The Gross Domestic Product (GDP) in Germany stagnated 0 percent in the third quarter of 2025 over the previous quarter. This dataset provides the latest reported value for - Germany GDP Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This ranking displays the results of the worldwide Made-In-Country Index 2017, a survey conducted to show how positively products "made in..." are perceived in various countries all over the world. During this survey, 77 percent of respondents from Germany perceived products made in Switzerland as "slightly positive" or "very positive". The survey indicates that Swiss products have the strongest reputation in Germany, followed by EU products.

Germany DE: GDP: Growth: Final Consumption Expenditure data was reported at 1.709 % in 2017. This records a decrease from the previous number of 2.560 % for 2016. Germany DE: GDP: Growth: Final Consumption Expenditure data is updated yearly, averaging 1.709 % from Dec 1971 (Median) to 2017, with 47 observations. The data reached an all-time high of 5.691 % in 1971 and a record low of -1.022 % in 1982. Germany DE: GDP: Growth: Final Consumption Expenditure data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Gross Domestic Product: Annual Growth Rate. Average annual growth of final consumption expenditure based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. Final consumption expenditure (formerly total consumption) is the sum of household final consumption expenditure (formerly private consumption) and general government final consumption expenditure (formerly general government consumption). This estimate includes any statistical discrepancy in the use of resources relative to the supply of resources.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted average;

This statistic displays the results of the worldwide Made-In-Country Index 2017, a survey conducted to show how positively products "made in..." are perceived in various countries all over the world. For this statistic, respondents were asked about attributes they associate with products made in Germany. 49 percent of respondents stated that they associate "high quality" with products from Germany.

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems. By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National coverage.

Analysis unit

Individuals

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world’s population (see table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer’s gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size was 1000.

Mode of data collection

Landline and Cellular Telephone

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

Germany’s GDP per capita stood at almost 54,989.76 U.S. dollars in 2024. Germany ranked among the top 20 countries worldwide with the highest GDP per capita in 2021 – Luxembourg, Ireland and Switzerland were ranked the top three nations. Rising annual income in Germany The average annual wage in Germany has increased by around 5,000 euros since 2000, reaching in excess of 39,000 euros in 2016. Germany had the tenth-highest average annual wage among selected European Union countries in 2017, ranking between France and the United Kingdom. Growing employment More than two thirds of the working population in Germany are employed in the service sector, which generated the greatest share of the country’s GDP in 2018. Unemployment in Germany soared to its highest level in decades in 2005, but the rate has since dropped to below 3.5 percent. The youth unemployment rate in Germany has more than halved since 2005 and currently stands around 6.5 percent.

This dataset contains the financial records of 12 major German companies, including top players like Volkswagen AG, Siemens AG, Allianz SE, BMW AG, BASF SE, Deutsche Telekom AG, Daimler AG, SAP SE, Bayer AG, Deutsche Bank AG, Porsche AG, and Merck KGaA. Covering quarterly data from 2017 to 2024, this dataset is designed to provide insights into key financial metrics, allowing for indepth analysis and modeling of corporate financial health, performance, and growth trends. this comprehensive dataset is highly suitable for tasks such as financial forecasting, risk analysis, profitability assessment, and performance benchmarking. Each entry represents one quarter’s financial snapshot for a company, enabling robust time series and cross-sectional analyses.

Data Sources:

Company: Name of the company to which the financial data corresponds (e.g., "Volkswagen AG"). This field categorizes the data and enables cross-company comparisons and individual company trend analysis.

Period: The specific quarter (in year-month format) when the financial data was recorded (e.g., "2017-03-31" for Q1 of 2017). This field is crucial for time-series analysis, allowing users to track financial trends and performance over time.

Revenue: The total revenue of the company for that quarter, measured in billions of Euros. This field provides insight into the company’s sales performance and market reach within each period.

Net Income: The net income (profit after all expenses) of the company for the given quarter, also in billions of Euros. Net income is a key indicator of a company’s profitability and financial efficiency.

