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DOI: 10.1177/0891242411430327

2012 26: 34 originally published online 23 December 2011Economic Development QuarterlyShelley McDonough Kimelberg and Lauren A. Nicoll

Business Location Decisions in the Medical Device Industry : Evidence From Massachusetts

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The production and supply of medical devices is a $117 billion industry in the United States, responsible for more than 360,000 domestic jobs in 2007 (U.S. Census Bureau, 2010b). Given the comparatively high salaries paid to employees, medical device firms, like businesses in the more broadly defined areas of life sciences and biotechnology, are seen as desirable targets by many local and regional economic devel-opment officials. This is particularly true in states such as Massachusetts, Minnesota, and California, which are among the top regional hubs for the industry (U.S. Census Bureau, 2010a). Likewise, the predicted increase in demand for med-ical products fueled by a rapidly aging population is expected to buffer the industry from the precipitous employment declines recently experienced by other manufacturing- intensive businesses (U.S. International Trade Commission [USITC], 2007). In light of these factors, the question of how medical device firms make location decisions is clearly of interest to those individuals and agencies tasked with attract-ing and retaining private investment in their communities.

An examination of the factors influencing site selection for medical device firms has important implications for loca-tion theory as well. The medical device industry, like others that include research and development (R&D) activities (characteristic of the postindustrial knowledge economy) as well as traditional manufacturing functions (characteristic of the industrial economy), challenge us to think in a more nuanced way about business location theory. Many studies

of the location decisions of manufacturing companies tend to emphasize traditional economic factors such as operating costs, taxes, infrastructure, and the like (see, e.g., Bartik, 1985; Buss, 2001; Hekman, 1992; Schmenner, 1982). In contrast, studies of firms in the knowledge economy often highlight the importance of factors thought to enhance the satisfaction and productivity of their human assets (i.e., employees), such as local amenities, desirable housing and quality schools, and access to collaborative public and pri-vate institutions and resources (see, e.g., Cohen, 2000; Salvesen & Renski, 2003). With medical device firms, it is not immediately clear which types of location factors—those associated with production activities or those associated with knowledge activities—would carry the most weight in the location decision.

This is not to suggest that in the extant literature the loca-tion factors presumed to be important in one sphere are ignored or deemed irrelevant in another. Indeed, several well-cited studies (e.g., Barkley & McNamara, 1994; Blair & Premus, 1987; Galbraith & DeNoble, 1988) belie such a

430327 EDQXXX10.1177/0891242411430327Kimelberg and NicollEconomic Development Quarterly

1Northeastern University, Boston, MA, USA

Corresponding Author:Shelley McDonough Kimelberg, Department of Sociology & Anthropology, Northeastern University, 535 Holmes Hall, 360 Huntington Avenue, Boston, MA 02115, USAEmail: [email protected]

Business Location Decisions in the Medical Device Industry: Evidence From Massachusetts

Shelley McDonough Kimelberg1 and Lauren A. Nicoll1

Abstract

Medical device firms, like firms in the high-technology sector, often include business functions characteristic of both the industrial economy and the knowledge economy. Thus, it is not immediately clear which kinds of locational attributes (i.e., traditional cost-based factors or those concerned with attracting and retaining knowledge workers) are most influential in the site selection process. This study draws on survey data from 48 medical device firms in Massachusetts, a state with one of the largest clusters in the industry, to examine the relative importance of various factors and conditions in the location decision. A second objective is to consider the extent to which the rating of a given location factor varies depending on whether the firm maintains manufacturing facilities in the state. The implications of the findings for both location theory and economic development efforts are discussed.

Keywords

industrial location, industry studies, location decisions, manufacturing



Kimelberg and Nicoll 35

rigid separation between “classic” location factors (i.e., those concerning minimization of cost) and “contemporary” location factors (i.e., those emphasizing quality of life). Rather, there is a tendency to think about the firms them-selves in more simplistic terms (e.g., manufacturing inten-sive or knowledge intensive) than is likely warranted. For instance, despite extensive offshoring and outsourcing, many vanguards of the knowledge economy in the United States do maintain some domestic manufacturing facilities (Lowe, 2007). Thus, it is instructive to consider how such firms that are not so easily categorized make decisions about where to locate their facilities.

This article draws on survey data from 48 medical device companies in Massachusetts to examine the relative impor-tance of various factors and conditions in the local site selec-tion process. Massachusetts ranks among the top five states in the United States for medical device employment and pay-roll, on both a per capita and an absolute level (U.S. Census Bureau, 2010a). It is thus a fitting place to study the factors that influence the location decisions of such firms. A second objective of the research is to consider the extent to which the rating of a given location factor varies depending on the types of facilities the company maintains in Massachusetts (specifically, whether or not the company has manufacturing operations in the state). If, for instance, medical device firms with manufacturing facilities in the state place a greater emphasis on cost-based location factors than firms without local manufacturing facilities, this would suggest a need to think about business location decisions not just in terms of the industry in which the firm competes but also in terms of the types of facilities in their portfolios.

In the sections that follow, we provide a brief review of the business location literature, with an emphasis on studies that examine the site selection process for either manufactur-ing or knowledge-based establishments. Next, we offer pro-files of the medical device industry in both the United States and in Massachusetts, and describe the methods used in the present research. Last, we summarize our findings and dis-cuss their implications for both theory and practice.

Location Factors and the Industrial EconomyThere is a substantial literature devoted to understanding the role of production factors in the location decisions of manu-facturing establishments (Blair & Premus, 1987; Schmenner, 1982). While classical location theory emphasized access to markets, labor, transportation, and raw materials as the key determinants of site selection (Christaller, 1933/1966; Hotelling, 1929; Lösch, 1954; Weber, 1909/1929), more recent research has expanded the focus to include factors such as taxes, unionization rates, business climate, and infra-structure (see, e.g., Arsen, 1997; Bartik, 1985; Forkenbrock & Foster, 1996; Halstead & Deller, 1997).

Despite disagreements over the relative importance deci-sion makers assign to various production factors (see, e.g., Buss, 2001, for a review of the evidence concerning taxes), contemporary site selection research has not abandoned the study of traditional location factors (e.g., Hu, Cox, Wright, & Harris, 2008). Indeed, as Henderson and McNamara (2000) demonstrate in their research on food manufacturing facilities, raw material costs, distribution costs, and transportation access continue to drive the location decision for many firms. Similarly, a recent study by Haddad, Taylor, and Owusu (2010) finds that factors such as rail access, population density, and proximity to blending terminals are influential in determining site selection for ethanol plants. A subset of the business loca-tion literature focusing on foreign multinational corporations suggests that production factors also influence where these firms will establish branch plants in the United States (Brush, Maritan, & Karnani, 1999; Friedman, Gerlowski, & Silberman, 1992; Woodward, 1992). For a helpful summary of business location factors included in past studies, see Karakaya and Canel (1998).

Location Factors and the Knowledge EconomyAlthough the realities of doing business in a global, postin-dustrial society have rendered some traditional location fac-tors far less relevant (if not obsolete) for many companies, geographic location is still a key component of competitive advantage (Porter, 2000). Rather than focusing on an area’s tax rates, labor costs, or infrastructure, however, business owners competing in the knowledge economy often locate near the epicenter of their industry to increase opportunities for collaboration with other firms and research institutions, foster innovation and entrepreneurship, and leverage institu-tionalized business and cultural practices (see, e.g., Allen & Potiowsky, 2008; Delgado, Porter, & Stern, 2010; Manning, 2008; Porter, 2000; Schoales, 2006).

