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Multinational enterprises, technological intensity and firm survival. Evidence from Italian manufacturing and services firms.


It is commonly argued that foreign multinational enterprises (FMNEs) are more footloose than non-multinational domestic firms (DOMFs) (1) i.e. they are more likely to exit the market. Theoretically, the expected relationship between foreign ownership and firm survival is ambiguous. The footloose characteristics of FMNEs may be explained by these firms' position within an international production network. Thus, they can easily relocate production between countries in response to adverse shocks in the host country. Using optimal portfolio theory, Flamm (1984) showed that U.S. multinationals rapidly adjust their operations to changes in host country environments based on particular country risks. The exit propensity may also depend on the nature of foreign direct investment (FDI) involved: if FDI is horizontal, multinationals are mainly motivated by market-seeking determinants and are thus less likely to be influenced by changes in production costs in host countries. Conversely, vertical foreign investment, which is primarily driven by cost-saving forces, is more sensitive in reacting to changes in production costs and may be more likely to cease as a result of sudden shocks (Inui et al. 2009). However, the "rooted" character of FMNEs with respect to DOMFs may be explained by a result that emerged from the finance literature, which analyses the effect of sunk entry costs on firm exit (Dixit and Pindyck 1994): the larger the amount of irrecoverable costs, the greater the value of waiting before making an exit decision. Thus, if the sunk costs of investing abroad are higher than those for establishing a purely domestic plant in the host country, foreign affiliates are less likely to exit. (2)

After controlling for several firm and industry differences, a large amount of empirical research found that FMNEs are more footloose than DOMFs (Colombo and Delmastro 2000 for Italy; Bernard and Sjoholm 2003 for Indonesia; Gorg and Strobl 2003 for Ireland; Girma and Gorg 2004 for the UK; Van Beveren 2007 for Belgium; Alvarez and Gorg 2009 for Chile; Inui et al. 2009 for Japan; Bandick 2010 for Sweden), whereas other studies found that foreign-owned firms have the same survival chances as DOMFs (Mata and Portugal 2002 for Portugal; Ozler and Taymaz 2007 for Turkey).

Recently, by investigating the survival of foreign subsidiaries in Denmark over the period 1995-2001, Kronborg and Thomsen (2009) showed that FMNEs have a higher survival probability than domestic enterprises. However, the foreign survival advantage was eroded by globalization. In this literature, an issue that was somewhat overlooked is which factors may moderate or strengthen the footloose behavior of FMNEs. In particular, it could be argued that, given the strong concentration of FMNEs in industries with higher research and development intensity and greater technological complexity, the technological intensity of an industry might moderate their footloose behavior. (3) This question is particularly crucial if we consider that within the survival literature the relationship between the level of technological intensity in a sector and the survival probability of firms active in that sector is not clear-cut (Audretsch 1995). On the one hand, being active in a highly innovative sector enables firms to create new products, stimulating growth and thereby their likelihood of survival. There is compelling evidence that product and process innovation are important for survival, because even incumbent firms must continuously innovate to mitigate the threat of disruption by new technologies (Christensen 1997). On the other hand, it has been argued that the risk of exit may be higher for firms in high-tech sectors because of the uncertainty associated with innovation (Ericson and Pakes 1995).

Our study aims to add some value to the survival literature and fill this research gap by trying to answer the following research questions: How does foreign ownership affect the survival of Italian firms? To what extent are FMNEs located in Italy more footloose than DOMFs? To what extent does the level of technological intensity in a sector differently affect the survival dynamics of FMNEs versus DOMFs? To this end, we apply survival methods and estimate a Cox proportional hazards model (CPHM) (Cox 1972) in which we look for the effect of foreign ownership on firm survival, controlling for firm- and industry-specific covariates.

Our study contributes to the empirical literature on firm survival in several ways. First, our empirical analysis is based on a very large dataset with a much wider coverage of the firm universe than that which was considered in previous empirical research on firm survival in Italy. Second, unlike most of the empirical literature, our sample of firms is not restricted to manufacturing industries but also covers the service sectors. Third, by decomposing firm activities into different technological classes, we shed some light on the impact that technological environment may have on Italian firms' survival according to their ownership status.

Data and Description

The empirical analysis included in this paper was conducted using firm-level data from the Analisi Informatizzata Delle Aziende (AIDA) database, a commercial dataset provided by Bureau Van Dijk that contains extensive firm-level information on Italian firms. In particular, AIDA provides detailed yearly information on a wide set of economic and financial variables, such as sales, costs, number of employees, value added, tangible fixed assets, start-up year, sector of activity, and legal and ownership status. From the ownership section of the data, we constructed a foreign ownership indicator for a firm. Specifically, a firm is considered to be foreign if it is majority owned, wholly owned or the main known shareholder is foreign.

Firm exit is defined as the termination of firm's activities. Information about this is provided by a 'legal status' variable in the AIDA dataset. It indicates whether a firm is active or inactive. An inactive firm is defined as a firm that is in liquidation, dissolved, or in receivership. By omitting all observations for which the necessary data are incomplete, we obtained an unbalanced panel of approximately one million observations covering the years 2004 to 2008. (4)

The advantage of using this dataset is twofold. First, it is highly representative of the entire universe of corporate companies (e.g., in 2007, our sample covers approximately 87 % of the total employees declared by the Italian National Institute of Statistics (ISTAT 2008)). Second, our dataset accurately reflects the actual size distribution of firms in the Italian economy characterised, as is well known, by a large number of small- and medium-sized enterprises. (5)

Before formally testing for the relationship between ownership and firm survival, we provide some descriptive statistics based on our data. Tables 1, 2, and 3 compare the distribution of Italian firms by ownership status, sector of activity and size, the latter measured by the number of employees.

