The short-run effects of knowledge intensive greenfield FDI on new domestic entry
The short-run effects of knowledge intensive greenfield FDI on new domestic entry
Sara Amoroso 0 1
Bettina Mu? ller 0 1
JEL Classification L 0 1
0 Centre for European Economic Research (ZEW) , L 7, 1, 68161 Mannheim , Germany
1 Joint Research Centre (JRC), European Commission , Calle Inca Garcilaso 3, 41092 Seville , Spain
Existing evidence on the impact of foreign direct investment on domestic economies remains ambiguous. Positive technology spillovers of foreign investment may be outweighed by negative crowding out effect due to increased competition. In this paper, we employ a unique country/sector-level data set to investigate the impact of what is considered the 'best' type of foreign investment-greenfield knowledge intensive FDI-on domestic entry. Our results suggest that, in the short run, this type of FDI is positively related to the entry rate in the host country, if the domestic sector is either dynamic, or highly R&D intensive. These sectors may be respectively characterized by lower entry costs, which encourage a 'trial-and-error' learning business approach, and by a higher level of absorptive capacity which increases the chance of technology transfer.
Foreign direct investments; New firm entry
Electronic supplementary material The online version of this article (doi:10.1007/s10961-017-9575-y)
contains supplementary material, which is available to authorized users.
Foreign direct investment (FDI) is expected to transfer knowledge and technology,
enhance productivity, competitiveness, and ultimately boost long-run growth of the
domestic economy. Many studies have investigated what factors and government strategies
attract such type of investment (Guim o?n 2009; Blonigen and Piger 2014), and what the net
benefits are. The empirical literature is, however, far from arriving at a unanimous opinion
on the net effects of FDI. In fact, the extent to which FDI is enhancing growth depends on
the degree of complementarity and substitution between foreign and domestic investment
(De Mello 1999; Munemo 2014). On the one hand, FDI has a complementary effect when
the surge in foreign capital is associated with positive technological spillovers that increase
the productivity of local enterprises and stimulate domestic investment and new local
entry. Studies have shown that the extent to which an economy can reap the benefits from
these knowledge externalities depends on its available stock of human capital (Borensztein
et al. 1998; van Pottelsberghe and Porterie 2001). On the other hand, FDI has a substitution
effect when the foreign investment crowds out equal amounts of investment from domestic
sources by competing in product or financial markets. The increased competitive pressure
might lead to the exit of local businesses and to a slow replacement of new local entry.
Most of the relevant literature looks at the impact of FDI on proxies of economic growth
such as capital accumulation, total factor productivity (TFP) growth, and gross domestic
product (GDP) growth. More recently, an increasing number of studies deals with the
relationship between FDI (cross-border mergers and acquisitions, M&A) and new local
firm?s entry or firm?s survival (De Backer and Sleuwaegen 2003; Ayyagari and Kosova?
2010; Munemo 2014; Danakol et al. 2017). The generation of new businesses?or
entrepreneurship in general?offers a new perspective to look at the effects of FDI on the
host economy. The entry of new domestic firms is often seen as a key driver of economic
growth and job creation, and it has become a primary goal for policy makers. As with the
relationship between FDI and other measures of growth, the interaction between FDI and
entrepreneurship is shaped by complex dynamics such as vertical and horizontal industry
spillovers (Markusen and Venables 1999) and business start-up regulations (Munemo
2014). Consequently, the empirical literature provides ambiguous predictions about the
relationship between FDI and entrepreneurship.
In addition, FDI inflows have different impacts on the host countries depending on the
types of FDI such as greenfield FDI1 (new foreign firm) or cross-border M&A (foreign
acquisition of an existing domestic firm). The traditional view on the impacts of FDI
suggests that greenfield FDI is expected to increase the productivity, employment and
capital formation of host countries, while cross-border M&A only involves a change from
local to foreign ownership of existing assets and production capacity (Norba?ck and Persson
2005; Johnson et al. 2006; Ashraf et al. 2016). Moreover, quite a few cases of cross-border
M&As have been criticized as speculative funds seeking only the arbitrage profits with no
value-adding contribution such as technology transfer or new investment for technological
innovation (Kim 2009).
Due to the lack of reliable data on greenfield FDI, the contribution of this type of
investment to economic growth has been underinvestigated.2 However, greenfield FDI
inflows in Europe account for more than 40% of total FDI. Figure 1 reports the values of
1 A greenfield investment is the creation of a subsidiary from scratch by non-resident investors (www.imf.
2 See Ashraf et al. (2016) for a recent review.
Fig. 1 Values of cross-border M&A and announced greenfield projects in Europe. Source UNCTAD?
both cross-border M&A and greenfield investment projects inflows in Europe. The
figure shows both the pre- and post-crisis fluctuations and the recent slow growth recovery
during 2014?2015. During the entire period 2003?2015, greenfield FDI and cross-border
M&A amounted to USD 2.2 and 2.8 trillion, respectively.
In this paper, we use a novel data set on greenfield FDI from the fDi Market (www.
fDimarkets.com) online database3 that allows us to investigate the less explored
(shortterm) effects of greenfield FDI on a specific dimension of the host economy, i.e. the entry
of new domestic firms. We constructed a unique sector/country-level panel data set by
matching information on greenfield FDI projects to domestic business birth rates from
Eurostat and additional data on sector and country control variables from the Organization
for Economic Cooperation and Development (OECD).
