GDP from Manufacturing in Brazil decreased to 28401.72 BRL Million in the first quarter of 2025 from 30431.24 BRL Million in the fourth quarter of 2024. This dataset provides the latest reported value for - Brazil Gdp From Manufacturing - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Gross Domestic Product (GDP) in Brazil was worth 2179.41 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Brazil represents 2.05 percent of the world economy. This dataset provides the latest reported value for - Brazil GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

With over 20 million businesses, all held in-house, BoldData has the largest supply of Brazilian B2B data. We can select your perfect target based on numerous interesting selections: from 3,000 industries to region, turnover, sector, contact person and the number of employees.

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Industrial Production in Brazil increased 3.30 percent in May of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Brazil Industrial Production - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Concept: Balance on goods accounts transactions on items that are result of productive activities. Goods are physical, produced items over which ownership rights can be established and whose economic ownership can be passed from one institutional unit to another by engaging in transactions. The Balance on goods is divided in exports and imports. The exports registers the selling of goods from residents to nonresidents and the imports registers the purchases of goods by residents from nonresidents. The trade balance is compiled by the Ministry of Development, Industry and Foreign Trade (MDIC) based on custom records of the Integrated Foreign Trade System (Siscomex), and are adjusted by the Central Bank with the goal of an ampler coverage. Information published by the MDIC are therefore incorporated to electric energy purchases that are not registered on Siscomex. Additionally, exports and imports are adjusted by the inclusion of operations in which the product is traded with a nonresident but doesn’t cross the border of the original country. Lastly, the trade balance incorporates international postal delivery registration. Source: Central Bank of Brazil – Department of Economics 22709-imports---balance-of-payments---monthly 22709-imports---balance-of-payments---monthly

Analysis of ‘Brazil Largest Companies’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/brazil-largest-companiese on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

From the Forbes Global 2000 list​ last updated on May 2013. Forbes publishes an annual list of the world's 2000 largest publicly listed corporations. ​The Forbes Global 2000 weigh​s​ sales, profits, assets and market value​ equally​ so companies can be ranked by size. Figures for all companies are in US dollars.

​Source: Economy Watch

This dataset was created by Finance and contains around 0 samples along with Assets ($billion), Sales ($billion), technical information and other features such as: - Profits ($billion) - Market Value ($billion) - and more.

How to use this dataset

• Analyze Assets ($billion) in relation to Sales ($billion)
• Study the influence of Profits ($billion) on Market Value ($billion)
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Acknowledgements

If you use this dataset in your research, please credit Finance

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--- Original source retains full ownership of the source dataset ---

Exports in Brazil decreased to 29146.70 USD Million in June from 30156.20 USD Million in May of 2025. This dataset provides the latest reported value for - Brazil Exports - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Concept: Number of branches of finance companies in Brazil

The 2003 Santarem dataset consists of 8 interconnected datasets and 1 linking file. The primary unit of analysis is the rural property or lot. Each lot in the sample contains a minimum of 1 household with a mean of 1.33 households per lot in the final sample. Within households, data were collected on subsets of individuals as well as additional properties used by the households in the study. These 2003 Santarem data come from interviews with farm families in an agricultural zone south of the city of Santarem in the Brazilian state of Para. Santarem is a relatively old settlement within the Brazilian Amazon that has experienced waves of regional settlement in the 1930s, mid-century, and the 1970s. The study region is adjacent to the confluence of the Amazon and Tapajos Rivers and the northern terminus of the BR-163 (the Cuiaba-Santarem Highway). BR-163 links intensive agropastoral production (particularly mechanized soybean farming) in the state of Mato Grosso to Santarem, where the multinational corporation Cargill runs a deepwater port (opened in 2003) for loading soybeans onto oceangoing ships. The opening of this port has accelerated the process of urbanization and led to a transformation from a landscape of small family farming to a landscape of mechanized agriculture (description adapted from VanWey, Leah K., and Kara B. Cebulko, 2007, Journal of Marriage and the Family 69: 1257-1270). The discourse on deforestation has focused on the alarming rates of deforestation in the Amazon Basin to the neglect of the dynamic and reciprocal influences between the human population and the environment. Deforestation is a process mediated by human intervention, from the act of clearing to how such a clearing is used and managed over time. It would be helpful to know whether observable rates of forest removal represent a stage in the developmental cycle of households or represents the simple and direct impact of increasing population in these environments. From the point of view of theory and method, it is necessary to develop new approaches that effectively link demographic process to the interactive relationship of population to specific aspects of an environmental matrix. This project addressed multiple scales, from household dynamics to landscape dynamics and has developed methods by which to scale between them. We hypothesize that as households occupy frontier areas past the first generation, they move from a strategy of managing their land under the constraints of available household labor to a strategy that gives greater recognition of the constraints posed by land quality and of the risks to their farm operation coming from external socioeconomic forces and biophysical constraints. In the first generation, the labor available to a household is determined by the size of the household making the initial trip to the frontier (primarily young couples is common in frontier regions) and later by the fertility of these initial migrants. As these initial migrants age and their children enter adulthood (thereby becoming the second generation), labor supply is determined by the reproductive and land use choices of these children. Given the precipitous decline in female fertility, other factors gain salience in the second generation: the suitability of the land for various uses, the availability of off-farm employment and educational opportunities (both locally and those requiring migration), and macroeconomic factors affecting the economic viability of farming. These decisions then directly determine the entries into and exits from the household. This study investigated five basic questions: (1) Does the changing availability of household labor over the household life cycle affect the trajectory of deforestation and land use change in the same way for later generations of Amazonian farmers as for first generation in-migrants? (2) What are the determinants of changing household labor supply? Specifically, what are the biophysical and socioeconomic determinants of entries into and exits from the household through fertility, migration, and marriage? (3) How are the decisions of households regarding land use and labor allocation constrained by soil quality, access to water supplies, interannual drought events (e.g. El Nino type events), and other resource scarcities? (4) Are there notable differences in land use choices made by la

