The impact of credit constraints on foreign direct
investment: evidence from firm-level data
Preliminary draft
Please do not quote
David Aristei*
Chiara Franco†
Abstract
This paper explores the role of financial constraints on the extensive margin
of foreign direct investment of manufacturing firms in seven European
countries during the crisis period. Using direct credit rationing indicators
and controlling for endogeneity issues, we find that difficulties in accessing
external finance significantly reduce the probability of expanding abroad
through FDI. Furthermore, we find that the impact of financial constraints
significantly varies according to FDI motivations, destination areas and
types of production activities.
Keywords: credit constraints, FDI, multinational firms
JEL Classification: F23; G20; C35
*
Department of Economics, University of Perugia, Via Pascoli, 20, 06123 Perugia (Italy); email:
[email protected].
†
Corresponding author, University of Pisa, Department of Political Science, Via Serafini, 3, 56126 Pisa
(Italy); email: [email protected].
1 Introduction
The decision of a firm to enter a foreign market involves extra costs and may affect its
external financing needs. As a consequence, difficulties in accessing external funds may
represent an additional barrier to firm’s foreign expansion. This holds also for multinational
firms investing abroad through foreign direct investments (FDI) although they are usually
larger and more productive than domestic firms.
Much of the empirical literature on financing constraints and firms’ internationalisation
patterns focus on explaining the exporting side of the issue (Wagner 2014), while few other
studies consider also the importing side and two-way trading (Aristei and Franco, 2014).
One of the crucial finding of those papers is that both extensive and intensive margins of
trade activities may suffer, although to a different extent, from credit constraints.
Instead, the role of financial frictions on firms’ foreign direct investments (FDI) has
received little attention, despite FDI decisions involve higher fixed costs than any other
international activity. De Maeseneire and Claeys (2012) provide evidence on the relevance of
external financing constraints for FDI projects of SMEs in Belgium. Buch et al. (2014)
analyse the extensive FDI margin of German firms and, consistently with their theoretical
model, find that both productivity levels and credit constraints significantly reduce the
probability of owning affiliates abroad. Desbordes and Wei (2014) recognise that the decline
in external finance availability partly explains the drop in FDI flows during the global
financial crisis and highlight the role of source countries’ financial development on extensive
and intensive FDI margins. Similarly, Manova et al. (2015) using Chinese export data find
that foreign affiliates are less credit constrained than domestic firms due to their access to
both internal capital markets and external capital markets in host countries.
In this paper, exploiting detailed firm-level data from the EFIGE survey and adopting the
same methodological approach we followed when investigating the effects of credit
constraints on trade (see Aristei and Franco, 2014), we add to this empirical literature by
providing cross-country evidence on the importance of credit access for firms’ FDI activities
during the crisis. To the best of our knowledge, this is the first study to disaggregate the
analysis by FDI characteristics, investigating whether the impact of credit rationing varies
according to FDI motivations, destination countries and type of activity. In Section 2 we
describe the econometric methods used while in section 3 the dataset is presented. Section 4
discuss about the results and Section 5 proposes a robustness analysis. Section 6 concludes.
2 Econometric Methods
As stated above, we use the same methodology adopted in the paper by Aristei and Franco
(2014) but with respect to the extensive margin of FDI only. Due to lack of data we are not
able to explore the intensive margin. As shown in Manova (2013), financing constraints may
hinder a firm’s ability to face the fixed costs of entering foreign markets. At the same time,
rationing probability reflects firm’s credit risk and depends on firm and credit market
characteristics. Financing constraints might thus be endogenous with respect to firm’s
international activities, due to simultaneity and omitted variable bias (Minetti and Zhu, 2011).
To tackle this issue, we consider a recursive bivariate probit model for the joint analysis of
FDI and credit rationing probabilities:
(1)
where xi and zi are vectors of control variables and
normal distribution with correlation
and
follow a bivariate standard
. Exogeneity of Ri can be formally tested by verifying
the significance of error correlation: when
, univariate probit estimates of
and
are
inconsistent.
