Territorial patterns of open e-government: evidence from Chilean municipalities

Figures & data

Table 1. Local open e-government index aggregation.

	Phase	Item	Weight
1st	Presence	Forms (downloadable documents)	0.25
		Municipal newsletter	0.25
		Online map	0.25
2nd	Urban information	Urban map	0.50
		Transport information	0.50
3rd	Interaction	Municipal social networks	0.75
		Municipal telephone	0.75
		Mobile	0.75
4th	Transaction	Online official procedures	1.00
		Follow-up	1.00
		Digital certificates	1.00
		Register of residents	1.00
		Online payment	1.00
5th	E-democracy	Citizen participation	1.25
6th	E-transparency	Transparency request response within 30 working days	1.00
		Transparency request response within 20 working days	1.25

Note: Adapted from Esteves (2005), Bayona and Morales (2017) and González-Bustamante, Carvajal, and González (2020). Some items in the original model, such as the presence of a search engine or the option of personalising the interface with the website, have been omitted so as to give priority to the incorporation of more important elements that are now of daily use, such as digital social networks. E-transparency items were incorporated as an additional phase.

Figure 1. a. Queen-style contiguity matrix. b. Matrix with Delaunay triangulation. c. Matrix with SOI model, highlighted in colour. d. Matrix based on neighbours by distance with k = 4. e. Matrix based on neighbours by distance with k = 2.

Note: Santiago Metropolitan Region has been used as an example. Based on data from IDE-Chile and SUBDERE (2018, 2020).

Table 2. Open e-government at the local level and spatial autocorrelation, Chile, 2019–2021.

Region	OEGi	Moran’s I	MCMC
Arica y Parinacota	4.229	−0.378
Tarapacá	5.226	0.311**
Antofagasta	5.778	−0.176	−0.176
Atacama	5.245	−0.320	−0.320
Coquimbo	5.686	−0.064	−0.064
Valparaíso	6.432	0.121	0.121
Santiago Metropolitan	6.722	0.092	0.092
O’Higgins	5.953	−0.223	−0.223
Maule	5.965	0.082	0.082
Ñuble	5.199	0.087	0.087
Biobío	5.554	0.058	0.058
Araucanía	5.319	0.150*	0.150*
Los Ríos	5.717	−0.198	−0.198
Los Lagos	5.389	0.203**	0.203*
Aysén	5.298	0.103	0.103
Magallanes	5.692	0.046	0.046

Note: Compiled with data from IDE-Chile and SUBDERE (2018, 2020) and CPLT (2022). The Arica y Parinacota and Tarapacá Regions have very few municipal districts to perform MCMC simulations. MCMC convergence plots can be found in the Supplementary Material file. Significance: * p ≤ 0.100; ** p ≤ 0.050; *** p ≤ 0.010.

Figure 2. Distribution of the open e-government index in the Santiago Metropolitan Region.

Note: Based on data from IDE-Chile and SUBDERE (2018, 2020) and CPLT (2022).

Table 3. Determinants of open e-government at the local level, Chile, 2019–2021.

	Model I	Model II	Model III	Model IV	Model V
Internet connections (log)	0.285*** (0.027)	0.193*** (0.073)	0.077 (0.075)	0.048 (0.074)	0.049 (0.074)
Poverty rate			−0.083*** (0.017)	−0.049*** (0.019)	−0.049** (0.019)
Budget (log)				0.382*** (0.098)	0.382*** (0.098)
Personnel professionalisation rate					0.002 (0.006)
Constant	3.773*** (0.206)	2.827*** (0.956)	3.511*** (0.937)	0.528 (1.193)	0.425 (1.241)
Population (log)	No	Yes	Yes	Yes	Yes
Population density (log)	No	Yes	Yes	Yes	Yes
Estimation method	OLS	OLS	OLS	OLS	OLS
VIF	1.324	1.331	1.421	1.486	1.486
B-P/Cook-Weisberg	0.031	0.001	1.364	1.443	1.437
Shapiro-Wilk	0.991**	0.991**	0.995	0.994	0.994
Moran’s I	0.081**	0.082**	0.033	0.016	0.018
N	345	345	345	345	345
R^2	0.245	0.249	0.296	0.327	0.327
Adj. R^2	0.242	0.242	0.288	0.317	0.315

Note: Beta coefficients, standard errors in parentheses. Compiled with data from IDE-Chile and SUBDERE (2018, 2020), CPLT (2022), SUBTEL (2022), DOS-MDSF and ECLAC (2021), SINIM (2022) and INE (2022). Significance: * p ≤ 0.100; ** p ≤ 0.050; *** p ≤ 0.010.

Table 4. Spatial models of Internet connections and open e-government at the local level, Chile, 2019–2021.

	SAR	SEM
Internet connections (log)	0.182** (0.072)	0.180** (0.076)
ρ (rho)	0.157** (0.066)
λ (lambda)		0.149** (0.072)
Constant	1.842* (1.015)	2.762*** (0.976)
Population (log)	Yes	Yes
Population density (log)	Yes	Yes
Estimation method	ML	ML
VIF	1.324	1.324
Moran’s I	−0.005	−0.002
N	345	345
Pseudo-R^2	0.261	0.258
Log likelihood	−566.041	−566.663
AIC	1,144.083	1,145.325

Note: Beta coefficients, standard errors in parentheses. Compiled with data from IDE-Chile and SUBDERE (2018, 2020), CPLT (2022), SUBTEL (2022) and INE (2022). Significance: * p ≤ 0.100; ** p ≤ 0.050; *** p ≤ 0.010.

Figure 3. a. Effect of monetary poverty on open e-government at the local level in Chile. b. Effect of budget measured with own municipal revenues on open e-government at the local level in Chile.

Note: Based on data from IDE-Chile y SUBDERE (2018, 2020), CPLT (2022), SUBTEL (2022), DOS-MDSF and ECLAC (2021), SINIM (2022) and INE (2022).

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Data availability statement

The data can be obtained on Zenodo: González-Bustamante, B., and D. Aguilar. 2023. “Data Set on Local Government Indicators in Chile (Version 0.21.15 – Late Sky)” (dataset, pre-release version). University of Oxford, Universidad de Santiago de Chile (USACH) and Training Data Lab. DOI: 10.5281/zenodo.7568387.