Determinants of Judicial Efficiency Change: Evidence from Brazil

Thiago A. Fauvrelle; Alessio Tony C Almeida

doi:10.1515/rle-2017-0004

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Determinants of Judicial Efficiency Change: Evidence from Brazil

Thiago A. Fauvrelle und Alessio Tony C Almeida

Veröffentlicht/Copyright: 16. Februar 2018

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Aus der Zeitschrift Review of Law & Economics Band 14 Heft 1

Abstract

Judicial efficiency matters for economic development. Nevertheless, the determinants of judicial productivity growth are not entirely understood. Using data of Brazil's state courts for the period of 2009 to 2014, this paper analyzes judicial productivity change and its possible determinants over time in a two stage approach. First, data envelopment analysis is used to calculate Malmquist productivity measures which are decomposed in: technical change (frontier-shift effect) and efficiency change (composed of pure efficiency change and scale efficiency change). In the second stage, fixed effect models are estimated to evaluate the associated factors with judicial productivity growth. The first stage results show a slight improvement in judicial productivity trend, which is defined mainly by efficiency change, since technical change deteriorated in the period. The second stage findings suggest the nonexistence of a trade-off between judicial quality and efficiency improvement. Moreover, judges’ remuneration, legal complexity and technological use are correlated with judicial productivity, however not always in the expected direction.

Keywords: judicial efficiency; Malmquist index; productivity; Brazilian courts

JEL Classification: K40; C23; H11

Acknowledgements

This article benefited from comments and suggestions from participants of the 11th Annual Conference on Empirical Legal Studies (Durham), 33rd Annual Conference of the European Association of Law and Economics (Bologna), the 3rd International Conference on Economic Analysis of Litigation (Montpellier), the 12th Italian Society of Law and Economics annual conference (Turin), 44 Encontro Nacional de Economia-ANPEC (Foz do Iguau) and graduate seminars at the University of Hamburg and Erasmus University Rotterdam. The authors also gratefully thank Stefan Voigt, Louis Visscher, Finn Førsund and Alexandre Samy de Castro, as well as the anonymous referees for their valuable comments. Any remaining errors are our own.

Appendix

Figure 5:

Brazilian Judiciary structure.

Figure 6:

Technical efficiency (lagged) versus Productivity change.

Source: Authors' elaboration from Courts in Figures, CNJ (2015).

Figure 7:

Percentage of Courts with advances (m>1) and deterioration (m<1) in judicial productivity between 2009 and 2014.

Source: Authors’ elaboration from Courts in Figures, CNJ (2015).

Figure 8:

Percentage of Courts with improvements in technical change, pure efficiency change and scale efficiency change between 2009 and 2014.

Source: Authors’ elaboration from Courts in Figures, CNJ (2015).

Table 5:

Technical efficiency (VRS) in Brazilian Courts – 2009 to 2014.

