Risk Diversi cation, Financial Integration and...

INSTITUT D'ETUDES POLITIQUES DE PARISÉcole doctorale

Risk Diversi�cation, Financial Integration

and Foreign Investors' Pro�le

Alessandro Giovannini

Mémoire présenté pour le Master enDiscipline : Economie

Mention : EPP, PhD Track

Directeur de mémoire : Nicolas Coeurdacier2013-2014

... sentirai la mia forza nel cammino

Risk Diversi�cation, Financial Integration

and Foreign Investors' Pro�le

Alessandro Giovannini

Department of Economics, Science Po

May 20, 2014

Abstract

This paper intends to test if investors diversify the risk due to domestic economic �uctuations, by

investing in foreign �rms and let them to specialize and exploit comparative advantages. The greater

specialization is then expected to increase the volatility of the owned �rm. We empirically assess to

which extent the volatility of �rms is due to activities of �rms under foreign ownership, both controlling

for idiosyncratic risk diversi�cation, physical distance between shareholding/controlled �rms and do-

mestic/foreign investors' pro�le. Following, Kalemli-Ozcan, Sørensen, and Volosovych (2010) we provide

empirical evidence that risk sharing increases the volatility of controlled �rms and therefore enhances spe-

cialization in production. To the best of our knowledge, this well-established and important theoretical

proposition has not been tested before at so disaggregated level.

To empirically test the diversi�cation hypothesis, we build a novel dataset of micro (at �rm level)

and macro data (at the level of states and regions) composed by 400.000 large and very large �rms in

the EU over the years 1985�2012, for a total of approximately 2.9 million observations. By calculating

a time-varying measure of �rm volatility based on three indicators (turnover, sales and number of em-

ployees) we show how the lower is the degree of covariation of GDP growth among the economies of the

shareholding/owned �rms, the higher is the volatility of �rms. Moreover, there is a direct and propor-

tional link between the degree of volatility and the physical distance between the two �rms. Finally, we

do not �nd evidence that also domestic investors invest in �rms located in other regions by looking for

diversifying their risk and hedge against home regional business cycle e�ects. This suggests that only

foreign shareholders are more prone to undertake risky investments and to force �rms to specialize more

when they invest abroad. These results are valid even considering several robustness checks in terms of

di�erent measures of volatility, types of �rms (listed, export oriented, etc.), and location of shareholders.

Keywords: Foreign Direct Investment, multinational companies, volatility, diversi�cation, Europe

JEL codes: F36, F23, G15

Introduction

Increasing macroeconomic output (and hence employment) could be considered as one of primary objectives

of economic policy, today, as in the past. But a sustained economic growth is not su�cient per-se. With the

recent economic crisis, the concerns related to the volatility of the macroeconomic environment have become

even more important in the political and economic debate. Therefore, reducing volatility has emerged as a

key issue for most policymakers and academics, as economic agents are assumed adverse to large �uctuations

in their incomes, prices and so on (Levasseur, 2011). Barlevy (2004) and Mendoza (1997) show a signi�cant

welfare loss as a result of higher macroeconomic volatility, while Ramey and Ramey (1995)among others

show a strong negative correlation between business cycle volatility and long-run growth.

Another key element showed by the crisis is the strong interconnection among economies created by the

deep �nancial integration occurred in the last three decades. The �ndings on whether �nancial integration

does generate growth bene�ts have been proved often not to be conclusive, as the relationship is not always

found to be strong or robust (Edison, Levine, Ricci, and Slok, 2002). However after the �nancial crisis of the

1980's and 1990's which followed capital account liberalization reforms, the attention has shifted to analyzing

the relationship between �nancial openness/integration and macroeconomic volatility.

For instance,Reinhart and Kaminsky (1999) argued that �nancial integration could be a source of greater

macroeconomic volatility, as it expose countries to sudden reversals of capital �ows: without adequate �-

nancial institutions to cope with them, the risk of the outbreak of the �nancial crisis is very high, resulting

in larger volatility of macroeconomic output. Even without considering the extreme episodes of macroeco-

nomic volatility such as �nancial crisis, it might also be the case that �nancial integration, associated with

weak domestic �nancial institutions, exacerbate existing distortions due to credit market imperfections, thus

yielding higher business cycle volatility.

Similarly to the empirical evidence on the relationship between �nancial integration and economic growth,

the one on �nancial integration and volatility has also often found quite inconsistent results. Recently, Prasad,

Rogo�, Wei, and Kose (2007) a�rm that, based on papers using a variety of regression models, di�erent

country samples, and time periods, the existing evidence leads to the conclusion that there is no systematic

empirical relationship between �nancial openness and output volatility.

On the one hand, the empirical approach does not seem to o�er a clear answer to the relationship between

�nancial integration and macroeconomic volatility. On the other hand, even the theoretical literature has

not been so far able to develop a consistent view. Until the 1950s, Foreign Direct Investments (FDI) were

entirely explained within the traditional theory of international capital movements. Like other forms of

international investment, FDI were seen as a response to di�erences in the rates of return on capital between

countries. In this perspective, the e�ect on macroeconomic volatility is quite unclear, if not nonexistent.

However, the de�ciencies of this approach appeared immediately clear. Brainard and Tobin (1992)

proposed a model in which FDI are simply one of the alternatives to portfolio investment. The rates of

return of the di�erent alternative investments are matched with an element of risk in the choice between

(imperfectly) substitutable assets to build an e�cient portfolio.

Also in the capital theory tradition is the risk diversi�cation hypothesis that explains the choice for

investing in �rms abroad. Rugman (1975) develops the argument that the international diversi�cation of

portfolios is a way of reducing the �rm's risk. This makes the Multinational Enterprises (MNEs) a vehicle for

geographical diversi�cation of investments. According to this view, foreign controlling investors are willing

to take more risk by forcing the owned �rms to specialize more. Therefore, this diversi�cation strategy is

expected to result in higher macroeconomic volatility.

1

Caves (1982) explains, however, that although the empirical evidence shows that investors recognize the

value of international diversi�cation, the diversi�cation of MNEs is more likely to result from investments

that were propelled by other motives. Indeed, Buckley (2002) recognizes how the geographical distribution

of the portfolios of existing MNEs, very much concentrated in highly correlated countries, is very di�erent

from what is suggested by the portfolio diversi�cation hypothesis.

This paper intends to enter in this debate, deepening the analysis on the e�ect of foreign �nance on

volatility and assessing whether FDI are driven by a diversi�cation motive to o�set business cycle e�ects

in the source country. To empirically test the diversi�cation hypothesis, we build a novel dataset of micro

(at �rm level) and macro data (at the level of states and regions). We use an unbalanced panel data

set composed by 400.000 large and very large �rms in the EU over the years 1985�2012, for a total of

approximately 2.9 million observations. In addition to key �nancial variables, for each �rm we have details

on its main shareholders, the geographical details of both shareholder and owned �rms (latitude, longitude

and distance between the two) and macroeconomic data on the nation/region in which it is located and

of the sector in which it operates. We use this macroeconomic data to derive two di�erent measures of

synchronization of business cycles between the country/region of the shareholder and that of the owned

�rm.

We then calculate a time-varying measure of �rm volatility based on three indicators: turnover, sales

and number of employees, capturing the �rms speci�c performance with respect to the overall trends in the

(regional and national) economy, from the economic sector that represents its main activity and considering

�rm speci�c characteristics (i.e. �rm �xed e�ects). We use this variability measure to test whether foreign

investors do actually hedge against home country business cycle e�ects, by investing in countries with low

level of synchronization of the macroeconomic cycle.

