stein,2009, for a discussion of benefits and costs of
capital regulation on market eciency).
3. The Model
The main problem of interest is the allocation of capital across countries by financial institu-
tions. This section first discusses the representative household and the representative firm. We then
introduce the financial institution’s problem and define the equilibrium.
3.1. Household
There are Ncountries in the economy. At the beginning of a period, representative households in
country i∈ {1,...,N}have siunits of savings. They invest the savings in the financial institution of
their country, for which they receive return riper unit savings at the end of the period. Households
also supply one unit of labor inelastically at wage wi. Uncertainty in the model is driven by the
country-specific productivity shocks Ai. Both riand wi, which depend on Ai, are stochastic and
vary by country. The amount of savings sivaries by country ias well. The number of households
Liin each country is also allowed to be dierent to capture heterogeneity in sizes of European
countries. Total savings in a country iis therefore SisiLi.
At the end of the period, households of country iconsume the return rifrom their savings si
and their wages wi. Households’ consumption ciis given as follows:
ci=wi+risi.(1)
The assumption of no new savings allows us to keep the household problem static. Our static
model also assumes that there is investment at the beginning and consumption out of output at
the end of the period. A dynamic model with consumption and investment in every period would
certainly be better. Since we are interested in a relatively short period of time, and since the model
solution has a large dimensionality from the number of countries in the EU countries, we believe
10
tractability justifies these assumptions.
3.2. Financial Institutions’s Problem and Financial Integration
In this model, the representative households of each country simply invest their savings in the
domestic financial institution of their own countries. We assume that the financial institution in
country iis owned by the households of the same country. The financial institution then allocates
the capital across the countries to maximize expected utility for their owners (Diamond,1984;
Eaton,1994;Niepmann,2015;Fillat et al.,2016).18 Even though we refer to a financial institution
in the model, in the end, we are building a multi-country model of capital flows in which most
bilateral flows are driven by the pursuit of higher returns and diversification of risk.
Let Ri j denote the gross investment returns a financial institution in country iobtains when
investing in country jand let φi j be the share of assets of a financial institution in country iinvesting
in country j. We impose the no-short-selling constraint that requires all φij to remain non-negative,
φi j 0.19 The financial institution’s problem in country iis to maximize the expected utility of
the representative household, subject to the budget constraint (Eq. 1), a constraint that allocation
fractions across the countries add up to unity, and the no short sale constraint. Thus, the financial
18We are making an assumption, needed for general equilibrium in terms of capital allocation, that these 15 coun-
tries constitute a closed economy. See Appendix B for a discussion of this assumption. To be additionally conservative,
in our counterfactual experiments, we will exclude Great Britain, Ireland, and Luxembourg. The first two countries
have economies that are more connected across the Atlantic Ocean with the U.S. and the Commonwealth nations. The
last country is a financial center. This is even though we estimate the model with all 15 countries. This will ensure that
we capture impact of financial frictions in Continental Europe and not financial frictions that, say, aect capital flow
to Great Britain and Ireland from the U.S.
19Short selling is not economically meaningful in our setup. φij <0 eectively will mean that country iis investing
a negative share of savings in country j, or in other words borrows from country j. We can achieve the same net capital
flows between countries iand jby having a positive investment from country jto country i,φji >0, while keeping
our no-short-selling constraint, φi j 0. Further, we do not observe negative investment shares in the data.
11
institution’s problem is characterized as follows:
max
Φi≡{φi1,...,φiN }
Eu(ci)
s.t. ci=wi+ Φ0
iRisi,
φii +X
j,i
φi j =1,
φi j 0,i,j.(2)
where Φi={φi1, . . . , φiN }is the vector of portfolio allocation and Ri={Ri1,...,RiN }is vector of
returns, and the expectations are taken over the joint distribution of the vector of {A1,...,AN}.
Thus, the final return of the household on total savings is ri= Φ0
iRi. We assume that the household
utility function is CRRA:
u(c)=c1γ
1γ
where γis the risk aversion coecient of the household.
Returns on domestic investment in country i,Rii, are endogenously determined based on supply
of capital Ki, labor force available Li, productivity Aiand wages wi(Eq. 4discussed next in the
firm’s problem). The financial institution’s cross-border returns Ri j depend on country j’s domestic
investment return Rjj and the level of financial integration of country j. As in Gertler and Kiyotaki
(2010); Gertler, Kiyotaki, and Queralto (2012), financial institutions invest in firms by purchasing
productive capital.
Financial frictions due to limited financial integration are captured by parameter θjthat varies
across countries. The parameter θjin eect captures the “haircut” on (i.e., reduction in) investment
return from country jdue to levels of financial integration of the country. The rate of cross-
border investment return in country jfor financial institutions in country idepends upon destination
country j’s level of financial integration:
Ri j =Rj jeθj,i,j.(3)
12
where the investment rate Rjj for each country jis endogenously determined based on demand of
capital from firms in the country and supply of capital from households who can choose among
countries. These endogenous returns together with the variance-covariance matrix of TFP shocks
generate capital flows in the model. These capital flows will be matched to those observed in data
using internally calibrated destination-specific haircuts, i.e. {θ1, . . . , θN}. When country jis fully
financially integrated with the rest of countries, θj=0; and when country j’s capital market is
completely isolated from the rest of the economies (i.e., θj→ ∞), investors can not earn any return
from their investment in country i, i.e. Ri j =0. In such an extreme case, no capital should flow
into the country and the foreign share of investment is 0. Cross-border investments may be more
beneficial as regulatory frictions decline (Houston, Lin, and Ma,2012).
Note that the financial frictions in our setup are only destination specific financial frictions. A
more general approach is to allow for source and destination financial frictions separately. Our
choice of only destination specific financial frictions is partially driven by computational limita-
tions. Further, if the capital flow is intermediated through a third country, the destination financial
frictions will still capture them. However, we feel satisfied with this assumption because as we
will see later in Section 4.3, the calibrated financial frictions correlate strongly with those obtained
by Fern´
andez et al. (2015) in their work on capital control.
3.3. Firm’s Problem
The representative firm in country ienjoys productivity Aiand faces local wage wiand capital
rental rate Rf
ii. The firm is assumed to have Cobb-Douglas production function, and maximizes
profits πby choosing rented capital Kias follows:
πi=max
Ki
AiKαi
iL1αi
iwiLiRf
ii Ki(4)
where Aiis the aggregate productivity shock in country iand αiis the output elasticity of capital.
Let A={Ai,...,AN}be the vector of productivity shocks for all N countries. We assume that
13
Afollows a multivariate log-normal distribution as follows:
Aln N(µ, ).
Here, we allow the productivity shocks between dierent countries to be correlated with each other.
This correlation can create cross-country linkages of financial returns based on the real economy.
In particular, if cov(Ai,Aj),0, the productivity shocks between countries iand jare correlated.
Thus, the representative financial institution in country imay have an incentive to invest in country
j’s production in order to reduce the risk of the portfolio of a household in country i.
The rate of return for the financial institution that rents capital in country iis:
Rii =1+Rf
ii δi
where δiis the capital depreciation rate in country i.
3.4. Equilibrium
We consider an equilibrium where a representative financial institution in country imakes the
allocation decision regarding the share of domestic and foreign investment. Given the level of
financial integration vector {θi|i[1,N]}, and similarly productivity shocks {Ai}, deposits per
capita {si}, labor supply {Li}and initial capital {Ki0}in each country i, the equilibrium consists of
a cross-country distribution of financial institutions’ asset allocation decisions, capital returns and
firms’ capital levels {φ
i1, . . . , φ
iN ,R
ii,K
i}and is defined as follows:
1. {φ
ii,{φ
i j}j,i}solve banks’ optimization problem as follows:
max
Φi≡{φi1,...,φiN }≥0
Eu(ci)
s.t. ci=wi+ Φ0
iRisi,
φii +X
j,i
φi j =1
14
2. φ
ii siLi+PN
j,iφ
ji sjLj+Ki0(1 δi)=K
ifor every country i.
3. w
i=(1 αi)Ai(K
i)αiLαi
ifor every country i.
4. R
ii =1+αiAi(K
i)αi1L1αi
iδifor every country i.
5. R
i j =R
j jeθjfor every country.
Asset returns are determined in equilibrium based on supply of capital Ki, labor force available
Li, productivity Aiand wages wi(Eq. 4).20 The incentives of the bank of country iin the model are
aligned with those of the households of the same country.21
4. Calibration and Model Solution
This section describes calibration details and equilibrium solution approach. To calibrate the
model, as discussed in Section 2, we use the data from the Penn World Tables, the Bank of In-
ternational Settlements and Lane and Milesi-Ferretti (2007). The original dataset in Lane and
Milesi-Ferretti (2007) was extended to include the data up to 2011 by Lane and Milesi-Ferretti.
