Transcript Innovation

Innovation activities and learning processes in the crisis.
Evidence from Italian international and interregional trade
in manufacturing and services
Emanuela Marrocu e Stefano Usai
University of Cagliari and CRENoS
Raffaele Brancati, Manuel Romagnoli
MET-Economia
SIE - SIEPI session
Napoli, 24 ottobre 2015
Motivation / 1
Research in international trade has changed dramatically over the
past fifteen years as its focus has shifted from industries and
countries to firms and products. This transformation was instigated
by the emergence of a wide range of micro-datasets exhibiting
sharp variation in firm outcomes and attributes, even within narrow
industries. Models developed in reaction to this challenge both
rationalize this heterogeneity and offer new insight into the ways in
which economies respond to international trade... (Bernard et al.,
2012).
Motivation / 2
Export is an issue of self-selection: exporters are more productive because only
the most productive firms are able to overcome the sunk costs of entering export
markets. The most successful model of such selection is the seminal Melitz (2003)
model, which has dominated recent research in the field.
Many research show that exporting firms are not only more productive but also
larger, more skill intensive, more innovative …
In particular, the positive association between a firm’s export status and its
innovation can be considered as a strong empirical regularity in both international
economics and the economics of innovation: a wide consensus has been reached
on the fact that firms introducing innovations are ex-post more likely to export.
Evidence for learning to export (especially inter-regionally) and on the
importance of other learning phenomena related to regional and sectoral
spillovers is sparser and not conclusive.
Theoretical foundations
Old (Hecksher-Ohlin) and New (Krugman) Trade theories do not consider
hetereogenous firms because
• in the former case profit maximising firms are just in the background and
micro foundations are modest or non existent
• in the latter case firms do not face fixed costs of exporting since trade costs
are just a proportion of revenues, and as a result all firms export
The business community would take it as axiomatic that entering export
markets incurs sunk costs: market research has to be done; option appraisals
completed; existing products have to be modified; new distribution networks
set up and so on.
Clerides et al. (1998) were among the first to model this explicitly in a discrete
choice framework. Later, Melitz (2003) builds a dynamic industry model with
heterogeneous firms operating in (Dixit-Stiglitz) monopolistically competitive
industries.
Melitz (2003)
•
Melitz (2003) builds a dynamic industry model with heterogeneous firms
operating in (Dixit-Stiglitz) monopolistically competitive industries.
•
Firms incur a fixed cost to export. However, each has to make a productivity
draw from an exogenous distribution which determines whether they produce
and export, and an endogenously determined productivity threshold
determines who does and does not export.
•
The interaction of these raises industry productivity.
– First, there is a rationalisation effect. Exporting increases expected profit,
which induces entry, pushes up the productivity threshold for survival and
drives out the least efficient firms. Clearly this raises average industry
productivity.
– Second, exporting allows the most productive firms to expand and causes
less productive firms to contract
Empirics on firm export performance
• Firm export performance has been measured in two ways
– Extensive margin, that is the decision to export or not
– Intensive margin, that is quota of export on sales
• Empirical studies exploiting firm-level data have provided wide
evidence supporting the role played by productivity and other
sources of firm heterogeneity in explaining firm export activity
See, among others, Bernard and Jensen, 2004, Sterlacchini, 1999; Basile, 2001;
Roper and Love, 2002; Lachenmaier and Woessmann, 2006; Becker and Egger,
2009; Cassiman et al., 2010; Cassiman and Golovko, 2011;
• For comprehensive surveys see Bernard et al (2007 and 2012), Greenaway and
Kneller (2007), and Wagner (2007 and 2012), International Study Group on
Exports and Productivity (ISGEP), 2008
Empirics on export performance and innovation
• Particularly, several recent papers have compared the export performance of
innovative and non innovative firms, concluding that there is a significant
positive correlation between innovation and exports (Basile, 2001; Castellani
and Zanfei, 2007; Cassiman and Golovko, 2011).
• Although it can be argued that such correlation is the result of exporting firms
been more prone to innovate (e.g. Aw et al, 2007; Bratti and Felice, 2012), the
evidence available so far provides strong support in favour of a causal effect
which goes mainly from innovation to exports, particularly in the case of
product innovations (Nassimbeni, 2001; Roper and Love, 2002; Nguyen et al,
2008; Caldera, 2010).
