Agglomeration Effects

in the Diffusion of Advanced Manufacturing Technologies

(Job Market Paper)

Joung Yeo Angela No

University of Toronto*

November 20, 2002

Abstract

Knowledge spillovers associated with the diffusion of new technologies have long been viewed as an important driver of economic growth. However, this claim has largely resisted econometric scrutiny because patterns of technology adoption are generally not observable. This paper overcomes this problem by exploiting a proprietary panel data set that reports the adoption of 22 advanced manufacturing technologies by 1,902 Canadian plants. The paper starts with documenting the fact that the adoption of these technologies is more highly concentrated geographically (i.e., agglomerated) than other forms of economic activity. Motivated by this fact, I test for knowledge spillovers by investigating how a plant’s probability of adopting a new technology depends on the presence of prior adopters. The results indicate that technology adoption is facilitated by the presence of prior adopters with four characteristics. First, they are adopters of the same technology (as opposed advanced technologies more generally). Second, they reside in the same region. Third, they are similar to the potential adopter in that they purchase a similar set of intermediate goods and services, and finally, they are dissimilar to the potential adopter in that they do not operate in the same product market (i.e., the same 4-digit SIC code). These results are robust to controlling for the effects of regional labour pooling, regional linkages to suppliers and buyers, as well as industry, region, and technology fixed effects. These findings strongly suggest that agglomeration in the adoption of new technologies is driven by knowledge spillovers. Key words: Technology adoption, Agglomeration, Knowledge spillovers, Micro-data JEL Classification: O3, R3

* I am grateful to Dan Trefler, Diego Puga and Nadia Soboleva for helpful comments. I would like to thank Adonis Yatchew and seminar participants at the University of Toronto. I also thank John Baldwin and Statistics Canada for providing the data, and Industry Canada for financial support. Any errors and omissions are my own. Author’s contact information: email: jyano@chass.utoronto.ca, phone: (416) 946-3584, fax: (416) 978-5519, homepage: http://www.chass.utoronto.ca/~jyano

2

1. Introduction

Knowledge spillovers associated with the diffusion of new technologies have long

been viewed as important drivers of modern economic growth (e.g., Rosenberg 1982;

Landes 1990; Romer 1990). Since knowledge spillovers both facilitate and are facilitated

by regional economic agglomerations (e.g., Marshall 1920; Krugman 1991a, 1991b;

Porter 1998), it is natural to wonder whether knowledge spillovers also lead to

agglomeration effects in the adoption of new technologies. Specifically, is the adoption of

new technologies more highly agglomerated than other forms of economic activity and, if

so, can this be explained by knowledge spillovers?

Figure 1 provides a simple answer to the first part of this question. It shows that

geographic concentration is higher for plants using advanced manufacturing technologies

than is for all plants in an industry.1 This fact is new to the literature and raises an

obvious question: What explains the high degree of geographic agglomeration among

adopters of advanced manufacturing technologies? One potential explanation appeals to

knowledge spillovers across technology adopters. Unfortunately, this is not an easy

hypothesis to explore. Firstly, we rarely observe knowledge spillovers directly.

Secondly, there are alternative potential explanations such as labour pooling, forward and

backward linkages between suppliers and purchasers (e.g., Krugman 1991a; Fujita,

Krugman and Venables 1999; Porter 1990), and local amenities which have nothing to do

with spillovers such as transportation infrastructure and even weather.2

In this paper I will investigate each of these explanations. While I find some

support for almost all of them, my most interesting result is that a plant is more likely to

adopt a specific technology (e.g., a flexible manufacturing cell) if that specific technology

has already been adopted by other plants in the same region. This effect cannot be traced

back to a spurious correlation operating at the industry level, the region level, or even the

industry-region level. For one, the result holds even after controlling for labour pooling,

backward linkages to suppliers, forward linkages with buyers, and even industry, region,

and technology fixed effects. For another, the result is strongest when prior adopters are

1 I expand on this in section 2. 2 See Hanson (2000) for discussion of the issues associated with identifying agglomeration effects.

3

in a different industry than the new adopter. In short, my finding strongly indicates that

there is communication across plants in the same region.

Such communication implies that there are localized, learning-based knowledge

spillovers as in Case (1992), Jaffe, Trajtenberg, and Henderson (1993), Powell and

Brantley (1992), and von Hippel (1988). More specifically, in the decision to adopt a new

technology, potential adopters often face uncertainties about implementation costs. Since

certain types of information about the implementation of a new technology are tacit,

learning about tacit information will happen through direct observation of early adopters,

demonstration effects, word-of-mouth, and other informal mechanisms. Hence, the rapid

and complete diffusion of a new technology will be facilitated by proximity to prior

adopters.3

The analysis in this paper is based upon a proprietary panel data set on the

adoption of 22 advanced manufacturing technologies by 1,902 Canadian plants. I use

this data to address the following questions. First, and most importantly, are there

regional knowledge spillovers linking prior adopters to potential adopters? And, does the

extent of spillovers depend on whether the prior adopters are ‘similar’ to the potential

adopters where similar is measured in terms of the pattern of input usage? Second, what

is the sectoral scope of agglomeration? That is, are agglomeration effects facilitated by

regional specialization in just a few industries as in Marshall (1920) or are they facilitated

by regional diversification as in Jacobs (1969)? Third, what is the technological scope of

knowledge spillovers? If prior adopters primarily adopt technology 2, does this impact a

potential adopter of any technology or only of technology 2?

Fourth, how does a region’s industrial structure (i.e., its mix of large and small

plants) contribute to the speed of learning in the region? Saxenian (1994) claims that

Silicon Valley, with its many small firms, provided a more flexible and open

environment than did Massachusetts’s Route 128 which only had few dominant players.

To test this, I examine how different organinzational forms -- small vs. large, and single-

plant vs. multi-plant firms-- differentially affect the diffusion of knowledge. Fifth, are

there any plant size- or plant status-related difference in the effects of regional

3 The geographic concentration of technology adopters may also be due to other agglomeration effects or ‘common environments’ that plants face. See section 3.

4

agglomeration on the adoption decision? Do small or single-plant firms benefit more

from regional agglomeration?

While the effect of agglomeration on technology adoption is conjectured to be

important in most discussions about agglomeration, there is very little related work.

There are three relevant strands of literature. The first strand of work is by Jaffe,

Trajtenberg, and Henderson (1993) who study the type of knowledge spillover that is

captured by patent citations. The use of patent citations to study knowledge spillovers

has three drawbacks. First, firms often do not patent (Levin et al. 1987; Rosenberg

1982). Second, not all patents contain valuable information, so they are not the best

measure of knowledge spillovers. Third, patents only describe a certain innovation side of

knowledge, and not all innovation activity lend themselves to patenting. In contrast, the

22 advanced manufacturing technologies that are my object of study are often general

purpose technologies which are valuable and universally accessible, therefore, they are

likely candidates for a study of knowledge spillovers.

The second strand in the literature is not about knowledge spillovers per se, but

about the importance of different sources of agglomeration (Rosenthal and Strange 2001;

Dumais, Ellison and Glaeser 2002; and Holmes 2002). In these papers, each source of

agglomeration is examined separately and, to the extent that knowledge spillovers are

considered, they are either imperfectly measured or treated as residuals.

The third related strand of literature examines the impact of agglomeration on

technology adoption. This is the literature most closely related to this paper. However, it

consists of only two studies, namely, Harrison, Kelley and Gant (1996) and Kelley and

Helper (1997). They examine the effects of location attributes on the adoption of

Computer Numerically Controlled (CNC) machines. Unfortunately, these studies are

more of case studies based on the adoption of one specific technology and examine only

a small number of plants in a small subset of industries. Specifically, they study only 342

plants in 21 industries. Further, they do not directly investigate knowledge spillovers or

isolate such spillovers from the effects of other location attributes. In short, they simply

examine the attributes of location that better facilitate technology adoption.

Consequently, identification of the effects of knowledge spillovers are left unanswered.

5

This paper is the first to empirically identify and separately estimate the impact of

knowledge spillovers and other sources of agglomeration on technology adoption.

The paper’s main finding is that technology adoption is facilitated by the presence

of prior technology adopters with 4 characteristics: (1) they are adopters of the same

technology (as opposed to adopters of advanced technology more generally); (2) they

reside in the same region; (3) they are similar to the potential adopter in that they

purchase a similar set of intermediate goods and services; and (4) they are dissimilar to

the potential adopter in that they do not operate in the same product market (i.e., the same

4-digit SIC code). This result holds even after controlling for the effects of regional

labour pooling, regional linkages to suppliers and buyers, as well as industry, region, and

technology fixed effects. These findings strongly suggest that agglomeration in the

adoption of new technologies is driven by knowledge spillovers.

A second important finding of this paper is that the regional industrial structure

affects the learning of technical knowledge in a region. Environments that are

characterized by smaller plants as opposed to larger plants, and single-plant firms as

opposed to multi-plant firms better facilitate the creation and the diffusion of knowledge.

This paper is the first to document that what is observed in Silicon Valley and Route 128

(Saxenian 1996) is not unique to those regions, but a broader experience (observed in

more general data). Finally, this paper finds that small and single-plant firms not only

better facilitate the diffusion of technical knowledge in a region, but also benefit more

from agglomeration economies.

The remainder of the paper is organized as follows. Section 2 documents the

higher concentration of technology adopting plants than all manufacturing plants.

Section 3 discusses the different sources of agglomeration externalities, and competing

theories of agglomeration economies. Section 4 describes the data sources. Section 5

explains the model specification and construction of variables. Section 6 presents the

results of agglomeration effects on technology adoption. Section 7 examines the

organization characteristics of plants that are more likely to adopt technologies. Section

8 concludes.

6

2. The Geographic Concentration of Advanced Technology Adopters

In this section, I examine the degree of geographic concentration of all plants in

manufacturing industries in Canada and those plants using advanced manufacturing

technologies. As a measure of the degree of agglomeration, I employ Ellison-Glaeser

index of concentration. The Ellison-Glaeser index is defined as:

( )2 2 2

1 1 1

2

1

( ) 1

1 1

M M N

i i i ji i j

N

jj

s x x zG H

H zγ = = =

=

− − −−≡ ≡− −

∑ ∑ ∑∑

.