Given that the success of knowledge-based firms often depends on their ability to attract and retain quality skilled workers, much of the location research in recent years has emphasized the role of quality-of-life factors in site selection (see, e.g., Dissart & Deller, 2000; Gottlieb, 1995; Granger & Blomquist, 1999; Green, 2001; Klier, 2006; Love & Crompton, 1999; Rast & Carlson, 2006; Salvesen & Renski, 2003). The notion that amenities such as cultural attractions, a vibrant nightlife, and natural beauty may influence where workers (and, thus, businesses) locate has received wide-spread popular and scholarly attention since the introduction of Richard Florida’s (2002) creative class thesis. Though the subject of considerable criticism and controversy (Glaeser, 2004; Hoyman & Faricy, 2009; Malanga, 2004; Markusen, 2006; Zukin, 2009), Florida’s argument that highly educated, high-income workers in the “creative economy” actively seek out exciting, diverse, tolerant, and “hip” communities



36 Economic Development Quarterly 26(1)

in which to live and work has proven extremely appealing to local officials, and thus, continues to be the subject of aca-demic research (e.g., Bieri, 2010).

Location Factors and Complex Industries: The Case of High-Technology FirmsAlthough the specific location factors emphasized in the manu-facturing economy and the knowledge economy may vary, this rough taxonomy has limited utility when it comes to describing the activities of certain types of firms. As Mellander (2009) argues in her study of occupations in the creative industries, researchers often conflate an industry’s products with its pro-cesses and fail to recognize how diverse the occupational distribution of a given industry can be. Indeed, she explains:

a large high-tech firm can include occupations such as accountants, software engineers, traditional manufac-turing jobs, health-care assistants, and service jobs at the food court. Each of these would be counted as employees in the high-tech industry, but in practice they have very different tasks to perform on an every-day basis. (p. 294)

Similarly, several studies of the location preferences of high-technology firms demonstrate the extent to which the disparate activities characteristic of such establishments demand attention to different types of factors or conditions

(see, e.g., Alcacer & Chung, 2007; Appold, 1991; Cohen, 2000; DeNoble & Galbraith, 1992; DeVol, 1999; Feldman, 1999; Hart, Denison, & Henderson, 1989; Haug, 1991; Galbraith, Rodriguez, & DeNoble, 2008; Malecki, 1987; Salvesen & Renski, 2003). In particular, while sharing some of the production needs of traditional manufacturing firms, high-technology firms also typically house R&D facilities that have unique labor requirements (i.e., highly educated and trained scientists and engineers) and specialized infor-mation flows (e.g., Feldman, 1999) more representative of the knowledge economy. Furthermore, DeNoble and Galbraith (1992) conclude that the business strategy pursued by any given firm may dictate which location factors are most relevant, as firms that seek to differentiate themselves from the competition may place a premium on locations that allow them to attract more skilled workers, whereas those competing on a cost basis may care more about traditional production-related factors (Karakaya & Canel, 1998).

In a similar vein, the mix of manufacturing and R&D activities common to many medical device firms raises inter-esting questions about the importance attached to various location factors. We turn now to overviews of the industry in the United States and Massachusetts.

Medical Devices in the United StatesThe medical device industry is generally defined as a field of producers and suppliers who specialize in implements used in the larger health services sector, from the simple,

Figure 1. Medical device industry employment, United States, 2007Source. U.S. Census Bureau (2010a).




such as syringes, to the complex, such as pacemakers or hearing aids. North American Industrial Classification System (NAICS) Industry Group 3391 (medical equipment and supplies manufacturing), which includes manufacturers of laboratory apparatuses and furniture (339111), surgical and medical instruments (339112), surgical appliance and supplies (339113), dental equipment and supplies (339114), and ophthalmic goods (339115), makes up a core part of the medical device industry. In addition, industry reports typi-cally include firms identified as electromedical and electro-therapeutic apparatus manufacturers (334510), in vitro diagnostic substance manufacturers (325413), and irradia-tion apparatus manufacturers (334517).

Based on 2007 County Business Patterns data (U.S. Census Bureau, 2010a), Figure 1 shows that 29% of U.S. firms in the medical device industry specialize in surgical appliance and supplies manufacturing, 27% in surgical and medical instrument manufacturing, and 16% in electromedi-cal and electrotherapeutic apparatus manufacturing, with the remainder split between the other industry groups. The majority of the establishments in this industry are small—one third of U.S. medical device establishments have 4 or fewer employees and about half have between 5 and 99 employees (see Table 1).

Jobs in the industry pay quite well. In 2007, the average U.S. salary for medical device employees was $58,921, nearly 50% more than the national per capita income of $39,392. This represents an increase of 48% over the 1997-2007 decade, compared with 54% for U.S. jobs as a whole during the same time period (U.S. Census Bureau, 2010b; U.S. Department of Commerce, Bureau of Economic

Analysis, 2010). Medical device workers are also more edu-cated than the U.S. labor force as a whole, with 9.2% holding an associate’s degree, 18.4% holding a bachelor’s degree, and 12% holding graduate or professional degrees, as com-pared with 7.8%, 15.8%, and 6.3%, respectively, of all U.S. workers (Clayton-Matthews, 2001).

Industry growth in the United States was relatively flat in the decade between 1997-2007, registering only modest increases in the number of establishments, from 5,998 to 6,007—a change of 0.15% (U.S. Census Bureau, 2010b). However, the U.S. Department of Labor, Bureau of Labor Statistics (2009) predicts that by 2018 there will be 359,500 jobs in the medical equipment and supplies manufacturing group (NAICS Code 3391) alone, reflecting a higher rate of growth (16.1% between 2008-2018) for the coming decade than the previous decade (from 301,300 jobs in 1998 to 309,700 in 2008—a change of 2.8%).

Based on recent purchasing trends and demographic shifts, this growth is likely to continue over the coming years. The world’s largest consumer of medical devices, the United States saw its consumption of these products increase “by an average annual rate of 6 percent during 2001-5, from $71 billion to $90 billion” (USITC, 2007). Furthermore, as the United States and world populations age at an increas-ingly faster pace, demand for medical devices is projected to rise. Venture capital activity seems to confirm these expecta-tions. United States investments in the industry in 2009 were more than 4 times the dollar amount invested in 1995 (PriceWaterhouseCoopers, 2010). Additionally, between 1997 and 2002 (the latest year of data available), the medical device industry in the United States invested between 10%

Table 1. Medical Device Industry Size by Number of Establishments and Employees, United States, 2007

NAICS code Industry description Establishments, total

Establishments by number of employees

1-4 5-99 ≥100 Employees, total

325413 In vitro diagnostic substances 244 42 144 58 27,215334510 Electromedical and

electrotherapeutic manufacturing599 182 288 129 58,111

334517 Irradiation equipment manufacturing 179 52 103 24 17,738339111 Laboratory apparatus and furniture

manufacturing378 91 236 51 19,273

339112 Surgical and medical instrument manufacturing

1,275 355 678 242 101,760

339113 Surgical appliance and supplies manufacturing

1,960 703 1,039 218 108,150

339114 Dental equipment and supplies manufacturing

774 333 408 33 16,581

339115 Ophthalmic goods manufacturing 604 249 306 49 24,492 Total, medical device industry 6,013 2,007 3,202 804 373,320

Source. U.S. Census Bureau (2010a).




and 13% of its sales revenue into R&D, providing a further basis for future innovation. By way of comparison, Japan and the European Union spend far less, 5.6% and 1.7% of sales, respectively (Pammolli et al., 2005).

Medical Devices in MassachusettsThe medical device industry is part of the broader life sci-ences cluster in Massachusetts, one of the state’s key strate-gic interests. The Massachusetts Life Sciences Center (MLSC), a quasi-public agency, is charged with promoting the life sciences within the state. In 2007, the governor of Massachusetts announced a 10-year, $1-billion life sciences economic development package consisting of programs including a small business matching grant fund, tax incen-tives, cooperative research between academic institutions and industry, and an early-stage company loan fund for busi-nesses within the life sciences (MLSC, 2010).

This initiative was intended to build on an existing strength in the industry. In 2007, the location quotient, or measure of the relative geographic density of the industry, was 3.1 for the United States and 6.8 for Massachusetts. In other words, the proportion of employees working in the medical device industry in Massachusetts was twice that of the nation as a whole (U.S. Census Bureau, 2010a). Furthermore, the average salary for these jobs was $68,332, double the per capita income for the state, and nearly $10,000 more than the national average for the industry (U.S. Census Bureau, 2010b).

