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The relationship between local income inequality and subjective well-being in Latin America is analyzed. To do this, daytime satellite images and the average happiness level of a set of more than one thousand urban agglomerations in the region were studied. Objective patterns of visual heterogeneity are extracted from the images using deep neural networks, and an inequality indicator around the selected centroid is computed from them. This inequality indicator offers certain advantages over traditional objective indicators (granularity and periodicity) and subjective indicators (scalability, consistency, and accuracy). A statistically significant negative correlation is found between these objective inequality indicators and the subjective happiness measure. This relationship is particularly strong for images with an approximate radius of 10 kilometers, suggesting that this spatial scale more closely captures the local environments in which social com parisons are formed. Moreover, a negative association between inequality and subjective well-being is observed across the entire distribution: urban areas with higher levels of inequality tend to report, on average, lower levels of happiness.

Caravaggio, L. (2026). Local Income Inequality and Subjective Well-Being in Latin America. Revista Economía Del Rosario, 28(1), 1–47. https://doi.org/10.12804/revistas.urosario.edu.co/economia/a.16048

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

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