About this dataset

Context

The dataset tabulates the population of White Earth by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for White Earth. The dataset can be utilized to understand the population distribution of White Earth by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in White Earth. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for White Earth.

Key observations

Largest age group (population): Male # 10-14 years (17) | Female # 40-44 years (13). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the White Earth population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the White Earth is shown in the following column.
• Population (Female): The female population in the White Earth is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in White Earth for each age group.

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 White Earth Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Globe by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Globe. The dataset can be utilized to understand the population distribution of Globe by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Globe. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Globe.

Key observations

Largest age group (population): Male # 40-44 years (386) | Female # 50-54 years (413). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Globe population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Globe is shown in the following column.
• Population (Female): The female population in the Globe is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Globe for each age group.

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 Globe Population by Gender. You can refer the same here

Context

I thought of consolidating and sharing this public data to see how the data science world uses it discover interesting patterns. The data has been collected from 2018 FIFA World Cup Russia Official App.

Content

The data will be updated after each match daily.

Note: On the column '1st Goal', any goal that was scored in the extra time will be denoted as 45 or 90 based on 1st or 2nd half of the game (ex. if 1st goal was scored in 45+2 mins then it will be mentioned as 45 instead of 47, likewise for the 2nd half)

Acknowledgements

Thanks to the FIFA 2018 World Cup App.

Inspiration

I thought of consolidating and sharing this public data to see how the data science world uses it discover interesting patterns. Can we predict the Man of the match award using this statistics before the official announcement that will be made right after the match?

This dataset provides values for RETIREMENT AGE MEN reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

As of April 2024, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger.

              Teens and social media

              As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
              Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.

The Gross Domestic Product (GDP) in Isle Of Man was worth 7.43 billion US dollars in 2022, according to official data from the World Bank. The GDP value of Isle Of Man represents 0.01 percent of the world economy. This dataset provides - Isle Of Man Gdp- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset contains data from WHO's data portal covering the following categories:

Adolescent, Ageing, Air pollution, Assistive technology, Child, Child mortality, Cross-cutting, Dementia diagnosis, treatment and care, Environment and health, Foodborne Diseases Estimates, Global Dementia Observatory (GDO), Global Health Estimates: Life expectancy and leading causes of death and disability, Global Information System on Alcohol and Health, Global Patient Safety Observatory, Global strategy, HIV, Health financing, Health systems, Health taxes, Health workforce, Hepatitis, Immunization coverage and vaccine-preventable diseases, Malaria, Maternal and newborn, Maternal and reproductive health, Mental health, Neglected tropical diseases, Noncommunicable diseases, Nutrition, Oral Health, Priority health technologies, Resources for Substance Use Disorders, Road Safety, SDG Target 3.8 | Achieve universal health coverage (UHC), Sexually Transmitted Infections, Tobacco control, Tuberculosis, Vaccine-preventable communicable diseases, Violence prevention, Water, sanitation and hygiene (WASH), World Health Statistics.

For links to individual indicator metadata, see resource descriptions.

This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems.

These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty.

Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis.

Hazards:

• coastal and river flooding (Ward et al, 2020; Baugh et al, 2024)
• extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)
• tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)

Exposure:

• population (Schiavina et al, 2023)
• built-up area (Pesaresi et al, 2023)
• roads (OpenStreetMap, 2025)
• railways (OpenStreetMap, 2025)
• power plants (Global Energy Observatory et al, 2018)
• power transmission lines (Arderne et al, 2020)

Contextual information:

• elevation (European Union and ESA, 2021)
• land-use and land cover (Copernicus Climate Change Service and Climate Data Store, 2019)
• administrative boundaries from geoBoundaries (Runfola et al., 2020)

The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people.

To learn more about related concepts, there is a free short course available through the Open University on Infrastructure and Climate Resilience. This overview of the course has more details.

