This dataset provides information on the unemployment rates for different demographic groups in the United States.

The data is sourced from the Economic Policy Institute’s State of Working America Data Library and economic research conducted by the Federal Reserve Bank of St. Louis.

The dataset contains unemployment rates for various age groups, education levels, genders, races, and more.

Interesting Task Ideas:

1. See how unemployment rates have changed for different groups of people over time.
2. Look into how education levels can affect unemployment rates.
3. Compare unemployment rates between different races / genders.
4. Check out how unemployment rates can vary across different age groups and genders.
5. Find out if there's a connection between education levels and unemployment rates within specific racial or gender groups.
6. Explore how economic downturns can impact unemployment rates for specific groups of people.
7. Use the data to create visuals that show how unemployment rates differ across all sorts of factors.

Don't forget to upvote this dataset if you find it useful! 😊💝

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Column Descriptions

Initial Jobless Claims in the United States decreased to 216 thousand in the week ending November 22 of 2025 from 222 thousand in the previous week. This dataset provides the latest reported value for - United States Initial Jobless Claims - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

• The dataset is part of Eurostat's collection of population and employment statistics. The code "NAMQ_10_PE" specifically refers to data related to employment and population trends in European countries and likely spans a range of years from 1980 to 2024.

Eurostat provides statistical data on various aspects of the labor market across Europe, including:

• Total Population – The total number of people residing in a particular country or region.
• Labor Force – The portion of the population that is either employed or actively looking for work.
• Employment Rate – The percentage of the working-age population that is employed.
• Unemployment Rate – The percentage of the labor force that is unemployed.
• Youth Employment Rate – The employment rate among young people (typically aged 15-24).

• Sectoral Employment – Employment distribution across various sectors like agriculture, industry, and services.

• **Details of the Dataset **

This dataset would typically cover European Union countries and potentially other European countries (depending on the specific version). The data likely spans multiple years (1980-2024) and provides insights into the demographic and economic changes in these countries over time.

-**Some example insights you might explore:**

Trends in Employment: Analyzing the employment and unemployment rates over time to see how they correlate with major economic events, such as the global financial crisis. Sectoral Shifts: Investigating how the structure of employment has shifted from agriculture and industry to services over the decades. Impact of Population Growth: Exploring how changes in population size relate to changes in employment, labor force participation, and unemployment.

• Link to Eurostat’s Dataset

You can access the Eurostat dataset directly using the following link:

• Eurostat – NAMQ_10_PE Dataset

This link takes you to Eurostat's Labor Force Survey (LFS) data, which includes datasets related to employment, unemployment, and other labor force indicators across EU countries. You can navigate and search for NAMQ_10_PE by using Eurostat’s filtering and search tools. Here, you can download data in various formats such as CSV, Excel, or TSV.

Dataset contains monthly counts, from 2001 to present, of individuals receiving regular unemployment insurance benefits, as well as the total amount of benefits received from New York State.

Data are provided for the state, 10 labor market regions, and counties. State counts can include everyone who receives benefits through New York State (including out-of-state residents) or only state residents who do so (excluding out-of-state residents).

Regular unemployment insurance includes: Unemployment Insurance (UI) Compensation, Compensation for Federal Employees (UCFE), Unemployment Compensation for Ex-Service Members (UCX), Shared Work (SW) and Self Employment Assistance Program (SEAP). It excludes federal extensions and 599.2 training.

ACS 1-year estimates are based on data collected over one calendar year, offering more current information but with a higher margin of error. ACS 5-year estimates combine five years of data, providing more reliable information but less current. Both are based on probability samples. Some racial and ethnic categories are suppressed to avoid misleading estimates when the relative standard error exceeds 30%.

Data Source: American Community Survey (ACS) 1- & 5-Year Estimates

Why This Matters

Employment is the main source of income for most people. For many families and individuals, unemployment threatens access to basic needs, such as food, housing, transportation, health care, and education, among others.

