Skip to main content

Advertisement

SpringerLink
Log in

Positive Geographical Spillovers of Human Capital on Student Learning Outcomes

  • Published:
Applied Spatial Analysis and Policy Aims and scope Submit manuscript

Abstract

Human capital spillovers arise when the presence of individuals with high levels of human capital makes others more productive. If the students’ higher achievement scores are associated with human capital spillovers, a social return to education is generated. In this context, this paper developed the rationale and statistic methods for examining the association between regional human capital stocks and student learning outcomes. We used PISA data for Spain from the 2015 wave. On average, 15-year-old students scored 486 points in mathematics, but there were statistically significant differences across the 17 regions of Spain (comunidades autónomas). Three-level regression modeling showed that an extra year of region-level average schooling is associated with PISA math scores 14.5 points higher, after controlling for student and school level variables. We used mediation analysis to test whether educational expectations mediated this effect of regional human capital on student achievement. The mediation analysis showed that students who live in regions with a high prevalence of well-educated residents expect more years of education and perform better on the PISA test. This paper also found that the current expenditure per student in the different comunidades autónomas does not explain the regional differences in math performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. Public schools are financed by regional governments. Also, an important percentage of private schools are subsidized by the public sector in each region.

  2. The picture was similar in reading and science.

  3. In economics, externalities (or spillover effects) are uncompensated third-party effects resulting from the production and consumption of goods and services. Positive externalities are uncompensated external benefits.

  4. We will use in this paper many more variables (defined in Table 5 in Appendix).

  5. The causal chain, X → M → Y, is represented by the two coefficients a and b, and the product, ab, is known as the indirect effect of X on Y that passes through M (Falk and Biesanz 2016).

  6. Referenced by 94,310 papers in Google Scholar on August 23, 2020.

  7. Only a single plausible value of the mathematics literacy scores was finally included due to convergence problems in three-level modeling. We used pv2math as a dependent variable. But “using one plausible value or five plausible values does not really make a substantial difference on large samples”’ (OECD 2009, p. 46).

  8. The regional per capita income was not considered due to the high correlation with the average years of education.

  9. Unobserved heterogeneity is conceptualized as a vector of missing variables acting through the error term. One might worry that omitted variables influence math literacy in ways that could bias results.

  10. The unobserved heterogeneity is corrected if the omitted variable is included in the regression but not correlated with the independent variables already included, something that we verified in Table 6 in Appendix.

  11. This variable retained the original values given by students. It has been considered as a quantitative variable of additional ISCED levels of education pursued by students. Otherwise, it is not possible to run the mediation analysis.

  12. The statistical analysis was performed with the statistical software STATA 14. PISA sampling weights were used in multilevel modeling. Using no weights could lead to misleading conclusions (Laukaityte and Wiberg 2018).

  13. The intercept-only model (or null model) doesn’t include covariates other than a constant.

  14. The significance indicates that the analysis of three levels is justified from the statistical point of view.

  15. The decrease in the variance component from 374.48 (estimated in Model 1) to 275.98 (Model 2) means that the regional human capital explains more than a quarter of the between-region variation in students’ math performance.

  16. “Grade repetition is relatively uncommon in lower secondary general programmes and is below 5% in most countries. However, the share of repeaters exceeds 10% in Argentina, Costa Rica, Luxembourg and Spain” (OECD 2018, p. 152). This percentage was 28% in our sample (see Table 5 in the Appendix).

  17. On the contrary, father’s education, family wealth, and achievement motivation, all of them show positive and statistically significant estimated coefficients (Table 2). At the school level, the shortage of school materials harms individual school results (Table 2).

  18. This coefficient is lower than that obtained in Table 2. Partial mediation exists if the effect of the independent variable on the dependent variable is reduced, but still significant, when the mediators are controlled (Baron and Kenny 1986).

  19. The coefficients comprising the indirect effect are the effect of the independent variable X on the mediator M, a (0.0667), and the effect of the mediator M on the outcome Y, b (10.7784).

