Historical vehicles tax-filing and tax-payment behavior in Antioquia - Colombia
Principal Investigator(s): View help for Principal Investigator(s) Juan Pablo Ramirez-Madrid, Universidad de Medellin; Manuela Escobar-Sierra, Universidad de Medellin; Isaias Lans-Vargas, Quipux; Juan Manuel Montes Hincapie, Universidad de Medellin
Version: View help for Version V1
Version Title: View help for Version Title Antioquia 2015 - 2020
Project Citation:
Ramirez-Madrid, Juan Pablo, Escobar-Sierra, Manuela, Lans-Vargas, Isaias, and Montes Hincapie, Juan Manuel. Historical vehicles tax-filing and tax-payment behavior in Antioquia - Colombia. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2021-10-16. https://doi.org/10.3886/E152221V1
Project Description
Summary:
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The data refers to the Department of Antioquia’s annual vehicle tax filing and payment services, available via a website (www.vehiculosantioquia.com), a mobile application, and physical offices. Also, the data includes the actions performed by the government influencing the services.
For these services, the Department has total control and can issue regulations ruling the process, the service design, technology selection, and the channels to offer the services. The Department of Antioquia has 1.5 million vehicles, and approximately 1.2 million are subject to this tax (motorcycles with engines smaller than 125 cm3, public transport vehicles, and government vehicles are exempt). The annual payment rate is 79%, with the remaining 21% becoming defaulters. Almost 25% of the Department’s annual income comprises overdue amounts, including penalties and interest.
This information was extracted from the operational database and contains the weekly consolidated data from 2015 to 2020 (11,235,510 million filing records and 5,006,830 million payments from 2015 to 2020, in 313 weeks), including service type (e-filing, e-payment), week (represented by the Sunday dates), number of services for overdue and current term per channel (current term physical, current term e-government, due term physical, and due term e-government). We studied the behavior of the weekly adoption of e-government by citizens (AEC) rate. Our AEC measurement was as follows: the number of services requested in the e-government channel divided by the total number of services requested.
In parallel, to provide context for the data, we collected data from physical records and documents, digital records, and relevant laws and regulations. We conducted several interviews with the service operation manager and staff from 2018 to 2021 to identify all actions related to the services performed for three years, starting from 1 January 2018 to 31 December 2020. Each action performed during the three years was classified according to the institutional factor groups, used as independent variables, and analyzed.
For these services, the Department has total control and can issue regulations ruling the process, the service design, technology selection, and the channels to offer the services. The Department of Antioquia has 1.5 million vehicles, and approximately 1.2 million are subject to this tax (motorcycles with engines smaller than 125 cm3, public transport vehicles, and government vehicles are exempt). The annual payment rate is 79%, with the remaining 21% becoming defaulters. Almost 25% of the Department’s annual income comprises overdue amounts, including penalties and interest.
This information was extracted from the operational database and contains the weekly consolidated data from 2015 to 2020 (11,235,510 million filing records and 5,006,830 million payments from 2015 to 2020, in 313 weeks), including service type (e-filing, e-payment), week (represented by the Sunday dates), number of services for overdue and current term per channel (current term physical, current term e-government, due term physical, and due term e-government). We studied the behavior of the weekly adoption of e-government by citizens (AEC) rate. Our AEC measurement was as follows: the number of services requested in the e-government channel divided by the total number of services requested.
In parallel, to provide context for the data, we collected data from physical records and documents, digital records, and relevant laws and regulations. We conducted several interviews with the service operation manager and staff from 2018 to 2021 to identify all actions related to the services performed for three years, starting from 1 January 2018 to 31 December 2020. Each action performed during the three years was classified according to the institutional factor groups, used as independent variables, and analyzed.
