100+ datasets found
  1. Replication dataset for PIIE WP 23-4, What caused the US pandemic-era...

    • piie.com
    Updated Jun 13, 2023
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    Ben S. Bernanke; Olivier Blanchard (2023). Replication dataset for PIIE WP 23-4, What caused the US pandemic-era inflation?by Ben Bernanke and Olivier Blanchard (2023). [Dataset]. https://www.piie.com/publications/working-papers/2023/what-caused-us-pandemic-era-inflation
    Explore at:
    Dataset updated
    Jun 13, 2023
    Dataset provided by
    Peterson Institute for International Economicshttp://www.piie.com/
    Authors
    Ben S. Bernanke; Olivier Blanchard
    Area covered
    United States
    Description

    This data package includes the underlying data files to replicate the data and charts presented in What caused the US pandemic-era inflation? PIIE Working Paper 23-4.

    If you use the data, please cite as: Bernanke, Ben, and Olivier Blanchard. 2023. What caused the US pandemic-era inflation? PIIE Working Paper 23-4. Washington, DC: Peterson Institute for International Economics.

  2. Replication dataset and calculations for PIIE WP 24-13 US Monetary Policy...

    • piie.com
    Updated May 28, 2024
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    David Reifschneider (2024). Replication dataset and calculations for PIIE WP 24-13 US Monetary Policy and the Recent Surge in Inflation by David Reifschneider (2024). [Dataset]. https://www.piie.com/publications/working-papers/2024/us-monetary-policy-and-recent-surge-inflation
    Explore at:
    Dataset updated
    May 28, 2024
    Dataset provided by
    Peterson Institute for International Economicshttp://www.piie.com/
    Authors
    David Reifschneider
    Description

    This data package includes the underlying data to replicate the charts and calculations presented in US Monetary Policy and the Recent Surge in Inflation, PIIE Working Paper 24-13.

    If you use the data, please cite as:

    Reifschneider, David. 2024. US Monetary Policy and the Recent Surge in Inflation. PIIE Working Paper 24-13. Washington: Peterson Institute for International Economics.

  3. T

    INFLATION RATE by Country in AMERICA

    • tradingeconomics.com
    csv, excel, json, xml
    Updated May 27, 2017
    + more versions
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    TRADING ECONOMICS (2017). INFLATION RATE by Country in AMERICA [Dataset]. https://tradingeconomics.com/country-list/inflation-rate?continent=america
    Explore at:
    excel, json, xml, csvAvailable download formats
    Dataset updated
    May 27, 2017
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    2025
    Area covered
    United States
    Description

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

  4. Replication dataset and calculations for PIIE WP 24-11 An analysis of...

    • piie.com
    Updated May 15, 2024
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    Ben S. Bernanke; Olivier Blanchard (2024). Replication dataset and calculations for PIIE WP 24-11 An analysis of pandemic-era inflation in 11 economies by Ben Bernanke and Olivier Blanchard (2024). [Dataset]. https://www.piie.com/publications/working-papers/2024/analysis-pandemic-era-inflation-11-economies
    Explore at:
    Dataset updated
    May 15, 2024
    Dataset provided by
    Peterson Institute for International Economicshttp://www.piie.com/
    Authors
    Ben S. Bernanke; Olivier Blanchard
    Description

    presented in An analysis of pandemic-era inflation in 11 economies, PIIE Working Paper 24-11.

    If you use the data, please cite as: Bernanke, Ben, and Olivier Blanchard. 2024. An analysis of pandemic-era inflation in 11 economies. PIIE Working Paper 24-11. Washington: Peterson Institute for International Economics.

  5. Replication dataset and calculations for PIIE WP 24-22 Fiscal policy and the...

    • piie.com
    Updated Dec 16, 2024
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    Karen Dynan; Douglas Elmendorf (2024). Replication dataset and calculations for PIIE WP 24-22 Fiscal policy and the pandemic-era surge in US inflation: Lessons for the future by Karen Dynan and Douglas Elmendorf (2024). [Dataset]. https://www.piie.com/publications/working-papers/2024/fiscal-policy-and-pandemic-era-surge-us-inflation-lessons-future
    Explore at:
    Dataset updated
    Dec 16, 2024
    Dataset provided by
    Peterson Institute for International Economicshttp://www.piie.com/
    Authors
    Karen Dynan; Douglas Elmendorf
    Area covered
    United States
    Description

    This data package includes the underlying data to replicate the charts, tables, and calculations presented in Fiscal policy and the pandemic-era surge in US inflation: Lessons for the future, PIIE Working Paper 24-22.