Empirics on export performance, innovation and regions
• Most studies that have analysed the link between innovation and firm exports
have somewhat neglected the role of space. However, aggregate regional data
show sharp disparities across regions in exports, that suggest a potential link in
a way or another to some regional characteristics.
• A number of more recent firm-level studies recognises the potential role played
by regional factors, and add them to the list of firm level characteristics when
explaining firm export performance.
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Andersson M, Weiss J F (2012): Sweden, 1997-2004
Koenig et al, (2010): France, 1998-2003
Becchetti e Rossi, (2001): Italy, 1989-1991
Antonietti and Cainelli, (2011): Italy, 1998-2003
Greenaway and Kneller, (2004): UK, 1998-2002
Farole and Winkler, (2013): multi-country
Rodríguez-Pose et al, (2013), Indonesia 1990-2005
Mukim (2012): India 1999-2004
Lopez-Baso and Motellon, (2013), Spain, 2004
Becchetti and Rossi, 2001
• Using a sample composed of over 3,800 manufacturing firms drawn from the
Mediocredito Centrale database (more than 11 employees) for the period
1989–1991, find that spatial agglomeration, captured by localization within
the boundary of an industrial district, increases average export intensity by 4%
points.
• Tobit estimates show that geographical agglomeration significantly increases
export intensity and export participation. The result is robust when controlled
for firm size, sector and geographical areas and for the separate and positive
effects of export subsidies and export “consortia” on export intensity.
Antonietti and Cainelli, 2011
• The dataset consists of a sample of Italian manufacturing firms drawn from
the VIII and IX waves of the Survey by Unicredit-Capitalia, which covers the
period 1998–2003. The master datasets gather information on 4,680 and
4,289 firms, but the final dataset is just 715 firms
• The model used in the paper is an ‘augmented’ version of the Crepon,
Durette-Mairesse model, developed to summarize the complex process
“that goes from the firm decision to engage in research activities to the use
of innovations in its production activities” (Crépon et al. 1998, p.116). It
comprises five main equations
• Estimates show that agglomeration economies play a role in shaping the
relationship between innovation, productivity and export performance. In
particular, urbanization economies do positively affect both R&D and also
the propensity to export and the relative export intensity
Aim and contribution
• We want to assess firm export performance of Italian firms, in both
manufacturing and production services, during the crises
• Incidentally, we start examining interregional openness
• We aim at understanding how much such a performance (in terms of extensive
and intensive margin) depends on
o endogenous determinants (within the firm, with a specific emphasis on
innovation and learning to export internationally and interregionally)
o Other learning phenomena related to exogenous factors, external to the
firms which relate either to the sector, to the region or to local industry
characteristics
• Contrary to previous contribution we deal with
• the extensive and intensive margin models as intertwined phenomena,
from an empirical and econometric point of view
• the initial conditions problem with an appropriate approach
Dataset / 1
We focus on the Italian case thanks to a new database (the MET survey) which has
collected information at the firm level in four waves, every two years, since 2007
The MET survey is designed to focus on firms’ structure and strategies (in particular R&D
and innovation activities, the internationalisation process and network phenomena) as
well as on their financial aspects.
• Population:
• All italian firms in industrial sector (manufacturing, energy, mining) and production services
(except finance and insurance, real estate, transportation for private consumption). It includes
data on micro and family firms
• Stratification criteria:
• Firm’s dimension (4 classes – 1-9, 10-49, 50-249, 250<);
• Regions (20 regions);
• Sectors (12 sectors in the manufacturing industry);
• Methodology:
• CATI (Computer Assisted Telephone Interview);
• CAWI (Computer Assisted Web Interview);
Dataset / 2
• Since we want to explain current performance with past determinants, firms
have to appear at least in two consecutive years to be included in our analysis
• MET data have been merged with CRIBIS data to collect information on some
important financial and economic indicators available in balance sheets.
MET-firms
Two-period Merge with
panel
CRIBIS
2007
24,896
2009
22,340
11,549
6,016
2011
25,090
13,901
5,797
2013
25,000
10,537
4,728
• The final sample for our analysis thus consists of 16,541 firms
Dataset/3: Size class and geographical distributions
Total
N. of obs.
Manufacturing
Production Services
% N. of obs.
