Introduction

It is difficult to imagine that a company can emerge and remain in the market without being part of a business ecosystem or outside its influence. Aspects such as the manufacturing specialization of a city or region, the local and national business environment, the number of companies participating in the different economic sectors and subsectors, the interdependence of sectors and subsectors, the formation of supply chains, and the size of the market seem to be related to the emergence and closure of businesses, and therefore to companies' life expectancy.

Metropolitan areas are complex ecosystems with high concentrations of businesses. Nevertheless, each metropolitan area possesses characteristics that differentiate it from its peers. The question that gives rise to this research is whether and to what extent the characteristics of metropolitan areas, understood as business ecosystems, exert any influence on variables associated with social and business development, such as poverty and employment.

Literature review

According to the National Institute of Geography and Statistics (Spanish: Instituto Nacional de Geografía y Estadística, ^{INEGI) (2014)}, there are 59 metropolitan areas in Mexico where 73% of the population resides and 77% of the GDP is produced. A metropolitan area is formed when a city "exceeds its politicaladministrative territorial limit to form an urban area located in two or more municipalities" (^{Sobrino, 2003}). The formation of metropolitan areas is a socio-spatial process whose dynamics are influenced by economic and social phenomena such as the social and spatial division of labor, the integration of regions into the flow of local and global economic value, supply chains, and globalization or selective specialization of local economies (^{Bernardes and Castillo, 2007}).

Different authors (^{Santos, 1977}; ^{Sanchez, 1991}; ^{Bernardes and Castillo, 2007}) agree that these are complex places, composed of different subspaces, whose administration is subject to the variability of public policies and private investment decisions. There is also the perception that, although each metropolitan area has unique characteristics, in countries such as Mexico and Brazil-still located on the "periphery" of the economic giants-their development faces major limitations in the absence of longterm territorial planning.

^{Iracheta (2010)} considers that metropolitan areas concentrate location advantages (economies) for social and economic actors that are superior to most neighboring cities, and offer better living conditions, provision of services, and equipment. He also notes that the disadvantages of metropolitan areas are a tendency toward disorganized and unsustainable growth, unequal provision of services- especially for poor social sectors-, lack of resources to address social needs, weak institutions, and lack of a framework for intergovernmental coordination.

Following ^{Iracheta's (2010)} rationale, the locational advantages offered by metropolitan agglomerations-derived from productive, sectoral, and spatial concentration processes, as pointed out by ^{Garza and Schteingart (2010)}-would presumably exert some kind of influence on phenomena such as poverty and economic informality. ^{Lezama (2014)} mentions that these are socially constructed spaces where social relations evolve, but inequality and inequity do so as well.

One of the ways in which the complexity of urban entrepreneurial ecosystems seems to relate to the phenomenon of poverty is how the configuration of labor markets means that access to employment would be one of the determinants for the population to improve their quality of life progressively, but that they can also quickly fall into poverty in situations of economic contraction, given their dependence on income from work (^{Aguilar-Zurita, Martínez and Armenta, 2018}).

A key variable for understanding the interactions between the economic and social subsystems is the phenomenon of informality. According to ^{Robles, Sánchez, and Beltrán (2019)}, it is the result, on the one hand, of the low development and productivity of an economy, and on the other, of the heavy demographic concentration in urban areas. Income inequality, lack of social security, and deficient tax collection would be among its consequences.

^{Robles et al. (2019)} state that the population increase in urban areas has a positive relation with informality. Consequently, a priority of public policies in these areas should be the generation of employment and not only mobility, public services and housing, which are the predominant concerns of metropolitan administrations.

The Sustainable Development Goals (SDGs) defined by the UN in 2015 are the framework of a new development agenda whose objectives are eradicating poverty, protecting the planet, and ensuring prosperity for all. In Mexico, the 2030 Agenda for Sustainable Development was adopted, and the Sustainable Cities Index, in which the original 17 goals were translated into 107 indicators, was established (^{Citibanamex-Centro de Investigación y Docencia Económica [CIDE] 2018}).

The reason why the monitoring of the SDGs in Mexico has an emphasis on metropolitan administration is the degree of population concentration in urban spaces, coupled with the processes and imbalances that generate chronic problems of poverty, vulnerability, inequality, and environmental impact (^{Citibanamex-CIDE, 2018}).

Terraza, Rubio, and Vera (2016) note that in urban growth processes, both the need for capital and the means of production converge, as well as the need to meet the aspirations of society that would otherwise be unrealizable. In this respect, they point out that if fairer growth prospects are sought, it is necessary to resort to new planning methodologies and, above all, to think about urban economies from other perspectives that are simultaneously open to local and global aspects. This way, the urban space favors the interaction of nano and micro enterprises with large companies, which could be achieved through managing and strengthening business ecosystems.

The metropolitan area as a business ecosystem

Each metropolitan area has ecosystemic characteristics and pressures that simultaneously drive business creation and closure. It follows that one way to explain the growing specialization and improved efficiency of companies in metropolitan environments is by using a biological metaphor, comparing organizations to biological beings, which are part of ecosystems, focusing on survival and seeking a balance between their subsystems and the environment (^{Montoya et al., 2012}).