Previous empirical findings could be viewed as inconclusive because they typically rely
on overall FDI inflows or only cross-border M&A. Our analysis complements the literature
on the entry effects of inward FDI, as we are the first to evaluate the effects of greenfield
investment on new domestic entry. Moreover, while the impact of FDI on firms and host
economies has been largely studied, knowledge about the impact of knowledge intensive
investment is limited. Although knowledge intensive activities such as R&D are still highly
concentrated in headquarters, evidence shows that knowledge intensive FDI has grown
substantially and more rapidly than the other types of FDI in the past decade (OECD 2008,
pp. 219?248; Hall et al. 2011). In contrast with general FDI, evidence on the impact of
foreign R&D stocks and the presence of foreign-owned high-tech firms points to a potential
unambiguous positive influence on the domestic economy (Coe et al. 2009; Keller and
Yeaple 2009). Indeed, more than other types of FDI, investments such as design and R&D
seem to favour the formation of specialized clusters and allow host locations to integrate in
more competitive global value chains (Cantwell and Piscitello 2000; Carlsson 2006). For
this reason, we narrow our focus on the effects of knowledge intensive greenfield FDI and
investigate whether the ?most desirable? type of FDI (greenfield and knowledge intensive)
is indeed a transmission channel of knowledge and entrepreneurial skills or if, as with other
types of foreign investment, there are the opposing effects on the creation of domestic
businesses found in the literature.
3 fDi Markets is an online database maintained by fDi Intelligence, a division of the Financial Times Ltd.
The main contribution of our paper consists in adding a new piece of evidence to the
FDI-entrepreneurship puzzle which may be extremely relevant from a policy perspective.
Indeed, national governments have adopted several competitive strategies to attract
greenfield FDI, and in particular knowledge intensive investment. Blomstr o?m et al. (2003)
observe that policymakers mainly compete for greenfield FDI, by means of subsidies and
incentives, as voters seem to reward politicians for attracting new investment projects
The remainder of the paper is organized as follows. The next section presents a brief
review of the literature. Section 3 describes the data and the methodological approach.
Section 4 discusses the results of the econometric estimations. Section 5 concludes.
2 Domestic entry and the role of FDI
The generally held assumption in the theoretical literature is that foreign firms not only
bring new investments that creates new jobs and boosts national income, but also spill over
some of their knowledge to local firms that are, as a consequence, able to increase their
productivity. The potential to increase productivity is conditional on structural
characteristics of the host economy, such as the absorptive capacity and the technological distance
to the source country of the FDI (G o?rg and Greenaway 2004; Go? rg and Strobl 2005).
Markusen and Venables (1999) and Rodr??guez-Clare (1996) explain that the potentially
favourable effects of FDI on the host country are the consequence of the generation of
??forward?? and ??backward linkages??4 (vertical and horizontal spillovers). Even though a
new FDI project creates additional competition in a local industry, the possible consequent
reduction in prices may be beneficial to customer firms. Rodr??guez-Clare (1996) shows that
FDI can lead to the development of a local industry which may become sufficiently
competitive to eventually drive the MNEs out of the market. Also, if the costs of
communication between the headquarters and the production plants are high, the entry of
foreign firms can increase the demand for local specialized input suppliers. Grossman
(1984), on the other hand, indicates that an inflow of foreign enterprises has a negative
effect on the supply of local entrepreneurs. In fact, the local entrepreneur is faced with the
choice between the entrepreneurial income and the wage employment at a multinational
firm, which typically has a higher wage premium to retain the employees and avoid
knowledge spillovers (Glass and Saggi 2002).
While the theoretical literature tends to be more cohesive, the empirical analyses of the
FDI spillovers on host economies have offered mixed results at best. In a review of 40
empirical studies, Go? rg and Greenaway (2004) reported that only eight studies employing
panel data find an unambiguous positive aggregate impact of FDI, and only for developed
Although earlier studies on the impact of FDI on domestic investment found a positive
impact for both OECD countries (De Mello 1999) and developing countries (Borensztein
et al. 1998), more recent work reports mixed evidence of no or negative effects on
domestic investments (Agosin and Machado 2005). More precisely, Wang (2010) finds that
4 A forward linkage is the indirect positive effect of FDI on a sector due to the increased competition in the
sector where FDI takes place. The tougher competition in the sector with higher foreign presence may result
in a reduction of prices and stimulate the demand of customer firms in other sectors. A backward linkage is
the increased demand for local products and services and this backward effect may also strengthen the local
supply industries (think of the ?East-Asia effect? on local suppliers).
there are negative contemporaneous effects on the ratio of gross fixed capital formation to
GDP, especially among developed economies, whereas he finds positive cumulative effects
for developing countries. Munemo (2014) investigates the impact of FDI and entry
regulation on domestic investment and shows that FDI crowds out domestic investment if the
entry costs are too high.
Other studies have investigated the impact of foreign investment on local firms?
productivity using firm-level data. Results point, in general, to the effects of negative
spillovers on the domestic firms for transition economies (Konings 2001; Javorcik and
Spatareanu 2008; Djankov and Hoekman 2000),5 Southern European economies (Barbosa
and Eiriz 2009; Garc??a et al. 2013), and developing economies (Aitken and Harrison
1999). However, the negative firm-level effect disregards the fact that technology transfer
is a process that requires time and learning resources. Liu (2008) finds that an increase in
FDI lowers the short-term productivity level but raises the long-term productivity growth
rate of domestic Chinese firms in the same industry. Furthermore, the effect of FDI may
differ between firms. As Iacovone et al. (2015) show for the case of Walmart entering the
Mexican market that firms in the lowest quartile of the size distribution are affected
negatively, while larger local firms may benefit from FDI.