In the empirical analysis, we will mainly focus on the marginal effect of Ri , which allows
evaluating the average treatment effect (ATE) of financial constraints on the extensive FDI
margin. Based on estimates of model (1), the ATE of Ri can be computed as:
(2)
3 Data
We use data from the “European Firms in a Global Economy” (EFIGE) survey, which provides
cross-sectional information on a representative sample of nearly 15000 manufacturing firms (with
more than 10 employees) in seven European countries (Austria, France, Germany, Hungary,
Italy, Spain and the United Kingdom) over the period 2007-2009. 1
Exploiting the detailed information on cross-border expansion of firms, we firstly define a
binary FDI indicator equal to 1 if firm runs at least part of its production activity abroad through
direct investment (i.e., through foreign affiliates/controlled firms). The questionnaire also
provides a breakdown of FDI destination areas and information on the types of production
1
See Altomonte and Aquilante (2012) for more details on the survey.
activities carried out abroad (finished products; semi-finished products/components; R&D,
engineering and design services; other business services). We are also able to distinguish
different types of FDI activities; specifically, we define binary indicators of Horizontal FDI
(when the outcome of production abroad is sold in the foreign country), Vertical FDI (when it
is re-imported in firm’s home country to be used in production, sold in the domestic market or
re-exported) and Export-platform FDI (when it is sold directly in third countries where the firm
does not produce or where other production facilities are located). From Table 1 we notice
some cross-country differences in FDI choices. Austria and the UK have the highest shares of
FDI-active firms, while when disaggregating by FDI motivations we notice that vertical FDI
is the most common form of foreign investment in France, Italy, Spain and the UK.
[Table 1 about here]
As in Aristei and Franco (2014), we consider two direct binary indicators of credit
rationing, capturing different intensities of financial constraints. Strong rationing identifies
rationed firms as those that applied for additional credit during the last year, but their
application was rejected. Weak rationing equals 1 for those firms which would have liked to
obtain more credit at the market interest rate, but they either did not apply or obtained credit
at a higher cost.
Figure 1 shows that FDI-active firms have a lower probability of being strongly and
weakly rationed. However, differences in observed frequencies with respect to firms not
engaged in FDI are statistically insignificant. This may be due to observed and unobserved
factors that simultaneously increase (or decrease) rationing and FDI probabilities, which should
be taken into account to properly analyse the effect of financing constraints.
[Figure 1 about here]
Our empirical specification replicates that of Aristei and Franco (2014). Both FDI and
rationing equations include firm’s age and employees (both included also as squared),
turnover, and dummies for part-time employment, quality certification, use of bank debt and
increased price margins over costs. We then control for firm’s ownership and management,
workforce skill composition, R&D and innovation activities over the last three years. Finally,
we include country and sector fixed effects and account for local development using average
TFP at the sectoral and regional level.2
To enhance parameter identifiability, we impose exclusion restrictions and include only
in the rationing equation the percentage of total debt held at the firm’s main bank, the length
of the relationship with this bank and a dummy for collateral requirements, assuming that
they directly affect firm’s access to credit, while they do not directly influence FDI decisions.
4 Results
Table 2 presents marginal effects of the bivariate probit models of extensive FDI margins and
credit rationing. Estimated error correlations are positive and significant, suggesting that the
unobserved factors affecting FDI and credit constraints are positively correlated and rationing
indicators cannot be considered as exogenous. Signs and significance of the coefficients of the
control variables in the FDI equation are all in line with expectations. Younger firms, those with
lower turnover and more dependent on bank financing have a higher rationing probability,
whereas no significant country differences emerge. Instrumental variables are highly significant
and increase rationing probability, supporting the validity of our identification strategy.3
[Table 2 about here]
Both strong and weak rationing indicators have a negative and significant impact on FDI
decisions. Estimated marginal effects point out that strong rationing reduces the probability of
carrying out any FDI activity by 6.57 percentage points, while weak rationing has a much
lower impact (2.85%).
We further elaborate on the role of financing constraints by disaggregating the analysis by
FDI destination areas, motivations and types of production activities. Table 3 presents results
distinguishing between single and multiple destinations: while weak rationing affects single
destination FDI, strongly rationed firms are hindered when investing in multiple host
countries. Conditional on carrying out FDI activities in EU countries, strong rationing also
reduces the likelihood of investing outside Europe, especially in China and India, the US and
Canada and Latin America. These results, which differ from those obtained by Aristei and
2
Table A1 in the Supplementary Appendix presents complete variable definitions.
