Linked versus Non‐linked Firms in Innovation:
The Effects of Economies of Network in Agglomeration in East Asia *Tomohiro Machikita † and Yasushi Ueki ‡May 1, 2009
Abstract. This paper proposes a new mechanism linking innovation and network in developing
economies to detect explicit production and information linkages and investigates the testable
implications of these linkages using survey data gathered from manufacturing firms in theIndonesia, Thailand, Philippines, and Vietnam. Linkages with local firms, foreign firms, andpublic organizations play a role in reducing the search costs of finding new suppliers andcustomers. We found that firms with more information linkages tend to innovate more, have ahigher probability of introducing new goods, introducing new goods to new markets using newtechnologies, and finding new partners located in remote areas. We also found that firms that
dispatched engineers to customers achieved more innovations than firms that did not. Thesefindings support the hypothesis that production linkages and face‐to‐face communicationencourage product and process innovation.

Keywords. Innovation; Linkages; Engineer Mobility; Indonesia; Philippines; Thailand; Vietnam
JEL Classification No. D83, L25, O31, O32, O33, R12*

Comments and suggestions are welcome. The authors gratefully thank Ken Imai who gave
generous comments and encouragement to polish our motivations in the early stage of thiswriting. The authors are also especially thankful to Truong Chi Binh, Ikumo Isono, Fuku
Kimura, Kitti Limskul, Mari‐len Macasaquit, Dionisious Narjoko, Nipon Poapongsakorn, Rajah
Rasiah, Yoshi Takahashi, Masatsugu Tsuji, and seminar participants in Zhejiang University,
China for their comments and discussions. This paper is based on research conducted under theinternational project ”Development of Regional Production and Logistic Networks in East Asia”
that was sponsored by the Economic Research Institute for ASEAN and East Asia (ERIA) in FY
2008. This project also has not been carried without cooperation from Center for Strategic andInternational Studies (CSIS) of Indonesia, Philippine Institute for Development Studies (PIDS),
Institute for Industry Policy and Strategy (IPSI), Ministry of Industry and Trade of Vietnam.
† Address: Institute of Developing Economies, Inter‐disciplinary Studies Center, Japan External
Trade Organization (IDE‐JETRO), 3‐2‐2 Mihama Wakaba Chiba 2618545, Japan. Email:machi@ide.go.jp.
‡ Address: Institute of Developing Economies, Bangkok Research Center, Japan External Trade
Organization, 14th Floor. of Nantawan Building, 161 Rajdamri Road, Bangkok 10330, Thailand.
Email: yasushi_ueki@ide‐jetro.org.

1. Introduction
This paper proposes a new mechanism linking innovations (product and process
innovation and creation of new markets) and networks in developing economies to
identify explicit linkages between production and information. It also investigates theempirical implications of this new mechanism using survey data gathered frommanufacturing firms in four megacities in East Asia. Our sampling countries and cities
are Indonesia (JABODETABEK area), the Philippines (CALABARZON area), Thailand(Greater Bangkok area), and Vietnam (Hanoi area). We collected firm‐level evidence oninnovations, linkages between production and information, and the respondent‐firms’
own characteristics using mail surveys and field interviews.
Why the particular focus on East Asia? The production network in East Asia contrasts
with the innovation system in the European Union (EU) and the United States (U.S.)
where universities and research institutes form the core of research and development(R&D) networks. Evidence from East Asia indicates a new role of production networks
in the upgrading of industry. The approach we will take to find the answers is simple
descriptive statistics and regressions.
In a model consisting of heterogeneous firms with R&D activity and linkages, themore productive firms introduce more innovations than less productive firms and are
more successful in introducing new goods to market, with only the most productive
firms able to introduce new goods and technologies in new markets. Linkages withlocal and foreign firms and public organizations help reduce the cost of finding new
suppliers and customers. Firms with more information linkages tend to innovate more
and are more likely to introduce new goods and technologies in new markets as well as
find new partners in remote areas. These findings support the hypothesis that
production and information linkages stimulate product and process innovation.
This paper also discusses the impact of small and hypothetical subsidies on the extent
of upgrading knowledge‐exploiting and knowledge‐creation (or knowledge‐exploring)
activities for firms in production networks. Likewise, it discusses the policy
implications of these findings and some theoretical background to evaluate the extent
of production‐related knowledge on industry upgrading.
There is a dearth of empirical researches that precisely capture the knowledgetransmission mechanism through inter‐firm communication. There is also a lack ofquantitative evidence that rigorously identifies the effects on product innovation of
production‐related knowledge based on process innovation or creation of new markets.
Since we need to quantify the contribution of production networks on innovation, thispaper collects detailed information about production linkages, product and processinnovation, and creation of new markets. This field survey‐based information provides
findings that are lacking in previous studies.
Most of the previous studies on the effects of geographic proximity on innovation
used the local average of R&D expenditures or the number of R&D engineers as
explanatory variable. These studies assumed that all firms in a local area benefit
equally from the local average of R&D activities in their empirical specification. Even if
this assumption were plausible on average, it is natural that the role of knowledge
flows on production linkages and volume of interactions would vary among linkages.
That is why we have to go beyond geographic proximity, collect information about
linkages directly, and carefully investigate the effects of each type of production
linkage on innovation.
To examine the role of local production linkages on product innovations, we need to
identify the extent of companies’ investment in R&D, the exact channels used to
upgrade existing products, the geographic extent of new‐market creation, and theemergence of local alliances to introduce a new product. We will build a simple model
to explain the large variation of product innovation across firms with and without
R&D activities or multiple production linkages. This simple theoretical framework will
be based on the reduced‐form regression model and will provide some interpretationsof the empirical estimates of the effect of two factors, i.e., the variety of production
linkages and engineer‐level communications, on innovations. Estimating the empiricalelasticity of production linkages or micro‐level communications on innovation would
enable us to detect the exact channels of process and product innovations and creationof new markets.
How do agglomeration economies affect firm‐level productivity and any changes inthis productivity? How does agglomeration affect productivity and growth indeveloping economies? How do geographic variations in competitiveness stimulatetechnological spillovers and enhance firm and industry performance? In the era ofglobalization, which entails reduction of trade costs across nations, the importance ofgeographic concentration of economic activities within a country has been growing.
The main reason for this is the combination of globalization and technologies that
promote increasing returns to scale (IRS). Globalization pushes industries that use IRS
technologies to relocate to a small number of countries where many consumers, input
suppliers, and other supporting industries are already in place. Manufacturing firms,
particularly those in IRS‐technology industries, are concentrated not only in a limited
number of countries but also in limited geographical areas within a country.
Globalization and economic integration make markets denser and more competitive.

Reduction in costs of cross‐border trade forces manufacturing firms in developing
countries to upgrade and innovate their products and processes.
This paper will investigate the role of production networks on industry upgrading by
documenting the spatial architecture of upstream and downstream firms in developing
economies and examining the network effects of innovations. Local network
externalities are a mechanism for understanding the relationship between production
networks and innovation. Lucas (1988) identified local knowledge spillovers asimportant sources of economic growth. Glaeser, Kallal, Scheinkman, and Shleifer (1992)
showed city‐level evidence of the role of knowledge spillovers. Conley and Udry (2009)
studied the role of communication networks in determining the importance of learningfrom others.
This paper also focuses on production networks to quantify the extent to which
information flows with customers or suppliers motivate a firm to innovate. The lack of
empirical studies and the potential heterogeneity in production‐ network availability
provide several empirical questions about the effects of innovation networks. The
specific question we are trying to answer is how production networks affect firms’
incentive to innovate when inter‐firm linkages become dense. Do firms tend toinnovate more if their innovation linkages are concentrated in single source or if theirinnovation sources are heterogeneous? How do firms innovate if communication with
their suppliers increases? Should firms respond to information flows from theirconsumers? This paper empirically explores these questions. The simple illustration of
the framework of paper is shown in Figure 1.
[Figure 1 here]
To summarize our introduction, we present the following two statistical findings that
this paper will attempt to explain. These findings are basically consistent with the
network‐based theory of agglomeration and innovation.
(1) There is positive effect or correlation between the variety of linkages and the variety
of innovations for firms with no R&D activities while there is no significant effect forfirms with R&D activities.
(2) Firms with face‐to‐face communications at the engineer level and firms withfrequent interactions with production partners are successful in implementing
innovations, particularly organizational reform toward external markets and process
innovations like creation of new markets and securing new sources of input.
The next section reviews related literature. Data will be described in Section 3. Section

4 compares the characteristics of firms with many different types of linkage and firms
without linkages. Determinants of firm’s innovation are examined in Section 5. The
effect of spatial architecture of production networks is examined in Section 6. Theinterpretations of the results and conclusion are discussed in Sections 7 and 8.

2. Related literature
A. Why firms agglomerate
We combine two different literatures to investigate the effects of production networks
on innovation and upgrading. First, we review agglomeration economies to define theeffects of production networks on a firm’s performance. Second, we survey firms’
product and process innovation responses to tougher market competition.
There are three kinds of forces in agglomeration economies: (1) technologicalexternalities; (2) pecuniary externalities; and (3) competition‐based selection process.
The first two forces often produce knowledge and information spillovers across firms,
sharing of the same intermediate goods and labor pooling (the Marshallian “thickmarket” effect), and IRS on the local input‐output level.
Rosenthal and Strange (2004) provide a fully comprehensive review of the causes andconsequences of agglomeration economies. In a recent attempt to quantify
agglomeration economies, Ellison, Glaeser, and Kerr (2009) find the significant
contribution of input‐output linkages to co‐agglomeration patterns instead of naturaladvantage which played a dominant role in previous studies, such as that done by
Ellison and Glaeser in 1999. Through these linkages, producers in denser areas are able
to obtain positive impacts from agglomeration economies.
Greenstone, Hornbeck, and Moretti (2008) present clear evidence of agglomeration
spillovers in a local area, focusing on the local cost linkages between customers andsuppliers to test the agglomeration‐induced productivity effect. They used the “Million
Dollar Plant” (i.e., a large, new manufacturing plant like Mercedes Benz or Toyota) in
winning and losing counties as evidence. The corporate real estate journal Site
Selection includes an article titled “The Million Dollar Plant” that describes how a large
plant decided where to locate. This article presents not only the county where theʺMillion Dollar Plantʺ chose to locate (the ʺwinning countyʺ) but also one or two“runner‐up” counties (the ʺlosing countiesʺ).
In the absence of an actual plant opening, they use the total factor productivity (TFP)
of incumbent plants in losing counties as a counterfactual for the TFP of incumbent

plants in winning counties. They examine agglomeration spillovers by estimating theimpact of the opening of “The Million Dollar Plant” on the TFP of incumbent plants inthe same county. The empirical result is consistent with theories of agglomeration andshows that the opening of the new plant induces incumbent plants in winning counties
to experience a significant and sharp relative increase in TFP compared to incumbent
plants in losing counties five years after plant opening.
The last force involves the competition‐driven selection process of agglomeration.
Denser markets here often mean markets with greater substitutability. It is relatively
easier for inefficient producers in denser areas to lose their market share and exit themarket than producers in less dense areas. Consequently, the average productivity offirms in denser markets is always higher. To consider this effect, Syverson (2004)
provides a simple and novel starting point. The specific mechanism Syverson raised isthe spatial substitutability in single product market, i.e., relatively inefficient producers
find it more difficult to operate profitably when it is easier for consumers to change
suppliers within a local area.
Combes, Duranton, Gobillon, Puga, and Roux (2009) present an empirical framework
to distinguish the agglomeration spillover effects of productivity from the selection
effects of (average) productivity improvement. This model suggests that the stronger
or tougher selection effect in denser markets left‐truncates the productivity distribution
while stronger or positive agglomeration effect right‐shifts the productivity
distribution.
B. How firms innovate in low‐wage countries
Tougher market competition arising from globalization and economic integration spurs
firms to be innovative to escape price competition. The main aim of this research is to
identify the pro‐competitive effects of trade liberalization on the incentive to innovate.
Bloom, Draca, and Van Reenen (2008) find positive impacts of increases in Chinese
imports on European firms’ investment in the use of information technology (IT) andtheir innovation (basedon patent counts). However, using datasets from emerging

economies, Gorodnichenko, Svejnar, and Terrell (2008) find a negative association
between firms’ subjective perception on the toughness of competition and innovation.
Teshima (2008) distinguishes process innovation from product innovation whenmarket competition becomes tougher. He utilizes new information about process andproduct innovation from Mexican plant‐level datasets to estimate the effects of tariffchanges on changes in expenditures for process and product R&D. He finds that
increased competition arising from the reduction of tariffs causes an increase in total

R&D expenditures for plants and process R&D rather than product R&D. This result
suggests that trade liberalization stimulates firms to be cost‐efficient, not necessarily to
produce new varieties of products.
Additionally, Brambilla (2006) compares the performance of foreign and domestic
firms in terms of the introduction of new varieties using firm‐level data for the Chinese
manufacturing sector during the country’s export boom period of 1998‐2000. The
empirical result implies that firms with more than 50 percent foreign ownership create
more than twice as many new varieties of products as private domestic firms. Foreign
firms are superior to domestic firms in terms of fixed cost of development and variable
cost of operation. This productivity difference between foreign and domestic firms also
explains the difference in the number of new product varieties released.
C. What is the benefit of linkages?
To determine the relationship between economies of agglomeration and innovations,
we investigate the role of linkages between firms and their economies of network. If
applicable to our context, economies of network can be broken down into the following
three categories: (1) production network; (2) transportation network and other networkof utilities; and (3) innovation network. We will also consider why industries areagglomerated in a specific space, mostly a hub/node of such networks.
In addition to the traditional arguments for economies of agglomeration (input andoutput linkages, labor market pooling, and idea spillovers), a town’s reputation in the
global supply chains and the world market also plays big role in developing economies(Banerjee and Duflo, 2005). Firms in the auto parts industry, for example, may be able
to find a better partner in the Eastern Seaboard area, which contains the largest
agglomeration of auto industry firms in East Asia, than in other areas in Thailand orother East Asian countries. Firms in the Eastern Seaboard area are familiar with theJust‐In‐Time (hereafter JIT) delivery system and have a reputation for providing
high‐quality auto parts. Such collective reputation among producers in developing
economies invites new entrants into the agglomeration.
A town’s reputation is formed by the nexus of linkages or production processes
between firms. If many producers have a good reputation for quality and timeliness,
local and global buyers will flock to the area for high‐quality goods. Other firms may
ask for new and more complex products. This is the key point of industry upgrading
for local firms in developing economies. Everybody benefits from producers that havelinkages to local and foreign markets. Collective reputation matters especially foryoung producers and those located in non‐established clusters. This paper will try tofind the innovation impact of linkages in concentrated areas in order to pinpoint thepositive and negative externalities of collective reputation in developing economies.
D. The role of MNEs
We should not forget about the presence of multinational enterprises (hereafter, MNEs)
in developing economies, especially in East Asia. Since Japanese MNEs have led theformation of production networks in the region, the relationship between production
networks and innovation intensity and its type should be varied according to thedegree of firms’ capital tie‐up with MNEs.
In Indonesia and Thailand, Ramstetter and Sjoholm (2006) try to answer the following
empirical questions: (1) why multinationals pay higher wages than their counterpartsin their host countries and whether the entry of multinationals raise wages fordomestic workers; (2) why multinationals have higher productivity and whether
multinationals

affecttheproductivityofdomestic

enterprises;(3)

whether

multinationals have a greater tendency to export than local firms. This paper
investigates the role of MNEs in associating cluster‐based production networks with
innovation.
The empirical questions we raise are based on Ramstetter and Sjoholm’s work (2006).
First, do MNEs or joint‐venture firms enjoy communications with customers or
suppliers located in neighboring or remote areas and do such communications with
MNEs or joint‐venture firms increase the innovations done by local firms withconnections to foreign capital firms? Second, do MNEs or joint‐venture firms tend to
introduce new goods or create new markets and do product and process innovations
made by MNEs or joint‐venture firms intensify innovations done by domestic firms?