The literature on firm entry/exit and occupational choice also deals with the impact of
FDI on domestic economies, and it points to both positive and negative effects of the
foreign presence on local economies. Extending Jovanovic?s 1994 model of firm formation
to allow for entry of foreign firms, the study of De Backer and Sleuwaegen (2003) predicts
that the foreign firms crowd out local ventures. They argue that since MNEs pay higher
wages and skim the labour market, the stronger rise in wages than in entrepreneurial
income stimulates people to become workers instead of entrepreneurs. However, their
empirical results also suggest that ??the importance of positive long-term structural
effects?learning, demonstration, networking and linkage effects?between foreign and
domestic firms can moderate or even reverse crowding out effects?? (De Backer and
Sleuwaegen 2003, pp. 16?17). Similar results are found in the study of Zajc Kejz?ar (2011)
who tests the effects of greenfield FDI and cross-border M&A and finds that only
greenfield FDI decrease the survival probability of Slovenian firms. However, the
crowding-out effect diminishes as the export propensity of local firms increases, while the
presence of foreign affiliates reduces the exit probability of their downstream local
customers, via positive forward linkages. Ferragina and Mazzotta (2014) investigate the
effects of forward and backward linkages between foreign and Italian firms and find no
clear evidence of an impact of foreign presence on the exit of local firms. They do,
however, find that FDI reduces the probability of exit of highly productive firms or of firms
in medium and low-tech sectors. These results may be explained by the differences in
The study of Bu? rke et al. (2008) develops an additional conceptual framework to
explain the contrasting positive industrial spillovers and the crowding out effects. They
hypothesize that the impact of FDI varies between dynamic and static industries. Dynamic
markets, typically characterized by a high rate of firm churn, are more innovative and
competitive. In these markets, foreign firms are more likely to displace domestic firms, as
foreign companies are more innovative and have more competitive technologies. In
contrast, static markets are associated with later stages of innovation diffusion. In static
5 In contrast, Javorcik (2004) finds positive FDI productivity spillovers via linkages with local suppliers in
upstream sectors for Lithuanian firms.
industries domestic firms are more imitative and have more scope to benefit from the
foreign technology spillovers.
More recently, a growing number of studies have recognized the importance of
exploring the relationship between FDI and entrepreneurship, as this may drive economic
growth (Acs 2006; Fritsch and Wyrwich 2017). According to the knowledge spillover
theory of entrepreneurship (Acs et al. 2009, 2013), the creation of new firms also provides
a ?conduit? with which knowledge brought into the country by FDI is transferred to the
local economy. By comparing Ireland and Wales, (Acs et al. 2012) hypothesise that,
depending on the type of FDI and the accompanying local entrepreneurship policies, FDI
may increase knowledge-based entrepreneurship in a country.
On the one hand, studies that have focused on one single country find evidence of
positive spillovers from FDI on domestic entrepreneurship [Liu et al. (2014), Anwar and
Sun (2015) for China; Ayyagari and Kosova? (2010) for the Czech Republic; Barrios et al.
(2005) for Ireland]. Similar to Barrios et al. (2005), Lee et al. (2014) show that the positive
effect only holds up to a certain degree of FDI intensity in a region. Beyond a certain
threshold, local entrepreneurship tends to diminish because of too high competition and
entry costs and a comparably high attractiveness of foreign firms as workplaces for
On the other hand, results from cross-country/industry analyses point to the opposite
direction. In particular, Albulescu and Ta?ma?s?ila? (2016) does not find any association
between inward or outward FDI on entrepreneurial activity in 16 European countries.
Colantone and Sleuwaegen (2010), using data on eight countries from the ??Business
Demography Statistics?? database by Eurostat, find a strong displacement exit and a slow
replacement entry due to trade exposure. Pathak et al. (2015) analyse cross-country Global
Entrepreneurship Monitor (GEM) survey data on entrepreneurs from 38 countries and find
that inbound FDI is negatively associated with five types of entrepreneurship (nascent,
new, early-stage, established, and high-growth). Similarly, Danakol et al. (2017), find a
negative impact of M&A on three different types of entrepreneurship, controlling for
corruption and cultural characteristics.
The few existing cross-country studies suggest a neutral or negative relation between
inward FDI and domestic entrepreneurship; however, we still do not know whether we
could generalize and conclude that all FDI inflows have the same impact on the formation
of new local businesses.
To address this issue, our study contributes to this latter emerging literature with the
analysis of the role of a specific type of FDI, namely the greenfield knowledge intensive
FDI. Greenfield FDI, differently from cross-border M&A, is considered to increase the
productivity of domestic firms, and knowledge intensive FDI activities, or FDI activities in
knowledge intensive sectors (Antonietti et al. 2015), are regarded as the investment with
the highest potential for knowledge spillovers for both international investors and local
competitors, universities and research institutes (Castellani and Pieri 2016; Castellani et al.
3 Data and descriptive statistics
This section describes our dataset (Sect. 3.1), the variables used (Sect. 3.2) and descriptive
statistics (Sect. 3.3).