This data set is a subset of the "Records of foreign capital" (Registros de capitais estrangeiros", RCE) published by the Central Bank of Brazil (CBB) on their website. The data set consists of three data files and three corresponding metadata files. All files are in openly accessible .csv or .txt formats. See detailed outline below for data contained in each. Data files contain transaction-specific data such as unique identifier, currency, cancelled status and amount. Metadata files outline variables in the corresponding data file. RCE_Unclean_full_dataset.csv - all transactions published to the Central Bank website from the four main categories outlined below Metadata_Unclean_full_dataset.csv RCE_Unclean_cancelled_dataset.csv - data extracted from the RCE_Unclean_full_dataset.csv where transactions were registered then cancelled Metadata_Unclean_cancelled_dataset.csvRCE_Clean_selection_dataset.csv - transaction data extracted from RCE_Unclean_full_dataset.csv and RCE_Unclean_cancelled_dataset.csv for the nine companies and criteria identified below Metadata_Clean_selection_dataset.csv The data include the period between October 2000 and July 2011. This is the only time span for the data provided by the Central Bank of Brazil at this stage. The records were published monthly by the Central Bank of Brazil as required by Art. 66 in Decree nº 55.762 of 17 February 1965, modified by Decree nº 4.842 of 17 September 2003. The records were published on the bank’s website starting October 2000, as per communique nº 011489 of 7 October 2003. This remained the case until August 2011, after which the amount of each transaction was no longer disclosed (and publication of these stopped altogether after October 2011). The disclosure of the records was suspended in order to review their legal and technical aspects, and ensure their suitability to the requirements of the rules governing the confidentiality of the information (Law nº 12.527 of 18 November 2011 and Decree nº 7724 of May 2012) (pers. comm. Central Bank of Brazil, 2016. Name of contact available upon request to Authors). The records track transfers of foreign capital made from abroad to companies domiciled in Brazil, with information on the foreign company (name and country) transferring the money, and on the company receiving the capital (name and federative unit). For the purpose of this study, we consider the four categories of foreign capital transactions which are published with their amount and currency in the Central Bank’s data, and which are all part of the “Register of financial transactions” (abbreviated RDE-ROF): loans, leasing, financed import and cash in advance (see below for a detailed description). Additional categories exist, such as foreign direct investment (RDE-IED) and External Investment in Portfolio (RDE-Portfólio), for which no amount is published and which are therefore not included. We used the data posted online as PDFs on the bank’s website, and created a script to extract the data automatically from these four categories into the RCE_Unclean_full_dataset.csv file. This data set has not been double-checked manually and may contain errors. We used a similar script to extract rows from the "cancelled transactions" sections of the PDFs into the RCE_Unclean_cancelled_dataset.csv file. This is useful to identify transactions that have been registered to the Central Bank but later cancelled. This data set has not been double-checked manually and may contain errors. From these raw data sets, we conducted the following selections and calculations in order to create the RCE_Clean_selection_dataset.csv file. This data set has been double-checked manually to secure that no errors have been made in the extraction process. We selected all transactions whose recipient company name corresponds to one of these nine companies, or to one of their known subsidiaries in Brazil, according to the list of subsidiaries recorded in the Orbis database, maintained by Bureau Van Dijk. Transactions are included if the recipient company name matches one of the following: - the current or former name of one of the nine companies in our sample (former names are identified using Orbis, Bloomberg’s company profiles or the company website); - the name of a known subsidiary of one of the nine companies, if and only if we find evidence (in Orbis, Bloomberg’s company profiles or on the company website) that this subsidiary was owned at some point during the period 2000-2011, and that it operated in a sector related to the soy or beef industry (including fertilizers and trading activities). For each transaction, we extracted the name of the company sending capital and when possible, attributed the transaction to the known ultimate owner. The name of the countries of origin sometimes comes with typos or different denominations: we harmonized them. A manual check of all the selected data unveiled that a few transactions (n=14), appear twice in the database while bearing the same unique identification number. According to the Central Bank of Brazil (pers. comm., November 2016), this is due to errors in their routine of data extraction. We therefore deleted duplicates in our database, keeping only the latest occurrence of each unique transaction. Six (6) transactions recorded with an amount of zero were also deleted. Two (2) transactions registered in August 2003 with incoherent currencies (Deutsche Mark and Dutch guilder, which were demonetised in early 2002) were also deleted. To secure that the import of data from PDF to the database did not contain any systematic errors, for instance due to mistakes in coding, data were checked in two ways. First, because the script identifies the end of the row in the PDF using the amount of the transaction, which can sometimes fail if the amount is not entered correctly, we went through the extracted raw data (2798 rows) and cleaned all rows whose end had not been correctly identified by the script. Next, we manually double-checked the 486 largest transactions representing 90% of the total amount of capital inflows, as well as 140 randomly selected additional rows representing 5% of the total rows, compared the extracted data to the original PDFs, and found no mistakes. Transfers recorded in the database have been made in different currencies, including US dollars, Euros, Japanese Yens, Brazilian Reais, and more. The conversion to US dollars of all amounts denominated in other currencies was done using the average monthly exchange rate as published by the International Monetary Fund (International Financial Statistics: Exchange rates, national currency per US dollar, period average). Due to the limited time period, we have not corrected for inflation but aggregated nominal amounts in USD over the period 2000-2011.

