Joo, et al., J Tourism Res Hospitality 2015, 4:1 http://dx.doi.org/10.4172/2324-8807.1000145 Case Report Journal of Tourism Research & Hospitality A SCITECHNOL JOURNAL Revitalizing Cities: Amenities, Economic Development, and the Attraction of Human Capital productive and skilled labor. What can cities and regions suffering from decline as a result of macroeconomic changes do to attract and retain a highly educated workforce and the individuals that drive innovation? These are the questions that have been at the forefront of urban policy debates for decades. Mijin Joo1* and Mark S. Rosentraub2 Even regions that were former manufacturing centers must attract far more educated workers to meet the demands of the highly mechanized factories that are less dependent on unskilled labor and more invested in robots and computers. In the past these areas relied on a series of comparative advantages often related to transportation efficiencies and a labor force appropriate for assembly line work. Those transportation advantages have dissipated. Robots and computerized systems have led to new levels of productivity and the need for workers with skills different from that in-demand for decades ago. That change in skill types and increasing levels of efficiency and the reliance on automation has reduced the overall demand for labor. These changes and the rise to prominence of other industries with demands for highly educated and creative workers have focused every region on enhancements to the “quality of life.” Quality of life amenities are code words for the lifestyles desired by the most educated workers. Exactly what constitutes a high quality of life remains highly subjective. 1Chung-Ang 2University University, Dongjak-Gu, Seoul, Korea of Michigan, Ann Arbor, Michigan, Korea Corresponding author: Mijin Joo Ph.D., Chung-Ang University, Dongjak-Gu, Seoul, Republic of Korea, E-mail: [email protected] Rec date: Aug 06, 2014 Acc date: Dec 01, 2014 Pub date: Jan 10, 2015 Abstract The focus on knowledge, innovation, and a highly educated workforce as the agents of economic transformation has reemphasized Marshall’s “ideas in the air” as the keys to successful long-term growth strategies. He was among the first to suggest that wealth occurs where creative, skilled labor concentrates. It is these individuals who generate the ideas that create new products, processes, innovations, and, most importantly, jobs. Recent empirical work has validated the importance of a well-educated labor for development. As a result, areas that seek to reverse periods of economic contraction are driven to policies and practices that help attract and retain highly productive and skilled labor. Some regions have capitalized on warm weather, mountains, or Bohemian life-style neighborhoods to attract well-educated labor. Other regions lacking those assets have turned to investments in sports facilities or other entertainment-oriented amenities to compete with areas with milder winters. Do those investments often funded by higher taxes have the potential to change labor migration patterns? That is the focus of the research reported to help community leaders design new public policies for redevelopment strategies. Two analyses are provided. The first focuses on the relationship between different sets of amenities and the movement of highly educated workers. The second looks for differences in migration patterns and the presence of different amenities related to the age of educated workers. The findings suggest some entertainment amenities are indeed useful for attracting workers, but additional research is needed. Introduction The focus on knowledge, innovation, and a highly educated workforce as the agents of economic transformation has reemphasized Marshall’s “ideas in the air” as the keys to successful longterm growth strategies [1]. Marshall’s ideas suggest that wealth occurs where creative, skilled labor is concentrated. It is these individuals who generate the ideas that create new products, processes, innovations, and, most importantly, jobs. Recent empirical work has validated the importance of a well-educated labor for development and wealth [2, 3]. As a result, areas that seek to reverse periods of economic contraction are driven to policies and practices that lead to the production or creation of environments that attract and retain highly Some regions, for example, have capitalized on environmental amenities (warm weather and year-round access to beaches, mountains, or Bohemian life-style neighborhoods). Other regions lacking those assets have turned to investments in large-scale changes to their built-environment to offer spectator-based amenities to attract and retain highly educated and skilled workers. Do those investments often funded by higher taxes have the potential to change labor migration patterns? That is the focus of the research reported here in an effort to increase the information available to community leaders who must frame and design new public policies to guide and support redevelopment strategies and efforts. The rising prominence of the quality of