Following the breakdown of ISTAT, we considered five size classes for firms (1-9 employees; 10-49 employees; 50-99 employees; 100-249 employees, and greater than 250 employees). Looking at Table 1, we see that DOMFs represent the largest percentage of Italian firms (more than 99 %), which are primarily made up of smaller firms (i.e., firms with less than 50 employees account for approximately 95 % of total DOMFs). The number of FMNEs is substantially smaller than that of indigenous firms resulting in a much larger average size for foreign firms. It has been argued by Schivardi and Torrini (2003) that the relatively small average size of Italian firms could be due to some peculiar features of the labor market caused by Italian institutional settings. Within the institutional setting, the following five types of regulations depend on firm size: employment protection legislation, mandatory quotas on hiring, firm level rights to organize union related institutions, firms' safety standards and rules relating to collective dismissal procedures. In particular, firms with more than 15 employees are subject both to legislation regulating dismissals, which increases firing costs (6) and the possibility of applying for wage supplementation schemes (cassa integrazione straordinaria) as an alternative to layoffs. (7)

Following the Eurostat-OECD classification (Eurostat 2006), in Tables 2 and 3 firms are grouped in four classes according to the level of sectoral technological intensity in which they operate. (8) Specifically, we aggregated manufacturing (services) sectors into i) high- and medium-high-technology industries (knowledge-intensive services) and ii) low- and medium-low-technology industries (less-knowledge-intensive services).

At the subsample level, we also observe a pattern of distribution of firms similar to the one observed at the aggregated level. More specifically, both in high- and medium-high-technology and low- and medium-low-technology industries, firms are prevalently small.

Table 4 reports the average exit rates of firms (measured by the number of exiting firms relative to the total number of firms). These rates are reported both as a total and according to ownership status. The figures suggest that, compared with the total sample and the services, the percentage of exit is larger in DOMFs with a rate of 6.00 and 6.50 %, respectively. In the manufacturing industries, the highest exit rate (5.83 %) is registered for FMNEs. Moreover, when we consider the different level of technology, we observe the expected higher exit rates for firms active in low- and medium-low-technology industries, independently of the ownership status. However, the results for the services sectors are surprising, as the firms active in less-knowledge-intensive services show an exit rate lower than the others.

Empirical Methodology

The empirical analysis is carried out by using survival methods. These methods allow us to control both for the occurrence of an event (i.e. whether a firm exits) and the timing of the event (that is, when the exit takes place). Therefore, these methods take into account the evolution of the exit risk and its determinants over time.

The central concept in survival analysis is the hazard function [h(t)] which is defined as the probability that a firm will exit the market in a moment t given that it survived until this period t and conditional on a vector of covariates [], which may include both time-varying and time-constant variables:

h(t, []) = [lim.sub.dt[right arrow]0] Pr (t [less than or equal to] T < + dt | T [greater than or equal to] 1, < + []) (1)

where T is a non-negative continuous random variable (duration). Specifically, we undertake a multivariate analysis in order to evaluate the effect of foreign ownership on the risk of exit controlling simultaneously for the effect of other variables considered. Thus, we estimate the following semi-parametric CPHM:

h(t, []) = [h.sub.o] {t)exp([beta]' []) (2)

where [h.sub.0] (t) stands for the (unspecified) baseline hazard when all of the covariates are set to zero, that is, the hazard function obtained for exp([beta]'X)= 1, and [beta]' is a corresponding vector of regression coefficients to be estimated. The logarithm expression of Eq. (2) gives us a linear model that can be estimated by the maximum likelihood method:

lnh(t) = ln[h.sub.0](t) + [beta] [] (3)

An interesting feature of the model, which motivates its name, is that the effect of a covariate operates in additive fashion on In [h.sub.0](t) so that a unit change in a covariate leads to a proportional effect on the hazard rate. Also, to interpret the estimates, we should note that hazard ratios lower (higher) than one imply that the hazard rate decreases (increases) and the corresponding probability of survival increases (decreases), with all other things being equal. In addition to a measure for foreign ownership (INW) measured by a dummy that takes a value of 1 if firm i is foreign-owned and 0 otherwise, our empirical model includes a set of both firm-specific (FX) and industry-specific (SX) control variables, and time dummies ([[delta]sub.t]]) to capture business cycle effects:

ln h(t) = ln [h.sub.0] (t) + [[beta].sub.1]IN [] + [[gamma]] F[] + [[theta].sub.jt] X [X.sub.jt] + [[delta].sub.t] + [v.sub.ijt] (4)

Firm-Specific Variables

With regard to firm-specific determinants that might affect survival, we first consider firm size (SIZE). Several studies provide evidence of a positive effect of size on the likelihood of survival (Audretsch and Mahmood 1995; Mata and Portugal 1994, 2002; Disney et al. 2003; Segarra and Callejon 2002). Two main arguments help explain the liability of smallness. First, the output levels of larger firms are more likely to be close to their industries' minimum efficient scales. Second, compared with small firms, large firms have easier access to capital markets and a greater capacity to recmit qualified workers.

Another important predictor of firm survival is age (AGE). Young firms are expected to have a higher risk of failure than older ones because they lack sufficient resources and capabilities, and have not yet established a stable set of relationships, both among internal members and with outside suppliers and buyers. However, several empirical

studies (Evans 1987; Agarwal and Gort 1996) show that age initially reduces exit hazard, and then raises it. This can be explained by the fact that the firms' stock of knowledge increases with time but at a decreasing rate, and the relationship between age and survival might not be monotonic, following instead an inverted-U pattern. So, in our model we also enter the squared term of age (AGE_SQ).