Instruments exogeneity has been tested by adding the instrumental variables in the FDI equation and testing for
their joint significance. Results for strong and weak rationing indicate that instrument exclusion cannot be
rejected, with p-values respectively equal to 0.9573 and 0.4829, supporting the hypothesis that the instruments
do not directly affect FDI choices.
3
Franco (2014) for export, suggest that FDI activities imply higher fixed costs that are not
common across destination areas.
[Table 3 about here]
Table 4 shows that the effect of financing constraints significantly varies with respect to
FDI motivations. Firms carrying out horizontal FDI are those suffering more both from
strong and weak rationing. A possible explanation for this result is that this form of FDI
involves shifting the entire production process to the host country thus requiring higher fixed
costs than vertical FDI. In the same way, firms investing abroad to produce finished product
are more affected by credit rationing than when they produce abroad semi-finished products.
[Table 4 about here]
Financial constraints do not significantly affect internationalisation of R&D activities.
This result supports the hypothesis that performing R&D activities abroad, despite the costs
of decentralised research units, implies lower external financing needs than other forms of
FDI, due to the fact that investing firms may benefit from the costs already sustained to carry
out knowledge intensive activities at home and exploit host country cost advantages.
5. Robustness analysis
Table 5 presents results of several robustness checks on the baseline estimates. They follow
the same steps and approach used in Aristei and Franco (2014). Firstly, we see whether there
is a change in results due to the choice of the set of identifying variables: we therefore include
in all specifications additional instruments encompassing overall riskiness and dependence on
external financing of each sector. As in Aristei and Franco (2014), using data from the Bureau
van Dijk Amadeus database, we compute earnings volatility over 2006-2008 (measured as the
sector-region average standard deviation of EBITDA) for firms with more than 10 employees.
We then match this variable with our data using EFIGE sectoral and regional identifiers. The
reason for which we choose this variable lays in the fact that firms’ financial constraints can be
different following the differences in industry/regional-specific riskiness as this may
reverberate on lenders’ risk taking behaviour as well as credit policies (Laeven and Levine,
2009). We further add two measures accounting for financial dependence at the sectoral and
regional level: the first indicator is a “subjective measure” obtained by averaging firm’s selfassessment of the external financing dependence of its industry (measured on an ordinal scale
ranging from 1 (‘not dependent at all’) to 5 (‘extremely dependent’)) by region and sector. One
of the drawback of this measure is that it may catch only firm’s perception on this industrylevel feature: to address this weakness, we also built an “objective” measure based on average
firms’ debt ratio at the sector-region level (computed on Amadeus data). As pointed out in
several studies (Kroszner et al., 2007; Dell’Ariccia, Detragiache, and Rajan (2008)) during a
financial crisis period, the sectors relying more on external financing are more vulnerable and
can experience reductions in growth rates. Therefore being part of such sectors can increase the
extent to which those firms are exposed to rationing probability. As shown in panels a1) and
a2), results obtained with these additional instruments largely confirm the evidence obtained in
the baseline specifications, supporting the robustness of our identification strategy.4
In panel b) we include controls for TFP and capital intensity that we do not include in the
benchmark models because of high number of missing values. Despite the estimation sample
significantly reduces, dropping from 14590 to 7194 firms, the effect of strong rationing
remains significant and even increases in absolute terms, confirming the relevance of both
real and financial constraints to firms’ foreign expansion, while the effect of weak rationing
turns out to be statistically insignificant.
[Table 5 about here]
Because of the fact that the inclusion of the share of bank debt over total debt (Bank
financing) as a proxy for firms’ financial conditions can be a determinant of a lower marginal
effect of rationing, in panel c) we show the results obtained by re-estimating extensive margins
equations by excluding the bank financing control. We find results that are consistent with
baseline estimates.
We rerun estimates including dummy variables indicating whether the firm strongly relies
on export credit (panel d)) and whether it has received any export incentive (panel e)), in order
to control for the possibility that the effects of rationing on foreign investment decisions may be
altered when international activities heavily relies on external support. Results obtained confirm
baseline estimates.
4
The additional instruments proved to be significant in the credit rationing equation of all the bivariate probit
models and non significant in explaining firms’ FDI decisions.