3. Data
We used the dataset from the Establishment Survey on Innovation and Production
Network for selected manufacturing firms in four countries in East Asia. We createdthis dataset in December 2008 in Indonesia, the Philippines, Thailand, and Vietnam.
The sample population is restricted to selected manufacturing hubs in each country(JABODETABEK area, i.e., Jakarta, Bogor, Depok, Tangerang and Bekasi for Indonesia,
CALABARZON area, i.e., Cavite, Laguna, Batangas, Rizal, and Quezon for thePhilippines, Greater Bangkok area for Thailand, and Hanoi area for Vietnam). A total of
600 firms agreed to participate in the survey: (1) 149 firms in Indonesia; (2) 203 firms in
the Philippines; (3) 112 firms in Thailand; and (4) 137 firms in Vietnam.

The sample industries consist of 17 manufacturers for each country. Since theaggregate composition of industries is different among the four countries, we focusedon just three major industries for each of the four countries: food processing, apparel,
and wood products for Indonesia; food processing, apparel, and electronics for thePhilippines; food processing, apparel, and chemical products for Thailand; chemical
products, machinery, and electronics for Vietnam.
Table 1 shows the number of observations by industry and country. The pooled
dataset from the four countries also suggests that the following industries have either
more than or approximately 5 percent share of our survey: apparel (105 firms), foodprocessing (80 firms), chemical products (59 firms), electronics (54 firms), metal (37firms), auto (32 firms), wood products (31 firms), machinery (30 firms), and paper
products (27 firms).
[Table 1 here]
Table 2 presents the summary statistics of the main variables. The average age of a
firm is 14 years, with a standard deviation of 12 years. Firm size is also much dispersed.
Average size is 293 employees, with a standard deviation of 456. Since our samplingstrategy covers whole manufacturing in each country, some firms have more than 2,000
employees while some firms are very small, with less than 20 employees. Of the total
number surveyed, approximately 60 percent are local firms; 13 percent, joint‐venturefirms; and 25 percent, MNEs.
A firm’s function is classified into any one of five categories here. Forty‐six percent of
the firms process raw materials. Twenty‐eight percent produce components and parts
while 71 percent produce final goods. A total of 24 percent procure raw materials while
and 43 percent do marketing activities.
We also collected information about the firms’ subjective evaluation of their current
internal and external environment and their assessment of their present situationcompared with the previous year’s (2007). Seventy‐seven percent of the firms said thatthe quality of products improved. Nearly 70 percent said that production defects werereduced. However, many firms in East Asia have to hurdle a number of challenges: (1)increase the value of exports; (2) increase the value of exports to developed countries;and (3) increase the number of export destinations. Less than 20 percent of sample
firms felt they could achieve these export‐market successes.
[Table 2 here]

Table 2 also presents our main interests: innovations and linkages. We classified
innovations into the following three categories: (1) product innovation (introduction of
new goods); (2) process innovations, including adoption of new technology andorganizational changes to improve product quality and cost efficiency; and (3) securing
new customers to sell to, and new suppliers to produce existing products for,efficiently.
While approximately 45 percent of the sample firms, on average, are able to do
product innovations in general, it appears that more firms find it difficult to achievecertain kinds of product innovations. Only 9 percent said they were able to introduce
new goods to new markets, while only 11 percent of were able to introduce new goods
using new technology. This situation may be due to the higher fixed costs of creating
new markets and using new technology in addition to the typical costs associated withproduct innovations.
In contrast, more than 50 percent of the firms were able to introduce process
innovations, such as (1) buying new machines; (2) improving existing machines; (3)introducing new know‐how on production processes; (4) earning certification from theInternational Standards Organization (ISO); and (5) introducing internal activities to
respond to changes in the markets.
Firms reported different experiences in the task of securing new customers andsuppliers depending on the locations and characteristics of the customers andsuppliers. The probability of securing a new local supplier or customer in a
metropolitan area in which the respondent is also located is higher (63 percent for
securing a new supplier and 65 percent for securing a new customer) than theprobability of securing a new supplier or customer outside the metropolitan area (56percent for securing a new supplier and 58 percent for securing a new customer).
Securing a new supplier or customer in other ASEAN countries is more difficult for thefour countries involved in the study (32 percent for securing a new supplier and 27
percent for securing a new customer). Sample firms also found it difficult to buy inputs
from, or sell products to, MNEs. Only 17 percent of the firms successfully secured new
multinational suppliers within a metropolitan area while only 16 percent were able to
do so outside the metropolitan area. Between the two tasks, however, firms found it
easier to sell products to MNEs than to buy inputs from them. Nearly 30 percent of thefirms successfully secured new multinational customers within an agglomeration area,
while 21 percent did so outside. Figure 2 summarizes the distribution of the number ofinnovations across firms in East Asia: the number of innovations across firms looks like

normal distribution with fat‐tail of zero innovation. The cross‐country difference of thenumber of innovations across firms is shown in Figure 4. Panels in Figure 4 suggestthat many firms in Philippines achieve zero innovations while some firms in Thailand
achieve many types of innovations.
The distribution of linkages is also presented in Figure 3. This figure summarizes the
distribution of the number of linkages across firms in East Asia. The number oflinkages across firms is quite far from normal distribution. Many firms have few
linkages and some firms achieve to keep many production‐ and intellectual linkages.
The first look at the relationship between linkages and innovations across countries isshown in four panels of Figure 5. However there is much heterogeneity in the linkages
effects of innovations, there is positive association between linkages and innovations.
The detailed statistics are presented in Table 2. The most striking evidence of technicaltransfer is that production‐related linkages are more cultivated than intellectual
linkages. For example, collaboration with joint ventures established by a sample firmwith other local firms and collaboration with a local supplier or customer were done by
32 percent and 41 percent of the firms, respectively. On the other hand, 27 percent of
the firms accepted technical assistance financed or provided by a government or publicagency while 23 percent engaged in technical cooperation projects with a local
university. Technology transfer between firms is prevalent, and University‐Industry
Linkages (hereafter, UIL) does not play a key role in technology transfer in East Asia.
Many firms also rely on internal sources for information on upgrading and innovation.
Thirty‐four percent of the surveyed firms depend on their own R&D departments as asource of information and R&D initiatives while 38 percent utilize their own sales
departments and sales agents as information sources. Fifty‐one percent of surveyed
firms use technological agreements with headquarters or affiliated firms; 62 percent
look to their own production and manufacturing departments when undertaking
upgrades.
Industries in the sample are primarily involved in manufacturing and exporting andare currently operating in East Asia. To keep pace with domestic demand and stay on
top of international competition, the firms adopt new technology, acquire neworganizational form to adapt to market changes, create new markets, find new inputs
to improve product quality and cost efficiency, and introduce new products. They
utilize the external environment and local/international markets to upgrade themselves.
Therefore, it is reasonable to say that they are more likely to adapt new technology andundertake organizational changes in response to the external environment and thedemands made by their respective local and international markets. Tables 3 and 4 show

the variety of innovations and linkages across countries and industries, respectively.
There is a large cross‐sectional dispersion of innovations not only across countries but
also across industries within a country. The variety of innovations for each firm is thesum of product innovations, process innovations including organizational changes,
and securing new customers and suppliers at firm‐level. The variety of linkages here is
the sum of sources of information and new technology for each firm. The sample
average (median) of variety of innovations for the pooled dataset is 8.96 (9) and thestandard deviation is 4.91. Firms in Thailand and Vietnam are above the sample
average (median) of innovations: 12.07 (12) and 10.83 (12), respectively. Standard
deviations of innovations across firms within each country are 4.58 for Thailand and3.6 for Vietnam.
The variety of linkages is also quite different across countries. The sample average(median) of linkages is 8.04 (6) for the pooled dataset. The standard deviation of
linkages is quite high at 8.77. Firms in the Philippines only have 1.9 linkages on
average while firms in Thailand have an average of 19 linkages. Indonesian firms have
7.63 linkages; Vietnamese firms, 8.62. The dispersion in linkages may be explained by
the difference in the composition of industries across countries and the difference inthe nature of production networks across industries. However main industries areconcentrated in food processing and apparel (textile and garment) in each country,
there is a large dispersion of linkages across countries for food processing and apparel.
[Figure 2 to 5 and Table 3 and 4 here]

4. Characteristics of linked versus non‐linked firms’ innovations
Innovative activities reflect several dimensions of industry upgrading. There is no
single measure to evaluate the success or failure of a firm’s policy of industry
upgrading. We drew up four different groups of measures: new goods, adoption of
new technologies and organizational structure, new source of procurement, andcreation of new markets. We map out the firm’s linkage to innovations and present
univariate comparisons of the outcome of the innovations with the status of thelinkages.
A. New varieties
Our first measure is the number and percentage of firms introducing new goods. We

define “linked” and “non‐linked” firms by the level of median linkages and found that
there is no significant difference between linked and non‐linked firms in terms ofintroducing new goods. Linked firms have different sources of information comparedwith non‐linked firms when they develop and introduce new goods. If the cost of
introducing new goods decreased as a function of the variety of linkages, linked firms
would have an advantage in the area of product innovations. Panel A of Table 5
suggests that there is no significant evidence that a linked firm’s success in introducing
new goods can be compared with a non‐linked firm’s results based on mean andmedian differences. There is also no significant evidence that a linked firm’s outcome inintroducing new goods in a new market and new goods based on new technologies canbe compared with non‐linked firm’s.
B. Adoption of new technologies and organizational structure
Aside from product innovations, the most striking evidence of industry upgrading is
the implementation of plant‐level process innovations. It was assumed that linked
firms tend to invest in process innovations if production‐related linkages reduced thecost of buying new machines, maintaining existing ones, and changing a firm’sorganizational form or structure. But contrary to the above‐mentioned assumption,
Panel B of Table 5 suggests that the percentage of improved existing machines is
actually lower for linked firms than non‐linked firms. It was found that there are no
significant differences between linked and non‐linked firms in terms of buying new
machines and introducing new know‐how on production methods, although
non‐linked firms seem to implement more process innovations.
These results suggest that it is easy for stand‐alone firms to reorganize machine‐based
production processes. As expected, linked firms are able to implement moreorganizational changes than non‐linked firms, and their success can be traced togetting ISO certification, using information and communication technologies (ICT),and introducing internal activities aimed at responding to changes in the market.
C. New sources of input
Finding new sources of inputs, raw materials, and parts could help upgrade
production processes and product quality and reduce production costs. Ultimately, it
could also result in new product varieties because it would help firms find new,possibly more cost‐efficient, and higher‐quality types of inputs. This measure can beconsidered as a market‐based innovation.
Panel B of Table 5 suggests that the probability of finding new suppliers is higher forlinked firms than non‐linked firms. Firms with many linkages could use these existing
linkages to procure new inputs. A firm’s direct linkages provide information not only
about its partners but also its partner’s linkages (this includes both the partner’s
partners and its competitors). If a firm’s direct linkages increase, its indirect linkages
will also increase.
The probability of securing new local suppliers within a firm’s immediate locationand in nearby areas are also higher for linked firms than non‐linked firms. Linked
firms enjoy both local and global linkages and are more likely to secure newmultinational suppliers within and outside of a concentrated area.
Linked and non‐linked firms differ in their importing activities as well. Linked firms
have more advantages in terms of securing new international suppliers thannon‐linked firms when they decide to import new parts and materials. If theimportation of new parts and materials from a particular country is too costly, a firm’s
linkages can help it to partially overcome this challenge by making it easier for the firm
to seek a new supplier in other countries.

D. Creation of new markets
Finally, creation of new markets also reflects a firm’s upgrading behavior. Our firstquestion here is whether or not existing linkages could help a firm create a new market
for existing and new goods. Our second and similar question is whether or not existing
linkages could help create a new international market. Panel D of Table 5 presents thedifference between linked and non‐linked firms in creating new markets.
Linked firms tend to secure new domestic customers more than non‐linked firms. In
particular, linked firms are able to secure new local customers within the area where
they and the new customers are both located. Linked firms also tend to find new
multinational customers in areas near where they (the linked firms) operate. There islarge difference between the ability of linked and non‐linked firms to export to markets
in East Asia (but outside the ASEAN), the EU, and U.S. and their ability to export to
ASEAN countries alone. This suggests that their linkages actually help linked firms
meet the challenge and difficulties of exporting to distant markets.
[Table 5 here]5. Determinants of innovation failure and success
A. The variety of innovations
In this section, we present the effects of linkages on innovations. The univariatecomparison reports in the last section do not control for factors that explain the successor failure of innovations. In this section, too, we present the results of the multivariate
test that controls for the country differences and other firm characteristics, such as
capital structure, age, number of employees, function, and R&D activities. We report
the determinants of the variety of innovations. Table 6 presents the baseline results of
the impacts of linkages on innovations. The dependent variable is the variety ofinnovations, i.e., the sum of product innovations, process innovations includingorganizational changes, and securing new customers and suppliers. The variety ofinnovations is approximated by normal distribution. Ordinary regression model wasused to explain the variety of innovations.
The independent variables include the following explanatory variable: number oflinkages is calculated by the sum of firm’s production linkages and intellectual linkages.
Rigorously speaking, we count the number of types of linkages. If the firm has a linkage
to a local or foreign customer or supplier, we count that as one type of local or foreign
production linkage. In addition, if the firm has a linkage to local or foreign university,
we also count that as another type of local or foreign intellectual linkage. This meansthat such a firm has two types of linkages. “Multinational Enterprises” is a dummy
variable equal to one for a firm that is wholly funded by foreign capital. Multinationals
can access global technology frontiers and belong to international markets. This is not
only a proxy of financial advantages for innovations but also a proxy of technology
advantages compared with local firms.
Age and employment size are also attributes of innovations. Aged firms have ahistory of established production linkages and accumulated innovations. There is alsoa difference in the types of innovations and innovation investments that large andsmall/medium firms make. Cross‐country differences can be attributed to thefundamental differences in the causes and consequences of innovations in response tomarket conditions.
The results are reported in Table 6. The coefficient for the variety of linkages is .189
with standard error of .027; it is statistically significant at the 1 percent level. Firms
with more types of linkages implement significantly more innovations than firms withfewer types of linkages, even after one controls for capital structure, age, size, andcountry differences. We separately estimate the innovation impacts of the variety oflinkages by R&D activities because there is a strong correlation between R&D activities
and the variety of linkages. The coefficient for the variety of linkages is .161 withstandard error of .041 for firms with R&D activities and .161 with standard error of .031
for firms without R&D activities. Both of them are statistically significant at the 1
percent level. The effects of being an MNE and the size of the firm are significant. The
variety of innovations achieved cannot be attributed to differences in the age of thesample firms. Cross‐country differences in the variety of innovations are apparent:
firms in Indonesia and the Philippines innovate less than those in Thailand. This
sample also reflects the difference between less developed countries in East Asia likeIndonesia and the Philippines and more developed countries like Thailand.

