Movie Scores vs. Unemployment Rate

Examining the Correlation between Cinema Popularity and Economic Stability

By [source]

About this dataset

This dataset examines the potential correlation between US unemployment rates and movie scores in order to explore how difficult economic times can influence how viewers rate films. With data spanning from 2009-2018, this dataset contains information on the yearly unemployment rate as well as the average movie score on a scale from 1-10 for that same year. Our goal is to investigate whether economic unrest and hardship have any effect on film ratings in order to shed light both on an often overlooked part of moviegoers' opinions, and also on our society's attitudes towards certain topics during times of crisis

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Research Ideas

• Predicting the success of a movie, given the economic conditions for that year.
• Determining how a year's unemployment rate affects viewers' overall opinion of movies from that same period.
• Analyzing whether people rate movies differently in times of economic difficulty than when the economy is booming

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: result.csv | Column name | Description | |:-------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------| | year | The year in which the movie was released. (Integer) | | UnEmployeeRate | The unemployment rate in the country during the year the movie was released. (Float) | | movieScore | The average score of the movie based on reviews from critic websites such as Rotten Tomatoes, IMDb etc., with 10 being highest rated movies. (Float) |

Acknowledgements

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

This data shows a summary of annual unemployment rates for cities within the metro Phoenix area and supports Tempe's Unemployment Rate performance measure. The performance measure page is available at 5.13 Unemployment Rate. Additional Information Source: https://www.bls.gov/Contact (author): Madalaine McConvilleContact E-Mail (author): madalaine_mcconville@tempe.govData Source Type: Excel tablePreparation Method: Extracted for selected citiesPublish Frequency: AnnualPublish Method: ManualData Dictionary

This dataset contains non-seasonally adjusted California Unemployment Rate by age groups, from the Current Population Survey (CPS). The age group ranges are as follows; 16-19 ; 20 - 24; 25 - 34; 35 - 44; 45 - 54; 55 -64; 65+. This data is based on a 12-month moving average.

This dataset is invaluable for data science applications due to its granularity and the historical depth it offers. With detailed monthly data on unemployment rates by age groups, data scientists can perform a myriad of analyses:

• Time Series Analysis: Analyzing trends over time to forecast future unemployment rates or identify cyclic patterns.
• Demographic Analysis: Understanding which age groups are most affected by unemployment and how this changes through economic conditions.
• Geographic Analysis: Although the dataset currently focuses on California, if similar data is available for other regions, comparisons can be made to understand regional economic differences.

The dataset can also be merged with other socioeconomic indicators like GDP, education levels, and industry growth metrics to examine broader economic narratives or policy impacts.

In 2025, it was estimated that over 163 million Americans were in some form of employment, while 4.16 percent of the total workforce was unemployed. This was the lowest unemployment rate since the 1950s, although these figures are expected to rise in 2023 and beyond. 1980s-2010s Since the 1980s, the total United States labor force has generally risen as the population has grown, however, the annual average unemployment rate has fluctuated significantly, usually increasing in times of crisis, before falling more slowly during periods of recovery and economic stability. For example, unemployment peaked at 9.7 percent during the early 1980s recession, which was largely caused by the ripple effects of the Iranian Revolution on global oil prices and inflation. Other notable spikes came during the early 1990s; again, largely due to inflation caused by another oil shock, and during the early 2000s recession. The Great Recession then saw the U.S. unemployment rate soar to 9.6 percent, following the collapse of the U.S. housing market and its impact on the banking sector, and it was not until 2016 that unemployment returned to pre-recession levels. 2020s 2019 had marked a decade-long low in unemployment, before the economic impact of the Covid-19 pandemic saw the sharpest year-on-year increase in unemployment since the Great Depression, and the total number of workers fell by almost 10 million people. Despite the continuation of the pandemic in the years that followed, alongside the associated supply-chain issues and onset of the inflation crisis, unemployment reached just 3.67 percent in 2022 - current projections are for this figure to rise in 2023 and the years that follow, although these forecasts are subject to change if recent years are anything to go by.

Introduction

The U.S. job market, with its dynamic trends and fluctuating unemployment rates, serves as an important barometer for the nation's economic health. All rates provided in this dataset are seasonally adjusted. Delving into the intricacies of unemployment rates by age and gender helps researchers, policymakers, and analysts uncover underlying patterns and address potential disparities.

Usage Examples

• Economic Research: Study the historical unemployment trends to gauge economic cycles.
• Policy Making: Inform labor market policies and interventions based on age or gender disparities.
• Business Strategy: Companies can analyze job market conditions when considering expansions or contractions.
• Academic Projects: Students and educators can use the dataset for case studies, dissertations, or classroom projects.

