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World Econ. 2019;00:1–34. wileyonlinelibrary.com/journal/twec | 1© 2019 John Wiley & Sons Ltd
Received: 26 September 2018 | Revised: 23 July 2019 | Accepted: 30 August 2019
DOI: 10.1111/twec.12866
O R I G I N A L A R T I C L E
Firms' efficiency and global value chains: An empirical investigation on Italian industry
Mariarosaria Agostino1 | Emanuele Brancati2 | Anna Giunta3 | Domenico Scalera4 | Francesco Trivieri1
1University of Calabria, Arcavacata di Rende, Italy2LUISS Guido Carli University, Roma, Italy3Roma Tre University and Rossi‐Doria Centre, Roma, Italy4University of Sannio, Benevento, Italy
K E Y W O R D Sefficiency, global value chains, Italian industry
1 | INTRODUCTIONThe establishment and growth of global value chains1 (GVCs) are key features of the process of inter-national integration of markets and vertical fragmentation of industries that has taken place in the last 25 years. Several aspects of this phenomenon have been examined by the economic literature: some studies have focused on macroeconomic consequences of the upsurge of GVCs and related policy is-sues (Amador & di Mauro, 2015; De Backer & Miroudot, 2014; Grossman & Rossi‐Hansberg, 2006; Koopman, Powers, Wang, & Wei, 2011; OECD, 2012; UNCTAD, 2013), while others have specifi-cally analysed the effects of firms' participation in GVCs or other production network (Baldwin & Venables, 2013; Costinot, Vogel, & Wang, 2013; Weisbuch & Battiston, 2007). In particular, the rela-tionship between firms' involvement in GVCs and their performance has often been investigated through productivity indicators (Agostino, Giunta, Nugent, Scalera, & Trivieri, 2015; Del Prete, Prete, Giovannetti, & Marvasi, 2017; Giovannetti, Marvasi, & Sanfilippo, 2015; Veugelers, Barbiero, & Blanga‐Gubbay, 2013), but rarely efficiency issues have been explicitly taken into account (Manello, Calabrese, & Frigero, 2016).
Yet, there are several reasons to believe that efficiency gains may accrue to firms from participation in GVCs, because of both productive and organisational rewards. The former are mainly linked to ver-tical specialisation and externalities connected to entering GVCs. Vertical specialisation (Hummels, Ishii, & Yi, 2001) allows firms to focus on specific activities and production stages within the chain, and exploit comparative advantages realised across the phases of the production process. In fact, on 1 The term ‘Global Value Chain’ denotes the entire complex of operations and transactions within and between firms, through which raw materials are transformed into intermediate products and then into final goods. For industrial products, the transformation carried out along GVCs involves many stages, ranging from design, manufacturing and assembly to marketing and distribution; these activities are frequently dispersed over a good number of different firms, regions and countries.
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the one hand, buyer/assembler or "final" firms (henceforth indicating firms selling to the end markets) may offshore less‐rewarding stages of production, and import new, cheaper and higher‐quality vari-eties of inputs so as to benefit from restructuring organisational processes thanks to a broader variety and better quality of imported inputs (Castellani, Serti, & Tomasi, 2010). On the other hand, suppliers (i.e., firms selling intermediate goods to other companies rather than to end customers) are given a chance to access international markets, increase production and exploit economies of scale. The international dimension of chains is particularly beneficial for small and medium‐sized enterprises (SMEs), allowing them to be no longer locked into supplying national buyers and exposed to sunk costs of specialising in a specific production (McLaren, 2000).
Concerning externalities, GVCs are recognised to be an environment where productivity spillovers take place, especially favouring SMEs and suppliers. Indeed, trade relationships with large actors of the chain (first of all, multinational enterprises, usually on the frontier of productivity, innovation and technology) provide a valuable opportunity to increase SME's productivity and efficiency through learning about technologies, and organisational and managerial practices (Saia, Andrews, & Albrizio, 2015). Lead firms stimulate suppliers by demanding better quality inputs, share knowledge and tech-nology with them and encourage the adoption of new practices (Criscuolo, Timmis, & Johnstone, 2015). Especially when such interfirm relationships imply some involvement in the strategic stages of production (mainly: product conception, R&D and design), SMEs and suppliers may enhance techni-cal knowledge and managerial skills, exploit new channels of innovation and penetrate new markets, thereby boosting their productivity and approaching the efficiency frontier (Gereffi, Humphrey, & Sturgeon, 2005; Humphrey & Schmitz, 2002; Pietrobelli & Rabellotti, 2011).
Efficiency gains may also arise from organisational changes induced by involvement in GVCs. The relationship between firm's organisation and performance has been object of a broad literature, mainly referring to highly influential theories on transaction costs and property rights. On that basis, several possible advantages of vertical integration have been pointed out, in terms of reduction of transaction costs (Klein, Crawford, & Alchian, 1978; Williamson, 1971, 1975), improved multitasking incentives (Holmström & Milgrom, 1991), alignment of control and incentives (Grossman & Hart, 1986; Hart & Moore, 1990) and better coordination (Hart & Holmström, 2010). On the other hand, vertical integra-tion is also costly and thus requires appropriate financial commitment (Acemoglu, Johnson, & Mitton, 2009); in addition, it tends to give managers weaker incentives to profit maximisation and imply higher bureaucratic costs (Grossman & Hart, 1986; Klein, 2005).2
By contrast, other scholars pointed out possible advantages of "hybrid organizational forms such as long‐term contracts, partial ownership agreements, franchises, networks, alliances, and firms with highly decentralized assignments of decision rights […] attempting to achieve some level of central coordination and protection for specific investments while retaining the high‐powered incentives of market relations" (Klein, 2005, p. 438).
The issue of hybrid organisational forms has been revived by the development of GVCs, which emphasises the potential benefits of firms' relationships, by relying on a wide and dense web of firms' transactions. Indeed, as pointed out by a recent strand of the literature (Gereffi et al., 2005; Humphrey & Schmitz, 2002; Pietrobelli & Rabellotti, 2011), GVCs can reduce the costs of uncertainty, incom-plete contracts and potential opportunistic behaviour by hinging on repeated collaboration, reputation and careful "selection of players." On the other hand, thanks to the developments of ICT, possible organisational diseconomies of long supply chains are controlled by the use of information and data sharing technologies (Park & King, 2007).
2 For an empirical assessment of the effects of vertical integration on efficiency and the distance from the frontier, see for instance Månsson (2004) and Pieri and Zaninotto (2013).
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But neither all firms' transactions are alike, nor the benefits equally shared by all the firms involved in the chain. The type of transaction, the firm positioning (Agostino, Giunta, Scalera, & Trivieri, 2016) and the GVC governance modes are factors heavily affecting the firms' performance (Brancati, Brancati, & Maresca, 2017). Leveraging the literature on transaction costs, "a typology can be elabo-rated that highlights multiple ways in which the relationship between firms in GVCs could be coordi-nated" (De Marchi, Maria, & Gereffi, 2017, p. 5). Such a typology poses market‐based relationships among firms and vertically integrated companies at opposite ends of the spectrum, and classifies as intermediate types of value chains those characterised by modular, captive and relational modes of governance (Gereffi et al., 2005). In a "modular" chain, orders to suppliers are relatively easy to be codified, generic machinery is used, switching costs on both sides are relatively low; in a "captive" chain small suppliers are heavily dependent on a few lead firms, and their tasks are confined to a lim-ited range; finally, a GVC is characterised by a "relational" governance when participation implies knowledge sharing, complex information, specific investments for both buyers and suppliers, and high switching costs.3
This paper aims at contributing to the firm‐level empirical literature on GVCs on several under-investigated aspects. First of all, we detect possible effects of participation in GVCs on firms' ability to locate closer to the efficiency frontier. To our knowledge, this is the first study analysing the link between GVC involvement and firms' efficiency. In fact, the impact of participation in GVCs on firms' efficiency has been formerly investigated only by Manello et al. (2016) on the Italian automotive sec-tor, whereas our paper deals with the entire Italian industry, thus providing results with a higher level of generalisation.
A relevant strand of the extant literature focused on partial measures of firms' productivity, such as labour productivity, or total factor productivity (TFP). However, interfirm differences or time changes in TFP can be driven by different factors, such as individual improvements in technical efficiency (moving closer to the production frontier), overall shifts of the technology frontier (due for example to technological progress), as well as movements along the frontier connected to changes in operational scale. The efficiency analysis performed in this paper explicitly focuses on pure technical efficiency, defined as firms' ability to maximise output for given resources and technology. By excluding sources of productivity changes due to technology progress and changes in scale, we aim at investigating to what extent improvements in a firm's pure technical efficiency can be ascribed to the participation in GVCs.
Furthermore, we explore heterogeneity across different forms of GVC governance. Our research hypothesis is that the impact of GVCs on firms' efficiency is especially beneficial when relational modes of participation are in place, so that through interfirm relationships within GVCs, SMEs can get involved in strategic stages of production and find incentives and opportunities to raise their effi-ciency. To allow for differential efficiency gains deriving from each specific mode of participation, we explicitly analyse the role of GVC governance by defining a "relational mode" as the one occurring when firms take part in product conception, R&D and design.
In addition, we also highlight other dimensions of heterogeneity, linked to firms' positioning in the GVC (suppliers vs. final firms) and time length of participation in the GVC. In this way, we can assess and compare the impact of participation in GVCs for final and supplier firms, and evaluate whether the effects of joining or leaving a chain on firms' efficiency take place immediately or need a longer time to emerge.
3 In accordance with this theoretical framework, in what follows we will distinguish between relational, conventional and broad‐sense participation in GVCs, where "conventional" is the participation in GVCs characterised by "modular" or "captive" governance and "broad‐sense" is participation in any kind of GVCs.