3.1 Dataset construction
For our empirical analysis, we created a data set from three data sources which allow us to
analyse the impact of knowledge intensive greenfield FDI on business entry in the host
country for the first time: the fDI Markets database of the Financial Times, the Structural
Business Statistics (SBS) of Eurostat, and industry level information from OECD
The fDi Markets is an on-line database maintained by fDi Intelligence, a division of the
Financial Times Ltd. Since 2003, fDi Intelligence collects available information from
company data and media sources on greenfield foreign direct investments and monitors
cross-border investments covering all sectors and countries worldwide. Data from the fDi
Markets database are used by the UNCTAD to present global investment trends in the
World Investment Reports series6 and have been used in publications by the Economist
Intelligence Unit and in recent academic research (e.g. Crescenzi et al. 2014; Paniagua and
Sapena 2014; Castellani and Pieri 2016; Castellani et al. 2016; Amoroso et al. 2015;
Antonietti et al. 2015). The data are provided at the project level and includes information
on the project date (the month when the FDI project started), the name of the investing
company, the source and destination addresses of investment at the city level, the estimated
amount of capital invested, the industry sector in which the investment takes place, and the
type of activity. The latter covers five different classes of knowledge intensive activities
which are design, development, and testing; education and training; headquarters
activities7; information and communication technologies and Internet infrastructure; and
research and development.
Our second data source is the structural business statistics (SBS). The SBS is a database
on firm dynamics maintained by Eurostat, which covers, among others, the number of firm
births, the number of firm closures and the number of active enterprises in the EU member
states. The data are available for the EU-28 countries since their respective entry into the
EU. As with all other statistics from Eurostat, the data for the SBS are assembled by
statistical agencies of member states and originate in the national business registers. The
national statistical offices collect the data based on internationally harmonised rules for
data collection and preparation. The data are available at the sector level. Based on this
information firms can be classified into different subgroups according to their sector
In order to control for factors that are found to be relevant for the formation of new
firms at the sector level we make use of two databases from the OECD. The first is the
OECD Main Science and Technology Indicators database (OECD-MSTI) which provides
information on the activities of the OECD countries in the field of science and technology
such as R&D expenditures. The second is the database for Structural Analysis (STAN)
which includes measures of output and inputs at the industry level. From this database, we
extract the value added, the gross operating surplus and mixed income, the gross capital
stock, the labour costs, the number of employees and the production value.
Our data sample covers the period between 2005 and 2012. The focus on this period is
mainly due to the availability of the relevant data at the time of the analysis. Because we
7 Headquarter activities in MNEs are high-skill activities such as R&D, marketing and management
(Bandick et al. 2014). Although the decision to open a headquarter abroad is mainly driven by low corporate
taxes, HQs are located in areas with similar industry specialization and with high levels of business services,
which are typically knowledge intensive (Falk 2012).
lag our independent variables by one year in order to avoid simultaneity bias, the data on
the cross-border greenfield projects from the fDi Markets database as well as the aggregate
production and R&D statistics from the OECD cover the period 2004?2011.
Matching and integrating heterogeneous data sources allows us to analyse the impact of
knowledge intensive greenfield FDI on the local entrepreneurship; it presents, however,
several challenges. One above all is the very small matched sample size (45?88
observations) deriving from the different patterns of missing observations across the various data
sets. To mitigate this problem, we decided to impute the missing values of the total number
of active firms in the SBS8 (needed to compute both foreign and domestic entry rates). To
impute the number of total active firms, we assume that the stock of firms has grown at a
constant rate between 2004 and 2012. We calculate the average growth rate with the
available data points, and then extrapolate the time series backwards. We checked whether
this data manipulation affects our results by comparing the outcomes of our estimations
with and without the imputed values. We do not detect any qualitative differences.9 For the
analyses, we consider all the observations for which data on the full set of variables
(dependent and independent) is available. The resulting sample of 454 observations covers
10 EU countries and 20 industries.
3.2 Variables specification
Table 1 shows the variables used in the analysis including their data sources. Our
dependent variable is what we call the ?domestic entry rate? which is defined as the number
of births of enterprises relative to the number of active enterprises in a given year and
sector (Ayyagari and Kosova? 2010; Kosova 2010). SBS data on enterprise births refer to
the birth of firms that are not foreign-controlled.10 Dividing the number of births by the
number of active firms makes the number of firm births comparable between economies of
Our focal right-hand-side variable is the foreign entry rate; it is defined similarly to
other studies (De Backer and Sleuwaegen 2003; Colantone and Sleuwaegen 2010; Zajc
Kejz?ar 2011) as ratio between the number of knowledge intensive greenfield FDI projects
from the fDi Markets database and the number of active firms in the host country, for each
We include a set of control variables that have been shown to be relevant for the
formation of new firms at the firm level. In particular, we control for the R&D intensity of
the sectors. In particular, we group sectors into low-tech and medium/high-tech sectors to
analyse whether the potential knowledge spillovers differ between the levels of R&D of
sectors. For this grouping we applied a classification from the OECD (see Table 2). R&D
provides a source of new ideas that entrepreneurial businesses can transform into new
products?i.e. R&D opens up new opportunities. Wennekers et al. (2002) report evidence
that technology change is one of the main reasons for expanded entrepreneurial
opportunities. In addition, Audretsch et al. (2008) show that a high regional R&D activity and
proximity to research institutions such as universities increase the opportunities to start
new knowledge-based businesses.
8 Data from SBS is still under construction. The values for the number of active firms are absent for the
earlier years of our period of observation. This, in turn, is mainly due to the notorious difficulty in
determining the number of firm exits which are needed for the calculation of the stock of the firms.
9 The results of the comparison are available upon request from the authors.
Table 1 Variable description
The ratio between the number of births of
enterprises and the total number of active
enterprises at time t in 20 business sectors
(except activities of holding companies)
and in 10 countries. Data are collected for
the reference period 2004?2012
The ratio between the number of knowledge
intensive greenfield projects and the total
number of active enterprises at time t.
