Paper to be presented at the DRUID Academy Conference 2018at University of Southern Denmark, Odense, Denmark

January 17-19, 2018

Network participation and innovation: evidence from MSMEs in Vietnamesemanufacturing sector

Elisa CalzaUNU-MERITUNU-MERIT

calza@merit.unu.edu

AbstractBy identifying knowledge flows as a main driver ofinnovation, firm-level empirical literature explains firmheterogeneity in innovation performance as the result ofdifferences in learning and knowledge generation anddiffusion. Even within clusters, where geographicproximity is believed to play a primary role (Asheimand Gertel, 2005), firms are not homogeneous and theystill behave differently, which may ultimately bereflected in how and how much they innovate(Boschma and Wal, 2007; Giuliani, 2007). In thisrespect, other dimensions of proximity (besidesgeographic) may act as channels for knowledgediffusion and innovation. Among these, social orrelational proximity (Boschma, 2005) represented by(social, business, family) networks embeddedness(Gebreeyesus and Mohnen, 2013) is attractingincreasing interest. Most of the existing empirical analyses relatingnetwork and innovation performance are based on theexperience of advanced economies, thus they do notconsider the specific conditions of entrepreneurialactivity in a developing country. At the same time, theliterature on networks in developing economies focusesmainly on their effects on other dimensions of firmperformance (e.g. value added) (Fafchamps and Minten2002; Barr, 2000; Howard, 2016), or on transactioncosts and information (Fafchamps, 2000). Hence, theempirical evidence on the impact of networks on

firm-level innovation performance in developingeconomies is still scarce (Gebreeyesus and Mohnen,2013). This work aims at addressing this research gap bybringing together these two strands of the literature: itinvestigates the determinants of technologicalinnovation outputs (product and process) amongVietnamese SMEs in the manufacturing sector byexploring the role of participation in networks,addressing in particular whether the size of a firm'snetwork impacts upon its innovation performance. Wefurther examine the importance of the ‘quality’ ofcontacts and the role of geographic proximity in drivinginnovative performance. Underlying the proposed analysis is the theoreticalargument that the inclusion and the degree ofembeddedness in networks can play a role in explaininginnovation heterogeneity. Different types of networkare likely to convey different information, and adifferent degree of participation in networks impliesthat firms are linked to different sets of players, whichare in turn carrying different sets of knowledge,representing potentially different opportunities as wellas constraints. Using 5 waves (2007-2015) of a panel survey onVietnamese MSMEs in the manufacturing sector (1,123firms per period in the balanced panel), a probabilitymodel for technological innovation (product and/or

process) is implemented. The main independentvariable accounting for network effects is the number ofcontacts. We also include other network-related factors(e.g. diversity of contacts, being part of a businessassociation, geographical distance from suppliers andcustomers), as well as controls (firm and entrepreneurcharacteristics, sector, province and time periods). Toaccount for possible endogeneity of network, wesimultaneously estimate the innovation probabilityequation (implementing a correlated random effectsprobit based on Mundlak (1978)) and a reduced formequation for network (implemented ascontrol-function). Initial results show that firms with a larger network aremore likely to innovate. This result is robust to differentestimation methods and specifications. We also findthat the number of contacts in different sectors, amongpoliticians and among women is positively correlatedwith innovation. In terms of ‘quality’ of network, beingpart of a business association is highly positivelycorrelated with innovation, while we do not find suchevidence for having a more diverse network. Ourresults do not provide any evidence of a curvilineareffect of the size of network on innovation in our data.

ReferencesAsheim, B. and M.S. Gertler (2005). ‘The Geographyof Innovation: Regional Innovation Systems’. In J.Fagerberg, D. Mowery, and R. Nelson (Eds.), TheOxford Handbook of Innovation, 291-317, Oxford:Oxford University Press. Barr, a. (2000). ‘Social Capital and TechnicalInformation Flows in the Ghanaian ManufacturingSector’. Oxford Economic Papers, 52(3): 539-59.Boschma, R.A. (2005). ‘Proximity and Innovation: ACritical Assessment’. Regional Studies, 39(1):61-74.Boschma, R. A. and L.J. Wal (2007). ‘KnowledgeNetworks and Innovative Performance in an IndustrialDistrict: the Case of Footwear District in the SouthItaly’. Industry and Innovation, 14(2): 177-99.Fafchamps, M. (2000). ‘Ethnicity and Credit in AfricanManufacturing’. Journal of Development Economics,61: 205-35.Fafchmps, M. and B. Minten (2002). ‘Returns to SocialNetwork Capital Among Traders’. Oxford EconomicPapers, 54(2): 173-206.Gebreeyesus, M. and P. Mohnen (2013). ‘InnovationPerformance and Embeddedness in Networks: Evidencefrom the Ethiopian Footwear Cluster’. WorldDevelopment, 41: 302-316Giuliani, E. (2007). ‘The Selective Nature ofKnowledge Networks in Clusters: Evidence from theWine Industry’. Journal of Economic Geography,7:139-68.

Howard, E. (2016). ‘Social Networks, GeographicProximity, and Firm Performance in Viet Nam’.WIDER Working Paper 2017/69. Helsinki:UNU-WIDER.Tybout, J.R. (2000). ‘Manufacturing Firms inDeveloping Countries: How Well Do They Do, andWhy?’. Journal of Economic Literature, 38(1): 11-44.

