SEARCH

SEARCH BY CITATION

Abstract

  1. Top of page
  2. Abstract
  3. Location Theory: Determinants of Site Selection
  4. Location Theory: Research Methods
  5. Data and Methods
  6. Results
  7. Discussion
  8. Limitations and Directions for Future Research
  9. References

This study examines two important issues concerning the evaluation of business location factors. First, in contrast to many analyses that seek to determine the influence of a single factor or set of factors on site selection, this study aims to measure the relative importance of a wide range of factors. Second, it investigates the extent to which the perceived importance of a given location factor varies based on the type of facility in question. While there is a substantial amount of research devoted to identifying industry-specific location factors, little is known about the influence that facility type has on the assessment of location criteria. Drawing on original survey data collected from real estate professionals in the U.S., we found significant differences in the mean ratings for more than half of the 39 location factors on the basis of facility type. In particular, “corporate/office” respondents were significantly more likely than “manufacturing” or “retail” respondents to assign higher ratings to “quality-of-life” location factors, such as crime rates, amenities, housing, and schools. We discuss the implications of these findings for future research on location theory.

There is a vast literature devoted to identifying and explaining the factors that determine where companies locate. Most of this research tends to fall into two broad categories: 1) studies that measure the influence of a specific factor or set of factors on firm location decisions; and 2) studies that explicate the location decision process for a specific business or industry.

Examples of the first type include analyses of the impact of taxes, subsidies, and incentives (e.g., Buss 2001; Gius and Frese 2002; Luger and Bae 2005; Schwartz, Pelzman, and Keren 2008); environmental regulations (e.g., Bartik 1988; Brosio 2008; Brunnermeier and Levinson 2004; Condliffe and Morgan 2008; Thomas and Ong 2004); quality of life and amenities (e.g., Deller, Lledo, and Marcouiller 2008; Dissart and Deller 2000; Gottlieb 1995; Granger and Blomquist 1999; Green 2001; Love and Crompton 1999; Nzaku and Bukenya 2005; Shaffer, Deller, and Marcouiller 2006); labor costs (Becker et al. 2005; Cheng 2006; Roberts and Smith 1992); and transportation and access (e.g., Arsen 1997; Forkenbrock and Foster 1996; Gkritza et al. 2008; Holl 2007; Luskin, Mallard, and Victoria-Jaramillo 2008; Targa, Clifton, and Mahmassani 2006).

Research of the second type includes studies of the location decisions of biotechnology firms (e.g., Feldman 2003; Ferrand et al. 2009; Goetz and Morgan 1995; Koo, Bae, and Kim 2009; Su and Hung 2009); companies in the automobile sector (e.g., Bilbao-Ubillos and Camino-Beldarrain 2008; Klier and McMillen 2008; Klier and Rubenstein 2010); call centers (e.g., Bishop, Gripaios, and Bristow 2003; Richardson and Gillespie 2003); and high-tech firms (e.g., Frenkel 2001; Hackler 2003a,b, 2004).

Both types of research have clear implications for policy and practice. Data that illustrate how individual economic, social, or political factors affect the likelihood that firms will locate in a given place can inform policy decisions and economic development initiatives at the local, regional, state, and national levels. Similarly, a deep understanding of the array of factors that need to be present before a specific firm or industry will establish operations in an area is necessary to help government officials determine whether their municipality or region is—or could be—a viable candidate for such investment.

While a focus on specific factors or specific industries is certainly valuable, neither approach addresses the more nuanced question of how a given location factor's importance varies based on the type or function of the facility in question. Kahn and Henderson (1992), for example, demonstrate that the perceived importance of several commonly studied location factors (e.g., labor costs, labor availability, and technical infrastructure) is different for family firms and nonfamily firms, thus highlighting the influence of ownership structure. Similarly, recent research has underscored the need to look beyond the broad industry or sector in which a company competes, and consider the actual business functions that take place in a given facility. As Mellander (2009) argues in her study of occupations in the creative industries, scholars frequently conflate an industry's products with its processes, and fail to recognize the diversity of tasks and activities that are performed on a daily basis in an individual firm. Indeed, large firms with multiple properties in their real estate portfolios often spread out discrete business functions—e.g., human resources, sales and marketing, production, distribution, and back office operations—among their different facilities (see, e.g., Nelson 2005, 2006 for a discussion of accounting firms). Because the needs of those facilities vis-à-vis things like labor, space, infrastructure, or parking are apt to be quite different, studies of location decisions that focus only on an industry (as opposed to a specific facility or project type) may be limited in what they can conclude about the practical importance of various location factors.

This study draws on original survey data collected from a sample of real estate professionals in the U.S. to examine the extent to which respondents' evaluations of individual location criteria differ depending on the nature or purpose of the facility in question. In doing so, this work adds to the existing body of literature on the site selection process in two ways. First, in contrast to many analyses concerned primarily with exploring the influence of an individual location factor (e.g., taxes, labor costs, amenities, or housing), this study aims to measure the relative importance of a wide range of factors. Second, by explicitly asking respondents to rate the importance of each location factor with respect to a specific type of facility or development project, it allows for an investigation of the degree to which the influence of a given location determinant is universal (e.g., all siting decisions place a premium on, for instance, labor availability) or context dependent. While the notion that the perceived importance of a given factor will vary based on facility type has an intuitive logic, this study seeks to test empirically where those differences are most pronounced.

In the sections that follow, we provide a brief review of location theory, with an emphasis on the array of factors examined in the extant literature, and the primary methods scholars have utilized to measure the influence of these factors on location outcomes. Next, we describe the data and analyses employed in the present research. Lastly, we present the findings and discuss their implications.

Location Theory: Determinants of Site Selection

  1. Top of page
  2. Abstract
  3. Location Theory: Determinants of Site Selection
  4. Location Theory: Research Methods
  5. Data and Methods
  6. Results
  7. Discussion
  8. Limitations and Directions for Future Research
  9. References

Early scholars of location theory were primarily interested in explaining the location patterns of manufacturing firms. Because most businesses in the early- and mid-twentieth century relied on the production and sale of goods, firms could achieve a competitive advantage by locating where the costs to produce those goods were minimized, and/or the profits derived from their sale were maximized. As a consequence, location theorists emphasized the importance of factors such as access to raw materials, transportation costs, labor costs, and access to markets (Christaller [1933] ; Hotelling 1929; Losch 1954; Weber [1909] ).

Over the years, costs have remained a central concern of location theory, with numerous studies exploring the influence of factors including state and local taxes (Buss 2001; Kolko, Neumark, and Mejia 2011; Rainey and McNamara 1999), financial incentives (Billings 2009; Bondonio and Greenbaum 2007; Hanson and Rohlin 2011), unions and right-to-work laws (Holmes 1998; Lafer and Allegretto 2011; Wilson 2002), minimum wage laws (Bartik 2004; Méjean and Patureau 2010), and infrastructure (Berechman, Ozmen, and Ozbay 2006; McCann and Shefer 2004). While the findings from these studies are largely mixed (see, e.g., Lynch 2004; McGuire 2003; Neumark and Kolko 2010), the belief that the “bottom line” drives the site selection process pervades the popular wisdom as well, as evidenced by heated debates over the multimillion dollar incentives and tax breaks awarded to corporations that agree to build or relocate a facility in a given municipality (see, e.g., Austin 2011; Egan 2011; Morley 2011; Weir 2011).

