The Impact of Inward FDI and Foreign Ownership on

Performance of German Multinational Firms

Christian Arndt, Anselm Mattes�

First Draft (August 31, 2007)

Abstract

Recent studies �nd that the ceteris paribus impact of inward FDI on the sec-

toral level of employment in Germany is negative. Adversely, �rm level studies

comparing foreign owned and domestic multinational �rms typically report only

small causal ownership e�ects on �rm performance.

This paper departs from the main body of the existing literature in the fact

that we try to operationalize the pure ownership e�ect by analyzing the e�ects of

ownership change and FDI on productivity and employment only on MNEs. In a

�rst empirical part of the paper we give aggregate country evidence for the case of

Germany with new data on the �rm level which link information about FDI and

domestic performance. In a second empirical part the impact of ownership change

on labor productivity and employment is analyzed within a matching-estimator

framework. Furthermore, we will use an econometric model to gauge the ceteris

paribus e�ect of ownership on employment and productivity.

JEL: F15, F21, F23

Keywords: FDI, foreign ownership, M&A, total factor productivity, labor pro-

ductivity, employment

1 Introduction

The e�ects of foreign direct investment (FDI) and the economic activities of multi-

national enterprises (MNE) are the subject of an increasing number of research

projects. Up to now a large part of economic research is dedicated to the home-

country e�ects of FDI abroad, speci�cally labor market e�ects (see e.g. Barba-

Navaretti and Venables, 2004, and see Buch et al., 2007b). Less research and

�Institute for Applied Economic Reasearch (IAW) Tuebingen, Germany. www.iaw.edu. This paper

has partly been written during visits of the authors to the research centre of the Deutsch Bundesbank.

The hospitality of the Bundesbank as well as access to its �rm-level database International Capital Links

('MiDi') are gratefully acknowledged. The project has bene�ted from �nancial support through the

German Science Foundation under the project "Multinational Enterprises: New Theories and Empirical

Evidence from German Firm-Level Data" (BU 1256/6-1) and under SFB-TR 15. We thank Claudia

Buch, Jörn Kleinert, Alexander Lipponer and Farid Toubal for hints and helpful discussions. Martin

Schlotter has provided most e�cient research assistance. All errors are in our own responsibility.

1

evidence exists on the domestic e�ects of inward FDI in general. Speci�cally, �rm

evidence on the micro level is scarce, despite the policy relevant question, whether

or not to attract FDI and whether to in�uence the �quality� of FDI. Recall also

the recently ongoing public debates on this topic in Germany. For some of the ex-

amples see Buch et al. (2007a), Murakami and Fukao (2006) and others. Further,

Mergers and Aquisitions (M&A) of MNEs account for a large part of inward FDI

activity and always attract major and often controversial resonance in the media

and public debates.

In this paper we analyze the impact of inward FDI and foreign ownership on

performance of domestic based multinational �rms as measured by productivity

and employment and present new evidence concerning Germany. We use a novel

data merge of FDI micro-level data (Microdatabase Direct Investment, MiDi) and

commercial data about domestic performance of MNEs in Germany (Dafne), sup-

plied by Bureau van Dijk.1

In general, foreign owned �rms are found to outperform purely domestic �rms.

Several studies emphasize that the productivity and pro�tability gap is not due

to foreign onwership per se but to gains from MNE-networks (see e.g. Pfa�ermayr

and Bellak, 2000, p. 31). Therefore, we restrict the total population of all domestic

based enterprises to the subsample of MNEs and compare domestic owned MNEs

(DMNE) with foreign owned MNEs (FMNE). The multinational structure of these

MNEs, both domestic and foreign owned, should lead ceteris paribus to a similar

performance potential. This allows us to isolate and measure the pure ownership

change e�ect. For an analogous argumentation see Pfa�ermayr and Bellak (2000,

p. 30). We de�ne foreign ownership as a foreign participation in an MNE's equity

of more than 20%. We do not discriminate between single and multiple foreign

owners.

German companies' total investment stock abroad (676 bill. e in 2004) is far

higher than foreign �rms' total FDI stock in Germany (see Deutsche Bundesbank,

2006, p. 6). But FDI in Germany led to a total amount of 345 bill. e of direct

and indirect FDI stock in 2004 and seems important enough to make one expect

signi�cant impacts, among others, on sectors and �rm performance (see Deutsche

Bundesbank, 2006, p. 42). The largest share of total inward FDI consists of

investments in already existing �rms. That is, ownership change via M&A or

foreign participation plays an important role in comparison to newly founded �rms.

Once more, the case of a foreign participation in domestic MNEs is a special one.

FDI in MNEs accounts for a great share in German inward FDI. In 2004, about

19% of German MNEs' total employment was located in multinationals with a

foreign parent company. As already mentioned, these M&As usually attract a lot

of publicity because of the number of jobs and amount of capital involved in each

single transaction is typically high.

Since the impact of foreign participation on the �rm's performance and behavior

is not clear from a theoretical view, the main questions we want to answer are the

following:

� Do productivity measures of FMNEs and DMNEs di�er?

1 http://www.bvdep.com/.

2

� Is there an impact of foreign ownership on productivity and employment on

the �rm level?

� Do increases in FDI, on the intensive margin, a�ect productivity and employ-

ment of German multinationals?