Liabilities: The total liabilities (debt and obligations) of the company for the quarter, in billions of Euros. This metric helps gauge the company’s financial leverage and debt exposure, essential for risk assessment.

Assets: The total assets (all owned resources with economic value) for the company in billions of Euros. This metric reflects the scale of the company’s holdings and resources available for operations and investments.

Equity: The shareholder equity calculated as Assets minus Liabilities, in billions of Euros. Equity indicates the residual value owned by shareholders and serves as a core metric for assessing financial stability and value creation.

ROA (%): Return on Assets (ROA), expressed as a percentage, calculated as (Net Income / Assets) * 100. ROA shows how efficiently a company is utilizing its assets to generate profit, an essential measure of operational effectiveness.

ROE (%): Return on Equity (ROE), expressed as a percentage, calculated as (Net Income / Equity) * 100. ROE is a key indicator of financial performance and profitability, reflecting the rate of return on shareholders' investment.

Debt to Equity: The ratio of Liabilities to Equity. This metric provides insights into the company’s capital structure and financial leverage, aiding in risk assessment by showing how much of the company’s operations are funded through debt compared to shareholder equity.

Graph and download economic data for Consumer Price Index: Housing for Germany (DEUCPIHOUAINMEI) from 1960 to 2017 about Germany, CPI, housing, price index, indexes, and price.

Inflation Rate in Germany remained unchanged at 2.30 percent in November. This dataset provides the latest reported value for - Germany Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Germany DE: IMF Account: Fund Position: USD: UFC: Outstanding Loans: Structural Adj. Facility, Poverty Reduction and Growth Facility & Trust Fund data was reported at 0.000 USD mn in 2017. This stayed constant from the previous number of 0.000 USD mn for 2016. Germany DE: IMF Account: Fund Position: USD: UFC: Outstanding Loans: Structural Adj. Facility, Poverty Reduction and Growth Facility & Trust Fund data is updated yearly, averaging 0.000 USD mn from Dec 1945 (Median) to 2017, with 73 observations. Germany DE: IMF Account: Fund Position: USD: UFC: Outstanding Loans: Structural Adj. Facility, Poverty Reduction and Growth Facility & Trust Fund data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Germany – Table DE.IMF.IFS: IMF Account: Fund Position: Annual.

Graph and download economic data for Consumer Price Index: Food for Germany (DEUCPIFODAINMEI) from 1960 to 2017 about Germany, food, CPI, price index, indexes, and price.

Time series data for the statistic Net_Bilateral_Aid_Germany_to_Seychelles. Indicator Definition:Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.The indicator "Net bilateral aid flows from a DAC donor (US$)" stands at 0.0085 Million usd as of 12/31/2017. Regarding the One-Year-Change of the series, the current value constitutes an increase of 0.0018 Million compared to the value the year prior.The Serie's long term average value is 0.319 Million usd. It's latest available value, on 12/31/2017, is -0.311 Million lower, compared to it's long term average value.The Serie's change from it's minimum value, on 12/31/2003, to it's latest available value, on 12/31/2017, is +0.5785 Million.The Serie's change from it's maximum value, on 12/31/1990, to it's latest available value, on 12/31/2017, is -1.45 Million.

This dataset encapsulates the financial records of 12 leading German companies, presenting a detailed compilation of quarterly data from 2017 to 2024. The dataset features top-tier corporations such as Volkswagen AG, Siemens AG, Allianz SE, BMW AG, BASF SE, Deutsche Telekom AG, Daimler AG, SAP SE, Bayer AG, Deutsche Bank AG, Porsche AG, and Merck KGaA. It offers an extensive view into critical financial metrics, fostering comprehensive analysis and modeling of corporate financial health, performance trends, and growth trajectories.

Designed for various analytical applications, this dataset is an invaluable resource for financial forecasting, risk analysis, profitability assessment, and performance benchmarking. Each record represents a single quarter's financial snapshot for a specific company, enabling users to conduct robust time-series analysis and cross-sectional evaluations. The dataset provides granular insights into revenue generation, profitability, asset management, and financial leverage, supporting informed decision-making and strategic planning.

