+Model
JPO-5866; No. of Pages 12
ARTICLE IN PRESS
Available online at www.sciencedirect.com
Journal of Policy Modeling xxx (2010) xxx–xxx
Firm participation in financial incentive programmes:
The case of subsidies for outward internationalisation
Mariasole Bannò ∗ , Francesca Sgobbi
University of Brescia, Department of Mechanical and Industrial Engineering, Via Branze, 38, 25123 Brescia, Italy
Accepted 9 August 2010
Abstract
This study explores the process of firms’ participation in financial subsidies supporting outward foreign
direct investments. Policy makers should be concerned about the existence of self-selection mechanisms
among eligible firms as they could fail to reach the target population. Using firm-level data on subsidised firms
and potential applicants, we show that firms self-select according to the balance between application costs
and expected benefits. These findings have interesting policy implications. First, participation rate among
the target group could be enhanced by lowering application costs. Second, in order to avoid deadweight
effects, expected benefits should carry a higher value for target firms.
© 2010 Society for Policy Modeling. Published by Elsevier Inc. All rights reserved.
JEL classification: H81; F23; D21; C25
Keywords: Policy evaluation; Firm participation process; Outward FDI
1. Introduction
Internationalisation is acknowledged as a determinant of competitiveness and a driver of
development for both the host and the home country (Dunning & Lundan, 2008; Te Velde,
2007; Westhead, Wright, & Ucbasaran, 2007). For these reasons, since the 1990s firms’ outward internationalisation is becoming an increasingly important target of public intervention in
most OECD countries (UNCTAD, 2001) so that governments have implemented several home
country measures (HCMs). These tools, which include financial support, investment insurance,
fiscal intervention, information provision and technical assistance (Sarmah, 2003), are launched
∗
Corresponding author. Tel.: +39 030 3715563.
E-mail addresses: [email protected] (M. Bannò), [email protected] (F. Sgobbi).
0161-8938/$ – see front matter © 2010 Society for Policy Modeling. Published by Elsevier Inc. All rights reserved.
doi:10.1016/j.jpolmod.2010.08.001
Please cite this article in press as: Bannò, M., & Sgobbi, F. Firm participation in financial incentive
programmes: The case of subsidies for outward internationalisation. Journal of Policy Modeling (2010),
doi:10.1016/j.jpolmod.2010.08.001
+Model
JPO-5866;
2
No. of Pages 12
ARTICLE IN PRESS
M. Bannò, F. Sgobbi / Journal of Policy Modeling xxx (2010) xxx–xxx
to mitigate information and co-ordination failures. The promotion of internationalisation seeks
to reduce economic and political risks, to overcome uncertainties and to alleviate any shortfall in
resources and capabilities in a company initiating the internationalisation process or seeking to
invest in an environment that is distant in geographical, cultural and institutional terms (Sarmah,
2003; Te Velde, 2007).
Despite the increasing importance of such policy tools, HCMs have so far been neglected
(Te Velde, 2007) and no evidence exists on the processes that drive the allocation of public
incentives among firms (Tanayama, 2007). The general push to collect evidence on the impacts
of policy measures has somehow diverted attention away from the problems surrounding the
allocation process.1 We argue that policy makers should be concerned about firm behaviour
when applying for a subsidy as the motivations of firms participation can reveal unexpected
barriers to firm involvement (Blanes & Busom, 2004; Heckman & Smith, 2004). In addition, the
participation process has also important implications for evaluation strategies as it reveals possible
misalignments between policy goals and allocation outcomes (Scheirer, 1994). The understanding
of firm behaviour helps to draw the counterfactual scenario and provides important clues about
what pieces of information are crucial for the effectiveness of the evaluation process (Heckman
& Smith, 2004; Moffitt, 1991). For these reasons we provide evidence about the drivers of firm
behaviour when filing a request for public financial support. On the basis of these findings, we
draw some policy implications for the promotions of foreign investments and, more generally,
the design of public incentives to private investment.
The empirical analysis is based on information on a sample of Italian firms that received at
least one financial incentive for international growth outside of the European Union between 1992
and 2008 and on a sample of potential applicants that internationalised without the support of any
public programme in the same period.
Our data show that firms self-select according to the balance between application costs and
expected benefits. Consequently, we argue that policy makers can reduce barriers to participation by lowering application costs and encourage additional investments by increasing expected
benefits for the target population.
The rest of this paper is structured as follows. The next section surveys the existing literature,
and formulates the hypotheses that drive the empirical analysis. The following section presents
the data and Section 4 describes the model tested in the empirical analysis. Section 5 illustrates
the results of the econometric estimates. Final comments and policy implications are reported in
Section 6.