In order to estimate the home-market e�ects of inward FDI, we use three dif-

ferent approaches. Firstly, as point of departure, we take into account aggregated

e�ects that may occur through intra-industry spill-overs (as e.g. the already men-

tioned IAW-study). Secondly, we want to ask, how does �rm performance change

after an initial foreign participation compared to the counterfactual situation, if

there would have been no foreign engagement. We use a propensity score matching

estimator to quantify this impact. Note, that in that �rst approach we control for

the initial conditions before ownership. Thirdly, we ask for the ceteris paribus ef-

fects of one additional entity of FDI on various �rm performance measures holding

further controls constant. We estimate a dynamic �xed e�ects model and partial

e�ects of the inward FDI stock as a continuous variable on �rm performance.

In general, using the two latter approaches at the same time allows to distin-

guish between pure ownership change e�ects (extensive margin) and the e�ects

of additional amounts of FDI (intensive margin). The econometric model allows

to hold �rm output constant when estimating employment e�ects. This permits

limited comparison to the �rst approach that will �nd negative employment e�ects

of inward FDI.

The paper proceeds as follows: First, we give a short overview over the existing

empirical and related theoretical studies. We describe our novel data and report

results with regard to the relevance of FMNEs and DMNEs which shall deliver new

and comparable evidence for the case of Germany. Then, we derive the results on

the semi-aggregated sectoral level. Finally, we compare central �gures for DMNEs

and FMNEs in Germany and have a closer look at �rm performance before and

after ownership change. Finally, we sketch the remainder of the analysis.

2 Related Literature

Barba-Navaretti and Venables (2004, pp. 151-162) present a basic conceptual

framework and an overview on the existing literature that has been dedicated

to measuring productivity and wage e�ects caused by ownership change through

foreign mergers and acquisitions.

Further, Pfa�ermayr and Bellak (2000) develop a conceptual framework for the

impact of foreign ownership change and present evidence on the case of Austria.

They �nd evidence for a higher level of productivity for foreign owned �rms but

argue that this is due to their membership in a MNE-network. In contrast, com-

paring foreign and domestic owned MNE doesn't reveal signi�cant di�erences in

productivity.

Karpaty (2004) considers e�ects of FDI on the productivity of �rms in the des-

tination country and states that direct e�ects may conduce to a higher productivity

of foreign owned �rms. He �nds that foreign-owned manufacturing �rms in Sweden

have a higher productivity compared to domestic �rms.

3

McGuckin and Nguyen (2000) examine labor market and wage e�ects of FDI

in the US with plant-level data for the U.S. manufacturing industries for the years

from 1977-1987. They �nd, that ownership changes are not a primary vehicle

for cuts in employment and wages. Furthermore, they �nd positive overall labor

market e�ects.

Piscitello and Rabbiosi (2005) analyze the impact of ownership change with

Italian data. They conduct paired t-tests and �nd that foreign acquisitions induce

productivity improvements. These higher levels of productivity could be (but are

not necessarily) related to labor downsizing.

Conyon et al. (2002) use �rm data for the United Kingdom from 1987 to 1996

and examine foreign �rm take-overs in the period from 1989 to 1994. The authors

�nd that foreign owned �rms pay equivalent employees 3.4 % more than domestic

�rms. Firms which are acquired by foreign companies exhibit an increase in labor

productivity of 13 %.

Bernard and Sjöholm (2003) analyze the probability of plant shutdowns in

Indonesia. The fact, that foreign owned plants are far less likely to close than

wholly-owned domestic plants is on grounds of a larger plant size rather than on

grounds of the nationality of ownership. Controlling for plant size and productivity

foreign plants in Indonesia are signi�cantly more likely to close than comparable

domestic establishments.

Murakami and Fukao (2006) analyze the impact of M&A on Japanese manu-

facturing �rms using a panel of �rm level data from the Japanese Basic Survey of

Business Activity of the Ministry of Economy, Trade and Industry for the period

from 1994 to 1998. They compare M&As between a foreign and a Japanese �rm

(out/in), on the one hand, and between two Japanese �rms, on the other hand

(in/in). They �nd that out/in �rms show higher labor productivity, pro�t-to-

sales ratios and R&D intensity than in/in �rms. Further, they show that �rms

acquired by foreign �rms see an improvement in their productivity after the acqui-

sition. They conclude that out/in M&As involve a transfer of business resources

of business know-how that helps to lift �rm productivity.

Evidence in the existing literature suggests that there is a positive productivity

e�ect of foreign ownership changes on domestic �rms. Little evidence exists on the

case of an ownership change of MNE that usually already have a comparatively

high productivity. In contrast, results on employment e�ects are mixed.

Few analyses deliver evidence on ownership change of MNEs. A notable ex-

ception is Spearot (2007), who develops a framework of cross-border M&A with

heterogeneous �rms. We analyze this question more in detail and narrow the gap

between the evidence based on semi-aggregated �rm-level data. Furthermore we

present �rst results on the case of Germany.

3 Theoretical hypotheses

3.1 Links between ownership status and �rm performance

In the literature there are di�erent rationales about how ownership changes and

FDI may a�ect �rm performance (see e.g. Bellak et al., 2006, pp. 32-33, Pfaf-

4

fermayr and Bellak, 2000, pp. 9-13, or Barba-Navaretti and Venables, 2004, pp.

151-185, for a survey on this question). These e�ects can be summarized as e�ects

due to a change of management, synergy and competitiveness e�ects, and market

power e�ects:

� E�ects due to a change of management: Three di�erent hypotheses can

be summarized as management e�ects that imply impacts from ownership

change: (i) A corporate governance system from one country may be more

e�cient than one from another country. (ii) Additionally, a new foreign owner

imposes a new management that matches better to the domestic MNE, which

in turn induces a productivity improvement. (iii) Related is the so called dis-

ciplining e�ect. This follows the idea that poorly run �rms have a low market

value from the perspective of a foreign investor with advantages in running

this �rm. Hence, it has incentives to acquire such a �rm. Exchanging the

management and installing a better one improves productivity and increases

market value.