Image Source Photo by Ron Lach : https://www.pexels.com/photo/woman-looking-for-jobs-in-newspaper-9832700/

Dataset Contents

This dataset, sourced from the FRED API, provides: - df_sex_unemployment_rates.csv: A breakdown of U.S. unemployment rates based on gender. - df_unemployment_rates.csv: Unemployment rates categorized by various age groups, ranging from young entrants (ages 16-17) to seasoned professionals (55 and above).

Together, these data files offer a comprehensive insight into the nuances of unemployment in the U.S., highlighting potential disparities in the job market across different age groups and between men and women.

The employment and unemployment indicator shows several data points. The first figure is the number of people in the labor force, which includes the number of people who are either working or looking for work. The second two figures, the number of people who are employed and the number of people who are unemployed, are the two subcategories of the labor force. The unemployment rate is a calculation of the number of people who are in the labor force and unemployed as a percentage of the total number of people in the labor force.

The unemployment rate does not include people who are not employed and not in the labor force. This includes adults who are neither working nor looking for work. For example, full-time students may choose not to seek any employment during their college career, and are thus not considered in the unemployment rate. Stay-at-home parents and other caregivers are also considered outside of the labor force, and therefore outside the scope of the unemployment rate.

The unemployment rate is a key economic indicator, and is illustrative of economic conditions in the county at the individual scale.

There are additional considerations to the unemployment rate. Because it does not count those who are outside the labor force, it can exclude individuals who were looking for a job previously, but have since given up. The impact of this on the overall unemployment rate is difficult to quantify, but it is important to note because it shows that no statistic is perfect.

The unemployment rates for Champaign County, the City of Champaign, and the City of Urbana are extremely similar between 2000 and 2023.

All three areas saw a dramatic increase in the unemployment rate between 2006 and 2009. The unemployment rates for all three areas decreased overall between 2010 and 2019. However, the unemployment rate in all three areas rose sharply in 2020 due to the effects of the COVID-19 pandemic. The unemployment rate in all three areas dropped again in 2021 as pandemic restrictions were removed, and were almost back to 2019 rates in 2022. However, the unemployment rate in all three areas rose slightly from 2022 to 2023.

This data is sourced from the Illinois Department of Employment Security’s Local Area Unemployment Statistics (LAUS), and from the U.S. Bureau of Labor Statistics.

Sources: Illinois Department of Employment Security, Local Area Unemployment Statistics (LAUS); U.S. Bureau of Labor Statistics.

The unemployment rate is the number of unemployed persons as a percentage of the labour force based on International Labour Office (ILO) definition. The labour force is the total number of people employed and unemployed. The MIP scoreboard indicator considers unemployed persons comprise persons aged 15 to 74 who: - are without work during the reference week; - are available to start work within the next two weeks; - and have been actively seeking work in the past four weeks or had already found a job to start within the next three months. Unit: rate. The indicative threshold of the indicator is 10%. In the table, values are also calculated by considering unemployed persons aged 15 to 24 and those aged 25 to 74.

Unemployment rates represent unemployed persons as a percentage of the labour force. The labour force is the total number of people employed and unemployed. Unemployed persons comprise persons aged 15 to 74 who were: a. without work during the reference week, b. currently available for work, i.e. were available for paid employment or self-employment before the end of the two weeks following the reference week, c. actively seeking work, i.e. had taken specific steps in the four weeks period ending with the reference week to seek paid employment or self-employment or who found a job to start later, i.e. within a period of, at most, three months. This table does not only show unemployment rates but also unemployed in 1000 and as % of the total population.

1990 to present (approximate 2 month lag) Virginia Labor Force and Unemployment estimates by Month by County.

Special data considerations: Period values of "M01-M12" represent Months of Year; "M13" is the Annual Average.

U.S. Bureau of Labor Statistics; Local Area Unemployment Statistics, table la.data.54.Virginia Data accessed from the Bureau of Labor Statistics public database LABSTAT (https://download.bls.gov/pub/time.series/la/)

Supporting documentation can be found on the U.S. Bureau of Labor Statistics website under Local Area Unemployment Statistics, Handbook of Methods (https://www.bls.gov/opub/hom/lau/home.htm)

Survey Description: Labor force and unemployment estimates for States and local areas are developed by State workforce agencies to measure local labor market conditions under a Federal-State cooperative program. The Department of Labor develops the concepts, definitions, and technical procedures which are used by State agencies for preparation of labor force and unemployment estimates.

These estimates are derived from a variety of sources, including the Current Population Survey, the Current Employment Statistics survey, the Quarterly Census of Employment and Wages, various programs at the Census Bureau, and unemployment insurance claims data from the State workforce agencies.

