When Method Redefines the Problem: Causal Identification and the Limits of Development Analysis in Latin America

Authors

DOI:

https://doi.org/10.35319/lajed.202645625

Keywords:

Empirical Credibility, development Economics, causal Identification, structural Processes, Latin America

Abstract

This article examines how economic literature has addressed the structural problems of development in Latin America in the context of the consolidation of empirical evidence standards centered on internal validity and causal identification. Drawing on a systematic review, the study analyzes how these orientations have shaped the formulation of research problems, the delimitation of empirically tractable phenomena, and the treatment of issues such as result generalization and external validity. The findings indicate that, while these advances substantially increased the empirical rigor of applied research, they also fostered an analytical fragmentation of structural development processes, displacing integrated long-term explanations. The article discusses the implications of this shift and underscores the need for pluralist approaches that are sensitive to institutional context in the analysis of Latin American development.

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Author Biography

Victor Octavio, Universidade Estadual Paulista (Unesp)

Es graduado en Economía y cuenta con especializaciones en Finanzas y en Data Science, además de una maestría en Ingeniería, con énfasis en Ingeniería Económica, por la Universidade Estadual Paulista (UNESP). Posee experiencia en los sectores público y privado, con actuación en las áreas de economía, finanzas y ciencia de datos. En el ámbito académico, desarrolla investigaciones en estas áreas, con especial atención a América Latina.

References

Andrews, I., Gentzkow, M. y Shapiro, J. M. (2017). Measuring the sensitivity of parameter estimates to estimation moments. Quarterly Journal of Economics, 132(4), 1553-1592. https://doi.org/10.1093/qje/qjx0232 .

Angrist, J. D. y Pischke, J.-S. (2010). The credibility revolution in empirical economics: How better research design is taking the con out of econometrics. Journal of Economic Perspectives, 24(2), 3-30. https://doi.org/10.1257/jep.24.2.3

Angrist, J. D. y Pischke, J.-S. (2017). Undergraduate econometrics instruction: Through our classes, darkly. Journal of Economic Perspectives, 31(2), 125-144. https://doi.org/10.1257/jep.31.2.125

Athey, S. (2018). The impact of machine learning on economics. The Economics of Artificial Intelligence. University of Chicago Press. https://doi.org/10.7208/chicago/9780226613475.003.0021

Athey, S. e Imbens, G. W. (2017). The state of applied econometrics: Causality and policy evaluation. Journal of Economic Perspectives, 31(2), 3-32. https://doi.org/10.1257/jep.31.2.3

---------- (2019). Machine learning methods that economists should know about. Annual Review of Economics, 11, 685-725. https://doi.org/10.1146/annureveconomics-080217-053433

Backhouse, R. E. y Cherrier, B. (2017). The age of the applied economist. History of Political Economy, 49 (Supplement), 1-33. https://doi.org/10.1215/00182702-4166239

Banerjee, A. V. y Duflo, E. (2008). The Experimental Approach to Development Economics, NBER Working Paper N° 14467. https://doi.org/10.3386/w14467

Bold, T., Kimenyi, M., Mwabu, G., Ng’ang’a, A. y Sandefur, J. (2018). Experimental evidence on scaling up education reforms in Kenya. Journal of Public Economics, 168, 1-20. https://doi.org/10.1016/j.jpubeco.2018.08.007

Busso, M., Fazio, M. V. y Levy, S. (2012). (In)formal and (un)productive: the productivity costs of excessive informality in Mexico. Inter-American Development Bank. http://dx.doi.org/10.18235/0011401

Deaton, A. (2020). Randomization in the tropics revisited. En Bédécarrats, F., Guérin, I. y Roubaud, F. (Eds.), Randomized control trials in the field of development: a critical perspective. Oxford University Press. https://doi.org/10.1093/oso/9780198865360.003.0002

---------- (2024). Rethinking my economics. F y D Magazine https://www.imf.org/en/publications/fandd/issues/2024/03/symposium-rethinking-economics-angus-deaton

Deaton, A. y Cartwright, N. (2018). Understanding and misunderstanding randomized controlled trials. Social Science & Medicine, 210, 2-21. https://doi.org/10.1016/j.socscimed.2017.12.005

Ferraz, C. y Finan, F. (2008). Exposing corrupt politicians: the effects of Brazil’s publicly released audits on electoral outcomes. The Quarterly Journal of Economics, 123(2), 703- 745. https://doi.org/10.1162/qjec.2008.123.2.703