	year						Total (2009-2014)
Court	2009	2010	2011	2012	2013	2014	Mean	SD	Min	Max	Count Eff*
Rio de Janeiro (RJ)	1.00	1.00	1.00	1.00	1.00	1.00	1.00	0.00	1.00	1.00	6
Roraima (RR)	1.00	1.00	1.00	1.00	1.00	1.00	1.00	0.00	1.00	1.00	6
Rio Grande do Sul (RS)	1.00	1.00	1.00	1.00	1.00	1.00	1.00	0.00	1.00	1.00	6
Sao Paulo (SP)	1.00	1.00	1.00	1.00	1.00	1.00	1.00	0.00	1.00	1.00	6
Acre (AC)	1.00	1.00	1.00	1.00	1.00	0.95	0.99	0.02	0.95	1.00	5
Mato Grosso do Sul (MS)	1.00	1.00	1.00	1.00	0.81	0.85	0.94	0.08	0.81	1.00	3
Rondonia (RO)	0.76	0.94	1.00	1.00	0.92	0.93	0.92	0.08	0.76	1.00	1
Distrito Federal (DF)	0.82	0.77	0.95	0.91	1.00	1.00	0.91	0.09	0.77	1.00	2
Minas Gerais (MG)	0.93	1.00	0.83	0.85	0.88	0.93	0.90	0.06	0.83	1.00	1
Para (PA)	1.00	0.93	0.78	0.84	0.90	0.96	0.90	0.07	0.78	1.00	1
Parana (PR)	0.87	0.86	0.97	0.78	0.84	0.99	0.89	0.07	0.78	0.99	0
Amazonas (AM)	0.77	0.77	0.88	1.00	1.00	0.86	0.88	0.09	0.77	1.00	2
Goias (GO)	0.90	0.75	0.73	0.92	0.98	1.00	0.88	0.11	0.73	1.00	1
Amapa (AP)	1.00	0.57	0.81	0.89	1.00	1.00	0.88	0.16	0.57	1.00	3
Sergipe (SE)	0.87	0.95	0.82	0.77	1.00	0.81	0.87	0.08	0.77	1.00	1
Maranhao (MA)	0.76	0.71	0.99	0.86	0.97	0.87	0.86	0.10	0.71	0.99	0
Alagoas (AL)	0.44	0.92	0.94	0.96	0.88	0.90	0.84	0.18	0.44	0.96	0
Rio Grande do Norte (RN)	1.00	0.74	0.82	0.88	0.76	0.72	0.82	0.10	0.72	1.00	1
Ceara (CE)	0.91	0.58	0.63	0.81	0.77	0.93	0.77	0.13	0.58	0.93	0
Tocantins (TO)	1.00	0.48	0.71	0.68	0.79	0.87	0.76	0.16	0.48	1.00	1
Santa Catarina (SC)	0.77	0.70	0.76	0.76	0.78	0.60	0.73	0.06	0.60	0.78	0
Bahia (BA)	0.96	0.84	0.60	0.69	0.57	0.62	0.71	0.14	0.57	0.96	0
Paraiba (PB)	0.54	0.52	0.77	0.61	0.91	0.87	0.70	0.15	0.52	0.91	0
Pernambuco (PE)	0.72	0.63	0.53	0.47	0.80	0.66	0.63	0.11	0.47	0.80	0
Espirito Santo (ES)	0.68	0.46	0.55	0.53	0.60	0.69	0.58	0.08	0.46	0.69	0
Mato Grosso (MT)	0.46	0.36	0.44	0.46	0.62	0.77	0.52	0.13	0.36	0.77	0
Piuai (PI)	0.30	0.36	0.30	0.38	0.55	0.77	0.44	0.17	0.30	0.77	0
Total	0.83	0.77	0.81	0.82	0.86	0.87	0.83	0.03	0.77	0.87	-

Source: Authors’ elaboration from Courts in Figures, CNJ (2015). Note: SD = standard deviation; *Count Eff = amount of times that the DMU was efficient in the period.

Table 6:

Scale efficiency in Brazilian Courts – 2009 to 2014.

