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Innovation and Urban Growth in Canada

May 29, 2017 | 0 comments

May 29, 2017 | Essays | 0 comments

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Innovation and Urban Growth in Canada

1.0       Introduction

This paper examines the relationship between innovation and urban growth from a Canadian metropolitan perspective. Urban growth is defined as the decline and growth of the economic agglomerations (web 1). health care sector. The act of patenting medical Innovation, on the other hand, is the process or act of introducing new methods, devices or ideas or something new. For a long time innovation has been viewed as one of the core drivers of change and economic growth (Schumpeter 39; Romer 71-102; Solow 312-320). Innovation according to Karlsson (112) leads to the emergence of new sectors of the economy, development of new products and jobs creation. Though innovation is indispensable to growth, it is also distributed unevenly across space.

The paper hopes to achieve two objectives. The major objective of this paper is to investigate the relationship between innovation and urban growth from a Canadian metropolitan perspective.  The second objective is to examine the likely effect of innovation to the urban growth. The central question that will guide the analysis is whether or not, at least in part, innovation is responsible, for the disparity of the rate of urban centers growth. The first part is significant as it underlines the probability that innovation may not be the sole factor that affects disparity in the urban centers growth rate in Canadian regions. Therefore, the paper developed a multi-dimensional model examining the relationship between innovation and urban growth while controlling other potential determinants.

The paper found out that innovation is significant in explaining the differences in urban growth patterns across Canadian urban centers. More innovative urban centers, are more unequal in terms of their distribution of employments. This results holds using various measures of urban centers growth and innovation, in addition to after controlling other series of other institutional, socio-demographic and economic variables. The finding of a statistically significant and positive relationship between innovation and urban centers growth is also robust to the alternative estimation of the instrumental variables specifications, and this alleviates the potential concerns of simultaneous or reverse causality between the two significant variables of interest.

The paper proceeds as follows. In section 2, it will review some related literature. Section 3 focuses on the theoretical framework of how innovation impacts urban growth, while section 4 introduces the data sources, estimation methods and empirical results. Section 5 presents conclusions and some suggestions for further research.

2.0       Literature review

There are extensive literature exploring the geographic dimensions of innovation and also seeks to give an explanation to the significance of local; context and proximity to innovative activity (Feldman and Kogler 381-410). The clustering of the economic activities within large urban centres provides benefits such as externalities to workers and firms that are available otherwise to those located in more remote and smaller areas. Such externalities may originate from localised economies for instance, within the same industry, or even urbanization industries for instance between industries. In their work, Duranton and Puga (1454-1477) developed a formal model  on how urbanized economies  function, arguing that  more cities that are diversified act as nurseries where companies can easily experiment with various  techniques of production, exchange with different providers of knowledge, and collaborate with other companies. Access to technological spillovers and knowledge is indeed a significant reason why cities are more favorable and conducive to innovation (Malecki 493-513). Gertler (221) suggest that being there, facilitates exchange of tacit types of knowledge and collaborative learning. Storper and Venables (351-370) stated that compact and dense urban environments makes it easier for people to interact face to face. Likewise, Blundell et al (210) asserted that different private and public research facilities and universities tend to be more concentrated in cities, and this is another factor that influences urban centers technological trajectories. Lastly, cities also provides the culture shock upon arriving in the United States. These students must understand the cultural and social amenities that are much sought after by the innovative and creative class of people (Florida 355-377).

Islam (1127-1170) looks at the relationship between innovation and urban growth and concludes that innovation is directly related to the growth of an urban center. While the urban economics literature is still unclear about the role of innovation in the urban growth process, it has been argued that innovation provides useful ingredients that further enhance technological progress (Arellano, Manuel, and Stephen 277-297). Using a system of simultaneous equations, Hamilton, (77) use a large sample of Canadian provincial data for the period 1995-2005 to analyze the aggregate impact of skilled labor and research expenditures on urban growth. They find evidence in support of a convergence process across the provinces in terms of both per-capita output and income. Their results show that, on average, skilled labor has a positive impact on growth, while the effect of research expenditures remains statistically insignificant.