Nationally, Black workers and workers of color, on average, experience persistently higher unemployment rates than white workers. Racist policies and practices, including segregation, employment discrimination, and inequities in the criminal justice system have undermined job security for workers of color.

The District's Response

Initiatives that support residents in career advancement and their efforts to secure sustainable employment through education and training support, such as Career MAP, Advanced Technical Centers (ATC), and the DC Infrastructure Academy, among other programs and services.

Administering federal and local safety net programs that provide temporary cash and health benefits to help residents experiencing unemployment and related economic hardship meet their basic needs, including unemployment insurance, Medicaid, TANF For District Families, SNAP, etc.

Programs to remove barriers employment for returning citizens, such as Pathways to Work and the Returning Citizens Access to Jobs Grant.

Unemployment rate, participation rate, and employment rate by educational attainment, gender and age group, annual.

Unemployment Rate in India remained unchanged at 5.20 percent in October. This dataset provides - India Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The number of people who are unemployed as a percentage of the active labour force (i.e. employed and unemployed).

Unemployment Insurance Benefits (NYS)

Beneficiary Counts and Benefit Amounts By Region and County

By State of New York [source]

About this dataset

This dataset provides a powerful opportunity to analyze and understand the effects of unemployment insurance in New York State from 2001 to present. It provides a comprehensive overview of the monthly counts for individuals receiving regular unemployment insurance benefits, as well as the total amount of benefits received from New York State. In addition, data are provided for all 10 labor market regions in the state, which enables an assessment of local labor markets and helps inform strategies for improving regional employment outcomes. Moreover, information on out-of-state residents receiving benefits is also included in these data – allowing a unique cross-border examination. Therefore, with this dataset on hand it is possible to gain insights into how recipients are being affected by economic trends across different sectors, cities and counties throughout New York State. With these insightful statistics at our disposal we can better understand who has been affected by financial ups and downs across our state over time – enabling us to take smarter steps forward!

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How to use the dataset

This dataset provides an in-depth look at the number of people receiving regular unemployment insurance benefits in New York State as well as the total amount of these benefits paid out by the state from 2001 through present. The data is broken down by state, labor market region, and county. It includes Unemployment Insurance (UI) Compensation, Compensation for Federal Employees (UCFE), Unemployment Compensation for Ex-Service Members (UCX), Shared Work (SW) and Self Employment Assistance Program (SEAP).

Research Ideas

• Employers in New York can measure the impact of their business decisions on unemployment insurance beneficiaries in their regions over a specific period of time. This can help them better assess the effectiveness of their decisions, and identify where there are gaps that need to be addressed or areas they should focus on.
• Education organizations and institutions can compare unemployment insurance beneficiary trends within counties vs regionally to identify in-demand job concentrations and create programming around those skills sets needed by employers.
• Policymakers can analyze this dataset to understand the current state of unemployment benefits, including frequency of claims, regional variations, and amount paid out per month in order to ensure an equitable distribution of resources throughout the state

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: unemployment-insurance-beneficiaries-and-benefit-amounts-paid-beginning-2001-1.csv | Column name | Description | |:------------------|:-----------------------------------------------------------------------------------| | Year | Year of the data. (Integer) | | Month | Month of the data. (String) | | Region | Region of New York State. (String) | | County | County of New York State. (String) | | Beneficiaries | Number of individuals receiving regular unemployment insurance benefits. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit State of New York.

Initial Claims for UI released by the CT Department of Labor. Initial Claims are applications for Unemployment Benefits. Initial Claims may not result in receiving UI benefits if the individual doesn't qualify. Claims data can be access directly from CT DOL here: https://www1.ctdol.state.ct.us/lmi/claimsdata.asp

The initial claims reported in these tables are "processed" claims to the extent that duplicates and "reopened" claims have been eliminated. The claim counts in this dataset may not match claim counts from other sources.

Claims are disaggregated by age, education, industry, race/national origin, sex, and wages.