		year						Total
Court	Scale*	2009	2010	2011	2012	2013	2014	2009-2014
AC		0.85	1.00	1.00	1.00	1.00	0.92	0.96
	constant	increasing	constant	constant	constant	constant	increasing
AL		0.74	0.71	0.68	0.68	0.87	0.83	0.75
	increasing	increasing	increasing	increasing	increasing	increasing	increasing
AM		0.80	0.79	0.72	0.88	1.00	0.76	0.83
	increasing	increasing	increasing	increasing	increasing	constant	increasing
AP		1.00	0.98	0.92	0.98	1.00	1.00	0.98
	undefined	constant	increasing	increasing	increasing	constant	constant
BA		0.73	0.85	0.92	0.88	0.90	0.99	0.88
	undefined	increasing	increasing	decreasing	decreasing	increasing	decreasing
CE		0.99	0.97	0.97	0.97	0.99	0.95	0.98
	increasing	increasing	increasing	increasing	increasing	increasing	increasing
DF		0.83	0.89	0.83	0.83	0.78	0.85	0.84
	decreasing	decreasing	increasing	increasing	decreasing	decreasing	decreasing
ES		0.93	0.94	0.98	0.98	0.96	0.95	0.96
	increasing	decreasing	decreasing	increasing	increasing	increasing	increasing
GO		0.94	1.00	0.99	0.96	1.00	1.00	0.98
	increasing	increasing	increasing	increasing	decreasing	increasing	constant
MA		0.86	0.80	0.85	0.83	0.80	0.84	0.83
	increasing	increasing	decreasing	increasing	increasing	decreasing	increasing
MG		0.81	0.77	0.86	0.86	0.83	0.86	0.83
	decreasing	decreasing	decreasing	increasing	increasing	decreasing	decreasing
MS		1.00	1.00	0.99	1.00	0.99	1.00	0.99
	constant	constant	constant	increasing	constant	decreasing	increasing
MT		0.93	0.95	0.99	0.99	1.00	0.99	0.98
	undefined	decreasing	increasing	decreasing	decreasing	increasing	increasing
PA		1.00	0.95	1.00	0.99	0.88	0.87	0.95
	increasing	constant	increasing	increasing	increasing	increasing	increasing
PB		1.00	0.98	0.99	0.99	0.88	0.93	0.96
	decreasing	decreasing	decreasing	increasing	increasing	decreasing	decreasing
PE		0.99	0.97	0.96	0.97	1.00	1.00	0.98
	increasing	increasing	increasing	increasing	increasing	increasing	increasing
PI		1.00	0.93	0.90	0.93	0.96	0.80	0.92
	increasing	increasing	increasing	increasing	increasing	increasing	increasing
PR		0.99	0.98	0.98	1.00	0.99	0.98	0.99
	increasing	increasing	increasing	increasing	increasing	increasing	increasing
RJ		1.00	1.00	1.00	1.00	1.00	1.00	1.00
	constant	constant	constant	constant	constant	constant	constant
RN		0.98	0.95	0.96	0.97	0.95	0.99	0.96
	decreasing	decreasing	decreasing	increasing	decreasing	decreasing	decreasing
RO		0.98	0.91	1.00	0.97	0.91	0.99	0.96
	increasing	increasing	increasing	constant	increasing	increasing	increasing
RR		0.63	0.62	0.53	0.67	0.55	0.90	0.65
	increasing	increasing	increasing	increasing	increasing	increasing	increasing
RS		1.00	1.00	1.00	1.00	1.00	1.00	1.00
	constant	constant	constant	constant	constant	constant	constant
SC		0.99	0.96	0.99	0.99	1.00	1.00	0.99
	increasing	increasing	increasing	increasing	increasing	increasing	increasing
SE		0.97	0.99	0.89	0.99	0.96	0.97	0.96
	increasing	increasing	decreasing	decreasing	increasing	increasing	increasing
SP		1.00	0.66	0.83	0.85	0.79	0.84	0.83
	decreasing	constant	decreasing	decreasing	decreasing	decreasing	decreasing
TO		0.70	0.90	0.85	0.90	0.95	0.93	0.87
	increasing	increasing	increasing	increasing	increasing	increasing	increasing
Total		0.91	0.91	0.91	0.93	0.92	0.93	0.92

Authors’ elaboration from Courts in Figures, CNJ (2015). Note: *Return to scale dominant over time.

Table 7:

Description of inputs, output and scores of judicial efficiency in Brazil by type of returns to scale (2009-2014).

	Returns to scale
Variable	Increasing	Constant	Decreasing	Total
Efficiency
Technical (VRS)	0.79	1.00	0.81	0.83
Scale	0.91	1.00	0.89	0.92

Inputs
Expenditure (constant 2014 R$ millions)	714.79	1,575.71	2,057.25	1,156.60
Judges	301	511	691	423
Staff	5,746	12,839	16,302	9,274
Workload	1,406,513	4,602,358	4,874,524	2,709,823

Output
Judgements	381,670	1,253,334	1,120,370	691,103

Source: Authors’ elaboration from Courts in Figures, CNJ (2015).

Table 8:

Malmquist index, Technical change (TC), Efficiency change(EC), Pure efficiency change (PEC) and Scale efficiency change (SC) – Average annual changes, 2009-2014.

				EC = PEC × SC
Court	Malmquist index	TC	EC	PEC	SC
AC	1,007	0,993	1,010	0,989	1,021
AL	1,211	0,984	1,228	1,211	1,015
AM	1,032	0,986	1,039	1,026	1,013
AP	1,041	0,993	1,042	1,044	0,999
BA	0,956	0,971	0,984	0,929	1,059
CE	0,994	0,971	1,028	1,036	0,993
DF	1,046	0,999	1,049	1,046	1,002
ES	0,990	0,974	1,026	1,022	1,004
GO	1,023	0,986	1,040	1,031	1,009
MA	1,029	0,975	1,047	1,045	1,002
MG	1,001	0,986	1,013	1,004	1,009
MS	0,952	0,979	0,971	0,972	1,000
MT	1,111	0,985	1,139	1,127	1,011
PA	0,940	0,971	0,969	0,997	0,972
PB	1,087	0,981	1,112	1,134	0,980
PE	1,012	0,991	1,029	1,024	1,005
PI	1,143	0,967	1,179	1,230	0,958
PR	1,009	0,980	1,032	1,035	0,997
RJ	1,023	1,023	1,000	1,000	1,000
RN	0,915	0,961	0,954	0,948	1,006
RO	1,033	0,983	1,051	1,046	1,004
RR	1,091	0,992	1,114	1,000	1,114
RS	0,983	0,983	1,000	1,000	1,000
SC	0,945	0,984	0,962	0,958	1,003
SE	0,992	0,988	1,003	1,000	1,004
SP	0,978	0,996	0,986	1,000	0,986
TO	1,029	0,971	1,068	1,038	1,029
Mean	1,015	0,983	1,040	1,033	1,007