life in economic development Business locations are still dependent on production costs (labor, capital, transportation, energy, communication, etc.). Increasingly, however, efficiencies with regard to certain inputs have increased the geographic options that exist with regard to the areas in which companies can locate. The increasing dependency of new businesses and those focused on new levels of productivity, and those concentrated in the fastest growing segments of the economy, are dependent on highly skilled/educated labor. This has elevated the financial returns to firms from locating in areas that offer the most coveted workers the quality of life they desire or demand. As a result, businesses in the post-industrial age can maximize their profit potential by locating in areas that offer access to a large pool of skilled and well-educated workers [4]. Areas or regions that rose to economic prominence as a result of the access they provided to transportation networks or raw materials and other factor inputs must now compete with the available mix of amenities that appeal to the labor needed by the growing predominance of finance, management, media, information technology, and advanced engineering firms. A failure to build an environment capable of attracting and retaining educated and skilled workers will lead to economic stagnation and decline [5]. With All articles published in Journal of Tourism Research & Hospitality are the property of SciTechnol and is protected by copyright laws. Copyright © 2015, SciTechnol, All Rights Reserved. Citation: Mijin J, Mark SR (2015) Revitalizing Cities: Amenities, Economic Development, and the Attraction of Human Capital. J Tourism Res Hospitality 4:1. doi:http://dx.doi.org/10.4172/2324-8807.1000145 numerous North American and Western European formermanufacturing centers mired in various stages of economic contraction community leaders in those areas are focused on ways to attract and retain human capital. Ironically, in many of these areas there is no shortage of institutions producing well-educated workers. Several highly ranked universities are located in some of America’s slowest growing states (e.g., The University of Michigan, Indiana University, the Ohio State University, the University of Illinois, the University of Chicago, etc.). The problem is not producing a large pool of talent; the challenge is to attract and retain this educated workforce after they finish their education. The quality of life and economic development Straubhaar [6], Florida [7] and Clark [8] point out that in the modern knowledge-based economy what affects the residential choices of skilled and educated workers has changed. Entertainment and other activities associated with enjoyment of arts, culture, and recreation influence the locations valued by well-educated workers. Clark [8] explains that human capital aggregations and amenities jointly create a location that is a desirable place to live and work. The extraordinary growth in America’s southern and western states also attests to the value workers place on areas with mild winters. What quality of life amenities can areas with less temperate climates offer to compete for skilled labor? Can improvements in the built-environment or what is often described as entertainment amenities offset the advantage of mild winters? This study was designed to offer some insight into the linkage between the presence of certain amenities and the concentration of a well-educated workforce. Many communities struggling to attract well-educated workers have made large investments in sports facilities, entertainment complexes, cultural centers, and other entertainmentrelated amenities despite the absence of data suggesting the value of these assets in changing the distribution of human capital. The research reported here is designed to help officials understand if investments in entertainment or other amenities have any impact on the migration of educated workers. Two analyses are provided in an effort to offer insight into the relationship between amenities and the distribution of well-educated human capital. The first focuses on the relationship between entertainment amenities and the movement of highly educated workers. The second analysis looks for differences in location choices and entertainment amenities related to the age of educated workers. These analyses are preceded by a brief review of recent research into the factors that attract skilled labor to particular areas and the concluding section looks at the implications of the findings for public policies and investments while also offering suggestions for future work. What Attracts Skilled Labor to an Area? From the Pull of Jobs to the Rising Importance of the Quality of Life Numerous early studies of labor migration identified the importance of employment opportunities and higher wage expectations as the factors that explained the attraction and retention of workers to specific areas [9]. The importance or primacy of employment opportunities and wages were sometimes described as the “pull factors” that predicted labor