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Our emphasis on SMEs suppliers in the manufacturing sector is justified by the key role these firms play in GVCs and their importance in the manufacturing structure of Italy and most industri-alised countries. As a matter of fact, suppliers are the majority of firms in most European countries and mostly relevant in terms of aggregate employment, productivity and trade (Agostino et al., 2016). It follows that a better understanding of the determinants of their performance, once involved in the GVC, is crucial for policymakers aiming at improving a country's competitiveness. Admittedly, sup-pliers generally suffer from a "productivity discount" (Kimura, 2002; Razzolini & Vannoni, 2011) and have been severely affected by the great crisis of 2007–08 (Accetturo & Giunta, 2016; Altomonte et al., 2012). However, some researchers have also pointed out significant heterogeneity in their be-haviour and performance (Bönte, 2008). According to Agostino et al. (2015), the "most capable sup-pliers" (i.e., the ones exporting and carrying out both product and process innovation) display labour productivity and TFP at least as high as final firms with comparable levels of abilities. Other studies (Veugelers et al., 2013) show that suppliers producing intermediate goods for GVCs gain a significant productivity premium compared to those producing for local buyers.
Our analysis takes advantage of the 2009, 2011 and 2013 waves of the MET survey on the Italian industry (details on the dataset are given in Section 2). Despite using the same dataset, our paper differentiates from Brancati et al. (2017) for several features. Beside diversities in the identification of different kinds of involvement in GVCs and the estimation techniques employed (see Section 3.3), the main difference concerns the key variable of our analysis and econometric investigation. Indeed, while Brancati et al. (2017) study innovativeness and sales' growth of firms involved in GVCs, we spe-cifically focus on firms' efficiency, a relevant performance indicator especially suitable to assess the effects of participation in GVCs. Second, unlike Brancati et al. (2017), we allow for further differential sources of heterogeneity in the impact of GVCs by explicitly considering both the positioning of the firm in the GVC and the time length of participation. Finally, we deepen the analysis on the sources of gains from involvement in GVCs and disentangle the effects of internationalisation from those of relational participation by separately studying the impact of belonging to a GVC in the subsample of firms (within or outside a GVC) involved in the upstream high‐value‐added stages of production (product concept, R&D, design).
The empirical investigation starts by identifying suitable proxies for firms' involvement and kind of involvement (relational or conventional) in GVCs, for which we employ information on import, export, participation in product conception and the existence of close and stable ties with foreign counterparts, as well as position of firms in GVCs. After retrieving a measure of technical efficiency, based on the data envelopment analysis (DEA) approach, we use DEA scores for both a graphical analysis, supported by non‐parametric tests, and econometric exercises aimed at assessing whether and how much participation in GVCs affects SMEs' technical efficiency. To account for potential en-dogeneity issues (more efficient firms could self‐select into GVCs), we design an analysis of propen-sity score matching (PSM) and perform a number of robustness checks based on truncated regression and Heckman models.
Our results consistently show that taking part in GVCs matters, as it allows Italian manufacturing SMEs to obtain significant efficiency gains. Benefits are greater for suppliers than final firms and larger in case of relational than conventional participation. Also, entering or leaving a GVC implies immediate gains (losses) in case a relational GVC is joined (left), while in the case of conventional participation, benefits (damages) need longer time to occur.
The remainder of the paper is as follows. Section 2 presents our dataset. Section 3 discusses the construction of our main variables of interest and illustrates the strategy for the empirical investiga-tion. Section 4 shows the results, while Section 5 summarises the main conclusions.
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2 | DATA
The main source of data is the 2009, 2011 and 2013 waves (each reporting data on the previous year) of the MET database on Italian firms, the widest survey conducted in a single European country. The original sample follows a disproportionate Bayesian scheme and is representative of the Italian econ-omy along each stratum of size, geographical region, and industry. Each wave is made of roughly 25,000 cross‐sectional observations and, unlike other surveys, provides information even on microsized companies with less than 10 employees. Because of our research question, we rule out large enterprises and restrict the analysis to the manufacturing sector only.4 Table 1 shows the composition of the operat-ing sample by firm size and geographical macroregion (Northern, Central and Southern Italy).
The questionnaire asks for a rich array of information that is then exploited to derive our main vari-able of interest (i.e., our proxy for GVC participation), the identification of relational GVCs, and the classification of suppliers and final firms. In particular, we exploit data (self‐assessments) on firms' internationalisation attitude (import and export activities), the type of good exported (semi‐finished or final) and the share of sales to order to other companies. Furthermore, we use information about the involvement of the firm in the conception, R&D and design stages of production, and the existence of close and stable ties with foreign counterparts as proxy for participation in a GVC. The latter pieces of information are directly derived from firms' answers to the questionnaire. This allows us to avoid making judgments on the type of relationship in place and participation in the GVC, and leave to the firm the assessment on whether their international link is non‐occasional and represents a critical activity for its own business.
Survey data are then matched with official balance‐sheet information provided by CRIF‐Cribis D&B. To carry out the econometric investigation and derive additional controls, we select companies with complete balance‐sheet information within the years of interest. Finally, some observations are dropped because of unreasonable values or 1% censoring aimed at limiting the influence of outliers. Overall, the final estimating sample is made of approximately 15,200 observations.
4 Ruling out services is due to a number of reasons. First, in international trade, services exhibit peculiarities in how they are transacted and regulated, and the way they affect downstream sectors (Heuser and Mattoo, 2017). Second, service crossing‐border transactions (i.e., other than touristic services) are mostly high‐value‐added services, related to conception, R&D and design stages of production, so that in (almost) no case service firms participate in conventional GVCs. Third, the MET survey collects information only on business services rather than on the whole service sector.
T A B L E 1 Composition of the sample by firm size and geographical macroregion
2009 (%) 2011 (%) 2013 (%)Size (number of employees)
Micro (1–9) 62.10 60.60 47.80Small (10–49) 28.40 28.00 38.50Medium (50–249) 9.60 11.40 13.60
Geographical macroregionNorth 42.50 44.40 39.00Centre 27.10 26.20 26.20South 30.40 29.40 34.80
Source: MET dataset. For further details, see Brancati et al. (2017).
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3 | THE EMPIRICAL INVESTIGATION
Our empirical investigation proceeds in three steps. First, we retrieve the dependent variable of our analysis (pure technical efficiency, EFF), defined as a firm's ability to maximise output given the available resources and technology.5 Second, we perform graphical analysis and non‐parametric tests to verify our working hypotheses about the effects of a firm's participation in GVCs on its efficiency. Third, we carry out an econometric analysis based on propensity‐score matching (PSM, Rosenbaum & Rubin, 1983) to account for the potential self‐selection of more efficient companies into GVCs and employ the bootstrapped truncated (Simar & Wilson, 2007) and Heckman (1979) estimators to pro-vide robustness to our results.
It is worth emphasising that the analysis is carried out on both the entire set of companies and some subsamples, in order to provide evidence on specific groups of firms, which may be differently affected by participation in GVCs. In particular, we separately consider: (a) firms involved in the conception, R&D and design stages of production; (b) firms entering or leaving a GVC in the period considered; (c) suppliers; and (d) final firms.
3.1 | Retrieving DEA efficiency scoresThe DEA approach (Charnes, Cooper, & Rhodes, 1978) has been extensively employed to ob-tain relative measures of (firm, sector, institution or country) efficiency based on the distance from a piecewise production frontier, representing the locus of technically efficient input–output combinations.
Indicating with N the number of firms belonging to each sector, if the ith firm employs K inputs to produce M outputs (represented by the vectors xi and qi, respectively), the K × N input matrix X and the M × N output matrix Q represent the data of all N firms in each sector.6 Assuming variable returns to scale (VRS), for each firm the following linear programming problem is solved:
where !∗ is a scalar in the interval (1, ∞), ! is a N×1 vector of constants, and N1 is an N×1 vector of ones. Since !∗=1 for firms located on the efficient frontier, and !∗>1 when the firm is inefficient, !∗−1 is defined between 0 and 1, with a value of 1 (<1) corresponding to a point on (below) the frontier.
Allowing for VRS implies that not all firms are assumed to be operating at their optimal scale, as it is especially plausible when focusing on SMEs. Thus, like Alvarez and Crespi (2003, p. 238), “we measure inefficiency that is caused only by an excessive use of inputs and not by inadequate plant size.” Furthermore, our efficiency measure is based on contemporaneous frontiers; that is, we con-sider as a benchmark for each observation in a given year all the other observations belonging to the same sector in the same year.
5 Alternatively, efficiency may also be defined as the ability to minimise the amount of inputs required to produce a given output level. To allow for different technologies across sectors, we carry out separate computations at the 3‐digit level of the ATECO classification, derived from the European NACE taxonomy.6 The output is approximated by sales, while capital, labour and raw materials are measured by their respective costs (Table 2 provides further details). Because of lack of data on inputs and outputs prices at the firm level, following Milana et al. (2013), we derive output (input) volumes by deflating nominal values. To this purpose, indexes defined at sector level drawn from the EU KLEMS database (https ://www.eukle ms.net/) are used.
(1)max !,"" s.t. Q!−"qi ≥0; xi−X!≥0; N1′!=1; !≥0,
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3.2 | The non‐parametric analysis of DEA score distributionsBy comparing the observed distributions of DEA scores for different samples and using the non‐parametric Kruskal and Wallis (1952) test, we assess whether firms' technical efficiency significantly varies according to participation and modes of participation in GVCs. To this purpose, DEA scores are plotted on the x‐axis and cumulative shares of firms on the y‐axis, ordered by level of efficiency (Figures 1‒5). A rightward (leftward) shift in the distribution of efficiency scores implies increasing (decreasing) efficiency. A significant result of the Kruskal–Wallis test can be then interpreted as evi-dence in favour of the hypothesis that efficiency performance is significantly different across different groups of firms.
