J Knowl Econ DOI 10.1007/s13132-015-0278-z

The “Knowledge City” and the “Experience City”: the Main, Mediating, and Moderating Effects of Education on Income and Economic Inequality Meir Russ 1 & Gaurav Bansal 1 & Adam Parrillo 2

Received: 20 March 2015 / Accepted: 6 July 2015 # Springer Science+Business Media New York 2015

Abstract The new knowledge economy and the experience economy are the two most recent techno-economic paradigms that appear to guide business executives and economic development practitioners and that frame the research of management and economic development academics. In this paper, we distinguished between knowledge cities and experience cities, and we performed a preliminary study of the association and alternative roles of education with income and inequality within urban areas. Specifically, we analyzed a number of competing models that investigate the main, mediating, and moderating effects of education on income and inequality in urban areas in the USA. Our findings suggest that education has a positive role in increasing income and, more importantly, in reducing inequality when we account for the concentration of knowledge and experience-based industries in the city. Distinctively, it is the knowledge-based sector that contributed significantly to this result. Keywords Income . Economic inequality . Education . Experience city . Knowledge city

Earlier versions of the paper were presented at a seminar at Ben Gurion University of the Negev, Beer Sheva, Israel, November 23, 2011, and at the IFKAD-KCWS 2012 5th Knowledge Cities World Summit conference, June 15, 2012, Matera, Italy.

* Meir Russ [email protected] Gaurav Bansal [email protected] Adam Parrillo [email protected] 1

Austin E. Cofrin School of Business, University of Wisconsin-Green Bay, Green Bay, WI, USA

2

Urban and Regional Studies, University of Wisconsin-Green Bay, Green Bay, WI, USA

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Introduction The new knowledge economy and the experience economy are the two most recent techno-economic paradigms that appear to guide business strategists and frame the research of strategic management and economic development academics. This paper’s intent is to untangle some of the confusion concerning the concept of creative spaces, focusing on the urban areas as a unit of analysis. For that, as a conceptual framework, we will draw upon knowledge management typologies and taxonomies developed from the firm’s strategic perspective in a business context (Russ et al. 2010). First, we will discuss the characteristics of modern urbanization and the scaling effect. Next, we distinguish between knowledge cities and experience cities (both are creative spaces) utilizing the distinctive knowledge bases needed to provide for the diverse competencies required by the two sectors. We will use sectoral definitions for the city’s type since we will be looking into education as a mitigating variable. The industry sectors used in this study will be drawn more as indicators rather than as a comprehensive measure since some of the sectors used by the literature are (or have the potential of being) a mix 1 of both types of cities. This paper will use the sectors of software, IT and R&D as indicators for knowledge cities, and arts, recreation, and accommodations as indicators for experience cities. This will be followed by a discussion about education and economic inequality. We are interested in the role of education for several reasons. First, education is seen as a major building block for human capital which in turn is seen as a necessary base for both the new knowledge-based economy and for the experienced economy. Education is also seen as an enabler for social mobility, a critical factor for a healthy democratic system, and a factor in helping societies adjust to economic change. On the other hand, at least in some cases, education is seen as a mechanism maintaining prevailing social and economic inequalities as well as introducing new forms of inequalities. Education might also play a moderating and/or mediating role affecting economic income and inequalities. These are just a few of the reasons education is seen as an important factor. Early research suggests that the higher the educational level, the more successful cities are, as well as the reverse; the more successful the city, the higher the education level. Research also indicates that the more creative a city is, the more attractive it is to the creative and educated class. Early research also hints that the more successful the city, the higher the economic inequality, which may suggest that some of the expectations regarding the equalizing role of education may be simplistic. Next, we will present our hypotheses, research methods, and findings. We will wrap up the paper with conclusions, implications, limitations, and recommendations for future research.

Urbanization, Size, and Income The world is experiencing a level of urban development unprecedented in human history. Since 2008, the majority of the people in the world are living in urban areas. 1

For example, see in Asheim and Hansen (2009), Table 2, p. 434, where engineers and architects belong to the same occupational group or where university art professors and engineering professors are listed in one occupational group.

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By 2050, two out of three people worldwide will be living in cities, and this pace of urbanization is increasing (Decker et al. 2007). On one hand, cities are considered to be the engines of innovation and wealth creation (e.g., Bettencourt et al. 2007). On the other hand, cities’ growth raises concerns of impact on poverty, on crime, and especially on the environment (e.g., Storper and Scott 2009). Regardless, there seems to be a scaling effect to city growth, both positive and negative, that is a function of the city population size (Bettencourt et al. 2007). The academic literature observes that larger cities produce more GDP per capita, more patents, and more innovation, though there are of course debates about the theories driving the scaling effects and the intensities of the effects (for example, Acs et al. 2002; Storper and Scott 2009; Shalizi 2011).To summarize, we hypothesize that the larger the city, the higher the income (wages) (see H1, Table 4) and the higher the inequality as compared to a smaller city (e.g., Behrens et al. 2010), (see H2,Table 4).

Knowledge Cities and Experience Cities Why Is the Distinction Between Knowledge and Experience Important? Confusion is evident in the academic literature when deliberating creative cities. Are creative, cultural, knowledge cities synonymous? Related? Different? (Florida 2002; Scott 2006; Musterd et al. 2007; Cooke and Lazeretti 2008). It is imperative to have a distinctive definition of what kinds/types of underlying skills are driving the growth of a specific urban area, especially considering the industry clusters aspects of economic development (e.g., Lorentzen 2009; Storper and Scott 2009; Florida et al. 2012).We are postulating that knowledge-based segments and experience-based segments are distinctively different. In other words, creative industries or cities could be experiencebased (art, tourism) but might not necessarily be knowledge-based (e.g., computer games) (Lorentzen 2009). For example, there is a well-accepted positive relationship between the creative segment and the growth rate of a population and the household income per capita (e.g., Florida 2002). McGranahan and Wojan (2007) take issue with such a broad and vague definition of the creative vocation (Florida 2002) and make a clearer distinction between creative occupations and what they call economic reproduction, low on creativity, but high on knowledge occupations. They find that after separating the two (which is consistent with this research perspective), their results have both a higher significance and a higher validity. Our conjecture in this study is also consistent with the three distinctive knowledge bases, the synthetic, analytic, and symbolic as proposed by Asheim and Hansen (2009) in the context of regional development. We use this taxonomy but take it one step further by deferring to the findings of Marrocu and Paci (2013), Burd (2012, 2013), and de Carvalho (2013) which suggested major similarities between the synthetic and analytic knowledge bases (what we would call the knowledge cities) but both being different from the symbolic knowledge base (what we would call the experience cities). As such, we will consider, based on the discussion above, two types of drivers for production or idea creation2 in 2

Those two drivers: knowledge and experience, seem to be complementary (but different) explanations or paradigms for economic development (Lorentzen 2009; Yigitcanlar et al. 2008).