As shown in Table 2, whereas California boasts the larg-est medical device workforce in the United States in terms of absolute numbers (69,772 employees), Massachusetts ranks third with 20,906 employees,1 just slightly behind Minnesota with 21,781 employees. On a per capita basis, however, both Minnesota and Massachusetts (along with Delaware, Utah, and Wisconsin) outrank the considerably more populous California. Given its comparatively high cost of living and highly educated workforce, Massachusetts’ annual payroll for medical devices ranks second (behind only California), in absolute dollar amount and first overall on a per capita basis (U.S. Census Bureau, 2010a).

Consistent with national trends, industry growth in Massachusetts has been relatively flat over the decade from 1997 to 2007, both in terms of the number of establishments and the number of employees. At the same time, the total value of product shipments and employee productivity has increased dramatically in the state (and the nation as a whole; see Table 3). Likewise, venture capital investments in Massachusetts increased nearly fivefold over the decade, from $52 million in Q1-Q4 1995 to $247 million in Q1-Q4 2009 (PriceWaterhouseCoopers, 2010). A recent study points to the impact of the medical device industry on the overall state economy: “Every hundred jobs is associated with another 79 jobs in Massachusetts, and every dollar of medical-device output is associated with an additional 45 cents of outputs from Massachusetts firms” (Clayton-Matthews & Loveland, 2004). These trends, coupled with the state’s strategic emphasis on the life sciences, point to the continued—and likely increased—presence of the medical device industry in Massachusetts in coming years.

Data and MethodSample

The Massachusetts Medical Device Industry Council (Mass MEDIC) is an advocacy organization that represents the interests of the medical technology industry at the state and federal levels, and provides a range of informational resources to its private sector members. It is the only organi-zation of its type in the New England region, and thus, is a useful source for data on issues affecting medical equipment and device companies located in and around Massachusetts.

In May 2008, we distributed an online survey to MassMEDIC’s “primary” membership list, composed solely of representatives from medical device firms in the state. Although membership in MassMEDIC is also open to indi-viduals and organizations that provide services to medical device companies (e.g., law firms) or have a financial stake in the industry (e.g., venture capitalists), for the purposes of this study it was important to ensure that respondents were drawn only from the population of interest, that is, leaders of

Table 2. Rankings of Top Five Medical Device States by Production Characteristics in 2007

Per capita Absolute

Employment Annual payroll Employment Annual payroll

1 Minnesota Massachusetts 1 California California2 Delaware Minnesota 2 Minnesota Massachusetts3 Utah Utah 3 Massachusetts New Jersey4 Massachusetts New Hampshire 4 Florida Florida5 Wisconsin California 5 Pennsylvania Pennsylvania

Source. U.S. Census Bureau (2010a).




medical device firms. The contacts on MassMEDIC’s pri-mary membership list are senior-level employees at their respective firms; the vast majority are “C-suite” employees (e.g., CEOs, COOs, etc.). In other words, they are the types of people most likely to play an influential role in important business decisions for the firm.

The survey was e-mailed to the primary membership list by the president of MassMEDIC.2 At the time of the study, this list contained representatives from approximately 300 firms in the state.3 A total of 48 respondents completed the survey, reflecting 16% of the medical technology firms in MassMEDIC’s core membership base.4 Although this response was lower than we would have liked, the president of MassMEDIC was pleased with the outcome and noted that it compared favorably with the response rates typically achieved in research efforts sponsored by the organization.5 At our request, the president did send a reminder e-mail 10 days after the initial solicitation (the number of respondents prior to the reminder was closer to 30), but given that other important organizational communications and research ini-tiatives were waiting in the queue, he was reluctant to pursue other follow-up measures beyond that point.

One of the main concerns with low response rates is the risk of systemic bias in the data. Studies of the use of web-based surveys have highlighted two potential problems, nei-ther of which is in clear evidence here (Solomon, 2001). The possibility of coverage bias or sampling bias—which would be concerns if segments of the target population had restricted access to e-mail or the Internet, or were inexperienced or uncomfortable with online activity—does not seem likely. The members of MassMEDIC are regular users of e-mail and the Internet, as these are the typical modes of communi-cation used to share organizational information with mem-bers. In addition, given the technical sophistication of the industry, the medical device community is likely more “wired” on the whole relative to other populations.

Similarly, although the overall response rate was on the low side, there does not seem to be cause to suspect nonre-sponse bias (i.e., that the nonrespondents in the sample differ in a meaningful way from those who did respond). The pro-file of the firms in the final sample closely mirrors that of MassMEDIC’s overall membership base, which itself is a good representation of the sector as a whole in the state (Clayton-Matthews, 2001; Goodman, 2007).6 According to a 2001 report of the sector in Massachusetts, the industry employing the most workers in the state is surgical and medical devices, followed by the electromedical and electrotherapeutic apparatus industry (Clayton-Matthews, 2001). Together, these two industries employ more than 60% of the workers in the medical device sector in Massachusetts (Goodman, 2007). Similarly, the largest percentage of firms in our sample (50%) reported a specialty in surgical and medical devices while the second largest segment focuses on electromedical apparatuses (35%).7 The remaining industries in the sector are markedly smaller, in both our sample, and in the state as a whole.8

Likewise, the majority of the firms in both Massachusetts and in our sample are relatively small: 73% of our respon-dents employ 50 or fewer people, whereas census data reveal that half of the state’s firms in 1998 employed fewer than 20 people (Clayton-Matthews, 2001). Consistent with the sector as a whole, the firms in our sample are mostly concentrated in the Greater Boston area, with comparatively fewer located in the western, northern, and southern regions of the state (Goodman, 2007).

The firms in the sample maintain a variety of facility types in Massachusetts, including those typically associated with the knowledge economy (e.g., R&D and corporate/office buildings) as well as traditional manufacturing. More than half (54%) reported that their company housed both manufacturing facilities and corporate/R&D facilities in Massachusetts. When completing the survey, most respon-dents were able to draw on recent experiences opening or relocating facilities in the state. More than a quarter (28%) opened or relocated a facility within the past year and another 36% reported doing so in the past 2 to 4 years. Slightly more

Table 3. Medical Device Industry Trends, Massachusetts and the United States, 1997-2007

United States Massachusetts

No. of firms 1997 5,998 264 2002 6,017 252 2007 6,007 261 No. of employees 1997 353,473 20,755 2002 375,772 22,605 2007 364,502 22,728 Average salary ($) 1997 39,840 47,674 2002 45,946 51,173 2007 58,921 68,332 Value of shipments ($1,000) 1997 65,146,783 4,158,347 2002 86,307,221 5,456,194 2007 117,466,464 8,059,711 Output per worker ($) 1997 184,305 200,354 2002 229,680 241,371 2007 322,266 354,616

Source. U.S. Census Bureau (2010b).




than half (51%) stated that it was at least “somewhat likely” that they would open or relocate another facility in Massachusetts within the next 2 years. Sample characteris-tics are summarized in Table 4.

Survey InstrumentRespondents were asked to rank the importance of 39 differ-ent factors in the location decision process using a 4-point scale (1 = unimportant, 2 = moderately important, 3 = impor-tant, 4 = very important). These factors fell into six broad topical categories: (a) labor, (b) permitting processes, (c) development and operating costs, (d) business environment, (e) transportation and access, and (f) quality of life/social environment. An open-ended section that followed allowed respondents the opportunity to (a) mention any additional factors that they believed were important to the location decision for medical device firms and (b) highlight what they thought were the five most critical factors influencing site decisions for such firms. The survey also included a brief series of questions concerning the number, size, location, and business functions of the firm’s facilities. The complete sur-vey instrument is presented in the appendix.