Consistent with the shift to a postindustrial economy, however, scholars have increasingly turned their attention to location factors whose impact on cost savings is less obvious, immediate, or direct. For example, cluster theory, Porter's (2000) widely cited account of the geographic co-location of firms, emphasizes that the benefits of agglomeration extend well beyond efficiency and cost minimization. Rather, in a knowledge-based economy, firms situate themselves within networks of competitors and collaborators in an effort to foster and capitalize on innovation (Delgado, Porter, and Stern 2010; Koo and Cho 2011; Muro and Katz 2010; Schoales 2006; Simmie 2004; Whittington, Owen-Smith, and Powell 2009; Yu and Jackson 2011). Similarly, organizational theorists have demonstrated how the embeddedness of firms in local production networks can lead firms to satisfice, rather than to seek out lower cost locations (Romo and Schwartz 1995). Other studies have highlighted the tendency of firms to make location decisions based on a desire to reduce uncertainty in their operating environment (Henisz and Delios 2001; Kimelberg 2010).

Given the importance of human capital to the knowledge economy, much of the recent research in location theory explores how firms select sites that will either appeal to their current workforce or attract new workers to the area, or where the requisite skilled labor already resides (Cohen and Soto 2007; Hanushek and Woessmann 2008; Laabas and Weshah 2011; Powell and Snellman 2004). Many of these studies employ some version of the broadly defined concept of “quality of life” to explain business location patterns. Factors such as the cost of housing, access to high-quality schools, desirable natural resources, convenient transportation options, and amenities ranging from restaurants and nightlife to cultural and sporting events figure prominently in the literature. Although widely criticized by many social scientists (Hoyman and Faricy 2009; Markusen 2006; Zukin 2009), Florida's (2002a) creative class thesis perhaps best embodies the shift in location theory toward the need for firms to attend to the preferences and demands of their employees. Florida's ideas have been embraced by many government officials and planning departments as a roadmap for attracting educated creative types to their cities and regions, thus, it is hoped, prompting businesses to follow suit (Bieri 2010; Gaskill 2011; Hickman 2011; MacGillis 2010).

In general, despite the number and variety of factors examined in the site selection literature, the vast majority of studies focus on measuring the impact of a single factor or set of factors on business location decisions. Few attempts have been made to assess the relative importance of a wide range of factors simultaneously (Karakaya and Canel 1998 is a notable exception).

Location Theory: Research Methods

  1. Top of page
  2. Abstract
  3. Location Theory: Determinants of Site Selection
  4. Location Theory: Research Methods
  5. Data and Methods
  6. Results
  7. Discussion
  8. Limitations and Directions for Future Research
  9. References

Site selection research typically employs one of two broad methodological approaches: 1) econometric models that measure the extent to which the location outcomes of specific types of companies or industries are predicted by various place-based characteristics, or 2) direct surveys or interviews of the decision makers involved in choosing a site for business activity (Blair and Premus 1987; Carlson 2000). There are advantages and disadvantages associated with each method. Statistical models allow the researcher to quantify the size, direction, and timing of the relationship between location factors and outcomes, and to capture the effects of factors that might be difficult to articulate in a standard survey instrument or interview protocol. However, statistical models are also dependent on the type and quality of the data available, and require the researcher to make a priori assumptions about which factors are likely to be significant. As a consequence, important factors may be overlooked, while those that are included may rely on indicators that are suboptimal for the task at hand (Carlson 2000).

In contrast, surveys and interviews typically allow for greater flexibility in terms of the factors considered, and the way in which they are presented to respondents. In addition, more qualitatively oriented research may make use of open-ended questions in an effort to glean details about the site selection process itself, or to uncover new location factors that warrant further examination (Carlson 2000). Survey-based research, however, relies on the respondent's knowledge and memory of the location decision, 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 and Premus 1987; Carlson 2000). While methodology choice is often largely a function of researcher preference and data availability, Carlson's (2000) direct comparison of the two approaches suggests that surveys and econometric models provide reasonably consistent results, at least for location factors that are more easily quantifiable.

Data and Methods

  1. Top of page
  2. Abstract
  3. Location Theory: Determinants of Site Selection
  4. Location Theory: Research Methods
  5. Data and Methods
  6. Results
  7. Discussion
  8. Limitations and Directions for Future Research
  9. References

In January 2005, we surveyed the memberships of two national real estate trade associations to examine the importance of various location factors in the site selection process. The first organization (organization A) includes commercial real estate brokers, developers, industrial location specialists, and other service providers whose companies initiate, execute, and/or support the commercial real estate process. Members of the second organization (organization B) include both external service providers as well as internal corporate real estate professionals (i.e., the “end users” of the real estate process, who typically run the in-house real estate functions for companies whose primary business is not real estate). We sought participation from members of both organizations in an effort to not only increase the likely sample size, but also to capture the perspectives of a wide range of decision makers who play an active role in evaluating and selecting locations for business facilities.

Potential respondents were contacted via an email request sent by the respective leadership of each organization. Emails included a URL that took respondents directly to the online survey, created in Zoomerang.1 While both organizations utilized their existing membership lists, calculating the overall response rate was somewhat challenging because of the different technical capabilities of each organization. Given that organization B frequently emails surveys and other research-related information to its members, they employed a more sophisticated tracking system. They confirmed that 1,689 emails were sent and that 371 (22 percent) of those were opened by the recipients. Of those who opened the email, 29 percent (n = 108) completed the survey.2 The email request to organization A members yielded fewer responses (n = 80).3 Consequently, we approached our contact there and asked to do a follow-up mail survey to the organization's New England chapter (its largest). In collaboration with the chapter's president, we drafted a letter that was sent to the 348 individuals on the New England membership list, along with a paper copy of the survey. This effort yielded an additional 43 responses (12 percent), for a total of 123 respondents from organization A, and a combined sample size of n = 231 for both organizations.4 Respondents represented a broad cross-section of the U.S.: roughly 25 percent work primarily in New England; 18 percent are focused mainly in the East Central states; 17 percent are in the Pacific region; another 16 percent each are in the West Central and Middle Atlantic regions; 14 percent work primarily in the South Atlantic; and 6 percent are focused in the Mountain states.5

Survey instrument

The survey instrument asked respondents to rate the importance of 39 different factors in the location decision process, using a four-point Likert scale (1 = unimportant; 2 = moderately important; 3 = important; and 4 = very important). The individual location factors were gleaned from a review of the extant literature on firm location decisions, as well as informal focus groups and interviews with various real estate professionals and business leaders. We pre-tested potential survey items with a group of developers and location specialists at an annual commercial real estate trade association meeting to ensure that the questions were clear and comprehensible.6

The survey items were grouped into six thematic categories: 1) business environment; 2) development and operating costs; 3) labor; 4) permitting processes; 5) quality of life/social environment; and 6) transportation and access. Each category was introduced with the following heading: In your opinion, how important is each of the following—as either an asset or a deterrent—in the decision to locate in a particular municipality or site within that municipality?

Given that we sought to understand not only the relative importance of a wide range of location factors but also how the evaluation of individual location factors varies depending on the type of facility in question, we asked the respondents to assign their ratings with a particular context in mind. Respondents selected from among six project types: 1) manufacturing plant; 2) office/headquarters; 3) retail; 4) research and development (R&D) facility; 5) general industrial/distribution; and 6) mixed-use facility. Thus, a respondent who picked, for example, the “manufacturing” option rated all of the location factors on the basis of their importance to selecting a site for a manufacturing plant. The majority of respondents (55 percent) chose to evaluate the survey items in the context of a commercial space for office or headquarters purposes. A little over one-fifth (21 percent) chose either the manufacturing or the general industrial category,7 10 percent selected retail, and another 10 percent selected a mixed-use project. Very few (only 3 percent) of respondents evaluated the location factors in the context of an R&D facility.