2. The evaluation of incentive allocation: background literature and research
hypotheses
Early literature framed the implementation process as a sequence of administrative routines that
would occur of and by themselves once policy measures were brought into effect by a legislative
act and agencies possessing administrative authority (Corbett & Lennon, 2002). This view has
1 The allocative problem can be decomposed into five steps (Heckman & Smith, 2004). Policy makers set the criteria
of eligibility, which will be implemented by the agencies in charge of incentive programme management. Based on their
awareness (i.e., the extent to which eligible subjects are informed about the existence of a suitable public measure), firms
decide whether to submit an application. Thus, firms self-select to participate in the allocation process. Finally, public
agencies make granting decisions by choosing which applications will be accepted and which firms will be enrolled in
the programme.
Please cite this article in press as: Bannò, M., & Sgobbi, F. Firm participation in financial incentive
programmes: The case of subsidies for outward internationalisation. Journal of Policy Modeling (2010),
doi:10.1016/j.jpolmod.2010.08.001
+Model
JPO-5866; No. of Pages 12
ARTICLE IN PRESS
M. Bannò, F. Sgobbi / Journal of Policy Modeling xxx (2010) xxx–xxx
3
been undermined as recent literature has recognised that implementation is a complex process
involving distinct actors, namely, government bodies, public agencies and firms, all of which are
characterised by contrasting goals due to their different objectives, power and capabilities (Corbett
& Lennon, 2002; Schilder, 2000).
Although several studies have ascertained the existence of significant differences between
firms benefiting and not benefiting from public subsidies and have observed low participation rate
by eligible firms (Giebe, Grebe, & Wolfstetter, 2006; Schilder, 2000), scholars have paid little
attention to the underlying causes and consequences. Thus, we know surprisingly little about
how potential applicants decide whether to apply. In the empirical literature on policy evaluation,
the inclusion of variables capturing firm participation in a public subsidy programme is in fact
intended only to control for possible selection bias rather than to explore firms’ behaviour (Blundell
& Costa Dias, 2000). Only two prior studies (Rolfe, Ricks, Pointer, & McCarthy, 1993; Mudambi,
1999) demonstrate that incentive preferences are a function of firm and investment characteristics,
while Blanes and Busom (2004) estimate reduced-form models of joint application and granting
decisions for R&D subsidies and Colombo, Grilli, and Verga (2007) investigate the determinants
of firms’ access to public subsidies and private venture capital.
In the case of public incentives to outward foreign direct investments (FDIs), the design of
successful policy measures should take into account the barriers to firms participation and provide
actual benefits to target firms (Amorim, Bannò, & Piscitello, 2010). However, if policy makers
design the incentive and identify the characteristics of potential benefiting firms, it is the beneficiaries themselves that decide whether and when to ask for public aid. Assuming that firms
are aware of the existence of public support, we argue that the decision to apply depends on the
balance between the costs of the application process and the expected benefits of participation. On
one side, non-negligible application costs compromise the effectiveness of a policy tool, as they
induce self-selection by eligible candidates. On the other side, expected benefits encourage participation, yet they risk attracting participants beyond the target group and generating deadweight
effects.
Based on the above discussion, our first research hypothesis claims that application costs significantly influence the decision to apply for a public incentive by affecting barriers to participation.
HP1. Managerial skills and funding experience induce self-selection by reducing application
costs.
The administrative burden and the effort of submitting an application can generate significant costs. Information gathering, reporting activities and form completion represent potential
obstacles to actual participation (Colombo et al., 2007; Sarmah, 2003). As managerial capabilities reduce the costs of applying and increase the likelihood of self-selection in submitting a
request (Blanes & Busom, 2004; González, Jaumandreu, & Pazó, 2005; Westhead et al., 2007),
we expect that larger and older firms will be more likely to apply for an incentive, due to their
greater managerial resources and competences (Duguet, 2004; Tanayama, 2007). Moreover, large
firms may enjoy an advantage in scouting for grant opportunities because their search costs can be
spread over larger revenues (Aschooff, 2009; Colombo et al., 2007). Additionally, firms that have
participated in the same or similar programmes might benefit from learning effects and use their
experience for submitting projects that are more suitable for funding (Aschooff, 2009; Duguet,
2004; Lerner, 2002; Tanayama, 2007).
The second research hypothesis argues that financial constraints and perceived risk both
encourage the decision to apply for a public incentive by increasing the expected benefits of
participation.
Please cite this article in press as: Bannò, M., & Sgobbi, F. Firm participation in financial incentive
programmes: The case of subsidies for outward internationalisation. Journal of Policy Modeling (2010),
doi:10.1016/j.jpolmod.2010.08.001
+Model
JPO-5866;
No. of Pages 12
4
ARTICLE IN PRESS
M. Bannò, F. Sgobbi / Journal of Policy Modeling xxx (2010) xxx–xxx
HP2. Financial constraints and the risk associated to the foreign project affect the propensity to
apply for a public incentive by increasing the benefits of participation.