Both firm-level productivity (PROD) and profitability (PROFIT) are likely to affect firm survival. In the first case several theoretical models (Jovanovic 1982; Melitz 2003) have predicted that the exit of firms is largely motivated by productivity differences at the firm level, whereas in the second case, empirical evidence has shown that profitable firms are more likely to survive since they are more able to generate positive cash flow and, thus, provide the necessary resources to develop firm-specific assets (Geroski 1995; George 2005; Esteve Perez and Manez Castillejo 2008). For both control variables, the expectation is that the survival rate of firms will be higher for more productive and more profitable firms, respectively.

Theoretically, we would expect more capital-intensive firms to be more likely to survive. In fact, firms with higher capital-labour ratios (KL) may have a lower ratio of variable to fixed costs. Given the basic shutdown rule that a firm will remain in operation as long as it can cover variable costs, firms with low variable cost production techniques may be more likely to withstand negative shocks than high variable cost production (Dorns et al. 1995). In addition, if capital is firm-specific, it may involve sunk costs and become a barrier to exit (Dixit 1989).

Finally, to control for some peculiar features of the Italian institutional settings of the labour market that could affect firm exit, we introduce a proxy of institutional protection (ISLM). We expect that ISLM negatively affects the likelihood of survival because firms that face a lower degree of institutional protection may be expected to have a higher risk of exit than firms that must comply with special labour regulations.

Industry-Specific Variables

To control for industry-specific conditions, we use a set of variables computed at three-digit level of the Italian Standard Industrial Classification System ATECO 2002. The first variable is the degree of market competition (HERE), approximated by the Flerfindhal concentration ratio. Firms in highly concentrated markets are subject to fiercely aggressive behavior by rivals, which may threaten the survival of incumbents. However, a higher market concentration may also lead to higher price-cost margins in the industry, which, ceteris paribus should increase a firm's probability of surviving. Empirical evidence is quite mixed, since some authors find a positive effect of industry concentration on firm survival (Audretsch 1995; McCloughan and Stone 1998; Segarra and Callejon 2002), while others do not find any statistically significant relationship (Wagner 1994; Mata and Portugal 1994; Strotmann 2007).

Economies of scale (MES) in an industry may also play an important role. Industries with a larger minimum efficient scale should have higher price-cost margins and, thus, a higher probability of firm survival. However, the minimum efficient scale should exert a positive influence on the hazard rate because the output of new firms is typically less than the minimum efficient scale level (Audretsch 1991). Empirical evidence concerning the influence of scale economies is also ambiguous. While some authors find that firms entering sectors with higher minimum efficient scale have a lower chance of survival (Audretsch and Mahmood 1995; Tveteras and Eide 2000; Gorg and Strobl 2003; Strotmann 2007), others do not find statistically significant relationships (Mata and Portugal 1994).

Finally, the degree of foreign presence in a particular sector is expected to have an impact on firm survival. As a matter of fact, there is a large body of literature that focuses on the externality effects that may arise from the presence of FMNEs in host economies. These FDI-related externalities (spillover effects) may have both a technological and a pecuniary nature. In the first case spillovers from FDI rely on real externalities since they represent the set of productivity benefits due to the technological transfer from parent firms to foreign affiliates which in turn leaks out to DOMFs. The second type of spillovers from FDI are expected to occur via the competition effect in the product market. Indeed, by entering a new market, FMNEs affect the competitive environment of the industry in which they operate. This increased competition is likely to affect DOMFs, both positively and negatively. On the one hand, they may react to foreign competition by using the existing technology more efficiently or by investing in new technology in order to remain competitive and maintain their market shares (Wang and Blomstrom 1992). On the other hand, since FMNEs are more productive than their local competitors, they produce at lower marginal costs than the host country's firms and thus have an incentive to increase output and attract demand away from these firms. This will result in some DOMFs ceasing their production or losing some market share (Aitken and Harrison 1999). Technological and pecuniary spillovers can occur both at horizontal and vertical levels (Javorcik 2004). In the first case, FDI-related externalities to DOMFs take place at the intra-industry level (HSPILL). In the second case, FDI spillovers occur through linkages at the backward level (BACKSPILL) (i.e. the relationships between FMNEs and their domestic suppliers) and/or at the forward level (FORWSPILL) (the relationships between FMNEs and their domestic customers of intermediate inputs). (9) Table 9 in the appendix summarises the explanatory variables used in estimations.

Estimation Results

Estimation Results for Manufacturing and Service Sectors

In this section, we present the regression results of the CPHM of Eq. (4) applied to our sample of Italian firms during the period from 2004 to 2008. The estimations are stratified by two-digit ATECO industrial classifications, which allow for equal coefficients of the covariates across strata (industries) but baseline hazards that are unique for each stratum (industry). For each regression, we report the hazard rates and associated robust standard errors, which are adjusted for clustering at the firm level (Table 5).

Table 5 shows that the hazard ratios for the coefficients on INWare higher than 1 and statistically significant at the 1 % level. This means that Italian firms with foreign ownership have an approximately 70 % increased risk of failure with respect to DOMFs when they operate in the manufacturing sectors (column i) and a 43 % higher risk when they belong to service sectors (column ii). Our finding supports the hypothesis that FMNEs are more "footloose" than national firms and is in line with several studies for different countries (Audretsch and Mahmood 1995; Colombo and Delmastro 2000; Bernard and Sjoholm 2003; Girma and Gorg 2004; Van Beveren 2007; Esteve Perez and Manez Castillejo 2008; Ferragina et al. 2012).