Baldwin et al (117-132) in their study indicated that in Canada, the externalities produced by the economic activities clustering across industries leads to significant productivity gains for the companies. Additionally, in investigating the different kinds of agglomeration economies, they found out that spillovers of highly localized knowledge are an important productivity differences determinant across cities. Similarly, Beckstead et al. (24) in their study observed that  agglomeration of economies confer advantages of wages as the earnings of the people living in large metropolitan areas of Canada are 25$ more compared to their counterparts in rural areas.

Innovation as a topic is much studies in its own right. Jacobs (49) in her book, “The Economy of Cities,” relates innovation to urban growth when asserted that “innovating economies develop and expand. The economies that continue only repeating the old work and do not add new forms of services and good, do not develop and by definition expand.” Furthermore, Jacobs (49) contrast the view of Adam Smith that economic growth is driven by specialization, arguing that innovation is generated by diversity. On the other hand, Glaeser (83) views cities or larger urban centers as centers for creation  and transmission of ideas and suggest that cities will grow when new ideas are produced and the cities’’ roles as centers  for intellectuals increases.

Romer (71-102) together with other new growth theorists state that innovation as a significant factor in the economic development. Lastly, Lucas (3-42) focused on the significance of human capital externalities innovation and the clustering of individuals

2.1       The rise of innovation districts

In their article, Bruce and Julie pointed out that following the great recession, a remarkable shift occurred in the spatial innovation geography. For the past five decades, the innovation landscape in United States has been dominated by places such as the Silicon Valley (para 1). However, a new complementary model of urban is emerging and giving rise to the “innovation districts.” Bruce and Julie defined innovation districts as geographic areas where companies and anchor institutions that are of leading edge connect and cluster with business incubators, startups and accelerators. They are compact physically, technically wired, transit accessible and provide mixed-use housing, retail and office (para 2).

Innovation districts are simply manifestation of the mega trends that alters the people’s and firms location preferences, and in the process re-conceiving the connection between place making, economy shaping and social networking. The most creative workers, firms and institutions crave proximity so that knowledge and ideas can be transferred more seamlessly and faster (para 3). Bruce and Julie also pointed out that the open innovation economy of United States rewards collaboration and this transforms how buildings and the whole districts are spatially arrayed and designed. The diverse population demand s better and more choices of where to live, play, work and this fuels demand for more neighborhoods that are walkable and where amenities, housing and jobs intermix (para 3).

Bruce and Julie further indicated that innovation districts have the potential of spurring productivity, inclusivity and sustaining economic development (para 5). During the time when the growth is sluggish, they provide a very strong foundation for creating and expanding of jobs and firms by assisting entrepreneurs, companies, investors, researchers and universities across disciplines and sectors, co-produce and co invent new discoveries for the market. Moreover, at a period of rising social inequality, they provide prospects of employment expansion and educational opportunities for the populations that are disadvantaged given that many districts comprises of the low and moderate income neighborhoods. In addition, at a time of extensive sprawl, inefficient land use and continued degradation of environment, they present the potential for denser employment and residential patterns, the repopulation of the cores of urban and leveraging of mass transit.

Generally innovation districts is composed of the ultimate mash up of educational institutions and entrepreneurs, schools and startups, medical innovations and mixed use development, bankable investments and bike sharing. All these are connected by powered energy, transit, fueled by caffeine and wired for digital technology.

 

3.0 Theoretical Framework

In this section, the paper reviewed a number of theories that establish a framework that will be helpful in analyzing the role of innovation on urban growth.

3.1 Urban Growth

Economic growth of a region is the increase in per capita income of the local population. There are three traditional non-geographical sources of economic growth: capital deepening, increase in human capital, technological progress and lastly the agglomeration of economies that is urbanization and localization of economies. Physical proximity increases the productivity by labor pooling, input sharing and knowledge spillovers.

Figure 1

The extra production gotten from variable input increase will decline eventually as more of the fixed inputs are used with the variable inputs.

3.2 Innovation           

Figure 2: urban economic growth from technological progress

In the initial equilibrium indicated by point (i) a workforce of a region is equally divided between two cities having 6 million workers innovation in one city shifts upwards its utility curve, and the innovative city moves to point (j) in the absence of migration.

Figure 3: urban economic growth from technological progress

Migration to the innovative city

Figure 4: urban economic growth from technological progress

We reach pint (b) (innovative city) in equilibrium and point (s) (other city). The innovation increases utility in both cities and shifts population to the innovative city

3.3 Innovation and Urban Growth

In both cities, they experience an upward shift of utility curve. Since there is no utility gap at the original populations, there is no migration. Moreover, there is increases in utility in both cities as both cities maintain the initial 6 million population.

