Firm-level drivers of export performance and external competitiveness in Italy.
Date: 2018-11-07
Institutions: Directorate-General for Economic and Financial Affairs
Subjects: Italy, economic indicator, export, export (EU), impact study, innovation

6

ISSN 2443-8022 (online)

Firm-level Drivers 
of Export Performance               
& External Competitiveness 
in Italy

Emanuele Brancati, Raffaele Brancati, 
Dario Guarascio, Andrea Maresca, 
Manuel Romagnoli and Antonello Zanfei

DISCUSSION PAPER 087 | SEPTEMBER 2018

EUROPEAN ECONOMY

EUROPEAN 
ECONOMY

Economic and 
Financial Affairs

European  Economy  Discussion  Papers  are  written  by  the  staff  of  the  European  Commission’s 
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European Commission 
Directorate-General for Economic and Financial Affairs 

Firm-level Drivers of Export Performance               
and External Competitiveness in Italy 

Emanuele  Brancati,  Raffaele  Brancati,  Dario  Guarascio,  Andrea  Maresca,         
Manuel Romagnoli and Antonello Zanfei 

Abstract 

This paper provides an in-depth study on the main firm-level drivers of external competitiveness during the 
recent  crisis  in  Italy.  We  contribute  to  the  debate  on  the  Italian  international  position  by  presenting 
evidence  based  on  a  unique  sample  survey  database  (the  MET  dataset).  Overall,  our  results  confirm  the 
high  degree  of  heterogeneity  of  the  Italian  corporate  sector  and  the  well-known  differences  between 
internationalised and domestic companies in terms of performance as well as structure and behaviour. In 
particular, the data highlight not only the correlation between internationalisation and innovative activities 
but  also  a  positive  change  of  attitude  of  Italian  firms  towards  these  strategies.  Our  analysis  shows  that, 
whilst  structural  factors  play  a  key  role  for  external  competitiveness  (size,  location,  industry,  etc.),  other 
critical  firm-level  aspects,  especially  those  related  to  strategic  profiles,  technological  capabilities,  and 
‘proactive’  behaviour,  trigger  superior  performances.  To  this  extent,  our  policy  suggestions  focus  on  the 
need to sustain and foster innovative activities to improve aggregate competitiveness. 

JEL Classification: F14; L25; O31; O33. 

Keywords: Italy; export performance; external competitiveness; internationalisation; innovation; extensive 
margins; intensive margins; firm-level study. 

Acknowledgements:  Report  prepared  for  the  Commission,  Directorate  General  for  Economic  and 
Financial Affairs (DG ECFIN), under contract ECFIN/117/2016/733754. 

Contact: Raffaele Brancati, Monitoraggio Economia e Territorio, met@pec.met-economia.it. 

The closing date for this document was August 2017. 

EUROPEAN ECONOMY                                                                                      Discussion Paper 087 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
CONTENTS 

1. 

Introduction ..................................................................................................................................... 5 

2. 

Background ..................................................................................................................................... 8 

3. 

Related literature ......................................................................................................................... 17 

2.1  Analysing competitiveness at the firm-level: the Italian case ........................................................ 17 

4. 

Data ............................................................................................................................................... 23 

5. 

Econometric strategy ................................................................................................................. 24 

6. 

Results ............................................................................................................................................ 27 

6.1  Extensive margins.................................................................................................................................... 27 

6.2  Intensive margins and extensions ........................................................................................................ 33 

7. 

Concluding remarks and policy suggestions .......................................................................... 36 

8. 

REFERENCES .................................................................................................................................. 40 

9. 

ANNEX: TABLES ............................................................................................................................. 53 

10.  APPENDIX ...................................................................................................................................... 70 

3 

 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
1.  INTRODUCTION1 

The  external  competitiveness  and  export  performance  of  the  Italian  economy  has  been  a  much 
researched and debated topic in recent years. The discussion centres on the appraisal of the national 
competitive position. On the one hand, some scholars emphasise that the decline of the Italian export 
market  share  follows  from  a  decreasing  degree  of  aggregate  competitiveness.  According  to  this 
perspective,  the  drop  in  the  share  of  Italian  exports  is  linked  to  the  deterioration  of  cost 
competitiveness  indicators  (e.g.  unit  labour  costs,  real  effective  exchange  rates  etc.),  mainly  due  to 
stagnant  productivity  (Hassan  and  Ottaviano,  2013).  This  issue  has  been  often  ascribed  to  the  high 
presence  of  micro  and  small  firms  within  the  Italian  industrial  sector  (Barba  Navaretti  et  al.,  2011; 
Bugamelli et al., 2012; Pellegrino and Zingales, 2014), coupled with a low propensity to innovate and 
undertake  R&D  activities  (Faini  and  Sapir,  2005;  Bugamelli  et  al.,  2012)  as  well  as  lower  positions 
along  the  global  value  chains  (Accetturo  and  Giunta,  2016).  Within  this  perspective  of  decreasing 
external competitiveness, the high degree of fragmentation of the industrial sector is also paired with 
an  insufficient  resorting  to  external  management,  often  bound  to  the  family  dimension  of  many 
business  companies  (Bugamelli  et  al.,  2012;  Pellegrino  and  Zingales,  2014;  Lippi  and  Schivardi, 
2014).  Moreover,  several  scholars  highlight  the  aggregate  disadvantages  connected  with  the 
institutional setting such as administrative inefficiencies (Giordano et al., 2015; Lanau and Topalova, 
2016), market rigidities (Calligaris et al., 2016; Gopinath et al., 2017), and the labour market reforms 
implemented in the 1990’s (Larch, 2004; Daveri and Parisi, 2015). 

Conversely,  an  alternative  view  underlines  the  presence  of  highly  competitive  segments  embedded 
within an extremely heterogeneous industrial system. Some scholars highlighted how the international 
performances  of  Italian  companies  experienced  stable  trends  over  recent  years  when  compared  with 
their main European competitors (Lanza and Quintieri, 2007; CSC, 2010, 2011). With this perspective, 
several  studies  stressed  the  ongoing  presence  of  major  restructuring  processes  connected  with  the 
repositioning  of  many  firms  within  market  segments  characterised  by  higher  value  added,  the 
improvement  of  the  product  quality,  and,  more  in  general,  an  increasing  effort  to  adapt  to  the  fast-
evolving competitive scenario (De Nardis and Pensa, 2004; De Nardis and Traù, 2005; Cipolletta and 
De  Nardis,  2012;  Arrighetti  and  Ninni,  2014).  These  pressures  also  challenged  traditional  Italian 
industrial  districts  triggering  profound  transformations  in  firms’  internationalisation,  innovation,  and 
organisation strategies (De Marchi et al., 2013; Brancati et al., 2017).  

Our  work  aims  at  contributing  to  this  widely  debated  topic  by  adopting  a  firm-level  perspective  to 
explicitly account for the heterogeneity of companies populating the Italian system. We contribute to 
the discussion by providing an in-depth study on the relevant microeconomic factors underpinning the 
Italian  external  competitiveness,  so  as  to  give  useful  insights  for  the  design  of  effective  policy 
schemes. There are several contributions of this work that are worth emphasising: 

First of all, compared to most papers, our analysis does not focus on a single specific aspect, but rather 
takes on a broad approach by encompassing a wide range of dimensions of external competitiveness. 
Those range from aggregate dynamics, to firm-level determinants of export probabilities, shares, and 
actual  performances.  This  is  a  critical  issue  if  potential  drivers  of  external  competitiveness  have 
heterogeneous  impacts  along  different  dimensions.  Moreover,  our  work  does  not  rely  on  a  unique 

1  This  paper  is  an  elaboration  of  a  broader  analysis  performed  by  a  large  study  group  composed  of  Emanuele  Brancati, 
Raffaele Brancati, Anna Giunta, Dario Guarascio, Lelio Iapadre, Andrea Maresca, Mario Pianta, Manuel Romagnoli, Stefano 
Usai, and Antonello Zanfei. 

5 

                                                            
 
driver of external competitiveness, but rather it puts an emphasis on the importance of behavioural and 
strategic factors by highlighting both their direct and indirect effects on export performances. In doing 
so, we explore several dimensions of heterogeneity aimed to pinpoint targeted segments for granular 
policies (i.e., identify companies with the highest expected gains  from the intervention). Finally, we 
exploit a large amount of information in the recent financial crisis based on different data sources. This 
is essential to dig deeper into the high degree of heterogeneity that characterise the Italian industrial 
system. Moreover, the use of up-to-date data is paramount in studying firms’ behaviours and strategies 
adopted during the financial crisis episode. Indeed, the recent period came with deep changes of firms’ 
strategies and performance leading to an increasing polarisation within the population of enterprises. 
In this complex and fast-evolving scenario, the use of newly-available datasets is critical to properly 
interpret  and  assess  these  structural  developments,  as  well  as  designing  a  set  of  effective  policy 
measures.  

On  the  top  of  aggregate  statistics  based  on  the  entire  population  of  Italian  manufacturing  firms 
(‘Rapporto  sulla  competitività  dei  settori’  dataset,  ISTAT)  we  take  advantage  of  a  newly-available 
survey  data,  the  MET  database  on  the  Italian  industry,  to  explicitly  analyse  firms’  strategic  profiles 
such as innovation, R&D, and networking relationships. This survey is specifically conceived to study 
a  massive  amount  of  firms’  characteristics  and  strategies,  with  a  particular  focus  on  their 
internationalisation  and  innovative  patterns.  The  sampling  scheme,  ensuring  a  very  high  degree  of 
representativeness  once  coupled  with  a  large  number  of  interviews,  is  stratified  along  three 
dimensions:  sector,  geographical  region,  and  size  class.  Unlike  many  other  firm-level  databases,  the 
MET  survey  also  accounts  for  firms  with  less  than  ten  employees  representing  the  vast  majority  of 
companies within the Italian industrial system. The survey is made of five waves (2008, 2009, 2011, 
2013, and 2015) and covers a time span starting right before the collapse of Lehman Brothers in 2008 
until recent years. This large amount of information is exploited to derive accurate results at the firm 
level  aimed  at  identifying  the  main  drivers  of  external  competitiveness  in  a  period  characterised  by 
deep changes in the economic scenario. 

Our  econometric  analysis  emphasises  the  importance  of  both  structural  characteristics  and  strategic 
behaviours in determining firms’ international performance. The empirical strategy, in order to correct 
for  unobserved  heterogeneity,  mainly  employs  random-effect  probit  models  (with  Mundlak 
correction)  and  within  estimator  (with  firm  and  time  fixed  effects)  to  dig  deeper  into  the  drivers  of 
external  competitiveness,  both  in  terms  of  extensive  and  intensive  margins  of  export.  Our  analysis 
delivers a set of results with some important policy implications.  

As  expected,  we  confirm  the  presence  of  a  self-selection  mechanism  of  more  productive  companies 
into international markets. Although productivity is associated to higher likelihood of exporting, once 
accounted  for  persistent  characteristics  that  do  not  vary  over  time,  its  role  is  found  to  be  largely 
reduced in explaining the change in the exporting status. On the other hand, productivity has a critical 
impact  on  firms’  international  performance  (i.e.,  intensive  margins)  and  decision  to  exit  the  foreign 
markets. Moreover, other characteristics play a critical role in firms’ export propensity, especially size 
and the affiliation to a corporate group. Finally, being an importer of intermediate products is largely 
associated to a higher probability of exporting, confirming how import policies may affect aggregate 
productivity, resource allocation, and industry export activity. 

A critical driver for external competitiveness is found to be linked to firms’ strategic behaviour and 
investment  in  dynamic  strategies.  The  innovation,  the  involvement  in  R&D  projects,  as  well  as 
undertaking  new  investments,  induce  a  significant  positive  effect  on  export  strategies  and 
performance.  The  introduction  of  innovations  is  even  more  important  in  reaching  extra-EU 

6 

 
destinations  and  in  affecting  firms’  switching  strategies  (i.e.,  the  choice  of  entry  or  exit  from  the 
international market). Interestingly, on the top of their direct impact, there are significant cumulative 
effects of dynamic strategies. First, the integration of innovation and R&D activities implies premia on 
both  the  extensive  and  intensive  margins  of  export.  The  coefficients  of  innovative  strategies,  paired 
with  R&D  investment,  is  two-to-three  times  larger  than  isolated  innovations.  Moreover,  the  path 
followed  by  the  set  of  dynamic  strategies  undertaken  in  the  past  (adding  or  reducing  the  set  of 
strategies,  i.e.,  upgrading/downgrading  paths)  has  additional  effects  on  firms’  international  attitude. 
Among  the  different  types  of  innovations,  new  products  dominate  other  forms  of  innovativeness 
(process  or  organizational/managerial),  especially  in  case  of  previously  non-exporting  companies. 
However,  we  find  process  and  organisational  innovations  to  have  an  additional  indirect  effect  on 
export by boosting firms’ productivity. 

Furthermore,  innovative  strategies  are  found  to  have  disproportionate  effects  for  the  international 
performance  of  (originally)  less  productive  and  small  companies,  pointing  at  innovativeness  as  a 
potential tool to fill the gap between large/productive companies and the set of less structured firms, a 
segment that represents an ideal target for policy measures. 

A simultaneous equation model also explores the main determinants of firms’ innovativeness. In this 
regard,  structural  characteristics  significantly  affect  the  introduction  of  innovations,  especially  for 
larger, younger, and more productive companies. The existence of R&D projects is clearly correlated 
to huge increases in the probability of introducing innovations. Importantly, this effect is not limited to 
the  investment  in  R&D  performed  within  the  firm  (internal  R&D),  but  extends  to  firms  outsourcing 
R&D  activities,  even  though  with  smaller  magnitudes.  This  evidence  emphasises  the  importance  of 
research-oriented  networks  to  overcome  the  lower  innovativeness  of  less  structured  companies. 
Another critical factor in driving firm innovative activity is related to the operating environment of a 
company, such as the affiliation to a corporate group and the establishment of close relationships with 
other  domestic  enterprises.  Finally,  financial  constraints  are  found  to  significantly  affect  a  firm’s 
capacity  to  innovate  and  the  establishment  of  close  ties  with  its  main  bank  is  documented  to  be 
extremely important in reducing informational asymmetries penalising innovative SMEs. 

Overall, we contribute to the debate on Italian external competitiveness by pointing out the importance 
of behavioural and strategic factors in  shaping firm-level competitive advantages. Whilst our results 
confirm  the  positive  effect  of  structural  characteristics,  such  as  size  and  productivity,  they  also 
highlight  the  existence  of  additional  factors  that  help  boost  firm  internationalisation.  Among  these, 
innovation  and  R&D  play  a  crucial  role  and  affect  external  competitiveness  both  directly  and  via 
productivity  improvements.  Even  though  the  positive  effect  of  innovative  activities  and  R&D  is 
certainly not new, we explore several new levels of heterogeneity, as well as indirect effects that may 
provide  a  sound  base  for  the  construction  of  granular  policy  implications.  In  particular,  the  analysis 
allows for the identification of segments of potentially innovative companies with the highest expected 
gains from international activity. The last section will provide a more in-depth discussion of the main 
policy implications. 

The  remainder  of  the  paper  is  organised  as  follows.  Section  2  further  analyses  the  background 
scenario. Section 3 reviews the related literature with a specific focus on firm-level drivers of external 
competitiveness.  Section  4  presents  the  data  used  in  the  empirical  analysis,  section  5  deals  with  the 
main identification challenges at the econometric level, while section 6 discusses the results. Finally, 
section 7 concludes the paper by presenting the main policy implications derived from the empirical 
analysis. 

7 

 
2.  BACKGROUND 

Italy has been suffering from a slow growth trend that can be traced back to the 90’s, long before the 
outbreak  of  the  global  recession.  This  dynamic  might  reflect  many  factors,  including  its  lower 
openness  to  the  international  environment  (even  in  comparison  with  other  European  countries  of 
similar size) and the resulting limited exposure to the beneficial effects of global integration.  

One of the most discussed weaknesses of the Italian economy is linked to its low and stagnant labour 
productivity, often ascribed to the very composition of the industrial structure characterised by a high 
diffusion  of  small  and  micro-sized  companies.  This  interpretation,  however,  does  not  seem  to  be 
consistent with Table 9.1 providing a cross-country comparison of the size/class distribution within the 
main European peer economies. The share of micro-sized companies (1-9 employees), over the entire 
population of manufacturing firms, whilst being much larger than the German one, is actually similar 
to  -and  slightly  below-  the  French  and  Spanish  ones.  However,  the  Italian  industrial  structure  is 
characterised by a lower (and decreasing) diffusion of large enterprises. 

When  considering  the  dynamics  of  labour  productivity  the  picture  significantly  changes.  Aggregate 
data  on  Italian  labour  productivity  hide  a  large  degree  of  heterogeneity  among  the  different  size 
classes.  Figure  2.1  plots  the  evolution  of  the  apparent  labour  productivity,  measured  as  gross  value 
added per employee, by size class across the European countries of interest. Overall, the productivity 
level of Italian manufacturing ranks in the last position, even though, in recent years, its trend has been 
in line with the other countries. The scarce aggregate productivity mainly follows from the conduct of 
micro  and  large  firms,  while  Italian  medium-sized  companies  show  relatively  positive  trends.  This 
suggests  that  the  productivity  gap  between  Italy  and  the  other  countries  is  largely  driven  by  the 
relatively negative performance of smallest enterprises. Thus, rather than by their high presence, Italy 
is  hindered  by  micro-sized  firms’  ability  to  reach  the  same  productivity  levels  of  their  European 
counterparts.  

8 

 
Figure 2.1 Apparent labour productivity by size class 

Source: EUROSTAT. Gross value added per person employed, thousands of Euros. 

This clearly translates in a differential external competitiveness, which is a result of the self-selection 
of more productive companies into international markets. In line with findings by many other studies 
including those that adopt different approaches, the productivity distribution of internationalised firms 
is  shifted  rightward  with  respect  to  the  bulk  of  the  distribution  referred  to  domestic  companies.2 
Furthermore, the general result holds even when the comparison is performed within each size class 
separately,  as  shown  by  Figure  2.2.  However,  this  correlation  seems  to  be  only  partially  connected 
with  the  one  linking  the  ability  to  export  and  firm  size.  In  fact,  starting  from  small  firms,  the 

2  Even  though  there  is  a  very  small  group  of  highly  productive  non-exporting  firms.  Unreported  kernel  density  functions 
show  an  overlap  of  domestically-oriented  activities  with  the  ones  of  internationalized  companies.  Whilst  the  reasons 
underpinning this outcome may be manifold -ranging from market niches through to rent positions-, this evidence does not 
affect the general result that companies reaching foreign markets tend to be more productive than those remaining within the 
national borders. 

9 

 
                                                            
 
percentiles  associated  with  the  group  of  exporters  exceed  those  associated  with  the  group  of  non-
exporting firms belonging to the subsequent class. To this extent, small exporters exhibit higher labour 
productivities than medium-size domestic companies and medium-sized exporters turn out to be more 
productive than large domestic enterprises. 

Figure 2.2 Labour productivity distribution by size 

Note: Data refer to the population of manufacturing sectors in 2014. 

Source: Elaborations on microdata from the Italian National Institute of Statistics (ISTAT). 

As such, the data suggest that the structural weakness of the Italian company has not to be only traced 
in the high number of micro-sized companies (indeed frequencies are similar to the Spanish or French 
economy),  but  rather  in  their  technical  inefficiency  vis-à-vis  other  countries.  The  latter  is  strictly 
related to poor performances in terms of innovative behaviours, human capital absorption, and degree 
of international openness. As largely emphasised by the evolutionary literature on industrial dynamics, 
technological  and  organisational  innovations  play  a  central  role  in  this  matter  for  they  represent  the 
way through which business organisations seek to gain an advantage over their competitors (Dosi and 
Nelson,  2010).  In  this  way,  they  represent  a  key  element  for  external  competitiveness  (Dosi  et  al., 
2015) as well as an indirect signal for the presence of dynamic capabilities, i.e. of those organisational 
abilities  of  coordinating  internal  and  external  resources  to  promptly  deal  with  the  changing 
surrounding  environment  (Teece  and  Pisano,  1994).  In  addition,  the  innovative  process  of 
competitiveness  is  a  multifaceted  and  complex  phenomenon  that  can  be  studied  from  different 
perspectives.  Each  of  the  perspectives  can  be  characterised  by  strengths  and  drawbacks  (for  a 
discussion  of  many  innovation  indicators  see  for  example  Smith,  2004  and  Castellani  and  Koch, 
2015). Thanks to the information contained in the MET database, the analyses in this document focus 
both on innovation output and input variables. The former refers to the final outcome of the process 

10 

 
 
while the latter to the factors involved in its ‘production’. The possibility of taking advantage of both 
types is very important for it allows to draw a thorough picture of the technological advancements of 
the Italian firms and, hence, of their level of competitiveness.  