In an effort to keep the length of the survey manageable (and thus increase the likelihood of response), we did not collect extensive demographic information on the firms. Admittedly, this limits some of the conclusions that can be drawn from our data. We cannot, for example, speak to how the age or size (as measured by revenues) of the firm influ-ences the importance of the location factors in question. Likewise, although we know whether the firms maintain facilities outside Massachusetts, we did not capture detailed information on where these facilities are located or what types of facilities they are. We discuss the implications of these and other methodological limitations in greater detail in the last section of the article.

ResultsLocation Factors and Medical Device Firms

Based on the survey data, the availability of an appropriate labor force in the region (mean = 3.77, on a scale from 1 to 4,) is the single most important factor driving site selection for medical technology firms. The availability of on-site parking (mean = 3.48), the timeliness of approvals and appeals (mean = 3.34), the crime rate in the local area (mean = 3.34), and state tax/financial incentives (mean = 3.30) round out the top five most highly rated factors.

At the other end of the spectrum, respondents agreed that the existence of a municipal minimum wage law (mean = 1.44), the presence of strong trade unions (mean = 1.48), access to railroads (mean = 1.61), the awareness of strong neighborhood organizations (mean = 2.15), and an informative

Table 4. Sample Characteristics (n = 48)

Percentage

Primary business Surgical and medical instruments 50 Electromedical and electrotherapeutic

apparatuses35

Ophthalmic goods 15 Surgical appliances and supplies 13 In vitro diagnostic substances 6 Laboratory apparatuses and furniture 4 Other (mix of existing categories,

subspecialties)8

Number of employees in the United States 0-50 62 50-100 6 100-200 2 200-500 10 500-1,000 4 >1,000 15 Number of employees in Massachusetts 0-50 73 50-100 2 100-200 4 200-500 6 500-1,000 6 >1,000 8 Regional presence Greater Boston 60 Central Massachusetts 15 Northeastern Massachusetts 13 Southeastern Massachusetts 9 Pioneer Valley 4 Berkshires 0 Cape and the Islands 0 Types of facilities in Massachusetts Research and development 79 Corporate/office 77 Manufacturing 58 Both manufacturing and R&D or corporate/

office54

No manufacturing 42 Length of time since company opened/relocated

facility

In the past 6 months 13 In the past year 15 In the past 2-4 years 36 In the past 5-10 years 17 >10 years ago 19 Likelihood of firm opening/relocating facility in

next 1-2 years

Very likely 23 Somewhat likely 28 Not sure 9 Unlikely 40




Table 5. Factors Influencing the Location Decision for Medical Technology Firms

Factor Mean ratinga

Availability of appropriate labor 3.77On-site parking 3.48Timeliness of approvals/appeals 3.34Crime rate in the area 3.34State tax/financial incentives 3.30Quality/capacity of infrastructure 3.21Rental rates 3.20Predictability/clarity of permitting

process3.19

Access to major highways 3.19Local tax/financial incentives 3.09Access to airports 3.06Complementary/supplemental business

services3.02

State tax rates 3.02Proximity to research institutions/

universities3.00

Cost of housing for employees 3.00Municipal reputation as a good place to

work2.99

Quality of local schools 2.98Competitive labor costs 2.96Traffic congestion 2.96Physical attractiveness 2.91Property taxes 2.89Fast-track permitting 2.89Municipal reputation for economic

development2.83

Municipal reputation as a good place to live

2.83

Zoning “by right” for proposed use 2.83Undesirable abutting land use 2.79Permitting ombudsman 2.72Critical mass of similar firms 2.72Land costs 2.70Awareness of brownfield

contamination2.52

Public transportation 2.50Proximity to restaurants and shops in

surrounding area2.40

Availability of cultural/sports amenities

2.38

Availability of customized workforce training

2.34

Informative municipal website 2.17Awareness of strong, active

neighborhood organizations2.15

Access to railroads 1.61Existence of strong trade unions 1.48Existence of a municipal minimum

wage law1.44

a. On a scale from 1 to 4, where 1 = unimportant, 4 = very important.

municipal website (mean = 2.17) are relatively unimportant when deciding where to locate medical technology facilities.

Falling somewhere in between are a number of other fac-tors frequently cited in the business location literature. These include, for example, housing costs (mean = 3.00), the qual-ity of local schools (mean = 2.98), labor costs (mean = 2.96), state and property taxes (mean = 3.02 and 2.89, respec-tively), the existence of a critical mass of similar firms in the area (mean = 2.72), and access to public transportation (mean = 2.50). Mean ratings for all 39 survey items are pro-vided in Table 5.

Although these data offer a snapshot of the relative impor-tance of various factors in the location decisions of medical device companies, it is certainly possible that there are other influential factors that were overlooked in the survey instru-ment. Thus, in Part III we asked respondents to identify any additional factors (i.e., items not included in Part II) that play a role in determining where medical device firms locate. Based on the responses, it appears that the survey effectively captured the key factors driving site selection for these busi-nesses. Only 27% of respondents (n = 13) named any addi-tional factors at all, and none of these new factors received multiple mentions.9 In fact, with the exception of a few spe-cific items (e.g., “the weather,” “access to capital,” “reputa-tion for innovation,” and “whether we have an existing presence in a given area”), most of the additional factors cited were variations or elaborations on existing survey items.

For the second question in Part III, respondents were asked to identify the five most critical factors in the location decision for medical technology firms. Since this was an open-ended question, respondents were free to nominate any factor or con-dition that they believed was among the most important. To enable analysis of these data, we created content categories and coded all the responses into one of these categories.

The qualitative responses reinforce the previous finding concerning the absolute primacy of the availability of a suit-able labor force in the location decision. Nearly all respon-dents (90%) mentioned this in their lists of the top five factors driving site selection for medical technology firms. No other single factor generated anywhere near the same degree of consensus on the open-ended question. (The next most frequently cited factor—“quality of life/amenities/good place to live”—garnered mention by only one third of the respondents.) The combined results from the factor rat-ings and the open-ended question provide convincing evi-dence that for a municipality to be considered a viable candidate for this type of business, a workforce with the req-uisite education and skills must be readily accessible.

Group Differences: Facility TypeNext, we sought to determine whether respondents’ ratings of the location factors in question differed on the basis of the type(s) of facilities these firms maintained in Massachusetts.




Although all but two respondents worked for firms that had corporate and/or R&D facilities in Massachusetts, only slightly more than half (54%) of these respondents reported that their companies also had manufacturing facilities in the state. In other words, of all the firms surveyed, 42% did not have any manufacturing plants in their local portfolio—all their properties were devoted to the types of knowledge-related activities characteristic of firms in the postindustrial economy. We thus divided the respondents into two categories: (a) those that had manufacturing facilities in the state (n = 28) and (b) those that did not have manufacturing facilities in the state (n = 20).

As demonstrated in Table 6, firms with and without man-ufacturing facilities rated most location factors similarly. Based on an independent means t test, there was no signifi-cant difference between the mean ratings of the firms with local manufacturing facilities and those without local manu-facturing facilities on 33 of the 39 factors. Nevertheless, the differences that did emerge are worth noting. Respondents who worked for firms with manufacturing facilities in Massachusetts were significantly more likely to rate strong trade unions (t = 2.18, p < .036), the existence of a municipal minimum wage law (t = 2.24, p < .030), land costs (t = 3.18, p < .003), and the quality and capacity of infrastructure (t = 3.11, p < .003) more highly than respondents whose firms did not maintain manufacturing facilities in the state. At the same time, these respondents were significantly less likely to attach as much importance to the site’s proximity to restau-rants and shops (t = −2.09, p < .042) or its proximity to research institutions and universities (t = −1.70, p < .096), though the latter finding is only marginally significant.

Group Differences: Size, Regional Location, and Recency of Location ActivityLast, we examined whether there were significant differ-ences in the factor ratings based on (a) the size of the firm, (b) its location within Massachusetts, or (c) how recently the firm had opened or relocated a facility in the state. A com-parison of ratings based on company size yielded only one notable difference. Larger firms (defined here as those employing at least 50 employees) were more likely than smaller firms (those with fewer than 50 employees) to place importance on land costs (t = 2.63, p < .014). There were no significant differences on the remaining location factors.