There isno significant difference between Vietnam and Thailand.
Table 7 presents the impacts of different types of linkages on innovations: (1) the
number of production linkages with customers, suppliers, and other linkages madethrough the labor market and the equipment supply chain; (2) the number ofintellectual linkages with universities, research institutes, business organizations, andpublic support agencies; (3) the number of the sample firms’ internal resources. The
Production linkages, intellectual linkages, and internal resources are also positive andhave a significant impact on the variety of innovations at the 1 percent level. More than
production and intellectual linkages, however, the innovations could be attributed tointernal resources.
It is natural that the innovation impacts of linkages are different among the types offunction. Table 8 reports the effects of linkages on the variety of innovations by thefirms’ functions. First, the coefficient for the variety of linkages is .293 with a standard
error of .063 for firms with procurement functions. Second, the coefficient for thevariety of linkages is .249 with a standard error of .038 for firms with a marketing
department. Third, the coefficient for the variety of linkages is .239 with a standard
error of .037 for firms that produce raw materials. Finally, the coefficients for thevariety of linkages are also positive and significant for firms that do final assembly andparts production, although the volume is less than that for the functions of producing
raw materials, parts procurement, and marketing.
[Table 6 to 8 here]
B. New varieties
To what extent are firms able to introduce new products with and without linkages? To
what extent are firms able to create new markets when they introduce new products?

To what extent do firms utilize new technologies when they introduce new products?
We test these questions here. Table 9 reports the effects of linkages on the number oftypes of introducing new product varieties. In our sample, each firm has following
three options: (1) introduce new goods or not, (2) introduce new goods to new markets
or not, and (3) introduce new goods based on new technologies or not. If a firm
achieves all types of introducing new varieties, it acquires three points. We used theOrdered Logit model to explain the determinants of the number of types of
introducing new varieties. As reported in Table 9, the number of types of introducing
new varieties is positive and significantly related to the variety of linkages. Twodecomposed linkages (production and intellectual linkages) and internal resources arealso positively related to the number of types of introducing new varieties. The
coefficient for the number of MNEs is negative and significant.
Table 10 presents the coefficients for linkages by R&D activities. The coefficient for all
types of linkages is not significantly different from zero when firms have R&D
activities. On the other hand, the coefficient for all types of linkages is .038 withstandard errors of .020 when firms do not have R&D activities, indicating that a firmwith many production linkages would be able to achieve more of the number of typesof introducing new varieties than a firm that does not have many linkages.
Table 11 presents the coefficients for production linkages by R&D activities. The
coefficient for production linkages is positive but this does not have significant impact
when firms have R&D activities. This is true in the case of intellectual linkages andinternal resources. Table 12 and 13 reports the coefficients for intellectual linkages andinternal resources as .115 with standard error of .055 and .212 with standard errorof .103, respectively, when firms do not have R&D activities. As shown in Table 14,
there are also functional differences in the number of types of introducing new
varieties. The coefficients for the variety of linkages are positive and significant whenfirms do marketing, production of raw materials, procurement, and final assembly.
[Table 9 to 14 here]
C. Adoption of new technologies and organizational structure
Process innovations also play a key role in upgrading business activities. Innovations
in business processes can be carried out in two ways. The first involves the adoption of
new technologies to improve efficiency or quality. The second involves changing theorganizational structure to respond to the external environment.
First, we report the result of adoption of new technologies. There are three types ofprocess innovations related to the adoption of new technologies inside the firm: (1)purchase of new machines or facilities with new functions; (2) improvement of existingmachines, equipment, or facilities; (3) introduction of new know‐how on production
methods. We call these “process innovations towards the firm” or innovations made forthe firm’s internal processes. Table 15 presents the impacts of process innovation on thevariety of linkages. Only “internal resources” has a positive and significant effect. The
coefficient for the number of internal resources is .110 with a standard error of .060 at
the 10 percent level. As reported in Table 16, the coefficient for the number of internal
resources is .064 with a standard error of .024 when firms do marketing activities.
Second, we report the result of changing the organizational structure to enable a firm
to respond to its external environment. There are three types of process innovations
related to this: (1) Certification by International Organization for Standardization (ISO);(2) Introduction of ICT to reorganize the business process; (3) Introduction of otherinternal activities to respond to changes in the market. We call these “process
innovations toward the outside market.” Table 17 reports the effect of the variety oflinkages on process innovations toward the outside market. The coefficient for thevariety of linkages is .054 with a standard error of .013 for all types of linkages. The
number of production linkages, intellectual linkages, and internal resources is also
positive and has a significant impact on process innovations. Table 18 presents theeffects of the variety of linkages on process innovations toward the outside market byfunctions. All the coefficients are positive and significant. The coefficients of the varietyof linkages for firms involved in producing raw materials, doing marketing, parts, andprocurement are relatively larger than for firms doing final assembly.
[Table 15 to 18 here]
D. New sources of input
It is important to secure new sources of input—both locally and overseas—in order toimprove quality and stimulate process innovations. The dependent variable is higher
for firms that are successful in doing this than for firms that fail to secure multiple
types of local and foreign trade partners.
Table 19 reports the impacts of the variety of linkages on securing new sources of
input. The coefficient for the variety of linkages is .061 with a standard error of .011.
This indicates that firms with more linkages tend to secure more new suppliers thanfirms with few linkages.
The most striking evidence in cross‐country comparison is Vietnamese firms that have

been able to secure more suppliers than Thai firms. This is partially reflected by thedifference in the industry composition between the two countries. Table 20 compares
the results of the impact of the variety of linkages on the number of secured new
suppliers by functions. All of coefficients are positive and significant. The coefficientsof the variety of linkages for firms that produce raw materials and do marketing
functions are relatively larger than for firms that produce parts and do final assembly.
[Table 19 to 20 here]
E. Creation of new markets
The creation of new local and international markets is very important in helping
upgrade business processes and, to a certain extent, spurring process innovations. The
dependent variable is higher for firms that are able to secure new local andinternational customers than for firms fail to do so. Table 21 reports the impacts of thevariety of linkages on securing new customers. The coefficient for the variety oflinkages is .076 with a standard error of .012, indicating that firms with many linkages
are more successful in securing new markets than firms with few linkages. Table 22compares the results of the impact of the variety of linkages on the number of secured
new suppliers by functions. All of coefficients are positive and significant. The
coefficients of the variety of linkages for firms with procurement and marketing
functions are relatively larger than for firms that make raw materials and parts and do
final assembly.
[Table 21 to 22 here]

6. The effect of spatial architecture of production networks
A. Production networks in space
This section focuses on five issues related to production linkages between the main
customer and supplier in a spatial economy: (1) capital tie‐up; (2) products type; (3) JIT
system; (4) exchange of engineers; (5) the relationship between production networks
and innovation networks.
We have two competing theories of spatial architecture of production network to
explain co‐location between two firms. First, if fixed search costs of production
partners (or setup and coordination costs of alliances) decrease with capital structurebetween firms, it is efficient for firms with capital tie‐up to form production linkages
with their affiliates. Second, if communication costs per meeting and informationexchanges increase with geographic distance between firms, these two firms will form
production linkages that will tend to co‐locate in one area. Capital tie‐up with affiliates
is a good proxy for the existence of production linkages. If both of the first and second
conjectures are appropriate in East Asia, firms with capital tie‐up tend to locate nearer
each other than firms without capital tie‐up.
That is, the geographic extent of input‐output linkage is locally limited for firms withcapital tie‐up than firms without capital tie‐up due to the needs of the JIT system or
frequent information exchanges for quality upgrading. This is a transport costs‐based
theory of co‐location. This explanation is also derived from standard spatial economy.
Less productive (less differentiated goods production) firms forge local or nearby
alliances while more productive firms do it globally. For given variable communication
costs of alliances, the geographic extent of input‐output linkages should be ruled out
by productivity. If communication costs increase, the probability of network formation
with remote firms could decrease.
Second, there is the enforceability‐based theory of agglomeration. This theory
emphasizes the monitoring effect of production networks from buyer to seller. If
buyers do not have a long‐term or tight relationship with the producers, such buyers
would have to frequently monitor and check product quality repeatedly. The cost ofcommunication is an increasing function of geographic distance between buyers andsellers. If this conjecture is right, for example, firms with capital tie‐up need not be
co‐located because these buyer and sellers would already know each other. The
geographic extent of input‐output linkage is locally limited for firms without capitaltie‐up compared to firms with capital tie‐up due to monitoring needs. This section
answers the following questions of production networks in space: (1) Are there any
differences in the input‐output linkages across firms and countries in East Asia; (2)How strong are the linkages between customers and suppliers; (3) Are production
linkages also important partners for innovations?
Table 23 presents data on geographic proximity of a firm to its main customer andsupplier in relation to capital tie‐up. Almost all of the sample firms do not have capitaltie‐up to their main customer and supplier. On average, firms with a capital tie‐up

tothe main customer and supplier are located more remotely from one another (514 km
from customer, 374 km from supplier) than firms that have no any capital tie‐up to
their main customer or supplier (394 km from main customer, 353 km from supplier ).
This result validates the theory of co‐location that some kind of monitoring occurs toenable firms to create a production network.
This result also holds for the types of goods that buyers and sellers produce. Table 24compares the geographic proximity of sample firms to their main customer andsupplier by the status of customized‐goods production. If the transport cost‐based
theory of co‐location is accurate, firms could buy standard goods from nearby
suppliers and sell their own products to nearby customers. On the other hand, if
customized goods are insensitive to transport costs, a firm can buy from a remote
producer and sell to a remote customer. Data show that on average, firms who buy
standard goods from a supplier and sell their own product to a customer who makes
standard goods are located near their main customer and supplier (353 km from main
customer, 206 km from supplier). Firms who buy customized goods from a supplierand sell their own products to a customer who makes customized goods are locatedfarther away from their main customer and supplier (467 km from customer, 432 km
from supplier).
It is natural for firms to create a JIT system with locally concentrated partners. Table
25 compares the geographic proximity of a firm to a main customer and supplier by the
introduction of the JIT system. Firms who have a JIT system with their main customer
and supplier are located nearer to their main trading partners than firms who have no
JIT system with their main partners (333 km from customer with JIT, 232 km fromsupplier with JIT versus 448 km from customer without JIT, 442 km from supplier
without JIT). The formation of the JIT system justifies co‐location based on transport
costs.
Exchanging engineers between firms is also a main proxy of exchanging
production‐related knowledge on production linkages. Table 26 compares thegeographic proximity of firms that accept engineers from their main trading partners
to these same partners to the geographic proximity of firms that choose not to do sowith their main partners. The results show that firms that decide to accept engineersfrom their main customers and suppliers tend to be located farther away from thesesame trading partners (669 km from customer and 567 km from supplier for firms that
accept engineers versus 318 km from customer and 237 km from supplier for firms that
do not accept engineers).
Table 27 compares the geographic proximity of firms that dispatch engineers to theirmain customers and suppliers from those same trading partners to the geographic
proximity of firms that do not dispatch engineers to their main partners. Firms save on
communication costs to remote areas by accepting engineers from their main
customers and suppliers if these trading partners are located far from them. This is also

true for firms that decide to dispatch engineers to their main partners. By doing this,firms can save on communication costs, especially if the partners are located in remoteareas (500 km from customer and 348 km from supplier for firms that dispatch
engineers versus 391 km from customer and 342 km from supplier for firms that do notdispatch engineers).
Finally, there is the overlap between production linkages and innovations network. As
reported in Table 28, many firms (341 out of 600) responded that their main consumers
are an important consideration in any decision to upgrade business activities andimplement innovations while many firms also said that their main supplier is not thatimportant a factor in any decision to upgrade and innovate their business processes
and products. Geographic proximity to main customer and supplier, on average, islocally limited for firms who consider their main supplier as an important factor in
upgrading and innovations (367 km from supplier if firms do not consider versus 141
km from supplier if firms consider).
[Table 23 to 28 here]
B. The effect of accepting/dispatching engineers and JIT system: face‐to‐face communications on
innovations
We report the following internal effects of linkages in order to understand theinformation flow on production linkages. First, exchanging engineers could stimulate
information flow based on face‐to‐face communication. Second, the formation of the
JIT system could provide the opportunity for frequent communication between
suppliers and customers. Since the last section reports on the effect of variety oflinkages on product and process innovations, we relate the internal information flow oflinkages to product and process innovations.
Table 29 reports the effects of accepting engineers from customers and suppliers on
the introduction of new products. The dependent variable is equal to one if each firm
introduces new products and is equal to zero otherwise. The independent variable,
accepting engineers from customers or suppliers, is equal to one if each firm accepts
engineers from the main customer or supplier. Marginal effects are presented. Other
control variables are MNEs, age, firm size, and country dummy variables. We
separately estimate the impacts of flows of engineers on product innovations by goods
characteristics, that is, customized‐ and standard‐goods production. As reported inTable 29, the coefficient for accepting engineers from suppliers is .329 with a standard
error of .105, and it is statistically significant at the 1 percent level. Thus, firms that

