Identification of areas with potential for promoting innovation

processes in Mexico1

Elvia Martínez-Viveros, Camilo Caudillo-Cos, Ana Bertha Coronel and Fernando López-Caloca

Centro de Investigación en Geografía y Geomática “Ing. Jorge L. Tamayo” A.C. www.centrogeo.org.mx

1. Introduction

The entry and positioning of countries and regions in the so called

‘knowledge economy’ is recognized by many field scholars by their intensive use of knowledge and technology in economic processes. “It refers to an emerging economy where productivity and growth are less

based on the abundance of natural resources than on the capacity to improve the quality of human capital and factors of production, and to

create new knowledge and ideas and incorporate them into equipment and people (David and Foray 2003 cited in Cooke et al 2007:26-27). Lundvall (1995) as cited in Hudson (1999:61) asserts that

“…contemporary capitalism has reached the stage at which knowledge is the most strategic resource and learning the most important process”. Knowledge intensive innovations are the key to the economic

success of persons, firms and places. In the past, it was thought that innovations emerged mainly from scientific and technological

institutions. Although there is a strong link between science and innovation, the translation of scientific results into innovations is not linear and it involves combinations of explicit formal knowledge and

tacit knowledge contained in people’s skills and abilities (Cooke et al 2007). Following Lundvall and OCDE, Morgan (2002: 493) describes

innovation as “an interactive process -between firms and the basic science infrastructure, between the different functions within the firm, between producers and users at the inter-firm level and between firms

and the wider institutional milieu- and this process should be conceived as a process of interactive learning in which a wide array of institutional

mechanisms can play a role” In the knowledge economy, geographic proximity is relevant. There is a

lot of literature on the role of spatial proximity in clusters of firms or industrial districts (examples may be found at Garnsey and Heffernan

2007, Rutten and Boekema 2007, Asheim et al 2006, Morosini 2004 or Lawson 1997). Also, the study of innovation has been approached through the dynamics of agglomerates that share access to skills and

resources and are supplemented by supporting institutions (Feldman 2000). At the place scale social actors share norms and values

1 This work is a first exploratory stage of a research project financed by an

Institutional Fund of the National Council of Science and Technology (CONACYT) in

Mexico. 2 These methods place each observation in a group and goes on iteratively including

nearest groups in a high order group. At each step, a level is added in the hierarchical

facilitating collaboration between persons, firms and sectors for their

mutual advantage; social capital and trust support the networks that link actors in interactive learning processes (Wolfe, 2002). From

evolutionary economics perspective, spatial proximity is a key element to explain economic activity location and agglomeration, the dynamics of development and growth processes, the reasons for the uneven

distribution of prosperity, or the role and effect of public policy (Javanovic 2009:3).

But localized innovation processes can be inserted and/or fed back into global economic processes, which in this knowledge era account for the

organization of the most strategic and profitable economic activities. The territorial articulation of global strategic activities has been described in terms of the leading role of global cities which control

strategic decisions and actions, leaving other urban spaces to specialize in different functions. Jointly they describe a new division of work and

incorporate territories in a global production process which is relatively independent from the nation states (Sassen 2001). Globalization both challenges and paves the road for regions and places to use their

unique capacities and become innovation and learning centers (Wolfe 2002). But regions and places inserted in economies of increasing returns pose a major challenge to the competitive positioning of the

peripheral or marginalized ones in knowledge intensive innovation processes.

The abundant literature about emblematic processes, products, technologies, places or regions of the knowledge economy constitutes

the empirical evidence that nourish the conceptualization of this current stage of capitalism. From this literature one main conclusion

may be derived: knowledge is concentrated in regions, cities, firms, universities or research centers. These units become involved in networks of formal or informal collaboration through which knowledge

circulates generating spillovers with regional, sectoral or global reach. But the heterogeneous distribution of knowledge gives rise to disparities in competences, productivity, income and opportunities between

regions, countries and social groups. Cooke et al assert the “…highly uneven and polarized character of knowledge generation, application

and innovation in geographical space. Innovative milieu, high-tec regions and knowledge based city regions are expressions of such asymmetries and knowledge monopolies.” (2007:46)

In less developed countries, like Mexico, evidence of intensive knowledge innovation processes is looked for in attempts to situate its

regions and places in the knowledge economy map and to identify features of its local and regional innovation systems (Corona 2005).

