This scatter chart displays female population (people) against inflation (annual %). The data is about countries.

In 2023, the usual median hourly rate of a female worker's wage in the United States was 18.11 U.S. dollars. Dollar value is based on 2023 U.S. dollars. This is a slight increase from the previous year, when women's median hourly wage was 17.9 constant 2023 U.S. dollars. The median hourly earnings of women in the U.S. not adjusted for inflation can be accessed here.

Graph and download economic data for Consumer Price Index for All Urban Consumers: Women's and Girls' Apparel in U.S. City Average (CUUR0000SAA2) from Jan 1947 to Jun 2025 about females, apparel, urban, consumer, CPI, inflation, price index, indexes, price, and USA.

In 2023, the median usual weekly earnings of employed women in the United States was 1,005 U.S. dollars. Dollar value is based on 2023 U.S. dollars. In 1979, the median weekly earnings of women in full-time employment was 717 constant 2023 U.S. dollars. Median weekly earnings for women unadjusted for inflation can be found here.

According to a Rakuten Insight survey conducted in March 2023, ** percent of female and ** percent of male respondents in Vietnam indicated that they reduced their frequency or stopped engaging in leisure activities, such as dining out, going to bars, and cinema, in response to inflation. Noticeably, more female respondents reported buying cheaper groceries compared to their male counterparts.

Graph and download economic data for Producer Price Index by Industry: Women's Handbag and Purse Manufacturing (PCU316992316992) from Dec 1975 to Dec 2022 about females, manufacturing, PPI, industry, inflation, price index, indexes, price, and USA.

This scatter chart displays female population (people) against inflation (annual %) in Georgia. The data is about countries per year.

Graph and download economic data for Producer Price Index by Industry: Women's Clothing Stores: Women's Clothing Store Services (DISCONTINUED) (PCU4481204481201) from Jun 2003 to May 2015 about females, apparel, services, PPI, industry, inflation, price index, indexes, price, and USA.

India IESH: RBI: Inflation Expectations: Female: One Year Ahead: Mean data was reported at 9.900 % in Nov 2018. This records an increase from the previous number of 9.700 % for Sep 2018. India IESH: RBI: Inflation Expectations: Female: One Year Ahead: Mean data is updated monthly, averaging 11.200 % from Sep 2008 (Median) to Nov 2018, with 46 observations. The data reached an all-time high of 13.800 % in Sep 2014 and a record low of 6.300 % in Mar 2009. India IESH: RBI: Inflation Expectations: Female: One Year Ahead: Mean data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Business and Economic Survey – Table IN.SC002: Inflation Expectations Survey of Households (IESH): Reserve Bank of India: Inflation Expectations: by Gender.

India IESH: RBI: Inflation Expectations: Female: Three Months Ahead: Standard Deviation data was reported at 4.300 % in Jun 2018. This stayed constant from the previous number of 4.300 % for May 2018. India IESH: RBI: Inflation Expectations: Female: Three Months Ahead: Standard Deviation data is updated monthly, averaging 4.100 % from Sep 2008 (Median) to Jun 2018, with 44 observations. The data reached an all-time high of 6.000 % in Sep 2009 and a record low of 2.520 % in Mar 2009. India IESH: RBI: Inflation Expectations: Female: Three Months Ahead: Standard Deviation data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Business and Economic Survey – Table IN.SC002: Inflation Expectations Survey of Households (IESH): Reserve Bank of India: Inflation Expectations: by Gender.

This scatter chart displays female population (people) against inflation (annual %) in Equatorial Guinea. The data is about countries per year.

This scatter chart displays female population (people) against inflation (annual %) in Greece. The data is filtered where the date is 2021. The data is about countries per year.

This scatter chart displays female population (people) against inflation (annual %) in Eastern Africa. The data is about countries.

According to a 2023 survey by Rakuten Insight on inflation in Thailand, over half of the female and male respondents indicated that they had to be cautious with their expenses. Around **** percent of the female and ** percent of male survey participants said they were not impacted at all by inflation.

India IESH: RBI: Inflation Expectations: Female: Current: Mean data was reported at 9.400 % in Sep 2018. This records an increase from the previous number of 8.800 % for Jun 2018. India IESH: RBI: Inflation Expectations: Female: Current: Mean data is updated monthly, averaging 10.500 % from Sep 2008 (Median) to Sep 2018, with 45 observations. The data reached an all-time high of 12.700 % in Sep 2014 and a record low of 5.400 % in Mar 2009. India IESH: RBI: Inflation Expectations: Female: Current: Mean data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Business and Economic Survey – Table IN.SC002: Inflation Expectations Survey of Households (IESH): Reserve Bank of India: Inflation Expectations: by Gender.

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 Davis. 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 Davis, the median income for all workers aged 15 years and older, regardless of work hours, was $45,837 for males and $32,301 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 30% between the median incomes of males and females in Davis. With women, regardless of work hours, earning 70 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Davis.

- Full-time workers, aged 15 years and older: In Davis, among full-time, year-round workers aged 15 years and older, males earned a median income of $94,742, while females earned $73,926, leading to a 22% gender pay gap among full-time workers. This illustrates that women earn 78 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 similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Davis, showcasing a consistent income pattern irrespective of employment status.

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

This dataset is about countries per year in Tunisia. It has 64 rows. It features 4 columns: country, inflation, and female population.

India IESH: RBI: Inflation Expectations: Female: One Year: Standard Deviation data was reported at 4.900 % in Jun 2018. This stayed constant from the previous number of 4.900 % for May 2018. India IESH: RBI: Inflation Expectations: Female: One Year: Standard Deviation data is updated monthly, averaging 4.200 % from Sep 2008 (Median) to Jun 2018, with 44 observations. The data reached an all-time high of 5.950 % in Sep 2009 and a record low of 2.660 % in Mar 2009. India IESH: RBI: Inflation Expectations: Female: One Year: Standard Deviation data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Business and Economic Survey – Table IN.SC002: Inflation Expectations Survey of Households (IESH): Reserve Bank of India: Inflation Expectations: by Gender.

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 China. 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 China, the median income for all workers aged 15 years and older, regardless of work hours, was $58,750 for males and $30,313 for females.

These income figures highlight a substantial gender-based income gap in China. Women, regardless of work hours, earn 52 cents for each dollar earned by men. This significant gender pay gap, approximately 48%, underscores concerning gender-based income inequality in the city of China.

- Full-time workers, aged 15 years and older: In China, among full-time, year-round workers aged 15 years and older, males earned a median income of $62,188, while females earned $69,375

Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.12 dollars for every dollar earned by men. This suggests that within full-time roles, womens median incomes significantly surpass mens, contrary to broader workforce trends.

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

This dataset is about countries per year in Serbia. It has 64 rows. It features 4 columns: country, inflation, and female population.