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urban areas: (1) the knowledge-science-learning, R&D, IT, cognitive-based, “footloose”; and(2) the experience, art, emotional-based, place-bound. We see the type of city as a spectrum with different proportions, or balance, between the two drivers, and not as a strict and simplistic classification of the city. Knowledge Cities We will next consider a few major facets of knowledge cities. In a knowledge-,skillbased economy, increasing numbers of cities are turning to utilizing knowledge to support their socio-economic growth as they are facing global competition and inequality in prosperity (Perry and May 2010; Florida et al. 2012). For instance, there is a positive association between the manifestation of national research universities, the percentage of knowledge-based jobs, and venture capital investments (Carlson and Chakrabarti 2007). Specifically, venture capital is associated with stimulating patent activity but is heavily concentrated in a very small number of major urban areas (Carlson and Chakrabarti 2007). The nature of the product or service in the knowledge city supports a broader, most frequently, global competition, resulting in concentration in large clusters in large urban areas. The consumption of such products is usually not locally bounded. The clusters benefit from global flow of knowledge, talent, and capital (Lorentzen et al. 2007, p. 12).But, knowledge cities should not be seen as synonymous with high-tech industries. Research suggests that medium-tech companies might be more important for the local economy than high-tech companies since their inputs and outputs are both locally or regionally proximate (Dolfsma and Leydesdorff 2008).The growing inequality in knowledge cities might result from the pulling up effect of the high end of the wage scale and/or from the growth of the low-paid sector, or both (e.g., Autor et al. 2006).This sector utilizes mostly (but not only) the synthetic and analytic knowledge base (Asheim and Hansen 2009) and the codified product knowledge base (Russ et al. 2010). Based on the discussion above, we expect that the more knowledge-baseintensive the city, the higher the income per capita (see H3, Table 4) and the higher the inequality (see H4, Table 4) (e.g., Acs et al. 2002; Donegan and Lowe 2008; Florida et al. 2012). Experience Cities The experience economy is contemplated as a new paradigm for business, whereby companies attribute “memorable experience” to their product or service offerings, which targets customers or users in a wide-ranging, intrinsic manner (Pine and Gilmore 1998). Such experience creates augmented economic value by engaging active and emotional involvement of the customers (Lorentzen et al. 2007). One additional facet of the experience-based economy is that it is “place bound” (Lorentzen et al. 2007). This is critically important, because it constrains the scope of the competition, and for the cities that are successful in creating name recognition, it minimizes the competition from other cities. Finally, there seems to be a positive association between the number of artists per capita in a city and higher education, population, and income growth in US cities (e.g., Rushton 2007), but such an association is weakly supported by academic research due to methodological issues (Markusen and Gadwa 2010). This

J Knowl Econ

sector utilizes mostly (but not only) the symbolic knowledge base (Asheim and Hansen 2009) and the tacit process knowledge base (Russ et al. 2010). Based on the discussion above, we expect that the more experience-based-intensive the city, the higher the income per capita (see H5, Table 4) and the inequality (see H6, Table 4). Production costs and efficiencies are distinctive in knowledge-based industries and in experience-based industries (wages, economies of scale, and learning curves). Also, idea exchanges have a predominantly different nature in science-based and art-based networks (explicit versus tacit nature of the underlying knowledge) (e.g., Houghton and Sheehan 2000; Russ et al. 2010).Furthermore, due to the increasing returns of knowledge (Romer 1986), we expect that the scaling effect of the knowledge-based urban areas in comparison to the experience-based urban areas will be higher on income (see H7, Table 4) and inequality (see H8, Table 4), ceteris paribus.

Education We will now discuss some major aspects of education within the context of our study. Human capital is a critical factor needed to support both the knowledge-based and experience-based cities’ growth and success (e.g., Glaeser and Saiz 2003). Berry and Glaeser (2005) assert that cities with higher levels of human capital are more attractive to populations with higher academic education. This would suggest that there is a positive association between human capital and income (Gottlieb and Fogarty 2003) (see H9, Table 4), as well as on inequality (e.g., Perry and May 2010) (see H10, Table 4). Abel and Gabe (2008) assert that human capital enhances income in urban areas directly and indirectly, first by increasing productivity, which then increases income. Next, higher concentrations of human capital result in knowledge spillovers and complementarity that further increase productivity and innovation. Such an increase in productivity and innovation further attracts knowledge-based firms to the area (Berry and Glaeser 2005). It also increases income which further attracts diverse (experience-based, social intelligence, and less skilled) participants (e.g., Florida et al. 2012). This may suggest that the impact of education on the knowledge aspects (see H11, Table 4) and on the experience aspects (see H12, Table 4) is positive and that ceteris paribus, it is higher on the knowledge aspects than on the experience aspects (see H13, Table 4). Growing and successful cities are also antecedents for a demand for human capital and have a special need for higher educated and skilled labor (e.g., Abel and Gabe 2008). Specifically, we hypothesize that the positive effects of knowledge-based and experience-based sectors on income and inequality are mediated by education (Model 2 below). More explicitly, we expect that the higher the intensity of the knowledge base in the city, the higher concentration of human capital in the city (e.g., Brint 2001) (see H14, Table 4). We also expect that the higher the intensity of the experience base in the city, the higher the concentration of human capital in the city (e.g., Karlsson et al. 2009) (see H15, Table 4). Further, we expect that ceteris paribus, the concentration of human capital is more strongly affected by the knowledge aspects than by the experience aspects (see H16, Table 4).