Results

  1. Top of page
  2. Abstract
  3. Location Theory: Determinants of Site Selection
  4. Location Theory: Research Methods
  5. Data and Methods
  6. Results
  7. Discussion
  8. Limitations and Directions for Future Research
  9. References

Overall mean ratings

According to the respondents, on-site access to parking for employees (mean = 3.52, on a scale from 1–4), real estate rental rates (3.48), the availability of appropriate labor (3.37), and the timeliness of approvals and appeals in the permitting process (3.33) are the most important factors driving the site selection process. In contrast, the existence of a municipal minimum wage law (1.95), access to railroads (2.16), an informative municipal website (2.18), and the presence of strong trade unions (2.24) are seen as relatively unimportant to the business location decision.

A number of other factors frequently cited in both the academic literature and the politically charged debates about the use of public funds fell in between these two extremes. Property taxes (3.13), state tax rates (3.04), and local financial incentives (3.10), while rated “important” by the average respondent, were not among those factors deemed most critical to the site selection process.8 Two commonly used measures of quality of life—access to good schools (2.74) and housing costs (2.80)—were even farther down the list. Mean ratings for all 39 location factors are provided in Table 1.

Table 1. Mean Location Factor Ratings
FactorMeanStandard deviationFactorMeanStandard deviation
  1. a

    This factor was asked only of organization B respondents. (N = 108).

  2. N = 231.

On-site parking for employees3.520.633Zoning by right2.920.929
Rental rates3.480.693Municipal reputation as a good place to live2.900.734
Availability of appropriate labor3.370.751Proximity to restaurants/shops2.890.744
Timeliness of approvals/appeals3.330.682Complementary/supplemental business servicesa2.840.672
Quality/capacity of infrastructure3.220.686Public transportation2.840.891
Traffic congestion3.210.662Cost of housing for employees2.800.779
State tax/financial incentivesa3.170.649Access to airportsa2.790.786
Land costs3.160.818Critical mass of similar firms2.780.856
Predictability/clarity of permitting3.150.877Awareness of brownfields2.760.984
Competitive labor costs3.150.823Quality of local schools2.740.95
Access to major highwaysa3.150.593Strong neighborhood organizations2.650.961
Property tax rates3.130.763Permitting ombudsman2.620.973
Crime rate in area3.130.732Customized workforce traininga2.510.767
Local tax/financial incentives3.100.808Proximity to research/universities2.370.961
Undesirable abutting land use3.090.814Sports/cultural amenities2.350.939
Fast track permitting3.070.799Strong trade unions2.241.054
State tax ratesa3.040.709Informative municipal website2.181.002
Physical attractiveness of area3.010.716Access to railroadsa2.160.949
Municipal reputation as a good place to work2.970.726Municipal minimum wage law1.950.983
Municipal reputation in economic development2.960.765 

Mean ratings by project type

Next, we disaggregated the mean ratings by project type to examine potential differences in the evaluation of specific location factors. Given that so few respondents opted to rate the factors in the context of an R&D facility, we dropped this project type so as not to bias the results. Thus, for the purposes of analysis, we focused on the mean ratings for three project types: office/headquarters, manufacturing/industrial, and retail.9 When disaggregated by project type, the mean ratings reveal both similarities and differences in the significance that each factor holds for office, manufacturing, and retail properties. Four survey items—1) on-site parking, 2) rental rates, 3) the timeliness of approvals and appeals, and 4) the quality and capacity of infrastructure—were among the top 10 highest rated factors for each of the project types, although the overall ranking of these factors within the top 10 varied by project type. Similarly, four other survey items—1) an informative municipal website, 2) the existence of a municipal minimum wage law, 3) the availability of sports and cultural amenities, and 4) proximity to research institutions and universities—were among the ten lowest rated factors for office, manufacturing, and retail properties alike.

Several differences are also worth noting. Respondents who evaluated the factors based on their importance to manufacturing facilities assigned a much higher rating to labor costs (3.52) than did respondents who answered with respect to office properties (3.12) or retail properties (2.78). Those respondents evaluating factors on the basis of retail projects rated active neighborhood organizations (3.09) much higher than did those evaluating manufacturing (2.62) or office projects (2.47), but they assigned a markedly lower rating to the availability of an appropriate labor force (2.70) than did respondents evaluating manufacturing (3.65) or office (3.40) projects. Finally, respondents who answered the survey with respect to an office property rated the crime rate in the area and the physical attractiveness of the area (3.22 and 3.19, respectively) more highly in importance than did respondents who selected manufacturing projects (2.90, 2.73) or retail projects (2.78, 2.74). The complete list of mean ratings by project type is provided in Table 2.

Table 2. Mean Location Factor Ratings, by Project Type
Office (N = 127)Manufacturing (N = 48)Retail (N = 23)
FactorMean (standard deviation)FactorMean (standard deviation)FactorMean (standard deviation)
  1. a

    This factor was asked only of organization B respondents.

Parking3.65 (0.541)Labor availability3.65 (0.635)Land cost3.48 (0.73)
Rental rate3.55 (0.653)Highwaya3.54 (0.519)Predictability3.39 (0.783)
Labor availability3.40 (0.727)Parking3.53 (0.687)Timely permits3.39 (0.583)
Timely permits3.28 (0.665)Labor cost3.52 (0.714)Traffic3.39 (0.583)
State incentivesa3.23 (0.590)Rental rate3.48 (0.652)Infrastructure3.35 (0.714)
Crime rate3.22 (0.712)Timely permits3.42 (0.739)Rental rate3.32 (0.716)
Traffic3.21 (0.65)State incentivesa3.38 (0.65)Highwaya3.18 (0.603)
Physical attractiveness3.19 (0.592)Predictability3.35 (0.838)Neighborhood orgs.3.09 (0.668)
Land cost3.17 (0.824)Infrastructure3.34 (0.635)Parking3.04 (0.767)
Infrastructure3.17 (0.703)Local incentives3.26 (0.793)Zoning by right2.96 (1.022)
Property tax3.15 (0.767)Property tax3.25 (0.668)Brownfields2.91 (0.949)
Highwaya3.14 (0.535)State tax ratea3.23 (0.725)Fast permits2.91 (0.811)
Undesirable abutters3.13 (0.823)Traffic3.23 (0.698)Undesirable abutters2.87 (0.92)
Local incentives3.13 (0.749)Fast permits3.15 (0.751)Similar firms2.87 (0.869)
Reputation/work3.13 (0.657)Land cost3.02 (0.872)Reputation/ec. dev.2.87 (0.815)
Labor cost3.12 (0.752)Undesirable abutters2.96 (0.721)State tax ratea2.82 (0.874)
Reputation/live3.09 (0.667)Business servicesa2.92 (0.494)State incentivesa2.82 (0.874)
Shops3.07 (0.632)Crime rate2.90 (0.751)Ombudsman2.78 (1.043)
Fast permits3.05 (0.828)Reputation/ec. dev.2.89 (0.787)Labor cost2.78 (0.998)
Airporta3.04 (0.633)Zoning by right2.87 (0.924)Local incentives2.78 (0.902)
Reputation/ec. dev.3.02 (0.769)Airporta2.85 (0.555)Crime rate2.78 (0.795)
State tax ratea3.00 (0.685)Reputation/work2.81 (0.734)Physical attractiveness2.74 (0.81)
Public transit2.98 (0.845)Customized traininga2.77 (0.599)Shops2.74 (0.81)
Predictability2.97 (0.925)Railroada2.77 (0.599)Property tax2.70 (0.926)
Local schools2.95 (0.879)Physical attractiveness2.73 (0.736)Labor availability2.70 (0.876)
Housing costs2.94 (0.721)Brownfields2.66 (1.069)Reputation/live2.57 (0.662)
Business servicesa2.93 (0.608)Housing costs2.63 (0.761)Reputation/work2.43 (0.896)
Zoning by right2.91 (0.955)Neighborhood orgs.2.62 (0.922)Union2.39 (1.076)
Similar firms2.88 (0.845)Union2.54 (1.129)Housing costs2.39 (0.783)
Brownfields2.78 (0.991)Public transit2.51 (0.882)Public transit2.35 (0.855)
Sports/culture2.58 (0.877)Reputation/live2.50 (0.772)Business servicesa2.27 (1.009)
Ombudsman2.57 (0.983)Similar firms2.50 (0.744)Municipal website2.13 (0.968)
Customized traininga2.55 (0.782)Ombudsman2.49 (0.882)Research2.04 (1.065)
Research2.49 (0.944)Shops2.48 (0.691)Local schools1.96 (0.928)
Neighborhood orgs.2.47 (1.001)Local schools2.39 (0.954)Customized traininga1.91 (0.701)
Municipal website2.26 (1.021)Research1.98 (0.887)Sports/culture1.87 (0.968)
Union2.21 (1.014)Minimum wage1.91 (0.812)Minimum wage1.83 (1.029)
Railroada2.21 (0.999)Municipal website1.90 (0.928)Airporta1.82 (0.751)
Minimum wage2.02 (1.035)Sports/culture1.90 (0.857)Railroada1.55 (0.688)

