The Brazilian government has adopted measures that aim to influence students’ spatial mobility. The extent and success of such measures require detailed knowledge of the mobility determinants. Gravity models are the appropriate tool for analyzing the flows of college students from their place of origin to their destination. To analyze the determinants of student flows, we estimate a negative binomial regression model with Brazilian data. The results show the deterrence effect of distance on mobility, as the total costs of entering a university increase with the distance between the place of origin and the destination institution. Places with lower living costs and smaller university centers (campuses) are attraction factors to students, as are the possibility of having non-reimbursable financing and a larger number of study programs.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
As of 2017, the IBGE (Brazilian Institute of Geography and Statistics) replaces the term mesoregion with an intermediate geographic region.
Foreign students were excluded from the sample, as the Census does not provide the place of origin of non-Brazilian residents.
For more details, see Barbosa (2020).
Abramo, G., Cicero, T., & D’angelo, C. A. (2011). The dangers of performance-based research funding in non-competitive higher education systems. Scientometrics, 87(3), 641–654.
Agasisti, T., & dal Bianco, A. (2007). Determinants of college student migration in italy: empirical evidence from a gravity approach. Paris: Congress of the European Regional Science Association.
Alm, J., & Winters, J. V. (2009). Distance and intrastate college student migration. Economics of Education Review, 28(6), 728–738.
Antonucci, D., & Manzocchi, S. (2006). Does Turkey have a special trade relation with the EU? A Gravity Model Approach. Economic Systems, 30(2), 157–169.
Bacci, S., & Bertaccini, B. (2020). Assessment of the university reputation through the analysis of the student mobility. Social Indicators Research, 156, 1–26.
Barbosa, M. L. O. (2020). A expansão desigual do ensino superior no Brasil. Curitiba: Appris.
Barbosa, M. E., & Pôssas, I. B. (2017). O Enem frente as diferenças socioespaciais: Uma análise comparativa de fatores espaciais com o desempenho médio do Enem (2006–2014). Revista Interface, 14, 38–52.
Barufi, A. M. B. (2012). Impactos do Crescimento de Vagas em Cursos Universitários sobre a Migração de Estudantes: Uma Análise Preliminar com o Censo Demográfico de 2010. TD Nereus, 13, 1–20.
Becker, G. S. (1962). Investment in human capital: A theoretical analysis. Journal of Political Economy, 70(5, Part 2), 9–49.
Becker, G. S. (1993). Human Capital: A theoretical and empirical analysis, with special reference to education (3rd ed.). Chicago.
Beine, M., Noël, R., & Ragot, L. (2014). Determinants of the international mobility of students. Economics of Education Review, 41, 40–54.
Biagi, B., Faggian, A., & Mccann, P. (2011). Long and short distance migration in Italy: The role of economic, social and environmental characteristics. Spatial Economic Analysis, 6(1), 111–131.
Cameron, A. C., & Trivedi, P. K. (2010). Microeconometrics using Stata: revised edition. Stata Press.
Cattaneo, M., Malighetti, P., Meoli, M., & Paleari, S. (2016). Regional Studies, 51(5), 750–764.
Chaves, V. L. J., & Amaral, N. C. (2016). Política de expansão da educação superior no Brasil-o PROUNI e o FIES como financiadores do setor privado. Educação Em Revista, 32(4), 49–72.
Ciriaci, D. (2014). Does university quality influence the interregional mobility of students and graduates? The Case of Italy. Regional Studies, 48(10), 1592–1608.
Columbu, S., Porcu, M., Primerano, I., Sulis, I., & Vitale, M. P. (2021). Geography of Italian student mobility: a network analysis approach. Socio-Economic Planning Sciences, 73, 10100918.
Cullinan, J., & Duggan, J. (2016). A school-level gravity model of student migration flows to higher education institutions. Spatial Economic Analysis, 11(3), 294–314.
Dotti, N. F., Fratesi, U., Lenzi, C., & Percoco, M. (2013). Local labor markets and the interregional mobility of Italian university students. Spatial Economic Analysis, 8(4), 443–468.
Dotzel, K. R. (2017). Do natural amenities influence undergraduate student migration decisions? The Annals of Regional Science, 59(3), 677–705.
Faggian, A., & Franklin, R. S. (2014). Human capital redistribution in the USA: The migration of the college-bound. Spatial Economic Analysis, 9(4), 376–395.