� Synergy and competitiveness e�ects: MNEs already have a above average pro-

ductivity due to ownership advantages that stem from �rm speci�c knowledge

(�knowledge capital�) or other assets like a worldwide known brand name. Ad-

ditionally, MNEs may exploit international di�erences in factor costs more

easily. If a MNE takes over another MNE there still can be substantial

synergy e�ects if there is a good match between their respective ownership

advantages, which improves �rm level productivity for both, parent and new

a�liate.

� Market power e�ect: Mergers and acquisitions can have a signi�cant e�ect

on the structure of the market the �rms are competing in. The two merged

�rms have a greater market power than the independent �rms. By exploiting

this market power e�ect, a MNE which becomes an a�liate can improve its

productivity.

These three theoretical arguments can be divided into two strands: The �rst

strand states that a change of ownership per se leads to an increase in performance.

This reasoning follows the disciplining and management matching theory. The

other strand follows the idea that foreign ownership per se doesn't change �rm

performance. An increase in performance is rather induced by participation in the

international network the new a�liate can bene�t from. This reasoning goes in

line with the synergy and competitiveness e�ect.

3.2 Operationalization of the pure ownership e�ect and pos-

sible sample selection bias

To shed more light on the �rst argument, we will compare �rm performance of

German owned MNEs with foreign owned MNEs in Germany in the empirical part.

The multinational structure of these MNEs, both domestic and foreign owned,

should result ceteris paribus in similar productivity measures. This allows us to

isolate and measure the pure ownership change e�ect and to eliminate the synergy

and competitiveness e�ects.

5

Figure 1 shows, that when a previously purely domestic �rm (PDE) obtains a

foreign parent, two things happen at the same time: First, there is a change in the

ownership structure. But second, also the �rms structure is changed, there are a

new multinational intra-�rm links, that may e�ect productivity. Furthermore, this

new multinational structure may consist not only in the new foreign parent itself,

but also in a multinational network via the foreign parent.

Figure 1: The pure ownership e�ect

Source: Own presentation

Figure 1 shows, that in the case of an new foreign owner of a previously domestic

MNE the main characteristic of the �rm structure remains unchanged: also before

a multinational structure has been existing.

Futhermore, we have to be concerned with the fact that �rms may choose to

invest only in the most e�cient and pro�table �rms ('cherry picking'). Another

hypothesis states, in contrast, that mostly poorly performing �rms are acquired,

because there is a potential to improve �rm performance by better management or

by other means (also see Bellak et al., 2006). This may cause a selection bias in

simple descriptive comparisons of domestic and foreign owned MNEs.

4 Data

For the analysis on semi-aggregated level in section 5 we use the Microdatabase

Direct Investment (MiDi), collected and maintained by the Deutsche Bundesbank

that covers all international capital links for Germany, comprising investments on

the �rm-level above certain thresholds since 1989. From 1996 onwards, MiDi allows

to pursue each record in time (micro-panel structure). Hence, we can pursue both,

companies holding FDI stocks (investors) as well as each single direct and indirect

investment enterprise (see Lipponer, 2006).

6

Figure 2: E�ect of ownership and MNE structure

Source: Own presentation

Our micro-data have been obtained from merging two previously separate data

sets. (1) The already described MiDi-data. In an earlier joint research project with

University of Tuebingen the panel identi�ers in MiDi have been matched to the

unique �rm-numbers from German Creditreform, which in turn are (2) contained

in German �rm-data supplied by the Bureau van Dyjk (Dafne). MiDi alone already

allows us to distinguish FMNEs and DMNEs. Due to its focus on the investment

enterprises MiDi contains far less detailed knowledge about the investor. The data

merge is intended to supply the previously lacking information on German investing

enterprises (FMNEs and DMNEs), but not for the total group of investment objects

in Germany. Hence, we have no further Dafne information about foreign owned

�rms without own investments abroad. Firstly, we use the Dafne information

to analyze �rm performance of German DMNEs in the years before and after a

possible ownership change. Secondly, Dafne delivers additional information about

both, FMNEs and DMNEs, that our analysis is focused on. Thirdly, MiDi gives

us the exact year of ownership change, that cannot be obtained from Dafne alone.

Up to our knowledge this novel dataset has been used before only by Kleinert and

Toubal (2006).

One potential drawback of the data is that we do not have exact information

about its representativity for the German economy. But at least, we have a total

number of about 90,000 domestic �rms and 7,000 MNEs, from which about 1,800

are foreign owned.

7

5 Country results for Germany

5.1 Relevance of FMNEs and DMNEs in the data

Table 1 in the appendix shows the relevance of FMNEs compared to MNEs and

compared to the total number of �rms in our data. The left part of the table con-

tains the number, the employment and the sales of FMNEs in relation to the total

of all MNEs (total number of MNEs = number of FMNEs + number of DMNEs).

Whereas about 30 % of the MNEs in our data are foreign owned, only about 14 %

of the total MNE-employment is found within foreign owned MNEs. Also, FMNE-

sales amount only to about 13 % of total MNE-sales. Hence, compared to DMNEs,

FMNEs are smaller in average, both, with regard to the number of employees and

with regard to their sales.

In relation to the total economy, FMNEs account for only about 1.9 % of the

total number of �rms in our data (see right part of Table 1). But at the same

time, these �rms make up about 7 % of total employment and 9 % of total sales

in the data. Consequently, compared to domestic companies FMNEs result to be

considerably bigger.

