Estimación de un índice de eficiencia para la rama judicial en Colombia: una aproximación mediante análisis de envoltura de datos

Keywords:

rama judicial, circuitos judiciales, eficiencia, programación, economía matemática, envoltura de datos

Abstract

En este artículo se propone una medida para la Rama Judicial en Colombia para cuantificar su eficiencia. Se explica por qué es importante la eficiencia, el método escogido para estimarla y la robustez de los resultados encontrados. También se utilizan herramientas de programación de computadores que reflejan su importancia en la economía matemática moderna, además de facilitar y brindar mayores libertades en los cálculos de modelos complejos.
Los resultados muestran el comportamiento de los circuitos judiciales escogidos a través de los índices encontrados. Adicionalmente, queda abierta la posibilidad de realizar investigaciones futuras que complementen el análisis de envoltura de datos.

References

Andrews D. & Pregibon, D. (1978). Finding the Outliers that Matter. Journal of the

Royal Statistical Society, 40(1), 85-93.

Azadeh, A., et al. (2011). An Integrated Data Envelopment Analysis-Artificial

Neural Network-Rough Set Algorithm for assessment of personnel efficiency.

Expert Systems with Applications, 38(3), 1364-1373.

Banker, R., Charnes, A. & Cooper, W. (1984). Some Models for Estimating

Technical and Scale Inefficiencies in Data Envelopment Analysis. Management

Science, 30(9), 1078-1092.

Bogetoft, P. & Otto, L. (2011). Benchmarking with DEA, SFA, and R. New York:

Stanford University.

Bogetoft, P. & Otto, L. (2015). Benchmark and Frontier Analysis Using DEA and

SFA. R Foundation for Statistical Computing, Vienna, Austria. Disponible en:

https://cran.r-project.org/web/packages/Benchmarking/index.html

Charnes, A., Cooper, W. & Rhodes, E. (1978). Measuring the Efficiency of Decision

Making Units. European Journal of Operational Research, 2, 429-444.

Charnes, A., Cooper, W., Seiford, L. & Stutz, J. (1982). A Multiplicative Model for

Efficiency Analysis. Socio-Economic Planning Sciences, 16(5), 223-224.

Coelli, T. (1996). A Guide to DEAP Version 2.1: A Data Envelopment Analysis (Computer)

Program. Centre for Efficiency and Productivity Analysis (CEPA) Working Paper No. 8.

Debreu, G. (1951). The Coefficient of Resource Utilization. Econometrica, 19(3),

-292.

Dimitrova-Grajzl, V., et al. (2012). Court Output, Judicial Staffing, and the

Demand for Court Services: Evidence from Slovenian Courts of First Instance.

International Review of Law and Economics, 32(1), 19-29.

Dimitrova-Grajzl, V., et al. (2016). Courts in a Transition Economy: Case

Disposition and the Quantity-Quality Tradeoff in Bulgaria. Economic Systems,

(1), 19-38. https://doi.org/10.1016/j.ecosys.2015.09.002

Farrel, M. (1957). The Measurement of Productive Efficiency. Journal of the Royal

Statistical Society, 120(3), 253-290.

Gholam, A. (2017). Impact of Outliers in Data Envelopment Analysis. Int. J.

Industrial Mathematics, 9(4), 319-332.

Gillespie, R. (1976). The Production of Court Services: Analysis of Scale Effects

and Other Factors. The Journal of Legal Studies, 5(2), 243-265.

Hernández, V. (2016). Análisis de la eficiencia relativa en el sistema judicial colombiano

como funciones de producción, mediante análisis envolvente de datos (DEA) (Tesis de

maestría). Universidad Distrital Francisco José de Caldas, Colombia.

Johnstone, I. & Titterington, D. (2009). Statistical Challenges of HighDimensional Data. Transactions. Series A, Mathematical, physical, and engineering

sciences, 367(1906), 4237-4253.

Kneip, A., et al. (2010). A computationally efficient, consistent bootstrap for inference

with non-parametric DEA estimators. Computational Economics, 38(4), 483-515.

Koopmans, T. (1951). An Analysis of Production as an Efficient Combination of Activities.

En T. Koopmans (Ed.), Activity Analysis of Production and Allocation (Cowles Commission for

Research in Economics, Monograph 13). New York: John- Wiley and Sons, Inc.

Murrel, P. (2001). Demand and Supply in Romanian Commercial Courts:

Generating Information for Institutional Reform. IRIS Center _ Maryland

University. https://doi.org/10.2139/ssrn.280428

20. Newman, P. (2016 [1987]). Convexity. En J. Eatwell & M. Milgrate The New

Palgrave: A Dictionary of Economics. London: Palgrave Macmillan.

North, D. (1992). Transaction Costs, Institutions, and Economic Performance. USA: ICS Press.

Paris, Q. (2011). Economic Foundations of Symmetric Programming. USA: Cambridge

University Press.

Rapposelli, A. & Nissi, E. (2012). Analyzing Industrial Accidents in European Countries

Using Data Envelopment Analysis. AIEL Series in Labour Economics, 6, 93-101.

Ricardo, R. (2016). La política de descongestión judicial 2009-2014, un costoso e ineficiente

esfuerzo. Revista de Derecho Público, 36. http://dx.doi.org/10.15425/redepub.36.2016.06

Seiford, L. & Zhu, J. (1999). An Investigation of Returns to Scale in Data Envelopment

Analysis. Omega: The International Journal of Management Science, 27(1), 1-11.

Shepherd, R. (1970). Theory of Cost and Production Functions. USA: Princeton

University Press.

Simar, L. & Wilson, P. (2002). Non-parametric tests of returns to scale. European

Journal of Operational Research, 139, 115-132.

Simm, J. & Besstremyannaya, G. (2016).rDEA: Robust Data Envelopment

Analysis (DEA) for R. R Foundation for Statistical Computing, Vienna, Austria.

Disponible en: https://cran.r-project.org/web/packages/rDEA/index.html

Voigt, S. (2016). Determinants of Judicial Efficiency: A Survey. Eur J Law Econ,

, 183-208.

Zhu, J. (2009). Quantitative Models for Performance Evaluation and Benchmarking:

Data Envelopment Analysis with Spreadsheets (Segunda ed.). USA: Springer.

Estimación de un índice de eficiencia para la rama judicial en Colombia: una aproximación mediante análisis de envoltura de datos

Keywords:

Abstract

References

Published

How to Cite

Issue

Section

Make a Submission