This text presents the evolution of the economic structure of manufacturing for the municipalities of the Mexican state of Guanajuato, using different methods from evolutionary economic geography literature i) to describe the evolution of specialization in manufacturing industries during the period 2003-2018; ii) to analyze the diversification strategies of the six most important manufacturing municipalities based on the level of economic complexity of the new branches in which they specialized; iii) to identify opportunities for the future diversification of manufacturing in the six municipalities, considering their most recent economic structure. The analysis may be relevant for the design of public policies to contribute to Guanajuato's economic and industrial development.

During the last 25 years, certain regions of Mexico have significantly transformed their productive structure. Literature¹ attributes the observed changes to the economic reforms that the country initiated in the 1980s, specifically, to the easing of restrictions in order to stimulate the flow of Foreign Direct Investment (FDI) and to trade agreements with other countries in order to encourage the trade of goods and services (mainly the North American Free Trade Agreement [NAFTA]).

During this period, the state of Guanajuato and its neighbors (Aguascalientes, Querétaro and San Luis Potosí) recorded outstanding economic growth (higher than the national average), driven mainly by the growth of the transportation equipment manufacturing subsector and the productive industries to which this subsector is linked.

Recently, in the context of international economic slowdown mainly caused by the Covid-19 pandemic, national and local governments in different countries were faced with the urgent need to develop industrial strategies to reactivate and boost their economies. These included smart specialization strategies, which aim to encourage productive diversification and technification and are based on the principle of prioritization, vertical logic, and on robust data analysis methods to identify optimal areas for intervention through innovation policies (Foray, 2014). Vertical policies follow a process of identification and selection of desirable areas of intervention, which involves choices of technologies, fields and subsystems that could be favored in the regional policy framework. In turn, robust data analysis methods should serve to identify the economic activities that, on a regional level, represent the best opportunities so that when implemented, they also help to develop the greatest number of other economic activities. All in order to build regional competitive advantages based on existing productive capacities (Foray and Goenaga, 2013).

This paper has two main objectives: 1) to describe and explain the productive evolution of the municipalities of Guanajuato, which have specialized in new economic branches of the manufacturing industry during the period 2003-2018, 2) to estimate different metrics of the economic complexity methodology to analyze the industrial diversification strategies that the municipalities have used during this period, as well as to identify opportunities for future specialization in the most industrialized municipalities of the state.

The rest of the chapter is organized as follows: Section 2 describes the transformation of the economic manufacturing structure of the municipalities of Guanajuato. The economic change is illustrated by the transformation of the main municipalities that have evolved to specialize in economic branches linked to the production of transportation equipment. Section 3 presents the analysis of the industrial diversification strategies of the six most important municipalities in the state (Celaya, Irapuato, Leon, Moroleón, San Francisco del Rincon and Silao). This analysis is based on the classification of the new industries in which these municipalities have specialized during the study period (as well as those in which they no longer specialized) based on different metrics of economic complexity. Section 4 analyzes these municipalities' most recent productive structures to identify opportunities for future specialization, which will allow for an economy based on industrial technification with sustained growth. Section 5 presents the final comments.

The economic structure of the state of Guanajuato in general and several of its municipalities in particular has evolved over the last two decades. The most economically important municipalities have specialized in more manufacturing activities, which has resulted in the state becoming one of the most important manufacturing producers in the country.

To illustrate the evolution of the manufacturing sector in the state, which is the most dynamic in the country, we used the following data in order to measure the productivity of the municipalities: Added Value (AV) divided by the Total Employed Population of each municipality by branch of economic activity.² The data is obtained from INEGI's Economic Censuses (EC) from 2004, 2009, 2014 and 2019. A matrix for each census lists the country's municipalities in the rows and the economic branches of manufacturing included in the North American Industrial Classification System (SCIAN) in the columns. Therefore, for each year, the matrix entries show the AV per worker for each municipality in each branch. These data matrices will be called Mt, where t indicates each year of the sample.

Evidence of the evolution of the productive capacities of the municipalities can be obtained thanks to a simple procedure widely used in the literature referring to regional economics. Using the described data matrices, we can obtain the number of economic branches each municipality specializes in.³ Table 1 shows the number of manufacturing branches in which each municipality specializes in the different years of the sample. It is clear that there is a general trend over time, and the municipalities increase their productive expertise, i.e., the number of branches of manufacturing in which they specialize increases. According to the 2019 EC, the state’s municipalities specialized, on average, in three new manufacturing branches compared to what was recorded in the 2004 EC.