Figure 2.3 presents the diffusion of innovators among exporters and domestically-oriented companies, 
both at the aggregate and at the size/class levels. The data are drawn from the pooled MET dataset and 
refer  to  the  entire  time  span  of  the  crisis  2008-2015.  The  height  of  the  bars  represents  the  share  of 
business  activities  that  have  introduced  at  least  one  type  of  innovation  (either  technological  or 
organisational) during the period of reference of each survey wave. 

In  line  with  the  empirical  evidence  (Bernard  and  Jensen,  2004;  Wagner,  2007;  Dosi  et  al.  2015), 
export  and  innovation  activities  appear  to  be  strongly  positively  correlated.  Indeed,  the  share  of 
innovators among the firms selling (part of) their products on foreign markets doubles the one detected 
among the domestically-oriented business activities (39 % against 19 %). This association holds even 
within each size class and seems to be particularly strong for micro-organisations. The percentage of 
innovators among the exporters belonging to this group is almost twice as much as the one detected 
among non-exporting micro-firms (33 % against 18 %).  

Moreover,  similarly  to  the  dynamics  of  productivity,  the  innovation-export  nexus  seems  to  be  only 
partly linked to the number of employees. As a matter of fact, despite the positive relationship between 
the  share  of  innovators  and  firm  size,  the  share  of  innovating  exporters  within  a  class  outstrips  the 
share  non-exporting  innovators  within  the  subsequent  class.  For  example:  small  exporters  tend  to 
innovate  more  frequently  than  medium-sized  domestically-oriented  firms  (45  %  versus  35  %).  As 
before,  then,  size  is  important  in  relative  terms.  Although  smaller  organisations  struggle  more  than 
larger  ones  to  access  and  manage  the  resources  and  capabilities  necessary  to  carry  out  innovative 
activities, innovations do not require a very large number of employees to be accomplished.  

Figure 2.3 Firms’ innovativeness by exporting status 

Note: Data refer to firms belonging to industrial sectors. ‘Innovators’ include all the business activities that stated of 
having introduced at least one type of innovation during the period of reference of the MET questionnaire. Types 
of innovation encompass: new products, new processes, new organisational, marketing and managerial 
practices. 

Source: MET sample survey, waves from 2008 to 2015 (pooled dataset). 

Table 9.2 provides further information on the export-innovation relationship by reporting the share of 
business activities per each type of innovation introduced. In particular, the analysis accounts for the 

11 

 
 
introduction of new products, new processes, and new organisational/managerial/marketing practices3. 
Overall, exporters’ higher propensity to innovate does not appear to be bound to a specific variety but 
it  is  equally detectable  in  each  single  category.  As  such,  companies  that  sell  (part  of)  their  products 
abroad  turn  out  to  be  more  inclined  than  other  ones  to  undertake  all  the  types  of  technological  and 
organisational  changes.  Provided  that,  it  is  however  worth  pointing  out  that  product  innovations  are 
the  most  frequent  type  of  innovation  introduced  both  by  exporters  and  by  domestic  firms  while 
process and organisational/managerial/marketing innovations lag behind.  

The  widest  gap  (in  percentage  points)  between  the  two  groups  of  companies  occurs  precisely  with 
regard  to  product  innovations.  Whilst  this  outcome  follows  from  a  multitude  of  phenomena,  it 
nevertheless highlights the fact that the introduction of new varieties of goods is extremely sensitive to 
demand  conditions,  i.e.  that  it  is  a  demand  pulled  process  (see  for  example  Guarascio,  Pianta,  and 
Bogliacino,  2016).  Indeed,  as  any  change  in  the  portfolio  of  firm’s  products  is  strictly  bound  to  the 
targeted  market,  the  large  bias  of  product  innovators  for  international  environments  represents  a 
further sign of the difficult conditions of the domestic market.  

At the same time, the strong correlation between the introduction of new products and firm’s ability to 
export  underlines  the  strategic  importance  of  this  type  of  innovative  activities  for  entering  and 
surviving fast-changing environments. In this regard, the literature has emphasised that the production 
of new varieties is a fundamental way whereby business companies may reach market niches and/or 
obtain  quasi-rents  erecting  barriers  against  their  competitors  (Dosi  et  al.,  1990;  Coad,  2009).  In 
addition,  the  permanence  on  a  specific  market  may  also  help  the  firm  to  learn  from  the  tastes  and 
needs of its customers as well as to get in touch with new technologies, spurring the introduction of 
new  products  and,  thus,  reinforcing  the  relationship  between  these  two  phenomena  (learning  by 
exporting). 

In  terms  of  process  and  organisational/managerial/marketing  innovations,  the  differences  in  the 
behaviours of exporters and non-exporting firms are less strong. Indeed, whilst the former introduces 
new  production  methods  and  new  management  practices  more  frequently  than  the  latter  do,  the 
propensities  to  innovate  of  the  two  groups  are  closer  than  what  happens  in  the  case  of  product 
innovation. This outcome is both due to lower shares of exporters undertaking these activities as well 
as  to  higher  percentages  of  domestic  businesses  changing  processes  and  practices  to  cope  with  the 
fallouts  of  the  crisis.  As  a  matter  of  fact,  among  large  and  medium  firms  that  remained  on  a 
local/national scale, the number of organisational and (to a certain degree) process innovators outstrip 
the number of companies that introduced new products. As such, this evidence suggests that process 
and organisational innovations may be employed either to compete on foreign markets or to survive 
within national borders.  

A  second  perspective  to  study  the  innovation-export  nexus  focuses  on  the  procedures  set  up  by  an 
organisation to improve its knowledge base. Indeed, the high degree of competitiveness characterising 
the  international  environments  require  business  activities  not  only  to  renew  their  range  of  products, 
production  processes  and  organisational  routines,  but also  to  manage  and  process  a  great  amount  of 
information  in  order  to  be  able  to  create  new  varieties  of  innovations.  These  activities  may  be  both 
formal  and  informal  depending  on  whether  the  innovative  effort  translates  into  dedicated  R&D 
routines, or it is carried out during other stages of the production process, as it is the case, for example, 

3 In the MET questionnaire the organizational, managerial and marketing innovations are grouped together. Although these 
categories refer to different aspects of firm’s organization, they represent three types of innovation that, at least in principle, 
do not require any investment to be carried out. Under this perspective, then, they can be treated as three similar phenomena.  

12 

 
                                                            
 
of  learning  by  doing  phenomena  or  of  patent  acquisitions  (Nelson  and  Winter,  1982).  Whilst  both 
types play an important role for firm’s technological advancements, codified R&D activities are often 
regarded as potentially more productive. Moreover, as already mentioned in the background scenario, 
the lack of a sufficient level of effort devoted to these procedures has been often pointed out as one of 
the major weakness of the Italian industrial system. 

To this extent, Figure 2.4 reports the MET estimates on the diffusion of R&D among exporters and 
non-exporting firms and on the amount of resources invested in these projects by these two groups. In 
particular,  the  height  of  the  blue  and  yellow  bars  indicates  the  percentage  of  business  activities  that 
carried  out  a  research  and  development  operation  during  the  reference  period  of  the  corresponding 
MET wave, whereas the red squares indicate the average R&D expenditure, expressed in terms of firm 
total turnover, during the year the survey was conducted. The data are drawn from the pooled MET 
dataset in order to embrace the entire span of the crisis (2008-2015), and the information reported in 
the chart is detailed by size class. 

As  for  the  innovation  output  variables,  the  ability  of  reaching  foreign  markets  and  the  tendency  of 
investing in research and development activities are strongly positively correlated. As a matter of fact 
the percentage of exporters engaged in R&D activities is five times greater than the one referred to the 
domestically  oriented  businesses  (24 %  versus  5 %).  The  propensity  of  exporters  of  being  more 
frequently  active  in  R&D  projects  holds  even  within  each  size  class,  even  though  the  extent  of  this 
difference increases along with the number of employees. In line with what was observed before, this 
evidence may be the result of two different dynamics. On the one hand, smaller exporters face higher 
difficulties  in  undertaking research  and  development  activities  due  to  the high costs  they  entail  and, 
thus,  many  of  them  may  fail  to  start  these  projects.  On  the  other  hand,  among  non-exporting 
companies, the incentive to devote resources to these operations does not increase with size as fast as 
among  their  exporting  counterparts.  As  a  result,  despite  the  relatively  large  size,  many  domestic 
companies do not undertake formal R&D activities. Once more the explanations for this evidence are 
manifold,  ranging  from  the  drop  of  the  domestic  aggregate  demand  to  the  lack  of  the  necessary 
resources to start a new research activity. 

In  accordance  with  these  interpretations,  the  ability  to  carry  out  research  projects  seems  to  be  only 
partially related to the size of the organisation. In fact, the share of exporters engaged in R&D within 
each  class  outstrips  not  only  the  corresponding  share  of  non-exporting  companies,  but  also  the  one 
referred  to  the  group  of  domestic  firms  falling  within  the  subsequent  class.  For  example:  small 
exporters  carry  out  R&D  activities  more  frequently  than  small  and  medium  domestic  firms  (31 %, 
11 % and 18 % respectively). 

Similar  results  can  be  detected  when  focusing  on  the  amount  of  resources  devoted  to  the  research 
activities rather than to their diffusion. Indeed, on average, business companies that sell (part of) their 
products  on  foreign  markets  invest  a  greater  share  of  their  revenues  in  R&D  projects  than  non-
exporting firms (1.52 % versus 0.44 %). In line with the evolutionary perspective (Dosi et al., 2015), 
then,  highly  competitive  organisations  are  associated  with  higher  efforts  to  win  the  technological 
competition  on  international  markets.  Moreover,  this  evidence  holds  true  not  only  within  each  size 
class, but also between each group of exporters and the group of non-exporting firms falling within the 
subsequent class. 

13 

 
Figure 2.4 R&D activity by exporting status and size class 

Note: Data refer to firms belonging to industrial sectors. The graph reports the diffusion of R&D activities and the 
effort in terms of R&D expenditure over total turnover among manufacturing firms broken down by size class and 
export status. Blue bars represent the share of business activities undertaking research and development 
activities (left axis), whereas red squares indicate the average weight of R&D expenditure on total revenue (right 
axis). 

Source: MET sample survey, waves from 2008 to 2015 (pooled dataset). 

The  unquestioned  importance  of  dynamic  strategies,  i.e.,  innovation,  R&D,  and  investment,  which 
needs to be further investigated at the econometric level, played an even more critical role in the light 
of the deep changes in the economic scenario characterising the time span under consideration. Italy 
has been strongly hit by the crisis and the Great Recession had a deep repercussion on the evolution of 
the population of firms as well as their business strategies.   

During the second phase of the crisis Italy witnessed a diverging trend between domestic and foreign 
demand. While  many European peers  started to recover from the fallout of the financial  crisis, Italy 
continued  to  struggle  in  the  face  of  a  dwindling  internal  demand.  Indeed,  the  outbreak  of  this  new 
downturn dampened the mild rebound started in the wake of 2009. As a result, after almost 10 years, 
the  overall  economic  performance  has  not  recovered  yet  and  the  aggregate  value  added  still  lags 
behind its pre-crisis levels, despite a tepid recovery displayed since 2015. 

The significant drop of domestic demand pushed an increasing number of firms to look abroad for new 
survival opportunities, especially micro-sized firms, leading to a sizable growth of both extensive and 
intensive margins of export.4 As a result, the long-lasting decline of Italian export shares stopped in 
2010. The value of Italian exports experienced a positive growth, which was roughly in line with the 
one for Germany, and even higher than other relevant peers such as France, UK, the Netherlands, and 
Belgium.5  

Nevertheless,  many  of  the  newly-internationalised  companies  turned  out  to  be  weak  performers 
(unstructured  and  not  competitive  enough)  and  unable  to  persistently  sell  on  foreign  markets.  Once 

4 ISTAT census data show that, between 2011 and 2014, the number of exporters went through a mild increase (+1.8 % 
within the entire population and +0.04 % within manufacturing sectors), while the number of business activities selling their 
products and/or services within national borders experienced a significant reduction (about 4.8 % for the whole economy and 
8.5 % among manufacturing sectors). 
5 This trend has been positively affected both by the geographical composition of the Italian export markets and by the 
favourable world demand for products of the Italian industrial specialization. 

14 

 
                                                            
 
again,  the  ability  to  succeed  on  the  international  markets  was  strictly  connected  to  the  degree  of 
international complexity entailing, among others, a higher innovative propensity. 

In the end, we can say that the crisis has acted on firms’ innovative activities in different ways. First of 
all, the uncertainty generated by the turmoil on domestic and international markets and the shortage of 
resources  to  whom  companies  could  access  dampened  both  the  propensity  to  innovate  and  the 
propensity  to  improve  technological  knowledge  by  means  of  codified  R&D  activities.  This  is 
especially true for the introduction of new varieties of products entailing a disproportionally high risk 
that could potentially undermine firms’ probability of survival. During the second phase of the crisis, 
however, the prolonged lack of an adequate domestic demand influenced companies’ expectations by 
spurring a larger and larger share of them to act proactively against the economic cycle. These factors 
may  have  generated  a  rebound  of  both  innovation  and  R&D,  as  shown  in  Figure  2.5.  This  general 
pattern  is  not  bound  to  a  specific  form  of  innovation  but  is  similar,  albeit  not  identical,  in  product, 
process, and organisational/managerial/marketing innovators.  

An additional effect of the crisis episode concerns the significant changes in firms’ mode to carry out 
innovative activities. During the 2008-2015 period, the share of innovators undertaking R&D projects 
have been steadily increasing, from 20% to 40% (Figure 2.5); this evidence is consistent across size 
classes and not isolated in a specific group of companies. In other words, business activities started to 
take into account that innovations require the support of codified knowledge in order to constitute a 
competitive  advantage  over  rivals.  Indeed,  this  evidence  points  at  a  deep  change  in  companies’ 
behaviours that were traditionally based on innovation modes relying on informal research activities, 
such as learning-by-doing.  

Figure 2.5 Types of innovators by exporting status 

Note: Data refer to firms belonging to industrial sectors. 
‘Innovators’ include all the business activities that stated of 
having introduced at least one type of innovation during 
the period of reference of the MET questionnaire. Types of 
innovation encompass: new products, new processes, new 
organisational, marketing and managerial practices. 

Source: MET sample survey, waves from 2008 to 2015 
(pooled dataset). 

Overall,  descriptive  statistics  showed  that,  whilst  size  and  productivity  play  a  key  role  for  external 
competitiveness,  there  are  also  other  fundamental  aspects  that  triggered  superior  performances.  In 
particular,  a  non-negligible  part  of  heterogeneity  stems  from  firms’  strategic  profiles,  technological 

15 

 
 
capabilities  and  ‘proactive’  behaviours,  especially  innovative  activities.  Paired  with  a  general 
improvement  in  Italy’s  external  performance,  the  sovereign  debt  crisis  came  with  a  decreasing 
diffusion  of  the  “traditional”  Italian  model  characterised  by  incremental  innovations  without  R&D 
activities.  Moreover,  after  an  initial  collapse  in  the  share  of  innovative  firms,  the  Italian  industrial 
system has been witnessing a gradual rebound in the diffusion of proactive firms. It is then natural to 
examine  whether  there  is  a  causal  nexus  linking  firms’  dynamic  strategies  and  export  performance 
behind these correlations.  

16 

 
 
 
3.  RELATED LITERATURE  

This section provides a thorough review of the academic literature on the patterns and determinants of 
firm-level competitiveness, with a specific focus on Italian firms. As a starting point, we acknowledge 
the  need  of  framing  the  notion  of  competitiveness  as  a  multidimensional  concept.  First, 
competitiveness has different meanings depending on whether it is considered at the micro, meso, and 
macro  levels.  Even  if  the  main  focus  is  on  micro-level  analyses,  we  need  to  account  for  the 
interdependency of firm-level competitiveness with sectoral and national characteristics and priorities. 
Second,  one  needs  to  distinguish  between  the  contextual  enablers  of  competitiveness,  i.e.  quality  of 
the  structural  conditions  underlying 
institutions,  business  environment,  and 
competitiveness, that can be measured in terms of innovative capacities, technological conditions, and 
productivity;  as  well  as  the  competitive  performance,  expressed  in  terms  of  exports,  FDIs,  market 
shares, and other indicators. Third, whichever the level of analysis, competitiveness issues are largely 
shaped  by  the  way  global  value  chains  (GVCs)  are  organised  and  governed.  All  these  facets  of 
competitiveness are addressed reviewing theoretical, empirical, and policy oriented contributions. 
With  specific  reference  to  Italy,  we  review  an  extensive  empirical  literature  that  has  investigated 
Italy's (long lasting) weak productivity dynamics (see, for example, Codogno, 2009). In addition, we 
provide  an  overview  of  contributions  focusing  on  the  heterogeneity  of  firms’  performance  in  Italy 
(Tiffin,  2014).  Finally,  we  take  into  account  the  importance  of  some  specific  features  of  the  Italian 
firms  such  as  corporate  governance  and  ownership  structure,  family  management,  and  managerial 
characteristics.  

infrastructures; 

2.1  ANALYSING COMPETITIVENESS AT THE FIRM-LEVEL: THE ITALIAN CASE  

What are the features of firms that successfully compete in international markets? To what extent do 
they  contribute  to  productivity  and  employment?  What  are  the  policies  that  can  improve  a  nation’s 
foreign  trade  performance?  These  are  some  of  the  questions  addressed  empirically  by  authors  as 
Ottaviano and Mayer (2007), Barba Navaretti et al. (2010) and Altomonte et al. (2011, 2012). In their 
work, Altomonte et al. (2011, p. 2) provide a definition of firm-level competitiveness: ‘…we define 
competitiveness as the ability of firms in a given country – not of the country itself – to mobilise and 
efficiently  employ  (also  beyond  the  country’s  borders)  the  productive  resources  required  to  offer 
goods  and  services.  The  factors  affecting  this  ability  range  from  firm-specific  variables  (such  as  the 
sector  of  activity,  size,  technology  and  so  on)  to  macro/institutional  ones  (e.g.,  price/cost  structure, 
investment environment and so on)’.  

In what follows we review the contributions that have tried to answer to the abovementioned questions 
focusing  on  Italian  firms’  competitiveness.  Since  the  mid-nineties,  the  Italian  productivity  trend  has 
been  sluggish  if  not  dramatically  weak  (Borin  and  Mancini,  2016;  Calligaris  et  al.,  2016).  This  is 
particularly  true  if  the  Italian  dynamics  is  compared  with  the  ones  of  its  main  competitors  such  as 
Germany.  In  this  respect,  stylised  facts  show  that,  in  Italy,  a  moderately  sustained  labour  market 
performance has come together with a weak real GDP growth. It emerged how a robust contribution of 
labour  utilisation  to  GDP  growth  has  been  more  than  offset  by  a  reduction  in  the  contribution  from 
labour  productivity,  resulting  in  weak  overall  GDP  growth.  Such  persistently  poor  dynamics  of 
productivity  has  been  analysed  by  a  large  number  of  contributions  aimed  at  identifying  the  drivers 
laying behind it (for a review, see Codogno, 2009 and Calligaris et al. 2016). In particular, efforts have 
been  put  forth  to  understand  to  what  extent  these  weaknesses  –  detected  at  the  aggregate  level  - 
negatively affect international performance when the micro level is accounted for.  

