INTRODUCTION

In the search for gains in economic growth and competitiveness, many countries have stimulated entrepreneurship through supportive public policies (Farinha et al. 2020; Salman, 2016). Policymakers, however, must define the type of enterprise, whose creation and development they intend to promote (Autio & Rannikko, 2016; González-Uribe & Reyes, 2021), and then prioritize the removal of barriers and strengthening of facilitators of entrepreneurship (Kaya & Persson, 2019).

Entrepreneurship is a dynamic process of resource mobilization for the exploration of opportunities, perceived in the operationalization of a new business, whose growth and performance potential is moderated by the behavior and competence of the entrepreneurs, and affected by contextual and institutional factors (Ács et al., 2014). From this perspective, therefore, policies must be formulated from a systemic view of entrepreneurship, which considers the interactions between individuals and the context (Ács et al., 2014).

The approach of entrepreneurial ecosystems makes it possible to identify such interactions and capture exchanges between the stock of entrepreneurial capital, knowledge and socioeconomic, political, institutional, industrial and technological contexts (Lafuente et al., 2016). Known as the National Entrepreneurship System (NSE), this approach enables policymakers to measure interactions between individuals and context, and to implement national entrepreneurship policies (Szerb et al., 2020).

To capture the complex and multifaceted interactions between entrepreneurs and context, and portray the NSE, Ács & Szerb (2009) formulated the Global Entrepreneurship Index (GEI), which became the first entrepreneurship composite index (ECI). The GEI differs from other indices, such as the Global Entrepreneurship Monitor (GEM) and Doing Business, because it is not limited to measuring just one aspect of entrepreneurship, such as the characteristics of entrepreneurs/businesses or contextual factors, but combines individual and contextual aspects, thereby providing both micro and macro perspectives.

Since 2011, researchers associated with the GEI have annually published studies on entrepreneurship at the country level, and the index has been used as an analytical framework to understand this phenomenon from a systemic perspective, in countries and/or regions (Ács & Correa, 2014; Atiase et al., 2018; Ha & Hoa, 2018, Szerb et al., 2014; Szerb & Trumbull, 2018).

Despite the use and wide dissemination of this ranking and, consequently, the indicators selected and organized to portray and monitor entrepreneurial ecosystems at the national level, there are still no studies that validate this methodology as a way of measuring entrepreneurship. Attempts to show the validity and reliability of this methodology were limited to correlation matrices between the indicators that make up the GEI (see Ács & Szerb, 2009). These tests attest to the correlation between the indicators, but do not emphasize their importance for the construction of the sub-indices, nor do they validate the methodology.

Given this gap, the question is: Are the Global Entrepreneurship Index (GEI) indicators valid and reliable, from a statistical point of view, to measure entrepreneurial activity at the country level? To answer this question and assess whether the GEI is, in fact, a valid methodology for measuring national entrepreneurship systems, in this study, a sophisticated statistical technique known as Structural Equation Modeling (SEM) was applied to the indicators of the GEI, through the SmartPLS software, as the SEM provides information that explains the relationships between multiple variables (Hair et al., 2017) and their factors/dimensions, thus performing the analysis of the measurement model. With this, it will be possible to identify if the GEI has plausibility, that is, if it can be validated as a methodology capable of measuring, in fact, what it proposes, that is, the entrepreneurial ecosystems.

In addition to this introduction, this article presents: an overview of entrepreneurship systems; the synthesis of the Global Entrepreneurship Index; the methodological procedures used; the results found, and the discussion derived from them; final considerations and suggestions for future studies.

NATIONAL SYSTEMS OF ENTREPRENEURSHIP IN PERSPECTIVE

In the NSE approach, the emphasis is on high-impact entrepreneurship – activity that contributes to the generation of jobs and the advancement of sectors in which technologies and market development are at less advanced stages (Ács et al., 2018a). In these cases, despite the uncertainty involved, entrepreneurs usually assume central roles in defining new technological trajectories, creating markets and establishing technological standards (Lattacher et al., 2021).