T A B L E 2 Description of the variables and main summary statistics
Variable Description Mean SD Min Max ObsEmployed to retrieve the efficiency measures
SALESa Total sales 6,798 12,400 0.366 158,000 20,016KAPa Tangible plus intangible assets
(including depreciation)2,433 4,603 0.020 53,600 20,016
RAWa Expenditure for raw materials 3,592 7,781 0.001 94,200 20,016EMPLOa Personnel expenditure 1,127 1,734 0.002 20,300 20,016
Entering the EFFICIENCY modelEFF Technical efficiency score, based on
DEA (assuming variable returns to scale)
0.53 0.21 0.002 0.968 15,205
BROAD Dummy coded 1 if a firm takes part in any kind of GVC
0.16 0.37 0 1 15,205
REL Dummy coded 1 if a firm takes part in a relational GVC
0.11 0.32 0 1 15,205
SUP Dummy coded 1 if the percentage of sales on order is 100%
0.28 0.45 0 1 15,205
SIZE a Total assets 6,646 8,472 323.690 32,700 15,205AGE Current year minus firm's year of
establishment22 13 4 48 15,205
INDEBT Total debt to total assets 62.5 21.6 20.5 95.5 15,205CASHFLOW Cashflow 4.26 5.48 −8.7 15.8 15,205GRADUATE Share of graduate employees 6.70 13.17 0 100 15,205HHIA Herfindahl–Hirschman index on
firms' assets (at 4‐digit NACE sectoral level)
0.08 0.08 0.01 0.349 15,205
PATENT Patent applications (per million inhabitants) to the EPO by priority year by NUTS 3 regions
81.11 72.75 1.30 532.40 15,205
GDPPC Provincial per capita GDP 28,084 7,513 14,374 52,499 15,205CNORTH Dummy coded 1 for firms operating
in Northern and Central regions0.76 0.43 0 1 15,205
aThousand euros, deflated values (price indexes defined at sectoral level, drawn from http://www.eukle ms.net/). Source: MET dataset.
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3.3 | The econometric analysis
3.3.1 | Propensity score matchingTo corroborate the results of our non‐parametric analysis, we employ propensity‐score matching tech-niques (PSM, henceforth) to dig deeper into causality nexuses linking GVC participation and firms' efficiency. From a methodological standpoint, firms' participation in global value chains is potentially endogenous to efficiency, especially in case of self‐selection of persistently more efficient companies into the chain (driven by observable and/or unobservable factors simultaneously affecting the two variables). This upward bias makes estimates of standard regression inconsistent and requires to be properly tackled to make inference on causality issues. Under the conditional independence assump-tion (CIA),7 the PSM method allows to retrieve the average treatment effect on the treated (ATT), 7 If CIA holds, the outcomes associated with treated and untreated units are independent of the treatment, conditional on the knowledge of observable factors affecting the sample selection. In other words, knowledge of observable factors restores the condition of randomisation.
F I G U R E 1 (a) All sample. Firms in GVC (gvc1) vs. firms not in GVC (gvc0). (b) Only suppliers. Firms in GVC (gvc1) vs. firms not in GVC (gvc0). (c) Firms changing status. Firms in GVC (gvc1) vs. firms not in GVC (gvc0)
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measuring the expected difference in efficiency (firm's technical efficiency score based on bootstrap DEA, described in Section 3.1) between treated and control firms.8
In our baseline analysis, we consider as “treated” the set of companies involved in a GVC (first in any kind of GVC; later in relational governance GVCs only) and regard non‐participants as “controls.” For the sake of robustness, we also provide estimates of ATT when the treatment variable is “entering” rather than “being in” a GVC, keeping the set of non‐GVC companies as a control.9 We employ an array of observable characteristics to pair companies with similar probability of belonging to (or
8 We do not use matching diff‐in‐diff estimators as our dataset does not allow to build up a panel. This leaves us no choice but to implement standard matching techniques, taking advantage of a rich set of time‐varying controls to capture most of heterogeneity driving firm participation in a GVC.9 Focusing on companies "entering" GVC may also allow to attenuate residual endogeneity not taken care of by the original matching design. Indeed, it might be argued that time‐invariant unobservables are more likely to affect assiduous participa-tion in a GVC (a stable condition) than accessing the chain.
F I G U R E 2 (a) All sample. Firms in relational GVC (rel1) vs. firms not in relational GVC (rel0). (b) Only suppliers. Firms in relational GVC (rel1) vs. firms not in relational GVC (rel0). (c) Firms changing status. Firms in relational GVC (rel1) vs. firms not in relational GVC (rel0)
rel1 rel0
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entering) a GVC, that only differ for their actual participation (or entry) in a value chain. By matching firms with similar ex‐ante probability of being treated (based on a propensity score retrieved in the first step), we get rid of differences affecting the two groups that may potentially drive the correlation between GVC participation and efficiency.
Before discussing the main observables characteristics employed in the matching, it is worth defin-ing the treatment variable. In order to classify companies into GVC participants and non‐participants, we take advantage of a rich information set from the MET survey. First of all, since semi‐finished products are employed in other firms' production process, we regard exporters of intermediate goods as involved in a GVC. In the same spirit, two‐way traders, that is, companies that both import and export, are fully internationalised and have a higher likelihood of belonging to a value chain (through
F I G U R E 3 All sample. Suppliers in GVC (s_g1)–Suppliers not in GVC (s_g0)–Final firms in GVC (f_g1)–Final firms not in GVC (f_g0)
s_g0 f_g0s_g1 f_g1
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F I G U R E 4 All sample. Suppliers relational (s_r1)–Suppliers no relational (s_r0)–Final firms relational (f_r1)–Final firms no relational (f_r0)
s_r0 f_r0s_r1 f_r1
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backward and/or forward linkages). A certain degree of uncertainty arises for partially internation-alised companies, since their openness to international trade may simply be driven by the search of cheaper inputs or new final markets. For this group of firms, we condition their participation in a GVC to the existence of significant and long‐lasting relationships with foreign counterparts, so focusing on non‐occasional links that represent a critical activity for a firm's business.
As not all companies belonging to a GVC establish relevant and close ties with other international firms, and since repeated interactions provide a suitable environment for knowledge exchange and effi-ciency gains, we also build an alternative indicator to identify “relational” GVCs. In particular, we consider a firm to be involved in a relational mode of participation if it is characterised by long‐lasting trade relationships with foreign companies and it is highly involved in the conception, R&D and design stages of production of the final good. This identification is different from Brancati et al. (2017) work-ing on the same Italian dataset, since we specifically focus our attention on relational forms of GVCs, that is, the ones granting the highest efficiency premium.10 By employing different treatment dummies, the matching analysis separately tests the effect of belonging to (or entering) any kind (conventional or relational) of GVC (BROAD) or a relational governance GVC (REL) against non‐participation.
We match participants and non‐participants along a set of several observable characteristics. First of all, because we specifically focus on the role of GVC participation in overcoming efficiency disadvantages of suppliers, we match treated and control groups along our proxy for supplier status (SUP) taking unit value if a firm sells its whole product to order to other companies. Additionally, we check an alternative proxy by employing a continuous variable measured by the share of sales to order to total sales (SUPR). We also base our matching on a set of controls made of lagged firm‐level regressors as well as provincial and industry‐level variables.11 As to firm‐level characteristics,
10 Brancati et al. (2017) provide a broader taxonomy than ours and show that other modes of participation in GVCs yield no premium for firms' innovativeness and performance compared to domestic companies. This evidence is in line with our findings.11 Our analysis is rooted in a large body of research which investigates the determinants of firms' efficiency by resorting to the interplay of individual firms' characteristics and external context factors (e.g., Alvarez & Crespi, 2003; Aw et al., 2000; Caves, 1992; Caves & Barton, 1990; Charoenrat & Harvie, 2014; Djankov & Murrell, 2002; Frydman et al., 1999; Sinani et al., 2007).
F I G U R E 5 Firms involved in product conception. Firms in relational GVC (rel1) vs. firms not in GVC (rel0)
rel1 rel0
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12 | AGOSTINO ET AL.
we control for (the log of) total assets as a proxy for firms' size (SIZE). Given that our dependent variable is technical efficiency purged from scale efficiency, the SIZE control is not meant to cap-ture economies of specialisation resulting from larger dimensions, but rather other potential effects of size on the ability of firms to successfully manage their input combinations. For instance, larger firms may have better access to finance, attract employees with higher skills and be more export‐ori-ented and thus more exposed to international competition and beneficial "learning by exporting" effects (Clerides, Lach, & Tybout, 1998). In the same vein, we also consider (the log of) firms' age (AGE): older firms may exploit potential "learning by doing" effects and enjoy easier access to credit, given their longer records; on the other hand, younger enterprises may be more motivated to build their reputation, more inclined to internationalisation and more capable of absorbing new technological knowledge. Further, we include the ratio of total debt to total assets in order to control for firm's indebtedness (INDEBT). Greater liquidity connected to indebtedness may smooth the production process, helping firms optimally utilise their productive capacity. However, debt may also entail agency costs as shareholders can behave opportunistically, at the expense of debt holders, by making decisions that do not necessarily enhance firm value or efficiency.12 Moreover, banks or suppliers may keep financing indebted customers, even when undertaking inefficient projects, to avoid their default and recuperate past loans (Carletti, 2004). In addition, we also control for CASHFLOW and the share of graduate employees (GRADUATE): firms' liquidity should enhance the capability to optimally manage production process, decreasing the completion time of projects (and hence their relative costs), while higher human capital should entail more efficiency and greater absorptive capacity, a fundamental prerequisite for firms to gain from GVC involvement (Agostino et al., 2015).
Furthermore, we control for some provincial characteristics for local development (provincial per capita gross domestic product, GDPPC) and local generation of knowledge (patent applications, PATENT). At sector level, we take into account the degree of industry concentration (by using the Herfindahl–Hirschman Index based on total assets HHIA), as higher competitive pressure should stim-ulate firms' efficiency.13 The geographical dummy CNORTH (distinguishing the Central and Northern regions of Italy) is considered to account for the structural differences of relatively lagging regions of Southern Italy. Lastly, industry effects and year dummies are included among controls. Tables 2 and 3 report the variables' summary statistics and the correlation matrix.