17 

 
In a recent study, Calligaris et al. (2016) pose a major emphasis on resource misallocation and on its 
effect on aggregate productivity dynamics. What emerges is a clear slowdown in Italy’s TFP dynamics 
since  the  middle  of  the  1990’s,  whereas  TFP  continued  to  grow  in  France  and  Germany  until  the 
global financial crisis. One of the factors which is connected to such productivity slowdowns has to do 
with a set of reforms undertaken in Italy form the mid-1990s onwards. In particular, the authors detect 
a relationship between such reform intervention and the resource misallocation put at the basis of the 
weak  TFP  performance.  Calligaris  et  al  (2016)  point  to  the  large  set  of  privatisations  –  directed  at 
privatising  former  state-owned  firms.  This  process  may  have  shifted  resources  from  industrial  or 
services activities with a high level of productivity to services characterised by low competition, high 
rents,  and  low  productivity.  Another  set  of  reforms  on  which  Calligaris  et  al  (2016)  point  their 
attention  are  the  intervention  that  aimed  at  increasing  flexibility  of  the  Italian  labour  market.  The 
authors  note  that,  in  Italy,  the  reform  has  created  a  ‘dual’  labour  market  where  some  workers  are 
highly  protected  while  others  are  highly  ‘flexible’.  Such  segmentation  may  eventually  lead  to  more 
labour misallocation and to a de-anchoring of wages from labour productivity (Manasse and Manfredi, 
2014).  

An additional element potentially driving the Italian weak competitive performance regards the poor 
market  selection  dynamics6.  That  is,  market  mechanisms  expected  to  kick  out  firms  when 
characterised by poor productivity performance seems to be not particularly effective in Italy (Landini, 
2016). Such poor market selection is commonly related to rigidities ensuring competitive rents - i.e., 
market  advantages  due  to  idiosyncrasies  of  the  firm’s  demand.  Comparing  the  dynamics  of  market 
selection during the Great recession in the EU, Landini (2016) finds that such dynamics is weakest in 
countries  –  i.e.  such  as  Italy  –  more  severely  hit  by  the  crisis,  thus  suggesting  that  policy  makers 
should  devote  more  attention  -  beside  measures  aimed  at  reducing  rigidities  in  product  market  -  to 
policies  that  foster  firm  competition  and  eliminate  the  sources  of  competitive  rents  (transportation 
costs or institutional elements such as long-term buyer-supplier ties7).  

Besides  a  poor  productivity  performance  registered  at  the  aggregate  level,  Italy  seems  to  be 
characterised  by  a  relevant  degree  of  heterogeneity  in  terms  of  competitiveness  and  international 
performance.  Particularly,  Italy  reveals  sustained  export  dynamics  during  the  last  two  decades,  a 
dynamic continuing even after the 2008 crisis (Tiffin, 2014). In this respect, Italy seems to be affected 
by  dualism:  on  the  one  hand,  firms  characterised  by  weak  productivity  performances  and  mainly 
relying  on  domestic  demand;  on  the  other,  internationalised  firms  showing  remarkable  competitive 
performances  and  ability  to  increase  their  market  shares  (on  these  topics,  see  Castellani  and  Zanfei, 
2007; D’Aurizio e Cristadoro, 2015; Cozza and Zanfei 2014; Bogliacino et al. 2016). 

In  acknowledging  the  existence  of  such  heterogeneity,  a  growing  number  of  studies  explored  the 
characteristics  of  Italian  exporters.  These  efforts  are  aimed  at  identifying  elements  explaining  the 
international  success  of  such  (small)  cluster  of  Italian  firms.  A  particular  emphasis  is  given  to:  the 
relative  importance  of  price  and  non-price  competitiveness  factors,  such  as  product  and  process 
innovation,  products  quality,  and  complexity  (Basile,  2001;  Castellani  and  Giovannetti,  2010; 
Bogliacino et al. 2016); the role of firm size, ownership structure and management strategies (Borin 
and Mancini, 2016); the linkages with local institutions and GVCs. 

Basile  (2001)  analyses  the  relationship  between  export  behaviour  and  innovation  capabilities 
considering three different years between 1991 and 1997. Studying both the probability for a firm to 

6 For a review of the recent empirical contributions analysing the dynamics of market selection in various economies see the 
previous sub-section. 
7 See Landini, 2016 p. 4. 

18 

                                                            
 
export  and  the  propensity  to  export  for  the  exporting  firms,  Basile  (2001)  studies  the  correlation 
between  the  latter  and  a  set  of  covariates  including  new  products,  new  processes,  labour  cost,  firm 
size, ownership characteristics, and geographical localisation of firms. The export performance of the 
set  of  Italian  firms  analysed  by  Basile  (2001)  turns  out  to  be  significantly  driven  by  product 
innovation. Contrarily, cost factors - as labour costs per unit of product - seem to play a marginal role. 
Another  set  of  contributions  explores  the  heterogeneity  of  Italian  firms’  performances  based  on  the 
evolutionary  approach  (Dosi  and  Nelson,  2010;  Dosi,  2012).  These  authors  show  that  firms' 
international  performance  is  characterised  by  persistent  heterogeneity,  and  by  cumulative  processes 
that  are  specific  to  firms  sharing  specific  characteristics  in  their  knowledge  base  and  business 
strategies  (Dosi  et  al.,  2010).  On  similar  lines,  Dosi  (2007)  explores  the  statistical  distribution  of 
Italian manufacturing firms’ performance variables. It emerges how the idiosyncratic components of 
firms  -  principally  their  innovation  efforts  -  drive  the  process  of  change  in  such  distributions. 
However, the process of market selection is confirmed to play a minor role in affecting the patterns of 
growth because differential efficiencies do not reward more successful firms in terms of growth. Using 
a large dataset on Italian manufacturing microdata produced by ISTAT, Dosi et al. (2012) find a strong 
intra-sectoral heterogeneity of firms along labour productivity and growth rates. 

Focusing on Italy, a large number of works focused on the differences between internationalised and 
domestically-oriented  firms  (see,  among  the  others,  Ferragina  and  Quintieri  (2000),  and  Castellani 
(2002)). Another stream of literature analyses heterogeneity focusing on firms performing FDIs and on 
multinationals  (Barba  Navaretti  and  Castellani,  2004;  Casaburi  et  al.,  2009;  Castellani  and  Zanfei, 
2007;  Castellani  and  Giovannetti,  2010,  Cozza  and  Zanfei,  2014).  Relying  on  a  matching  model, 
Barba  Navaretti  and  Castellani  (2004)  compare  productivity  dynamics  of  a  group  of  firms  observed 
after  their  decision  of  locating  their  production  abroad;  against  a  control  group,  which  is  not 
internationalised.  Internationalised  firms  result  to  substantially  outperform  the  control  group.  These 
results  are  supported  by  the  findings  in  Castellani  and  Zanfei  (2004)  showing  that  internationalised 
companies  perform  relatively  better  in  terms  of  both  value  added  and  TFP.  Furthermore,  Castellani 
and Giovannetti (2010) find that firms’ innovative performance is positively correlated with the degree 
of  internationalisation.  In  their  contribution,  Barba  Navaretti  et  al.  (2004)  test  whether  the  attitude 
towards  internationalisation  and  offshoring  is  one  of  the  drivers  of  better  economic  performance  of 
Italian firms. They focus on MNFs aiming at identifying the effects of investments in “cheap-labour” 
countries on firm performance – measured in terms of sales and employment growth. Implementing a 
propensity-score  matching  model,  the  authors  find  no  evidence  of  a  negative  effect  of  outward 
investments to cheap labour countries. Firms that invest in cheap labour countries seem to enhance the 
efficiency of home activities, with also positive long-term effects on output and employment growth. 
Focusing again on the impact of foreign investments on Italian  firms’ performance,  Barba Navaretti 
and Castellani (2004) find that investing abroad significantly enhances domestic performance.  

The role of labour force quality and skills as drivers of Italian firms’ competitiveness is explored by 
Castellani  and  Giovannetti  (2010).  In  their  paper,  the  authors  examine  the  relationship  between  the 
share of high-skill workers and TFP dynamics. The analysis is conducted exploiting an original dataset 
on  Italian  firms  and  distinguishing  between  R&D  workers,  workers  in  managerial  and  clerical 
occupations. According to their results, TFP differences are not only the outcome of different constant 
in  the  production  function  between  international  and  non-international  firms;  but  they  rather  reflect 
differences in the slopes of the production function. The interpretation of this result points to the fact 
that MNEs are both more capital intensive and exhibit higher returns to capital. Furthermore, they find 
that managers and capital are complements in the productivity of multinational firms. This element is 

19 

 
consistent  with  the  idea  that  multinational  firms  have  superior  organisational  capabilities  and 
managerial practices. 

From a policy perspective, the evidence reported above highlights the importance of FDI inflows and 
market  selection  as  elements  favouring  the  enlargement  of  the  cluster  of  competitive  firms.  In 
particular, the presence of MNEs can result in cooperation and knowledge spillover towards domestic 
firms.  Similarly,  a  larger  number  of  internationalised  firms  may  work  as  an  incentive  to  push  other 
firms  towards  more  intense  international  activities.  On  the  other  hand,  product  market  regulation, 
administrative and judicial efficiency may emerge as crucial factors favouring FDI inflows and MNEs’ 
investments (Ciriaci et al., 2016). In this respect, structural reforms aimed at cutting down rigidities in 
the product market or at spurring judicial efficiency may affect positively the rate of FDI inflows.  

Borin and Mancini (2016) investigate if and to what extent Italian firms’ internationalisation strategies 
affect their productivity. The authors use a propensity-score matching technique to properly identify 
the effects of internationalisation on firms’ performance. The effect appears to depend on the country 
of destination of the investment and on the industry of the firm. More specifically, MNEs investing in 
advanced economies seem to be characterised by better performance along a broad array of measures 
(turnover,  value  added,  employment  and  capital).  D’Aurizio  and  Cristadoro  (2015)  perform  a 
descriptive analysis comparing firms according to their ‘international status’. The set of Italian firms 
included  in  their  sample  are  distinguished  between  those  selling  their  products  only  domestically, 
firms exporting but not making any FDI, as well as firms both exporting and performing FDIs. The 
analysis  highlights  remarkable  heterogeneities  across  the  three  groups.  Firms  belonging  to  the 
exporters-FDI  cluster  are  characterised  by  a  relatively-stronger  performance  in  terms  of  sales,  value 
added, and employment.  Such cluster is followed by the one including exporters while firms selling 
their  products  only  domestically  show  poorer  performance  with  respect  to  all  the  considered 
indicators.   

The  international  performance  of  firms  has  been  analysed  with  respect  to  a  number  of  additional 
factors playing a significant role in shaping the Italian economic context. A branch of literature deals 
with the links between family management and international performance of Italian firms. Expanding 
on previous contributions by Corbetta (1995) and Montemerlo (2000, 2005), Cerrato and Piva (2012) 
exploit a large sample of Italian manufacturing SMEs, and show that the involvement of the owning 
family in management negatively influences export propensity. However, they also find that once the 
choice  to  go  international  has  been  made,  the  degree  of  internationalisation  of  the  firm  is  not 
significantly different with respect to the composition of the management team. An analogous exercise 
is  carried  out  in  Bannò  et  al.  (2015)  empirically  studying  a  set  of  293  Italian  MNEs.  Bannò  et  al. 
(2015)  attempt  to  answer  to  the  following  question:  does  family  management  affect  the 
internationalisation capabilities of Italian MNEs? The authors find that family management – intended 
as both ownership and management held by family members – has a negative impact on MNEs’ ability 
to compete in international markets. On the contrary, the involvement of family’s new generations as 
managers seems to have a positive effect on internationalisation. 

A  related  issue  is  the  education  of  managers  and  the  impact  the  latter  has  on  companies’ 
competitiveness  and  performance.  Focusing  on  middle  managers,  Feltrinelli  et  al.  (2015)  investigate 
through  an  econometric  model  if  training  activities  improve  managerial  practices  being,  in  turn,  a 
driver of companies’ competitive advantage. Their econometric exercise relies on a sample of Italian 
manufacturing firms observed over the 2006-2011 period and reporting information on both balance 
sheet data and internal training. Using GMM techniques, Feltrinelli et al. (2015) find robust evidence 
in  favour  of  a  positive  relationship  between  middle  managers  vocational  training  and  a  set  of 

20 

 
performance measures as ROI, ROE, and TFP. Similar findings are in Cucculelli and Micucci (2008) 
where the impact of the founder–chief executive officer (CEO)  succession is analysed. Their results 
show  that  the  maintenance  of  management  within  the  family  has  a  negative  impact  on  a  firm's 
performance.  All  these  evidences  confirm  the  findings  in  Bianco  et  al.  (2013)  in  exploring  the 
differential  sensitivity  to  uncertainty  of  family  and  non-family  firms  in  Italy.  It  emerges  that  family 
firms’  investments  are  significantly  more  sensitive  to  uncertainty  than  non-family  firms.  They 
interpret  such  evidence  as  related  to  the  greater  opacity  of  family  firms  and  to  their  higher  risk 
aversion,  rather  than  to  the  degree  of  sunk  fixed  capital  as  argued  by  the  previous  literature  on 
investment decisions. 

The relation between quality of institutions and Italian firms’ competitiveness is examined, among the 
others, in Daniele and Marani (2011) and in Lasagni et al. (2015). The aim of both contributions is to 
test the hypothesis that macro factors, particularly the quality of institutions, play a role in explaining 
the heterogeneity of performance within Italian firms. More specifically, Daniele and Marani (2011) 
test whether the presence of organised crime – an element signalling strong weaknesses and fragility 
of institutions – affects the inflow of FDIs in Italian provinces. This element is particularly relevant 
since - as it has been shown previously - foreign ownership and participation in GVCs is a key element 
driving  the  international  performance  of  firms  localised  in  Italy.  They  find  that  organised  crime  is 
negatively  and  significantly  associated  with  firm  FDIs.  This  result  holds  even  when  an  indicator  of 
financial incentives for investment is included in the model. 

Nifo  and  Vecchione  (2012)  propose  a  multidimensional  Institutional  Quality  (IQ)  indicator  merging 
governance quality indexes stemming from official sources and surveys conducted by public, private 
and  non-governmental  institutions.  Lasagni  et  al.  (2015)  exploit  this  indicator  to  empirically  test  if 
institutions’ quality contributes to explain firms’ heterogeneity in TFP. Analysing a sample of Italian 
manufacturing  companies  observed  between  1998  and  2007,  the  authors  find  that  firms  showing  a 
relatively stronger TFP dynamics are those localised in areas where the IQ displays higher levels. That 
is,  in  line  with  previous  contributions  exploring  this  relationship,  a  good  institutional  framework 
contributes to spur firms’ competitiveness.  

The  factors  analysed  above  –  i.e.,  quality  of  institutions,  education  of  managers,  and  family 
management – testify the presence of relevant country specificities affecting the performance of Italian 
firms.  In  this  respect,  policy  actions  directed  at  fostering  firms’  competitiveness  should  be  designed 
accounting  for  such  characteristics.  Concerning  the  quality  of  institutions,  administrative  efficiency 
turns out to be the major issue in Italy. The role of institutions quality in affecting competitiveness has 
been recently analysed by Giordano et al. (2015). They study the effect of public sector efficiency on 
firm  productivity  using  data  from  more  than  400.000  firms  across  Italy’s  provinces.  Public  sector 
inefficiencies turns out to significantly reduce private firms’ productivity. Remarkably, Giordano et al. 
(2015) find that if public sector efficiency rose to the frontier in all provinces firm productivity may 
therefore increase up to 22 percent while gross value added per employee costs could rise from 2 to10 
percent. For the average firm, output would expand by 3 percent. However, the presence of inefficient 
institutions  having  a  negative  effect  on  firms’  performance  is  found  to  be  largely  uneven  across 
regions and provinces. From this point of view, policy actions should focus particularly on the areas 
lagging behind in terms of both institutions’ quality and economic dynamics. Regarding the education 
of managers, an effective policy strategy to enhance the latter may be based on instruments favouring 
both  firms’  partnerships  and  internationalisation  (the  positive  effects  of  internationalisation  are 
detected  by  Bogliacino  et  al.  2016).  In  fact,  opening  up  firms’  organisation  to  international 
collaborations  and  foreign  competition  is  likely  to  stimulate  the  acquisition  of  new  managerial 

21 

 
practices.  Internationalisation,  moreover,  may  emerge  as  an  effective  tool  to  soften  the  distortions 
related to family management practices.           

The role of financial markets and credit rationing as factors affecting export performance is studied by 
Minetti and Zhu (2011). They estimate the impact the effect that credit rationing exerts on Italian firm 
exports. They show that the probability of exporting is 39% lower for rationed firms and that rationing 
reduces foreign sales by more than 38%. Moreover, it emerges that credit rationing impacts more than 
proportionally  on  foreign  sales  rather  than  on  domestic  turnover.  The  role  of  credit  rationing  is 
explored also by Caggese and Cunat (2013). They study the interaction between financing constraints 
and  export  activity.  It  turns  out  that  credit  constraints  reduce  productivity  gains  deriving  from  trade 
liberalisation, as constrained firms are less likely to enter the export market. 

A  different  stream  of  literature  focuses  on  the  linkage  between  labour  market  institutions  and 
companies’  productivity.  The  main  hypothesis  is  that  different  labour  market  institutions  can  have 
heterogeneous  effect  on  firms’  competitiveness  strategies.  Focusing  on  labour  market  flexibility 
Saltari  and  Travaglini  (2009)  and  Fana  et  al.  (2016)  connected  the  Italian  poor  productivity 
performance to the flexibilisation introduced in Italy from the late nineties onwards. These arguments 
are in line with the findings in Lucidi and Kleiknecht, (2009). Both Saltari and Travaglini (2009) and 
Fana  et  al.  (2016) point  to  the  fact  that  the  flexibilisation  of  labour  markets  may have  pushed  firms 
towards strategies of cost competitiveness rather than to gain market shares through innovation. In this 
respect, the hypothesis of a negative relationship between labour market flexibility and the diffusion of 
technological competitiveness strategies is in line with the results reported in Daveri (2006). Exploring 
a sample of Italian firms, Daveri (2006) shows that the weak propensity towards innovation is at the 
roots  of  the  poor  productivity  performance  of  these  firms.  All  in  all,  shape  and  dynamics  of  Italian 
firms' competitiveness seems to be characterised, on one side, by a significant degree of heterogeneity 
(a  cluster  of  innovative  and  internationalised  firms  capable  of  significant  performances  on  the 
international  markets  and  another  one  displaying  poor  productivity  and  relying  mostly  on  domestic 
demand).  

22 

 
 
 
 
4.  DATA 

The econometric analysis exploits the large amount of information obtained by combining 2006-2014 
balance sheet data (Crif-Cribis D&B) with the wide set of firm-specific data of the MET survey. 

This  survey  is  specifically  conceived  to  study  a  massive  amount  of  firms’  characteristics  and 
strategies, with a particular focus on their internationalisation and innovative patterns. The sampling 
scheme,  ensuring  a  very  high  degree  of  representativeness  once  coupled  with  a  large  number  of 
interviews,  is  stratified  along  three  dimensions:  sector,  geographical  region,  and  size  class.  Unlike 
many other firm-level databases, the MET survey also accounts for firms with less than ten employees 
representing the vast majority of companies within the Italian industrial system. The survey is made of 
five  waves  (2008,  2009,  2011,  2013,  and  2015)  and  covers  a  time  span  starting  right  before  the 
Lehman  collapse  until  recent  years.  The  population  of  interest  includes  the  enterprises  operating 
within either Industry (construction excluded) or Production Services sectors (38 NACE Rev.2 3 digit 
sectors),  with  a  total  number  of  interviews  of  120,000  companies,  roughly  24,000  observations  per 
wave (see Table 9.3). 

