This panel dataset contains quarterly series on inflation targets, bands, and track records for 41 inflation targeting countries from 1990 to 2024. Data on inflation targets and bands are collected through each central bank’s historical documents and rules-based track record measures are calculated by the author to assess actual inflation outcomes with respect to the central banks’ stated policy objectives. The dataset supports research work in Zhang (2025), Zhang and Wang (2022), and Zhang (2021). Please cite the papers when using the data.

Z. Zhang, Does inflation targeting track record matter for asset prices? Evidence from stock, bond, and foreign exchange markets, Journal of International Financial Markets, Institutions and Money, Volume 101, 2025, 102141. Z. Zhang, S. Wang, Do actions speak louder than words? Assessing the effects of inflation targeting track records on macroeconomic performance, 2022, IMF Working Papers 2022/227.
Z. Zhang, Stock returns and inflation redux: An explanation from monetary policy in advanced and emerging markets, 2021, IMF Working Papers 2021/219.

About this dataset

Context

The dataset tabulates the median household income in Banks. It can be utilized to understand the trend in median household income and to analyze the income distribution in Banks by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Banks, OR Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Banks, OR: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Banks, OR
• Banks, OR households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Banks median household income. You can refer the same here

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 supports the research exploring the impact of monetary policy instruments on the Colombian economy, focusing on the classical dichotomy and monetary neutrality. The analysis delves into how monetary policy, including instruments such as interest rates and money supply, influences both nominal and real variables in the economy. It also highlights the relationship between monetary policy and economic stability, particularly how central banks manage inflation and economic growth. Key sections explore the separation between nominal and real variables as explained by the classical dichotomy, and the principle of monetary neutrality, which argues that changes in money supply affect nominal variables without impacting real economic factors.

The dataset is structured around a combination of theoretical insights and simulations that analyze the effectiveness of monetary neutrality in the Colombian context, given both domestic and international economic challenges such as the war in Ukraine and agricultural sector disruptions. Through simulations, the dataset demonstrates the effects of monetary expansion on variables like inflation, production, and employment, providing a framework for understanding current economic trends and proposing solutions to socio-economic challenges in Colombia.

This dataset contains news headlines relevant to key forex pairs: AUDUSD, EURCHF, EURUSD, GBPUSD, and USDJPY. The data was extracted from reputable platforms Forex Live and FXstreet over a period of 86 days, from January to May 2023. The dataset comprises 2,291 unique news headlines. Each headline includes an associated forex pair, timestamp, source, author, URL, and the corresponding article text. Data was collected using web scraping techniques executed via a custom service on a virtual machine. This service periodically retrieves the latest news for a specified forex pair (ticker) from each platform, parsing all available information. The collected data is then processed to extract details such as the article's timestamp, author, and URL. The URL is further used to retrieve the full text of each article. This data acquisition process repeats approximately every 15 minutes.

To ensure the reliability of the dataset, we manually annotated each headline for sentiment. Instead of solely focusing on the textual content, we ascertained sentiment based on the potential short-term impact of the headline on its corresponding forex pair. This method recognizes the currency market's acute sensitivity to economic news, which significantly influences many trading strategies. As such, this dataset could serve as an invaluable resource for fine-tuning sentiment analysis models in the financial realm.

We used three categories for annotation: 'positive', 'negative', and 'neutral', which correspond to bullish, bearish, and hold sentiments, respectively, for the forex pair linked to each headline. The following Table provides examples of annotated headlines along with brief explanations of the assigned sentiment.

Examples of Annotated Headlines


    Forex Pair
    Headline
    Sentiment
    Explanation




    GBPUSD 
    Diminishing bets for a move to 12400 
    Neutral
    Lack of strong sentiment in either direction


    GBPUSD 
    No reasons to dislike Cable in the very near term as long as the Dollar momentum remains soft 
    Positive
    Positive sentiment towards GBPUSD (Cable) in the near term


    GBPUSD 
    When are the UK jobs and how could they affect GBPUSD 
    Neutral
    Poses a question and does not express a clear sentiment


    JPYUSD
    Appropriate to continue monetary easing to achieve 2% inflation target with wage growth 
    Positive
    Monetary easing from Bank of Japan (BoJ) could lead to a weaker JPY in the short term due to increased money supply