The categories loans, cash in advance (anticipated payment for exports), financed import, and leasing/rental, are those used by the Central Bank of Brazil in their published data. They are denominated respectively: “Loans” (“emprestimos” in original source) - : includes all loans, either contracted directly with creditors or indirectly through the issuance of securities, brokered by foreign agents. “Anticipated payment for exports” (“pagamento/renovacao pagamento antecipado de exportacao” in original source): defined as a type of loan (used in trade finance)“Financed import” (“importacao financiada” in original source): comprises all import financing transactions either direct (contracted by the importer with a foreign bank or with a foreign supplier), or indirect (contracted by Brazilian banks with foreign banks on behalf of Brazilian importers). They must be declared to the Central Bank if their term of payment is superior to 360 days.“Leasing/rental” (“arrendamento mercantil, leasing e aluguel” in original source) : concerns all types of external leasing operations consented by a Brazilian entity to a foreign one. They must be declared if the term of payment is superior to 360 days.More information about the different categories can be found through the Central Bank online. (Research Data Support provided by Springer Nature)

Concept: Number of electronic service outposts of finance companies in Brazil Source: Information System on Entities Related to the BCB 25005-number-of-electronic-service-outposts-of-finance-companies-in-brazil 25005-number-of-electronic-service-outposts-of-finance-companies-in-brazil

Concept: Number of headquarters of finance companies in Brazil

The Gross Domestic Product (GDP) in Brazil expanded 1.40 percent in the first quarter of 2025 over the previous quarter. This dataset provides - Brazil GDP Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

GDP from Services in Brazil decreased to 201813.29 BRL Million in the first quarter of 2025 from 210207.12 BRL Million in the fourth quarter of 2024. This dataset provides the latest reported value for - Brazil Gdp From Services - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Manufacturing Production in Brazil increased 2.30 percent in May of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Brazil Manufacturing Production - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Gross Domestic Product (GDP) in Brazil expanded 2.90 percent in the first quarter of 2025 over the same quarter of the previous year. This dataset provides - Brazil GDP Annual Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Crude Oil Production in Brazil increased to 3620.81 BBL/D/1K in March from 3488.10 BBL/D/1K in February of 2025. This dataset provides the latest reported value for - Brazil Crude Oil Production - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Business Confidence in Brazil decreased to 47.30 points in July from 48.60 points in June of 2025. This dataset provides - Brazil Business Confidence - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Wages in Manufacturing in Brazil decreased to 3356 BRL/Month in May from 3360 BRL/Month in April of 2025. This dataset provides - Brazil Wages In Manufacturing Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Brazil Automobile Industry: Share in Industrial GDP data was reported at 18.700 % in 2012. This records a decrease from the previous number of 18.900 % for 2011. Brazil Automobile Industry: Share in Industrial GDP data is updated yearly, averaging 14.600 % from Dec 1966 (Median) to 2012, with 47 observations. The data reached an all-time high of 20.600 % in 1975 and a record low of 10.600 % in 1990. Brazil Automobile Industry: Share in Industrial GDP data remains active status in CEIC and is reported by National Association of Automobile Manufacturers. The data is categorized under Brazil Premium Database’s Automobile Sector – Table BR.RAI001: Automobile Industry. The annual data brings together statistical information of the Brazilian automobile industry on motor vehicles and agricultural machinery