Let us now turn to the other firm- and industry-specific characteristics. Looking at Table 5, we observe that the results are generally in accordance with our expectations and with the results of (other) previous studies. In particular, a robust and expected finding across the two different samples is that older and more productive firms are found to have a lower risk of exit than younger and less productive firms. This result is in line with the predictions of the industrial organisation selection models literature (Jovanovic 1982; Hopenhayn 1992) and is highly standard in the empirical evidence on firm survival (Dunne et al. 1988; Mata and Portugal 2002; Esteve Perez and Manez Castillejo 2008; among others). However, we find that in both manufacturing and services sectors, the control variable of the square term of age is greater than 1 and statistically significant. The existence of a non-monotonic relationship between age and survival suggests that a liability of adolescence could be in place: the risk of exit is relatively low when firms are young, whereas it increases later on in the firm's lifecycle. Firm size has a positive effect on firm survival only in the service sectors (i.e., small firms face a higher hazard of exit compared with large firms). Higher firm profits correspond to a lower risk of failure. Similar to the results of several previous studies (Esteve Perez and Manez Castillejo 2008; Ozler and Taymaz 2007), the positive effect of this variable may be attributed to the relationship of higher profits with higher efficiency or a higher degree of market power. The impact of capital-intensity differs among the two macro-sectors. While in the manufacturing industries, capital-intensive firms have an almost 8% higher probability of exit than less capital-intensive firms, in the services higher capital-intensity increases firm survival. (10) Finally, the hazard ratios for the variable ISLM seem to indicate that a lower degree of institutional protection increases the risk of failure both in the manufacturing and service sector. In order to check if the preponderance of small firms in our sample could drive the results, we re-estimated Eq. (4) including an interaction variable between INW and SIZE (JNW SIZE). This variable, which should measure the different impact of size on firm survival for FMNEs, is never statistically significant.

Regarding the industry-specific covariates, our results show that firms in industries with a higher level of minimum efficient scale have a higher probability of exit. Corresponding to the studies by Audretsch and Mahmood (1995) and Gorg and Strobl (2003), the negative effect of this variable may be explained as follows. Firms may face more obstacles in achieving an efficient production scale and may suffer a cost disadvantage vis-a-vis the most efficient firms in the market. Our results also indicate that higher levels of industry concentration correspond to an increased probability of firm exit. This result suggests that firms in highly concentrated sectors are subject to stronger competition, which increases their likelihood of exit. (11) With regard to the spillover effect, our results suggest, on the one hand, the absence of both horizontal and backward spillovers, and, on the other, the existence of positive forward spillovers but only in the service sectors. This may be due to the fact that MNEs in upstream industries provide inputs to domestic firms that were previously unavailable in the country, or make them technologically more advanced or less expensive, or ensure that they are accompanied by the provision of complementary services.

The result that only service sectors seem to benefit from FDI vertical spillovers is in line with Ayyagari and Kosova (2010) for the Czech Republic and with the empirical evidence concerning Italy, which indicates a weak presence of FDI-related externalities at the intra-industry level in manufacturing industries (Imbriani and Reganati 2002, 2004; Reganati and Sica 2007; Castellani and Zanfei 2007; Imbriani et al. 2014; Ferragina and Mazzotta 2013), but positive vertical spillovers in the service sector (Pittiglio et al. 2008).

Estimation Results for Sectors with Different Technological Intensity

Due to the large size of our database, we are able to better verify the existence of some sector-specific characteristics that may interact with the different covariates in explaining the probability of firm survival in the Italian economy. Thus, we re-estimate our model disaggregating manufacturing and service sectors according to the level of technological intensity. Specifically, we aggregate manufacturing (service) sectors into: i) high- and medium-high-technology industries (knowledge-intensive services) and ii) low- and medium-low-technology industries (less-knowledge-intensive services).

The estimates in Tables 6 and 7 reveal that being controlled by FMNEs is associated with a higher probability of exit, regardless of the technological level of the sector of activity in which it operates. Thus, within both low and high-tech industries, foreign ownership exerts a strongly negative influence on the survival of firms suggesting that the behaviour and strategies of MNEs differ from those of DOMFs. In particular, in the low- and medium-low-technology industries, the hazard of exit is approximately 67 % higher for FMNEs than DOMFs. In the high- and medium-high-technology industries, the chance of exit is approximately 60 % higher for FMNEs compared to DOMFs. Similar results are obtained in the services: FMNEs belonging to the less-knowledge-intensive services have a 46 % greater risk of exiting compared with DOMFs belonging to the same sector, whereas they exceed by almost 45 % the exit risk of DOMFs within knowledge-intensive services.

Turning to the other firm- and industry-specific characteristics, we observe that the results are generally in accordance with our previous results at the more aggregate level. In particular, older and more productive firms are found to have a lower risk of exit than younger and less productive firms. Again, the relationship between age and survival follows an inverted-U pattern across all the subsamples. Firm size has a positive effect on firm survival only in the low- and medium-low-technology industries and less-knowledge-intensive services.

Higher firm profits correspond to a lower risk of failure only for firms belonging to high- and medium-high-technology industries and to knowledge-intensive services.

Finally, we find that ISLM turns out to affect survival negatively. Less institutionally protected firms face higher hazards of exit than do more institutionally protected firms while the interaction variable INW_SIZE confirms the finding obtained at aggregate level resulting not statistically significant.

Regarding the industry-specific covariates, our results show that firms in industries with a higher minimum efficient scale have a higher probability of exit across all the subsamples with the exception of knowledge-intensive services. Higher levels of industry concentration correspond to an increased probability of firm exit in both high- and medium-high-technology industries and knowledge-intensive services, while they are associated with a higher probability of firm survival in the low- and medium-low-technology industries and less-knowledge-intensive services.

