About this dataset

Context

The dataset tabulates the population of China by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for China. The dataset can be utilized to understand the population distribution of China by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in China. 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 China.

Key observations

Largest age group (population): Male # 15-19 years (52) | Female # 20-24 years (65). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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 China population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the China is shown in the following column.
• Population (Female): The female population in the China 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 China 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 China Population by Gender. 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 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

About this dataset

Context

The dataset tabulates the population of China town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for China town. The dataset can be utilized to understand the population distribution of China town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in China town. 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 China town.

Key observations

Largest age group (population): Male # 25-29 years (307) | Female # 55-59 years (294). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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 China town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the China town is shown in the following column.
• Population (Female): The female population in the China town 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 China town 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 China town Population by Gender. You can refer the same here

China Intentional Homicides: Female: per 100,000 Female data was reported at 0.506 Ratio in 2014. China Intentional Homicides: Female: per 100,000 Female data is updated yearly, averaging 0.506 Ratio from Dec 2014 (Median) to 2014, with 1 observations. China Intentional Homicides: Female: per 100,000 Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s China – Table CN.World Bank: Health Statistics. Intentional homicides, female are estimates of unlawful female homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.; ; UN Office on Drugs and Crime's International Homicide Statistics database.; ;

China Women Business and the Law Index Score: scale 1-100 data was reported at 78.125 NA in 2023. This stayed constant from the previous number of 78.125 NA for 2022. China Women Business and the Law Index Score: scale 1-100 data is updated yearly, averaging 62.500 NA from Dec 1970 (Median) to 2023, with 54 observations. The data reached an all-time high of 78.125 NA in 2023 and a record low of 56.875 NA in 1988. China Women Business and the Law Index Score: scale 1-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s China – Table CN.World Bank.WDI: Governance: Policy and Institutions. The index measures how laws and regulations affect women’s economic opportunity. Overall scores are calculated by taking the average score of each index (Mobility, Workplace, Pay, Marriage, Parenthood, Entrepreneurship, Assets and Pension), with 100 representing the highest possible score.;World Bank: Women, Business and the Law. https://wbl.worldbank.org/;;1. For the reference period, WDI and Gender Databases take the data coverage years instead of reporting years used in WBL (https://wbl.worldbank.org/). For example, the data for YR2020 in WBL (report year) corresponds to data for YR2019 in WDI and Gender Databases. 2. The 2024 Women, Business and the Law (WBL) report has introduced two distinct datasets, labeled as 1.0 and 2.0. The WBL data in the Gender database is based on the dataset 1.0. This dataset maintains consistency with the indicators used in previous WBL reports from 2020 to 2023. In contrast, the WBL 2.0 dataset includes new areas of childcare and safety. For those interested in exploring the WBL 2.0 dataset, it is available on the WBL website at https://wbl.worldbank.org.

Explore gender statistics data focusing on academic staff, employment, fertility rates, GDP, poverty, and more in the GCC region. Access comprehensive information on key indicators for Bahrain, China, India, Kuwait, Oman, Qatar, and Saudi Arabia.

academic staff, Access to anti-retroviral drugs, Adjusted net enrollment rate, Administration and Law programmes, Age at first marriage, Age dependency ratio, Cause of death, Children out of school, Completeness of birth registration, consumer prices, Cost of business start-up procedures, Employers, Employment in agriculture, Employment in industry, Employment in services, employment or training, Engineering and Mathematics programmes, Female headed households, Female migrants, Fertility planning status: mistimed pregnancy, Fertility planning status: planned pregnancy, Fertility rate, Firms with female participation in ownership, Fisheries and Veterinary programmes, Forestry, GDP, GDP growth, GDP per capita, gender parity index, Gini index, GNI, GNI per capita, Government expenditure on education, Government expenditure per student, Gross graduation ratio, Households with water on the premises, Inflation, Informal employment, Labor force, Labor force with advanced education, Labor force with basic education, Labor force with intermediate education, Learning poverty, Length of paid maternity leave, Life expectancy at birth, Mandatory retirement age, Manufacturing and Construction programmes, Mathematics and Statistics programmes, Number of under-five deaths, Part time employment, Population, Poverty headcount ratio at national poverty lines, PPP, Primary completion rate, Retirement age with full benefits, Retirement age with partial benefits, Rural population, Sex ratio at birth, Unemployment, Unemployment with advanced education, Urban population

Bahrain, China, India, Kuwait, Oman, Qatar, Saudi Arabia

Follow data.kapsarc.org for timely data to advance energy economics research.