( )2

i iiG s x≡ −∑ is the spatial Gini coefficient, where ix is location i’s share of

employment in a particular industry. 2

1j

j

H z=

= ∑ is the Herfindahl index of the j plants in

the industry, with zj representing the employment share of the jth plant. Let s1, s2 ,…, sM

be the shares of an industry’s employment in each of M geographic areas, and x1, x2,….,

xM be the shares of total employment in each of M areas.

This index corrects for the random concentration arising from industrial structure

that the spatial Gini does not control for.4 It measures the excess concentration beyond

what would be expected to occur randomly. It takes on a value of zero when an industry

is as concentrated as one would expect to result from a random location process, and

takes a positive value when an industry is concentrated more than what one would expect

to occur randomly.

Figure 1 exhibits the Ellison-Glaeser index of concentration for all manufacturing

plants and advanced technology adopting plants as of 1993 for 2-digit manufacturing

industries at the Economic Region level. The grey bars show the concentration of all

plants in each industry. All 2-digit manufacturing industries have a positive value of the

index, indicating excess geographic concentration. This is a well-documented fact in the

4 The ( )21 ii

x− ∑ term is included so that the index � has the property that E(� )=0 when neither

agglomerative spillovers nor natural advantage is present (see Ellison and Glaeser 1997 for details). A positive value of the spatial Gini can arise when an industry is made up of a small number of large plants and there is no agglomerative force leading to concentration. For a perfectly competitive industry with a

7

literature (see e.g. Krugman 1991b; Ellison and Glaeser 1997). What is more interesting,

however, is the degree of concentration among adopters of advanced technologies, which

is shown by the black bars. It indicates that technology adopters not only exhibit excess

concentration in every industry, but for most industries, the adopters of advanced

technologies are substantially more concentrated than overall plants. This fact has never

been documented in the literature.5 While a positive value of the index does not

necessarily indicate the presence of agglomeration externalities, it is what one would

expect if the regional agglomeration externalities are indeed present. How can this higher

degree of agglomeration among technology adopters be explained? Is it because there

are localized knowledge spillovers across technology adopting plants? Or alternatively,

is it because other agglomeration economies or ‘common environment’ which attract

plants to certain regions also facilitate technology adoption?

3. The Effects of Agglomeration Externalities

In this section, I discuss various aspects of the regional agglomeration that may

affect technology adoption in a region. In section 3.1, the potential explanations for the

observed agglomeration of technology adopters are discussed. In section 3.2, I explain

the competing theories on the economic environment settings that facilitate

agglomeration economies. In section 3.3, I discuss how the organization of economic

activity within a region may affect technology adoption.

3.1 The Sources of Agglomeration Economies

The three main sources of regional agglomeration economies come from

knowledge spillovers, specialized skilled labour, and input sharing (Marshall 1920). I

discuss how each of these sources of agglomeration may facilitate technology adoption in

a region.

large umber of small plants, the spatial Gini measures concentration without any contamination associated with industrial organization. 5 Audretsch and Feldman (1996) show that innovative activity is substantially more concentrated than overall production and that industries that emphasize R&D tend to be more spatially concentrated. A related result is obtained by Jaffe et al (1993), who show that patent citations are highly spatially concentrated.

8

i. Knowledge Spillovers

Probably the most important benefit of clustering is knowledge spillovers.6 There

are two types of knowledge. One is explicit or codified knowledge that can be effectively

expressed using symbolic forms of representation. The other is tacit knowledge that

defies such representation (Reber 1995; Barbiero (n.d.)). Tacit knowledge has come to

be recognized as a central component of the learning economy, and a key to innovation

and value creation. The more easily codifiable (tradable) knowledge can be accessed, the

more crucial tacit knowledge becomes for sustaining or enhancing the competitive

position of the firm (Maskell and Malmberg 1999). Consequently, tacit knowledge is

considered a prime determinant of the geography of innovative activity, since its central

role in the process of learning-through-interacting tends to reinforce the local over the

global (Gertler 2002).

In implementing new technologies, plants face many kinds of uncertainties

regarding costs and benefit analysis, adaptation difficulties, and employee training.

Information on these would reduce some of these uncertainties associated with new

technologies and enable plants to better assess the risks and expectations. Since certain

information regarding technology implementations are tacit (e.g., detailed engineering

characteristics or particular organizational changes in order to fully exploit a technology

capabilities), learning about this type of knowledge will depend on direct observation of

early adopters, demonstration, word-of-mouth, or on other informal mechanisms.

Therefore, the presence of plants that have already adopted new technologies facilitates

knowledge spillovers to other plants in the region. Further, the feedback loop of

knowledge spillovers from technology adopters will facilitate, and are facilitated by, the

regional agglomeration (Case 1992; Jaffe, Trajtenberg, and Henderson 1993; Powell and

Brantley 1992; von Hippel 1988).

ii. Specialized Skilled Labour

Firms may also want to locate in clusters to take advantage of specialized skilled

labour in the region. When a cluster of specific and related industries is developed, the

6 A regional cluster is defined as a group of firms in the same industry, or in closely related industries, that are in close geographic proximity to one another.

9

region draws the specialized human capital into the region. With many plants in the

region, specialized labour faces lower risk in finding other jobs and similarly plants find

it easier to hire workers with the necessary skills. Furthermore, mobility of the

specialized skilled labour within a sector-by-region acts as a carrier of knowledge, and

speeds the diffusion of knowledge. Hence, a region with an abundance of specialized

skilled labour is more likely to facilitate technology adoption.

iii. Upstream and Downstream Linkages

Nearby suppliers and firms in related businesses foster the rapid flow of

information, scientific collaboration, and joint development efforts, and can more readily

influence their suppliers’ technical efforts, accelerating the pace of innovation.7

Furthermore, highly applied technology and specialized skills are difficult to codify,

accumulate, and transfer. Kelley (1993) finds that firms whose customers provide them

with technical information are more likely to adopt programmable automation. Also,

having specialized inputs readily available in a region increases the flexibility and the

competition at the same time as it reduces the risk born by the suppliers and the buyers.8

The advantages of clusters also arise from the characteristics of the local market.

Local customers offer high visibility, ease of communication, and the opportunity for

joint working relationships. Presence of sophisticated and demanding local customers, or

customers with unusually intense needs for the specialized varieties also in demand

elsewhere, or demanding buyers pressure companies to meet high standards, provide a

window into evolving customer needs. Hence, it motivates companies to innovate and

move to more advanced segments. The presence of local suppliers, coupled with local

demand conditions, promotes adoption of technologies in clusters of interconnected

industries (Porter 1990).

7 Both theoretical and empirical work emphasize input suppliers and output demander relationships as an important channel of externalities. See Porter (1999) and Helper (1995). 8 Dore (1983, 1986) argues that the greater security and trust involved in arm’s-length customer supplier firm relationships lead to more investment and a more rapid flow of information. Lane (1991), Piore and Sabel (1984), and Carlssona and Jacobsson (1994) show that vertical integration promotes the adoption of technologies.

10

3.2 The Scope of Agglomeration Economies: Specialization versus Diversification

Given the underlying microfoundations of agglomeration economies discussed in

the previous section, in what kind of economic setting, do these agglomeration economies

best facilitate technology adoption? Is technology adoption more likely in places where

there are clusters of similar activities, or where there is a diversity of different activities?

The Marshall-Arrow-Romer (MAR) assumes that spillovers are limited to occur within

the relevant industry, and the transmission of knowledge spillovers across industries is

assumed to be non-existent or at least trivial. It argues that localization (or

specialization) would facilitate higher knowledge spillovers since firms in the same

industry would use common technologies or face similar problems and hence pay more

attention to one another.9 Each firm learns from other firms through conversation,

reverse engineering, and rapid inter-firm movement of highly skilled labour, and these

facilitate the fast dissemination of ideas among neighboring firms. A good example

would be the auto industry in Detroit.10

On the other hand, Jacobs (1969) focuses on diversification as a crucial source of

externalities.11 Jacobs argues that diversification derives externalities from the

opportunities to interact with others in the same or different industries, making it easier to

copy a practice being used by industry peers and to modify a practice from an outside

industry. Also, with a labour force with a broader mix of skills, including new skills

conducive to working with emerging production technologies, diversity may be the

crucial force for technical information flows (Harrison, Kelley and Gant 1996). A good

example supporting this view is New York.12

9 Localization economies are externalities associated with the presence, in a locality, of a mass of other producers in the same industry or sector 10 There are number of studies that find evidence of localization economies. Henderson (1999) finds that localization (MAR) scale externalities arise from the number of local own industry plants, or points of information spillovers. Also, Rosenthal and Strange (1999) find that localization externalities have much greater impact than urbanization externalities on the agglomeration of economic activities. 11 Diversification economies are externalities associated with the presence of firms from various industries, extensive infrastructure, or a large pool of labour in a given location. 12 Rosenberg (1962) supports this view in a study of the spread of machine tools across industries and describes how an idea is transmitted from one industry to another. Also, Feldman and Audretsch (1999) find that diversity across complementary economic activities sharing a common science base is more conducive to innovation than is specialization. Harrison, Kelley and Gant (1996) find that while both types of externalities are important in the adoption of CNC machines, diversification is more important.

