Desigualdade de renda local e bem-estar subjetivo na América Latina

DOI:  https://doi.org/10.12804/revistas.urosario.edu.co/economia/a.16048

Barra lateral de artigos

PDF (Español (España))

Publicado: jul 16, 2026
Edição: v. 28 n. 1 (2025):
Seção Artigos


Palavras-chave: felicidade bem-estar desigualdade coesão social

Conteúdo do artigo principal

Autores

  Leonardo Caravaggio
Universidad de Buenos Aires

Resumo

Analisa-se a relação entre a desigualdade de renda local e o bem-estar subjetivo, na América Latina. Para isso, foram estudadas imagens de satélite diurnas, e o valor médio de felicidade de um conjunto de mais de mil aglomerações urbanas na região. A partir das imagens, extraem-se padrões objetivos de heterogeneidade visual, utilizando redes neurais profundas, a partir dos quais se calcula um indicador de desigualdade em torno do centroide selecionado. Esse indicador de desigualdade apresenta certas vantagens em relação aos indicadores tradicionais objetivos (granularidade e periodicidade) e subjetivos (escalabilidade, consistência e precisão). Observa-se uma correlação negativa estatisti camente significativa entre esses indicadores objetivos de desigualdade e o indicador subjetivo de felicidade. Essa relação é particularmente acentuada para as imagens com um raio aproximado de 10 quilômetros, o que sugere que essa escala espacial capta de forma mais precisa os ambientes locais nos quais se formam as comparações sociais. Da mesma forma, observa-se uma associação negativa entre a desigualdade e o bem-estar subjetivo ao longo de toda a distribuição: os aglomerados com maiores níveis de desigual dade tendem a apresentar, em média, níveis mais baixos de felicidade.

Como Citar
Caravaggio, L. (2026). Desigualdade de renda local e bem-estar subjetivo na América Latina. Revista De Economía Del Rosario, 28(1), 1–47. https://doi.org/10.12804/revistas.urosario.edu.co/economia/a.16048
Referências

Abbate, N. F., Gasparini, L., Ronchetti, F., & Quiroga, F. (2024, noviembre). High resolution income estimates using satellite imagery: A deep learning approach applied in Buenos Aires (Working Paper n.° 4701). Asociación Argentina de Economía Política. https://doi.org/10.1109/CLEI64178.2024.10700489

Alesina, A., Di Tella, R., & MacCulloch, R. (2004). Inequality and happiness: Are Europeans and Americans different? Journal of Public Economics, 88(9-10), 2009–2042. https://doi.org/10.1016/j.jpubeco.2003.07.006

Amiel, Y. (1998). The subjective approach to the measurement of income inequality. En J. Silber (Ed.), Handbook of income inequality measurement (pp. 227–241). Kluwer Academic Publishers. https://doi.org/10.1007/978 94-011-4413-1_8

Ayush, K., Uzkent, B., Burke, M., Lobell, D., & Ermon, S. (2020). Generating interpretable poverty maps using object detection in satellite images. arXiv. https://doi.org/10.24963/ijcai.2020/608

Bruni, L., & Porta, P. L. (2005). Economics and happiness: Framing the analysis. Oxford University Press. https://doi.org/10.1093/0199286280.001.0001

Bruni, L., & Stanca, L. (2008). Watching alone: Relational goods, television and happiness. Journal of Economic Behavior & Organization, 65(3), 506–528. https://doi.org/10.1016/j.jebo.2005.12.005

Cacioppo, J. T., & Patrick, W. (2008). Loneliness: Human nature and the need for social connection. W. W. Norton & Company. Ciaschi, M. (2021). Análisis distributivo utilizando información satelital: el caso de Argentina. Estudios Económicos, 38(77), 5–38. https://doi. org/10.52292/j.estudecon.2021.2116

Cowell, F. A. (2011). Measuring inequality (3.a ed.). Oxford University Press. https://doi.org/10.1093/acprof:osobl/9780199594030.001.0001