Case studies documented as innovations in firms, places or regions in Mexico can be related to: 1) decision and control centers localized abroad and led by transnational firms, which point to the subordinated

insertion of ‘the local’ into ‘the global’ (Carrillo and Hualde 2006 or

Casalet et al 2008,); 2) examples of success stories in the export of products manufactured by multinational corporations, such as

automobiles, where the locus of mainstream innovation lies abroad (Unger 2004); or 3) examples of success in international markets of products which market advantage is derived by the low cost of labor.

(Carrillo 2007). Maldonado explores Mexico’s place in the knowledge based economy and asserts that, since 2006, Mexico has not improved in OCDE’s, World Bank’s or UNESCO’s indicators, such as science and

technology public expenditure, number of international students attracted by the country, patents, published articles, rates of

researchers per inhabitant or internet users (Maldonado 2010). Mexico is a country characterized by inequality. The development gap

between regions, places or social groups is wide and inequalities in availability and access to all kinds of resources have prevailed over a

long period of time. Innovation processes in the country, relevant from the knowledge economy point of view, are very rare. While this situation is not encouraging, it is worth to acknowledge that the country holds

potentiality in its human resources and in the natural and cultural wealth of its many and varied regions and places. Although local or regional systems of innovation cannot be easily detected along its

territory, some authors have advanced the possibility to detect regions and places prone to innovation. For instance, Casas and Luna saw this

propensity in what they called ‘knowedge emergent spaces’ (Casas and Luna 2001).

The work we present here is a first exploratory stage of a project conducted at CentroGeo (a Mexican public research institution). This

stage was set about to integrate a comprehensive view of urban areas with potential for promoting innovation processes in Mexico. The purpose is to help incorporate a territorial view in the design of public

policies aimed to generate local or regional spatial environments prone to innovation. The relevance of this view is supported by the key role that spatial proximity plays in innovation (as has been argued above),

but also because territories provide a base for coordinating public policies that usually are sector biased. Policies and their instruments

designed from different institutional silos and aimed at different goals converge territorially. It is in territorial contexts where science and technology, competitiveness, public infrastructure investments or

industrial development, among other relevant policies can be coordinated by a territorial innovation policy and combined and synergized by emerging governance arrangements.

We characterize spaces with potential for promoting innovation

processes by the joint convergence in urban space of a dense economic activity linked to industries with a relatively high use of a knowledge base and of factors that facilitate or are building blocks of the

knowledge economy. According to Wolfe (2002) factors reinforcing local

agglomeration effects are: highly qualified local labor, unique support services for local firms, trust relationships in networks of suppliers and

buyers and the emergent interactive learning in local and regional contexts. Main among such factors are those related to human and social capital. Framed in the various angles of knowledge creation and

knowledge transfer, human and social capital have been approached as background to innovative activity (Mourad and De Clercq 2004).

Human capital expresses individuals’ abilities, skills and knowledge and is a source of competitive advantage for themselves, the organizations in

which they work and the places where they belong to (Florin and Schultze 2000). Several studies highlight the positive association between human capital and productivity, employment or innovation

(Dakhli and De Clercq 2004, Cannon 2000, Maskell and Malmberg 1999, Black and Lynch 1996, Simon 1988). Human capital can be

developed through formal education, on the job training, work experience or health care and physical training (Prais 1995). People with high educational level and wide practical experience who have invested

time, energy and resources for the improvement of their skills are able to get a better personal well being as well as for the society as a whole (Gardstein and Justman 2000).