accept engineers from main suppliers are likely to experience significantly higher
probability of product innovation than firms that do not accept engineers from main
suppliers. This effect holds true if the main customers and suppliers produce standard
goods. Overall, product innovation is positively related to accepting engineers frommain suppliers and dispatching engineers to main customers.
Table 30 presents the innovation impacts of dispatching engineers to main customers
and suppliers. The dependent variable is product innovation. This is equal to one if
each firm introduces new varieties and is equal to zero if otherwise. The independent
variable, dispatching engineers to customers or suppliers, is equal to one if each firm
dispatches engineers to the main customers or suppliers. As reported in Table 30, thecoefficient for dispatching engineers to main customers is .153 with a standard errorof .080 if the main customer produces customized goods. The coefficient for
dispatching engineers to main suppliers is .248 with a standard error of .100 if the main
supplier produces standard goods. These results suggest that the acceptance ofengineers from the main supplier and the dispatching of engineers to the main partners
are positively important for product innovations.
Let us move to process innovations. Table 31 presents the impact of accepting
engineers from the supplier on improving existing machines. The coefficient for
accepting engineers from the supplier is ‐.140 with a standard error of .081 if the main
customer produces customized goods. The coefficient for accepting engineers from the
supplier is .173 with standard error of .080 if the main customer produces standard
goods. The coefficient for accepting engineers from the supplier is ‐.242 with standard
error of .094 if the main supplier produces customized goods. The coefficient for
accepting engineers from the supplier is .191 with standard error of .053 if the main
supplier produces standard goods. These results indicate that if the main partners
produce customized goods, it is not easy to improve existing machines for firms that
accept engineers from suppliers. On the other hand, if the main partners produce
standard goods, accepting engineers from main suppliers stimulates the improvementof existing machines.
Table 32 reports the result of dispatching engineers to the main partners on improving
existing machines. The coefficient for dispatching engineers to the customer is .139
with a standard error of .074 if the main customer produces customized goods. The
coefficient for dispatching engineers to the customer is .174 with a standard errorof .089 if the main supplier produces customized goods. The coefficient for dispatchingengineers to the supplier is .157 with a standard error of .060 if the main supplierproduces standard goods. Thus, firms that dispatch engineers to customers andsuppliers could experience significantly higher probability of process innovations
toward internal firm‐improving existing machines. In summary, process innovation
toward internal production efficiency is negatively related to accepting engineers fromsuppliers if production linkages are connected to produce customized goods. On theother hand, process innovation is positively related to accepting engineers fromsuppliers if production linkages are connected to produce standard goods. Process
innovation is also positively related to dispatching engineers to customers if
production linkages are connected to produce customized goods.
Table 33 presents the effect of accepting engineers from suppliers for firms that are
working on getting ISO certification. The first column indicates that the coefficient for
accepting engineers from the main supplier is .250 with a standard error of .060. Thus,
firms that accept engineers from the main supplier have a significantly higher
probability of getting ISO certified. This is true if the main customer and supplierproduce customized and standard goods, respectively. Table 34 reports the effect of
dispatching engineers to the main customer. The coefficient for dispatching engineers
to customers is .193 with a standard error of .067, indicating that firms that dispatch
engineers to customers have a significantly positive impact of getting ISO certified,
which is considered as a process innovation towards the external market.
Making investments to deal with disequilibria is another kind of process innovation.
The dependent variable is equal to one if a firm invests in internal activities that will
help it adjust to changes in the market. As reported in Table 35, the coefficient for
accepting engineers from the supplier is .332 with a standard error of .053. Thus, firmsthat accept engineers from suppliers are more likely to make investments that will
enable them to adjust to changes in the market. Table 36 shows that the coefficient for
dispatching engineers to the customer is .218 with a standard error of .059 while thecoefficient for dispatching engineers to the supplier is .150 with a standard error of .073.
The impacts on process innovation of the practice of dispatching engineers is higher forfirms that dispatch engineers to customers than for firms that dispatch engineers to
suppliers in the face of market disequilibria or market turbulence. In summary, process
innovation aimed at enabling a firm to respond to changes in the external market
environment is positively related to the practice of accepting engineers from suppliersand dispatching engineers to main customers.
Finally, the formation of a JIT system is also a proxy of information exchanges on
production linkages. Table 37 reports the impacts of forming a JIT system with themain customer and supplier on earning ISO certification, which is a type of process
innovation towards the external market. The independent variables of forming a JIT

system with the customer or supplier are equal to 1 if a firm forms a JIT system forproduction and distribution with its main customer or supplier, respectively, and areequal to zero otherwise. Table 37 shows that the coefficient for a JIT system with thecustomer is .245 with a standard error of .100 if the customer produces a standard
product. The coefficient for a JIT system with the supplier is .225 with a standard errorof .098 if the supplier produces a customized product. These results indicate that firmsthat form a JIT system with a customer have a significantly higher probability ofgetting ISO certified than firms that do not have a JIT system with their main customer.
Table 38 presents the impact of forming a JIT system with a customer on a firm’s
ability to adjust to changes in the market. The empirical question here is whether a JIT
system provides information flows in the face of market changes or market turbulence.
The coefficient for a JIT system with the customer is .206 with a standard error of .102 if
the customer produces a standard product, indicating that the firm that forms a JIT
system with a customer has a higher probability of investing in internal activities thatwill help it adjust to changes in the market. Overall, a process innovation that helps afirm adjust to changes in the market environment, for example, ISO certification ormarket turbulence, is positively related to JIT system with a customer.
[Table 29 to 38 here]

7. Reasons firms with many linkages and firms with direct information
flows from partners are more successful
Empirical evidence based on both univariate comparisons and multivariate tests
suggest two findings about the impacts of linkages on innovations. First, firms withmany linkages achieve many types of product and process innovations. In particular,
compared to firms that do not have many linkages, firms with many linkages achieved
many types of organizational changes in response to changes in the market
environment and market‐based process innovations, such as earning ISO certification,
investment in ICT to communicate to trade partners, investment in internal activities toadjust to market turbulence, and securing new suppliers and customers. Second,
information flows, especially face‐to‐face communication and frequent exchanges in
information, play an important role in achieving product and process innovations. In
particular, compared to firms that do not accept engineers from main partners or
dispatch engineers to main partners, firms that interact with main partners are more

likely to introduce new product varieties, organizational changes in response tochanges in the market environment, and market‐based process innovations.
A. The value of knowledge diversity
One reason for the success of firms with many linkages is that each type of linkage
provides unique information about upgrading business processes and changes in themarket. Linkages variable is constructed by two different types of linkages: production
and intellectual linkages. The former means linkages with several production partners
that are located within or between areas of concentration. The latter means linkages
with universities and research and public business organizations within or between
areas of concentration. The empirical results also imply that two extremely different
types of linkages complement product and process innovations. These linkages do notcancel out each other’s contributions. The empirical results clearly suggest that the
combination of two different sources of knowledge is valuable for innovations.
B. Accuracy arising from interactions
Although the number of types of linkages increases the number of types of productand process innovations, internal resources have the most important impact for
innovations. Product and process innovations are, by nature, a process of trial anderror. One of the reasons why many types of linkages are beneficial to innovations isthat the number of types of linkages and internal resources are interpreted using
instruments that help produce more accurate information compared to trial and error.
If firms have many types of production linkages, the number and diversity of linkages
would insure accuracy when firms invest in innovations. This is supported by theempirical evidence that the variety of linkages increases the number of types of
product innovations when firms do not do R&D activities while the variety of linkages
does not increase the number of types of product innovations when firms experimentwith R&D. If firms do not already have an instrument for internal trial and error, they
can learn about other firms’ trials and errors only through external linkages. Firms with
sufficient internal resources or with R&D activities could acquire this information bythemselves.
C. Information flows from customer and supplier linkages
Firms with direct information flow from partners tend to be more successful because of
the value brought by face‐to‐face communication and frequent interaction. Accepting
engineers from the main supplier insures the transfer of knowledge relating to raw

materials, parts, and components. If the suppliers are based in a more competitivemarket, the main supplier has to pay the costs of knowledge transfer, i.e., dispatchingengineers to the main customer. Dispatching engineers to the main customer also
insures the transfer of knowledge about production processes and market changes.
Since it is critically important for firms to acquire the most accurate information aboutmarket changes, the supplier dispatches the engineers from an upstream to adownstream level. The empirical results suggest that there are backward linkages of
information flows from customer to supplier. Because most suppliers are keen toacquire ISO certification to help them expand their market, they need to communicate
face to face with their main customer to pay the costs of dispatching engineers. The JIT
system also provides an opportunity for frequent interactions between customers andsuppliers. Frequent interactions insure the accuracy of information about market
changes.

8. Conclusion
In East Asia, a complex production network has been constructed utilizing wage
disparity and lower transportation costs across countries in the region. Lowertransportation costs between regions foster the fragmentation of production processes
over borders. In particular, the intermediate process is more complex, skill intensive,and higher paid while the final process is easier to build, unskilled‐labor intensive, andlower paid. On the other hand, since both inter‐firm supplier‐customer relationships
and intra‐firm upstream and downstream processes face higher transportation costs,
firms with capital tie‐up to their main trading partners tend to co‐locate near one
another.
From the viewpoint of spatial economy, it is unclear whether geographic proximity
between firms tends to spur knowledge transfer between upstream and downstream
processes within a concentrated area. On one hand, co‐location stimulates frequent
communication between firms. On the other hand, the mobility of engineers(dispatching of workers to partners and accepting of workers from partners) between
firms was shown to be more frequent for firms located in remote areas than nearer
their main trading partners. Empirical work was needed to provide a solution. To
detect the origin and destination of knowledge flow between upstream anddownstream processes, we collected information on engineer mobility andimplementation of the JIT system to estimate the strength of ties.

The empirical results suggest that firms with many linkages, with face‐to‐facecommunication at the engineer level, and with frequent interaction with production
partners are able to innovate successfully, particularly in the areas of organizational
reform toward external markets and market‐based process innovations like creation of
new markets and securing new sources of input. We offer the following threehypotheses as a possible explanation for these results: (1) Many types of linkages or
combinations of different types of linkages provide the value of knowledge diversity;(2) Many types of linkages provide the opportunity to get accurate information aboutother firms’ trials and errors for firms without their own R&D department or sufficientinternal resources; (3) Face‐to‐face communication and frequent interaction withproduction partners provide a chance to acquire deep and correct information aboutchanges in the market and market turbulence.
Finally, we derive two policy suggestions based on these empirical results. First,
policy resources should target firms that have a few production and intellectual
linkages, particularly small‐ and medium‐sized firms in East Asia. Linked firms receive
benefits from partners while providing important information about market changes totheir other partners, especially their supplier. It is also important to devote policy
resources to the implementation of a JIT system. If there are some obstacles toimplementing a JIT system that will help firms upgrade, public assistance can betapped to create such a network. Economies of network based on production linkages
could create such externality.
Second, policy resources should be allocated to the reduction of obstacles to engineer
mobility in East Asia. Since engineer mobility happens at the local and international
levels, (1) insuring free mobility of engineers or simplifying immigration proceduresand (2) creating common certification of engineers’ skills in East Asia could stimulate
the upgrading of firms and industries through face‐to‐face communication at thedifferent stages of product and process innovation.

References
Brambilla, I., 2006, Multinationals, Technology, and the Introduction of Varieties ofGoods, NBER Working Paper No. 12217.
Bloom, N., Draca, M., and van Reenen, J., 2008, Trade Induced Technical Change? The
Impact of Chinese Imports on IT and Innovation, Manuscript, Stanford University and
London School of Economics.
Comebs, P.P., Mayer, T., and Thisse, J‐F., 2008, Economic Geography: The Integration ofRegions and Nations, Princeton University Press, New Jersey.
Combes, P.P., Duranton, G., Gobillon, L., Puga, D., and Roux, S., 2009, The Productivity
Advantages of Large Markets: Distinguishing Agglomeration from Firm Selection,
Manuscript, GREQAM, INED, University of Toronto, IMDEA and CREST.
Conley, T., and Udry, C., 2008, Learning about a New Technology: Pineapple in Ghana,forthcoming, American Economic Review.
Ellison, G., and Glaeser, E L., 1999, The Geographic Concentration of Industry: DoesNatural Advantage Explain Agglomeration?, American Economic Review Papers andProceedings, 89 (2), 311‐316.
Ellison, G., Glaeser, E L., and Kerr, W R., 2009, What Causes Industry Agglomeration?
Evidence from Coagglomeration Patterns, Manuscript.
Glaeser, E L., Kallal, H D., Scheinkman, J A., and Shleifer A., Growth in Cities, Journal of
Political Economy, 1992, 100(6): 1126‐52.
Gorodnichenko,Y., Svejnar, J., and Terrell, K., 2008, Globalization and Innovation in
Emerging Markets, NBER Working Paper No. 14481.
Greenstone, M., Hornbeck, R., and Moretti, E., 2008, Identifying Agglomeration
Spillovers: Evidence from Million Dollar Plants, NBER Working Paper No. 13833.
Karpoff, J., 2001, Public versus Private Initiative in Arctic Exploration: The Effects ofIncentives and Organizational Structure, Journal of Political Economy, 109(1): 38‐78.
Lucas, R E. Jr., 1988, On the Mechanics of Economic Development, Journal of Monetary
Economics, Vol. 22, No. 1: 3‐42.
Ramstetter E. D., and Sjoholm F., 2006, Multinationals Corporations in Indonesia andThailand: Wages, Productivity, and Exports, Palgrave Macmillan, Basingstorke.
Rosenthal, S. and Strange, W., 2004, Evidence on the Nature and Sources of
Agglomeration Economies, in: Henderson, J.V. and J.‐F. Thisse (eds.) Handbook ofRegional and Urban Economics, Vol. 4, North‐Holland: Elsevier, 2713‐2739.
Syverson, C, 2004, “Market Structure and Productivity: A Concrete Example” Journal of
Political Economy, 112(6): 1181–1222.
Teshima, K., 2008, Import Competition and Innovation at the Plant Level: Evidence
from Mexico, Manuscript, Columbia University.

Figure 1: A Framework of Product and Process Innovation

Economies of Innovation

Innovation
New ProductNetworks

Survives
Process

New Entry

Production(Entry costs)

Linkages

Innovation
Firm‐level
Knowledge

Intellectual
Linkages

Stock

FailureInnovation
FailureExits

R&D
Investments

Product
DeathsNote: Given local innovation system and firm’s exogenous productivity, each firm chooses to thenumber of information linkages and frequency of commuting to partner to maximize present
value of firm.

Figure 2: Number of Innovations across Firms in East Asia010Frequency
20
30

40

50

Number of Innovations, Pooled

05Source: ERIA Survey 2008.

1015
Number of Innovation

20

Figure 3: Number of Linkages across Firms in East Asia

050Frequency
100

150

200

Number of Linkages, Pooled

05Source: ERIA Survey 2008.

1015
num_link

20

25

Figure 4: Number of Innovation across Firms and Countries

Number of Innovations, Philippines

0

0

5

5

Frequency
1015Frequency
10

15

20

25

20

Number of Innovations, Indonesia

05Source: ERIA Survey 2008.

10
Number of Innovation

15

20

0

5

Source: ERIA Survey 2008.

15

20

Number of Innovations, Vietnam

0

0

5

5

Frequency
1015Frequency
10

20

15

25

Number of Innovations, Thailand

10
Number of Innovation

05

Source: ERIA Survey 2008.

1015
Number of Innovation

20

0

5

Source: ERIA Survey 2008.