4.1. External Calibration of Productivity and Demographic Parameters
Table 2reports the relevant statistics of the 15 countries in the European Union that we focus on.
These 15 countries were in the European Union before the year 2000. Our sample selection ensures
that we have a long enough time series prior to 2007 for each country and thus can calculate the
variance and covariance matrix of their joint productivity. The data period covered is from 2000
to 2011. One year after the introduction of Euro in 1999, we consider the year 2000 to be a good
20One could use a partial equilibrium approach and utilize asset returns series over time directly. In such a case,
a possibility would be that a partial equilibrium model may overestimate the returns from diversification given the
exogenous nature of returns, and then also overestimate the financial frictions given that the data will not support the
level of diversification estimated by the model with exogenous returns. Another issue is that given that cross-border
capital flows are composed of multiple asset classes through multiple investor types, it is hard to convincingly calculate
a correct measure of returns. Hence, we think using TFP data and allowing returns to be endogenous provides us a
way to avoid these dicult questions.
21Exploring the impact of an incentive misalignment between households and financial institutions, specifically
due to macro-prudential restrictions, is beyond the scope of this work.
15
starting point. 2011 is the latest year for which data is available from Penn World Tables (PWT)
8.1.
We use the PWT 8.1 data for each country ito obtain capital depreciation rate δi, the share of
labor compensation in GDP αi, the value of capital stock Ki, output in each country Yi, investment
level Iiin each country. Labor supply Liis the product of the number of persons employed and
the average annual hours worked by the employed person. We calculate the amount of savings
Siin country iusing investment and current account data. The reason we do this is because not
all capital stock may be available for reallocation to other countries, in a relatively short period
of time. In a dynamic model, one could imagine all capital being able to freely move, but there
would be a capital adjustment cost, and adjustment would be gradual. In our model, the amount of
savings Siis not meant to be a measure of wealth but that of allocatable resources. Another possible
approach to calibrating savings could have been capital stock plus net foreign assets adjusted for
what portion of capital is available for cross-border reallocation. The value of initial capital in
country iis calculated as Ki0=(KiIi)/(1δi).22 We calibrate TFP parameter for each country and
each period as the residual in the production function, Ai=Yi/Kαi
iL1αi
i. We also calibrate our
parameters using an alternative approach, that we discuss in detail in Section 5. In the alternative
approach, we take TFP as given and calculate the output for each country Yi. The results remain
quantitatively and qualitatively similar in that case as well. We normalize Yiand Liin 2005 in the
Great Britain in our sample to 1. Additional details regarding variable construction are in Appendix
A.
Table 2summarizes our estimates of the parameter values for the two subperiods that we are
interested in, 2000-2007 and 2008-2011. The table reports significant dierences in the reported
characteristics. The relative prosperity and productivity, with respect to the U.S., also varies signif-
icantly. Table E.10 in the Appendix reports the cross-country variance-covariance matrix of loga-
22As discussed in Section 3, the final rented capital observed in a country is the sum of initial capital available to
the country and the sum of investments made by the resident financial institutions of all 15 European countries out of
their savings. The resident financial institutions of each country, in turn, make those investments based on return-risk
trade-owhere return on capital is endogenously determined based on production function parameters of the target
country and investments by other representative financial institutions of the other countries.
16
rithm of TFP levels in the 15 European countries for the full sample period. It is notable that there
is substantial variation in the variance of the logarithm of TFP levels across nations. Denmark
has one of the lowest levels at 1.47 percentage points standard deviation, whereas Luxembourg
which is an active conduit for allocation of capital within Europe has the highest reported level at
13.71 percentage points standard deviation. The German standard deviation of the logarithm TFP
is almost half of that of France, at 3.87 percentage points standard deviation. The TFPs in most
economies are positively correlated, which is expected, with the only exception being Portugal and
Denmark. Among the larger economies, the German TFP is more correlated with that of Great
Britain (0.70) than of France (0.35). The TFPs of Italy and Spain are even less correlated with that
of Germany, having correlations of 0.21 and 0.23 respectively.
4.2. Equilibrium Solution Algorithm
The optimal allocation of capital for each country is dependent on the optimal allocation of the
capital of all other N1 countries, where N=15 countries in our case. This is because the
return on investment is dependent on total capital invested, which is dependent on the allocation of
other countries. In other words, every country is allocating across Ncountries based on a N×N
variance-covariance matrix of productivity shocks, and based on the N×1 return vector for each
country which is dependent on allocation of capital of other countries into the target country and
the haircut faced by each country in investing in the target country.
Given the model parameters, we find the equilibrium of the model by solving the system of
equilibrium conditions that consist of the first-order optimality conditions for the financial institu-
17
tions and market-clearing conditions:
siEu0(ci)(Ri j RiN )+λi j λiN =0,i,j,
λi jφi j =0, λi j 0, φi j 0,i,j,
ci=wi+ Φ0
iRisi,i,
φii +X
j,i
φi j =1,i,
φ
ii siLi+
N
X
j,i
φ
ji sjLj+Ki0(1 δi)=K
i,i,
w
i=(1 αi)Ai(K
i)αiLαi
i,i,
R
ii =1+αiAi(K
i)αi1L1αi
iδi,i,
R
i j =R
j jeθj,i,j.
This system includes the budget constraints and market clearing conditions from Section 3.4, and
the first-order conditions that describe the solution to the investment allocation problem of the
representative financial institutions. Because of the presence of the short-selling constraints (we
allow representative banks to take only non-negative positions in member countries), these first-
order conditions take the form of the Kuhn-Tucker inequalities, with λi j denoting the corresponding
Kuhn-Tucker multipliers. We use Zangwill and Garcia (1981) approach to replace the Kuhn-
Tucker inequalities with equations by an appropriate change of variables, and solve the resulting
system of non-linear equations, given the values of θj.23
Given the high dimensionality of the problem, we approximate the expectations in the system
of equilibrium conditions specified above using monomial integration rules which were introduced
by Judd (1998) to economics literature, and described in detail in Maliar and Maliar (2014). For a
given vector µand matrix Σwhich, given our assumption of log-normality, fully characterize the
distribution of {A1,...,An}, monomial integration rules construct a relatively small set of nodes and
23See Appendix C for more details.
18
weights distributed within a multidimensional hypercube. The computational expense of mono-
mial rules grows polynomially with the dimensionality of the problem, which makes them ideal
for problems of large dimensions such as the present one.
The equilibrium solution output is a matrix of allocation of capital by all countries in all other
countries conditional on certain haircuts. The calibration of these financial frictions is discussed in
Section 4.3.
4.3. Internal Calibration of Financial Friction Parameters
We calibrate destination-specific haircuts (i.e., financial frictions {θ1, . . . , θN}) that can justify the
observed post-crisis capital flows for each country by targeting moments on the share of foreign
investments before and after the financial crisis. Table 1reports for each country and each year the
gross cross-border financing that the country receives and claims outstanding in a certain year as a
fraction of the sum of total claims outstanding in terms of lending by domestic financial institutions
and gross cross-border financing. Data used to calculate the fractions are obtained from the BIS as
described in detail in Section 2.
To calibrate the N×1 vector of financial frictions which generate N×1 optimal foreign in-
vestment shares for Ncountries, we utilize a fully parallelizable global optimization algorithm as
suggested in Guvenen (2011). The algorithm combines a global search stage with a local stage.
The algorithm uses the Sobol sequence to obtain a series of “quasi-random” starting points for
the global search stage. Then it conducts a local search from that starting point using the Nelder-
Mead’s downhill simplex algorithm. A Sobol sequence provides a way to search thoroughly and
at the same time, eciently across the parameter space of frictions. The algorithm can be easily
parallelized by allowing each central processing unit core to do a separate local search. To reduce
the dimensionality of the problem, and to have an exact match in terms of moments, we keep the
haircuts eθiequal for a target country irrespective of which country is investing into it. For exam-
ple, the haircut faced by a German financial institution and a French financial institution on their
investments in Greece is the same.24
24Ideally, we would like to match bilateral flows for the 15 ×15 matrix of countries. However, we do not have
19
The first column of Table 3reports the calibrated financial frictions eθiafter the financial crisis
using our model and data. The second and third columns of the table compare model simulated
moments and targeted moments from data. The average values for the period 2008–2011 are
reported in the “Data” column of Foreign Shares panel. We note that the data moments regarding
the foreign share of total investment is well matched by the model generated moments. On average,
the model tracks foreign investment shares in the data within 0.68%. The reported distance is
calculated as a sample standard deviation. As an out of sample test, we also tabulate the model
generated aggregate investment level in a country and the data on investment obtained from PWT
8.1. As seen in the fourth and fifth columns in Table 3, our model predicted aggregate investment
shares closely track the corresponding moments in the data.