migration patterns. Jobs and higher wages were usually found in areas where transportation assets reduced the costs of acquiring raw materials and transporting them to production facilities and finished products to markets. Workers followed businesses to those areas. Volume 4 • Issue 1 • 145 As noted, once similar transportation and other factor input costs could be found in multiple areas firms had more flexibility in the choices that could be made regarding locations. That flexibility has contributed to a focus on the quality of life for labor as a factor of production that can ensure a firm would always have access to the talent needed for profitability. Increasingly the quality of life that a region offers to its residents has become a primary factor in selecting a location for a business. Florida [7] and Clark [8] have suggested that in the modern knowledge-based economy highly skilled workers are as focused on preferences for entertainment or other lifestyle amenities as they are on wages. Business leaders understand this changing environment and choose to locate in areas that have a reputation for offering skilled labor a desirable quality of life. Depending on the researcher (or pundit) the relative importance of pecuniary gains or amenities could be seen to be at parity or one set of benefits could be more valuable than another. Regardless of the relative weight workers played on amenities or wages it is clear that the quality of life available to highly regarded employees is now part of the portfolio of factor inputs that predict the locations preferred by employers. The importance of quality of life amenities has convinced community leaders in areas with longer winters, colder average temperatures, and a lack of access to mountain areas or other highly regarded physical amenities to invest in entertainment amenities. The goal of these public investments has been to create a built environment that achieves a level of competitive balance relative with the quality of life available in other areas. The role of amenities could assume in attracting talent was initially discussed by [10,11] decades ago. They also found that a more temperate climate helped to create a pool of talented workers and that could mean that communities in more severe climates might be encouraged to consider enhancements to their built-environments. Other researchers also offered early evidence of the role of amenities in the choice of locations preferred by educated workers [12] Graves, et. al. There was also growing evidence that those areas with warmer climates seemed to prosper for than other regions. Areas in colder climates became “rust belts” of decline and this ushered in a frenzy of activity to enhance the supply of built-environment amenities to offset a perceived lower quality of life. Numerous cities, for example, built sports facilities for professional sports teams as well as centers for the performing arts to highlight a commitment to the quality of life [13]. The focus on the quality of life as a tool for economic development raises several important questions. For example, what are the components of the quality of life that firms and workers value in their choice of a location in which to live and work? Which particular amenity is the most valued, and how much more valuable is any amenity relative to another in terms of the attraction of more highly-educated workers? In which amenity should a government invest relative to magnitude of the shift in migration patterns that would be engendered? While these research questions were being discussed others challenged the view that the quality of life was a new magnet for human capital, Herzog, Schlottmann, and Johnson [14] concluded earnings were still the principal factor directing the migration decisions of talented human capital. Florida [7] and Clark [8], however, continued to stress the importance of amenities and their research illustrated that the distribution of educated and skilled workers was related to what it takes to makes an area a desirable place to live. Figure 1 depicts the different perspectives researchers have debated regarding the relationship between the factors that effect the migration • Page 2 of 7 • Citation: Mijin J, Mark SR (2015) Revitalizing Cities: Amenities, Economic Development, and the Attraction of Human Capital. J Tourism Res Hospitality 4:1. doi:http://dx.doi.org/10.4172/2324-8807.1000145 of labor. In the traditional view underscored by those who deemphasize the role of amenities, classical factors of production and not the quality of life predict local choices and the presence of jobs in a region’s economy. The concentration of jobs is also related to higher wage packages. A different approach, labeled human capital theory elevates amenities but suggests the simple presence of firms and the salaries offered to employees are more important than amenities. Amenity theory elevates the quality of life factors to a superior position relative to pecuniary elements changing the areas where firms choose to locate (Figure 1). to the building of amenities be given to public