The  employed  calibration  estimators  allow  the  survey,  under  predetermined  circumstances,  to 
reproduce known population parameters as well as to recover possible sample biases following from 
the (non-random) missing responses’ distribution. The computation of the calibration estimators takes 
also  advantage  of  a  set  of  auxiliary  information  (other  than  the  one  employed  to  identify  the  strata) 
drawn  from  the  population  of  interest.  This  auxiliary  information  constitutes  a  further  binding 
constraint  the  final  sample  has  to  reproduce.  All  the  constraints  are  drawn  from  ISTAT  Italian 
Statistical Business Register (ASIA) while the calibration procedure makes use of an ad-hoc iterative 
algorithm.  As  for  the  longitudinal  part  of  the  sample,  a  specific  calibration  estimator  has  been 
computed to account for firms interviewed in two succeeding waves. 

In  addition  and  starting  from  the  wave  2009,  the  sampling  scheme  employs  Bayesian  techniques  in 
order to ensure high precision estimates on a selected range of topics that represent the core part of the 
survey (R&D, innovation and internationalisation strategies among others). These techniques draw on 
a tree-based classification model able to detect, in the preceding wave, the strata showing higher rates 
of  enterprises  undertaking  these  relevant  activities.  This  procedure  has  required  further  constraints 
leading to an oversampling of the targeted strata. Clearly, such sample bias is accounted for during the 
calibration estimator’s procedure. 

23 

 
 
 
5.  ECONOMETRIC STRATEGY 

The  econometric  strategy  aims  at  analysing  several  dimensions  of  firms’  external  competitiveness, 
ranging from probabilities of export, entrance, or exit, to international performance and productivity 
dynamics. The baseline equation tests the effect of different drivers of external competitiveness on the 
extensive m rgins of e

f exporting) according to the following specification: 

rt (i. ., probability o

xpo

a

e

Pr(cid:4666)(cid:1851)(cid:3036)(cid:3047) = 1(cid:4667) = Φ(cid:4666)(cid:2010)(cid:4593)(cid:1852)(cid:3036)(cid:3047)(cid:2879)(cid:2869) + (cid:2019)(cid:3047) + (cid:1855)(cid:3036) + (cid:2013)(cid:3036)(cid:3047)(cid:4667)							( 1 ) 

where  (cid:1851)(cid:3036)(cid:3047)  is  either  a  dummy  dependent  variable  identifying  export  activity  (Export),  or  other 
dimensions of firms’ internationalisation. Equation 1 is a standard reduced form including a rich set of 
regressors  ((cid:1852)(cid:3036)(cid:3047)(cid:2879)(cid:2869))  to  capture  structural  characteristics  (productivity,  size,  age,  capitalisation,  group 
belonging, degree of vertical integration, cost of labour), financial issues (leverage, trade credit, bank 
debt,  ROA,  sales,  cash  flow)  and  especially  firms’  behaviours  (innovativeness,  R&D,  investments, 
affiliation  to  networks,  import  propensity,  and  human  capital).  We  also  include  time  effects  ((cid:2019)(cid:3047))  to 
capture common shocks and cyclical components that vary over time. Finally, (cid:1855)(cid:3036) is a factor controlling 
for firms’ unobserved heterogeneity, also accounting for permanent industrial/geographical effects (12 
controls  for  the  firms’  belonging  industry,  20  for  region,  and  110  for  geographical  province).  More 
details on the definition of all the variables employed can be found in the Appendix. 

The common factors and industrial or geographical components may have critical effects within this 
context.  Indeed,  the  economic  cycle,  aggregate  investment,  as  well  as  fluctuations  of  industrial 
demand and the external environment (among other common, industrial, or regional factors) are likely 
to have significant impact on both the extensive and intensive margins of export. Even though these 
factors are removed (through time fixed effects and sectorial/geographical interacted time controls) to 
obtain  unbiased  estimates  useful  for  policy  indications,  they  allegedly  had  a  prominent  role  in  the 
definition of a firm’s international performance.8  

There are two main issues we need to take into account for assessing the effect of different drivers on 
firms’ external competitiveness. The first one has to do with reverse causality, whereby characteristics 
and  behaviours  do  not  foster  export  performance  but  instead  result  from  the  (a  priori)  successful 
penetration  into  new  markets.  The  second  interrelated  point  is  the  clear  self-selection  of  more 
productive and dynamic companies into international environments. Because of the lack of a natural 
experiment allowing to define a strictly exogenous set of instruments, we try to address these issues in 
several alternative ways. 

First  of  all,  we  alleviate  problems  of  reverse  causality  by  ruling  out  simultaneity  bias.  Matching 
current export status with lagged regressors partially solves reverse causation but may leave residual 
endogeneity in case of relevant unobserved heterogeneity or high persistence of Y and Z. 

Accounting for firms’ unobserved heterogeneity is extremely important to derive policy implications 
requiring  an  identification  of  causal  relationships.  In  general,  unobserved  heterogeneity  refers  to 
relevant variables (in addition to the ones under study) that are unobserved (or not measurable) but are 
correlated with the observed measures employed. If some firms’ characteristics that affect both the left 
and  right-hand  side  variables  are  omitted  because  they  are  not  observable,  the  regressors  will  be 
correlated with the error term and the regression coefficients will be biased (the direction of the bias 

8 We avoid exploring heterogeneities along industrial or geographical components as they hide unobserved shocks that may 
reflect other common characteristics across firms that are spuriously correlated with the belonging stratum. 

24 

                                                            
 
will then depend on the correlation between the omitted, dependent, and explanatory variables). For 
instance, our research aims at studying the effect of firms’ innovative strategies on their international 
propensity.  On  the  top  of  the  rich  set  of  observable  characteristics  included  in  all  regressions,  there 
will  always  be  some  factors  that  the  econometrician  cannot  measure,  such  as  the  specific  skills  or 
attitude  of  a  firm’s  manager.  Eventually,  the  latter  may  drive  both  a  broader  openness  to  the 
international markets and a higher propensity to innovate. If we neglected this component, we would 
get a positive effect of innovation on export that is simply due to unobserved heterogeneity driving the 
relationship of interest. This issue can be generalised to any unobserved factor having effects on any of 
the  variables  included  in  the  specification.  The  major  motivation  for  employing  panel  data  is  the 
ability  to  control,  at  least,  for  the  possibly  correlated,  time-invariant  heterogeneity;  that  is,  the 
longitudinal model will be purged from any unobserved and observed characteristic that is persistent 
over the considered time span. 

Dealing with unobserved heterogeneity in a binary-response framework is not trivial. On the one hand, 
standard random effects (RE) models impose unrealistic assumptions on the type of heterogeneity that 
takes place (i.e., (cid:1855)(cid:3036) must be uncorrelated with the entire set of regressors). On the other, fixed-effects 
models,  that  do  not  impose  any  hypothesis  on  (cid:1855)(cid:3036),  are  computationally  difficult  and  introduce  an 
incidental  parameter  problem  leading  to  inconsistent  estimates.  Our  strategy  is  in-between  the  two 
approaches and relies on RE-probit models augmented with the time average of each regressor (i.e., 
Mundlak-type controls). 

The standard RE-probit model on the full set of co

variates 

Pr(cid:4666)(cid:1851)(cid:3036)(cid:3047) =

1|(cid:1852) (cid:2879)(cid:2869), (cid:1855)(cid:3036)(cid:4667) = Φ

(cid:3036)(cid:3047)

(cid:4666)(cid:2010)(cid:4593)(cid:1852)(cid:3036)(cid:3047)(cid:2879)(cid:2869) + (cid:1855)(cid:3036) + (cid:2013)(cid:3036)(cid:3047)(cid:4667)						( 2 ) 

(cid:2870)(cid:4667), which implies the assumption of strict independence between firm-specific 
imposes (cid:1855)(cid:3036)|(cid:1852)(cid:3036) ∼ (cid:1840)(cid:4666)0, (cid:2026)(cid:3030)
unobserved heterogeneity ((cid:1855)(cid:3036)) and the full set of regressors. Because this unrealistic hypothesis would 
generate  biased  results  in  our  setup,  we  follow  Wooldridge  (2010)  and  relax  it  by  focusing  on  the 
effect of each variable in terms of deviations from its time average. This allows us to purge the model 
from  persistent  heterogeneity  across  firms  and  to  derive  cleaner  policy  implications.9  Moreover,  to 
further control for persistence of (cid:1851)(cid:3036)(cid:3047), we also provide results for the subset of firms with (cid:1851)(cid:3036)(cid:3047)(cid:2879)(cid:2869) = 0. 

In the unlikely case that residual heterogeneity is still affecting our findings, we run several additional 
robustness  checks.  We  account  for  the  introduction  of  unrestricted  firm  fixed  effects  through  linear 
probability (within estimators) models purging all the firms’ characteristics that are stable over time. 
Finally,  we  further  take  care  of  self-selection  by  employing  matching  techniques  (Coarsened  Exact 
Matching) to recover a subsample of companies with the same ex ante probability of export. We then 
re-estimate our baseline specification for the new subsample of balanced firms to make inference on 
the treatment effect and provide additional robustness to our results. 

The  last  econometric  issue  that  is  worth  discussing  is  the  possibility  of  correlated  shocks.  In  other 
words, if there is a polarisation of companies within specific industries or geographical areas, the set 
of parameters (cid:2010)(cid:4593) may be affected by the firms’ reaction to unobserved shocks and no longer reflect the 

9  Our  estimator  can  be  viewed  as  a  Mundlak  (1978)  version  of  the  Chamberlain  (1980)’s  assumption  on  the  correlation 
between  (cid:1855)(cid:3036)  and  Z,  which  requires  the  milder  hypothesis  of:  (cid:1855)(cid:3036)|(cid:1852)(cid:3036) ∼ (cid:1840)(cid:4666)(cid:2032) + (cid:2016)(cid:4593)(cid:1852)̅(cid:3036), (cid:2026)(cid:3028)
(cid:2870)  is  the  variance  of  (cid:1853)(cid:3036)  in 
(cid:1855)(cid:3036) = (cid:2032) + (cid:2016)(cid:4593)(cid:1852)̅(cid:3036) + (cid:1853)(cid:3036), and (cid:1852)̅(cid:3036) is the time-average of (cid:1852)(cid:3036)(cid:3047)(cid:2879)(cid:2869) (see Brancati et al., 2017). This approach is equivalent to a fixed-
effects  model  in  which  the  heterogeneity  is  projected  on  the  time-mean  of  the  regressors  ((cid:1852)̅(cid:3036)),  allowing  to  write  the  latent 
∗ = (cid:2032) + (cid:2010)(cid:4593)(cid:1852)(cid:3036)(cid:3047)(cid:2879)(cid:2869) + (cid:2016)(cid:4593)(cid:1852)̅(cid:3036) + (cid:1853)(cid:3036) + (cid:1857)(cid:3036)(cid:3047),  with  (cid:1857)(cid:3036)(cid:3047) ∼ (cid:1840)(cid:4666)0,1(cid:4667).  As  usual,  the  estimator  hinges  on  the  exogeneity  of 
variable  as  (cid:1851)(cid:3036)(cid:3047)
(cid:1852)(cid:3036)(cid:3047)(cid:2879)(cid:2869) conditional on (cid:1855)(cid:3036). We verify this hypothesis by adding the vector (cid:1852)(cid:3036)(cid:3047) to our specification and testing the significance of 
its estimates (as proposed by Wooldridge, 2010). The test never rejects the null, thus providing at least some justification for 
the strict exogeneity assumption. 

(cid:2870)(cid:4667),  where  (cid:2026)(cid:3028)

25 

 
                                                            
 
impact of some specific drivers. To tackle this concern, we enrich our baseline specifications with an 
extensive  set  of  time-fixed  effects  specific  for  firm  belonging  industry  (12  macro  industries  ×  5 
periods), region (20 ×	5), and geographical province (110 × 5). This broad set of fixed effects permits 
us to control for most of the unobserved shocks induced by the Great Recession. 

Further  analyses  are  largely  in  line  with  the  baseline  specification.  We  take  advantage  of  within 
estimators and quantile regression models (with Mundlak correction) for the intensive margins, as well 
as linear probability models with firm and time fixed effects.10 

10 In RE-Tobit models the dependent variable is allowed to be censored. In our setup, the share of turnover from exported 
products is bounded in [0,100]. Alternatively, we employ within estimator with firm and time fixed effects accounting for 
continuous  dependent  variables. If  the latter is  applied to  a  dummy  variable, a  linear  probability model  is  estimated. Even 
though  such  a  model  (born  for  continuous  measures)  does  not  impose  any  constraint  on  the  admissible  values  of  the 
dependent  variable,  it  has  the  advantage  of  avoiding  any  restriction  on  the  type  of  fixed  effects  that  are  included.  Thus,  it 
provides  useful  indications  on  the  robustness  of  the  results.  The  estimates  from  linear  probability  models  can  be  directly 
interpreted in terms of marginal effects (i.e., any estimated coefficient (cid:2010)(cid:4632) represents the percentage change in the probability 
of Y=1 as a result of a one-unit increase in the value of the regressor).  

26 

 
 
                                                            
 
6.  RESULTS 

6.1 

  EXTENSIVE MARGINS 

This  section  presents  the  results  of  the  analysis  on  firms’  probability  of  export  (i.e.,  extensive 
margins).  Table  9.4  shows  some  preliminary  evidence  obtained  through  pooled  probit  estimators 
(estimated coefficients with robust standard errors are reported). Even though this approach neglects 
firms’ unobserved heterogeneity and reverse causality issues, it  may still provide useful guidance in 
the  establishment  of  clean  correlations  between  exporting  status  and  firms’  characteristics  and 
behaviours. 

There are several issues that are worth mentioning. First, more productive companies are found to be 
largely associated with higher probabilities of export: a one-standard deviation increase in productivity 
is associated to a 1.5% higher probability of export. This evidence is broadly in line with the dominant 
literature on firms’ internationalisation. However, given the type of econometric analysis performed, 
that neglects persistent heterogeneity across firms, little can be said on whether this effect is linked to 
reverse causality, the ex-ante self-selection of more productive companies into international markets, 
or even learning by exporting phenomena. The role of productivity on firms’ probability of export will 
be further explored in the following analyses. 

Second, structural characteristics play a critical role for firms’ exporting status. In particular, firm size 
and  the  belonging  to  corporate  groups  are  associated  with  increases  in  a  company’s  probability  of 
exporting.  On  the  other  hand,  firms’  age  is  found  to  be  largely  insignificant,  and  the  coefficients  of 
firms’ degree of capitalisation is very unstable across specifications. 

A  prominent  effect  is  found  for  firms’  strategic  behaviours.  Being  an  importer  of  intermediate 
products is largely associated to a higher probability of exporting (33% probability). This result is in 
line  with  the  extant  literature  emphasising  how  import  policies  may  affect  aggregate  productivity, 
resource allocation, and industry export activity along both the extensive and intensive margins (see 
for  instance  Amiti  and  Konings,  2005;  Halpern,  Koren,  and  Szeidl,  2015;  Kasahara  and  Lapham, 
2013; Kasahara and Rodrigue, 2008). Importantly, the introduction of innovations, the involvement in 
R&D projects, as well as undertaking new investments, are strongly correlated with export strategies. 
At  this  stage  though,  the  estimates  do  not  allow  to  make  inference  on  the  direction  of  causality 
underlying these relationships. 

As expected, past performances are positively related to firms’ exporting status, but, once again, this 
analysis  does  not  shed  light  on  whether  the  direction  of  causality  is  reversed  (i.e.,  the  access  to 
international markets allowed firms to experience higher sales growth). 

Consistently  with  the  recent  trends  in  international  trade  that  brought  the  surge  of  Global  Value 
Chains, increasing fragmentation of the production process (i.e., low degree of vertical integration) is 
associated with a positive probability of internationalisation, allegedly capturing a higher propensity to 
participate in the production on global scale. Furthermore, large weights of labour cost (on total costs 
of production) are negatively affecting a firm’s probability of export, possibly capturing a component 
of cost competitiveness (once controlling for the level of capital intensity). 

Financial issues play a critical role too. Highly leveraged companies, rationed out of the credit market, 
have  a  lower  capacity  of  penetrating  international  markets,  possibly  because  of  the  difficulties  in 

27 

 
tapping external funds. In line with this finding, a firm’s ability to access bank debt and trade debt is 
positively associated to export activity. Finally, the quality of human capital (defined as the share of 
graduated employees) fosters firms’ international propensity, while the existence of relevant domestic 
networks seems to be negatively related to firm export status. The last finding  may be linked to the 
lower demand shock experienced by domestic companies involved in stable networks. 

These  results,  are  stable  across  a  number  of  robustness  checks  controlling  for  more  detailed 
geographical components (province fixed effects), for correlated shocks at the region and/or industry 
levels through the introduction of industry*time and region*time fixed effects. Finally, the estimates 
are  virtually  unchanged  if  we  focus  on  the  subsample  of  companies  operating  in  the  manufacturing 
sector only (in untabulated robustness checks).  

The  sample  size  varies  considerably  across  columns  because  additional  variables  require  detailed 
information  in  the  balance  sheet  data  that  sometimes  is  not  reported.  This  is  a  common  problem  in 
every econometric estimation that has to deal with the trade-off between sample size and problems of 
model  misspecification.  By  adding  more  and  more  variables,  the  so-called  omitted-variable  bias  is 
reduced, but the price to pay is a reduced size of the sample. However, the unbiasedness of the results 
is  ensured  by  the  stability  of  the  coefficients  presented.11  Vice  versa,  little  can  be  said  on  unstable 
coefficients whose changes can be either driven by sample selection or due to omitted variable bias. 
This is why, in the following estimations we focus our discussion only on features and strategies that 
across specifications are found to have consistent effects on firms’ internationalisation. 

Since  these  results  may  be  affected  by  persistent  unobserved  heterogeneity  across  firms  (such  as 
managers’ attitudes and skills), Table 9.5 accounts for this issue by presenting RE-probit models with 
Mundlak correction (columns 1 to 4) and within estimators with firm and time fixed effects (columns 
5 to 8).  

Once purged the model from persistent heterogeneity across firms, the role of productivity in fostering 
export activity is found to be strongly reduced. This result is unchanged across the two estimators and 
is  qualitatively  similar  if  we  employ  TFP  as  an  alternative  measure  of  productivity  (Levinsohn  and 
Petrin,  2003).  We  note  that  the  comparison  of  Table  9.4  and  Table  9.5,  is  implicitly  confirming  the 
self-selection hypothesis (largely emphasised by the literature), whereby persistently more productive 
companies are self-selecting into international markets, driving the positive association in Table 9.4. 
However,  once  this  persistent  heterogeneity  is  accounted  for,  the  effect  of  productivity  tends  to  be 
small or even insignificant. Because columns 1 to 4 of Table 9.5 include the Mundlak correction, the 
estimates have to be interpreted as effects of changes in each measure from its time average. In other 
words,  the  model  is  purged  from  any  persistent  characteristic,  including  any  high/low  level  of 
productivity that is stable  over time. Once accounted for this issue, and directly controlled for other 
structural and strategic determinants, the variation in firms’ productivity (on average) is not sufficient 
to explain the change in their exporting status. On the one hand, this is because productivity tends to 
be quite sticky over time. On the other, most of the variation of productivity in the short run is caused 
by  the  strategies  undertaken  by  the  company  and  its  operating  environment,  which  are  added  as 
separate regressors in our specification as they represent the drivers of productivity and implementable 
objectives for policy. The insignificance of the estimates implies that the remaining component does 
not have enough variation to induce any change in the international attitude. 

11 Indeed, the progressive insertion of additional variables, and the resulting loss of observations, does not pose 
any threat as long as the key coefficients of interests keep having the same sign, statistical significance, and 
similar magnitudes. This indicates that the selection imposed on the model is close to be orthogonal to the main 
regressors. 

28 

                                                            
 
This result is largely confirmed by the linear probability models in columns 5 to 8 (especially in the 
richest specifications), that do not impose any restriction on the type of unobserved heterogeneity that 
takes place. This finding does not have to be interpreted as an evidence that productivity does not play 
any  role  in  firms’  exporting  status.  The  significant  effect  documented  in  Table  9.4  suggests  a  clear 
positive  association  between  productivity  and  export.  The  insignificance  portrayed  in  Table  9.5  is 
exclusively indicating that most of the effect is due to a self-selection of persistently more productive 
companies  into  the  international  environment  rather  than  variations  in  the  level  of  productivity  (not 
driven by dynamic strategies) causing a change in the export propensity. 