    If you use the data, please cite as:

    Dynan, Karen, and Douglas Elmendorf. 2024. Fiscal policy and the pandemic-era surge in US inflation: Lessons for the future. PIIE Working Paper 24-22. Washington: Peterson Institute for International Economics.

  6. N

    Parksley, VA annual median income by work experience and sex dataset: Aged...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
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    Neilsberg Research (2025). Parksley, VA annual median income by work experience and sex dataset: Aged 15+, 2010-2023 (in 2023 inflation-adjusted dollars) // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/a52f0996-f4ce-11ef-8577-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 27, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Virginia, Parksley
    Variables measured
    Income for Male Population, Income for Female Population, Income for Male Population working full time, Income for Male Population working part time, Income for Female Population working full time, Income for Female Population working part time
    Measurement technique
    The data presented in this dataset is derived from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates. The dataset covers the years 2010 to 2023, representing 14 years of data. To analyze income differences between genders (male and female), we conducted an initial data analysis and categorization. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series (R-CPI-U-RS) based on current methodologies. For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    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 Parksley. 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 Parksley, the median income for all workers aged 15 years and older, regardless of work hours, was $33,500 for males and $49,028 for females.

    Contrary to expectations, women in Parksley, women, regardless of work hours, earn a higher income than men, earning 1.46 dollars for every dollar earned by men. This analysis indicates a significant shift in income dynamics favoring females.

    - Full-time workers, aged 15 years and older: In Parksley, among full-time, year-round workers aged 15 years and older, males earned a median income of $47,569, while females earned $62,443

    Contrary to expectations, in Parksley, women, earn a higher income than men, earning 1.31 dollars for every dollar earned by men. This analysis showcase a consistent trend of women outearning men, when working full-time or part-time in the town of Parksley.

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

  7. Replication dataset and calculations for PIIE WP 21-15, Forbes, Kristin,...

    • piie.com
    Updated Sep 21, 2021
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    Kristin Forbes; Joseph E. Gagnon; Christopher G. Collins (2021). Replication dataset and calculations for PIIE WP 21-15, Forbes, Kristin, Joseph E. Gagnon, and Christopher G. Collins, Low Inflation Bends the Phillips Curve around the World: Extended Results, PIIE Working Paper, September 2021. [Dataset]. https://www.piie.com/publications/working-papers/2021/low-inflation-bends-phillips-curve-around-world-extended-results
    Explore at:
    Dataset updated
    Sep 21, 2021
    Dataset provided by
    Peterson Institute for International Economicshttp://www.piie.com/
    Authors
    Kristin Forbes; Joseph E. Gagnon; Christopher G. Collins
    Description

    This data package contains underlying data to replicate the calculations, charts, and tables in Low Inflation Bends the Phillips Curve around the World: Extended Results, PIIE Working Paper 21-15.

    If you use the data, please cite as: Forbes, Kristin, Joseph E. Gagnon, and Christopher G. Collins, Low Inflation Bends the Phillips Curve around the World: Extended Results, PIIE Working Paper 21-15, September 2021, Peterson Institute for International Economics.

  8. N

    Milano, TX annual median income by work experience and sex dataset: Aged...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
    Share
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    Neilsberg Research (2025). Milano, TX annual median income by work experience and sex dataset: Aged 15+, 2010-2023 (in 2023 inflation-adjusted dollars) // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/a528548b-f4ce-11ef-8577-3860777c1fe6/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 27, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Texas, Milano
    Variables measured
    Income for Male Population, Income for Female Population, Income for Male Population working full time, Income for Male Population working part time, Income for Female Population working full time, Income for Female Population working part time
    Measurement technique
    The data presented in this dataset is derived from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates. The dataset covers the years 2010 to 2023, representing 14 years of data. To analyze income differences between genders (male and female), we conducted an initial data analysis and categorization. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series (R-CPI-U-RS) based on current methodologies. For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    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 Milano. 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 Milano, the median income for all workers aged 15 years and older, regardless of work hours, was $27,412 for males and $34,028 for females.

    Contrary to expectations, women in Milano, women, regardless of work hours, earn a higher income than men, earning 1.24 dollars for every dollar earned by men. This analysis indicates a significant shift in income dynamics favoring females.