Our results show how the lower is the degree of covariation of GDP growth among the economies of the

shareholding/owned �rms, the higher is the volatility of �rms in terms of employment, operating revenues

and sales.Moreover, there is a direct and proportional link between the degree of volatility and the physical

distance between the two �rms. Finally, we investigate whether foreign investors are di�erent from national

investors, by analyzing synchronization of the macroeconomic cycle at regional level. We do not �nd evidence

that also domestic investors invest in �rms located in other regions by looking for to diversify their risk and

hedge against home regional business cycle e�ects. This suggests that only foreign shareholders are more

prone to undertake risky investments and to force �rms to specialize more when they invest abroad. These

results are valid even considering several robustness checks in terms of di�erent measures of volatility, types

of �rms (listed, export oriented, etc.), and location of shareholders.

The paper proceeds as follows. Section 1 revises the empirical literature on �nancial integration and

volatility, both at micro and macro level. Section 2 provides a simple theoretical model of international

portfolio choice for a representative investor. Section 3 describes the data and empirical methodology used

to build the main variables of interest for our analysis. Section 4 presents our main results and robustness

checks. Section 5 concludes.

2

1 Financial integration, risk diversi�cation and volatility

The expected impact of international �nancial integration on volatility is not perfectly explained by the

economic theory. Under standard preferences, agents are risk averse and trade in international assets should

allow them to smooth consumption over time. Accordingly, opening capital accounts and increase �nancial

integrations into the global capital market, would increase the overall welfare of the country. However, the

opening of the capital account could create two opposite e�ects. It could decrease volatility by promoting

production base and risk diversi�cation, but at the same time, the output volatility can rise if foreign owners

are likely to invest in more risky investments as a result of their internationally diversi�ed portfolio.

Compared to the rich literature dealing with the empirical impact of �nancial integration on economic

growth, the number of empirical studies focusing on macroeconomic volatility is much more limited and most

of these studies are based on analysis at macro level. The relationship between business cycle volatility and

the existence of barriers on international mobility of goods and capital was �rstly examined by Razin and

Rose (1994). They estimate the following regression model:

σj,i = α+ βj,CFCi + βj,KFKi + εj,i

where σ is the standard deviation of the de-trended variables of interest (output, consumption and

investment, that is j = Y,C, I ) and FCi, FKi are, respectively, the measures of current account and capital

account openness. The empirical results of this analysis suggest that neither the degree of capital mobility,

nor the degree of goods mobility have an impact on the volatility of the series.

The sources of output growth volatility was also analyzed by Easterly, Islam, and Stiglitz (2001). Con-

sidering two long periods, 1960-1978 and 1979-1997 and a broad sample of countries, they demonstrate that

trade openness exposes a country to greater volatility, while the increasing level of private capital �ows

and their volatility have not a signi�cant impact on output growth instability. Nevertheless, the level of

�nancial development is found to have signi�cant smoothing e�ect on output growth. But this impact is

nonlinear: deep and more integrated �nancial systems appear to have reduced volatility, but only up to a

certain threshold (around 100% of GDP for private credit).

Among the �rst to theoretically address the issue, Sutherland (1996) predicted that easing �nancial

market integration would increases the volatility of a number of variables when shocks originate from the

money market but decreases the volatility of most variables when shocks originate from real demand or

supply. Buch, Doepke, and Pierdzioch (2005) tested this prediction, focusing on OECD countries using

annual data from 1960 to 2000, and proposing the following empirical speci�cation:

σi,t = α0,i + α1,i + β1σcontrolsi,t + β2FOi,t + ui,t

where σi,t is the standard deviation of the cyclical component of real GDP computed over 5-years time

periods, and FOi,t is a measure of �nancial openness. They found that link between �nancial openness and

business cycle volatility depends on the nature of the underlying shock, as the model predicted: �nancial

openness magnify monetary shocks and dampen budgetary shocks. Moreover, they found that this relation

between business cycle volatility and �nancial openness changed over time.

Kose, Rogo�, Prasad, and Wei (2003) examine the volatility of output growth, of consumption growth

and also at the relative consumption volatility (ratio of consumption volatility to output volatility), for 76

countries over the period 1960-1999. The dummy variable for capital account restrictions and the amount

3

of private capital �ows represent the two �nancial openness indicators at the basis of their analysis. The

results con�rm the smoothing impact of �nancial development. Financial openness does not seem to have

a signi�cant positive sign when regressed on output and consumption volatility, but, looking at the relative

consumption volatility, its impact is strongly signi�cant and non-linear. They also more deeply analyse

this non-linearity and �nd that the stabilizing e�ects of �nancial openness is realized only after a certain

threshold �nancial openness is attained (around 50% of GDP). This result resists also controlling for the

interaction e�ects between international �nancial integration and domestic �nancial development in their

relationship to macroeconomic volatility.

Bekaert, Harvey, and Lundblad (2006)examine the e�ects of equity market liberalization on GDP and

consumption growth variability Excluding the 1997-2000 years, dominated by the consequences of the South-

East Asia crisis, we �nd an economically and statistically signi�cant decrease in both GDP and consumption

growth variability post liberalization. When the 1997-2000 years are taken into account, the negative volatil-

ity response of consumption growth is weakened and is no longer signi�cant for a sample of emerging markets,

but remains signi�cant for a larger set of countries

Similarly, Bekaert, Harvey, and Lundblad (2006) examine the e�ects of equity market liberalization on

GDP and consumption growth variability �nding economically and statistically signi�cant decrease in both

GDP and consumption growth variability post capital account liberalization. These results, however, are

highly dependent on the exclusion of the 1997-2000 years, dominated by the consequences of the South-East

Asia crisis. Once these years are considered, in fact, the negative volatility response of consumption growth

is weakened and it is no longer signi�cant for a sample of emerging markets (but remains signi�cant for a

larger set of countries).

More recently, the empirical studies have started to move from analyzing macro data to more detailed

micro data. Thesmar and Thoenig (2004) introduce �nancial market development as one of the driving

forces behind the rise in �rm level uncertainty, focusing on the role of risk sharing among investors. They

start from the assumption that �nancial globalization and stock market development, by broadening the

pool of potential investors, promote risk sharing. In this case �rms listed on the stock exchange are more

prone to to adopt more pro�table and riskier strategies. But in equilibrium, where all �rms compete on

the labour and product markets, non-listed �rms are induced to bear more risk in order to maintain their

market shares in front of more aggressive listed �rms, leading to a pervasive increase in sales volatility

and labour market reallocation. They support this prediction by analyzing French data during the period

of stock market reforms (1984-1990): a larger pool of investors encourages the adoption by listed �rms of

riskier business strategies but whose pro�ts are larger on average. They also �nd supporting evidence of

the general equilibrium e�ect, �nding that the e�ect of �nancial liberalization on �rm level uncertainty goes

beyond those �rms directly involved in stock market activities and a�ect also non-listed �rms.

Another example of micro-founded study on macroeconomic volatility is made by Correa and Suarez

(2009) who exploit the staggered timing of state deregulation of interstate banking in the United States

in the 1980s and early 1990s. They claim that upward trend in volatility found by other studies (such as

Stock and Watson, 2002 and McConnell and Quiros, 2000) is temporarily halted or even reversed during

the deregulation of the years they focus on. Their empirical analysis shows, in fact, that the increase in

�rm volatility may have been steeper without interstate banking deregulation. In other words, �rms' level

volatility has increased despite of not because of banking deregulation.

