1. INTRODUCTION

2-. THEORETICAL FRAMEWORK

3-. METHODOLOGY

3.1. Variables used in the model

To compose the research universe, based on internet search programs, the active websites of all municipalities in the State of Maranhão were collected, constituting the primary source of data for the study, totaling 217 municipalities that make up the State. . The study did not consider 6 municipal websites due to the non-existence, deactivation, construction, maintenance of part of the electronic sites or even because they were configured as blogs, which were (...). Thus, the final sample resulted in the analysis of 211 municipalities that make up the state of Maranhão.

After this search, a calculation of the transparency index of the websites of the municipalities of Maranhão was carried out to develop the digital transparency model, which was adopted for this study, using the studies of Amorim (2012), Diniz et al., (2016) and Araújo et al., (2020), who considered data, information, services and transparency, comprised in dimensions of the website evaluation process, in addition to analysis categories and indicators.

In the statistical analysis, the multiple regression methodology was used, which, according to Fávero (2017), allows the construction of models that can describe, even in a reasonable way, the relationships between the variables under analysis, according to the author, a regression model multiple is a measure of variable Y that is explained by the joint variation of variables X considered in the model. The Rcmdr software, v. 4.3.0.

To demonstrate the proposed objective, empirical tests were carried out and the variables that could explain the study were initially tested. To this end, alternative multiple linear regressions were carried out to analyze the significant variables, and to be able to determine the most representative betas that were related to the level of transparency.

The Demographic Census carried out by the Brazilian Institute of Geography and Statistics (IBGE) in 2018 identified 217 cities that belong to the State of Maranhão. For the study, the sample consisted of 211 municipalities. The selection criterion was to have a population estimate of 5,000 inhabitants, as identified in table 2. Of the municipalities in Maranhão that make up the sample, 9 are large (over 100,000 inhabitants), 15 are medium-sized (50,001 to 100,000 inhabitants) and 189 small ones (up to 50,000 inhabitants).

Table 1. Large, medium and low-sized cities in the state of Maranhão.

Table 1. Large, medium and low-sized cities in the state of Maranhão.

