ITH: an open database on Italian Tenders 2016-2023

Abstract

Governments procure large amounts of goods and services to help them implement policies and deliver public services; in Italy, this is an essential sector, corresponding to about 12% of the gross domestic product. Data are increasingly recorded in public repositories, although they are often divided into multiple sources and not immediately available for consultation. This paper provides a description and analysis of an effort to collect and arrange a legal public administration database. The main source of interest involves the National Anti-Corruption Authority in Italy, which describes more than 3 million tenders. To improve usability, the database is integrated with two other relevant data sources concerning information on public entities and territorial units for statistical purposes. The period identified by domain experts covers 2016-2023. The analysis also identifies key challenges that arise from the current Open Data catalogue, particularly in terms of data completeness. A practical application is described with an example of use. The final dataset, called Italian Tender Hub (ITH), is available in a repository with a description of its use.

Fig. 1

Workflow with data collection, database creation via SQL script, testing, and publication of the final relational database on Zenodo. Full size image available at https://github.com/roberto-nai/ScientificData2024.

Fig. 2

(a) A page of the ANAC website for downloading CSV files (by year and month); (b) CSV file data preview. Full size image available at https://github.com/roberto-nai/ScientificData2024.

Fig. 3

Data model of the ITH database. Full size image available at https://github.com/roberto-nai/ScientificData2024.

Fig. 4

Example of an SQL query to import a CSV file into the corresponding database table; the text in blue is related to the standard SQL syntax while the text in orange (between the quotation marks) and black (without quotation marks) is the customisation of the query parameters. Full size image available at https://github.com/roberto-nai/ScientificData2024.

Fig. 5

Example of an SQL query to extract the tenders of the year 2016 by merging various tables; the text in blue is related to the standard SQL syntax while the text in black is the customisation of the query parameters. Full size image available at https://github.com/roberto-nai/ScientificData2024.

Fig. 6

Distribution of NUTS (geographical location of CAs) and CA types; (a) distribution of the 5 most present NUTS; (b) aggregation of NUTS by area; (c) distribution of the 5 most present CA types. Full size image available at https://github.com/roberto-nai/ScientificData2024.

Fig. 7

Map of Italy with darker colours for regions with higher investments and lighter colours for regions with lower expenditures, by CA category (sourced from BDAP). Full size image available at https://github.com/roberto-nai/ScientificData2024.