This  interpretation  is  confirmed  in  Table  9.6  where  we  model  a  firm’s  entry  in  the  international 
markets by focusing on the sample of previously non exporting companies. This result suggests that 
the  productivity  premium  of  internationalised  companies  is  mainly  confined  to  firms  that  are 
persistently  operating  in  the  international  markets.  Vice  versa,  because  of  the  stickiness  of  the 
productivity dynamics, changes in productivity do not seem to be capable of explaining a firm’s entry 
choice once persistent heterogeneity is purged by the model. 

Noticeably, firms’ dynamic strategies keep playing a critical role in their international propensity. The 
introduction of innovations, the involvement in R&D projects, and firms’ investments have a positive, 
large,  and  significant  effect  on  the  probability  of  export  even  if  the  persistent  heterogeneity  across 
firms  is  properly  accounted  for  in  Table  9.5  (roughly  2%  higher  probability  for  the  introduction  of 
innovations  and  investments).  This  is  confirmed  in  Table  9.6,  showing  that  a  firm’s  innovativeness 
significantly increases its probability to start exporting. The (apparently small) coefficient of R&D has 
to  be  interpreted  as  the  effect  of  a  1%  increase  in  the  expenditure  in  R&D.  For  instance,  a  firm 
increasing R&D expenditure up to 10% of its total sales has a 3% higher probability of exporting. 
Other estimates are largely insignificant, suggesting that the results of Table 9.4 were mostly driven by 
persistent  differences  or  sticky  characteristics  that  do  not  vary  over  time  and  may  be  spuriously 
associated  with  unobserved  factors.  This  is  the  case  for  profitability  measures  and  indicators  of 
vertical  integration  that  tend  to  be  quite  smoothed  across  consecutive  years.  On  the  other  hand, 
financial  issues  (also  quite  persistent)  are  likely  to  have  indirect  effects  through  firms’  investment 
strategies; this issue will be further explored in the upcoming analyses. Finally, the insignificance of 
the variable human capital is also likely to come from the explicit control for R&D, which is capturing 
most of its positive effect. 

Table  9.7  explores  heterogeneity  along  the  (maximum)  geographical  extension  of  the  destination 
market. To this end, we run two distinct regressions, one for a firm’s probability of exporting within 
the EU area (Export(EU)), and the other for exporting beyond the Euro zone. The two sets of results 
confirm the critical role played by firms’ strategy in affecting their exporting status. Interestingly, the 
effect  of  innovation  is  found  to  be  way  more  important  for  farer  markets,  with  a  magnitude  that  is 
roughly twice as much as the impact for export activity within the Euro zone. On the other hand, the 
effect of R&D seems to be somewhat reduced.  

Given the prominent role played by firms’ dynamic strategies, in Table 9.8 we examine whether, on 
the top of their direct impact, the upgrading/downgrading paths of a firm have additional effects on its 
international  attitude.  In  other  words,  two  identical  companies  having  the  same  set  of  dynamic 
strategies, may differ in terms of international propensity depending on the pattern undertaken in the 
recent past (i.e., stable, increasing, or decreasing number of dynamic activities). To this end, we enrich 
the  baseline  specification  of  Table  9.5  with  the  change  in  the  number  of  dynamic  strategies 
(innovation,  R&D,  and  investment)  between  t-2  and  t-1.  This  measure  (Upgrading)  takes  positive 
values  if  the  company  incremented  its  array  of  dynamic  behaviours,  is  zero  if  the  firm  experienced 

29 

 
constant  strategies,  and  takes  negative  values  in  case  of  a  reduction  in  the  number  of  dynamic 
activities (i.e., downgrading).  

Results clearly show that, besides the positive effects of innovation, R&D, and investment, the path of 
dynamic strategies undertaken in the recent past has significant effects. In particular, integrating more 
and  more  strategies  is  associated  with  a  positive  premium  leading  to  a  higher  probability  of  export 
(ranging between 4% and 8%). Furthermore, the same coefficient suggests that a reduction in the array 
of  dynamic  behaviours  tends  to  lower  firms’  likelihood  of  exporting.  We  note  that  these  results 
highlight disproportionate benefits for firms that in the past presented low or reduced innovativeness. 
For this set of companies, the introduction of one or more dynamic activities would allow to (partially) 
fill the gap with the group of most dynamic firms. 

Table  9.9  replicates  the  analysis  in  Table  9.5  by  providing  a  breakdown  for  product,  process,  and 
organisational/managerial innovations to explore some heterogeneities along the type of improvement 
introduced.  As  expected,  product  innovations  dominate  other  forms  of  innovativeness  (process  or 
organisational/managerial  innovations).  This  strict  dominance  is  partly  related  to  the  fact  that  new 
products are the main form of innovation which is not reflected in the level of productivity (which we 
control  for).  Importantly,  the  comparison  of  column  1  and  3  (or  2  and  4),  shows  that  the  impact  of 
product innovations is especially relevant for new exporters (columns 3 and 4), with a magnitude that 
is roughly twice the impact on the entire sample (4% increase in the probability to start exporting). 

However, besides its direct effect on export, a firm’s innovativeness may also have indirect effects by 
boosting  firms’  productivity,  especially  process  and  organisational  innovations.  To  explore  this 
additional  channel  Table  9.10  shows  this  indirect  effect  by  testing  the  role  of  innovations  on  firm 
productivity growth. Columns 1 and 2 present the results from within estimators with firm and time 
fixed  effects.  As  expected,  higher  innovativeness  is  linked  to  more  pronounced  productivity  growth 
(+4%  growth  rate)  that  may,  in  turn,  further  foster  a  firm’s  probability  of  export.  Interestingly,  this 
indirect  effect  is  largely  driven  by  less  radical  forms  of  improvements  such  as  process  and 
organisational/managerial  innovations  (column  2),  while  the  introduction  of  new  products  does  not 
seem to be linked to any productivity growth (i.e., they only induce direct effects documented in Table 
9.9).  

To deal with the possible reverse causality affecting the estimates, we also take advantage of matching 
techniques  in  columns  3  and  4.  We  employ  Coarsened  Exact  Matching  to  recover  a  subsample  of 
firms with similar characteristics (size, age, geographical location, industrial features, and, especially, 
with  the  same  productivity  at  the  beginning  of  the  sample,  2008)  that  only  differ  for  the  actual 
introduction of innovations (the treatment variable). We then re-estimate the regressions in columns 1 
and 2 on the new (balanced) sample employing matching weights. Once again, firms’ innovativeness 
is found to have a positive and significant effect on their productivity growth, with a dominant role for 
process and organisational/managerial innovations (associated to a 15% higher productivity growth).  

Noticeably, import choices significantly affect productivity growth too. This evidence is largely in line 
with  the  empirical  and  theoretical  literature,  suggesting  that  productivity  gains  from  importing 
intermediates may allow some importers to start exporting, leading to a resource reallocation along the 
intensive margin (Amiti and Konings, 2005; Halpern, Korn, and Szeidl, 2015; Kasahara and Rodrigue, 
2005; Kasahara and Lapham, 2013).  

The analysis presented so far, despite providing clear indications on the role of dynamic strategies for 
firms’ internationalisation patterns, misses to acknowledge two important issues. The first one has to 
do with the simultaneity of the innovation and export decisions. While we always match current export 

30 

 
activity with lagged innovative choices, there is still the possibility of significant correlation between 
the two equations that may invalidate the coefficients of interest. For instance, firms’ may choose to 
invest  in  innovations  to  penetrate  international  markets.  If  this  is  the  case,  the  two  choices  are 
simultaneously determined and this issue should be properly accounted for. The second point has to 
deal  with  the  identification  of  the  determinants  of  firms’  innovative  strategies,  which  are  especially 
useful to provide some policy guidance. 

To this aim, we account for the simultaneity of the phenomena by employing bivariate probit models 
(with Mundlak correction). The specification estimates firms' probability of exporting conditionally on 
its (lagged) innovative status. The model can be summarised by the following system of equations: 

(cid:1831)(cid:1876)(cid:1868)(cid:1867)(cid:1870)(cid:1872)(cid:3036)(cid:3047) = 1	(cid:1861)(cid:1858)	(cid:1831)(cid:1876)(cid:1868)(cid:1867)(cid:1870)(cid:1872)(cid:3036)(cid:3047)
(cid:4682)
(cid:1831)
(cid:1876)(cid:1868)(cid:1867)(cid:1870)(cid:1872)(cid:3036)(cid:3047)

(cid:1876)(cid:1868)(cid:1867)(cid:1870)(cid:1872)(cid:3036)(cid:3047) = 0	

(cid:1861)(cid:1858)	(cid:1831)

∗ = (cid:2016)(cid:1835)(cid:1866)(cid:1866)(cid:1867)(cid:1874)(cid:1853)(cid:1872)(cid:1861)(cid:1867)(cid:1866)(cid:3036)(cid:3047)(cid:2879)(cid:2869) + (cid:2010)(cid:2869)
(cid:3021)(cid:1850)(cid:2869)(cid:3036)(cid:3047)(cid:2879)(cid:2869) + (cid:2013)(cid:2869)(cid:3036)(cid:3047) > 0	
∗ = (cid:2016)(cid:1835)(cid:1866)(cid:1866)(cid:1867)(cid:1874)(cid:1853)(cid:1872)
(cid:3021)(cid:1850)(cid:2869)
(cid:2010)(cid:2869)
(cid:3036)(cid:3047) ≤ 0
+ (cid:2013)(cid:2869)

(cid:3036)(cid:3047)(cid:2879)(cid:2869) +

(cid:1861)(cid:1867)(cid:1866)

(cid:3036)(cid:3047)(cid:2879)(cid:2869)

(cid:1835)(cid:1866)(cid:1866)(cid:1867)(cid:1874)(cid:1853)(cid:1872)(cid:1861)(cid:1867)(cid:1866)(cid:3036)(cid:3047)(cid:2879)(cid:2869) = 1	(cid:1861)(cid:1858)	(cid:1835)(cid:1866)(cid:1866)(cid:1867)(cid:1874)(cid:1853)(cid:1872)(cid:1861)(cid:1867)(cid:1866)(cid:3036)(cid:3047)(cid:2879)(cid:2869)
(cid:4682)
= 0	(cid:1861)(cid:1858)	(cid:1835)(cid:1866)(cid:1866)(cid:1867)(cid:1874)(cid:1853)(cid:1872)(cid:1861)(cid:1867)(cid:1866)(cid:3036)(cid:3047)(cid:2879)(cid:2869)
(cid:1835)(cid:1866)(cid:1866)

(cid:1867)(cid:1874)(cid:1853)(cid:1872)(cid:1861)(cid:1867)(cid:1866)(cid:3036)(cid:3047)(cid:2879)(cid:2869)

∗

∗

= (cid:2010)(cid:2870)
= (cid:2010)(cid:2870)

(cid:3021)(cid:1850)(cid:2870)(cid:3036)(cid:3047)(cid:2879)(cid:2870) + (cid:2013)(cid:2870)(cid:3036)(cid:3047)(cid:2879)(cid:2869) > 0	
(cid:3021)(cid:1850)(cid:2870)(cid:3036)(cid:3047)(cid:2879)(cid:2870) + (cid:2013)(cid:2870)(cid:3036)(cid:3047)(cid:2879)(cid:2869) ≤ 0

∗

∗   and 
where  (cid:1831)(cid:1876)(cid:1868)(cid:1867)(cid:1870)(cid:1872)(cid:3036)(cid:3047)  and  (cid:1835)(cid:1866)(cid:1866)(cid:1867)(cid:1874)(cid:1853)(cid:1872)(cid:1861)(cid:1867)(cid:1866)(cid:3036)(cid:3047)(cid:2879)(cid:2869)  are  the  observed  (dummy)  dependent  variables,  (cid:1831)(cid:1876)(cid:1868)(cid:1867)(cid:1870)(cid:1872)(cid:3036)(cid:3047)
	are latent variables, while (cid:2013)(cid:2869)(cid:3036)(cid:3047) and (cid:2013)(cid:2870)(cid:3036)(cid:3047)(cid:2879)(cid:2869) are the two error terms, assumed to be i.i.d. 
(cid:1835)(cid:1866)(cid:1866)(cid:1867)(cid:1874)(cid:1853)(cid:1872)(cid:1861)(cid:1867)(cid:1866)(cid:3036)(cid:3047)(cid:2879)(cid:2869)
as a bivariate normal with unitary variance and correlation coefficient (cid:2025) = (cid:1855)(cid:1867)(cid:1870)(cid:1870)(cid:4666)(cid:2013)(cid:2869), (cid:2013)(cid:2870)(cid:4667). The system of 
equations allows to estimate the impact of innovativeness on export, and to jointly analyse the drivers 
of innovative strategies. (cid:1850)(cid:2869)(cid:3036)(cid:3047)(cid:2879)(cid:2869) is the standard set of regressors in Table 9.5, while (cid:1850)(cid:2870)(cid:3036)(cid:3047)(cid:2879)(cid:2870) is a vector 
of  determinants  for  innovations,  including  R&D  choices  (internal  vs  outsourcing),  structural 
characteristics  (size,  age,  capitalisation,  and  productivity),  proxies  for  demand  conditions  (past  sales 
growth),  operating  environment  (group  belonging  and  participation  in  local  networks),  as  well  as 
measures aimed at capturing firms’ financial conditions.  

Table  9.11  presents  the  main  results.  First,  even  after  accounting  for  the  simultaneity  of  the 
phenomena, and perfectly controlling for third factors that jointly affect export and innovation, firms’ 
innovative strategies are found to have strong and significant effects on their international propensity. 
In other words, the main results of the previous analyses are found to be robust and not to depend on 
simultaneity issues. 

The  analysis  on  the  determinants  of  firms’  innovativeness  present  interesting  results.  As  expected, 
structural characteristics are significantly affecting the introduction of innovations, especially in case 
of larger, younger, and more productive companies. Similarly, past sales growth, which can be thought 
of  as  a  proxy  for  demand  trends,  is  positively  associated  to  firms’  innovativeness.  The  existence  of 
R&D  projects  is  clearly  correlated  to  huge  increases  in  the  probability  of  introducing  innovations. 
Importantly,  this  effect  is  not  limited  to  the  investment  in  R&D  performed  within  the  firm  (internal 
R&D), but extends to firms outsourcing R&D activities, even though with smaller magnitudes (+8% 
vs.  +27%  probability).  This  is  a  relevant  result,  as  the  outsourcing  of  R&D  projects  is  widely 
employed by very small firms that are not structured enough to undertake internal research projects.12 
Another important factor in driving firms’ innovativeness is related to the operating environment of a 
firm. Belonging to a corporate group significantly increases the probability of innovating by roughly 
5%, as well as the insertion into domestic networks with other companies (4.5%). This result is largely 
in  line  with  the  dominant  literature  on  domestic  districts,  whereby  close  proximity  with  other 

12 The variable human capital is largely insignificant, possibly because R&D measures are capturing most of its effect. 

31 

 
 
 
                                                            
 
companies  may  foster  the  innovative  process  through  iterated  exchanges  of  knowledge  flows.  As 
before, the insignificance  of human capital may be driven by its persistence (we always account for 
Mundlak correction) as well as controls for R&D capturing most of its effect. 

Finally, columns 1 and 2 emphasise the critical role played by financial constraints in the development 
of innovative projects. By their very own nature, innovative firms, especially SMEs, are more likely to 
suffer from financial problems. Because of their informational opaqueness, their little tangible assets 
to  pledge  as  collateral,  and  the  riskiness  of  their  strategies,  most  potentially  innovative  firms  are 
credit-rationed  and  face  relevant  obstacles  in  financing  their  investments.  In  this  regard,  the  type  of 
innovation to be financed, the characteristics of the firm, and its relationship with the lender bank play 
a crucial role in the actual capability of introducing innovations. 

We analyse this effect in two alternative ways. In column 1, we employ a synthetic measure of firms’ 
creditworthiness  (or  bankability),  defined  as  the  first  principal  component  (Creditworthiness)  of 
several  financial  rations  that  are  traditionally  used  by  banks  to  compute  internal  credit  scores 
(leverage, ability to pledge collateral, age, size, cash flow to total assets ratio, sales to total assets ratio, 
proxies  for  rollover  risk,  …).13  As  expected,  this  measure  is  positively  associated  with  firms’ 
innovativeness, possibly because of its correlation with firm access to external credit.  

As  an  alternative  measure,  column  2  employs  the  predicted  probability  of  being  financially 
constrained.  In  particular,  the  MET  survey  contains  information  on  the  existence  of  positive  net 
present value investment projects that were not undertaken because of a lack of financial means. This 
measure  can  be  thought  of  as  a  direct  proxy  for  financial  constraints  (dummy  variable)  and  is 
employed in Table 9.12 as a dependent variable.  

Besides  including  some  structural  characteristics,  we  employ  a  set  of  measures  that  are  traditionally 
used  by  the  literature  as  proxies  for  relationship  lending.  Relationship  lending  represents  the 
informational privilege that a bank accumulates over time by establishing close ties with its borrower 
so  to  overcome  problems  of  informational  asymmetry,  especially  when  dealing  with  innovative 
projects.  The  amount  of  soft  information  gathered  by  banks  can  be  critical  in  determining  credit 
access, especially for opaque SMEs whose hard information is not enough to correctly evaluate firm 
creditworthiness.  In  other  words,  the  existence  of  close  ties  with  the  lender  bank  significantly  helps 
overcoming credit constraints.  

Given the unobservability of the stock of prior information accumulated by the bank, we employ three 
main  measures  to  capture  this  phenomenon.  The  first  one,  bank  distance,  is  the  physical  distance 
between the belonging province of a company and the headquarter of the lending bank (in log-Km). 
This measure is correlated with the “informational distance” between the lender and the borrower; the 
larger the distance, the higher banks’ difficulties in transmitting soft information to the headquarters. 
Similarly,  banks'  degree  of  hierarchisation  (here  proxied  by  bank  size,  i.e.,  the  log  of  banks’  total 
assets) significantly affects the transmission of soft information gathered from delocalised branches to 
the upper levels. Finally, the higher the number of banks each firm is borrowing from, the weaker the 
relationship  that  is  established  with  the  lender,  and  the  lower  the  amount  of  soft  information  that  is 
allegedly gathered.  

Table  9.12  presents  results  that  are  in  line  with  a  priori  expectations  and  emphasise  the  critical  role 
played  by  relationship  lending  in  reducing  a  firm’s  probability  of  being  financially  constrained. 

13 The first principal component accounts for 65% of the total variance and loads on all the coefficients unambiguously 
indicating increases in creditworthiness. 

32 

                                                            
 
Finally, in column 2 of Table 9.11 we employ the predicted probability from Table 9.12 to show the 
detrimental effect of financial constraints on firms’ innovativeness, which is always very negative and 
extremely significant, suggesting that finance may represent a severe obstacle for financing innovative 
activities of SMEs.  

Before moving to the analysis of the intensive margins of export, it is worth to provide some evidence 
on firms’ exit. Because of the sensible drop in domestic demand, a large fraction of companies entered 
the international environment in the aftermath of 2011. At the same time, a significant flow of firms 
exited  international  markets  within  the  same  period.  Table  9.13  digs  deeper  into  this  issue  by 
modelling a firm’s probability of exiting the international market after 2011. These firms tend to be on 
average  smaller,  younger,  and,  especially,  less  productive.  After  accounting  for  unobserved 
heterogeneity, firms’ productivity did not play any role in firms’ entrance, as it has a very negative and 
significant effect on exit strategies (a one-standard deviation increase in productivity is linked to a -
1.4% probability of exiting the international markets). These combined results point at and outflow of 
companies that attempted to succeed in the international environment despite being less structured and 
fragile.  Importantly,  the  adoption  of  dynamic  strategies,  especially  innovation  and  investment,  may 
help overcoming structural characteristics by significantly lowering a firm’s probability of exit (by -
8.5% and -4.7%, respectively).  

