Skip to main content
Have a personal or library account? Click to login
The Contextual Relationship with Access to Justice: Parameters That Impact the Brazilian Courts Cover

The Contextual Relationship with Access to Justice: Parameters That Impact the Brazilian Courts

International Journal for Court Administration
Volume 17 (2026): Issue 1
By: ,  ,   and    
Open Access
|Jun 2026
Figures & tables

Figures & Tables

Table 1

Variables used in the study.

VARIABLES TYPEDESCRIPTION – VARIABLECODE OF JUSTICE IN NUMBERS**
Predictor variables
Actual access to the judiciary

  • New cases (per 100 thousand inhabitants) – (Y1)

casos_novos
Potential access to the judiciary

  • Number of magistrates working in the court

    (per 100 thousand inhabitants) – (Y2)

mag_por_pop
Explanatory variables
People

  • Number of magistrates working in the Court

    (per 100 thousand inhabitants) – J/POP

mag_por_pop

  • Number of civil servants working in the Court

    (per 100 thousand inhabitants) – Staff

f4a

  • Number of outsourced workers in the Court* – OW

tfauxt
Administrative structurevaras_por_pop
ICTs

  • Expenses with the acquisition of ICTs* – ICT1

dinf1

  • Cost of ICTs* – ICT2

dinf2
Education

  • The educational attainment rate – EDU

escolarizacao

  • Illiteracy rate – ILLT

analfabetismo
Income

  • Gross Domestic Product* – GDP

gdp
Control variables

  • Population (per 100 thousand inhabitants) – POP

h2

  • Workload – W

k

  • Total Court Expenses – TCE

gt

  • Rate of households with piped water – RHP

agua_canalizada

  • Rate of live births – LB

nasc_vivos

  • Height-for-age deficit – HAD

DAÍ

  • Weight Deficit for Age – WAD

DPI

  • Gini coefficient – Gini

coef_gini

  • Rate of women in the population – RW

taxa_mulher

  • Rate of people over 50 years of age – MA

idade_50

  • Rate of nonwhite people in the population – NWP

cor_raca_nao_branca

  • Average processing time until decision – APT

tempo_medio_decisao

  • Number of judges – J

f2

[i] * Variables transformed through logarithmic transformation (Box & Cox, 1964), ** (CNJ, 2021).

Figure 1

Histogram of the dependent variable (processes per 100 thousand inhabitants).

Figure 2

Histogram of the dependent variable (magistrates per capita).

Figure 3

Distribution of correlations and bivariate analyses.

Table 2

Multiple linear regression results.

MODELS
Y1Y2
(1)(2)(3)(4)
J/POP43,847,012.0000***
(8,979,453.0000)		45,585,121.0000***
(7,659,770.0000)		–12,400.0000
(13,099.4700)
Staff14.6482**
(6.7953)		14.5351**
(6.1299)		0.0443***
(0.0070)		0.0412***
(0.0057)
OW0.0633
(0.1040)		–0.0002
(0.0001)		–0.0003***
(0.0001)
Courtrooms553,252.2000
(10,763,276.0000)		–13,332.8000
(13,449.4100)		–22,784.0600**
(11,109.5800)
ICT10.0011
(0.0010)		0.0000
(0.000001)
ICT20.0027***
(0.0009)		0.0030***
(0.0006)		0.000001
(0.000001)		0.000001
(0.000001)
EDU–499.8882**
(202.3117)		–363.9731**
(146.5130)		–0.2434
(0.2645)
ILLT65.3607
(63.8436)		0.1174
(0.0797)
GDP–0.0000**
(0.0000)		–0.0000*
(0.0000)		–0.0000*
(0.0000)		–0.0000*
(0.0000)
POP4.0934*
(2.3854)		3.1541
(2.1547)		0.0048
(0.0030)		0.0037
(0.0025)
W0.0907**
(0.0355)		0.0973***
(0.0338)		–0.00004
(0.00005)
TCE–0.0000***
(0.0000)		–0.0000***
(0.0000)		–0.0000***
(0.0000)		–0.0000***
(0.0000)
RHP21,238.1100**
(9,206.0110)		11,540.2800**
(5,138.1140)		31.0433***
(11.4247)		19.4942***
(4.1361)
LB16,492.2100*
(9,447.0280)		16,066.5000*
(8,514.2040)		–1.5280
(12.1620)
HAD177.3723
(114.1018)		179.3684*
(105.4078)		–0.0693
(0.1460)
WAD–518.1639*
(260.3605)		–328.9689
(221.8510)		–0.1604
(0.3369)
Gini–8,722.1500
(9,518.4850)		–15.4828
(11.9064)
RW13,208.1000
(19,284.3400)		–30.8830
(24.0630)
MA–24,855.6500**
(11,756.2100)		–13,261.4100
(8,243.2560)		–12.8704
(15.2095)
NWP–4,494.3790*
(2,303.2390)		–1,765.5630
(1,312.6850)		–4.0666
(2.9395)
APT–0.3572
(0.6226)		0.0006
(0.0008)
J–161.6616*
(95.7380)		–183.6477**
(88.2937)
New Cases–0.0003*
(0.0002)		–0.0004***
(0.0001)
Constant–9,794.7120
(12,584.2000)		4,708.3700
(8,575.5490)		–5.9164
(15.8970)		–11.5679***
(3.8664)
Observation89898989
R20.94860.94480.89640.8855
Adjusted R20.93150.93340.86190.8725
RSE946.0964 (df = 66)932.8215 (df = 73)1.1910 (df = 66)1.1443 (df = 79)
F-statistic55.4003*** (df = 22; 66)83.2423*** (df = 15; 73)25.9567*** (df = 22; 66)67.8899*** (df = 9; 79)