Authors’ elaboration from Courts in Figures, CNJ (2015). Note: Pure efficiency change is calculated using DEA-VRS and efficiency change by DEA-CRS. Scale efficiency change (SC), based on Färe et al. (1994b), is defined by: SC=ECPEC.

Table 9:

Matrix of correlations between covariates

	Variables↓→	A	B	C	D	E	F	G
A	Reversal rate	1.0000
B	Remuneration	-0.1273	1.0000
C	Investment	0.0430	-0.2280*	1.0000
D	Technology investment	-0.2225*	-0.0122	0.0456	1.0000
E	Electronic filing	0.0952	0.1642	-0.0180	0.2115*	1.0000
F	GDP per capita	-0.0632	-0.0001	-0.0026	0.1045	-0.1756*	1.0000
G	Criminal cases	-0.0209	0.0883	0.1216	0.1170	0.0017	-0.4549*	1.0000

Authors’ elaboration. Note: *p–value<0.05.

References

Akerlof, G.A., and Yellen J.L.. 1990. “The Fair Wage-Effort Hypothesis and Unemployment,” 105 (2)Quarterly Journal of Economics 255–283.10.2307/2937787Suche in Google Scholar

Beenstock, M., and Haitovsky Y.. 2004. “Does the Appointment of Judges Increase the Output of the Judiciary?” 24 (3)International Review of Law and Economics 351–369.10.1016/j.irle.2004.10.006Suche in Google Scholar

Beer, C.C. 2006. “Judicial Performance and the Rule of Law in the States,” Mexican 48(3)Latin American Politics and Society 33–61.10.1111/j.1548-2456.2006.tb00355.xSuche in Google Scholar

Bhattacharya, U., and Daouk H.. 2009. “When no Law is Better than a Good Law,” 13 (4)Review of Finance 577–627.10.1093/rof/rfp011Suche in Google Scholar

Blank, J., B.v.H. Martin van der Ende, and R. Jagtenberg. 2004. “Bench Marking in an International Perspective: An International Comparison of the Mechanisms and Performance of the Technical Report May, ECORYS Nederland BV (comissioned by the Netherlands Council for the Judiciary), Rotterdam.Suche in Google Scholar

Bogetoft, P., and Otto L.. 2011. “Statistical Analysis in dea,” in Benchmarking with DEA, SFA, and R, pages 155–196. Springer.10.1007/978-1-4419-7961-2_6Suche in Google Scholar

Buscaglia, E., and Ulen T.. 1997. “A Quantitative Assessment of the Efficiency of the Judicial Sector in America,” Latin 17(2)International Review of Law and Economics 275–291.10.1016/S0144-8188(97)00007-0Suche in Google Scholar

Carmignani, A., and Giacomelli S.. 2010. “Too Many Lawyers? Litigation in Courts,” Italian Civil Banca D’Italia, Working Paper No 745, pages 1–28.Suche in Google Scholar

Castro, M.F., and Guccio C.. 2014. “Searching for the Source of Technical Inefficiency in Italian Judicial Districts: Investigation,” An Empirical 38(3)European Journal of Law and Economics 369–391.10.1007/s10657-012-9329-0Suche in Google Scholar

Caves, D.W., Christensen L.R., and Diewert W.E.. 1982. “The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity,” 50 (6)Econometrica 1393–1414.10.2307/1913388Suche in Google Scholar

Charnes, A., Cooper W., and Rhodes E.. 1978. “Measuring the Efficiency of Decision Making Units,” 2 (6)European Journal of Operational Research 429–444.10.1016/0377-2217(78)90138-8Suche in Google Scholar

Choi, S.J., Gulati G.M., and Posner E.A.. 2009. “Are Judges Overpaid?: A Skeptical Response to the Judicial Salary Debate,” 1 (1)Journal of Legal Analysis 47–117.10.4159/jla.v1i1.3Suche in Google Scholar