Network participation and innovation: evidence fromVietnamese MSEMs in manufacturing sector

Elisa Calza∗

December 7, 2017

DRAFT - DO NOT CIRCULATE, DO NOT CITE

Abstract

JEL Classifications:Keywords: Entrepreneurship, Innovation

Emerging economies, Viet NamSocial networks

∗UNU-MERIT/MGSoG, Maastricht, The Netherlands, Contact: calza@merit.unu.educalza@merit.unu.edu

1 Introduction

The heterogeneity of firm-level innovation performance is a widely investigated topic in thediscipline of economics of innovation. This interest can be understood when considering thepositive effects of technological innovation on firm performance and productivity, effects thathave been deeply studied and well recognized in the empirical literature (Crepon et al.Crepon et al., 19981998;Mairesse and MohnenMairesse and Mohnen, 20102010; HallHall, 20112011). Thus, understanding what makes firms more likelyto innovate - and thus, potentially, to grow or foster their productivity - is a relevant issue,especially for developing and emerging countries.The analysis of the determinants of innovation for firms operating in a developing or emergingeconomies has considered a wider set of proxies for channels of knowledge diffusion. In thisrespect, geographic proximity has been studied as playing a primary role on innovation activitieswithin firm clusters. However, even when geographically close, firms are not homogeneous andthey still behave differently, which may ultimately be reflected in how and how much theyinnovate. Other dimensions of proximity may act as channels for knowledge diffusion andinnovation. Among these, social networks and social interactions have been gaining increasingattention as a channel for information and knowledge diffusion. However, most of existingempirical studies on developing and emerging economies explore the relationship between socialnetworks and firm productivity or profit performance, while the empirical evidence on socialnetworks and firm-level innovation performance is still scarce, and limited to the experience ofclusters in more advanced economies.This paper aims at contributing to address this gap in the literature by investigating the sourcesof the heterogeneity of innovation performance from the perspective of knowledge diffusion andtransfer among entrepreneurial actors operating in a developing and emerging economy. Wefollow the argument that the inclusion in networks allows firms to get in contact with a newand different set players, who are carrying different typed of information respect to what theyown, and this may represent an important input for innovation activity, especially among microand small firms operating in a developing and emerging economy. Using a rich panel databaseof a MSMEs survey conducted in Vietnamese manufacturing sector between 2007 and 2015,we perform an empirical analysis of the determinants of technological innovation (product andprocess) among Vietnamese SMEs in the manufacturing sector, focusing on the role playedby the participation into networks. The term network is here used as synonymous for ‘socialnetwork’ as it has been defined by Nichter and GoldmarkNichter and Goldmark (20092009): ‘micro-level relationshipsbetween agents in an economy...to refer to relationships between individuals’. These couldinclude, but not be restricted to, business relations, as well as family, neighborhood, or villagerelationships. We find evidence of a positive and significant effect of network size on theprobability of engaging in innovation activities. By testing different specifications, our resultsalso show that also participating in a more diverse network, as well as being member of ‘higherquality’ networks, has a further positive effect on innovation.The determinants of innovation performance and the role played by network have not beenempirically investigated for the case of Vietnamese MSMEs. Thus, our work also contributes tobetter understand the innovation process for micro, small, and medium enterprises in the specificcontext of the Vietnamese manufacturing sector. We believe that this study is particularlyimportant for the country, potentially providing some insights for the design of entrepreneurialpolicies aiming at fostering the performance of MSMEs. The Vietnamese government isnowadays keen on implementing policy actions in this direction, aiming at the strengthening ofdomestic technological capabilities to avoid the risk of getting stuck in a middle income trap.The paper is structured as follows. Section 22 provides a brief review of the relevant literature,based in which we formulate some hypotheses to be tested in the empirical analysis. Section 33describes the used database, presenting also the description and some summary statics of the

1

used variables. Section 44 discuss the model and explains the implemented estimation strategy.Section 55 comments the obtained results, followed by a brief discussion and some concludingremarks in Section 66.

2 Literature and hypotheses

2.1 Literature background

Absorptive capacity and firm capabilities have long been recognized as the grounds of learningand innovation performance in firms (Nelson and WinterNelson and Winter, 19821982; Cohen and LevinthalCohen and Levinthal, 19901990;LallLall, 19921992). They represent also an important source of firm heterogeneity: firms areheterogeneous in what they know, how they learn, how they use their knowledge, whichultimately may reflect in how and how much they innovate.With the increased availability of micro-data, a well established empirical literature hasinvestigated the determinants of innovation activities and outputs, trying to match thetheoretical importance of capabilities and absorptive capacity with the search of adequateindicators. In this respect, the studies related to the CDM model (Crepon et al.Crepon et al., 19981998)contributed largely to a better understanding of the determinants of technological innovation,assigning a pivotal role to RD investments in accounting for advanced technological capabilities(together with other ‘conventional’ controls to account for more general firm-level capabilities,such as firm size and age) (Mairesse and MohnenMairesse and Mohnen, 20102010). However this approach was developedon the basis of the experience of firms in advanced economies, and it has been criticized dueto its emphasis on RD activities, which makes it not so adequate for the analysis of innovationin small enterprises and in firms in developing countries. In a developing economy productiveactors tend not to work on frontier technology and rarely rely on conducting RD to push forwardinnovation (Hall et al.Hall et al., 20092009), being their learning more related to imitation, adaptation andabsorption of technologies developed somewhere else (Chudnovsky et al.Chudnovsky et al., 20062006).In order to better account for the heterogeneity in innovation performance in a developingcontext, the empirical analysis of firm-level innovation has also emphasized other factorsassociated with firm-level absorptive capacity, capabilities, and learning, such as the humancapital of the firm proxied by manager education, experience or/and employee training(Goedhuys et al.Goedhuys et al., 20142014; GebreeyesusGebreeyesus, 20112011; Gebreeyesus and MohnenGebreeyesus and Mohnen, 20132013), or the role ofknowledge diffusion and knowledge spillovers. Innovation is based on the interactions andknowledge flows between market and economic entities (Asheim and GertlerAsheim and Gertler, 20052005), andknowledge spillovers are likely to play a particularity important role in a developing countrycontext, where firms are operating far from the best practice frontier (Howard et al.Howard et al., 20152015;HowardHoward, 20172017), and where transaction costs and information asymmetries are more diffused(TyboutTybout, 20002000).The research into knowledge spillover and knowledge transfer across economic entities stemsfrom the tradition of studies on industrial agglomeration and clusters. These studies emphazisethe importance of geographic proximity on firm competitive and innovaiton performancewith respect to isolated firms, mainly thanks to a ‘cluster effect’ (Asheim and GertlerAsheim and Gertler, 20052005)associated with collective efficiency and the transmission of tacit knowledge. However, therelative importance of geographical agglomeration has started to get questioned. This isnot exclusively due to the appearance of digital technologies and internet, which may havemade physical distance less relevant and whose impact on how knowledge and informationare transmitted is still debated, but mainly due to the fact that geographical proximity perse seems not to be able to explain the intra-cluster heterogeneity in learning, knowledge