Further, we �nd considerable di�erences in the relevance of FMNE-�rms when

we go down to the industry-level. In some industries foreign ownership is a very

common feature of German MNEs. E.g. in the case of wholesales, light industries

as well as in the case of machinery, electronics and automobiles more than 30 %

of the MNEs are foreign owned. In contrast, in the �nance sector, which is very

well protected, this is only true for about 6 % of the MNEs. Especially in the light

industries sector and in the case of wholesales, FMNE-sales account for 89 % and

57 % of the total MNE-sales. These big �rms also matter for the total economy:

With regard to the total economy, FMNEs in the light industries and in the sales

sector still make up about 49 % and 24 % of total sales.

5.2 Size distribution of FMNEs

Table 2 shows some stylized facts about the size distribution and concentration of

FMNEs with regard to total sales.

In the case of DMNEs the 44 top 1 %-�rms with regard to total sales account

for about 71 % of total sales. In the case of FMNEs this concentration still is

higher: the 19 FMNEs belonging to the top 1% make up 88 % percent of total

shares.

5.3 Distribution of the time spent as FMNE in the data

Figure 3 collects three histograms for the distribution of the time (in years) that

three types of �rms (FMNEs, DMNEs and domestic �rms, DOMs) can be observed

in the data. For the FMNEs, the DMNEs, as well as for the domestic �rms the

second class (two years) turns out to be the modal class, followed by the �rst

class (exactly one year). For subsequent increasing numbers of years the estimated

densities decay with exception of the last class: For the �rms that exist during the

total number of years in the data we �nd a local maximum of the density curve.

8

The most remarkable di�erence between the groups we �nd between MNEs

and domestic companies. Relative to the other classes, we �nd more MNEs in

the upper part of the distribution than DOMs, meaning that MNEs relatively live

longer compared to domestic �rms.

5.4 Comparing performance measures controlling for industry-

�xed e�ects

Table 3 shows results for the regression equation

log yit = �+ � � FDIdum+ � inddum+ uit; (1)

where log yit is a perfomance measure in logs, � is a constant, FDIdum is a

dummy variable (=1 for FMNEs, = 0 for DMNEs), inddum is a vector of industry-

�xed e�ects and uit is an error term. We are interested in �̂, which gives us the

expected conditional di�erence in the dependent variable for FMNEs compared to

DMNEs controlling for the industry-�xed e�ects, that are captured by the industry-

dummies inddum.

The results show that in the case of quite some of the selected performance

measures the FMNE-dummy is signi�cant: Again, FMNEs turn out to be smaller

compared to DMNEs, als well with regard to employees, capital, and sales.

Furthermore, with regard to some of the selected productivity measures, FMNEs

show up to be more productive: They have signi�cant higher TFP values (signi�-

cant on the 10%-level), as well as a higher Labor Productivity (again signi�cant on

the 10%-level). Further, with regard to our within-TFP estimate they even show

higher degrees of productivity on the 1 %-level. Finally, with regard to wages we

do not �nd signi�cant di�erences between FMNEs and DMNEs.

5.5 Distribution of TFP for FMNEs vs. DMNEs

Figure 4 shows kernel-density estimates of TFP grouped for FMNEs (dashed line)

and DMNEs (solid line).

On the one hand, both lines show several common features, e.g. we �nd a

maximum at an TFP-value of about 2.2. On the other hand, we �nd two main

di�erences, which partly indicate in opposite directions: On the one side, in the

range of TFP-values from about 2.2 to 3 we �nd considerable higher densities

for FMNEs, suggesting slightly higher productivities within this range. But on

the other side, we �nd a local maximum at about 3.8 in the case of the domestic

MNEs, which show that we must have some DMNEs with considerable high degrees

of TFP.

6 Results for Germany on the aggregated Level

An analysis on the aggregated level hides a lot of �rm heterogeneity. But, semi-

aggregated data on the sectoral and regional level may reveil labor market reactions

9

that also include indirect e�ects via intra-industry spill-overs and market compe-

tition. See Kolasa (see e.g. 2006) for a similar argumentation and an analysis of

determinants of spill-overs in the case of Poland.

For this analysis we needed to impose a harmonized threshold level for the total

time covered by MiDi (see Lipponer, 2006). That means, we eliminated changes in

our dependent variables which are due to changes in reporting limits by dropping

all observations that are not covered by the most stringent reporting requirements.

Then, the data has been aggregated on the sectoral level, resulting in data for a

total of 23 industries and 13 years. We de�ne FDI as the total stock of direct and

indirect investment hold by holding companies. Furthermore, only the part of a

�rm's capital stock is counted as FDI that is attributable to the foreign investor.

At the industry-level, we use standard NACE sectors which allow combining our

FDI data with industry-level data obtained from the German Statistical O�ce.

The original MiDi database contains information on more than 100 industries,

following NACE Rev. 1 categories, and these can be aggregated into 37 broader

industries. Of these, we use only standard manufacturing and services industries.

We drop industries such as agriculture, mining and quarrying, public institutions,

or households. Out of the industries dropped, holding companies are particularly

important. The �nal set of industries includes 13 manufacturing and 9 services

industries.

We use a �xed-e�ects IV-Estimator for the function:

Lit = �1i�2dt + �1Kit + �2Yit + �3wit + FDIinwardit + �it; (2)

where:

� Kit is log aggregated domestic capital,

� Yit is log aggregated value added,

� wit is log aggregated hourly wages,

� FDIinit is log aggregated FDI-stock in Germany,

� FDIoutit is log aggregated German FDI-stock abroad,

� FDIcount�init is log number of investment objects in Germany, and

� FDIcount�outit is log number of German investment objects abroad.