To test the external validity of the estimated financial frictions, we utilize an index of overall
restrictions created by Fern´
andez et al. (2015). Fern´
andez et al. (2015) creates a dataset of capital
control restrictions on both the inflows and outflows of 10 categories of assets for 100 countries
over the period of 1995–2013. We find that, for the post-crisis sample period of 2008–2011 in
continental Europe,25 the cross-sectional correlation between the overall restrictions index and our
estimated financial frictions is 22.9 percent. Figure 3reports the correlations. The two vectors
are demeaned before calculating correlation. Luxembourg is not in the dataset of Fern´
andez et al.
(2015), and hence is excluded in the reported correlation. We also exclude Portugal which has
a very high capital control index number compared to Spain, even though our estimates suggest
Spain and Portugal are similar in terms of capital control. GDP weighted cross-sectional correla-
tion estimate is 0.787, as shown in the right panel of Figure 3.
access to the matrix of bilateral ratios for each country, we only have aggregate data separated at foreign/domestic
level. Hence, we do not have enough moments available for matching bilateral flows. Further, it is computationally
much more challenging to expand the set of moments from 15 to 225. Because of this limitation on the number of
moments matched, we cannot be confident that the model does a good job matching bilateral flows – we can only
speak to cross-border flows aggregated at the country level.
25As discussed earlier, we exclude the Great Britain, Ireland and Luxembourg in the counterfactuals in the next
section, and hence we exclude them in the external validity test as well. This is even though we estimate the model
with all 15 countries. This approach ensures that we capture the impact of financial frictions in continental Europe and
not financial frictions that aect capital flow to Great Britain and Ireland from, say, the U.S., as the two island nations
are relatively more connected to the U.S. and the Commonwealth nations. A similar reason applies for Luxembourg
which is a financial center within Europe.
20
As another supporting evidence for the model, we calculate the correlation of the model gen-
erated current account numbers with those obtained from data at the beginning of the period. The
correlation between model generated current account numbers and data is 0.692. Given that Greece
was facing unique economic challenges during this period, we also calculate the correlation with-
out Greece, which is 0.696. Note that investment level moments and therefore current account
moments were un-targeted.26
4.4. Change in Financial Frictions from Before to After the Crisis
This section investigates how calibrated financial friction changed over time. To answer this ques-
tion, we also calibrate the financial frictions for the pre-2007 period, i.e. the period before the
financing crisis. Table 4shows the calibrated frictions before the financial crisis and those cal-
ibrated post financial crisis. The next two columns also show the change in financial frictions
post-crisis with equal weights and with GDP weights respectively. We note that overall financial
frictions have increased between the two periods. This supports findings in the literature on capital
controls (Fern´
andez et al.,2015).
The GDP weighted eθfor European countries pre-crisis is 0.9831, and post-2007 is 0.9739.
The friction in this case is 1 eθ, which is the loss due to limited financial integration. Therefore,
the haircut increases from 1.69% to 2.61% from before the financial crisis to after the crisis. This
is a 54.3% increase in financial frictions post crisis.27
As we see, the standard deviation in haircuts on investment returns are approximately 2.43
percent before the crisis and 1.68 percent after the crisis for the continental European countries
(except Luxembourg). This shows the significant importance of financial frictions, which with
such limited variation are able to generate large eects on output, as Section 5shows next. It
is important to note that cross-sectional variation is important for allocation of capital within the
26Our experiment is intended to match reasonably well the current accounts at the beginning of the period essen-
tially by construction. Table D.9 in Appendix D shows that, as expected, the dierence between the current account
generated by the model and the current account from the data is equal to the negative of the dierence between the
investment in the model and in the data.
27This number is calculated as 1eθPost
1eθPre 1=0.543.
21
period, but at the same time change in financial frictions from the pre-crisis period is important for
determining the overall impact of financial frictions on the economy.
5. Counterfactual Experiment Results
We conduct two counterfactual experiments to measure the benefits of reduction in supply side
frictions due to financial integration.
5.1. Impact of Financial Disintegration on European Recovery
In order to quantify the eects of financial integration on outcomes and capital investment, we
compare the pre-financial crisis world with a counterfactual world where the financial integra-
tion level is that of the post-crisis level Θpost-2007 and the real side of the economy remained at the
pre-crisis level {αpre2007,Apre2007, δpre2007,Lpre2007,Spre2007 ,Kpre2007
0}. The dierence in outputs
between these two scenarios measures the real eects of changes in financial integration. The rea-
son we conduct this counterfactual as opposed to the opposite where we take estimated financial
frictions from the pre-2007 period and use the data from the post-2007 period is because signif-
icant economic and regulatory interventions were conducted post financial crisis. The pre-2007
period data is relatively more undisturbed. Further, we are interested in financial frictions from the
post-2007 period, some of which have been imposed by regulatory authorities themselves through
“ringfencing” of economies. Hence, applying net frictions calibrated from the post-crisis period
on the undisturbed pre-crisis period economic data is the appropriate exercise in our opinion.
Panel A of Table 5reports the results. The first counterfactual reports the output (Y), wage
(W) and return on capital (R) for continental European economies if they faced the same financial
frictions as before the financial crisis. The financial frictions have been calibrated using the model
and data from the post-financial crisis period as described in Section 4. The results reported are
scaled versions of the same characteristics for each country in the pre-crisis period. We focus
on continental Europe in the counterfactuals because the United Kingdom is a major financial
center and Ireland has been going through structural changes over the last decade. Further, the two
English-speaking island nations are relatively more connected to the U.S. and the Commonwealth
22
nations. Therefore, capital from around the world flew into these countries before the crisis and left
after the crisis. This is not the case in continental Europe which is more or less a closed economy.
We also drop Luxembourg because it is a financial center within continental Europe.
Overall, Panel A of Table 5shows that if financial frictions post-crisis existed in the pre-crisis
period, all else being equal, the GDP of continental European countries (ex-Luxembourg) will be
0.54 pp lower.28 This estimate is in line with other estimates of the impact on GDP of banking
sector overhaul (See, for example Slovik and Courn´
ede,2011, who estimate that the impact of
Basel III on GDP growth is between -0.05 to -0.15 percentage point). All else being equal includes
pre-2007 levels of depreciation rate, labor, TFP, output elasticity of capital, initial capital available
to each country, and investable savings (See Panel A of Table 2). In addition, labor wages would
have been 0.72 percentage lower across continental Europe before the financial crisis if the same
level of financial frictions as those calibrated post-financial crisis existed. The marginal product of
capital would also have been 0.71 percent lower.
Focusing on individual countries, the GDP of Germany, which is 27 percentage of the continen-
tal European economy in our data period, would have been 1.02 percentage lower. France, with 19
percent of the continental European economy, would have suered a 0.25 percentage lower output.
Greece, a country that faced a significant uncertainty post-financial crisis regarding its future in
Europe, would have suered a 0.63 percentage lower output if similar financial constraints existed
before the crisis. However, the aggregate results are GDP-weighted and thus are not driven by
Greece, which is only 2.6 percent of the European output in our sample.
5.2. Benefits of an Integrated Financial System
In this counterfactual experiment, we evaluate the eects of a fully integrated financial system by
comparing the calibrated model economies with a counterfactual scenario where all countries are
fully integrated financially. We implement this counterfactual simulation by setting θi=0 for every
28Table E.11 in the Appendix shows that the average GDP growth rate for the post-crisis period (2008–2011) is
eectively zero. It also shows that the growth rate of the continental European Union countries has been approximately
2.39% percentage points per year for the two-decade period of 1991–2011.
23
country i∈ {1,...,N}. When θi=0, country iis fully integrated with the rest of the European
Union countries. In other words, there are no haircuts due to financial frictions θon investment
returns for any financial institution residing in any country, irrespective of the investment being
domestic or foreign. The dierence in outputs between an integrated Europe with θi=0 and the
actual European data provides an estimate of the gains from financially integrative policies.