J Knowl Econ

This also may suggest a moderating role (Model 3 below) for the effect of educational attainment on economic income and inequality (e.g., Peng and Lin 2009). For example, Honore and Hu (2004) find that investment in education has a higher return in the lower end of income distribution. This might suggest that ceteris paribus, the interaction of human capital with the knowledge-based sector is higher than with the experience-based sectors on income (see H17, Table 4) and on inequality(H18, Table 4). We will test that role in models 2 and 3 discussed below.

Economic Inequality We will now discuss some major aspects of economic inequality within the urban context of this research. Some see inequality as positive, for example, as an incentive for individuals to invest in education, take entrepreneurial risks, and increase savings (e.g. Li and Zou 1998). Simultaneously, however, inequality may cause social unrest, alter consumption patterns at the bottom of income distribution, and/or pose threats to economic stability and the environment (e.g., Donegan and Lowe 2008). Growth in urban areas is leading to increased inequality; the larger the city, the larger the inequality (e.g., Baum-Snow and Pavan 2009). Additionally, the broader the scope of skills heterogeneity, the bigger the inequality (Behrens and Robert-Nicoud 2010) since the competition is global and the talent (entrepreneurial and technological) will move to the more attractive location (cluster, city). So, as mentioned earlier, we expect that the larger the city, the bigger the inequality (see H2, Table 4). A corresponding theory, resulting in a complementary inference, is proposed by Autor and Dorn (2009). Their model (supported by findings) suggests that the underlying cause for the growth in inequality is the relative growth of low-skill service and high-skill jobs, and the decline of the routine-skilled (medium paid) jobs replaced by computerization. This may also suggest that the higher the income, the higher the inequality (see H19, Table 4).

Control Variables Based on earlier studies three control variables were also considered in this study: 1. Immigration Immigration, on one hand, has a negative impact on inequality, by providing a cheap, less educated labor force (e.g., Donegan and Lowe 2008). On the other hand, immigration has a positive effect since it provides entrepreneurial talent (e.g., Saxenian 1999) as well as a highly skilled and educated labor force (e.g., Hall et al. 2011). 2. Rent Housing supply, price, and/or elasticity also seem to effect migration in and out of cities (e.g., Glaeser and Gottlieb 2009). 3. Financial Sector The financial sector seems to play a critical role in the growth of the cities’ economy and in its effect on economic inequality (Warf 2004). For example, Lee

J Knowl Econ

(2011) suggested that in Europe, the financial segment has a significant impact on economic inequality. 4. Antecedent Population Change Preceding population growth trends will indicate growing and declining urban areas. Such antecedents will have impact on urban dynamics and economic fundaments in cities (e.g., Glaeser et al. 2006; Hwang and Quigley 2006).

Hypotheses To summarize, this paper presents a number of specific hypotheses (see Table 4 below) suggesting that the larger the city, the higher the income and inequality (H1 and H2) and the higher the percentage of knowledge-based and experience-based segments in the urban area (and its size), the higher the income (H3 and H5) and the higher the percentage and the income, the higher the inequality (H4, H6, H7, H8, H19), while this relationship is mitigated directly by the educational level of the urban area (H4, H15) and indirectly by the educational level of the urban area effecting the knowledge and the experience base (H17 and H18).In this research, we test three models of mitigation: the direct effect of education on economic income and inequality, a mediating role of education, and a moderating role of education. Models Model 1 analyzes the main effect of education (2007) on the knowledge-based sector, the experience-based sector (both in 2008), and the income and economic inequality in Metropolitan Statistical Areas (MSAs) (2009). Model 2 analyzes the mediating effect of education (2008) on the knowledge-based sector, the experience-based sector (both in 2007), and the income and economic inequality in MSAs (2009). Models 3 and 3a analyzes the moderating effect of education (2007) on the knowledge-based sector, the experience-based sector (both in 2007), and the income and economic inequality in MSAs (2009).For testing the moderation effect, we use the median split model for the above and below median of education, similar to the method used for example by Ray et al. (2005). All models are analyzed while controlling for the size of the MSA (2007), the financial sector, the housing market, and the immigration (all in 2009), and the antecedent population growth (2005–2007).

Methodology The partial least squares (PLS) method was utilized to estimate and test the research model. PLS is a common structural equation modeling a data analysis technique that is frequently applied in business research including various knowledge-based studies (e.g., Bontis and Fitz-enz 2002). PLS was selected over covariance-based techniques (e.g., LISREL) since it places smaller restrictions on data distribution and normality (Chin 1998). PLS-SEM results are robust even if data is not normally distributed and

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particularly when formative measures are used (Ringle et al. 2009). PLS-SEM provides more flexibility when formative measures are involved (Hair et al. 2012). Covariance-based SEM can handle formative indicators, but then to ensure model identification, researchers must follow rules that require specific constraints. In covariance-based SEM, at least two reflective items or two unrelated constructs measured with reflective indicators need to be specified as outcomes of the formatively measured construct to enable the estimation of the paths linking the formative indicators to the construct and the variance of the error term (see Diamantopoulos 2011 for more details). The significance of the paths was determined using the T-statistics calculated with bootstrapping technique. All constructs were formative. PLS also seem to have a number of advantages over covariance based SEM techniques in terms of the estimation of interaction effects (Chin et al. 2003). Jarvis et al. (2003) note that for the error terms of formative constructs to be identified in a covariance-based SEM, it is necessary that they emit paths to (a) at least two unrelated latent constructs with reflective indicators, (b) at least two theoretically appropriate reflective indicators, and (c) one reflective indicator and one latent construct with reflective indicators. Component-based PLS does not pose such restrictions. Cenfetelli and Bassellier’s (2009) guidelines for model interpretations were followed (details available per request).

Data Description The data utilized in this analysis were collected from the US Bureau of the Census. The data spans all 363 metropolitan statistical areas (MSA) as defined by the Census Bureau for the years 2007, 2008, and 2009. However, due to missing values over this period of time, the usable Nis 357. Information from two separate databases, the American Community Survey (ACS) and County Business Patterns (CBP) series, was acquired and compiled to create the database for this paper; these individual datasets will be elaborated upon in the description for each analysis variable. Following the discussion by Chapple et al. (2004) and by Shalizi (2011),this paper uses a mix of ratio and absolute, as well as a number of establishments and economic output indicators. We also identify explicitly the industries we are using as indicators for the two economic sectors. Since this study is a broad and initial examination of the interaction of these variables across all MSAs, more generalized, higher level industry variables are utilized because, as industry data at a more intimate level is acquired, whether annual payroll or employment numbers, there is a significant amount of missing data across the dataset. We decided to use the most recent available data (at the time the research was conducted) despite the complexities that might result from using data for the years when the American economy was in crisis. This might be a limitation of the study; as such, we would recommend repeating the study using data for later years when it becomes available. Knowledge Economy Intensity The Location Quotient (LQ) of the Information Sector (NAICS Code 51): the total annual payroll for the Information sector for a metropolitan statistical area divided by the average receipts for the same sector across all metropolitan statistical areas.