While we check the consistency of our results to different matching algorithms, we report results obtained through 5, 10 or 20 nearest neighbours (NN).14 We then infer the effect of participation (or entry) in a GVC on efficiency by comparing the expected difference in firm's technical efficiency score (based on bootstrap DEA) between treated and control groups (both along our broad measure and the narrow proxy for relational governance).
12 We do not mention organisation inefficiencies due to agency costs connected to the separation between ownership and control (Jensen, 1986; Jensen & Meckling, 1976) because they should be negligible for Italian SMEs, featuring a strongly concentrated ownership structure (e.g., Costi & Messori, 2005; Giacomelli & Trento, 2005).13 Higher HHIA values do not necessarily imply lower competition in an industrial sector. Indeed, while according to the structure–conduct–performance paradigm, concentration may foster collusive behaviour among firms and therefore reduce the degree of competition, the efficient–structure hypothesis claims that a greater concentration is associated with higher market competition, as the most efficient firms are spurred to increase their market shares at the expense of less efficient competitors.14 Kernel and local linear matching analyses provide consistent results but in a few instances show an unbalance in one control variable. These additional results are available on request.
| 13AGOSTINO ET AL.
TABL
E 3
Corre
lation
matr
ix
SU
PBR
OAD
REL
SIZE
AGE
IND
EBT
CASH
FLO
WG
RAD
UAT
EH
HI
PATE
NT
GD
PPC
CNO
RTH
SUP
1
BRO
AD−0
.011
1
RE
L0.0
090.8
166
1
SIZE
−0.07
70.2
120.0
981
AGE
−0.01
90.0
690.0
590.3
161
IN
DEB
T−0
.010
−0.03
8−0
.028
−0.11
0−0
.268
1
CA
SHFL
OW
0.006
0.007
0.003
0.016
−0.04
8−0
.281
1
GRA
DU
ATE
−0.04
70.0
930.0
380.1
860.0
39−0
.033
0.016
1
H
HIA
−0.03
10.0
07−0
.008
0.050
0.027
−0.03
00.0
110.0
531
PA
TENT
0.029
0.070
0.034
0.108
0.146
−0.02
10.0
30−0
.004
−0.04
51
GD
PPC
0.036
0.087
0.047
0.160
0.211
−0.06
00.0
330.0
48−0
.019
0.733
1
CNO
RTH
0.054
0.089
0.057
0.149
0.201
−0.03
70.0
330.0
26−0
.033
0.740
0.804
1
14 | AGOSTINO ET AL.
3.3.2 | Robustness checksWe perform two main sets of robustness checks to corroborate our findings. First of all, like other papers investigating the impact of internal firms' characteristics and external factors on efficiency (e.g., Agostino, Ruberto, & Trivieri, 2018; Alvarez & Crespi, 2003; Cummins & Xie, 2013; Kwoka & Pollitt, 2010; Sufian, 2009), we adopt a two‐stage DEA estimator. In particular, to ensure valid in-ference, we carry out the double (smoothed) bootstrap procedure proposed by Simar and Wilson (2007): we first bootstrap DEA scores to obtain bias‐corrected efficiency scores and then regress them on covariates by employing bootstrapped regressions.15
To single out the importance of involvement in GVCs, the mode of participation in GVCs, and the firm positioning in the chain (supplier vs. final firm) as determinants of technical efficiency, we sepa-rately estimate the following truncated regression models, while controlling for other factors affecting the response variable:
where indices i and t refer to firms and time periods (2008, 2010 and 2012), respectively. The dependent variable is the firm's technical efficiency score based on bootstrap DEA, described in Section 3.1 and the 1. On the right‐hand side, the dummies BROAD (Equation 2), REL (Equation 3) and SUP are our key explanatory variables and CTRL indicates a vector of controls including all the variables used in the matching analysis of Section 3.3.1.
Second, we also run a set of robustness tests based on Heckman's (1979) model, addressing the bias potentially associated with unobservable characteristics.16 To identify the model, the selection equa-tion is specified as the main process, adding as exclusion restrictions the average values of two import and export dummies (DIMP and DEXP, taking unit value if the firm imports or exports, respectively) computed in the stratum composed of firms operating in the same industry (defined at the NACE 2‐digit level) and the same province, for each year considered.17 Alternatively, as a further check, we employ as exclusion restrictions literacy and urbanisation regional rates at the end of nineteenth cen-tury, drawn from Tabellini (2010).18
15 Further details on the bootstrapped procedure as well as on the so called "separability condition" (Badin et al., 2014; Daraio et al., 2018), underlying the two‐stage DEA models, are provided in the 1.
(2)EFFit =!0 +!BROADBROADit +!SUPSUPit +∑
!kCTRLkit +"it,
(3)EFFit =!0 +!RELRELit +!SUPSUPit +∑
!kCTRLkit +"it,
16 When adopting the Heckman estimator, the residuals from a probit model—the selection process—are used to construct a selection bias control factor (inverse Mill's ratio), which is used as an additional regressor in Equations (2) and (3).17 This choice is justified by the fact that the overall industry importing and exporting activities somehow reflect exogenous scope and dynamics of world demand and supply of intermediate products, which are reasonably correlated with firms' participation in GVCs.18 Precisely, we use the regional share of persons able to read and write around 1,880 (LITE), and the rate of urbanisation (URB), defined as the share of regional population that lived in cities with over 30,000 inhabitants in 1850. The argument underlying this choice is based on recent literature recognising the quality of local institutions as a significant determinant of the probability of firms' participation in GVCs (Accetturo et al., 2017: Dollar & Kidder, 2017). Since in turn present institutional differences across European regions are deemed to depend on historical educational endowments and prosperity (Glaeser et al., 2004; Rodríguez‐Pose & Di Cataldo, 2014; Tabellini, 2010), regional heterogeneity in historical educational endowments and urbanisation should be correlated with firms' participation in GVCs today.
| 15AGOSTINO ET AL.
4 | RESULTS
4.1 | Non‐parametric analysis of DEA score distributionsPairwise or multiple comparisons of the efficiency scores (EFF) cumulative distributions of different groups of SMEs are presented in Figures 1–5.
EFF distributions of firms belonging and not belonging to GVCs are contrasted in Figure 1. Figure 1a considers the whole sample while Figure 1b and c make the comparison (GVC vs. non‐GVC) for the subsample of suppliers, and firms changing their status (i.e., from non‐participant to participant or vice versa) during the considered time, respectively. The motivation for specifically looking at the latter group is that observing those firms allows for the evaluation of whether the effects of joining (or leav-ing) a GVC materialise in a short (or longer) span of time. In all cases, the solid line representing the distribution of efficiency scores of firms participating in GVCs lies below the dashed line of non‐partic-ipating companies. However, the Kruskal–Wallis test confirms statistical significance of differences in efficiency distributions only for the whole sample and suppliers. Comparing firms participating and not participating in relational GVC, Figure 2 yields a similar outcome, as the solid lines are consistently on the right of the dashed ones. What is more important, in this case the Kruskal–Wallis test is always sig-nificant, suggesting that participation in relational GVCs always brings about an efficiency gain, rapidly appearing even for firms changing their status from non‐participant to participant.
Figure 3 depicts efficiency scores distributions of four groups of companies. The thick and thin solid lines, respectively, indicate the EFF distributions of final firms and suppliers participating in GVCs, while the dashed and dotted lines, respectively, indicate final firms and suppliers not belonging to GVCs. The comparison clearly shows that final firms perform better than suppliers, except when the latter are involved in a GVC and the former are not. Figure 4 replicates the same analysis taking into consideration participation in relational GVCs. The ranking remains unaltered, but in this case the distributions of sup-pliers involved in relational GVCs and final firms not involved overlap for efficiency scores greater than 0.5. Finally, Figure 5 contrasts relational GVC firms with domestic firms participating in the conception and definition of final products. Clearly, the former show higher efficiency, as the distribution of their efficiency scores is located more to the right. For all the comparisons in Figures 3–5, the Kruskal–Wallis test confirms that efficiency distributions are significantly different across the different groups of firms.
4.2 | PSMTable 4 reports the PSM results, based on the comparison between treated units (GVC firms) and their non‐GVC counterfactual, defined as the 5 or 10 or 20 nearest neighbours, in terms of propensity scores. The estimated values of ATT are computed on the whole sample for the benchmark and an alternative specification (OTH SPEC),19 with reference to both the broad measure of participation (BROAD) and the relational participation (REL). Moreover, in order to gain more insights on the rela-tive effectiveness of different kinds of participation in GVCs, ATT is calculated for four additional different subsamples: firms involved in the conception of the final product, firms entering or leaving GVCs, suppliers and final firms. In the latter two subsets, we also distinguish firms involved in the conception of the final product and firms entering or leaving GVCs.20
19 OTH SPEC employs SUPR in place of SUP and the Herfindahl index computed on sales (HHIS) instead of HHIA. It also includes the squared terms of both SIZE and AGE.20 It is worth noticing that results in Table 4 are consistent even if we employ robust standard errors developed by Abadie and Imbens (2006, 2011).
16 | AGOSTINO ET AL.