2

generation and diffusion, and innovation. Even within clusters, firms still behave and performdifferently, thus are heterogeneous despite their geographical proximity (Giuliani and BellGiuliani and Bell, 20052005;Boschma and Ter WalBoschma and Ter Wal, 20072007; ?).Other dimensions of proximity need to be considered in order to properly understand firmlearning and innovation performance, first of all social or relational proximity (BoschmaBoschma, 20052005;Boschma and Ter WalBoschma and Ter Wal, 20072007). The inclusion and the degree of embeddedness in networks canplay a role in explaining innovation heterogeneity within the same clusters: different typesof network are likely to carry different information content that may affect firm performancedifferently, and uneven participation in networks means that firms are connected in differentiatedways that link them to different sets of players and present them with sharply differentopportunities and constraints.This does not imply that geographical proximity is not relevant; instead, that it should not beassessed in isolation but in relation other dimensions of proximity. Gebreeyesus and MohnenGebreeyesus and Mohnen(20132013) argue that ‘the effect of clusters on innovation may be (...) partially influenced by networkposition.’ In other words, the impact of geographical proximity (such as in clusters) may alsodepend on the degree and quality of embededness in networks, suggesting a sort of interactionbetween different types of proximities. In the same way, the effect of the embeddedness innetworks may also differ from firm to firm depending on their absorptive capacity: firms withan higher absorptive capacity may find it easier to establish links with external sources ofknowledge (Giuliani and BellGiuliani and Bell, 20052005) and are better able to exploit the knowledge from outside(Cohen and LevinthalCohen and Levinthal, 19901990).The empirical literature on entrepreneurship in developing countries has recognized theimportance of social network mainly in relation with firm performance and growth, or toovercome obstacles related to transaction costs, contract enforcement, and lack of regulation,which are typical in environments with pervasive market failures as in the case of developing andemerging economies. ‘Having an extensive social network is a valuable asset that can help anentrepreneur obtain access to information (e.g., leads about profitable business opportunities)as well as resources (e.g.,credit).’(Nichter and GoldmarkNichter and Goldmark, 20092009). FafchampsFafchamps (20002000) shows thepreference of Ghanaian firms for doing business with individuals they already know, and thatnetworks are crucial in determining access to credit. In another study Fafchamps and MintenFafchamps and Minten(20022002) find that larger networks are associated with higher value added for agricultural traders inMadagascar. This is similarly to what BarrBarr (20002000) finds for the case of small-scale manufacturersin Ghana, among those the ones with larger and more diverse sets of networks are moreproductive. Turning more specifically to Viet Nam, as far as we know HowardHoward (20172017) is theonly existing study focusing on the correlation between social networks, geographical proximityand firm performance (value added) using a panel of MSMEs in manufacturing over years 2011-2013-2015, finding that larger and ‘more quality’ social networks seem to be more positivelycorrelated with firm productivity level than geographical proximity.Most of the existing empirical studies on network and innovation focus instead on the experienceof firms in advance economies, whose clusters are characterized by RD and by the presenceof more advanced, more capable leading firms acting as ‘technological/knowledge gatekeeper’(Boschma and Ter WalBoschma and Ter Wal, 20072007). On the contrary in a developing context clusters are more likelyto be formed by small and micro locally owned firms in traditional sectors and that are active onthe local market, which tend to generate little new knowledge and technology and rely mainly onimitating and adapting existing one developed elsewhere (MarkusenMarkusen, 19961996). Thus, the existingempirical literature on network and innovation is of limited relevance for the analysis of thissame topic in a developing context.We are not aware of many empirical studies on network embededness and innovationperformance in developing countries, with the exception of the work of Gebreeyesus and MohnenGebreeyesus and Mohnen(20132013) that provides positive evidence of the effect of embeddedness in networks on firminnovative performance in the case of the Mercato footwear cluster in Ethiopia, finding that local

3

business relations are the major channels through which marketing and technical knowledge flowinto the analyzed footwear cluster, where trade is mainly conducted within the analyzed footweardistrict, and where linkages with firms outside the district - even through subcontracting - tendto be minimal, with most of the firms obtaining information from informal relations with theirlocal business partners (Gebreeyesus and MohnenGebreeyesus and Mohnen, 20132013). An empirical analysis of the effect ofsocial networks on firm innovation is still missing for Vietnamese manufacturing sector. Giventhe current interest of the Vietnamese government, this could provide new insights for the designand implementation of innovation and entrepreneurship policies for micro and small enterprises.

2.2 Hypotheses

Drawing from the presented literature, we formulate some hypothesis about the role ofparticipating into a social network - intended as the set of individuals the firm is regularlyin contact with and that is useful to perform the business operations - on the likelihood tointroduce a new process or an improved product11. We refer to Gebreeyesus and MohnenGebreeyesus and Mohnen (20132013)and explicitly formulate our main hypothesis about the role of networks, which we argue tobe ‘the main channel of knowledge flows’ for micro and small firms in developing countries, asfollows:

Hypothesis 1: The larger the network a firms in participating in, the larger is theprobability that the firm introduces and innovation.