10
Number of Innovation

15

20

Figure 5: The Linkages Effect of Innovation across Countries
No. Innovations vs. No. Linkages, Philippines00

Number of Innovations
5
10
15

Number of Innovations
5
10
15

20

20

No. Innovations vs. No. Linkages, Indonesia

0

5

10
15
No. of Linkages

Number of Innovation

20

25

0

Fitted Values

5

10
15
No. of Linkages

Number of Innovation

Source: ERIA Survey 2008

20

25

Fitted Values

Source: ERIA Survey 2008

No. Innovations vs. No. Linkages, Vietnam

00

Number of Innovations
5
10
15

Number of Innovations

20

20

No. Innovations vs. No. Linkages, Thailand

0

5

10
15
No. of Linkages

Number of Innovation
Source: ERIA Survey 2008

20
Fitted Values

25

0

5

10
15
No. of Linkages

Number of Innovation
Source: ERIA Survey 2008

20
Fitted Values

25Table 1: Number of Observations by Industry and Country
Pooled sample
Freq. Percent
1 Food80
13.31
2 Apparel
105
17.47
3 Wood31
5.16
4 Paper27
4.49
5 Coal3
0.5
6 Chemical
59
9.82
7 Nonmetal
91.5
8 Iron24
3.99
9 Nonferrous
1
0.17
10 Metal
37
6.16
11 Machinery30
4.99
12 Computers
6
1
13 Electronics
54
8.99
14 Precision
6
1
15 Auto32
5.32
16 Transport
8
1.33
17 Other
89
14.81
Total
601
100

Cum.
13.31
30.78
35.94
40.43
40.93
50.75
52.25
56.24
56.41
62.56
67.55
68.55
77.54
78.54
83.86
85.19
100

Indonesia
Freq. Percent Cum.
25
16.78
16.78
36
24.16
40.94
16
10.74
51.68
13
8.72
60.4
1
0.67
61.07
5
3.36
64.43
N.A.
7
4.7
69.13
N.A.
2
1.34
70.47
2
1.34
71.81
N.A.
2
1.34
73.15
N.A.
4
2.68
75.84
2
1.34
77.18
34
22.82
100
149
100

Philippines
Freq. Percent Cum.
35
17.24
17.24
43
21.18
38.42
7
3.45
41.87
5
2.46
44.33
N.A.
21
10.34
54.68
8
3.94
58.62
5
2.46
61.08
1
0.49
61.58
16
7.88
69.46
8
3.94
73.4
1
0.49
73.89
30
14.78
88.67
1
0.49
89.16
12
5.91
95.07
1
0.49
95.57
9
4.43
100
203
100

Thailand
Freq. Percent Cum.
15
13.39
13.39
17
15.18
28.57
4
3.57
32.14
4
3.57
35.71
2
1.79
37.5
15
13.39
50.89
N.A.
1
0.89
51.79
N.A.
11
9.82
61.61
5
4.46
66.07
4
3.57
69.64
2
1.79
71.43
1
0.89
72.32
8
7.14
79.46
3
2.68
82.14
20
17.86
100
112
100

Vietnam
Freq. Percent Cum.
5
3.65
3.65
9
6.57
10.22
4
2.92
13.14
5
3.65
16.79
N.A.
18
13.14
29.93
1
0.73
30.66
11
8.03
38.69
N.A.
8
5.84
44.53
15
10.95
55.47
1
0.73
56.2
20
14.6
70.8
4
2.92
73.72
8
5.84
79.56
2
1.46
81.02
26
18.98
100
137
100

Table 2: Summary Statistics of Firm Characteristics
Obs Mean Std. Dev. Min Max
Firm Characteristics
Age
589 14.202
12.392080
Full‐time Employees
602 293.879 456.483
10 2000
Local Firms
605 0.617
0.487
01
Joint Venture Firms
605 0.132
0.339
01Multinational Enterprise
605 0.251
0.434
01
Production (raw material processing)
605 0.463
0.499
01
Production (components and parts)
605 0.281
0.450
01
Production (final products)
605 0.712
0.453
01Procurement of raw materials, parts, or supplies
605 0.250
0.433
01Marketing, sales promotion
605 0.433
0.496
01
R&D activities (1 if Yes, 0 otherwise)
605 0.221
0.416
01
IndustryFood
605 0.132
0.339
01Apparel605 0.174
0.379
0
1
Wood
605 0.051
0.221
0
1
Paper
605 0.045
0.207
0
1
Coal
605 0.005
0.070
01
Chemical
605 0.098
0.297
01
Nonmetal
605 0.015
0.121
0
1
Iron
605 0.040
0.195
01
Nonferrous
605 0.002
0.041
0
1
Metal
605 0.061
0.240
01Machinery
605 0.050
0.217
01Computers
605 0.010
0.099
01Electronics
605 0.089
0.285
01
Precision
605 0.010
0.099
0
1
Auto
605 0.053
0.224
01Transport
605 0.013
0.114
0
1
Other
605 0.147
0.355
0
1

Table 2: Summary Statistics of Firmʹs Perception for Own Performances
Obs Mean Std. Dev. Min Max
605 0.560
0.497
01
1 Sales amount increases
605 0.499
0.500
01
2 Profit increased
605 0.372
0.484
01
3 Number of employees increased
605 0.243
0.429
01
4 Value of exports increased
605 0.188
0.391
01
5 Value of exports to developed countries increased
605 0.175
0.380
01
6 Number of export destination increased
605 0.631
0.483
01
7 Productivity of operation improved
Qualityofproducts
improved
605
0.775
0.418
0
1
8
605 0.699
0.459
01
9 Product defects were reduced
605 0.455
0.498
01
10 Production cost decreased
605 0.579
0.494
01
11 Lead‐time was reduced

Table 2: Summary Statistics of Innovations

1
2
3
4
5
6
1
2
3
1
2
3
4
5
6

Obs
Number of Innovations
605
Number of Types of Innovations
605
Number of Types of Product Innovations
Number of Types of Process Innovations in
605
Production Method605
Number of Types of Securing New Supplier
605
Number of Types of Securing New Customer
605
Number of Types of Organizational Changes
Product Innovations
605
Introduction of New Good605
Introduction of New Good to New Market
Introduction of New Good with New Technology 605
Process Innovations
605
Bought New Machines605
Improved Existing Machines
Introduced New Know‐how on Production Method 605
605
Adopted an international standard (ISO or
605
Introduced ICT and reorganized business
Introduced other internal activities to respond to605changes in the market?

Mean

Std. Dev. Min Max

8.970
0.671

4.913
0.870

0
0

21
3

1.752
2.549
2.742
1.469

1.220
2.061
2.128
1.198

0
0
0
0

3
7
7
3

0.458
0.096
0.117

0.499
0.295
0.322

0
0
0

1
1
1

0.529
0.673
0.550
0.531
0.342

0.500
0.470
0.498
0.499
0.475

0
0
0
0
0

1
1
1
1
1

0.597

0.491

0

1

Table 2: Summary Statistics of Innovations

1
2
3
4
5
6
7
1
2
3
4
5
6
7

Obs
Securing New Supplier
Secured a new local supplier (100% local capital) in
605
survey city
Secured a new local supplier (100% local capital) in605
the country outside survey city
Secured a new Multinational Company (MNC)(100% foreign capital) or joint venture (JV)605
supplier in survey city
Secured a new MNC or JV supplier in the country
605
outside survey city
Secured a new supplier in other ASEAN countries 605
Secured a new supplier in other countries in East
605
Asia (China, Japan, Korea, Taiwan)
Secured a new supplier in other foreign countries 605
Securing New Customer
Secured a new local customer (100% local capital)
605
in survey city
Secured a new local customer (100% local capital)
605
in the country
Secured a new MNC or JV customer in survey city 605
Secured a new MNC or JV customer in the country 605
605
Secured a new customer in other ASEAN
Secured a new customer in other countries in East
605
Asia (China, Japan, Korea, Taiwan)
605
Secured a new customer in other foreign countries

Mean

Std. Dev. Min Max

0.636

0.481

0

1

0.567

0.496

0

1

0.174

0.379

0

1

0.162
0.327

0.369
0.470

0
0

1
1

0.380
0.302

0.486
0.460

0
0

1
1

0.653

0.476

0

1

0.580
0.307
0.218
0.271

0.494
0.462
0.413
0.445

0
0
0
0

1
1
1
1

0.347
0.365

0.476
0.482

0
0

1
1

Table 2: Summary Statistics of Linkages
Obs Mean
Number of Linkages and Internal Resources
605 8.064
1 Number of Linkages
605 5.893
2 Number of Production Linkages
605 2.172
3 Number of Intellectual Linkages
605 1.917
4 Number of Internal Sources
Production Linkages
Joint venture established by your firm with other1
605 0.326
local firms
605 0.412
2 Local supplier or customer (100% local capital)
Local competitor (Firms in the same business3
605 0.236
which is neither supplier nor customer)
Local firm in the different business which is4
605 0.226
neither supplier nor customer
605 0.448
5 Licensing technologies from other local firms
605 0.233
6 Local consultant hired by your firm
Joint venture established by your firm with other7
605 0.383
foreign‐owned firms
Foreign‐owned (or multinational) supplier or
8
605 0.450
customer
Foreign‐owned competitor (Firms in the same9605 0.321
business which is neither supplier nor customer)
Foreign‐owned firm in the different business10
605 0.294
which is neither supplier nor customer
605 0.236
11 Licensing technologies from other MNCs
605 0.193
12 International consultant hired by your firm
605 0.559
13 Recruitment of mid‐class personnel
Recruitment of personnel retired from MNCs and14
605 0.243
large firms
605 0.362
15 Technical information obtainable from patents
Introduction of “foreign‐made” equipment and16
605 0.509
software605 0.461
17 Reverse engineeringIntellectual Linkages
Technical assistance financed/provided by1
605 0.278
government/public agency
Technical assistance financed/provided by local
2
605 0.302
business organization
Research consortium organized with the support
3
605 0.235
of government
Research consortium organized with the support
4
605 0.225
of local business organization
Business consortium organized with the support
5
605 0.236
of government
Business consortium organized with the support
6
605 0.233
of local business organization
Technical cooperation with (or assistance from)7
605 0.233
local university or R&D instituteTechnical cooperation with (or assistance from)8605 0.217
foreign university or R&D institute605 0.213
9 Academic society and academic journalInternal Resources
605 0.339
1 Own R&D department
605 0.448
2 Own Sales department or sales agent
605 0.618
3 Own production or manufacturing department
Technological agreement with the headquarters or
4
605 0.512
affiliated firmStd. Dev. Min Max
8.783
5.884
3.457
1.602

0
0
0
0

26
17
9
4

0.469
0.493

0
0

1
1

0.425

0

1

0.419
0.498
0.423

0
0
0

1
1
1

0.487

0

1

0.498

0

1

0.467

0

1

0.456
0.425
0.395
0.497

0
0
0
0

1
1
1
1

0.429
0.481

0
0

1
1

0.500
0.499

0
0

1
1

0.448

0

1

0.460

0

1

0.424

0

1

0.418

0

1

0.425

0

1

0.423

0

1

0.423

0

1

0.412
0.410

0
0

1
1

0.474
0.498
0.486

0
0
0

1
1
1

0.500

0

1

Table 2: Summary Statistics of the Relationship with Main Customer and SupplierObs Mean Std. Dev. Min Max
Relationship with Customer
605 0.638
0.481
01
1 Main Customer makes Customized Good
584
400.069
438.087
51000
2 Geographic Proximity to Customer (km)605 0.451
0.498
01
3 JIT with Customer
605 0.107
0.310
01
4 Capital Tie‐up with Customer
3.489
0.5 105 Duration of the Relationship with Customer (year) 590 6.412
605 0.339
0.474
01
6 Accept Engineers from Customer
605 0.215
0.411
01
7 Dispatch Engineers to Customer
605 0.668
0.471
01
8 Customer is Important Partner for Innovation
Relationship with Supplier605 0.554
0.498
01
1 Main Supplier makes Customized Good545 343.418 413.176
5 10002 Geographic Proximity to Supplier (km)605 0.362
0.481
01
3 JIT with Supplier605 0.112
0.316
01
4 Capital Tie‐up with Supplier
3.587
0.5 105 Duration of the Relationship with Supplier (year) 570 6.233
605 0.273
0.446
01
6 Accept Engineers from Supplier605 0.170
0.376
01
7 Dispatch Engineers to Supplier
605
0.117
0.322
0
1
8 Supplier is Important Partner for Innovation

Table 3: Number of Innovations by Industry and Country

1 Food
2 Apparel
3 Wood
4 Paper
5 Coal
6 Chemical
7 Nonmetal
8 Iron9 Nonferrous
10 Metal
11 Machinery
12 Computers
13 Electronics
14 Precision
15 Auto16 Transport
17 Other
Total

Philippines
Pooled sample
Indonesia
ThailandMean Median S.D. Min Max Mean Median S.D. Min Max Mean Median S.D. Min Max Mean Median S.D.
8.07
8
4.91
0
21
5.48
4
4.16
0
15
8.11
8
5.03 0
21
11.2
11
4.2
6.29
6
4.68
0
21
5.5
4.5
3.45
0
15
4.4
4
3.95 017 11.35
11
4.24
6.87
7
3.58
0
13
6.81
6
3.62
0
13
6.14
5
5.24 0
13
7.25
7
1.26
9.7
9
4.46
2
21
7.92
8
3.8
2
16
11.2
12
2.59 814 13.25
13.5
6.85
N.A
12.67
13
0.58 12
13
13
13
.
13
13
12.5
12.5
0.71
10.37
11
4.14
2
21
9
8
4.47
4
15
9.1
8
3.99 219 11.87
11
4.85
N.A
N.A
8.44
8
5.64
1
19
8.63
8.5
6
1
19
8.42
8.5
4.7
0
17
6.29
6
5.5
0
17
7.6
10
5.77 0
13
7
7
.
N.A
6
6
.
6
6
6
6
.
6
6
12
12
4.99
0
20
5
5
2.83
3
7
9.94
10
4.95 020 16.82
17
2.79
10.8
12.5
4.37
1
17
14
14
1.41 1315
8.63
7
6.48 1
17
10.4
11
1.95
N.A
12.33
14
6.89
3
20
14
14
.
14
14
10
9.5
7.16
10.63
10
3.57
1
19
7
7
0
7
7
10.2
9.5
4.33 1
19
11
11
2.83
N.A
10.67
12
3.39
6
14
7
7
.
7
7
11
11
.
10.25
10.5
5.71
0
21
9.5
10.5
3.11
5
12
7.5
6
5.79 019 16.13
15.5
3.83
10.38
10
3.46
6
17
10.5
10.5
2.12
9
12
11
11
.
11
11 12.67
12
4.04
8.78
8
5.02
1
21
5.85
5
4.49
1
21
6.67
5
4.33 2
15
11.1
12
4.29
8.96
9
4.91
0
21
6.44
6
4.14
0
21
7.84
8
5.01 021 12.07
12
4.58