Micro level

Entrepreneurship represents a link between technical knowledge and products and services, and the entrepreneur is the one who explores market opportunities and brings relative balance to the markets (Lafuente et al., 2020). Therefore, this individual must be able to recognize opportunities and exploit them, adding value to the economic environment (Chang & Chen, 2020). The motivations for entrepreneurship vary between the need to generate income, due to the lack of jobs, and the possibility of improving the income already received (Cervelló-Royo et al., 2020) among others.

The transformation of these opportunities into real businesses depends on the attitudes, preferences (Beynon et al., 2020) and aspirations of individuals, that is, the perspectives of contribution to the socioeconomic contexts in which they are inserted (Cieślik et al., 2018). In addition to these factors, to recognize and exploit opportunities and establish competitive advantages, entrepreneurs need to have skills and education (Amorós et al., 2021; Tavassoli et al., 2021).

Entrepreneurs should not be considered as isolated agents, even at the micro level, because they often access networks to obtain tangible and intangible resources (Lassalle et al., 2020), as well as being influenced by culture, through of values and norms that act as catalysts or barriers to their entrepreneurial behavior (Bogatyreva et al., 2019). Culture influences the degree of openness of entrepreneurs to share ideas and experiences with other people with similar interests, and can, in this way, even influence the career options of individuals (Asante & Affum-Osei, 2019). This denotes that analyzing entrepreneurship from a systemic perspective is inevitable.

Macro level

Literature has advanced towards understanding the contextual conditions that affect entrepreneurial activity (Sternberg et al., 2019). One of the first aspects considered is associated with the institutional context, understood as the collective of formal and informal norms that shape the behavior of individuals in economic systems (Ács et al., 2018a).

The creation of companies also involves the dynamics of productive structures, such as changes or maturity of technologies, industrial growth (Malerba & Pisano, 2019), characteristics of market demand and the competitive environment (Zheng & Du, 2020), and availability of complementary resources (Stam & Van de Ven, 2021).

These macro conditions are linked to countries' levels of development. For example, growth in gross domestic product (GDP) per capita can promote entrepreneurial activity by providing demand sophistication (Fredström et al., 2021; Hamdi-Kidar & Vellera, 2018). On the other hand, some researchers negatively relate general entrepreneurial activity to GDP per capita growth (Ali et al., 2020; Udimal et al., 2020), due to the possibility that opportunity costs are higher in the employment-entrepreneurship relationship, and the intensity of competition inhibits potential (Cervelló-Royo et al., 2020).

Complementary resources are relevant to the quality and configuration of the entrepreneurial environment, such as: (a) the availability of financing or financial resources (Dutta & Meierrieks, 2021); and the managerial experience provided by investors to entrepreneurs (Omri, 2020).

In this sense, science, technology and innovation institutions (universities, technology transfer offices, R&D institutes, science parks and incubators) are elements that influence the creation of technology-based tecnológica (Buenstorf & Costa, 2018; Fuster et al., 2019; Sousa-Ginel et al., 2021; Xie et al., 2018), providing human, financial and administrative support (Huynh et al., 2017; Steruska et al., 2019).

Entrepreneurship systems

Combining micro and macro perspectives, the NSE approach assesses the trajectory of new business development, by observing the systemic factors that influence entrepreneurial activity (Lafuente et al., 2020); recognize the bottlenecks that inhibit entrepreneurship; and identify areas that need intervention (Szerb et al., 2020).

Such an approach emerges from the critique of the National Innovation Systems (NIS) of the 1990s, one of the main trends in industrial economics in innovation studies (Edquist, 1997; Freeman, 1995; Lundvall, 1992; Nelson, 1993), whose merits in understanding which factors affect the emergence and diffusion of innovations are undeniable. They influenced a whole generation of scholars and policy makers in terms of propositions related to the formulation, implementation and evaluation of science, technology and innovation policies. These, in part, according to their precursors, were responsible for the economic recovery of countries after the Second World War (Dosi, 1982; Freeman, 1995; Kline & Rosenberg, 1986).