Easterlin, R. A. (1974). Does economic growth improve the human lot? Some empirical evidence. En P. A. David & M. W. Reder (Eds.), Nations and hou seholds in economic Growth: Essays in honor of Moses Abramovitz (pp. 89–125). Academic Press. https://doi.org/10.1016/B978-0-12-205050-3.50008-7

Ebert, U., & Welsch, H. (2009, septiembre). How do Europeans evaluate income distributions? An assessment based on happiness surveys. Review of Income and Wealth, 55(4), 803–819. https://doi.org/10.1111/ j.1475-4991.2009.00347.x

Estudio Latinobarómetro. (2024). Corporación Latinobarómetro: Oleada 2024. https://www.latinobarometro.org/latinobarometro-2024 Fields, G. S. (1994). Data for measuring poverty and inequality changes in the developing countries. Journal of Development Economics, 44(1), 87–102. https://doi.org/10.1016/0304-3878(94)00007-7

Frey, B. S., Benesch, C., & Stutzer, A. (2007). Does watching tv make us happy? Journal of Economic Psychology, 28(3), 283–313. https://doi.org/10.1016/j.joep.2007.02.001

Galbraith, J. K. (1998). The affluent society. Mariner Books. Grinblatt, M., Keloharju, M., & Ikäheimo, S. (2008). Social influence and consumption: Evidence from the automobile purchases of neighbors. The Review of Economics and Statistics, 90(4), 735–753. https://doi.org/10.1162/rest.90.4.735

Haidt, J. (2024). The anxious generation: How the great rewiring of childhood cau sed an epidemic of mental illness. Penguin Press. https://doi.org/10.56315/ PSCF9-25

Haidt Hall, O., Ohlsson, M., & Rögnvaldsson, T. (2022). A review of explainable AI in the satellite data, deep machine learning, and human poverty domain. Patterns, 3(10), artículo 100600. https://doi.org/10.1016/j.pat ter.2022.100600

He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep residual learning for image recognition. arXiv. https://arxiv.org/abs/1512.03385

Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., Le, Q. V., & Adam, H. (2019). Searching for MobileNetV3. arXiv. https://doi.org/10.48550/arXiv.1905.02244

Jean, N., Burke, M., Xie, M., Davis, W., Lobell, D., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790–794. https://doi.org/10.1126/science.aaf7894

Kuhn, P. J., Kooreman, P., Soetevent, A. R., & Kapteyn, A. (2010). The effects of lottery prizes on winners and their neighbors: Evidence from the dutch postcode lottery (iza Discussion Paper n.° 4950). Institute of Labor Econo mics (iza). https://doi.org/10.2139/ssrn.1631085

Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin Transformer: Hierarchical vision transformer using shifted windows. arXiv. https://doi.org/10.48550/arXiv.2103.14030

Luttmer, E. F. P. (2005, agosto). Neighbors as negatives: Relative earnings and well-being. The Quarterly Journal of Economics, 120(3), 963–1002. https://doi.org/10.1093/qje/120.3.963

Marmot, M. (2017). The health gap: The challenge of an unequal world. Bloomsbury Publishing. https://doi.org/10.1093/ije/dyx163

Meyer, B. D., Mok, W. K. C., & Sullivan, J. X. (2015, november). Household surveys in crisis. Journal of Economic Perspectives, 29(4), 199–226. https://doi.org/10.1257/jep.29.4.199

Mirza, M. U., Xu, C., van Bavel, B., van Nes, E. H., & Scheffer, M. (2021). Global inequality remotely sensed. Proceedings of the National Aca demy of Sciences, 118(18), artículo e1919913118. https://doi.org/10.1073/pnas.1919913118

Murthy, V. H. (2020). Together: Why social connection holds the key to better health, higher performance, and greater happiness. Harper Wave. Muñetón-Santa, G., & Manrique-Ruiz, L. C. (2023). Predicting multidimen sional poverty with machine learning algorithms: An open data source approach using spatial data. Social Sciences, 12(5), artículo 296. https:// doi.org/10.3390/socsci12050296

Naik, N., Kominers, S. D., Raskar, R., Glaeser, E., & Hidalgo, C. A. (2017). Computer vision uncovers predictors of physical urban change. Proceed ings of the National Academy of Sciences of the United States of America, 114(29), 7571–7576. https://doi.org/10.1073/pnas.1619003114