% N. of obs.
%
micro
small
medium
large
Total
5,622
6,953
3,144
822
16,541
34.0
42.0
19.0
5.0
100.0
3,112
4,795
1,979
485
10,371
30.0
46.2
19.1
4.7
100.0
2,510
2,158
1,165
337
6,170
40.7
35.0
18.9
5.5
100.0
North West
North East
Centre
South
Islands
Total
3,397
4,226
4,770
2,977
1,171
16,541
20.5
25.6
28.8
18.0
7.1
100.0
2,219
2,943
2,678
1,841
690
10,371
21.4
28.4
25.8
17.8
6.7
100.0
1,178
1,283
2,092
1,136
481
6,170
19.1
20.8
33.9
18.4
7.8
100.0
Some descriptive statistics/1
All firms
(16,541 obs.)
Mean
Innovators
(5,067 obs.)
Mean
Exporters
Non exporters
Exporters
Non exporters
At time t
export propensity
export share (%)
39%
13.7
54%
19.4
6,510
34.9
10,031
-
2,715
36.2
2,352
-
At time t-2
export propensity
inter-regional trade propensity
Innovation - all types
Innovation - main product
Innovation - process
Innovation - organization
Productivity - va per worker
Productivity - tfp
R&D intensity
RD_D
Leverage
Employees
Age
Group
Local network
37%
60%
38%
17%
19%
23%
10.61
5.8
1.4
14%
12.0
68.1
19.4
19%
41%
47%
66%
71%
32%
37%
44%
10.6
6.0
2.3
24%
11.3
107.1
19.2
27%
46%
74%
79%
45%
22%
24%
26%
10.64
6.1
2.2
24%
10.0
93.5
20.9
26%
39%
12%
47%
33%
13%
16%
21%
10.58
5.6
0.9
8%
13.3
51.6
18.4
15%
42%
79%
81%
70%
36%
38%
42%
10.65
6.2
3.1
34%
11.7
135.2
20.6
32%
42%
10%
49%
73%
27%
36%
47%
10.59
5.7
1.4
13%
10.9
74.7
17.7
21%
51%
All firms
Innovators
Some descriptive statistics/2
Piemonte
Valle d'Aosta
Lombardia
Trentino Alto Adige
Veneto
Friuli Venezia Giulia
Liguria
Emilia Romagna
Toscana
Umbria
Marche
Lazio
Abruzzo
Molise
Campania
Puglia
Basilicata
Calabria
Sicilia
Sardegna
1277
143
1563
629
1910
352
414
1335
1563
506
687
2014
247
244
1059
568
278
581
850
321
exporters Export
%
intensity
50%
17.18
32%
10.45
54%
20.76
42%
16.34
45%
17.18
59%
23.42
46%
17.92
42%
14.38
43%
17.08
33%
9.82
47%
16.50
29%
8.75
45%
16.04
27%
6.60
30%
9.19
39%
11.98
27%
6.36
15%
2.58
25%
6.82
22%
4.56
size
85.89
33.73
79.52
110.81
57.55
102.85
59.09
85.10
46.73
58.20
44.82
58.26
87.16
19.90
42.61
73.92
32.40
33.34
34.56
35.17
R&D
innovators
intensity
%
1.77
35%
0.40
24%
1.80
32%
1.24
31%
1.61
36%
1.10
34%
1.36
33%
2.02
30%
1.47
33%
1.35
35%
1.30
31%
1.09
30%
1.23
27%
0.69
23%
1.37
22%
1.18
32%
0.46
33%
0.28
20%
0.86
29%
0.71
20%
Some descriptive statistics/3
Exporters Export Innovators
productivty
R&D
%
intensity
%
size
(log)
intensity
North-West
North-East
Centre
South
Islands
3397
4226
4770
2977
1171
50%
45%
36%
30%
24%
19%
17%
13%
9%
6%
41%
42%
39%
31%
29%
88.0
76.1
62.3
54.3
40.0
10.55
10.63
10.66
10.49
10.72
1.8
1.5
1.3
1.0
1.0
Empirical model: the extensive and the intensive margin
The extensive margin model:
Pr(Expirst= 1) = Pr(ai + b1exp_inirst-2 b2exp_irirst-2 + dinnirst-2 + gprodirst-2+
fR&Dirst-2 + mXirst-2 + reg_d + ind_d + time_d + eirst ) =