Vietnam
Min Max Mean Median S.D. Min Max
5
20
11.4
12
1.52
9
13
4
21
8.89
8
5.51
0
18
6
9
8
8
1.83
6
10
5
21
10
12
4.3
5
15
N.A
12
13
4
21
11
12.5
3.27
4
14
7
7
.
7
7
7
7
10.27
9
3.44
5
17
N.A
11
20 11.25
11.5
1.58
9
14
8
13 11.67
13
3.52
4
16
3
18
20
20
.
20
20
9
13
11.6
12
1.96
7
14
11
11
11.5
13
3.7
6
14
11
21
8.88
8
4.26
4
16
9
17
6.5
6.5
0.71
6
7
4
20 11.54
12
4.08
1
20
3
21 10.83
12
3.6
0
20

Table 4: Number of Linkages by Industry and Country

1 Food
2 Apparel
3 Wood
4 Paper
5 Coal
6 Chemical
7 Nonmetal
8 Iron9 Nonferrous
10 Metal
11 Machinery
12 Computers
13 Electronics
14 Precision
15 Auto16 Transport
17 Other
Total

Pooled sample
Indonesia
Philippines
Thailand
VietnamMean Median S.D. Min Max Mean Median S.D. Min Max Mean Median S.D. Min Max Mean Median S.D. Min Max Mean Median S.D. Min Max
6.86
4
8.41
0
26
8.32
7
6.9
0
20
2.69
0
5.81 0
26
13.4
12
11.86
0
26
9.2
9
0.84
8
10
6.67
3
8.56
0
26
5.86
5.5
5.22
0
20
0.58
0
1.56 0
8
22.18
26
6.9
6
26
9.67
10
1.5
8
12
6.74
4
8.87
0
26
5.38
2.5
7.87
0
26
0.86
0
2.27 0
6
21
26
10
6
26
8.25
9
1.5
6
9
8.22
6
8.4
0
26
7.08
5
6.17
0
20
0
0
0
0
0
23.5
26
5
16
26
7.2
7
1.3
6
9
N.A
N.A
6.67
4
4.62
4
12
4
4
.
4
4
8
8
5.66
4
12
10.05
8
10.1
0
26
14.6
12
11.13 126
408.15 0
26
19.2
26
11.53
0
26
8.22
8
1.52
6
12
N.A
N.A
3.44
0
5.34
0
15
2.75
0
5.26 0
15
9
9
.
9
9
7.96
8
7.96
0
26 13.29
10
12.37 026
1.4
03.13 0
7
0
0
.
0
0
8.27
8
1.56
6
11
N.A
N.A
0
0
.
0
0
N.A
0
0
.
0
0
9.7
7
9.98
0
26
7.5
7.5
0.71
7
8
1.13
0
2.83 011 23.91
24
1.22 22
26
7.88
8
1.46
6
10
8.17
9
7.1
0
26
17.5
17.5
10.61 10250.88
02.47 0
7
12.4
11
11.84
0
26
9.4
10
1.55
7
11
N.A
9
5.5
10.77 0
26
0
0
.
0
0
11
9
12.68
0
26
10
10
.
10
10
6.65
7
7.08
0
26
13.5
13.5
3.54 1116
4.03
07.87 0
26
16
16
14.14
6
26
8.95
9
1.96
6
14
N.A
10.33
8.5
8.64
0
26
0
0
.
0
0
26
26
.
26
26
9
8.5
2.16
7
12
7.75
6
9.66
0
26
9.25
5.5
11.47 0
26
0
0
0
0
0
17.88
26
11.39
0
26
8.5
8.5
2.14
6
12
12.13
7.5
10.29 026
151515.56 4
26
0
0
.
0
0
17.33
20
10.26
6
26
7.5
7.5
0.71
7
8
10.01
8
9.07
0
26
6.65
5.5
6.82
0
26
0
0
0
0
0
22.3
26
8.16
0
26
8.42
8.5
1.65
5
12
8.04
6
8.77
0
26
7.63
6
7.49
0
26
1.89
0
5.11 0
26
19
26
9.97
0
26
8.62
9
1.68
5
14

Table 5: Innovation Outcomes by Linkages and Mean Differences
Linkages:
Linkages:
Over
All
Under
MedianMedian8.970
7.142
10.878
Number of Types of Innovations
A. Product Innovations
0.671
0.628
0.716
Number of Product Innovations
1 Introduction of New Good
0.458
0.440
0.476
Introduction of New Good to New
0.096
0.084
0.108
2
MarketIntroduction of New Good with New
3
0.117
0.104
0.132
Technology

t‐Statistics‐10.101‐1.249‐0.893‐1.000‐1.077

Table 5: Innovation Outcomes by Linkages and Mean Differences (Continued)
Linkages:
Linkages:
All
Under
Over
t‐Statistics
MedianMedianB. Process Innovations
Number of Types of Process
1.752
1.832
1.669
1.643
Innovations in Production Method
1 Bought New Machines
0.529
0.557
0.500
1.395
0.673
0.706
0.639
1.757
2 Improved Existing Machines
Introduced New Know‐how on
0.550
0.570
0.530
0.967
3
Production Methods
Number of Types of Organizational
4
1.469
1.159
1.794
‐6.758Changes
Adopted an international standard
5
0.531
0.430
0.635
‐5.145(ISO or others)?
Introduced ICT and reorganized
0.342
0.246
0.443
‐5.2006
business processes by it?
Introduced other internal activities to
7
0.597
0.482
0.716
‐6.030
respond to changes in the market?

Table 5: Innovation Outcomes by Linkages and Mean Differences (Continued)
Linkages:
Linkages:
All
Under
Over
t‐Statistics
MedianMedianC. Securing New Suppliers
Number of Types of Securing New
2.549
1.893
3.233
‐8.446
Supplier
Secured a new local supplier (100%
1
0.636
0.515
0.764
‐6.577
local capital) in survey city
Secured a new local supplier (100%
0.567
0.472
0.666
‐4.8762
local capital) in the country outside
Secured a new Multinational Company
3 (MNC) (100% foreign capital) or joint
0.174
0.110
0.240
‐4.271
venture (JV) supplier in survey city
Secured a new MNC or JV supplier in4
the country outside survey city
Secured a new supplier in other5
ASEAN countries
Secured a new supplier in other6
countries in East Asia (China, Japan,
Secured a new supplier in other7
foreign countries

0.162

0.117

0.209

‐3.122

0.327

0.207

0.453

‐6.657

0.380

0.259

0.507

‐6.482

0.302

0.214

0.395

‐4.953

Table 5: Innovation Outcomes by Linkages and Mean Differences (Continued)
Linkages:
Linkages:
All
Under
Over
t‐Statistics
MedianMedianD. Securing New Customers
Number of Types of Securing New
2.742
1.819
3.706
‐12.157
Customer
Secured a new local customer (100%
1
0.653
0.518
0.794
‐7.440local capital) in survey city
Secured a new local customer (100%
2
0.580
0.443
0.723
‐7.251
local capital) in the country
Secured a new MNC or JV customer in
3
0.307
0.126
0.497
‐10.758
survey city
Secured a new MNC or JV customer in
4
0.218
0.126
0.314
‐5.737the country
Secured a new customer in other
5
0.271
0.175
0.372
‐5.575ASEAN countries
Secured a new customer in other6 countries in East Asia (China, Japan,
0.347
0.191
0.510
‐8.736
Korea, Taiwan)
Secured a new customer in other7
foreign countries

0.365

0.239

0.497

‐6.802

Table 6: Number of Linkages and Number of Innovations by R&D
OLS

(1)

(2)

(3)

Dependent variables: Number of
Innovations

All

With R&D

Without
R&D

0.189**[0.027]1.635**[0.464]0.030+[0.017]0.003**[0.000]‐3.925**[0.570]‐1.725**[0.663]0.08[0.628]7.363**[0.647]587
0.35992

0.161**[0.041]‐0.129[1.267]0.039[0.027]0.003**[0.001]‐3.502**[1.077]‐0.837[0.979]0.355[1.204]9.111**[0.961]128
0.308770.161**[0.031]2.431**[0.518]0.005[0.021]0.002**[0.000]‐3.688**[0.718]‐2.346**[0.821]‐0.292[0.793]
7.527**[0.811]459
0.35505

Number of Linkages
Multinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam
Constant

Observations
R‐squared
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

Table 7: Number of Linkages and Number of Innovations
OLS

(1)

(2)

(3)

(4)

Dependent variables: Number of
Innovations

All

All

All

All

Number of Linkages

0.189**[0.027]

Number of Production Linkages

0.283**[0.039]Number of Intellectual Linkages

0.428**[0.074]

Number of Internal Resources
Multinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam
Constant

1.635**[0.464]0.030+[0.017]0.003**[0.000]‐3.925**[0.570]‐1.725**[0.663]0.08[0.628]7.363**[0.647]587
0.35992

1.619**[0.462]0.029+[0.017]0.003**[0.000]‐4.104**[0.538]‐1.933**[0.630]‐0.862[0.595]7.601**[0.605]587
0.36141

Observations
R‐squared
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

1.697**[0.471]0.032+[0.017]0.003**[0.000]‐4.181**[0.608]‐2.214**[0.688]1.007[0.742]7.870**[0.686]587
0.34310.989**[0.134]
1.908**[0.451]0.026[0.017]0.002**[0.000]‐4.113**[0.583]‐2.431**[0.630]‐0.951[0.608]7.605**[0.642]587
0.36584

Table 8: Number of Linkages and Number of Innovations by Functions
OLS

(1)

(2)

(3)

(4)

(5)

(6)

Dependent variables: Number of
Innovations Last 3 years

All

Raw materials

Parts

Final
Assembling

Procurement

Marketing

0.189**[0.027]1.635**[0.464]0.030+[0.017]0.003**[0.000]‐3.925**[0.570]‐1.725**[0.663]0.080[0.628]7.363**[0.647]587
0.360

0.239**[0.037]2.406**[0.645]0.046+[0.024]0.002**[0.001]‐3.874**[1.112]‐1.531[1.187]‐0.222[1.175]6.737**[1.157]272
0.407

0.162**[0.045]1.810*[0.834]0.016[0.034]0.002**[0.001]‐5.053**[1.110]‐2.117[1.343]‐1.164[1.235]8.907**[1.270]167
0.402

0.191**[0.037]1.428**[0.535]0.048*[0.019]0.002**[0.000]‐3.797**[0.772]‐1.488[0.912]0.793[0.827]7.059**[0.898]419
0.346

0.293**[0.063]‐0.332[0.976]‐0.026[0.049]0.005**[0.001]‐4.518**[1.667]0.606[1.770]‐0.535[1.846]6.382**[1.760]146
0.318

0.249**[0.038]2.691**[0.549]0.037*[0.018]0.003**[0.001]‐2.814*[1.261]1.411[1.361]
0.092[1.298]6.041**[1.401]253
0.500

Number of Linkages
Multinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam
Constant

Observations
R‐squared
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

Table 9: Number of Linkages and Number of Product Innovations
Ordered Logit

(1)

(2)

(3)

(4)

Dependent variables: Number of
Innovations in Introducing New Product(0, 1, 2, 3)

All

All

All

All

Number of Linkages

0.031**[0.012]

Number of Production Linkages

0.042*[0.017]

Number of Intellectual Linkages

0.088*[0.035]

Number of Internal Resources
Multinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam

‐0.589*[0.238]0.007[0.007]0.001**[0.000]‐0.388[0.294]0.303[0.314]‐0.636*[0.321]587‐0.587*[0.238]0.007[0.007]0.001**[0.000]‐0.453[0.282]0.218[0.300]‐0.807**[0.298]587

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

‐0.585*[0.239]0.008[0.008]0.001**[0.000]‐0.35[0.304]0.335[0.323]‐0.368[0.376]587

0.298**[0.065]‐0.512*[0.238]0.007[0.007]0.001**[0.000]‐0.172[0.281]
0.523+[0.281]‐0.686*[0.294]587

Table 10: Number of Linkages and Number of Product Innovations by
R&D
Orderd Logit
(1)
(2)
(3)
Dependent variables: Number of
Innovations in Introducing New Product(0, 1, 2, 3)
Number of Linkages
Multinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam

All

With R&D

Without
R&D

0.031**[0.012]‐0.589*[0.238]0.007[0.007]0.001**[0.000]‐0.388[0.294]0.303[0.314]‐0.636*[0.321]587

0.007[0.019]0.115[0.523]‐0.005[0.011]0[0.000]‐0.786[0.622]‐0.12[0.461]0.521[0.455]128

0.038+[0.020]‐0.45[0.311]0.006[0.010]0.001**[0.000]0.038[0.447]0.541[0.529]‐0.63[0.485]459

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

Table 11: Number of Production Linkages and Number of ProductInnovations by R&D
Orderd Logit(1)(2)
Dependent variables: Number of
Innovations in Introducing New Product(0, 1, 2, 3)
Number of Production Linkages
Multinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam(3)

All

With R&D

Without
R&D

0.042*[0.017]‐0.587*[0.238]0.007[0.007]0.001**[0.000]‐0.453[0.282]0.218[0.300]‐0.807**[0.298]587

0.009[0.029]0.117[0.522]‐0.005[0.011]0[0.000]‐0.799[0.617]‐0.134[0.455]0.485[0.439]128

0.047[0.028]‐0.446[0.310]0.006[0.010]0.001**[0.000]‐0.091[0.411]
0.362[0.484]‐0.873*[0.427]459

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

Table 12: Number of Intellectual Linkages and Number of ProductInnovations by R&D
Orderd Logit
(1)
(2)
(3)
Dependent variables: Number of
Innovations in Introducing New Product(0, 1, 2, 3)
Number of Intellectual Linkages
Multinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam

All

With R&D

Without
R&D

0.088*[0.035]‐0.585*[0.239]0.008[0.008]0.001**[0.000]‐0.35[0.304]0.335[0.323]‐0.368[0.376]587

0.02[0.057]0.117[0.520]‐0.005[0.012]0[0.000]‐0.766[0.636]‐0.11[0.475]0.584[0.530]128

0.115*[0.055]‐0.455[0.313]0.006[0.010]0.001**[0.000]0.116[0.453]0.637[0.529]‐0.239[0.587]459

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

Table 13: Number of Internal Sources and Number of Product Innovations
by R&D
Orderd Logit
(1)
(2)
(3)
Dependent variables: Number of
Innovations in Introducing New Product(0, 1, 2, 3)
Number of Internal Sources
Multinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam

All

With R&D

Without
R&D

0.298**[0.065]‐0.512*[0.238]0.007[0.007]0.001**[0.000]‐0.172[0.281]
0.523+[0.281]‐0.686*[0.294]587

0.233[0.153]‐0.042[0.516]‐0.002[0.013]0[0.000]‐0.735[0.597]0.064[0.418]0.386[0.454]128

0.212*[0.103]‐0.422[0.310]0.007[0.010]0.001**[0.000]0.014[0.399]0.466[0.453]‐0.790+[0.426]459

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

Table 14: Number of Linkages and Number of Product Innovations by Functions
Ordered Logit

(1)

(2)

(3)

(4)

(5)

(6)

Dependent variables: Number of
Innovations in Introducing New Product(0, 1, 2, 3)All