Finally, one of the most recent paper on this line of research is the study of Kalemli-Ozcan, Sørensen, and

Volosovych (2010) on the e�ects of deeper �nancial integration on �rms' volatility. Using a novel panel data

4

set for 16 European countries composed by 4.7 million �rms, they show how a �rm whose largest owner is

foreign has about 30% more volatile valued added. This volatility is also transmitted to the aggregate level,

as they �nd that the micro-level patterns carry over to the �macro-regional� level: the variability shown

by the �rms contained in their dataset is in fact able to explain about one-third of the variation shown in

EUROSTAT macro-based data.

This paper follows the works of Kalemli-Ozcan, Sørensen, and Volosovych (2010) and Correa and Suarez

(2009) in analyzing the role played by �nancial globalization in de�ning the volatility of �rms and, at the

same time, it investigates the link between these results and the desire of investors to diversify their risk by

investing abroad and thus hedging against home country business cycle e�ects.

2 A simple theoretical model of shareholder value

The main argument in favor of greater integration of �nancial markets at the global level is to encourage

the mechanism for spreading risk among regions and countries and to allow agents to being insured against

idiosyncratic shocks. Accordingly, the diversi�cation of ownership, can also allow �rms to specialize more

thereby exploiting comparative advantage further. Saint-Paul (1992) propose a model for �rms' technological

choice and �nancial markets integration, in which there exists a basic trade-o� between the gains from

specialization due to comparative advantage in production and a lower variance of output. More recently

Thesmar and Thoenig (2009) use a theoretical model to study the e�ect of international capital integration

on the volatility of publicly-traded and privately-held �rms and �nd that an increase in risk sharing, through

capital market integration can generate opposite trends in volatility for private and listed �rm.

To better understand the theoretical foundations of the links between �rm volatility, risk diversi�cation

and FDI, we consider here a simple model of FDI portfolio choice by looking at the shareholder value. Let

consider a multinational �rm that has to choose between allocating its investment between the source and

foreign countries.

Let de�ne a standard representative shareholder who faces a typical budget constraint in each period

given by:

Ct +Bt+1 +Kt = Yt + (1 + r)Bt + πt (1)

where income is derived from: other sources of income (Yt), , interest from risk-free bonds (Bt, paying

1+ r consumption goods at time t) and pro�ts from the multinational (πt). This income is divided between

consumption (Ct), total level of FDI investment (Kt), and accumulation of new risk-free bonds (Bt+1). In

the two period case, taking initial values as given, marginal propensity to consume could be generalized as

follows:

MPC =C1

C1 +B2 +K2=

C1Y1 + (1 + r)B1 + π1

therefore:

C1 = MPC (Y1 + (1 + r)B1 + π1)

= MPC (Y1 + (1 + r)B1) +MPC (π1) = C̃1 + απ1

where α equals the MPC and C̃1represent the consumption funded from other sources.

Following Blanchard and Fischer (1989), the returns generated by the �targeted� �rm each state of nature

5

are weighted by the marginal utility of consumption in that state. In a multinational setting, this discount

factor is a function of the state of the business cycle in the shareholder's home country. Shareholders therefore

look for �rms that are able to produce pro�ts counter-cyclical to the source country's business cycle, i.e.

when the marginal utility of consumption for shareholders is highest. Therefore:

U′(Ct) = E

[(1 + θ)

−1(Vt+1 + πt+1

Vt

)U′(Ct+1)

](2)

where θ is the representative shareholder's subjective discount factor, (Vt+1 + πt+1)/Vt is the rate of return

from holding the �rm for one period (π represents pro�ts of the �rm), C is consumption, U(·) is the utilityfunction, which is assumed to has diminishing marginal utility of consumption (U

′() > 0 and U

′′(C) < 0).

By multiplying by Vt on both sides of (1) and solving recursively forward, the expected value of a �rm, could

be summarized as follows:

E[Vt] = E

[ ∞∑i=1

(1 + θ)−i U

′(Ct+i)

U ′(Ct)πt+i

](3)

Equation (2) shows discount factor places a higher value on a �xed level of pro�ts earned when consump-

tion is relatively low, and vice-versa. Considering the easiest case of only two two periods, and assuming

that shareholders invest in time 0 and earn pro�ts only in time 1, equation (2) could be rewritten as

E[V1] = E

[(1 + θ)

−1 U′(C1)

U ′(C0)

]× E [π] + Cov

[(1 + θ)

−1 U′(C1)

U ′(C0),π

](4)

where the covariance term implies a higher value of pro�ts that arrive when the marginal utility of consump-

tion is particularly high. Similarly to Cushman (1985) and Dennis and Laincz (2005), this value function

could be linearized assuming that shareholders have quadratic utility of the form:

U =(aC0 − bC20

)+ (1 + θ)

−1 (aC1 − bC21

)where a, b > 0, leading to the following ratio:

U′(C1)

U ′(C0)= (1 + θ)

−1(a− 2bC1a− 2bC0

)assuming that E(C1) = C0and treating �rst period consumption as given, equation (3) could be rewritten

as:

E[V1] = (1 + θ)−2E [π] + Cov

[(1 + θ)

−2(a− 2bC1a− 2bC0

),π

](5)

The expected shareholder value de�ned in equation (4) could then we rewritten as:

E[V1] = (1 + θ)−2E [π] + Cov

[(1 + θ)

−2

(a− 2bC̃1 − 2bαπ

a− 2bC0

),π

]

E[V1] = φ1E [π]− φ2Cov[C̃, π

]− φ3V ar [π] (6)

where φ1 = (1 + θ)−2, φ2 =

2b(1+θ)−2

a−2bC0 and φ3 = αφ2all strictly positive. From equation (6) emerge the main

elements behind the decisions of shareholders in the allocation of the FDI: i) the expected return (E [π]);

6

ii) the risk minimization (V ar [π]); and more interestingly for this analysis, iii) the desire to hedge against

home country business cycle e�ects. When Cov[C̃, π

]< 0 pro�ts from foreign �rm are a good hedge, as the

they are high when consumption is otherwise low. Therefore, by holding a diversi�ed portfolio (i.e. holding

shares of �rms located in countries where the business cycles has a low correlation with the home country),

investors could shift investment towards high return projects, that could lead to higher values .

3 Data

To test whether investors do actually diversify their risk by hedging against home country business cycle

e�ects, we need to aggregate a large amount of information from di�erent sources, very often not easily

compatible. The dataset thus obtained is a novel set of micro (at �rm level) and macro data (at the level of

states and regions) for a total of 2.2 million observation for around 30 variables.

3.1 Financial data

Financial data are taken from Bureau van Dijk Amadeus �rm-level panel dataset. It contains �nancial

information on over 11 million public and private companies in 41 European countries (including Eastern

Europe and Balkans), combining data from over 30 specialist regional information providers. The information

collected by national providers are homogenized applying uniform formats, allowing accurate cross-country

comparisons. Amadeus includes consolidated and unconsolidated information about the balance sheets and

pro�t/loss statements, reporting standardized annual accounts. We select large and very large corporations

in the EU in the period 1985-2012, obtaining an unbalanced panel of approximately 2.9 million observations,

implying that almost 400.000 �rms are present in the period considered.

Each company in the data base includes wide range of the standardized �nancial accounting information.

A standard company report includes: 25 balance sheet items, 20 �nancial ratios, 26 pro�t and loss account

items. Following Kalemli-Ozcan, Sørensen, and Volosovych (2010), our analysis is focused only three indi-

cators: turnover (operation revenue, �opre�), sales (�turn�) and number of employees (�empl�). Therefore

all the �rms that do not have at leas one of three outcome variable non-missing in a give year are deleted.