Population Range	n (%)	Counties
Over 100,000 – large	2 (7.25%)	São Luís, Imperatriz, São José de Ribamar, Timon, Caxias, Paço do Lumiar, Codó, Açailândia and Bacabal
Between 10,001 and 100,000-medium size	25 (36.23%)	Ferries, Santa Inês, Barra do Corda, Pinheiro, Chapadinha, Santa Luzia, Buriticupu, Grajaú, Itapecuru-Mirim, Coroatá, Barreirinhas, Tutoia, Vargem Grande, Viana, Zé Doca, Lago da Pedra, Coleho Neto, Presidente Dutra, Araioses, São Bento, Rosário, Estreito, Tuntum, Bom Jardim, São Mateus do Maranhão, Colinas, Pedreiras, Penalva, Santa Rita, Brejo, Turiaçu, Parnarama, São Domingos do Maranhão, Bom Jesus das Selvas, Matões, Monção, Urbano Santos, Pindaré -Mirim, Vitória do Mearim, Curupuru, Arame, Alto Alegre do Pindaré, Vitorino Freire, Raposa, Arari, Buriti, Timbiras, Humberto de Campos, São Bernardo, Miranda do Norte, Icatu, Alto Alegre do Maranhão, Anajatuba, Aldeias Altas, Itinga do Maranhão, São João dos Patos, Santa Quitéria do Maranhão, Turilândia, Governador Nunes Freire, Santa Luzia do Paruá, Pedro do Rosário, Carolina, Porto Franco, Buriti Bravo, João Lisboa, Carutapera, Matinha, Dom Pedro, Peritoró, São Vicente Ferrer, Alcântara, Trizidela do Vale, Cantanhade, Centro Novo do Maranhão, Pio XII, Maracaçumé, Paraibano, Bequimão, Paulo Ramos, Mirador, Nova Olinda do Maranhão, São João Batista, Riachão, Cândido Mendes, Magalhães de Almeida, Palmeirândia, Olho d'Água das Cunhãs, Pastos Bons, Morros, Cajari, Formosa da Serra Negra, Passagem Franca, Presidente Sarney, São Raimundo das Mangabeiras, São Luís Gonzaga do Maranhão, Barão do Grajaú, São Benedito do Rio Preto, Pirapemas, Bacuri, São João do Soter, Governador Edison Lobão, Gonçalves Dias, Poção das Pedras, Esperantinópoliks, Apicum-Açu, Bacabeira, Mata Roma, Governador Eugênio Barros, Matões do Norte, Jenipapo dos Vieiras, Bom Lugar, Lago Verde, Conceição do Lago-Açu , Joselândia, Itaipava do Grajaú, Paulino Neves, Santo Amaro do Maranhão, São João do Caru, Anapurus, Fortuna, Buritirana, Araguanã, Primeira Cruz, Mirinzal, Olinda Nova do Maranhão, Cidelândia, Santo Antônio dos Lopes, Nina Rodrigues, Campestre do Maranhão, Peri Mirim, Senador La Rocque, Igarapé do Meio, Satubinha, Centro do Guilherme, Vila Nova dos Martírios, Santa do Maranhão, Davinópolis, Presidente Juscelino, São Pedro da Água Branca, Fortaleza dos Nogueiras, Água Doce do Maranhão, São Francisco do Maranhão, Loreto, Axixá, Guimarães, Lima Campos, Godofredo Viana, São Francisco do Brejão, Lagoa Grande do Maranhão, Duque Bacelar, Igarapé Grande, Lagoa do Mato, Bela Vista do Maranhão, Presidente Vargas, Alto Paranaíba, São João do Paraíso and Cajapió.
Between 5,000 and 10,000-low sized	39 (56.52%)	Capinzal do Norte, Lago do Junco, Governador Archer, Cedral, Fernado Falcão, Serrano do Maranhão, Governador Newton Bello, Jatobá, Cachoeira Grande, Boa Vista do Gurupi, Brejo de Areia, Montes Altos, Lagoa dos Rodrigues, Central do Maranhão, Tasso Fragoso, Feira Nova do Maranhão, Milagres do Maranhão, Governador Luiz Rocha, Marajá do Sena, Santa Filomena do Maranhão, São José dos Basílios, Lajeado Novo, Belágua, São Domingos do Azeitão, Presidente Médici, Amapá do Maranhão, Luís Domingues, São Roberto, Afonso Cunha, Graça Aranha, Junco do Maranhão, Porto Rico do Maranhão, Tufilândia, Sambaíba, Sucupira do Riachão, Bacurituba, Benedito Leite, Nova Colinas, São Raimundo do Doca Bezerra, São Pedro dos Crentes and São Félix de Balsas.

Source: Prepared by the authors, based on IBGE (2018).

Table 1 presents information on (…) ? I didn't really understand, because the quantity is different from the values described above, the total in the table also doesn't match the total in the sample. There is also no discussion about the painting, it was not even called in the text for presentation. If it is a sample, we can see another way of presenting it, without mentioning all the cities, because there are many, leaving this for the appendix.

In this research, the same indicators were used, shown in tables 3 and 4. To identify of service or no, started at the portal from the transparency, after at page initial, in map of site It is, per last, appealed The tool in search, when it is it was made available and working correctly. In this examination, the indicators that were highlighted received a score of 1, and those that were not, received a score of 1. 0.

Regarding data collection, it was carried out between the months of June and July 2021. To identify the sites researched, the search platform was used online website from Google (www.google.com.br), adopting as the standard expression “City Hall Municipal of [name of municipality]”. All electronic pages had the extension “.gov” in your address It is they were considered valid.

The collection of evidence for the construction of the Transparency Level (NT) variable occurred through analysis of information from the portals of each of the municipalities. On a Firstly, the content was accessed by accessing the page's direct links . When the information was not found, the search service was used, if this was available. available at the portal.

The collection of explanatory variables for the level of transparency took place through several sites: Population and GDP were collected from the Foundation for Economics and Statistics website (FEE) (2018 period); IDHM data (2018 data) can be found on the Atlas Brasil website and the IFDM was collected in the FIRJAN System itself, referring to the year 2018, which was the last available; population data were collected from the IBGE website and refer to the year 2018. It should be noted that the most recent data for each of the variables, in order to measure their relationship with the variable dependent.

With regard to variables, the dependent variable of this research is the Level of Transparency (NT), what he was built with base at the study in Ax (2013) and Brocco et al (2018), It is it will be used as a dependent variable, in order to investigate the explanatory factors that can be correlated to the your results. Such variable gathers 16 items in disclosure mandatory required by the LRF and 16 required by the LAI. In Table 2, all you 32 items that make up O NT It is Yours respective legal bases.