Raw materials

Parts

Final
Assembling

Procurement

Marketing

0.031**[0.012]‐0.589*[0.238]0.007[0.007]0.001**[0.000]‐0.388[0.294]0.303[0.314]‐0.636*[0.321]587

0.058*[0.024]‐0.614[0.412]0.013[0.011]0.001+[0.000]0.697[0.578]1.614*[0.723]‐0.162[0.687]272

0.015[0.024]‐0.841+[0.434]0.004[0.021]0.001**[0.000]‐1.052+[0.601]0.530[0.666]‐0.438[0.645]167

0.037**[0.014]‐0.797**[0.292]0.013[0.009]0.001**[0.000]‐0.622+[0.342]0.107[0.354]‐0.568[0.358]419

0.041+[0.023]‐0.314[0.467]‐0.014[0.018]0.001*[0.000]‐0.606[0.765]‐0.887[0.624]‐1.362+[0.750]146

0.069**[0.024]‐1.648**[0.428]0.024*[0.012]0.001*[0.000]‐0.019[0.808]1.109[0.900]‐0.071[0.824]253

Number of Linkages
Multinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

Table 15: Number of Linkages and Number of Process Innovations
Ordered Logit
(1)
(2)
(3)
Dependent variables: Adopted a New
Production Method (0, 1, 2, 3)
Number of Linkages

All

All

All

0.017
[0.017]0.027[0.029]Number of Intellectual Linkages
Number of Internal Resources

Age
Full‐time Employees
Indonesia
Philippines
VietnamAll

0[0.011]

Number of Production Linkages

Multinational Enterprises(4)‐0.766**[0.228]0.013+[0.007]0.002**[0.000]‐0.632*[0.262]‐0.042[0.295]‐1.330**[0.318]587‐0.767**[0.228]0.013+[0.007]0.002**[0.000]‐0.643*[0.261]‐0.056[0.296]‐1.388**[0.306]587

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

‐0.764**[0.229]0.013+[0.007]0.002**[0.000]‐0.648*[0.257]‐0.071[0.283]‐1.269**[0.346]587

0.110+[0.060]‐0.738**[0.231]0.012+[0.007]0.002**[0.000]‐0.536*[0.269]0.041[0.278]‐1.338**[0.309]587

Table 16 : Number of Linkages and Number of Process Innovations by Functions
Ordered Logit

(1)

(2)

(3)

(4)

(5)

(6)

Dependent variables: Adopted a New
Production Method (0, 1, 2, 3)All

Raw materials

Parts

Final
Assembling

Procurement

Marketing

0.012[0.011]‐0.766**[0.228]0.013+[0.007]0.002**[0.000]‐0.632*[0.262]‐0.042[0.295]‐1.330**[0.318]587

0.030[0.019]‐1.190**[0.349]0.019*[0.009]0.001**[0.000]‐0.763[0.577]‐0.085[0.639]‐1.266*[0.599]272

0.027[0.024]‐0.200[0.443]0.020[0.017]0.001**[0.000]‐1.047+[0.582]‐0.151[0.667]‐1.479*[0.668]167

0.006[0.015]‐0.838**[0.261]0.014+[0.007]0.001**[0.000]‐0.579+[0.339]0.000[0.378]‐1.187**[0.385]419

0.042[0.026]0.102[0.415]‐0.013[0.018]0.002**[0.001]‐0.656[0.607]0.673[0.732]‐1.657*[0.783]146

0.064**[0.024]‐1.981**[0.369]0.024*[0.010]0.001**[0.000]‐0.631[0.988]0.560[1.061]‐0.963[1.012]
253

Number of Linkages
Multinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

Table 17: Number of Linkages and Number of Process Innovations
Ordered Logit

(1)

(2)

(3)

(4)

Dependent variables: Number ofBusiness Process Improvement (Min:0,
Max:3)

All

All

All

All

Number of Linkages

0.054**[0.013]

Number of Production Linkages

0.084**[0.020]

Number of Intellectual Linkages

0.113**[0.036]

Number of Internal Resources
Multinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam

1.272**[0.219]0.002[0.007]0.001**[0.000]‐2.041**[0.308]‐0.907**[0.328]‐0.991**[0.319]587

1.266**[0.220]0.002[0.007]0.001**[0.000]‐2.087**[0.299]‐0.939**[0.317]‐1.255**[0.308]587

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

1.286**[0.219]0.003[0.007]0.001**[0.000]‐2.118**[0.315]‐1.092**[0.330]‐0.766*[0.361]587

0.333**[0.062]
1.390**[0.225]0.001[0.007]0.001**[0.000]‐2.003**[0.301]‐1.001**[0.286]‐1.232**[0.299]587

Table 18 : Number of Linkages and Number of Product Innovations by Functions
Ordered Logit

(1)

(2)

(3)

(4)

(5)

(6)

Dependent variables: Number ofBusiness Process Improvement (Min:0,
Max:3)

All

Raw materials

Parts

Final
Assembling

Procurement

Marketing

0.054**[0.013]
1.272**[0.219]0.002[0.007]0.001**[0.000]‐2.041**[0.308]‐0.907**[0.328]‐0.991**[0.319]587

0.099**[0.023]
1.735**[0.324]‐0.004[0.010]0.001**[0.000]‐0.906[0.631]
0.187[0.730]‐0.491[0.655]272

0.079*[0.031]1.303**[0.377]‐0.005[0.018]0.001**[0.000]‐2.395**[0.649]‐0.460[0.655]‐1.757**[0.627]167

0.052**[0.017]1.200**[0.266]0.012[0.008]0.001**[0.000]‐1.944**[0.404]‐0.894*[0.418]‐0.644[0.415]419

0.072+[0.037]1.022*[0.415]0.010[0.015]0.002**[0.000]‐2.011*[0.920]‐0.151[0.927]‐0.536[0.827]146

0.088**[0.021]1.974**[0.358]0.001[0.010]0.001**[0.000]‐3.021*[1.193]‐1.197[1.224]‐2.202+[1.215]253

Number of Linkages
Multinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

Table 19: Number of Linkages and Number of Process Innovations
Ordered Logit

(1)

(2)

(3)

(4)

Dependent variables: Number ofSecuring New Suppliers of Raw
Materials and Parts (Min:0, Max:7)

All

All

All

All

Number of Linkages

0.061**[0.011]

Number of Production Linkages

0.091**[0.016]

Number of Intellectual Linkages

0.139**[0.029]Number of Internal Resources
Multinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam

1.041**[0.219]0.008[0.008]0.001**[0.000]‐0.905**[0.246]‐0.191[0.263]0.985**[0.273]587

1.032**[0.218]0.007[0.008]0.001**[0.000]‐0.961**[0.242]‐0.253[0.259]0.687*[0.269]587

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

1.061**[0.220]0.009[0.008]0.001**[0.000]‐0.964**[0.246]‐0.319[0.266]
1.296**[0.300]587

0.347**[0.060]1.152**[0.219]0.005[0.008]0.000*[0.000]‐0.880**[0.259]‐0.29[0.280]0.733**[0.280]587

Table 20: Number of Linkages and Number of Securing New Suppliers by Functions
Ordered Logit

(1)

(2)

(3)

(4)

(5)

(6)

Dependent variables: Number ofSecuring New Suppliers of Raw
Materials and Parts (Min:0, Max:7)

All

Raw materials

Parts

Final
Assembling

Procurement

Marketing

0.061**[0.011]
1.041**[0.219]0.008[0.008]0.001**[0.000]‐0.905**[0.246]‐0.191[0.263]0.985**[0.273]587

0.077**[0.018]1.336**[0.298]0.018+[0.010]0.000[0.000]‐1.007+[0.599]‐0.190[0.617]0.784[0.631]272

0.041*[0.019]1.272**[0.418]0.008[0.019]0.000[0.000]‐0.771[0.539]‐0.050[0.586]0.843[0.592]167

0.063**[0.016]
1.069**[0.273]0.014[0.010]0.000[0.000]‐0.748*[0.339]‐0.063[0.368]1.339**[0.383]419

0.110**[0.024]0.037[0.382]‐0.017[0.018]0.002**[0.000]‐1.582*[0.649]0.034[0.673]0.215[0.719]146

0.075**[0.024]2.017**[0.356]0.008[0.015]0.001[0.001]‐0.536[0.848]0.997[0.864]1.483+[0.850]253

Number of Linkages
Multinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

Table 21: Number of Linkages and Number of Securing New Customer
Ordered Logit
(1)
(2)
(3)
Dependent variables: Number ofSecuring New Customers (Min:0, Max:7)
Number of Linkages

All

All

All

0.116**[0.018]

Number of Intellectual Linkages

0.163**[0.032]

Number of Internal Resources

Age
Full‐time Employees
Indonesia
Philippines
VietnamAll

0.076**[0.012]

Number of Production Linkages

Multinational Enterprises(4)

0.486*[0.221]
0.011[0.007]0.000**[0.000]‐1.502**[0.257]‐1.328**[0.283]0.445+[0.261]587

0.467*[0.220]0.011[0.007]0.000*[0.000]‐1.574**[0.247]‐1.406**[0.274]0.068[0.258]587

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

0.518*[0.222]0.012+[0.007]0.001**[0.000]‐1.597**[0.268]‐1.516**[0.296]
0.783**[0.301]587

0.307**[0.062]0.562**[0.213]0.01[0.007]0.000+[0.000]‐1.665**[0.277]‐1.745**[0.296]0.007[0.271]587

Table 22: Number of Linkages and Number of Securing New Customer by Functions
Ordered Logit

(1)

(2)

(3)

(4)

(5)

(6)

Dependent variables: Number ofSecuring New Customers (Min:0, Max:7)

All

Raw materials

Parts

Final
Assembling

Procurement

Marketing

0.076**[0.012]0.486*[0.221]
0.011[0.007]0.000**[0.000]‐1.502**[0.257]‐1.328**[0.283]0.445+[0.261]587

0.077**[0.021]0.747*[0.337]0.015[0.009]0.000[0.000]‐2.369**[0.699]‐2.236**[0.725]‐0.419[0.675]272

0.049*[0.024]0.293[0.397]0.004[0.017]0.000[0.000]‐2.558**[0.529]‐2.190**[0.657]‐0.157[0.540]167

0.077**[0.016]
0.561*[0.262]0.017*[0.008]0.000[0.000]‐1.480**[0.337]‐1.203**[0.372]0.605+[0.357]419

0.106**[0.030]‐0.709+[0.405]‐0.008[0.020]0.001*[0.000]‐1.039[0.765]0.345[0.831]
0.747[0.828]146

0.117**[0.027]1.737**[0.306]0.016+[0.009]0.001**[0.000]‐1.257[1.012]0.410[1.026]0.459[0.987]253

Number of Linkages
Multinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

Table 23: Geographic Proximity to Customer/Supplier by Capital Tie‐up with Customer/SupplierWith Customer

With Supplier

No

No

Yes

No

No

Yes

Yes

Yes

Variable (km)Geographic Proximity to Consumer
Geographic Proximity to Supplier
Geographic Proximity to Consumer
Geographic Proximity to Supplier
Geographic Proximity to Consumer
Geographic Proximity to Supplier
Geographic Proximity to Consumer
Geographic Proximity to SupplierObs Mean
498
394.2
462
353.0
23
301.2
19
236.9
23
428.0
23
182.5
40
514.2
41
374.8

S.D. Min Max
435.2
5 1000415.1
5 1000392.3
5 1000351.8
5 1000471.8
5 1000316.7
5 1000471.9
5 1000449.7
5 1000

Table 24: Geographic Proximity to Customer/Supplier by Customized Goods Transaction with
Customer/SupplierWith Customer With Supplier
Variable (km)Obs Mean
S.D. Min Max
Geographic Proximity to Consumer
182 353.9
428.8
5 1000No
No
Geographic Proximity to Supplier154 206.0
330.7
5 1000Geographic Proximity to Consumer
80 276.5363.8
5 1000Yes
No
Geographic Proximity to Supplier
67 217.8
339.3
5 1000Geographic Proximity to Consumer
26 332.7385.0
5 1000No
Yes
Geographic Proximity to Supplier28 462.1
437.4
5 1000Geographic Proximity to Consumer
296 467.8
456.1
5 1000Yes
Yes
Geographic Proximity to Supplier296 432.1
438.3
5 1000

Table 25: Geographic Proximity to Customer/Supplier by JIT with Customer/SupplierWith Customer With Supplier
Variable (km)Obs MeanGeographic Proximity to Consumer
307 448.9
No
No
Geographic Proximity to Supplier289 442.8
Geographic Proximity to Consumer
71 391.3
Yes
No
Geographic Proximity to Supplier45 172.5
Geographic Proximity to Consumer
15 294.6
No
Yes
Geographic Proximity to Supplier
18 369.2
Geographic Proximity to Consumer
191 333.1
Yes
Yes
Geographic Proximity to Supplier193 232.0

S.D. Min Max
445.9
5 1000435.4
5 1000442.4
5 1000341.9
5 1000440.9
5 1000439.9
5 1000415.9
5 1000348.1
5 1000Table 26: Geographic Proximity to Customer/Supplier by Accept Engineers from Customer/Supplier
From Customer From Supplier
No

No

Yes

No

No

Yes

Yes

Yes

Variable (km)Geographic Proximity to Consumer
Geographic Proximity to Supplier
Geographic Proximity to Consumer
Geographic Proximity to Supplier
Geographic Proximity to Consumer
Geographic Proximity to Supplier
Geographic Proximity to Consumer
Geographic Proximity to SupplierObs Mean359 318.5
331 237.6
64 319.3
57 368.6
23 282.8
23 501.4
138 669.4
134 567.0

S.D. Min Max
403.2
5 1000340.1
5 1000404.1
5 1000404.7
5 1000389.2
5 1000454.1
5 1000443.5
5 1000474.8
5 1000

Table 27: Geographic Proximity to Customer/Supplier by Dispatch Engineers to Customer/Supplier
To Customer

To Supplier

No

No

Yes

No

No

Yes

Yes

Yes

Variable (km)Geographic Proximity to Consumer
Geographic Proximity to Supplier
Geographic Proximity to Consumer
Geographic Proximity to Supplier
Geographic Proximity to Consumer
Geographic Proximity to Supplier
Geographic Proximity to Consumer
Geographic Proximity to SupplierObs Mean439 391.4
407 342.2
48 295.5
41 361.1
20 454.0
23 315.8
77 500.6
74 348.7

S.D. Min Max
434.3
5 1000409.5
5 1000397.3
5 1000418.8
5 1000463.7
18 1000
406.0
5 1000464.3
5 1000439.9
5 1000