Norton, M. I., & Ariely, D. (2011, febrero). Building a better America—one wealth quintile at a time. Perspectives on Psychological Science, 6(1), 9–12. https://doi.org/10.1177/1745691610393524

Perez-Truglia, R. (2020, april). The effects of income transparency on well being: Evidence from a natural experiment. American Economic Review, 110(4), 1019–1054. https://doi.org/10.1257/aer.20160256

Pradhan, N., & Agrawal, A. (2025, febrero). Mapping fine-scale socioeco nomic inequality using machine learning and remotely sensed data. pnas Nexus, 4(2), artículo pgaf040. https://doi.org/10.1093/pnasnexus/pgaf040

Putnam, R. D. (2000). Bowling alone: The collapse and revival of american com munity. Simon & Schuster. https://doi.org/10.1145/358916.361990

Reynal-Querol, M., & García-Montalvo, J. (2021, mayo). Measuring inequality from above (Working Papers n.° 1252). Barcelona School of Economics. https://ideas.repec.org/p/bge/wpaper/1252.html

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., & Fei-Fei, L. (2015). ImageNet large scale visual recognition challenge. Internatio nal Journal of Computer Vision, 115(3), 211–252. https://doi.org/10.1007/s11263-015-0816-y

Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. En Proceedings of the ieee/cvf Conference on Computer Vision and Pattern Recognition (pp. 4510–4520). ieee. https://doi.org/10.1109/CVPR.2018.00474

Sapolsky, R. M. (2017). Behave: The biology of humans at our best and worst. Penguin Press. Savage, M. (2021). The return of inequality. Social change and the weight of the past. Harvard University Press. https://doi.org/10.4159/9780674259652

Shanahan, D. F., Lin, B. B., Gaston, K. J., Bush, R., & Fuller, R. A. (2014). Socio economic inequalities in access to nature on public and private lands: A case study from Brisbane, Australia. Landscape and Urban Planning, 130, 14–23. https://doi.org/10.1016/j.landurbplan.2014.06.005

Sommet, N., Fillon, A., Rudmann, O., Rossi, A., & Ehsan, A. (2025, noviembre). No meta-analytical effect of economic inequality on well-being or mental health. Nature, 649, 926–937. https://doi.org/10.1038/s41586 025-09797-z

Stiglitz, J. E. (2012). El precio de la desigualdad: el 1 % de la población tiene lo que el 99 % necesita. Taurus. Suel, E., Muller, E., Bennett, J. E., Blakely, T., Doyle, Y., Lynch, J., Fecht, D., Elliott, P., & Ezzati, M. (2023, junio). Do poverty and wealth look the same the world over? A comparative study of 12 cities from five high income countries using street images. EPJ Data Science, 12, artículo 19. https://doi.org/10.1140/epjds/s13688-023-00394-6

Uhlaner, C. J. (1989). Relational goods and participation: Incorporating sociability into a theory of rational action. Public Choice, 62(3), 253–285. http://www.jstor.org/stable/30025077

Wilkinson, R., & Pickett, K. (2009). Desigualdad: Un análisis de la (in)felicidad colectiva (R. Sanahuja, Trad.). Turner. (Obra original publicada en 2009). Wilkinson, R., & Pickett, K. (2019). The inner level: How more equal societies reduce stress, restore sanity and improve everyone’s well-being. Penguin Books.

Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M., & Luo, P. (2021). SegFormers: Simple and efficient design for semantic segmentation with transformers. arXiv. https://arxiv.org/abs/2105.15203

Estatísticas de consulta

Downloads

Não há dados estatísticos.
Métricas de publicação

0




 

Creative Commons License

Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial 4.0 International License.

Los autores conservan los derechos de autor y garantizan a la revista el derecho de ser la primera publicación del trabajo al igual que licenciado bajo una Creative Commons Attribution License que permite a otros compartir el trabajo con un reconocimiento de la autoría del trabajo y la publicación inicial en esta revista.