11

3.3 The Role of Industrial Structure in Learning in a Region

The advantages of clusters come from not only by the size and density of cluster,

but also by how the economic activities within a cluster are organized. Saxenian (1994)

argues that industrial systems - which includes three dimensions of local institutions and

culture, industrial structure, and corporate organization - play an important role in

innovativeness of a region. In Saxenian’s (1994) comparative study of California’s

Silicon Valley and Massachusetts’ Route 128 as clusters of electronics and high-

technology, she characterizes Silicon Valley as being flexible and entrepreneurial with

many small venture capitals and Boston as being relatively rigid and hierarchical with

few dominant players. She claims that the ascendance of Silicon Valley can be attributed

to its open and flexible environment. Similarly, Jacobs (1969) and Chinitz (1961) also

suggest that urban efficiencies depend not just on numbers (i.e., city or industry size) but

also on the nature of urban interactions.13

4. The Data

Data for the analysis come from numerous sources. The main source is the 1993

Survey of Innovation and Advanced Technology. It is a unique, confidential and

proprietary data set that surveys approximately 2,500 plants covering an entire

manufacturing sector across Canada. The survey collects information on various aspects

of innovation and adoption of advanced manufacturing technologies. In particular, the

survey reports information on each plant’s adoption of twenty-two advanced

manufacturing technologies. These twenty-two advanced manufacturing technologies are

categorized into 6 different technology groups. These technologies are ‘general-purpose

technologies’ in that they are not specific to any particular industry, but can be used in

13 However, the effects of the industrial system on technology adoption have never been tested empirically. Rosenthal and Strange (2001) find a related result, that the industrial structure affects the agglomeration of economic activities.

12

the production process of any industries.14 These technologies are listed in Table 3 along

with the incidence of use as of 1993.

A very important aspect of this survey is that it reports information on years of

use for each technology by each plant. Using this information on the time dimension of

technology adoption, I construct a panel data set. I construct a panel consists of three

periods from 1984-1987, 1987-1989, and 1990-1992. Use of time intervals enables me to

get around the recollection problem that may arise from the retrospective nature of panel

data.15 In addition, it allows me to have enough variations in regional economies over

different periods.16

Additional information on these plants are obtained from the Annual Survey of

Manufactures (ASM). The ASM is a longitudinal database of Canadian manufacturing

plants which collects information for approximately 35,000 to 40,000 manufacturing

plants each year.17 Among the 2,500 plants surveyed in Survey of Innovation and

Advanced Technology, 1,902 plants are also surveyed in the ASM. Detailed information

on the plants such as geographical location, employment, output, country of ownership,

plant age, and multi-plant status are taken from the ASM for these 1,902 plants.

To measure the regional economic environment, both the ASM and the Census of

Population datasets are used. All variables characterizing the local manufacturing

activities are for the Census Division in which the plant is located, and based on the

information from the ASM. Variables characterizing regional demographic information

are obtained from the Census of Population.18 Information drawn from the Census of

Population include variables such as occupation, education level, and the degree of study.

Other supplementary data come from the National Input Output Tables for years

1983-1992. I use the National Input Output Tables at the most detailed level available,

which consists of 145 3- and 4-digit manufacturing SIC.19 The tables record the value of

14 The concept of General Purpose Technology (GPT) used here is not as broad as the one used in Bresnahan and Trajtenberg (1995). 15 Plants may round the number of years a given technology has been in use. For example, plants may report 5 years instead of 4 or 6 years, and 10 years instead of 9 or 11 years. There are peaks at 5 and 10 years, and lower numbers of new technology adoptions are reported for 4, 6, 9 and 11 years. 16 There are small changes in the economic environment over a period of year. 17 This consists of most manufacturing plants in Canada. 18 The Census of Population is surveyed every 5 years, and for each Census year, there are about 5 million observations that contain detailed information on each individual. 19 The level of National Input Output tables used are ‘w-level’.

13

intermediate inputs and outputs each industry buys and sells to other industries. Based on

this information, forward and backward linkages are calculated.

The units of geography employed in this paper are Economic Regions and Census

Divisions. An Economic Region is a region, within a province or territory, comprised of

one or more of Census Divisions.20 While boundaries tend to stay constant over the years

for most Census Divisions, it is not true for some, especially in the late 1980s.21 In

order to consistently measure the effects of regional economies, I use a constant Census

Division code of 1976.22

5. Estimation and Specification Issues

5.1. Model Specification

I examine the effects of the presence of prior adopters on plant p’s adoption of

WHFKQRORJ\� 2� DW� WLPH� t, after controlling for various aspects of the regional economies.

The dependent variable is

1 if plant in industry in region adopts technology at time

0 otherwisep irt

p i r tADOPTION τ

τ =

where p indexes plant, i indexes industry, r indexes region, 2 indexes technology and t

indexes time. There are three intervals of time periods employed in this study. The first

period is 1985-1987, the second period is 1988-1990 and the third period is 1990-1992.

In each period, a plant makes a choice to adopt or not to adopt technology 2. For each

plant-technology pair, if plant p adopts technology 2 in period 1, the adoption decision is

no longer applicable for plant p for the two subsequent periods. In this case, the

20 There are ten provinces and two territories in Canada, and their boundaries remain constant over time. Each province and territory is divided into number of economic regions. There are total of 68 Economic Regions and 290 Census Divisions across provinces and territories in 1991. 21 There were major re-constructions in census division boundaries in the provinces of Quebec and British Columbia in the late 1980’s. 22 Constant Census Division code based on 1976 has been assigned to all plants in all years using Map Info, by matching postal codes.

14

observation on plant p’s adoption of technology 2 is omitted from the sample for the those

periods.23

The decision to adopt technology 2 by plant p depends on the following factors:

plant characteristics, exogenous time-, technology-, industry- and location- specific

characteristics, the regional agglomeration economies, and knowledge spillovers from

prior adopters. I will explain each of these factors in turn in more detail.

First, plant characteristics are important determinants in technology adoption

decisions. Investigating the effects of the regional agglomeration without conditioning

plant specific characteristics bias the results of the location attributes. While plant

characteristics are included in all regressions, I will discuss the theories regarding

organizational characteristics and their estimates in section 7. In this section, I will

discuss only the external factors in order to focus on the effects of agglomeration on

technology adoption.

The second set of variables are time, technology, industry and location specific

characteristics. Technology adoption may be more likely in the later periods due to

general reductions in the price of technology, better information flow, and/or recognition

of the importance of advanced technologies. To account for time effects, I include two

dummy variables, one for period 2 (1987-1989), and the other for period 3 (1990-1992).

There are technology specific effects. These 22 advanced manufacturing

technologies varies greatly in their usefulness and value to plants, in their price, and in

the difficulties of implementation. For example, computer aided design and engineering

(CAD/CAE), which is the most widely used technology among the 22, is more likely to

have the greater impact on the productivity of plant than automated storage and retrieval

system (AS/RS). Consequently, certain technologies are more likely to be adopted than

others. To control for technology specific effects, dummy variables for each technology

are employed.

The take-up rate of technology varies significantly across industries. Industries

are substantially different in terms of the need for extensive automation as well as the

benefits arising from implementing such advanced manufacturing technologies. For

23 Also, those plants that are born after 1987 or 1990 would not be present in the first and/or the second periods. The sample is therefore an unbalanced panel.

15

example, the need for advanced technologies in the aircraft and aircraft parts industry can

be quite different from that in the sign and display industry. Industry specific

characteristics are captured with dummy variables for each 3-digit SIC industry.

The last set of exogenous characteristics I control for are location specific effect.

As suggested by geographic concentration of economic activities, certain regions attract

more economic activity, and for the same reasons, adoption of technology may be more

likely in these regions. An example of this kind of location fixed effect is the presence of

universities in a region. To control for location specific characteristics, fixed effects for

the Economic Region are included. All of these variables control for all five dimensions

employed in this study: plant, technology, industry, location and time.

Controlling for all these dimensions, I can separately estimate the agglomeration

effects. The third set of variables captures the regional agglomeration effects. I include

three indicators for different sources of agglomeration economies: specialized skilled

labour, forward and backward linkages, and the scale of economic activities in a region.

The calculation of these variables are discussed in the next section.

The last set of variables I include are the number of plants that have already

adopted technolog\�2�LQ�WKH�UHJLRQ�SULRU�WR�WLPH�t. This is to capture the information and

knowledge from the prior adopters that is difficult to obtain from a distance.

The estimating equation for plant p’s adoption of technology 2 at time t is

0 1 , 1 2 , 1 3 , 1

4 , 1 5 , 1 6 , 1

7 , 1 8 , 1 9

10

Pr( ) ( _ _ _

_

_ _

_ _ )

p irt ir t ir t ir t

ir t r t ir t

ir t p t ir

i r i t p irt

ADOPTION F TECH S TECH M TECH D

EMP REGION ENGINEER INPUT

OUTPUT X Avg IND REGION

Avg IND TECH

τ τ τ τ

τ τ τ

α β β β

β β β

β β β

β δ γ ϕ λ ε

− − −

− − −

− −

= + + +

+ + +

+ + +

+ + + + + +

where F represents the logistic cumulative distribution. I estimate this using logit model

to capture the “fat tail” of the distribution (i.e., there is a larger proportion of not-adopting

any technology at time t).

5.2 Construction of Variables

In this section, I explain the construction of variables used in the model.

16

i. Measurement of Similarities Across Industries in terms of their Input Purchases

The knowledge spillovers from prior adopters of technology 2 in the same region

may differ based on the ‘relatedness’ between plants. One of the common criticism of

earlier geographic studies is in the use of highly aggregated industry units, typically at the

2-digit Standard Industrial Classification (SIC) scheme to empirically define ‘related’

industries.24 2-digit SIC are not appropriate for capturing the similarities of industries.,

for example, SIC 39 includes both Broom, Brush and Mop Industry (in SIC 399) and

Jewellery and Silverware Industry (in SIC 392).

In context of studying the effects of knowledge spillovers on technology adoption,

the ‘relatedness’ across industries can be better measured by the similarities in input

purchases. The patterns of input purchases are observed at 145 3- and 4-digit SIC

industries based on the National Input Output tables. For each industry i, its correlations

with every other industry j based on input purchases, ijρ , are calculated. For each

industry i, all other industries are categorized into three groups base on the correlations.

Industries with a correlation equal or greater than 0.50 are categorized as Similar

industries, industries with a correlation between 0.50 and 0.2 are categorized as

Moderately similar industries, and the rest of industries are categorized as Different

industries.25 For each industry, the groups of similar, moderately similar and different

industries are not symmetric, nor of equal size.26

ii. Technology Users

For each technology τ, irtTτ is natural log of number of plants in industry i in

region r that have already adopted technology 2 as of period t.