Table 1 displays some summary statistics of the three variables (considered in logs), while Table A.1 in the

Appendix A reports the coverage by county of each indicator in fore the 2004-2012 years.

Table 1: Summary Statistics for Variable of Interest

Variable Mean Std. Dev Min Max

Sales 16.96 2.03 0 29.34

Operation revenue 16.87 2.16 0 29.35

Number of employees 4.15 1.80 0 13.38

Similarly to Kalemli-Ozcan, Sørensen, and Volosovych (2010), the distribution of these variables almost

follows log-normal. Figure 1 shows the distribution for 2007 (Skewness: -0.05 and Kurtosis :4.61) and, even

considering separate years, it does not change much over time (similarly, Figure 2 shows the distribution for

year 2000 of log sales). As our empirical analysis is focused only on large and very large �rms, there is no

need to winsorize variables at the bottom of the distribution, as the potential impact of outliers in this sense

does not seem particularly relevant.

7

Figure 1: Distribution of Firm-level Log Operating Revenue (2007)

Figure 2: Distribution of Firm-level Log Sales (2000)

Finally, the Amadeus data set contains also the relatively detailed NACE 2-digit level. Although NACE

4-digit level is the most detailed level at which the European national classi�cation schemes are harmonized

across countries, EUROSTAT database do not present National Sectoral Accounts at 4-digit level, but only

a 2-digit. As NACE 2-digit level di�erentiates across about 100 class of �rms, this allows us to control for

sector speci�city in our analysis and in the de�nition of the volatility. However, EUROSTAT data do not

reports regional account for NACE 4-digit level. Therefore, in the de�nition of the volatility with controls

at regional level, we regroup activities according to NACE 1-digit level, as reported in Table 3.

Table 3: Presence of Firms in the Sample According to NACE Activities

Code NACE activities Rev2 Freq. Percent Cum.

A Agriculture, forestry and �shing 29,485 1.05 1.05

B-E Industry (except construction) 51,913 1.84 2.89

C Manufacturing 591,373 21.01 23.9

F Construction 214,102 7.61 31.51

G-IWholesale and retail trade, transport,

801,621 28.48 59.99accommodation and food service activities

J Information and communication 97,442 3.46 63.46

K Financial and insurance activities 196,775 6.99 70.45

8

L Real estate activities 251,485 8.94 79.38

M-NProfessional, scienti�c and technical activities;

437,771 15.55 94.94administrative and support service activities

O-QPublic administration, defense, education,

94,501 3.36 98.29human health and social work activities

R-UArts, entertainment and recreation;

48,007 1.71 100other service activities; activities of household

Following Correa and Suarez (2009), but di�erently from Kalemli-Ozcan, Sørensen, and Volosovych

(2010), we calculate a time-varying measure of �rm volatility as follows

Yijt = αi + µt + ηXi,t−1 + ρZjt + δWkt + υijt (7)

where i, j, k, and t index �rm, state (or region), industry, and year, respectively and αi are �rm-�xed e�ects;

µt are time e�ects, Xijt−1 is a �rm-speci�c control de�ned by total assets; Zjt represents the growth rate

of state (region) per capita product; and Wkt is the growth of Value Added at the industry level (NACE

2-digit level) in the state (or region). Equation (7) is estimated for three variables of interest (Yijt=opre,

turn, empl) and V OLijt, is de�ned as the absolute value ofυ̂ijt. In this way, this measure of volatility is able

to capture the �rms speci�c performance with respect to the overall trends in the (regional and national)

economy and from the industrial sector that represents its main activity. In the end, we also compute the

average volatility by year & NACE level and by year & country to build control variables for the empirical

analysis.

Several studies in the literature on �rm-level volatility, such as Comin and Philippon (2005), usually use

the standard deviation of the outcome of interest. Figures 3 shows how the distribution of the operating

revenue is close to log-normal (as also the distribution of the other two variables of interest). However, we

need that our volatility measure change our time and computing standard deviations on small time spans

is not the best empirical strategy to follow. In the robustness check, nevertheless, we control if the results

obtain according to our principal methodology are valid also considering the standard deviation measures

on the pooled sample.

Figure 3: Distribution of Firm-level Standard Deviation of Log Operating Revenue

9

3.2 Ownership data

To understand the impact of domestic/foreign ownership, we merge the balance sheet information with the

details of the shareholders for each �rm. The ownership data are also taken from Bureau van Dijk Amadeus,

but from a di�erent dataset with respect to the �nancial data. For this reason, not all �rms reported in the

�nancial dataset have the respective information on ownership. Moreover, ownership informational are often

missing in the Amadeus dataset, although this problem is less relevant for large and very large enterprises

here considered. What is relevant in terms of our analysis is are the geographical of shareholding companies,

an information that is not always reported. However, Table 4 shows how the summary statistics of the three

variables here following considered (considered in logs) are almost equal for the �rms for which ownership

country location is available and for those that is not.

Table 4: Summary Statistics for Variables of Interest According to Availability of Ownership Country

Location

Variable Mean Std. Dev Min Max

Ownership country location is available

Sales 16.99 1.90 0 29.33



Ownership country location is not available

Sales 16.92 2.16 0 28.00



Total

Sales 16.96 2.03 0 29.34



It is often the case that a �rm is owned by more than one �rm. As, for the analysis we need to control for

the macroeconomic condition in the country (region) of shareholding �rm, it is crucial to have just one entry

for each �rm. We therefore compute for each �rm the sum of direct plus indirect control of each shareholder

and we keep the largest shareholder for each �rm. Nevertheless, it could be the case that the �rms is a joint

venture owned by one or more shareholders with the same quota: in this case we keep the shareholder that

reports the largest amount in terms of property assets. However, for robustness check control, we also keep

track of several indicators for each �rm: the total number of shareholders, the share of �rm owned by a

foreign (and domestic) shareholder, only the direct share of the largest shareholder.

Finally, Figures 4 and 5 show the data set used for the analysis has a good coverage in terms of intercon-

nections between countries, both for EU and not EU located shareholders. Only �rms located in some small

countries (Cyprus, Malta) have a poor coverage in terms of geographical origin of their largest shareholders.

However also in these extreme cases, we have still signi�cant amounts of geographical diversi�cation: for

Cyprus, for instance, domestic shareholders amount to 601, EU shareholders to 112 (mostly from Greece,

Great Britain and Netherlands) and not-EU shareholders to 157.

10

Figure 4: Shareholders based in EU country and the location of the controlled �rms

ATBEBGCYCZDEDKEEESFI

FRGBGRHUIEITLTLULVMTNLPLPTROSESI

SK

Sha

reho

lder

Cou

ntry

ISO

cod

e

AT

BE

BG

CY

CZ

DE

DK

EE

ES FI

FR

GB

GR

HU IE IT LT LU LV

MT

NL

PL

PT

RO SE SI

SK

Country ISO code

Figure 5: Shareholders not based in EU country and the location of the controlled �rms

Sha

reho

lder

Cou

ntry

ISO

cod

e

AT

BE

BG

CY

CZ

DE

DK

EE

ES FI

FR

GB

GR

HU IE IT LT LU LV

MT

NL

PL

PT

RO SE SI

SK

Country ISO code

11

3.3 Geographical data

Both the �nancial and shareholders Amadeus databases contain information on the country, city and region

of �rms: nevertheless, while country details are always reported, city and regional values are often missing.