= F(ai + b1exp_inirst-2 b2exp_irirst-2 + dinnirst-2 + gprodirst-2+ fR&Dirst-2 +
mXirst-2 + reg_d + ind_d + time_d + eirst)
The intensive margin model:
Exp on sales = ai + dinnirst-2 + gprodirst-2+ fR&Dirst-2 + mXirst-2 + reg_d +
ind_d + time_d + eirst
i = firm
r = 5 macro-regions
s = 2 macro-sectors
t = 2009, 2011, 2013
X is a set of firm characteristics
Empirical model / the variables
Dependent variables
• Pr(Expirst): binary indicator
• Exp on sales (%)
Both for international and interregional trade
Independent variables:
Innovative efforts
• Inn: dummy which takes value one when the firm innovates and zero otherwise
• Inn prod (main product innovation)
• Inn proc (process innovation)
• Inn org (organisation innovation)
• R&D: expenditure on R&D over sales
Learning processes
• Past (international) export
• Past (inter-regional) export
• Export spillovers (quota of exporters in local sector)
• Regional public R&D
• Regional private R&D
• Group
• Local network
Empirical model / the variables
Firms characteristics (X)
• Productivity
• value added per employee
• tfp (for a smaller sample)
• Leverage
• Age
• Size
Dummies
• Macro-reg_d
• Macro-sec_d
• Time_d
Main hypothesis
1. We try to assess the role of experience and learning by including the lag of
the dependent variable in the model
2. Most importantly and differently from all previous contribution we consider
the experience in ”exporting” beyond regional borders (interregional trade)
3. We try to go beyond the use of dummies to take into account other learning
processes which go beyond the firm level which may influence firms’ ability
to export. We explore a set of potential phenomena.
Estimation method
Extensive margin
- Pooled model (Logit and Probit)
- Random Effects model (Logit and Probit)
-
Correction for endogeneity induced by the dynamic term. In Wooldridge (2005) the
distribution of the unobserved effects is modelled conditional on the initial value of the
dependent variable and the mean of the exogenous variables (in our case only firm age)
Intensive margin
-
Tobit II-Heckman (Probit and Linear)
-
-
With exclusion restrictions (the sunk costs variables), the two processes are considered
correlated but it has very restrictive restrictions and it does not consider bounded values
Two part model (Probit/Logit and Linea/Beta)
-
Linear and beta distribution for the positives: beta accounts also for the bounded features
of the dependent variable (0-1]. Both do not account for the correlation between the two
processes
Estimation strategy
1.
Estimate the benchmark model for the extensive margin
2.
Test for robustness with respect to alternative measures of innovation and
subsamples.
3.
Post-estimation stage: assess the conditional probability of exporting
4.