TABL
E 4
Prop
ensit
y sco
re ma
tching
Trea
tmen
tSa
mple
NN
5N
N 1
0N
N 2
0
ATT
tp‐V
alue
psR2
ATT
tp‐V
alue
psR2
ATT
tp‐V
alue
psR2
BRO
ADW
hole
0.009
1.849
.032
0.000
0.008
1.600
.055
0.000
0.008
1.685
.046
0.000
REL
Who
le0.0
101.8
24.03
40.0
010.0
122.1
80.01
50.0
010.0
122.1
75.01
50.0
00BR
OAD
Who
le, O
TH S
PEC
−0.00
1−0
.122
.548
0.002
0.002
0.356
.359
0.002
0.002
0.388
.348
0.002
REL
Who
le, O
TH S
PEC
0.011
1.766
.038
0.002
0.012
2.105
.018
0.002
0.013
2.238
.013
0.001
REL
Conc
ept
0.016
2.528
.006
0.001
0.014
2.223
.007
0.001
0.012
1.998
.023
0.001
BRO
ADIn
Out
0.005
0.624
.268
0.000
0.005
0.685
.245
0.000
0.004
0.555
.291
0.000
REL
InOu
t0.0
080.9
56.16
90.0
010.0
101.2
00.11
50.0
010.0
101.1
89.11
70.0
00BR
OAD
SUP1
0.013
1.359
.087
0.001
0.016
1.685
.047
0.001
0.015
1.580
.057
0.001
BRO
ADSU
P00.0
050.8
44.20
00.0
010.0
050.8
32.20
30.0
000.0
040.8
00.21
20.0
00RE
LSU
P10.0
333.0
10.00
10.0
010.0
272.6
09.00
50.0
010.0
313.0
57.00
10.0
01RE
LSU
P00.0
121.8
07.03
50.0
020.0
111.7
80.03
80.0
010.0
111.7
77.03
80.0
01RE
LCo
ncep
t_SUP
10.0
322.8
60.00
20.0
010.0
333.0
18.00
10.0
010.0
333.1
44.00
10.0
00RE
LCo
ncep
t_SUP
00.0
121.7
05.04
50.0
000.0
142.0
16.02
20.0
000.0
142.1
27.01
70.0
00BR
OAD
InOu
t_SUP
10.0
191.2
49.10
60.0
020.0
110.7
47.22
70.0
010.0
110.7
32.23
30.0
01BR
OAD
InOu
t_SUP
00.0
050.5
26.29
80.0
010.0
030.3
04.38
20.0
010.0
020.2
39.40
50.0
00RE
LIn
Out_S
UP1
0.025
1.422
.078
0.004
0.029
1.729
.042
0.002
0.027
1.629
.052
0.002
REL
InOu
t_SUP
00.0
151.4
02.08
10.0
010.0
141.3
87.08
20.0
010.0
131.2
92.09
90.0
01No
tes:
NN
stand
s for
neare
st ne
ighbo
urs;
ATT
for a
verag
e trea
tmen
t effe
ct on
the t
reated
; psR
2 is t
he ps
eudo
‐R2 r
elated
to th
e pro
pens
ity sc
ore e
stima
tion o
n the
matc
hed s
ample
, part
icipa
nts an
d ma
tched
non‐
parti
cipan
ts. B
alanc
ing te
sts (t
tests
of th
e diff
erenc
e in t
he co
varia
tes' m
eans
betw
een c
ontro
l and
trea
ted gr
oups
) and
comm
on su
ppor
t grap
hs ar
e rep
orted
in th
e App
endix
.
| 17AGOSTINO ET AL.
The first four rows show that being involved in a GVC brings about significant positive efficiency gains in case of relational participation, while for broad‐sense participation evidence is mixed across the number of nearest neighbours considered. As a matter of fact, the average difference in efficiency between firms participating and non‐participating in GVCs is always greater than 1% and statistically significant with reference to the relational mode of participation (REL), while the gain is lower and sometimes insignificant for generic participation (BROAD).
A scrutiny of rows 5 to 17 of Table 4 permits to draw some additional conclusions. First, the re-ported values of ATT are consistently higher for REL than BROAD across all the considered specifica-tions, thus confirming the greater relevance of the relational mode of participation in GVCs on firms' efficiency. Second, there is a specific contribution to extra efficiency due to the international scope of interfirm partnership in the conception of final products (row 5). Third, efficiency differences be-tween firms participating and non‐participating in GVCs are insignificant in the subsample of firms entering or leaving GVCs (rows 6 and 7). Fourth, efficiency differences between firms participating and non‐participating in GVCs are always greater for suppliers than for final companies (rows 8 to 17). Also, when splitting the sample between suppliers and final firms, REL becomes statistically sig-nificant even for firms entering or leaving GVCs (rows 16 and 17). Fifth, the highest efficiency gains from participation in GVCs (on average 3.3%) are enjoyed by the most capable suppliers involved in the conception stages of final products.
To sharpen our findings and check that they are not driven by unobserved‐persistent characteristics associated with the permanent selection into GVCs, the PSM analysis is replicated for firms entering (rather than belonging to) the chain. Table 5 presents results. In this case, the two treatment variables are BROAD_enter and REL_enter, dummy indicators that take unit value if the firm enters a GVC (in the broad or relational form, respectively) during the span of time we consider and 0 for firms not belonging to GVCs. This alternative exercise provides consistent results and documents even stronger effects of the involvement in GVCs.
To verify the common support between treatment and comparison groups, we visually inspect the density distribution of the propensity score in both groups (graphs are available upon request). Besides, to check balancing, as is customary, we run a t test for each confounder used in the propen-sity score estimation, confirming that in both the analyses on firms belonging to GVCs and entering GVCs, the after‐matching means of treated and control units are never significantly different from each other (see Tables A1 and A2).
4.3 | Truncated regression and Heckman modelsThe results obtained by the bootstrapped truncated estimation (Simar & Wilson, 2007) of Equations (2) and (3), reported in Tables 6 and 7, show a remarkable consistency with the findings presented so far.
Columns (1) and (2) of Table 6 display the outcome of the benchmark specification. Preliminarily, it is worth noting that control variables show signs consistent with expectations and in most cases statistically significant.21 In particular, the coefficients of SIZE, INDEBT, CASHFLOW and HHIA are positive, suggesting that larger size, greater liquidity and higher concentration in the operating indus-try may foster firms' efficiency. While the effect of AGE on efficiency appears to be negative and slightly significant, the macrovariables GDPPC and CNORTH assume the expected positive and sig-nificant sign. These results essentially hold across all the regressions reported in Tables 6–8.
21 The only exceptions are human capital (GRADUATE) and patent applications (PATENT) which are not significant.
18 | AGOSTINO ET AL.
TABL
E 5
Prop
ensit
y sco
re ma
tching
Trea
tmen
tSa
mple
NN
5N
N 1
0N
N 2
0
ATT
tp‐V
alue
psR2
ATT
tp‐V
alue
psR2
ATT
tp‐V
alue
psR2
BRO
AD_e
nter
Who
le0.0
152.0
31.02
10.0
010.0
162.3
00.01
00.0
000.0
172.5
31.00
60.0
00RE
L_en
ter
Who
le0.0
172.0
06.02
20.0
010.0
182.2
08.01
30.0
010.0
182.3
37.00
90.0
00BR
OAD
_ent
erW
hole,
OTH
SPE
C0.0
121.4
98.06
60.0
020.0
121.6
06.05
30.0
020.0
131.7
58.03
90.0
01RE
L_en
ter
Who
le, O
TH S
PEC
0.022
2.559
.005
0.003
0.020
2.424
.007
0.003
0.017
2.074
.019
0.002
REL_
ente
rCo
ncep
t0.0
222.5
73.00
50.0
010.0
172.0
35.02
10.0
010.0
172.0
37.02
10.0
01BR
OAD
_ent
erSU
P10.0
382.8
19.00
20.0
000.0
322.4
57.00
70.0
010.0
302.4
10.00
80.0
01BR
OAD
_ent
erSU
P00.0
141.6
32.05
10.0
010.0
081.0
13.15
60.0
010.0
101.2
62.10
30.0
01RE
L_en
ter
SUP1
0.035
2.475
.006
0.001
0.031
2.209
.014
0.001
0.036
2.683
.004
0.001
REL_
ente
rSU
P00.0
191.8
43.03
30.0
010.0
181.8
76.03
00.0
010.0
161.7
14.04
40.0
00RE
L_en
ter
Conc
ept_S
UP1
0.044
2.949
.001
0.003
0.046
3.188
.001
0.002
0.040
2.859
.002
0.001
REL_
ente
rCo
ncep
t_SUP
00.0
201.9
79.02
40.0
010.0
181.8
23.03
40.0
000.0
191.9
30.02
70.0
00No
tes:
NN
stand
s for
neare
st ne
ighbo
urs;
ATT
for a
verag
e trea
tmen
t effe
ct on
the t
reated
; psR
2 is t
he ps
eudo
‐R2 r
elated
to th
e pro
pens
ity sc
ore e
stima
tion o
n the
matc
hed s
ample
, part
icipa
nts an
d ma
tched
non‐
parti
cipan
ts. B
alanc
ing te
sts (t
tests
of th
e diff
erenc
e in t
he co
varia
tes' m
eans
betw
een c
ontro
l and
trea
ted gr
oups
) and
comm
on su
ppor
t grap
hs ar
e rep
orted
in th
e App
endix
.
| 19AGOSTINO ET AL.