The integration of economic units into business ecosystems is a topic that has been widely explored. Authors such as ^{Williamson (1975)} state that reducing transaction costs is one of the main motivations for companies to integrate, which also leads to improved productivity since collectivity allows companies to improve their efficiency (^{Montoya et al., 2012}) by favoring their degree of specialization. Among the business groupings that have been widely studied are clusters (^{Kothandaraman and Wilson, 2001}) and productive agglomerations (^{Teixeira and Ferraro, 2009}).

Along these lines, ^{Moore (2005)} proposes the notion of organizational ecology to characterize the economic communities in which leading producers, suppliers, customers, and competitors interact. By developing specialized functions, they carry out co-evolutionary processes; that is, they help beneficial relations between two species to evolve.

^{Montoya et al. (2012)} point out that in this type of ecosystem the agents, i.e., the companies, are related by competition, cooperation, or mutualism, and that in general, there is an intention to access markets, solve problems, or access technology that motivates companies to integrate.

^{Duranton and Puga (2000)} point out several key aspects of diversification and specialization in cities, including the fact that both characteristics coexist. To calculate the degree of specialization, they propose a Specialization Index that quantifies a sector's share in local employment. For diversification, they propose the inverse of the Hirshman-Herfindal index, which consists of the sum of the squares of firms' market shares in a given sector.

Regarding highly specialized cities, ^{Duranton and Puga (2000: 534)} point out that behind their economic structure lies a strong dependence on natural resources. They also note that large cities tend to be more diversified, while cities with similar levels of specialization have similar sizes, and that the growth of a city is related to specialization and diversification.

In another paper, ^{Duranton and Puga (2019: 43)} propose a model of how cities and urbanization interact with aggregate income and economic growth. They identify patterns in which residents of less productive localities are incentivized to move to more productive localities; however, this trend is constrained because city residents impose regulations limiting the arrival of new residents. Thus, when modeling the heterogeneity of localities, they propose considering both agglomeration economies and urban costs, and warn that limiting the size of cities also limits the benefits o agglomeration.

Associating industrial composition with diversity, ^{Park (2020)} warns that industrial diversity tends to increase in technology-intensive industries but decreases in traditional ones. He also found that vertical markets are strongly correlated with diversity in building space and that high land prices hinder agglomeration and may have a negative association with diversity.

Analyzing the formation of clusters of knowledge-intensive companies, ^{Pérez-Campuzano (2021)} finds that variables such as educational level influence the location of companies but not necessarily the number of companies. In contrast, the presence of other companies and the transportation and mobility variables influence the number of companies that are set up in these clusters.

Another factor related to economic diversity and poverty levels is the rate of entrepreneurship. In this regard, Lee and Rodríguez-Pose (2020) warn that the effect of entrepreneurship on poverty levels in cities depends on the sectors in which it operates. For example, when it is concentrated in tradable sectors with other cities or regions, it can generate positive multiplier effects and impact poverty levels, while if it is focused on non-tradable sectors, it can saturate local markets, neutralizing its effects on poverty.

Socio-ecological systems and structural coupling

A perspective that can also be used to explain the behavior and survival capacity of organizations is that of socio-ecological systems and structural coupling. This theoretical approach proposes that there is a web of relations around the resources that are necessary for human life where social and environmental variables interact (^{Ostrom, 2009}). Accordingly, interactions do not only occur in the social sphere but are also related to physical space.

Among the concepts incorporated into the socio-ecological system approach are selforganization, which designates mechanisms that respond to the preconditions of the system from which its structure can be modified, and attractors or states of self-organized stability (^{Gunderson and Holling, 2002}; Urquiza and Cadenas, 2015).

Another concept that can help understand a social-ecological system is resilience, which in its general form is understood as the capacity of a system to adapt to changing conditions in its environment and to resist or recover from impacts without losing its integrity. In the case of a social-ecological system, it seems to be related to the diversity of the elements that make it up, in such a way that a greater variety of elements is a major advantage in stressful and risky situations (Urquiza and Cadenas, 2015).

In this context, reviewing the concepts and metrics used to estimate the specific diversity of communities is relevant. It is important to note that, although the subject has been widely debated and has also led to semantic and conceptual problems (Hurblert, 1971), specific diversity is considered an emerging property that is related to the variety of communities and is derived from two components: the variety or richness of species and equitability, which is the distribution of abundance among the number of species.

The ^{Margalef (1956)} index expresses the specific abundance, that is, the relation between the number of species (S) and the total number of individuals observed (n).

The Shannon-Wiener (H) and Simpson (D) indices measure diversity, incorporating specific richness and equitability in a single value. The Shannon-Wiener index derives from information theory and measures the information content per individual in samples obtained at random from an extensive community, so that diversity is understood as the degree of uncertainty in predicting to which species an individual taken at random from a sample of S species and N individuals corresponds (^{Pla, 2006}).