ObjectivesRegular sports participation is a gendered phenomenon in China. Women have reported much higher constraints than men on time, partner, psychology, knowledge, and interest. This study explores personal, family, lifestyle, and health factors associated with sports participation.Study DesignThis study is a cross-sectional study.MethodsData were collected from the national reprehensive China Family Panel Studies (CFPS) database (2018) to analyze personal information, family background, lifestyle, and health in relation to women's sports participation. Multiple classification logistic regression was used to quantify the association between independent variables and sports time.ResultsWomen with high personal income and education, who were unmarried, in faster economic development areas have more awareness and more time for sports participation. Women who were overweight and self-rated as unattractive spent less time on sports participation. Women with a small family population and no children have more time for sports participation. Less time on the internet and moderate sleep contribute to active sports participation. Women with chronic diseases and high medical costs are less likely to participate in sports.ConclusionsNegative body aesthetic perception, the burden of family environment, modernization of lifestyle, and the normalization of sub-health are essential factors affecting women's sports participation. The government should understand the inner and outer barriers to women's participation in sports, develop policies and regulations to protect and support women's sports participation, and guide and monitor the effective implementation of women's sports activities.

Time series data for the statistic The age at which men and women can retire with partial pension benefits is the same (1=yes; 0=no) and country China. Indicator Definition:The indicator measures measures whether the age at which men and women can retire and receive partial pension benefits is the same, or if the age at which men and women can retire and receive partial benefits is not mandated. Partial pension benefits refer to a reduced or proportional minimum old-age pension payable to workers who did not accumulate enough work experience or contributions or have not reached the statutory age to qualify for a minimum old-age pension. If transitional provisions gradually increase, decrease or equalize the statutory retirement age, the answer reflect the age according to the report's data collection cycle, even if the law provides for changes over time.

The contribution of women to China’s economic growth and development cannot be overemphasized. Women play important social, economic, and productive roles in any economy. China remains one of the countries in the world with severe gender inequality and sex ratio at birth (SRB) imbalance. Severe gender inequality and disenfranchisement of girls with abnormally high sex ratios at birth reflect deep-rooted sexism and adversely affect girls’ development. For China to achieve economic growth, women should not be ignored and marginalized so that they can contribute to the country’s growth, but the sex ratio at birth needs to be lowered because only women can contribute to growth. Thus, this study empirically predicts an asymmetric relationship between gender inequality, sex ratio at birth and economic growth, using NARDL model over the period 1980–2020. The NARDL results show that increases in gender inequality and sex ratio at birth significantly reduce economic growth in both the short and long term, while reductions in gender inequality and sex ratio at birth significantly boost economic growth in both the short and long term. Moreover, the results show the significant contribution of female labor force participation and female education (secondary and higher education) to economic growth. However, infant mortality rate significantly reduced economic growth. Strategically, the study recommends equal opportunities for women in employment, education, health, economics, and politics to reduce gender disparities and thereby promote sustainable economic growth in China. Moreover, policymakers should introduce new population policy to stabilize the sex ratio at birth, thereby promoting China’s long-term economic growth.

This Dataset shows major roadways throughout the mainland of china. Data was found online at http://www.people.fas.harvard.edu/~chgis/ on May 15th.

Much of the existing knowledge regarding the impact of beauty ideals and body positive social media content on women’s body image is based on the Western cultural context. This limits our understanding of the issue in other cultures, such as China, among others. Therefore, to address this gap, this study examined young Chinese women’s perspectives on beauty ideals and body positivity in social media through a qualitative investigation. Female university students in China (N = 24) participated in individual interviews. A thematic analysis revealed four primary themes: (1) characteristics of mainstream beauty ideals in Chinese social media; (2) impact of beauty ideals on young women; (3) perspectives on the content and roles of body positivity; (4) influences of body positive social media content on young women. These findings indicate that young Chinese women are aware of the beauty ideals in social media and their negative impact on their body image. Furthermore, young Chinese women generally expressed a favorable outlook on body positivity but noted its limitations.