Table 7 in the appendix reports the results. We �nd a positive and signi�cant

�̂1, hence K is a complement of labor. �̂2 is positive and highly signi�cant. The

hourly sectoral wage w has a negative and signi�cant impact. A higher sectoral

wage hence implies a reduction of total sectoral employment. We �nd, that ̂ is

negative and signi�cant on the 1 % level. Note, that we hold Y constant and conse-

quently do not take into account possible impact of FDI on the level of production.

That means that ceteris paribus the stock of inward FDI has a negative e�ect on

the level of employment. On the one hand, this could be caused by employment

reductions in the investment �rm. On the other hand, this may be caused by spill-

over e�ects that increase sectoral productivity. Furthermore, a higher level of FDI

may rise the intensity of competition in a considerable way. In order to clarify this,

micro analysis is called for.

10

Aggregated results often are criticized for potential aggregation bias. Hence,

we continue on the �rm level.

7 Measuring Productivity

Basically there are two widely used measures of �rm productivity, total factor

productivity (TFP) and labor productivity.

In the �rst line we are interested in the total development of productivity fol-

lowing the ideas of Olley and Pakes (1996). But we will also use OLS estimation in

order to check for the robustness of the results. Further we will also use labor pro-

ductivity because of four reasons: First, it shall serve as a further robustness check,

second because data on labor and output are often found more reliable compared

to data about capital (see e.g. ?) and third, we will be able to compare results on

labor productivity to a larger set of existing studies. Fourth, from a theoretical

point of view, given the �nding that employment ceteris paribus decreases with

rising inward FDI on sectoral level it is of a special interest whether there are dif-

ferent developments of TFP and labor productivity in the MNE that were subject

to an ownership change. Stagnating TFP and rising labor productivity indicate

an increase in productivity that is due to a rising capital/labor ratio what in turn

could be due to reduced employment.

As a measure of �rm level productivity we use labor productivity calculated as

LPit =V Ait

Lit

; (3)

where V A = [(net sales)� (inputs other than labor and capital)] and Lit is the

number of employees of �rm i in year t.

Secondly, we employ the concept of total factor productivity. This enables us

to account for di�erent optimal factor input compositions in various industries and

gives additional insight whether a di�erent capital/labor ratio or other factors drive

the changes in labor productivity. Additionally this allows better comparison with

other empirical studies. In order to control for possible bias that may stem from

sample selection and endogeneity, we employ the estimation technique proposed

by Olley and Pakes (1996) and use investment to control for correlation between

input levels and the unobserved �rm-speci�c productivity process. Given the lack

of information on fuel and electricity consumption in the data we seem not to be

able to use intermediate inputs as proxies for investment, as has been proposed by

Levinsohn and Petrin (2003).

8 Descriptive results

8.1 Comparing FMNEs and DMNEs

As point of departure of the empirical analysis we compare FMNEs and DMNEs

with regard to our central performance measures, labor productivity and employ-

ment, as well as further �rm speci�c �gures (see table 4).

Taking means of a total of 10,041 observations from 1996 to 2004 of FMNEs and

DMNEs separately, reveals considerable di�erences between FMNEs and DMNEs

11

in Germany. Further, we test the null that these di�erences are zero by using a

panel robust test statistic.

First, purely DMNEs are bigger in di�erent senses: On average, they have a

larger size in terms of employees, value added, sales and tangible assets. Addition-

ally, they are older in terms of years since foundation of the �rm. In contrast, point

estimates of pro�ts and productivity are higher for FMNEs, but in these cases we

do not �nd signi�cant di�erences.

The multivariate analyses in section 8 will address the question in more detail,

if employment is lower in FMNEs � also given selected covariates. Furthermore, we

will address the question, if the di�erence in productivity still will stay insigni�cant.

8.2 Pre- and post ownership change performance

Table 5 shows the distribution of the ownership changes in single years from 1997

to 2004. Remember, that we de�ne foreign ownership as a foreign participation in

an MNE`s equity of more than 20 %.

In order to get a �rst glance at the impact of an ownership change we present a

table that shows the development of several performance indicators of MNEs that

are subject to inward FDI for the years before and after the ownership change (see

table 6).

Apparently, most of the reported �gures seem to boost after ownership change

in the years t > 0. This is specially true for value added and intangible assets, lat-

ter typically being accounted for after the MNEs market value has been disclosed

by the ownership change. Further, our point estimates show a rising labor pro-

ductivity, sales, equity and �xed assets. In contrast, employment, pro�ts, tangible

assets and wages per capita seemingly stay unchanged.

The change in productivity at this stage of analysis seems not to be due to a

lower level of employment after ownership change, but rather due to a higher level

of value added. Note that in the case of employment we only �nd a very small

impact of ownership change. Hence, these results on �rm level are in contrast to

our �rst �ndings on the semi-aggregated level that suggested a link between higher

sectoral productivity and lower sectoral employment levels.

The changing number of observations that enter the calculation of the means

indicate, that we do not have observations for all previous and subsequent years

for the total of 172 �rms which experience an ownership change in t = 0. This

fact may be due to item non-response as well as the fact that some �rms have a

lower probability to survive. Moreover, this �rst inspection does not control for the

possibility, that variables like productivity etc. may in�uence �rm survival. But

this �rst bivariate inspection suggests, that there may be a remarkable impact on

various �gures that coincides with ownership change.