To establish uniform labor force concepts and definitions in all States and areas consistent with those used for the U.S. as a whole, monthly national estimates of employment and unemployment from the Current Population Survey are used as controls (benchmarks) for the State labor force statistics.

Summary Data Available: Monthly labor force and unemployment series are available for approximately 7,500 geographic areas, including cities over 25,000 population, counties, metropolitan areas, States, and other areas.

For each area, the following measures are presented by place of residence:

• Total civilian labor force,
• Total number of people employed,
• Total number of people unemployed, and
• Unemployment rate

Data Characteristics: Rates are expressed as percents with one decimal place. Levels are measured as individual persons (not thousands) and are stored with no decimal places.

VITAL SIGNS INDICATOR
Unemployment (EC3)

FULL MEASURE NAME
Unemployment rate by residential location

LAST UPDATED
December 2022

DESCRIPTION
Unemployment refers to the share of the labor force – by place of residence – that is not currently employed full-time or part-time. The unemployment rate reflects the strength of the overall employment market.

DATA SOURCE
California Employment Development Department: Historical Unemployment Rates
1990-2010
Spreadsheet provided by CAEDD

California Employment Development Department: Labor Force and Unemployment Rate for California Sub-County Areas - https://data.edd.ca.gov/Labor-Force-and-Unemployment-Rates/Labor-Force-and-Unemployment-Rate-for-California-S/8z4h-2ak6
2010-2022

California Employment Development Department: Local Area Unemployment Statistics (LAUS) - https://data.edd.ca.gov/Labor-Force-and-Unemployment-Rates/Local-Area-Unemployment-Statistics-LAUS-/e6gw-gvii
1990-2022

U.S. Bureau of Labor Statistics: Local Area Unemployment Statistics (LAUS) - https://download.bls.gov/pub/time.series/la
1990-2021

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Unemployment rates produced by the CA Employment Development Department (EDD) for the region and county levels are not adjusted for seasonality (as they reflect annual data) and are final data (i.e., not preliminary). Unemployment rates produced by U.S. Bureau of Labor Statistics (BLS) for the metro regions are annual and not adjusted for seasonality; they reflect the primary metropolitan statistical area (MSA) for the named region, except for the San Francisco Bay Area which uses the nine-county region. The unemployment rate is calculated based on the number of unemployed persons divided by the total labor force. Note that the unemployment rate can decline or increase as a result of changes in either variable.

The unemployment rate in the United States falls slowly in expansions, and it may not reach its previous low point before the next recession begins. Based on this feature, I document that the frequent recessions prior to 1983 are associated with an upward trend in the unemployment rate. In contrast, the long expansions beginning in 1983 are associated with a downward trend. I then estimate a two-variable vector autoregression (VAR) that includes the unemployment rate and a recession indicator. Long-horizon forecasts from this VAR conditioned on no future recessions project that the unemployment rate will go to 3.6 percent after a long period with no recessions.

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

Youth unemployment stood at 9.7 percent in February 2025. Seasonal adjustment is a statistical method for removing the seasonal component of a time series that is used when analyzing non-seasonal trends. The unemployment rate by state can be found here, and the annual national unemployment rate can be found here. Youth unemployment in the United States The United States Bureau of Labor Statistics track unemployment of persons between the ages of 16 and 24 years each month. In analyzing the data, the Bureau of Labor Statistics performed a seasonal adjustment—removing seasonal influences from the time series, such that one month’s rate of unemployment could be analyzed in comparison with another month’s rate of unemployment. During the period in question, youth unemployment ranged from a high of 9.9 percent in April 2021, to a low of 6.5 percent in April 2023. The national youth unemployment rate can be compared to the monthly national unemployment rate in the United States, although youth unemployment tends to be much higher due to higher rates of participation in education. In May 2023, U.S. unemployment was at 3.7 percent, compared with 7.4 percent amongst those 16 to 24 years old. Additionally, as of May 2023, Nevada had the highest state unemployment rate of all U.S. states, at 5.4 percent.

This dataset contains the Local Area Unemployment Statistics (LAUS), annual averages from 1990 to 2024. The Local Area Unemployment Statistics (LAUS) program is a Federal-State cooperative effort in which monthly estimates of total employment and unemployment are prepared for approximately 7,600 areas, including counties, cities and metropolitan statistical areas. These estimates are key indicators of local economic conditions. The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor is responsible for the concepts, definitions, technical procedures, validation, and publication of the estimates that State workforce agencies prepare under agreement with BLS. Estimates for counties are produced through a building-block approach known as the "Handbook method." This procedure also uses data from several sources, including the CPS, the CES program, state UI systems, and the Census Bureau's American Community Survey (ACS), to create estimates that are adjusted to the statewide measures of employment and unemployment. Estimates for cities are prepared using disaggregation techniques based on inputs from the ACS, annual population estimates, and current UI data.