officials. Community leaders need information that describes the relative value of any factor or amenity. Simply put what amenity or set of amenities should local governments foster in an effort to attract and retain an educated work force? Or, is it best to simply induce the firms likely to offer the highest wages to locate in their communities? In this study, amenities that create entertainment options for residents and visitors were analyzed to understand their importance for the attraction of human capital. These amenities can clearly contribute to the quality of life by producing numerous and varied entertainment options [16]. What constitutes entertainment, for this analysis, was separated into those large facilities built by numerous communities for professional sports teams and centers for the performing arts. What was also included were measures of entertainment that are more neighborhood-based or far smaller in scale to assess some of the insights made by Clark [8] and others who askew the focus on large-scale (sometimes called “big ticket” amenities because of their cost) and instead recommend the building of Bohemian-style neighborhoods (and thus more in line with theories advanced by Jane Jacobs [15]). Conceptual Model Figure 1: The relationships between firms, human capital, and amenities The amenities studied here were those classified as part of the builtenvironment designed to offer entertainment options to residents. They included neighborhood-scale attractions and larger regional assets (sports facilities, museums, etc.). The larger-scale amenities were classified into three areas or types: (1) sports-related, (2) cultural, and (3) amusements (including casinos). Those proponents of the overarching (or leading) value of amenities should actually categorize the relationship as hypothetical since the level of empirical research already performed is too small to sustain the paramount role of quality of life factors in labor migration. Reflecting that observation, the research reported here is designed to add to the understanding of the value of amenities in the migration of highly educated labor. The work is undertaken in an effort to provide additional empirical tests of the ideas suggested by several and underscored by Florida and Clark’s work. There is evidence that businesses are interested in having convenient access to large pools of educated workers. What constitutes an amenity that appeals to this to this labor pool, however, is far less certain or precise. For example, built-environment amenities can mean sports facilities for major league teams, or it can also mean high quality schools, subjective evaluations of public safety, or neighborhoods that offer easy access to music, restaurants, or pubs. Precision in definitions of each of the components or elements that contribute to the quality of life is necessary. In addition, attention must be directed to the quantification of amenities. How many amenities are needed to attract more highly educated workers? Without precision of this nature the quality of life can be seen to mean anything that improves an environment or makes people happy. If the quality of life is everything then it runs the risk of being nothing that can be quantified and translated into specific policy targets. As a result what needs to be a part of any study is the inclusion of numerous different sets of amenities to see which ones seem to be associated with the migration of labor while also controlling for numerous other factors. Only when that is done can policy recommendations relative Volume 4 • Issue 1 • 145 Figure 2: The Conceptual Model Human capital was measured by years of education and migration decisions were analyzed for entry-level or younger workers, for middle-aged workers, and for older workers. These different segments were considered since it is possible entry-level workers focused on their first job may place less emphasis on entertainment options. Slightly older workers might be more influenced by amenities related • Page 3 of 7 • Citation: Mijin J, Mark SR (2015) Revitalizing Cities: Amenities, Economic Development, and the Attraction of Human Capital. J Tourism Res Hospitality 4:1. doi:http://dx.doi.org/10.4172/2324-8807.1000145 to their emerging families (education), and older workers with less family-formation responsibilities might be more focused on entertainment options [3,17,18]. Migration was defined as a move from one place to another place for a period of at least one year. The conceptual model that guided the research to understand the influence of built tourist amenities is described in Figure 2. Variables and Data Dependent variable Human capital was the dependent variable measured by educational attainment. It is recognized that educational attainment is but one measure of human capital and does not capture training and experience. Florida R [19] too has argued that education is not the best measure of human capital and suggests a focus on what people do as an index of talent. Educational attainment was used in this analysis as it less controversial, far