Inflation Rate in the United States remained unchanged at 2.70 percent in July. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in Turkey decreased to 32.95 percent in August from 33.52 percent in July of 2025. This dataset provides the latest reported value for - Turkey Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Consumer price inflation statistics are important indicators of how the UK economy is performing. They are used in many ways by individuals, government, businesses and academics. Inflation statistics impact on everyone in some way as they affect interest rates, tax allowances, benefits, pensions, savings rates, maintenance contracts and many other payments. This article provides information about the users and uses of consumer price inflation statistics, and user experiences of these statistics, including the new CPIH and RPIJ measures. In addition, it also provides information on the characteristics of the different measures of consumer price inflation in relation to their potential use. Source agency: Office for National Statistics Designation: National Statistics Language: English Alternative title: consumer price inflation statistics

The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset contains year-wise macroeconomic indicators for over 200 countries from 2010 to 2025, extracted programmatically using the World Bank Open Data API.

It includes key indicators critical for policy makers, economists, data scientists, and financial analysts. The data has been cleaned, structured, and exported as a CSV — making it ready for analysis, dashboards, and forecasting models.

📦 Included Indicators

• Inflation (Consumer Price Index %)
• GDP (Current USD)
• GDP per Capita
• GDP Growth (% Annual)
• Unemployment Rate
• Real Interest Rate
• Public Debt (% of GDP)
• Government Expense and Revenue
• Current Account Balance
• Gross National Income
• Tax Revenue

📊 Columns Overview

📈 Use Cases

• Economic trend visualization
• Country comparison dashboards
• Machine learning forecasting models
• Macroeconomic policy analysis

📡 Data Source

• API: World Bank Open Data
• Extracted using: Python, requests, pandas, SQLAlchemy

🧠 Ideal For

• Data scientists
• Policy researchers
• Students in economics or finance
• Kaggle forecasting competitions

✅ Format

• CSV format (UTF-8)
• 200+ countries × 15 years × 13 indicators = ~40,000+ rows

About the Dataset

The breakeven inflation rate represents a measure of expected inflation derived from 10-Year Treasury Constant Maturity Securities (BC_10YEAR) and 10-Year Treasury Inflation-Indexed Constant Maturity Securities (TC_10YEAR). The latest value implies what market participants expect inflation to be in the next 10 years, on average. Starting with the update on June 21, 2019, the Treasury bond data used in calculating interest rate spreads is obtained directly from the U.S. Treasury Department.

United States CSI: Expected Inflation: Next Yr: Up by 1-2% data was reported at 29.000 % in May 2018. This stayed constant from the previous number of 29.000 % for Apr 2018. United States CSI: Expected Inflation: Next Yr: Up by 1-2% data is updated monthly, averaging 18.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 34.000 % in Oct 2016 and a record low of 1.000 % in May 1980. United States CSI: Expected Inflation: Next Yr: Up by 1-2% data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The questions were: 'During the next 12 months, do you think that prices in general will go up, or go down, or stay where they are now?' and 'By what percent do you expect prices to go up, on the average, during the next 12 months?'

Inflation Rate in Russia decreased to 8.10 percent in August from 8.80 percent in July of 2025. This dataset provides - Russia Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset shows the average interest rates for U.S. Treasury securities for the most recent month compared with the same month of the previous year. The data is broken down by the various marketable and non-marketable securities. The summary page for the data provides links for monthly reports from 2001 through the current year. Average Interest Rates are calculated on the total unmatured interest-bearing debt. The average interest rates for total marketable, total non-marketable and total interest-bearing debt do not include the U.S. Treasury Inflation-Protected Securities.

Zimbabwe ZW: Real Interest Rate data was reported at 5.728 % pa in 2016. This records a decrease from the previous number of 7.576 % pa for 2015. Zimbabwe ZW: Real Interest Rate data is updated yearly, averaging 34.675 % pa from Dec 1980 (Median) to 2016, with 32 observations. The data reached an all-time high of 572.936 % pa in 2007 and a record low of 4.257 % pa in 1980. Zimbabwe ZW: Real Interest Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank.WDI: Interest Rates. Real interest rate is the lending interest rate adjusted for inflation as measured by the GDP deflator. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.; ; International Monetary Fund, International Financial Statistics and data files using World Bank data on the GDP deflator.; ;

The Average Interest Rates on U.S. Treasury Securities dataset provides average interest rates on U.S. Treasury securities on a monthly basis. Its primary purpose is to show the average interest rate on a variety of marketable and non-marketable Treasury securities. Marketable securities consist of Treasury Bills, Notes, Bonds, Treasury Inflation-Protected Securities (TIPS), Floating Rate Notes (FRNs), and Federal Financing Bank (FFB) securities. Non-marketable securities consist of Domestic Series, Foreign Series, State and Local Government Series (SLGS), U.S. Savings Securities, and Government Account Series (GAS) securities. Marketable securities are negotiable and transferable and may be sold on the secondary market. Non-marketable securities are not negotiable or transferrable and are not sold on the secondary market. This is a useful dataset for investors and bond holders to compare how interest rates on Treasury securities have changed over time.