  20. The missing values in the mathematics items are due to the fact that students answer only a limited number of questions from the total test item pool. See OECD (2017b) for more details.

  21. The “repest” macro was developed by OECD researchers Avvisati and Keslair (2017).

  22. http://www.psych.mcgill.ca/perpg/fac/falk/tutorials/mediation/pcalc/

References

  • Aaronson, D. (1998). Using sibling data to estimate the impact of neighborhoods on children’s educational outcomes. Journal of Human Resources, 33(4), 915–946.

    Google Scholar 

  • Agasisti, T., & Cordero-Ferrera, J. M. (2013). Educational disparities across regions: a multilevel analysis for Italy and Spain. Journal of Policy Modeling, 35(6), 1079–1102.

    Google Scholar 

  • Ainsworth, J. W. (2002). Why does it take a village? The mediation of neighborhood effects on educational achievement. Social Forces, 81(1), 117–152.

    Google Scholar 

  • Ainsworth, J. W. (2010). Does the race of neighborhood role models matter? Collective socialization effects on educational achievement. Urban Education, 45(4), 401–423.

    Google Scholar 

  • Ainsworth-Darnell, J. W., & Downey, D. B. (1998). Assessing the oppositional culture explanation for racial/ethnic differences in school performance. American Sociological Review, 63, 536–553.

    Google Scholar 

  • Akerlof, G. A., & Kranton, R. E. (2000). Economics and identity. Quarterly Journal of Economics, 115(3), 715–753.

    Google Scholar 

  • Akerlof, G. A., & Kranton, R. E. (2002). Identity and schooling: some lessons for the economics of education. Journal of Economic Literature, 40(4), 1167–1201.

    Google Scholar 

  • Anderson, C. A. (1987). Social selection in education and economic development. World Bank.

  • Avvisati, F., & Keslair, F. (2017). REPEST: Stata module to run estimations with weighted replicate samples and plausible values. https://econpapers.repec.org/software/bocbocode/S457918.htm.

  • Bandura, A., Barbaranelli, C., Caprara, G. V., & Pastorelli, C. (1996). Multifaceted impact of self-efficacy beliefs on academic functioning. Child Development, 67(3), 1206–1222.

    Google Scholar 

  • Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.

    Google Scholar 

  • Bartholomae, S., Kiss, D. E., Jurgenson, J. B., O’Neill, B., Worthy, S. L., & Kim, J. (2019). Framing the human capital investment decision: examining gender bias in student loan borrowing. Journal of Family and Economic Issues, 40(1), 132–145.

    Google Scholar 

  • Becker, G. S. (1964). Human capital. New York: Columbia University Press.

    Google Scholar 

  • Biesanz, J. C., Falk, C. F., & Savalei, V. (2010). Assessing mediational models: testing and interval estimation for indirect effects. Multivariate Behavioral Research, 45, 661–701.

    Google Scholar 

  • Campbell, R. T. (1983). Status attainment research: end of the beginning or beginning of the end? Sociology of Education, 56, 47–62.

    Google Scholar 

  • Catsambis, S., & Beveridge, A. A. (2001). Does neighborhood matter? Family, neighborhood, and school influences on eighth-grade mathematics achievement. Sociological Focus, 34(4), 435–457.

    Google Scholar 

  • De la Fuente, A. (2011). Human capital and productivity. Nordic Economic Policy Review, 2(2), 103–132.

    Google Scholar 

  • Dietz, R. D. (2002). The estimation of neighborhood effects in the social sciences: an interdisciplinary approach. Social Science Research, 31(4), 539–575.

    Google Scholar 

  • Durlauf, S. N. (2004). Neighborhood effects. In Handbook of regional and urban economics (Vol. 4, pp. 2173-2242). Elsevier.

  • Falk, C. F., & Biesanz, J. C. (2016). Two cross-platform programs for inferences and interval estimation about indirect effects in mediational models. SAGE Open, 6(1), 1–13.