The actual cost of going abroad may vary across firms as a result of differences in the availability and cost of financial resources. As discussed in the recent literature on small and medium
enterprises (SMEs), the market for investment capital is subject to significant imperfections,
which often result in financial constraints (Beck, Demirgüç-Kunt, Levine, & Maksimovic, 2005).
In addition, firms face higher difficulties in accessing capital to finance international projects
due to the volatile and asymmetric information typical of those projects. Financial market imperfections can consequently curb investment projects and limit a firm’s capability to engage in
FDIs (De Maeseneire & Claeys, 2007; van Tongeren, 1998). Subsidies help internationalising firms to overcome their financial constraints and reduce the cost of the internationalisation
process. Consequently, we expect a positive relationship between the financial constraints perceived by a firm and the probability of self-selection in applying for public funds (Duguet,
2004).
Supporting institutions usually share with firms the economic and political risks of foreign
projects and gather information to reduce the economic risks related to the unfamiliar context
abroad and the so-called “liability of foreignness” (Te Velde, 2007; Zaheer, 1995). Thus, we
expect that firms will submit the projects characterised by the highest destination country risk to
public agencies and finance the least risky ones internally or through the private capital market
(Mudambi, 1999). Institutional differences between the home and the host countries amplify
the difficulties in gathering, organising and interpreting the information necessary for successful
entry (Henisz, 2004). Investors are consequently more likely to enter countries characterised by
a similar culture, similar institutional structures and a stable policy system. When the above
conditions are not met, public aid is perceived as a means of lowering systematic, country-level
risk.
Country risk has both direct and indirect effects on the probability of applying for a grant. A
higher country risk directly encourages firms to look for public support and indirectly increases
application rates by discouraging high-commitment entry modes (Quer, Claver, & Rienda, 2007)
such as greenfield projects and foreign majority stakes, which involve higher levels of commitment, higher transaction costs and higher investment costs (Mudambi, 1999). On the contrary,
the existence of previous FDIs diversifies risk and makes a firm less bounded by risk exposure as successful experience mitigates the perception of risk. Moreover, past experience in
countries characterised by high political hazard reduces a firm’s sensitivity to this type of risk
in subsequent entry decisions (Henisz, 2004) and lowers the propensity to apply for a public
incentive.
3. The data
3.1. The Italian agencies supporting outward FDIs: Simest and Finest
Italian HCMs are mainly implemented by Simest and Finest, two public agencies working under
guidelines issued by the central government or by local public administrations. Simest, established
as a limited company in 1990 (Law 100/1990), is a public–private partnership controlled by the
Italian Government (76%). It promotes the competitiveness of the Italian industry and service
sectors by providing funding and advice to outward Italian investors. Finest was founded in
Please cite this article in press as: Bannò, M., & Sgobbi, F. Firm participation in financial incentive
programmes: The case of subsidies for outward internationalisation. Journal of Policy Modeling (2010),
doi:10.1016/j.jpolmod.2010.08.001
+Model
JPO-5866; No. of Pages 12
ARTICLE IN PRESS
M. Bannò, F. Sgobbi / Journal of Policy Modeling xxx (2010) xxx–xxx
5
1992 as an investment company (Law 19/1991). It operates in North-Eastern Italy, and its main
shareholders are local public administrations2 and Simest.
This paper focuses on Law 100/1990 (executed by Simest) and Law 19/1991 (executed by
Finest), which promote the provision of capital loans at interest rates below the market rate that
are not paid back in case of failure of the foreign project (Law 394/1981). Public agencies can
directly acquire up to 25% of the equity of a foreign venture, and benefiting firms agree to buy back
the agency equity share within 8 years.3 Although the agencies can accept all types of investment
proposals, priority is given to initiatives by SMEs investing in Eastern Europe. Projects in the
same sector as the parent company are encouraged, while the support programmes exclude FDIs
in the European Union and FDIs that entail the divestment of R&D, sales or production activities
in Italy (Law 80/2005).
In recent years, Simest and Finest filed between 150 and 250 applications per year. Since
the beginning of their operation, the two agencies have approved over 1000 investment projects
outside the European Union and acquired shareholdings in Italian foreign affiliates with a total
value of more than one billion Euros.
3.2. The dataset
The dataset used in the empirical analysis combines four different sources of data: Reprint, a
database that provides a census of outward and inward FDIs in Italy beginning in 1986 (Mariotti
& Mutinelli, 2008); the balance sheets of Simest and Finest, which provide information about
the incentives granted to outward Italian FDIs; and AIDA, an archive developed by Bureau van
Djick that provides structural and financial data for Italian public limited companies. The dataset
obtained by merging the above sources includes information on 501 FDIs backed by public
incentives between 1992 and 2008 and 831 FDIs that received no support from Simest or Finest
in the same period. The 1332 identified projects involved 912 firms, 52% of which received at
least one public financial incentive in the examined period.