Differences by project type: Analysis of variance (ANOVA) and Tukey's honestly significant difference (HSD) results

To determine whether these and other differences observed between the facility type ratings were statistically significant, we conducted an ANOVA. Of the 39 site location factors, there were significant differences in the level of importance assigned to 21 of them based on the project type selected by the respondent. This suggests that there is some uniformity in factor ratings across facility types—in other words, the perceived importance of a number of factors does not vary, regardless of whether the respondent evaluated an office, manufacturing, or retail project. However, for the remaining location factors (more than half of the survey items), project context does matter, as evidenced by statistically significant differences between the respondent ratings. Those factors where the differences in perceived importance were most pronounced include the availability of appropriate labor [F(2,195) = 13.546, p < 0.000]; labor costs [F(2,195) = 8.069, p < 0.000]; a location's reputation as a good place to work [F(2,194) = 10.892, p < 0.000]; a location's reputation as a good place to live [F(2,195) = 15.222, p < 0.000]; access to public transportation [F(2,194) = 8.787, p < 0.000]; on-site parking for employees [F(2,193) = 9.741, p < 0.000]; access to airports [F (2,94) = 17.604, p < 0.000]; the physical attractiveness of the area [F (2,193) = 11.082, p < 0.000]; the availability of sports and cultural amenities [F (2,195) = 14.134, p < 0.000]; and the quality of the local schools [F (2,191) = 15.363, p < 0.000]. The complete ANOVA results are presented in Table 3.

Table 3. Analysis of Variance
FactorF
  1. a

    This factor was asked only of organization B respondents.

  2. * p < 0.05; ** p < 0.01; *** p < 0.001.

Airporta17.604***
Brownfields0.515
Business servicesa4.985**
Crime rate5.749**
Customized traininga4.364*
Fast permits0.681
Highwaya3.044
Housing costs7.063**
Infrastructure1.371
Labor availability13.546***
Labor cost8.069***
Land cost2.367
Local schools15.363***
Local incentives2.890
Minimum wage0.507
Municipal website2.370
Neighborhood orgs.4.178*
Ombudsman0.715
Parking9.741***
Physical attractiveness11.082***
Predictability4.564*
Property tax4.330*
Public transit8.787***
Railroada5.181**
Rental rate1.233
Reputation/ec. dev.0.636
Reputation/live15.222***
Reputation/work10.892***
Research6.186**
Shops13.953***
Similar firms3.843*
Sports/culture14.134***
State tax ratea1.002
State incentivesa2.642
Timely permits0.791
Traffic0.727
Undesirable abutters1.544
Union1.850
Zoning by right0.061

Next, we conducted a Tukey's HSD test to determine exactly which pairs of means were statistically different from one another. For each of the three facility types, we identified the location factors for which the assigned rating was significantly higher than the rating given for either or both of the other two facility types. Table 4 lists all of these factors, along with the comparison project(s) (in parentheses) and the level of significance of the difference in means.

Table 4. Tukey's Honestly Significant Difference: Factors Rated Significantly Higher, by Project Type
Office (O)Manufacturing (M)Retail (R)
  1. * p < 0.05; ** p < 0.01; *** p < 0.001.

Availability of appropriate labor (R)***Availability of appropriate labor (R)***Strong neighborhood organizations (O)*
Customized workforce training (R)*Competitive labor costs (O)** (R)*** 
Property tax rates (R)*Customized workforce training (R)* 
Critical mass of similar firms (M)*Property tax rates (R)* 
Municipal reputation as a good place to work (M)* (R)***Complementary/supplemental business services (R)* 
Proximity to research/universities (M)**On-site parking for employees (R)** 
Complementary/supplemental business services (R)**Access to airports (R)*** 
Public transportation (M)** (R)**Access to railroads (R)** 
On-site parking for employees (R)***Predictability/clarity of permitting (O)* 
Access to airports (R)***  
Proximity to restaurants/shops (M)***  
Municipal reputation as a good place to live (M)*** (R)**  
Cost of housing for employees (M)* (R)**  
Crime rate in area (M)* (R)*  
Physical attractiveness of area (M)*** (R)**  
Sports/cultural amenities (M)*** (R)**  
Quality of local schools (M)** (R)***  

As shown in the table, respondents who evaluated the location factors with respect to a retail project rated only one item—strong neighborhood organizations—more highly than did other respondents. In contrast, respondents evaluating office projects and those evaluating manufacturing projects both rated six different factors—the availability of labor, the availability of customized workforce training, property tax rates, complementary/supplemental business services, on-site parking for employees, and access to airports—more highly than did retail respondents.

Respondents who answered the survey in the context of a manufacturing project rated two factors significantly higher than did respondents evaluating office projects: the predictability of the permitting process and the cost of labor. (The rating for the latter factor was also highly significant when compared with retail respondents.)

Perhaps most noteworthy are the number of location factors rated significantly higher by respondents who evaluated office projects. The majority of these factors fell into two broad categories: quality of life and business environment. Respondents who answered the survey with respect to office properties assigned much higher ratings to things like the crime rate in the area, the cost of housing, the physical attractiveness of the area, the availability of cultural and sports amenities, public transportation, the quality of local schools, and the reputation of the municipality as a good place to live than did either manufacturing or retail respondents. Similarly, office respondents assigned significantly higher ratings to business-related factors like the proximity to research institutions and universities, the reputation of the municipality as a good place to work, and a critical mass of similar firms.