Some interesting results emerge when we look at the FDI-related effect variables. In the manufacturing sector, DOMFs operating in the high- and medium-high-technology industries are the ones that benefit from horizontal spillovers: the foreign presence acts to increase the survival of domestic firms. On the one hand, this supports the idea that domestic firms must be technologically advanced in order to benefit from FDI technology spillovers. On the other hand, our results show that in low-tech and medium-low-technology industries the presence of FDI in the same industry generates significant and negative effects on the durations of DOMFs. In this case, the negative spillover effects imply that competition between FMNEs and DOMFs strongly dominate the learning effects of DOMFs from FMNEs. Similar results were found by Kosova (2010) for the Czech Republic and by Gorg and Strobl (2003) for Ireland.

Table 6 also shows that the results for vertical spillovers are less straightforward. On the one hand, in high- and medium-high-technology industries the foreign presence across downstream industries negatively affects the survival of DOMFs, suggesting that inputs produced locally by FMNEs might be less adapted to local requirements and cause difficulties when integrated into the production chain. On the other hand, in the low- and medium-low-technology industries, we find that foreign presence has a positive effect on the duration of DOMFs both across upstream and downstream industries. Finally, looking at the service sectors, we find that FDI-related effects on the survival of DOMFs are present only in the less-knowledge-intensive services. In particular, the presence of FMNEs in the same industry generates significant and negative effects, while DOMFs benefit from vertical spillovers both across upstream and downstream industries.


This paper has empirically investigated the relationship between foreign ownership and firm survival in Italy. To achieve this goal, we conducted an analysis for the period from 2004 to 2008 based on CPHM. This study has allowed us to determine whether foreign firms are more likely to exit the market than DOMFs and the extent to which the exit probability of foreign firms depends on the technological environment in which they operate. The analysis reveals that manufacturing and service firms owned by FMNEs are more likely to exit the market than national firms. By decomposing firm activities into different technological classes, we still find that foreign ownership affects the survival prospects of Italian manufacturing and service firms, even if there are some differences between less and more technologically- or knowledge-intensive sectors. In particular, our findings reveal that the chance of exit of foreign firms compared to domestic firms is higher in the low- and medium-low technology industries and less-knowledge-intensive services than in the highland medium-high-technology and knowledge-intensive industries, respectively.

In addition, we find that the presence of FDI in the same industry generates significant and negative effects on the survival of DOMFs which operate in low- and medium-low technology industries and less-knowledge-intensive services. This can be explained by the fact that competition between FMNEs and DOMFs strongly dominates the learning effects of DOMFs from FMNEs. On the other hand, indigenous firms operating in high- and medium-high-technology industries are the ones that benefit from horizontal spillovers. Positive vertical spillovers through both backward and forward linkages prevail among low- and medium-low technology industries and less-knowledge-intensive services while in high and medium-high-technology industries, foreign presence across downstream industries negatively affects the survival of DOMFs.

Overall, our study enriches our understanding of the determinants of firms' survival in Italy and allows us to draw some insights in terms of policy design. First, the much higher hazard ratios for FMNEs in low- and medium-low-technology manufacturing sectors seem to support the hypothesis that the exit behaviour of foreign firms is indeed related to the role of opportunity costs which are generally quite relevant in low-tech industries, and to the role of sunk costs of setting up production which (on average) are higher in less traditional sectors, ceteris paribus.

Second, our findings seem to suggest that there is a peculiar behaviour of firms in service sectors where FMNEs exhibit exit rates lower than in manufacturing. These results suggest that peculiar features of services might play an important role. In fact, there is a wide range of services which are more likely to be non-tradable and therefore, which can be supplied to local markets by foreign and domestic firms only through the location in those markets. Therefore, it is not a surprising result that activities by FMNEs are less volatile.

Finally, our study enriches our understanding of the determinants of firms' survival in Italy and suggests a number of policy implications. In order to increase firm survival the indications show the importance of adopting ownership-specific incentive policies. Policies should also be calibrated according to the sector involved, taking into account the very different features of manufacturing and service activities which need to be further investigated with regard to their different sensitiveness to variables and the policy of firm attraction and internationalisation. In this context, incentives to foreign investors appear to be a strategy that is more appropriate in the service sectors, where there are higher positive spillovers on survival induced by the presence of foreign firms. In order to raise the probability of survival, policy makers should also target some firm-specific characteristics that are crucial determinants of performance gaps in survival, primarily, size and productivity. Our findings should be taken into account in current policies of FDI attraction and of firm internationalisation via FDI.

DOI 10.1007/s11293-014-9441-3

Table 8 Eurostat-OECD classification ATECO 2002

Manufacturing Industries             Services

High- and medium-high-technology     Knowledge-intensive services
                                     61 Water transport
24 Manufacture of chemicals and
  chemical products                  62 Air transport

29 to 35 Manufacture of machinery    64 Post and telecommunications
  and equipment n.e.c.;
  Manufacture of electrical and      65 to 67 Financial intermediation
  optical equipment; Manufacture
  of motor vehicles, trailers and    70 to 74 Real estate, renting and
  semi-trailers; Manufacture of        business activities
  other transport equipment
                                     80 Education