Knowledge intensive greenfield FDI are
defined as cross-border greenfield
investment projects in R&D, design,
development and testing, education and
training, headquarters activitiesa, and
information and communication
technologies. The data are at project level,
however we aggregate the total number of
projects per sector and year, as the other
data sources are aggregated at sector- or
A dummy with value of one if firms churn
rate?sum of domestic entries and exits,
divided by the total number of active
firms?is larger than 15%, zero otherwise
A dummy with value of one if the NACE
sector is a medium/high-tech sector, zero if
it is a low-tech sector. (For the division of
the sectors into medium/high-tech and
lowtech sectors see next Table 2)
An approximation of the price-cost margin,
pp c, is given by multiplying and diving by
the demanded quantity q to obtain profits
over sales. For this calculation, we divide
the gross operating surplus and mixed
income by the production value
The capital to labour ratio, given by the gross ??
capital stock divided by the labour costs
fDi Markets database
Authors? calculations based on the
variables provided in the OECD
a Headquarter activities in MNEs are high-skill activities such as R&D, marketing and management
(Bandick et al. 2014). Although the decision to open a headquarter abroad is mainly driven by low corporate
taxes, HQs are found to be located in areas with similar industry specialization and with high levels of
business services, which are typically knowledge intensive (Falk 2012)
Moreover, researchers studying domestic entry and exit argue that firm entry is the
response to profitable opportunities that depend on price-cost margins (profitability) and on
the economic growth of the domestic industry (Hause and Du Rietz 1984; Shapiro and
Khemani 1987; Geroski 1989; De Backer and Sleuwaegen 2003). Past profitability signals
profitable opportunities to domestic entrepreneurs and a rapid growth of the sectoral GDP
Table 2 Industry grouping
16 Wood/wood products
17t18 Paper and printing
Wholesale and retail
Hotels and restaurants
05t09 Mining and quarrying
Manufacture of coke and refined petroleum products; chemicals
and chemical products; basic pharmaceutical products and
pharmaceutical preparations; rubber and plastic products
Manufacture of other non-metallic mineral products
24t25 Manufacture of basic metals and fabricated metal products,
except machinery and equipment
26t28 Manufacture of machinery and equipment n.e.c.
29t30 Manufacture of motor vehicles, trailers, semi-trailers and of
other transport equipment
31t32 Manufacture of furniture; other manufacturing
35t39 Electricity, gas, steam and air conditioning supply; Water
supply; sewerage, waste management and remediation
49t53 Transportation and storage
58t63 Information and communication
64t66 Financial and insurance activities except activities of holding
68t82 Administrative and support service activities
84t99 Education; human health and social work activities; arts,
entertainment and recreation; other service activities
of the domestic market indicates a large market potential which leads to a high entry rate of
firms. The sectoral growth rate also allows us to control for other sector specific cyclical
effects which may impact entry. We therefore include profit over sales as an approximation
of the price-cost margin and the GDP growth at the sectoral level in the regression.
Theoretical and empirical models have shown that entry into a market can be deterred in
the presence of high entry costs in the form of capital requirements (Khemani and Shapiro
1986). To capture this effect we control for the capital intensity of an industry in the
In addition, we follow the idea of B u?rke et al. (2008) and analyse differences between
dynamic and static industries. For this, we group the industries by their firm churn rate. The
Table 3 Summary statistics
churn rate is commonly defined as the number of firm entries plus exits relative to the stock
of firms. We calculate the firm churn rate using the number of firm births, firm deaths and
active firms provided by Eurostat and define an industry to be dynamic (static) if the firm
churn rate is above (below) the threshold value of 15% across all industries.
Overall, we expect the coefficients of our control variables to be consistent with the
findings of the literature, i.e. new firms enter in rapidly growing industries, with relatively
high price-cost margins and low entry costs, and where there is a relatively high level of
technological opportunity or R&D intensity (Acs and Audretsch 1989).
3.3 Descriptive statistics
Table 3 shows summary statistics of our sample. The average domestic firm birth rate is
7.12%, while the number of knowledge intensive greenfield foreign projects is 0.02%.
Fig. 2 Trend over time of domestic entry rate, firm turnover and foreign entry rate (yearly averages, %).
Source Own calculations based on fDi Market data
Table 4 Domestic, foreign entry
rates, and firm turnover by
country (averages, %)
Table 5 Means of variables by sector type
* Mean-difference test significant at 1%-level. The classification of the industries into low-tech and
medium/high-tech can be taken from Table 2 in the Appendix. Static (dynamic) industries are those with a firm
churn rate below (above) the median churn rate in the sample
Although having a comparatively low average value, the foreign entry rate is among the
variables with the highest dispersion. The coefficient of variation (SD/mean) of this
variable is 4 which makes it the second most dispersed variable. The variable with the
highest dispersion is GDP growth (coefficient of variation 17.12). The coefficient of
variation of the other variables ranges between 0.52 (firm churn rate) and 1.19 (capital
Figure 2 reports the averages over years and sectors of domestic and foreign entry rates
as well as firm churn rate. The period of the European financial crisis 2007/2008 visually
appears to have changed the trend in the series of all three variables. Both domestic entry
and firm churn rate increased noticeably after these years. In contrast, the entry rate of new
knowledge intensive foreign firms significantly went down in the years after the crisis.
Table 4 reports the averages over years and sectors of domestic and foreign entry rates,
as well as the sectoral churn rate by country. There is observed variation among the
countries. The domestic entry rate varies from 3.92% in Belgium to 15.04% in Estonia.