Discussion

  1. Top of page
  2. Abstract
  3. Location Theory: Determinants of Site Selection
  4. Location Theory: Research Methods
  5. Data and Methods
  6. Results
  7. Discussion
  8. Limitations and Directions for Future Research
  9. References

In this study, we examined two important questions concerning business location decisions. First, when comparing a wide range of location factors, which emerge as the most significant? The extant literature highlights numerous potential determinants of site selection, ranging from cost-based factors, to strategy-driven factors, to those tied to human capital needs. Yet few studies have attempted to measure the importance of multiple disparate location factors simultaneously. The second objective of this research was to explore the extent to which the perceived importance of a given location factor is influenced by the specific type of facility or development project in question. While there is a substantial amount of work devoted to identifying industry-specific location factors, little is known about the role that facility type plays in the evaluation of individual location factors. We argue that the distinction between industry and facility type is an important one for location research. For instance, if a biotechnology firm maintains some of its core business functions (e.g., corporate/administrative, R&D, manufacturing, and distribution) in separate facilities, then it is reasonable to assume that the location decisions involving those individual properties would privilege very different criteria. In other words, the firm might place a premium on proximity to universities when searching for a facility to house its R&D operations but care more about access to railroads when evaluating potential locations for its distribution plant.

With respect to the first research question, our findings present a mixed story concerning the importance of traditional location factors based on cost minimization. While local rental rates received the second-highest overall rating from respondents, other commonly cited economic factors—e.g., state tax rates, property taxes, local tax or financial incentives, and labor costs—were rated lower. In fact, two cost-based factors—strong trade unions and the existence of a municipal minimum wage law—were actually among those items rated least important by the respondents. The perceived insignificance of the latter may be explained by the dearth of such laws in existence; at the time of this research, only four U.S. cities—Washington, D.C., Santa Fe, San Francisco, and Albuquerque—had citywide minimum wage laws in effect (Sonn 2006).10 The low rating attributed to the existence of strong labor unions, however, is perhaps more surprising, especially given the attention paid to unions in both academic research and public discourse.

Similarly, the data reveal mixed results concerning the role of human capital in the site selection process. While the availability of an appropriate labor force in the area was one of the top-rated location criteria, the respondents assigned much lower ratings to a host of factors often thought to be necessary to attract and retain the needed human capital. Indeed, a number of criteria frequently discussed under the umbrella of “quality-of-life factors”—housing costs, good schools, access to shops and restaurants, and the availability of cultural or sports activities—were deemed only moderately important by the respondents. Much more central to the location decision, according to this survey, are several factors that have not received the same amount of popular or scholarly interest to date, including the adequacy of employee parking, general infrastructure concerns, and issues related to the development process itself (i.e., the timeliness of approvals and appeals, and the predictability of the permitting process).

Based on the overall mean ratings, then, these findings present a complex account of the relative importance of a wide range of site selection criteria. It is apparently not the case that traditional economic factors have lost all relevance to business location decisions; some cost-based factors appear to be quite important, while others are decidedly less so. Similarly, despite the clear role that the availability of labor plays in the site selection process, it does not appear that many characteristics commonly associated with the postindustrial economy—for instance, quality of life measures or agglomeration—have supplanted traditional production factors in the location decision. However, an examination of the data disaggregated by project type offers some potential explanations for these ambiguous findings.

Per the objectives of the second research question, we sought to determine the extent to which evaluations of location factors were influenced by the type of project or facility in question. For over half of the survey items, there were significant differences in the mean rating assigned to the factor based on project type. In general, respondents who answered the survey with respect to retail projects assigned lower overall mean ratings to the factors (median average rating = 2.78; range = 1.55–3.48) than did either office respondents (median average rating = 3.05; range = 2.02–3.65) or manufacturing respondents (median average rating = 2.87; range = 1.90–3.65). In only one case—the awareness of strong, active neighborhood organizations—was the rating assigned by retail respondents significantly higher than the rating of one of the comparison groups (office). While we can only speculate, one plausible explanation for these findings is that the specific location factors included in this survey do not address some of the most important determinants of site selection for retail facilities. Indeed, as other studies have suggested, population density and demographics—neither of which was featured in the present research—are among the primary drivers of retail location (see Porter 1995).11

Additionally, it is instructive to consider some of the location factors deemed less important by retail respondents. In particular, the rating for labor availability (2.70) is noteworthy when compared with the high ratings that office (3.40) and manufacturing (3.65) respondents assigned to that factor. This suggests that when searching for a site for a retail establishment, the size and skill set of the native labor pool play less of a role than they do for office or manufacturing location decisions. Given that labor is often considered relatively fungible in many retail outlets—workers are frequently part time, minimum-wage earners—this is not entirely surprising. The comparatively lower score given to on-site parking for employees (3.04 versus 3.53 for manufacturing and 3.65 for office) can likely be explained by the fact that the parking concerns of retail outlets are typically focused more on customers rather than employees. Similarly, the relative unimportance of access to airports for retail (1.82) versus, for instance, office (3.04) makes sense in light of the fact that office or corporate employees are far more likely to need to travel by air than are retail employees.

While manufacturing respondents rated a number of factors higher than did retail respondents, in only two cases did they place more importance on a factor than did office respondents. First, manufacturing respondents rated labor costs significantly higher (3.52) than did office (3.12) respondents (or, for that matter, retail respondents). This is consistent with location theory's emphasis on cost minimization in the industrial economy. Interestingly, however, while manufacturing respondents rated labor unions more highly than did office respondents (2.54 versus 2.21), the difference between the two groups' evaluations was not statistically significant. Second, manufacturing respondents assigned a higher rating to predictability in the permitting process than did office respondents (3.35 versus 2.97). One possible explanation for this finding concerns the degree to which different types of projects require special permits or zoning variances, or are likely to generate opposition. For example, to the extent that a manufacturing facility might require changes to the existing building or infrastructure, or raise concerns about pollution, more so than an office or corporate facility, heightened attention to the permitting process would seem warranted.

The most pronounced differences in this study, however, were between the evaluations of respondents who answered the survey with respect to an office or corporate facility and those of the comparison groups. Office respondents were significantly more likely to cite the importance of all of the location factors under the thematic heading of “quality of life” than were either manufacturing or retail respondents. Given that, in general, corporate or office facilities are more likely to employ workers considered part of the knowledge economy than are manufacturing or retail facilities, these data supplement existing research highlighting the influential role that employee preferences and satisfaction play in the site selection process for postindustrial firms (Clark et al. 2002; Florida 2002b; Storper and Scott 2009). Whether this is because decision makers believe that they need to choose locations with, for instance, low crime rates, good schooling and housing options, attractive natural and man-made amenities, and well-known reputations to attract desired workers to their facility—or because the employees they would like to hire have already chosen to live in such communities—is not clear from these data. Regardless, the emphasis placed on these factors is notable when compared with the ratings from the manufacturing and retail respondents.

Also consistent with the literature on location decisions in the postindustrial economy were the higher ratings that office respondents assigned to business-related factors like the proximity to research institutions and universities, the reputation of the community as a good place to work, and the existence of a critical mass of similar firms in the area. These data lend additional support to previous research highlighting the importance placed on information flows, networks, and opportunities for collaboration and innovation by knowledge-based firms.

In sum, these findings indicate that while there are some discernible patterns concerning the types of factors that are likely to be relevant—or not relevant—to most business location decisions, notable differences exist in the importance attached to specific criteria on the basis of facility type. In contrast to other studies that have focused on an individual location factor or a particular industry, the present research emphasizes the need to be cognizant of the degree to which the significance of a given factor may vary—not simply by industry (e.g., biotech vs. automotive) but also by the unique needs of the facility in question. Because it is common for a business to maintain separate facilities in its real estate portfolio to handle distinct functions—e.g., back-office operations, corporate activities, or production and warehousing of goods—location research ought to be mindful of the ways in which the site selection process might unfold differently in each case. By asking respondents to rate location factors with respect to some common facility types (e.g., manufacturing or industrial, office or corporate headquarters, retail), this study represents an initial attempt to utilize such a targeted approach.