Mourad and De Clerq assert that “the central proposition in the social

capital literature is that networks of relationships constitute, or lead to, resources that can be used for the good of the individual or the collective”. (2004:110). The social capital of an individual encompasses

personal relationships. The one of an organization implies working collaborations or collective action. At society level, the social capital is

embedded in networks, norms and confidence structures that expedite coordination and cooperation for collective benefit. Social capital is a key factor in human capital formation (Coleman 1988, Seragelding and

Dasgupta 2001). In this work, dense economic activity is represented by means of

agglomerates of industries that make a relatively high use of a knowledge base. We are aware of the difference between clustered

economies and the mere agglomerations of firms; and that mere agglomerates say very little about the networks and kinds of linkages through which knowledge may be transmitted or exchanged and

transformed into innovations. But we believe that the integration of comprehensive views of spaces with factors favoring innovation may help planning and policy processes to further local social capital and

the development and consolidation of local and regional clusters that may impinge upon the emergence of significant innovations. As it is

commonly done, we use educational level in order to measure human capital; but we also measured it by means of occupations or professions related to knowledge intensive activities; because in performing such

activities people engage in learning processes that increase their human

capital. Following Van Damme, we selected the city level “to look at the interface between human capital formation and utilization” (2009:14).

We saw each city as a market and we tried to detect the heterogeneity in the distribution of knowledge in their occupational structure, in the overall assimilation of human capital, and in the presence and

formation of people with a high educational level. Then, the differentiation of these cities’ characteristics was underpinned on indicators of the territorial structure of: 1) knowledge intensive

occupations and professional services, 2) market use of human capital, and 3) availability and formation of people with high levels of education.

We also tried to detect the spatial distribution among cities of some variables used as proxies of social capital. Indicators considered in these conceptual blocks were used as independent variables explaining

the agglomeration of knowledge based industrial activities (Figure 1.1).

Figure1.1 Conceptual blocks for explaining the agglomeration of knowledge based activities.

2. Variables and attributes.

Territorial context.- Territories considered in this work comprise the 100 cities which represent 63.4% of Mexico’s population at 2010 (Map 2.1). They are classified according to its size as shown on Table 2.1.

Table 2.1 Territorial context

Type of city Number of cities

Population 2010

Annual population

growth rate 2000-2010

Metropolitan areas 11 41,020,204 -1.38

Agglomerates of knowldege based industries

Knowledge intensive occupations and services

Market use of human capital

Stock and formation of high level human capital

Social capital

Metropolitan cities 45 21,351,762 -1.90 Medium sized dynamic

cities

44 8,848,774 2.15

Total 100 71,220,740 Source: Cities’ classification: CONAPO, INEGI and SEDESOL 2007. Cities’ data: INEGI: 2011c)

Map 2.1 Mexico’s urban system

Source: Own elaboration with data (INEGI: 2011c).

Knowledge based industries.- An OCDE report classifies industries according to the use they make of technology (Hatzichronoglou 1996).

Industries from the Industrial Classification System for North America (INEGI 2007) were chosen to fill this report’s classification of technology use. The attributes considered for this variable were: high and medium-

high use of technology. The industries selected for each of them are summarized in Table 2.2.

Knowledge intensive occupations.- From INEGI’s classification of

occupations (INEGI, 2011-2:13-28), a selection was made of those which performance require the use of a relatively more intensive knowledge base. They were grouped in 7 attributes: strategic decision

and ownership, top and middle management, market, finance and administration, management of manufacture, information and

communication technologies, researchers, and creative professionals. Table 2.2 Sectors and/or branches classified per attribute of knowledge

based industries

Technology use Industry

High Information and communication technologies (ICT)

Electronic equipment

Pharmaceutical industries

Medium-High Electric equipment

Automobile Chemical products

Source: Own elaboration from the Industrial Classification System for North America

(INEGI 2007)

Knowledge intensive services.- Based upon the Industrial Classification

System for North America (INEGI 2007), economic units providing scientific, technological and professional services were selected as a

variable. These professional services were classified in the following attributes: legal services, accounting services, engineering and architecture, design, research and development, specialized

consultancy, informatics and advertising. Market use of human capital.- This was approached by the 2010 census

data of the human capital activity rate measured as the ratio between the number of persons of an age group and an educational level who are

working and all persons of the same age group and educational level. Age groups considered for this variable are: 25 to 34 (young), 35-54 (adults). Two levels of education were considered: people who started or

finished university (or equivalent level) and people who started or finished graduate studies.

High level human capital availability and formation.- This human capital was measured using 2010 census data of people with a

university or graduate degree or enrolled in a university or graduate program.