Austria, Belgium, and the Netherlands have the highest inflows of knowledge intensive
greenfield FDI projects (4 per 10 thousand active firm in the period of observation). The
firm churn rate varies from 6.49% for Belgium to 27.84% for Estonia.
Table 5 displays and tests the difference in the means of the variables by R&D level and
by firm churn rate. The domestic entry rates do not differ significantly between medium/
high-tech and low-tech sectors. However, the foreign entry rates are higher in medium/
high-tech sectors than in low-tech sectors. Also, as expected, the price-cost margin, the
Table 6 Correlation table
* Correlation coefficient significant at 1%-level
capital intensity, and the sectoral GDP growth are higher in the more technology intensive
Comparing static and dynamic sectors, domestic entry is higher in dynamic sectors than
in static ones. In contrast, the foreign entry rate is lower in dynamic sectors. This is perhaps
a consequence of the fact that multinational companies seem to be concentrated in
countries with a lower firm churn rate (see Table 4). There is no significant difference in
the price-cost margin, capital intensity, and GDP growth between static and dynamic
Table 6 displays the correlation coefficients for all the variables used in the empirical
specification. Domestic entry is negatively correlated with foreign entry rates and (highly)
positively correlated with firm turnover, profitability (price-cost margin), and capital
intensity. Foreign entry in knowledge intensive activities is positively correlated with
higher level of technological capabilities, and negatively correlated with the level of firm
churn rate, which formally supports the observation from Table 4 that knowledge intensive
FDI projects tend to be carried out in countries with low firm churn.
4 Econometric analysis and results
4.1 Empirical model
To investigate the impact of greenfield foreign entry on domestic entry, we regress the
domestic entry rate of sector j of country i at time t on the past value of domestic entry rate,
knowledge intensive greenfield foreign entry rate and a set of control variables, which have
been identified by the literature as key determinants of entry. The empirical model is as
DomEntryijt ? a ? bDomEntryijt 1 ? cForEntryijt 1 ? d0Xjit 1 ? jit;
where X is a vector of lagged control variables at sector-level, including the growth rate of
gross domestic product (Dgdp), profitability (pcm), capital intensity (CapInt), the firms
churn rate dummy (Dyn), and the level of technology (Tech). ijt is a composite error term
which includes year, country and sector fixed effects, and a remainder error assumed to
follow an iid normal distribution. The coefficient b measures the impact of greenfield
foreign entry rate (or the relative foreign entry rate) on the domestic entry rate.
Given the ambiguous results found in the literature concerning the sign of the impact of
foreign entry, we test whether this coefficient vary by sector classification. In particular,
following some of the predictions in the literature, we interact the greenfield foreign entry
The churn rate dummy, Dyn, a dummy taking value of one if the sector has a churn rate
above 15% (dynamic sector), and zero otherwise (static sector)
The technological classification of the sector, Tech, a dummy taking value of 1 if the
sector is a medium/high-tech one, zero otherwise
4.2 Estimation technique
Our data set is an unbalanced panel with observations clustered by country, sector and
year. Our regression analysis is based on generalized least square (GLS) accounting for
Table 7 Results from a feasible generalized
DomEntryijt ? a ? bDomEntryijt 1 ? cForEntryijt 1 ? d0Xjit 1 ? jit
panel heteroskedasticity (the variance varies for each country-sector pair). All control
variables are lagged by one year to potentially correct for issues of reverse causality.
Nonetheless, the endogeneity deriving from the characteristics of the host market that
affect the choice of FDI location and type of investment may yield inconsistent results.
Figure 2 reports both new domestic and new foreign greenfield entry rates, and the firm
churn rate. While the churn rate and the domestic entry rate follow a similar increasing
trend over time, the greenfield investments seem to have dramatically dropped after the
crisis. However, there could be unobserved confounders (omitted variables) that are
correlated with both the domestic entry rate and the foreign entry rate (e.g. government
regulations, labour market arrangements, macroeconomic stability, etc.).
We account for these potential endogeneity biases and rely on the instrumental variable
approach. Namely, we use a 2-stage least squares (2SLS) estimator. In line with Danakol
et al. (2017), we use the weighted average geographical distance11 between source and
host country as instrument, as this is a priori not linked to the domestic entry rate, while it
largely influences the location decisions of foreign affiliates (Carr et al. 2001). In the first
stage the foreign entry rate is regressed on the weighted distance, country, sector and year
fixed effects, while in the second stage, we estimate Eq. (1) where we replace the lagged
foreign entry rate with the predicted values of the first stage.
4.3 Estimation results
Table 7 presents the estimation results of Eq. (1). The first column (1) reports the estimated
regression coefficients of a simple specification where the domestic entry depends only on
past greenfield foreign entry rate and country, sector, and year dummies. The coefficient
associated with past foreign investment is positive and significant. A one percentage point
increase in the foreign entry rate corresponds to a 3 percent increase in the domestic entry
Column 2 displays the regression coefficients from a full specification that takes into
account the past domestic new entry rate, sectoral knowledge capabilities, physical capital
intensity, and the profitability and the growth of sectors? output. The marginal effect of past
foreign investment in knowledge intensive activities on new domestic entry is not
significant when taking these factors into consideration.
On the one hand, our results differ from those of Colantone and Sleuwaegen (2010) and
De Backer and Sleuwaegen (2003) who find entry-discouraging effects due to increased
trade exposure in manufacturing industries of eight European countries. On the other hand,
results for the estimated coefficients of the control variables confirm the empirical and
theoretical findings of the relevant literature.