Panel B of Table 5reports the results of the counterfactual economy where there are no haircuts
for cross-border financing. The panel shows that if financial frictions could be eliminated in the
pre-crisis period, all else being equal, the GDP of continental European countries would have been
0.37 pp higher. As before, all else being equal includes pre-2007 levels of depreciation rate, labor,
TFP, output elasticity of capital, initial capital available to each country, and investible savings
(See Panel A of Table 2). In addition, labor wages would have been 0.26 percentage higher across
continental Europe before the financial crisis in an economy with a completely integrated financial
system. The marginal product of capital would also be higher by 0.22 percent.
In contrast to the case when post-crisis financial frictions have hurt Germany, which is the
largest economy in continental Europe, if financial frictions are eliminated, we find that Germany
per se is not a major beneficiary (it gains 0.03 percentage point in output). However, French output
increases by 0.77 percentage point and Spanish economic output increases by 90 basis points.
5.3. Welfare Eects of Financial Frictions
Sections 5.1 and 5.2 discussed the costs of financial disintegration and benefits of financial integra-
tion in terms of output. In this section, we estimate the welfare eects of these two counterfactu-
als. The approach we utilize is similar to that in Conesa, Kitao, and Krueger (2009). Specifically,
we ask the following question: by what percentage gdo we have to change consumption in the
benchmark case, where benchmark consumption is given by cBfor the household to be indierent
between living in the benchmark case and living in the counterfactual case where consumption is
cCF . This percentage gin our case given a CRRA utility function is given by:
g=u(cCF )
u(cB)
1
1γ
1,(5)
24
where risk aversion coecient γ=2.
Table 6reports the welfare consequences in terms of consumption equivalence described above.
Columns (1) and (3) report the ratio of un-weighted and GDP-weighted consumption (in percent-
age) lost due to financial frictions that were imposed in the post-crisis period. Columns (2) and (4)
report consumption equivalent gains compared to the pre-crisis benchmark consumption in case
that financial frictions could be completely eliminated. Column (3) of the table shows that in terms
of consumption equivalence, the aggregate welfare eect on European economies is 0.20 percent-
age point. The benefits of eliminating financial frictions are reported in the final column at 0.81
percentage points in terms of consumption in the pre-crisis period.
5.4. Robustness Tests
This section conducts two robustness tests with alternative time periods and productivity measure.
Change in Financial Frictions from the Financial Crisis till 2015
Section 4.4 calibrates two frictions for each country (pre-2007 and 2007 to 2011). An important
concern may be that choice of periods may understate the change in barriers. The change in foreign
investment from 2007 to 2015 is possibly larger than the change from the pre-2007 to post-2007
periods. To address this concern, this section makes a comparison of the 2006 to 2008 period with
the 2011-15 period regarding financial frictions and output.29
Table 7reports the estimated frictions in years 2006-08 and those estimated in years 2011-15.
The next two columns report the change in financial frictions for years 2011-15 with equal weights
and with GDP weights respectively. The haircut increases from 2.42% to 3.18% from 2006-08 to
years 2011-15, a 31.2 percent increase in financial frictions. The estimated financial friction for
the 2011-15 is higher than that of the pre-2007 period in Section 5.1. The final column reports
the output (Y) for continental European economies (except Luxembourg) if they faced the same
financial frictions as in period 2011-15 in years 2006-08. It shows that had the financial frictions
29One issue that we face is that while we have cross-border financing data available for 2011-2015, the economic
data from Penn World Tables 8.1 and Lane and Milesi-Ferretti (2007) stops in 2011. Therefore we use 2010-11
economic data and 2011-15 financial data for the exercise.
25
in Continental Europe observed in the period from 2011–15 existed in the period 2006-08, then the
aggregate output would have been 79 basis points lower. Thus, the output fall is larger than that
estimated in Section 5.1.
Alternative Measure of Shocks
In an alternative approach to calibration, in place of estimating TFP parameter for each coun-
try as a residual in the production function, we download productivity data from the PWT 8.1.30
Using this data, we calculate outputs which are somewhat dierent from the output provided in the
data. We then estimate financial frictions and use estimated frictions to conduct the counterfactual
experiments. Table E.12 in the Appendix shows that results remain similar: if financial frictions
post-crisis existed in the pre-crisis period, we find that the output of the continental European econ-
omy would be 0.47 basis points lower. Further, Panel B shows that if financial frictions could be
eliminated in the pre-crisis period, the GDP of continental European countries would have been 41
basis points higher.
Overall, our results show that additional gains for the European economy can be obtained over
and beyond restoration of financial integration to the levels before the crisis. Our work does not
take into account the costs of financial integration which include systemic risk experienced during
the financial crisis. We just want to underscore the benefits of financial integration in this work, so
that policymakers can make the appropriate trade-o.
6. Conclusion
The European experiment of political and economic integration is unparalleled. The financial
crisis, the resulting worldwide tremors, the sovereign debt crises across Europe and various geopo-
litical problems including the recent British vote for exiting the union have created significant
challenges for the European policymakers.
30Data for the U.S., total factor productivity at constant national prices, is available as “rtfp”, which normalizes
AUS =1 for 2005. Data for other countries is available as “ctfp” relative to the U.S. that year. We obtain the values
for Aifor each country as the product of these two series.
26
This paper seeks to highlight the benefits of financial integration that Europe enjoyed before
the crisis and the costs of financial disintegration since then. Our analysis shows that financial
disintegration leads to 0.54 pp drop in GDP since the crisis. In contrast, if financial frictions are
reduced, even beyond what existed before the financial crisis, we find that European GDP could
increase by an additional 0.37 pp. Thus, we argue that while financial separation is useful to limit
contagion during a crisis, such ringfencing comes at certain costs borne by firms and consumers at
other times.
Two important limitations of our work are worth mentioning. First, we employ a static model
which does not take into account the relative dynamics of the European economies beyond the
productivity inter-relationships that we consider. While a static model may be sucient for our
purposes in this work, it may not be sucient for future work which may consider, say, long-
term equilibrium implications of variation in financial frictions across countries. We also do not
consider systemic risk in this model, because other researchers have investigated it very carefully.
The policymakers face a trade-o. Our work seeks to assist them and the European Union in
general on their path to a more prosperous union.
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0.1 .2 .3 .4 .5
Fraction of GDP
1980q1 1985q1 1990q1 1995q1 2000q1 2005q1 2010q1 2015q1
Year quarter
EU countries U.S.
100 120 140 160 180
Scaled business lending
2006q1 2009q1 2012q1 2015q1
Year quarter
EU countries U.S.
(a) Cross-border Financing and Commercial & Industrial Lending around the Crisis
.1 .2 .3 .4 .5 .6
Cross-border lending
1990q1 1995q1 2000q1 2005q1 2010q1 2015q1
Time
Germany France
United Kingdom Spain
Italy Netherlands
.2 .25 .3 .35 .4
Cross-border lending
2000q1 2005q1 2010q1 2015q1
Time
Germany France
Spain
(b) Cross-border Financing over time
Figure 1: Cross-border Financing in Europe around the Crisis
Note: The left figure of Panel (a) reports the cross-border claims outstanding as a fraction of GDP against the respective
economies. Data are obtained from BIS Statistics Explorer Table C3 (See http://stats.bis.org/statx/srs/table/c3,
details in Section 2). GDP data are obtained from PWT 8.1. The right figure of Panel (a) reports business loans in EU
countries and in the U.S. normalized to 100 in the year 2006. Data are obtained from ECB Statistical Data Warehouse (See
https://sdw.ecb.europa.eu/browse.do?node=8549726) and the Federal Reserve Bank of St. Louis (FRED Economic
Data, See https://fred.stlouisfed.org/series/BUSLOANS/). Panel (b) of the figure reports the incoming cross-border
claims outstanding against each country as a fraction of total claims outstanding by all financial institutions who have claims
outstanding (domestic and foreign) against the respective country in the respective years. Data from BIS is used to calculate
cross-border financing (BIS Statistics Explorer Table C3, details in Section 2).
31
Figure 2: Cross-border Financing before the crisis (2007) and recently (2014)
The figure reports the incoming cross-border claims outstanding against each country as a fraction of total claims outstanding by all financial institutions who have claims outstanding
(domestic and foreign) against the respective country in the respective years. Data from BIS is used to calculate cross-border financing (BIS Statistics Explorer Table C3, details in
Section 2). Cross-border financing data on Israel, Norway, Russia, and Turkey are for reference only. Those nations are not part of the 15 EU countries analysed in this paper.