On the other hand, authors related to the conception of the NSE, which later evolved and established itself with the use of the concept of entrepreneurial ecosystems, argue that the theory of the NIS neglected the role of the individual and entrepreneurial action as central elements in the generation of innovations, as it emphasized the firm (the company) as the main, if not exclusive, agent or locus of the innovative process (Bruns et al., 2017; Isenberg, 2010; Mason & Brown, 2014; Roundy et al., 2018; Spigel & Harrison, 2018; Stam, 2015).

According to these authors, Schumpeter’s pioneering insights, in the model known as “Schumpeter – Mark I” (Schumpeter, 1949, 2011, [1934]), in which the innovation process was described as one of creative destruction and led by the entrepreneur, were progressively forgotten. For Schumpeter, in that model, the innovation trigger was not triggered by the controlling actors of the production process in search of the new, but by the entrepreneur (entrepreneur) who, when realizing the potential of such inventions, assumed the risk of development, that is, of the transformation of inventions into innovations for extraordinary profit. Thus, according to this author, the entrepreneur “educates” customers in the demand for new products and processes.

Thus, in view of the exploratory nature of entrepreneurial activity and the limitations of NIS in incorporating entrepreneurs, the entrepreneurial ecosystem approach defines that (Ács et al., 2014, p. 479):

1. A National System of Entrepreneurship is the dynamic, institutionally embedded interaction between entrepreneurial attitudes, ability, and aspirations, by individuals, which drives the allocation of resources through the creation and operation of new ventures.

The central idea of the NSE, then, is to bring the entrepreneur back to the literature on innovation and economic development, through the revaluation of individual action as the center of the processes of innovation and economic prosperity. On the other hand, it is interesting to note how the resumption of focus on entrepreneurship influenced the literature that deals with innovation and growth strategy, from the perspective of the company, leading to the development of the field of studies on corporate entrepreneurship (Kuratko, 2009; Kurato & Covin, 2008; Landström et al., 2015).

Due to the tendential limitation of institutions and consolidated companies in generating ruptures in development trajectories, due to the fear of harming their positions in the economic scenario, the approach of entrepreneurial ecosystems considers innovation as the result of the interaction between institutional actors (context) and individuals. Thus, in the absence of initiative to recognize opportunities and to mobilize resources for new businesses, the institutional context is not capable of influencing the aspirations of individuals regarding the creation of high-impact companies. On the other hand, an inadequate institutional context results in companies that do not significantly contribute to the socioeconomic scenario (Kuratko, 2009; Kurato & Covin, 2008; Landström et al., 2015). It is worth mentioning, in this sense, that a systematic review of the literature on entrepreneurial ecosystems, from the perspective of the importance of its measurement, is presented by Leendertse et al. (2021).

In short, the entrepreneurial ecosystem approach considers the creation of companies as the result of a process influenced by interdependent systemic factors, which affect the life cycle and performance of start-up firms (Ács et al., 2018a). To this end, it assesses the development trajectories of high-impact organizations in countries, based on contextual and individual aspects of entrepreneurship (Szerb et al., 2020). In terms of policy formulation, such an approach goes beyond “market failures”, as it encompasses social and systemic aspects that affect entrepreneurship (Inácio Jr. et al., 2021).

GLOBAL ENTREPRENEURSHIP INDEX (GEI)

The GEI was developed to measure the quality of entrepreneurial systems at the country level, through measures of attitudes, skills and entrepreneurial aspirations, individually (micro level), as weights in adjusting the importance of contextual and institutional factors (macro level), in regulating the quality of the dynamics of entrepreneurship.

The GEI conceptual model assumes that entrepreneurs allocate resources to exploit perceived opportunities. However, the resource mobilization process is influenced by contextual/institutional factors (Lafuente et al., 2020). Examples of this are the quality and dynamics of interactions between science, technology and innovation institutions (STI) and entrepreneurs, which regulate the potential for innovation. In this way, low interaction directly impacts the ability to innovate in new ventures. Therefore, macro-level factors are institutional and contextual moderators of entrepreneurs' attitudes, skills and aspirations.