12

9 Methodology

9.1 Matching Estimator Approach

The matching estimator approach starts from the simple idea to compare �treated�

and �untreated� subpopulations, where in our case treatment is foreign owner-

ship change and we de�ne a treatment indicator w1 = 1. We handle DMNEs as

�untreated� (w1 = 0).2 As mentioned already, in an alternative setting we will

de�ne treatment as an additional amount of FDI (w2 = 1, if �FDIt > 0). We

would want to compare MNEs� performance y (productivity, employment) with

treatment (y1) and without treatment (y0), and estimate the Average Treatment

E�ect, ATE = E[y1 � y0], in other words, the e�ect of ownership change on the

performance of a randomly drawn MNE.3 Unfortunately, we neither can observe

y1 and y0 for each multinational at the same time, nor can we assume that treat-

ment is distributed randomly among �rms: More speci�cally, we have to assume,

that M&As are correlated with �rm-performance (e.g. �cherry picking�). Hence,

E[y1�y0] 6= E[(y1jw = 1)�(y0jw = 0)], and we cannot compare mean performance

of FMNEs and DMNEs directly.

But, if given a set of observable covariates X, multinational ownership change

happens randomly, or in other words, conditional on X, w and (y1; y0) are inde-

pendent, then

E[ykjX;w] = E[ykjX]; k = 1; 2; (4)

(see e.g. Wooldridge, 2002, p. 607). Once we �partial out� the observables

in X, w and (y1; y0) are uncorrelated. This is called �selection on observables� or

�ignorability of treatment� given the observed covariates inX. Then ATE = E[y1�

yxjX]. More speci�cally, Rosenbaum and Rubin (1983) have used the alternative

formulation ATT = E[y1 � yxjp(X)], where p(X) is called the propensity score:

p(X) = Pr(w = 1jX): (5)

The MNE-speci�c covariates X determine the probability of ownership change.

For an estimate of ATT we would simply have to average treated and untreated

�rms with the same p(X), respectively. To cope with the problem, that we will �nd

no �rms with exactly identical score p(X) in general, di�erent matching strategies

have been proposed in the literature.4 In a �rst step, we will use nearest neighbour

matching, where each treated FMNE is assigned to an untreated DMNE-�twin�, as

far as the di�erence in propensity score is below a certain threshold.

With exception of age, TFP and market share we transform all variables into

their log deviations from the respective industry mean. In case of negative devi-

ations we take logs of the absolute deviations and multiply by �1. Note, that we

set all absolute deviations smaller than one to the value of one. In contrast to

2 Note that we drop the MNE speci�c subscripts i in this part of the summary.

3 yj(w = 1) = y1 and yj(w = 0) = y0.

4 Besides Propensity score matching (see Rosenbaum and Rubin, 1983), there are caliper matching

(see Cochran and Rubin, 1973), kernel-based matching (see Heckman et al., 1997 and Heckman

et al., 1998), mahalanobis distance matching (see Rubin, 1980).

13

Bellak et al. (2006) we not only use labor productivity but also our estimated TFP

measures as rhs variables.

9.2 Panel-econometric Model

In order to deepen the �rm level analysis we will also estimate the dynamic �xed-

e�ects baseline model

yit = �i + �yi;t�1 + �0xit + FDIinwardit + �it; (6)

which can be estimated by GMM-methods. All variables are transformed in

logs. yit is a performance variable for MNE i and year t, the �i are �rm speci�c

e�ects, � captures �rst order serial correlation, x is a column vector of controls and

FDIinwardit is the amount of direct investment attributable to �rm i in the year t

that is held by foreigners. We are interested in ̂ that estimates the elasticity of

MNE performance to FDI.

10 Conclusions and outlook

Economic research still has not answered the question whether or not to attract

FDI and whether to in�uence the quality of FDI in a satisfying way. We started

from the stylized fact that foreign owned �rms are found to outperform purely

domestic �rms. More in detail, several studies emphasize that this productivity

and pro�tability gap is not due to foreign onwership per se but to gains from MNE-

networks. Hence, with a novel data merge of FDI and commercial data about

domestic performance of MNEs in Germany at hand, we analyzed the impact of

inward FDI and foreign ownership on performance of domestic based multinational

�rms as measured by productivity and employment and presented new evidence

on the case of Germany.

First, on a semi-aggregated level, we �nd evidence for considerable spill-overs

on the sectoral level that apparently boost productivity within industries. We then

go beyond the aggregated level and compare domestic and foreign owned MNEs.

We �nd, that DMNEs are bigger in di�erent senses: On average, they have a

bigger size in terms of employees, value added and tangible assets. Furthermore,

they are older in terms of years since foundation of the �rm. Most interestingly,

we �nd higher levels of total factor productivity in foreign owned multinationals

compared to domestically controlled multinational �rms. For labor productivity

we �nd signi�cant results only in the regression approach.

But looking at how MNE �gures change before and after ownership change,

our point estimates show rising labor productivity, sales, equity and �xed assets.

In contrast, employment, pro�ts, tangible assets and wages per capita seemingly

stay unchanged. Hence, these results on the �rm level are in contrast to our �rst

�ndings on the semi-aggregated level that suggested a link between higher sectoral

productivity and lower sectoral employment levels. That means that at this stage

of analysis the impact of ownership change on productivity seems not to be due

to a lower level of employment after ownership change, but rather due to a higher

level of value added.

14

Finally, we propose an additional productivity measure, the Matching Esti-

mator, and a econometric labor demand model to scrutinize the causal e�ect of

ownership change on labor productivity and employment on the level of German

MNEs and sketched the further work that still is to be done within this study.

Further research beyond the borders of this study will have to be done, especially

with regard to the question, in how far our results are speci�c to ownership change

in the case of MNEs as in contrast to ownership changes in the case of previously

purely domestic enterprises.