Data Fields and Their Significance: Company: This field identifies the company associated with the financial data, such as "Volkswagen AG" or "Siemens AG." It categorizes the data for cross-company comparisons and trend analysis of individual organizations.

Period: Representing the specific quarter in year-month format (e.g., "2017-03-31" for Q1 2017), this field is critical for tracking temporal trends in financial performance, allowing users to analyze year-over-year or quarter-over-quarter changes.

Revenue: Captured in billions of Euros, revenue reflects the total sales performance of a company for the given quarter. It provides insights into the company’s market reach and the demand for its products or services during each period.

Net Income: Expressed in billions of Euros, net income denotes the company’s profit after all expenses for the quarter. This metric is a cornerstone of profitability analysis, reflecting the financial efficiency and success of operational strategies.

Liabilities: Recorded in billions of Euros, liabilities represent the total debt and obligations of a company for a specific quarter. This data is essential for understanding the company’s financial leverage and assessing its exposure to financial risks.

Assets: Assets, measured in billions of Euros, encompass all resources owned by a company with economic value. This metric reflects the scale and capacity of the company’s operations and investments, serving as a benchmark for evaluating organizational size and financial resourcefulness.

Equity: Equity is calculated as Assets minus Liabilities and is expressed in billions of Euros. This metric represents the residual value available to shareholders, offering insights into financial stability and value creation within the organization.

ROA (Return on Assets): ROA, expressed as a percentage, is derived from the formula ( Net Income / Assets ) × 100 (Net Income/Assets)×100. It measures the company’s ability to generate profit from its assets, providing a lens into operational efficiency.

ROE (Return on Equity): Calculated as ( Net Income / Equity ) × 100 (Net Income/Equity)×100, ROE, expressed as a percentage, highlights the profitability of a company from shareholders' investments, serving as a key performance indicator.

Debt to Equity Ratio: This ratio, representing the proportion of Liabilities to Equity, sheds light on the company’s capital structure. It is crucial for understanding financial leverage, revealing the balance between debt financing and shareholder equity in the company's operations.

This comprehensive dataset is tailored to meet the needs of analysts, researchers, and industry professionals, facilitating in-depth studies and decision-making processes. By encompassing a diverse range of financial metrics over an extended time frame, it provides a rich foundation for examining the dynamics of corporate performance in one of the world's most robust economies.

Germany CC: Development of Number of Employees During Last 3 Months data was reported at 22.600 Balances in May 2021. This records an increase from the previous number of 16.900 Balances for Apr 2021. Germany CC: Development of Number of Employees During Last 3 Months data is updated monthly, averaging 23.200 Balances from Jan 2005 (Median) to May 2021, with 197 observations. The data reached an all-time high of 39.000 Balances in Dec 2017 and a record low of -3.200 Balances in Jul 2009. Germany CC: Development of Number of Employees During Last 3 Months data remains active status in CEIC and is reported by Ifo Institute - Leibniz Institute for Economic Research at the University of Munich. The data is categorized under Global Database’s Germany – Table DE.S027: Business Survey: Service Sector: IFO Institute: WZ 2008.

Time series data for the statistic Net_Bilateral_Aid_Germany_to_Chile. Indicator Definition:Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovienia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.The indicator "Net bilateral aid flows from a DAC donor (US$)" stands at 24.63 Million usd as of 12/31/2017. Regarding the One-Year-Change of the series, the current value constitutes an decrease of -110.54 Million compared to the value the year prior.The Serie's long term average value is 23.61 Million usd. It's latest available value, on 12/31/2017, is 1.02 Million higher, compared to it's long term average value.The Serie's change from it's minimum value, on 12/31/1979, to it's latest available value, on 12/31/2017, is +38.76 Million.The Serie's change from it's maximum value, on 12/31/2014, to it's latest available value, on 12/31/2017, is -137.83 Million.

The services sector is the largest in Germany and has been generating a steady share of around 63 percent since the late 2010s.