These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow:

• snkit helps clean network data
• nismod-snail is designed to help implement infrastructure exposure, damage and risk calculations

The open-gira repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis.

For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at https://east-africa.infrastructureresilience.org/ and is described in detail in Hickford et al (2023).

References

• Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: 10.5281/zenodo.3628142
• Baugh, Calum; Colonese, Juan; D'Angelo, Claudia; Dottori, Francesco; Neal, Jeffrey; Prudhomme, Christel; Salamon, Peter (2024): Global river flood hazard maps. European Commission, Joint Research Centre (JRC) [Dataset] PID: data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif
• Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: 10.4121/12705164.v3
• Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: 10.4121/14510817.v3
• Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.006f2c9a (Accessed on 09-AUG-2024)
• Copernicus DEM - Global Digital Elevation Model (2021) DOI: 10.5270/ESA-c5d3d65 (produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved)
• Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; resourcewatch.org/
• Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries
• Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: 10.1029/2020EF001616
• Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details
• OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2025) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at global.infrastructureresilience.org
• Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA
• Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. DOI: 10.1371/journal.pone.0231866.
• Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: 10.5281/zenodo.8147088
• Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE
• Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: www.wri.org/publication/aqueduct-floods-methodology.

Prediction of Insurance Charges Using Age, Gender, BMI

A Study of Customers Insurance Charges

By Bob Wakefield [source]

About this dataset

This dataset contains detailed information about insurance customers, including their age, sex, body mass index (BMI), number of children, smoking status and region. Having access to such valuable insights allows analysts to get a better view into customer behaviour and the factors that contribute to their insurance charges. By understanding the patterns in this data set we can gain useful insight into how age,gender and lifestyle choices can affect a person's insurance premiums. This could be of great value when setting up an insurance plan or marketing campaigns that target certain demographics. Furthermore, this dataset provides us with an opportunity to explore deeper questions such as what are some possible solutions for increasing affordability when it comes to dealing with high charges for certain groups?

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset can be used to predict insurance charges based on the age, sex, and BMI of a customer. The data has been gathered from a variety of sources and contains information such as age, gender, region and bmi values for each customer.

To make use of this dataset you will first need to understand the different variables present in it so you can understand which ones have an impact on predicting insurance charges. Age is expectedly one of the most important variables as younger or older customers may pay less or more respectively for their coverallsure policies. Similarly sex is also influential as traditionally gender roles dictate premiums with men paying more than women for the same coverage on many policies historically speaking. Lastly bmi should also be taken into account when making any predictions regarding insurance costs due to varying factors such as risk factors associated with obesity being taken into consideration by premium pricing decisions made by insurers.

Once having understood how all these elements influence pricing decisions it is then time to explore potential predictive models that could accurately calculate an appropriate amount/estimation based off what you know about a customer's characterisitcs. You may find regression based models most useful here however there are other options out there too so make sure you spend enough time researching before designing your systems architecture entirely around one particular model type.

The data provided should provide all that's required in order to ascertain these correlations between features however further refinements could result from additional customer related features being inputted such as driving history or past claims experience etc but again this information may not have been kept/provided within this dataset!

In conclusion this dataset provides a decent starting point for predicting accurate numerical output using various combinations of characteristic related inputs - have fun creating something amazing!

Research Ideas

• Using age, sex and bmi to create an algorithm for assessing life insurance costs.
• Predicting costs for certain patients based on their sex, age, bmi and region to help doctors decide what treatments work best financially for them.
• Creating a cost calculator that takes into account the patient’s age, sex, smoker status, region of residence and other factors to accurately predict the medical bills a person will pay in a year

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: insurance.csv | Column name | Description | |:--------------|:---------------------------------------------------| | Age | The age of the customer. (Integer) | | Children | The number of children the customer has. (Integer) | | Smoker | Whether or not the customer is a smoker. (Boolean) | | Region | The region the customer lives in. (String) | | Charges | The insurance charges for the customer. (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 Bob Wakefield.