Further additional robustness checks include the re-estimation of benchmark models
including a variable measuring whether firm’s turnover and/or workforce have decreased during
the last year. In this way we take into account that the negative coefficient of rationing may
capture the sharp decrease in sales and employment over the crisis period. Robust results are
found also in this case.
Finally, we check whether results are affected by the underrepresentation of Austria and
Hungary inside the EFIGE dataset (respectively 443 and 488 observations): empirical
findings are not significantly affected by the country composition of the sample.
6. Conclusions
This paper provides empirical evidence on the role of access to finance for FDI activities of
European manufacturing firms. Our findings point out that credit rationing significantly lowers
the overall extensive FDI margin. Furthermore, the impact of financial constraints significantly
varies according to destination areas, FDI motivations and types of production activities.
Specifically, investing in multiple host countries, carrying out horizontal FDI activities and
producing finished products abroad, are affected to a greater extent by external financing
difficulties, and in particular by credit denial, than other forms of cross-border expansion.
References
Altomonte, C., Aquilante, T., 2012. The EU-EFIGE/Bruegel-Unicredit dataset. Bruegel
Working paper, 2012/13.
Aristei D., Franco, C., 2014, The role of credit constraints on firms’ exporting and importing
activities, Industrial and Corporate Change 23(6), 1493–1522.
Buch, C.M., Kesternich, I., Lipponer, A., Schnitzer, M., 2014. Financial constraints and foreign
direct investment: firm-level evidence. Review of World Economics 150 (2), 393-420.
Dell'Ariccia, G., Detragiache, E. and Rajan, R. (2008). The real effect of banking crises.
Journal of Financial Intermediation 17(1), 89-112.
De Maeseneire, W., Claeys, T., 2012. SMEs, foreign direct investment and financial
constraints: The case of Belgium, International Business Review 21(3), 408–424
Desbordes, R., Wei, S.-J., 2014. Credit conditions and foreign direct investment during the
global financial crisis. Policy Research Working Paper Series 7063, The World Bank.
Kroszner, R.S., Laeven, L. and Klingebiel, D. (2007). Banking crises, financial dependence,
and growth. Journal of Financial Economics 84(1), 187–228.
Laeven, L. and Levine, R. (2009). Bank governance, regulation and risk taking. Journal of
Financial Economics 93, 259–275
Manova, K., 2013. Credit constraints, heterogeneous firms, and international trade. Review of
Economic Studies 80 (2), 711–744.
Manova, K., Wei, S.-J., Zhang, Z., 2015. Firm exports and multinational activity under credit
constraints. Review of Economics and Statistics 97 (3), 574–588.
Minetti, R., Zhu, S.C., 2011. Credit constraints and firm export: Microeconomic evidence
from Italy. Journal of International Economics 83 (2), 109-125.
Wagner, J., 2014. Credit constraints and exports: A survey of empirical studies using firm
level data. Industrial and Corporate Change 23 (6), 1477-1492.
Tables
Table 1 – Extensive margins of foreign direct investment
Percentage distribution of FDI-active firms:
FDI (any type) Horizontal FDI
Vertical FDI
Country
N. of firms
AUT
FRA
GER
HUN
ITA
SPA
UK
419
2965
2965
487
3009
2832
2013
7.16
3.88
5.67
1.98
2.45
2.74
6.60
4.51
1.88
4.97
1.02
1.59
1.55
3.53
4.29
3.53
4.57
0.82
2.18
1.80
4.98
3.61
2.25
3.34
0.41
1.09
1.06
3.87
Total
14590
4.00
2.66
3.27
2.21
Notes: percentage frequencies are computed using sample weights.