Figure 1 spatially illustrates the progress of specialization in the municipalities during the period. This figure has four categories: i) number 0 (lighter color) shows municipalities that had a setback or zero progress in relation to the number of new branches; ii) number 1 denotes municipalities that added one or two new branches (minimum progress); iii) number 2 indicates municipalities that specialized in three, four or five new branches (moderate progress); and, finally, iv) number 3 indicates municipalities that added six or more branches of manufacturing (maximum progress). It is clear that, in general, the municipalities closest to the so-called industrial corridor specialize in the largest number of manufacturing activities.⁴ Leon is the most important municipality with specialization in almost 60% of the branches of manufacturing, followed by Irapuato and Celaya with 49 and 45%, respectively; San Francisco del Rincon with 31%, and Moroleón and Silao with 27%, which completes the municipalities that specialize in more than 25% of branches. In general, municipalities in the north and south of the state tend to specialize in fewer manufacturing branches.

The growth of the transportation equipment manufacturing subsector was an important driver of growth in the state of Guanajuato during the period analyzed. This is reflected in the branches of manufacturing in which some of the municipalities in the state specialize. Table 2 shows the main economic branches that provide inputs and intermediate products to the branches of the transportation equipment subsector (3361 Manufacture of automobiles and trucks; 3362 Manufacture of vehicle bodyworks and trailers; 3363 Manufacture of parts for motor vehicles; 3364 Manufacture of aerospace equipment; 3365 Manufacture of railway equipment; 3366 Manufacture of boats; and 3369 Manufacture of other transportation equipment).

Identifying the value chains of the region's key industries is a fundamental aspect of the smart specialization strategy and the generation of greater added value. Examples on an international level show that the most successful linkages go backward in the chain, not forwards. Developing linkages that are in demand locally will make it possible to increase productive know-how in the region. However, this will only be achieved if the linkages are based on local industry and educational and research centers can provide specialized technology and human capital.

As shown in Figure 2, during the period 2003-2018, Silao was the municipality that provided evidence of the substantial change in the economic structure of the state. This was not only because of the number of new economic branches in which it specialized (16) but also because many of them were directly related to the production of transportation equipment. The most closely related were listed first: 3261, 3312, 3313, 3329, 3359 and 3361. It also specialized in other branches associated with the manufacture of transportation equipment: 3169 Manufacture of other leather, fur and leather substitute products; 3252 Manufacture of synthetic resins and rubber, and chemical fibers; 3255 Manufacture of paints, coatings and adhesives; 3315 Casting of metal parts; 3327 Machining of parts and manufacture of screws; 3328 Metal coatings and finishes; and 3339 Manufacture of other machinery and equipment for general industry.

The next municipality with significant transformation is Irapuato. It specialized in some of the branches mentioned in Table 2, such as 3261, 3312, 3313 and 3363, in addition to 3252 Manufacture of synthetic resins and rubbers, and chemical fibers; 3326 Manufacture of wire, wire products and springs; 3328 Metallic coatings and finishes; and 3339 Manufacture of other machinery and equipment for general industry.

To generate sustained economic growth, countries and regions of the world transform their productive structures to produce more diverse goods with greater added value; in other words, they develop new comparative advantages.

Although the diversification in production observed in the industrial corridor of Guanajuato complies with this principle of development, the generation of greater added value based on innovation and the generation of new knowledge implies that new industries have a more technological base and more specialized human capital. On the other hand, as suggested by evolutionary economic geography studies, the opportunities for diversification in regional production towards more high-tech industries are usually determined by their accumulated historical production capacities. In other words, the development of new industries is based on the principle of path dependence (Boschman and Frenken, 2018; Hidalgo et al., 2007; Hausmann et al., 2014).

In this context, two relevant questions regarding the state’s economic development are: 1) is the diversification in production of the municipalities based on said path dependency? and 2) do the new industries encourage the accumulation of more sophisticated, higher added value productive knowledge in the municipalities?

The economic complexity methodology is one of the most widely used techniques to classify economies and economic activities based on the productive knowledge they possess or require. Based on its use, it is possible to design intelligent specialization strategies that take local comparative advantages as a starting point (see Annex 3 for a detailed methodology for calculating economic complexity). As shown in international literature and literature applied to Mexico, the Index of Economic Complexity (ICE) of regions and activities or products is closely related to aspects of development, such as economic growth, income, inequality, and even carbon emissions (Hidalgo and Hausmann, 2009; Hidalgo, 2021; Chávez et al. 2017, among others).