ECPO joins several important digital collections of the early Chinese press and puts them into a single overarching framework. In the first phase, several databases on early women’s periodicals and entertainment publishing were created: “Chinese Women’s Magazines in the Late Qing and Early Republican Period” (WoMag), “Chinese Entertainment Newspapers” (Xiaobao), and databases hosted at the Academia Sinica in Taiwan. These systems approach the material in two ways: in the intensive approach we record all articles, images, advertisements, and related agents and assign them to a complete set of scanned pages, while in the extensive approach we record the main characteristic features of publications. ECPO is distinguished from other existing databases of Chinese periodicals in that it not only provides image scans but also preserves materials often excluded in reprint, microfilm, or digital (even full-text) editions, such as advertising inserts and illustrations. In addition, it aims at incorporating metadata in both English and Chinese, including keywords and biographical information on editors, authors and individuals represented in illustrations and advertisements in the journals. As the material basis of the database consists mostly of image scans, the project has been running experiments on one Republican newspaper to explore approaches toward full-text generation. Computer-aided processing of image scans of historical periodicals is still challenging with the current state of technology, in particular, because processing standards for Latin-script newspapers do not apply to the Chinese context. It is only with new approaches in machine learning that it is now possible to transform material that was previously inaccessible just a few years ago. However, many challenges remain. Extremely complex layouts resulting in difficulties for reliable automatic detection of page segmentation have prevented full-text generation for these newspapers even within China. The application of artificial intelligence requires a ground truth data set. This error-free, manually corrected text with structural information is used for evaluation and training of software models for text and layout recognition. In the fall of 2021, the project successfully implemented OCR on a newspaper 晶報 Jing bao (The Crystal) sample with a character error rate below 3% (Henke 2021). On that basis, the project is now expanding and generalizing its approach. With additional funding recently received from the Research Council Cultural Dynamics in Globalized Worlds for the first half of 2022, the project is currently producing a new data set. The project’s aim is to offer a solution to automatically produce full text from Republican newspapers using neural networks and machine learning. The project’s current work will further develop its original aims and contribute to the field of research as a whole. With the disclosure of the project’s network models and data sets, its results can be reproduced and evaluated, and others can adopt its approaches in the field. Although processing non-Latin-script is still a challenge in many cases, the project hopes its work may serve as good practice examples for such initiatives. The data set provides a first and complete extract of all metadata edited by the project so far. Future versions will also incorporate the fulltext produced in our OCR pipeline.

The dataset is a file of the raw interview scripts with my interviewees during the fieldwork conducted between 2021.6 to 2022.2.

This thesis investigates how urban middle-class working women with two children make sense of work, childcare, and self under the universal two-child policy of China. This thesis also explores how the idea of individual and family interact in these women's construction of a sense of self. On January 1st, 2016, the one-child policy was replaced by the universal two-child policy, under which all married couples in China are allowed to have two children. In the scholarships of motherhood, it is widely documented across cultures that it is a site of patriarchal oppression where women are expected to meet the unrealistic ideal of intensive mothering to be a good mother, suffer from the motherhood wage penalty and face more work-family conflict than fathers. Emprical studies of China also came to similar conclusions and such findings are not only widely regonized in scholarship but is also widespread in popular discourse in China. Despite that marriage and having children is still universal for the generation of the research target, women born in the 1970s and 1980s, due to compounding influence fo the one-child policy, increasing financial burden of raising a child etcs, having only one child has become widely acceptable and normal. Given this context, this study intend to investigate how these middle-class women, who are relatively empowered and resourceful, come to a decision that is seemingly against their own interest. Moreover, unlike in the west where the issue of childbearing and childcaring is mainly an issue of the conjugal couple and the gender realtions is at the center of the discussion, in China, extended family, especially grandparents also play a role in both the decision making process and the subsequent childcare arrangement. Therefore, to study the second-time mothers’ childcare and work experiences in contemporary urban China, we also need to situate them, as individuals, in their family. To investigate how they make sense of childcare and work is also to understand the tension between individual and family. By interviewing twenty-one parents from middle-class family in Guangzhou with a second child under six years old, this study finds that these urban working women with two children consider themselves as an individual unit and full-time paid employment is something that cannot be given up since it is the means of securing that independent self . However, they did not prioritize their personal interest to that of other family members, especially the elder child and thus the decision of having a second child is mainly for the sake of the elder child. Moreover, grandparents played an essential role to provide a childcare safety net, without which, these urban working women would not be able to work full-time and maintain the independent self as they defined it. The portrayal of these women’s experiences reflected the individualization process in China where people are indivdualized without individualism, and family are evoked as strategy to achieve personal as well as family goals. The findings of this study contributs to theories of motherhood by adding an intergenerational perspective to the existing gender perspective and also contributes to the studies of family by understanding the relation and interaction between individual and family in thse women’s construction of sense of self in the context of contemporary China.

PLEASE, CITE AS Kalabikhina IE, Kuznetsova PO, Zhuravleva SA (2024) Size and factors of the motherhood penalty in the labour market: A meta-analysis. Population and Economics 8(2): 178-205. https://doi.org/10.3897/popecon.8.e121438

Explanatory note 1: List of papers used in the meta-analysis - see the file "Meta_regression_analysis_papers".

The data is presented in WORD format.

Explanatory note 2: Set of data used in the meta-analysis - see the file "Meta_regression_analysis_table".