Abstract copyright UK Data Service and data collection copyright owner. This is a qualitative data collection. Research has documented the return of paid domestic labour in the Global North. Studies have shown how it is migrant workers who are supplying much of this domestic labour, and the concept of global care chains has been developed to capture this. The research has tended to focus on how women are situated within these global care chains. This project, however, aims to illuminate and make sense of some of the ways in which men are positioned within the relationship between globalisation, migration and social reproduction. The project will focus on situations in which families buy-in the labour of migrant handymen to undertake traditionally male tasks of social reproduction such as home maintenance and gardening. The project combined quantitative and qualitative research methods. A range of existing data sets was analysed in order to provide a descriptive statistical portrait of the prevalence and characteristics of the migrant handyman phenomenon in the UK. In-depth face-to-face interviews with migrant handymen and labour-using households were conducted in order to explore themes such as why, how and with what consequences households use migrant handymen, and the processes by which migrants come to be inserted in this type of work.

The "LLM World of Words" (LWOW) [1] is a collection of datasets of English free association norms generated by various large language models (LLMs). Currently, the collection consists of datasets generated by Mistral, LLaMA3, and Claude Haiku. The datasets are modeled after the "Small World of Words" (SWOW) (https://smallworldofwords.org/en/project/) [2] English free association norms, generated by humans, consisting of over 12,000 cue words and over 3 million responses. The purpose of the LWOW datasets is to provide a way to investigate various aspects of the semantic memory of LLMs using an approach that has been applied extensively for investigating the semantic memory of humans. These datasets, together with the SWOW dataset, can be used to gain insights about similarities and differences in the language structures possessed by humans and LLMs.

What are free associations?

Free associations are implicit mental connections between words or concepts. They are typically accessed by presenting humans (or AI agents) with a cue word and then asking them to respond with the first words that come to mind. The responses represent implicit associations that connect different concepts in the mind, reflecting the semantic representations that underly patterns of thought, memory, and language. For example, given the cue word "woman", a common free association response might be "man", reflecting the associative mental relation between these two concepts.

How can they be used?

Free associations have been extensively used in cognitive psychology and linguistics as a tool for studying language and cognitive information processing. They provide a way for researchers to understand how conceptual knowledge is organized and accessed in the mind. Free associations are often used to built network models of semantic memory by connecting cue words to their responses. When thousands of cues and responses are connected in this way, the result is a complex network model that represents the complex organization of semantic knowledge. Such models enable the investigation of complex cognitive processes that take place within semantic memory, and can be used to study a variety of cognitive phenomena such as language learning, creativity, personality traits, and cognitive biases.

Validation of the datasets with semantic priming

The LWOW datasets were validated using data from the Semantic Priming Project (https://www.montana.edu/attmemlab/spp.html) [3], which implements a lexical decision task (LDT) to study semantic priming. The semantic priming effect is the cognitive phenomenon that a target word (e.g. nurse) is more easily recognized when it is prompted by a related prime word (e.g. doctor) compared to an unrelated prime word (e.g. doctrine). We simulated the semantic priming effect within network models of semantic memory built from both the LWOW and the SWOW free association norms by implementing spreading activation processes within the networks [4]. We found that the final activation levels of prime-target pairs correlated significantly with reaction time data for the same prime-target pairs from the LDT. Specifically, the activation of a target node (e.g. nurse) is higher when a related prime node (e.g. doctor) is activated compared to an unrelated prime node (e.g. doctrine). These results demonstrate how the LWOW datasets can be used for investigating cognitive and linguistic phenomena in LLMs, demonstrating the validity of the datasets.