Limitations and Directions for Future Research

  1. Top of page
  2. Abstract
  3. Location Theory: Determinants of Site Selection
  4. Location Theory: Research Methods
  5. Data and Methods
  6. Results
  7. Discussion
  8. Limitations and Directions for Future Research
  9. References

Scholars have long debated the merits of different methodological approaches to the study of location decisions (see Carlson 2000; Jeppesen and Folmer 2001). On balance, it is reasonable to say that there are advantages and disadvantages associated with both econometric models and qualitative research. In addition to commonly cited concerns about surveys and interviews (e.g., social desirability bias and recollection problems), however, one issue worth considering in the present study is the possibility that respondents' evaluations of location factors for their selected project context (e.g., manufacturing) might be influenced by their general experience with site selection for other facility types (e.g., retail trade). In other words, while we asked the respondents to focus only on how they would rate each individual factor's importance in their chosen context, we cannot be certain that they did so. Future research might address this concern by having the same individuals rate the importance of location factors for different types of facilities.

Additional studies might also seek to examine the extent to which other respondent-specific factors affect the ratings of the various survey items. For example, we purposely surveyed real estate professionals from across the U.S. in an effort to increase the generalizability of the findings, and to minimize the likelihood that a given factor rating merely reflects the specific or unique conditions in a particular municipality or region. However, it would be instructive to examine how the perceived importance of a given location factor varies based on the city or state in which the respondent works. For instance, do respondents in right-to-work states evaluate the importance of unions differently than do respondents in states that do not have right-to-work laws on the books? Similarly, while we see the inclusion of respondents from different professional backgrounds as a strength of this study, in that it allows for a more comprehensive view of the location decision, it would be interesting to determine the extent to which a respondent's role (e.g., developer versus corporate end-user) is correlated with the assessment of any given location factor.

Finally, it is worth noting that several years have passed since these data were collected. Certainly, with any type of survey research, it is prudent to question how a set of findings might change over time in response to shifting economic, political, or structural conditions. For instance, recent debates over the fate of public-sector unions, while perhaps not directly relevant to private-sector location decisions, have nevertheless placed renewed emphasis on the issue of American labor unions, and thus might reasonably affect respondents' perceptions in the future surveys. Given that this study was conducted just prior to the advent of U.S and global financial crises, however, it seems particularly important to reflect on the ways in which those critical events might affect the site selection process in general, and real estate professionals' assessments of location determinants in particular. A lower tolerance for risk, difficulty accessing capital, or a significantly higher unemployment rate, for example, could all have a significant impact on how decision makers evaluate potential sites for a business facility.

Notes
  1. 1

    While we discussed the benefits and drawbacks of email versus mail surveys, our contacts at both organizations felt strongly that email was the preferred and typical mode of contact for their membership. Likewise, in both instances, our contacts stated that their email lists were updated more frequently—and were thus more accurate—than their mailing lists.

  2. 2

    We proposed a follow-up email message or mail survey in an effort to boost the response rate, but our contact declined, citing heavy email traffic, and the fact that we had already surpassed the 5 percent response rate that they typically seek from research efforts such as this.

  3. 3

    Unfortunately, organization A was not able to provide a count of the number of emails that were distributed or opened, so it was not possible to calculate a response rate for this population.

  4. 4

    While the inability to calculate an overall response rate is less than ideal, we do not believe that there is evidence of systemic problems in the data (Dillman and Bowker 2001; Solomon 2001). Coverage bias or sampling bias—which would be concerns if segments of the target population had restricted access to email or the Internet, or were inexperienced or uncomfortable with online activity—does not seem likely. The members of both organizations are regular users of email and the Internet, as these are the typical modes of communication used to share information about organizational activities with members. In addition, the commercial real estate community is likely more “wired” on the whole relative to other populations. Similarly, there does not seem to be reason to suspect non-response bias (i.e., that the non-respondents in the sample differ in a meaningful way from those who did respond). First, the distribution of the respondents in terms of business function and geography roughly parallels the distributions within each organization as a whole. Second, because the content of the survey—i.e., the factors that matter in firm location decisions—was, by definition, relevant to all of the potential respondents' daily jobs, the salience of the topic to the population as a whole seems clear.

  5. 5

    Respondents were allowed to select more than one region (hence the total percentages exceed 100 percent). Some respondents also reported working outside the U.S. (in Canada).

  6. 6

    Approximately 20 individuals took the pretest and participated in debriefing sessions to discuss their comments and suggestions on the instrument. Based on the feedback received, we made a few minor modifications to the survey, mostly concerning phrasing and language choice.

  7. 7

    These two categories were originally combined in the first draft of the survey. At the request of one of the organizations, which had an interest in exploring potential differences between manufacturing and general industrial facilities, we separated them out. Given the similarity between the two categories, however, we opted to collapse them into a single category here for analytical purposes.

  8. 8

    These findings were confirmed in an open-ended question featured at the end of the survey. Respondents were asked to list the “five most critical” factors in the location decision. Only 13 percent listed “taxes” among their top five factors, while 18 percent included “incentives” on that list. In contrast, 73 percent mentioned “rents or lease costs,” and 70 percent mentioned “the availability of labor” as one of the five most critical factors in the location decision. Complete findings from the open-ended section are available from the authors upon request.

  9. 9

    Although the sample size for mixed-use facilities was adequate (n = 23), we also dropped these respondents' ratings from the group analyses for two reasons. First, given the emphasis on housing in mixed-use developments, we were concerned that factor ratings might reflect the perceived importance of these criteria to housing rather than to business facilities. Second, many of the potential uses of mixed-use facilities would include project types that we were already considering separately (e.g., office space). The decision to list mixed-use as an option on the survey was made at the request of one of the participating organizations. These data are available from the authors upon request.

  10. 10

    Considerably more cities and counties have implemented living wage ordinances, but the reach of these laws is more limited, typically affecting only those businesses that have contracts with the city, or receive subsidies from the city (Sonn 2006).

  11. 11

    Additional qualitative research conducted by one of this study's authors confirmed the importance of population density and demographics to retail location decisions. Interview respondents frequently referred to the number of “rooftops” in the surrounding area when assessing a potential site for a retail establishment.