6.2 

INTENSIVE MARGINS AND EXTENSIONS 

Table 9.14 presents the result for the intensive margins of export, which is captured with two different 
measures. The first one is the share of sales from exported products (as a percentage of total sales, in 
columns 1 and 2), which, however, may provide unclear indications in case of relevant shocks to the 
domestic demand (i.e., this measure may increase for companies that keep exporting the same value 
while  reducing  at  the  same  time  the  overall  domestic  sales).  To  overcome  this  drawback,  we  also 
present results on the growth rate of export sales (in columns 3 and 4) as an alternative proxy. 

While accounting for unobserved heterogeneity made productivity irrelevant in determining a firm’s 
probability  of  export,  productivity  plays  a  critical  role  for  the  intensive  margins.  Increasing 
productivity  allows  firms  to  better  compete  on  the  international  markets,  resulting  into  increasing 
share of exported products and positive trends in export growth (a one standard deviation increase in 
productivity leads to a 4% increase in the growth rate of exported products). 

Importantly, the effect of firms’ innovativeness on export is not limited to the extensive margins, but 
extends to the performance on the international markets, and causes a 8.3% increase in export growth. 
As before, columns 2 and 4 show a strict dominance of product innovations compared to alternative 
forms of improvements. 

Importantly, the effect of dynamic strategies on extensive and intensive margins of export tend to be 
multiplicative. Table 9.15 shows the differential impact of innovation on export depending on whether 
or not the firm paired the improvement with investment in R&D activities. The analysis sheds light on 
significant  heterogeneities  based  on  the  differential  degree  of  innovativeness  embedded  in  the  new 
products or processes developed. Innovation strategies that are combined with R&D projects increase 
a firm’s probability of exporting twice as much as the introduction of less radical innovations (4% vs 
1.7%). In the same vein, they increase the growth rate of exported value by roughly 12%, compared to 
the  6.6%  of  innovation  without  R&D.  These  effects  are  always  over  and  above  the  mere  effect  of 
R&D  (which  is  controlled  for  in  any  regression).  Given  the  large  diffusion  of  the  phenomenon  of 
“innovation without R&D” in Italy, this result identifies a set of suitable targets for industrial policies 

33 

 
by  pointing  at  the  importance  of  pushing  companies  to  integrate  innovation  strategies  with  R&D 
activities in order to improve international performances.  

Table  9.16  presents  estimates  from  quantile  regressions  on  the  share  of  exported  products.  The 
analysis  aims  at  shedding  light  on  potential  nonlinearities  in  the  effect  of  firms’  innovativeness  and 
other factors on export. In other words, if there are structural differences between marginal exporters 
and  firms  exporting  a  large  share  of  their  production,  quantile  regressions  allow  testing  for  these 
heterogeneities. 

Results  highlight  that  research  activities  are  fundamental  to  improve  the  performance  on  foreign 
markets except in case of firms that are already large exporters. In particular, the positive role played 
by R&D projects increases along with the degree of dependency from international markets. For large 
exporters,  however,  this  effect  turns  out  to  be  low  and  insignificant,  probably  due  to  the  fact  that 
highly  internationalised  firms  lean  on  other  types  of  strategies  (e.g.  outsourcing,  market  power 
exploitation, strict connection with foreign markets) to gain/preserve the competitive advantage over 
their rivals. 

In  a  similar  vein,  the  introduction  of  new  products/processes/organisational  practices  is  a  relevant 
strategy  for  companies  with  a  low  share  of  revenues  stemming  from  export  activities.  Indeed,  the 
estimated coefficients point out that the first half of the export share conditional distribution associated 
with  innovators  is  shifted  rightward  with  respect  to  the  first  half  of  the  conditional  distribution 
associated  with  non-innovating  firms.  On  the  contrary,  innovation  plays  a  minor  role  for  large 
exporters, probably because these business activities have already reached an established position on 
foreign markets.  

Clearly, large exporters may face tougher competition in some industries, but to further increase the 
share of exported products innovations seem not to be a critical issue. In particular, productivity is a 
fundamental determinant of the magnitude of the international success, as emphasised by coefficients 
that  are  strictly  increasing  along  with  the  intensive  margins.  In  a  similar  vein,  the  number  of 
employees  and  the  share  of  exported  turnover  are  significantly  and  positively  correlated,  with  a 
coefficient that increases (at least until the 90th percentile) along the distribution of intensive margins. 
In  between  the  75th  and  90th  percentiles  the  importance  of  size  dwindles  in  magnitude  but  not  in 
significance. Moreover the number of employees remains more important for large exporters than for 
the marginal and small ones. 

Overall,  the  estimates  of  quantile  regression  suggest  that  R&D  and  innovation  are  fundamental  for 
penetrating foreign markets. Once the  firm has reached a significant degree of dependence from the 
international  environment,  however,  the  organisational  complexity  and  efficiency  (i.e.  size  and 
productivity) stand out as the key elements for further improvements of competitive advantages. 

Having  established  the  effect  of  dynamic  strategies  on  firms’  international  propensity,  we  turn  the 
attention to potential non-linearities in the effects of interest. In particular, smaller and less productive 
firms may have disproportionate benefits from the introduction of innovations and the investment in 
R&D  projects.  To  test  for  this  heterogeneity,  Table  9.17  and  Table  9.18  interact  the  effect  of 
innovation and R&D with  dummy indicators of high and low productivity and large  and small  size. 
The thresholds used to identify the companies are listed in the third rows (33rd or 25th percentile of the 
2008 cross-sectional distribution of size and productivity).  

Both the extensive (columns 1 and 2) and the intensive (columns 3 and 4) margins of export present 
significant  non-linearities  pointing  at  larger  benefits  for  smaller  and  less  productive  companies  (for 

34 

 
instance, innovations of less productive companies are linked to a 5.3% increase in the probability of 
export  and  17%  higher  export  sales  growth,  which  are  reduced  to  1.7%  and  7.1%  in  case  of 
innovations  undertaken  by  a  productive  firm).  This  is  a  critical  finding  as  it  identifies  innovative 
strategies  as  a  potential  tool  to  fill  the  gap  between  large/productive  companies  and  the  set  of  less 
structured firms that are ideal targets for policy measures. 

Finally, before moving the attention to a focus on global value chains, Table 9.19 explores differential 
performance  in  the  second  part  of  the  crisis  (after  2011)  across  persistently  internationalised 
companies and new entrants.  

The  drop  in  domestic  demand  has  pushed  a  second  wave  of  internationalisation  for  companies  that 
were originally domestic. The self-selection hypothesis emphasises the existence of a productivity gap 
between long-lasting exporters and the new entrants. This analysis highlights substantial differences in 
the international performance across the two groups. Table 9.19 focuses on exporters only and shows 
that  new  entrants  experienced  significantly  lower  performance  in  terms  of  exported  values  (the 
benchmark  is  the  subset  of  persistently  internationalised  companies).  Column  2  instead  shows  the 
overall  performance  of  new  entrants  and  persistently  internationalised  companies  compared  to  the 
domestic ones. Interestingly, the entrance in the international markets allowed firms to increase their 
overall sales by roughly 6.7%, with a growth rate that is significantly larger than the one experienced 
by  companies  that  were  already  internationalised  (2.2%).  This  result  confirms  the  relative  poor 
performance of domestic companies, largely affected by the drop in domestic demand, and emphasises 
the  significant  role  played  by  international  strategies  in  offsetting  the  collapse  of  domestic  sales. 
Moreover,  even  if  new  entrants  were  characterised  by  lower  exported  values,  their  relative  higher 
growth  rates  of  sales  indicate  a  process  of  catching  up  if  the  international  strategy  is  pursued 
continuously.  

35 

 
 
 
7.  CONCLUDING REMARKS AND POLICY SUGGESTIONS 

The  aim  of  this  paper  is  to  better  understand  the  drivers  of  competitiveness  of  Italian  industrial 
companies and their business activity during the Great Recession. Our work highlights the well-known 
structural  factors  underlying  external  competitiveness,  but  emphasised  the  major  role  played  by 
knowledge-creating activities, such as R&D and innovations, as well as several relevant dimensions of 
heterogeneity. The main findings and policy suggestions can be grouped into the following four major 
areas of contents. 

Productivity 

Our analysis confirms the critical role played by productivity for international competitiveness: a one 
standard deviation increase in productivity is associated with a 1.5% higher probability to export. This 
evidence is consistent with the literature that emphasises a self-selection of companies in international 
markets. Moreover, productivity has a critical impact on firms’ intensive margin to trade and on the 
decision  to  exit  the  foreign  markets  (+4%  export  sales  growth  and  -2%  probability  of  going  back 
domestic).  This  is  a  critical  issue  since  firms’  exit  from  international  markets  has  been  very  sizable 
(roughly  20%  of  companies  previously  active  abroad  between  2011  and  2014)  even  in  times  of 
relatively low domestic demand.  

However, once accounted for persistent characteristics that do not vary over time (including the higher 
average  productivity  of  some  companies),  productivity  is  found  to  be  largely  insignificant  in 
explaining  the  change  in  firms’  exporting  status  for  domestic  enterprises.  The  new  entry  into 
international  markets,  also  in  absence  of  R&D  and  innovation,  has  a  particularly  virtuous  effect  on 
sales performance. 

In  the  light  of  our  findings,  productivity  represents  an  essential  driver  for  the  success  and  the 
permanence  on  the  international  markets,  but  is  not  found  to  be  a  significant  driver  of  new 
internationalisation  choices  (i.e.,  new  entrances).  In  this  regard,  policy  measures  should  account  for 
such  a  heterogeneous  effect.  Specifically,  policy  measures  should  be  able  to  differentiate  between 
interventions aimed at increasing the number of internationalised companies, which is a relevant issue 
for  the  growth  of  competitiveness  through  learning-by-exporting  mechanisms,  and  those  measures 
oriented  to  strengthening  the  international  position  and  performance  of  previously-internationalised 
companies. 

Research and innovation 

Besides  structural  characteristics,  a  prominent  effect  is  found  for  firms’  strategic  behaviours  and 
investment in dynamic strategies. The introduction of innovations, the involvement in R&D projects, 
as well as undertaking new investments, induce a significant effect on export strategies (roughly +2% 
in export probability) and performance (innovations induce on average a 8% increase of export sales 
growth).  

The  introduction  of  innovations  is  even  more  important  in  reaching  extra-EU  destinations  and  in 
affecting  firms’  switching  strategies;  i.e.,  entrance  (+2.3%)  and  exit  (-8.5%)  from  the  international 
markets.  Besides  their  direct  impact,  there  are  significant  cumulative  effects  of  dynamic  strategies. 
First,  the  integration  of  innovation  and  R&D  activities  implies  premia  on  both  the  extensive  and 
intensive margins of export. The coefficients of innovative strategies paired with R&D investment is 

36 

 
two  to  three  times  larger  than  isolated  innovations  (4.1%  vs  1.7%  in  the  probability  of  export,  and 
+11.7%  vs  +6.6%  in  export  sales  growth).  Moreover,  the  path  followed  by  the  set  of  dynamic 
strategies undertaken in the past (adding or reducing the set of strategies, i.e., upgrading/downgrading 
paths) has additional effects on firms’ international attitude (roughly, +5% probability of exporting).  

Among  the  different  types  of  innovations,  new  products  dominate  other  forms  of  innovativeness 
(process or organisational/managerial), especially in case of previously non-exporting companies (4% 
higher probability of exporting). This is because new products are the main form of innovation which 
is not reflected in the level of productivity. However, we find process and organisational innovations 
to  have  an  additional  indirect  effect  on  export  by  boosting  firms’  productivity  (+2%  productivity 
growth, +15% if based on matching techniques), while the introduction of new products does not seem 
to be linked to any productivity growth. 

Innovative  strategies  are  found  to  have  disproportionate  effects  for  the  international  performance  of 
(originally) less productive and small companies: 17% and 21% increase in export growth compared 
to  7%  and  8%  increase  for  more  productive  and  larger  firms,  respectively.  This  result  may  point  to 
innovative strategies as a potential tool to fill the gap between large/productive companies and the set 
of less structured firms, which are ideal targets for policy measures. 

A  simultaneous  equation  model  (bivariate  probit)  also  emphasises  the  main  determinants  of  firms’ 
innovativeness. In this regard, structural characteristics are significantly affecting the introduction of 
innovations,  especially  for  larger,  younger,  and  more  productive  companies.  The  existence  of  R&D 
projects  is  clearly  correlated  with  a  large  increase  in  the  probability  of  introducing  innovations. 
Importantly,  this  effect  is  not  limited  to  the  investment  in  R&D  performed  within  the  firm  (internal 
R&D), but extends to firms outsourcing R&D activities, even though with smaller magnitudes (+8% 
vs  +27%  probabilities).  Another  important  factor  in  driving  firms’  innovativeness  is  related  to  the 
operating  environment  of  a  firm,  such  as  the  affiliation  to  a  corporate  group  (+5%)  and  the 
establishment  of  close  relationships  with  other  domestic companies  (i.e.,  networks,  +4.5%).  Finally, 
financial  constraints  are  found  to  significantly  affect  a  firm’s  capacity  to  innovate  and  the 
establishment of close ties with the main bank is documented to be extremely important in reducing 
informational asymmetries penalising innovative SMEs. 

(new 

directly 

products 

influencing 

In  terms  of  potential  policy  recommendations,  we  confirm  the  critical  role  played  by  R&D  and 
innovations 
and 
process/organisational innovations operating through higher productivity growth). Our analysis sheds 
lights on some pertinent factors. First, we show the disproportionate effect of dynamic strategies for 
more  fragile  (smaller  and  less  productive)  companies  that  are  paired  with  a  premium  linked  to 
integrated  strategies  (innovation  and  R&D)  and  the  documented  need  to  reduce  the  discontinuity  of 
proactive  behaviours,  typically  characterising  more  fragile  (and  often  smaller)  companies.  These 
factors should be all taken into account when designing specific policies. 

competitiveness, 

international 

Similarly, financial constraints severely limit the activity of potentially innovative firms. The role of 
financial  constraints  goes  beyond  generic  frictions  in  the  credit  market,  such  as  informational 
asymmetries that can affect the entire set of relationships of SMEs with the banking system. Financial 
constraints  can  be  dramatically  exacerbated  in  the  presence  of  direct  funds  to  innovative  projects 
(characterised  by  higher  uncertainty,  opacity,  and  typically  associated  to  investment  in  immaterial 
capital that cannot be pledged as collateral). 

37 

 
 
Firm size and corporate groups 

The  dominant  academic  view  on  the  Italian  productive  system  emphasises  the  excessive  number  of 
micro-sized  companies  within  the  economy,  as  well  as  a  very  fragmented  industry  paired  with  a 
reduced  diffusion  of  large  enterprises.  Our  analyses  and  the  available  comparisons  allow  to  draw  a 
more detailed picture of the industrial system. 

The few existing academic econometric studies on firm-level information are not adequately capable 
of  capturing  the  role  of  micro-sized  companies,  because  they  exclude  very  small  firms  from  the 
sample design due to their mainly survey data. 

Aggregate descriptive statistics seem to document that the main weakness of the Italian system is not 
due to the high diffusion of micro firms (whose share is not far from the one in France and Spain), but 
rather to their relatively poor performance. Our analysis suggests that the heterogeneity of the set of 
dynamic  strategies  leads,  even  within  the  same  size  class,  to  substantially  different  economic 
outcomes  and  efficiency  levels.  Moreover,  within  the  class  of  small  firms  there  is  a  significant 
difference  between  companies  below  and  above  ten  employees.  For  the  latter  (>10), descriptive  and 
empirical  evidence  emphasises  higher  performance  of  Italy  compared  to  other  European  peer 
economies. 

The econometric analyses always confirm the positive role of size and affiliation to corporate groups 
(respectively, +3% and +2% probability of exporting). In this regard, however, innovative strategies 
seem to display disproportionate incremental effects for smaller companies.  

Obviously, firms’ size  cannot be easily implemented  as a specific objective for policies, at least not 
directly.  It  is  however  clear  from  the  empirical  results  that  policy  measures  oriented  to  the 
reinforcement of innovative and knowledge-creating activities and the specific financial constraints to 
their implementation may explicitly help smaller firms that are willing to undertake dynamic paths.  

Industrial networks and global value chains 

The role played by networks of firms and industrial districts is central in the economic literature about 
the Italian industrial sector. It has often been argued as a factor capable of offsetting the inefficiencies 
of  small  firms  by  allowing  them  to  reach  a  larger  scale  at  the  network  level  compared  to  the  small 
dimension of the single units. Similarly, firms’ involvement in Global Value Chains is often cited as a 
determinant  factor  in  explaining  success  and  weakness  on  the  international  markets,  depending  on 
their  diffusion  and  the  different  modes  of  participation  of  the  firms  involved.  Our  analysis  confirms 
the  role  of  the  environment  and  the  involvement  in  local  networks  to  increase  firm  innovativeness, 
through the opportunity of knowledge exchanges.14  

Overall and given that our analysis does not specifically explore the effect of macroeconomic policies 
and structural reforms, the key potential policy suggestions from our work and empirical results can be 
summarised as follows: 

• 

• 

the support to firms’ R&D and innovation remains one of the main focus of policy measures 
for competitiveness. The choice of instruments has to explicitly consider needs and constraints 
of the targeted businesses; 
similarly,  policies  aimed  at  increasing  innovative  propensity  should  carefully  account  for 
qualitative upgrading of both the labour force and managers; 

14 For an extensive discussion of the results (on the same dataset) see Brancati et al. 2017. 

38 

                                                            
 
• 

• 

• 

of 

three 

(R&D, 

strategies 

innovation 

fundamental 

an essential issue of the work is represented by the research of differential effects due to the 
integration 
and 
dynamic 
internationalisation), whereby the effect of integrating dynamic strategies by firms following a 
path of modernisation that is not yet completed appears to be very significant. The completion 
of this strategy and the integration of activities is characterised - in our view - as one of the 
main goal of a strategy to support firms’ growth and to improve international competitiveness. 
Policy measures must be specifically targeted to these goals; 
similarly,  the  elimination  or  reduction  of  discontinuity  in  strategic  business  activities  (for 
example with reference to the intermittent presence on foreign markets or with discontinuous 
innovative  activities  and  R&D),  in  particular  of  smaller  enterprises,  seems  to  represent  an 
“effective”  policy  target.  The  discontinuity  can  be  directly  influenced  by  appropriate  and 
specific policy measures; 
financial constraints still constitute a substantial limitation to the growth of the most dynamic 
companies  and  to  the  improvement  of  their  development  strategies;  paradoxically,  financial 
constraints  are  stronger  for  companies  oriented  towards  innovation  and  R&D  than  the  static 
ones  because  of  the  significant  market  and  technological  risk  and  for  the  higher  credit 
demand: access to credit (as well as other financial instruments) could usefully have a specific 
orientation towards innovative projects, while the practice of current policy for credit access 
and equity support is more in the sense of general targeting.  

39 

 
 
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52 

 
 
 
 
9.  ANNEX: TABLES 

Table 9.1 Composition by size classes (manufacturing) 

Employees:

0-9

EU 28

Germany

Spain

France

Italy

82.9%

65.1%

84.1%

87.6%

82.9%

10-19

8.0%

17.4%

7.4%

5.4%

9.9%

20-49

50-249

250 or more

5.1%

7.7%

5.5%

4.1%

4.8%

3.4%

7.8%

2.5%

2.4%

2.1%

0.8%

2.0%

0.4%

0.6%

0.3%

Source: Elaboration on Eurostat data. % of companies within the national population. 

Table 9.2 Types of innovations by size classes and exporting status 

Product innov ation

Process innov ation

Organisational / managerial / 

marketing innov ation

Domestic (%)

Exporters (%)

Domestic (%)

Exporters (%)

Domestic (%)

Exporters (%)

Micro

Small

Medium

Large

Total

11.8

18.6

20.0

17.2

12.6

24.6

33.6

43.0

55.8

29.2

7.60

16.9

18.1

26.1

8.60

15.2

24.5

33.1

44.7

19.8

7.40

16.3

21.9

30.4

8.40

13.4

22.6

31.1

41.5

17.9

Note: Data refer to firms belonging to industrial sectors. The types of innovation introduced are not mutually 
exclusively, i.e. a firm may have introduced a new product, a new process and a new set of organisational, 
managerial and marketing practices at the same time. 