Nigeria’s inflation has been higher than the average for African and Sub-Saharan countries for years now, and even exceeded 16 percent in 2017 – and a real, significant decrease is nowhere in sight. The bigger problem is its unsteadiness, however: An inflation rate that is bouncing all over the place, like this one, is usually a sign of a struggling economy, causing prices to fluctuate, and unemployment and poverty to increase. Nigeria’s economy - a so-called “mixed economy”, which means the market economy is at least in part regulated by the state – is not entirely in bad shape, though. More than half of its GDP is generated by the services sector, namely telecommunications and finances, and the country derives a significant share of its state revenues from oil.

Because it got high

To simplify: When the inflation rate rises, so do prices, and consequently banks raise their interest rates as well to cope and maintain their profit margin. Higher interest rates often cause unemployment to rise. In certain scenarios, rising prices can also mean more panicky spending and consumption among end users, causing debt and poverty. The extreme version of this is called hyperinflation: A rapid increase of prices that is out of control and leads to bankruptcies en masse, devaluation of money and subsequently a currency reform, among other things. But does that mean that low inflation is better? Maybe, but only to a certain degree; the ECB, for example, aspires to maintain an inflation rate of about two percent so as to keep the economy stable. As soon as we reach deflation territory, however, things are starting to look grim again. The best course is a stable inflation rate, to avoid uncertainty and rash actions.

Nigeria today

Nigeria is one of the countries with the largest populations worldwide and also the largest economy in Africa, with its economy growing rapidly after a slump in the aforementioned year 2017. It is slated to be one of the countries with the highest economic growth over the next few decades. Demographic key indicators, like infant mortality rate, fertility rate, and the median age of the population, all point towards a bright future. Additionally, the country seems to make big leaps forward in manufacturing and technological developments, and boasts huge natural resources, including natural gas. All in all, Nigeria and its inflation seem to be on the upswing – or on the path to stabilization, as it were.

This paper investigates both the effects of domestic monetary policy and external shocks on fundamental macroeconomic variables in six fast growing emerging economies: Brazil, Russia, India, China, South Africa and Turkey—denoted hereafter as BRICS_T. The authors adopt a structural VAR model with a block exogeneity procedure to identify domestic monetary policy shocks and external shocks. Their research reveals that a contractionary monetary policy in most countries appreciates the domestic currency, increases interest rates, effectively controls inflation rates and reduces output. They do not find any evidence of the price, output, exchange rates and trade puzzles that are usually found in VAR studies. Their findings imply that the exchange rate is the main transmission mechanism in BRICS_T economies. The authors also find that that there are inverse J-curves in five of the six fast growing emerging economies and there are deviations from UIP (Uncovered Interest Parity) in response to a contractionary monetary policy in those countries. Moreover, world output shocks are not a dominant source of fluctuations in those economies.

Inflation Rate in Japan decreased to 3.10 percent in July from 3.30 percent in June of 2025. This dataset provides the latest reported value for - Japan Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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When examining the intricate relationship between economic conditions and purchasing decisions, the utilization of practice datasets can offer invaluable insights. This particular artificial dataset comprises three main components: a dimension table of ten companies, a fact table documenting purchases from these companies, and a set of data points regarding economic conditions. These elements are meticulously designed to mimic real-world scenarios, enabling analysts to dissect and understand how fluctuations in the economy can influence the purchasing behavior of different types of companies.

The dimension table serves as the foundation, listing ten distinct companies, each potentially operating in varied sectors. This diversity allows for a comprehensive analysis across a spectrum of industries, highlighting sector-specific sensitivities to economic changes. The fact table of purchases acts as a historical record, offering detailed insights into the buying patterns of these companies over time. Analysts can observe trends, frequencies, and the magnitude of purchases, correlating them with the economic conditions presented in the third component of the dataset.