Increase in human capital increases per capita income of a city because workers become more productive and also there is an increase in the rate of technological progress. The external benefits from human capital increase include:

  • Complementary labor across skill levels
  • Proximity to top level researchers is an important factor in the birth of the biotechnology companies
  • Wage benefits increase from 1% in the college share of the cities: high school graduates (1.6%), high school dropouts (1.9%) and college graduates (0.4%).

 

4.0 Empirical Analysis

4.1 Methodology

The empirical analysis is based on the theoretical framework in the preceding section. Data limitations preclude fully employing the full model. Even though the models cannot be estimated fully, the paper still use it as a framework as best as it can.

First, it examined the relationship between innovation and urban growth using a simple correlation (or regression) analysis to understand the strength (or nature) of the relationship between the proxy variables for urban growth and innovation.

4.2 Data Sources

Population and employment data are from Statistics Canada’s Labor Force Survey. GDP data are in 2002 basic prices and are sourced from the Conference Board of Canada.  The analysis covers the 2001-2011 period.

4.3. Descriptive Statistics

Table 1 below shows the mean, minimum and maximum values for the following variables:

  1. GDP
  2. Population
  • Employment
  1. Employment share of professional, scientific and technical services
  2. Employment share of manufacturing
  3. Employment share of educational services
  • Employment share of the information, culture and recreation sector

 

The census metropolitan Areas (CMA) listed in the tables are census geographic units or country subdivisions found in Canada. CMAs are used by the statistics bureau of the federal government of Canada (Statistics Canada) in conducting the five year census of the country. CMAs are formed by one or many adjacent municipalities centered at the core or the population center (Pendakur 34). According to Pendakur (44), a CMA must have a total population of 100,000 and above, of which 50,000 and above live in the core. That explains why Regina and Saskatoon in the table below are CMAs and Moose Jaw and Prince Albert are not CMAs.

Table 1: The Mean, Minimum and Maximum Values For the Variables

 

Variable   GDP ($M 2002 prices) Population Employment Employment share of professional, scientific and technical services Employment share of manufacturing Employment share of educational services Employment share of the information, culture and recreation sector
Regina CMA Minimum 6,587 197,600 104,500 6,400 5,000 7,100 6,300
Maximum 8,679 218,700 122,800 7,400 6,700 8,300 7,700
Mean 7,633 208,150 113,650 6,900 5850 7,700 7000
Saskatoon CMA Minimum 7,643 233,000 115,300 5,500 9,100 10,900 4,800
Maximum 10,513 272, 000 144, 700 10,200 10,300 14,800 4,800
Mean 9,078 252, 500 130,000 7850 19,400 12, 850 4,800
Moose Jaw

 

 

 

Minimum X 26,800 15,400 1,400 X X X
Maximum  X 27,000 16,700 1,600 X X X
Mean X 26,900 16,050 1,500 X X X
Prince Albert

 

 

 

Minimum X 31,300 19,500 2.4 X X X
Maximum X 33,900 22,300 2.4 X X X
Mean X 32,600 20,900 2.4 X X X

 

*These numbers are not available

From the table above representing four CMAs in Canada, it is evident that since the year 2001 to 2011, these urban regions have recorded increasing statistical figures in the GDP, population, employment, Employment share of professional, scientific and technical services, employment share of manufacturing, employment share of educational services, and finally on employment share of the information, culture and recreation sector. GDP growth and population growth are good measures of urban growth. However, innovation can be measured by either or all of the following:

  1. Employment share of the professional, scientific and technical services sector
  • Employment share of the manufacturing sector
  • Employment share of the educational services sector
  1. Employment share of the information, affects both the organization and its employees. The effects of culture and recreation sector

The statistical figures presented in the table correlate with various literature that there is a relationship between innovation and urban growth. Saskatoon CMA leads with the highest figures in population, followed closely by Regina CMA. However, Moose Jaw and Prince Albert does not have higher figures compared to the two CMAs.