Graph and download economic data for Consumer Price Index: Total Food Excluding Restaurants for Germany (CPGDFD02DEA659N) from 1960 to 2017 about restaurant, Germany, food, goods, CPI, price index, indexes, and price.

Graph and download economic data for Retail Trade Sales: Total Car Registrations for Germany (SLRTCR01DEA180N) from 1960 to 2017 about car registrations, Germany, retail trade, sales, and retail.

Political issues (Issues). Political attitudes and behaviour. Opinion formation during election campaigns.

Topics: Political interest; satisfaction with democracy; Big Five (psychological self-characterisation); intention to participate in elections; intended vote on BTW (first and second vote); election decision (intended, hypothetical): Consideration Set for second vote; current assessment of personal economic situation and the economic situation in Germany; sympathy scale for selected parties (CDU, CSU, SPD, FDP, Bündnis 90/Die Grünen, Die Linke, AfD); satisfaction with the performance of the federal government (scale); satisfaction with the performance of the individual governing parties (CDU, CSU, SPD); willingness to take risks; sympathy scale for top politicians (Angela Merkel, Sigmar Gabriel, Horst Seehofer, Christian Lindner, Katrin Göring-Eckardt, Katja Kipping, Frauke Petry); problem-solving competence of the parties; political knowledge (voting rights in Germany, first-second vote, 5% hurdle); self-assessment on the left-right continuum (scalometer); personal value orientations according to the Schwartz model; positionissues (ego): socio-economic dimension (lower taxes and less welfare benefits vs. more welfare state benefits vs. more taxes), opportunities for foreigners to move in, integration of foreigners (should be able to adapt to German culture vs. be able to live according to their own culture), climate protection (priority for combating climate change, even if it harms economic growth vs. priority for economic growth, even if it makes combating climate change more difficult), security and privacy (for strong state intervention vs. against strong state intervention), European integration (push for European unification vs. European unification is already going too far); attitudes towards efficiency and electoral norms; political positions (adoption of children for same-sex partnerships, deportation of economic refugees, Islamic communities should be monitored by the state, state measures to reduce income disparities, referenda at federal level, restrictions on the exercise of the Islamic faith); political positions on current issues (state and economy, expansion of state powers in fighting crime, Islam fits into German society); most important source of political information (television, newspaper, radio, Internet, personal conversations, others); average Internet use (general, politically current); current use and reception frequency of TV news (Tagesschau/Tagesthemen (ARD), Heute/Heute Journal (ZDF), RTL Aktuell, Sat. 1 News, others); current use and reception frequency of daily newspapers (Bild-Zeitung, Frankfurter Rundschau, Frankfurter Allgemeine Zeitung, Süddeutsche Zeitung, die tageszeitung, Die Welt, others); current use and reception frequency of weekly magazines in print and online versions (Der Spiegel, Focus, Die Zeit, Stern); voter participation and decision on the BTW 2013 election; frequency of political conversations; number of interlocutors; relationship to individual interlocutors and the interlocutors´ election intentions; party identification as well as the duration, strength and type of party identification; disenchantment with politics (parties only want voters´ votes, most party politicians are trustworthy and honest, even simple party members can contribute ideas, without professional politicians our country would be governed worse, citizens have hardly any possibilities to influence politics, parties are only about power, parties exert too much influence in society, parties consider the state as a self-service shop); assessment of differences in governmental policies of parties and assessment of differences between parties in general; national identity; assessment of components of national identity; temporary work; fear of losing a job; fear of losing a business; subjective class affiliation.

Additionally in the second wave: Political knowledge (assignment of politicians/parties, unemployment rate); assessment of justice within the German society; assessment of one´s own share in the German standard of living; foreign policy orientation (use of military force never justified, FRG should concentrate on problems in the country, FRG should act in agreement with the USA, necessity of a common stance of FRG and allies in crises, FRG should play a more active role in world politics, war sometimes necessary to protect national interests, FRG should provide security on its own, F...