The coefficient measuring the impact of industry capital intensity (a proxy for potential
entry costs) on entry rate has the expected negative sign. In particular, while capital
intensity affects positively the probability of firms? survival (Doms et al. 1995; Bernard
and Jensen 2002), the role of capital intensity as a barrier to entry is well recognized
(Khemani and Shapiro 1986) and observed in the empirical literature (Scarpetta et al.
2002; De Backer and Sleuwaegen 2003; Feizpour and Moradi 2014). Anwar and Sun
(2015) control for capital intensity at firm level rather than industry level and find
contrasting results. Industry price-cost margin and gross product growth have a modest
positive effect on domestic entry rate. A 10% increase in the lagged profit margin or past
industry growth is associated with 0.4 or 0.2% increase in the entry rate, respectively.
In columns (3), (4) and (5) we include the interaction between knowledge intensive
foreign entry and the firm churn rate dummy (3), the technological level of the sector (4),
and both (5). When interacting the knowledge intensive foreign entry with the business
churn dummy, we find that as foreign entry rate increases, the domestic entry rate of
dynamic sectors increases 8% faster than that of static sectors. Higher industry turnover is
typically associated with lower barriers to business. This, in turn, attracts both domestic
and foreign businesses. Contrarily, static industries may be characterized by older
incumbents and higher entry costs. In these industries, MNEs have larger capital to face the
barriers to entry and would eventually discourage the entry of local businesses. Our results
contrast the ??surprising?? positive effects in static industries found by Bu? rke et al. (2008, p.
11 We use foreign capital expenditure to weight the geographical distance. Following a common practice in
the literature, we take the great circle distance between nations? capital cities where a sphere is used to
approximate the shape of the earth.
403); however, their study takes into consideration the survival of new firms, while we
look at the formation of new domestic businesses. Also, they rely on firm-level data for just
one country, UK, and proxy the foreign presence using the share of employment by MNEs,
without distinguishing between the knowledge intensity of their activities.
In column (4), we include the interaction of knowledge intensive foreign entry rate and
technology intensity.12 In line with the predictions of Acs et al. (2013), we find that the
impact of foreign entry on domestic entry depends on the level of technology intensity:
new foreign entry in high- and medium-tech sectors yields a 14% faster increase in the
domestic entry rate (compared to low-tech sectors). Indeed, higher technological
capabilities increase the level of absorptive capacity and the pool of unexplored knowledge,
which is then commercialized and transformed into economic knowledge (new products
and services). Different from the specification in column (3), the marginal effect of the
foreign entry rate is negative and statistically significant (-3%). Anwar and Sun (2015) do
not find any evidence of such positive R&D-entry relationship, however their measure of
technological intensity is at the firm-level. Although the theoretical argument of Audretsch
et al. (2005) and Acs et al. (2013)?regional knowledge capacity attracts FDI and results
in knowledge intensive entrepreneurship?could apply also to firm-level R&D, the pool of
latent entrepreneurs may benefit from the sectoral level of R&D. According to Feldman
(1999), sectoral R&D is a proxy for the quality of human capital in the form of scientific
and technical expertise. In addition, the level of sectoral R&D intensity accounts for
intrasectoral knowledge spillovers which may contribute to create the right climate to enable
potential entrepreneurs to spot the profit opportunity and start a business.
These results are also in line with those of Go? rg and Strobl (2005), who argue that firms
in high-tech sectors have a greater absorptive capacity and are able to benefit from the
foreign technology spillovers, but they contrast with the results of Ferragina and Mazzotta
(2014) who find that firms in high-tech sectors do not benefit from horizontal FDI while in
low- and medium-tech sectors they do. Indeed, the presence of multinationals in high-tech
sectors may increase the competitive pressure for domestic firms causing a costly
reallocation of economic resources within and across sectors.
Finally, in column (5), we consider both the interactions with the sectoral turnover and
the technological intensity. The results confirm that the new foreign knowledge intensive
entry corresponds to 6.5 and 16% faster new domestic entry rates in dynamic and high/
medium-tech sectors, respectively. The marginal effect of greenfield FDI is
Table 8 reports the results from 2SLS estimations, which point to a lack of association
between new foreign and domestic entry rates. Below the estimation results, we present the
results from the first stage, where the variable ForEntry is regressed on the logarithm (and
squared logarithm) of the weighted geographical distance between the investing origin
countries and the destination country. We also report a set of test statistics to assess the
goodness of fit (F-statistic and R2), the endogeneity of the foreign entry rate, and the
relevance of the instruments used (test of underidentification). Using the 2SLS approach,
differently from Danakol et al. (2017), we do not find any significant effect of the
greenfield foreign entry rate on the domestic entry rate. In addition, the hypothesis of
endogeneity of the knowledge intensive gFDI is always rejected, making the feasible GLS
our preferred estimator for this empirical model.
12 The official classification adopted by the Eurostat http://ec.europa.eu/eurostat based on the average R&D
intensities of the manufacturing and service sectors. So, for example, the sector of rubber and plastic
products is defined as medium-low-tech for all years and all countries.