The analysis found that the ICE of municipalities and industrial branches is positively linked to the measure of productivity (average added value per employee). In the case of municipal economic complexity, as shown in Figure 3, a positive relationship exists between municipal ICE and productivity in a sample of more than 2,400 municipalities in Mexico. This indicates that municipalities with greater economic complexity in the country, with more productive knowledge, also on average have higher productivity. In turn, the ICI in Mexico also has a positive relationship with productivity in a sample of 86 branches (see Figure 4). This indicates that the branches of economic activity that require the most productive knowledge are those with the highest productivity levels.

In this context, to answer the first question about the path of productive municipal diversification, estimates were made of the capacity distance of each municipality in the country from the economic branches in which it does not specialize. This measure begins by considering the comparative advantages of the municipality to calculate based on the proximity between industries. This metric indicates a municipality's capacity distance with regard to developing a new industry considering its current productive structure. Specifically, if a municipality is already specialized in the branches related to a new industry, the distance measure will be short, close to 0; otherwise, the distance will be long, close to 1 (for more details on the methodology, see Annex 4).

Figure 5 presents the results of this analysis. The horizontal axis shows the measurement of the capacity distance for each municipality based on the productive structure presented in the 2004 EC. The vertical axis gives the complexity index of the branches of manufacturing calculated in the 2019 EC. The shapes of the marks indicate the changes in the municipality's specialization: i) "+" marks show those new branches in which the municipality specialized from 2004 to 2019; ii) "x" marks show those branches in which the municipality was specialized in 2004, but no longer specializes in 2019; iii) "•" dots show the remaining branches of manufacturing that continue to represent opportunities for diversification in production.

In general, we can see how the municipalities of Celaya, Irapuato and Silao followed aggressive strategies for diversification in production (whether or not explicit) by specializing in manufacturing branches with a high level of complexity and a high-capacity distance (upper right quadrant). Meanwhile, the municipalities of León and San Francisco del Rincón used less risky strategies by diversifying towards industries close to their average capacity distance (a strategy more in line with the path dependency principle) and with different levels of complexity with respect to the average 2019 ICE. Finally, Moroleón followed a more conservative strategy, diversifying into industries with a reduced capacity distance from its productive structure with a below-average 2019 ICE (lower left quadrant).

The strategies followed by the municipalities help to understand the results obtained in comparative terms on a national level. In this sense, to answer the second question, it is important to analyze whether the diversification in production registered in the 2004 and 2019 ECs had any impact on the level of sophistication of the productive structures of the leading industrial municipalities of Guanajuato with respect to the rest of the municipalities in Mexico. For this purpose, the ICE was estimated for more than 2,400 municipalities using the productivity variable described above. This index makes it possible to classify productive municipal structures based on their diversity (number of economic branches in which they specialize) and the ubiquity of the branches in which they specialize (number of municipalities that also specialize in those branches). This allows us to obtain an indicator in order to evaluate the amount of productive knowledge possessed by the municipality.

As shown in Table 3, some municipalities in the state managed to climb several places in the ranking of municipal economic complexity, while others had setbacks. Specifically, Celaya rose from 39th place out of a total of 2,447 municipalities in 2004 to 22nd place in 2019; Irapuato went from 64th place to 50th place; Silao from 143rd place to 104th place; and San Francisco del Rincón from 147th place to 139th place in the same period. On the other hand, the municipality of León fell in the ranking from 29th to 35th place, and the municipality of Moroleón, despite its progress in relation to diversification during the period, dropped from 215th to 281st place.

Based on the previous analysis, this section aims to identify opportunities for future diversification into more sophisticated manufacturing branches in each municipality. To this end, measures of economic complexity were estimated for the most current period for which data is available.

Specifically, the horizontal axis of figure 6 presents the measurement of the capacity distance of each municipality from each manufacturing branch; the vertical axis refers to the complexity index of each manufacturing branch. Finally, for the purposes of the analysis, the color and size of the dots in the graphs refer to the level of productivity of the manufacturing branch in the municipality (larger, darker dots refer to branches with higher productivity).