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16

Appendix

17

Table1:RelevanceofforeignownedMNErelativeto

MNEsandtotalnumberof�rm

sin

Germ

any,sharesoveryears

1996-2004

FMNEsasshare

ofMNEs

FMNEsasshare

ofall�rm

sin

thedata

Percentage

Employment

Sales

Percentage

Employment

Sales

Sector

of�rm

sshare

share

of�rm

sshare

share

Total

29.6%

14.3%

13.8%

1.9%

7.3%

9.7%

Lightindustries

37.0%

39.4%

89.4%

3.3%

16.8%

49.1%

Heavyindustries

27.9%

19.6%

16.2%

3.8%

12.8%

14.0%

Machinery,electronics,automobile

32.3%

16.6%

9.0%

6.3%

12.5%

7.8%

Utilities,construction

21.1%

7.8%

1.4%

0.3%

1.8%

0.3%

Sales

41.1%

23.8%

56.8%

1.8%

6.5%

23.7%

Transport,communication,businessservices

24.1%

7.3%

2.0%

1.9%

3.6%

1.1%

Finance

6.0%

0.5%

3.5%

0.3%

0.2%

1.3%

Realestate

20.6%

29.5%

16.8%

0.3%

2.5%

0.0%

Utilities,Construction

21.1%

7.8%

1.4%

0.3%

1.8%

0.3%

Manufacturing

31.6%

19.0%

15.5%

4.6%

13.1%

13.0%

Services

28.1%

9.1%

9.6%

1.5%

4.0%

4.9%

DMNE:DomesticMultinationalEnterprises,FMNE:ForeignMultinationalEnterprises.

Allvalues

exceptagein

EURO,age

inyears.1997-2004.***indicatessigni�cance

onthe1%

level,**onthe5%,and*onthe10%

level.Source:

Own

calculations.MiDi-Dafne-Merge.

18

Table 2: Shares of Top-x% Firms with regard to sales, shares

over years 1996 - 2003

DMNE FMNE DOM

Obs Top1 44 19 883

Top5 217 92 4,417

Top10 433 185 8,834

Total obs 4,333 1,855 88,355

sales Top1 1,250,235 248,612 871,548

Top5 1,763,718 282,752 872,875

Top10 1,764,065 282,916 873,365

Total sales 1,764,731 283,244 874,061

shares Top1 70.85% 87.77% 99.71%

Top5 99.94% 99.83% 99.86%

Top10 99.96% 99.88% 99.92%

Total shares 100.00% 100.00% 100.00%

19

Figure 3: Time spent as ...

Source: Own calculations

20

Table3:Regressionresults,pooledestim

ation,1996-2003

-1-2

-3-4

-5-6

-7-8

-9

Employees

Capital

Sales

TFP(O

&P)

Lab.Prod.

prodOLSA

prodOLSB

prodwithin

wagem

ean

DummyFMNE

-2,006.283***

-2.838e+

08***

-2.701e+

08***

0.053*

288,377.382*

0.018

0.027

0.251***

42,370.79

0=DMNE

00

0-0.065

-0.052

-0.236

-0.119

0-0.24

sector=

=1

361.526

56639137.558***

2.044e+

08***

-0.726***

931,498.831**

-0.204**

-0.212**

-1.119***

40,762.024***

-0.279

0-0.002

0-0.018

-0.016

-0.013

0-0.002

sector=

=2

1,231.731**

1.605e+

08***

1.934e+

08***

-2.069***

-6,130.90

0.03

-0.168**

-2.017***

548.554

-0.022

-0.003

-0.003

0-0.693

-0.629

-0.027

0-0.777

sector=

=3

3,205.475***

3.179e+

08***

7.894e+

08***

-1.036***

7,475.95

0.124*

0.026

-0.666***

877.746

00

00

-0.814

-0.09

-0.677

0-0.854

sector=

=4

1,114.158*

6.288e+

08***

39532523.5

0.504***

-308.294

0.146*

-0.017

-1.071***

19,332.68

-0.052

0-0.358

0-0.984

-0.067

-0.791

0-0.241

sector=

=5

256.897

52271197.678***

1.151e+

08**

-1.197***

115,730.80

-0.127*

-0.277***

-1.187***

88,204.49

-0.411

0-0.042

0-0.431

-0.087

-0.003

0-0.31

sector=

=6

3,228.228***

7.055e+

08***

2.677e+

08***

0.360**

11,344.60

0.118**

0.085

-0.692***

7,418.845***

-0.004

00

-0.039

-0.381

-0.041

-0.125

0-0.001

sector=

=7

-746.922

86812474.4

-6.542e+

07***

5.419***

51,096.687*

4.371***

1.180***

-1.155***

16,746.237**

-0.117

-0.412

-0.005

0-0.073

00

-0.006

-0.02

sector=

=8

-663.864*

-3.347e+

07*

-2.782e+

07*

0.276**

-9,422.45

0.206

0.231*

0.681***

-1,173.43

-0.072

-0.077

-0.063

-0.036

-0.537

-0.148

-0.092

-0.002

-0.557

Constant

1,651.977***

92366477.510***

85402878.263***

3.788***

-66,091.790*

1.012***

1.107***

3.829***

-9,595.85

-0.001

-0.007

-0.004

0-0.081

00

0-0.321

Observations

4400

6772

6188

3371

4083

3371

3371

3371

4389

R-squared

0.011

0.007

0.011

0.325

0.012

0.324

0.114

0.125

0.002

DMNE:DomesticMultinationalEnterprises,FMNE:ForeignMultinationalEnterprises.