A comparison of ratings from firms in the Greater Boston area and those outside Greater Boston revealed five statisti-cally significant findings. Firms located outside the Boston area were more likely to cite the importance of a permitting ombudsman in the municipality (t = 2.36, p < .024), land costs (t = 2.08, p < .044), the existence of on-site parking for employ-ees (t = 2.42, p < .019), property tax rates (t = 3.53, p < .001), and state tax rates (t = 3.44, p < .001) than their counterparts in Greater Boston.

Last, an examination of mean factor ratings based on the recency of location activity produced several interesting dif-ferences. Firms that had opened or relocated a facility in Massachusetts relatively recently (defined here as within the past 4 years) were significantly more likely to assign higher ratings to a municipality’s proximity to restaurants and shops (t = 2.09, p < .044), access to highways (t = 2.46, p < .021), and the physical attractiveness of the area (t = 2.37, p < .024). In contrast, firms that had not made a local move within the past 4 years placed greater importance on competitive labor costs (t = 2.85, p < .007) and land costs (t = 3.06, p < .004). Although these data are only preliminary, the findings con-cerning regional location and recency of location activity seem worthy of future attention. Given the questions that motivated the present study, the emphasis on “quality of life” factors among the firms who had recently engaged in the site selection process is particularly interesting. Complete group means data from this section are available from the authors on request.

DiscussionThe most significant finding from this study concerns the importance that medical device firms place on the avail-ability of an appropriate workforce in a given region when making location decisions. Both the mean ratings and the open-ended survey question indicate that respondents see this as the most critical factor influencing site selection, thus lending additional support to previous research high-lighting labor as a major determinant of location decisions (e.g., Cohen, 2000; Cortright, 2001; Dumais, Ellison, & Glaeser, 1997; Fulton & Shigley, 2001). Although the present study did not ask respondents to name the specific criteria that they look for when assessing the labor pool in a certain area, the education profile of the industry’s workforce suggests that regions with a highly educated population are likely to be particularly attractive to medi-cal device firms. This is consistent with the emphasis on human capital characteristic of businesses in the knowl-edge economy (see, e.g., Almazan, deMotta, & Titman, 2007; Berry & Glaeser, 2005; Florida, 2002; Glaeser & Kohlhase, 2004).

Beyond the clear consensus around the importance of labor, however, the broader question concerning the relative weight assigned to production-related factors and knowledge-related factors in the site selection process is considerably more complicated.10 Although the lowest rated items (i.e., those receiving a mean score of less than 2), are all traditional economic factors—access to railroads, strong trade unions in the area, the existence of a municipal minimum wage law—some of the most highly rated items, concerning issues such as state and local taxes, infrastructure, and rental rates, are also production-related location factors. Similarly, although among the most highly rated items are several factors




commonly cited in the knowledge economy literature for their quality of life and agglomeration benefits (crime rates, access to highways and airports, proximity to research institu-tions and universities, housing costs), other factors presumed to be important for the purposes of attracting highly skilled and educated workers—public transportation, proximity to

restaurants and shops, the availability of cultural/sports amenities—received far lower ratings. These findings sug-gest that location decisions in the medical device industry, driven largely by labor needs, are also influenced by a range of other factors captured by both classic and contemporary location theories.11

Table 6. Factors Influencing the Location Decision for Medical Technology Firms With and Without Manufacturing Facilities in Massachusetts

Factor

Mean rating

Manufacturing (n = 28)

No manufacturing (n = 20) t Significance (two-tailed)

Availability of appropriate labor 3.79 3.75 0.256 .799On-site parking 3.50 3.45 0.290 .773Timeliness of approvals/appeals 3.29 3.42 −0.713 .479Crime rate in the area 3.26 3.45 −1.08 .286State tax/financial incentives 3.04 3.00 −0.018 .986Quality/capacity of infrastructure 3.48 2.85 3.11 .003***Rental rates 3.04 3.42 −1.56 .125Predictability/clarity of permitting process 3.21 3.16 0.290 .773Access to major highways 3.15 3.25 −0.529 .599Local tax/financial incentives 3.11 3.05 0.247 .806Access to airports 2.96 3.20 −1.07 .291Complementary/supplemental business services 2.93 3.15 −0.924 .360State tax rates 3.04 3.00 0.169 .867Proximity to research institutions/universities 2.78 3.30 −1.70 .096*Cost of housing for employees 3.00 3.00 0.000 1.00Municipal reputation as a good place to work 2.93 3.05 −0.566 .574Quality of local schools 3.11 2.80 1.15 .256Competitive labor costs 3.00 2.90 0.439 .662Traffic congestion 2.86 3.10 −1.04 .303Physical attractiveness 2.81 3.05 −1.07 .291Property taxes 2.96 2.80 0.675 .503Fast-track permitting 2.89 2.89 −0.007 .994Municipal reputation for economic development 2.93 3.05 1.20 .238Municipal reputation as a good place to live 2.78 2.90 −0.586 .561Zoning “by right” for proposed use 2.93 2.67 0.920 .366Undesirable abutting land use 2.86 2.70 0.669 .507Permitting ombudsman 2.71 2.74 −0.079 .937Critical mass of similar firms 2.63 2.85 −0.784 .437Land costs 3.07 2.20 3.18 .003***Awareness of brownfield contamination 2.62 2.40 0.716 .478Public transportation 2.50 2.50 0.000 1.00Proximity to restaurants and shops in area 2.21 2.65 −2.09 .042**Availability of cultural/sports amenities 2.30 2.50 −0.974 .335Availability of customized workforce training 2.48 2.15 1.15 .258Informative municipal website 2.25 2.06 0.657 .514Awareness of strong, active neighborhood

organizations2.07 2.26 −0.706 .484

Access to railroads 1.56 1.70 −0.719 .476Existence of strong trade unions 1.68 1.20 2.18 .036**Existence of a municipal minimum wage law 1.61 1.20 2.24 .030**

*p < .10. **p < .05. ***p < .01.




A second objective of this study was to examine the extent to which the ratings assigned to various location factors dif-fered depending on whether the respondent’s firm main-tained manufacturing facilities in Massachusetts. Although we found no significant differences on the majority of survey items, it is instructive to consider the six location factors that did register statistically significant group differences, as these comport with what location theory might suggest about the way that production-intensive and knowledge-intensive activities fuel a preference for certain locational attributes. Respondents whose firms had manufacturing facilities in Massachusetts were more likely to attach importance to some classic production-related location factors—trade unions, minimum wage laws, land costs, and infrastructure—and less likely to do so for two factors commonly associated with the knowledge economy—proximity to restaurants and shops, and proximity to research institutions and universities. Granted, the absolute importance attributed to most of these items (with the exception of the quality of the infrastructure and proximity to research institutions) by both groups was still rather low. Nevertheless, the data provide initial evi-dence that the facility types in a firm’s portfolio—and not merely the industry in which the firm competes—may influ-ence how decision makers evaluate given location factors (see, Nelson, 2005, 2006, for a discussion of how accounting firms locate specific business functions in different places).

Limitations and Directions for Future ResearchOne of the greatest challenges in conducting location research is determining the most appropriate method for the task. Studies in this area typically use one of two broad strat-egies: surveys of the decision makers involved in the site selection process, or econometric models that measure the extent to which certain local or regional characteristics pre-dict the location patterns of firms (Blair & Premus, 1987; Carlson, 2000). Each approach has obvious strengths and weaknesses. Studies that make use of statistical models, for example, are constrained by the available data, which may not include enough microlevel information to be of practical value, or may lack useful measures for some qualitative aspects of the location decision. Survey-based research, on the other hand, relies on the respondent’s knowledge and memory of the decision-making process, both of which may be limited (or subject to conscious manipulation if the respondent believes that his or her answers may have some influence on industrial recruitment policies; Blair & Premus, 1987; Carlson, 2000). The present study, then, like other location research using surveys is vulnerable to the latter set of concerns. It is unclear, for instance, whether the differ-ences observed between firms that had recently made a move in Massachusetts and those that had not is indicative of an actual increased emphasis on quality-of-life factors in

the medical device industry, or the result of recollection dif-ferences between the two groups.