References

  1. Top of page
  2. Abstract
  3. Location Theory: Determinants of Site Selection
  4. Location Theory: Research Methods
  5. Data and Methods
  6. Results
  7. Discussion
  8. Limitations and Directions for Future Research
  9. References
  • Arsen, D. 1997. Is there really an infrastructure/economic development link? In Dilemmas of urban economic development: Issues in theory and practice, ed. R. Bingham and R. Mier , 8298. Thousand Oaks, CA: Sage.
  • Austin, J. 2011. Urban renewal: A tale of two cities. http://www.CDAPress.com, June 20. http://www.cdapress.com/news/local_news/article_0a3f6ad0-9b67-11e0-ac6a-001cc4c03286.html (accessed July 2011).
  • Bartik, T. 1988. The effects of environmental regulation on business location in the United States. Growth and Change 19(3): 2244.
  • Bartik, T. 2004. Thinking about local living wage requirements. Urban Affairs Review 40(2): 269299.
  • Becker, S., K. Ekholm, R. Jackle, and M. Muendler. 2005. Location choice and employment decisions: A comparison of German and Swedish multinationals. Review of World Economics 141(4): 693731.
  • Berechman, J., D. Ozmen, and K. Ozbay. 2006. Empirical analysis of transportation investment and economic development at state, county, and municipality levels. Transportation 33(6): 537551.
  • Bieri, D. 2010. Booming bohemia? Evidence from the U.S. high-tech industry. Industry and Innovation 17(1): 2348.
  • Bilbao-Ubillos, J., and V. Camino-Beldarrain. 2008. Proximity matters? European Union enlargement and relocation of activities: The case of the Spanish automotive industry. Economic Development Quarterly 22(2): 149166.
  • Billings, S. 2009. Do enterprise zones work? An analysis at the borders. Public Finance Review 37(1): 6893.
  • Bishop, P., P. Gripaios, and G. Bristow. 2003. Determinants of call centre location: Some evidence for UK urban areas. Urban Studies 40(13): 27512768.
  • Blair, J., and R. Premus. 1987. Major factors in industrial location: A review. Economic Development Quarterly 1(1): 7285.
  • Bondonio, D., and R. Greenbaum. 2007. Do local tax incentives affect economic growth? What mean impacts miss in the analysis of enterprise zone policies. Regional Science and Urban Economics 37(1): 121136.
  • Brosio, G. 2008. Regional growth and environmental regulation: Friends or enemies? In Public policy for regional development, ed. J. Martinez-Vazquez and F. Vaillancourt , 198214. New York: Routledge.
  • Brunnermeier, S., and A. Levinson. 2004. Examining the evidence on environmental regulations and industry location. Journal of Environment and Development 13(1): 641.
  • Buss, T. 2001. The effect of tax incentives on economic growth and firm location decisions: An overview of the literature. Economic Development Quarterly 15(1): 90105.
  • Carlson, V. 2000. Studying firm locations: Survey responses vs. econometric models. Journal of Regional Analysis and Policy 30(1): 122.
  • Cheng, S. 2006. The role of labor cost in the location choices of Japanese investors in China. Papers in Regional Science 85(1): 121138.
  • Christaller, W. [1933] 1966. Central places in southern Germany. Translated by C. Baskin. Englewood Cliffs, NJ: Prentice Hall.
  • Clark, T., R. Lloyd, K. Wong, and P. Jain. 2002. Amenities drive urban growth. Journal of Urban Affairs 24(5): 493515.
  • Cohen, D., and M. Soto. 2007. Growth and human capital: Good data, good results. Journal of Economic Growth 12(1): 5176.
  • Condliffe, S., and O. Morgan. 2008. The effects of air quality regulations on the location decisions of pollution-intensive manufacturing plants. Journal of Regulatory Economics 36: 8393.
  • Delgado, M., M. Porter, and S. Stern. 2010. Clusters and entrepreneurship. Journal of Economic Geography 10(4): 495518.
  • Deller, S., V. Lledo, and D. Marcouiller. 2008. Modeling regional economic growth with a focus on amenities. Review of Urban and Regional Development Studies 20(1): 121.
  • Dillman, D., and D. Bowker. 2001. The web questionnaire challenge to survey methodologists. http://www.websm.org/uploadi/editor/Dillman.pdf (accessed July 2011).
  • Dissart, J., and S. Deller. 2000. Quality of life in the planning literature. Journal of Planning Literature 15(1): 135161.
  • Egan, M. 2011. Talent trumps tax incentives in corporate relocations. Fox Business, April 1. http://www.foxbusiness.com/industries/2011/03/31/corporate-relocation-hinges-talent/ (accessed July 2011).
  • Feldman, M. 2003. The locational dynamics of the US biotech industry: Knowledge externalities and the anchor hypothesis. Industry and Innovation 10(3): 311328.
  • Ferrand, Y., C. Kelton, K. Chen, and H. Stafford. 2009. Biotechnology in Cincinnati: Clustering or colocation? Economic Development Quarterly 23(2): 127140.
  • Florida, R. 2002a. The rise of the creative class. New York: Basic Books.
  • Florida, R. 2002b. The economic geography of talent. Annals of the Association of American Geographers 92(4): 743755.
  • Forkenbrock, D., and N. Foster. 1996. Highways and business location decisions. Economic Development Quarterly 10(3): 239248.
  • Frenkel, A. 2001. Why high-technology firms choose to locate in or near metropolitan areas. Urban Studies 38(7): 10831101.
  • Gaskill, S. 2011. Attracting the creative class: Local companies weigh in on the importance of talent. http://www.carolinabusinessconnection.com/cbc/article.html?id=17293# (accessed July 2011).
  • Gius, M., and P. Frese. 2002. The impact of state personal and corporate tax rates on firm location. Applied Economics Letters 9(1): 4749.
  • Gkritza, K., K. Sinha, S. Labi, and F. Mannering. 2008. Influence of highway construction projects on economic development: An empirical assessment. Annals of Regional Science 42(3): 545563.
  • Goetz, S., and R. Morgan. 1995. State-level locational determinants of biotechnology firms. Economic Development Quarterly 9(2): 174184.
  • Gottlieb, P. 1995. Residential amenities, firm location and economic development. Urban Studies 32(9): 14131436.
  • Granger, M., and G. Blomquist. 1999. Evaluating the influence of amenities on the location of manufacturing establishments in urban areas. Urban Studies 36(11): 18591873.
  • Green, G. 2001. Amenities and community economic development: Strategies for sustainability. Journal of Regional Analysis and Policy 31(2): 6175.
  • Hackler, D. 2003a. High-tech growth and telecommunications infrastructure in cities. Urban Affairs Review 39(1): 5986.
  • Hackler, D. 2003b. High-tech location in five metropolitan areas. Journal of Urban Affairs 25(5): 625640.
  • Hackler, D. 2004. The information technology industry and telecommunications: An empirical analyses of cities in the Minneapolis-St. Paul and Phoenix metropolitan areas. Journal of Urban Technology 11(3): 3559.
  • Hanson, A., and S. Rohlin. 2011. The effect of location-based tax incentives on establishment location and employment across industry sectors. Public Finance Review 39(2): 195225.
  • Hanushek, E., and L. Woessmann. 2008. The role of cognitive skills in economic development. Journal of Economic Literature 46(3): 608668.
  • Henisz, W., and A. Delios. 2001. Uncertainty, imitation, and plant location: Japanese multinational corporations, 1990–1996. Administrative Science Quarterly 46(3): 443475.
  • Hickman, P. 2011. The great reset: Economic opportunities and challenges in Shreveport-Bossier. http://www.nlacf.org/members/blog_view.asp?id=514551&tag=aspen+ideas+mini-festival (accessed July 2011).
  • Holl, A. 2007. Transport network development and the location of economic activity. In Essays on transport economics, ed. P. Coto-Millan and V. Inglada , 341361. New York: Physica-Verlag Heidelberg.
  • Holmes, T. 1998. The location of industry: Do states' policies matter? Regulation 23(1): 4750.
  • Hotelling, H. 1929. Stability and competition. Economic Journal 39(1): 4157.
  • Hoyman, M., and C. Faricy. 2009. It takes a village: A test of the creative class, social capital, and human capital theories. Urban Affairs Review 44(3): 311333.