    USDJPY
    Dollar rebounds despite US data. Yen gains amid lower yields 
    Neutral
    Since both the USD and JPY are gaining, the effects on the USDJPY forex pair might offset each other


    USDJPY
    USDJPY to reach 124 by Q4 as the likelihood of a BoJ policy shift should accelerate Yen gains 
    Negative
    USDJPY is expected to reach a lower value, with the USD losing value against the JPY


    AUDUSD

RBA Governor Lowe’s Testimony High inflation is damaging and corrosive

    Positive
    Reserve Bank of Australia (RBA) expresses concerns about inflation. Typically, central banks combat high inflation with higher interest rates, which could strengthen AUD.

Moreover, the dataset includes two columns with the predicted sentiment class and score as predicted by the FinBERT model. Specifically, the FinBERT model outputs a set of probabilities for each sentiment class (positive, negative, and neutral), representing the model's confidence in associating the input headline with each sentiment category. These probabilities are used to determine the predicted class and a sentiment score for each headline. The sentiment score is computed by subtracting the negative class probability from the positive one.

Graph and download economic data for Inflation, consumer prices for the United States (FPCPITOTLZGUSA) from 1960 to 2024 about consumer, CPI, inflation, price index, indexes, price, and USA.

This article introduces the most comprehensive dataset on de jure central bank independence (CBI), including yearly data from 182 countries between 1970 and 2012. The dataset identifies statutory reforms affecting CBI, their direction, and the attributes necessary to build the Cukierman, Webb and Neyapty index. Previous datasets focused on developed countries, and included non-representative samples of developing countries. This dataset’s substantially broader coverage has important implications. First, it challenges the conventional wisdom about central bank reforms in the world, revealing CBI increases and restrictions in decades and regions previously considered barely affected by reforms. Second, the inclusion of almost 100 countries usually overlooked in previous studies suggests that the sample selection may have substantially affected results. Simple analyses show that the associations between CBI and inflation, unemployment or growth are very sensitive to sample selection. Finally, the dataset identifies numerous CBI decreases (restrictions), whereas previous datasets mostly look at CBI increases. These data’s coverage not only allows researchers to test competing explanations of the determinants and effects of CBI in a global sample, but it also provides a useful instrument for cross-national studies in diverse fields, such as liberalization, diffusion, political institutions, democratization, or responses to financial crises.

About this dataset

Context

The dataset tabulates the median household income in Banks. It can be utilized to understand the trend in median household income and to analyze the income distribution in Banks by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Banks, AL Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Banks, AL: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Banks, AL
• Banks, AL households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Banks median household income. You can refer the same here

Explanation of CPI and the Dataset:

What is CPI?

CPI (Consumer Price Index) measures the average change in prices over time that consumers pay for a basket of goods and services. It is a key indicator of inflation and is used by governments and central banks to monitor price stability and for inflation targeting. Components: The construction of CPI involves two main components: Weighting Diagrams: These represent the consumption patterns of households. Price Data: This is collected at regular intervals to track changes in prices.

Role of the Central Statistics Office (CSO):

The CSO, under the Ministry of Statistics and Programme Implementation, is responsible for releasing CPI data. The indices are released for Rural, Urban, and Combined sectors for all-India and individual States/UTs.

Dataset Alignment:

Sectors: The dataset includes a "Sector" column that categorizes data into "Rural," "Urban," and "Rural+Urban," aligning with the CPI data released by the CSO. Time Period: The "Year" and "Name" (which appears to represent months) columns in the dataset track the data over time, consistent with the monthly release schedule by the CSO starting from January 2011. State/UT Data: Each column corresponding to a state or union territory likely represents the CPI values for that region. The numeric values under each state/UT column represent the CPI index values, with a base of 2010=100. Purpose: This data can be used to analyze inflation trends, price stability, and the impact on economic policies, such as adjustments to dearness allowance for employees. Practical Use of This Data: Inflation Analysis: By examining the changes in CPI values across different states, analysts can study regional inflation trends and compare them to the national average. Policy Making: Governments and central banks can use this data to design and adjust policies aimed at controlling inflation, targeting specific regions or sectors that are experiencing higher inflation. Wage Indexation: Companies and governments can use CPI data to adjust wages and allowances in line with inflation, ensuring that purchasing power is maintained.