,

ln ( * )irt p p irtp i r

T w Iττ τ

∈

= ∑

24 e.g. Rosenthal and Strange 2001. 25 The benchmark for this grouping is chosen based on the distribution of correlations. The distribution of correlations exhibits an asymmetric weak tri-modal pattern. There are a small percentage of industries in the high range of correlations, another group concentrated between 0.2 and 0.5, and the remainder in the lower end of the distribution. 26 The average size of each group of industries in terms of the number of 3-digit SIC industries it contains are presented in appendix table A1, as well as the size of 2-digit SIC industry.

17

where 1 if plant in industry in region uses technology at time

0 otherwisep irt

p i r tI τ

τ

τ =

.

w is a plant weight that is provided in the survey to make the sample representative of the

population. The unit of geography used in the calculation of number of technology users

is the Economic Region. Economic Region is used in this case to maintain enough

observations in each region to preserve the representativeness.

Then, the number of plants in similar industries that have already adopted

technology 2 at time t, is calculated simply as,

_ lnirt jrtj F

TECH S Tτ τ∈

=

∑

where i and j indexes industry, F represents groups of industries that are categorized as

similar industries for each industry i. The number of plants in moderately similar

industries and in different industries that have adopted technology 2 at time t are

calculated likewise.

ii. Variables on Other Agglomeration Effects

These measures of the local economies are for the Census Division (CD) in which

the plant is located. The scale of manufacturing activity in a region, EMP_REGION is

measured by the natural log of employment in the manufacturing sector in the Census

Division the plant is located. Similar to technology adopters, EMP_REGION can be

decomposed into three groups: employment in similar industries, moderately similar

industries, and different industries.

Specialized skilled labour in a region, ENGINEER, is calculated as the proportion

of people in population with science or engineering degrees in the Census Division.27

& rtrt

rt

Scientists EngineersENGINEER

population

=

The presence of input suppliers for industry i in region r at time t is calculated as

27 Information on the major of study is obtained from Census of Population. This information has been collected since 1986. Hence, for observations for period one (1985-1987) information from the Census of Population in 1986 is used. Using labour information for 1986 for the first period would not make much difference since changes in the proportion of people with science or engineering degrees tend to be very small.

18

ln jrtirt jit

j i jt

EINPUT I

E≠

=

∑

where jitI is the value of industry i’s inputs that come from industry j at time t, jstE is

industry j’s employment in region r at time t, and jtE is total employment in industry j at

time t. Since, the National Input Output table provides the flow of input and output at the

national level, value at the regional level are calculated by using the proportion of

employment in census division divided by national employment. Similarly, the presence

of output purchasers for industry i in region r at time t is calculated as

ln jrtirt jit

j i jt

EOUTPUT O

E≠

=

∑

where jitO is the value of industry i’s outputs that go to industry j.

6. Results

This section presents and discusses the estimates of results of the logit model with

plant p’s adoption of technology τ at time t as the dependent variable. In this section, the

effects of agglomeration on technology adoption are investigated on several dimensions.

Specifically, section 6.1 presents the effects of prior adopters of technology on the

adoption decision of other plants after controlling for the effects of other agglomeration

as well as other exogenous effects. I investigate the spillover effects of prior adopters on

potential adopters allowing the effects to vary by their similarities. Section 6.2 discusses

the issues associated with identifying the “spillover effects” from other effects. Section

6.3 examines the sectoral scope of agglomeration effects. Section 6.4 tests for the

technological scope of knowledge spillovers. Section 6.5 investigates the effects of

industrial structure on the speed of learning in a region.

6.1. Effects of Technology Spillovers and Agglomeration on Technology Adoption

This section estimates the effects of nearby technology adopters and other

regional agglomeration economies on a plant’s technology adoption decision, after

19

controlling for other exogenous effects operating at various levels. In examining the

effects of local prior adopters, I allow the effects of prior adopters to vary depending on

their similarity of input purchases with potential adopters. The key variables of interest

are the measures of prior adopters of technology 2 in three different groups in the same

region: prior technology adopters in similar industries, TECH_S; prior technology

adopters in moderately similar industries, TECH_M; and prior technology adopters in

different industries, TECH_D. Other regional agglomeration externalities that may affect

a plant’s adoption decision are captured by EMP_REGION, INPUT, OUTPUT, and

ENGINEER. Other regressors included are fixed effects and control variables (which

will be discussed in section 6.2), and plant characteristics (which will be discussed in

detail in section 7).

The results are presented in table 4. The coefficient on prior adopters in similar

industries is estimated to be positive and significant. This indicates that the probability

that a plant adopts a technology increases with the number of similar plants in the same

region that have already adopted the same technology. An elasticity of 0.008 means that

doubling ‘the number of technology adopters in similar industries in a region’ increases

the probability of technology adoption in the region by 0.8%. A plant located in a region,

such as Toronto, with about 50 times more ‘adopters in similar industries’ than some

other regions, are 40% more likely to adopt a given technology holding everything else

constant.28 The coefficient on TECH_M is positive and significant, with an elasticity of

0.006 which is smaller than that of TECH_S. The coefficient on adopters in different

industries is estimated to be negative.

The significant effects of local prior technology adopters in three different groups,

after controlling for effects that operate at various levels, strongly indicate the presence of

communication across plants in the same region. This finding is strongly suggestive of

learning-based knowledge spillovers. Further, a very clear decaying pattern of spillover

effects indicates that the effects of prior adopters differ depending on their similarities

with potential adopters. The more similar are the prior and the potential adopters, the

greater are the spillover effects.

28 It is not unusual that certain regions have 50 times more “technology adopters in similar industries” than other regions. However, for confidentiality reasons, I cannot reveal the identity of specific regions.

20

Regional Employment, EMP_REGION, has a positive and significant coefficient,

with an elasticity of 0.012. In other words, a plant located in a region with manufacturing

employment of 300,000 has 12% higher probability of adopting a given technology

compared to a plant located in a region with manufacturing employment of 30,000, given

everything else equal. This suggests that the scale of regional economic activity

positively affect technology adoption in a region. The coefficient on ENGINEER is

estimated to be highly significant with an elasticity of 1.07. This implies that one

percentage point change in the share of scientists and engineers in the population, say

from 4.1% to 5.1% (or, 24% change in the share) increases the probability that a plant

adopts a given technology by 26%.29 This implies that regional specialized skilled labour

is an important determinant of technology adoption. This finding supports the claim that

having an abundance of people who have technological knowledge and know-how

increases the absorptive capacity of the region, and hence increases the likelihood of the

technology adoption in the region.30 The coefficient on the “local presence of input

suppliers in a region” is positive and significant. This means that the adoption of

technology is enhanced by the presence of input suppliers in the region.31 The elasticity

of 0.014 indicates that the magnitude of the effect of local suppliers is similar to the

effect of regional employment.

It is worth noting that the elasticity of prior adopters in similar industries is about

the two thirds of the effects of the regional employment or the input suppliers. Because

there are no previous studies on the effects of knowledge spillovers on technology

adoption, there is no benchmark to evaluate the magnitude of spillover effects from

technology adopters estimated in this paper. However, the relative magnitude of

elasticity of TECH_S as compared to EMP_REGION and INPUT suggests that the

‘spillover effects’ from technology adopters are fairly big.

29 The average share of scientists and engineers in a region is 4.1%, and the variation in a region is fairly small. Since an elasticity in the logit model captures the change in probability due to a percentage change of an independent variable at a local point, the interpretation of elasticities in the logit should be done with care. With the S-shaped cumulative distribution function, an increase in the probability diminishes when moving to the higher value of a variable. 30 This finding is consistent with that of Dumais, Ellison and Glaeser (1997), that labor pooling is one of the most significant externalities of agglomeration. 31 The importance of local suppliers is documented also in Helper and Kelley (1997).

21

6.2 Identification Issues

This section discusses the issues associated with identifying spillover effects from

local prior adopters of technology 2. Column (1) of table 5 presents the results where

time-, technology, industry-, and region- fixed effects are included. Since the effects of

prior adopters operate at the technology-industry-region-time level, this specification

controls for each of the effect separately. The natural experiment that one would like to

test next is whether they are capturing other effects that operate at the industry-region, or

the industry-technology level. I test for the hypothesis of industry-location specific

effects, by implementing a control variable. AVG_IND_REGION is an average adoption

rate of advanced technologies overall among plants in industry i in region r . This

captures the effects that are common to plants in the same industry in the same region,

across technologies. For example, R&D subsidy electrical and electronic products

industries in Ottawa region would have this type of effect by increasing the overall

investment in these industries, but not necessarily increasing the adoption of a specific

technology over and above. Column 2 reports the results. The estimates of technology

adopters stay virtually the same, indicating that the “spillover effects” in column (1) are

not driven by the industry-region specific effects.

Similarly, the industry-technology specific effects are controlled by including

AVG_IND_TECH, an average adoption rate of technology 2 by industry i across regions.

This controls for effects that are common to plants in the same industry adopting the

same technology. For example, plants in the aircraft industry are more likely to take up

Computer Aided Design (CAD) than plants in the petroleum products industries. The

results are presented in the final column. All three coefficients stay significant, and the

magnitudes tend to stay similar, with a small decline in the effect of “prior adopter in

similar industries”. This indicates that the “spillover effects’ presented column (2) may

have captured the industry-technology specific effects to a certain degree. Hence, I use

specification in column (3), which controls for both the industry-region and the industry-

technology effects, as my main regression (note that this is the one presented the previous

section also). The significant coefficients obtained in this specification provides a strong

22

support that the effects captured by prior adopters are not driven by industry-location, or

industry-technology fixed effects.

As a final check, I run linear probability models with more extensive fixed

effects. While a linear probability model is inappropriate for examining a binary

dependent variable, it allows the inclusion of all the fixed effects needed to test for the

alternative hypotheses. I include industry-region and industry-technology fixed effects,

instead of AVG_IND_REGION and AVG_IND_TECH to fully capture the effects at these

levels. The results are presented in table A2 in the Appendix. I obtain the similar results.