[i] Notes: Y1 and Y2 are the first and second dependent variables, respectively. R² (coefficient of determination) represents the proportion of variance in the dependent variable explained by the model; values closer to 1 indicate greater explanatory power. Adjusted R² accounts for the number of predictors included, reducing the risk of overestimating model fit when irrelevant variables are added. RSE is an acronym for Residual Standard Error, which measures the average deviation of residuals from the regression line, with smaller values indicating better model fit. The F-statistics evaluate whether the model as a whole significantly explains variation in the dependent variable; higher values with statistical significance (***) suggest that the predictors jointly contribute to the model. (df) indicates degrees of freedom associated with the model. Significance levels: *p < 0.1; **p < 0.05; ***p < 0.01.

Table 3

Summary of hypotheses and their results (potential and effective access).

HYPOTHESISDESCRIPTIONRESULT
H1The number of judges positively impacts actual access to the judiciary.

  • – Not rejected

  • – Significant

H2The number of public servants positively impacts the potential access to the Judiciary.

  • – Not rejected

  • – Significant

H2BThe number of public servants positively impacts actual access to the judiciary.

  • – Not rejected

  • – Significant

H3The number of outsourced civil servants positively impacts the potential access to the Judiciary.

  • – Rejected

  • – Not Significant

H3BThe number of outsourced civil servants positively impacts actual access to the judiciary.

  • – Rejected

  • – Not Significant

H4The number of judicial units positively impacts the potential access to the judiciary.

  • – Rejected

  • – Not Significant

H4BThe number of judicial units positively impacts the actual access to the judiciary.

  • – Rejected

  • – Not Significant

H5Investment in technology positively impacts potential access to the judiciary.

  • – Rejected

  • – Not Significant

H5BInvestment in technology positively impacts actual access to the Judiciary.

  • – Rejected

  • – Not Significant

H6The cost of technology positively impacts the potential access to the judiciary.

  • – Rejected

  • – Not Significant

H6BThe cost of technology positively impacts actual access to the Judiciary.

  • – Not rejected

  • – Significant

H7The educational attainment rate positively impacts the potential access to the Judiciary.

  • – Rejected

  • – Not Significant

H7BThe educational attainment rate positively impacts actual access to the judiciary.

  • – Rejected

  • – Reverse the order of the relationship

H8The illiteracy rate negatively impacts the potential access to the judiciary.

  • – Rejected

  • – Not Significant

H8BThe illiteracy rate negatively impacts actual access to the judiciary.

  • – Rejected

  • – Not Significant

H9Population income positively impacts potential access to the judiciary.

  • – Rejected

  • – Reverse the order of the relationship

H9BPopulation income positively impacts actual access to the judiciary.

  • – Rejected

  • – Reverse the order of the relationship

DOI: https://doi.org/10.36745/ijca.648 | Journal eISSN: 2156-7964
Journal RSS Feed
Language: English
Page range: 6 - 6
Published on: Jun 12, 2026
Published by: International Association for Court Administration
In partnership with: Paradigm Publishing Services
Keywords:
public administration,
administration of justice,
access to justice,
court

© 2026 Renato Máximo Sátiro, Marcos de Moraes Sousa, Pedro Miguel Alves Ribeiro Correia, Woska Pires da Costa, published by International Association for Court Administration
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 17 (2026): Issue 1
Previous article
Next article