There are, in addition, some limitations that are more spe-cific to the design of this study. First, the sample size is quite small for survey-based research. Although we are comfort-able that our sample is a reasonable representation of the population of medical device firms in Massachusetts, a data set of only 48 responses does limit the types of statistical analyses that are feasible or appropriate. In particular, the findings from our group comparisons, although interesting and relevant for theoretical purposes, should be interpreted cautiously. In addition, although our data offer insight into how Massachusetts firms evaluate various location factors, the small sample precludes generalization of our findings to the site selection process for medical device firms nation-wide or internationally.

Second, the survey instrument did not include a number of questions that would have allowed for multivariate analy-ses of the location decision. A primary goal of this study was to understand the perceived importance of a wide range of detailed location factors (as opposed to a truncated list of general factors). At the same time, we were cognizant of the need to keep the survey a reasonable length to improve the likelihood of response. Consequently, we opted for depth with the location factor items, but this did come at the expense of a number of relevant demographic questions that would have further informed our findings. For instance, since we only asked respondents to report the types of facili-ties that their firms maintain in Massachusetts, it is possible that these companies own—and, potentially, have made location decisions concerning—facilities in other states or countries. Thus, even those respondents whose firms do not have manufacturing facilities in Massachusetts (i.e., the “no manufacturing” group) may have experience selecting loca-tions for manufacturing plants outside the state and reflected on those experiences when rating the location factors on the survey. Future research should seek to clarify the sources of subjects’ respondents’ perceptions by asking them to identify specific projects or location decisions, and evaluate site selection criteria on the basis of those cases. In particular, capturing two different sets of ratings—for example, one for location decisions affecting a firm’s manufacturing facilities and one for decisions concerning its R&D operations or cor-porate headquarters—would allow for a more direct and rig-orous analysis of the effect that a property’s intended business function has on the importance attached to various locational attributes.

Similarly, although we asked how recently firms had opened or relocated a facility in the state, it would also be instructive to know how old the company is or at what stage in the life cycle it is (start-ups, e.g., likely think about the location decision quite differently than more established firms do), as well as the total number of facilities of each type the firm maintains in its portfolio. Whether the firm




rents or owns its properties also has clear implications for how decision makers evaluate various location factors, as many of the factors in question may be experienced differ-ently (or not at all) depending on ownership status. Thus, although our findings offer preliminary evidence regarding the importance of a number of key factors in the site selec-tion process for medical device firms, additional research is needed to better establish how firm-specific characteristics influence location outcomes.

ConclusionIn this article, we have argued that industries with a mix of production-related and knowledge-related activities, such as medical devices, offer an interesting case study for loca-tion theory and research. Beyond these scholarly pursuits, however, the data presented here also have implications for government officials or economic development staff who wish to attract medical device firms to their commu-nity. The findings clearly emphasize the need for local leaders to understand the labor requirements of medical device firms, and to realistically assess the education and skill levels in their region’s workforce, to determine whether the medical device industry would be a good fit. A number of other factors—including the clarity and effi-ciency of the development process, crime rates, taxes and incentives, rental rates, infrastructure quality, and trans-portation access—appear to be important, but are unlikely to pique a firm’s interest if the requisite labor needs cannot be met.

With the passage of the health care reform bill in 2010, the location decisions of medical device firms are likely to come under additional scrutiny. (The legislation mandates an excise tax on the sale of medical devices, effective January 1, 2013.) Although industry leaders have raised concerns that the newly imposed tax may increase the need to shift more business overseas,12 it remains to be seen whether the addi-tional costs will have an appreciable impact on the way in which medical device firms evaluate and select new sites for their operations.

AppendixSurvey Instrument

Part I1. How recently has your company opened or relo-

cated a MedTech facility in the United States?a. In the past six monthsb. In the past yearc. In the past 2 to 4 yearsd. In the past 5 to 10 yearse. More than 10 years agof. Not sure

2. How recently has your company opened or relo-cated a MedTech facility in Massachusetts?

a. In the past six monthsb. In the past yearc. In the past 2 to 4 yearsd. In the past 5 to 10 yearse. More than 10 years agof. Not sure

3. How likely is it that your company will open or relocate a MedTech facility in Massachusetts in the next 1 to 2 years?

a. Very likelyb. Somewhat likelyc. Unlikelyd. Not sure

4. What types of MedTech facilities does your com-pany maintain in Massachusetts? (Check all that apply.)

a. Research and developmentb. Manufacturingc. Corporate/officed. Other, please specify

5. Which of the following best describes your com-pany’s primary business? (Check all that apply.)

a. In vitro diagnostic substancesb. Electromedical and electrotherapeutic appara-

tusesc. Irradiation apparatusesd. Laboratory apparatuses and furnituree. Surgical and medical instrumentsf. Surgical appliances and suppliesg. Ophthalmic goodsh. Other, please specify

6. Approximately how many employees does your company currently employ in the United States?

a. 0-50b. 50-100c. 100-200d. 200-500e. 500-1,000f. More than 1,000

7. Approximately how many employees does your company currently employ in Massachusetts?

a. 0-50b. 50-100c. 100-200d. 200-500e. 500-1,000f. More than 1,000

8. In which region(s) in Massachusetts does your company have MedTech facilities?

a. Berkshiresb. Pioneer Valleyc. Central Massachusetts




d. Greater Bostone. Northeastf. Southeastg. Cape and the Islands

Part II The following section lists a number of factors that compa-nies may take into account when deciding where to locate a MedTech facility. Please rate the importance of each factor, based on your experience with a current or recent siting or relocation decision.

In your opinion, how important is each of the following—as either an asset or a deterrent—in the decision to locate a MedTech facility in a particular municipality or site within that municipality?

Labora. Availability of appropriate labor pool in the regionb. Competitive labor costsc. Existence of a municipal minimum wage lawd. Existence of strong trade unions in the municipalitye. Availability of customized workforce trainingPermitting processesa. Predictability/clarity of permitting processb. Timeliness of approvals and appealsc. Permitting ombudsman (single point of contact in

municipality)d. Zoning “by right” for proposed usee. “Fast track” or concurrent permittingf. Municipal reputation for economic developmentg. Fully informative municipal websiteh. Awareness that there are strong, active neighbor-

hood organizations in the municipalityDevelopment/operating costsa. Property tax ratesb. State tax ratesc. Local tax/financial incentivesd. State tax/financial incentivese. Land costsf. Real-estate rental ratesg. Quality and capacity of basic infrastructure (e.g.,

water, sewer)h. Awareness of brownfield contaminationBusiness environmenta. Critical mass of similar firms in areab. Municipal reputation as a good place to workc. Proximity to research institutions and universitiesd. Availability of complementary/supplemental busi-

ness servicesTransportation and accessa. Public transportationb. On-site parking for employeesc. Access to airports

d. Access to major highwayse. Access to railroadsf. Traffic congestiong. Proximity to restaurants and shops in surrounding

areah. Undesirable abutting land useQuality of life/social environmenta. Municipal reputation as a good place to liveb. Cost of housing for employeesc. Crime rate in the aread. Physical attractiveness of areae. Availability of cultural/sports/recreational amenitiesf. Quality of local primary and secondary schools

Part III1. Please list any other factors not included above

that play an important role—as either an asset or a deterrent—in the decision to locate a MedTech facility in a particular municipality or site within that municipality.

2. Taking into account all factors discussed above, please identify the five factors that, in your expe-rience, play the most critical role in the location decision.