That is, the estimates of technology adopters tend to stay same and robust with the

inclusion of additional fixed effects.32 These results strongly suggest that these effects of

prior adopters can not be traced back to a spurious correlation operating at other levels,

nor are they driven by the lack of controls and fixed effects.

6.3. The Sectoral Scope of Agglomeration Effects: Diversification vs. Specialization

In the previous section, the effects of agglomeration on technology adoption were

examined. In this section, I examine how far these agglomeration effects are

transmitted, i.e., what is the scope of these agglomeration effects? The first column of

table 6 presents a specification that examines the effects of prior technology adopters and

the regional employment depending on their similarities to potential adopters. The

coefficients on employment show that the regional agglomeration of employment in

moderately similar industries has a positive effect on technology adoption, while the

agglomeration of similar industries or different industries do not have that effect. This

suggests that while the agglomeration of moderately similar industries in a region

facilitates technology adoption, agglomeration of very similar industries or very different

industries do not increase the probability of technology adoption. This supports Jocob’s

claim of diversification economies. Plants benefit more from having a diverse set of

industries which bring new ideas and practices to one place. However, the learning is

maximized when they are not too different. Technology adoption is more likely in a

32 While the linear probability model can provide a meaningful comparison, the interpretation of coefficients are inappropriate.

23

region where there are plants that are different enough to learn from, but similar enough

for the knowledge to be relevant. This suggests that not only does the size of regional

agglomeration matters, but more importantly, the industries which agglomerate matters.

I then examine the technology adoption along the dimension of product market.

Firms within the same product market face different incentives and concerns than plants

in other industries. On the one hand, firms may have an incentive to keep certain

information from competitors in the same final product market, hence the knowledge

flow among them could be hampered. On the other hand, firms have incentive to ‘move

forward’, facing competitive pressure from others. To investigate whether being in the

same product market has differential effect on technology adoption, I divide the ‘similar

industry’ group into two subgroups –the first includes plants in the same product market,

as defined by own 4-digit SIC industry, and the second includes plants in the rest of

industries in similar industry group.

Column (3) presents the estimates of the product market decomposition

regression. Prior adopters in own 4-digit industry have a smaller estimate than the rest of

similar industries. However, the estimated effects of employment in own 4-digit industry

is bigger than that of the similar industries net of own 4-digit industry. The weaker effect

of technology adopters in the same product market is suggestive of the ‘hampered

knowledge spillover’ effects from plants’ incentive to keep information from

competitors. The stronger effect of employment in the same product market is suggestive

of the ‘away-from-competition’ effects. Being faced with competitors nearby, plants are

motivated to adopt advanced technologies to place themselves in a more competitive

position. While the estimated effects are consistent with the above potential explanation,

there is lack of detailed information in the data that would enable me to identify the

underlying forces that are driving these results.

6.4. The Technological Scope of Knowledge Spillovers

In the previous sections, the effect of local plants that have already adopted a

VSHFLILF�WHFKQRORJ\�2�RQ�RWKHU�SODQWV¶�DGRSWLRQ�GHFLVLRQ�RI�WHFKQRORJ\�2 are examined. In

this section, I examine how a plant’s adoption decision of technology 2 is affected by the

24

presence of other plants that have adopted any technology. In other words, I explore the

technological scope of knowledge spillovers from prior adopters. Specifically, are

knowledge spillovers of one particular technology limited to the adoption of the same

technology, or are they extended to the adoption of other technologies? This

investigation also serves as an identification test of whether knowledge spillover effects

of prior adopters come through a channel that is specific to each technology, or come

through a channel that are common to all technologies, in which case, they may capture

effects that operate at the industry-region level.

Table 7 presents the results for three different specifications. The first column

reports the benchmark result, which examines the effects of nearby plants that have

adopted technology 2 on other plants’ adoption of the same technology. The second and

the third column present the results where a plant’s decision to adopt technology 2 can

depend on other plants that have adopted any of the 22 technologies. In the second

column, I divide all local prior technology adopters into two groups: adopters of the same

group of technology as 2, and adopters of different groups of technologies. In the third

column, I decompose all local prior adopters of technology into three groups: adopters of

the same technology 2, adopters of the same group of technologies other than technology

2, and adopters of different groups of technologies.

The number of local adopters of the same group of technologies other than

technology 2 in similar industry is calculated as

1 1_ lnG

irt gjrtj S g

SameGroupTech S Tττ

− −∈ ≠

=

∑∑

where g indexes technologies and j indexes industries. G includes technologies that are in

the same technology group as technology 2, and S includes the similar industries to

industry i. Similarly, the number of adopters of different group of technologies in similar

industries is calculated as

1_ lnirt ajrtj S a G

DiffGroupTech S Tτ −∈ ∉

=

∑∑

where a indexes technologies, and G includes technologies in the same technology group

DV� 2�� � 7KH� QXPEHU� RI� DGRSWHUV� RI� VDPH� JURXS� RI� WHFKQRORJLHV� DQG� GLIIHUHQW� JURXS� RI�

technologies in moderately similar- and different industries are calculated likewise.

25

The estimating equation for the third specification is

0 , 1 2 , 1 3 , 1

4 , 1 5 , 1 6 , 1

7 , 1 8 , 1 9 ,

Pr( ) (p irt 1 ir t ir t ir t

ir t ir t ir t

ir t ir t ir t

ADOPTION F TECH_S + SameGroupT_S DiffGroupT_S

TECH_M + SameGroupT_M DiffGroupT_M

TECH_D + SameGroupT_D DiffGroupT_D

τ τ τ τ

τ τ τ

τ τ τ

α β β β

β β β

β β β

− − −

− − −

− −

= + +

+ +

+ + 1

4 , 1 5 , 1 6 , 1 7 , 1

8 , 1

_

)

ir t ir t ir t ir t

pir t r i t p irt

EMP REGION ENGINEER INPUT OUTPUT

X τ τ

β β β β

β δ γ ϕ λ ε

−

− − − −

−

+ + + +

+ + + + + +

Since technology adopters in similar industries have the greatest effect, I will

focus on the effects of prior adopters in similar industries here. The first three estimates

in column 3 exhibits a clear pattern that spillover effects decay with the technological

distance. A 1% increase in the number of prior adopters of technology 2 increases the

probability that other plants in similar industries in the same region adopt that technology

2 by .015%. The effect of adopters of other technologies in the same technology group is

positive, but smaller in magnitude with an elasticity of .0005. The effect of adopters of

different group of technologies is very small and negative with an elasticity of -.00003.

The spillover effects are stronger with the technological proximity. The closer are the

technology adopted by prior adopters and technology to be adopted by potential adopters,

the greater the spillover effects. This result establishes the point that, along with the

similarity in the pattern of input purchases as discussed in the previous section, the

technological proximity is an important factor identifying the channel of knowledge

spillovers.

In addition, the significantly smaller effects from prior adopters adopting other

technologies confirms the earlier finding that the strong positive effect of adopters of the

same technology in similar industries is not driven by factors common to industry-region,

but driven by factors that work at technology-industry-region-time level.

6.5. The Effect of Industrial Structure on Learning in a Region

26

This section examines the effect of industrial structure on learning and knowledge

flows in a region. The nature and the productivity of a regional economic system do not

depend just on numbers and quantity, but also on the nature of urban interactions. To test

the effects of the industrial system in learning, as claimed by Saxenian (1994), I address

two questions regarding the industrial structure of the regional economic environment.

First, I examine whether small plants are more open and flexible, and hence better

facilitate the knowledge than do large plants. If so, an increase in an additional worker at

small plants would enhance knowledge spillovers more than an additional worker at large

plants would. To evaluate this hypothesis, I re-estimate the main regression in section

6.1, with the regional employment divided into two groups: employment at small plants

and employment at large plants. Employment at small plants in a region is

_ ( * )Srt pt pt

p r

SMALL REGION Emp I∈

= ∑ where

1 if employment at plant 100 at

0 otherwiseSpt

p tI

< =

.

The employment at large- plants are calculated similarly, where large plants are defined

as those with employment equal or greater than 100. The estimating equation is

0 1 , 1 2 , 1 3 , 1

4 , 1 5 , 1

6 , 1 7 , 1 8 , 1 9 , 1

10 11

Pr( ) ( _ _ _

_ _

_ _ _ _

p irt ir t ir t ir t

r t r t

r t ir t ir t pir t

ir

ADOPTION F TECH S TECH M TECH D

SMALL REGION LARGE REGION

ENGINEER INPUT OUTPUT X

AVG IND REGION AVG IND TE

τ τ τ τα β β β

β β

β β β β

β β

− − −

− −

− − − −

= + + +

+ +

+ + + +

+ +

)

i

r i t p irt

CH τ

τ τδ γ ϕ λ ε+ + + + +

Table 8A presents the results. The estimate of the effect of employment at small

plants, SMALL_REGION, is 0.0562 and significant while the estimate of employment at

large plants is 0.0175 and insignificant. The elasticity indicates that a 1% increase in

employment at small plants increases the probability of technology adoption by .013%

while an increase in employment at large plants has an insignificant effect. This suggests

that small plants tend to engage more in interactions with neighbours and take more

active roles in local milieu, and hence act as better agencies of creation and diffusion of

knowledge and information. This finding is consistent with Saxenian’s explanation of

27

the ascendance of Silicon Valley over Route 128. This is the first to document that the

effects of industrial structure on learning of technical knowledge in a region is evident in

a broader pattern of data, and not unique to the case of Silicon Valley and Route 128.33

Second, I examine whether single-plant firms facilitate knowledge transfer better

than do multi-plants firms. On the one hand, knowledge spillover from multi-plant firms

are expected to be restricted since their decision-makings are constrained and influenced

by their corporation, and their interactions may heavily involve their corporation for

information than from their neighbours. On the other hand, these multi-plant firms are

expected to provide greater knowledge base since they often have better access to

resources elsewhere and hence contain more knowledge and information that are not

available in the region. Hence, the effect of plant status on knowledge spillover is

ambiguous. To investigate this, the regional employment is divided into two groups:

employment at multi-plant firms, and employment at single-plant firms. Employment at

multi-plant firms are calculated as

_ ( * )Mrt pt pt

p r

MULTI REGION Emp I∈

= ∑ where

1 if plant is part of a multi-plant firm at

0 otherwiseMpt

p tI

=

.