Simpson's diversity index indicates the probability of finding two individuals of different species in two successive random extractions without replacement (^{Bouza, 2005}). It is expressed in the following equation:

where pi is the proportional abundance of the ith species and represents the probability that an individual of species i is present in a sample, so the sum of pi equals 1. Therefore,

Values close to 0 in Simpson's Diversity Index would indicate the dominance of a few species, while values close to 1 would indicate greater diversity, less dominance of certain species, and greater ecosystem stability.

Methodology

The research was quantitative, correlational in scope, and cross-sectional in design.

The research questions are:

The research aim is to determine whether the diversity of lines of business in a metropolitan area influences poverty levels and the capacity to generate or retain jobs.

For this research, the subsectors of economic activity will be considered as the equivalent of the species of an ecosystem, and the number of economic units of each subsector per metropolitan area will be the equivalent of the number of individuals of each species per ecosystem. The basis for determining the subsectors (or species) was the Industrial Classification System for North America (Spanish: Sistema de Clasificación Industrial para América del Norte, SCIAN 2018), and the data on the number of units or individuals were taken from INEGI's National Directory of Economic Units (DENUE).

The research consisted of collecting data and calculating and correlating the variables mentioned in the research questions, as shown in Table 1.

Discussion

^{Fontenla (2018)} describes ecosystems as ensembles where diversity can be seen as an emergent phenomenon derived from complexity. In this sense, diversity can be understood as a measure of the complexity of ecosystems and vice versa (^{Morin, 2008}). Now, if diversity is an epiphenomenon of complexity, this suggests that a system where species richness and abundance are combined also represents a greater number of interactions among themselves and with the environment. This is consistent with the notion that the greater the diversity, the better the conditions for maintaining the system's integrity, even if its behavior is difficult to predict (^{Levin, 1998}; ^{Marion et al. 2015}).

If this analogy is transferred to the economic field, diversity could be interpreted as a property that allows business ecosystems to maintain their integrity in the face of external shocks, such as economic recessions. In this regard, a more diverse business ecosystem can be more effective in limiting job losses and business closures since strong interdependence allows companies to survive thanks to their integrated production processes.

In this research, Simpson's Diversity Index has been used to evaluate how the productive diversity of metropolitan areas affects their capacity to generate jobs and reduce poverty.

The correlations found between variables such as Diversity, Employment Supply, Moderate Poverty, and Extreme Poverty seem to be consistent with ^{Iracheta (2010)} and ^{Garza and Schteingart (2010)}, in the sense that productive and spatial concentration translates into good economies and, therefore, advantages for economic actors. This is also consistent with the thesis of ^{Moore (2005)}, who states that in business concentrations, there is a co-evolution derived from mutually beneficial relations between businesses. An interesting finding would be that diversity impacts not only the generation of wealth but also the progressive reduction of income inequality by being positively related to poverty reduction.

On the other hand, according to the modeling of variables performed, when Simpson's Diversity Index is high, the effect of GDP variations on employment, moderate poverty, and extreme poverty variables is lower than when diversity is low. This is consistent with what Urquiza and Cárdenas (2015) proposed, in the sense that diversity is related to the system's resilience.

The Informality Rate variable requires a separate mention. A finding of the present research is that it has a significant statistic relation of negative sign with Simpson's Diversity Index, which indicates that the less diverse the business ecosystem, the greater the tendency of the population to resort to precarious livelihood options. This is consistent with Robles, Sánchez, and Beltrán (2018), who found heterogeneous features according to the geographic region.

Conclusions

The analysis of the information collected implies the following answers to the research questions:

Simpson's Diversity Index has a significant positive correlation (0.557) with the variation in the number of workers permanently affiliated to the IMSS in the metropolitan areas studied and a significant but negative correlation (-0.753) with the Informality Rate. This indicates that diverse business ecosystems are more effective in creating jobs and reducing informality. It is worth mentioning that the size of a metropolitan area has no relation to diversity or the informality rate, as no significant coefficients are found when correlating these variables.

Simpson's Diversity Index also has a significant negative correlation with the Extreme Poverty Index (-0.651) and the Moderate Poverty Index (-0.762). This shows that diversity is a property of business ecosystems that influences the reduction of inequality in income.

Using Simpson's Diversity Index, Informality Rate, State GDP Variation, and Years of Schooling as predictors, it is possible to model the capacity of a metropolitan area to generate employment and reduce moderate and extreme poverty rates over a certain period. The parameters of the developed models show that the selected predictors explain most of the variability of the mentioned results.

The possible uses of the information gathered in this article can be classified into two parts: those related to public policies and those related to specific investment decisions. In the case of public policies, it is important to highlight the importance of creating government programs that promote integrated production, the diversification of lines of business, the lengthening of local supply chains and, in general, the development of capabilities for entrepreneurs to enter niches that allow them to increase the diversity of business ecosystems. Valuable input for decision-making would be the creation of information systems showing the least developed subsectors in a business ecosystem and the capabilities or competencies that need to be developed to enter them.

Concerning specific investment decisions, entrepreneurs need to have access to information on the lines of business where there are opportunities for entry and access to the training required to venture into these areas.