Social capital.- Due to the lack of relevant and reliable data in Mexico, the operationalization of this concept is difficult. Here we used two

variables as proxies: 1) the percentage of civil society associations in a city as the share of the total associations of this kind in the cities included in the study. According to Industrial Classification System for

North America they can be: organizations of producers, traders and service providers, labor unions, professional associations, sports and

leisure organizations and civil associations (INEGI 2007), and 2) the activity rate of old people participating in the labor market.

3. Agglomerates The identification of spatial concentration of knowledge based economic

activity may help detecting emergence of increasing returns economies, the characteristics of the places in which they emerge and their

similarities and/or connections with other places. It also can tell us something about regional inequality.

In order to analyze if knowledge intensive productive activities tend to cluster spatially and whether this groupings associate with variables contributing to innovation prone environments, at this data exploration

stage, agglomerates of manufacturing firms classified by knowledge base and of firms that render professional, scientific and technological

services were built. We departed from a geographical data base of firms with more than 50 people working in them. We used a hierarchical agglomerative clustering algorithm from CrimeStatIII.2 The nearest

neighbor was used as a criterion for grouping firms located at a distance less than 4 or 8 kilometers. Clustering started with a minimum

of three firms and in all instances we explored, first order clusters were obtained, all of them significantly different from non random spatial arrangements. Table 3.1 summarizes these results and they can be

viewed in Maps 3.1 and 3.2.

2 These methods place each observation in a group and goes on iteratively including

nearest groups in a high order group. At each step, a level is added in the hierarchical

segmentation of data. The result is a sequence of groupings and the user chooses the

desired grouping level.

Map 3.1 Agglomerates of knowledge based industries and knowledge intensive professional services (Results from hierarchical clustering algorithm).

Source: Own elaboration with data from (INEGI, 2011a)

Map 3.2. Examples of agglomerates in selected cities.

Table 3.1 Agglomerates knowledge based industries and knowledge

intensive services Technology use Distance

(Km)

Starting

points

Number of

agglomerates

Total

number of

firms

Clustered

firms

HIGH

ICT 8 3 4 65 27

Electronic 8 3 23 325 183

Pharmaceutics 8 3 27 194 146

MEDIUM HIGH

Electric 8 3 25 352 252 Automobile 4 3 154 2062 1507

Chemical 8 3 41 650 448

Professional services

Legal 4 3 73 1793 1526

Accounting 4 3 86 1809 1593 Engineering &

Architecture

4 3 75 1197 919

Design 8 3 13 275 201

Informatics 8 3 24 561 486

Specialized consulting

4 3 54 908 705

R&D 8 3 5 134 45

Advertising 8 3 53 1171 1056

Source: own elaboration using INEGI 2007 and INEGI 2011-a

4. The variables spatial patterns

In order to detect and display spatial patterns of the variables enlisted in section 2, we used location quotients which are “ratios of an

industry’s share of the economic activity of the economy being studied to that industry share of another economy” (Isserman 1977:34). Local

economies under our study are the ones represented by one hundred Mexican cities, and we took as reference economy the aggregate of those cities. The purpose was to identify the structure of specializations in our

cities regarding the variables listed in section 2, and to be able to compare these structures between cities. Location quotients, as Growe

(2010) asserts, eliminate the ‘size effect’ in the comparisons. For knowledge intensive occupations, location quotients gave rise to

different patterns of specialization in the Mexican urban system. Map 4.1 shows the patterns resulting in the country’s 11 metropolitan areas (Map 4.1). The largest of them shows a quite similar and diversified

occupational structure. It is worth noticing the weight of creative occupations, finance, ICTs and R&D in Valle de Mexico, the biggest

metropolitan area of the country and its political center. Monterrey shows a similar occupational structure and surprisingly also San Luis Potosí, which, with only 1,036,000 inhabitants and a manufacturing

bias, is emerging as an entrepreneurial space where culture is important, but which needs to make greater efforts in ICTs. This pattern

shows the interdependencies between occupations in a culturally and

economically more diversified urban environment. Deviations from such

pattern show deficits or surpluses resulting from the geographic, political or demographic positioning of each city. Guadalajara and

Querétaro deviate from the pattern in terms of the larger concentration of strategic command and control occupations, which may point to the manufacturing industries under the command and control of a local

entrepreneurship. Also, in Map 4.1 a balanced occupational pattern can be seen in some

urban concentrations with less inhabitants but lying within the limits of the largest metropolitan areas. The occupational pattern of cities

situated at the border with USA reflects the importance of the maquiladora industry and the insertion of these territories in American electronic clusters. The lack of CEO’s and management occupations

outstands, particularly in Ciudad Juárez, where violence and insecurity may be a decisive factor to move command centers to the other side of the border. Finally, it is worth mentioning that the distribution of people

occupied in research and development is relatively balanced across these 11 cities; which might be evidence of the increase in educational

opportunities in higher education and of the emergence and growth of research centers in these cities.