32
Figure 3: Financial Friction in European Economies
Note: The figure tests the external validity of the estimated financial frictions, with respect to an index of overall restrictions
created by Fern´
andez et al. (2015) (FRU). Using the Annual Report on Exchange Arrangements and Exchange Restrictions by IMF,
Fern´
andez et al. (2015) creates a dataset of capital control restrictions on both inflow and outflows of 10 categories of assets for
100 countries over the period of 1995–2013. We find that, for the post-crisis sample period of 2008-2011 in continental Europe, the
cross-sectional correlation between the overall restrictions index and our estimated financial frictions is 22.9 percent (left panel).
The two vectors are demeaned before calculating correlation to avoid spurious correlation. Luxembourg is not in the dataset of
Fern´
andez et al. (2015), and hence is excluded in the reported correlation. We also exclude Portugal which has a very high capital
control index number compared to Spain, even though our estimates suggest Spain and Portugal are similar in terms of capital
control. The right panel shows that GDP weighted cross-sectional correlation estimate is 78.7 percentage points.
33
Table 1: Cross-border Financing: 15 European Union Countries
Note: This table reports the incoming cross-border claims outstanding against each country as a fraction of total claims outstanding by all financial institutions who have claims outstanding
(domestic and foreign) against the respective country in the respective years. Data from BIS is used to calculate cross-border financing (BIS Statistics Explorer Table C3, details in
Section 2).
Country 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Austria 0.315 0.309 0.298 0.293 0.293 0.318 0.314 0.326 0.305 0.274 0.272 0.263 0.271 0.256 0.280 0.283
Belgium 0.374 0.401 0.386 0.423 0.464 0.512 0.541 0.549 0.451 0.401 0.403 0.381 0.377 0.359 0.359 0.383
Denmark 0.235 0.223 0.194 0.210 0.191 0.219 0.253 0.269 0.253 0.242 0.243 0.244 0.243 0.242 0.234 0.262
Finland 0.339 0.364 0.331 0.306 0.335 0.343 0.391 0.399 0.433 0.423 0.460 0.506 0.447 0.449 0.463 0.488
France 0.289 0.305 0.286 0.281 0.305 0.339 0.353 0.358 0.328 0.296 0.300 0.281 0.267 0.260 0.276 0.291
Germany 0.277 0.286 0.276 0.269 0.260 0.284 0.265 0.277 0.277 0.261 0.285 0.276 0.287 0.252 0.272 0.298
Greece 0.424 0.402 0.397 0.378 0.392 0.389 0.377 0.375 0.353 0.346 0.267 0.210 0.269 0.274 0.302 0.235
Ireland 0.892 0.919 0.915 0.763 0.720 0.705 0.699 0.739 0.796 0.457 0.390 0.381 0.343 0.360 0.377 0.389
Italy 0.259 0.265 0.254 0.250 0.258 0.279 0.283 0.289 0.255 0.231 0.203 0.168 0.157 0.149 0.163 0.164
Luxembourg 0.797 0.804 0.811 0.829 0.828 0.840 0.856 0.873 0.702 0.637 0.658 0.658 0.551 0.537 0.528 0.519
Netherlands 0.370 0.375 0.378 0.359 0.378 0.399 0.397 0.421 0.376 0.344 0.327 0.343 0.340 0.320 0.323 0.335
Portugal 0.491 0.530 0.539 0.570 0.563 0.572 0.572 0.546 0.487 0.434 0.356 0.311 0.279 0.247 0.260 0.262
Spain 0.318 0.327 0.352 0.366 0.376 0.394 0.382 0.369 0.360 0.322 0.277 0.237 0.205 0.198 0.200 0.203
Sweden 0.937 0.936 0.305 0.341 0.320 0.335 0.351 0.363 0.314 0.306 0.300 0.307 0.293 0.304 0.314
United Kingdom 0.472 0.493 0.483 0.492 0.501 0.516 0.521 0.549 0.553 0.471 0.482 0.470 0.454 0.432 0.404 0.410
34
Table 2: Summary Statistics of 15 EU Countries
Note: The set of countries are those that joined the European Union by 1 January 1995. The next set of countries joined on 1st
May 2004. Our sample selection ensures that we have long enough time series observations prior to 2007 for each country and thus
can calculate the variance and covariance matrix of their joint productivity. Data period is from 2000 to 2011. One year after the
introduction of Euro in 1999, we consider the year 2000 to be a good starting point. This is because 2011 is the latest year for which
data is available from Penn World Tables (PWT) 8.1 (Lane and Milesi-Ferretti,2007). (i) Capital depreciation rate δis the “delta”
series from PWT 8.1. (ii) Labor supply Lis the product of the number of persons employed (“emp” series) and the average annual
hours worked by the employed person (“avh” series in PWT). (iii) The share of labor compensation αin GDP is obtained from “labsh”
series in PWT, (iv) the value of capital stock Kis obtained from “ck” series, and (v) output in each country, Yis the “cgdpo” series.
(vi) Investment level Ifor each country per year is obtained from “csh” and “cgdpo” series. (vii) The value of initial capital in country
iis calculated as Ki0=(KiIi)/(1 δi). (viii) We estimate TFP parameter for each country and each period as the residual in the
production function, Ai=Yi/Kαi
iL1αi
i.Yiand Liin 2005 in Great Britain in our sample is set to 1. (ix) Savings, S, is the sum of
current account and investment. The table reports significant dierences in the reported characteristics. The relative prosperity and
productivity, with respect to the U.S., also varies significantly.
Panel A: Years 2000–2007
δL TFP αK Y S K0I
AUT 0.0430 0.1410 0.6273 0.3794 0.4654 0.1391 0.0352 0.4501 0.0347
BEL 0.0428 0.1375 0.7410 0.3757 0.5183 0.1671 0.0435 0.4972 0.0423
DEU 0.0377 1.1746 0.7140 0.3642 4.1276 1.3241 0.2701 4.0191 0.2599
DNK 0.0405 0.0903 0.6846 0.3512 0.2904 0.0930 0.0231 0.2792 0.0226
ESP 0.0339 0.6480 0.6059 0.3468 1.9358 0.5668 0.1640 1.8254 0.1718
FIN 0.0358 0.0854 0.5963 0.4092 0.2875 0.0835 0.0212 0.2775 0.0199
FRA 0.0351 0.8125 0.7241 0.3715 2.8709 0.9316 0.1963 2.7725 0.1954
GBR 0.0423 0.9913 0.7461 0.3649 2.0414 0.9600 0.1743 1.9436 0.1794
GRC 0.0368 0.2049 0.4531 0.4733 0.4067 0.1279 0.0317 0.3867 0.0342
IRL 0.0388 0.0734 0.8614 0.5130 0.1444 0.0877 0.0208 0.1281 0.0212
ITA 0.0414 0.9112 0.5335 0.4600 3.3247 0.8757 0.2259 3.2297 0.2285
LUX 0.0434 0.0097 0.7665 0.4222 0.0404 0.0135 0.0045 0.0379 0.0042
NLD 0.0383 0.2429 0.7821 0.3802 0.8181 0.3006 0.0618 0.7903 0.0580
PRT 0.0428 0.2064 0.4331 0.3181 0.3747 0.1077 0.0288 0.3588 0.0312
SWE 0.0484 0.1468 0.7797 0.3478 0.3276 0.1511 0.0326 0.3125 0.0301
Panel B: Years 2008–2011
δL TFP αK Y S K0I
AUT 0.0417 0.1452 0.5844 0.3931 0.6372 0.1517 0.0404 0.6238 0.0394
BEL 0.0435 0.1485 0.6401 0.3804 0.7667 0.1774 0.0534 0.7456 0.0536
DEU 0.0367 1.1927 0.6731 0.3910 5.3457 1.4433 0.2972 5.2598 0.2791
DNK 0.0414 0.0917 0.6837 0.3119 0.4149 0.1004 0.0259 0.4068 0.0249
ESP 0.0342 0.6665 0.5673 0.3494 3.5068 0.6752 0.1978 3.4182 0.2056
FIN 0.0344 0.0893 0.5615 0.3946 0.4118 0.0917 0.0229 0.4032 0.0225
FRA 0.0345 0.8271 0.6497 0.3756 4.3790 1.0048 0.2386 4.2849 0.2417
GBR 0.0403 0.9966 0.6622 0.3673 3.0760 0.9972 0.1843 3.0090 0.1883
GRC 0.0391 0.2073 0.4185 0.4825 0.5713 0.1415 0.0285 0.5614 0.0318
IRL 0.0404 0.0702 0.7075 0.4705 0.2854 0.0959 0.0176 0.2788 0.0179
ITA 0.0406 0.9189 0.4859 0.4486 4.5110 0.9112 0.2266 4.4602 0.2318
LUX 0.0424 0.0116 0.5846 0.4960 0.0609 0.0154 0.0053 0.0583 0.0050
NLD 0.0372 0.2515 0.7178 0.3878 1.2232 0.3332 0.0721 1.2004 0.0674
PRT 0.0403 0.1997 0.4113 0.3378 0.5730 0.1172 0.0289 0.5644 0.0313
SWE 0.0486 0.1583 0.7064 0.3642 0.4520 0.1639 0.0378 0.4381 0.0352
Table 3: Calibration of Financial Integration Haircuts
Note: This table reports estimation of financial frictions obtained using post financial crisis data and the model in the paper. The
estimation of friction is using moments on foreign share of financing. The aggregate investment moments are reported to show out of
sample validity. Discounts are reported relative to the minimum discount estimated (Portugal), where numbers reported are eθ.