The statistical disparity between the cities is because Regina and Saskatoon are bigger cities with higher levels of research and development activities, compared to Moose Jaw and Prince Albert.  Additionally, the existence of University of Regina and University of Saskatoon which are centers of research and development, and are leading innovation centers has led to the growth of the cities. The population umber of the two CMA can also be explained from the fact that there is high percentage of the employed persons, employed professionals, employed people in the manufacturing which uses innovation and technology, employed people in educational services where there is knowledge spillover, research and innovation and higher number employed in recreation and culture centers where people interact, share knowledge and ideas. On the other hand, Moose Jaw and Prince Albert has minimal population because of lack of technological advanced and innovative manufacturing industries that can employ many people, lack of research educational institutions and no employment in the information culture and recreational sector. The statistical figures in the figures support the argument that innovation promotes urban growth.

4.3.1    Retail sales

The most notable trend in the table is the increasing data for the employment share for the professional, scientific and technical services sector, which is considered a sector that uses technology and innovation. The corresponding increase in data from this section also correlates with the increase in GDP of the two CMAs. On the other handing Moose Jaw and Prince Albert, the employment sector for the professionals in scientific and technical services sector is stagnant and this also corresponds to the insignificant figure of the GDP of these two cities. This indicates that innovation is directly related to the growth of an urban centre. The growth of GDP in Regina CMA and Saskatoon CMA also correlates with the retail sales as shown in figure 5 and 6. Increase in retail sales is an indication of high population, vibrant economy, high purchasing power and active manufacturing industry (Akai and Sakata 93-108).

Figure 5

 

Figure 6

 

4.4 Research Hypothesis

Based on the explanations from the preceding sections, I expect an increase in employment in the manufacturing sector to lead to a corresponding increase in GDP (and therefore, population)

4.5 Correlation Analysis

In computing the correlation analysis, the study used the correlation coefficient for the following pairs. The results are represented in table 2

 

 

GDP vs. Employment share of professional, scientific and technical

GDP vs. Employment share of manufacturing

GDP vs. Employment share of educational services

GDP vs. Employment share of information, culture and recreation

Table 2: Regina CMA

 

Year GDP at basic prices ($M 2002 prices) per year Employment share of professional, scientific and technical Employment share of manufacturing Employment share of educational services Employment share of information, culture and recreation
2001 6587 6400 5000 7100 6300
2002 6673 6100 6200 7400 6700
2003 6815 6200 5500 7600 7900
2004 7014 5300 5600 8400 8100
2005 7210 5500 6500 7700 7400
2006 7375 4800 6600 8700 7400
2007 7607 5400 6500 8600 7400
2008 7840 6100 6700 7300 7500
2009 7771 6100 7200 7600 7100
2010 8182 6500 7100 7400 6600
2011 8679 7400 6700 8300 7700

 

Table 3: Saskatoon CMA

 

Year GDP at basic prices ($M 2002 prices) per year Employment share of professional, scientific and technical Employment share of manufacturing Employment share of educational services Employment share of information, culture and recreation
2001 7643 5500 10300 10900 4800
2002 7636 6600 10300 11700 4800
2003 7849 6200 9400 13400 5300
2004 8219 6400 10400 13000 5400
2005 8575 7000 11800 13300 5900
2006 8819 8000 11100 11100 6000
2007 9256 8900 11300 14800 5600
2008 9914 8600 12100 13600 5900
2009 9588 9100 10700 15500 6700
2010 10,034 9000 9900 15100 5100
2011 10,513 10200 9100 14800 4800

 

Correlation analysis increase in a variable input will has been done only on two metropolitan cities (Regina CMA and Saskatoon CMA) since there was no available data on the employment per industry for the Prince Albert city and Moose Jaw.

The data presented in table 2 and table 3 shows the relationship between GDP from year 2001 to 2011 against the data on employment from different sectors.  The data from the two metropolitan areas between GDP and corresponding data of the employment categories displays positive correlation. This means that as the GDP figures increases, there is corresponding increase in the data for the Employment share of professional, scientific and technical, employment share of manufacturing, employment share of educational services, and also employment share of information, culture and recreation.

However, there is a noticeable trend in the relationship between GDP and the corresponding employment share of professional, scientific and technical in Regina CMA between 2001 and 2007 which shows negative correlation. This implies that as the GDP was rising, the number of employment share of professional, scientific and technical decreased. The numbers increased from 2007 to 2011 to show a positive relationship again.

