Net pension wealth represents a critical concept in evaluating the overall financial security and adequacy of pension benefits received by retirees. It is a comprehensive measure that takes into account not only the present value of the flow of pension benefits but also the impact of taxes and social security contributions that retirees must pay on their pensions. This key indicator serves as a vital tool for assessing the effectiveness of pension systems in providing retirees with a stable and sufficient income during their post-working years. Calculating net pension wealth involves accounting for various factors that influence the value of pension benefits over time. One of the most critical elements considered is the present value of the expected flow of pension benefits throughout a retiree's lifetime. By discounting these future pension benefits to their present value, taking into account factors such as inflation and interest rates, analysts can assess the overall economic worth of the pension entitlements. Moreover, net pension wealth is sensitive to factors such as life expectancy and the age at which individuals choose to start receiving their pensions. Longer life expectancy can lead to more extended periods of pension receipt, affecting the overall value of net pension wealth. Similarly, the age at which people decide to begin taking their pensions can influence the duration and level of pension payments, impacting net pension wealth calculations. Measuring net pension wealth as a multiple of annual net earnings by gender provides insights into potential disparities in retirement income between men and women. Gender-specific differences in earnings, workforce participation, and pension contributions can lead to varying levels of net pension wealth for male and female retirees. Understanding these discrepancies is essential for policymakers striving to promote gender equality and equitable pension outcomes.

Short-term interest rate forecasts provide valuable insights into the expected values of three-month money market rates, a critical component of short-term borrowing and lending activities. These forecasts, measured as a percentage, play a crucial role in financial decision-making, monetary policy formulation, and economic planning. Analysts and economists generate short-term interest rate forecasts by conducting a comprehensive assessment of the economic climate at both individual country and global levels. This process involves utilizing a combination of model-based analyses and statistical indicator models to develop reliable and forward-looking projections. The forecasting of short-term interest rates requires a thorough examination of various economic factors and financial indicators that influence interest rate movements. Macroeconomic variables, such as GDP growth, inflation rates, employment figures, and consumer spending patterns, are among the key determinants of short-term interest rates. Additionally, factors like central bank policies, global economic trends, geopolitical events, and changes in investor sentiment play pivotal roles in shaping interest rate expectations. While short-term interest rate forecasts are valuable tools for decision-making, they are subject to inherent uncertainties and limitations. Economic conditions and financial markets can be influenced by unexpected events, policy changes, and other unforeseen factors, making the accuracy of forecasts challenging to achieve. Therefore, short-term interest rate forecasts should be interpreted as informed estimates rather than precise predictions.

This dataset contains stock market data for prominent energy companies operating within the Refinery and Distribution, namely MPC, PSX and VLO. It encompasses 5 years of trading data, including daily opening and closing prices, the highest and lowest prices recorded during the day, and the trading volume for each respective day. This dataset, which provides the stock performance analysis of major companies in the Refinery and Distribution sector such as MPC, PSX, and VLO, holds significant value for investors, analysts, and researchers. Due to its inclusion of daily stock price changes, this dataset is a crucial tool for those evaluating both short-term and long-term investment strategies for these companies. Analysts interested in assessing the performance of leading companies in the Refinery and Distribution sector, including MPC, PSX, and VLO, can actively utilize this comprehensive dataset. Encompassing data on the differences between opening and closing prices, highest and lowest values, trading volume, and other technical analysis tools, this dataset provides essential information for understanding sector trends and making company comparisons. Investors and financial experts seeking to examine and evaluate the stock performance of MPC, PSX, and VLO in the Refinery and Distribution sector will greatly benefit from this valuable dataset. Through this data, investors can make more informed decisions, reduce risks, and discover potential opportunities. This comprehensive dataset serves as a reliable and detailed guide for future investment strategies, offering essential insights into the stock performance of these significant companies.

The money market statistical reporting (MMSR) dataset, collected on the basis of transaction-by-transaction data from a sample of euro area reporting agents, provides information on the secured, unsecured, foreign exchange swap and overnight index swap euro money market segments. For the unsecured and secured segments all statistics are calculated in reference to each reserve maintenance period (MP), i.e. reflecting transactions with a trade date within the relevant MP. For the OIS segment the series on spot transactions are calculated with reference to each MP, while the statistics on forward-dated transactions are calculated with reference to each quarter. The euro short-term rate (€STR) is based on MMSR data. Depending on the series, aggregated total nominal amounts, daily average nominal amounts and weighted average rates are published for each MP or quarter. The published breakdowns differ across market segments and include series by transaction type, counterparty sector and maturity.