The GEI is composed of 14 indicators aggregated into three sub-indices: (1) attitudes (ATT) – associated with indicators that measure the entrepreneurial behavior of the adult population (from 18 to 64 years old) in a country; (2) skills (ABT) – provide a picture of the characteristics of entrepreneurs with high impact potential, measured by metrics associated with motivation to undertake, the technological intensity of the business, the level of education of the entrepreneurs, and the uniqueness of a product and /or service offered, compared to competitors; and (3) entrepreneurial aspirations (ASP) – which deal with high-impact entrepreneurial activity, which spreads new technologies, and generates wealth and jobs (Table 1). This impact is measured by the entrepreneurial ambitions of: internationalization, growth (of jobs), technological innovation, and the availability of venture capital to finance the expansion of new businesses.

Each of the 14 indicators is the result of the combination of an individual variable and an institutional variable, both of which capture the context of each of the participating countries. The individual variables – obtained from the annual adult population survey (APS), carried out by the GEM (Bosma et al., 2020) – describe the entrepreneurial behavior of the population and the characteristics of early-stage entrepreneurship. The institutional variables, in turn, made available by international organizations (World Economic Forum, Heritage Foundation, World Bank, Unesco, among others), identify the context of entrepreneurs (market dynamics, existence of services and policies to support entrepreneurship, quality of teaching and research institutions, among others).

By combining variables representing the micro and macro levels, the GEI goes beyond the existing entrepreneurship indices, which are limited to measuring only one of the levels, such as the Doing of Business and the National Expert Survey, which assess only the constraints of entrepreneurship; and the GEM and Kauffman surveys, which look only at entry rates and entrepreneurial attitudes.

Like the GEI, the Index of Dynamic Entrepreneurship (IDE) also measures the micro and macro levels of entrepreneurship but does not provide diagnoses about the entrepreneurial attitudes of the population and the quality of entrepreneurship, that is, whether they are innovative, technology-based and/or or export-oriented.

The GEI results in a composite indicator, developed according to the international recommendations of the OECD, discussed in detail in its manual “Handbook on Constructing Composite Indicators: Methodology and User Guide” (OECD, 2008).

A prominent element in the GEI is the so-called “penality for bottleneck”, which is not found in any other aggregate indicator. It is the procedure based on the assumption that the performance of a system is dependent on its bottleneck, that is, the overall performance is determined by its weakest link. Thus, countries with greater imbalances among the ten pillars have, as a result, a smaller GEI.

METHODOLOGY

Data

To evaluate the training model of the GEI, data from the index itself, available on the website http://thegedi.org, belonging to The Global Entrepreneurship and Development Index (GEDI), responsible for studies on entrepreneurship, were used.

The sample consists of 137 countries, referring to the GEI 2018 report (Ács et al., 2018a), and for each one, data were collected concerning the 14 entrepreneurship indicators of the GEI 2017 and 2018, and from all available countries, creating its own database (which can be accessed at: https://doi.org/10.25824/redu/IXXOZ4).

The use of data from two editions was necessary to verify the consistency of the model, in relation to the two measurements in time, and if there are differences between the evaluation periods (2017 and 2018).

Structural Equations Modeling (SEM)

To achieve the research objective, a multivariate statistical treatment of data was applied through the SEM. According to Hair et al. (2017), the SEM is suitable for assessing how well a model is able to explain the multifaceted aspects of a phenomenon. To perform it, the SmartPLS 3.0 software was used, which, according to Gudergan et al. (2008), analyzes the suitability of models, as it organizes a Confirmatory Factor Analysis (CFA) to assess how much each indicator fits a model. (Hair et al., 2017).

To apply the SEM in the GEI, the first step is to build a diagram of causal relationship paths, representing the three sub-indices of entrepreneurship (ATT, ABT and ASP), called constructs or latent variables (LV) by the SEM literature.