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The Location Quotient (LQ) of the Professional, Scientific, and Technical Services Sector (NAICS Code 54): the total annual payroll for the Professional, Scientific, and Technical Services sector for a metropolitan statistical area divided by the average receipts for the same sector across all metropolitan statistical areas. Knowledge Intensity−Info+Prof/Total Estab Ratio: the total number of establishments for both the Information and Professional, Scientific, and Technical Services sectors for a metropolitan area divided by the total number of establishments for these same sectors across all metropolitan statistical areas.

Experience Economy Intensity The Location Quotient (LQ) of the Arts, Entertainment, and Recreation Sector (NAICS Code 71): the total annual payroll for the Arts, Entertainment, and Recreation sector for a metropolitan statistical area divided by the average receipts for the same sector across all metropolitan statistical areas. Experience Intensity−Number of Establishments in Arts: the total number of establishments for the Arts, Entertainment, and Recreation sector for a metropolitan area divided by the total number of establishments for the same sector across all metropolitan statistical areas. Figure 1 below illustrates our findings for experience and knowledge location quotients for 2009. Figure 1a displays the entire dataset including the largest two MSAs, while Fig. 1b displays the details of the smallest MSAs. The trend line indicates a positive relationship between knowledge and experience sectors of the economy which is generally expected considering the interrelationships between the two, as indicated in the literature. Interestingly, the slope of the trend line describes a relationship in which the rise in the experience sectors is higher than that of the knowledge sectors and the smallest cities. Further, the third variable, relative city size, is superimposed on the chart as the plot points. Given this, according to the scatterplot, there appears to be support to the notion that larger cities have more successful experience and knowledge sectors. Even more, there appears to be a “steeper” relationship that the trend line displayed, a relationship where there is a larger increase in the knowledge sectors as compared to the experience sectors. Overall, the smallest cities appear to be weighted toward experience sectors, and medium to large size cities are weighted toward knowledge sectors.

Education Level The Percentage of Population over 25 Years of Age with a Bachelor’s Degree: total number of people with a bachelor’s degree divided by the total number of people over 25 years of age. The Percentage of Population over 25 Years of Age with a Master’s Degree: total number of people with a master’s degree divided by the total number of people over 25 years of age.

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a

Experience LQ vs. Knowledge LQ by Total Populaon 50 45 40 R² = 0.753

Knowledge LQ

35 30 25

20 15 10 5 0 0

5

10

15

20

25

30

35

40

45

50

Experience LQ

b

Experience LQ vs. Knowledge LQ by Total Populaon 4

R² = 0.753 3.5

Knowledge LQ

3 2.5 2 1.5 1 0.5

0 0

0.5

1

1.5

2

2.5

3

3.5

4

Experience LQ

Fig. 1 Experience LQ versus knowledge LQ by city total population (data for 2009)

Income Inequality The Household Gini Index: the Gini measure of inequality for a metropolitan statistical area as provided by the Census. The Income Quintile Ratio: the lowest quintile of mean household income divided by the highest quintile of mean household income.

Income-Indicator of City Success Per Capita Income: the average per capita income as provided by the census.

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Median Household Income Per Capita: the median household income as provided by the census.

City Size LOG TOT_POP: the logarithm of total population. Total for all receipts ($1000): the total receipts for all NAICS economic sectors in thousands of dollars.

Control Variables Finance (financial sector): 0=below the mean of financial sector receipts, 1=above the mean of financial sector receipts. Housing (median contract rent): 0=below the mean contract rent, 1=above the mean contract rent. Migration (geographical mobility): 0=below the mean of the number of people that immigrated from abroad, 1=above the mean of the number of people that immigrated from abroad. Population growth (antecedent): Population growth was used as a control factor by examining the relationship between change in population growth (between 2005 and 2007) and inequality, as well as income. Population growth is comprised of population change from 2005 to 2007.

Findings We employ the partial least squares (PLS) method to test our hypotheses in order to move toward the development of a model that illustrates the relationship between income and inequality with the other variables. N generally equals 363 representing all metropolitan statistical regions defined by the US Bureau of the Census for years 2007, 2008, and 2009. However, due to missing data, the usable Nis 357 for all years. Descriptive statistics and the correlation matrix are available per request. We will now describe the findings for the three models we proposed. Model 1 Model 1 analyzed the main effect of education (2007) on the knowledge-based sector, the experience-based sector (both in 2008), and the income and economic inequality in MSAs (2009) (see Fig. 2 and Table 1).

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Research Model 1

Control Variables

.20*** Educaon (2007) .74***

.28***

Income (2009) R2= .59

.21***

.27*** .22**

Knowledge (2008) R2=.53

Finance (2009)

Rent (2009)

.53**

-.33** .32*** Experience (2008) R2=.09 Abbreviaon: * P value < .05 ** p value < .01 *** p value < .001 Immi: Immigraon Not significant

-.18** -.28**

Immi (2009)

Inequality (2009) R2=.24 .16*

Pop Change (2005-2007)

-.33** Size (2007)

Fig. 2 Education main effects

Education explains 53 % in the knowledge-based sector and 9.0 % in the experience-based sector of the MSA economic activities. Both knowledge-based sectors and education (along with the control variables) explain 59 % in the household income within the MSA economy, with the knowledge sector playing the more important role. Education was also found to have a direct impact and mediated impact on income. Household income and knowledge-based sector (along with the control variables) explain 24 % of the economic inequality within the MSA, with income playing the major (intensifying inequality) role, while knowledge-based sector is reducing the inequality. The experience-based sector seems not to contribute directly to the inequality of the MSA; however, it seems that the impact of experience on inequality is partially mediated through income. It appears that the effect of education on income is mediated more strongly via the knowledge sector than via the experience sectors. Among the control variables, the financial sector and housing sector contribute positively, while population growth contributes negatively to income. Population change also increases inequality, whereas the size of the MSA lowers inequality. Immigration seems to have no effect on income and inequality within the MSA. Model 2 Model 2 analyzed the mediating effect of education (2008) on the knowledge-based sector, the experience-based sector (both in 2007), and the income and economic inequality in MSAs (2009) (see Fig. 3 and Table 2). The knowledge-based and the experience-based sectors explain 53 % in education within the MSA. Both knowledgeand experience-based sectors and education (along with the control variables) explain 60 % in the household income within the MSA economy, with knowledge sector and education (as opposed to experience) playing a more important role. Household