The claim counts in this dataset may not match claim counts from other sources.

Unemployment claims tabulated in this dataset represent only one component of the unemployed. Claims do not account for those not covered under the Unemployment system (e.g. federal workers, railroad workers or religious workers) or the unemployed self-employed.

Claims filed for a particular week will change as time goes on and the backlog is addressed.

Continued Claims for UI released by the CT Department of Labor. Continued Claims are total number of individuals being paid benefits in any particular week.

Claims are disaggregated by age, education, industry, race/national origin, sex, and wages.

The claim counts in this dataset may not match claim counts from other sources.

Unemployment claims tabulated in this dataset represent only one component of the unemployed. Claims do not account for those not covered under the Unemployment system (e.g. federal workers, railroad workers or religious workers) or the unemployed self-employed.

Claims filed for a particular week will change as time goes on and the backlog is addressed.

For data on initial claims at the town level, see the dataset "Initial Claims for Unemployment Benefits by Town," here: https://data.ct.gov/Government/Initial-Claims-for-Unemployment-Benefits-by-Town/twvc-s7wy

For data on continued claims see the following two datasets:

"Continued Claims for Unemployment Benefits in Connecticut," https://data.ct.gov/Government/Continued-Claims-for-Unemployment-Benefits-in-Conn/f9e5-rn42

"Continued Claims for Unemployment Benefits by Town," https://data.ct.gov/Government/Continued-Claims-for-Unemployment-Benefits-by-Town/r83t-9bjm

Historical series of Characteristics of the Insured Unemployed Reports (ETA-203) including monthly data by state breaking out insured unemployment by claimant characteristics including age, gender, race, occupation and industry. The report collects characteric information on individuals filing a continued claim for Unemployment Insurance reflecting unemployment during the week which includes the 12th of the month. The data in this dataset is intended to align with the unemployment data collected through the monthly Consumer Population Survey.

Unemployment Rate in Germany remained unchanged at 6.30 percent in November. This dataset provides the latest reported value for - Germany Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Abstract

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

The Department of Statistics (DOS) carried out four rounds of the 2016 Employment and Unemployment Survey (EUS). The survey rounds covered a sample of about fourty nine thousand households Nation-wide. The sampled households were selected using a stratified multi-stage cluster sampling design.

It is worthy to mention that the DOS employed new technology in data collection and data processing. Data was collected using electronic questionnaire instead of a hard copy, namely a hand held device (PDA).

The survey main objectives are: - To identify the demographic, social and economic characteristics of the population and manpower. - To identify the occupational structure and economic activity of the employed persons, as well as their employment status. - To identify the reasons behind the desire of the employed persons to search for a new or additional job. - To measure the economic activity participation rates (the number of economically active population divided by the population of 15+ years old). - To identify the different characteristics of the unemployed persons. - To measure unemployment rates (the number of unemployed persons divided by the number of economically active population of 15+ years old) according to the various characteristics of the unemployed, and the changes that might take place in this regard. - To identify the most important ways and means used by the unemployed persons to get a job, in addition to measuring durations of unemployment for such persons. - To identify the changes overtime that might take place regarding the above-mentioned variables.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

Covering a sample representative on the national level (Kingdom), governorates, and the three Regions (Central, North and South).

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

THE CLEANED AND HARMONIZED VERSION OF THE SURVEY DATA PRODUCED AND PUBLISHED BY THE ECONOMIC RESEARCH FORUM REPRESENTS 100% OF THE ORIGINAL SURVEY DATA COLLECTED BY THE DEPARTMENT OF STATISTICS OF THE HASHEMITE KINGDOM OF JORDAN

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

----> Raw Data

A tabulation results plan has been set based on the previous Employment and Unemployment Surveys while the required programs were prepared and tested. When all prior data processing steps were completed, the actual survey results were tabulated using an ORACLE package. The tabulations were then thoroughly checked for consistency of data. The final report was then prepared, containing detailed tabulations as well as the methodology of the survey.