National CNJ - Council of Justice}. 2015. Courts in Figures,” CNJ, BrasíCNJ, Braslia-DF.Suche in Google Scholar

Coase, R.H. 1960. “The Problem of Social Cost,” 3 The Journal of Law and Economics 1–44.10.1086/466560Suche in Google Scholar

Dakolias, M. 1999. “Court Performance Around the World: A Comparative Perspective},” 2 (1)Yale Human Rights and Development Journal 87–142.10.1596/0-8213-4436-6Suche in Google Scholar

Dalton, T.A., and Singer J.M.. 2014. “Bigger Isn’t Always Better: An Analysis of Court Efficiency Using Hierarchical Linear Modeling,” 34 (3)Pace Law Review 1169–1189.Suche in Google Scholar

Deyneli, F. 2012. “Analysis of Relationship Between Efficiency of Justice Services and Salaries of Judges with Two-Stage DEA Method,” 34 (3)European Journal of Law and Economics 477–493.10.1007/s10657-011-9258-3Suche in Google Scholar

Dimitrova-Grajzl, V., Grajzl P., Slavov A., and Zajc K.. 2016. “Courts in a Transition Economy: Disposition Case and the Quantity–Quality Tradeoff in Bulgaria,” 40 (1)Economic Systems 18–38.10.1016/j.ecosys.2015.09.002Suche in Google Scholar

Djankov, S., La Porta R., Lopez-de Silanes F., and Shleifer A.. 2003. “Courts,” 118 (2)The Quarterly Journal of Economics 453–517.10.1162/003355303321675437Suche in Google Scholar

Elbialy, N., and García-Rubio M.A., 2011. “Assessing Judicial Efficiency of Egyptian First Instance Courts: A DEA Analysis,” MAGKS Papers on Economics, Working Paper No 19.Suche in Google Scholar

Färe, R., and Grosskopf S.. 1996. Intertemporal Production Frontiers: with Dynamic DEA. Norwell: Kluwer Aca.10.1007/978-94-009-1816-0Suche in Google Scholar

Färe, R., Grosskopf S., and Lovell C.A.. 1994. Production Frontiers, Cambridge University Press.10.1007/978-1-4612-2626-0_1Suche in Google Scholar

Färe, R., Grosskopf S., Norris M., and Zhang Z.. 1994b. “Productivity Growth, Technical Progress, and Efficiency Change in Countries,” Industrialized 84(1) The American Economic Review 66–83.Suche in Google Scholar

Farrell, M.J. 1957. The Measurement of Productive Efficiency. 120 (3)Journal of the Royal Statistical Society. Series A (General) 253–290.10.2307/2343100Suche in Google Scholar

Fleming Jr., J. 1961. “Burdens of Proof,” 47 (51)Virginia Law Review 51–70.10.2307/1071154Suche in Google Scholar

Friedman, D.D. 2000. Law’s Order: What Economics Has to Do with Law and Why It Matters. New Jersey: Princeton University Press.10.1515/9781400823475Suche in Google Scholar

Ginsburg, T. 2000. “Does Law Matter for Economic Development? Evidence from East Asia,” 34 (3)Law and Society Review 829–856.10.2307/3115145Suche in Google Scholar

Glaeser, E., and Shleifer A.. 2002. “Legal Origins,” 117 (November)The Quarterly Journal of Economics 1193–1229.10.1162/003355302320935016Suche in Google Scholar

Gorman, M.F., and Ruggiero J.. 2009. “Evaluating U.S. Judicial District Prosecutor Performance Using DEA: Are Disadvantaged Counties More Inefficient?,” 27 (3)European Journal of Law and Economics 275–283.10.1007/s10657-008-9093-3Suche in Google Scholar

Kittelsen, S.A.C., and Forsund F.R.. 1992. “Efficiency Analysis of Norwegian District Courts,” 3 (3)Journal of Productivity Analysis 277–306.10.1007/BF00158357Suche in Google Scholar

Kourlis, R.L., and Gagel P.A.. 2008. “Reinstalling the Courthouse Windows : Using Statistical Data to Promote Judicial Transparency and Accountability in Federal and Courts,” State 53(5)Villanova Law Review 951–972.Suche in Google Scholar

Landes, W. 1971. “An economic Analysis of the Courts,” 14 (1)The Journal of Law and Economics61–107.10.1086/466704Suche in Google Scholar

Landes, W., and Posner R.. 1979. “Adjudication as a Private Good,” 8 (2)The Journal of Legal Studies 235–284.10.1086/467609Suche in Google Scholar