J Knowl Econ Table 1 The main effects of education Outer model weights Original sample estimate

Mean of subsamples

Standard error

T stats

INC91

0.79

0.78

0.22

3.64

INC92

0.23

0.24

0.24

0.96

INEQ91

0.22

0.15

0.40

0.55

INEQ92

1.19

1.11

0.37

3.22

SIZE71

0.86

0.85

0.09

9.12

SIZE72

0.19

0.20

0.11

1.70

FIN9

1.00

1.00

0.00

0.00

RENT9

1.00

1.00

0.00

0.00

IMMI9

1.00

1.00

0.00

0.00

EXP81

0.66

0.47

1.25

0.52

EXP82

−0.01

0.19

1.30

0.01

EXP83

0.67

0.64

0.13

5.34

KNOW81

0.54

0.55

0.24

2.26

KNOW82

−0.47

−0.48

0.23

2.01

KNOW83

0.96

0.97

0.03

32.88

EDN71

0.65

0.65

0.08

8.24

EDN72

0.42

0.41

0.08

5.05

CHANGE71

1.00

1.00

0.00

0.00

Source: own elaboration

income, education, experience, and knowledge-based sector (along with the control variables) explain 23 % of the economic inequality within the MSA, with income playing the major (intensifying inequality) role, while knowledge-based sector and education are found to reduce the inequality. The experience-based sector seems to contribute directly to income and education, but not directly to inequality of the MSA. The effect of experience on inequality is mediated completely by education (and also by income). However, the effect of knowledge-based sector on both income and inequality is only partially mediated by education. Education plays a significant role in household income and also in reducing inequality. Among the control variables, as earlier, the financial sector and housing sector contribute positively to income, while population growth contributes negatively to income; population growth also adds to inequality, whereas size of the MSA lowers inequality. Immigration again seems to have no effect on income and inequality within the MSA. Model 3 Model 3 analyzed the moderating effect of education (2007) on the knowledge-based sector, the experience-based sector (both in 2007), and the income and economic inequality in MSAs (2009) (see Fig. 4 and Table 3). To examine the moderating impact of education (2007) in the MSA, we divided the dataset into high (H) and low (L)

J Knowl Econ Control Variables

Research Model 2 .21*** Knowledge (2007) .70***

Income (2009) R2=.60

.29***

.19** Rent (2009)

.31*** .20**

Educaon (2008) R2=.53

Finance (2009)

.52**

-.28** .09*

-.17*

-.33**

Experience (2007) Abbreviaon: * P value < .05 ** p value < .01 *** p value < .001 Immi: Immigraon Not significant

Immi (2009)

Inequality (2009) R2=.23 .14*

Pop Change (2005-2007)

-.35*** Size (2007)

Fig. 3 Education mediating effects

education based on the median factor scores of the education variables. We examined research model 3 separately for high and low education groups and compared the path coefficients for the resulting submodels (model 3 with high education and model 3 with low education) using t tests and structural moderation. Structural moderation exists when a path is significant for one group and is insignificant for the other. We found that in the case of high education, the positive impact of knowledge on inequality is fully mediated by income, whereas education has a direct negative impact on inequality. Experience sector and the size of the MSA are seen to have impact neither on income nor on inequality of the MSA. Income seems to increase inequality. Among the control variables, as earlier, housing sector contributes positively to income, finance, immigration, and population change that seem to have no effect on income and inequality within the MSA. The high education submodel explains 59 % in the household income and 35 % of the economic inequality within the MSA economy. In the case of the low education model, we found that the impact of education and experience sectors on inequality in the MSA is fully mediated by income. The knowledge sector has a direct impact on inequality in the MSA. Size of the MSA along with other control variables (finance sector, housing sector, immigration, and population change) seems to have no significant impact on income and inequality. Income significantly and positively impacts inequality; however, the path is significantly stronger for the high education group than it is for the low education group. The low education submodel explains 55 % in the household income and 32 % of the economic inequality within the MSA economy. Model 3 is also consistent with the significant main effects of the knowledge and experience sectors on inequality and on income reported in models 1 and 2. The model supports the hypothesis suggesting that there is a moderating effect of education on the

J Knowl Econ Table 2 The mediating effects of education Outer model weights Original sample estimate

Mean of subsamples

Standard error

T stats

INC91

0.83

0.83

0.24

3.48

INC92

0.20

0.18

0.27

0.72

INEQ91

0.18

0.10

0.40

0.46

INEQ92

1.16

1.07

0.38

3.05

SIZE71

0.86

0.84

0.09

9.31

SIZE72

0.19

0.20

0.11

1.74

FIN9

1.00

1.00

0.00

0.00

RENT9

1.00

1.00

0.00

0.00

IMMI9

1.00

1.00

0.00

0.00

EXP71

0.57

0.49

1.08

0.53

EXP72

0.10

0.19

1.10

0.09

EXP73

0.64

0.61

0.14

4.68

KNOW71

0.52

0.55

0.23

2.30

KNOW72

−0.48

−0.52

0.22

2.20

KNOW73

0.98

0.98

0.04

27.93

EDN81

0.64

0.63

0.08

7.84

EDN82

0.43

0.44

0.08

5.10

CHANGE71

1.00

1.00

0.00

0.00

Source: own elaboration

Research Model 3 (Median Split)

Control Variables

Finance (2009) Knowledge (2007)

H: .32* L: ns

Income (2009) R2= H: .59; L: .55

H: ns L: .25** H: ns L: .42**

Educaon (2007)

Experience (2007) Abbreviaon: * P value < .05 ** p value < .01 *** p value < .001 ns not significant Immi: Immigraon Structural moderaon T-test Moderaon Not significant