50 years+ of historical inflaction, interest and unemployment rates by country

data source: https://data.worldbank.org cover image credit: https://www.pexels.com/photo/one-dollar-bill-3943739/

Labour force participation rate: number of people aged 15+ in the labour force as a percentage of the working-age population (aged 15+). The notion of labour force refers to people who are employed or unepmployed (according to International Labour Organization). Unemployment rate: number of people aged 15+ who are unemployed as a percentage of people in the labour force. The notion of unemployment refers to people who are 1) not in employment, 2) available to work, 3) actively looking for work.

Find more Pacific data on PDH.stat.

Unemployment Rates by State

Unemployment rates in US states from 1976 to 2018

By Throwback Thursday [source]

About this dataset

This dataset, titled Unemployment by State 1976 - 2018, provides comprehensive information on unemployment rates in each state within the United States from 1976 to 2018. The data was sourced from the Bureau of Labor Statistics, ensuring reliability and relevance.

The dataset includes a variety of columns that provide valuable insights into the unemployment situation in each state during this time period. These columns include:

1. Month and Year: This column represents the specific month and year for which the data was recorded. It enables researchers to analyze trends and changes in unemployment rates over time.

2. State: This categorical column indicates the name of the individual state for which the unemployment data is recorded. It allows users to compare and contrast unemployment rates between different states throughout this time frame.

3. Civilian Population: This numeric column represents the total number of individuals who were not part of the military or institutionalized, and were at least 16 years old during a particular month and year. It serves as a reference point for understanding workforce size when analyzing employment trends.

4. Total Labor Force: This numeric column represents the total number of individuals who were either employed or actively seeking employment during a specific period in a particular state. It provides an accurate measure of workforce participation that can be used alongside other variables for further analysis.

5. Percent of Population: This numeric column reflects what percentage of a state's civilian population made up its total labor force during a given month and year, providing insights into labor market dynamics on a proportional scale.

6. Employed - Total: This numeric column gives an aggregate count representing how many individuals were employed within each state during a certain period, enabling analysis of employment opportunities across states over time.

7. Employed - Percent of Population: Expressed as a percentage, this variable indicates what proportion of each state's civilian population was employed during specific months/years under consideration. It helps measure the efficiency of state economies in utilizing available workforce resources.

8. Unemployed - Total: This numeric column represents the total number of individuals who were unemployed during specific months and years within each state. It serves as a quantifiable indicator of labor market fluctuations and economic challenges faced by different states at various times.

9. Unemployed - Percent of Labor Force: This numeric column reveals the percentage of total labor force members who were unemployed during specific periods, providing an insight into the severity of unemployment rates relative to overall workforce size.

With this comprehensive dataset, researchers can conduct in-depth analyses on unemployment rates across different states

How to use the dataset

This dataset provides comprehensive information on unemployment rates in each state from 1976 to 2018. The data is obtained from the Bureau of Labor Statistics and covers various aspects related to unemployment.

Here is a step-by-step guide on how to effectively utilize this dataset:

• Understand the columns: Familiarize yourself with the different columns present in the dataset. Each column represents a specific attribute related to unemployment rates, such as the month and year of the data recorded, state name, civilian population, total labor force, percentage of population employed or unemployed, and more.

• Filter by state: If you are interested in analyzing specific states, use the State column to filter and extract data for those particular states. This will help you focus your analysis on regions that are most relevant to your research or area of interest.

• Analyze trends over time: Utilize the Month and Year column (excluding dates) for understanding long-term trends in unemployment rates across different states. This can be done by creating line plots or bar charts comparing unemployment rates for multiple states over time.

• Compare employment percentages: The columns Employed - Percent of Population and Unemployed - Percent of Labor Force provide valuable insights into employment trends at both individual state as well as national levels. Use these percentages to compare different states' employment performances against each other or within a specific timeframe.

• Calculate raw numbers: The columns E...

Dataset replaced by: http://data.europa.eu/euodp/data/dataset/5wwzZYnxIK3aSTAZdXaGg Unemployment rates represent unemployed persons as a percentage of the labour force. The labour force is the total number of people employed and unemployed. Unemployed persons comprise persons aged 15 to 74 who were: a. without work during the reference week, b. currently available for work, i.e. were available for paid employment or self-employment before the end of the two weeks following the reference week, c. actively seeking work, i.e. had taken specific steps in the four weeks period ending with the reference week to seek paid employment or self-employment or who found a job to start later, i.e. within a period of, at most, three months. This table does not only show unemployment rates but also unemployed in 1000 and as % of the total population.