Internal Panel A: Targeted Mom. Panel B: Untargeted Mom.
Calib. Pars. Foreign Inv. Share Agg. Inv.
Discount Model Data Model Data
AUT 0.9773 0.3049 0.3083 0.0387 0.0394
BEL 0.9843 0.4532 0.4563 0.0526 0.0536
DEU 0.9611 0.2670 0.2743 0.2116 0.2791
DNK 0.9942 0.2176 0.2243 0.0262 0.0249
ESP 0.9955 0.3524 0.3605 0.2287 0.2056
FIN 0.9769 0.3456 0.3510 0.0304 0.0225
FRA 0.9769 0.3016 0.3145 0.1775 0.2417
GBR 0.9537 0.4989 0.5034 0.2147 0.1883
GRC 0.9518 0.3803 0.3918 0.0322 0.0318
IRL 0.9272 0.7910 0.7940 0.0529 0.0179
ITA 0.9792 0.2589 0.2671 0.2429 0.2318
LUX 0.9621 0.8251 0.8298 0.0226 0.0050
NLD 0.9623 0.3836 0.3846 0.0687 0.0674
PRT 1.0000 0.5505 0.5479 0.0415 0.0313
SWE 0.9490 0.5013 0.5036 0.0357 0.0352
|model-data|0.00677
36
Table 4: Financial frictions over time
Note: The table reports the estimated frictions before the financial crisis and those estimated post financial crisis. The next two columns
also show the change in financial frictions post crisis with equal weights and with GDP weights respectively. The GDP weighted eθfor
European countries pre-crisis is 0.9831, and post-2007 is 0.9739. The friction in this case is 1 eθ, which is the loss due to limited financial
integration. Therefore, the haircut increases from 1.69% to 2.61% from before the financial crisis to after the crisis, a 54.3 percent increase
in financial frictions post crisis.
Pre-2007 Post-2007 Change GDP wted.
change
AUT 0.9846 0.9773 -0.0074 -0.0010
BEL 0.9900 0.9843 -0.0057 -0.0010
DEU 1.0000 0.9611 -0.0389 -0.0515
DNK 0.9996 0.9942 -0.0054 -0.0005
ESP 0.9955 0.9955 0.0000 0.0000
FIN 0.9738 0.9769 0.0030 0.0003
FRA 0.9851 0.9769 -0.0082 -0.0077
GRC 0.9148 0.9518 0.0370 0.0047
ITA 0.9549 0.9792 0.0243 0.0213
NLD 0.9807 0.9623 -0.0184 -0.0055
PRT 0.9995 1.0000 0.0005 0.0001
SWE 0.9748 0.9490 -0.0258 -0.0039
Sum -0.0448
GDP Loss 0.0169 0.0261 -0.0092
37
Table 5: Counterfactuals: European Economy without Financial (dis)integration
Note: This table reports the two counterfactuals conducted. The first counterfactual reports the output (Y), wage (W) and return on capital
(R) for continental European economies (ex-Luxembourg) if they faced the same financial frictions before the financial crisis. The financial
frictions have been estimated using the model and data from the post financial crisis period. The second counterfactual reports the same
economic characteristics in case the European economy faced no financial frictions.
Panel A: Post-crisis frictions Panel B: No fin. frictions
Y W R Y W R
AUT 0.9928 0.9928 0.9930 0.9986 0.9992 0.9991
BEL 0.9844 0.9842 0.9844 1.0024 1.0025 1.0025
DEU 0.9898 0.9897 0.9898 1.0003 1.0003 1.0000
DNK 0.9968 0.9963 0.9965 1.0011 1.0006 1.0000
ESP 1.0018 1.0018 1.0010 1.0090 1.0091 1.0088
FIN 0.9976 0.9976 0.9983 1.0024 1.0017 1.0025
FRA 0.9975 0.9975 0.9975 1.0077 1.0076 1.0074
GRC 0.9937 0.9942 0.9940 0.9984 0.9991 0.9987
ITA 1.0001 1.0000 1.0000 1.0043 1.0044 1.0049
NLD 0.9900 0.9901 0.9900 1.0027 1.0026 1.0021
PRT 0.9889 0.9896 0.9892 1.0000 1.0000 1.0000
SWE 0.9841 0.9839 0.9844 1.0020 1.0015 1.0019
Total 0.9946 0.9928 0.9929 1.0037 1.0026 1.0022
38
Table 6: Welfare eects of financial frictions
Note: This table estimates the welfare losses from the financial (dis)integration of European economies post crisis, and also the possible
welfare gains in counterfactual economy with no financial frictions. Columns (1) and (3) report the ratio of un-weighted and GDP weighted
consumption (in percentage) lost due to financial frictions that were imposed in the post-crisis period. Columns (2) and (4) report consump-
tion equivalent gains compared to the pre-crisis benchmark consumption in case that financial frictions could be completely eliminated.
Un-weighted GDP-Weighted
Post-crisis No fin. Post-crisis No fin.
frictions frictions frictions frictions
(1) (2) (3) (4)
AUT -0.2280% 0.4958% -0.0065% 0.0141%
BEL -0.1833% 1.0347% -0.0063% 0.0354%
DEU -1.0359% 0.0783% -0.2810% 0.0212%
DNK -0.1597% 0.0682% -0.0030% 0.0013%
ESP 0.0000% 0.1226% 0.0000% 0.0142%
FIN 0.1004% 0.9505% 0.0017% 0.0163%
FRA -0.2240% 0.9031% -0.0427% 0.1723%
GRC 1.3260% 3.3288% 0.0347% 0.0872%
ITA 0.8480% 1.8650% 0.1521% 0.3345%
NLD -0.5083% 0.9766% -0.0313% 0.0601%
PRT 0.0152% -0.3702% 0.0003% -0.0082%
SWE -0.7149% 1.8219% -0.0221% 0.0564%
Average -0.0637% 0.9396%
Total -0.2041% 0.8050%
39
Table 7: Financial frictions and output growth for 2006-08 and 2011-15.
Note: This table reports the estimated frictions in years 2006-08 and those estimated in years 2011-15. The next two columns
show the change in financial frictions post crisis with equal weights and with GDP weights respectively. The GDP weighted eθ
for Continental European countries (except Luxembourg) for years 2006-08 is 0.9758, and for years 20011-15 is 0.9682. The
friction in this case is 1 eθ, which is the loss due to limited financial integration. Therefore, the haircut increases from 2.42% to
3.18% from before the financial crisis to after the crisis, a 31.2 percent increase in financial frictions post crisis. The final column
reports the output (Y) for continental European economies (except Luxembourg) if they faced the same financial frictions as in
period 2011-15 in years 2006-08.
2006-08 2011-15 Change GDP weighted Counterfactual
change Output
AUT 0.9801 0.9753 -0.0048 -0.0007 0.9914
BEL 0.9812 0.9846 0.0034 0.0006 0.9903
DEU 0.9675 0.9560 -0.0115 -0.0167 0.9894
DNK 0.9893 0.9945 0.0053 0.0005 1.0220
ESP 1.0000 0.9940 -0.0060 -0.0040 0.9928
FIN 0.9777 0.9748 -0.0028 -0.0003 1.0000
FRA 0.9823 0.9758 -0.0065 -0.0064 0.9882
GRC 0.9579 0.9497 -0.0082 -0.0012 0.9900
ITA 0.9875 0.9734 -0.0141 -0.0130 0.9940
NLD 0.9597 0.9637 0.0039 0.0013 1.0027
PRT 0.9995 1.0000 0.0005 0.0001 0.9879
SWE 0.9478 0.9455 -0.0023 -0.0004 0.9871
Sum -0.0402 0.9921
GDP Loss 0.0242 0.0318
40
The Real Eects of Financial (Dis)integration:
A Multi-Country Equilibrium Analysis of Europe
(Internet Appendix)
Appendix A. Data
Appendix A.1. Cross-border Capital Flow Data
We utilize data from the Bank for International Settlements (BIS) to investigate how cross-border
financing has changed in Europe. Table C3 from BIS Statistics Explorer provides detailed in-
formation about issuance and amounts outstanding by residence and nationality of issuer for 137
countries.31 The dataset breaks down claims outstanding by domestic and international claims
against domestic firms, i.e. cross-border financing for each country. We obtain series [2,1] and
series [16,1] for amounts of total and cross-border claims outstanding respectively for each coun-
try for each year in our sample period.32 Cross-border transactions occur either directly between
a foreign capital provider and a firm, or between a foreign capital provider’s subsidiary inside the
country and the firm. In the latter case, an accounting identity on the books of the domestic sub-
sidiary of the foreign capital provider ensures that for every domestic asset on the books of the
subsidiary (which are claims against domestic firms), there is a cross-border liability (to the parent
capital provider) outstanding in the books as well. Series [16,1] of Table C3 thus provides the sum
of these two types of international capital provision.