TABL
E 6
Sima
r and
Wils
on (2
007)
estim
ation
. Who
le sa
mple
12
34
56
7
Benc
hO
thCo
ncep
tIn
Out
BRO
ADRE
LBR
OAD
REL
BR
OAD
REL
BRO
AD0.0
098*
*
0.003
7
0.0
070
.0
46
.499
.339
RE
L
0.015
9***
0.0
158*
**0.0
152*
**
0.018
3**
.0
04
.010
.006
.0
29IN
TL1
0.002
80.0
006
.5
00.8
83
INTL
2
0.0
181*
**0.0
143*
*
.003
.012
SU
P−0
.0170
***
−0.01
72**
*
−0
.0020
−0.00
64−0
.0139
.000
.000
.696
.419
.112
SIZE
0.052
1***
0.052
3***
−0.31
30**
*−0
.3167
***
0.045
3***
0.059
1***
0.051
2***
.000
.000
.000
.000
.000
.000
.000
AGE
−0.00
52*
−0.00
54*
0.007
10.0
068
−0.01
43**
*−0
.0192
***
−0.01
37**
.078
.072
.747
.767
.001
.002
.049
IND
EBT
0.000
7***
0.000
7***
0.000
7***
0.000
7***
0.000
4***
0.000
4**
0.000
4*.0
00.0
00.0
00.0
00.0
01.0
34.0
64CA
SHFL
OW
0.005
6***
0.005
6***
0.005
5***
0.005
5***
0.005
2***
0.006
1***
0.005
9***
.000
.000
.000
.000
.000
.000
.000
GRA
DU
ATE
0.000
10.0
001
0.000
10.0
001
0.000
2−0
.0001
0.000
0.3
14.3
12.5
81.5
56.1
12.6
27.9
37H
HIA
0.408
8***
0.409
4***
0.313
0***
0.381
9***
0.358
5***
.000
.000
.000
.000
.000
(Con
tinue
s)
20 | AGOSTINO ET AL.
12
34
56
7
Benc
hO
thCo
ncep
tIn
Out
BRO
ADRE
LBR
OAD
REL
BR
OAD
REL
PATE
NT−0
.0018
−0.00
18−0
.0021
−0.00
200.0
072*
*−0
.0050
−0.00
66.4
99.5
16.3
76.4
22.0
15.3
52.2
71G
DPP
C0.0
540*
**0.0
543*
**0.0
570*
**0.0
573*
**0.0
455*
**0.0
232
0.009
7.0
00.0
00.0
00.0
00.0
01.3
24.7
20CN
ORT
H0.0
313*
**0.0
312*
**0.0
345*
**0.0
343*
**0.0
204*
*0.0
782*
**0.0
909*
**.0
00.0
00.0
00.0
00.0
23.0
00.0
00SU
PR
−0
.0001
***
−0.00
01**
*
.001
.000
SI
ZEsq
0.012
0***
0.012
1***
.0
00.0
00
AGEs
q
−0
.0018
−0.00
18
.640
.663
H
HIS
0.495
9***
0.496
2***
.0
00.0
00
TREN
D
−0
.0089
***
−0.00
88**
*
.000
.000
Ob
serv
ation
s15
,205
15,20
515
,205
15,20
58,4
833,2
782,6
36M
odel
test
2,798
.472,7
71.22
3,542
.623,3
62.40
2,949
.3468
8.10
452.8
4.0
00.0
00.0
00.0
00.0
00.0
00.0
00
Note
s: T
he de
pend
ent v
ariab
le is
EFF.
For
the d
escri
ption
of va
riable
s, se
e Tab
le 2.
Supe
rscrip
ts **
*, **
and *
deno
te sta
tistic
al sig
nifica
nce a
t the
1%, 5
% an
d 10%
leve
l, res
pecti
vely.
In it
alics
are r
e-po
rted t
he p‐
value
s of t
he te
sts. Y
ear a
nd se
ctor d
ummi
es al
ways
inclu
ded b
ut no
t rep
orted
. SIZ
E, A
GE,
PAT
ENT
and G
DPP
C are
in lo
g‐ter
ms. S
IZE,
AG
E, IN
DEB
T an
d CAS
HFL
OW
are l
agge
d onc
e. In
Colu
mns (
3) an
d (4)
, SU
PR is
the s
hare
of sa
les to
orde
r to t
otal s
ales;
HH
IS is
the H
erfind
ahl i
ndex
comp
uted o
n sale
s.
TABL
E 6
(Con
tinue
d)
| 21AGOSTINO ET AL.
TABL
E 7
Sima
r and
Wils
on (2
007)
estim
ation
. Sep
arate
subs
ample
s (su
pplie
rs an
d fina
l firm
s)
12
34
56
78
910
GVC
Rel
Conc
ept
InO
utIn
Out
SUPP
LIER
SFI
NAL
SUPP
LIER
SFI
NAL
SUPP
LIER
SFI
NAL
SUPP
LIER
SFI
NAL
SUPP
LIER
SFI
NAL
BRO
AD0.0
174*
0.009
9*
0.0
101
0.006
7
.0
68.0
69
.4
70.4
12
RE
L
0.0
262*
*0.0
142*
*0.0
297*
**0.0
115
0.028
5*0.0
144
.016
.028
.004
.119
.073
.129
SIZE
0.054
7***
0.050
1***
0.055
0***
0.050
4***
0.044
4***
0.051
8***
0.056
8***
0.058
2***
0.041
4***
0.053
4***
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
AGE
−0.00
99−0
.0084
**−0
.0102
*−0
.0086
**−0
.0225
***
−0.00
51−0
.0261
**−0
.0201
***
−0.01
70−0
.0151
*.1
00.0
17.0
98.0
16.0
02.3
06.0
42.0
05.2
05.0
74IN
DEB
T0.0
005*
**0.0
007*
**0.0
005*
**0.0
007*
**0.0
002
0.000
5***
0.000
30.0
004*
*0.0
004
0.000
5*.0
09.0
00.0
05.0
00.3
24.0
00.4
61.0
39.3
42.0
71CA
SHFL
OW
0.005
2***
0.005
5***
0.005
2***
0.005
5***
0.004
7***
0.006
0***
0.005
0***
0.006
3***
0.005
9***
0.005
9***
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
GRA
DU
ATE
−0.00
010.0
002
−0.00
010.0
002
0.000
00.0
001
0.000
3−0
.0004
0.000
5−0
.0002
.663
.203
.685
.204
.963
.594
.611
.227
.539
.503
HH
IA0.4
164*
**0.4
204*
**0.4
190*
**0.4
206*
**0.3
531*
**0.3
358*
**0.3
861*
**0.4
363*
**0.3
400*
**0.4
469*
**.0
00.0
00.0
00.0
00.0
00.0
00.0
00.0
00.0
01.0
00PA
TENT
−0.00
130.0
002
−0.00
130.0
002
0.011
1*0.0
033
−0.00
74−0
.0014
−0.00
86−0
.0018
.796
.946
.795
.945
.066
.405
.471
.819
.431
.798
GD
PPC
0.038
8*0.0
595*
**0.0
389*
0.059
9***
0.017
10.0
686*
**−0
.0040
0.023
9−0
.0350
0.018
0.0
89.0
00.0
86.0
00.5
15.0
00.9
29.3
89.4
70.5
66CN
ORT
H0.0
238
0.031
4***
0.023
90.0
313*
**0.0
202
0.013
30.1
011*
**0.0
659*
**0.1
102*
**0.0
749*
**.1
28.0
00.1
26.0
01.2
70.2
49.0
02.0
00.0
02.0
00 (Con
tinue
s)
22 | AGOSTINO ET AL.
12
34
56
78
910
GVC
Rel
Conc
ept
InO
utIn
Out
SUPP
LIER
SFI
NAL
SUPP
LIER
SFI
NAL
SUPP
LIER
SFI
NAL
SUPP
LIER
SFI
NAL
SUPP
LIER
SFI
NAL
Obse
rvati
ons
4,333
10,87
24,3
3310
,872
2,303
6,180
943
2,335
800
1,836
Mod
el tes
t80
3.28
2,290
.3279
1.85
2,490
.0882
1.12
1,336
.8320
4.04
636.7
915
9.97
405.4
6.0
00.0
00.0
00.0
00.0
00.0
00.0
00.0
00.0
00.0
00
Note
s: T
he de
pend
ent v
ariab
le is
EFF.
For
the d
escri
ption
of va
riable
s, se
e Tab
le 2.
Supe
rscrip
ts **
*, **
and *
deno
te sta
tistic
al sig
nifica
nce a
t the
1%, 5
% an
d 10%
, res
pecti
vely.
In it
alics
are r
epor
ted
the p‐
value
s of t
he te
sts. Y
ear a
nd se
ctor d
ummi
es al
ways
inclu
ded b
ut no
t rep
orted
. SIZ
E, A
GE,
PAT
ENT
and G
DPP
C are
in lo
g‐ter
ms. S
IZE,
AG
E, IN
DEB
T an
d CAS
HFL
OW
are l
agge
d onc
e.
TABL
E 7
(Con
tinue
d)
| 23AGOSTINO ET AL.
Focusing on our key explanatory variables, BROAD and REL come out to be significantly positive. Regardless of the participation mode, being involved in a GVC implies a gain in the efficiency score; however, a relational mode of participation grants a higher reward confirming that the beneficial ef-fects of GVCs take place especially when SMEs get directly involved in strategic stages of production, thus having higher opportunities of knowledge exchange, organisational improvement and efficiency gains.22 Concerning the impact of being a supplier, the SUP coefficient comes out to be negative and statistically significant. This result supports the view that suppliers are characterised by a lower per-formance than final firms (Agostino et al., 2015; Razzolini & Vannoni, 2011).23
Columns (3) and (4) provide coherent results when adopting the alternative specification (OTH SPEC) described in Footnote 19.24 To disentangle the effect of simpler forms of firm's engagement in international markets, two dummy variables are included (INTL1, equal to 1 when the firm either ex-ports or imports, and zero otherwise, and INTL2 equal to 1 if the firm is both an exporter and importer, and zero otherwise). In this specification, a larger difference in the coefficients' values of the key re-gressors BROAD and REL comes up, with the former losing statistical significance.25 This result is likely due to the presence of the additional INTL variables, which are highly positively correlated with BROAD and thus blur the effect of the latter on firms' efficiency. This evidence permits to argue that while the variable BROAD basically captures the benefits of international openness, REL keeps mag-nitude and significance of its impact on firms' efficiency even after controlling for international open-ness, because it is mainly related to relational participation in GVCs, that is to those interfirm relationships aimed at the conception of the final good, which are actually able to engender upgrading in organisation and management, transfers of knowledge and skills, and thus gains which go beyond those associated with simpler forms of engagement in international markets.