easier to measure, is readily available from secondary data sets, and is commonly used as a derived shadow measure of skills or a better educated workforce. A review of Index numerous studies of human capital found that 11 relied on educational attainments as the best available proxy (Table 1) for skill level. Independent variables Amenities were defined by with three groups or categories of activities: (1) sports, (2) culture (museums) and (3) amusement. These amenities were classified using the numbering system developed by the United States Department of Commerce (North American Industry Classification System or NACIS). Sports-related amenities were defined by spectator sports (NAICS codes 7112 and 7113). Museums, historical sites, and performing arts companies were a second type of built amenities and were defined by NAICS codes 7121 and 7111. The third type of amenities included cinemas, big box retail stores, themed restaurants, record and video superstores, simulation theatres, virtual reality arcades, gaming establishments, and other amusement (NAICS codes 7131, 7132, and 7139). Care was taken to divide by total establishments to reduce problems with multicollinearity and heteroskedasticity. Occupation High Factor Technology Education Professional Scientists Engineers High School Or above 25 or above Male White 10 3 1 1 X X 1986 X Gottlib 1995 X Arora, Florida, Gates and Kamlet 2000 X Kordrzychi 2001 X Florida 2002 Florida 2002 X Clark 2003 X Glaeser and Saiz 2004 X Hansen, Ban and Huggins 2003 X Heuer 2010 X Shapiro 2006 Gottlieb Joseph and Artnz Mellender Florida 2006 2007 X X X 2006 and 1 College or above Herzog, Scholottmann, Johnson X 2 Race 4 X 2 Sex Total number of Studies X 1 Age X X X X X X X X Table 1: Measuring human capital: a survey from earlier studies Volume 4 • Issue 1 • 145 • Page 4 of 7 • Citation: Mijin J, Mark SR (2015) Revitalizing Cities: Amenities, Economic Development, and the Attraction of Human Capital. J Tourism Res Hospitality 4:1. doi:http://dx.doi.org/10.4172/2324-8807.1000145 Control variables The control variables used were divided into four categories: (1) weather-related factors (2) local public services, and (3) regional economic condition. Average daily temperatures in January and mean precipitation levels measured weather. There are a number of variables to measure local public services. Based on a review of several studies [20-22] crime, education and house value variables were selected as measures of the value of local public services. The level of crime was Variable measured by local violent crime rates (a measure of the performance of the police and other civic institutions to advance civil behavior). The ratio of students per teacher in public schools was a measure of the quality of schools (another important amenity). Median value of owner-occupied-housing units was used to measure the house values. Employment levels and median income controlled regional economic conditions. A “regions variable” controlled for the faster growth levels in the southern and western parts of the United States. Category Detail Date Source Educated Migrants Rate The Number of Migrants over college degree/ Total Population IPUMS (http://usa.ipums.org/) Young Migrants Rate The Number of Migrants between age 25 and 45/Total Population Middle Migrants Rate The Number of Migrants between age 45 and 55/Total Population Older Migrants Rate The Number of Migrants between over age 55/ Total Population Income Median household income American Community survey 2005, 2008 Log (Employment) Total employment County Business Patterns, 2005, 2008 House Value Median value of owner-occupied-housing units American 2005,2008 Crime Violent crime Crime in the United States, FBI, 2005, 2008 Pupil ratio Teacher per pupil IES (National Center for Educational Statics) 2005, 2008 Annual precipitation Annual precipitation (inches) Average January temperature Average daily temperature (degrees Fahrenheit) Dependent Variable Age Economic Factor Social Factor Weather Independent Variable County and 2000,2008 community City Data survey Book (Amusement Parks and Arcades (7131)/Total Establishments Amusement Rate (Gambling Industries Establishments (7132) )/ Total (Other Amusement and Recreation Industries (7139) )/Total Establishments (Performing Arts Companies (7111) )/Total Establishments Amenities County Business Patterns 2005, 2008 Cultural Rate (Museums, Historical Sites, and Similar Institutions (7121) )/ Total Establishments (Spectator Sports Establishments (7112) )/ Total Sports Rate (Promoters of Performing Arts, Sports, and Similar Events (7113) )/Total Establishments Table 2: Variables and sources Data Panel data from 2005 and 2008 were used. Income and employment data came from American Community Survey and County Business Patterns produced by the US Bureau of the Census. The enumeration of students per public school teachers was from the National Center Volume 4 • Issue 1 • 145 for Education Statics (NCES) and local crime rates came from the Federal Bureau of Investigation’s national crime database. Housing values were from the US