----> Harmonized Data

• The SPSS package is used to clean and harmonize the datasets.
• The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency.
• All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
• A post-harmonization cleaning process is then conducted on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.

This data has been taken from LGInform at http://lginform.local.gov.uk/ data reference ID 5470 The figures show the numbers of people claiming unemployment benefits aged between 25-49 and living in Plymouth. The data is monthly and shows data ranging from Jan 2013 to May 2017. Number of people claiming unemployment related benefits, aged 25-49 - This is the total number of people aged 24-49 claiming unemployment related benefits (Claimant Count). The Claimant Count is a measure of the number of people claiming benefits principally for the reason of being unemployed, based on administrative data from the benefits system. From April 2015, the Claimant Count includes all Universal Credit claimants who are required to seek work and be available for work, as well as all Jobseeker's Allowance (JSA) claimants, between May 2013 and March 2015, the Claimant Count includes all out of work Universal Credit claimants as well as all JSA claimants prior to this the Claimant Count is a count of the number of people claiming JSA. The Claimant Count includes people who claim unemployment related benefits but who do not receive payment. For example some claimants will have had their benefits stopped for a limited period of time by Jobcentre Plus. Some people claim JSA in order to receive National Insurance Credits. The Claimant Count does not attempt to measure unemployment, which is a concept defined by the International Labour Organisation (ILO) as all those who are out of work, actively seeking work and available to start work. However, since the people claiming benefits are generally a particular subset of the unemployed, the Claimant Count can provide a useful indication of how unemployment is likely to vary between areas and over time. The Claimant Count estimates provide the best available estimates of the number of people claiming unemployment related benefits in the UK. Source name: Nomis Collection name: Claimant county by sex and age Polarity: No polarity Polarity is how sentiment is measured "Sentiment is usually considered to have "poles" positive and negative these are often translated into "good" and "bad" sentiment analysis is considered useful to tell us what is good and bad in our information stream

Number of unemployed individuals who have received FPUC payments and total amount paid (regular UI + FPUC paid amount) for time period recorded. Data is collected from the Department of Employment Services (DOES). Data is typically at least 24 hours behind.

Unemployment rates of 25- to 29-year-olds, by educational attainment, Canada and jurisdictions. This table is included in Section E: Transitions and outcomes: Labour market outcomes of the Pan Canadian Education Indicators Program (PCEIP). PCEIP draws from a wide variety of data sources to provide information on the school-age population, elementary, secondary and postsecondary education, transitions, and labour market outcomes. The program presents indicators for all of Canada, the provinces, the territories, as well as selected international comparisons and comparisons over time. PCEIP is an ongoing initiative of the Canadian Education Statistics Council, a partnership between Statistics Canada and the Council of Ministers of Education, Canada that provides a set of statistical measures on education systems in Canada.

The statistic shows the unemployment rate in India from 1999 to 2024. In 2024, the unemployment rate in India was estimated to be 4.2 percent. India's economy in comparison to other BRIC states India possesses one of the fastest-growing economies in the world and as a result, India is recognized as one of the G-20 major economies as well as a member of the BRIC countries, an association that is made up of rapidly growing economies. As well as India, three other countries, namely Brazil, Russia and China, are BRIC members. India’s manufacturing industry plays a large part in the development of its economy; however its services industry is the most significant economical factor. The majority of the population of India works in this sector. India’s notable economic boost can be attributed to significant gains over the past decade in regards to the efficiency of the production of goods as well as maintaining relatively low debt, particularly when compared to the total amount earned from goods and services produced throughout the years. When considering individual development as a country, India progressed significantly over the years. However, in comparison to the other emerging countries in the BRIC group, India’s progress was rather minimal. While China experienced the most apparent growth, India’s efficiency and productivity remained somewhat stagnant over the course of 3 or 4 years. India also reported a rather large trade deficit over the past decade, implying that its total imports exceeded its total amount of exports, essentially forcing the country to borrow money in order to finance the nation. Most economists consider trade deficits a negative factor, especially in the long run and for developing or emerging countries.