Fig. 4 Education moderating effects

Rent (2009)

H: .71*** L: .55* H: ns L: ns

H: ns L: -.36* H: -.30* L: ns

H: .32*** L: ns

Immi (2009) Inequality (2009) R2= H: .35; L: .32 Pop Change (2005-2007)

Size (2007)

J Knowl Econ Table 3 The moderating effects of education Outer model weights High education Original sample estimate

Mean of subsamples

Low education Standard error

T stats

Original sample estimate

Mean of subsamples

Standard error

T stats

KNOW71

1.46

1.35

0.60

2.43

0.47

0.42

0.76

0.62

KNOW72

−1.11

−1.02

0.62

1.79

−0.06

−0.11

0.76

0.07

0.73

0.71

0.11

6.87

0.73

0.77

0.20

3.67

−0.08

−0.20

0.83

0.09

−0.18

−0.37

0.71

0.25

KNOW73 INEQ91 INEQ92

0.93

0.74

0.74

1.25

0.85

0.56

0.74

1.15

EXP71

0.95

1.24

1.79

0.53

−0.33

−0.17

0.83

0.40

EXP72

0.06

−0.34

1.81

0.03

1.09

0.81

0.91

1.20

EXP73

−0.08

−0.03

0.40

0.19

0.50

0.52

0.29

1.73 0.00

IMMI9

1.00

1.00

0.00

0.00

1.00

1.00

0.00

FIN9

1.00

1.00

0.00

0.00

1.00

1.00

0.00

0.00

SIZE71

0.80

0.71

0.33

2.44

−0.12

0.25

0.63

0.19

SIZE72

0.27

0.32

0.33

0.81

1.08

0.68

0.66

1.63

RENT9

1.00

1.00

0.00

0.00

1.00

1.00

0.00

0.00

INC91

−0.17

−0.25

0.66

0.25

0.68

0.58

0.50

1.37

INC92

1.14

1.13

0.56

2.04

0.39

0.39

0.50

0.79

EDN71

0.48

0.49

0.45

1.07

0.89

0.84

0.25

3.55

EDN72

0.67

0.54

0.43

1.57

0.21

0.20

0.31

0.67

CHANGE71

1.00

1.00

0.00

0.00

1.00

1.00

0.00

0.00

Source: own elaboration

knowledge and experience sectors as well as in the relationship between income and inequality. The moderation role is also seen for the main effect of the housing sector on income—such that housing sector is positively associated with income for the high education group and not for the low education group. Model 3a Model 3a analyzed specifically the intensity of the moderating effect of education (2007) with the knowledge-based sector and the experience-based sector (both in 2007) on the income and economic inequality in MSAs (2009) (see Fig. 5). To examine the strength of the moderating impact of education (2007) in the MSA, we divided (as in model 3) the dataset into high (H) and low (L) education based on the median factor scores of the education variables. We examined the research model 3 separately for high and low education groups and compared the path coefficients for the resulting submodels (model 3a with high education and model 3a with low education) using t tests and structural

J Knowl Econ

Fig. 5 The intensity of education moderating effects

moderation. To test for the intensity of the moderation, the direct effects of education were excluded from the model (see Fig. 5). Our findings suggest that education has a higher impact when moderated with the knowledge-based sector on income than when education is moderated with the experience-based sector. We found that, in the case of the low education model, experience has a positive impact on income, while knowledge does not have a significant impact. Then, in the case of the high education model, the effects reverse, meaning that knowledge has a significant impact on income, but experience does not. This suggests that the higher the education and the higher the intensity of the knowledge sector, the higher the income of the city. We did not find such moderation with regard to inequality. Both models of education, when moderated with the knowledge sector, have a positive impact on inequality, while the moderation with the experience sector has no significant effect on inequality. Hypothesis Testing Table 4 below summarizes the hypothesis testing as proposed in this study. Hypothesis 1 and 2 examined the impact of the size of the urban area. The first hypothesis (H1) suggested that the larger the city, the higher the income (wages). This hypothesis was not supported in all three models. In all three models, the relationship between size and income is not significant. The second hypothesis suggested that the larger the urban area, the higher the inequality (H2). This hypothesis was rejected in all three models. Our study found to the contrary in two of the three models. When the knowledge and experience sectors, education, and the other control variable are considered, size has a positive impact on equality. The third hypothesis (H3) proposed that the more

J Knowl Econ

knowledge-base-intensive the city is, the higher the income per capita. We found support for this hypothesis in all three models, in conjunction with the moderating and mediating effects of education. The fourth hypothesis (H4) purported that the more knowledge-base-intensive the city is, the higher the inequality in the city. Similar to the earlier hypothesis (H2) that tested inequality, this one was rejected as well. In all three models, when the two sectors, education and the other control variable, are considered, the intensity of the knowledge base sector in the city has a positive impact on equality. The fifth hypothesis (H5) insinuated that the more experience-base-intensive the city is, the higher the income per capita. We found support for this hypothesis in all three models, in conjunction with the moderating and mediating effects of education. In a similar fashion, the sixth hypothesis (H6) indicated that the more experience-baseintensive the city is, the higher the inequality in the city. We could not find support for this hypothesis in any of the three models. The seventh (H7) hypothesis is postulating that the scaling of income in the knowledge city is higher than in the experience city, ceteris paribus. This hypothesis was not supported in models 1 and 2 (the path coefficients are in the direction as hypothesized; however, there is no significant difference)but was supported for the high education submodel in model 3.The eighth hypothesis (H8) is proposing that the scaling of inequality in the knowledge city is higher than in the experience city, ceteris paribus. This hypothesis was rejected in all three models. Our study found to the contrary. In all three models, when the two sectors (knowledge and experience), education and the other control variable, are considered, the scaling of inequality in the knowledge city is higher and more positive than in the experience city. The ninth hypothesis (H9) put forward the positive direct impact of education on income. We found support for this hypothesis in all three models, in conjunction with the moderating and mediating effects of the two sectors. The tenth hypothesis (H10) suggested the positive direct effect of education on inequality. In all three cases, this hypothesis was rejected, as education was found to have a statistically significant positive impact on equality. The 11th hypothesis (H11) is proposing that the higher the education, the higher the knowledge base. This hypothesis was tested only in model 1, and the findings are statistically significant. The 12th hypothesis (H12) is proposing that the higher the education, the higher the experience base. This hypothesis was also tested only in model 1, and the findings are statistically significant. This would suggest that higher education has a positive role in attracting both knowledge-based and experience-based sector’s companies and jobs. The 13th hypothesis (H13) is claiming that the higher the education, the higher the knowledge base (in comparison to experience base), ceteris paribus. This hypothesis was also tested only in model 1, and the findings support our hypothesis. The next three hypotheses are tested in the context of the mediating effects of education on income and inequality. All hypotheses were tested in model 2 only. Specifically, the 14thhypothesis (H14) is proposing that the higher the knowledge base, the higher the education; the findings support our hypothesis. The 15th hypothesis (H15) is suggesting that the higher the experience base, the higher the education; this hypothesis was supported as well. And lastly, the 16th hypothesis (H16) is insinuating that the effect of knowledge base on higher education is larger than the effect of experience base, ceteris paribus; the findings support our hypothesis.