Investigating gender biases

To demonstrate how this dataset can be used to investigate gender biases in LLMs compared to humans, we conducted an analysis using network models of semantic memory built from both the LWOW and the SWOW free association norms. We applied a methodology that simulates semantic priming within the networks to measure the strength of association between pairs of concepts, for example, "woman" and "forecful" vs. "man" and "forceful". We applied this methodology using a set of female-related and male-related primes, and a set of female-related and male-related targets. This analysis revealed that certain adjectives like "forceful" and "strong" are more strongly associated with certain genders, shedding light on the types of stereotypical gender biases that both humans and LLMs possess.

Technical notes

The free associations were generated (either via API or locally, depending on the LLM) by providing each LLM with a set of cue words and the following prompt: "You will be provided with an input word. Write the first 3 words you associate to it separated by a comma." This prompt was repeated 100 times for each cue word, resulting in a dataset of 11,545 unique cues words and 3,463,500 total responses for each LLM.

How to access and use the datasets

The LWOW datasets for Mistral, Llama3, and Haiku can be found in the LWOW_datasets folder, which contains two subfolders. The .csv files of the processed cues and responses can be found in the processed_datasets folder while the .csv files of the edge lists of the semantic networks constructed from the datasets can be found in the graphs/edge_lists folder.

Since the LWOW datasets are intended to be used in comparison to humans, we have further processed the original SWOW dataset to create a Human dataset that is aligned with the processing that we applied to the LWOW datasets. While this human dataset is not included in this repository due to the license of the original SWOW dataset, it can be easily reproduced by running the code provided in the reproducibility folder. We highly encourage you to generate this dataset as it enabales a direct comparison between humans and LLMs. The Human dataset can be generated with the following steps:

• Go to the SWOW research page (https://smallworldofwords.org/en/project/research) [2] and download the English processed data (SWOW-EN18). Save this .csv file with the name "SWOW-EN.R100.csv" in the reproducibility/data/original_datasets folder.
• Run the python file FA_data_Cleaning.py saved in the reproducibility folder. This will generate a .csv of the processed Human dataset, which will be saved in the reproducibility/data/processed_datasets folder. Note that this python script will also regenerate the .csv files of the processed LWOW datasets (the same that can be found in the LWOW_datasets/processed_datasets folder).
• Run the python file FA_build_Networks.py saved in the reproducibility folder. This will generate a .csv of the edge list of the semantic network constructed from the Human dataset, which will be saved in the reproducibility/data/graphs/edge_lists folder. Note that this python script will also regenerate the .csv files of the same edges lists of the LLM networks (the same that can be found in the LWOW_datasets/graphs/edge_lists folder). This python script will also produce igraph versions of all the semantic networks.

How to reproduce the data and analyses

To reproduce the analyses, first the required external files need to be downloaded:

• Go to the SWOW research page (https://smallworldofwords.org/en/project/research) [2] and download the English data SWOW-EN18. Save this .csv file with the name "SWOW-EN.R100.csv" in the reproducibility/data/original_datasets folder.
• Go to the Semantic Priming Project (https://www.montana.edu/attmemlab/spp.html) [3] and download the LDT Priming Data. Save this .csv file with the name "primingLDT_data.csv" in the reproducibility/data/LDT_analyses folder.

Once the files are saved in the correct folders, follow the instructions in each script, which can be found in the reproducibility folder. The scripts should be run in the following order:

1. FA_data_Generation.py: generates the raw LLM datasets
2. FA_data_Cleaning.py: processes the original SWOW dataset and the raw LLM datasets
3. FA_build_Networks.py: builds the semantic networks from the datasets
4. FA_analyses_LDT_Gender.py and FA_spreadr.r: implements spreading activation processes within the networks in order to validate the datasets and investigate gender biases

Do you want to know more? Read the Preprint!