4. The model and the variables
The difficulty of analysing the allocation process lies in the complexity of observing firms’
application behaviour separately from the grant allocation decisions made by a public agency
(Blanes & Busom, 2004). The most frequent limitation faced by researchers is the impossibility
of identifying unsuccessful applications and the characteristics of rejected projects. This constraint
hinders the distinction of the agency selection criteria from the factors driving firm behaviour.
The present empirical analysis has to adjust for missing information on rejected applications and,
as in previous studies (see, e.g., Blanes & Busom, 2004), this limitation forces us to combine the
application by a firm and the allocation process by an agency into a single step. In the empirical
analysis, we try to relax this limitation by adding some control variables that account for the
agency screening rules to the determinants of firm behaviour.4
The empirical analysis is based on a probit regression where the dependent variable,
D Incentive, is a dummy variable equal to 1 if a firm has launched an FDI project with the
2 Participating local administrations include the regional governments of Friuli-Venezia Giulia and Veneto and the
autonomous province of Trento.
3 Since 2005, Simest and Finest are permitted to acquire up to 49% of the equity of a foreign venture.
4 It is worth noting that he probability of including a rejected application in the control group amounts to a small 2.5%.
Please cite this article in press as: Bannò, M., & Sgobbi, F. Firm participation in financial incentive
programmes: The case of subsidies for outward internationalisation. Journal of Policy Modeling (2010),
doi:10.1016/j.jpolmod.2010.08.001
+Model
JPO-5866;
No. of Pages 12
6
ARTICLE IN PRESS
M. Bannò, F. Sgobbi / Journal of Policy Modeling xxx (2010) xxx–xxx
Table 1
Variables and data sources.
Variable
Dependent variable
D Incentive
Independent variables
SME
Large firm
Log sales
Overall experience
Funding experience
Financial constraints
Country risk
International experience
Greenfield
Majority
East Europe
Diff industry
Cohort 02 08
Industry dummies
a
Description
Source
Dummy variable taking value 1 for FDIs
backed by public incentives in t0 and 0
otherwise
SIMEST and FINEST balance sheets
Dummy variable taking value 1 if firm
turnover is less than 50 million euros in t0–1
and 0 otherwise
Dummy variable taking value 1 if firm
turnover is more than 100 million euros in
t0–1 and 0 otherwise
Logarithm of firm sales in t0–1 (euros)
Logarithm of firm age in t0–1 (years)
Dummy variable taking value 1 if the firm
owned at least one FDI backed by a public
incentive in t0–1 and 0 otherwise
Ratio between debt and total assets in t0–1
Country risk rating on a scale from 1 to 5
Logarithm of the number of FDIs held in
t0–1 , 0 if no FDI existed
Dummy variable taking value 1 if the foreign
affiliate is a greenfield project and 0
otherwise
Dummy variable taking value 1 if the foreign
affiliate is majority-owned by the parent
company in t0 and 0 otherwise
Dummy variable taking value 1 if the FDI
destination country is in Eastern Europe and
0 otherwise
Dummy variable taking value 1 if the FDI is
in a different sector than the parent company
and 0 otherwise
Dummy variable taking value 1 if the FDI
was launched between 2002 and 2008 and 0
otherwise
Ten dummy variables taking value 1 for
different industries
AIDA
AIDA
AIDA
AIDA
SIMEST and FINEST balance sheets
AIDA
SACEa
REPRINT
REPRINT
REPRINT
REPRINT
REPRINT, AIDA
REPRINT
REPRINT
SACE is an Italian leading credit management organisation.
support of a public financial incentive and 0 otherwise. The model is:
D Incentivei = ␣ Firm behaviouri + ␤ Control variablesi + εi
In order to take into account lagged effects on the outcome of the application and allocation process, all independent variables are calculated for the year before the FDI start-up or the
nearest available year. The variables used to explain firm behaviour and the agency allocation
process (Table 1) were selected based on the hypotheses described in Section 3 and on policy
guidelines (Law 100/1990 and Law 19/1991), which state that agencies should favour SMEs,
investments in Eastern Europe and firms operating in the same sector as the parent company. In
addition, a dummy variable controls for temporal heterogeneity caused by the greater availabilPlease cite this article in press as: Bannò, M., & Sgobbi, F. Firm participation in financial incentive
programmes: The case of subsidies for outward internationalisation. Journal of Policy Modeling (2010),
doi:10.1016/j.jpolmod.2010.08.001
+Model
JPO-5866; No. of Pages 12
ARTICLE IN PRESS
M. Bannò, F. Sgobbi / Journal of Policy Modeling xxx (2010) xxx–xxx
7
Table 2
Comparison between benefiting firms and non-benefiting firms.