The data is presented in EXCEL format.

Description of table headers:

estimate_number - Number of the estimate

paper_number - Number of the paper

paper_name - Paper (year and first author)

paper_excluded - Paper was excluded from the final sample

survey - Data source

table_in_paper - Number of the table with the regression results in the paper

coeff - Regression coefficient for parenthood variable (estimate)

se - SE of the estimate

t - t-value of the estimate

ols - Estimate is obtained using the OLS method

fixed_effects - Estimate is obtained using the fixed effects method

panel - Model considers panel data (for several years)

quintile - Estimate is obtained using the quintile regression method

other - Estimate is obtained using other methods

selection_into_motherhood - Estimate is obtained allowing for selection into motherhood

hackman - Estimate is obtained allowing for selection into employment (Heckman procedure)

annual_earnings - Annual earnings are considered in the model

monthly_wage - Monthly wage is considered in the model

daily_wage - Daily wage is considered in the model

hourly_wage - Hourly wage is considered in the model

min_age_kid - Child's age (minimum)

max_age_kid - Child's age (maximum)

motherhood - Model uses a dummy variable of the presence of children

num_kids - Model uses a variable of the number of children

kid1 - Model uses a variable of the presence of one child

kid2p - Model uses a variable of the presence of two or more children

kid2 - Model uses a variable of the presence of two children

kid3p - Model uses a variable of the presence of three or more children

kid3 - Model uses a variable of the presence of three children

kid4p - Model uses a variable of the presence of three or more children

race/nationality - Model includes a race/ethnicity variable

age - Model includes the age variable

marstat - Model includes the marital status variable

oth_char_hh - Model includes any other variables of other household characteristics

settl_type - Model includes a variable of the type of settlement (urban, rural)

region - Model includes a variable of the region of the country

education - Model includes information on the level of education

experience - Model includes a variable of work experience

pot_experience - Model includes a variable of potential work experience, to be calculated from the data on age and number of years of education

tenure - Model includes a variable of the duration of employment at the current job

interruptions - Model includes a variable of employment interruptions (related to motherhood)

occupation - Model includes an occupation variable

industry - Model includes a variable of the industry of employment

union - Model includes a variable of trade union membership

friendly_conditions - Model includes a variable of the favourable working conditions for mothers (flexible schedule, possibility to work from home, etc.).

hours - Model includes a variable of the number of hours worked

sector - Model includes a variable of the type of employer ownership (public or private)

informal - Model includes a variable of informal employment

size_ent - Model includes a variable of the employer size

min_age_woman - Woman's age (minimum)

max_age_woman - Woman's age (maximum)

mean_age_woman - Woman's age (mean)

restricted - Sample is limited

private - Model considers only private sector employees

state - Model considers only public sector employees

full_time - Model considers only full-time workers

part_time - Model considers only part-time workers

better_educated - Model considers only women with a high level of education

lower_educated - Model considers only women with a low level of education

married - Model includes only married women

single - Model includes only single women

natives - Model includes only native women (born in the country)

immigrants - Model includes only immigrant women (born abroad)

race - Model includes only women of a particular race

min_year - Time period (minimum year)

max_year - Time period (maximum year)

journal - Type of publication

usa - Sample includes women from the USA

western_europe - Sample includes women from Western Europe (Belgium, France, Germany, Luxembourg, the Netherlands, Switzerland)

north_europe - Sample includes women from Northern Europe (Denmark, Finland, Norway, Sweden)

south_europe - Sample includes women from Southern Europe (Greece, Italy, Portugal, Spain)

east_centre_europe - Sample includes women from Central or Eastern Europe (Czechia, Hungary, Poland, Russia, Serbia, Ukraine)

china - Sample includes women from China

Russia - Sample includes women from Russia

others - Sample includes women from other countries

country - Country name

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 Grove. 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 Grove, the median income for all workers aged 15 years and older, regardless of work hours, was $69,583 for males and $44,851 for females.

These income figures highlight a substantial gender-based income gap in China Grove. Women, regardless of work hours, earn 64 cents for each dollar earned by men. This significant gender pay gap, approximately 36%, underscores concerning gender-based income inequality in the town of China Grove.

- Full-time workers, aged 15 years and older: In China Grove, among full-time, year-round workers aged 15 years and older, males earned a median income of $71,500, while females earned $53,571, leading to a 25% gender pay gap among full-time workers. This illustrates that women earn 75 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 China Grove.