  • Jeppesen, T., and H. Folmer. 2001. The confusing relationship between environmental policy and location behaviour of firms: A methodological review of selected case studies. Annals of Regional Science 35: 523546.
  • Kahn, J., and D. Henderson. 1992. Location preferences of family firms: Strategic decision of “home sweet home”? Family Business Review 5(3): 271282.
  • Karakaya, F., and C. Canel. 1998. Underlying dimensions of business location decisions. Industrial Management and Data Systems 98(7): 321329.
  • Kimelberg, S. 2010. Can we seal the deal?: An examination of uncertainty in the development process. Economic Development Quarterly 24(1): 8796.
  • Klier, T., and D. McMillen. 2008. Clustering of auto supplier plants in the United States. Journal of Business and Economic Statistics 26(4): 460471.
  • Klier, T., and J. Rubenstein. 2010. The changing geography of North American motor vehicle production. Cambridge Journal of Regions, Economy and Society 3(3): 335347.
  • Kolko, J., D. Neumark, and M. Mejia. 2011. Public policy, state business climates, and economic growth. NBER Working Paper No. 16968. Cambridge, MA: National Bureau of Economic Research.
  • Koo, J., and K. Cho. 2011. New firm formation and industry clusters: A case of the drugs industry in the U.S. Growth and Change 42(2): 179199.
  • Koo, J., J. Bae, and D. Kim. 2009. What does it take to become a biotech hot spot? Environment and Planning. C, Government and Policy 27(4): 665683.
  • Laabas, B., and R. Weshah. 2011. Economic growth and the quality of human capital. http://www.mpra.ub.uni-muenchen.de/28727 (accessed July 2011).
  • Lafer, G., and S. Allegretto. 2011. Does “right-to-work” create jobs? Answers from Oklahoma. EPI Briefing Paper No. 300. Washington, DC: Economic Policy Institute.
  • Losch, A. 1954. The economics of location (English translation). New Haven, CT: Yale University Press.
  • Love, L., and J. Crompton. 1999. Role of quality of life in business (re)location decisions. Journal of Business Research 44(3): 211222.
  • Luger, M., and S. Bae. 2005. The effectiveness of state business tax incentive programs: The case of North Carolina. Economic Development Quarterly 19(4): 327345.
  • Luskin, D., E. Mallard, and I. Victoria-Jaramillo. 2008. Potential gains from more efficient spending on Texas highways. Annals of Regional Science 42(3): 565590.
  • Lynch, R. 2004. Rethinking growth strategies: How state and local taxes and services affect economic development. Washington, DC: Economic Policy Institute.
  • MacGillis, A. 2010. The ruse of the creative class. American Prospect, January 4. http://www.prospect.org/cs/articles?article=the_ruse_of_the_creative_class (accessed July 2011).
  • Markusen, A. 2006. Urban development and the politics of a creative class: Evidence from a study of artists. Environment and Planning A 38(10): 19211940.
  • McCann, P., and D. Shefer. 2004. Location, agglomeration, and infrastructure. Papers in Regional Science 83(1): 177196.
  • McGuire, T. 2003. Do taxes matter? Yes, no, maybe so. State Tax Notes 28(10): 885890.
  • Méjean, I., and L. Patureau. 2010. Firms' location decisions and minimum wages. Regional Science and Urban Economics 40(1): 4559.
  • Mellander, C. 2009. Creative and knowledge industries: An occupational distribution approach. Economic Development Quarterly 23(4): 294305.
  • Morley, H. 2011. Panasonic deal stirs criticism of N.J.'s tax-credit programs. The Record, March 13. http://www.northjersey.com/news/117875959_The_incentive_gamble.html (accessed July 2011).
  • Muro, M., and B. Katz. 2010. The new “cluster movement”: How regional innovation clusters can foster the next economy. Washington, DC: Brookings Institution.
  • Nelson, M. 2005. Rethinking agglomeration economies and the role of the central city. Journal of Planning Education and Research 24(3): 331341.
  • Nelson, M. 2006. Interpreting producer service suburbanization: The public accounting industry in Chicago and Minneapolis-St. Paul. Urban Geography 27(1): 4571.
  • Neumark, D., and J. Kolko. 2010. Do enterprise zones create jobs? Evidence from California's enterprise zone program. Journal of Urban Economics 68(1): 119.
  • Nzaku, K., and J. Bukenya. 2005. Examining the relationship between quality of life amenities and economic development in southeast USA. Review of Urban and Regional Development Studies 17(2): 89103.
  • Porter, M. 1995. The competitive advantage of the inner city. Harvard Business Review 73(3): 5571.
  • Porter, M. 2000. Location, competition and economic development: Local clusters in a global economy. Economic Development Quarterly 14(1): 1534.
  • Powell, W., and K. Snellman. 2004. The knowledge economy. Annual Review of Sociology 30: 199220.
  • Rainey, D., and K. McNamara. 1999. Taxes and the location decision of manufacturing establishments. Applied Economic Perspectives and Policy 21(1): 8698.
  • Richardson, R., and A. Gillespie. 2003. The call of the wild: Call centers and economic development in rural areas. Growth and Change 34(1): 87108.
  • Roberts, K., and P. Smith. 1992. The effect of labor cost differences on the location of economic activity under the U.S.-Canada free trade agreement. Economic Development Quarterly 6(1): 5263.
  • Romo, F., and M. Schwartz. 1995. The structural embeddedness of business decisions: The migration of manufacturing plants in New York State, 1960 to 1985. American Sociological Review 60(6): 874907.
  • Schoales, J. 2006. Alpha clusters: Creative innovation in local economies. Economic Development Quarterly 20(2): 162177.
  • Schwartz, D., J. Pelzman, and M. Keren. 2008. The ineffectiveness of location incentive programs: Evidence from Puerto Rico and Israel. Economic Development Quarterly 22(2): 167179.
  • Shaffer, R., S. Deller, and D. Marcouiller. 2006. Rethinking community economic development. Economic Development Quarterly 20(1): 5974.
  • Simmie, J. 2004. Innovation and clustering in the globalised international economy. Urban Studies 41(5–6): 10951112.
  • Solomon, D. 2001. Conducting web-based surveys. Practical Assessment, Research and Evaluation 7(19). http://www.pareonline.net/getvn.asp?v=7&n=19 (accessed July 2011).
  • Sonn, P. 2006. Citywide minimum wage laws: A new policy tool for local governments. New York: Brennan Center for Justice.
  • Storper, M., and A. Scott. 2009. Rethinking human capital, creativity, and urban growth. Journal of Economic Geography 9(2): 147167.
  • Su, Y., and L. Hung. 2009. Spontaneous vs. policy-driven: The origin and evolution of the biotechnology cluster. Technological Forecasting and Social Change 76(5): 608619.
  • Targa, F., K. Clifton, and H. Mahmassani. 2006. Influence of transportation access on individual firm location decisions. Transportation Research Record: Journal of the Transportation Research Board. Washington, DC: Transportation Research Board of the National Academies.
  • Thomas, W., and P. Ong. 2004. Environmental regulations and industrial competitiveness: An analysis of air pollution control regulations on the wood furniture industry in Southern California. Economic Development Quarterly 18(3): 220235.
  • Weber, A. [1909] 1929. Alfred Weber's theory of the location of industries. Trans. C. Friedrich. Chicago, IL: University of Chicago Press.
  • Weir, D. 2011. City's controversial tax break for Twitter is the key to remaining a center of innovation. http://www.7x7.com, April 12. http://www.7x7.com/tech-gadgets/citys-controversial-tax-break-twitter-key-remaining-center-innovation (accessed July 2011).
  • Whittington, K., J. Owen-Smith, and W. Powell. 2009. Networks, propinquity, and innovation in knowledge-intensive industries. Administrative Science Quarterly 54(1): 90122.
  • Wilson, W. 2002. The effect of right-to-work laws on economic development. Midland, MI: Mackinac Center for Public Policy.
  • Yu, J., and R. Jackson. 2011. Regional innovation clusters: A critical review. Growth and Change 42(2): 111124.
  • Zukin, S. 2009. Naked city: The death and life of authentic urban places. New York: Oxford University Press.