As a supplement of the analysis of the spatial occupational structure, we explored knowledge intensive services; particularly the ones represented by the firms lying in the agglomerates described in section

3. Here we may interpret a pattern in evolution that can be read departing from five services, that in greater or lesser extent are

clustered in the 11 cities (legal, accounting, engineering and architecture, informatics and advertising), and which are the only ones clustered in San Luis Potosí and La Laguna. Then, we can trace a first

step to specialized consulting services clustered in Querétaro, Toluca and Juárez, followed by a second step, including design services –found

in Puebla-Tlaxcala, Monterrey, Tijuana and León- and ending with research and development which only are agglomerated in Mexico and Guadalajara (Map 4.2). One could make the hypothesis that only in

these two cities specialized firms in this domain have been established as a result of both: knowledge spillovers, derived from formal universities and research and development institutions, and market

demands created by firms’ needs to outsource specialized functions.

Map 4.1 Location quotient patterns of knowledge intensive occupations in Mexican metropolitan areas.

Source: Own elaboration with data from INEGI 2011c.

Map 4.2 Location quotient patterns of agglomerated knowledge based professional services.

Source: Own elaboration with data from INEGI 2011a

Map 4.3 Location quotient patterns of technology use.

Source: Own elaboration with data from INEGI 2011a.

Patterns derived from location quotients of knowledge intensive firms in

the eleven cities can also be described in steps, starting from cities like León, La Laguna and San Luis Potosí, where high technology industries

are absent; followed by Puebla-Tlaxcala, Toluca, Guadalajara and Monterrey where high technology industries gain relevance and concluding in cities where low technology firms lose importance in favor

of high technology ones. Mexico’s metropolitan zone displays a more balanced structure with relevant concentrations of all firms included in the knowledge use classification (Map 4.3).

Following Growe (2010) the functional importance of cities respect to

each variable was calculated as the sum of their attributes location quotients. This measure reflects the entire importance of each city in the variable. This was done for knowledge based occupations and

knowledge intensive professional services. The functional importance of the 11 cities regarding these variables is shown on Graph 4.1. They are

displayed together with the location quotients of agglomerates of high technology firms and the sum of those of medium high and high technology firms. It is worth noticing that higher values of functional

importance attach to a specialization of the variable in one or two of its attributes, while low values accrue to a more balanced or diversified structure. Hence, for example, Querétaro’s high value in the functional

importance of knowledge based occupations is derived from the fact that CEO’s concentrate the most in this city; also, Guadalajara’s high

functional importance of knowledge intensive services is due to the concentration of research and development services in this city. Meanwhile, Monterrey lies near the average of functional importance

which may be due to a more balanced pattern of knowledge based occupations and to a deficit presence of agglomerates of research and

development professional services in this city. Human capital activity rate allows answering if those who are working

have a higher educational level in their group age. In such a case one could say that the market is making good use of the available human capital. Location quotients patterns for this variable in the 11 Mexican

metropolitan areas are displayed in Graph 4.2. This graph shows the human capital contained in persons that can be situated into two

echelons of educational attainments. The first one corresponds to those who have achieved a university degree or are enrolled in a university program; the second corresponds to those who have a graduate degree

or are enrolled in a graduate program. In general terms we observe that the market assimilates more this human capital from the young generation than from the adult one. This in itself poses a situation

reflecting human capital waste. Although we deal with the same level of formal education in both age groups, it is expected that human capital

would increase with age: adults have spent more time in the labor market, therefore they have accumulated more human capital as a result of their working experience and possibly of on the job training.

Another explanation could be the lower cost implied by employing

young people than by employing adults.