Before modeling, however, confirmatory tetrad analysis (CTA) was performed for each LV to assess whether the measurement model was formative or reflective (Gudergan et al., 2008). The results (Appendix 1) show that all constructs (ATT, ABT and ASP) have non-null tetrads, indicating that the measurement model is formative (Hair et al., 2017), as predicted in theory for building formative indicators (OECD, 2008).

After confirming the nature of the model, the path diagram was constructed (Figure 1), based on the measurement scheme of formative models. For that, all constructs were connected using the weighting scheme “factor weighting”, where each arrow is estimated as a correlation between the latent variables

Evaluation of formative measurement model

To evaluate the formative measurement model (FMM), according to the recommendations of Hair et al. (2017), three steps were followed:

1. 1. evaluation of the convergent validity of the FMM, carried out through the correlation of a formative orientation construct with a single global item - the average value of the indicators of this same construct (to be convergent, the result must be 0.70 or higher);
2. 2. evaluation of the indicators' collinearity, to identify whether there is redundancy between them – which occurs through high correlations (≥ 5) between indicators of the same construct; and
3. 3. evaluation of the significance, relevance and level of contribution of each of the formative indicators of the construct, through the bootstrapping technique, which measures the external weight of each indicator (relative importance) and its external load (absolute importance).

When the weight of an indicator is significant (it has a statistical t value greater than 1.65, 1.96 or 2.57), there is empirical support for it to remain in the FMM (Hair et al., 2017). On the other hand, if the weight is not significant, but the value of the external load is relatively high and statistically significant (≥ 0.50), the indicator must be maintained. However, if the external weight is not significant and the external load is relatively low (<0.50), the indicator can be removed from the FMM.

RESULTS

The convergent validity of the latent variables was verified by redundancy analysis of each construct, with the original formative construction marked as “original”, and the single-item construction as “global”. All path coefficients were higher than the recommended limit of 0.70, which indicates that the LVs exhibit convergent validity (Figure 2).

The assessment of the collinearity of the indicators was performed using the variance inflation factor (VIF), and resulted in values lower than 5.0 - indicative of the absence of redundancy, which does not require the correction of collinearity problems, the removal or merging of indicators (Table 2).

Regarding the results of the evaluation of the indicators and the relative importance (external weights), in terms of significance and relevance, only three of them showed low significance regarding the t statistic. They are: Pillar_1 (-0.084); Pillar_4 (0.052); and Pillar_10 (0.037). As for the absolute importance, represented by the values of external loads, all indicators showed values greater than 0.50, thus proving to be significantly relevant for the FMM (Table 2).

To achieve the results related to the consistency of the measurement model, based on data from two GEI editions (2017 and 2018), the permutation method was used, by calculating the MICOM test (measurement invariance of composite models), which evaluated the invariance of the FMM (Bido & Da Silva, 2019) in the years analyzed (Table 3 e Table 4). All permutation p-values are non-significant (p>0.05), which confirms the inexistence of differences between the two observed periods (Table 3 e Table 4).

DISCUSSION

The results of the FMM evaluation show that all LVs have convergent validity; however, with regard to the significance and relevance of the indicators, Pilar_1, Pilar_4 and Pilar_10 have non-significant external weights. Even so, they have significant external loads (>0.5), which suggests the permanence of the indicators in the FMM, as long as there is empirical support for this.

Pillar_1 is composed of the variables “opportunity recognition” and “business and property”. The first (individual) measures the population's perception of opportunities to undertake; and the second (institutional), provided by the Economic Freedom Index (EFI), captures the effects of the regulatory environment on entrepreneurship and private property rights.

When looking at the EFI database, it can be noted that developed countries are characterized by the quality of the regulatory environment; the GEM database, in terms of the individual variable, shows that underdeveloped and developing countries have greater opportunities to undertake (from the point of view of the adult population), compared to developed countries.