    Google Scholar 

  • Federal Reserve Board (2018). Report on the economic well-being of US households in 2017. Washington, DC: Federal Reserve Board of Governors. Retrieved from https://www.federalreserve.gov/publications/files/2018-report-economic-well-being-us-households-201905.pdf

  • Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge: Cambridge University Press.

    Google Scholar 

  • Greene, J. P., & Winters, M. A. (2007). Revisiting grade retention: An evaluation of Florida’s test-based promotion policy. Education Finance and Policy, 2(4), 319–340.

    Google Scholar 

  • Heckman, J. J. (2000). Policies to foster human capital. Research in Economics, 54(1), 3–56.

    Google Scholar 

  • Hox, J. J. (2010). Multilevel analysis: Techniques and applications (2nd ed.). New York: Routledge.

    Google Scholar 

  • Hox, J. J., & Maas, C. J. M. (2002). Sample sizes for multilevel modeling. In J. Blasius, J. J. Hox, E. de Leeuw, & P. Schmidt (Eds.), Social science methodology in the new millennium: Proceedings of the fifth international conference on logic and methodology (2nd ed.). Opladen, Germany: Leske + Budrich.

    Google Scholar 

  • Johnson, O. (2008). Ecology in educational theory: thoughts on stratification, social mobility & proximal capital. Urban Review, 40(3), 227–246.

    Google Scholar 

  • Johnson, O. (2012). A systematic review of neighborhood and institutional relationships related to education. Education and Urban Society, 44(4), 477–511.

    Google Scholar 

  • Johnson, C. L., Gutter, M., Xu, Y., Cho, S. H., & DeVaney, S. (2016). Perceived value of college as an investment in human and social capital: views of generations X and Y. Family and Consumer Sciences Research Journal, 45(2), 193–207.

    Google Scholar 

  • Laukaityte, I., & Wiberg, M. (2018). Importance of sampling weights in multilevel modeling of international large-scale assessment data. Communications in Statistics-Theory and Methods, 47(20), 4991–5012.

    Google Scholar 

  • Lei, L. (2018). The effect of neighborhood context on children’s academic achievement in China: exploring mediating mechanisms. Social Science Research, 72, 240–257.

    Google Scholar 

  • Lucas Jr., R. E. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22(1), 3–42.

    Google Scholar 

  • MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593–614.

    Google Scholar 

  • Mandell, L., & Klein, L. S. (2007). Motivation and financial literacy. Financial Services Review, 16(2), 105–116.

    Google Scholar 

  • Manski, C. F. (1993). Identification of endogenous social effects: the reflection problem. Review of Economic Studies, 60(3), 531–542.

    Google Scholar 

  • Manski, C. F. (2000). Economic analysis of social interactions. Journal of Economic Perspectives, 14(3), 115–136.

    Google Scholar 

  • Moretti, E. (2004a). Human capital externalities in cities. In J. V. Henderson, & J. F. Thisse (Eds.), Handbook of regional and urban economics (1st ed., Vol. 4, pp. 2243–2291). Amsterdam: Elsevier.

  • Moretti, E. (2004b). Estimating the social return to higher education: evidence from longitudinal and repeated cross-sectional data. Journal of Econometrics, 121(1–2), 175–212.

    Google Scholar 

  • Morris, T., Dorling, D., & Davey Smith, G. (2016). How well can we predict educational outcomes? Examining the roles of cognitive ability and social position in educational attainment. Contemporary Social Science, 11(2–3), 154–168.

    Google Scholar 

  • Morrison, K., & On No, A. L. (2007). Does repeating a year improve performance? The case of teaching English. Educational Studies, 33(3), 353–371.

    Google Scholar 

  • Nieuwenhuis, J., & Hooimeijer, P. (2016). The association between neighbourhoods and educational achievement, a systematic review and meta-analysis. Journal of Housing and the Built Environment, 31(2), 321–347.

    Google Scholar 

  • OECD. (1998). Human capital investment: An international comparison. Paris: OECD, Centre for Educational Research and Innovation.