This table contains 13 series, with data from 1949 (not all combinations necessarily have data for all years). Data are presented for the current month and previous four months. Users can select other time periods that are of interest to them.

Recently, the inflationary impacts of climate change shocks have emerged among key constraints to price and financial stability. In line with this development, some Central banks are incorporating climate change risks in their surveillance activities. Thus, this study examines the asymmetric inflationary impact of climate change shocks on food and general consumer prices in Algeria, Egypt, Nigeria, and South Africa. The study employs a panel quantile via the moment’s method and a wavelet coherency analysis for monthly from 2000M01 to 2023M12. The empirical results reveal that, first, there is a dynamic interconnectedness between climate change shocks and inflation. Secondly, the results show that climate change shocks have an inflationary impact on food and general consumer prices. However, the magnitude and direction of the impact depend on the prevailing inflationary regime. Finally, the analysis shows that climate change shocks raise inflation uncertainty. Collectively, these findings imply that climate change shocks are key sources of inflationary pressures and uncertainty, posing significant challenges to central banks’ inflation management. One implication of these findings is that central banks in these countries will likely face extreme difficulty stabilising inflation since monetary policy instruments are mainly demand management, and thus may be ineffective in dealing with climate change shocks. In line with the findings, the study recommends that these countries should enhance their inflation surveillance and monetary policy strategies but considering the potential climate change risks.

About this dataset

Context

The dataset tabulates the Banks median household income by race. The dataset can be utilized to understand the racial distribution of Banks income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Banks, AL median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in Banks, AL (2021, in 2022 inflation-adjusted dollars)

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Banks median household income by race. You can refer the same here

Inflation Rate in Brazil increased to 5.35 percent in June from 5.32 percent in May of 2025. This dataset provides - Brazil Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

South African monthly The FTSE/JSE All Share Index data was procured from Bloomberg and the nominal effective exchange rate (NEER) from South African Reserve Bank (SARB) database, where the data has been seasonally adjusted specifying 2015 as the base year. Volatility measures in these markets are generated through a multivaraite EGARCH model in the WinRATS software. South African monthly consumer price index (CPI) data was procured from the International Monetary Fund’s International Financial Statistics (IFS) database, where the data has been seasonally adjusted, specifying 2010 as the base year. The inflation rate is constructed by taking the year-on-year changes in the monthly CPI figures. Inflation uncertainty was generated through the GARCH model in Eviews software. The following South African macroeconomic variables were procured from the SARB: real industrial production (IP), which is used as a proxy for real GDP, real investment (I), real consumption (C), inflation (CPI), broad money (M3), the 3-month treasury bill rate (TB3) and the policy rate (R), a measure of U.S. EPU developed by Baker et al. (2016) to account for global developments available at http://www.policyuncertainty.com/us_monthly.html.

Inflation Rate in Russia decreased to 9.40 percent in June from 9.90 percent in May of 2025. This dataset provides - Russia Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in Japan decreased to 3.50 percent in May from 3.60 percent in April 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.

The benchmark interest rate in Russia was last recorded at 20 percent. This dataset provides the latest reported value for - Russia Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Envestnet®| Yodlee®'s Bank Statement Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

The benchmark interest rate in Brazil was last recorded at 15 percent. This dataset provides - Brazil Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset supports the thesis The U.S. Dollar in Crisis: The Role of Asset-Backed Digital Currencies in Its Transformation by Nicolin Decker. It provides empirical data and econometric models to analyze the feasibility of Asset-Backed Digital Currencies (ABDCs) as a stabilizing alternative to fiat monetary systems. Spanning historical macroeconomic data (1970–2024) and projected ABDC circulation trends (2026–2036), the dataset includes inflation-adjusted monetary indicators, crisis response simulations, and global trade impact assessments. Key analyses incorporate Vector Autoregression (VAR), Monte Carlo simulations, Granger causality tests, and DSGE modeling to evaluate ABDC's effect on inflation control, liquidity stability, and financial resilience. The dataset is structured for full reproducibility, ensuring rigorous validation of ABDC’s role in modernizing global monetary policy.