Benefiting firms (501)
Non-benefiting firms (831)
Sign.
Firms’ self-selection variables
SMEc (%)
Large firmc (%)
Log salesa (euros)
Overall experiencea (log duration)
Funding experiencec (%)
Financial constraintsa (%)
Country riskb (median)
International experiencec (log FDI)
Greenfieldc (%)
Majorityc (%)
61.58
22.31
7.44
1.27
17.82
74.32
3
0.36
57.38
90.49
53.42
34.41
7.46
1.20
4.57
63.46
2
1.20
40.30
86.53
***
Control variables
East Europec (%)
Diff industryc (%)
Cohort 02 08c (%)
60.79
31.68
56.63
19.98
61.01
38.99
***
***
n.s.
***
***
***
***
***
***
**
***
***
a
t-Test between the two categories (mean).
Mann–Whitney test between the two categories (median).
c Proportion test between the two categories (%).
* Significant at the 10% level.
** Significant at the 5% level.
*** Significant at the 1% level.
b
ity of public funding from 2002 onwards. We also include industry dummies as further control
variables.5
Table 2 reports the descriptive statistics for the explanatory variables and provides preliminary
tests of the differences between firm-specific and project-specific features of FDIs launched with
and without public financial support. The highly significant differences between the two groups
provide preliminary evidence of the opportunity to investigate the likelihood of obtaining an
incentive based on firm behaviour.
5. Results of the empirical analysis
Estimated results of the probit regressions are shown in Table 3.6 As previously argued, a firm
chooses to apply for public intervention if the expected benefits exceed the application costs.
The proxies for application costs and expected benefits support the existence of self-selection
mechanisms among firms.
The hypothesis that application costs induce self-selection and partially crowds out the target population is supported. Firms with higher managerial skills and firms with past successful
applications to the same programme are more likely to obtain an incentive. Overall experience
and past funding experience reduce application costs through learning processes. This finding
5 Ten industry dummies were considered: services; wood products; raw materials; chemical and pharmaceutical; building
and construction; electronics; industrial machinery; automotive; food, tobacco and beverages; textile; with plastic and
rubber as the baseline.
6 Due to the comparably high correlation between the variables SMEs and Log sales, two distinct specifications were
tested (respectively, Model 1 and Model 2).
Please cite this article in press as: Bannò, M., & Sgobbi, F. Firm participation in financial incentive
programmes: The case of subsidies for outward internationalisation. Journal of Policy Modeling (2010),
doi:10.1016/j.jpolmod.2010.08.001
+Model
JPO-5866;
No. of Pages 12
8
ARTICLE IN PRESS
M. Bannò, F. Sgobbi / Journal of Policy Modeling xxx (2010) xxx–xxx
Table 3
Probit model, participation in financial incentive programmes.
Probit regression dependent variable: D Incentive
Model 1
Firms’ self-selection variables
SME
Large firm
Log sales
Overall experience
Funding experience
Financial constraints
Country risk
International experience
Greenfield
Majority
Control variables
East Europe
Diff industry
Cohort 02 08
Industry dummies
Cons
*
**
***
Model 2
Coeff.
Std. err.
e␤
1.338
2.726
1.010
1.050
0.528
1.206
1.285
0.033
0.288**
1.004***
0.009***
0.048**
−0.639***
0.181
0.252*
0.048
0.112
0.149
0.002
0.023
0.067
0.115
0.135
1.034
1.334
2.729
1.009
1.049
0.528
1.198
1.287
1.946
0.763
1.411
0.672***
−0.268***
0.344***
Coeff.
Std. err.
e␤
−0.123
−0.048
0.129
0.143
0.884
0.953
0.291***
1.003***
0.010***
0.049**
−0.638***
0.187*
0.251**
0.111
0.150
0.002
0.023
0.068
0.114
0.135
0.666***
−0.271***
0.344***
0.094
0.098
0.091
Yes
−1.981***
0.313
Number of obs = 1336
LR chi2 (22) = 652.37
Prob > chi2 = 0.000
Pseudo R2 = 0.369
0.138
0.094
0.097
0.091
Yes
−2.284***
0.443
Number of obs = 1336
LR chi2 (22) = 654.00
Prob > chi2 = 0.000
Pseudo R2 = 0.369
1.958
0.765
1.411
0.102
Significant at the 10% level.
Significant at the 5% level.
Significant at the 1% level.
suggests that policy makers could improve the allocation of public incentives by lowering the
application costs, for example by supporting unskilled and inexperienced companies.