Table 8b presents the results of this model. Estimates of the other variables are

suppressed to conserve space. The estimated effect of employment at single-plant firms

is positive and significant while the estimated effect of employment at multi-plant firms

is negative and significant. The elasticity indicates that a 1% increase in employment at

single-plant firms in a region increase the probability that a plant adopts a technology by

.022% while a same increase in employment at multi-plant firms decrease the probability

by .011%. This result suggest that given the level of regional employment, an

environment that is comprised of more single-plant firms than multi-plant firms

facilitates technology adoption in a region. This supports the view that single-plant firms

that do not have outside resources tend to rely more on resources that are available

locally, and interact more. Consequently, the diffusion of knowledge is faster and more

33 Rosenthal and Strange (1999) find that small- and medium-sized plants have a larger effect on agglomeration than larger plants. This, however, is not a direct test of the hypothesis of Saxenian, since

28

likely in regions characterized by single-plant firms. This finding also supports

Saxenian’s claim.34 These two results found in this section provide strong evidence that

what are observed in Silicon Valley and Route 128 are also applicable in more general

setting.

7. Organizational Characteristics

A complementary body of theory on the differential capacity of firms to absorb

and make good use of new technical information emphasizes differences in internal

expertise, access to financial resources, and organizational routines. These affect each

firm’s expected profitability – that is, the incremental returns to investing in the new

technology – which in turn gives rise to the observed uneven pattern of adoption (Cohen

and Levinthal 1990; Dosi 1988; Malerba 1992; Nelson and Winter 1982). As Nelson

(1995) points out, some new technologies will never be taken by certain firms, they will

not “learn”. Many of those that do not learn will fall back or exit the industry altogether,

as new young firms enter with their production and work organizations built around the

new technological paradigm from the very beginning.

Plant learning – and ability to act on that information – will also vary by the level

of organizational resources, scale of the production process, appropriateness of the few

technology to that plants’ core production process, and sources of information, that may

have nothing to do with geography per se. For example, local plants may learn as much

from their non-local parent firms and from their networks as from proximate institutions

(Badaracco 1991; Caves 1989; Dunning 1994; Harrison 1994; Mowery 1988; Powell

1990; Scherer et al. 1975).

In sum, whether the underlying processes by which innovative behaviour of

individual firms are enhanced mainly by virtue of agglomeration within a sector or as a

consequence of the diverse institutional character of the region, the capacity of an

organization to learn from external sources is a function of many structural factors, of

she emphasizes the role of industrial structure on technical learning, rather than its role on industry agglomeration of a region. 34 Rosenthal and Strange (1999) did not find any evidence that multi-plant and single-plant firms significantly differ in terms of promoting regional agglomeration.

29

which locational context is only one. Indeed, for certain types of organizations, such as

the branches of multi-locational corporation or members of a production network, the

characteristics of the local business environment may be of minor importance to the

external learning process. Only by comparing the probabilities of adoption of a new

technology among businesses having the same capabilities and resources, but which are

situated within different locational contexts, I can obtain unbiased estimates of the

relative importance of location per se. All the estimation presented in the previous

section include plant characteristics, although only local attributes are discussed. In this

section, I present the organizational characteristics of plants that are more likely to adopt

technologies, and that are more likely benefit from locational context.

7.1 Theories of Firm Capabilities

I first discuss the organizational characteristics of organizations that are more

likely to adopt technologies, and explain the variables used.

Capabilities and Resources

The willingness and the ability of an organization to adopt new technology

depends in part on its capabilities and resources to adapt existing organizational routines

and to engage in a learning by doing process (Kelley and Helper 1997). Heterogeneity in

the size of enterprises and the scale of operations to which the technology applies affects

the expected profitability of the innovation among firms, explaining why some firms will

adopt new technologies while others fail to do so (Dosi, 1988). Size affects the expected

profitability of an innovation in a number of ways. Romeo (1975) and Globerman (1975)

observe that large firms are more likely to be early users of numerically controlled

technology. To measure the capabilities and resources of a plant, I use a plant’s

employment, SIZE.

Even when there are no technical constraints on the scale at which a new

technology is deployed, liquidity constraints may increase the costs of making the

investment for firms with small revenue streams. A large, relatively resource-rich

organization can afford to embark on a number of experiments with process innovations,

30

without risking its survival even if only some of these experiments are successful (Cohen

and Levin 1989; March 1981). Moreover, the costs of employing specialists to increase

the probability of successful implementation of a new technology is lower for larger

firms who can spread fixed costs over greater output. For these reasons, larger

enterprises are expected to be more likely to adopt a new process technology. To capture

the additional disadvantages faced by small plants, I include a dummy variable, SMALL,

for small plants that have employment less than 20.

Scope

Economies of scope (joint production or distribution) are those resulting from the

use of processes within a single operating unit to produce or distribute more than one

product (Chandler 1990). With economies of scope, the cost of adopting technology is

shared across products. Also, the usefulness of technologies in the production of each

commodity may vary. Hence, with more product lines, it is more likely that some

technologies will be adopted. Furthermore, adoption of advanced manufacturing

technologies allows more flexibility in the production of many commodities. For these

reasons, I expect plants with a greater scope to be more likely to adopt technologies. I

measure the scope by the number of commodities produced in a plant, COMMODITY.

Diversity of Information Channels

An important source of heterogeneity in firms’ capabilities to learn about a new

technology developed outside the firm stems from the diversity and salience of

management’s linkages to external sources of information about a new technology.

Hence, organizations with a more diverse set of connections in its learning network are

more likely to find out about the benefits of a new technology in the first place, and to

have better information about costly problems of implementation and the methods and

techniques prior adopters have used to avoid or minimize these costs (Kelley and Helper

1997).

Similarly, for an organization that is exposed to diverse sets of industries, there

are a larger number of available sources of information, and opportunities to learn about

31

potential knowledge. To proxy for the diversity of an organization’s information

channel, I use the number of 4-digit industries in which a plant operates, SEGMENT.

Ownership

Many studies find that foreign controlled plants are more productive than

domestic plants, and other studies suggest that the lagged productivity of Canadian plants

are due to lower technology uptakes (Baldwin and Diverty 1995). In addition, foreign

ownership means that an organization has an internal access to the parent firm elsewhere,

which implies an access to certain resources and information that are not available to

domestic owned plants. To account for the effects of foreign ownership, a dummy

variable for foreign controlled plant, FOREIGN, is used.

Plant-Status

Being a part of a multi-plant firm often allows better access to resources, knowledge and

information that are not available in the region. To control for plant status, a dummy

variable for single-plant firms, SINGLE, is used.

Size- and Status-related Difference in Plant’s Susceptibility to Agglomeration Economies

I expect smaller plants to be more susceptible to outside influences from their

network and geographic location. The reasons are two-fold. First, a plant belonging to a

larger firm is more likely to serve a national market, so regional demand fluctuations will

have less effect on its output (Kelley and Helper 1997). For large firms, investment

decisions may depend on factors that may have little to do with that business unit’s

locales or local management’s connections to sources of information external to the firm.

Second, the smaller the firm, the more I would expect it to be susceptible to external

influences. That is what sociologists term “mimetic isomorphism”, the tendency of

organizations to adopt practices and technologies in imitation of other enterprises like

themselves (DiMaggio and Powell, 1983).

I also expect single-plant firms to be more susceptible to the regional economic

environment they locate in. A multi-plant firm’s investment decisions are more likely to

be influenced by corporate strategy (Schere et al., 1975).

32

7.2. Results on Organizational Characteristics

Table 9 presents the estimates for plant characteristics. The baseline regression

includes plants characteristics, local technology adopters, regional employment, other

agglomeration effects, and regional-, technology- and industry- fixed effects. As before,

other variables and fixed effects are suppressed to conserve the space. The first column

presents the estimates for the baseline regression, and the second column presents the

elasticity.

The employment size of a plant is positively correlated with technology adoption.

A 1% increase in plant employment increases the probability of adopting technology by

0.22%. This is consistent with the theory that organizational capabilities and resources

are one of the most important factors in the adoption of technology. The number of

commodities produced at plant, which captures the scope of a plant, has a negative effect

on technology adoption, after controlling for plant size. A diversity in information

channels has a positive effect. This suggests that knowledge and information play an

important role in technology adoption decision. Furthermore, the single-plant firm

dummy variable has a negative and significant coefficient, indicating that single-plant

firms are less likely to adopt technologies than multi-plant type of plants even after

controlling for plant size. The benefit of being part of multi-plant firm not only come

from the size, but also from the information and resources available from elsewhere.

Also, the probability of adopting technology is higher among foreign owned plants. This

confirms previous findings that foreign owned plants have higher technology uptake

rates.

I then examine whether the benefit of locational attributes and knowledge

spillovers condition on the plant size. Particularly, do small firms benefit more from

knowledge spillovers, from experience of prior adopters or from the regional

agglomeration? If so, then it is more important for small plants to locate in regions that

are more likely to facilitate inter-firm learning. The third column reports the results on

this hypothesis. I test for size-related differences in the effects of knowledge spillovers

by interacting plant size dummy, SMALL, with TECH_S. The positive coefficient on the

interaction between these two variable, SMALL*TECH_S suggest that the learning

33

advantages from local prior adopters are greater for the small plants. Similarly, the

positive coefficient on the interaction between SMALL and EMP_REGION suggest that

small plants benefit more from locating in regions with large manufacturing employment.

With respect to agglomeration economies and knowledge spillovers, I find size-related

differences, that small plants are more susceptible to the external environment they face.