This is, therefore, a limitation of the GEM, given that the research considers any type of entrepreneurship, not necessarily productive, innovative and/or knowledge intensive. Thus, there is no differentiation between perceived opportunities in high-tech sectors and low-productivity opportunities (such as selling homemade cakes, for example, due to the scarcity of quality jobs and the need to earn income to survive). This methodological option, therefore, provides distorted results of what could be understood as productive or high-impact entrepreneurship.

Although there is theoretical support for maintaining Pillar_1, since opportunities are the core of entrepreneurship (Wood & Mckinley, 2017), some authors (Neill et al., 2017; Welter & Alvarez, 2015) argue that it is necessary to distinguish them between discovery and creation. On the other hand, measuring only the opportunities perceived in technology sectors would generate new results, being able to identify the ability of an economic system to favor entrepreneurs (Radosevic & Yoruk, 2013).

Although the focus on the perception of entrepreneurial opportunities in technological sectors penalized less developed countries, a change in this indicator would minimize the distortions of results, when comparing countries at different stages of development. For example, the United States has 0.73 points in the variable “recognition of opportunities”, while Chad, the last place out of 137 countries, has a performance of 0.94, surpassing the United States, the first place in the GEI ranking. In this context, this variable may indicate that Chad's economic system generates more opportunities (perceived by the population of that country) to undertake than the US system. These results distortions would be minimized if the indicator captured other aspects of opportunity perception, especially those related to technology.

Pillar_4, consisting of the individual variables “known entrepreneurs” and institutional “agglomeration”, assesses the ease of meeting an entrepreneur, the ability to connect individuals, the degree of urbanization and the quality of the countries' infrastructure. Several researchers (Audretsch & Belitski, 2017; Belitski & Desai, 2016; Bosma & Sternberg, 2014; Stam & Van de Ven, 2021) claim, in this sense, that urban environments, with quality infrastructure, drive innovative and high-quality entrepreneurship growth, due to agglomeration economies (Krugman, 1991). Thus, knowledge flows are denser in cities, where different skills and resources are concentrated, and they become more accessible. In addition, these environments facilitate market access (Lattacher et al., 2021).

Likewise, entrepreneurs known by the majority of the population become models and successful references for potential entrepreneurs, acting as leaders, in strengthening the NSE (Stam & Van de Ven, 2021; Sternberg et al., 2019).

There is, therefore, theoretical support for maintaining Pillar_4, despite the low significance of the indicator, which is associated with the methodological limitations of the GEM, for which any individual involved in the creation of a new business is considered an entrepreneur, whether he is dedicated to an activity with the potential to become a unicorn, be it a subsistence activity.

This becomes clearer when comparing the United States and Mauritania (number 136 in the ranking of 137 countries), regarding the variable “known entrepreneurs”. The first country has a score of 0.57; and the second, 1.0 point, indicating that there are more entrepreneurs in Mauritania than in the United States.

Although less developed countries have larger “stocks” of entrepreneurs than more developed ones (Ács et al., 2014), quantity does not represent quality, as they are primarily need-oriented entrepreneurs (Autio & Fu, 2014; Lederman et al., 2014), with low visibility and/or less known than innovative entrepreneurs. As a result, they find it more difficult to access the same resources and knowledge flows held by an innovative new (Kantis et al., 2018; Ordeñana et al., 2019).

With regard to Pillar_10, the low external weight can be attributed to the characteristics of the GEI database, which measures entrepreneurship in 137 countries, most of which are characterized by economic systems based on factors of production or efficiency, which are known dependence on the global economy and the importation of technology. Only a small number of countries diffuse the technology, through knowledge-intensive activities and business sophistication and demand (WEF, 2017). Thus, when using a relatively large database on entrepreneurial innovation, in which most countries have few innovative initial entrepreneurs, it is expected to obtain low significance.

Pillar_10, then, due to the institutional variable “technology transfer”, which measures the quality of a country’s science and technology system (Ács et al., 2018b), has theoretical support, which guarantees the their stay in the FMM. According to some researchers (Belitski & Desai, 2016; De Bernardi & Azucar, 2020; Malerba & McKelvey, 2020; Radosevic & Yoruk, 2013), the quality of the innovation environment drives the creation of new innovative companies. On the other hand, the individual variable “new product”, which measures whether entrepreneurs introduce new products to some of their customers, combined with the institutional variable that assesses the “quality of the S&T system”, represent an advance in relation to the indexes of entrepreneurship that provide a diagnosis of the quality of the innovation environment, but do not inform whether innovation actually occurs.