Based on this approach, opportunities for future specialization and diversification are inferred from these metrics in this order: 1) prioritizing branches of higher complexity; 2) prioritizing branches in which the municipality has higher productivity (productivity ≠ 0); 3) prioritizing branches with a lower capacity distance. In this way, three types of strategy are inferred in which the six most industrialized municipalities of Guanajuato could be located:

The municipalities of Celaya and León, the most complex in the state, present opportunities to consolidate manufacturing branches of high complexity and affinity with their current productive structure (upper left quadrant). In this respect, the suggested strategy consists of promoting their specialization in these branches and making them more competitive on a national level. This requires exploring opportunities for productive diversification along the value chains, which will not necessarily be found within the manufacturing industries. Additionally, as leading industrial municipalities, they can seek to consolidate their productive structures and key industries by taking advantage of the specialization of neighboring municipalities. As the literature suggests, the analysis of opportunities for specialization and productive diversification towards more sophisticated industries in the tertiary sector will be important at this level of their productive structure.

The most important opportunities for specialization for the municipalities of Irapuato, Silao and San Francisco del Rincón are located in the low-risk quadrants (upper and lower left quadrants), where some medium complexity branches that exhibit similarities with their current productive structure are located. It is also important to note that the manufacturing branches with the highest productivity in these municipalities are located in the lower right quadrant but are of very low complexity. Therefore, a second proposal would be to increase the technology of these industries backward along their value chain, identifying those supplier industries that are more similar to their productive structure.

The most immediate opportunities in the municipality of Moroleón, the least complex of the six municipalities under analysis, are in the specialization of manufacturing branches on which its economy bases its productivity. These branches are related to the current productive structure, with low complexity (lower left quadrant), so a two-fold strategy is suggested. Firstly, achieve specialization in these industries through increased technology; secondly, and in parallel, leverage its medium-term vision in a higher-risk strategy based on developing more sophisticated industries that are not currently present in the municipality. The selection of these industries should consider the principle of affinity with its current productive structure and the industries’ complexity and, more strategically, should aim to match the productive structures of the leading industrial municipalities through the needs of their value chains.

In general, it is suggested that the strategy to generate greater added value and innovation in key manufacturing industries in the region will depend, to a large extent, on the development of local suppliers along the entire value chain of these industries. As mentioned earlier, international success stories show that backward linkages in the value chain generate better results. Therefore, as part of the smart specialization strategy, the links in the chain most dependent on global value chains need to be identified in order to reduce this dependence in the medium term on local suppliers and generate greater benefits from agglomeration economies. Likewise, identifying artificial intelligence technologies, remote management, manufacturing and/or harvesting robots, etc., in each of the links will be crucial in order to prioritize projects and investments in research and development, generation of technology-based startups, training of specialized human capital (current and future), etc.

Finally, table 4 presents the prioritization of the five manufacturing branches in which each municipality should specialize and around which innovation should be generated based on the principles of complexity, productivity and capacity distance.

This article describes the evolution of the manufacturing industry in the municipalities of the state of Guanajuato for the period considered in the 2004-2019 ECs. It is the sector that gave the most economic impulse to the state in this period.

Estimates reveal that the state's municipalities advanced in the number of manufacturing economic branches they specialize in. In 2018 they specialized, on average, in three new branches compared to 2003. The municipalities of the industrial corridor are not only the most specialized in the number of industrial branches in this sector but also presented the greatest progress in the period.

Celaya, Irapuato, Silao and San Francisco del Rincón stand out because they specialize in the most economically complex branches, i.e., economic activities requiring more productive knowledge. This allowed these municipalities to advance in the ranking of municipalities by level of complexity and by 2018, they possessed economic structures capable of carrying out economic activities that produced goods with higher added value. The municipalities of León and Moroleón, despite having specialized in new branches, suffered a setback in the same classification of municipalities on a national level. This implies that some municipalities in the country made more significant progress.

Finally, the methods used made it possible to identify manufacturing branches in which the municipalities can specialize in the future. Given their economic structure in 2018, there are economic activities that require capacities similar to those already present in the municipalities due to the economic activities they already carry out. Given that they have a small capacity distance, the diversity of these branches in which municipalities can specialize shows that the municipalities remain diverse.

The SCIAN divides the manufacturing sector into the following 21 subsectors and 86 branches:

To transform the productivity matrix (VA per worker), Mt, into what will be called the specialization matrix , a matrix of zeros and ones,⁵ the definition of Location Quotient (LC) is used. For the 2019 EC:

(1)

where VA_m,a is the productivity of municipality m, in branch a; is the productivity of all economic branches in municipality m; is the productivity of all municipalities in each economic branch a; finally, is the productivity of all branches in all municipalities.