To improve our understanding of the role of social networks for innovation, we furthercomplement the scope of the analysis of our first hypothesis with some additional hypothesesabout the ‘quality’ of the network. Different types of contacts may carry different sets ofknowledge and to convey different information. Hence, by contributing to not only to expandbut also to diversify the knowledge base of a firm, the participation in a more diversifiednetwork may positively affect the ability to innovate. This is expressed as follows:

Hypothesis 2: Firms with a more diversified network are more likely to get in contactwith different forms of knowledge, and thus become more likely to implement an innovation,controlling for the size of their network.

Another way to proxy for ‘network quality’ is to consider who is actually part of thenetwork, and whether some of the individuals a firm is connected to can actually be more usefuland helpful for firm business operations. In this respect, being member of a business associationhas been used in the literature to proxy for participation in ‘higher quality’ networks (Howard2016), following the argument that the size of the network may matter less in terms of firmperformance if the ‘quality of the contacts’ is larger. It follows that:

Hypothesis 3: Being part of a business association implies being connected with moreuseful contacts for firms’ business operation, thus it increases the probability that a firmengages in innovative activities than those are not in a business association.

1 See Table 11 for a more precise definition and description of the used variables.

4

3 Data and summary statistics

3.1 Data

The data used in the present study come from the Small and Medium Scale ManufacturingEnterprise (SME) survey conducted since 2005 every second year among Vietnamese enterprisesoperating in the manufacturing sector22. The survey was conducted in 10 provinces: Ho ChiMinh City (HCMC), Hanoi, Hai Phong, Long An, Ha Tay, Quang Nam, Phu Tho, Nghe An,Khanh Hoa, and Lam Dong. The random sample was stratified by ownership type to include:household establishments, private enterprises, collectives or cooperatives, and limited liabilityand joint stock companies.The current sample is a direct continuation of the sample obtained in 2005: apart from theenterprises interviewed in 2005 that still operate, the sample contains enterprises added toreplace those that in the meantime have stopped operating. It originally included only firmsactive in manufacturing sectors and with less than 300 employees; however, due to changes ofsector over time, it may be possible that some firms still appear in the list of manufacturingfirms even though they in fact moved to services, or that some firms have grown larger than themaximum threshold of 300 employees. Firms that operate in agriculture or mining, and withthe participation of foreign or state capital are excluded from the dataset.The questionnaire includes a rich set of information on enterprise characteristics and practices,such as number and structure of workforce, technology and innovation, owner characteristics,together with unique information on firm’s social network defined as the number of contacts:in the same sector, in a different sector, among bank officials, politicians and civil servants,women, as well as the number of costumers and suppliers.For the presented empirical analysis we use a database obtained by merging the five waves of thesurvey collected between 2007 and 201533, which (after cleaning)44 corresponds to an unbalancedsample of 13,163 firms that have appeared at least in one of the survey waves between 2007and 2015. Among these, we consider the 1,123 firms that participated in all consecutive surveywaves since 2007, and that thus constitute the balanced sample.

3.2 Descriptives

Table 11 reports the definition of the main variables used in the empirical analysis. Table 22presents the basic summary statistics of the main variables used (mean and standard deviation)over different sample compositions (per year, total unbalanced and balanced).The Innovation variable includes process innovation and product improvement. It presents a

2 The survey has been conducted in collaboration between the Central Institute for EconomicManagement (CIEM) of the Ministry of Planning and Investment of Vietnam (MPI), the Instituteof Labor Science and Social Affairs (ILSSA) of the Ministry of Labor, Invalids and Social Affairsof Vietnam (MOLISA), the Development Economics Research Group (DERG) of the University ofCopenhagen and the United Nations University World Institute for Development Economics Research(UNU-WIDER). For more information about the surveys and data, see CIEM and ILSSACIEM and ILSSA (20162016).

3 We were planning to use all six waves available of the MSMEs survey between 2005 and 2015 (with990 firms in the balanced sample, for a total of 5940 observations across six periods). We decidedto drop the first wave because some variables (especially related to innovation and network) present‘far-from-(within)average‘ figures. See Appendix for more details on the construction of some variables.

4 See Appendix A for more detailed on the cleaning process performed on the data to obtain the variablesNetwork and Employment.

5

declining pattern after 2011, dropping from 48 per cent in 2007 to 15 per cent in 201555. Theaverage values for network size are more persistent over time: the average number of contactsacross time falls between a 33 and 39 individual contacts, with an average of 36 in the balancedpanel 66. More than 1/3 of firms have a diversified network - thus containing at least onecontact from the same sector, from a different sector, from politicians or civil servants, or frombank officials -, while around 11-10 per cent of firms are member of a business association.Looking at general firm characteristics, the average firm size (based on employment) declinesover time from an average of almost 15 (2007) to 12 (2015) regular employees. Given the smallaverage firm size, and considering that the large majority of firms in the considered samplecan be classified as micro and small firms77, it is thus not surprising that between 80 and 77per cent of firms fall in the category of household enterprises. The average firm age raises overtime from 14 to 20 years. The observed increase in the average firm age over time is mainlydue to the fact that we are considering firms in the balanced panel; however, the last columnsin Table 22 show that the average firm age does not change much between the balanced andthe unbalanced sample. About manager or owner (the respondent) characteristics, the shareof male respondents decreases from almost 70 to 60 per cent between 2007 and 2015, while theaverage age is between 44.7 and 46.5, being larger in the last waves (again, this may be due tothe panel structure of the sample)88.Looking at the variables used as covariates in the network equation (first stage), it is interestingto note the rapid increase in Internet access during the last decade, which reached 36 per centof considered firms in 2015. The access to infrastructure, such as road, rail or port, is largeand has been also increasing over the considered time period. An increasing number of firmsdeclare to conduct individual price negotiation with their costumers, while the share of firmsthat use personal contacts as main criteria to select suppliers is not constant over time andoscillates between 26 and 43 per cent.

5 We do not have enough elements to explain the change in innovation outcomes. We can, however,hypothesize two factors: the international 2008 crisis, and a data collection issue (e.g. a change in howthe questions related to innovation are asked.