Luskin, M.L., and Luskin R.C.. 1986. “Why so Fast, Why so Slow: Explaining Case Processing Time,” 77 (1)Journal of Criminal Law and Criminology 190–214.10.2307/1143594Suche in Google Scholar

Maher, B.S. 2010. “The Civil Judicial Subsidy,” 85 (4)Indiana Law Journal 1527–1556.Suche in Google Scholar

Malmquist, S. 1953. “Index Numbers and Indifference Surfaces,” 4 (2)Trabajos de Estadistica 209–242.10.1007/BF03006863Suche in Google Scholar

Mitsopoulos, M., and Pelagidis T.. 2010. “Greek Appeals Courts' Quality Analysis and Performance,” 30(1) European Journal of Law and Economics 17–39.10.1007/s10657-009-9128-4Suche in Google Scholar

Mora-Sanguinetti, J.S., and Garoupa N.. 2015. “Do Lawyers Induce Litigation? Evidence from Spain, 2001 – 2010,” 44(October) International Review of Law and Economics 29–41.10.1016/j.irle.2015.06.003Suche in Google Scholar

Murrel, P. 2001. “Demand and supply in romanian commercial courts: Generating information for institutional reform,” SSRN Working Paper no. 280428.10.2139/ssrn.280428Suche in Google Scholar

North, D.C. 1994. “Economic Performance Through Time,” 84 (3)The American Economic Review 359–368.Suche in Google Scholar

Palumbo, G., Giupponi G., Nunziata L., and Mora-Sanguinetti J.. 2013. “The Economics of Civil Justice: New Cross-country Data and Empirics,” OECD Economics Department Working Papers, No. 1060.Suche in Google Scholar

Pedraja-Chaparro, F., and Salinas-Jimenez J.. 1996. “An Assessment of the Efficiency of Spanish Courts Using DEA,” 28 (11)Applied Economics 1391–1403.10.1080/000368496327651Suche in Google Scholar

Posner, R.A. 1993. “What do Judges Maximize? (The Same Thing Everyone Else Does),” 3 Supreme Court Economic Review 1–41.10.1086/scer.3.1147064Suche in Google Scholar

Posner, R.A. 2000. “Is the Ninth Circuit Too Large? A Statistical Study of Judicial Quality,” 29 (2)The Journal of Legal Studies 711–719.10.1086/468090Suche in Google Scholar

Ramanathan, R. 2003. An Introduction to Data Envelopment Analysis: A Tool for Performance Measurement, volume 1. New Delhi: Sage Publications.Suche in Google Scholar

Ramello, G., and Melcarne A.A.. 2015. “Judicial Independence, Judges’ Incentives and Efficiency,” 11(2) Review of Law & Economics 149–169.10.1515/rle-2015-0024Suche in Google Scholar

Rosales-López, V. 2008. “Economics of Court Performance: An Empirical Analysis,” 25 (3)European Journal of Law and Economics 231–251.10.1007/s10657-008-9047-9Suche in Google Scholar

Schneider, M.R. 2005. “Judicial Career Incentives and Court Performance: An Empirical Study of the German Labour Courts of Appeal,” 20 (2)European Journal of Law and Economics 127–144.10.1007/s10657-005-1733-2Suche in Google Scholar

Shepherd, J. 2011. “Measuring Maximizing Judges: Empirical Legal Studies, Public Choice Theory, and Judicial Behavior,” 68 (2007)University Of Illinois Law Review 101–114.Suche in Google Scholar

Vereeck, L., and Muhl M.. 2000. “An Economic Theory of Court Delay,” 10 (3)European Journal of Law and Economics 243–268.10.1023/A:1026547515846Suche in Google Scholar

Voigt, S. 2016. “Determinants of Judicial Efficiency: A Survey,” 42 (2)European Journal of Law and Economics 1–31.10.1007/s10657-016-9531-6Suche in Google Scholar

Bank World. 2002. “Initiatives in Legal and Reform,” Judicial Technical report, World Bank, Washington, D.C.Suche in Google Scholar

Yeung, L.L., and Azevedo P.F.. 2011. “Measuring Efficiency of Brazilian Courts with Data Envelopment Analysis (DEA),” 22 (4)Ima Journal of Management Mathematics 343–356.10.1093/imaman/dpr002Suche in Google Scholar

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https://doi.org/10.1515/rle-2017-0004

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judicial efficiency; Malmquist index; productivity; Brazilian courts