In line with hypothesis two, expected benefits such as the availability of alternative financial
sources and the mitigation of risk exposure induce self-selection among target firms. Firms with
financial constraints are more likely to participate in public financial programmes. This evidence
suggests that firms with limited access to private capital markets may renounce to their foreign
projects and severely constrain their growth potential (De Maeseneire & Claeys, 2007). Financial
incentives are likely to produce a reallocation in the composition of financial sources (Atzeni &
Carboni, 2008) so that the availability of funds at low interest rate improves firm capability to
invest in new activities that would not otherwise be carried out.
Also firms investing in risky countries are more likely to enjoy financial incentives, confirming
that the riskiness of the foreign environment significantly affects firm application behaviour insofar
public aid is perceived as a means of lowering systematic, country-level risk. Coherently, firms
with past FDIs are less bounded by risk diversification and consequently less interested in asking
for public aid.
The acquisition of a majority share in the foreign venture as well as the willingness to invest
in a greenfield project increase the odds of obtaining a subsidy, revealing that the greater is the
commitment, the more prominent is the phenomenon of firm self-selection. A policy implication
Please cite this article in press as: Bannò, M., & Sgobbi, F. Firm participation in financial incentive
programmes: The case of subsidies for outward internationalisation. Journal of Policy Modeling (2010),
doi:10.1016/j.jpolmod.2010.08.001
+Model
JPO-5866; No. of Pages 12
ARTICLE IN PRESS
M. Bannò, F. Sgobbi / Journal of Policy Modeling xxx (2010) xxx–xxx
9
of these findings is that support to high-commitment entry mode in foreign markets (i.e., majority
stake or greenfield project) may alleviate the perceived risk and consequently induce additional
investment.
Despite policy guidelines in favour of initiatives by small firms, the coefficient of the dummy
variable SME is not significantly different from zero. Although the statistical analysis reported in
Table 2 suggests that smaller companies have greater chances of receiving financial benefits, the
probit models show that, after taking into account other determinants, the probability of accessing
the benefit does not change with firm size.7 This finding casts doubts on the effectiveness of the
observed HCM in achieving the target population, namely SMEs. Our results suggest that, when
targeted to SMEs, incentives might need additional measures in order to support project planning
and application.
Consistent with the guidelines stated by the relevant laws, the other control variables display
significant and positive coefficients for initiatives in Eastern Europe and in the same business
sector as the parent company.
Overall, we may conclude that our findings indicate a partial effectiveness of the incentive
allocation process. On one side the existence of barriers to firms participation partially crowds
out small, inexperienced or unskilled firms. On the other side, the examined HCM succeeds in
promoting additional investments by generating benefits to financed constrained firms and to firms
with high level of commitment in the foreign project.
6. Policy implications of the empirical results
An implementation process is often unforeseeable and difficult to monitor. This difficulty partly
explains why the literature has so far paid little attention to understanding firms’ behaviour when
applying for public subsidies (Blanes & Busom, 2004). This article provides new evidence on this
topic and yields substantial insights on program equity and on the design of non-experimental
program evaluations. In particular, this is the first paper that explicitly addresses the participation
process with regard to HCMs. Given that many developed countries adopt HCMs, the Italian
experience provides an interesting policy case. In addition, although focused on outward investment policy, with appropriate adjustments the proposed model is suitable to be extended to the
assessment of other types of public fund allocation processes.
Our findings suggest that, after controlling for agency selection criteria, application costs
significantly affect differences in participation status caused by firm self-selection. In particular,
the policy makers’ objective of encouraging SMEs’ participation is not fully achieved. This
result contrasts with the idea that the mere increase of available funds will inevitably lead to
larger benefits. In fact, an increased amount of resources may only partially affect self-selection
mechanisms in the eligible population. The existence of significant application costs suggests
that, rather than increasing the amount of public funds, measures for reducing application costs,
such as help desks or information networks, could increase participation rates by eligible firms,
especially SMEs. Moreover, as suggested by Sarmah (2003), greater transparency, minimisation
of bureaucracy and simplification and standardisation of application procedures can contribute to
increase application rates.
7 This finding holds both when size is proxied for by a continuous variable (Log sales) and when separate dummies for
smaller and larger firms allow for a non-linear relationship between firm size and the probability of receiving a financial
incentive.