Context

This is a paid research survey to explore the linkage between mental illness and unemployment. NAMI has conducted multiple surveys verifying the high unemployment rate among those with mental illness, but this is the only survey to date which targets causation (why they are unemployed). Statistical significance of the variance has long since been proven by previous, larger samples.

You are free to visualize and publish results, please just credit me by name.

Collection methodology

I received several messages about methodology of collection because various people would like to use this data for papers.

• I paid respondents on Survey Monkey in a general population sampling. I did not target any specific demographic as not to get skewed results. Survey Monkey stratifies the sample according to certain characteristics like income and location.

• I know that the general population sampling went well because the number of people self identifying as having a mental illness is consistent with larger samples.

• Although we disqualified people without a mental illness, they were still given the complete survey. That means that the data contains sampling of people with and without mental illness and a yes/no indicator.

Potential area's to investigate

• Linkage between unemployment and education level
• The effect of a gap on your resume on income level
• Symptom/side effects impact on employment
• The effectiveness of social welfare programs
• The linkage between gaps in your resume and hospitalizations due to mental illness

Content

***Sample size:** n = 334; 80 w/ mental illness - this proportion is approximately equal to estimates of the general population diagnosed with mental illness (typically estimated at 20-25% according to various studies).*

Questions:

I identify as having a mental illness Response
Education  Response
I have my own computer separate from a smart phone Response
I have been hospitalized before for my mental illness  Response
How many days were you hospitalized for your mental illness Open-Ended Response
I am currently employed at least part-time Response
I am legally disabled  Response
I have my regular access to the internet  Response
I live with my parents Response
I have a gap in my resume  Response
Total length of any gaps in my resume in months.  Open-Ended Response
Annual income (including any social welfare programs) in USD  Open-Ended Response
I am unemployed Response
I read outside of work and school  Response
Annual income from social welfare programs Open-Ended Response
I receive food stamps  Response
I am on section 8 housing  Response
How many times were you hospitalized for your mental illness  Open-Ended Response

I have one of the following issues in addition to my illness:
  Lack of concentration
  Anxiety
  Depression
  Obsessive thinking
  Mood swings
  Panic attacks
  Compulsive behavior
  Tiredness

Age Response
Gender Response
Household Income  Response
Region Response
Device Type Response

Important data transformation note

When comparing the actual rate to government statistics, it is important to take into account the labor force participation rate (the % of people who are legally considered to be in the workforce). People not included in the unemployment statistic, like discouraged workers (for example the mentally ill) will be "not participating" in the workforce.

Other studies

1. Nami: https://www.nami.org/Press-Media/Press-Releases/2014/Mental-Illness-NAMI-Report-Deplores-80-Percent-Une

2. NIH: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4182106/

Abstract

The cleaned and harmonized version of the survey data produced and published by the Economic Research Forum represents 100% of the original survey data collected by the Central Agency for Public Mobilization and Statistics (CAPMAS)

In any society, the human element represents the basis of the work force which exercises all the service and production activities. Therefore, it is a mandate to produce labor force statistics and studies, that is related to the growth and distribution of manpower and labor force distribution by different types and characteristics.

In this context, the Central Agency for Public Mobilization and Statistics conducts "Quarterly Labor Force Survey" which includes data on the size of manpower and labor force (employed and unemployed) and their geographical distribution by their characteristics.

By the end of each year, CAPMAS issues the annual aggregated labor force bulletin publication that includes the results of the quarterly survey rounds that represent the manpower and labor force characteristics during the year.

----> Historical Review of the Labor Force Survey:

1- The First Labor Force survey was undertaken in 1957. The first round was conducted in November of that year, the survey continued to be conducted in successive rounds (quarterly, bi-annually, or annually) till now.