                                     85 Health and social work

                                     92 Recreational, cultural and
                                       sporting activities

Low- and medium-low-technology       Less-knowledge-intensive services
                                     50 to 52 Wholesale and Retail
15 to 22 Manufacture of food           trade
  products, beverages and tobacco;
  Textiles and textile products;     55 Hotels and restaurants
  Leather and leather products;
  Wood and wood products; Pulp,      60 Land transport; transport via
  paper and paper products,            pipelines;
  Publishing and printing;
                                     63 Supporting and auxiliary
                                       transport activities;
                                       activities of travel agencies

23 Manufacture of coke, refined      75 Public administration and
  petroleum products and nuclear       defence; compulsory social
  fuel;                                security

                                     90 Sewage and refuse disposal,
                                       sanitation and similar
25 to 28 Manufacture of rubber
  and plastic products; Minerals,    93 Other service activities
  basic metals and fabricated
  metal products; other non-
  metallic mineral products;

36 Furniture

Table 9 Definition of variables used in Eq. (4)

Variables   Description

INW         Foreign multinational ownership is measured by a dummy
              that takes a value of 1 if firm i is foreign-owned and
              0 otherwise
SIZE        Firm size is given by the firm i total number of
              employees at time t
AGE         Firm age is defined as the difference between year of
              observation t and the official year of incorporation of
              the firm.
PROD        Firm productivity is defined as firm i net value added
              per employee at time t
KL          Firm capital-intensity is given by firm i fixed assets
              over total employment at time t
PROFIT      Profit is the operating margin for total sales of firm i
              at time t.
ISLM        The institutional setting of the labour market is a dummy
              variable that takes a value of 1 if firm i has a number
              of employees less than 15, and 0 otherwise.
HERF        The degree of market competition is measured by the
              Herfindahl index). It is constructed as:

                            [N.summation over (i = 1)]

MES         The minimum efficient scale is measured by the average
              value of shipments of the largest establishments
              producing more than 50 % of industry shipments divided
              by industry shipments (Comanor and Wilson 1967)
HSPILL      Foreign presence in the same sector is measured by the
              share of foreign firms' output in total sector output:

            [HSPILL.sub.jt] = [summation over i[member of]j,i = MNEs]
            [OUTPUT.sub.ijt]/[summation over = MNEs] [OUTPUT.sub.ijt]

BACKSPILL   Backward vertical spillovers to domestically-owned firms
              that supply their inputs to foreign-owned enterprises
              is given by:

             [BACKSPILL.sub.jt] = [summation over k,k[not equal to]j]
                         [[gamma]sup.jkt] [HSPILL.sub.kt]

              where [[gamma].sub.jkt] is the proportion of the j's
              output supplied to sourcing sectors k

FORWSPILL   Forward vertical spillovers to domestically-owned firms
              that buy inputs from foreign-owned enterprises is
             measured by:

             [FORSPILL.sub.jt] = [summation over l,l[not equal to]j]
                        [[delta].sub.ljt] []

              where [[delta].sub.ljt] is the proportion of sector j's
              inputs purchased from upstream sectors l.

Acknowledgments We would like to thank an anonymous referee for providing us with constructive comments and suggestions.

We owe special thanks to Carla Carlucci for her support with the database construction and to Umberto De Marco for his assistance with the AIDA data collection. Any errors or omissions remain the responsibility of the authors.

Published online 21 December 2014


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R. Pittiglio ([mail])

Second University of Napoli, Capua, CE, Italy


F. Reganati

Sapienza, University of Roma, Roma, RM, Italy


(1) A firm is identified as a domestic non-multinational enterprise (DOMF) if it has no subsidiaries in countries other than Italy and it is not foreign-owned.

(2) However, the literature is controversial on this point. Some authors posit that multinationals should face higher sunk costs when establishing a new firm because new firms are typically more skill- and capital-intensive than incumbent firms. Other authors claim that multinationals, such as multi-unit enterprises, are likely to benefit from lower sunk costs in terminating a plant's operations due to the greater efficiency of their internal factor markets in re-deploying the production equipment and labour force of the closed plant (Baden-Fuller 1989).

(3) In one recent study on Portugal, Varum et al. (2010) have shown that foreign firms operating in more technology-intensive industries face lower hazards during crises.

(4) We focus on this period because, since 2004, AIDA showed wider coverage of corporate enterprises operating in both the manufacturing and services sectors.

(5) Approximately 95 percent of the firms present in our database have less than 50 employees compared with the official statistics of 98.5 % in 2006 (ISTAT 2008).

(6) In 1970, the Statute dei Lavoratori (Law No. 300) established that in the case of unfair dismissals, all firms with more than 15 employees had to hire back workers and pay the wages foregone.

(7) Firms undergoing temporary crisis may access these schemes instead of firing part of their workforce. Wages are temporarily paid by supplementation funds and the employment spell is not broken.

(8) For a detailed list of these sectors along with their Italian Standard Industrial Classification (ATECO) codes, see Table 8 in the appendix.

(9) All nominal variables included in the database were deflated using an appropriate producer price index provided by ISTAT, which also provided the annual input-output table used to construct inter-industry linkages. Simple correlation coefficients were also calculated among the variables to assess whether multicollinearity was present. Correlations between the independent variables were generally low. Results are available upon request.

(10) Similar results for manufacturing were found by Bandick (2010) for Sweden and by Ozler and Taymaz (2007) for Turkey.