Further, I examine whether there are plant status-related differences with respect

to the regional agglomeration. Similar to the case with size-related difference, I ask

whether single-plant firms benefit more from the experience of prior adopters and the

regional agglomeration. I test this hypothesis by interacting plant status dummy, Single,

with other locational variables. The positive coefficient on SINGLE*TECH_S suggests

that the effect of knowledge spillovers on technology adoption are greater for single-

plant firms. Similarly, SINGLE*EMP_REGION has a positive coefficient. This implies

that the advantage from locating in a region with a high level of manufacturing activity is

greater for single-plant firms.

To examine whether this size-related difference with respect to agglomeration

economies are capturing the effect of status-related difference, and vice versa (since

small plants tend to be single-plant firm, and single-plant frims tend to be small), I

include both sets of variables. The results are reported in the last column in table 9.

Three of four interaction terms are significant. When both the size- and status-related

difference are accounted for, the size-related advantage on knowledge spillovers and

regional agglomeration, and status-related advantage on the regional agglomeration

remain to be important.

8. Conclusion

The nature and utility of knowledge are at the heart of the economics of R&D,

innovation and technological change. How knowledge is created and diffused, and how

learning takes place are still inside the black box. By examining the various aspects of

regional economies, this paper attempts to explore the role of agglomeration in

technology adoption. Specifically, this paper separates the effects of knowledge

34

spillovers from other agglomeration externalities and local amenities, and identifies a

very narrow channel through which knowledge is diffused.

The key finding of this paper is that a plant’s decision to adopt a specific

technology is facilitated by the presence of other plants in similar industries and in the

same region that have already adopted the same technology. This paper finds that

knowledge spillovers from prior technology adopters are bounded along the three

dimensions: geographical proximity, production process proximity, and technological

proximity. This implies a very narrow channel for knowledge spillovers which has not

been previously identified.

Another key finding of this paper is that the industrial structure of a region plays

an important role in the speed of learning in a region. I find that, after controlling for

regional employment, environments that are characterized by smaller plants as opposed

to larger plants, and single-plant firms as opposed to multi-plant firms, are more likely to

facilitate the diffusion of knowledge and learning, and hence technology adoption is more

likely in such places. This suggests that small and single-plant firms are better agencies

in the creation and the diffusion of new ideas and information than their larger or multi-

plant counterparts. This paper is the first to show that this is a broader pattern observed

in the data, not just an unique comparative experience of Silicon Valley and Route 128.

Finally, I find that there are size- and status-related differences with respect to the

regional agglomeration. The learning advantages from the experience of local prior

adopters and the regional agglomeration are greater for the small and single-plant firms.

35

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40

Figure 1 The Geographic Concentration of Activity:

All Plants vs. Advanced Technology Adopters

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

Machinery

Other Manufacturing

Food

Non-Metallic Mineral

Chemical

Electrical & Electronic

Clothing

Fabricated Metal

Printing, Publishing

Tobacco

Transport'n Equip't

Plastic

Primary

Textile

Paper

Ref'd Petrol'm & Coal

Wood

Beverage

Leather

Rubber

Primary Textile

Furniture & Fixture

Indu

stry

(2

digi

t SIC

)

Index of Geographic Concentration

All plants Technology Adopters

41

Table 1. List of Advanced Manufacturing Technologies and Incidence of Technology Use by Plants

Name of Advanced Manufacturing Technologies 1993 1984

Design and Engineering Computer Aided Design (CAD) and/or Computer Aided Engineering (CAE) 27.1 1.3 CAD output used to control manufacturing machines (CAD/CAM) 12.9 0.6 Digital representation of CAD output used in procurement Activities 6.0 0.2

Fabrication and Assembly Flexible Manufacturing Cell (FMC) or Systems (FMS) 6.8 0.2 Numerically Controlled and Computer Numerically Controlled (NC/CNC) machines 15.0 2.8 Materials working laser 2.4 0.0 Pick and places robots 3.5 0.3 Other robots 3.0 0.0

Automated Material Handling Automated Storage and Retrieval System (AS/RS) 3.0 0.2 Automated Guided Vehicle Systems (AGVS) 1.1 0.0

Inspection and Communications Automated sensor-based equipment used for inspection/testing of incoming or in-process materials 6.1 1.0 Automated sensor-based equipment used for inspection/testing of final product 6.8 1.4 Local area network for technical data 10.5 0.4 Local area network for factory use 8.1 1.0 Inter-company computer network linking plant to subcontractors, suppliers, and/or customers 7.5 0.1 Programmable controller 17.1 1.9 Computer used for control on the factory floor 15.6 1.5

Manufacturing Information Systems Materials Requirement Planning(MRP) 15.7 1.3 Manufacturing Resource Planning (MRP II) 8.5 0.2

Integration and Control Computer Integrated Manufacturing (CIM) 6.1 0.5 Supervisory Control and Data acquisition (SCADA) 7.5 1.0 Artificial Intelligence and/or expert systems 1.5 0.0

42

Table 2. Descriptive Statistics of Variables

Year 1990 Year 1987 Year 1984 Variable Name Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

Plant Characteristics

Employment of plant 61.0 746 76.7 949 88.7 1030 No. of sic-4 ind. in which a plant operates 1.98 14.7 2.20 14.4 2.85 19.1 No. of commodity produced 2.41 13.1 1.93 11.2 2.83 12.6 % of multi-plant firm 0.16 1.39 0.19 1.43 0.22 1.44 %of foreign owned plant 0.14 1.32 0.17 1.35 0.20 1.38

Technology Spillovers

No. of tech. users in similar industries 21.8 230 6.23 79.3 3.27 59.8 No. of tech. users in moderately similar industries

16.0 152 6.90 79.5 1.19 25.7

No. of tech. users in different industries 127.0 737 54.3 331.6 14.2 115

Employment

Employment in Census Division (in 1000s) 72.8 351.2 73.5 348.7 73.2 321.8 Employ. at small plants in CD (in 1000s) 9.4 42.9 7.9 34.9 8.8 36.9 Employ. at large plants in CD (in 1000s) 42.5 210 45.8 220.3 45.6 201.3

Other Agglomeration Economies

Value of output in upstream ind. in CD 40.5 289.3 36.6 247.4 25.5 171.6 Value of output in downstream ind. In CD 39.9 316.1 35.6 267.1 26.1 196.3 % of scientists & engineers in CD (%) 4.1 2.7 4.1 2.7 4.4 3.5

Note: 1. Variables are weighed by ‘establishment weight’ provided in 1993 Innovation and Technology Survey.

43

Table 3. Variable Names and Definitions Variable Name Definition

POTENTIAL CHANNELS OF AGGLOMERATION EXTERNALITIES

A. Technology Spillovers

Tech users in similar industries no. of plants in similar industries in region r that use tech 2 Tech users in own sic4 no. of plants in own sic4 industry i in region r that use tech 2 Tech users in similar industries excl. own sic4 no. of plants in similar industries excluding own sic-4 industry i in region r that use tech 2

Tech users in moderately similar industries no. of plants in moderately similar industries in region r that use tech 2 Tech users in different industries no. of plants in different industries in region r that use tech 2

B. Employment Effects

Regional Employment employment in region r Emp in similar industries employment in similar industries in region r

Emp in own sic4 employment in own sic4 industry in region r Emp in similar ind excl. own sic4 employment in similar industries excluding own sic4 industry i in region r

Emp in moderately similar industries employment in moderately similar industries in region r Emp in different industries employment in different industries in region r

C. Other Agglomeration Effects

INPUT output of upstream suppliers of industry i in region r OUTPUT output of downstream consumers of industry i in region r ENGINEER share of scientists and engineers in population in region r LOCALIZATION INDEX share of industry i’s employment over national share DIVERSIFICATION INDEX weighted share of 4-sic in region r

D. Other Controls

AVG_IND_REGION mean adoption rate of overall technologies in industry i in region r AVG_IND_TECH mean adoption rate of technology 2 in industry i across regions

ORGANIZATIONAL CHARACTERISTICS

SIZE total employment of plant COMMODITY number of commodity produced DIVERSITY no. of 4-digit industries in which a plant operates SMALL dummy variable for small plant (emp<20) FOREIGN dummy variable for foreign owned plant SINGLE dummy variable for single-plant firm

44

Table 4

Technology Spillovers and Agglomeration Effects on Technology Adoption

Dependent Variable: p irtADOPTION τ

Main Regression

Variable Name Coefficient Std. Error Elasticity Technology Spillovers (Tech users in similar ind)2LU�W-1 .0313* .0030 .008 (Tech users in moderately similar ind)2LU�W-1 .0243* .0030 .006 (Tech users in different ind)2LU�W-1 -.0167* .0043 -.004 Other Agglomeration Effects EMP_REGIONUï�W-1 .0530* .0086 .012 INPUTiUï�W-1 .0824* .0079 .014 OUTPUTiUï�W-1 -.1313* .0087 -.013 ENGINEERUï�W-1 4.3097* .9711 1.069 Observations 105,902 Log Likelihood 67,893 Notes:

1) Standard errors are in parentheses. 2) $

2 statistically significant at p < 0.05 3) Uï denotes Census Division in which plant p is located. 4) Also included are plant characteristics, fixed effects and control variables

(AVG_IND_REGION, AVG_IND_TECH).

45

Table 5 Technology Spillovers with Various Controls

Dependent Variable: p irtADOPTION τ

(1) Ind-, Location Fixed

Effects

(2) (Ind × Region) Controls

(3) Main Regression

Variable Name Coefficient Elasticity Coefficient Elasticity Coefficient Elasticity

Technology Spillovers

(Tech users in Similar ind)2LU�W-1 .0463* (.0029)

.012 .0484* (.0029)

.012 .0313* (.0030)

.008

(Tech users in Moderately similar ind)2LU�W-1 .0244* (.0029)

.006 .0242* (.0029)

.006 .0243* (.0030)

.006

(Tech users in Different ind)2LU�W-1 -.0208* (.0043)

-.005 -.0207* (.0043)

-.005 -.0167* (.0043)

-.004

Fixed Effects

TIME ( t ) ¥ ¥ ¥ TECHNOLOGY (τ ) ¥ ¥ ¥ INDUSTRY (SIC-3 : i ) ¥ ¥ ¥ REGION (Economic Region: r ) ¥ ¥ ¥

Other control Variables

AVG_IND_REGION ( i × r ) ¥ ¥ AVG_IND_TECH ( i ×�2 ) ¥

Observations 105,902 105,902 105,902 Log Likelihood 66,976 67,118 67,893

Notes: 1) Standard errors are in parentheses. 2) �$2 statistically significant at p < 0.05

3) Also included are plant characteristics and agglomeration effects.