The series provide us the gross stock of claims outstanding against the domestic firms of a
country period by period. Since, we use the data for calibrating a model of multi-country portfolio
allocation, the stock of claims outstanding is appropriate in our opinion. Furthermore, since we
allow cross-border claims to be present bilaterally for each country-pair in the model, gross claims
31See http://stats.bis.org/statx/srs/table/c3.
32See http://stats.bis.org/statx/srs/tseries/DEBT_SEC2/Q.DE.3P.1.1.1.A.A.TO1.A.A.A.A.A.
I?t=c3&c=DE&p=20134&i=2.1 and http://stats.bis.org/statx/srs/tseries/DEBT_SEC2/Q.DE.3P.1.1.
C.A.A.TO1.A.A.A.A.A.I?t=c3&c=DE&p=20142&i=16.1 respectively for the data on amounts outstanding per pe-
riod per country, with an example case of Germany.
1
outstanding as provided by the data are appropriate for calibration in our case. Both series are
in billions of U.S. dollars. The dataset is rich in additional data including gross issuance, net
flows, breakdown of amounts outstanding by banks compared to other financial and non financial
corporations, and currency of claims outstanding.
Appendix A.2. Country Level Data
We use the PWT data for country level data. (i) Capital depreciation rate δis obtained from “delta”
series; (ii) Labor supply Lis the product of the number of persons employed (“emp” series) and
the average annual hours worked by the employed person (“avh” series in PWT); (iii) To obtain the
values for αi, we use the data on the share of labor compensation in GDP (“labsh” series in PWT).
(iv) The value of capital stock Kis obtained from “ck” series, and (v) output in each country, Yis
the “cgdpo” series. (vi) Investment level Ifor each country per year is obtained from “cshi” and
“cgdpo” (Y) series.
We calculate the value of initial capital in country ias Ki0=(KiIi)/(1 δi). In our model
we assume that at the beginning of the period, each country has a certain amount of savings, Si,
which the economic agents in this country decide how to invest. We do not model the consumption
versus savings decision in that time period but use current account and investment data to determine
allocatable resources. This is because all capital stock may not be available for reallocation to other
countries in a relatively short period of time. Thus, savings is not a measure of wealth but that of
allocatable resources in our model. Finally, TFP parameter for each country and each period is the
residual in the production function, Ai=Yi/Kαi
iL1αi
i. In an alternative approach, we take TFP
as given and calculate the output for each country Yi. We normalize Yiand Liin 2005 in the Great
Britain in our sample to 1.
Appendix A.3. Importance of Equity Flows compared to Debt Markets in Europe
In our work, we consider the ratio of cross-border claims (which may include all types of securities
such as debt, equity or more complicated contracts) obtained by domestic firms as a fraction of to-
tal financing obtained by firms, (i.e. within country liabilities and liabilities obtained from across
the border) to calibrate financial frictions. In this section, we investigate the importance of equity
2
financing in general and specifically if households take equity positions to oset exposure to coun-
tries taken through debt position. First, we believe that given that in Europe, financing is mostly
done by banks, looking at debt to identify the ratio (not level) of domestic and foreign investment
is acceptable. Second, we would like to stress that we only use the BIS Statistics Explorer data
from Table C3 to measure the ratio rather than the level of foreign investment in a country. The
BIS data is an established source for financial flow data (See Niepmann,2015;Goldberg,2016,
among others).
Next, we need to show, that equity financing correlates with debt financing. Otherwise, equity
financing may oset debt financing and the use of ratio of cross-border lending obtained only from
debt financing data may not be a good assumption. We investigate the importance of European
equity markets for our results, and to verify our assumption that equity and debt flows correlate.
Table A.8 shows the summary statistics of the foreign debt and equity liabilities data of the Euro-
pean countries in our sample during the sample period 2000-2011. Using the updated data from
Lane and Milesi-Ferretti (2007), we find that, for our sample period in our set of European coun-
tries, the correlation between foreign debt and equity liabilities as a fraction of domestic capital
is very high at 0.9323. The correlation between year over year changes in debt as a fraction of
capital, and equity as a fraction of capital is 0.5753. Thus, it is not the case that equity exposures
are osetting debt exposures to a country in the data. Furthermore, the average ratio of equity with
respect to debt is approximately 26.45% in our sample. Thus, debt financing is the primary source
of financing for the European countries.
3
Table A.8: Debt and Equity Data from Penn World Tables
Note: Panel A reports the mean level of Debt/K, Equity/K and Equity/Debt in our sample of 15 European countries over the
sample period (2000–2011). Panel B reports the correlation over time between debt and equity levels and changes respectively
for the countries. 2011 is the latest year for which data is available from Penn World Tables (PWT) 8.1 and Lane and Milesi-
Ferretti (2007). The table reports significant dierences in the reported characteristics. The relative prosperity and productivity,
with respect to the U.S., also varies significantly.
Panel A: Country level liabilities
Debt/K Equity/K Equity/Debt
AUT 0.4747 0.1242 0.2591
BEL 0.6807 0.4058 0.6213
DNK 0.4062 0.1169 0.2894
FIN 0.2980 0.0794 0.2705
FRA 0.4011 0.1111 0.2776
DEU 0.3683 0.0806 0.2189
GRC 0.3583 0.0357 0.1040
IRL 3.0156 0.6045 0.2138
ITA 0.2356 0.0329 0.1407
LUX 8.6741 8.4490 1.0048
NLD 0.7720 0.2647 0.3431
PRT 0.4551 0.1010 0.2224
ESP 0.3003 0.0990 0.3278
SWE 0.5352 0.2367 0.4410
GBR 1.2657 0.1668 0.1323
Mean 0.5357 0.1417 0.2645
Panel B: Additional Statistics
Debt-Equity Corr. 0.9323
Debt-Equity Change Corr. 0.5753
4
Appendix B. Europe as (an almost) Closed Economy
As we discuss in Section 3, we consider general equilibrium in terms of capital allocation
within the 15 European countries. A natural question is whether the assumption that Europe is a
closed economy is too strong. Figure B.4 plots current account as a fraction of output of Germany
and the remaining European countries in our dataset for the sample period 2000-2011. Data are
obtained from Lane and Milesi-Ferretti (2007), which provides data on foreign assets and foreign
liabilities for a large sample of countries for the period 1970-2011.
The left panel shows that current account as a fraction of GDP for our set of countries is on av-
erage approximately 0.5 percentage points. The second panel shows that there is a strong negative
correlation between the current account of Germany and the remaining 14 countries, suggesting
that capital is getting mostly allocated within the sample of countries. These observations provide
confidence in our model’s assumption where countries allocate capital only within the set of 15
European countries.
Figure B.4: Current Account of European Economies
Note: The figure plots current account as a fraction of output of Germany and the remaining European countries in our dataset
for the sample period 2000-2011. Data are obtained from Lane and Milesi-Ferretti (2007).
5
Appendix C. Computational Algorithm
We find the solution by solving the system of equilibrium conditions that include the first-
order optimality conditions for the financial institutions and market-clearing conditions which is
described in Section 4.2. Because of the presence of the short-selling constraints, this system
includes Kuhn-Tucker inequalities. We use the “Garcia-Zangwill trick” (See Zangwill and Gar-
cia,1981) to replace these inequalities with equations. This approach is essentially a change of
variables, and works as follows. We fix a positive integer k2 (we use k=2), and define:
α+=(max[0, αGZ ])k, α=(max[0,αGZ ])k.
Note that α+0, α0 and α+α=0.