In the same vein, to disentangle the specific contribution to extra efficiency due to the interna-tional scope of interfirm partnership within the subset of firms taking part in the conception of final products, Equation (3) is also estimated on this subsample of firms. Consistent with the outcome of PSM analysis reported in row 5 of Tables 4 and 5, results shown in column (5) of Table 6 point out that even with respect to this selected group of firms, an efficiency premium to international openness connected to participation in relational GVCs is granted.
In columns (6) and (7), the subsample of firms changing their status (i.e., those becoming or ceas-ing to be involved in a GVC) is considered. Results are thoroughly consistent with the indications pro-vided by both the non‐parametric and the PSM analysis. Insignificance of BROAD (column (6)) points out that entering or leaving a GVC does not yield any gain or loss (respectively); together with evi-dence of column (1), this suggests that entering (exiting) a GVC becomes fruitful (harmful) only after some time. However, the coefficient of REL in column (7) is significant: if the mode of participation
22 Computing marginal effects allows to evaluate the efficiency gain associated to BROAD participation at about 1%, and that connected to REL participation around 1.4%.23 Since the baseline model uses explanatory variables defined at different aggregation levels, we also employ clustered and hierarchical models. The results, available on request, further corroborate our findings: participation in GVC leads to win a return in terms of higher efficiency, and this gain is consistently higher for the involvement in relational GVCs.24 A time trend (TREND) is also included among controls. The coefficients of supplier status and control variables remain essentially unaltered. In particular, the coefficient of SUPR is about 100 times lower than that of SUP because of the different range of definition of the two variables. The estimated impact of SIZE at the average value of total assets is around 0.064, quite close to the values shown in columns (1) and (2).25 A very similar outcome arises when using the same specification as in columns (1) and (2) and only adding the INTL dummies.
24 | AGOSTINO ET AL.
TABL
E 8
Heck
man e
stima
tion r
esult
s
12
34
56
78
BRO
ADRE
LBR
OAD
REL
Sele
ctM
ain
Sele
ctM
ain
Sele
ctM
ain
Sele
ctM
ain
BRO
AD
0.006
7
0.008
4**
.1
06
.045
REL
0.0
125*
**
0.013
7***
.0
09
.005
SIZE
0.188
2***
0.043
2***
0.055
1***
0.044
4***
0.231
6***
0.042
9***
0.101
1***
0.042
0***
.000
.000
.000
.000
.000
.000
.000
.000
AGE
−0.02
17−0
.0042
0.059
3**
−0.00
50*
−0.04
48**
−0.00
410.0
332
−0.00
60**
.340
.102
.016
.057
.041
.130
.165
.038
IND
EBT
−0.00
060.0
006*
**−0
.0005
0.000
6***
−0.00
12*
0.000
6***
−0.00
090.0
006*
**.3
47.0
00.5
19.0
00.0
75.0
00.2
12.0
00CA
SHFL
OW
−0.00
380.0
049*
**−0
.0026
0.004
8***
−0.00
44*
0.004
9***
−0.00
310.0
049*
**.1
29.0
00.3
30.0
00.0
72.0
00.2
46.0
00G
RAD
UAT
E0.0
047*
**0.0
001
0.001
40.0
001
0.005
3***
0.000
10.0
022*
*0.0
001
.000
.513
.180
.353
.000
.651
.037
.644
HH
IA−0
.1497
0.352
0***
−0.26
560.3
533*
**−0
.2119
0.351
6***
−0.33
91**
0.361
4***
.356
.000
.131
.000
.176
.000
.048
.000
PATE
NT0.0
293
−0.00
230.0
180
−0.00
220.0
762*
**−0
.0023
0.058
6***
−0.00
41.1
41.2
87.3
94.3
58.0
00.4
68.0
05.1
70G
DPP
C−0
.0816
0.045
6***
−0.17
44*
0.046
9***
−0.08
070.0
462*
**−0
.1870
**0.0
503*
**.3
32.0
00.0
55.0
00.3
30.0
00.0
37.0
00CN
ORT
H0.0
263
0.027
3***
0.019
40.0
272*
**0.0
083
0.026
9***
−0.00
340.0
257*
**.6
60.0
00.7
60.0
00.8
94.0
00.9
60.0
00
(Con
tinue
s)
| 25AGOSTINO ET AL.
12
34
56
78
BRO
ADRE
LBR
OAD
REL
Sele
ctM
ain
Sele
ctM
ain
Sele
ctM
ain
Sele
ctM
ain
SUP
0.018
3−0
.0149
***
0.049
3−0
.0152
***
0.003
2−0
.0149
***
0.035
1−0
.0160
***
.536
.000
.116
.000
.912
.000
.250
.000
LITE
0.005
5***
0.0
058*
**
.000
.0
00
URB
−0.00
81**
*
−0.00
67**
*
.001
.0
09
DIM
P0.8
856*
**
0.594
0***
.0
00
.000
D
EXP
1.125
4***
1.0
816*
**
.000
.0
00
MIL
LS
−0.00
98*
−0
.0098
*
−0.01
17
−0.03
93
.059
.0
89
.665
.1
46Ob
serv
ation
s15
,205
15,20
515
,205
15,20
515
,205
15,20
515
,205
15,20
5M
odel
test
3,5
56.12
3,2
28.69
3,5
44.33
3,2
25.63
.0
00
.000
.0
00
.000
Note
s: Fo
r the
desc
riptio
n of v
ariab
les, s
ee T
able
2. Su
persc
ripts
***,
** an
d * de
note
statis
tical
signif
icanc
e at t
he 1%
, 5%
and 1
0% le
vel, r
espe
ctive
ly. In
itali
cs ar
e rep
orted
the p
‐valu
es of
the t
ests.
Ye
ar an
d sec
tor du
mmies
alwa
ys in
clude
d but
not r
epor
ted. S
IZE,
AG
E, P
ATEN
T an
d GD
PPC
are in
log‐
terms
. SIZ
E, A
GE,
IND
EBT
and C
ASH
FLO
W ar
e lag
ged o
nce.
In co
lumns
from
(1) t
o (4)
, ex-
clusio
n res
tricti
ons a
re the
avera
ge va
lues o
f the
dumm
ies im
port
and e
xpor
t (D
IMP
and D
EXP)
comp
uted i
n eac
h yea
r at s
ector
al an
d pro
vincia
l lev
el. In
colum
ns fr
om (5
) to (
8), e
xclus
ion re
strict
ions
are li
terac
y (LI
TE) a
nd ur
banis
ation
(URB
) reg
ional
rates
at th
e end
of ni
netee
nth ce
ntury
, draw
n fro
m Ta
belli
ni (2
010)
.
TABL
E 8
(Con
tinue
d)
26 | AGOSTINO ET AL.
in the GVC is a relational one, the gain (or loss) is immediate, and the firm climbs up (or slips down) the efficiency frontier as soon as it changes its status of participant or non‐participant in a GVC.
Furthermore, to assess whether participation in GVCs delivers different effects on the efficiency of suppliers and final firms, we run additional regressions of Equations (2) and (3) separately on the subsamples of suppliers and final firms.
Looking at Table 7, the estimated coefficients of BROAD and REL are positive and in most cases statistically significant. Remarkably, in all cases the impact of participation is larger for REL than BROAD and greater for suppliers than final firms (for the latter, only two times out of five coefficients are 10% statistically significant) suggesting that the former group of firms benefits more from partic-ipation in GVCs, especially relational GVCs.26
Finally, Table 8 reports the Heckman estimates, obtained with different set of exclusion restric-tions, as described in Section 3.3.2.27 Estimates are again fully consistent with the results of PSM and truncated regressions commented above. In particular, involvement in relational GVC always displays a greater influence on the efficiency of firms than generic participation in GVC.
5 | CONCLUDING REMARKS
This paper investigates the effect of participation in GVCs on firms' efficiency and explores some di-mensions of heterogeneity in this impact across different firms, according to GVC governance, position-ing in the chain and time length of participation. Our analysis takes advantage of survey data providing information on involvement in GVCs for a large set of Italian industrial SMEs between 2008 and 2012. We employ a DEA approach to retrieve a measure of firms' technical efficiency (i.e., DEA efficiency scores) and estimate the impact of involvement in GVCs on firms' efficiency through propensity score matching techniques. To account for possible residual endogeneity, we also focus on new entrants. Furthermore, we provide robustness checks by using truncated regression and Heckman estimators.
The results of our empirical investigations contribute to the GVCs firm‐level literature confirming our research hypotheses: SMEs' participation in GVCs leads to win a return in terms of higher effi-ciency; this gain is systematically higher for the involvement in relational GVCs, and the benefit is especially noteworthy for suppliers.
Our findings might be useful to design suitable intermediate targets for industrial policy. Globalisation has implied a shock for many developed economies, and particularly for Italy, be-cause of its peculiar industrial structure, and the dwarfism of manufacturing firms. In that country, the average size of enterprises is around 3.7 employees, micro firms (<9 employees) represent 95% of the total, only 22% of the industrial employees work in large firms (250 and more em-ployees). Due to its productive fragmentation, Italian firms are often positioned in GVCs as sup-pliers (Agostino et al., 2016). In this context, in order to push manufacturing SMEs towards the
26 These findings are confirmed when the dummy SUP is alternatively interacted with BROAD and REL (results available upon request). In fact, while the average difference in efficiency between suppliers and final firms not belonging to GVCs is negative and statistically significant, the average difference in efficiency between suppliers and final firms taking part in GVCs is statistically insignificant. When considering relational participation in GVCs, this result is even clearer. Summarising, in general suppliers appear to suffer from a productivity disadvantage relative to other firms; however, the involvement in conventional GVCs and especially relational GVCs help them to fill the efficiency gap.27 Using the sets of instruments described in Section 3.3.2, we also estimated 2SLS models, finding further confirmation of our main results. It is worth mentioning that the Sargan test always supports the validity of our instrumenting set (i.e., the null hypothesis that all instruments are uncorrelated with the error term is never rejected).
| 27AGOSTINO ET AL.
efficiency frontier, the policymakers may facilitate SMEs' links to GVCs by attracting foreign direct investment and fostering SMEs' internationalisation, through increased flows of specific information on foreign markets. Moreover, as involvement in relational GVCs enhances the impact on firms' efficiency, the policymakers should also aim at favouring the development of an ade-quate capability of SMEs to participate in this kind of networks, which in turn implies to enhance SMEs absorptive capacity and ability to fulfil the complexity of requests coming from final firms in globalised markets.