First, we use the city variable to calculate the distance between the owned and shareholding �rms. However,

the database does not contain geodata details (Latitude and Longitude) of each city; we therefore use the

GeoNames geographical database, which covers all countries and contains over eight million place names

and the related geographical details. The name of the cities, however, are often misspelled in the Amadeus

database and/or reported in national language: for instance the entry �Rome� (the capital of Italy) is reported

as �Roma�, �Rome�, �Rom�, �Rom.�. This problem does not allow a perfect matching between the GeoNames

database and the Amadeus databases.

We therefore employ probabilistic record matching program that employs a modi�ed bigram string com-

parator to match similar entries when the matching is imperfect. Bigrams help provide the conditional

probability of a letter Lx given the presence of the similar letter Lx̃ , when the relation of the conditional

probability is applied:

P (Lx|Lx̃) =P (Lx̃, Lx)

P (Lx̃)

Once obtained these matches it is possible to link to each owned and shareholding �rms with the respective

Latitude and Longitude data of their city. Using the �haversine� formula we can therefore calculate the great-

circle distance between two point, that is the shortest distance over the earth's surface. Once converted

Latitude and Longitude data form decimal to radians (multiplying by π and dividing by 180), the distance

between the point i and j can be derived as follows:

a = sin2(θi − θj

2

)+ cos θi ∗ cos θj ∗ sin2

(φi − φj

2

)c = 2 ∗ arctan

(√a,√1− a

)where θi,j is the Latitude and φi,j is the Longitude. The variable distance DIST i,j is equal to earth's

radius R times c (we use the average radius equal to 6.371km). Around 1.2 millions of owned �rms have

their shareholders located in the same city; the average distance between the two �rms is 745 km while the

maximum distance is 12.286 km (about the distance between Paris and the Land of Fire - Tierra del Fuego

- in Argentina)

Moreover the region variable reported in the dataset changes signi�cance according to the di�erent

countries contained in the Amadeus dataset. For instance, in Finland the variable �region� stands for NUTS

3 level, in Romania for NUTS 2 and in the case of the United Kingdom neither for NUTS 1, 2 or 3. For the

purpose of our analysis, however, the understanding of the region of each �rm (both owned and shareholding)

is essential. We therefore use the geographical data obtained by the GeoNames database to standardize the

dataset, although for some countries is not possible to obtain NUTS, as shown by Table 5 that reports for each

county the NUTS level considered in the database. Finally, as many �rm entries do not show information on

city, but sometimes report information on regions, we input in these cases the average coordinates of that

region, so to compute in any case the distance between the owned and shareholding �rms.

12

Table 5: Nomenclature of Territorial Units for Statistics by Country

Country NUTS level Country NUTS level Country NUTS level

Austria 2 Germany 1 Netherlands 2

Belgium 1 Great Britain 3 Poland 2

Bulgaria 3 Greece / Portugal /

Cyprus / Hungary 2 Romania 2

Czech Republic / Ireland / Sweden 2

Denmark 2 Italy 2 Spain 2

Estonia / Latvia / Slovenia 2

Finland 3 Lithuania / Slovakia /

France 2 Malta /

3.4 Macroeconomic data

We enrich the �rm-level database with a large number of macroeconomic variable at national and regional

level. Domestic Product per capita and Value Added at the industry level (NACE 2-digit level) comes from

from EUROSTAT yearly national accounts, while regional Domestic Product per capita and regional Value

Added at the industry level (NACE 1-digit level) comes from from EUROSTAT Regional statistics by NUTS

classi�cation.

To compute the synchronization of the macroeconomic cycle between countries and between region, we

derive di�erent measure of synchronization. First, following Kalemli-Ozcan, Papaioannou, and Peydró (2010)

and Giannone and Reichlin. (2010), we measure business cycle synchronization with the divergence de�ned

as the absolute value of real GDP growth di�erences between country i and j in year t.

SY NC1i,j,t =| [(GDPi,t −GDPi,t−1) /GDPi,t−1]− [(GDPj,t −GDPj,t−1) /GDPj,t−1] |

Moreover line with Alesina, Barro, and Tenreyro (2002) and Barro and Tenreyro (2007), we compute for

every pair of countries i,j and for every year the following second-order autoregression:

GDPi,tGDPj,t

= βo + β1GDPi,t−1GDPj,t−1

+ β2GDPi,t−2GDPj,t−2

+ uijt

The estimated residual, ûijt, measures the relative GDP that could not be explained by the two lags of

relative GDP. The extent of GDP co-movement SY NC2i,j,t could therefore be measured by the adjusted

R squared regression. A lower value indicates lower correlation of GDP movements between two countries.

The macroeconomic data for computing these two indexes at county level are from International Monetary

Fund World Economic Outlook: this leads to 2.2 million of entries for SY NC1i,j,twith an average value of

-1.8 and a same number of entries for SY NC2i,j,twith an average value of 0.77 (excluding the entries for the

same countries) . For the regional data, instead, we use both IMF and EUROSTAT Regional statistics. As

many �rms have missing values in the regional dimension, but not in the national speci�cation, we compute

the two indicators both for pair of regions and pair of countries.

13

4 Empirical Analysis

4.1 Empirical Strategy

To estimate the e�ect of interstate bank entry deregulation on volatility we follow Morgan, Rime, and

Strahan (2004) and Correa and Suarez (2009), using a two-stage procedure. In the �rst stage we calculate a

time-varying measure of �rm volatility as de�ned by equation (7). his measure of volatility has the advantage

to correctly detect the idiosyncratic component of �rms' volatility, removing the e�ects of the overall trends

in the economy, and of those of the industrial sector that represents its main activity. Moreover, measuring

volatility using absolute deviation (as opposed to squared deviations) implies that volatility and growth are

conveniently expressed in the same units (Morgan, Rime, and Strahan, 2004).

In the second stage we use this measure to determine whether this measure is a�ected by the presence of

a foreign investors, by the degree of synchronization between the domestic business cycles of the owned �rms

and that of the shareholders (according to the two measures of SY NCi,j,t) and by the distance between

shareholders and owned �rm (DIST i,j). To increase the robustness of the analysis, we not only control for

�rm speci�c characteristic (Xi,t−1) but we also add two regressors, to control for the average �uctuation at

the state or region (V ol(Zjt)) and industry level (V ol(Wkt)) for the speci�c year. Finally, we consider also

year �xed-e�ects (µt) to account for the e�ect of global shocks and other business cycle factors that a�ect the

volatility of �rms and country (regional) �xed e�ects.We therefore estimate the following equation, taking

logs of each variable:

V ol(Yijt) = αi + µt + βSY NCi,j,t + γDIST i,j + ηXi,t−1 + ρV ol(Zjt) + δV ol(Wkt) + �ijt (8)

To test whether �rm volatility increase/decreases conditional to the the pro�le of the foreign investor, we

examine the sign of β and γ in equation (8). This equation is initially tested considering national boundaries,

that is the degree of synchronization is computed at national level: if the shareholder is domestic, SY NC1 = 0

while SY NC2 = 1. On the contrary, if the shareholder is foreign and the GDP of its country has a very low

degree of synchronization with that of the country where the controlled �rm is registered, SY NC1 goes to

+∞ while SY NC2 goes to 0.Moreover, we also test whether the degree of synchronization and the distance plays a role also at national

level. We therefore consider as boundaries not only the national borders, but also the regional one and the

distance at national level. In this setting SY NC1 = 0 and SY NC2 = 1 only if the shareholding �rm and

the owned �rm are in the same region. To test whether the national border plays a role giving a certain level

of synchronization and a certain distance from the owned �rm, we estimate the following equation in logs:

V ol(Yijt) = αi + µt + β1FOR ∗ SY NCi,j,t + β2NAT ∗ SY NCi,j,t + γDIST i,j+ηXi,t−1 + ρV ol(Zjt) + δV ol(Wkt) + �ijt (9)

in which FOR and NAT are two dummy variables equal to 1 in the case in which investors are foreign or

national, respectively. By creating the interactions terms of the SYNC measure with these dummies we are

able to understand whether only foreign investors do actually invest in �rms located in other regions by

looking for to diversify their risk and hedge against home regional business cycle e�ects. In this case only

β1 would be negative as before.