The economic conditions data is pivotal, as it encompasses a variety of indicators that can affect purchasing decisions. These may include inflation rates, interest rates, GDP growth, unemployment rates, and consumer confidence indices, among others. By examining the interplay between these economic indicators and the purchasing data, analysts can identify patterns and causations. For instance, an increase in interest rates might lead to a decrease in capital-intensive purchases by companies wary of higher borrowing costs.

Through this dataset, researchers can employ statistical models and data analysis techniques to uncover how economic fluctuations impact corporate purchasing decisions. The findings can offer valuable lessons for businesses in terms of budgeting, financial planning, and risk management. Companies can use these insights to make informed decisions, adjusting their purchasing strategies in anticipation of or in response to economic conditions. This proactive approach can help businesses maintain stability during economic downturns and capitalize on opportunities during favorable economic times.

Ultimately, this practice dataset not only aids in academic and educational pursuits but also serves as a practical tool for business analysts, economists, and corporate strategists seeking to better navigate the complex dynamics of the economy and its effects on corporate purchasing behaviors.

This paper models the dynamics of Chinese yuan–denominated long-term interest rate swap yields. It shows that the short-term interest rate exerts a decisive influence on the long-term swap yield after controlling for various macrofinancial variables, such as core inflation, the growth of industrial production, the percent change in the equity price index, and the percentage change in the Chinese yuan exchange rate. The autoregressive distributed lag approach is applied to model the dynamics of the long-term swap yield. The findings reinforce and extend John Maynard Keynes’s conjecture that in advanced countries, as well as emerging market economies such as China, the central bank’s actions have a decisive role in setting the long-term interest rate on government bonds and over-the-counter financial instruments, such as swaps.

This data set presents annual data on public sector deficits, the monetary sector, and the financial sector for a large and varying sample of member countries of the OECD and developing countries. It includes an in-depth examination of eight countries Argentina, Chile, Colombia, Ivory Coast, Ghana, Morocco, Pakistan, and Zimbabwe. These cases are analyzed within a comprehensive theoretical framework elaborated for the study and in conjunction with cross-country data from a larger set. It includes the following variables and periods: Consolidated Public Sector Surplus or Deficit, 1970-90 Seignorage, 1965-89 Inflation Rates, 1965-90 Real Interest Rates, 1965-89 Taxes from Financial Repression in Ten Countries, 1980-89

Monetary policy is generally regarded as a central element in the attempts of policy makers to attenuate business-cycle fluctuations. According to the New Keynesian paradigm, central banks are able to stimulate or depress aggregate demand in the short run by adjusting their nominal interest rate targets. The effects of interest rate changes on aggregate consumption, the largest component of aggregate demand, are well understood in the context of this paradigm, on which the canonical "workhorse'' model used in monetary policy analysis is grounded. A key feature of the model is that aggregate consumption is fully described by the amount of goods consumed by a representative household. A decline in the policy rate for instance implies that the real interest rate declines, the representative household saves less and hence increase its demand for consumption. At the same time, general equilibrium effects let labour income grow causing consumption to increase further. However, the mechanism outlined above ignores a considerable amount of empirically-observed heterogeneity among households. For example, households with a higher earnings elasticity to interest rate changes benefit more from a rate cut than those with a lower elasticity; households with large debt positions are at a relative advantage over households with large bond holdings; and households with low exposure to inflation are relatively better off than those holding a sizeable amount of nominal assets. As a result, the contribution to the aggregate consumption response differs substantially across households, implying that monetary expansions and tightenings produce relative "winners'' and relative "losers''. The aim of the project laid out in this proposal is to give a disaggregated account of the heterogeneous effects of monetary-policy induced interest rate changes on household consumption and a detailed analysis of the channels underlying them. Additionally, it seeks to draw conclusions about the determinants of the strength of the transmission mechanism of monetary policy. To do so, it relies on a large panel comprising detailed data from the universe of all households residing in Norway between 1993 and 2015 supplemented with additional micro-data provided by the European Commission. I will be assisted by two project partners, Pascal Paul who is a member of the Research Department of the Federal Reserve Bank of San Francisco and Martin Holm who is affiliated with the Research Unit of Statistics Norway and the University of Oslo. In addition, I would like to collaborate with and help train a doctoral student based at the University of Lausanne on this project. Existing empirical studies of the consumption response to monetary policy at the micro level rely on survey data. Therefore, they are subject to a number of severe data limitations. The surveys employed typically have either no or only a short panel dimension, suffer from attrition, include only limited information on income and wealth, are top-coded, and contain a significant amount of measurement error. The administrative data set provided to us by Statistics Norway suffers from none of these issues, implying that we are in a unique position to evaluate the household-level effects of policy rate changes. In a first step, we use forecasts published by the Norwegian central bank to derive monetary policy shocks that are robust to the simultaneity problem inherent in the identification of the effects of monetary policy following Romer and Romer (2004). We then confront the micro-data with the estimated shocks to study the consumption response along different segments of the income and wealth distribution and to test the importance of heterogeneity in labour earnings, financial income, liquid assets, inflation exposure and interest rate exposure among others. The findings will be of high relevance as they will not only allow us to evaluate channels hypothesised in the analytical literature, improve our understanding of the monetary policy transmission mechanism and its distributional consequences but also serve as a benchmark for structural models built both by theorists and practitioners.