Table 2. – Compliance Indicators of the Fiscal Responsibility Law.

Table 2. – Compliance Indicators of the Fiscal Responsibility Law.

Parameters	LRF Compliance Indicators	Legal base
LRF (16 points)	1) Current Multi-Year Plan (PPA)	Art. 48
	2) Previous Multi-Year Plan (PPA)	Art. 48
	3) Current Budget Guidelines Law (LDO)	Art. 48
	4) Previous Budget Guidelines Law (LDO)	Art. 48
	5) Current Annual Budget Law (LOA)	Art. 48
	6) Previous Annual Budget Law (LOA)	Art. 48
	7) Summary Budget Execution Report (RREO)	Art. 48
	8) Fiscal Management Report (RGF)	Art. 48
	9) Simplified version RREO	Art. 48
	10) Simplified version RGF	Art. 48
	11) Accountability report	Art. 48
	12) Prior Accounting Opinion	Art. 48
	13) Encouraging popular participation and public hearings in the process of drafting and discussing the PPA, LDO and LOA	Art. 48
	14) Real-time budget and financial execution information	Art. 48
	15) Disclosure of the dates of public hearings during the process of drafting and discussing the PPA, LDO and LOA	Art. 9
	16) Disclosure of the dates of the four-monthly public hearings to evaluate the fiscal goals	Art. 9

Source: Silva (2013) and Araujo et al (2020).

In the indicators referring to the PPA, the PPA was considered “current” for the period from 2014 to 2017, and “previous” was considered to be the period from 2010 to 2013 or 2006 to 2009. In the indicators pertinent to LDO and LOA, it was validated as “current” to the laws relevant to the year in 2017, It is “previous” any one of period in 2000 The 2016. In the real-time information indicator , they were admitted when they complied with Decree No. 7,185/2010 and were available until the first business day following the accounting record date. Table 3 presents the indicators associated with LAI.

Table 3. – Compliance Indicators of the Access to Information Law.

Table 3. – Compliance Indicators of the Access to Information Law.

Parameters	LAI Compliance Indicators	Legal base
LAI (16 points)	1) Clear indication of access to the Access to Information Law (LAI)	Art. 5
	2) Indication of means for requesting information	Art. 6
	3) Creation of the citizen information service - SIC	Art. 9
	4) Information regarding your skills	Art. 8
	5) Information regarding the organizational structure	Art. 8
	6) Disclosure of the address, telephone numbers and opening hours of the unit(s)	Art. 8
	7) Information regarding transfers or transfers of resources	Art. 8
	8) Information about expenses incurred	Art. 8
	9) Information on tenders, notices and results of competitions	Art. 8
	10) Information about concluded contracts	Art. 8
	11) Information for monitoring programs, actions, projects and works	Art. 8
	12) Availability of “answers and questions” most frequently asked FAQ	Art. 8
	13) Availability of research tools	Art. 8
	14) Permission to record electronic reports	Art. 8
	15) Updating the information made available	Art. 8
	16) Availability of access to information for people with disabilities	Art. 8

Source: Silva (2013) and Araujo et al (2020).

Then, the websites were simultaneously reanalyzed for usability and accessibility, according to the criteria established by Amorim (2012), Amorim and Almada (2016), which are listed in tables 3 and 4.

To quantify the NT, a score is assigned to each of the items listed above: 1 point for the highlighted items; no points for items not highlighted. To determine the level of transparency of a municipality, the number of items highlighted must be divided by the total number of items (32 items), which results in a percentage in decimal that can vary from 0 to 1; the closer to 1, the more transparent the municipality is considered. The formula is demonstrated below In table 4, as well as the determining factors selected from the studies empirical analyzed in the theoretical framework.

Table 4. – Summary of variables used at the study.

Table 4. – Summary of variables used at the study.

Type	Variable	Definition
Dependent	NT	Variable quantitative what varies from 0 to 1, obtained from from the score of 32 items in disclosure mandatory required for the LRF It is for the LAI.
Explanatory	GDP per capita	Indicator of level in activity economic of the municipality.
	total GDP	Indicates the level of total economic activity in the municipality.
	Population	AND O number in population in each County.
	HDI	Index what give me O level in human development on one County, The leave in indicators in education, longevity It is income.
	IFDM	Index what it has O goal in to accompany O human development, economic It is Social in each County.

Source: elaboration own.

4-. ANALYSIS

Table 1. Regression Results.

5-. CONCLUSIONS

References

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