Source: MET sample survey, waves from 2008 to 2015 (pooled dataset). 

Table 9.3 Composition of the MET database by firms’ size class. 

Micro (1-9)

Small (10-49)

Medium (50-249)

Large (>250)

Observ ations

2008

38.4%

38.4%

19.5%

3.0%

2009

60.0%

26.0%

10.5%

3.5%

2011

61.6%

24.7%

10.6%

3.1%

2013

48.1%

33.6%

13.5%

4.8%

24896

22340

25090

25000

2015

 46.9%

34.6% 

 14.7%

 3.7%

23071

53 

 
 
 
 
 
 
 
 
 
 
 
Table 9.4 Probability of export: baseline regression 

Dependent v ariable:

(1)

(2)

Export

(3)

(4)

(5)

(6)

Productiv ity

0.0151* * *

0.0116* * *

0.0167* * *

0.0281* * *

0.0208* * *

0.0186* * *

Size

Age

(0.0016)

(0.0016)

(0.0022)

0.0612* * *

0.0464* * *

0.0488* * *

(0.001)

-0.001

(0.002)

(0.001)

0.001

(0.002)

(0.001)

-0.001

(0.002)

Capitalisation

0.0038* * *

0.0035* * *

0.0038* * *

(0.0046)

0.056* * *

(0.003)

-0.009* *

(0.004)

(0.0056)

0.056* * *

(0.003)

-0.0107* * *

(0.004)

-0.006* * *

-0.006* * *

(0.0022)

0.058* * *

(0.001)

-0.003

(0.002)

0.001

(0.001)

0.019* * *

(0.005)

0.346* * *

(0.006)

(0.001)

0.042* * *

(0.005)

0.360* * *

(0.005)

(0.001)

(0.001)

0.0325* * *

0.0281* * *

(0.005)

0.330* * *

(0.005)

(0.005)

0.358* * *

(0.006)

(0.002)

0.006

(0.008)

0.336* * *

(0.011)

(0.007)

0.004* * *

(0.000)

0.0100

(0.007)

-0.011*

(0.005)

0.017* *

(0.009)

-0.041

(0.056)

(0.002)

0.003

(0.008)

0.343* * *

(0.011)

0.053* * *

(0.008)

0.004* * *

(0.000)

0.009

(0.007)

-0.010*

(0.005)

0.021* *

(0.009)

-0.023

(0.056)

0.0558* * *

0.0544* * *

0.0531* * *

0.0516* * *

(0.004)

0.004* * *

(0.000)

(0.004)

0.004* * *

(0.000)

(0.004)

0.004* * *

(0.000)

0.0125* * *

0.0123* * *

0.0132* * *

(0.004)

0.001

(0.002)

0.004

(0.004)

0.0109

(0.018)

(0.004)

-0.019* * *

(0.003)

0.0108* *

(0.004)

0.028

(0.019)

Group

Import

Innov ation

R&D share

Inv estment

Sales

Sales growth

ROA

Vertical integration

Cost of labor

Lev erage

Net acc. payable

Bank debt

Human capital

Domestic network

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

(0.003)

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

--

-0.253* * *

-0.222* * *

-0.232* * *

(0.011)

-0.004

(0.004)

--

--

--

--

--

--

--

--

--

--

(0.0325)

-0.074*

(0.0404)

(0.032)

-0.063

(0.0404)

-0.0272* * *

-0.027* * *

(0.004)

0.112* * *

(0.0211)

0.102* * *

(0.024)

--

--

--

--

(0.004)

0.111* * *

(0.0211)

0.107* * *

(0.024)

0.145* * *

(0.021)

-0.031* * *

(0.007)

Constant

-1.280* * *

-1.293* * *

-1.503* * *

-1.588* * *

-0.825* * *

-0.724* * *

(0.072)

(0.073)

(0.092)

(0.092)

(0.193)

(0.195)

Cont rols

Region

Industry

Time

Mundlak

Observ ations

Pseudo R2

yes

yes

yes

no

50242

0.217

yes

yes

yes

no

50242

0.239

yes

yes

yes

no

41757

0.239

yes

yes

yes

no

41756

0.249

yes

yes

yes

no

13420

0.272

yes

yes

yes

no

13209

0.278

Note: Pooled probit models (marginal effects). The dependent variable is the extensive margin of export (Export), 
a dummy variable identifying exporting companies. *, **, *** denote, respectively, significance at 10%, 5%, and 1% 
level. Robust standard errors in parentheses. 

Source: yearly balance-sheet data (Cribis D&B) and MET surveys 2008, 2009, 2011, 2013, and 2015. All measures are 
defined in Appendix.  

54 

 
 
Table 9.5 Probability of export: controlling for unobserved heterogeneity 

RE-Probit with Mundlak

Linear probability model

Export

(1)

-0.001

(0.002)

(2)

0.0011

(0.002)

(3)

-0.0002

(0.0017)

(4)

0.002

(0.002)

(5)

0.006*

(0.003)

0.0168* * *

0.0305* * *

0.0177* * *

0.0317* * *

0.023* * *

Dependent v ariable:

Estimator:

Productiv ity

Size

Age

Capitalisation

Group

Import

Innov ation

(0.003)

-0.0012

(0.002)

0.001

(0.001)

0.0119*

(0.006)

0.084* * *

(0.004)

0.011* *

(0.0045)

(0.004)

-0.002

(0.002)

0.002

(0.002)

0.0202* *

(0.008)

0.073* * *

(0.005)

(0.0032)

(0.0044)

0.000

(0.002)

0.0012

(0.0013)

0.0127*

(0.0068)

0.084* * *

(0.005)

-0.001

(0.002)

0.002

(0.002)

0.022* * *

(0.008)

0.074* * *

(0.005)

0.0131* * *

0.0133* * *

0.0163* * *

(0.005)

(0.004)

(0.005)

0.002* * *

(0.001)

R&D share

0.0021* * *

0.0021* * *

0.0021* * *

(0.001)

(0.001)

(0.001)

Inv estment

0.0143* * *

0.0157* * *

0.0125* * *

0.0153* * *

ROA

Vertical integration

Cost of labor

Sales

Sales growth

Lev erage

Net accounts  payable

Bank debt

Constant

Cont rols

Region

Industry

Time

Region* Time

Industry* Time

Mundlak

Firm FE

Observ ations

Pseudo R2

R2

(0.0045)

-0.007

(0.045)

0.0122

(0.017)

0.0112

(0.029)

--

--

--

--

--

--

--

--

--

--

-2.55* * *

(0.244)

yes

yes

yes

no

no

yes

--

23.932

0.757

--

(0.005)

-0.007

(0.045)

0.0116

(0.029)

0.0257

(0.045)

-0.005

(0.007)

-0.0027

(0.005)

-0.0020

(0.0044)

0.0105

(0.022)

-0.043*

(0.024)

-2.88* * *

(0.368)

yes

yes

yes

no

no

yes

--

14318

0.765

--

(0.005)

0.125

(0.195)

0.009

(0.017)

0.011

(0.029)

--

--

--

--

--

--

--

--

--

--

-2.93* * *

(0.306)

yes

yes

yes

yes

yes

yes

--

23932

0.768

--

(0.005)

-0.026

(0.045)

0.0119

(0.029)

0.006

(0.046)

-0.007

(0.007)

-0.005

(0.005)

-0.001

(0.0047)

0.003

(0.022)

-0.042*

(0.024)

-3.41* * *

(0.432)

yes

yes

yes

yes

yes

yes

--

14318

0.776

--

(6)

0.008

(0.011)

0.054* * *

(0.012)

-0.002

(0.006)

0.007*

(0.004)

0.040* * *

(0.015)

0.178* * *

(0.012)

0.018*

(0.009)

0.003* * *

(0.001)

0.023* *

(0.010)

0.034

(0.088)

-0.017

(0.054)

0.090

(0.089)

-0.001

(0.012)

-0.009

(0.009)

0.003

(0.008)

0.008

(0.040)

-0.126* *

(0.048)

0.09

(0.150)

yes

yes

yes

no

no

--

yes

15327

--

0.042

(7)

0.004

(0.004)

0.020* * *

(0.004)

0.000

(0.003)

0.000

(0.001)

0.028* * *

(0.008)

0.245* * *

(0.006)

0.015* * *

(0.005)

0.003* * *

(0.000)

0.024* * *

(0.005)

-0.004

(0.011)

-0.005

(0.017)

0.002

(0.004)

--

--

--

--

--

--

--

--

--

--

0.21* * *

(0.049)

yes

yes

yes

yes

yes

--

yes

51688

--

0.073

(8)

0.006

(0.011)

0.050* * *

(0.012)

-0.005

(0.006)

0.005

(0.004)

0.046* * *

(0.015)

0.177* * *

(0.012)

0.019*

(0.010)

0.003* * *

(0.001)

0.023* *

(0.010)

0.068

(0.090)

-0.004

(0.057)

0.143

(0.094)

0.003

(0.012)

-0.004

(0.010)

0.000

(0.009)

0.003

(0.041)

-0.086*

(0.050)

0.14

(0.158)

yes

yes

yes

yes

yes

--

yes

14318

--

0.066

(0.004)

-0.000

(0.003)

0.000

(0.001)

0.019* *

(0.008)

0.250* * *

(0.006)

0.016* * *

(0.005)

0.003* * *

(0.000)

0.025* * *

(0.005)

-0.001

(0.011)

-0.019

(0.017)

0.001

(0.004)

--

--

--

--

--

--

--

--

--

--

0.18* * *

(0.047)

yes

yes

yes

no

no

--

yes

55441

--

0.059

Note: RE-probit models with Mundlak correction (marginal effects in columns 1-4) and linear probability models 
with firm and time fixed effects (estimates and marginal effects in columns 5-8). The dependent variable is the 
extensive margin of export (Export), a dummy variable identifying exporting companies. *, **, *** denote, 
respectively, significance at 10%, 5%, and 1% level. Robust standard errors in parentheses. 

Source: yearly balance-sheet data (Cribis D&B) and MET surveys 2008, 2009, 2011, 2013, and 2015. All measures are 
defined in Appendix.  

55 

 
 
 
 
 
 
Table 9.6 Probability of export: previously non-exporting companies 

Dependent 

v ariable:

Innov ation

R&D share

Inv estment

Productiv ity

Size

Age

Import

(1)

0.022* * *

(0.007)

0.002* * *

(0.001)

0.0121*

(0.007)

0.0011

(0.002)

export

(2)

0.022* * *

(0.006)

0.002* * *

(0.001)

0.008

(0.006)

0.010* *

(0.004)

(3)

0.023* * *

(0.007)

0.002* * *

(0.001)

0.010

(0.006)

0.002

(0.002)

(4)

0.019* * *

(0.006)

0.003* * *

(0.001)

0.009

(0.006)

0.006

(0.004)

0.018* * *

0.018* * *

0.019* * *

0.015* * *

(0.004)

-0.003

(0.004)

0.099* * *

(0.006)

(0.005)

-0.001

(0.004)

0.321* * *

(0.011)

(0.004)

-0.002

(0.004)

0.102* * *

(0.006)

(0.005)

0.001

(0.004)

0.310* * *

(0.011)

Constant

-1.806* * *

-0.197* * *

-1.451* * *

-0.171* * *

(0.323)

(0.052)

(0.422)

(0.053)

Cont rols

Region

Industry

Time

Region* Time

Industry* Time

Mundlak

Firm FE

Observ ations

Pseudo R2

R2

yes

yes

yes

no

no

yes

--

11599

0.554

--

yes

yes

yes

no

no

--

yes

32889

--

0.124

yes

yes

yes

yes

yes

yes

--

11599

0.582

--

yes

yes

yes

yes

yes

--

yes

30540

--

0.164

Note:  RE-probit  models  with  Mundlak  correction  (marginal  effects  in  colums  1  and  3),  and 
linear  probability  models  with  firm  and  time  fixed  effects  (estimates  and  marginal  effects  in 
columns 2 and 4). The dependent variable is the extensive margin of export (Export), a dummy 
variable  identifying  exporting  companies.  *,  **,  ***  denote,  respectively,  significance  at  10%, 
5%, and 1% level. Robust standard errors in parentheses. 

Source:  yearly  balance-sheet  data  (Cribis  D&B)  and  MET  surveys  2008,  2009,  2011,  2013,  and 
2015. Additional regressors (untabulated) follow the specification in Table 9.4. All measures are 
defined in Appendix.  

56 

 
 
 
 
 
 
Table 9.7 Probability of export by geographical extension 

Dependent 

v ariable:

Innov ation

R&D share

Inv estment

Export(EU)

Export(extraEU)

(1)

0.010* * *

(0.002)

0.002* * *

(0.000)

0.007* * *

(0.002)

(2)

0.021* * *

(0.002)

0.001* * *

(0.000)

0.005* *

(0.002)

Constant

-3.040* * *

-2.068* * *

(0.212)

(0.213)

Cont rols

Region

Industry

Time

Region* Time

Industry* Time

Mundlak

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Observ ations

Pseudo R2

14318

0.795

14318

0.749

Note:  RE-probit  models  with  Mundlak  correction  (marginal  effects).  The 
dependent  variable  is  the  extensive  margin  of  export  by  maximum 
identifying 
geographical  extention.  Export(EU) 
companies  exporting  (at  most)  in  the  EU  area,  while  Export(extraEU)  is  a 
dummy variable identifying companies exporting in farer countries. The two 
dependent variables are defined to be mutually explusive. *, **, *** denote, 
respectively, significance at 10%, 5%, and 1% level. Robust standard errors in 
parentheses. 

is  a  dummy  variable 

Source: yearly balance-sheet data (Cribis D&B) and MET surveys 2008, 2009, 
2011,  2013,  and  2015.  Additional  regressors  (untabulated)  follow  the 
specification in Table 9.4. All measures are defined in Appendix.  

57 

 
 
 
 
 
 
 
 
Export

RE-Probit with Mundlak

Linear probability model

Table 9.8 Upgrading dynamic strategies 

Dependent 

v ariable:

Estimator:

Upgrading

(1)

0.046* * *

(0.004)

(2)

0.048* * *

(0.004)

Innov ation

0.0396* * *

0.0441* * *

R&D share

Inv estment

Productiv ity

(0.006)

0.002* * *

(0.000)

0.016* * *

(0.005)

0.001

(0.002)

(0.006)

0.002* * *

(0.000)

0.016* * *

(0.005)

0.002

(0.002)

Constant

-3.050* * *

-3.619* * *

(0.373)

(0.439)

Cont rols

Region

Industry

Time

Region* Time

Industry* Time

Mundlak

Firm FE

yes

yes

yes

no

no

yes

--

Observ ations

Pseudo R-squared

R-squared

14318

0.771

--

yes

yes

yes

yes

yes

yes

--

14318

0.783

--

(3)

0.078* * *

(0.007)

0.063* * *

(0.010)

0.004* * *

(0.001)

0.023* *

(0.010)

0.010

(0.011)

0.061

(0.149)

yes

yes

yes

no

no

--

yes

14318

--

0.055

(4)

0.075* * *

(0.007)

0.062* * *

(0.011)

0.004* * *

(0.001)

0.023* *

(0.010)

0.007

(0.011)

0.115

(0.157)

yes

yes

yes

yes

yes

--

yes

14318

--

0.077

Notes:  RE-probit  models  with  Mundlak  correction  (marginal  effects  in  columns  1  and  2)  and  linear 
probability models with firm and time fixed effects (estimates and marginal effects in columns 3 and 4). 
Upgrading  is  the  change  in  the  number  of  dynamic  strategies  (Innovation,  R&D,  and  Investment) 
between t-2 and t-1, with the support [-3,+3]. The dependent variable is the extensive margin of export 
(Export), a dummy variable identifying exporting companies. *, **, *** denote, respectively, significance 
at 10%, 5%, and 1% level. Robust standard errors in parentheses. 

Source:  yearly  balance-sheet  data  (Cribis  D&B)  and  MET  surveys  2008,  2009,  2011,  2013,  and  2015.  All 
measures are defined in Appendix.  

58 

 
 
 
 
 
 
 
 
 
Table 9.9 Probability of export: heterogeneities by innovation type 

Dependent v ariable:

Export

Entire

Prev iously non exporting

Sample:

R&D share

Product innov ation

Process innov ation

(1)

0.002* * *

(0.000)

0.0121* * *

(0.003)

0.007*

(0.004)

(2)

0.004* * *

(0.000)

0.020* * *

(0.006)

0.014*

(0.007)

Organizational innov ation

0.006*

0.013* *

Constant

Cont rols

Region

Industry

Time

Region* Time

Industry* Time

Mundlak

Firm FE

Observ ations

Pseudo R-squared

R-squared

(0.003)

-3.023* * *

(0.192)

(0.006)

0.227* * *

(0.049)

yes

yes

yes

yes

yes

yes

--

56955

0.762

--

yes

yes

yes

yes

yes

--

yes

57375

--

0.033

(3)

0.002* * *

(0.000)

0.021* * *

(0.005)

0.007

(0.006)

0.009*

(0.005)

(4)

0.003* * *

(0.001)

0.040* * *

(0.008)

0.005

(0.009)

0.007

(0.007)

-1.623* * *

-0.167* * *

(0.270)

(0.053)

yes

yes

yes

yes

yes

yes

--

30301

0.551

--

yes

yes

yes

yes

yes

--

yes

37370

--

0.144

Notes:  RE-probit  models  with  Mundlak  correction  (marginal  effects  in  columns  1  and  4)  and  linear 
probability models with firm and time fixed effects (estimates and marginal effects in columns 2 and 
4).  The  dependent  variable is  the  extensive  margin  of  export  (Export),  a  dummy variable  identifying 
exporting companies. The left panel reports estimates performed on the entire sample, while the right 
panel  refers  to  the  subsample  of  previously-non-exporting  companies  in  t-1.  *,  **,  ***  denote, 
respectively, significance at 10%, 5%, and 1% level. Robust standard errors in parentheses. 

Source:  yearly  balance-sheet  data  (Cribis  D&B)  and  MET  surveys  2008,  2009,  2011,  2013,  and  2015. 
Additional  regressors  (untabulated)  follow  the  specification  in  Table  9.4.  All  measures  are  defined  in 
Appendix.  

59 

 
 
 
 
 
 
 
 
 
Table 9.10 Indirect effect of innovation on productivity 

Dependent v ariable:

Productiv ity growth

Estimator:

Within estimator

Matching & Within estimator

Innov ation

Product innov ation

Process innov ation

Organizational innov ation

Import

Constant

Cont rols

Region

Industry

Time

Firm FE

Matching & weights

(1)

0.041* *

(0.019)

--

--

--

--

--

--

0.023* *

(0.009)

-0.117

(0.101)

yes

yes

yes

yes

--

(2)

--

--

0.011

(0.016)

0.018*

(0.007)

0.022* *

(0.007)

0.022* *

(0.009)

-0.117

(0.101)

yes

yes

yes

yes

--

Observ ations

R-squared

13.827

0.033

13.827

0.034

(3)

0.233* * *

(0.037)

--

--

--

--

--

--

0.321* * *

(0.038)

(4)

--

--

0.066

(0.049)

0.155* * *

(0.059)

0.133* *

(0.056)

0.328* * *

(0.038)

10.476* * *

10.491* * *

(0.019)

(0.019)

yes

yes

yes

yes

yes

5.519

0.021

yes

yes

yes

yes

yes

5.519

0.023

Notes:  Within  estimator  with  firm  and  time  fixed  effects  (estimates  and  marginal  effects).  The  dependent 
variable  is  the  growth  rate  of  productivity  (value  added  per  worker).  In  columns  3  and  4  the  analysis  is 
performed  after  matching  techniques  (Coarsened  Exact  Matching)  identifying  a  subsample  of  companies 
with the same characteristics (size, age, ex ante productivity, region, sector, …) that only differ for the actual 
introduction of innovations (the treatment variable). Estimates in columns 3 and 4 are performed employing 
the matching weights. *, **, *** denote, respectively, significance at 10%, 5%, and 1% level. Robust standard 
errors in parentheses. 