Inflation Rate in Brazil decreased to 5.13 percent in August from 5.23 percent in July of 2025. This dataset provides - Brazil Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Data about World Development Indicators measured from 1960 to 2022, extracted from the World Bank database. It includes macro-economical, social, political and environmental data from all the countries and regions the world bank has data about.

It contains information about 268 countries and regions, including 48 features, all numerical. Several entries are missing for different reasons, so you may want to extract only the columns you are interested in.

The columns included in this dataset are:

• country: The country or geographic region.
• date: Date of the measurement. This column along with country can be used as index.
• agricultural_land%: Agricultural land as a % of land area of the country/region.
• forest_land%: Forest area as the % of land area of the country/region.
• land_area: Land area, measured in km^2.
• avg_precipitation: Average precipitation in depth, measured in mm per year.
• trade_in_services%: Trade in services as a % of GDP.
• control_of_corruption_estimate: Index that makes an estimate of the control of corruption.
• control_of_corruption_std: Standard error of the estimate of control of corruption.
• access_to_electricity%: Percentage of the population that has access to electricity.
• renewvable_energy_consumption%: Renewable energy consumption as a % of total final energy consumption.
• electric_power_consumption: Electric power consumption, measured in kWh per capita.
• CO2_emisions: CO2 emisions measured in kt.
• other_greenhouse_emisions: Total greenhouse gas emissions, measured in kt of CO2 equivalent.
• population_density: Population density, measured in people per km^2 of land area.
• inflation_annual%: Inflation, consumer prices, as annual %.
• real_interest_rate: Real interest rate (%).
• risk_premium_on_lending: Risk premium on lending (lending rate minus treasury bill rate, %).
• research_and_development_expenditure%: Research and development expenditure, as a percentage of GDP.
• central_goverment_debt%: Central government debt, total , as a % of GDP.
• tax_revenue%: Tax revenue as a % of GDP.
• expense%: Expense as a % of GDP.
• goverment_effectiveness_estimate: Index that makes an estimate of the Government Effectiveness.
• goverment_effectiveness_std: Standard error of the estimate of Government Effectiveness.
• human_capital_index: Human Capital Index (HCI) (scale 0-1).
• doing_business: Ease of doing business score (0 = lowest performance to 100 = best performance).
• time_to_get_operation_license: Days required to obtain an operating license.
• statistical_performance_indicators: Statistical performance indicators (SPI): Overall score (scale 0-100).
• individuals_using_internet%: Percentage of population using the internet.
• logistic_performance_index: Logistics performance index: Overall (1=low to 5=high).
• military_expenditure%: Military expenditure as a % of GDP.
• GDP_current_US: GDP (current US$).
• political_stability_estimate: Index that makes an estimate of the Political Stability and Absence of Violence/Terrorism.
• political_stability_std: Standard error of the estimate of Political Stability and Absence of Violence/Terrorism.
• rule_of_law_estimate: Index that makes an estimate of the Rule of Law.
• rule_of_law_std: Standard error of the estimate of Rule of Law.
• regulatory_quality_estimate: Index that makes an estimate of Regulatory Quality.
• regulatory_quality_std: Standard error of the estimate of Regulatory Quality.
• government_expenditure_on_education%: Government expenditure on education, total, as a % of GDP.
• government_health_expenditure%: Domestic general government health expenditure as a % of GDP.
• multidimensional_poverty_headcount_ratio%: Multidimensional poverty headcount ratio (% of total population).
• gini_index: Gini index.
• birth_rate: Birth rate, crude (per 1,000 people).
• death_rate: Death rate, crude (per 1,000 people).
• life_expectancy_at_birth: Life expectancy at birth, total (years).
• population: Total population.
• rural_population: Rural population.
• voice_and_accountability_estimate: Index that makes an estimate of Voice and Accountability.
• voice_and_accountability_std: Standard error of the estimate of Voice and Accountability.
• intentional_homicides: Intentional homicides (per 100,000 people).