Faggian, A., & Royuela, V. (2010). Migration flows and quality of life in a metropolitan area: The case of Barcelona-Spain. Applied Research in Quality of Life, 5(3), 241–259.
Faggian, A., Mccann, P., & Sheppard, S. (2007). Human capital, higher education and graduate migration: An analysis of Scottish and Welsh students. Urban Studies, 44(13), 2511–2528.
Frenette, M. (2004). Access to college and university: Does distance to school matter? Canadian Public Policy/analyse De Politiques, 30(4), 427–443.
Fusco, W. (2005). Capital cordial: a reciprocidade entre os imigrantes brasileiros nos Estados Unidos. Campinas: Tese (Doutorado) – Instituto de Filosofia e Ciências Humanas, Universidade Estadual de Campinas.
Gibbons, S., & Vignoles, A. (2012). Geography, choice and participation in higher education in England. Regional Science and Urban Economics, 42(1–2), 98–113.
Graves, P. E. (1980). Migration and climate. Journal of Regional Science, 20(2), 227–237.
Greenwood, M. J., & Hunt, G. L. (1984). Migration and interregional employment redistribution in the United States. The American Economic Review, 74(5), 957–969.
Haas, H. (2010). Migration and development: A theoretical perspective. International Migration Review, 44(1), 227–264.
Haynes, K. E., & Fotheringham, A. S. (1985) Gravity and Spatial Interaction Models. Reprint. Edited by Grant Ian Thrall. WVU Research Repository 2020.
Imeraj, L., Willaert, D., Finney, N., & Gadeyne, S. (2018). Attraction and retention of graduates: A more-than-economic approach. Regional Studies, 52(8), 1086–1097.
Instituto Nacional De Pesquisas E Estudos Educacionais Anísio Teixeira (INEP). Censo da Educação Superior 2017. Brasília, 2019. Available at: < http://portal.inep.gov.br/web/guest/microdados>. Acesso em: 27 Nov. 2019.
Instituto Brasileiro De Geografia E Estatística. (1990). Divisão Regional do Brasil em Mesorregiões e Microrregiões Geográficas. Rio de Janeiro.
Kerstenetzky, C. L. (2006). Políticas Sociais: Focalização ou universalização. Revista De Economia Política, 26(4), 564–574.
Li, L. D., & Chagas, A. L. S. (2017). Efeitos do SISU sobre a migração e a evasão estudantil. In XV Encontro Nacional da Associação Brasileira de Estudos Regionais e Urbanos. Anais... São Paulo: Aber.
Liu, Y., Shen, J., Xu, W., & Wang, G. (2017). From school to university to work: Migration of highly educated youths in China. Annals of Regional Science, 59(3), 651–676.
Lopes, A. D. (2017). Affirmative action in Brazil: How students’ field of study choice reproduces social inequalities. Studies in Higher Education, 42(12), 2343–2359.
Machado, C., & Szerman, C. (2015) .The Effects of a Centralized College Admission Mechanism on Migration and College Enrollment: Evidence from Brazil. In 37th Meeting of the Brazilian Econometric Society. Anais... Florianópolis: SBE Meetings.
Massey, D. S. (1990). Social Structure, Household Strategies, and the Cumulative Causation of Migration. Population Index, 56(1), 3–26.
Mccowan, T. (2005). O crescimento da educação superior privada no Brasil: Implicações para as questões de eqüidade, qualidade e benefício público. Education Policy Analysis Archives, 13, 1–20.
Mello Neto, R. D., Medeiros, H. A. V., Paiva, F. S., & Simões, J. L. (2014). O impacto do Enem nas políticas de democratização do acesso ao Ensino Superior Brasileiro. Comunicações, 21(3), 109–123.
Meneghel, S. M. (2018). Considerações sobre o atual sistema de ensino superior no brasil. Pesquisa e Debate Em Educação, 7(1), 340–348.
Miranda, P. R., & Azevedo, M. L. N. (2020). Fies e Prouni na expansão da educação superior brasileira: Políticas de democratização do acesso e/ou de promoção do setor privado-mercantil? Educação & Formação, 5(3), 1–19.
Montmarquette, C., Cannings, K., & Mahseredjian, S. (2002). How do young people choose college majors? Economics of Education Review, 21(6), 543–556.
Parey, M., & Waldinger, F. (2010). Studying abroad and the effect on international labour market mobility: Evidence from the introduction of ERASMUS. The Economic Journal, 121(551), 194–222.