Finan, F., Olken, B. y Pande, R. (2017). The personnel economics of the developing state. Handbook of Economic Field Experiments, 2, 467-514. https://doi.org/10.1016/bs.hefe.2016.08.001

Gelman, A. e Imbens, G. (2013). Why ask why? Forward causal inference and reverse causal questions. NBER Working Paper N° 19614. https://doi.org/10.3386/w19614

Grant, M. J. y Booth, A. (2009). A typology of reviews: an analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26(2), 91-108. https://doi.org/10.1111/j.1471-1842.2009.00848.x

Heckman, J. J. (2010). Building bridges between structural and program evaluation approaches to evaluating policy. Journal of Economic Literature, 48(2), 356-398. https://doi.org/10.1257/jel.48.2.356

Heckman, J. J. y Pinto, R. (2015). Econometric mediation analyses: Identifying the sources of treatment effects from experimentally estimated production technologies with unmeasured and mismeasured inputs. Econometric Reviews, 34(1-2), 6-31. https://doi.org/10.1080/07474938.2014.944466

Imbens, G. W. (2020). Potential outcome and directed acyclic graph approaches to causality: Relevance for empirical practice in economics. Journal of Economic Literature, 58(4), 1129-1179. https://doi.org/10.1257/jel.20191597

Ioannidis, J. P. A., Stanley, T. D. y Doucouliagos, H. (2017). The power of bias in economics research. Economic Journal, 127(605), F236-F265. https://doi.org/10.1111/ecoj.12461

Leamer, E. E. (2010). Tantalus on the road to asymptopia. Journal of Economic Perspectives, 24(2), 31-46. https://doi.org/10.1257/jep.24.2.31

Levy, S. y Schady, N. (2013). Latin America’s social policy challenge: education, social insurance, redistribution. Journal of Economic Perspectives, 27(2), 193-218. https://doi.org/10.1257/jep.27.2.193

Mazzuca, S. (2021). Latecomer state formation: political geography and capacity failure in Latin America. EEUU: Yale University Press.

Mazzuca, S. L. y Munck, G. L. (2014). State or democracy first? Alternative perspectives on the state-democracy nexus. Democratization, 21, 1221-1243. https://doi.org/10.1080/13510347.2014.960209

Paré, G., Trudel, M.-C., Jaana, M. y Kitsiou, S. (2015). Synthesizing information systems knowledge: A typology of literature reviews. Information & Management, 52(2), 183-199. https://doi.org/10.1016/j.im.2014.08.008

Petticrew, M. y Roberts, H. (2006). Systematic reviews in the social sciences: A practical guide. Blackwell Publishing.

Piketty, T., Saez, E. y Zucman, G. (2018). Distributional national accounts: Methods and estimates for the United States. Quarterly Journal of Economics, 133(2), 553-609. https://doi.org/10.1093/qje/qjx043

Pritchett, L., Woolcock, M. y Andrews, M. (2013). Looking like a state: techniques of persistent failure in state capability for implementation. Journal of Development Studies, 49(1), 1-18. https://doi.org/10.1080/00220388.2012.709614

Ravallion, M. (2020). Should the randomistas (continue to) rule? In Bédécarrats, F., Guérin, I. y Roubaud, F. (Eds.), Randomized control trials in the field of development: A critical perspective. Oxford University Press. https://doi.org/10.1093/oso/9780198865360.003.0003

Rodrik, D. (2008). Second-best institutions. American Economic Review, 98(2), 100-104. https://doi.org/10.1257/aer.98.2.100

---------- (2014). When ideas trump interests: Preferences, worldviews, and policy innovations. Journal of Economic Perspectives, 28(1), 189-208. https://doi.org/10.1257/jep.28.1.189

Vivalt, E. (2020). How much can we generalize from impact evaluations? Journal of the European Economic Association, 18(6), 3045-3089. https://doi.org/10.1093/jeea/jvaa019

Cuando el método redefine el problema: identificación causal y límites del análisis del desarrollo en América Latina

Published

2026-05-15

How to Cite

Octavio, V. (2026). When Method Redefines the Problem: Causal Identification and the Limits of Development Analysis in Latin America. Latin American Journal of Economic Development, 24(45), 239–256. https://doi.org/10.35319/lajed.202645625