Abramski, K., et al. (2024). The "LLM World of Words" English free association norms generated by large language models (https://arxiv.org/abs/2412.01330)

Funding & Legal

• SoBigData.it which receives funding from the European Union – NextGenerationEU – National Recovery and Resilience Plan (Piano Nazionale di Ripresa e Resilienza, PNRR) – Project: “SoBigData.it – Strengthening the Italian RI for Social Mining and Big Data Analytics” – Prot. IR0000013 – Avviso n. 3264 del 28/12/2021;
• EU NextGenerationEU programme under the funding schemes PNRR-PE-AI FAIR (Future Artificial Intelligence Research).
• The HumaneAI-Net project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952026.
• COGNOSCO grant funded by Università di Trento (Grant ID: PS 22_27).

For speaking requests and enquiries, please contact:

• Katherine Abramski : katherine.abramski@phd.unipi.it
• Giulio Rossetti : giulio.rossetti@isti.cnr.it
• Massimo Stella : massimo.stella-1@unitn.it

References

[1] Abramski, K., et al. (2024). The" LLM World of Words" English free association norms generated

This dataset is about book subjects. It has 7 rows and is filtered where the books is Scapa Flow : the reminiscences of men and women who served in Scapa Flow in the two World Wars. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

The Macquarie Island Station Area GIS Dataset is a topographic and facilities data base covering Australia's Macquarie Island Station and its immediate environs. The database includes all man made and natural features within the operational area of the station proper. Attributes are held for many facilities including, buildings, site services, communications, fuel storage, aeronautical and management zones. The spatial data have been compiled from low level aerial photography, ground surveys and engineering plans. Detail attribution of hydraulic site services includes make, size and engineering plan number.

The dataset conforms to the SCAR Feature Catalogue which includes data quality information.

The data is included in the data available for download from a Related URL below. The data conforms to the SCAR Feature Catalogue which includes data quality information. See a Related URL below. Data described by this metadata record has Dataset_id = 25. Each feature has a Qinfo number which, when entered at the 'Search datasets & quality' tab, provides data quality information for the feature.

Changes have occurred at the station since this dataset was produced. For example some buildings and other structures have been removed and some added. As a result the data available for download from a Related URL below is updated with new data having different Dataset_id(s).

Context: Kanye West Rap Verses (243 Songs, 364 Verses)
Content: All verses are separated by empty lines. The data has been cleaned to remove any unnecessary words or characters not part of the actual verses.
Acknowledgements: The lyrics are owned by Kanye West and his label, but the dataset was compiled by myself using Rap Genius.
Past Research: Ran the data through a RNN to try to generate new verses that sounded similar to Kanye's existing verses.
Inspiration: It'll be interesting to see what analysis people can do on this dataset. Although it's pretty small, it definitely seems like a fun dataset to mess around with.

Note: Below is a list of all the songs used for verse extraction. Songs labeled with (N) were excluded due to either not containing rap verses (only choruses), or me not being able to locate the actual lyrics.

