Wages in the United States increased 4.46 percent in January of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Wages and Salaries Growth - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Average weekly earnings for the whole economy, for total and regular pay, in real terms (adjusted for consumer price inflation), UK, monthly, seasonally adjusted.

Wages in Italy increased 3.80 percent in January of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Italy Hourly Wage Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Wages in Russia increased 11.30 percent in December of 2024 over the same month in the previous year. This dataset provides - Russia Wage Growth- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Wages in Argentina increased 2.80 percent in December of 2024 over the same month in the previous year. This dataset provides - Argentina Wage Growth- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Average hourly and weekly wage rate, and median hourly and weekly wage rate by North American Industry Classification System (NAICS), type of work, gender, and age group.

We extend time-series models that have so far been used to study price inflation (Stock and Watson 2016) and apply them to a micro-level data set containing worker-level information on hourly wages. We construct a measure of aggregate nominal wage growth that (i) filters out noise and very transitory movements, (ii) quantifies the importance of idiosyncratic factors for aggregate wage dynamics, and (iii) strongly co-moves with labor market tightness, unlike existing indicators of wage inflation. We show that our measure is a reliable real-time indicator of wage pressures and a good predictor of future wage growth.

Wages in Netherlands increased 5.50 percent in February of 2025 over the same month in the previous year. This dataset provides - Netherlands Hourly Wage Growth YoY- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Wages in China increased to 120698 CNY/Year in 2023 from 114029 CNY/Year in 2022. This dataset provides - China Average Yearly Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

In April 2025, the UK minimum wage for adults over the age of 21 in will be 12.21 pounds per hour. For the 2025/26 financial year, there will be four minimum wage categories, three of which are based on age and one for apprentice workers. Apprentices, and workers under the age of 18 will have a minimum wage of 7.55 pounds an hour, increasing to ten pounds for those aged 18 to 20. When the minimum wage was first introduced in 1999, there were just two age categories; 18 to 21, and 22 and over. This increased to three categories in 2004, four in 2010, and five between 2016 and 2023, before being reduced down to four in the most recent year. The living wage The living wage is an alternative minimum wage amount that employers in the UK can voluntarily pay their employees. It is calculated independently of the legal minimum wage and results in a higher value figure. In 2023/24, for example, the living wage was twelve pounds an hour for the UK as a whole and 13.15 for workers in London, where the cost of living is typically higher. This living wage is different from what the UK government has named the national living wage, which was 10.42 in the same financial year. Between 2011/12 and 2023/24, the living wage has increased by 4.80 pounds, while the London living wage has grown by 4.85 pounds. Wage growth cancelled-out by high inflation 2021-2023 For a long period between the middle of 2021 and late 2023, average wage growth in the UK was unable to keep up with record inflation levels, resulting in the biggest fall in disposable income since 1956. Although the UK government attempted to mitigate the impact of falling living standards through a series of cost of living payments, the situation has still been very difficult for households. After peaking at 11.1 percent in October 2022, the UK's inflation rate remained in double figures until March 2023, and did not fall to the preferred rate of two percent until May 2024. As of November 2024, regular weekly pay in the UK was growing by 5.6 percent in nominal terms, and 2.5 percent when adjusted for inflation.

Wages in Poland increased 7.90 percent in February of 2025 over the same month in the previous year. This dataset provides - Poland Wage Growth- actual values, historical data, forecast, chart, statistics, economic calendar and news.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Trinidad. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Trinidad, the median income for all workers aged 15 years and older, regardless of work hours, was $35,805 for males and $18,409 for females.

These income figures highlight a substantial gender-based income gap in Trinidad. Women, regardless of work hours, earn 51 cents for each dollar earned by men. This significant gender pay gap, approximately 49%, underscores concerning gender-based income inequality in the city of Trinidad.

- Full-time workers, aged 15 years and older: In Trinidad, among full-time, year-round workers aged 15 years and older, males earned a median income of $48,050, while females earned $27,833, leading to a 42% gender pay gap among full-time workers. This illustrates that women earn 58 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Trinidad, showcasing a consistent income pattern irrespective of employment status.

https://i.neilsberg.com/ch/trinidad-tx-income-by-gender.jpeg" alt="Trinidad, TX gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2022
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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/.