2- Starting the October 2006 round, the fieldwork of the labor force survey was developed to focus on the following two points: a. The importance of using the panel sample that is part of the survey sample, to monitor the dynamic changes of the labor market. b. Improving the used questionnaire to include more questions, that help in better defining of relationship to labor force of each household member (employed, unemployed, out of labor force ...etc.). In addition to re-order of some of the already existing questions in much logical way.

3- Starting the January 2008 round, the used methodology was developed to collect more representative sample during the survey year. this is done through distributing the sample of each governorate into five groups, the questionnaires are collected from each of them separately every 15 days for 3 months (in the middle and the end of the month)

----> The survey aims at covering the following topics:

1- Measuring the size of the Egyptian labor force among civilians (for all governorates of the republic) by their different characteristics. 2- Measuring the employment rate at national level and different geographical areas. 3- Measuring the distribution of employed people by the following characteristics: gender, age, educational status, occupation, economic activity, and sector. 4- Measuring unemployment rate at different geographic areas. 5- Measuring the distribution of unemployed people by the following characteristics: gender, age, educational status, unemployment type "ever employed/never employed", occupation, economic activity, and sector for people who have ever worked.

The raw survey data provided by the Statistical Agency were cleaned and harmonized by the Economic Research Forum, in the context of a major project that started in 2009. During which extensive efforts have been exerted to acquire, clean, harmonize, preserve and disseminate micro data of existing labor force surveys in several Arab countries.

Geographic coverage

Covering a sample of urban and rural areas in all the governorates.

Analysis unit

1- Household/family. 2- Individual/person.

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The cleaned and harmonized version of the survey data produced and published by the Economic Research Forum represents 100% of the original survey data collected by the Central Agency for Public Mobilization and Statistics (CAPMAS)

Sample Design and Selection

The sample of the LFS 2006 survey is a simple systematic random sample.

Sample Size

The sample size varied in each quarter (it is Q1=19429, Q2=19419, Q3=19119 and Q4=18835) households with a total number of 76802 households annually. These households are distributed on the governorate level (urban/rural).

A more detailed description of the different sampling stages and allocation of sample across governorates is provided in the Methodology document available among external resources in Arabic.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire design follows the latest International Labor Organization (ILO) concepts and definitions of labor force, employment, and unemployment.

The questionnaire comprises 3 tables in addition to the identification and geographic data of household on the cover page.

----> Table 1- Demographic and employment characteristics and basic data for all household individuals

Including: gender, age, educational status, marital status, residence mobility and current work status

----> Table 2- Employment characteristics table

This table is filled by employed individuals at the time of the survey or those who were engaged to work during the reference week, and provided information on: - Relationship to employer: employer, self-employed, waged worker, and unpaid family worker - Economic activity - Sector - Occupation - Effective working hours - Work place - Average monthly wage

----> Table 3- Unemployment characteristics table

This table is filled by all unemployed individuals who satisfied the unemployment criteria, and provided information on: - Type of unemployment (unemployed, unemployed ever worked) - Economic activity and occupation in the last held job before being unemployed - Last unemployment duration in months - Main reason for unemployment

Cleaning operations

----> Raw Data

Office editing is one of the main stages of the survey. It started once the questionnaires were received from the field and accomplished by the selected work groups. It includes: a-Editing of coverage and completeness b-Editing of consistency

----> Harmonized Data

• STATA is used to clean and SPSS is used harmonize the datasets.
• The harmonization process starts with a cleaning process for all raw data files received from the Statistical Agency.
• All cleaned data files are then merged to produce one data file on the individual level containing all variables subject to harmonization.
• A country-specific program is generated for each dataset to generate/ compute/ recode/ rename/ format/ label harmonized variables.
• A post-harmonization cleaning process is then conducted on the data.
• Harmonized data is saved on the household as well as the individual level, in SPSS and then converted to STATA, to be disseminated.

Unemployment Rate in France increased to 7.70 percent in the third quarter of 2025 from 7.60 percent in the second quarter of 2025. This dataset provides the latest reported value for - France Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.