Acknowledgments

The authors would like to thank Barry Bluestone, Don Zizzi, and Tom Sommer, MassMEDIC’s president, for their assistance with this research. We are also grateful to three anonymous reviewers for their very helpful feedback on an earlier draft of this article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

1. This figure is lower than the number reported in Table 3 because the County Business Patterns and the Economic Census employee data are collected using different methods. The County Business Patterns data are derived from adminis-trative employment and payroll records, which the U.S. Census Bureau acknowledges results in a certain amount of underre-porting. The Economic Census data are collected by surveys to all establishments, plus administrative data for a sample of small employers and nonemployers.

2. We felt confident that an e-mail coming directly from the presi-dent, indicating his approval of the project, would generate a stronger response rate than an e-mail from a university to which most members likely have no attachment.




3. We were unable to confirm exactly how many of the e-mails were actually received or opened. However, given that this e-mail list is the primary means of contact for the organization, it is maintained regularly, and thus it is likely that the number of “undeliverables” was minimal. If anything, by assuming a sample population of 300 firms, it is possible that we have understated our response rate slightly.

4. In one case, two different individuals from the same firm com-pleted the survey. Because we did not want to arbitrarily select one set of responses from this firm, we decided to retain both respondents in the sample. Admittedly, this is not ideal. However, given the small sample size, we were reluctant to drop any data.

5. Research on the response rates of e-mail or web-based surveys relative to mail surveys is mixed. A few studies have found better response rates with e-mail surveys; others have found lower response rates relative to mail surveys, or no effective difference between the methods (Kaplowitz, Hadlock, & Levine, 2004). Overall, studies have found response rates to vary from roughly 6% to more than 60%, depending on the context and population (Fricker & Schonlau, 2002; Kaplowitz et al., 2004).

6. The president of MassMEDIC estimates that the overwhelming majority (likely 80% to 90%) of medical device firms in the state are members of MassMEDIC.

7. Respondents were allowed to select more than one industry type; thus, the total for all industries exceeds 100%.

8. One important difference worth noting is the absence in our sample of any firms in the irradiation apparatuses industry. Thus, our findings may not apply to this industry, which employs approximately 11% of the medical device workers in Massachusetts.

9. The low response to this question was likely not due to reluc-tance or unwillingness to answer an open-ended question, as the vast majority of respondents (85%) answered the second question in Part III, which also required a write-in response.

10. One curious result concerns the availability of on-site parking. Although this factor received the second-highest rating among the 39 items in the survey, only 2% of respondents mentioned it in the open-ended section of the questionnaire, where respon-dents were asked to identify the top five most critical location factors. One possible explanation for this discrepancy is that although parking is considered a very important asset, it is not necessarily a “deal breaker” or “deal maker.” In other words, the availability of parking, in and of itself, may not be enough to make a municipality a desirable candidate for business, nor is a lack of parking sufficient to disqualify an otherwise attrac-tive location.

11. A third category of location factors, those related to the devel-opment process itself as opposed to business operations, is also worth mentioning. Two of these factors—the timeliness of approvals and appeals, and the predictability and clarity of permitting processes—were among the most highly rated items in the survey. For a discussion of the meaning that real-estate professionals attribute to these factors, see Kimelberg (2010).

12. See, for example, www.massdevice.com for discussions with industry leaders on the topic of the excise tax and its potential impacts.

References

Alcacer, J., & Chung, W. (2007). Location strategies and knowl-edge spillovers. Management Science, 53, 760-776.

Allen, J., & Potiowsky, T. (2008). Portland’s green building cluster: Economic trends and impacts. Economic Development Quar-terly, 22, 303-315.

Almazan, A., deMotta, A., & Titman, S. (2007). Firm location and the creation and utilization of human capital. Review of Eco-nomic Studies, 74, 1305-1327.

Appold, S. (1991). The location processes of industrial research laboratories. Annals of Regional Science, 25, 131-144.

Arsen, D. (1997). Is there really an infrastructure/economic devel-opment link? In L. R. Bingham & R. Mier (Eds.), Dilemmas of urban economic development (pp. 82-98). Thousand Oaks, CA: SAGE.

Barkley, D., & McNamara, K. (1994). Manufacturers’ location decisions: Do surveys provide helpful insights? International Regional Science Review, 17, 23-47.

Bartik, T. (1985). Business location decisions in the United States: Estimates of the effects of unionization, taxes and other char-acteristics of states. Journal of Business & Economic Statistics, 3, 14-22.

Berry, C., & Glaeser, E. (2005). The divergence of human capital levels across cities. Papers in Regional Science, 84, 407-444.

Bieri, D. (2010). Booming Bohemia? Evidence from the U.S. high-technology industry. Industry and Innovation, 17, 23-48.

Blair, J., & Premus, R. (1987). Major factors in industrial location: A review. Economic Development Quarterly, 1, 72-85.

Brush, T., Maritan, C., & Karnani, A. (1999). The plant location deci-sion in multinational manufacturing firms: An empirical analysis of international business and manufacturing strategy perspec-tives. Production and Operations Management, 8, 109-132.

Buss, T. (2001). The effect of tax incentives on economic growth and firm location decisions: An overview of the literature. Eco-nomic Development Quarterly, 15, 90-105.

Carlson, V. (2000). Studying firm locations: Survey responses vs. econometric models. Journal of Regional Analysis & Policy, 30, 1-22.

Christaller, W. (1966). Central places in southern Germany (C. W. Baskin, Trans.). Englewood Cliffs, NJ: Prentice Hall. (Original work pub-lished 1933)

Clayton-Matthews, A. (2001). The medical device industry in Mas-sachusetts. Lowell: University of Massachusetts Donahue Institute.

Clayton-Matthews, A., & Loveland, R. (2004). Medical devices: Supporting the Massachusetts economy. Lowell: University of Massachusetts Donahue Institute.

Cohen, N. (2000). Business location decision-making and the cit-ies: Bringing companies back. Washington, DC: Brookings Institution, Center on Urban and Metropolitan Policy.




Cortright, J. (2001). New growth theory, technology and learn-ing: A practitioner’s guide. Review of Economic Literature and Practice: No. 4. Washington, DC: U.S. Economic Development Administration.

Delgado, M., Porter, M., & Stern, S. (2010). Clusters and entrepre-neurship. Journal of Economic Geography, 10, 495-518.

DeNoble, A., & Galbraith, C. (1992). Competitive strategy and high technology regional/site location decisions: A cross-country study of Mexican and U.S. electronic component firms. Journal of High Technology Management Research, 3, 19-37.

DeVol, R. (1999). America’s high-tech economy: Growth, devel-opment, and risks for metropolitan areas. Santa Monica, CA: Milken Institute.

Dissart, J., & Deller, S. (2000). Quality of life in the planning litera-ture. Journal of Planning Literature, 15, 135-161.

Dumais, G., Ellison, G., & Glaeser, E. (1997, November). Geo-graphic concentration as a dynamic process (NBER Working Papers 6270). Cambridge, MA: National Bureau of Economic Research.

Feldman, M. (1999). The new economics of innovation, spillovers and agglomeration: A review of empirical studies. Economics of Innovation and New Technology, 8, 5-25.

Florida, R. (2002). The rise of the creative class. New York, NY: Basic Books.

Forkenbrock, D., & Foster, N. (1996). Highways and business location decisions. Economic Development Quarterly, 10, 239-248.

Fricker, R., & Schonlau, M. (2002). Advantages and disadvantages of Internet research surveys: Evidence from the literature. Field Methods, 14, 347-367.

Friedman, J., Gerlowski, D., & Silberman, J. (1992). What attracts foreign multinational corporations? Evidence from branch plant location in the United States. Journal of Regional Sci-ence, 32, 403-418.

Fulton, W., & Shigley, P. (2001). Little chips, big dreams. Govern-ing Magazine, May, 20-27.

Galbraith, C., & DeNoble, A. (1988). Location decisions by high tech-nology firms: A comparison of firm size, industry type and insti-tutional form. Entrepreneurship Theory and Practice, 13, 31-48.

Galbraith, C., Rodriguez, C., & DeNoble, A. (2008). SME com-petitive strategy and location behavior: An exploratory study of high-technology manufacturing. Journal of Small Business Management, 46, 183-202.