Please cite this article in press as: Bannò, M., & Sgobbi, F. Firm participation in financial incentive
programmes: The case of subsidies for outward internationalisation. Journal of Policy Modeling (2010),
doi:10.1016/j.jpolmod.2010.08.001
+Model
JPO-5866;
No. of Pages 12
10
ARTICLE IN PRESS
M. Bannò, F. Sgobbi / Journal of Policy Modeling xxx (2010) xxx–xxx
Despite the above shortcomings, our results support the effectiveness of the examined financial incentive in targeting additional investments. Most studies ex-post test the entity of the
additionality by developing a counterfactual for benefiting firms (for a review, see Lenihan,
2004). However, only few studies analyse the characteristics of benefiting firms to predict and
control for the deadweight effect (see, e.g., Feldman & Kelley, 2006). Three aspects of our
analysis suggest that HCMs based on public equity participation are moving in the desirable
direction of additionality. First, several studies have highlighted that SMEs are often financially
constrained (Beck et al., 2005), so the provision of financial support may help firms to overcome those limits, especially in bank-based countries like Italy. Second, the examined policy
tool seems to be effective in supporting first FDI experiences as the probability of receiving
an incentive is stronger for non-internationalised firms. Third, the examined HCM encourages
projects characterised by greater commitment and risk (Young-Han, 2009) and suggests that
public support to FDIs may have contributed to reducing capital market failures and the uncertainty and risk associated with an unfamiliar host country (Te Velde, 2007; Westhead et al.,
2007).
Since the financial incentive reduces the relative price of capital, risk shifting can be expected
to occur. If institutions absorb too much risk, investing firms may be induced to further increase
the risk of their foreign projects and to restrict their applications to their most risky projects.
The policy implication is that careful consideration regarding incentive allocation procedures is
necessary in order to discourage risky behaviours by firms (Giebe et al., 2006).
In general terms, the outcome of a policy tool depends not only on the amounts invested,
but also on how the available resources are allocated to the target population. Under the budget constraints that typically affect governments, targeting the resources to the right subject
favours the implementation of the most efficient policy (Imai, 2007). The understanding of
the causes underlying participation processes and possible misalignments permits to collect
reliable evidence on the incentive’s direct and indirect effects (Heckman & Smith, 2004) and
consequently design incentive tools that promise higher benefits to the members of the target
group.
The proposed analysis reveals the complex nature of the implementation process and suggests
that application costs and expected benefits significantly affect the allocation and the outcome of
a support programme. Of course, better data would permit improving the proposed analysis. In
particular, repeated observations of the same firms across all stages of the participation process
would permit a detailed analysis of firm self-selection behaviour, agencies’ selection processes
and the consistency of the selected projects with policy goals.
Additional efforts related to the ex ante assessment of both firm and project characteristics
may provide the agencies in charge of incentive assignment with better operative tools. Finally, it
is worth mentioning that public incentive without monitoring can lead to economic inequalities
(i.e. crowding out) and induce inefficiencies. In summary, we believe that the analysis of the
implementation process is worthwhile both for the evaluation of subsidy effects and for practical
policy making.
References
Aschooff, B. (2009). Who gets the money? The dynamics of R&D project subsidies in Germany. ZEW Discussion Paper,
No. 08-018.
Atzeni, G. E., & Carboni, O. A. (2008). The effects of grant policy on technology investment in Italy. Journal of Policy
Modeling, 30(3), 381–399.
Please cite this article in press as: Bannò, M., & Sgobbi, F. Firm participation in financial incentive
programmes: The case of subsidies for outward internationalisation. Journal of Policy Modeling (2010),
doi:10.1016/j.jpolmod.2010.08.001
+Model
JPO-5866; No. of Pages 12
ARTICLE IN PRESS
M. Bannò, F. Sgobbi / Journal of Policy Modeling xxx (2010) xxx–xxx
11
Amorim, C., Bannò, M., & Piscitello, L. (2010). Internationalisation, financial incentives and firm growth: Evidence from
Italy. In Proceedings of AIB-UK, Academy of international business, regionalism and globalization.
Beck, T., Demirgüç-Kunt, A., Levine, R., & Maksimovic, V. (2005). Financial and legal constraints to growth: Does firm
size matter? Journal of Finance, 60(1), 137–177.
Blanes, J. V., & Busom, I. (2004). Who participates in R&D subsidy programs? The case of Spanish manufacturing firms.
Research Policy, 33(10), 1459–1476.
Blundell, R., & Costa Dias, M. (2000). Evaluation methods for non-experimental data. Fiscal Studies, 21(4), 427–468.
Colombo, M., Grilli, L., & Verga, C. (2007). High-tech start-up access to public funds and venture capital: Evidence from
Italy. International Review of Applied Economics, 21(3), 381–402.
Corbett, T., & Lennon, M. C. (2002). Implementation analysis: An evaluation approach whose time has come. Washington,
DC: The Urban Institute Press.
De Maeseneire, W., & Claeys, T. (2007). SMEs, FDI and financial constraints. Working Paper, Vlerick Leuven Gent
Management School Working Paper Series, No. 2007-25.
Duguet, E. (2004). Are R&D subsidies a substitute or a complement to privately funded R&D? Evidence from
France using propensity score methods for non-experimental data. Review d’Economie Politique, 114(2), 263–
292.