+

+

+

+

+

[A]>[C]

[B]>[D]

+

+

H3—The higher the knowledge intensity (KI), the higher the income [A]

H4—The higher the knowledge intensity (KI), the higher the inequality [B]

H5—The higher the experience intensity (EI), the higher the income [C]

H6—The higher the experience intensity (EI), the higher the inequality [D]

H7—Scaling of income in knowledge city, higher than in experience city, ceteris paribus

H8-Scaling of inequality in knowledge city, higher than in experience city, ceteris paribus

H9—The higher the education (Ed), the higher the income

H10—The higher the education (Ed), the higher the inequality

PLS

PLS

PLS/structural moderation

PLS/absolute value comparison/ structural moderation

PLS

PLS

PLS

PLS

PLS

Rejected, not signf.

Model 3

Accepted(for high education group)

Rejected, not signf.

Accepted

Rejected, not signf.

Accepted for low education group

Reverse supported, negative Rejected for high education group; reverse supported for low education group; negative

Accepted

Reverse supported, negative Rejected, not signf.

Rejected, not signf.

Model 2

Reverse supported, negative

Accepted

Reverse supported, negative

Accepted for low education group; not supported for high education group Reverse supported, negative Reverse supported, negative

Accepted

Reverse supported, negative Rejected, not signf. for high education group; reverse supported for low education group

Rejected, not signf. (the path Rejected, not signf. (the path Accepted for high education group (structural test); reverse coeff are different; coeff are different; supported for low education however, there is no however, there is no group (structural test) significant difference) significant difference)

Rejected, not signf.

Accepted

Reverse supported, negative

Accepted

Reverse supported, negative

Rejected, not signf.

+

H1—The larger the city, the higher the income

H2—The larger the city, the higher the inequality

PLS

Model 1

Expected Method relationship

Table 4 Study hypotheses, models to be tested, and summary

J Knowl Econ

+

H15—The higher the experience intensity (EI), the higher the education (Ed), [H]

PLS/structural moderation

PLS/structural moderation

PLS

Ed*KI> Ed*EI

Ed*KI> H18—The moderating effect of education and Ed*EI knowledge intensity (Ed*KI) on inequality is higher than the moderating effect of education and experience intensity (Ed*EI)

H19—The higher the income, the higher the inequality +

Source: own elaboration

H17—The moderating effect of education and knowledge intensity (Ed*KI) on income is higher than the moderating effect of education and experience intensity (Ed*EI)

PLS/absolute value comparison

H16—The effect of knowledge intensity (KI) on ed- [G]>[H] ucation (Ed) is higher than the effect of experience intensity (EI) on education, ceteris paribus

PLS

PLS

+

H14—The higher the knowledge intensity (KI), the higher the education (Ed), [G]

PLS PLS/absolute value comparison

+

H12—The higher the education (Ed), the higher the experience intensity (EI) [F]

PLS

H13—The effect of education (Ed) on knowledge [E]>[F] intensity (KI) is higher than the effect of education on experience intensity (EI), ceteris paribus

+

Expected Method relationship

H11—The higher the education (Ed), the higher the knowledge intensity (KI) [E]

Table 4 (continued)

Accepted

N/A

N/A

N/A

N/A

N/A

Partially accepted

Accepted

Accepted

Model 1

Accepted

N/A

N/A

Accepted

Accepted

Accepted

N/A

N/A

N/A

Model 2

Accepted

Partially accepted in model 3a

Accepted (in both models 3 and 3a)

N/A

N/A

N/A

N/A

N/A

N/A

Model 3

J Knowl Econ

J Knowl Econ

The next two hypotheses are tested in the context of the moderating effects of education on income and inequality. Specifically, the 17th hypothesis (H17) is proposing that the moderating effect of education and knowledge intensity on income is higher than the moderating effect of education and experience intensity. This hypothesis was supported; the moderating effect of the high education model with knowledge-based sectors is significantly higher than with experience-based sectors, while in the lower education model, the moderating effect with the experience-based sector had a higher income. The 18th hypothesis (H18) is insinuating that the moderating effect of education and knowledge intensity on inequality is higher than the moderating effect of education and experience intensity. This hypothesis was partially accepted; the moderating effect of both the high and low education models with the knowledge-based segment is significantly and positively effecting equality when the moderation with the experience sector is not significant, suggesting that education does not have a scaling effect on equality. The 19th and last hypothesis (H19) proposed the positive association of income with inequality. This hypothesis was supported by all three models. Next, we would like to summarize our findings by discussing the effects of education on the knowledge base sector and the experience base sector as was found in this study. Education seems to draw both the knowledge-based and experience-based sectors (model 1). Education also seems to have a significant positive impact on household income (supported by the three models) when accounting for the direct effect of the two sectors, city size, and the other control variables. The most significant impact of education is on economic inequality. Education has a positive role in all three models on reducing economic inequality. Model 3 shows that the high education group in particular is significantly more effective in directly lowering inequality than the low education group, which lowers inequality through income. Finally, we will summarize the effects of the two sectors (knowledge and experience) on education, income, and equality in our study. The knowledge-based sector seems to be a major creator of demands for higher education, as well as a major direct contributor (model 2) and mediator (model 1) to household income. The knowledge-based sector also has a positive impact of increasing income and reducing economic inequality directly (model 2), and as a mediator (model 1) and moderator (model 3).Specifically, the above median higher education interaction with the knowledgebase seems to have a positive effect on income, while the below median higher education interaction with the knowledge base, seems to have a positive effect on inequality. The experience-based sector seems to have minimal effect as a creator of demands for higher education (model 2). Also, the experience-based sector seems to have no impact on economic inequality. On the other hand, the experience-based sector has a positive, direct impact on household income in all three models. Interestingly, it is the below median higher education interaction with the experience-based sector that seems to have a positive effect on household income in model 3. Lastly, judging by the variability explained, it seems that model 3 has the highest R2 and, as such, seems to be the most conclusive.