(11) The empirical evidence of the effect of market concentration on firm survival is mixed. Gdrg and Strobl (2003) found a positive effect, and Mata and Portugal (1994) and (Strotmann 2007) found a negative effect,
Table 1 Distribution of Italian firms by size and ownership status
(percentages, sample average)

               FMNEs   DOMFs   Total

Total sample    0.60   99.40   100.00
Size 1-9       24.28   69.00    68.68
Size 10-49     36.80   25.59    25.71
Size 50-99     12.51    3.40     3.25
Size 100-249   14.80    1.33     1.62
Size >250      11.61    0.68     0.74

Author's elaborations on AIDA database

Table 2 Distribution of Italian manufacturing firms by size and
ownership status (percentages, sample average)

                                               FMNEs   DOMFs   Total

Manufacturing industries                        0.76   99.24   100.00
Size 1-9                                       13.87   54.10    53.70
Size 10-49                                     32.08   37.05    37.10
Size 50-99                                     15.50    5.57     5.72
Size 100-249                                   21.98    2.45     2.53
Size >250                                      16.57    0.83     0.95
High- and medium-high-technology industries     1.61   98.39   100.00
Size 1-9                                       14.61   54.36    53.74
Size 10-49                                     31.02   35.64    35.55
Size 50-99                                     14.56    5.18     5.30
Size 100-249                                   21.87    3.64     3.96
Size >250                                      17.94    1.18     1.45
Low- and medium-low-technology industries       0.42   99.58   100.00
Size 1-9                                       12.73   54.05    53.89
Size 10-49                                     33.72   37.55    37.53
Size 50-99                                     18.03    4.93     4.98
Size 100-249                                   21.06    2.77     2.85
Size >250                                      14.46    0.70     0.75

Source: Author's elaborations on AIDA database

Table 3 Distribution of Italian services firms by size and ownership
status (percentages, sample average)

                                     FMNEs   DOMFs   Total

Services                              0.52   99.48   100.00
Size 1-9                             31.58   77.07    76.82
Size 10-49                           39.45   19.08    19.20
Size 50-99                           10.62    1.96     2.01
Size 100-249                          9.98    1.28     1.33
Size >250                             8.37    0.61     0.64
Knowledge-intensive services          0.51   99.49   100.00
Size 1-9                             30.37   76.63    76.36
Size 10-49                           39.05   18.74    18.88
Size 50-99                           11.99    2.58     2.64
Size 100-249                          9.59    1.24     1.28
Size >250                             9.00    0.81     0.84
Less-knowledge-intensive services     0.53   99.47   100.00
Size 1-9                             32.73   77.01    76.77
Size 10-49                           39.29   19.63    19.74
Size 50-99                            9.57    1.82     1.86
Size 100-249                         10.43    1.06     1.11
Size >250                             7.98    0.48     0.52

Author's elaborations on AIDA database

Table 4 Exit rate by ownership status and sector (percentages, sample

                                               FMNEs   DOMFs   Total

Manufacturing industries                       5.83    4.75    4.74
High- and medium-high-technology industries    4.80    4.54    4.58
Low- and medium-low-technology industries      7.50    4.74    4.81
Services                                       5.82    6.50    6.50
Knowledge-intensive services                   6.84    6.72    6.72
Less-knowledge-intensive services              5.39    6.12    6.11
Total sample                                   5.77    6.00    6.00

Author's elaborations on AIDA database

Table 5 Estimation results: Cox proportional hazard model

                 Manufacturing industries    Services

                 (ATECO 15-37)               (ATECO 40-99)

                 (i)           (ii)          (iii)         (iv)

Firm-specific variables
  INW            1.7313        1.7439        1.4352        1.4137
                 (0.125) ***   (0.138) ***   (0.079) ***   (0.079) ***
  AGE            0.9779        0.9779        0.9795        0.9795
                 (0.002) ***   (0.002) ***   (0.001) ***   (0.001) ***
  AGE_SQ         1.0003        1.0003        1.0003        1.0003
                 (0.000) ***   (0.000) ***   (0.000) ***   (0.000) ***
  SIZE           1.0000        1.0000        0.9999        0.9999
                 (0.000)       (0.000)       (0.000) **    (0.000) **
  PROD           0.9442        0.9442        0.9870        0.9870
                 (0.013) ***   (0.013) ***   (0.002) ***   (0.002) ***
  KL             1.0783        1.0783        0.9802        0.9802
                 (0.034) **    (0.034) **    (0.008) **    (0.008) **
  PROFIT         0.9979        0.9980        0.9973        0.9972
                 (0.001) *     (0.001) *     (0.001) ***   (0.001) ***
  ISLM           1.3047        1.3049        1.2098        1.2082
                 (0.023) ***   (0.023) ***   (0.016) ***   (0.016) ***
  INW_SIZE                     1.0000                      1.0002
                               (0.000)                     (0.000)

Industry-specific variables
  MES            1.0245        1.0245        1.0379        1.0379
                 (0.011) **    (0.011) **    (0.006) ***   (0.006) ***
  HERF           1.0001        1.0001        1.0001        1.0001
                 (0.000) ***   (0.000) ***   (0.000) ***   (0.000) ***
  FIS PILL       0.4906        0.4905        1.6785        1.6754
                 (0.337)       (0.337)       (0.625)       (0.624)
  BACKSPILL      8.9801        9.0277        0.8274        0.8285
                 (24.923)      (25.061)      (0.595)       (0.596)
  FORWSPILL      1.5441        1.5465        0.0566        0.0565
                 (0.697)       (0.698)       (0.026) ***   (0.026) ***

Time dummies     Yes           Yes           Yes           Yes
No. of obs.      281,341       281,341       725,731       725,731
No. of           110,217       110,217       306,758       306,758
No. of           17,483        17,483        56,537        56,537
Wald test        446.61        446.73        619.67        618.82

Source: Author's own elaborations on AIDA database

***, **, * indicate statistical significance at the 1, 5 and 10 %
levels. Robust standard errors, adjusted for clustering at the firm
level, are in parentheses. Tests based on the null that the
coefficients are equal to 1.