Estimate the benchmark model for the intensive margin
Extensive margin models/1
Linear
Probability
Model
Pooled Logit
Pooled Logit
Innovation
0.013 **
0.110 **
0.096 *
0.063 **
0.129 **
0.072 **
R&D intensity
0.001 **
0.009 **
0.009 **
0.005 **
0.011 **
0.006 **
Past export
0.558 ***
2.312 ***
2.292 ***
1.393 ***
1.973 ***
1.175 ***
Past inter-regional trade
0.055 ***
0.378 ***
0.382 ***
0.217 ***
0.440 ***
0.247 ***
Export spillovers
0.001 ***
0.005 **
0.004 *
0.003 **
0.006 **
0.003 **
Regional public R&D
-0.025 **
-0.160 *
-0.244 ***
-0.090 *
-0.195 *
-0.109 *
Regional private R&D
0.022 ***
0.174 ***
0.169 ***
0.100 ***
0.214 ***
0.120 ***
Group
0.006
0.017
-0.014
0.012
0.022
0.014
Local network
-0.007
-0.035
-0.031
-0.020
-0.051
-0.029
Pooled Probit
Random
Effects Logit
model
Random
Effects Probit
model
Innovative efforts
Learning processes
Firm characteristics
Productivity - va per worker
0.026 ***
0.196 ***
Productivity - tfp
0.111 ***
0.236 ***
0.132 ***
0.131 ***
Size
0.026 ***
0.174 ***
0.075 ***
0.100 ***
0.215 ***
0.121 ***
Age
-0.004
-0.856 ***
-0.947 ***
-0.466 ***
-0.956 ***
-0.528 ***
Leverage
-0.007 ***
-0.045 **
-0.013
-0.024 **
-0.055 **
-0.029 **
Constant
-0.186 ***
-4.452 ***
-3.035 ***
-2.590 ***
-5.301 ***
-3.001 ***
Extensive margin models: average marginal effects
Linear
Probability
Model
Innovative efforts
Innovation
Innovation - non past exporters
Innovation - past exporters
Random
Random
Pooled Logit Pooled Probit Effects Logit Effects Probit
model
model
0.0133
0.0133
0.0133
0.0149
0.0140
0.0164
0.0153
0.0145
0.0166
0.0153
0.0140
0.0176
0.0157
0.0145
0.0176
R&D intensity - past exporters
0.0024
0.0024
0.0024
0.0021
0.0021
0.0022
0.0021
0.0021
0.0022
0.0022
0.0021
0.0023
0.0023
0.0022
0.0024
Learning processes
Past export
0.5585
0.4654
0.4696
0.3527
0.3647
0.0551
0.0551
0.0551
0.0529
0.0482
0.0610
0.0539
0.0500
0.0607
0.0537
0.0472
0.0651
0.0555
0.0499
0.0651
0.00043
0.00043
0.00043
0.00033
0.00030
0.00039
0.00033
0.00031
0.00038
0.00032
0.00028
0.00039
0.00033
0.00029
0.00039
-0.0089
-0.0089
-0.0089
-0.0076
-0.0070
-0.0087
-0.0075
-0.0070
-0.0085
-0.0080
-0.0071
-0.0097
-0.0083
-0.0074
-0.0097
0.0097
0.0097
0.0097
0.0108
0.0104
0.0114
0.0109
0.0107
0.0115
0.0115
0.0109
0.0126
0.0119
0.0114
0.0128
R&D intensity
R&D intensity - non past exporters
Past inter-regio trade
Past inter-regio trade - non past exporters
Past inter-regio trade - past exporters
Export spillovers
Export spillovers - non past exporters
Export spillovers - past exporters
Regional public R&D
Regional public R&D - non past exporters
Regional public R&D - past exporters
Regional private R&D
Regional private R&D - non past exporters
Regional private R&D - past exporters
Extensive margin models: average marginal effects
Linear
Probability
Model
Pooled Pooled
Logit Probit
Random
Random
Effects
Effects Probit
Logit model
model
Firm characteristics
Productivity
Productivity - non past exporters
Productivity - past exporters
0.0446
0.0446
0.0446
0.0469
0.0501
0.0414
0.0472
0.0500
0.0423
0.0497
0.0531
0.0439
0.0508
0.0539
0.0454
Size
Size - non past exporters
Size - past exporters
0.0393
0.0393
0.0393
0.0394
0.0424
0.0343
0.0399
0.0425
0.0354
0.0432
0.0467
0.0371
0.0442
0.0474
0.0388
Age
Age - non past exporters
Age - past exporters
-0.0023
-0.0023
-0.0023
-0.0611 -0.0583
-0.0488 -0.0481
-0.0824 -0.0760
-0.0602
-0.0457
-0.0853
-0.0596
-0.0469
-0.0817
Extensive margin models per type of innovation
Product
Pooled Logit