Recommended for further research

This dataset is a part of the main dataset for Trinidad median household income by gender. You can refer the same here

Wages in Bosnia And Herzegovina increased 9.30 percent in February of 2025 over the same month in the previous year. This dataset provides - Bosnia And Herzegovina Wage Growth- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Replication files for "Job-to-Job Mobility and Inflation" Authors: Renato Faccini and Leonardo Melosi Review of Economics and Statistics Date: February 2, 2023 -------------------------------------------------------------------------------------------- ORDERS OF TOPICS .Section 1. We explain the code to replicate all the figures in the paper (except Figure 6) .Section 2. We explain how Figure 6 is constructed .Section 3. We explain how the data are constructed SECTION 1 Replication_Main.m is used to reproduce all the figures of the paper except Figure 6. All the primitive variables are defined in the code and all the steps are commented in code to facilitate the replication of our results. Replication_Main.m, should be run in Matlab. The authors tested it on a DELL XPS 15 7590 laptop wih the follwoing characteristics: -------------------------------------------------------------------------------------------- Processor Intel(R) Core(TM) i9-9980HK CPU @ 2.40GHz 2.40 GHz Installed RAM 64.0 GB System type 64-bit operating system, x64-based processor -------------------------------------------------------------------------------------------- It took 2 minutes and 57 seconds for this machine to construct Figures 1, 2, 3, 4a, 4b, 5, 7a, and 7b. The following version of Matlab and Matlab toolboxes has been used for the test: -------------------------------------------------------------------------------------------- MATLAB Version: 9.7.0.1190202 (R2019b) MATLAB License Number: 363305 Operating System: Microsoft Windows 10 Enterprise Version 10.0 (Build 19045) Java Version: Java 1.8.0_202-b08 with Oracle Corporation Java HotSpot(TM) 64-Bit Server VM mixed mode -------------------------------------------------------------------------------------------- MATLAB Version 9.7 (R2019b) Financial Toolbox Version 5.14 (R2019b) Optimization Toolbox Version 8.4 (R2019b) Statistics and Machine Learning Toolbox Version 11.6 (R2019b) Symbolic Math Toolbox Version 8.4 (R2019b) -------------------------------------------------------------------------------------------- The replication code uses auxiliary files and save the pictures in various subfolders: \JL_models: It contains the equations describing the model including the observation equations and routine used to solve the model. To do so, the routine in this folder calls other routines located in some fo the subfolders below. \gensystoama: It contains a set of codes that allow us to solve linear rational expectations models. We use the AMA solver. More information are provided in the file AMASOLVE.m. The codes in this subfolder have been developed by Alejandro Justiniano. \filters: it contains the Kalman filter augmented with a routine to make sure that the zero lower bound constraint for the nominal interest rate is satisfied in every period in our sample. \SteadyStateSolver: It contains a set of routines that are used to solved the steady state of the model numerically. \NLEquations: It contains some of the equations of the model that are log-linearized using the symbolic toolbox of matlab. \NberDates: It contains a set of routines that allows to add shaded area to graphs to denote NBER recessions. \Graphics: It contains useful codes enabling features to construct some of the graphs in the paper. \Data: it contains the data set used in the paper. \Params: It contains a spreadsheet with the values attributes to the model parameters. \VAR_Estimation: It contains the forecasts implied by the Bayesian VAR model of Section 2. The output of Replication_Main.m are the figures of the paper that are stored in the subfolder \Figures SECTION 2 The Excel file "Figure-6.xlsx" is used to create the charts in Figure 6. All three panels of the charts (A, B, and C) plot a measure of unexpected wage inflation against the unemployment rate, then fits separate linear regressions for the periods 1960-1985,1986-2007, and 2008-2009. Unexpected wage inflation is given by the difference between wage growth and a measure of expected wage growth. In all three panels, the unemployment rate used is the civilian unemployment rate (UNRATE), seasonally adjusted, from the BLS. The sheet "Panel A" uses quarterly manufacturing sector average hourly earnings growth data, seasonally adjusted (CES3000000008), from the Bureau of Labor Statistics (BLS) Employment Situation report as the measure of wage inflation. The unexpected wage inflation is given by the difference between earnings growth at time t and the average of earnings growth across the previous four months. Growth rates are annualized quarterly values. The sheet "Panel B" uses quarterly Nonfarm Business Sector Compensation Per Hour, seasonally adjusted (COMPNFB), from the BLS Productivity and Costs report as its measure of wage inflation. As in Panel A, expected wage inflation is given by the average wage inflation over the previous four quarters. Growth rates are annualized quarterly values. The sheet "Panel C" uses semiannual manufacturing...

Average weekly earnings at sector level including manufacturing, finance and services, Great Britain, monthly, non-seasonally adjusted. Monthly Wages and Salaries Survey.

Wages in Spain increased 3.52 percent in December of 2024 over the same month in the previous year. This dataset provides - Spain Wage Growth- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Average weekly earnings at sector level headline estimates, Great Britain, monthly, seasonally adjusted. Monthly Wages and Salaries Survey.

Wages in Portugal increased 9.10 percent in January of 2025 over the same month in the previous year. This dataset provides - Portugal Wage Growth- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Wages in Estonia increased 8.30 percent in December of 2024 over the same month in the previous year. This dataset provides - Estonia Wage Growth- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

S&P 500: A Bull or a Bear?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data