ORCIDFrancesco Trivieri https://orcid.org/0000-0001-7648-2139
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How to cite this article: Agostino M, Brancati E, Giunta A, Scalera D, Trivieri F. Firms' efficiency and global value chains: An empirical investigation on Italian industry. World Econ. 2019;00:1–34. https ://doi.org/10.1111/twec.12866
APPENDIX
Simar and Wilson (2007)
As discussed in the main text, within the literature employing two‐step DEA procedures, Simar and Wilson (2007) propose a statistical model where the second‐stage regression is meaningful. In par-ticular, under the crucial separability condition (i.e., the environmental variables only affect the distri-bution of efficiency and do not affect production possibilities), they propose two types of algorithm. Algorithm 1 consists of obtaining estimates of the efficiency scores "i in the first step and then regress them on environmental variables zi, using a bootstrapped truncated regression. The algorithm 2, ap-plied in this paper, involves bootstrapping DEA scores in order to obtain bias‐corrected scores and then regressing these bias‐corrected scores on zi using a bootstrapped truncated regression. More formally, the algorithm 2 consists of the following steps:
I Compute the DEA input‐oriented efficiency scores "i for each firm, using the linear pro-gramming problem in Equation (1) in the main text;
II Use the maximum‐likelihood method to estimate the truncated regression of "i on zi, to provide an estimate " of β, and an estimate of "# of !";
III For each firm i=1,… ,n repeat the next four steps (1–4) B times to obtain a set of bootstrap esti-mates
{
"∗ib
; b=1,… ,B}
:
| 31AGOSTINO ET AL.
1. draw !i from the N(
0,"2#
)
distribution with left truncation at 1− "zi;2. compute !∗
i= #zi+$i;
3. construct a pseudo data set (
x∗i,q∗
i
)
where x∗i= xi and q∗
i=qi"i∕"
∗i;
4. compute a new DEA estimate !∗i on the set of pseudo data set
(
x∗i,q∗
i
)
;IV For each firm, calculate the bias‐corrected estimate #i = #i− biasi where biasi =
1
B
∑B
b=1#∗
ib− #i;
V Use the maximum‐likelihood method to estimate the truncated regression of #i on zi, to provide an estimate #i and #i of β and !";
VI Repeat the next three steps (1–3) B2 times to obtain a set of bootstrap estimates ;
1. for i=1,… ,n, !i is drawn form N(
0,#)
with left truncation 1− #zi;2. for i=1,… ,n, compute !∗∗
i= $zi+%i;
3. the maximum‐likelihood method is again used to estimate the truncated regression of !∗∗i
on zi, providing estimates
(
#∗
,$∗)
;
VII Use the bootstrap results to construct confidence intervals.
Applying the Kneip, Simar, and Wilson (2016) test to check the separabil i ty condit ion
To check the separability condition recalled above, we resort to a basic test based on Kneip et al. (2016) and Daraio, Simar, and Wilson (2015) and Daraio, Simar, and Wilson (2018). Building on new central limit theorems (CLTs), Kneip et al. (2016) develop tests of differences in mean of DEA (or free‐disposal hull, FDH) efficiency scores across groups of producers (as well as tests of convexity of production sets, and of the nature of returns to scale). Daraio et al. (2018) extend these results to the case of conditional DEA, namely in the presence of environmental variables that are neither inputs nor outputs in the production process, and are supposed to affect efficiency. When such contextual factors are discrete, it is possible to overlook the additional complication due to the presence of a bandwidth parameter optimisation in the conditional estimator and apply the Kneip et al. (2016) test of equivalent mean efficiency across groups of producers.
Following Daraio et al. (2015) illustration for the case of a dichotomous external variable, we randomly shuffle the observations and divide them into two groups of size n1 = n/2 and n2 = n−n1. Then, we compute the DEA unconditional efficiency scores (which do not consider external factors) for group 1. For group 2, conditional efficiency estimates are obtained by splitting observations into two subgroups according to whether GVC = 0 or GVC = 1, and applying in each subgroup the same unconditional efficiency estimator used with group 1. Finally, we apply the difference‐in‐means test of Kneip et al. (2016), computing bias correction terms separately for observations in the subgroup where GVC = 0 and the subgroup where GVC = 1. Applying the difference‐in‐means test of Kneip et al. (2016) separately to each sector, we can conclude that in 86% of the cases, the null hypothesis of separability is not rejected. The entire procedure is then replicated for the other key dummy variable SUP. It is worth noting that, since the splitting above described reduces the number of observations available, we encounter computational problems in several sectors/years. Therefore, except for the most numerous sectors, we proceed applying the test to each sector pooling observations over the years.
{(
#∗
b,$
∗
b, b=1,… ,B2
)}
32 | AGOSTINO ET AL.
T A B L E A 1 Balancing tests
Mean t test
Treated Control t p > |t|BROAD
SUP U 0.27398 0.28709 −1.32 .1870M 0.27398 0.27927 −0.41 .6790
SIZE U 15.612 14.879 26.76 .0000M 15.612 15.614 −0.04 .9690
AGE U 3.0168 2.8936 8.51 .0000M 3.0168 3.024 −0.4 .6860
INDEBT U 60.622 62.862 −4.71 .0000M 60.622 60.328 0.48 .6290
CASHFLOW U 4.2804 4.1754 0.85 .3980M 4.2804 4.2768 0.02 .9820
GRADUATE U 9.486 6.1596 11.52 .0000M 9.486 9.3963 0.21 .8350
HHIA U 0.08208 0.08043 0.88 .3770M 0.08208 0.08168 0.17 .8640
PATENT U 4.0374 3.8038 8.59 .0000M 4.0374 4.0458 −0.27 .7900
GDPPC U 10.26 10.197 10.72 .0000M 10.26 10.262 −0.2 .8450
CNORTH U 0.84634 0.7428 11.05 .0000M 0.84634 0.85 −0.36 .7210
RELSUP U 0.29642 0.2835 1.12 .2620
M 0.29642 0.28731 0.59 .5550SIZE U 15.346 14.953 12.15 .0000
M 15.346 15.378 −0.79 .4300AGE U 3.0223 2.8996 7.32 .0000
M 3.0223 3.0266 −0.2 .8380INDEBT U 60.797 62.719 −3.48 .0000
M 60.797 60.67 0.17 .8620CASHFLOW U 4.2552 4.1843 0.49 .6220
M 4.2552 4.2818 −0.14 .8860GRADUATE U 8.1047 6.5167 4.73 .0000
M 8.1047 8.0892 0.03 .9740HHIA U 0.07879 0.08094 −1 .3190
M 0.07879 0.07976 −0.36 .7220
(Continues)
| 33AGOSTINO ET AL.
Mean t test
Treated Control t p > |t|PATENT U 3.9595 3.8264 4.22 .0000
M 3.9595 3.9904 −0.79 .4310GDPPC U 10.242 10.203 5.74 .0000
M 10.242 10.25 −0.95 .3410CNORTH U 0.82699 0.75087 6.99 .0000
M 0.82699 0.84048 −1.07 .2860Note: For the description of variables, see Table 2.
T A B L E A 1 (Continued)
T A B L E A 2 Balancing tests on new entrants
Mean t test
Treated Control t p > |t|BROAD_ENTR
SUP U 0.315 0.287 1.890 .059M 0.315 0.315 −0.020 .985
SIZE U 15.451 14.879 14.080 .000M 15.451 15.477 −0.490 .626
AGE U 3.004 2.894 5.140 .000M 3.004 3.001 0.110 .911
INDEBT U 60.894 62.862 −2.790 .005M 60.894 60.829 0.070 .944
CASHFLOW U 4.153 4.175 −0.120 .903M 4.153 4.217 −0.260 .795
GRADUATE U 9.148 6.160 7.100 .000M 9.148 9.249 −0.150 .878
HHIA U 0.081 0.080 0.300 .766M 0.081 0.082 −0.110 .913
PATENT U 3.888 3.804 2.070 .038M 3.888 3.882 0.130 .897
GDPPC U 10.231 10.197 3.850 .000M 10.231 10.232 −0.110 .915
CNORTH U 0.828 0.743 6.020 .000M 0.828 0.829 −0.090 .925
REL_ENTRSUP U 0.353 0.284 4.200 .000
M 0.353 0.330 0.970 .333
(Continues)
34 | AGOSTINO ET AL.
Mean t test
Treated Control t p > |t|SIZE U 15.280 14.953 7.000 .000
M 15.280 15.305 −0.420 .676AGE U 3.011 2.900 4.620 .000
M 3.011 3.022 −0.350 .728INDEBT U 60.856 62.719 −2.350 .019
M 60.856 60.924 −0.060 .949CASHFLOW U 4.085 4.184 −0.480 .633
M 4.085 4.067 0.070 .947GRADUATE U 8.303 6.517 3.700 .000
M 8.303 8.415 −0.160 .875HHIA U 0.082 0.081 0.470 .638
M 0.082 0.084 −0.350 .723PATENT U 3.794 3.826 −0.710 .480
M 3.794 3.811 −0.280 .779GDPPC U 10.216 10.203 1.290 .197
M 10.216 10.217 −0.100 .917CNORTH U 0.810 0.751 3.740 .000
M 0.810 0.820 −0.530 .594Note: For the description of variables see Table 2.
T A B L E A 2 (Continued)