The presented data concurs with the literature review indicating that increase in number of professional, educational services, manufacturing and information sharing leads to urban economic growth because of increased innovation.

 

 

4.6 Regression Analysis

In order to assess formally the relationship of innovation to urban growth, the paper will specify the following regression model

URBit =α+β1 In INNOVit2 In POPit +δECONit +η EDUt + ϕSOCDEMOGit+τtit…… (1)

Where the dependable variable URBit is the measure of urban growth for the metropolitan or urban area i at time t.  On the right hand of the equation 1, the first variable is the measure of innovation (INNOVit) as earlier defined. POPit represents the size of the population of an urban areas and has been used here for general agglomeration forces as a proxy. There are several literature that links the city size to innovation (Wolfe and Allison 170-182). Majorly the argument is that increasing the size leads to potential innovation differences between cities. Based on the preliminary presented evidence, the study expect the coefficient estimates for the following variables (β1 and β2) to be positive.

Despite the fact that the study is very much interested in these two variables, the paper will also control other factors that may explain why urban growth has risen over the decade. Among these include the covariates reflecting institutional, socio-demographic, and economic characteristics of every city. ECONit is an economic variable that controlling demand size factors such as labor force percentage employed in government services and manufacturing industries. Stable and well-paying jobs in manufacturing firms contributes to the GDP. Moreover, government sector also contributes to the GDP and urban growth. EDUt is the Employment share of professional, scientific and technical at time t (or any of the others)

SOCDEMOGit is also a vector variable that is socio-demographic representing aspects of the supply side of the metropolitan economies. Some of the factors included here are the percentage measures of the labor force of the metropolitan area that are minority visibly, the population percentage aged >65 and <15 to reflect age structure variations of the cities in addition to the variables that control female participation rats differences across cities. Pendakur and Pendakur (2011) indicated that in Canada, visible minority is defined as individuals who are non-white or non-Caucasian in color. Therefore, aboriginal people have been included because of their small number compared to the majority groups.

τt is a fixed time effects for the years 2001 to 2011 capturing general trend of urban growth in metropolitan areas. εit on the other hand is a composite  error term represented as (=νi+ μit) where νi represents the differences that are not measured across cities fixed over a specific period of time and μit on the other hand represents the idiosyncratic errors varying across metropolitan areas over time. Therefore, model presented in equation 1 captures the potential unobserved factors that may influence urban growth patterns.

Table 4:  Regression analysis results for the selected run

Variable Population

(POP)

Economic Variable

(ECON)

Employment of professional, scientific and technical services (EDU) Socio-Demographic

(SOCDEMOG)

GDP
Coefficient 0.329 5.114 1.364 -18.053 19.159
Standard Error 0.108 1.690 0.195 3.094 3.068
t-Statistic 3.067 3.027 7.013 -5.838 6.247
Probability 0.0028*[1] 0.0032* 0.0000* 0.0000* 0.0000*

 

As shown in table 4 above, the Employment share of professional, scientific and technical of a city and the economic variables are positively correlated to the innovation capacity of a an urban center. On the other hand, socio-demographic variables are negatively correlated with the innovation. From the logical argument, the results obtained are in agreement with study’s initial predictions and hence acceptable.

The initial hypothesis of the study was based, a priori, on the signs of high population and employment rates amongst professional, scientific and technical. The study proved the initial hypothesis because of higher population and higher share of employment in the professional, scientific and technical services sector, which both lead to an increase in GDP. This is because innovation triggers economic growth, and this means more people will move into the city. Again, urban growth theory concurs with the argument as discussed in the theoretical section

 

5.0       Conclusion

The research examined the relationship between innovation and urban growth. Urban growth has been shown in many metropolitan areas or cities in Canada from2001 to 2011. Much of the urban growth has been seen within urban hierarchy of the country. The study found that urban growth has increased over the decade and is more pronounced compared to small cities.

The key question of the study was whether innovation played a role in explaining the rising patterns in urban centres growth. To shed some light, the study estimated many regression models in 4 cities in Canada. Using statistics from Statistics Canada’s Labor Force Survey, the results strongly supported the hypothesis of positive link between urban growth and innovation. The demographic and industrial composition of the cities also was significant. Increase in number of professional and technicians, high number of employees in manufacturing, educational and information and cultural industries appear to have also contributed to urban growth.

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[1] The results are significant at the 1% level.

 

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