14

4.2 Results

We �rst perform a speci�cation search through simple cross-sectional regressions. Columns (1)-(6) of Table 6

report of Ordinary Least Squares (OLS) estimates using synchronization in GDP growth rates (SY NC1and

SY NC2) as the independent variable, for all the three variables of interest.

All the two synchronization measures have the expected sings: SYNC1 positively while SYNC2 is nega-

tively correlated to the degree of �rms variability. All the coe�cients are signi�cant at standard con�dence

levels: this suggests that across the 1489 pairs of countries, the lower is the degree of covariation of GDP

growth among economies the higher is the volatility of �rms in terms of employment, operating revenues and

sales. Moreover, the average level of volatility of the NACE sector of �rms' activity and the level of national

GDP variability do play a signi�cant role in explaining the variability of the single �rm.

Table 6: Cross-sectional estimates

(1) (2) (3) (4) (5) (6)

Dependent Variable: Log Volatility of

Turnover Op. Revenues Employment

Log SYNC1 0.02*** 0.02*** 0.01***

(0.003) (0.003) (0.003)

Log SYNC2 -0.18*** -0.18*** -0.11***

(0.009) (0.009) (0.009)

Log Sectoral Volatility 1.00*** 0.88*** 1.01*** 0.94*** 0.95*** 0.79***

(0.006) (0.014) (0.005) (0.012) (0.008) (0.018)

Log Country Volatility 0.93*** 0.69*** 0.90*** 0.69*** 0.94*** 0.84***

(0.008) (0.016) (0.007) (0.015) (0.006) (0.012)

Log Total Assets -0.02*** -0.01*** -0.01*** -0.01*** -0.01*** -0.00

(0.001) (0.002) (0.001) (0.002) (0.001) (0.002)

Observations 1,211,538 1,211,566 1,433,800 1,433,844 1,260,666 1,260,666

Notes: Year dummies not reported. Standard errors in parentheses; ***, ** and * denote signi�cance at

1%, 5%, 10% level.

We also examine whether our results re�ect distance between countries (a key variable in explaining

�nancial and trade intensity, see Loungani, Mody, and Razin, 2002). Table 7 shows how the previous results

are still valid even controlling for the distance (the number of observations is slightly lower, as previously

discussed). The geographical distance between the foreign shareholder and the controlled �rm is positive

and highly signi�cant for all three variables of interest. More distant foreign owners may be more prone to

diversify more, specialize more and undertake more risky investments.

15

Table 7: Cross-sectional estimates including distance

(1) (2) (3) (4) (5) (6)



Log SYNC1 0.02*** 0.02*** 0.01***

(0.003) (0.003) (0.003)

Log SYNC2 -0.16*** -0.17*** -0.10***

(0.011) (0.010) (0.010)

Log Distance 0.01*** 0.01*** 0.01*** 0.01* 0.01*** 0.01*

(0.001) (0.003) (0.001) (0.003) (0.001) (0.003)


(0.007) (0.014) (0.007) (0.012) (0.011) (0.018)


(0.010) (0.018) (0.010) (0.016) (0.008) (0.012)

Log Total Assets -0.01*** -0.01*** -0.01*** -0.01*** -0.01*** -0.00

(0.001) (0.002) (0.001) (0.002) (0.001) (0.002)

Observations 1,159,285 1,159,301 1,368,366 1,368,398 1,204,411 1,204,411


1%, 5%, 10% level.

Moving from the simple OLS regression, we investigate also the dynamic speci�cation, exploring the

dynamic patterns in the data. We test equation (8) by analyzing a unbalanced panel of more than 180

millions �rms and see whether if increasing/decreasing synchronization of business cycles goes hand-in-hand

with increase propensity of investors to undertake more risky projects.

The results reported in Table 8 con�rm this predictions, as all the relevant coe�cients have the sings

analyzed before and all remain signi�cant at standard con�dence level. The coe�cient of SYNC1 (Column 2

and 6) now is signi�cant at 10% level for turnover and employment, contrary to the cross-sectional speci�ca-

tion. These results are still valid also by controlling for possible year and/or country speci�city (the dummy

variables are not reported in the table) and by distance.

Also in this case, the control for average sectoral volatility is expected to correctly identify the direction

of the causality, i.e. controlling for the fact that foreign investors have preferences in investing in more

volatile sectors. Similarly, the inclusion of the average GDP volatility ensures that foreign investors invest in

those countries they expect to be more volatile. Finally, all the two measures are time varying, controlling

therefore for the speci�city of each year.

16

Table 8: Dynamic estimates including distance and country/year dummy variables

(1) (2) (3) (4) (5) (6)



Log SYNC1 0.01* 0.01*** 0.01*

(0.003) (0.002) (0.002)

Log SYNC2 -0.10*** -0.11*** -0.07***

(0.014) (0.014) (0.014)

Log Distance 0.01*** 0.01* 0.01*** 0.01 ** 0.01** -0.01

(0.002) (0.006) (0.002) (0.003) (0.002) (0.006)


(0.010) (0.020) (0.009) (0.017) (0.013) (0.023)


(0.012) (0.022) (0.011) (0.019) (0.009) (0.015)

Log Total Assets -0.02*** -0.01*** -0.01*** -0.01*** -0.01*** -0.01**

(0.002) (0.003) (0.002) (0.003) (0.002) (0.003)

Observations 1,159,285 1,159,301 1,368,366 1,368,398 1,204,411 1,204,429

Number of id 183,094 183,089 224,783 224,777 217,283 217,276


1%, 5%, 10% level.

Finally we move from national dimension to the regional one. This allows to control whether foreign

investors are di�erent from the national one in pursuing more risky strategies for a given level of �desire� of

hedging against home business cycle e�ects. By computing SYNC2 at regional level, as also the sectoral and

regional volatility as derived by equation (8), we can test whether for a given level of synchronization of the

regional cycles of the shareholder and owned �rms, and for a given distance, foreign controlled �rms show a

higher level of variability.

Columns (1), (3) and (5) of Table 9 show how, considering both foreign and domestic investors, they

seem not to invest in �rms located in other regions by looking for to diversify their risk and hedge against

home regional business cycle e�ects. However, we want to test if this insigni�cance of the coe�cient is lead

by the presence of domestic investors or by the fact that the analysis has moved to regional level.

Columns (2), (4) and (6) show how the interaction variable for foreign shareholders de�ned in equation

(9) is negative and signi�cant at usual con�dence levels. We therefore �nd evidence of the existence of a

national border e�ect in the de�nition of �rms' strategies. This suggests, that foreign shareholders are more

prone to undertake risky investments and probably to specialize more, while domestic investors invest in

other regions for reasons di�erent from that of specialization and therefore are less prone to take more risk.

Nevertheless, the introduction of the interaction variables for foreign and domestic investors reduces the

magnitude and the signi�cance of the distance variable (that remains however signi�cant at 10%). This is

naturally expected, as foreign shareholders are usually also the more distant, so the distance e�ect is well

captured by the interaction term.