Process
Organizatio
Random Effects Logit model
Product
Process
Organizatio
Innovative efforts
Innovation by type
0.153 **
0.054
0.051
0.173 **
0.063
0.061
R&D intensity
0.008 **
0.010 **
0.011 **
0.010 **
0.012 **
0.013 **
Learning processes
Past export
2.313 ***
2.319 ***
2.320 ***
1.974 ***
1.979 ***
1.980 ***
Past inter-regional trade
0.380 ***
0.378 ***
0.377 ***
0.442 ***
0.439 ***
0.439 ***
Export spillovers
0.005 **
0.005 **
0.005 **
0.006 **
0.006 **
0.006 **
Regional public R&D
-0.157 *
-0.161 *
-0.161 *
-0.191 *
-0.195 *
-0.195 *
Regional private R&D
0.173 ***
0.173 ***
0.173 ***
0.212 ***
0.213 ***
0.212 ***
Group
0.021
0.020
0.019
0.026
0.025
0.024
Local network
-0.030
-0.026
-0.027
-0.044
-0.039
-0.040
Firm characteristics
Productivity - va per worker
0.197 ***
0.197 ***
0.197 ***
0.237 ***
0.237 ***
0.237 ***
Size
0.176 ***
0.176 ***
0.175 ***
0.217 ***
0.217 ***
0.216 ***
Age
-0.847 ***
-0.849 ***
-0.846 ***
-0.945 ***
-0.947 ***
-0.943 ***
Leverage
-0.046 **
-0.045 **
-0.046 **
-0.056 **
-0.055 **
-0.055 **
Constant
-4.464 ***
-4.414 ***
-4.409 ***
-5.307 ***
-5.255 ***
-5.250 ***
Log-likelihood
Number of observations
-7,177.16
16,541
-7,179.77
16,541
-7,179.75
13,781
-7,158.86
16,541
-7,161.33
16,541
-7,161.29
16,541
y=0
0<y<1
y=1
Intensive margin models
Tobit II model - two steps
Pooled models
Selection
Probit
Two-part model
Selection Share
Probit
Linear
Two-part model
Selection
Share
Probit
Beta
Innovative efforts
Innovation
0.063 **
-0.003
0.063 ** -0.001
0.063 **
R&D intensity
0.005 **
0.001 *
0.005 **
0.005 ** -0.0001
Learning processes
Past export
1.393 ***
1.393 ***
1.393 ***
Past inter-regional trade
0.217 ***
0.217 ***
0.217 ***
Export spillovers
0.003 **
0.001 ***
0.003 **
0.002 *** 0.003 **
0.005 ***
Regional public R&D
-0.090 *
-0.031 **
-0.090 *
-0.036 ** -0.090 *
-0.197
Regional private R&D
0.100 ***
0.022 **
0.100 *** 0.025 *** 0.100 *** -0.176
Group
0.012
0.012
0.012
0.013
Local network
-0.020
-0.022 ***
-0.020
-0.023 *** -0.020
Firm characteristics
Productivity - va per worker
0.111 ***
0.004
0.111 *** 0.006
Size
0.100 ***
0.022 ***
0.100 *** 0.026 *** 0.100 ***
Age
-0.466 ***
-0.006
-0.466 *** -0.008
Leverage
-0.024 **
-0.010 ***
-0.024 ** -0.011 *** -0.024 **
Constant
-2.590 ***
0.269 ***
-2.590 *** 0.214 *** -2.590 *** -0.405
Lambda Mills
-0.039 ***
Implied rho
-0.149
E(share|X, Z)
E(share|X, Z, share>0)
Share
Linear
0.137
0.306
0.001 **
0.137
0.321
0.012
0.111 ***
-0.027
0.007
-0.111 ***
0.015
0.075 ***
-0.466 *** -0.076 **
-0.042 **
0.169
0.429
10031
6271
239
Main results/extensive model_1
• Firm decision to export depends significantly on its innovative activity both in
terms on input (R&D expenditure) and output (innovativeness)
– As far as the latter aspect, product innovation always proves significantly correlated to
export whilst process and organization innovation are never significant
• There are also significant learning phenomena
–
–
–
–
There is quite an important learning to export (both at interregional at international level)
There is also a role for a sort of local specialisation effect of exporting activity
No role for group and local network
In particular we find that private R&D has a positive and significant effect whilst public R&D
has a negative even though not significant impact
• As in previous contributions we find out that probability to export depends on
some firm specific features
– Productivity, first of all, but also Size, Age and Leverage.