    - Full-time workers, aged 15 years and older: In Milano, among full-time, year-round workers aged 15 years and older, males earned a median income of $26,969, while females earned $53,500

    Contrary to expectations, in Milano, women, earn a higher income than men, earning 1.98 dollars for every dollar earned by men. This analysis showcase a consistent trend of women outearning men, when working full-time or part-time in the city of Milano.

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

  9. N

    Bolton, MS annual median income by work experience and sex dataset: Aged...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
    Share
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    Cite
    Neilsberg Research (2025). Bolton, MS annual median income by work experience and sex dataset: Aged 15+, 2010-2023 (in 2023 inflation-adjusted dollars) // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/a504a18f-f4ce-11ef-8577-3860777c1fe6/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 27, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Bolton
    Variables measured
    Income for Male Population, Income for Female Population, Income for Male Population working full time, Income for Male Population working part time, Income for Female Population working full time, Income for Female Population working part time
    Measurement technique
    The data presented in this dataset is derived from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates. The dataset covers the years 2010 to 2023, representing 14 years of data. To analyze income differences between genders (male and female), we conducted an initial data analysis and categorization. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series (R-CPI-U-RS) based on current methodologies. For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    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 Bolton. 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 Bolton, the median income for all workers aged 15 years and older, regardless of work hours, was $18,963 for males and $22,895 for females.

    Contrary to expectations, women in Bolton, women, regardless of work hours, earn a higher income than men, earning 1.21 dollars for every dollar earned by men. This analysis indicates a significant shift in income dynamics favoring females.

    - Full-time workers, aged 15 years and older: In Bolton, among full-time, year-round workers aged 15 years and older, males earned a median income of $31,938, while females earned $32,292

    Contrary to expectations, in Bolton, women, earn a higher income than men, earning 1.01 dollars for every dollar earned by men. This analysis showcase a consistent trend of women outearning men, when working full-time or part-time in the town of Bolton.

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

  10. Replication dataset and calculations for PIIE WP 19-6, Low Inflation Bends...

    • piie.com
    Updated Apr 1, 2019
    Share
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    Joseph E. Gagnon; Christopher G. Collins (2019). Replication dataset and calculations for PIIE WP 19-6, Low Inflation Bends the Phillips Curve, by Joseph E. Gagnon and Christopher G. Collins. (2019). [Dataset]. https://www.piie.com/publications/working-papers/low-inflation-bends-phillips-curve
    Explore at:
    Dataset updated
    Apr 1, 2019
    Dataset provided by
    Peterson Institute for International Economicshttp://www.piie.com/
    Authors
    Joseph E. Gagnon; Christopher G. Collins
    Description

    This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in Low Inflation Bends the Phillips Curve, PIIE Working Paper 19-6.

    If you use the data, please cite as: Gagnon, Joseph E., and Christopher G. Collins. (2019). Low Inflation Bends the Phillips Curve. PIIE Working Paper 19-6. Peterson Institute for International Economics.

  11. Replication dataset and calculations for PIIE WP 25-1 Why did inflation rise...

    • piie.com
    Updated Jan 17, 2025
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    Joseph E. Gagnon; Asher Rose (2025). Replication dataset and calculations for PIIE WP 25-1 Why did inflation rise and fall so rapidly? Lessons from the Korean War by Joseph E. Gagnon and Asher Rose (2025). [Dataset]. https://www.piie.com/publications/working-papers/2025/why-did-inflation-rise-and-fall-so-rapidly-lessons-korean-war
    Explore at:
    Dataset updated
    Jan 17, 2025
    Dataset provided by
    Peterson Institute for International Economicshttp://www.piie.com/
    Authors
    Joseph E. Gagnon; Asher Rose
    Description

    This data package includes the underlying data to replicate the charts, tables, and calculations presented in Why did inflation rise and fall so rapidly? Lessons from the Korean War, PIIE Working Paper 25-1.

    If you use the data, please cite as:

    Gagnon, Joseph E., and Asher Rose. 2025. Why did inflation rise and fall so rapidly? Lessons from the Korean War. PIIE Working Paper 25-1. Washington: Peterson Institute for International Economics.

  12. N

    Marlboro, Vermont annual median income by work experience and sex dataset:...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
    Share
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    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Neilsberg Research (2025). Marlboro, Vermont annual median income by work experience and sex dataset: Aged 15+, 2010-2023 (in 2023 inflation-adjusted dollars) // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/a526932c-f4ce-11ef-8577-3860777c1fe6/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 27, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Marlboro, Vermont
    Variables measured
    Income for Male Population, Income for Female Population, Income for Male Population working full time, Income for Male Population working part time, Income for Female Population working full time, Income for Female Population working part time
    Measurement technique
    The data presented in this dataset is derived from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates. The dataset covers the years 2010 to 2023, representing 14 years of data. To analyze income differences between genders (male and female), we conducted an initial data analysis and categorization. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series (R-CPI-U-RS) based on current methodologies. For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    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 Marlboro 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 Marlboro town, the median income for all workers aged 15 years and older, regardless of work hours, was $11,429 for males and $13,889 for females.