46

Table 6 Sectoral Scope of Technology Spillovers and Agglomeration Effects

Dependent Variable: p irtADOPTION τ

(1) Similarity in

Input Purchases

(2) Product Market Decomposition

Variable Name Coeff. Elast. Coeff. Elast.

Technology Spillovers

(Tech users in Similar ind)2LU�W-1 .0344* (.0030)

.009

(Tech users in own sic4)2LU�W-1 .0283* (.0038)

.007

(Tech users in similar ind excl own sic4)2LU�W-1 .0342* (.0030)

.009

(Tech users in Moderately similar ind)2LU�W-1 .0208* (.0030)

.005 .0203* (.0030)

.005

(Tech users in Different ind)2LU�W-1 -.0172* (.0043)

-.004 -.0166* (.0043)

-.004

Employment Effects

(Regional Employment) r’,,t-1 (Emp in Similar ind)ir’,t-1 -.0708*

(.0079) -.016

(Emp in own sic4)ir’,t –1 -.0197* (.0065)

-.005

(Emp in Similar ind excl. own sic4)ir’,t-1 -.0503* (.0038)

-.012

(Emp in Moderately similar ind)ir’,t-1 .0052* (.0039)

.0013 .0572* (.0039)

.014

(Emp in Different ind)ir’,t-1 .0050 (.0073)

.0012 .0137 (.0073)

.003

Observations 105,902 105,902 Log Likelihood 68,123 68,282

Notes: 1) Standard errors are in parentheses. 2) �$

2 statistically significant at p < 0.05 3) Uï denotes Census Division in which plant p is located. 4) Also included are plant characteristics, other agglomeration effects, control variables and

fixed effects.

47

Table 7 Technological Scope of Spillovers

Dependent Variable: p irtADOPTION τ

(1) Same

Technology

(2) Technology

Group

(3) All

Technology

Variable Names Coeff. Elast. Coeff. Elast. Coeff. Elast.

Plants in Similar industries that have adopted

Tech in Same group .0351* (.0028)

.009

Same tech .0484* (.0029)

.012 .0591* (.0032)

.015

Other tech in same group .0019 (.0030)

.0005

Tech in different group -.0001* (.00002)

-.00003 -.0001* (.00002)

-.00003

Plants in Moderately similar industries that have adopted

Tech in same group -.0002 (.0029)

-.00005

Same tech .0242* (.0029)

.006 .0504* (.0032)

.012

Other tech in same group -.0210* (.0030)

-.005

Tech in different group -.0002* (.00002)

-.00005 -.0002* (.00003)

-.00005

Plants in Different industries that have adopted

Tech in same group -.1089* (.0049)

-.026

Same tech -.0207* (.0043)

.005 -.0029 (.0045)

-.0007

Other tech in same group -.0818* (.0041)

-0.019

Tech in different group -.0002* (-7E-07)

-.00005 -.0002* (-7E-07)

-.00005

Observations 105,902 105,902 105,902 Log Likelihood 67,118 68,302 68,895

Notes: 1) Standard error are in parentheses. 2) �$

2 statistically significant at p < 0.05 3) Also included are plant characteristics, other agglomeration effects and fixed effects.

48

Table 8A Effects of Industrial Structure on Technology Adoption: Small vs. Large Plants

Dependent Variable: p irtADOPTION τ

(1)

Main Regression

(2) Regional Employment

Regression

(3) ‘Related-industry’

Employment Regression

Variable Name Coefficient Elasticity Coefficient Elasticity Coefficient Elasticity

Employment Effects

(Regional Employment) r’,t-1 .0626* (.0087)

.014

(Regional Emp at small plants)ir’,t-1 .0562* (.0102)

.013

(Regional Emp at large plants)ir’,t-1 .0075 (.0072)

.002

(Emp at small plants in related ind)ir’,t-1 .0643* (.0073)

.015

(Emp at large plants in related ind)ir’,t-1 .0043 (.0033)

.001

(Emp in different ind) ir’,t-1 -.0083 (.0081)

-.002

Observations 105,902 105,902 105,902 Log Likelihood 67,893 67,903 67,982

Notes: 1) Standard errors are in parentheses. 2) * $2 statistically significant at p < 0.05 3) Small plants are plants with employment less than 100, and large plants are those with employment 100 or more. 4) Related industries include both ‘similar’ and ‘moderately similar’ industries. 5) Uï denotes Census Division in which plant p is located. 6) Also included are plant characteristics, prior adopters, other agglomeration effects, control variables and fixed effects.

49

Table 8B Effects of Industrial Structure on Technology Adoption: Single-plant vs. Multi-plant Firms

Dependent Variable: p irtADOPTION τ

(1)

Main Regression

(2) Regional Employment

Regression

(3) ‘Related-industry’

Employment Regression

Variable Name Coefficient Elasticity Coefficient Elasticity Coefficient Elasticity

Employment Effects

(Regional Employment) r’,t-1 .0626* (.0087)

.014

(Regional Emp at single-plant firms)ir’,t-1 .1149* (.0122)

.022

(Regional Emp at multi-plant firms)ir’,t-1 -.0441* (.0112)

-.011

(Emp at single-plant firms in related ind)ir’,t-1 .0440* (.0067)

.011

(Emp at multi-plant firms in related ind)ir’,t-1 -.0213* (.0035)

-.005

(Emp in different ind) ir’,t-1 .0173* (.0073)

.004

Observations 105,902 105,902 105,902 Log Likelihood 67,893 67,954 67,936

Notes: 1) Standard errors are in parentheses. 2) �$

2 statistically significant at p < 0.05 3) Related industries include both ‘similar’ and ‘moderately similar’ industries. 4) Uï denotes Census Division in which plant p is located. 5) Also included are plant characteristics, prior adopters, other agglomeration effects, control variables and fixed effects.

50

Table 9 Organizational Characteristics

Dependent Variable: p irtADOPTION τ

Main Regression

Variable Name

Coeff.

Elasticity

Small Plant

Single Plant Firm

Small & Single Plant

Plant Characteristics

SIZE (total employment)

.5566* (.0075)

.2214 .5588* (.0075)

.5625* (.0075)

.5671* (.0076)

SEGMENT (# of sic-4 industry in which a plant operates)

.1018* (.0082)

.0084 .1032* (.0082)

.1007* (.0082)

.1050* (.0082)

COMMODITY (# of commodity a plant produces)

-.1246* (.0086)

-.0140 -.1274* (.0086)

-.1257* (.0086)

-.1291* (.0086)

SMALL (=1) -.0296 (.0203)

-.0974* (.0211)

-.0240 (.0204)

-.8201* (.0991)

FOREIGN (=1) .0688* (.0152)

.0775* (.0152)

.0614* (.0153)

.0696* (.0153)

SINGLE (=1) -.1766* (.0163)

-.1760* (.0164)

-.5973* (.0867)

-.4456* (.0898)

Agglomeration effects based on plant characteristics

SMALL*TECHUSER_S

.0551* (.0043)

.0397* (.0048)

SMALL*EMP_REGION .1087* (.0088)

.0734* (.0097)

SINGLE*TECHUSER_S .0164* (.0040)

.0064 (.0042)

SINGLE*EMP_REGION .0395* (.0084)

.0260* (.0087)

Observations 105,902 105,902 105,902 105,902 Log Likelihood 67,893 68,047 67,948 68,145

Notes:

1) Standard errors are in parentheses. 2) �$

2 statistically significant at p < 0.05 3) Also included are plant characteristics, other agglomeration effects, control variables and

fixed effects. 4) Plants are small if employment is less than 20.

51

Appendix

Table A1

Summary Statistics of Sizes of ‘Related Industries’

Industry Category

Avg. No. of SIC-3

Industries

Standard Deviation

Minimum

Maximum Similar industries 6.65 5.61 1 20 Moderately similar industries 8.89 4.02 0 33 Different industries 92.46 9.60 69 107 SIC-2 Industry 4.95 2.61 1 9

52

Table A2 Linear Probability Models with Fixed Effects

Dependent Variable: p irtADOPTION τ

Variable Name

Economic Region (ER)

Census Division(CD)

ER × SIC-3

CD × SIC-3

Tech × SIC-3

Technology Use Linkage

(Tech users in similar ind)2LU�W-1 .0019* (7.08)

.0018* (6.57)

.0016* (5.85)

.0018* (6.71)

.0015* (5.35)

(Tech users in moderately similar ind)2LU�W-1 .0014* (5.21)

.0015* (5.42)

.0015* (5.63)

.0015* (5.51)

.0014* (5.38)

(Tech users in different ind)2LU�W-1 .00002 (.07)

.00003 (-0.44)

.0002 (0.55)

.00007 (0.22)

.0003 (.39)

Fixed Effects

TIME ( t ) √ √ √ √ ¥ TECHNOLOGY (τ ) √ √ √ ¥ ¥ INDUSTRY (SIC-3 : i ) √ √ REGION (Economic Region: r) √ REGION (Census Division: r′ ) √ REGION-INDUSTRY (SIC-3*ER : i × r ) ¥ REGION-INDUSTRY (SIC-3*CD: i × r′ ) √

INDUSTRY-TECHNOLOGY (SIC-3*Tech : i × 2�)

¥

Observations 105,902 105,902 105,902 105,902 105,902 R-square .051 .059 .061 .072 .059 Notes:

1) t-statistics are in parentheses. 2) * t statistically significant at p < 0.05 3) Also included are plant characteristics and other agglomeration effects