DomEntryijt ? a ? bDomEntryijt 1 ? cForEntryijt 1
Robust standard errors in parentheses
*** p \ 0.01; ** p \ 0.05; * p \ 0.1
5 Discussion and conclusions
Foreign direct investment (FDI) is an essential part of an international economic system
and potentially a major catalyst for economic development. The catalytic potential arises
from the fact that FDI is usually accompanied with a transfer of technology and knowledge
from the country of origin to the host country. In particular, this applies to knowledge
intensive FDI where R&D activities are outsourced to another country than the country of
Yet, the benefits from FDI do not accrue automatically and evenly across countries and
sectors. The main reason is that most of the knowledge is not codified but tacit. Thus, some
transformative mechanism is needed to ensure that the knowledge transfer actually takes
place. One such mechanism is the start-up of new firms. Individuals might be inspired to
new business ideas by the knowledge existing in foreign firms and set up a new firm
thereby transferring the knowledge from the foreign firm to the local economy.
In this paper, we constructed a unique country/sector-level panel data set to analyse how
knowledge intensive greenfield FDI affects domestic economy, looking at the impact on
the generation of new local businesses. Our main contribution to the literature on FDI
spillovers is that we explore the empirically underinvestigated relationship between
greenfield FDI and domestic entry. Compared to previous studies that rely on the largely
available information on M&A, we build our analyses on the less explored data on
greenfield knowledge intensive FDI, which is considered to be the ?best? type of FDI with
the highest potential of positive effects for the host country.
We find that the short-run impact of this type of FDI on domestic entrepreneurship is
generally positive as one would expect from greenfield knowledge intensive investment,
and that the benefits from this type foreign entry are larger in more dynamic and
technologically intensive sectors, than in static and low-tech sectors. The observed positive
effects for dynamic and high- and medium-tech sectors validate some of the hypotheses
advanced by the literature on knowledge spillover theory of entrepreneurship (Audretsch
et al. 2006; Acs et al. 2012), and resonate with similar empirical studies (Liu et al. 2014;
Antonietti et al. 2015), as in these sectors knowledge may spill over faster.
The knowledge transfer process, however, does not exclude the possibility to observe
some initial negative effects on the domestic entry rate, due to adjustments. Potential
entrepreneurs have to first learn about new business opportunities resulting from the FDI
project by engaging with this new source of knowledge.
Moreover, as Acs et al. (2013) argue, for knowledge spillovers to take place, linkages to
the source of knowledge are necessary. This is due to the tacit character of most of the
knowledge. Individuals have to enter into direct exchange with knowledge carriers to take
up new insights. Two possible ways in which this can occur are if individuals from the
domestic workforce become employees in the affiliates of the foreign firm or if they serve
as business partners (Fu 2012). In both ways, the process of knowledge spillover takes
Although we are able to make an important step forward to analyse the effect of the
most desirable type of FDI, our analysis has its limitations, due to the lack of appropriate
data. First, we consider only the short-run effect of knowledge intensive greenfield FDI on
the local start-up rate. Admittedly, knowledge spillovers from FDI to the local economy is
a complex matter which is not fully captured by considering only one year lag in the
regressions. However, testing the effects of greenfield FDI with a longer lag specification
would result in a smaller sample size. Second, we only estimate the direct effect of FDI and
do not consider indirect effects through backward and forward linkages, although these
have been shown to be relevant in the literature. In fact, we only have quite rough
information on the sector in which the FDI occurs (at most on the two-digit level). This
implies that we cannot distinguish between vertical and horizontal spillovers, and therefore
cannot map the linkage structure. Third, as discussed in Sect. 3, we imputed the data on the
stock of active firms. We tested the importance of the bias from imputing the total number
of active firms and we found it to be negligible. Fourth, because of the low number of full
observations available, we use a parsimonious regression model. In particular, we only
included the capital intensity to capture entry costs. There are, of course, other factors that
influence domestic entry, such as red tape or institutional factors (e.g., corruption, the
degree of enforcement of property rights, etc.). However, like other studies before ours, we
assume these factors are captured by country and sector dummies.
Nevertheless, we think that some policy implications can be derived from our results.
Based on the positive spillovers expected from greenfield or knowledge intensive foreign
investment, national governments have adopted several competitive strategies to attract
FDI, often racing to bottom of labor and fiscal standards (Javorcik 2004; Olney 2013).
While technologies and knowledge may eventually spill over on the host economy, policy
makers should also consider that, in the short run, these spillovers mainly concern the
entrepreneurial activity in high-tech and dynamic sectors. In terms of industrial policy, this
suggests that the focus on leading sectors?where both incumbent and younger
entrepreneurial firms have representatives?requires an alignment of industrial and firm-level
policies. New industrial policies (e.g., targeted policies in advanced manufacturing, green
economy, etc.) can be seen as public sector interventions aimed at changing the distribution
of resources across economic sectors, while firm-level policies play an important role in
reallocating resources to market segments that might become more productive. In this
regard, in addition to generic enterprise policies (e.g., strengthening innovation framework,
improve access to risk and human capital, etc.), a specific policy for leading sectors that
focuses on the interaction between national and local governments, research institutes, and
young innovative enterprises, may increase the return to public funds to R&D.
Further research efforts should explore the long-run effects of knowledge intensive FDI,
the role of country-specific institutions, and the type of entrepreneurship that is affected by
foreign investment. Our future research agenda therefore includes the analysis of greenfield
FDI and its impact on different types of start-ups, controlling for information on
knowledge infrastructure at the regional level.
Acknowledgements We are grateful to Georg Licht, Vivien Procher, Alex Coad, Giacomo Damioli, Daniel
Vertesy, Cristiana Benedetti Fasil, and also to the editor Al Link and the anonymous reviewer for many
helpful comments on a previous version of the paper. Any remaining errors are ours alone. The views
expressed are purely those of the authors and may not in any circumstances be regarded as stating an official
position of the European Commission.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution,
and reproduction in any medium, provided you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license, and indicate if changes were made.
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