Export-platform FDI
Table 2 – Extensive margin of FDI and credit rationing: marginal effects
(1)
Age
Employees
R&D Workforce
High Skill Workforce
Labour Flexibility
Individual First Shareh
Foreign First Shareh
Group
Centralised Decisions
Family CEO
Innovation
R&D Investment Share
Turnover
Increased Margins
Quality Certified
Bank Financing
Mean TFP
Main Bank Share
Main Bank Length
Collateral
Austria
France
Hungary
Italy
Spain
UK
(2)
Strong Rationing
FDI
Weak Rationing
FDI
-0.0003***
(0.0001)
0.0002***
(0.0001)
0.0047
(0.0048)
-0.0009
(0.0038)
0.0029
(0.0030)
-0.0072
(0.0048)
-0.0059
(0.0081)
-0.0008
(0.0058)
0.0008
(0.0035)
-0.0041
(0.0035)
0.0045
(0.0036)
0.0004
(0.0002)
-0.0051**
(0.0023)
0.0029
(0.0073)
0.0057*
(0.0035)
0.0004***
(0.0000)
-0.0291**
(0.0139)
0.0004***
(0.0001)
0.0005***
(0.0001)
0.0617***
(0.0159)
-0.0127
(0.0168)
-0.0040
(0.0082)
-0.0055
(0.0122)
0.0346***
(0.0085)
0.0335***
(0.0096)
-0.0570***
(0.0090)
0.0001
(0.0001)
0.0001*
(0.0000)
0.0196***
(0.0050)
0.0100**
(0.0040)
0.0114***
(0.0038)
0.0063
(0.0047)
0.0149***
(0.0056)
0.0230***
(0.0050)
-0.0093**
(0.0036)
0.0076
(0.0046)
0.0124***
(0.0034)
0.0006***
(0.0002)
0.0188***
(0.0020)
0.0067
(0.0056)
0.0108***
(0.0040)
0.0001*
(0.0001)
0.0144
(0.0124)
-0.0004***
(0.0001)
0.0001
(0.0001)
0.0058
(0.0074)
0.0076
(0.0063)
0.0045
(0.0049)
-0.0000
(0.0071)
-0.0215**
(0.0099)
-0.0065
(0.0088)
-0.0063
(0.0057)
-0.0043
(0.0061)
0.0202***
(0.0060)
0.0005
(0.0004)
0.0005
(0.0034)
-0.0110
(0.0120)
0.0028
(0.0058)
0.0006***
(0.0001)
0.0074
(0.0180)
0.0015***
(0.0001)
0.0017***
(0.0002)
0.0919**
(0.0409)
-0.0396**
(0.0174)
-0.0395***
(0.0114)
-0.0500***
(0.0142)
0.0253**
(0.0100)
0.0344***
(0.0097)
-0.0043
(0.0095)
0.0001
(0.0001)
0.0001*
(0.0000)
0.0187***
(0.0045)
0.0104***
(0.0040)
0.0110***
(0.0036)
0.0068
(0.0045)
0.0137***
(0.0053)
0.0224***
(0.0047)
-0.0095***
(0.0036)
0.0077*
(0.0046)
0.0124***
(0.0034)
0.0005***
(0.0002)
0.0186***
(0.0020)
0.0055
(0.0054)
0.0102***
(0.0037)
0.0001*
(0.0001)
0.0185
(0.0117)
-0.0023
(0.0042)
-0.0062
(0.0067)
-0.0112
(0.0139)
-0.0112
(0.0068)
-0.0018
(0.0078)
0.0042
(0.0057)
-0.0657***
(0.0236)
Strong rationing
-0.0021
(0.0042)
-0.0063
(0.0065)
-0.0121
(0.0138)
-0.0144**
(0.0059)
-0.0053
(0.0069)
0.0078
(0.0052)
Weak Rationing
Number of firms
Log-Likelihood
Notes:
-0.0285*
(0.0155)
0.5492***
(0.1397)
14590
-3935.79
0.3086***
(0.1099)
14590
-5743.22
Table reports average marginal effects. For Age and Employees, reported marginal effects take into account that both the variables are also
entered with a quadratic term. Robust standard errors, clustered at the regional level, are reported in parentheses below the estimates. All
estimates are obtained using sample weights and include (unreported) sectoral controls.