Sample

from

1996-2003.Robust

p-values

inparentheses.***indicatessigni�cance

onthe1%

level,**onthe5%,and*onthe10%

level.Source:

Own

calculations.MiDi-Dafne-Merge.

21

Figure 4: Kernel densities for TFP of FMNEs and DMNEs

Source: Own calculations

22

Table 4: Performance indicators for domestic and foreign

owned MNE in Germany, mean over years 1996 -

2004

obs DMNE obs FMNE

Employment 3 558 3 528 1 057 1 692 **

Pro�t 5 379 20 600 000 1 640 134 000 000

Value added 4 810 269 000 000 1 496 70 800 000 **

TFP 2 629 472 762 135 *

Labor Productivity 3 259 21 352 1 006 375 854

Sales 4 906 374 000 000 1 511 254 000 000

Equity 5 415 206 000 000 1 644 389 000 000

Debts 5 415 270 000 000 1 644 107 000 000

Debt-equtiy ratio 5 342 19 1 617 6

Total assets 5 415 610 000 000 1 644 551 000 000

Fixed assets 5 414 427 000 000 1 644 318 000 000

Intangible assets 3 380 12 900 000 1 148 3 337 118

Tangible assets 5 414 139 000 000 1 644 15 300 000 *

Financial assets 5 414 280 000 000 1 644 300 000 000

Material 5 281 101 000 000 1 624 171 000 000

Age 5 302 36 1 627 30

Wages per capita 3 550 5 786 1 055 40 827 ***

Outward FDI 5 415 197 450 1 644 87 974

DMNE: Domestic Multinational Enterprises, FMNE: Foreign Multina-

tional Enterprises. All values except age in EURO, age in years. 1997 -

2004. *** indicates signi�cance on the 1 % level, ** on the 5 %, and *

on the 10 % level. Source: Own calculations. MiDi-Dafne-Merge.

Table 5: Number of ownership changes in Germany (1997 -

2004)

Year Ownership changes

1997 18

1998 9

1999 27

2000 23

2001 23

2002 35

2003 28

2004 10

Source: Own calculations.

MiDi-Dafne-Merge.

23

Table 6: MNE performance before and after ownership

change in Germany (1997 - 2004)

t = �2 t = �1 t = 0 t = 1 t = 2 t = 3

MNEs = Obs 87 117 172 155 117 88

Employment 1.837 1.834 1.713 1.583 1.758 1.711

Obs 64 72 115 117 84 67

Pro�t 14 374 9 527 16 618 14 566 18 307 9 938

Obs 87 117 171 155 117 88

Value added 12 501 15 717 14 007 80 670 108 390 115 863

Obs 82 109 152 139 107 80

Total Factor Productivity 2.04 1.82 2.03 2.27 2.43 2.31

Obs 53 59 77 80 54 43

Labor Productivity 8.24 7.96 7.65 16.69 24.63 23.85

Obs 63 69 104 107 77 61

Sales 146 307 129 044 114 659 243 462 360 233 233 821

Obs 83 111 154 142 107 80

Equity 110 821 83 948 96 383 151 012 217 930 287 741

Obs 87 117 172 155 117 88

Debts 129 365 108 351 124 718 150 671 199 523 271 270

Obs 87 117 172 155 117 88

Fixed assets 170 850 144 170 131 241 230 294 326 483 439 502

Obs 87 117 172 155 117 88

Intangible assets 226.863 74.276 498.087 1986.602 2573.736 4209.057

Obs 32 60 114 112 97 76

Tangible assets 18 090 11 581 13 488 16 813 23 096 20 011

Obs 86 114 169 152 117 88

Financial assets 152 676 132 585 117 423 212 033 301 270 415 856

Obs 87 117 172 155 117 88

Material 112 505 110 620 91 879 153 661 230 318 107 234

Obs 86 114 169 152 117 88

Wages per capita 7.652 6.022 10.951 6.699 9.533 20.647

Obs 64 71 114 116 84 67

All variables in thousands. All values in EURO. t is years after ownership change. Source:

Own calculations. MiDi-Dafne-Merge.

24

Table 7: FDI impact on sectoral employment (1991-2003)

(1) (2) (3) (4)

Kit 0.55*** 0.52*** 0.54*** 0.50***

(0.08) (0.07) (0.08) (0.08)

Yit 0.43*** 0.33*** 0.34*** 0.33***

(0.07) (0.07) (0.06) (0.06)

wit -0.39*** -0.56*** -0.53*** -0.60***

(-0.11) (-0.1) (-0.09) (-0.09)

FDIinit -0.07***

(-0.02)

FDIoutit 0.01

(0.02)

FDIcount�init -0.01

(-0.03)

FDIcount�OUTit 0.04

(0.03)

dt 0.04** 0.04** 0.04** 0.05**

(0.02) (0.02) (0.02) (0.02)

obs 268 268 268 266

industries 23 23 23 23

R2 0.75 0.75 0.75 0.76

Kit: Log domestic capital

Yit: Log value added

wit: Log hourly wages

FDIinit : Log aggregated FDI-stock in Germany

FDIoutit : Log aggregated German FDI-stock abroad

FDIcount�init : Log number of investment objects in Germany

FDIcount�outit : Log number of German investment objects

abroad

Reported standard errors are robust to serial correlation. Sig-

ni�cant at the 1 % (***), 5 % (**), 10 % (*) level. Fixed-

e�ects IV-Estimates. i industries, t years. The row vector of

excluded variables is z = [Ki;t�1; Yi;t�1; wit�1; FDIxxi;t�1]. Own

calculations. Aggregated data calculated from MiDi and o�cial

OECD-STAN data (see http://www.oecd.org).

25