Graph 4.1 Functional importance of knowledge occupations, services and technology use

Source: Own elaboration

Graph 4.2 Location quotients of human capital activity rates in Mexican Metropolitan areas.

Source: Own elaboration

But the assimilation in the market of highly educated human capital

varies among cities. The rate of activity of this human capital in the young generation shows great contrast: young people are the ones most

assimilated into the market of 7 cities and the least assimilated into the

0.00

2.00

4.00

6.00

8.00

10.00

12.00

Functional importance of knowledge intensive services

Functional importanceof knowledge based occupations

Functional importance of medium high and high technology firms

Location quotient of high technology firms

0.8

0.85

0.9

0.95

1

1.05

1.1

Youngs in university block

Youngs in graduate block

Adults in university block

Adults in graduate block

market of 4 cities. The tendency to use more human capital from the

university block than from their graduate block in many cities happens probably because many persons enrolled in graduate programs are full

time students, therefore not incorporated actively in the labor market. The market assimilation of high level human capital in the young generation is concentrated in the metropolitan cities of Querétaro,

Tijuana and León, while San Luis Potosí, Toluca y León peak in this variable for adults.

In addressing the topic of the market use of highly trained human capital the question arises about the stock of this capital available to

cities. Cities where the per capita rate of people with high educational level is higher, tend to have a greater weight in the spatial concentration of this human capital. Graph 4.3 show this trend for the

11 Metropolitan Zones of the Mexican Urban System. It is worth noticing, for example, that the largest Mexican cities –Valle de México,

Guadalajara and Monterrey- hold a relatively high position in the density of highly trained people and in their spatial concentration, but they are not the leading part. For example, Querétaro is the peak in the

concentration of highly trained persons per inhabitant. This goes hand in hand with the use this city makes of highly trained people, as can be seen on Graph 4.1. But also it is worth noticing that Tijuana and León

rate the lowest, both in the internal density of highly trained people and in their spatial concentration, but as seen on Graph 4.1 these cities

rate high in the use of this human capital. That is, they make the most with the human capital they have available.

Social capital is a very difficult concept to measure. We were aware that a suitable variable would have been the membership of local

associations of civil society. They are the ones that work voluntarily for the sake of the association’s purpose or mission, therefore reflecting local social capital. However we could only get data on the number of

people formally working at these associations. Hence, we use as a proxy the percentage of associations in each city and as we shall see below this proved to be a poor proxy. The activity rate of old people was

also included as proxy of social capital; which is measured by the percentage of this age group participating in the labor market. Resulting

from the demographic dynamic and from recurrent economic crises, people in Mexico have been displaced from the labor market earlier than the normal retirement age. We venture the hypothesis that places in

which labor market values old people, regardless of their educational level, might have denser social networks and a culture that values experience.

Graph 4.3 Highly educated people (rate per 1000 inhabitants vs location

quotients)

Source: Own elaboration

5. Regression model

Regression models were run in order to test the spatial convergence of agglomerations of knowledge based industries and factors related to the presence of knowledge in the structure of occupations and professional

services, the market assimilation of human capital, the stock of highly trained human capital and the social capital. First an ordinary least

square regression model was run and then spatial interaction (or autocorrelation) was incorporated by means of a spatial lag model.

The dependent variable was the agglomeration of technology based industries, measured by the functional importance of agglomerates of

industries classified in medium high and high technology use. As the model’s independent variables we included the functional importance of knowledge based occupations and professional services and location

quotients of: highly trained human capital activity rates for people in young and adult generations; availability and formation of high level human capital in the city; the share of civil society associations, and the

activity rate of old people. Models’ results are shown in Table 5.1.

Querétaro

San Luis Potosí Puebla-Talxcala

Monterrey

Valle de México

Guadalajara

Toluca

La Laguna

Juárez

León

Tijuana

100

120

140

160

180

200

0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3

rate

per

10

00

inh

abit

ants

Location quotients

The ordinary least square regression explains almost 51% of the

dependent variable variation. At 5% significance level, five variables are significant. Two of them are the functional importance of knowledge

service agglomerates and of knowledge occupations. The other three are the location quotients of: the human capital activity rate of highly educated adults, the share of young people with a university degree or

enrolled in university and the share of people (all ages) with or in graduate studies. At 10% significance level the activity rate of old people could be incorporated into the model.

