This dataset looks at the number of Brazilian municipalities where mining is a key economic activity and those where mining is a key job provider. We also listed municipalities that have mining as both a main economic activity and the main employer in a given town. We have also generated maps to visualize the impact of mining in Brazil as a key economic activity and/or job provider. Similarly, we have listed the same information for all the municipalities that received oil royalties in 2019, and the top 100 municipalities whose wealth was mainly generated by agribusiness in 2022. By detailing the economic and employment situation in Brazilian extractive municipalities (mining, oil, and crops), we demonstrate that company towns in Brazil (locations that are highly dependent on one key economic activity for employment generation) are few. Extractive industries may no longer be labour intensive, although economic dependency may follow indirectly, such as because of tax generation. We also offer data on national elections to demonstrate that there is not a clear correlation between economic activity and voting patterns, which may be explained by low employability. The main objective of this dataset is to generate a discussion on the need to redefine company towns in Brazil.

The first resource used to collect the data presented here is the Annual List of Social Information – RAIS database – which provides information on employment and companies. RAIS contains social information about each employee of a given firm, such as salary, race, gender, and length of employment. This data must be provided annually by every company based in Brazil. According to the Ministry of Labour and Social Security, every establishment must provide, through the Annual Social Information List (RAIS), information regarding each of its employees, in accordance with Decree No. 10,854, of November 10, 2021. Each line in the RAIS represents a single firm. It contains information such as the number of employees per firm, the address of the firm, and the National Classification of Economic Activities (CNAE 2.0). For the year 2020, the database contains 8,196,730 lines, from all Brazilian companies that delivered information to the annual RAIS. The CNAE is a categorization system that identifies the economic activities carried out by companies in Brazil. Each economic activity is represented by a unique code, which helps with the identification and legal and fiscal framework of companies. Based on the RAIS database, in Table 1 economic activities related to the mineral extraction sector were selected, namely: Section B, codes 05 – Extraction of mineral coal; 07 – Extraction of metallic minerals; 08 – Extraction of non-metallic minerals; and 09 – Activities to support the extraction of minerals. Subsequently, in Table 2 municipalities were selected where one of the aforementioned sectors was the largest employer, measured by the percentage of employees hired in relation to the total number of employees in the municipality.

Another database used to compose our dataset was the voting data of the Superior Electoral Court – Tribunal Superior Eleitoral (TSE). It contains information on the total votes per Brazilian municipality in the Brazilian general elections by all candidates who participated in the election: Federal Deputies, Senators of the Republic, State Governors, and President of the Republic. Data from the 2022 general elections, from the second round for President of the Republic, were used.

Table 1 presents the municipalities where in 2020 the mining sector had the highest percentage of employment generation in relation to other economic sectors in the municipality. In total, there were 37 such municipalities, distributed across 8 Brazilian states. Then, as mentioned previously, the results were examined alongside TSE voting data.

Regarding Table 2, another source of data used was Gross Domestic Product – municipal GDP 2020, published annually by the Brazilian Institute of Geography and Statistics (IBGE). In it, the Institute presents which economic activity contributes most to the municipality’s added value. Therefore, we selected the municipalities where the mining industry produces the highest added value to GDP. Table 2 presents the results: 55 Brazilian municipalities have this characteristic. Municipalities from Tables 1 and 2 have columns presenting TSE voting data.

Table 3, in turn, combines Tables 1 and 2. It presents the municipalities in which mineral extraction activity is both the main employer and generates the highest added value to GDP.

Maps 1, 2, and 3 plot the locations of the municipalities found in Tables 1, 2, and 3, respectively.

Table 4 presents the Brazilian municipalities that received oil royalties in Brazil in 2019. Municipalities entitled to royalties are those that are in some way affected by the oil industry, the information regarding current revenues is consolidated by Secretaria do Tesouro Nacional (National Treasury of Brazil) and oil royalties’ information is provided by the Agencia Nacional do Petroleo (ANP) National Oil Agency. In addition, the voting pattern for the President of the Republic in each municipality that received royalties was also presented.

Table 5 presents the 100 top Brazilian municipalities with the highest Agricultural Production Value, according to the IBGE Monthly Agricultural Production survey for the year 2020 as described by the Ministry of Agriculture. TSE voting data was added for each of those municipalities.

Usage notes

Company towns are usually referred to in the literature as locations highly economically dependent on the production of one company that generates most employment opportunities in a region; this economic activity therefore has major political influence in the area. Our dataset presents municipalities with strong oil, mining, and agribusiness economic presence but that is not always followed by employment generation. From this data, users can see that economic markers such as GDP and employment creation indicate a company town pattern in Brazil where income generation and direct employment are not strongly correlated. Thus, the dominant economic activity in those towns does not determine electoral results.