Información de la revista
Vol. 17. Núm. 3.
Páginas 238-248 (mayo - junio 2003)
Respuestas rápidas
Compartir
Compartir
Descargar PDF
Más opciones de artículo
Vol. 17. Núm. 3.
Páginas 238-248 (mayo - junio 2003)
Open Access
Evaluar intervenciones sanitarias sin experimentos
Evaluating health interventions without experiments
Visitas
5229
M. Vera-Hernández
Autor para correspondencia
uctpamv@ucl.ac.uk
http://www.homepages.ucl.ac.uk/~uctpamv

Correspondencia: Dr. Marcos Vera Hernández. Department of Economics, University College London. Gower Street, London WC1E 6BT. Londres. Reino Unido. Tel. +44-207-679-5808. Fax. +44-207-916-2775
Department of Economics, University College London. Londres. Reino Unido
Este artículo ha recibido

Under a Creative Commons license
Información del artículo
Resumen

En el presente artículo se revisa la bibliografía reciente en evaluación cuantitativa de intervenciones no experimentales, poniendo especial énfasis en su aplicación a la economía y la gestión sanitarias. En particular, se han descrito las técnicas de matching y de doble diferencia combinada con matching. El parámetro elegido como objeto de la estimación es la ganancia media para los participantes en la intervención, bajo la hipótesis de heterogeneidad en las ganancias no observables que produce la intervención entre los individuos elegibles. Se ha llevado a cabo una exposición no técnica de las metodologías descritas con el espíritu de fomentar al lector una lectura más profunda de la bibliografía relevante.

Palabras clave:
Estadística
Evaluación de programas
Reforma sanitaria
Abstract

This paper summarizes recent literature on quantitative techniques for the evaluation of non experimental reforms. We closely look at the application of the methods to health economics and health management. The methods of matching and difference in differences combined with matching have been analysed in greatest detail. We have focused our attention on the estimation of the average treatment for the treated as the relevant parameter to be estimated. Along the paper, we have assumed that gains from the reform are heterogeneous in non observable variables across eligible individuals. The methods are described in a non technical manner to motivate further reading.

Key words:
Statistics
Program evaluation
Health care reform
El Texto completo está disponible en PDF
Biblografía
[1.]
J. Heckman, R. Lalonde, J. Smith.
The econometrics of active labor market programs.
Handbook of labor economics. Vol. 3, pp. 1865-2097
[2.]
R. Blundell, M. Costa Dias.
Evaluation methods for non-experimetal data.
Fiscal Studies, 21 (2000), pp. 427-468
[3.]
R. Blundell, M. Costas Dias.
Alternative approaches to evaluation in empirical microeconomics.
Portuguese Economic Journal, 1 (2002), pp. 91-115
[4.]
Sianesi B. PSMATCH: Stata module to perform various types of propensity score matching [accedido el 28/10/2002]. Disponible en: http://econpapers.hhs.se/software/bocbocode/s418602.htm
[5.]
Ichino A. Stata programs for the ATT estimation based on propensity score matching [accedido el 28/10/2002]. http://www.iue.it/Personal/Ichino/Welcome.html#pscore
[6.]
J. Newhouse.
Free for all? Lessons from the Rand Health Insurance Experiment.
[7.]
Gertler P, Boyce S. An experiment in incentive-based welfare: the impact of PROGRESA on health in Mexico, Haas School of Business, July 2001 [accedido el 28/10/2002]. http://faculty.haas.berkeley.edu/gertler/working_papers/PROGRESA%204-01.pdf
[8.]
R. Lalonde.
Evaluating the econometric evaluations of training programs with experimental data.
American Economic Review, 76 (1986), pp. 604-620
[9.]
G. Burtless.
The case for randomized field trials in economic and policy research.
Journal of Economic Perspectives, 9 (1995), pp. 63-84
[10.]
J. Heckman, V. Hotz.
Choosing among altenative nonexperimental methods for estimating the impact of social programs: the case of manpower training.
J Am Stat Assoc, 84 (1989), pp. 862-874
[11.]
J.A. Hausman, D.A. Wise.
Social experimentation.
[12.]
J. Heckman, R. Robb.
Alternative methods for evaluating the impact of interventions. An overview.
Journal of Econometrics, 30 (1985), pp. 239-267
[13.]
J. Heckman, R. Robb.
Alternative methods for evaluating the impacts of interventions.
Longitudinal analysis of labor market data, pp. 156-246
[14.]
P. Chiappori, F. Durand, P. Geoffard.
Moral hazard and the demand for physician services: first lessons from a French natural experiment.
European Economic Review, 42 (1998), pp. 499-511
[15.]
B. Gray.
Do Medicaid physician fees for prenatal services affect birth outcomes?.
Journal of Health Economics, 20 (2001), pp. 571-590
[16.]
M. Bertrand, E. Duflo, S. Mullainathan.
How much shoud we trust differences-in-differences estimates.
pp. 01-34
[17.]
W. Cochrane, D. Rubin.
Controlling bias in observational studies.
Sankyha, 35 (1985), pp. 417-446
[18.]
J. Jalan, M. Ravallion.
Estimating the benefit incidence of an antipoverty program by propensity score matching.
Journal of Econometrics, 112 (2003), pp. 153-173
[19.]
C. Meghir, M. Palme.
The effect of a social experiment in education.
The Institute for Fiscal Studies, Working Paper n.° 01/11, (2001),
[20.]
R. Blundell, M. Costa Dias, C. Meghir, J. Van Reenen.
Evaluating the employment impact of a mandatory job search assistance program.
The Institute for Fiscal Studies Working Paper n.° 01/20, (2001),
[21.]
J. Heckman, H. Ichimura, P. Todd.
Matching as an econometric evaluation estimator: evidence from evaluating a job training programme.
Review of Economic Studies, 64 (1997), pp. 605-654
[22.]
J. Heckman, H. Ichimura, P. Todd.
Matching as an econometric evaluation estimator.
Review of Economic Studies, 65 (1998), pp. 261-294
[23.]
P. Rosenbaum, D. Rubin.
The central role of the propensity score in observational studies for causal effects.
Biometrika, 70 (1983), pp. 41-55
[24.]
J. Smith, P. Todd.
Does matching overcome Lalonde's critique of nonexperimental estimators?.
University of Pennsylvania Working Paper, (November 2000),
[25.]
J. Heckman, J. Smith.
Evaluating the welfare state.
Frisch centenary,
[26.]
J. Heckman, H. Ichimura, J. Smith, P. Todd.
Characterizing selection bias using experimental data.
Econometrica, 66 (1998), pp. 1017-1098
[27.]
J. Heckman.
Sample selection as a specification error.
Econometrica, 47 (1979), pp. 153-161
Copyright © 2003. Sociedad Española de Salud Pública y Administración Sanitaria
Descargar PDF
Idiomas
Gaceta Sanitaria
Opciones de artículo
Herramientas
es en

¿Es usted profesional sanitario apto para prescribir o dispensar medicamentos?

Are you a health professional able to prescribe or dispense drugs?