Table 5.1 Results for ordinary least square regression model.

Model properties

R2 0.5087

Adjusted R2 0.4596

LogLikely -80.3806

AIC 180.7612

SIC 206.8129

RSS 29.2215

F value 10.3551

P value 0.00

Sig-Sq 0.3247

Sig-Sq(ML) 0.2922

Model parameters

Independent variables Coefficient Std

error Z value

Probability

Intercept 3.1047 1.4338 2.1654 0.033**

HC

Activity

rate

With or in university: 25-34 -1.2836 0.8061 -1.5925 0.115

With or in graduate: 25-34 0.3878 0.4070 0.9527 0.343

With or in graduate: 35-54 -2.2083 1.0914 -2.0234 0.045**

Share of people with or in university:

25-34 -0.7624 0.3428 -2.2238

0.029**

Share of people with or in graduate studies -0.3998 0.1923 -2.0786

0.040**

Functional imp. of k. services 0.0760 0.0275 2.7569 0.007**

Funct. importance of k. occupations 0.1192 0.0333 3.5806 0.001**

Share of associations (% in the city) 0.0281 0.0285 0.9846 0.327

Activity rate of people 54 64 0.7294 0.3812 1.9137 0.06*

**Significance level 0.05 *Significance level 0.10

The null hypothesis regarding the normal distribution of the model’s error was not rejected. This pointed to the pertinence to perform the tests for spatial autocorrelation reported in Table 5.2.

Table 5.2 Regression diagnostics for spatial dependence Model properties

Test MI/DF Value Probability

Moran’s I (error) 0.373424 6.764457 0.000000**

Lagrange Multiplier (error) 1 36.98399 0.000000**

Robust LM (error) 1 11.0192 0.000902**

Lagrande Multiplier (lag) 1 34.57925 0.000000**

Robust LM (lag) 1 8.61446 0.003335**

Lagrange Multiplier (SARMA) 2 45.59854 0.000000**

** significance level 0.05

The low probability of Moran’s I error shows spatial autocorrelation in residual terms. The significance of both Lagrange multipliers would

point to the pertinence to prove spatial error models. However, Ward and Skrede assert that: “It is difficult to discriminate between spatially

lagged y and a spatial error model purely on statistical

grounds...[when].. the log likelihoods for the two models are very similar ..[and] both models entail the same number of parameters, there is no clear basis for saying that the one is more parsimonious than the other

and, hence, there is little empirical guidance to tell the two models apart in terms of fit to the data alone.” (2008:69-70). Then they conclude that the model’s choice must be framed in each research

conceptual context.

In our case, the log likelihood for the spatial error model (-68.4773) resulted very similar to the spatially lagged one (-70.5487) and both of them where run with the same number of independent variables. Then,

in order to choose a spatial model on conceptual grounds, two assumptions were opened:

1. The value of the agglomeration of medium and high technology

based industries for a city (yi) influences the value of this dependent

variable for neighboring cities and these last, in turn, feed-back on to yi. This means that industries agglomerate in one city as opposed as in one of its neighbors because of specific characteristics found in

the first one. Under this assumption the spatially lagged model is suitable.

2. A portion of the error in the dependent variable value for a city and

its neighbors is due to the value of some spatially agglomerated

unknown variable. This means that the model’s errors are spatially correlated and are due to the omission in the model of independent

variables. Under this assumption the spatial error model is suitable. Obviously we cannot entirely rule out the second assumption, as

models are almost by definition mere approximations for explaining the behavior of a dependent variable. In this case variables selected as proxies for social capital were neither significant at 5% level for the

ordinary least square nor for the spatial error model and we believe this problem arose because we could not find data for a suitable measure of

this kind of capital. But although we have to acknowledge that social capital may lie as source of error, we decided not choose this model and opt for the spatially lagged one because cities are not isolated areas;

they interact in complex ways and feedback loops are expected to be going on. What happens in one city may impact in others, being these close neighbors or distant cities.