The Local Area Unemployment Statistics (LAUS) program is a federal-state cooperative effort which produces monthly estimates of produces monthly and annual employment, unemployment, and labor force data for approximately 7,000 areas including Census regions and divisions, States, counties, metropolitan areas, and many cities.

This dataset includes data for all 50 states, the District of Columbia, and Puerto Rico. To only see data for Connecticut, create a filter where "State name" is equal to "Connecticut".

For more information on the LAUS program and data visit: https://www.bls.gov/lau/

For more information from the CT Department of Labor visit: https://www1.ctdol.state.ct.us/lmi/LAUS/default.asp

Labour force survey (LFS) Purpose and short description The Labour Force Survey (LFS) is a household sample survey, conducted throughout the year. It is based on the responses of approximately 110,000 persons aged 15-89. Its main objective is to classify the population of 15-89 years into three groups (employed, unemployed and inactive persons on the labous market) and to provide descriptive and explanatory data on every category. This survey is also carried out in the other EU Member States and is coordinated by Eurostat, the statistical office of the European Union. In Belgium, the LFS is organised by Statbel. The objective is to obtain comparable information at European level, in particular as regards employment and unemployment rates as defined by the International Labour Office (ILO), but also to collect and disseminate data that are otherwise not available, for example about the mobility of workers, the reasons for working part-time, the various forms of part-time employment, the occupation, the educational level of the working age population, ... . Survey population Members of private households aged 15-89. Sample frame Demographic data from the National Register. Data collection method and sample size Data are collected through face-to-face interviews for the first wave of the survey. Since 2017, there have been three (shorter) follow-up waves to which households respond online or by telephone. Households with only inactive persons older than 64 can also be interviewed by telephone. Every year, around 34,000 households take part in this survey. Response rate On average, the response rate in the first wave of the survey is around 68% and in the follow-up waves between 90% and 95%. Periodicity Quarterly Release calendar Results availability: around 3 months after the end of the reference period. Forms Labour Force Survey 2025 (PDF, 1 Mb) Definitions regarding employment and unemployment The survey is harmonised at European level. The definitions regarding employment and unemployment that are mentioned are those of the International Labour Office (ILO) to allow international comparison. People with a job (employed people) comprise all people who during the reference week performed some work ‘for wage or salary’ or ‘for profit’ regardless of the duration (even if this was only one hour), or who had a job but were temporarily absent. For example, one can be temporarily absent for holidays, illness, technical or economic reasons (temporary unemployment),.... Family workers are also included in the category ‘employed’. Since 2021, people who have been temporarily unemployed for an uninterrupted period of more than three months are counted as unemployed or inactive, and no longer as employed. The unemployed comprise all people who: (a) during the reference week were without work, i.e. were not in paid employment or self-employment; (b) were available for work, i.e. were available for paid employment or self-employment within two weeks after the reference week; (c) were actively seeking work, i.e. had taken specific steps during the last four weeks including the reference week to seek paid employment or self-employment, or who had found a job to start within a maximum period of three months. Please note: The ILO unemployment figures are unrelated to any possible registration with the VDAB, Actiris, FOREM or the ADG, or to the receipt of unemployment benefits from ONEM (National Employment Office). As a result, they cannot be compared with administrative unemployment figures. The labour force is made up of the employed and the unemployed. The economically inactive population comprises all people who were not considered as employed or unemployed. The employment rate represents employed persons as a percentage of the same age population. The employment rate as part of the Europe 2020 Strategy represents the share of persons employed in the population aged 20 to 64. The unemployment rate represents the share of unemployed people in the labour force (employed + unemployed) within a given age group. The economic activity rate represents the share of the labour force (employed + unemployed) in the total population within a given age group. The above indicators (employment rate, unemployment rate and economic activity rate) are the most important indicators for international comparisons of the labour market evolution. Low-skilled people are people who have at best a lower secondary education diploma. Medium-skilled people have obtained an upper secondary education diploma, but no higher education diploma. High-skilled people have a higher education diploma. Metadata Employment, unemployment, labour market (NL-FR) Labour force survey (LFS) (NL-FR) Survey methodology Modifications to the Labour Force Survey (LFS) in 2021 LFS: Methodological improvements to the Labour Force Survey 2017 (PDF, 99 Kb) LFS: Presentation of the survey until 2016 (NL-FR) LFS: Presentation of the survey from 2017 (NL-FR) Note on the occasion