Source: yearly balance-sheet data (Cribis D&B) and MET surveys 2008, 2009, 2011, 2013, and 2015. Additional 
regressors (untabulated) follow the specification in Table 9.4. All measures are defined in Appendix.  

60 

 
 
 
 
 
 
 
 
 
Table 9.11  Innovation and export: simultaneous equation model 

Export equation:

Innov ation

R&D

Inv estment

Innovation equation:

External R&D

Internal R&D

Creditworthiness

Probability (financial constraints)

Human capital

Size

Productiv ity

Capitalisation

(1)

(2)

0.493* * *

0.504* * *

(0.020)

(0.019)

0.003* * *

0.002* * *

(0.000)

(0.000)

0.029* * *

0.022* * *

(0.007)

(0.007)

0.061* * *

0.082* * *

(0.011)

(0.012)

0.247* * *

0.273* * *

(0.009)

(0.010)

0.016* * *

(0.005)

--

--

--

--

-0.419* * *

(0.167)

0.008

0.023

(0.017)

(0.017)

0.027* * *

0.027* * *

(0.003)

(0.003)

0.013* * *

0.0115* * *

(0.003)

(0.003)

-0.001

0.001

(0.001)

(0.001)

Age

-0.008* *

-0.0259* * *

Sales growth

Local network

Group

Cont rols

Region

Industry

Time

Mundlak

(0.003)

(0.005)

0.018* *

0.021* *

(0.008)

(0.008)

0.045* * *

0.040* * *

(0.006)

(0.006)

0.050* * *

0.044* * *

(0.008)

(0.009)

yes

yes

yes

yes

yes

yes

yes

yes

Observ ations

Rho

20.684

20.684

-0.641* * *

-0.641* * *

Notes:  bivariate  probit  models  with  Mundlak  correction  (marginal  effects).  The  top  panel  reports  estimates 
from  the  export  equation  (untabulated  regressors  follow  the  specification  in  Table  9.4).  The  bottom  panel 
presents  the  innovation  equation.  External  and  Internal  R&D  are  dummy  variables  identifying  companies 
outsourcing R&D or performing R&D internally. Creditworthiness is a synthetic proxy for firms’ creditworthiness 
computed  as  the  first  principal  component  of  several  financial  characteristics  that  may  affect  a  bank’s 
decision  to  lend  (leverage,  collateral  availability,  size,  rollover  risk,  age).  Prob(financial  constraints)  is  the 
predicted probability of being financially constrained as computed in  the  following  table  (9.12). Rho is  the 
estimated correlation of the error terms of the two equations. *,  **,  *** denote, respectively, significance at 
10%, 5%, and 1% level. Robust standard errors in parentheses. 

Source:  yearly  balance-sheet  data  (Cribis  D&B)  and  MET  surveys  2008,  2009,  2011,  2013,  and  2015.  All 
measures are defined in Appendix. 

61 

 
 
 
Table 9.12  Probability of being financially constrained 

Dependent v ariable:

Financial Constraints

Age

Size

Bank distance

Bank size

(1)

-0.032* * *

(0.001)

-0.015* * *

(0.002)

0.0104* * *

(0.000)

0.002* *

(0.001)

Number of banking relationships

0.003* * *

Observ ations

Pseudo R2

(0.001)

73865

0.122

Notes:  Probit  model  (marginal  effects,  in  units  of  standard  deviations  in  columns  1-2).  The 
dependent  variable  is  a  direct  measure  of  financial  constraints  (self  reported)  identifying  firms 
having  positive  net-present-value  projects  that  were  not  undertaken  because  of  a  lack  of 
financial  resources.  Bank  distance  is  the  physical  distance  (log-Km)  between  the  belonging 
province  of  a  firm  and  the  headquarter  of  its  lending  bank  (in  case  of  multiple  banking 
relationships,  an  average  has  been  applied).  Bank  size  is  the  log  of  total  assets  of  the  lending 
bank.  N  banking  relationships  is  the  number  of  banking  relationships  in  place.  *,  **,  ***  denote, 
respectively, significance at 10%, 5%, and 1% level. Robust standard errors in parentheses. 

Source: yearly balance-sheet data (Cribis D&B) and MET surveys 2008, 2009, 2011, 2013, and 2015. 
All measures are defined in Appendix.  

62 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 9.13  Firms’ exit after 2011 

Dependent v ariable:

Exit

Innov ation

R&D share

Inv estment

Productiv ity

Size

Age

(1)

--

--

--

--

--

--

-0.014* *

(0.004)

(2)

-0.085* * *

(0.011)

-0.001

(0.001)

-0.047* * *

(0.010)

-0.012* *

(0.005)

-0.056* * *

-0.043* * *

(0.002)

-0.014* * *

(0.004)

(0.003)

-0.015* *

(0.006)

Import

-0.0208* * *

-0.193* * *

Constant

-1.806* * *

-0.197* * *

(0.012)

(0.014)

(0.323)

(0.052)

Cont rols

Region

Industry

Time

Region* Time

Industry* Time

Mundlak

Observ ations

Prseudo R2

yes

yes

yes

no

no

yes

4951

0.540

yes

yes

yes

no

no

yes

4951

0.541

Notes: RE-probit models with Mundlak correction (marginal effects). The dependent 
variable is a dummy measure identifying firms exiting the international markets after 
2011  (Exit).  *,  **,  ***  denote,  respectively,  significance  at  10%,  5%,  and  1%  level. 
Robust standard errors in parentheses. 

Source:  yearly  balance-sheet  data  (Cribis  D&B)  and  MET  surveys  2011,  2013,  and 
2015.  Additional  regressors  (untabulated)  follow  the  specification  in  Table  9.4.  All 
measures are defined in Appendix.  

63 

 
 
 
 
 
 
 
Table 9.14  Intensive margins of export 

Dependent v ariable:

Export share

Export sales growth

Productiv ity

Size

Age

R&D share

Innov ation

Product innov ation

Process innov ation

Organizational Innov ation

Export sales

Constant

Cont rols

Region

Industry

Time

Region* Time

Industry* Time

Firm FE

Observ ations

R2

(1)

0.450* * *

(0.131)

1.255* * *

(0.157)

-0.095

(0.102)

0.107* * *

(0.016)

0.584* * *

(0.187)

--

--

--

--

--

--

--

--

4.701* * *

(1.778)

yes

yes

yes

yes

yes

yes

(2)

0.454* * *

(0.131)

1.258* * *

(0.157)

-0.098

(0.102)

0.102* * *

(0.016)

--

--

0.956* * *

(0.235)

0.028

(0.262)

0.408*

(0.223)

--

--

4.541* *

(1.778)

yes

yes

yes

yes

yes

yes

(3)

0.038* *

(0.018)

0.035

(0.024)

(4)

0.038* *

(0.018)

0.035

(0.024)

-0.049* * *

-0.049* * *

(0.014)

0.007* * *

(0.001)

0.083* * *

(0.019)

--

--

--

--

--

--

(0.014)

0.007* * *

(0.001)

--

--

0.061* * *

(0.023)

0.012

(0.026)

0.040*

(0.023)

-0.434* * *

-0.434* * *

(0.013)

7.213* * *

(0.571)

(0.013)

7.211* * *

(0.572)

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

67108

0.008

67108

0.009

16954

0.184

16954

0.184

Notes:  within  estimators  with  firm  and  time  fixed  effects  (estimates  and  marginal  effects).  The 
dependent variable is the intensive margin of export defined as  the share  of exported sales on 
total turnover (Export share in columns, 1 and 2) or as the growth rate of exported value (Export 
sales growth, in columns 3 and 4). *, **, *** denote, respectively, significance at 10%, 5%, and 1% 
level. Robust standard errors in parentheses. 

Source: yearly balance-sheet data (Cribis D&B) and MET surveys 2008, 2009, 2011, 2013, and 2015. 
Additional regressors (untabulated) follow the specification in Table 9.4. All measures are defined 
in Appendix.  

64 

 
 
 
 
 
 
Table 9.15  Cumulative effects of dynamic strategies: innovation with or without R&D 

Dependent v ariable:

Export

Export sales growth

Estimator:

RE-Probit with Mundlak

Linear probability model

Within estimator

Innov ation with R&D

(1)

0.036* * *

(0.004)

Innov ation without R&D

0.010* * *

R&D share

Productiv ity

(0.003)

0.002* * *

(0.000)

0.001

(0.001)

(2)

0.041* * *

(0.007)

0.017* * *

(0.004)

0.002* * *

(0.000)

0.002

(0.001)

Constant

-3.021* * *

-1.657* * *

(0.193)

(0.270)

Cont rols

Region

Indust ry

Time

Region* Time

Industry* Time

Mundlak

Firm FE

yes

yes

yes

no

no

yes

--

yes

yes

yes

yes

yes

yes

--

(3)

0.055* * *

(0.009)

0.017* * *

(0.006)

0.003* * *

(0.000)

0.004

(0.004)

0.228* * *

(0.049)

yes

yes

yes

no

no

--

yes

(4)

0.055* * *

(0.013)

0.025* * *

(0.006)

0.003* * *

(0.001)

0.004

(0.004)

-0.169* * *

(0.053)

yes

yes

yes

yes

yes

--

yes

(5)

0.117* * *

(0.027)

0.066* * *

(0.021)

0.006* * *

(0.002)

0.038* *

(0.018)

7.214* * *

(0.571)

yes

yes

yes

yes

yes

--

yes

Observ ations

56955

30301

57375

37370

16954

Pseudo R2

R2

0.764

--

0.776

--

--

0.033

--

0.144

--

0.184

Notes:  RE-probit  models  with  Mundlak  correction  (marginal  effects  in  columns  1  and  2)  and  within 
estimator with firm and time fixed effects (estimates and marginal effects in columns 3, 4, and 5). The 
dependent variable is the extensive margin of export (Export), a dummy variable identifying exporting 
companies in columns 1 to 4, or the growth rate of exported value (Export sales growth in column 5). 
Innovation  with  or  without  R&D  are  dummy  variables  identifying  innovative  firms  performing  or  not 
performing  R&D  activity.  *,  **,  ***  denote,  respectively,  significance  at  10%,  5%,  and  1%  level.  Robust 
standard errors in parentheses. 

Source: yearly balance-sheet data  (Cribis D&B) and MET surveys 2008, 2009, 2011, 2013, and 2015. All 
measures are defined in Appendix.  

65 

 
 
 
 
 
 
 
 
 
 
 
 
Table 9.16  Marginal vs. large exporters 

Dependent v ariable:

Export share

Quantile:

R&D

Innov ation

Productiv ity

Size

Constant

Cont rols

Region

Indust ry

Time

Region* Time

Industry* Time

Mundlak

q10

(1)

0.047* * *

(0.009)

0.439* * *

(0.127)

0.414* * *

(0.059)

0.869* * *

(0.066)

-3.488* * *

(0.685)

yes

yes

yes

yes

yes

yes

q25

(2)

0.077* * *

(0.021)

0.467* * *

(0.151)

0.536* * *

(0.116)

1.215* * *

(0.190)

0.210

-1477

yes

yes

yes

yes

yes

yes

q50

(3)

0.118* * *

(0.035)

0.838* * *

(0.292)

1.906* * *

(0.214)

4.329* * *

(0.107)

q75

(4)

0.174* *

(0.070)

0.432

(0.508)

2.502* * *

(0.425)

4.651* * *

(0.296)

q90

(5)

0.089

(0.084)

0.070

(0.880)

2.544* * *

(0.378)

3.423* * *

(0.301)

-4.836* * *

20.253* * *

54.684* * *

-1851

-5256

-4880

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

yes

Observ ations

28016

28016

28016

28016

28016

Notes: quantile regressions  with Mundlak correction  (estimates).  The dependent variable is the intensive 
margin of export defined as the share of exported sales on total turnover (Export share). The estimation is 
performed  on  the  subsample  of  exporters  only.  Additional  regressors  (untabulated)  follow  the 
specification  in  Table  9.4.  *,  **,  ***  denote,  respectively,  significance  at  10%,  5%,  and  1%  level.  Robust 
standard errors in parentheses. 

Source:  yearly  balance-sheet  data  (Cribis  D&B)  and  MET  surveys  2008,  2009,  2011,  2013,  and  2015.  All 
measures are defined in Appendix. 

66 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Export

Export sales growth

RE-Probit with Mundlak

Within estimator

Table 9.17  Disproportionate effects for less productive firms 

Dependent v ariable:

Estimator:

Threshold (percentile):

33rd

(1)

Innov ation* Medium-High productiv ity

0.017* * *

(0.003)

R&D share*  Medium-High productiv ity

0.002* * *

Innov ation* Low productiv ity

R&D share* Low productiv ity

(0.000)

0.053* * *

(0.003)

0.004* * *

(0.000)

25th

(2)

0.016* * *

(0.003)

0.002* * *

(0.000)

0.051* * *

(0.003)

0.004* * *

(0.000)

Constant

Cont rols

Region

Industry

Time

Region* Time

Industry* Time

Mundlak

Firm FE

Observ ations

Pseudo R2

R2

-3.164* * *

-3.174* * *

(0.240)

(0.240)

yes

yes

yes

yes

yes

yes

--

37710

0.767

--

yes

yes

yes

yes

yes

yes

--

37710

0.767

--

33rd

(3)

0.071* * *

(0.020)

0.007* * *

(0.002)

0.175* * *

(0.035)

0.012* * *

(0.004)

6.992* * *

(0.588)

yes

yes

yes

yes

yes

--

yes

16424

--

0.206

25th

(4)

0.080* * *

(0.020)

0.007* * *

(0.002)

0.161* * *

(0.041)

0.011* * *

(0.004)

6.968* * *

(0.588)

yes

yes

yes

yes

yes

--

yes

16424

--

0.205

Notes: RE-probit models with Mundlak correction (marginal effects in columns 1-2) and within 
estimator with firm and time fixed effects (estimates and marginal effects in columns 3-4). The 
dependent variable is the extensive margin of export (Export), a dummy variable identifying exporting 
companies in columns 1 and 2, or the growth rate of exported value (Export sales growth in columns 3 
and 4). Medium-High or Low productivity are dummy variables identifying firms whose ex ante 
productivity is above or below the threshold (33rd or 25th percentile in columns 1 and 3, or 2 and 4, 
respectively) of the productivity distribution at the beginning of the sample. *, **, *** denote, 
respectively, significance at 10%, 5%, and 1% level. Robust standard errors in parentheses. 

Source: yearly balance-sheet data (Cribis D&B) and MET surveys 2008, 2009, 2011, 2013, and 2015. All 
measures are defined in Appendix. 

67 

 
 
 
 
 
 
 
 
 
 
 
Table 9.18  Disproportionate effects for small firms 

Dependent v ariable:

Export

Export sales growth

Estimator:

RE-Probit with Mundlak

Within estimator

Threshold (percentile):

33rd

(1)

Innov ation*  Medium-Large

0.0124* * *

(0.003)

R&D share*  Medium-Large

0.002* * *

(0.000)

25th

(2)

0.011* * *

(0.003)

0.002* * *

(0.000)

Innov ation* Small

0.0442* * *

0.0481* * *

R&D share* Small

(0.003)

0.003* * *

(0.000)

(0.003)

0.003* * *

(0.000)

Constant

-3.115* * *

-3.110* * *

(0.212)

(0.212)

Cont rols

Region

Industry

Time

Region* Time

Industry* Time

Mundlak

Firm FE

Observ ations

Pseudo R2

R2

yes

yes

yes

yes

yes

yes

--

45401

0.759

--

yes

yes

yes

yes

yes

yes

--

45401

0.759

--

33rd

(3)

0.078* * *

(0.020)

0.007* * *

(0.002)

0.219* * *

(0.049)

0.024* * *

(0.007)

7.017* * *

(0.588)

yes

yes

yes

yes

yes

--

yes

16424

--

0.206

25th

(4)

0.089* * *

(0.019)

0.007* * *

(0.002)

0.141* *

(0.068)

0.030* * *

(0.011)

7.002* * *

(0.588)

yes

yes

yes

yes

yes

--

yes

16424

--

0.205

Notes: RE-probit models with Mundlak correction (estimates in columns 1-2) and within estimator with 
firm and time fixed effects (estimates and marginal effects in columns 3-4). The dependent variable is 
the extensive margin of export (Export), a dummy variable identifying exporting companies in columns 
1 and 2, or the growth rate of exported value (Export sales growth in columns 3 and 4). Medium-Large 
or Small are dummy variables identifying firms whose ex ante size is above or below the threshold (33rd 
or 25th percentile in columns 1 and 3, or 2 and 4, respectively) of the size distribution at the beginning 
of  the  sample.  *,  **,  ***  denote,  respectively,  significance  at  10%,  5%,  and  1%  level.  Robust  standard 
errors in parentheses. 

Source: yearly balance-sheet data (Cribis D&B) and MET surveys 2008, 2009, 2011, 2013, and 2015. All 
measures are defined in Appendix. 

68 

 
 
 
 
 
 
 
 
Table 9.19  Persistent vs. new exporters: differential performance 

Dependent v ariable:

ln(exports)

Total sales growth

Sample:

Internationalized

Entire

New entrants

-0.407* * *

0.067* * *

(1)

(2)

Persistently internationalized

Constant

Cont rols

Region

Industry

Time

Region* Time

Industry* Time

Firm FE

Observ ations

R-squared

(0.046)

--

--

2.530* * *

(0.176)

yes

yes

yes

yes

yes

yes

9.627

0.674

(0.020)

0.022*

(0.012)

-0.761* * *

(0.067)

yes

yes

yes

yes

yes

yes

19.003

0.305

Notes: within estimator with firm and time fixed effects (estimates and marginal effects). 
The dependent variable is the value of exported products in logn (ln(exports) in column 
1)  or  the  growth  rate  of  total  sales  between  the  average  of  2008-11  and  2012-2015 
(Total  sales  growth  in  column  2).  New  entrants  is  a  dummy  variable  identifying 
companies  that  started  export  activity  after  2011  (and  were  only  domestic  before). 
Persistently  internationalised  is  a  dummy  measure  identifying  companies  that  were 
exporters  throughout  the  entire  time  span.  The  sample  varies  across  columns  and  is 
indicated in the second row. *, **, *** denote, respectively, significance at 10%, 5%, and 
1% level. Robust standard errors in parentheses. 

Source: yearly balance-sheet data (Cribis D&B) and MET surveys 2008, 2009, 2011, 2013, 
and  2015.  Additional  regressors  (untabulated)  follow  the  specification  in  Table  9.4.  All 
measures are defined in Appendix. 

69 

 
 
 
 
 
 
 
10.   APPENDIX 

Table 10.1  Variable definition 

Variable

Definition

Export

Export(EU)

Dummy v ariable for exporting companies

Dummy v ariable for companies exporting only in the EU area

Export(extraEU)

Dummy v ariable for companies exporting beyond EU area

Export share

Export sales

Share of sales from exported products

Value of exported products

Export sales growth

 ln(export sales)

Two-way

Import

FDI

Production

Productiv ity

Size

Age

Dummy v ariable for two-way traders (exporting & importing)

Dummy v ariable for importers

Dummy v ariable for firms with foreign direct inv estments

Dummy v ariable for firms with foreign production processes abroad

ln(v alue added / # employees)

ln(# employees)

ln(1 + age)

Capitalisation

ln(physical capital / # employees)

Group

Innov ation

Dummy v ariable for group membership

Dummy v ariable for the introduction of innov ations (independently of the type)

Product innov ation

Dummy v ariable for the introduction of product innov ations

Process innov ation

Dummy v ariable for the introduction of process innov ations

Organisational innov ation

Dummy v ariable for the introduction of organisational-managerial innov ations

R&D

R&D share

Sales

Dummy v ariable for the existence of R&D projects

Expenditure in R&D (as a share of sales)

Sales / total assets

Sales growth

 ln(sales)

ROA

Net income / total assets

Vertical integration

Value added / total turnov er

Cost of labour

Lev erage

Cost of labour / total cost of production

Total assets / equity

Net accounts payable

(Accounts payable - accounts receiv able) / total assets

Bank debt

Bank debt / total assets

Human capital

% of graduated employees (tertiary education)

Domestic network

Dummy v ariable for local domestic relationships with other companies

70 

 
 
 
 
 
 
 
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