17

Table 9: Dynamic estimates at regional level, including distance and region/year dummy variables

(1) (2) (3) (4) (5) (6)



Log SYNC2 -0.01 -0.00 -0.00

(0.006) (0.005) (0.006)

Log Distance 0.01* 0.01* 0.01** 0.00 0.01* 0.01**

(0.003) (0.003) (0.003) (0.003) (0.003) (0.003)

NAT*Log SYNC2 0.01 0.00 0.00

(0.008) (0.007) (0.007)

FOR*Log SYNC2 -0.03** -0.02* -0.02*

(0.009) (0.008) (0.008)


(0.018) (0.020) (0.017) (0.018) (0.025) (0.018)

Log Regional Volatility 0.79*** 0.57*** 0.74*** 0.79*** 0.89*** 0.73***

(0.017) (0.019) (0.017) (0.017) (0.015) (0.017)

Log Total Assets 0.00 -0.00 0.01* 0.00 -0.00 0.01***

(0.003) (0.003) (0.003) (0.003) (0.003) (0.003)

Observations 496,945 496,945 504,751 504,751 437,086 437,086

Number of id 110,885 110,885 113,083 113,083 104,754 104,754


1%, 5%, 10% level.

4.3 Robustness check

Table 9 explores if the results are robust to various choices made in the de�nition of the empirical speci�cation.

We report only tests for the regional level speci�cation of volatility in turnover, as it appears to be the more

stringent model tested. However, the robustness checks on the other models speci�ed before do lead to

similar results.

As �rms size is already controlled by the total assets, we want to control for �rm age: Column (1) of

Table 10 shows that when we condition on size and age, the relation between volatility and synchronization

of macroeconomic cycles for foreign �rms is decreasing and signi�cant at 10% level.

We want also to control if the variability of the �rms is lead by other factors. The economic literature

has often investigated whether listed �rms are more volatile than others (Comin and Philippon 2005 and

Thesmar and Thoenig 2004). We therefore add a dummy variable for listed �rms in Column (2): its coe�cient

is signi�cant at 1% level and positive; nevertheless, our synchronization coe�cient remain negative and

statistically signi�cant at 10% level, supporting the previous �ndings. Similarly, we check whether being an

export oriented �rms is able to explain most of the variability of the �rm. We therefore derive an indicator of

export-orientation by computing the share of turnover coming from exports over total turnover of the �rm.

Column (3) shows how actually this regressor is signi�cant at 1% level and positive, that is the more export

18

oriented �rms are also those who are more volatile. Nevertheless, the synchronization measure remains

statistically signi�cant at 10% level.

We test also if the shareholding �rms located in the EU have less propensity towards risky investment,

due to the common membership in the single market. The dummy variable for EU shareholders reported

in Column (4) is negative (as expected) but not statistically signi�cant and the synchronization coe�cient

remain negative and statistically signi�cant at 5% level.

Table 10: Dynamic robustness check estimates at regional level: extra variables

(1) (2) (3) (4) (5) (6)

Dependent Variable: Log Volatility of Turnover

Log Distance 0.00 0.00 0.01 0.00 0.00 0.00

(0.003) (0.003) (0.011) (0.004) (0.003) (0.003)

NAT*Log SYNC2 0.01 0.00 0.03 0.00 0.00 0.00

(0.007) (0.007) (0.015) (0.007) (0.007) (0.007)

FOR*Log SYNC2 -0.02* -0.02* -0.02* -0.02* -0.02* -0.02*

(0.010) (0.008) (0.010) (0.009) (0.008) (0.008)


(0.019) (0.018) (0.077) (0.018) (0.018) (0.018)

Log Regional Volatility 0.77*** 0.79*** 0.94*** 0.78*** 0.78*** 0.78***

(0.017) (0.017) (0.059) (0.017) (0.017) (0.017)

Log Total Assets 0.01** -0.00 0.00 0.00 0.00 0.00

(0.003) (0.003) (0.008) (0.003) (0.003) (0.003)

Number of Years -0.01***

(0.000)

Listed Firm 0.16***

(0.006)

Exporting Firm 0.03***

(0.007)

Shareholder is EU �rm -0.02

(0.016)

Shareholder in same sector -0.03

(0.019)

Indipendence Indicator -0.00

(0.002)

Observations 448,994 496,945 496,945 496,945 496,945 496,945

Number of id 101,645 110,885 110,885 110,885 110,885 110,885


1%, 5%, 10% level.

We also control whether the higher/lower of volatility is due to investing in di�erent sectors. By using

NACE-4 digit details on shareholding and owned �rms, we compute a dummy variable if the sector of the

19

two �rms is the same. We expect that in case of di�erences in the sector, an higher degree of volatility.

The coe�cient reported in Column (5) is indeed negative, but statistically not signi�cant. The degree of

synchronization between the regions of the two �rms do actually explain most of the change in volatility of

controlled �rm for �rms owned by foreign investors.

In addition, we check whether the degree of independence of owned �rm by the shareholding is able to

explain part of the variability. The Amadeus Ownership Database quali�es companies according to their

degree of independence with regard to their shareholders. The Independence Indicators are noted as A, B,

C, D and U, with further sub quali�cations, moving from more independent (A+) to less independent (D).

Column (7) shows how this indicator is not statistically signi�cant and its inclusion does not have e�ects on

the signi�cance and direction of the synchronization measure for foreign investors.

Finally, as discussed in section 3.1, we test if our results are robust to the change in measure of the

volatility. We therefore use the Standard Deviation of the three variables of interest in a cross section

speci�cation, as this measure is not time variant. As for the other measures of volatility, we also compute

the sectoral and regional average Standard Deviation for each variable, in order to correctly detect the

speci�c volatility of the �rms.

The results shown by Table 11 con�rm the role played by the degree of synchronization of the economic

activity of the regions of shareholding and owned �rms: the coe�cient is negative and statistically signi�cant

at 1% level as in the previous speci�cations for foreign investors. Moreover, the coe�cient of distance, instead,

is more signi�cant than before (at 1% level) and with positive sign.

Table 11: Cross sectional robustness check estimates at regional level: di�erent measures of volatility

(1) (2) (3)



Log Distance 0.02*** 0.02*** 0.02***

(0.001) (0.001) (0.002)

NAT*Log SYNC2 0.00 0.01 0.02

(0.004) (0.004) (0.008)

FOR*Log SYNC2 -0.02*** -0.03*** -0.02*

(0.006) (0.006) (0.009)

Log Sectoral Volatility 0.71*** 0.83*** 0.72***

(0.011) (0.010) (0.015)

Log Regional Volatility 0.38*** 0.32*** 0.36***

(0.008) (0.008) (0.010)

Log Total Assets 0.02*** 0.03*** -0.01***

(0.001) (0.001) (0.002)

Observations 905,911 958,476 868,852


1%, 5%, 10% level.

20

5 Conclusions

Rational portfolio choice theory predicts that investors diversify some of their domestic economic �uctuations

by investing in foreign �rms and let them to specialize more thereby exploiting comparative advantage

further. The greater specialization is then expected to increase the volatility of the owned �rm. Following,

Kalemli-Ozcan, Sørensen, and Volosovych (2010)we provide empirical evidence that risk sharing enhances

specialization in production. To the best of our knowledge, this well-established and important theoretical

proposition has not been tested before in such a way at micro level.