J Knowl Econ

Model Summary The first two proposed models seem to explain more variability in household income than in economic inequality, with the major role being played primarily by knowledge sector and education. Education seems to have both direct and mediating roles in enhancing household income and reducing inequality. It appears that the knowledgebased sector and the experience-based sector partially mediate the impact of education on income and inequality, whereas education mediates the impact of the knowledgebased sector on income and inequality, as well as partially mediates the impact of the experience-based sector on income, and completely mediates the effect on inequality. It is interesting to note that there is a positive and reinforcing cycle in the two models between education and the knowledge-based sector, both with regard to income and equality. Similar reinforcement can be observed in the case of the experience base and education with regard to income but not in the case of equality. The third model seems to also explain more variability in household income than in economic inequality. It is interesting to note that at the lower level of education, it is the experience sector that has a positive impact on income, but not a direct impact on inequality, while at the higher level of education, it is the knowledge base sector that has the direct impact on income and on equality. It is somewhat surprising that there is no significant effect of size on income and a negative effect of size on inequality (as in models 1 and 2)and no significant effect of population growth on income (as in models 1 and 2). The fact that the competitive housing market and the financial sector have a positive association with income supports earlier studies as well. Finally, education seems to be an attractor, as well as mediator and moderator, for the role of the knowledge-based and experience-based sectors, while as mediator and moderator, its role is intriguing especially with regard to economic inequality.

Conclusions and Implications The main themes of this research are the following: As expected, the knowledge and experience sectors and education are all positively associated with growth in income. Education is adding additional income above and beyond the impact of the two sectors. Interestingly, both education and the knowledge sector play a positive role in reducing inequality, similar to the findings in European Smart Cities (e.g., Deakin 2014). The dynamics of the three variables provide for a much more complex picture and will require additional analysis, as well as serve as additional building blocks for the dynamic aspects of the triple helix model (e.g., Cooke and Leydesdorff 2006). This discussion could be enriched by incorporating the different types of knowledgebase that individuals and companies require to build their competencies upon. One useful taxonomy could be the codified product or process, and tacit product or process (Russ et al. 2010) that are needed to succeed in the knowledge and/or experience industries and cities. To summarize, as we expected, partitioning between the two different sectors in the city economy, the knowledge-based and the experience-based, provided for an

J Knowl Econ

improved understanding of their relationship to income and inequality. Also, the role of education in this mix is complex and sometimes counterintuitive (see another example in Caragliu et al. 2014). Specifically, its positive impact on the reduction of economic inequality should be noticed. There are few policy implications resulting from our study. If household income and economic equality are the primary concerns of economic policy, the knowledgeintensive sector and higher education should be well supported, especially at the high end of education. Such public support is particularly important in smaller and peripheral cities to overcome the liability of size and periphery (e.g., Russ and Jones 2011).This is not to say that support of the experience sector is not important since it has a positive contribution to income, especially at the lower end of education, but such contribution is of less significance to income and should not be expected to have a major contribution for reducing income inequality. A potential weakness of this study is that we sampled only one or two high level (two digits) segments in each of the experience and knowledge sectors, respectively. Future studies may want to utilize a more comprehensive sample of the sectors, as well as more detailed (four or six digits) codes and examine case studies of MSAs that displayed similar effects within the model presented. This issue, the level of data selected, was explained previously in the data descriptions (see page 10), where more specific industry datasets contained a significant amount of missing data. Another limitation of this study is the small number (four) of control variables used. Since this study is a first attempt on the subject, a proof of concept, we intended to limit the number of control variables at this early stage of research to validate and concentrate on the main concepts that are at the center of this study. As for next steps, we would suggest a focus on three aspects. First, recently, Albouy and Seegert (2010) suggested that cities might actually be underpopulated (to benefit from economies of scale or scaling) and that there might be too many of them. Our findings suggest that there is a need to enrich this discussion by looking into at least three types of cities: the global centers (high on knowledge, experience, and financial/ trade services), the large knowledge centers (established around industry clusters and heterogeneous, nested in complex local and global networks), and smaller experience centers that benefit from locally bounded amenities. This addition of the type of the urban area should be also considered when regional and urban policy aspects are contemplated. If balancing between the experiential and the knowledge base sectors is needed only as a support for the growth of the knowledge base sector, which has a positive impact on income and economic equality, and if investment in education has a similar positive impact, policies supportive of such initiatives should be strongly encouraged. Second, the subject of this study is by definition complex due to the involvement of multiple actors at different levels: cities (embedded in regions), industries and firms (embedded in clusters), and individuals (embedded in firms). Multilevel studies could facilitate improvement of our understanding of the role of education on income increase and economic inequality reduction. And lastly, the specific interactions and synergies between the types of knowledge base (synthetic and analytic, and/or the codified, tacit, product, and process) and the knowledge base as specific to the industry clusters require additional research, since different industries may epitomize distinct dynamics.

J Knowl Econ Acknowledgments Earlier versions of the paper were presented at the IFKAD-KCWS 2012 5th Knowledge Cities World Summit conference, June 15 2012, Matera, Italy, and at a seminar at Ben Gurion University of the Negev, Beer Sheva, Israel. The authors want to thank the participants for their constructive comments. The authors wish to thank Kelly Anklam for her assistance in editing the paper. The authors also thank Dafna Schwartz, Eugene Pierce, Wynne Chin, and Nnaoke Ufere for their advice and expertise. The first author wishes to acknowledge the Frederick E. Baer Professorship in Business and the Philip J. and Elizabeth Hendrickson Professorship in Business for partial financial support. As always, the mistakes are ours. Conflict of Interest

No conflict of interest.

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