Métodos de evaluación de políticas públicas
-
1
Universidad Complutense de Madrid
info
ISSN: 0210-9107
Year of publication: 2022
Issue: 172
Pages: 18-29
Type: Article
More publications in: Papeles de economía española
Abstract
Public policies are implemented with the intention of achieving certain goals. The effects of such policies can be measured using various impact evaluation techniques, providing crucial information regarding the extent to which a policy has achieved such goals. In this paper, we review the main methods that are most frequently applied in academic research to evaluate the impact of public policies
Bibliographic References
- Abadie, A. (2021). Using synthetic controls: Feasibility, data requirements, and methodological aspects. Journal of Economic Literature, 59(2), pp. 391-425.
- Abadie, A. y Cattaneo, M. D. (2018). Econometric methods for program evaluation. Annual Review of Economics, 10, pp. 465-503.
- Albert, R., Escot, L. y Fernández-Cornejo, J. A. (2011). A field experiment to study sex and age discrimination in the Madrid labour market. The International Journal of Human Resource Management, 22(2), pp. 351-375.
- Angrist, J. D. y Keueger, A. B. (1991). Does compulsory school attendance affect schooling and earnings? The Quarterly Journal of Economics, 106(4), pp. 979-1014.
- Artés, J. (2014). The rain in Spain: Turnout and partisan voting in Spanish elections. European Journal of Political Economy, 34, pp. 126-141.
- Artés, J. y Jurado, I. (2018). Government fragmentation and fiscal deficits: a regression discontinuity approach. Public Choice, 175(3), pp. 367-391.
- Athey, S. e Imbens, G. W. (2017). The state of applied econometrics: Causality and policy evaluation. Journal of Economic Perspectives, 31(2), pp. 3-32.
- Athey, S. e Imbens, G. W. (2022). Design-based analysis in difference-in-differences settings with staggered adoption. Journal of Econometrics, 226(1), pp. 62-79.
- Bagues, M. F. y Esteve-Volart, B. (2010). Can gender parity break the glass ceiling? Evidence from a repeated randomized experiment. The Review of Economic Studies, 77(4), pp. 1301-1328.
- Bertrand, M. y Mullainathan, S. (2004). Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. American Economic Review, 94(4), pp. 991-1013.
- Callaway, B. y Sant’Anna, P. HC. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225, pp. 200-300.
- Cattaneo, M. D., Titiunik, R., Vázquez-Bare, G. y Keele, L. (2016). Interpreting regression discontinuity designs with multiple cutoffs. The Journal of Politics, 78(4), pp. 1229-1248.
- Cook, T. D. (2008). «Waiting for life to arrive»: a history of the regression-discontinuity design in psychology, statistics and economics. Journal of Econometrics, 142(2), pp. 636-654.
- Curto-Grau, M., Solé-Ollé, A. y Sorribas-Navarro, P. (2018). Does electoral competition curb party favoritism? American Economic Journal: Applied Economics, 10(4), pp. 378-407.
- Diamond, A. y Sekhon, J. S. (2013). Genetic matching for estimating causal effects: A general multivariate matching method for achieving balance in observational studies. Review of Economics and Statistics, 95(3), pp. 932-945.
- Díaz-Serrano, L. y Meix-Llop, E. (2016). Do schools discriminate against homosexual parents? Evidence from a randomized correspondence experiment. Economics of Education Review, 53, pp. 133-142.
- Fichera, E., Mora, T., López-Valcárcel, B. G. y Roche, D. (2021). How do consumers respond to «sin taxes»? New evidence from a tax on sugary drinks. Social Science & Medicine, 274, 113799.
- Finkelstein, A., Taubman, S., Wright, B., Bernstein, M., Gruber, J., Newhouse, J. P., Al l en, H., Baicker, K. y Oregon Health Stud y Group (2012). The Oregon health insurance experiment: evidence from the first year. The Quarterly Journal of Economics, 127(3), pp. 1057-1106.
- García-Pérez, J. I. e Hidalgo-Hidalgo, M. (2017). No student left behind? Evidence from the Programme for School Guidance in Spain. Economics of Education Review, 60, pp. 97-111.
- García-Vega, M., Kneller, R. y Stiebale, J. (2021). Labor Market reform and innovation: Evidence from Spain. Research Policy, 50(5), 104213.
- Heckman, J. J., Ichimura, H. y Todd, P. (1998). Matching as an econometric evaluation estimator. The Review of Economic Studies, 65(2), pp. 261-294.
- Heij, C., De Boer, P., Franses, P. H., Kloek, T. y Van Dijk, H. K. (2004). Econometric methods with applications in business and economics. Oxford University Press.
- Imbens, G. W. (2014). Instrumental variables: an econometrician’s perspective (n.º w19983). National Bureau of Economic Research.
- Imbens, G. W. (2015). Matching methods in practice: Three examples. Journal of Human Resources, 50(2), pp. 373- 419.
- King, G. y Nielsen, R. (2019). Why Propensity Scores Should Not Be Used for Matching. Political Analysis, 27(4), pp. 435-454.
- Krueger, A. B. (1999). Experimental estimates of education production functions. The Quarterly Journal of Economics, 114(2), pp. 497-532.
- Lee, D. S. y Lemieux, T. (2010). Regression discontinuity designs in economics. Journal of Economic Literature, 48(2), pp. 281-355.
- Martens, E. P., Pestman, W. R., de Boer, A., Belitser, S. V. y Klungel, O. H. (2006). Instrumental variables: application and limitations. Epidemiology, 260-267.
- Peña-Longobardo, L. M., Oliva-Moreno, J., Zozaya, N., ArandaReneo, I., Trapero-Bertran, M., Laosa, O., Sinclair, A. y Rodríguez-Mañas, L. (2021). Economic evaluation of a multimodal intervention in pre-frail and frail older people with diabetes mellitus: the MID-FRAIL project. Expert Review of Pharmacoeconomics & Outcomes Research, 21(1), pp. 111-118.
- Rabe-Hesketh, S. y Skrondal, A. (2008). Multilevel and longitudinal modeling using Stata. STATA press.
- Radice, R., Ramsahai, R., Grieve, R., Kreif, N., Sadique, Z., y Sekhon, J. S. (2012). Evaluating treatment effectiveness in patient subgroups: a comparison of propensity score methods with an automated matching approach. The International Journal of Biostatistics, 8(1), p. 25.
- Rosenbaum, P. R. y Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), pp. 41-55.
- Rubin, D. B. (1973). The use of matched sampling and regression adjustment to remove bias in observational studies. Biometrics, 29(1), pp. 185-203.
- Sánchez-Braza, A. y Pablo-Romero, M. D. P. (2014). Evaluation of property tax bonus to promote solar thermal systems in Andalusia (Spain). Energy Policy, 67, pp. 832-843.
- Sant’Anna, P. H. y Zhao, J. (2020). Doubly robust differencein-differences estimators. Journal of Econometrics, 219(1), pp. 101-122.
- Thistlethwaite, D. L. y Campbell, D. T. (1960). Regressiondiscontinuity analysis: An alternative to the ex post facto experiment. Journal of Educational Psychology, 51(6), p. 309.