    Contrary to expectations, women in Marlboro town, women, regardless of work hours, earn a higher income than men, earning 1.22 dollars for every dollar earned by men. This analysis indicates a significant shift in income dynamics favoring females.

    - Full-time workers, aged 15 years and older: In Marlboro town, among full-time, year-round workers aged 15 years and older, males earned a median income of $57,692, while females earned $58,194

    Contrary to expectations, in Marlboro town, women, earn a higher income than men, earning 1.01 dollars for every dollar earned by men. This analysis showcase a consistent trend of women outearning men, when working full-time or part-time in the town of Marlboro town.

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

  13. N

    Banks township, Carbon County, Pennsylvania annual median income by work...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
    Share
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    Neilsberg Research (2025). Banks township, Carbon County, Pennsylvania annual median income by work experience and sex dataset: Aged 15+, 2010-2023 (in 2023 inflation-adjusted dollars) // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/a500c3d5-f4ce-11ef-8577-3860777c1fe6/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 27, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Pennsylvania, Banks Township, Carbon County
    Variables measured
    Income for Male Population, Income for Female Population, Income for Male Population working full time, Income for Male Population working part time, Income for Female Population working full time, Income for Female Population working part time
    Measurement technique
    The data presented in this dataset is derived from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates. The dataset covers the years 2010 to 2023, representing 14 years of data. To analyze income differences between genders (male and female), we conducted an initial data analysis and categorization. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series (R-CPI-U-RS) based on current methodologies. For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    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 Banks township. 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 Banks township, the median income for all workers aged 15 years and older, regardless of work hours, was $34,375 for males and $36,442 for females.

    Contrary to expectations, women in Banks township, women, regardless of work hours, earn a higher income than men, earning 1.06 dollars for every dollar earned by men. This analysis indicates a significant shift in income dynamics favoring females.

    - Full-time workers, aged 15 years and older: In Banks township, among full-time, year-round workers aged 15 years and older, males earned a median income of $41,458, while females earned $45,926

    Contrary to expectations, in Banks township, women, earn a higher income than men, earning 1.11 dollars for every dollar earned by men. This analysis showcase a consistent trend of women outearning men, when working full-time or part-time in the township of Banks township.

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

  14. N

    Fort Supply, OK annual median income by work experience and sex dataset:...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
    Share
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    Cite
    Neilsberg Research (2025). Fort Supply, OK annual median income by work experience and sex dataset: Aged 15+, 2010-2023 (in 2023 inflation-adjusted dollars) // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/a515a71c-f4ce-11ef-8577-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 27, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Oklahoma, Fort Supply
    Variables measured
    Income for Male Population, Income for Female Population, Income for Male Population working full time, Income for Male Population working part time, Income for Female Population working full time, Income for Female Population working part time
    Measurement technique
    The data presented in this dataset is derived from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates. The dataset covers the years 2010 to 2023, representing 14 years of data. To analyze income differences between genders (male and female), we conducted an initial data analysis and categorization. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series (R-CPI-U-RS) based on current methodologies. For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    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 Fort Supply. 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 Fort Supply, while the Census reported a median income of $50,833 for all male workers aged 15 years and older, data for females in the same category was unavailable due to an insufficient number of sample observations.

    Given the absence of income data for females from the Census Bureau, conducting a thorough analysis of gender-based pay disparity in the town of Fort Supply was not possible.

    - Full-time workers, aged 15 years and older: In Fort Supply, among full-time, year-round workers aged 15 years and older, males earned a median income of $60,208, while females earned $55,625, resulting in a 8% gender pay gap among full-time workers. This illustrates that women earn 92 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the town of Fort Supply.