6 Looking at the composition of the contacts categories that form this variable, it can be added thatin all considered waves the number of ‘contacts in different sectors‘ represent the largest share of thetotal number of contacts, with an average value between 19 and 23 individuals. This average valueis more than double than the average of number of contacts in the category ‘contacts in the samesector‘, which remains between 7 and 9. The average number of contacts among bank officials andpoliticians is in general lower (between 1 and 1.45 per cent for bank officials, and between 1.5 and 1.9for politicians), while the contacts in the residual category ‘others’ increase other time (from 3.3 to6.2) but it still lower than ‘contacts in same sector’.

7 Following the World Bank definition of micro, small, and medium enterprises based on the number ofemployees

8 It has to be noticed that, however, there is no guarantee that the respondent is always the same person.By looking at the data, some firms seem not to have the same manager across the whole time period,and thus individual-related factors cannot be fully considered as time-invariant variables.

6

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Av.

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50.3

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830.

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10.7

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7C

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ty0.

280.

450.

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370.

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110.

310.

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310.

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270.

100.

290.0

90.2

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00.2

90.0

90.2

8E

mp

loym

ent

14.7

527

.30

14.4

328

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14.0

328

.80

12.3

824

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12.1

625.6

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loym

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(log

)1.

911.

121.

871.

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811.

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761.

081.6

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01.8

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11.8

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age

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610

.75

16.3

411

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15.0

09.

0817

.96

9.31

19.9

49.2

916.7

010.3

214.6

910.3

9F

irm

age

(log

)2.

420.

692.

570.

672.

550.

572.

770.

492.9

00.4

32.6

40.6

02.4

60.6

9M

anag

er/o

wn

erag

e45

.15

10.2

246

.52

10.2

647

.30

10.3

748

.53

10.6

949.6

310.9

247.4

210.6

145.8

610.6

9M

anag

er/o

wn

erag

e(l

og)

3.78

0.23

3.81

0.23

3.83

0.23

3.86

0.23

3.8

80.2

33.8

30.2

33.8

00.2

4M

anag

er/o

wn

erge

nder

0.69

0.46

0.67

0.47

0.64

0.48

0.62

0.49

0.6

10.4

90.6

40.4

80.6

30.4

8H

ouse

hol

dan

dpri

vate

sole

pro

pri

etor

ship

0.82

0.39

0.80

0.40

0.79

0.41

0.78

0.42

0.7

70.4

20.7

90.4

10.7

30.4

5P

artn

ersh

ip/C

olle

ctiv

e/C

oop

erat

ive

0.03

0.18

0.03

0.16

0.03

0.16

0.03

0.16

0.0

30.1

70.0

30.1

70.0

30.1

7L

SC

0.15

0.36

0.17

0.38

0.18

0.39

0.19

0.40

0.2

10.4

00.1

80.3

80.2

50.4

3S

up

pid

0.31

0.46

0.26

0.44

0.39

0.49

0.43

0.50

0.3

60.4

80.3

50.4

80.3

50.4

8P

rice

neg

0.13

0.34

0.18

0.38

0.13

0.34

0.17

0.37

0.2

10.4

10.1

60.3

70.1

60.3

6In

tern

et0.

170.

380.

230.

420.

310.

460.

270.

450.3

60.4

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70.4

40.3

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fras

tru

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re0.

820.

390.

830.

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330.8

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6

Ob

serv

atio

ns

1123

5614

13163

Sou

rce:

auth

ors’

elab

orat

ion

bas

edon

CIE

Man

dIL

SS

AC

IEM

and

ILS

SA

(2016

2016).

8

Finally, to preliminary assess the existence of differences in terms of network size for firms thatengage in innovation activity, we compare the distributions of the used network variable acrossinnovative and non-innovative firms. As showed by Figure 11, the distribution of the networkvariables for innovative firms (continuous line) is shifted to the right of the distribution ofnetwork for non-innovative firms (dotted line), thus confirming that innovative firms do tendto have larger networks. This allow us to point out the presence of a possible ‘structuraldifference’ between innovative and non-innovative firms from in terms of their participationinto networks.

Figure 1: Distribution of network size: innovative and non-innovative firms

0.2

.4.6

.8kd

ensi

ty h

_net

_tot

2

0 2 4 6 8x

kdensity h_net_tot2 kdensity h_net_tot2

4 Model and empirical strategy

We formulate the binary choice model for having implemented a technological innovation sincethe last survey as:

INN∗jt = βXjt + εjt (1)

where INN∗jt

is the latent variable underlying the dichotomous response of having implementeda technological innovation, which depends on the vector of explanatory variables Xjt thatcontains measures for network Njt , a set of time-varying (exogenous) variables (Wjt) accountingfor firm and respondent characteristics, and time-invariant controls (Zjt), such as sector andprovince dummies. The relevant empirical literature proposes different measures for network; forexample, the linkages within business network, such as the contacts with suppliers and buyers,have been often used. However, a broader definition of network beyond trade based relationshipsis consistent with the argument that a diversity of contacts may channel a variety of informationand experiences, favoring innovation activity. Hence, following Gebreeyesus and MohnenGebreeyesus and Mohnen (20132013)and Howard2016, we measure ‘the intensity of network’ as the number of individuals the firmshas a regular contact with, defined as the sum of contacts among business people in the same

9

sector, in a different sector, bank officials, politicians and civil servants, and others99 - thus,irrespectively or beyond to the connections through the exchange of goods.We only observe the innovation binary outcome as:

INNprobitjt

=

{1 if INN∗

jt= βXjt + εjt > 0

0 otherwise(2)

where the error term εjt is assumed to be normally distributed (εjt ∼ N(0, 1)). However, itmay contain time-invariant unobservable characteristics (cjt). Endogeneity issues surge whenthese unobservable individual specific effects are correlated with the observed covariates (Xjt)(e.g. if unobservable managerial ability is correlated with other determinants of innovation,such as the number of business contacts). The availability of panel data allows us to addressthis source of endogeneity. With small T the implementation of fixed effect (FE) probit byadding individual dummies would, however, produce inconsistent estimations of the parametersof interest (β) due to the incidental parameters problem (WooldridgeWooldridge, 20102010). This could beavoided by applying a conditional maximum likelihood estimation (CMLE), but this methodcan be implemented for nonlinear models only in special cases, and relies on strong conditionalindependence assumptions (WooldridgeWooldridge, 20102010). A good compromise for the estimation ofparameters in nonlinear panel data models is represented by the correlated random effect (CRE)model (MundlakMundlak, 19781978). This method allows to specify the unobserved effect (cjt) as function ofthe vector of the averages across time periods of the time-variant variables contained in Xjt . Inpractice, it allows for correlation between the unobserved effect and the explanatory variables byadding the within group means of the time-variant covariates in the binary equation 22, using thesignificance of their coefficients as a test for the existence of this correlation (with the implicitassumption that the time-independent variables are uncorrelated with the unobserved effect cjt)(WooldridgeWooldridge, 20102010).By implementing the CRE model, the expression for the latent variable in Equation 11) can bereplaced by:

INN∗jt = β1Njt + β2Wjt + β3Zjt + ξ1Wjt + ξ2Njt + εjt (3)

The CRE model could be estimated using MLE if the assumption of serial independence holds;in fact, MLE returns inconsistent results if this assumption fails. Serial independence maybe a strong assumption in case of repeated observations, where serial correlation is common.To relax this strong assumption, we can estimate the CRE model with a pooled probitmethod, but this would lead to more inefficient estimations. A possible solution to recoverefficiency and relax independence is to use ‘generalized estimating equations’ (GEE), whichis more efficient and as robust as pooled probit when dependence within clusters is assumed(if independence is assumed, the GEE is equivalent to the pooled probit and less efficientthan MLE), by allowing to formulate alternative assumptions about the correlation structure(WooldridgeWooldridge, 20102010). We follow GebreeyesusGebreeyesus (20152015) and estimate the CRE as GEE probit withan ‘exchangeable’ correlation structure (Papke and WooldridgePapke and Wooldridge, 20082008).Another concern comes from the fact that the network variable could be correlated with theerror term. In this case endogeneity raises from simultaneity or from reverse causality betweena regressor and the dependent variable (e.g. here, if firms tend to gain a larger network dueto the fact that they are also more innovative). To avoid biased and inconsistent parameterestimations, we could look for a relevant instrument and implement an IV estimation to correctthis endogeneity problem. As pointed out by Gebreeyesus and MohnenGebreeyesus and Mohnen (20132013), finding a goodinstrument is often challenging, and may also lead to less precise estimations. An alternativeapproach is to implement a control function (CF) method (GebreeyesusGebreeyesus, 20152015). The CF consists

9 The questionnaire asks ‘How many people do you currently (present) have regular contact with?(Contact at least once every 3 months, which you find useful for your business operations)’.

10

of a two-step procedure: first, a reduced form equation is estimated (using OLS) for theendogenous explanatory variable, which in our case is the measure for network; second, theresiduals of the reduced form equation are used as additional regressor in the main structuralequation (here, the probability equation for innovation), together with the original endogenousvariable. This sequential estimation assumes causality to run from network to innovation, andno eventual feedback loops from innovation to network are considered. In practice, we followGebreeyesusGebreeyesus (20152015) and Papke and WooldridgePapke and Wooldridge (20082008) in addressing both endogeneity sourcesof individual unobserved hetereogeneity and of reverse causality by combining the CRE and theCF approaches in the presence of a continuous endogenous explanatory variable1010. The reducedform equation for network and the structural equation for innovation can then be formulatedas follows:

Njt = γ1Mjt + γ2Wjt + γ3Zjt + η1Mjt + η2Wjt + υ1jt (4)

INN∗jt = β1Njt + β2Wjt + β3Zjt + ξ1Wjt + υ̂1jt + υ2jt (5)

where in reduced form Equation 44 Mjt denotes a set of variables that are exogenous to themodel (thus, uncorrelated with the error term in the structural equation, and that can helpidentification), which in our case includes: a dummy that takes the value of 1 if the firmidentifies suppliers mainly via personal contact (Howard 2016), a dummy for having individualprice negotiation with costumer (Gebreeyesus and MohnenGebreeyesus and Mohnen, 20132013), and two dummies related tothe availability of access to information (Internet and Infrastructure)1111. In Equation 55 υ̂1jt isthe residual from the reduced form equation, while υ2jt is the new idiosyncratic error term1212.In both equations, variables with bar are group means of time-varying variables to allow themto be correlated with the firm unobserved heterogeneous characteristics.

5 Results

Table 33 shows the estimation results (probit coefficients) for the innovation equation forthe balanced sample1313. The results of the pooled probit and of the CRE regression model(estimated with MLE and GEE) are reported as benchmark in columns (1), (2) and (3).These methodologies give qualitatively similar results, providing a positive and significantcoefficient for the network variable; the sign and significance of the other covariates is alsorather consistent across the models. However, it has to be noticed that the magnitude ofthe network coefficient decreases in the CRE or Mundlak-augmented regression models. Thisshows that, when individual heterogeneity is properly accounted for, the effect of network issmaller. This is not surprising: the significance of the average of the network variable revealsthe presence of significant correlation between the main independent variable and individualunobservable effects.Columns (4) and (5) present the results of the estimations of the complete CRE-CF model(with MLE and GEE with boostrapped standard errors). We again find evidence of a positive

10 As noted by GebreeyesusGebreeyesus (20152015), because of the two-step procedure, we apply bootstrapped standarderrors (standard 50 replications) when estimating the second stage (or structural equation forinnovation).