Main results/extensive model_2
• Firms which have already paid sunk costs of entry have an increase in
probability of exporting in a range between 35 and 45%
• Past interregional export counts for another 5%, as much as productivity
• Innovation counts for 1.5%, almost six times the impact of investing in R&D
• If a firm has not already faced sunk costs, that is, it is a non exporter, then
being more productive, bigger and younger increases the probability of
exporting
Main results/intensive model
• We have found that, once sunk costs are taken aside, and we focus on the
determinants of the intensity of export, results are significantly different:
–
–
–
–
Innovation is no longer significant, nor is productivity
R&D is still significant but not robust across models
Leverage, size and age are still very important
And there is still a role for some specific regional/sectoral effects,
especially export spillovers role is quite robust while that of private R&D
is not
Speculative policy implications
• The degree of local industry internationalization and private R&D
expenditures at the regional level represent two valid objectives to boost
export activities even though the first effect is really small. Indeed, policies
directly affecting new exporters may trigger a domino effect.
• Some role of policy measures devoted to reduce financial and structural
constraints for SMEs. The combination of diseconomies of scale due to size
and the negative spillovers coming from the orientation towards local
networks still represent an important impediment to export activity which can
be addressed by specific policy interventions
• This recipe is also suggested by the fact that to enter a new market “for the
first time” firms need to be more productive, larger and younger: policies may
either focus on these kind of firms or helping young small firms to become not
only more productive but larger
Extension 1: Extensive margin models for sub periods
Pooled logit
Random effects logit
2009-11
2011-2013
2009-11
2011-2013
Innovative efforts
Innovation
0.085
0.022
0.098
0.022
R&D intensity
0.002
0.026 ***
0.003
0.026 ***
Learning processes
Past export
2.228 ***
2.496 ***
1.911 ***
2.496 ***
Past inter-regional trade
0.386 ***
0.362 ***
0.451 ***
0.362 ***
Export spillovers
0.000
0.012 ***
0.000
0.012 ***
Regional public R&D
-0.145
-0.140
-0.169
-0.140
Regional private R&D
0.130 **
0.103 *
0.158 **
0.103 *
Group
-0.025
0.100
-0.030
0.100
Local network
-0.050
-0.018
-0.067
-0.018
Firm characteristics
Productivity - va per worker
0.211 ***
0.142 ***
0.246 ***
0.142 ***
Size
0.210 ***
0.157 ***
0.250 ***
0.157 ***
Age
-0.809 ***
-0.495
-0.877 ***
-0.495
Leverage
-0.044 *
-0.049 *
-0.052 *
-0.049 *
Constant
-4.677 ***
-3.963 ***
-5.454 ***
-3.963 ***
Productivity - tfp
Extension 2: Extensive margin models for interregional flows
Linear
Probability
Pooled
Logit
Pooled
Probit
Random
Effects
Innovative efforts
Innovation
0.001
0.025
0.015
0.026
0.014
R&D intensity
0.001 *
0.010 **
0.005 **
0.011 **
0.006 **
Learning processes
Past inter-regional trade
0.372 ***
1.291 ***
0.790 ***
1.057 ***
0.642 ***
Past export
0.044 ***
0.248 ***
0.139 ***
0.263 ***
0.148 ***
Export spillovers
0.001 ***
0.008 ***
0.005 ***
0.009 ***
0.005 ***
Regional public R&D
0.010
0.067
0.032
0.071
0.034
Regional private R&D
0.019 ***
0.073 *
0.047 **
0.086 **
0.053 **
Group
0.014 *
0.100 *
0.060 *
0.114 *
0.068 **
Local network
0.004
0.016
0.009
0.024
0.012
Firm characteristics
Productivity - va per worker
0.021 ***
0.123 ***
0.075 ***
0.140 ***
0.083 ***
Size
0.032 ***
0.174 ***
0.110 ***
0.205 ***
0.125 ***
Age
0.011 **
-0.469 **
-0.322 ***
-0.551 ***
-0.353 ***
Leverage
0.003
0.014
0.008
0.013
0.007
Constant
0.128 ***
-2.138 ***
-1.322 ***
-2.409 ***
-1.450 ***
Log-likelihood
Number of observations
Random
Effects
-9,135.55
-8,733.25
-8,730.47
-8,724.15
-8,721.77
16,541
16,541
16,541
16,541
16,541