***, ** and * denote significance at 1, 5 and 10 percent levels, respectively.
Table 3 – FDI destinations: marginal effects of credit rationing
FDI Destinations:
Strong Rationing
Weak Rationing
Single Destination
-0.0123
(0.0165)
-0.0788***
(0.0280)
-0.0565**
(0.0272)
-0.0532**
(0.0250)
-0.0393
(0.0254)
-0.0477**
(0.0230)
-0.0179*
(0.0096)
-0.0371***
(0.0134)
-0.0516***
(0.0078)
-0.0190**
(0.0088)
-0.0010
(0.0164)
-0.0083
(0.0130)
-0.0229*
(0.0136)
-0.0084
(0.0051)
-0.0032
(0.0074)
-0.0034
(0.0037)
-0.0087*
(0.0049)
-0.0034
(0.0045)
Multiple Destinations
EU
Outside EU
– Other European (Non EU) Countries
– China & India
– Other Asian Countries
– USA & Canada
– Latin America
Notes: Table reports average marginal effects of strong and weak rationing indicators. Robust standard errors, clustered at the
regional level, are reported in parentheses below the estimates.
***, ** and * denote significance at 1, 5 and 10 percent levels, respectively.
Table 4 –FDI motivations and types of production activities: marginal effects of credit rationing
FDI motivations
– Horizontal FDI
– Vertical FDI
– Export-platform FDI
Types of production activities
– Finished products
– Semi-finished products/components
– R&D, engineering and design services
– Other business services
Strong Rationing
Weak Rationing
-0.0502***
(0.0241)
-0.0319**
(0.0163)
-0.0303***
(0.0087)
-0.0177*
(0.0094)
-0.0149
(0.0138)
-0.0119
(0.0089)
-0.0648**
(0.0273)
-0.0341*
(0.0199)
-0.0082
(0.0104)
-0.0022
(0.0028)
-0.0364**
(0.0141)
-0.0100
(0.0096)
-0.0076*
(0.0045)
-0.0011
(0.0085)
Notes: Table reports average marginal effects of strong and weak rationing indicators. Robust standard errors, clustered at the
regional level, are reported in parentheses below the estimates.
***, ** and * denote significance at 1, 5 and 10 percent levels, respectively.
Table 5 – Robustness analysis
a) Including additional instruments
a1) EBITDA volatility at the regional-sector level and
“self-assessed” sectoral financial dependence
Strong Rationing
-0.0641***
a2) EBITDA volatility at the regional-sector level and
“objective” sectoral financial dependence
Strong Rationing
(0.0225)
Weak rationing
-0.0284*
(0.0237)
Weak rationing
(0.0158)
b) Controlling for TFP and capital intensity
Strong Rationing
-0.1051***
(0.0323)
-0.0635***
-0.0278*
(0.0157)
c) Excluding bank financing control
Strong Rationing
-0.0599***
(0.0231)
Weak rationing
-0.0237
Weak rationing
(0.0459)
-0.0276*
(0.0157)
d) Controlling for significantly relying on export credit e) Controlling for having received export incentives
Strong Rationing
-0.0650***
Strong Rationing
(0.0238)
Weak rationing
-0.0276*
(0.0232)
Weak rationing
(0.0162)
f) Controlling for turnover and/or workforce decrease
Strong Rationing
-0.0650***
-0.0290*
-0.0286*
(0.0158)
g) Excluding Austria and Hungary
Strong Rationing
(0.0223)
Weak rationing
-0.0640***
-0.0622***
(0.0241)
Weak rationing
(0.0156)
-0.0266*
(0.0162)
Notes: Robust standard errors, clustered at the regional level, are reported in parentheses below the estimates. Estimates are obtained using
sample weights. All regressions, except c), include the same controls used in the baseline models. In panel a), we include as
additional instruments the sector-region average standard deviation of EBITDA for firms with more than 10 employees (computed
on Amadeus data) and a “self-assessed” (a1)) and an “objective” (a2)) measure of sectoral financial dependence. In panels d), e)
and f) additional dummies are included to control for relying on export credit, for having received export incentives and for
turnover and workforce decrease in the last year, respectively. Sample size reduces to 7194 and 13684 observations for estimations
reported in panels b) and g), respectively.
***, ** and * denote significance at 1, 5 and 10 percent levels, respectively.
Figures
Figure 1 – Percentage distribution of strongly and weakly rationed firms (conditional on
applying for or willing to increase credit) by FDI status
Appendix
Table A1 – Control variables: definitions and descriptive statistics
Variable
Definition
Mean
Std. Dev.