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

  15. Replication dataset and calculations for PIIE Working Paper 25-7 The role of...

    • piie.com
    Updated Apr 30, 2025
    Share
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    Joseph E. Gagnon; Steven Kamin (2025). Replication dataset and calculations for PIIE Working Paper 25-7 The role of long histories of “lived experience” in the COVID-era inflationary surge by Joseph E. Gagnon and Steven Kamin (2025). [Dataset]. https://www.piie.com/publications/working-papers/2025/role-long-histories-lived-experience-covid-era-inflationary-surge
    Explore at:
    Dataset updated
    Apr 30, 2025
    Dataset provided by
    Peterson Institute for International Economicshttp://www.piie.com/
    Authors
    Joseph E. Gagnon; Steven Kamin
    Description

    This data package includes the underlying data to replicate the charts, tables, and calculations presented in The role of long histories of “lived experience” in the COVID-era inflationary surge, PIIE Working Paper 25-7.

    If you use the data, please cite as:

    Gagnon, Joseph E., and Steven Kamin. 2025. The role of long histories of “lived experience” in the COVID-era inflationary surge. PIIE Working Paper 25-7. Washington: Peterson Institute for International Economics.

  16. Replication dataset for PIIE WP 23-1, Industrial policy for electric vehicle...

    • piie.com
    Updated May 2, 2023
    Share
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    Chad P. Bown (2023). Replication dataset for PIIE WP 23-1, Industrial policy for electric vehicle supply chains and the US-EU fight over the Inflation Reduction Act by Chad P. Bown (2023). [Dataset]. https://www.piie.com/publications/working-papers/2023/industrial-policy-electric-vehicle-supply-chains-and-us-eu-fight
    Explore at:
    Dataset updated
    May 2, 2023
    Dataset provided by
    Peterson Institute for International Economicshttp://www.piie.com/
    Authors
    Chad P. Bown
    Area covered
    United States, European Union
    Description

    This data package includes the underlying data files to replicate the data and charts presented in Industrial policy for electric vehicle supply chains and the US-EU fight over the Inflation Reduction Act, PIIE Working Paper 23-1.

    If you use the data, please cite as: Bown, Chad P. (2023). Industrial policy for electric vehicle supply chains and the US-EU fight over the Inflation Reduction Act, PIIE Working Paper 23-1. Peterson Institute for International Economics.

  17. N

    Aurora, NC annual median income by work experience and sex dataset: Aged...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    Share
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    Click to copy link
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    Close
    Cite
    The citation is currently not available for this dataset.
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 27, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    North Carolina, Aurora
    Variables measured
    Income for Male Population, Income for Female Population, Income for Male Population working full time, Income for Male Population working part time, Income for Female Population working full time, Income for Female Population working part time
    Measurement technique
    The data presented in this dataset is derived from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates. The dataset covers the years 2010 to 2023, representing 14 years of data. To analyze income differences between genders (male and female), we conducted an initial data analysis and categorization. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series (R-CPI-U-RS) based on current methodologies. For additional information about these estimations, please contact us via email at research@neilsberg.com
    Description
    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 Aurora. 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 Aurora, while the Census reported a median income of $19,545 for all female workers aged 15 years and older, data for males in the same category was unavailable due to an insufficient number of sample observations.

    Because income data for males was not available from the Census Bureau, conducting a comprehensive analysis of gender-based pay disparity in the town of Aurora was not possible.

    - Full-time workers, aged 15 years and older: In Aurora, among full-time, year-round workers aged 15 years and older, males earned a median income of $55,417, while females earned $31,500, leading to a 43% gender pay gap among full-time workers. This illustrates that women earn 57 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

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

  18. N

    Elizabeth, LA annual median income by work experience and sex dataset: Aged...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
    Share
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    Close
    Cite
    Neilsberg Research (2025). Elizabeth, LA annual median income by work experience and sex dataset: Aged 15+, 2010-2023 (in 2023 inflation-adjusted dollars) // 2025 Edition [Dataset]. https://www.neilsberg.com/insights/elizabeth-la-income-by-gender/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 27, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Elizabeth
    Variables measured
    Income for Male Population, Income for Female Population, Income for Male Population working full time, Income for Male Population working part time, Income for Female Population working full time, Income for Female Population working part time
    Measurement technique
    The data presented in this dataset is derived from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates. The dataset covers the years 2010 to 2023, representing 14 years of data. To analyze income differences between genders (male and female), we conducted an initial data analysis and categorization. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series (R-CPI-U-RS) based on current methodologies. For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    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 Elizabeth. 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 Elizabeth, while the Census reported a median income of $12,250 for all female workers aged 15 years and older, data for males in the same category was unavailable due to an insufficient number of sample observations.

    Because income data for males was not available from the Census Bureau, conducting a comprehensive analysis of gender-based pay disparity in the town of Elizabeth was not possible.