Raab, J., Knoben, J., Aufurth, L., & Kaashoek, B. (2018). Going the distance: The effects of university–secondary school collaboration on student migration. Papers in Regional Science, 97(4), 1131–1149.
Rietveld, P., Zwart, B., van Wee, B., & van den Hoorn, T. (1999). On the relationship between travel time and travel distance of commuters: Reported versus network travel data in the Netherlands. The Annals of Regional Science, 33(3), 269–287.
Rossi, F. (2010). Massification, competition and organizational diversity in higher education: Evidence from Italy. Studies in Higher Education, 35(3), 277–300.
Sá, C., Florax, R. J. G. M., & Rietveld, P. (2004). Determinants of the regional demand for higher education in the Netherlands: A Gravity Model Approach. Regional Studies, 34(4), 375–392.
Seeber, M., Lepori, B., Agasisti, T., Tijssen, R., Montanari, C., & Catalano, G. (2012). Relational arenas in a regional Higher Education system: Insights from an empirical analysis. Research Evaluation, 21(4), 291–305.
Sen, A., & Smith, T. E. (2012). Gravity models of spatial interaction behavior. Springer.
Schultz, T. W. (1961). Investment in human capital. American Economic Review, 51(1), 1–17.
Schultz, T. W. (1973). O capital humano: Investimentos em educação e pesquisa. Zahar Editores.
Silveira, F. L., Barbosa, M. C. B., & Silva, R. (2015). Exame Nacional do Ensino Médio (Enem): Uma análise crítica. Revista Brasileira De Ensino De Física, 37(1), 1101.
Singleton, A. D., Wilson, A. G., & O’brien, O. (2012). Geodemographics and spatial interaction: An integrated model for higher education. Journal of Geographical Systems, 14(2), 223–241.
Sjaastad, L. A. (1962). The costs and returns of human migration. Journal of Political Economy, 70, (5, Part 2), 80–93.
Soares, R. S., & Lobo, C. (2017). Centralidades municipais e regionais na oferta do ensino superior no Brasil. Cadernos Do Leste, 17(17), 107–118.
Spiess, C. K., & Wrohlich, K. (2010). Does distance determine who attends a university in Germany? Economics of Education Review, 29(3), 470–479.
Suhonen, T. (2014). Field-of-study choice in higher education: Does distance matter? Spatial Economic Analysis, 9(4), 355–375.
Terribili Filho, A., & Nery, A. C. B. (2009). Ensino superior noturno no Brasil: História, atores e políticas. Revista Brasileira De Política e Administração Da Educação, 25(1), 61–81.
Todaro, M. P. (1969). A migração da mão-de-obra e o desemprego urbano em países subdesenvolvidos. In H. A. Moura (Ed.), Migração interna: textos selecionados, Fortaleza (p. 145–172, 722).
Tuckman, H. P. (1970). Determinants of college student migration. Southern Economic Journal, 37(2), 184–189.
Türk, U. (2019). Socio-economic determinants of student mobility and inequality of access to higher education in Italy. Networks and Spatial Economics, 8, 1–24.
van Bouwel, L., & Veugelers, R. (2013). The determinants of student mobility in europe: The quality dimension. European Journal of Higher Education, 3(2), 172–190.
Wang, P., Cockburn, M. L., & Le, N. I. (1996). Mixed poisson regression models with covariate dependent rates. Biometrics, 52, 381–400.
Widiputera, F., de Witte, K., & van den Brink, H. M. (2017). The attractiveness of programmes in higher education: An empirical approach. European Journal of Higher Education, 7(2), 153–172.
This paper is financed by National Funds of the FCT – Portuguese Foundation for Science and Technology within the project «UIDB/03182/2020.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
According to Cameron and Trivedi (2010), the countfit command implemented in Stata 14.0 allows for a comparison of the estimates’ adequacy obtained for Poisson (PRM), negative binomial (NBR), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) models. Furthermore, this command compares the four models using the BIC and AIC criteria and the Vuong test. The command guides the choice of the preferred model and provides evidence that supports the appropriate choice. Table 6 summarizes the implemented tests that support the use of the negative binomial model over the other models.
About this article
Cite this article
Pelegrini, T., Sá, C. & França, M.T.A. Factors associated with the mobility of college students in Brazil: an analysis using a gravity model. High Educ 85, 203–223 (2023). https://doi.org/10.1007/s10734-022-00829-5