Each matrix entry is defined as follows:

A municipality, m, specializes in economic activity a if the proportion of the productivity of economic activity a, with respect to the total productivity of the municipality, is equal to or greater than the comparable proportion of the state.

Once the CL_m,a matrix is specified, it is used to define two dimensions, diversity and ubiquity, of the economic structure of the municipalities. These dimensions will be used to calculate the ICE of the municipalities and of the branches of economic activity, as well as measures of proximity between branches and measurements of distance between the municipalities and the industrial branches in which each municipality is not specialized. Diversity and ubiquity are defined as follows,

Diversity:		(2)
Ubiquity:		(3)

For calculations using the 2019 EC data, diversity is a vector of 1 column and 2,463 rows showing the number of different economic branches each municipality specializes in. Each entry is the sum of the rows of the CL_m,a matrix whose values are between 1 and 86. Diversity is considered the basic measure of the amount of productive knowledge possessed by each municipality, manifested through its productive variety.

Ubiquity is a vector of 1 row and 86 columns showing the number of specialized municipalities in each economic branch. Each entry is the sum of the columns of the CL_m,a matrix whose values are between 1 and 2,463. In this methodology, ubiquity to contributes important information to the diversity measure to infer the type of productive capacities possessed by each municipality.

The MR combines the two measures that describe the productive structure of each of the country's states, diversity and ubiquity, and this combination provides us with the economic complexity indexes of the municipalities and economic branches. These measures consist of iteratively calculating the average value of the previous values of diversity and ubiquity, starting with their initial values defined in equations (2) and (3).

Equations (4) and (5) describe the iterative process to obtain the subsequent diversity and ubiquity values, respectively. In the first case, the CL_m,a matrix is multiplied by the initial ubiquity vector and divided by the initial diversity values. In the second case, the CL_m,a matrix is multiplied by the initial diversity vector and divided by the initial ubiquity values. Formally, the iterative process is defined as follows:

(4)

(5)

for N ≥ 1, where N refers to the iteration number. In each iteration, the ranking of the municipalities is observed according to k_m,N and the iterations continue until the ranking of the states does not change in three consecutive iterations (a fixed point is reached). The final values at k_m,N rank the states according to their level of economic complexity. Based on the final economic complexity values of the municipalities k_m,N the complexity of the economic branches is calculated using equation (5) to obtain a final k_a,N.

This measurement is calculated based on the proximity between economic branches. The proximity between two branches is a measurement that quantifies the set of similar knowledge or capacities shared by that pair of branches. Formally, it is the conditional probability that a municipality specializes in branch a, given that it specializes in a'. Using the CL_m,a matrix, the proximity between economic branches a and a' is calculated as follows:

(6)

Where and represent the ubiquity of the economic branch a and a', respectively.

In this case, for the 2019 EC data, matrix is 86x86, and each of its entries have values between 0 and 1.⁶ Values closer to 1 indicate that branches a and a' share a greater number of capacities and thus have a greater proximity. Values close to 0 indicate that the two branches do not share many productive capacities or are not very close.⁷ Matrix will be used to calculate the measure of distance that each municipality has in relation to the most complex economic branches produced in Mexico.

To determine the potential of each municipality to develop new productive branches, a measure is required that quantifies the distance, in terms of capacities, of each one in relation to the branches in which they are not yet specialized. The measure of distance refers to the similarity between the capacities required by a pair of goods based on the probability that they are jointly produced with comparative advantage. To quantify this similarity, it is inferred that if two goods share the majority of the capacities required to be produced, municipalities specializing in the first will have a greater probability of specializing in the second. Thus, the measure of distance in terms of capacities is based on the measurement of proximity, i.e., the joint probability that a municipality that produces the first good will also produce the second.

Specifically, the measure of capacity distance is the sum of the proximities connecting new branch a with all economic activities in which municipality m is not specialized. This measure is obtained by dividing it by the sum of the proximities of all branches to branch a. Formally, it is defined as follows:

(7)

If a municipality specializes in economic branches very close to economic branch a, in which it does not specialize, then the capacity distance will be small, close to 0. On the other hand, if the municipality specializes in economic branches not close to branch a, then the capacity distance with branch a will be larger, close to 1.