Bureau of the Census’ American Community Survey. The built amenities data were obtained from County Business Patterns. Small amenities included restaurants, bookshops, and food • Page 5 of 7 • Citation: Mijin J, Mark SR (2015) Revitalizing Cities: Amenities, Economic Development, and the Attraction of Human Capital. J Tourism Res Hospitality 4:1. doi:http://dx.doi.org/10.4172/2324-8807.1000145 service businesses while big amenities were placed into one of three broad categories: amusements, art, or sports. Migration information was obtained from the Integrated Public Use Micro Data Series (IPUMS) maintained by the Minnesota Population Center at the University of Minnesota. The American Community Survey samples in 2005 and 2008 from IPUMS were selected to compare the two different years. IPUMS data do not provide any detailed migration category; immigration and non-movement was analyzed based on metropolitan area of residence data from a previous year provided by respondents. The focus on 2005 and 2008 permitted a look at recent changes. Assessments of other years and time periods were considered. A focus on changes between 1980, 1990, and 2000 could not be done; the required data could not be properly aligned for those years making it impossible to properly assess changes. Focusing only on 2005 and 2008 raises the possibility that an insufficient time period was used to identify changes. This limitation will be addressed but suffice to note at this point that the data used still included a large number of people who moved from one location or area to another. Further, the time period selected is a number of years after numerous cities had made substantial investments in sport facilities and cultural centers. If migration was influenced by these “big ticket” investments it should be evident. All of the data were realigned to conform to the definitions of MSAs specified on the IPUMS web site using the 1990 definitions or specifications. Economic, social and natural/built amenities data between 2005 and 2008 were collected at the county level and were aligned to the migration data from IPUMS. When conflicts emerged counties were excluded from the analysis. Research model A research model using panel analysis was constructed to measure the relationship between the movement of educated workers and amenities. First, several basic appropriate tests for autocorrelation, multi-correlation, and heteroskedasticity issues were performed. The Durbin–Watson statistic confirmed that there is no serious autocorrelation problem. To detect multi-correlation, a variance inflation factor (VIF) that measures how much multicollinearity has increased was used. After the test, variables with multi-collinearity issues such as population were removed from the model. Because employment was correlated with the amenity variables the measure of employment was transformed by using its log to resolve the multicorrelation issue. Residual plots and Levene‘s test determined that there was a heteroscedastic issue. After several transformation tests, the dependent variable was transformed (natural log) to eliminate any heteroscedasticity issues. Second, to construct the appropriate model for panel data, some statistical tests were needed. Panel data refers to any database of individuals for whom there are repeated observations across a sequence of time periods [23]. Park HM [24] explained that a fixed effect model assumes differences in intercepts across groups or time periods, whereas a random effect model explores differences in error variances. He explained that a one-way model should include one dummy variable such as a cross-sectional variable (e.g., firm, city, and county) or time-series variable (e.g., month and year) while a two way model may have two sets of dummy variables (e.g., firm and year). By using the SAS statistical program, the group and time effect in the data were tested and it turns out that there was no serious time effect but the group effect existed. The Hausman test was also used to identify whether the fixed or random effects model should be used. After the Volume 4 • Issue 1 • 145 Hausman test, a one-way fixed effect model was finally chosen to analyze the relationship between amenities and the movement of human capital. There are several one-way fixed group effect models (e.g., the least squares dummy variable model (LSDV), the within effect model, and the between effect model). In this study, LSDV model was chosen because the LSDV model is relatively easy to estimate. This LSDV, however, becomes problematic when there are many groups or subjects in the panel data (Park, 2009). To minimize degrees of freedom the various Metropolitan Statistical Areas (MSAs) were placed into four geographic zones - South, West, Northeast, and Midwest. The model used involved migration rates in MSAs in the United States and enumerated amenity levels: Ln (M)=o + β1 C + β2 A +ε , Where, Ln (M)=Log of migrant rate in MSAs; C=Control variables in MSAs; A=Built tourist amenities rate variables in MSAs and ε=error t. 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