The geographic space covered by the project considers the urban system

throughout the country and the scale and the distance between cities

gave rise to disjoint spaces. In order to implement the spatially lagged

regression model, we introduced a weight matrix. Each element of the matrix represents a neighbor. The neighbors are arranged by distance,

the first is the nearest and the last the farthest. Trials with few neighbors did not give rise to spatial dependence; but if the number of members exceeded seven, the space considered encompassed a very

large territory. Hence, we chose 6 neighbors and the result was analyzed together with a map of the centroids and we found that the resulting arrangement rendered a good approximation of regional

neighborhoods.

In consideration of the above arguments, we ran a spatial lag regression model, which explained 59.1% of the dependent variable variation and

which with a 5% significance level included the spatial term as a quite significant independent variable. This model combines significantly the spatial interaction and the following independent variables: the

functional importance of knowledge service agglomerates and of knowledge occupations and the share of people with university degree

or enrolled in university (Table 5.3). When space entered the model the dependent variable variation was no longer explained by the share of people with or in graduate studies or by the human capital activity rate

for graduate adults; although at 10% significance level this last variable remained in the equation. Also, at 10% significance level, the share of associations could be incorporated into the model.

Table 5.3 Result from the spatial lag regression model

Model properties

R2 0.5913

Squarecorr 0.6008

LogLikely -70.5487

AIC 163.0973

SIC 191.7542

Sig-Sq(ML) 0.2375

Model Parameters

Independent variable Coefficient Std

error Z value

Probability

Rho 0.0333 0.0086 3.8617 0.0001**

Intercept 2.1978 1.2351 1.7794 0.0752**

HC

Activity

rate

With or in university: 25-34 -1.1014 0.6901 -1.5959 0.1105

With or in graduate: 25-34 0.3226 0.3491 0.9241 0.3554

With or in graduate: 35-54 -1.5711 0.9518 -1.6507 0.0988*

Share of people with or in university:

25-34 -0.5699 0.2934 -1.9420

0.0521**

Share of people with or in graduate studies -0.2394 0.1685 -1.4205

0.1555

Functional imp. of k. services 0.0766 0.0236 3.2509 0.0012**

Funct. importance of k. occupations 0.0865 0.0293 2.9563 0.0031**

Share of associations (% in the city) 0.0417 0.0246 1.6979 0.0895*

Activity rate of people 54 64 0.5345 0.3277 1.6311 0.1029

**significance level 0.05 *significance level 0.10

Conclusions

This work departed from the purpose to integrate a comprehensive view

of areas with potential for promoting innovation in Mexican cities. This view may aid policy makers and territorial planners to tailor actions to create and synergize networks of knowledge generation and transfer,

which involve formal explicit knowledge created in science and technology institutions and tacit knowledge derived from know-how and skills from firms and relevant local stakeholders.

We explored local quotients of the variables included in this research

and used the country’s eleven largest metropolitan areas to show their trends. We found cities grouping in patterns and although groupings differ with each variable, we could trace cities that tend to group

frequently regardless of the variable.

Spaces prone to innovation were defined in places where industries using high and medium high technologies agglomerate. Then, by means of an ordinary least square regression we explained such agglomerates

by the specialization/diversification of knowledge intensive occupations and services, the labor market assimilation of highly educated adults, the availability and formation of young people in the university

educational echelon and of people in the graduate one.

Space proved to be an explaining factor of knowledge based activity agglomerates. The significance of the lag model allowed concluding that the spatial heterogeneity of the dependent variable is associated to

independent variables and to other spatial interactions (social, economic or cultural) between the cities. This model ruled out as

significant independent variables the human capital activity rate of adults in the graduate echelon and the stock and formation of graduates.

These are only preliminary results of a longer term research project. Many issues will have still to be incorporated into the study. Among

them, a more detailed analysis of the structure of specialization and diversification of occupational and services markets is still pendant.

Also, in the knowledge economy literature, innovation is equaled to products and services resulting from leading technology and frontier research. For countries like Mexico, it makes more sense to approach it

from the point of view of alternative technologies and local know-how, skills and abilities. Also, an alternative approach to innovation could include processes oriented towards the solution of social problems or

delivery of social services; hence new local processes of innovations may be incorporated in our scope.

Finally, social capital is a key factor in the knowledge economy. However the measurement of its presence and density in Mexican

spatial contexts is a challenge which still needs to be tackled, both

conceptually and empirically.

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