Using a novel dataset of micro (at �rm level) and macro data (at the level of states and regions) composed

by 400.000 large and very large �rms in the EU over the years 1985�2012, we empirically assess to which extent

the volatility of �rms is due to activities of �rms under foreign ownership, both controlling for idiosyncratic

risk diversi�cation, distance between shareholding/controlled �rms and domestic/foreign investors' pro�le.

Our main �nding is that, all else equal, the lower is the degree of covariation of GDP growth among the

economies of the shareholding/owned �rms, the higher is the volatility of �rms. Moreover, the higher is the

physical distance between the two �rms, the higher is the degree of volatility.

Finally, we do not �nd evidence that also domestic investors invest in �rms located in other regions by

looking for diversifying their risk and hedge against home regional business cycle e�ects. This suggests that

only foreign shareholders are more prone to undertake risky investments and to force �rms to specialize

more when they invest abroad. These results are valid even considering several robustness checks in terms

of di�erent measures of volatility, types of �rms (listed, export oriented, etc.), and location of shareholders.

Consistently with Coeurdacier and Guibaud (2011), we interpret our �nding as evidence in favor of

the empirical validity of portfolio choice theory at the international (and sub-national) level. Moreover, our

results suggest that foreign investors' decision are driven by their willingness to hedge domestic risk exposure,

by investing in foreign �rms and let them to specialize more (hence the greater volatility).

It would be interesting to con�rm these estimates by computing not only synchronization measures at

national/regional level, but also at sectoral level, thus looking at the di�erent types of motivation behind

the investment decisions (horizontal versus vertical FDI) and by expanding the coverage of the regional level

for more EU countries.

21

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24

Appendix A

Table A.1: Number of observations for the variables of analysis by country (2004-2012)

cntrycde variable 2004 2005 2006 2007 2008 2009 2010 2011 2012

AT turn 837 1216 2557 4095 4598 4517 4533 5216 4487

opre 908 1336 2772 4428 4884 4809 4875 5657 4892

empl 155 291 1155 3221 4109 4113 4044 4687 4523

BE turn 7769 7933 8675 9060 9571 9949 10439 10898 10801

opre 7152 7464 8325 8879 9433 9855 10368 10944 10840

empl 7839 7948 8685 9060 9223 9539 9645 9724 9503

BG turn 2115 2287 2443 2698 2770 2846 2930 3049 3002

opre 2115 2287 2443 2698 2770 2846 2930 3049 3002

empl 2365 2502 2496 2930 3008 3055 3144 3127 3267

CY turn 0 0 0 0 0 0 0 0

opre 72 68 199 234 251 256 217 65

empl 59 59 155 184 195 181 145 44

CZ turn 5103 5571 5968 6534 6893 7011 7032 6654 4266

opre 5106 5571 5974 6543 6909 7031 8150 7937 4266

empl 4756 5200 5603 6108 5292 6445 7552 7493 4307

DE turn 11182 16145 21777 26736 27926 28584 29730 30098 14852

opre 11690 17133 23757 31351 32513 33024 34084 34547 18866

empl 5815 12801 27703 40605 41991 42740 42544 42750 21794

DK 0 0 0 0 0

3373 3978 4169 4288 4362

4211 4781 4799 4856 4605

EE turn 812 859 905 949 982 973 988 996 975

opre 796 837 880 934 967 964 985 989 963

empl 708 724 738 759 814 744 732 737 725

ES turn 22060 23194 24326 23311 23363 24443 24892 23938 17291

opre 22311 23462 24658 24643 24454 25455 25847 24829 17855

empl 18651 19600 20776 21950 22150 23242 23584 22680 16383

FI turn 3042 3253 3563 4139 4565 4760 4976 5014 4762

opre 3053 3264 3569 4154 4590 4784 5014 5050 4794

empl 2673 2803 3151 3518 3507 3730 3829 4099 4011

FR turn 33576 34655 35844 37295 38371 38358 38790 37965 32998

opre 33579 34672 35931 38469 40456 43502 44841 44630 38948

empl 24363 24399 23483 23244 21417 19754 21299 19045 14020

GB turn 0 0 0 0 0 0 0 0 0

opre 23742 25190 27180 29917 32267 39141 45886 47882 48957

empl 24000 25133 27081 30068 32468 36324 40621 42440 43427

GR turn 2270 2321 2404 2520 2605 2655 2715 2654 2407

opre 2270 2321 2404 2520 2605 2655 2715 2654 2407

25

empl 1644 1671 1711 1768 1878 2187 2319 2319 2281

HU turn 2653 2798 2823 3161 3298 3579 3623 3617 3633

opre 2691 2842 2851 3196 3334 3622 3674 3673 3682

empl 152 548 998 2597 2611 3272 3280 3171 3421

IE turn 0 0 0 0 0 0 0 0 0

opre 1065 1387 1740 2310 2441 2515 2738 2830 2454

empl 5 129 944 1839 1976 1958 2028 1997 1563

IT turn 30116 31112 33122 37584 38440 38886 39681 39478 35778

opre 30124 31123 33129 37592 38878 38899 39682 39478 35780

empl 21458 22803 27108 28778 29517 29779 29907 34048 31535

LT turn 760 842 910 936 950 1034 1360 1344 888

opre 761 849 912 1317 1355 1201 1411 1346 891

empl 777 862 921 1432 1470 1509 1543 1352 893

LU turn 299 343 461 589 698 827 929 841 537

opre 402 581 870 1195 1543 2119 2207 1918 1049

empl 5 3 13 157 279 469 619 605 387

LV turn 803 841 895 938 953 989 1067 1083 1079

opre 803 841 895 938 953 989 1067 1083 1079

empl 800 860 908 974 978 1005 1058 1075 1052

MT turn 164 202 262 326 630 738 738 664 291

opre 164 202 262 326 630 738 738 664 291

empl 8 22 58 166 206 211 216 203 111

NL turn 3087 3506 3937 4670 5269 5703 6062 6330 4486

opre 3554 4147 4892 5973 7078 7890 7983 8339 5973

empl 5661 7425 8863 11358 11849 14465 12806 13895 8920

PL turn 6728 7188 8853 10108 10921 11440 11516 11263 6700

opre 6736 7198 8906 10232 11068 11570 11690 11336 6738

empl 6419 6881 7027 7690 8477 15757 9825 5943 2119

PT turn 3292 4575 4715 5014 5139 5205 5123 5059 4580

opre 3379 4775 4940 5219 5359 5566 5748 5635 5073

empl 592 636 4410 4679 4810 4925 4854 4801 4435

RO turn 3972 4216 3756 5069 5230 5522 5649 5796 5666

opre 4004 4264 3793 5069 5230 5522 5649 5796 5666

empl 3991 4240 3751 5061 5230 5522 5649 5796 5666

SE turn 9369 10467 11445 12519 13908 14512 15056 15560 15566

opre 9459 10483 11446 12519 13915 15362 15914 16447 16444

empl 9868 10444 11266 12279 13643 15093 15531 16015 15937

SI turn 938 970 1028 827 798 812 1226 1196 18

opre 939 972 1031 831 798 817 1243 1219 18

empl 920 954 1031 855 861 844 1112 1070 337

SK turn 1176 1536 1682 1886 1874 1935 1967 1883 1110

opre 1196 1548 1698 1947 1957 1942 2252 2160 1110

26

empl 727 1380 1578 1769 1837 1805 2013 1924 362

Total turn 152123 166030 182351 200964 209752 215278 221022 220596 176173

opre 177999 194821 215326 243399 259994 277047 292116 294597 246465

empl 144352 160318 191517 223020 233996 253463 254734 255997 205628

27

Risk Diversi cation, Financial Integration and...

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