    Google Scholar 

  • OECD. (2009). PISA data analysis manual: SPSS (2nd ed.). OECD Publishing.

  • OECD (2015). PISA in focus. Does math make you anxious? – 2015/02 (February).

  • OECD. (2016). PISA 2015 results (volume I): Excellence and equity in education. Paris: OECD Publishing.

    Google Scholar 

  • OECD. (2017a). PISA 2015 results (volume IV): Students' financial literacy. Paris: OECD Publishing.

    Google Scholar 

  • OECD. (2017b). PISA 2015 technical report. Paris: OECD Publishing.

    Google Scholar 

  • OECD. (2018). Education at a glance 2018: OECD indicators. Paris: OECD Publishing.

  • OECD. (2019). PISA 2018: Insights and interpretations. Paris: OECD Publishing.

  • Oliver-Rullán, X., & Rosselló-Villalonga, J. (2019). Does the economic productive structure determine early school leaving rates? Evidence from Spanish regions, 2000-2013. Estudios sobre Educación, 36, 181–205.

    Google Scholar 

  • Psacharopoulos, G. (1981). Returns to education: an updated international comparison. Comparative Education, 17(3), 321–341.

    Google Scholar 

  • Psacharopoulos, G. (1985). Returns to education: a further international update and implications. Journal of Human Resources, 20(4), 583–604.

    Google Scholar 

  • Psacharopoulos, G. (1994). Returns to investment in education: a global update. World Development, 22(9), 1325–1343.

    Google Scholar 

  • Psacharopoulos, G., & Patrinos, H. A. (2004). Returns to investment in education: a further update. Education Economics, 12(2), 111–134.

    Google Scholar 

  • Rauch, J. E. (1993). Productivity gains from geographic concentration of human capital: evidence from the cities. Journal of Urban Economics, 34, 380–400.

    Google Scholar 

  • Ricci, F., & Zachariadis, M. (2013). Education externalities on longevity. Economica, 80(319), 404–440.

    Google Scholar 

  • Romer, P. M. (1986). Increasing returns and long-run growth. Journal of Political Economy, 94(5), 1002–1037.

    Google Scholar 

  • Samuelson, P. A., & Nordhaus, W. D. (2010). Economics (19th ed.). Boston: McGraw-Hill.

  • Schultz, T. W. (1961). Investment in human capital. American Economic Review, 51(1), 1–17.

    Google Scholar 

  • Sharkey, P., & Elwert, F. (2011). The legacy of disadvantage: multigenerational neighborhood effects on cognitive ability. American Journal of Sociology, 116(6), 1934–1981.

    Google Scholar 

  • Smith, T. E. (1991). Agreement of adolescent educational expectations with perceived maternal and paternal educational goals. Youth & Society, 23, 155–174.

    Google Scholar 

  • Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290–312.

    Google Scholar 

  • Trusty, J. (1998). Family influences on educational expectations of late adolescents. Journal of Educational Research, 91, 260–270.

    Google Scholar 

  • Wilson, W. J. (1996). When work disappears: The world of the new urban poor. New York: Alfred A. Knopf.

    Google Scholar 

  • Wilson, R. A., & Briscoe, G. (2004). The impact of human capital on economic growth: a review. Impact of education and training. Third report on vocational training research in Europe: background report. Luxembourg: EUR-OP.

  • Wilson, P. M., & Wilson, J. R. (1992). Environmental influences on adolescent educational aspirations. Youth & Society, 24, 52–70.

    Google Scholar 

  • Winters, J. V. (2013). Human capital externalities and employment differences across metropolitan areas of the USA. Journal of Economic Geography, 13(5), 799–822.

    Google Scholar 

  • Wodtke, G. T., & Parbst, M. (2017). Neighborhoods, schools, and academic achievement: a formal mediation analysis of contextual effects on reading and mathematics abilities. Demography, 54(5), 1653–1676.

    Google Scholar 

  • Wu, C. L., & Bai, H. (2015). From early aspirations to actual attainment: the effects of economic status and educational expectations on university pursuit. Higher Education, 69(3), 331–344.