Table 28: Geographic Proximity to Customer/Supplier by Importance of Customer/Supplier as Innovation
Partner
Customer
Supplier
Variable (km)Obs Mean
S.D. Min Max
Geographic Proximity to Consumer
173 369.9
420.5
5 1000No
No
Geographic Proximity to Supplier
162 389.8
426.1
5 1000Geographic Proximity to Consumer
341 444.5
450.0
5 1000Yes
No
Geographic Proximity to Supplier
312 367.8
427.5
5 1000Geographic Proximity to Consumer
15 244.7
358.0
18 1000
No
Yes
Geographic Proximity to Supplier
16
90.4
107.7
5
350
Geographic Proximity to Consumer
55 261.9399.1
5 1000Yes
Yes
Geographic Proximity to Supplier
55 141.8
229.3
5 1000Table 29: Engineer Acceptance from Customers/Suppliers and Introduction of New Good
Probit (Marginal Effects)
Dependent variables: Introduction of
New Good (Yes/No)
Accept Engineers from Customer
Accept Engineers from SupplierMultinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam

(1)

(2)

(3)

(4)

(5)

All

Customermakes
Customized
Product

CustomermakesStandard
Product

Suppliermakes
Customized
Product

SuppliermakesStandard
Product‐0.039[0.067]0.104[0.069]‐0.179**[0.059]0.001[0.002]0.000**[0.000]‐0.213**[0.059]‐0.068[0.062]‐0.249**[0.070]587‐0.024[0.085]0.059[0.083]‐0.234**[0.069]0.003[0.002]0.000**[0.000]‐0.174*[0.075]‐0.103[0.085]‐0.253**[0.087]376‐0.017[0.115]0.329**[0.105]‐0.041[0.110]‐0.004[0.003]0.000[0.000]‐0.348**[0.099]‐0.053[0.091]0.334*[0.149]211

0.024[0.097]‐0.038[0.090]‐0.162*[0.077]0.002[0.003]0.000**[0.000]‐0.230**[0.075]‐0.133[0.089]‐0.320**[0.089]325‐0.076[0.098]0.343**[0.081]‐0.077[0.103]‐0.001[0.003]0.000**[0.000]‐0.217*[0.095]‐0.093[0.083]0.217+[0.132]
262

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

Table 30: Engineer Dispatch to Customers/Suppliers and Introduction of New Good
Probit (Marginal Effects)
Dependent variables: Introduction of
New Good (Yes/No)
Dispatch Engineers to Customer
Dispatch Engineers to SupplierMultinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam

(1)

(2)

(3)

(4)

(5)

All

Customermakes
Customized
Product

CustomermakesStandard
Product

Suppliermakes
Customized
Product

SuppliermakesStandard
Product

0.122+[0.067]0.124[0.077]‐0.158**[0.056]0.001[0.002]0.000**[0.000]‐0.223**[0.059]‐0.107+[0.063]‐0.265**[0.064]587

0.153+[0.080]0.124[0.098]‐0.224**[0.065]0.003[0.002]0.000**[0.000]‐0.191*[0.076]‐0.158+[0.083]‐0.278**[0.080]376

0.054[0.133]0.104[0.132]0.020[0.103]‐0.003[0.003]0.000*[0.000]‐0.321**[0.101]‐0.047[0.091]0.303+[0.162]211

0.116[0.093]0.046[0.108]‐0.170*[0.070]0.002[0.003]0.000*[0.000]‐0.234**[0.075]‐0.153+[0.088]‐0.321**[0.082]325

0.078[0.106]
0.248*[0.100]‐0.044[0.101]‐0.001[0.003]0.000*[0.000]‐0.204*[0.095]‐0.097[0.082]0.178[0.141]
262

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

Table 31: Engineer Acceptance from Customers/Suppliers and Improved Existing Machines
Probit (Marginal Effects)
Dependent variables: Improved Existing
Machines (Yes/No)
Accept Engineers from Customer
Accept Engineers from SupplierMultinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam

(1)

(2)

(3)

(4)

(5)

All

Customermakes
Customized
Product

CustomermakesStandard
Product

Suppliermakes
Customized
Product

SuppliermakesStandard
Product

0.050[0.062]‐0.059[0.065]‐0.219**[0.061]
0.003[0.002]0.000**[0.000]‐0.053[0.067]‐0.056[0.064]‐0.293**[0.082]587

0.082[0.083]‐0.140+[0.081]‐0.277**[0.074]0.004[0.003]0.000**[0.000]‐0.114[0.094]‐0.115[0.104]‐0.351**[0.103]376‐0.023[0.100]0.173*[0.080]‐0.089[0.113]0.000[0.003]0.000*[0.000]‐0.073[0.104]‐0.030[0.080]0.048[0.159]211

0.116[0.101]‐0.242*[0.094]‐0.198*[0.085]0.006+[0.003]0.000**[0.000]0.046[0.093]‐0.031[0.109]‐0.263*[0.113]325

0.004[0.074]0.191**[0.053]‐0.146[0.106]‐0.001[0.002]0.000**[0.000]‐0.190+[0.097]‐0.126+[0.068]‐0.063[0.136]
262

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

Table 32: Engineer Disptach to Customers/Suppliers and Improved Existing Machines
Probit (Marginal Effects)
Dependent variables: Improved Existing
Machines (Yes/No)
Dispatch Engineers to Customer
Dispatch Engineers to SupplierMultinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam

(1)

(2)

(3)

(4)

(5)

All

Customermakes
Customized
Product

CustomermakesStandard
Product

Suppliermakes
Customized
Product

SuppliermakesStandard
Product

0.118+[0.060]0.115+[0.065]‐0.237**[0.058]0.002[0.002]0.000**[0.000]‐0.062[0.067]‐0.089[0.064]‐0.298**[0.077]587

0.139+[0.074]0.106[0.087]‐0.316**[0.068]0.003[0.003]0.000**[0.000]‐0.118[0.095]‐0.152[0.104]‐0.348**[0.096]376

0.020[0.121]
0.136[0.099]‐0.061[0.110]0.001[0.003]0.000*[0.000]‐0.060[0.101]‐0.041[0.081]
0.004[0.180]211

0.173+[0.089]0.048[0.112]‐0.278**[0.074]0.005[0.003]0.000**[0.000]0.054[0.092]‐0.036[0.107]‐0.227*[0.101]325

0.027[0.076]0.157**[0.060]‐0.114[0.103]‐0.001[0.002]0.000**[0.000]‐0.183+[0.095]‐0.125+[0.069]‐0.086[0.152]
262

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

Table 33: Engineer Acceptance from Customers/Suppliers and Adopted ISO
Probit (Marginal Effects)
Dependent variables: Adopted ISO(Yes/No)
Accept Engineers from Customer
Accept Engineers from SupplierMultinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam

(1)

(2)

(3)

(4)

(5)

All

Customermakes
Customized
Product

CustomermakesStandard
Product

Suppliermakes
Customized
Product

SuppliermakesStandard
Product

0.069[0.065]0.250**[0.060]0.240**[0.058]‐0.001[0.002]0.000**[0.000]‐0.361**[0.061]‐0.331**[0.062]‐0.270**[0.078]587

0.057[0.084]0.249**[0.073]0.247**[0.071]‐0.002[0.003]0.000**[0.000]‐0.413**[0.078]‐0.476**[0.079]‐0.361**[0.097]376

0.131[0.112]0.261*[0.111]
0.242*[0.111]‐0.002[0.003]0.001**[0.000]‐0.344**[0.103]‐0.199*[0.094]0.002[0.230]211

0.023[0.092]0.279**[0.077]0.242**[0.079]‐0.002[0.003]0.000**[0.000]‐0.355**[0.090]‐0.408**[0.098]‐0.279*[0.109]325

0.138[0.095]0.196+[0.101]0.269**[0.094]‐0.002[0.003]0.000**[0.000]‐0.364**[0.079]‐0.297**[0.081]‐0.208[0.133]262

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

Table 34: Engineer Dispatch to Customers/Suppliers and Adopted ISO
Probit (Marginal Effects)
Dependent variables: Adopted ISO(Yes/No)
Dispatch Engineers to Customer
Dispatch Engineers to SupplierMultinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam

(1)

(2)

(3)

(4)

(5)

All

Customermakes
Customized
Product

CustomermakesStandard
Product

Suppliermakes
Customized
Product

SuppliermakesStandard
Product

0.193**[0.067]0.087[0.082]0.323**[0.053]‐0.002[0.002]0.000**[0.000]‐0.362**[0.060]‐0.350**[0.061]‐0.213**[0.076]587

0.190*[0.079]0.025[0.101]
0.342**[0.062]‐0.003[0.002]0.000**[0.000]‐0.422**[0.077]‐0.490**[0.077]‐0.315**[0.095]3760.226+[0.124]0.178[0.136]0.289**[0.107]‐0.002[0.004]0.001**[0.000]‐0.324**[0.103]‐0.224*[0.095]‐0.055[0.254]211

0.198*[0.082]0.005[0.110]0.353**[0.067]‐0.002[0.003]0.000**[0.000]‐0.367**[0.088]‐0.446**[0.095]‐0.246*[0.106]325

0.197+[0.109]0.207+[0.116]
0.291**[0.093]‐0.001[0.003]0.000**[0.000]‐0.356**[0.080]‐0.310**[0.080]‐0.254+[0.137]262

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

Table 35: Engineer Acceptance from Customers/Suppliers and Adjust Changes in the Market
Probit (Marginal Effects)
Dependent variables: Introduced
Internal Acitivities to Adjust Changes in
the Market (Yes/No)
Accept Engineers from Customer
Accept Engineers from SupplierMultinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam

(1)

(2)

(3)

(4)

(5)

All

Customermakes
Customized
Product

CustomermakesStandard
Product

Suppliermakes
Customized
Product

SuppliermakesStandard
Product

0.061[0.066]
0.332**[0.053]0.140*[0.062]‐0.001[0.002]0.000**[0.000]‐0.612**[0.051]‐0.370**[0.066]‐0.407**[0.081]587

0.102[0.080]0.336**[0.065]0.103[0.078]‐0.002[0.003]0.000**[0.000]‐0.584**[0.073]‐0.386**[0.098]‐0.457**[0.100]376‐0.051[0.112]0.368**[0.084]0.201+[0.114]0.003[0.003]0.000*[0.000]‐0.695**[0.061]‐0.379**[0.090]0.042[0.249]211

0.138[0.091]0.308**[0.077]0.153+[0.082]0.000[0.003]0.000**[0.000]‐0.553**[0.083]‐0.397**[0.109]‐0.400**[0.111]325‐0.025[0.094]0.367**[0.065]0.147[0.102]‐0.002[0.003]0.000[0.000]‐0.667**[0.056]‐0.374**[0.080]‐0.346*[0.135]262

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

Table 36: Engineer Dispatch to Customers/Suppliers and Adjust Changes in the Market
Probit (Marginal Effects)
Dependent variables: Introduced
Internal Acitivities to Adjust Changes in
the Market (Yes/No)
Dispatch Engineers to Customer
Dispatch Engineers to SupplierMultinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam

(1)

(2)

(3)

(4)

(5)

All

Customermakes
Customized
Product

CustomermakesStandard
Product

Suppliermakes
Customized
Product

SuppliermakesStandard
Product

0.218**[0.059]0.150*[0.073]0.255**[0.053]‐0.001[0.002]0.000**[0.000]‐0.613**[0.050]‐0.399**[0.066]‐0.343**[0.083]587

0.228**[0.067]0.096[0.093]0.256**[0.063]‐0.003[0.003]0.000**[0.000]‐0.595**[0.071]‐0.406**[0.098]‐0.382**[0.103]376

0.113[0.125]0.282**[0.104]0.252*[0.105]0.004[0.003]0.000*[0.000]‐0.681**[0.062]‐0.408**[0.089]‐0.107[0.283]211

0.215**[0.079]0.117[0.103]0.305**[0.065]‐0.001[0.003]0.000**[0.000]‐0.560**[0.081]‐0.449**[0.106]‐0.312**[0.113]325

0.236**[0.089]0.198+[0.103]0.175+[0.099]‐0.002[0.003]0.000[0.000]‐0.658**[0.056]‐0.385**[0.080]‐0.423**[0.129]262

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

Table 37: JIT with Customers/Suppliers and Adopted ISO
Probit (Marginal Effects)
Dependent variables: Adopted ISO(Yes/No)JIT with Customer
JIT with SupplierMultinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam

(1)

(2)

(3)

(4)

(5)

All

Customermakes
Customized
Product

CustomermakesStandard
Product

Suppliermakes
Customized
Product

SuppliermakesStandard
Product

0.122+[0.068]‐0.041[0.071]0.310**[0.053]‐0.002[0.002]0.000**[0.000]‐0.375**[0.063]‐0.322**[0.063]‐0.149+[0.079]587

0.106[0.095]0.027[0.092]0.331**[0.063]‐0.002[0.002]0.000**[0.000]‐0.464**[0.077]‐0.483**[0.079]‐0.265**[0.097]376

0.245*[0.100]‐0.204+[0.113]0.252*[0.104]‐0.002[0.003]0.001**[0.000]‐0.301*[0.118]‐0.153[0.100]0.174[0.202]211

0.225*[0.098]‐0.015[0.100]0.350**[0.068]‐0.002[0.003]0.000**[0.000]‐0.466**[0.092]‐0.493**[0.092]‐0.196+[0.108]325

0.071[0.092]‐0.054[0.099]0.278**[0.089]‐0.001[0.003]0.000**[0.000]‐0.344**[0.084]‐0.241**[0.082]‐0.116[0.152]
262

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.

Table 38: JIT with Customers/Suppliers and Adjust Changes in the Market
Probit (Marginal Effects)
Dependent variables: Introduced
Internal Acitivities to Adjust Changes in
the Market (Yes/No)
JIT with Customer
JIT with SupplierMultinational Enterprises
Age
Full‐time Employees
Indonesia
Philippines
Vietnam

(1)

(2)

(3)

(4)

(5)

All

Customermakes
Customized
Product

CustomermakesStandard
Product

Suppliermakes
Customized
Product

SuppliermakesStandard
Product

0.117+[0.066]‐0.042[0.067]0.240**[0.052]‐0.001[0.002]0.000**[0.000]‐0.606**[0.053]‐0.361**[0.067]‐0.269**[0.085]587

0.085[0.090]0.030[0.087]0.235**[0.064]‐0.003[0.003]0.000**[0.000]‐0.608**[0.072]‐0.378**[0.099]‐0.314**[0.103]376

0.206*[0.102]‐0.178[0.111]0.238*[0.100]0.003[0.003]0.000**[0.000]‐0.661**[0.070]‐0.347**[0.095]0.147[0.202]211

0.147[0.099]0.014[0.098]0.295**[0.065]0.000[0.003]0.000**[0.000]‐0.603**[0.085]‐0.457**[0.106]‐0.257*[0.113]325

0.114[0.090]‐0.089[0.095]0.180*[0.091]‐0.002[0.003]0.000*[0.000]‐0.637**[0.060]‐0.325**[0.083]‐0.276+[0.155]262

Observations
Notes:
Robust standard errors in brackets.
+ significant at 10%; * significant at 5%; ** significant at 1%
Reference country is Thailand.