    - Full-time workers, aged 15 years and older: In Elizabeth, among full-time, year-round workers aged 15 years and older, males earned a median income of $94,667, while females earned $56,250, leading to a 41% gender pay gap among full-time workers. This illustrates that women earn 59 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

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

  19. N

    Loyalton, CA annual median income by work experience and sex dataset: Aged...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
    Share
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    Close
    Cite
    Neilsberg Research (2025). Loyalton, CA annual median income by work experience and sex dataset: Aged 15+, 2010-2023 (in 2023 inflation-adjusted dollars) // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/a524d66c-f4ce-11ef-8577-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 27, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Loyalton, California
    Variables measured
    Income for Male Population, Income for Female Population, Income for Male Population working full time, Income for Male Population working part time, Income for Female Population working full time, Income for Female Population working part time
    Measurement technique
    The data presented in this dataset is derived from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates. The dataset covers the years 2010 to 2023, representing 14 years of data. To analyze income differences between genders (male and female), we conducted an initial data analysis and categorization. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series (R-CPI-U-RS) based on current methodologies. For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    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 Loyalton. 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 Loyalton, while the Census reported a median income of $14,091 for all female workers aged 15 years and older, data for males in the same category was unavailable due to an insufficient number of sample observations.

    Because income data for males was not available from the Census Bureau, conducting a comprehensive analysis of gender-based pay disparity in the city of Loyalton was not possible.

    - Full-time workers, aged 15 years and older: In Loyalton, among full-time, year-round workers aged 15 years and older, males earned a median income of $85,682, while females earned $59,375, leading to a 31% gender pay gap among full-time workers. This illustrates that women earn 69 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

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

  20. N

    Macwahoc Plantation, Maine annual median income by work experience and sex...

    • neilsberg.com
    csv, json
    Updated Feb 27, 2025
    + more versions
    Share
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    TwitterTwitter
    Email
    Click to copy link
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    Close
    Cite
    Neilsberg Research (2025). Macwahoc Plantation, Maine annual median income by work experience and sex dataset: Aged 15+, 2010-2023 (in 2023 inflation-adjusted dollars) // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/a5256e60-f4ce-11ef-8577-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 27, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Maine, Macwahoc
    Variables measured
    Income for Male Population, Income for Female Population, Income for Male Population working full time, Income for Male Population working part time, Income for Female Population working full time, Income for Female Population working part time
    Measurement technique
    The data presented in this dataset is derived from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates. The dataset covers the years 2010 to 2023, representing 14 years of data. To analyze income differences between genders (male and female), we conducted an initial data analysis and categorization. Subsequently, we adjusted these figures for inflation using the Consumer Price Index retroactive series (R-CPI-U-RS) based on current methodologies. For additional information about these estimations, please contact us via email at research@neilsberg.com
    Dataset funded by
    Neilsberg Research
    Description
    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 Macwahoc plantation. 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 Macwahoc plantation, while the Census reported a median income of $28,859 for all male workers aged 15 years and older, data for females in the same category was unavailable due to an insufficient number of sample observations.

    Given the absence of income data for females from the Census Bureau, conducting a thorough analysis of gender-based pay disparity in the plantation of Macwahoc plantation was not possible.

    - Full-time workers, aged 15 years and older: In Macwahoc plantation, among full-time, year-round workers aged 15 years and older, males earned a median income of $65,625, while females earned $34,583, leading to a 47% gender pay gap among full-time workers. This illustrates that women earn 53 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

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

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Ben S. Bernanke; Olivier Blanchard (2023). Replication dataset for PIIE WP 23-4, What caused the US pandemic-era inflation?by Ben Bernanke and Olivier Blanchard (2023). [Dataset]. https://www.piie.com/publications/working-papers/2023/what-caused-us-pandemic-era-inflation
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Replication dataset for PIIE WP 23-4, What caused the US pandemic-era inflation?by Ben Bernanke and Olivier Blanchard (2023).

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Dataset updated
Jun 13, 2023
Dataset provided by
Peterson Institute for International Economicshttp://www.piie.com/
Authors
Ben S. Bernanke; Olivier Blanchard
Area covered
United States
Description

This data package includes the underlying data files to replicate the data and charts presented in What caused the US pandemic-era inflation? PIIE Working Paper 23-4.

If you use the data, please cite as: Bernanke, Ben, and Olivier Blanchard. 2023. What caused the US pandemic-era inflation? PIIE Working Paper 23-4. Washington, DC: Peterson Institute for International Economics.

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