    Google Scholar 

  • Zhan, C. (2017). Institutions, social norms, and educational attainment. Education Economics, 25(1), 22–44.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Applied Economics, University of Granada Business School, Campus Cartuja, 18071, Granada, Spain

    Manuel Salas-Velasco, Dolores Moreno-Herrero & José Sánchez-Campillo

Authors
  1. Manuel Salas-Velasco
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dolores Moreno-Herrero
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. José Sánchez-Campillo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manuel Salas-Velasco.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Statistical Procedures and Data at the Regional Level

PISA 2015 collected data in representative samples of 15-year-old students across the 17 Spanish regions (Table 4, column [6]). As we said, PISA measures the mathematical literacy of a 15-year-old to formulate, employ and interpret mathematics in a variety of contexts to describe, predict and explain phenomena, recognizing the role that mathematics plays in the world. A mathematically literate student acknowledges the role that mathematics plays in the world to make well-founded judgments and decisions needed by constructive, engaged, and reflective citizens (OECD 2016). As with all item response scaling models, student proficiencies were not observed. They were missing data that had to be inferred from the observed item responses.Footnote 20 There are several possible alternative approaches for making this inference. Students’ scores were calculated using an imputation method referred to as plausible values (PVs), which are a selection of likely proficiencies for students who attained each score. For each scale and subscale, ten plausible values per student were included in the database. Using these ten plausible values on math literacy, mean values by region along with standard errors, the value of z, and p values were obtained with the “repest” Stata module (estimation with weighted replicate samples and plausible values).Footnote 21 See Table 4, columns [2], [3], [4], and [5], respectively.

Table 4 Mathematics performance in PISA 2015, average years of education, and spending per student in the different regions of Spain
Full size table

In the comparison of Spain average to regional averages, tests of statistical significance were used to establish whether the observed differences from the Spain average were statistically significant. The tests for significance used were standard t-tests. In PISA, education system groups are independent. We judge that a difference is “significant” if the probability associated with the t-test is less than 0.05. If a test is significant, this implies that the difference in the observed means in the sample represents a real difference in the population. In simple comparisons of independent averages, such as the average score of education system 1 with that of education system 2, the following formula was used to compute the t-statistic

$$ t=\frac{\left({est}_1-{est}_2\right)}{\sqrt{se_1^2+{se}_2^2}} $$

where est1 and est2 are the estimates being compared (e.g., averages of education system 1 and education system 2) and se12 and se22 are the corresponding squared standard errors of these averages. The significance tests were shown in columns [7] and [8] in Table 4.

Variable Description

Table 5 Explanation of variables and descriptive statistics
Full size table

Correlation Matrix

Table 6 Correlation matrix between the independent variables
Full size table

Testing the Indirect Effect

Mediation can be assessed by testing the significance of the indirect or mediated effect computed as the product of the regression coefficient estimates a and b (i.e., a x b) (MacKinnon et al. 2007). Traditionally, the mediation has been tested by using the Sobel’s test (1982) to examine the reduction of the effect of the independent variable on the dependent variable, after accounting for the mediating variables. Since it is no longer recommended due to low power (Falk and Biesanz 2016), modern approaches have been proposed. For example, the partial posterior approach (Biesanz et al. 2010) provides a p value for the indirect effect a x b interpretable in the same way as the p value from Sobel’s test (1982). Falk and Biesanz (2016) provide an easy-to-use calculator for the partial posterior method. Using this software,Footnote 22 we got a p value = 0.040; thus, the indirect or mediated effect (i.e., a x b = 0.7191) was significant at the 5% level.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Salas-Velasco, M., Moreno-Herrero, D. & Sánchez-Campillo, J. Positive Geographical Spillovers of Human Capital on Student Learning Outcomes. Appl. Spatial Analysis 14, 415–443 (2021). https://doi.org/10.1007/s12061-020-09366-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12061-020-09366-z

Keywords

JEL Classification

Search

Navigation