11 As test for the exogeneity of these variables, we added these variables to the specification of theprobability equation for innovation and they do not turn out to be significant.

12 Note that now we remove from the structural equation the group mean of the endogenous variable(Njt).

13 The results of the first stage equation are reported in Appendix B.

11

and significant effect of network on innovation, being the coefficient obtained with the GEEmethods slightly smaller but of higher significance level. The coefficient of the predictedresidual from the first stage is negative and statistically significant, confirming the endogeneityof the network variable to the model. Thus, the result of both columns (4) and (5) allow us toconfirm our first hypothesis: the larger the network, the more likely is that the firm will engagein innovation activities. The effect of network obtained when endogeneity is accounted for isnow much larger than in the original models.About the other covariates, we effect of firm size (as number of regular employees) on thelikelihood to have introduced an innovation is positive, while we do not find evidence of asignificant effect of firm age once endogeneity is properly accounted for (the coefficient of agewas significant in the first models). Interestingly, household enterprises are more likely tointroduce an improved product or a new process than cooperatives and joint stock companiesonce we control for the size of network. Individual characteristics of the entrepreneur (age andgender) seem not to matter for innovation, on average and everything else being equal.Columns (6) and (7) in Table 33 show the results for the CRE-CF model (GEE with boostrap)when other network-related variables are added to the original specification for the innovationequation (the first stage equation remains the same). To test Hypothesis 2, we add a dummyvariable for network diversity (column (6)). We find that hving a more diversified networkpositively affects the likelihood to innovate; at the same time, the coefficient of the mainnetwork variable is still positive and significant, even if of a smaller magnitude with respect towhat obtained with the CRE-CF where diversity of network is not accounted for (column (5)).Column (7) shows the estimations when a dummy variable for being member of a businessassociation is also included, consistently with Hypothesis 3. Being a member of a businessassociation has a large positive coefficient on innovation. Despite this large effect, the effect ofnetwork size is still significant and positive, even if now it presents a smaller magnitude and itis significant at 10 per cent.

12

Tab

le3:

Inn

ovat

ion

and

net

wor

k:

mai

nes

tim

atio

nre

sult

s

Dep

enden

tvariable:innovation

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Pooled

CRE

probit

CRE

probit

CRE-C

FCRE-C

FCRE-C

FCRE-C

Fprobit

(MLE)

(GEE)

(MLE)

(GEE)

(GEE)

(GEE)

Network

0.114***

0.072**

0.069**

0.678**

0.652***

0.578*

0.512*

(0.026)

(0.032)

(0.030)

(0.302)

(0.250)

(0.333)

(0.280)

Employmen

t(log)

0.323***

0.233***

0.225***

0.142**

0.137**

0.140*

0.145***

(0.025)

(0.051)

(0.050)

(0.066)

(0.056)

(0.077)

(0.050)

Firm

age(log)

-0.082**

-0.080

-0.076

-0.057

-0.054

-0.056

-0.062

(0.039)

(0.058)

(0.052)

(0.059)

(0.052)

(0.046)

(0.059)

Manager/owner

age(log)

-0.425***

-0.268*

-0.257*

-0.214

-0.205

-0.197

-0.206

(0.091)

(0.153)

(0.141)

(0.156)

(0.156)

(0.160)

(0.154)

Manager/owner

gen

der

0.110**

0.109*

0.104*

0.102

0.098

0.104

0.101

(0.045)

(0.066)

(0.063)

(0.066)

(0.065)

(0.066)

(0.069)

Partnersh

ip/Collective/

Cooperative

-0.325**

-0.357***

-0.340**

-0.373***

-0.353**

-0.364***

-0.523***

(0.150)

(0.133)

(0.151)

(0.134)

(0.141)

(0.124)

(0.154)

LSC

-0.098

-0.149**

-0.140*

-0.227**

-0.216***

-0.215**

-0.214**

(0.069)

(0.074)

(0.073)

(0.089)

(0.074)

(0.104)

(0.084)

Employmen

t(log)(avg.)

0.110*

0.102*

0.127**

0.119**

0.077

0.030

(0.059)

(0.057)

(0.059)

(0.058)

(0.064)

(0.044)

Firm

age(log)(avg.)

0.008

0.009

-0.004

-0.002

0.009

0.002

(0.080)

(0.077)

(0.080)

(0.074)

(0.074)

(0.078)

Manager/owner

age(log)(avg.)

-0.253

-0.239

-0.279

-0.264

-0.234

-0.273

(0.198)

(0.188)

(0.198)

(0.224)

(0.191)

(0.204)

Manager/owner

gen

der

(avg.)

0.011

0.008

0.007

0.005

0.008

0.021

(0.091)

(0.088)

(0.091)

(0.097)

(0.079)

(0.087)

Network

(avg.)

0.152**

0.148**

(0.065)

(0.062)

Contactsdiversity

0.149***

0.141***

(0.045)

(0.052)

Contactsdiversity

(avg.)

0.283**

0.291***

(0.110)

(0.095)

BusinessAssociation

0.254***

(0.089)

BusinessAssociation(avg.)

0.278*

(0.152)

Residuals

(network)

-0.575*

-0.553**

-0.506

-0.443

(0.303)

(0.251)

(0.335)

(0.278)

Constant

0.362

0.192

0.165

-1.648

-1.602

-1.515

-0.958

(0.363)

(0.530)

(0.503)

(1.352)

(1.148)

(1.498)

(1.305)

Sector

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Province

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Tim

eYes

Yes

Yes

Yes

Yes

Yes

Yes

lnsig2u,co

nstant

-2.326***

-2.324***

(0.254)

(0.255)

Obs.

5551

5551

5551

5518

5518

5518

5518

Firms

1123

1123

1123

1123

1123

1123

1123

dfm

27

32

32

32

29

33

34

Sou

rce:

auth

ors’

elab

orat

ion

bas

edon

CIE

Man

dIL

SS

AC

IEM

and

ILS

SA

(2016

2016).