Age
Years since firm’s establishment
34.126
30.573
Employees
Total number of employees
51.229
80.757
R&D Workforce
Equals 1 if the share of R&D employees is higher than the corresponding
national average; 0 otherwise
0.109
0.311
High Skill Workforce
Equals 1 if the share of graduate employees is higher than the
corresponding national average; 0 otherwise
0.278
0.448
Labour Flexibility
Equals 1 if firm uses part-time employment or fixed-term contracts; 0
otherwise
0.593
0.491
Individual First Shareh
Equals 1 if the first shareholder is an individual or a group of individuals;
0 otherwise
0.768
0.422
Foreign First Shareh
Equals 1 if the first shareholder is foreign; 0 otherwise
0.079
0.27
Group
Equals 1 if the firm belongs to any kind of group (national or foreign); 0
otherwise
0.193
0.395
Centralised Decisions
Equals 1 if the CEO/owner takes most of the decisions in every area; 0
otherwise
0.698
0.459
Family CEO
Equals 1 if the CEO is the individual (or a member of the family) who
owns/controls the firm; 0 otherwise
0.64
0.48
Innovation
Equals 1 if the firm has carried out any product or process innovation; 0
otherwise
0.642
0.479
R&D Investment Share
R&D investment as a percentage of total turnover
3.452
7.663
Turnover
Turnover classes, from 1 (‘less than 1 million euro’) to 7 (‘more than 250
million euro’)
2.816
1.252
Increased Margins
Equals 1 if the size of price margin over costs has increased during the last
year; 0 otherwise
0.063
0.244
Quality Certified
Equals 1 if firm has any form of quality certification; 0 otherwise
0.571
0.495
Bank Financing
Percentage of short and medium-long bank debt over total debt
15.123
29.442
Mean TFP
Average TFP at the sectoral and regional level
-0.025
0.204
Notes: descriptive statistics are computed using sample weights.
Table A2 – Extensive margins of FDI and credit rationing: univariate probit results
Credit rationing
Age
Employees
R&D Workforce
High Skill Workforce
Labour Flexibility
Individual First Shareh
Foreign First Shareh
Group
Centralised Decisions
Family CEO
(1)
(2)
Strong rationing
Weak rationing
0.0081
0.0071
(0.0094)
(0.0053)
0.0001
0.0001
(0.0001)
(0.0001)
0.0001
0.0001*
(0.0000)
(0.0000)
0.0183***
0.0183***
(0.0043)
(0.0044)
0.0101***
0.0099***
(0.0038)
(0.0038)
0.0104***
0.0104***
(0.0035)
(0.0035)
0.0068
0.0066
(0.0044)
(0.0044)
0.0144***
0.0144***
(0.0052)
(0.0052)
0.0224***
0.0225***
(0.0046)
(0.0046)
-0.0090***
-0.0089***
(0.0034)
(0.0034)
0.0074*
0.0074*
Innovation
R&D Investment Share
Turnover
Increased Margins
Quality Certified
Bank Financing
Mean TFP
Austria
France
Hungary
Italy
Spain
(0.0044)
(0.0044)
0.0113***
0.0112***
(0.0031)
(0.0031)
0.0005**
0.0005***
(0.0002)
(0.0002)
0.0183***
0.0182***
(0.0020)
(0.0019)
0.0062
0.0062
(0.0053)
(0.0053)
0.0098***
0.0098***
(0.0037)
(0.0037)
0.0000
0.0000
(0.0000)
(0.0000)
0.0189
0.0187
(0.0115)
(0.0117)
-0.0015
-0.0014
(0.0040)
(0.0040)
-0.0049
-0.0047
(0.0064)
(0.0064)
-0.0105
-0.0106
(0.0132)
(0.0132)
-0.0150**
-0.0148**
(0.0059)
(0.0059)
-0.0068
-0.0068
UK
Number of firms
Log-likelihood
(0.0070)
(0.0069)
0.0073
0.0071
(0.0052)
(0.0051)
14590
14590
-1891.37
-1890.90
Notes: Table reports average marginal effects. For Age and Employees, reported marginal effects take into account
that both the variables are also entered with a quadratic term. Robust standard errors, clustered at the regional
level, are reported in parentheses below the estimates. All estimates are obtained using sample weights and
include (unreported) sectoral controls.
***, ** and * denote significance at 1, 5 and 10 percent levels, respectively.