Scope of Project
Subject Terms:
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Colombia;
Latin America;
e-government adoption;
citizens
Geographic Coverage:
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Latin America
Time Period(s):
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1/1/2015 – 12/31/2020
Collection Date(s):
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1/1/2015 – 12/31/2020
Universe:
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11,235,510 million filing records and 5,006,830 million payments from 2015 to 2020
Data Type(s):
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administrative records data;
aggregate data
Collection Notes:
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The TXT is in CVS format. We uploaded the data in SPSS for statistical analysis.
Methodology
Response Rate:
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We had access to the universe of transactions.
Sampling:
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We had access to the universe of transactions.
Data Source:
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- The operational database analysis.
- In parallel, to provide context for the data, we collected data from physical records and documents, digital records, and relevant laws and regulations. We conducted several interviews with the service operation manager and staff from 2018 to 2021 to identify all actions related to the services performed for three years, starting from 1 January 2018 to 31 December 2020. Each action performed during the three years was classified according to the institutional factor groups, used as independent variables, and analyzed. We related these actions with the AEC rate behavior. For context, the findings were discussed with the staff. All the data from different sources support the credibility of the findings since they allow triangulation and capture contextual complexity
- In summary, we observed actions for knowledge deployment, conformance to the environment (fulfilling demand-side expectations with software updates, quality of service, and quality of software), mobilization, coercive pressure (laws and regulations for due dates, discounts and penalties, and measures in 2020 related to COVID-19).
- In parallel, to provide context for the data, we collected data from physical records and documents, digital records, and relevant laws and regulations. We conducted several interviews with the service operation manager and staff from 2018 to 2021 to identify all actions related to the services performed for three years, starting from 1 January 2018 to 31 December 2020. Each action performed during the three years was classified according to the institutional factor groups, used as independent variables, and analyzed. We related these actions with the AEC rate behavior. For context, the findings were discussed with the staff. All the data from different sources support the credibility of the findings since they allow triangulation and capture contextual complexity
- In summary, we observed actions for knowledge deployment, conformance to the environment (fulfilling demand-side expectations with software updates, quality of service, and quality of software), mobilization, coercive pressure (laws and regulations for due dates, discounts and penalties, and measures in 2020 related to COVID-19).
Collection Mode(s):
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mixed mode
Scales:
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NA
Weights:
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1. The adoption of e-government by citizens (AEC) measurement was as follows: the number of services requested in the e-government channel divided by the total number of services requested. We have AEC for e-payment (NIVELADOPTPAGOS) and AEC for e-filing (NIVELADOPTLIQ).
2. We consolidated the campaigns for every week, adding those records promoting e-government and subtracting records promoting physical offices. We then normalized the values for this variable (CAMPANNASTOTAL).
3. Conformance to the environment: We used a cumulative variable that added one to the value for every software update (SWUPDACUM).
4. Payment (coercive pressure): We used this variable to establish the value to pay (PAGOPERIODO); 0.8 for the 20% discount period, 0.9 for the 10% discount period, 1.0 for the no-discount period, and 1.0 plus monthly interest rate for the weeks after the due date.
2. We consolidated the campaigns for every week, adding those records promoting e-government and subtracting records promoting physical offices. We then normalized the values for this variable (CAMPANNASTOTAL).
3. Conformance to the environment: We used a cumulative variable that added one to the value for every software update (SWUPDACUM).
4. Payment (coercive pressure): We used this variable to establish the value to pay (PAGOPERIODO); 0.8 for the 20% discount period, 0.9 for the 10% discount period, 1.0 for the no-discount period, and 1.0 plus monthly interest rate for the weeks after the due date.
5. Laws and regulations (coercive pressure): For this variable (Leyes_reglas), we used 1 if any law or regulation showed influence in that week, and 0 if it did not.
6. COVID-19 (coercive pressure): For this variable (COVID19), we used 1 for the weeks the lockdown lasted, and 0 for the other week
Unit(s) of Observation:
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Weekly service behavior for tax filing and tax-payment
Geographic Unit:
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Service channel (physical office, online office)
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