To assess the NSE, for example, the IDE uses metrics, such as the S&T platform and the business structure, to assess the quality of the S&T system and the technological maturity of established companies (Kantis et al., 2018). However, it is not possible to correlate these metrics with the proportion of innovative entrepreneurs, as the FDI only includes an entrepreneurship indicator, which measures the growth orientation of new businesses, not whether they are innovative.

Assessing the proportion of early entrepreneurs who innovate is relevant to support decision makers in formulating policies that minimize market failures associated with knowledge externalities, inducing investments and facilitating the dissemination of knowledge (Ács et al., 2016).

CONCLUSION

In this research, it was asked whether the GEI indicators are relevant and significant to measure the performance of the NSE. To obtain the answers, the SEM and the evaluation procedures related to the FMM were used – configuration through which the indicators form/cause the construct (phenomenon). The results showed that all constructs have convergent validity and the indicators are not redundant.

When evaluating based on external weights, Pillar_1, Pillar_4 e Pillar_10 showed low significance. On the other hand, in terms of relevance, given the value obtained in the examination of external loads, the indicators proved to be significant.

As it is a relevant metric to assess the quality of entrepreneurship, this article suggests the permanence of Pillar_10. However, it is necessary to replace the individual variables of Pillar_1 and Pillar_4, in which entrepreneurship encompasses any attempt to create a new business, including from autonomous activities to the expansion of an existing business (GEM, 2017). Because of this, they provide distorted results, based only on quantity, not measuring the quality and impacts of entrepreneurship.

From the perspective of these variables, therefore, as a suggestion to the developers of indicators or policymakers, this article recommends the elaboration of more specific questions, in order to avoid, as a parameter to measure the visibility of entrepreneurs, the use of negligible forms of entrepreneurship. for economic growth or that do not constitute references for new business models (Feld, 2012; Ordeñana et al., 2019).

By validating the GEI and suggesting the replacement of some components, this research methodologically contributes to studies proposing regional and/or local applications of the aforementioned index. To avoid distorted results, however, it is recommended to limit the scope of evaluation to effectively productive forms of entrepreneurship.

In addition, this study contributes to the entrepreneurship literature, as it is the first to assess the validity of the GEI, using the SEM method. So far, research has correlated the index with indicators – a procedure that does not allow identifying whether it is valid as a methodology or as a model for measuring NSE.

For future research, we suggest the validation of other entrepreneurship indices, such as FDI; and the evaluation of the GEI over time, using techniques such as data envelopment analysis (DEA) and the Malmquist index (Bogetoft, 2012), to measure the efficiency of the countries' EE.

Acknowledgements

The authors are grateful for the contributions of the evaluators.

Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

Conflict of interest statemen

The authors declare that there is no conflict of interest.

Authors’ statement of individual contributions

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Author notes

Corresponding author: avancci.eduardo@gmail.com

Additional information

Article ID: 2050

JEL Code: L26, M10

Editor-in-Chief1or Adjunct2: ¹Dr. Dennys Eduardo Rossetto, Skema Business School

Handling Editor: Dr. Jefferson Lopes La Falce, FUMEC University

Executive Editor: M. Eng. Patrícia Trindade de Araújo

Translation / Proofreading: Tamiris de Souza Figueira Silva

Funding: This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) -Finance Code 001

Cite as: Dionisio, E. A; Silva, D; Carvalho, R. de Q; Inacio Junior, E. (2022). Evaluation of the Global Entrepreneurship Index validity. Iberoamerican Journal of Entrepreneurship and Small Business, 11(2), Article e2050. https://doi.org/10.14211/ibjesb.e2050

Additional information

redalyc-journal-id: 5615

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