Glaeser, E. (2004). Book review of Florida’s The Rise of the Cre-ative Class. Retrieved from http://www.economics.harvard.edu/faculty/glaeser/files/Review_Florida.pdf

Glaeser, E., & Kohlhase, J. (2004). Cities, regions, and the decline of transport costs. Papers in Regional Science, 83, 197-228.

Goodman, M. (2007, May 1). The medical device industry in Mas-sachusetts: An updated profile. Retrieved from http://www.massmedic.com/docs/goodman07.pdf

Gottlieb, P. (1995). Residential amenities, firm location and eco-nomic development. Urban Studies, 32, 1413-1436.

Granger, M., & Blomquist, G. (1999). Evaluating the influence of amenities on the location of manufacturing establishments in urban areas. Urban Studies, 36, 1859-1873.

Green, G. P. (2001). Amenities and community economic develop-ment: Strategies for sustainability. Journal of Regional Analysis & Policy, 31, 61-75.

Haddad, M. A., Taylor, G., & Owusu, F. (2010). Locational choices of the ethanol industry in the Midwest corn belt. Economic Development Quarterly, 24, 74-86.

Halstead, J., & Deller, S. (1997). Public infrastructure in economic development and growth: Evidence from rural manufacturers. Community Development, 28, 149-169.

Hart, S., Denison, D., & Henderson, D. (1989). A contingency approach to firm location: The influence of industrial sector and level of technology. Policy Studies Journal, 17, 599-623.

Haug, P. (1991). The location decisions and operations of high tech-nology organizations in Washington state. Regional Studies, 25, 525-541.

Hekman, J. (1992). What are businesses looking for? Federal Reserve Bank of Atlanta Economic Review, 67, 6-19.

Henderson, J., & McNamara, K. (2000). The location of food man-ufacturing plant investments in corn belt counties. Journal of Agricultural and Resource Economics, 25, 680-697.

Hotelling, H. (1929). Stability and competition. Economic Journal, 39, 41-57.

Hoyman, M., & Faricy, C. (2009). It takes a village: A test of the creative class, social capital, and human capital theories. Urban Affairs Review, 44, 311-333.

Hu, W., Cox, L., Wright, J., & Harris, T. (2008). Understanding firms’ relocation and expansion decisions using self-reported factor importance rating. Review of Regional Studies, 38, 67-88.

Kaplowitz, M., Hadlock, T., & Levine, R. (2004). A comparison of web and mail survey response rates. Public Opinion Quarterly, 68, 94-101.

Karakaya, F., & Canel, C. (1998). Underlying dimensions of busi-ness location decisions. Industrial Management & Data Sys-tems, 98, 321-329.

Kimelberg, S. (2010). “Can we seal the deal?”: An examination of uncertainty in the development process. Economic Develop-ment Quarterly, 24, 87-96.

Klier, T. (2006). Where the headquarters are: Location patterns of large public companies, 1990-2000. Economic Development Quarterly, 20, 117-128.

Lösch, A. (1954). The economics of location: A pioneer book in the relations between economic goods and geography (W. H. Woglom, Trans., with W. F. Stolper, Trans.; 2nd ed.). New Haven, CT: Yale University Press.

Love, L., & Crompton, J. (1999). Role of quality of life in business (re)location decisions. Journal of Business Research, 44, 211-222.

Lowe, N. (2007). Job creation and the knowledge economy: Les-sons from North Carolina’s life science manufacturing initia-tive. Economic Development Quarterly, 21, 339-353.

Malanga, S. (2004). The curse of the creative class. City Journal, Winter, 36-45.




Malecki, E. J. (1987). The R&D location decision of the firm and “creative” regions–A survey. Technovation, 6, 205-222.

Manning, S. (2008). Customizing clusters: On the role of western multinational corporations in the formation of science and engi-neering clusters in emerging economies. Economic Develop-ment Quarterly, 22, 316-323.

Markusen, A. (2006). Urban development and the politics of a cre-ative class: Evidence from a study of artists. Environment and Planning, 38, 1921-1940.

Massachusetts Life Sciences Center. (2010). MLSC programs. Retrieved from http://www.masslifesciences.com/programs.html

Mellander, C. (2009). Creative and knowledge industries: An occu-pational distribution approach. Economic Development Quar-terly, 23, 294-305.

Nelson, M. (2005). Rethinking agglomeration economies and the role of the central city: The public accounting industry in Chi-cago and Minneapolis-St. Paul. Journal of Planning Education and Research, 24, 331-341.

Nelson, M. (2006). Reinterpreting producer service suburbaniza-tion: The public accounting industry in Chicago and Minneap-olis-St. Paul. Urban Geography, 27, 45-71.

Pammolli, F., Riccaboni, M., Oglialoro, C., Magazzini, L., Baio, G., & Salerno, N. (2005, July). Medical devices competitive-ness and impact on public expenditure. Rome, Italy: Competi-tiveness, Markets and Regulation. Retrieved from http://www .cermlab.it/_documents/MD_Report.pdf

Porter, M. E. (2000). Location, competition, and economic devel-opment: Local clusters in a global economy. Economic Devel-opment Quarterly, 14, 15-34.

PriceWaterhouseCoopers. (2010). MoneyTree™ report, historical trend data, medical devices and equipment. Retrieved from https://www.pwcmoneytree.com/MTPublic/ns/index.jsp

Rast, J., & Carlson, V. (2006). When Boeing landed in Chicago: Lessons for regional economic development. State and Local Government Review, 38, 1-11.

Salvesen, D., & Renski, H. (2003, January). The importance of quality of life in the location decisions of new economy firms. Chapel Hill: Center for Urban and Region Studies, University of North Carolina at Chapel Hill.

Schmenner, R. (1982). Making business location decisions. Englewood Cliffs, NJ: Prentice Hall.

Schoales, J. (2006). Alpha clusters: Creative innovation in local economies. Economic Development Quarterly, 20, 162-177.

Solomon, D. (2001). Conducting web-based surveys. Practical Assessment, Research & Evaluation, 7. Retrieved from http://pareonline.net/getvn.asp?v=7&n=19

U.S. Census Bureau. (2010a). 2007 County Business Patterns (NAICS) [Table]. Retrieved from http://censtats.census.gov/

U.S. Census Bureau. (2010b). Economic Census, Manufacturing: Industry Series: Detailed Statistics by Industry: 1997, 2002, 2007 [Table]. Retrieved from http://factfinder.census.gov/

U.S. Department of Commerce, Bureau of Economic Analysis. (2010). Regional economic information system, per capita personal income. Table SA1-3. Retrieved http://www.bea.gov/regional/spi/default.cfm?selTable=summary

U.S. Department of Labor, Bureau of Labor Statistics. (2009). Employment and output by industry 1998, 2008, and pro-jected 2018 [Table]. Retrieved from http://www.bls.gov/emp/ep_table_207.htm

U.S. International Trade Commission. (2007, March). Medical devices and equipment: Competitive conditions affecting U.S. trade in Japan and other principal foreign markets (Publication 3909). Washington, DC: Author. Retrieved from http://www .usitc.gov/publications/docs/pubs/332/pub3909.pdf

Weber, A. (1929). Alfred Weber’s theory of the location of indus-tries (C. J. Friedrich, Trans.). Chicago, IL: University of Chi-cago Press. (Original work published 1909)

Woodward, D. P. (1992). Locational determinants of Japanese man-ufacturing start-ups in the United States. Southern Economic Journal, 56, 690-708.

Zukin, S. (2009). Naked city: The death and life of authentic urban places. New York, NY: Oxford University Press.

Bios

Shelley McDonough Kimelberg is an assistant professor of soci-ology at Northeastern University. She teaches and conducts research in the areas of urban sociology, economic development, urban education, and poverty.

Lauren A. Nicoll is a PhD student in the Department of Sociology and Anthropology at Northeastern University with an interest in urban sociology.



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