Dunning, J. H., & Lundan, S. M. (2008). Multinational enterprises and the global economy (2nd ed.). Cheltenham,
England: Edward Elgar.
Feldman, M. P., & Kelley, M. R. (2006). The ex ante assessment of knowledge spillovers: Government R&D policy,
economic incentives and private firm behaviour. Research Policy, 35(10), 1509–1521.
Giebe, T., Grebe, T., & Wolfstetter, E. (2006). How to allocate R&D (and other) subsidies: An experimentally tested
policy recommendation. Research Policy, 35(9), 1261–1272.
González, X., Jaumandreu, J., & Pazó, C. (2005). Barriers to innovation and subsidy effectiveness. RAND Journal of
Economics, 36(4), 930–950.
Heckman, J. J., & Smith, J. (2004). The determinants of participation in a social program: Evidence from a prototypical
job training program. Journal of Labor Economics, 22(2), 243–298.
Henisz, W. J. (2004). The institutional environment for international business. In P. J. Buckley (Ed.), What is international
business? (pp. 85–109). New York: Palgrave.
Imai, K. (2007). Targeting versus universalism: An evaluation of indirect effects of employment guarantee scheme in
India. Journal of Policy Modeling, 29, 99–113.
Lenihan, H. (2004). Evaluating Irish industrial policy in terms of deadweight and displacement: A quantitative methodological approach. Applied Economics, 36(3), 229–252.
Lerner, J. (2002). When bureaucrats meet entrepreneurs: The design of effective public venture capital programmes. The
Economic Journal, 112(477), 73–84.
Mariotti, S., & Mutinelli, M. (2008). Italia Multinazionale 2008. Le partecipazioni italiane all’estero ed estere in Italia.
Soveria Mannelli: Rubbettino.
Moffitt, R. (1991). Program evaluation with non-experimental data. Evaluation Review, 15(3), 291–314.
Mudambi, R. (1999). Multinational investment attraction: Principal-agent considerations. International Journal of the
Economics of Business, 6(1), 65–79.
Quer, D., Claver, E., & Rienda, L. (2007). The impact of country risk and cultural distance on entry mode choice: An
integrated approach. Cross Cultural Management: An International Journal, 14(1), 74–87.
Rolfe, R., Ricks, D., Pointer, M., & McCarthy, M. (1993). Determinants of FDI incentive preference of MNEs. Journal
of International Business Study, 24(2), 335–355.
Sarmah, P. (2003). Home country measures and FDI: Implications for host country development. Jaipur: Centre for
Competition, investment and economic regulation (CUTS). Jaipur Printers.
Scheirer, M. A. (1994). Designing and using process evaluation. In J. S. Wholey, H. Hatry, & K. Newcomer (Eds.),
Handbook of practical program evaluation (pp. 40–68). San Francisco: Josey Bass.
Schilder, A. (2000). Government failures and institutions in public policy evaluation: The case of Dutch technology policy.
Assen: Van Gorcum & Comp. B.V.
Tanayama, T. (2007). Eligibility, awareness and the application decision: An empirical study of firm participation in an
R&D subsidy program. HECER Discussion Paper, No. 161.
Te Velde, D. W. (2007). Understanding developed country efforts to promote foreign direct investment to developing
countries: The example of the United Kingdom. Transnational Corporations, 16(3), 83–104.
UNCTAD. (2001). Home country measures. New York, Geneva: United Nations Publication.
van Tongeren, W. F. (1998). Microsimulation of corporate response to investment subsidies. Journal of Policy Modeling,
20(1), 55–75.
Please cite this article in press as: Bannò, M., & Sgobbi, F. Firm participation in financial incentive
programmes: The case of subsidies for outward internationalisation. Journal of Policy Modeling (2010),
doi:10.1016/j.jpolmod.2010.08.001
+Model
JPO-5866;
12
No. of Pages 12
ARTICLE IN PRESS
M. Bannò, F. Sgobbi / Journal of Policy Modeling xxx (2010) xxx–xxx
Westhead, P., Wright, M., & Ucbasaran, D. (2007). Internationalization of small and medium-sized enterprises (SMEs)
and international entrepreneurship: A critique and policy implications. Regional Studies, 41(7), 1013–1030.
Young-Han, K. (2009). Cross-border M&A vs. greenfield FDI: Economic integration and its welfare impact. Journal of
Policy Modeling, 31(1), 87–101.
Zaheer, S. (1995). Overcoming the liability of foreignness. Academy of Management Journal, 38(2), 341–363.
Please cite this article in press as: Bannò, M., & Sgobbi, F. Firm participation in financial incentive
programmes: The case of subsidies for outward internationalisation. Journal of Policy Modeling (2010),
doi:10.1016/j.jpolmod.2010.08.001