Table 6 Estimation results: Cox proportional hazard model

                 Low- and medium-low-        High- and medium-high-
                 technology industries       technology industries

                 (i)           (ii)          (iii)         (iv)

Firm-specific variables
  INW            1.6722        1.6609        1.600         1.6044
                 (0.150) ***   (0.152) ***   (0.095) ***   (0.102) ***
  AGE            0.9851        0.9851        0.9795        0.9795
                 (0.002) ***   (0.002) ***   (0.002) ***   (0.002) ***
  AGE SQ         1.0002        1.0002        1.0003        1.0003
                 (0.000) ***   (0.000) ***   (0.000) ***   (0.000) ***
  SIZE           0.9998        0.9998        1.0000        1.0000
                 (0.000) **    (0.000) **    (0.000)       (0.000)
  PROD           0.9369        0.9369        0.9879        0.9879
                 (0.008) ***   (0.008) ***   (0.002) ***   (0.002) ***
  KL             0.9892        0.9892        0.9816        0.9816
                 (0.027)       (0.027)       (0.017)       (0.017)
  PROFIT         0.9989        0.9989        0.9978        0.9978
                 (0.003)       (0.003)       (0.001) ***   (0.001) ***
  ISLM           1.2738        1.2733        1.2570        1.2569
                 (0.027) ***   (0.027) ***   (0.019) ***   (0.019) ***
  INWS1ZE                      1.0001                      1.0000
                               (0.000)                     (0.000)

Industry-specific variables
  MES            1.0292        1.0292        1.0264        1.0264
                 (0.012) **    (0.012) **    (0.011) **    (0.011) **
  HERF           0.9999        0.9999        1.0001        1.0001
                 (0.000) **    (0.000) **    (0.000) **    (0.000) **
  HSPILL         10.8261       10.8160       0.2402        0.2405
                 (5.176) ***   (5.172) ***   (0.190) *     (0.190) *
  BACKSPILL      0.0021        0.0021        15.5908       15.5488
                 (0.002) ***   (0.002) ***   (36.709)      (36.603)
  FORWSPILL      0.2633        0.2632        2.3771        2.3780
                 (0.100) ***   (0.100) ***   (1.233) ***   (1.234) *

Time dummies     Yes           Yes           Yes           Yes
No. of obs.      201,054       201,054       80,287        80,287
No. of obs.      79,008        79,008        31,258        31,258
No. of           12,653        12,653        4,830         4,830
Wald test        358.21        357.8         439.49        439.64

Source: Author's own elaborations on AIDA database

***, **, * statistical significance at the 1, 5 and 10 % levels.
Robust standard errors, adjusted for clustering at the firm level,
are in parentheses. Tests based on the null that the coefficients are
equal to 1.

Table 7 Estimation results by knowledge intensity in the services:
Cox proportional hazard model

                 Less-knowledge-intensive    Knowledge-intensive
                 services                    services

                 (i)           (ii)          (iii)         (iv)

Firm-specific variables
  INW            1.4625        1.4524        1.453         1.4252
                 (0.106) ***   (0.106) ***   (0.101) ***   (0.105) ***
  AGE            0.9814        0.9813        0.980         0.9799
                 (0.002) ***   (0.002) ***   (0.002) ***   (0.002) ***
  AGE_SQ         1.0003        1.0003        1.000         1.0003
                 (0.000) ***   (0.000) ***   (0.000) ***   (0.000) ***
  SIZE           0.9999        0.9998        1.000         1.0000
                 (0.000) *     (0.000) **    (0.000)       (0.000)
  PROD           0.9473        0.9473        0.988         0.9883
                 (0.004) ***   (0.004) ***   (0.002) ***   (0.002) ***
  KL             0.9927        0.9927        0.967         0.9666
                 (0.009)       (0.009)       (0.023)       (0.023)
  PROFIT         0.9978        0.9979        0.998         0.9982
                 (0.004)       (0.004)       (0.001) ***   (0.001) **
  ISLM           1.2672        1.2632        1.218         1.2178
                 (0.020) ***   (0.020) ***   (0.023) ***   (0.023) ***
  INW_SIZE                     1.0003                      1.0000
                               (0.000) **                  (0.000)

Industry-specific variables
  MES            1.0529        1.0530        0.993         0.9933
                 (0.007) ***   (0.007) ***   (0.011)       (0.011)
  HERF           0.9999        0.9999        1.0002        1.0002
                 (0.000) **    (0.000) **    (0.000) ***   (0.000) ***
  HSPILL         4.0436        4.0369        3.036         3.0359
                 (1.434) ***   (1.432) ***   (3.344)       (3.343)
  BACKSP1LL      0.0502        0.0501        0.495         0.4947
                 (0.032) ***   (0.031) ***   (0.789)       (0.789)
  FORWSPILL      0.3453        0.3445        0.604         0.6038
                 (0.109) ***   (0.108) ***   (0.386)       (0.386)

Time dummies     Yes           Yes           Yes           Yes
No. of obs.      358,902       358,902       224,112       224,112
No. of           148,866       148,866       96,384        96,384
No. of obs.      26,309        26,309        17,894        17,894
Wald test        692.42        692.55        279.09        279.13

Source: Author's own elaborations on AIDA database

***, **, * indicate statistical significance at the 1, 5 and 10 %
levels. Robust standard errors, adjusted for clustering at the firm
level, are in parentheses. Tests based on the null that the
coefficients are equal to 1.
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Author:Pittiglio, Rosanna; Reganati, Filippo
Publication:Atlantic Economic Journal
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Geographic Code:4EUIT
Date:Mar 1, 2015
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