13

6 Discussion and concluding remarks

Using 5 waves (2007-2015) of a panel survey on Vietnamese MSMEs in the manufacturingsector, we investigate the effect of network participation on firms’ innovation activity. Proxingnetwork with the number of regular contacts, we find evidence that the size of networks has apositive and significant effect on the probability of a firm to engage in innovation activities.This result is consistent across different methods accounting for different source of endogeneity,such as the implementation of CRE (MundlakMundlak, 19781978) together with the control function (CF)method.We also find evidence that the ‘quality’ of the network also matters for innovation. In factour findings about the effect of network are robust to the addition of other network-relatedvariables, such as the dummies for being member of a business association and having amore diverse network of contacts. Both these variables are found to be important in affectinginnovation, but their addition to the model does not wash away the effect of the size of network,which continues to be positive and significant even in more sophisticated models.Besides expanding our understanding of the determinants of technological innovation outputs(product and process) among Vietnamese SMEs by providing empirical evidence in supportof the argument that the participation in networks plays a role in explaining innovationheterogeneity, this work also contributes to address the existing gap in the empirical literatureon firm in developing countries, which has typically overlooked the analysis relationship betweennetwork and innovation in favor of the study of network and firm performance. Furthermore,this works provides some initial insight to the re-thinking of policies aimed at fosteringinnovation among micro and small firms operating in developing or emerging economies. Amore specific analysis on the relevance of network connections versus geographical proximitywould be necessary, as well as a deeper investigation on how and which features of networksare more relevant for firm-level innovation activities.

14

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16

7 Appendix

7.1 Appendix A

Notes about the construction of variables

Network has been obtained as the total number of contacts in different contact categories(business people in the same sector, business people in a different sector, bank officials (formaland informal creditors), politicians and civil servants, others). The variable has been cleanedby eliminating extremely large values (e.g. we kept values around 1000 as maximum reasonablevalue in largest contact category, such as business people in the same or in a different sector).There is only one observation with network size corresponding to zero, which is left in thesample. The loss of observations due to cleaning is minimal, since this implied the replacementwith missing values of just 1 or 2 observations for every wave (even less, considering that mostof the ‘atypical‘ values were not in the balanced panel). The values of the network variable aretransformed with a inverse hyperbolic sine transformation.

Employment is calculated as the number of full-time regular employees, adding thepart-time regular employees with a weight of 0.6. We are not considering any form of casualemployment. In most periods this regular employment represents the 90 per cent of totalemployment (with the exception of 2009, where it was only 80 per cent due to a sharp decreaseof regular employment in that year, while a more stable patter for the casual one). To obtaina more homogeneous sample of micro and small entrepreneurs, we eliminate (replace withmissing) the observations with less than one regular employment (around 500 observationsin the whole unbalanced panel).The value of the regular employment for the single-employeevenues will turn to zero when we take the logarithm of regular employment1414. (The samehappens with the log of total employment).For this same purpose, we also eliminate large firms (with more than 300 regular employees)when these are: a) present only in the unbalanced panel (thus, they do not appear in everyperiod); b) present in the balanced panel and their large size is clearly the result of a MA (e.g.the firm went up from 20 to 400 employees in the consecutive period). The firms that are in theconsidered balanced panel and those growth into a large dimension is due to an entrepreneurialgrowth process were left in the dataset, these corresponding to only two firms. Moreover,we replace atypical values and extremely large (e.g. for revenues and number of contacts,especially, which could be entry errors) with missing, and we replace revenues those value iszero with one, in order to be able to convert into log values without loosing the correspondingindividual.

14 An alternative is to replace the value of 1 with 1.1, not to obtain a zero with the log.

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7.2 Appendix B

Network equation: first stage

Table 44 shows the results of the reduced form for the network equation, whose estimatedresiduals are then used as an additional regressor in the the structural equation for innovation.

Table 4: Network equation: first stage

Dependent variable: network (1) (2) (3)Pooled CRE probit CRE probitprobit (MLE) (GEE)

Supplid 0.059*** 0.043** 0.043**(0.020) (0.022) (0.022)

Priceneg 0.002 0.059** 0.059**(0.027) (0.029) (0.029)

Internet 0.122*** 0.103*** 0.103***(0.035) (0.038) (0.038)

Infrastructure 0.128*** 0.122*** 0.122***(0.030) (0.039) (0.039)

Employment (log) 0.137*** 0.068** 0.068**(0.016) (0.028) (0.028)

Firm age (log -0.032 0.030 0.030(0.022) (0.032) (0.032)

Manager/owner age (log) -0.078 0.023 0.024(0.049) (0.071) (0.071)

Manager/owner gender 0.015 0.036 0.036(0.022) (0.030) (0.030)

Partnership/Collective/Cooperative -0.027 -0.032 -0.032(0.069) (0.070) (0.070)

LSC 0.130*** 0.096** 0.097**(0.045) (0.047) (0.047)

Suppid (avg.) 0.082 0.083(0.056) (0.056)

Priceneg (avg.) -0.284*** -0.284***(0.077) (0.077)

Internet (avg.) 0.024 0.024(0.069) (0.069)

Infrastructure (avg.) -0.009 -0.010(0.063) (0.063)

Employment (avg.) 0.085** 0.085***(0.033) (0.033)

Firm age (avg.) -0.090** -0.090**(0.042) (0.042)

Manager/owner age (avg.) -0.115 -0.115(0.094) (0.094)

Manager/owner gender (avg.) -0.041 -0.041(0.045) (0.044)

Constant 4.104*** 4.258*** 4.258***(0.189) (0.239) (0.238)

Sector Yes Yes YesProvince Yes Yes YesTime Yes Yes Yes

Obs. 5518 5518 5518Firms 1123 1123df m 30 38 38r2 a 0.13

Source: authors’elaboration based on CIEM and ILSSACIEM and ILSSA (20162016).

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