Mercy
Niggas in Paris
Clique
Bound 2
No Church in the Wild
Father Stretch My Hand Pt. 1
New Slaves
Blood on the Leaves
Black Skinhead
Don't Like
Monster
All Day
Father Stretch My Hand Pt. 2
I Am a God
Famous
No More Parties in LA
I'm In It
Hold My Liquor
Facts
Power
Cold
New God Flow
Gotta Have It
Blame Game
Wolves
FML
Runaway
Can't Tell Me Nothing
Waves
Dark Fantasy
Gorgeous
Gold Digger
Devil in a New Dress
Otis
So Appalled
All Falls Down
Highlights
All of the Lights
On Sight
Who Gon Stop Me
Guilt Trip
Murder to Excellence
30 Hours
Send It Up
Through the Wire
Stronger
Illest Motherfucker Alive
Flashing Lights
Last Call
Homecoming
H·A·M
The Morning
Lost In The World
Saint Pablo
Freestyle 4
Feedback
Jesus Walks
Good Morning
The One
Good Life
Touch the Sky
Diamonds from Sierra Leone
Never Let Me Down
Big Brother
New Day
Hell of a Life
To the World
Hey Mama
Heard 'Em Say
White Dress
Heartless
Champion
That's My Bitch
Everything I Am
Gone
Made in America
I Wonder
Spaceship
Get Em High
Christian Dior Denim Flow
We Don't Care
Family Business
See Me Now
The Glory
Welcome to the Jungle
Looking For Trouble
Drive Slow
The Joy
The New Workout Plan
Champions
Love Lockdown
Primetime
We Major
Roses
School Spirit
Addiction
Lift Off
Barry Bonds
Bittersweet Poetry
Welcome to Heartbreak
Drunk and Hot Girls
Two Words Slow Jamz
Paranoid
Crack Music
Classic (Nike Air Force Remix)
RoboCop
Breathe In Breathe Out
Late
Bring Me Down
Christmas in Harlem
Celebration
Good Night
Lord Lord Lord
Chain Heavy
Eyes Closed
Don't Look Down
Take One for the Team
Mama's Boyfriend
Apologize
We Can Make It Better
When I See It
Because of You (Remix)
Home
Throw Some D's (Remix)
Livin' in a Movie
Another You
Impossible
Back Niggaz
Birthday Song
Back to Basics
Line for Line
What You Do To Me
In Common (Remix)
Pussy Print
Guard Down
Piss On Your Grave
Jukebox Joints
SMUCKERS
All Your Fault
Can't Stop
Drunk in Love (Remix)
Welcome to the World
Blazing
Glenwood
Ayyy Girl
We Fight We Love (Remix)
Anyone But Him
Erase Me
Diamonds (Remix)
Hate
Ego (Remix)
Alright
I'm the Shit (Remix)
Flight School
Teriya-King
Punch Drunk Love (The Eye)
Therapy
Digital Girl
Promise Land
It's Over
Go Hard
Beat Goes On
Everyone Nose
Down
In the Mood
Southside
My Drink n My 2 Step (Remix)
Still Dreaming
Tell Me When to Go (Remix)
Fly Away
They Say
Paid the Price
Call Some Hoes
The Way That You Do
Welcome Back (Remix)
Confessions Pt. 2 (Remix)
My Baby
Gettin' It In
I Changed My Mind
Selfish
Higher
Talk About Our Love
I See Now
Getting Out the Game
03 'til Infinity
So Soulful
Oh Oh
U Know
Candy
The Good, the Bad and the Ugly
Changing Lanes
The Bounce
Let's Get Married (Remix)
Pretty Girl Rock (Remix)
That Part
U Mad
Blessings
I Won
I Wish You Would
Marvin & Chardonnay
E.T.
Forever
The Big Screen
Supernova
Make Her Say
Run This Town
Gifted
Walkin' on the Moon
Knock You Down
Stay Up! (Viagra)
Put On
American Boy
Pro Nails
I Still Love H.E.R.
Wouldn't Get Far
Number One (With Pharrell)
Grammy Family
Extravaganza
Brand New
Wouldn't You Like 2 Ryde
This Way
Us Placers
Don't Stop!
Sanctified
Hurricane 2.0
Start It Up
In for the Kill (Remix)
Deuces (Remix)
Alors on Danse (Remix)
Live Fast Die Young
Maybach Music 2
Swagga Like Us (Remix)
Lollipop (Remix)
Plastic
Finer Things
Anything
Buy U a Drank (Remix)
This Ain't a Scene, It's an Arms Race (Remix)
Pusha Man
Selfish
Real Love
Hold On (Remix)