Next, we reformulate the equilibrium system of equations as:
siEu0(ci)(Ri j RiN )+α+
i j α+
iN =0,i,j,
α
i j φi j =0,i,j,
ci=wi+ Φ0
iRisi,i,
φii +X
j,i
φi j =1,i,
φ
ii siLi+
N
X
j,i
φ
ji sjLj+Ki0(1 δi)=K
i,i,
w
i=(1 αi)Ai(K
i)αiLαi
i,i,
R
ii =1+αiAi(K
i)αi1L1αi
iδi,i,
R
i j =R
j jeθj,i,j.
The resulting system is equivalent to the one in Section 4.2 but does not contain inequalities. This
allows us to use a non-linear solver to obtain a solution.33 We approximate the integrals that
33We use Knitro as a non-linear solver. The commercial software link is https://www.artelys.com/en/
optimization-tools/knitro.
6
represent the expectations in the above equations using the monomial integration rules described
in detail in Maliar and Maliar (2014).
7
Appendix D. Current Account and Investment
Table D.9 reports the model generated current account and investment levels. It then compares
the dierence in current account values from data and model and the same for investment levels
respectively. Data are from Penn World Tables (PWT) 8.1 and Lane and Milesi-Ferretti (2007).
As expected, by construction, the dierences for each country in terms of model and data match
of current account and investment levels sum upto zero.
Table D.9: Dierence between model and data generated Current Account and Investment
Note: The table reports the model generated current account, investment levels, the dierence in current account values from
data and model and the same for investment levels respectively. Data are from Penn World Tables (PWT) 8.1 and Lane and
Milesi-Ferretti (2007).
CA (model) I (model) CA I
AUT 0.0017 0.0387 0.0007 -0.0007
BEL 0.0008 0.0526 0.0010 -0.0010
DEU 0.0856 0.2116 0.0675 -0.0675
DNK -0.0003 0.0262 -0.0013 0.0013
ESP -0.0309 0.2287 -0.0231 0.0231
FIN -0.0075 0.0304 -0.0079 0.0079
FRA 0.0611 0.1775 0.0642 -0.0642
GBR -0.0304 0.2147 -0.0264 0.0264
GRC -0.0037 0.0322 -0.0004 0.0004
IRL -0.0353 0.0529 -0.0350 0.0350
ITA -0.0163 0.2429 -0.0111 0.0111
LUX -0.0173 0.0226 -0.0176 0.0176
NLD 0.0034 0.0687 -0.0013 0.0013
PRT -0.0126 0.0415 -0.0102 0.0102
SWE 0.0021 0.0357 -0.0005 0.0005
Appendix E. Additional Tables and Figures
8
Figure E.5: Capital Control Restrictions and output growth rates in European Economies
Note: Using the Annual Report on Exchange Arrangements and Exchange Restrictions by IMF, Fern´
andez et al. (2015) creates a
dataset of capital control restrictions on both inflow and outflows of 10 categories of assets for 100 countries over the period of
1995–2013. The figure reports the overall restrictions index over the last 20 years in the 14 European Countries in our sample
for which Fern´
andez et al. (2015) (FRU) report their measure. Luxembourg is not in the dataset of Fern´
andez et al. (2015). The
second panel reports the output growth rates for the Continental EU countries for the two decade period of 1991–2011.
9
Table E.10: Variance-covariance for log(TFP)
Note: This table reports the variance-covariance matrix of logarithm of Total Factor Productivity for the 15 EU countries in the sample for the whole sample period 2000–2011. Reported
numbers are scaled as follows for readability: σi j ×103.
iso AUT BEL DEU DNK ESP FIN FRA GBR GRC IRL ITA LUX NLD PRT SWE
AUT 1.449 2.586 1.289 0.169 1.030 1.168 1.751 2.153 1.518 3.903 1.365 4.580 1.552 0.801 2.022
BEL 2.586 6.342 1.715 0.244 3.301 2.490 5.094 4.801 3.857 8.625 4.612 10.056 3.194 1.985 3.557
DEU 1.289 1.715 1.499 0.132 0.414 1.006 0.937 1.696 1.004 3.098 0.552 3.494 1.329 0.525 1.877
DNK 0.169 0.244 0.132 0.219 0.146 0.172 0.211 0.150 0.209 0.655 0.273 0.062 0.063 -0.024 0.263
ESP 1.030 3.301 0.414 0.146 2.222 1.094 3.183 2.322 2.263 4.470 3.037 4.747 1.445 1.126 1.276
FIN 1.168 2.490 1.006 0.172 1.094 1.363 1.908 2.076 1.480 3.306 1.766 4.014 1.432 0.697 1.669
FRA 1.751 5.094 0.937 0.211 3.183 1.908 4.780 3.695 3.266 6.613 4.540 7.812 2.359 1.731 2.180
GBR 2.153 4.801 1.696 0.150 2.322 2.076 3.695 3.905 2.988 6.850 3.286 7.839 2.660 1.497 3.067
GRC 1.518 3.857 1.004 0.209 2.263 1.480 3.266 2.988 3.102 6.137 2.861 5.273 1.857 1.081 2.125
IRL 3.903 8.625 3.098 0.655 4.470 3.306 6.613 6.850 6.137 15.956 5.611 11.963 4.278 2.059 5.934
ITA 1.365 4.612 0.552 0.273 3.037 1.766 4.540 3.286 2.861 5.611 4.761 6.853 2.002 1.567 1.746
LUX 4.580 10.056 3.494 0.062 4.747 4.014 7.812 7.839 5.273 11.963 6.853 18.812 5.636 3.746 5.973
NLD 1.552 3.194 1.329 0.063 1.445 1.432 2.359 2.660 1.857 4.278 2.002 5.636 2.026 1.181 2.141
PRT 0.801 1.985 0.525 -0.024 1.126 0.697 1.731 1.497 1.081 2.059 1.567 3.746 1.181 0.941 0.918
SWE 2.022 3.557 1.877 0.263 1.276 1.669 2.180 3.067 2.125 5.934 1.746 5.973 2.141 0.918 3.082
10
Table E.11: European Economy Average Output Growth Rates
Note: This table reports the average output growth rates in the Continental EU economies during the sample period using Penn
World Tables 8.1 data.
2000-2007 2008-2011 1991-2011
AUT 0.0236 0.0069 0.0275
BEL 0.0231 0.0055 0.0236
DEU 0.0242 0.0026 0.0237
DNK 0.0223 0.0044 0.0236
ESP 0.0477 -0.0025 0.0362
FIN 0.0310 -0.0031 0.0224
FRA 0.0242 0.0041 0.0211
GRC 0.0350 -0.0134 0.0272
ITA 0.0146 -0.0087 0.0152
NLD 0.0378 -0.0020 0.0313
PRT 0.0222 -0.0014 0.0267
SWE 0.0278 0.0046 0.0244
GDP Weighted avg. 0.0265 0.00001 0.0239
11
Table E.12: Alternative Calibration and Counterfactuals: European Economy without Financial (dis)integration
Note: This table reports the two counterfactuals conducted using an alternative approach regarding calibration, where TFP data
directly downloaded from Penn World Tables 8.1 is used rather than estimating it. The first counterfactual reports the output
(Y), wage (W) and return on capital (R) for continental European economies if they faced the same financial frictions before the
financial crisis. The financial frictions have been estimated using the model and data from the post financial crisis period. The
second counterfactual reports the same economic characteristics in case the European economy faced no financial frictions.
Panel A: Post-crisis frictions Panel B: No fin. frictions
Y W R Y W R
AUT 0.9934 0.9935 0.9933 1.0004 1.0006 1.0000
BEL 0.9863 0.9864 0.9864 1.0046 1.0047 1.0045
DEU 0.9937 0.9937 0.9937 1.0034 1.0034 1.0031
DNK 0.9981 0.9980 0.9980 1.0019 1.0021 1.0030
ESP 1.0006 1.0006 1.0010 1.0065 1.0065 1.0071
FIN 1.0018 1.0020 1.0016 1.0073 1.0072 1.0072
FRA 0.9990 0.9990 0.9991 1.0084 1.0084 1.0082
GRC 0.9805 0.9805 0.9806 0.9854 0.9853 0.9856
ITA 0.9965 0.9966 0.9967 1.0017 1.0017 1.0020
NLD 0.9943 0.9941 0.9940 1.0070 1.0070 1.0068
PRT 0.9846 0.9845 0.9847 0.9932 0.9931 0.9933
SWE 0.9895 0.9894 0.9895 1.0080 1.0079 1.0082
Total 0.9953 0.9954 0.9954 1.0041 1.0040 1.0041
12
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