Higher education’s influence on social networks and entrepreneurship in Brazil

Abstract

Developing and middle-income countries increasingly emphasize higher education and entrepreneurship in their long-term development strategy. Thus, our work focuses on the influence of higher education institutions (HEIs) on startup ecosystems in Brazil, an emerging economy. As traditional data to perform this type of study, such as surveys, are challenging to get, we propose an alternative approach. Given the growing capability of social media databases such as Crunchbase and LinkedIn to provide startup and individual-level data, we draw on computational methods to mine data for social network analysis. Our approach enables different types of analysis. First, we describe regional variability in entrepreneurial network characteristics. Second, we examine the influence of elite HEIs in economic hubs on entrepreneur networks. Third, we investigate the influence of the academic trajectories of startup founders, including their courses of study and HEIs of origin, on the fundraising capacity of startups. We find that HEI quality and the maturity of the ecosystem influence startup success. We also observe that elite HEIs have a powerful influence on local entrepreneur ecosystems. Surprisingly, while the most nationally prestigious HEIs in the South and Southeast have the longest geographical reach, their network influence remains local. This means that investments in entrepreneurship, in the Brazilian context, tend to remain concentrated in wealthier cities, and may actually reinforce or increase regional inequalities. We also find that the startup ecosystem in the wealthier South and Southeast is more diverse in terms of sectors, which is more advantageous to economic development. Our approach can be helpful, especially in countries with limited studies of the interaction between startups and institutional factors supporting them. In terms of policy recommendations, we would recommend more investment at the regional level in terms of cultivating entrepreneurship, given the limited spillover from wealthier regions.

Appendices

Appendix A: Full university names and abbreviations

• Universidade de São Paulo (USP)

• Universidade Anhembi Morumbi (UAM)

• Unidervsidade Estadual Paulista (UNESP)

• Universidade Presbiteriana Mackenzie (MACKENZIE)

• Faculdade Milton Campos (FDMC)

• Faculdade de Administração de Brasília (FAAB)

• Fundação Getúlio Vargas de São Paulo (FGV/SP)

• Instituto de Ensino e Pesquisa (INSPER)

• Instituto Brasileiro de Mercado de Capitais (IBMEC)

• Associação Internacional de Educação Continuada (AIEC)

• Universidade Federal do Minas Gerais (UFMG)

• Universidade Federal do Rio de Janeiro (UFRJ)

• Universidade Federal do Santa Catarina (UFSC)

• Universidade Federal do Grande do Sul (UFRGS)

• Universidade Federal do Estado do Rio de Janeiro (UNIRIO)

• Pontifícia Universidade Católica de Paraná (PUC/PR)

• Pontifícia Universidade Católica de São Paulo (PUC/SP)

• Pontifícia Universidade Católica de Minas Gerais (PUC/MG)

• Pontifícia Universidade Católica de Rio Grande do Sul (PUC/RS)

Appendix B: Startup sectors by region

See Figs. 14, 15, 16, 17, and 18.

Fig. 14

South. Others: biotechnology, entertainment, industry/energy, education, financial, services, real state, health

Fig. 15

Southeast. Others: consumer products, tourism

Fig. 16

Center-West. Others: biotechnology, agribusiness, HR tech, education, consumer products, financial services, retail/wholesale, health

Fig. 17

Northeast. Others: HR Tech, lawtech, financial services, entertainment, real state, retail/wholesale, industry/energy, professional services, education

Fig. 18

North. Others: category unidentified

Appendix C: Summary of all ecosystems studied

See Table 5.

Table 5 Summary of all ecosystems studied

Appendix D: Classification and framework details

1.1 Classification

The classifications we used in this regard were to outline the startup ecosystems, the startups’ market segments, the classification of HEIs regarding teaching and research quality, and the classification of courses.

The first concept is the startup ecosystem, a set of startups located in the same city. Ecosystems were classified as “mature” or “emerging,” depending on the number of startups. Mature ecosystems were those whose number of startups was more significant than the national average. It is important to note that mature ecosystems are found in the capitals of the South and Southeast states, Brazil’s wealthiest regions.

The startup industry was extracted from CrunchBase’s raw data and refined through a topic mining algorithm called LDA, which is part of the natural language processing (NLP) discipline.

The HEIs were classified as “elite or not” based on the IGC, which is the official index of the Ministry of Education to assess the quality of HEIs. They were also ranked in universities, colleges, and university centers. A more detailed description of the composition of the IGC is presented as follows:

CPC—An indicator that assesses the course of study on a scale from 1 to 5. For the calculation, the following are considered: Enade Concept (student performance in the Enade test—nationwide test); Difference Indicator between Observed and Expected Performance (IDD); faculty (information from the Higher Census on the percentage of masters, doctors, and work regime) and students’ perception of their training process (information from the Enade Student Questionnaire).

IGC—An indicator that evaluates the educational institution. The following are part of the IGC calculation: the average of the CPCs of the last three years of Enade (2016, 2017, and 2018) related to the evaluated courses of the institution; the average of the evaluation concepts of the master’s and doctoral programs awarded by the Coordination for the Improvement of Higher Education Personnel (Capes), in the last available triennial evaluation; and distribution of students among the different levels of education, undergraduate and graduate courses.

The classification of bachelor’s, master’s, Ph.D.’s, MBA, or extension courses was carried out according to the courses’ descriptions in the Linkedin profile.

1.2 Framework

The framework consists of acquiring public data available on the Web, and processing and calculating complex network metrics, among other metrics. The following items are the main steps of our framework:

• 1. Load the raw data from Crunchbase.

• 2. Loading of raw Linkedin data.

• 3. Loading of raw IGC data.

• 4. Extraction of categories of startups employing LDA.

• 5. Extraction of courses from Linkedin profiles.

• 6. Consolidation of clean data on a single basis.

• 7. Enrichment of the base with georeferencing data from startups.

• 8. Enrichment of the base with IGC from HEIs.

• 9. Construction of networks.

• 10. Calculation of centrality metrics.

• 11. Calculation of startup fundraising.

• 12. Data analysis and insights discussion.

Our framework was created from the combination of different elements identified in traditional models (Schaeffer et al 2018; Conceição et al 2017; Avnimelech and Feldman 2015), models based on analysis of social networks (Diánez-González and Camelo-Ordaz 2019; Hayter 2015; Soetanto and Van Geenhuizen 2015; Lyu et al 2019; Minguillo and Thelwall 2015; Porter et al 2005) and alumni networks (Rubens et al 2011) to create informal links between universities and startups, and investigation of the influence of HEIs in these network structures.

Appendix E: HEI quality and the maturity of the ecosystem

This is the algorithm used for the task studying the HEI quality and the maturity of ecosystems:

• 1. For each startup, calculate the fundraising (k).

• 2. For each ecosystem, tick “Elite HEI” if there is at least one HEI with maximum IGC (IGC = 5).

• 3. Calculate startup fundraising CDFs and break them down into emerging ecosystems with or without elite HEIs, and mature ecosystems with or without elite HEIs.

• 4. Plot the comparative CDFs between these four possibilities: mature ecosystem (with or without elite HEI) and emerging ecosystem (with or without elite HEI).