(N) Coldest Winter
(N) Ultralight Beams
(N) Only One
(N) I Love Kanye
(N) Why I Love You
(N) Fade
(N) Welcome to the Jungle
(N) Amazing
(N) Say You Will
(N) Street Lights
(N) See You in my Nightmares
(N) Awesome (Freestyle)
(N) Rosalind Ballroom
(N) Pinocchio Story
(N) God Level
(N) Bad News
(N) I Feel Like That
(N) My Way Home
(N) I'll Fly Away
(N) All We Got
(N) M.P.A.
(N) Mula
(N) The Summer League
(N) Nobody
(N) Rollin'
(N) Touch It
(N) We Alright
(N) Punch Drunk Love (The Eye)
(N) More
(N) Take It as a Loss
(N) Figure It Out
(N) One Man Can Change the World
(N) Thank You
(N) Pride N Joy
(N) Everybody
(N) The Corner
(N) Down and Out
(N) The Food (N) Welcome 2 Chicago

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Earth. The dataset can be utilized to gain insights into gender-based income distribution within the Earth population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Earth, among individuals aged 15 years and older with income, there were 380 men and 236 women in the workforce. Among them, 217 men were engaged in full-time, year-round employment, while 79 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 8.29% fell within the income range of under $24,999, while 34.18% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 23.96% of men in full-time roles earned incomes exceeding $100,000, while none of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

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.

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 Earth median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Blue Earth County. The dataset can be utilized to gain insights into gender-based income distribution within the Blue Earth County population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Blue Earth County, among individuals aged 15 years and older with income, there were 27,065 men and 26,869 women in the workforce. Among them, 13,222 men were engaged in full-time, year-round employment, while 10,271 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 7.21% fell within the income range of under $24,999, while 13.32% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 19.80% of men in full-time roles earned incomes exceeding $100,000, while 8.16% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

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.

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 Blue Earth County median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Black Earth. The dataset can be utilized to gain insights into gender-based income distribution within the Black Earth population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Black Earth, among individuals aged 15 years and older with income, there were 677 men and 591 women in the workforce. Among them, 290 men were engaged in full-time, year-round employment, while 233 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 5.52% fell within the income range of under $24,999, while 3.86% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 15.86% of men in full-time roles earned incomes exceeding $100,000, while 13.30% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

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.

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 Black Earth median household income by race. You can refer the same here

About this dataset

Context

The dataset tabulates the United States population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for United States. The dataset can be utilized to understand the population distribution of United States by age. For example, using this dataset, we can identify the largest age group in United States.

Key observations

The largest age group in United States was for the group of age 25-29 years with a population of 22,854,328 (6.93%), according to the 2021 American Community Survey. At the same time, the smallest age group in United States was the 80-84 years with a population of 5,932,196 (1.80%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the United States is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of United States total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 United States Population by Age. You can refer the same here

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 Black Earth town. 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 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Black Earth town, the median income for all workers aged 15 years and older, regardless of work hours, was $68,125 for males and $58,750 for females.

Based on these incomes, we observe a gender gap percentage of approximately 14%, indicating a significant disparity between the median incomes of males and females in Black Earth town. Women, regardless of work hours, still earn 86 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

- Full-time workers, aged 15 years and older: In Black Earth town, among full-time, year-round workers aged 15 years and older, males earned a median income of $93,000, while females earned $78,542, leading to a 16% gender pay gap among full-time workers. This illustrates that women earn 84 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Remarkably, across all roles, including non-full-time employment, women displayed a lower gender pay gap percentage. This indicates that Black Earth town offers better opportunities for women in non-full-time positions.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-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 2023
• 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 Black Earth town median household income by race. You can refer the same here

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 Earth. 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 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Earth, the median income for all workers aged 15 years and older, regardless of work hours, was $37,763 for males and $16,019 for females.

These income figures highlight a substantial gender-based income gap in Earth. Women, regardless of work hours, earn 42 cents for each dollar earned by men. This significant gender pay gap, approximately 58%, underscores concerning gender-based income inequality in the city of Earth.

- Full-time workers, aged 15 years and older: In Earth, among full-time, year-round workers aged 15 years and older, males earned a median income of $49,236, while females earned $35,750, leading to a 27% gender pay gap among full-time workers. This illustrates that women earn 73 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Earth.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-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 2023
• 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 Earth median household income by race. You can refer the same here