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

BackgroundSex and gender are crucial variables in coronavirus disease 2019 (COVID-19). We sought to provide information on differences in clinical characteristics and outcomes between male and female patients and to explore the effect of estrogen in disease outcomes in patients with COVID-19.MethodIn this retrospective, multi-center study, we included all confirmed cases of COVID-19 admitted to four hospitals in Hubei province, China from Dec 31, 2019 to Mar 31, 2020. Cases were confirmed by real-time RT-PCR and were analyzed for demographic, clinical, laboratory and radiographic parameters. Random-effect logistic regression analysis was used to assess the association between sex and disease outcomes.ResultsA total of 2501 hospitalized patients with COVID-19 were included in the present study. The clinical manifestations of male and female patients with COVID-19 were similar, while male patients have more comorbidities than female patients. In terms of laboratory findings, compared with female patients, male patients were more likely to have lymphopenia, thrombocytopenia, inflammatory response, hypoproteinemia, and extrapulmonary organ damage. Random-effect logistic regression analysis indicated that male patients were more likely to progress into severe type, and prone to ARDS, secondary bacterial infection, and death than females. However, there was no significant difference in disease outcomes between postmenopausal and premenopausal females after propensity score matching (PSM) by age.ConclusionsMale patients, especially those age-matched with postmenopausal females, are more likely to have poor outcomes. Sex-specific differences in clinical characteristics and outcomes do exist in patients with COVID-19, but estrogen may not be the primary cause. Further studies are needed to explore the causes of the differences in disease outcomes between the sexes.

BackgroundAn increasing number of studies suggest that environmental pollution may increase the risk of vitamin D deficiency (VDD). However, less is known about arsenic (As) exposure and VDD, particularly in Chinese pregnant women.ObjectivesThis study examines the correlations of different urinary As species with serum 25 (OH) D and VDD prevalence.MethodsWe measured urinary arsenite (As3+), arsenate (As5+), monomethylarsonic acid (MMA), and dimethylarsinic acid (DMA) levels and serum 25(OH)D2, 25(OH)D3, 25(OH) D levels in 391 pregnant women in Tianjin, China. The diagnosis of VDD was based on 25(OH) D serum levels. Linear relationship, Logistic regression, and Bayesian kernel machine regression (BKMR) were used to examine the associations between urinary As species and VDD.ResultsOf the 391 pregnant women, 60 received a diagnosis of VDD. Baseline information showed significant differences in As3+, DMA, and tAs distribution between pregnant women with and without VDD. Logistic regression showed that As3+ was significantly and positively correlated with VDD (OR: 4.65, 95% CI: 1.79, 13.32). Meanwhile, there was a marginally significant positive correlation between tAs and VDD (OR: 4.27, 95% CI: 1.01, 19.59). BKMR revealed positive correlations between As3+, MMA and VDD. However, negative correlations were found between As5+, DMA and VDD.ConclusionAccording to our study, there were positive correlations between iAs, especially As3+, MMA and VDD, but negative correlations between other As species and VDD. Further studies are needed to determine the mechanisms that exist between different As species and VDD.

Background:Between January and April 2020, China implemented differentiated prevention and control strategies across the country, based on the severity of the COVID-19 epidemic/pandemic in different regions. These strategies included lockdowns, social distancing, and the closure of public places. These measures may have affected dietary intake to varying degrees. This study aimed to assess variations in food intake and diet quality among pregnant women according to regional severity and related control measures during the most severe period of COVID-19 restrictions in 2020.MethodsA total of 3,678 pregnant women from 19 provinces/municipalities in mainland China were analyzed in this nationwide, multi-center study. Food intake data were obtained and assessed using a validated food frequency questionnaire (FFQ). Diet quality was quantified using the Diet Balance Index for Pregnancy (DBI-P), which included high bound score (HBS, excessive dietary intake), low bound score (LBS, insufficient dietary intake), and diet quality distance (DQD, dietary imbalance). Linear trend tests and multivariable regression analyses were performed to examine the association between food intake, DBI-P and the severity of pandemic.ResultsThe median daily intake of vegetables, fruit, livestock/poultry meat, dairy, and nuts decreased (p < 0.05) according to low, moderate, and high severity of the pandemic, while no significant differences in cereals/potatoes, eggs, and fish/shrimp intake. The median daily intake of cereals/potatoes exceeded the recommended ranges, and the daily intake of eggs and fish/shrimp was below recommended ranges regardless of the pandemic severity (p < 0.05). Regarding diet quality, HBS decreased (lower excessive consumption) (p = 0.047) and LBS increased (greater insufficient consumption) (p = 0.046) with increased severity of the pandemic. On multivariable analyses, moderate and high pandemic severity were related to lower HBS risk (OR = 0.687, OR = 0.537) and higher LBS risk (β = 1.517, β = 3.020) when compared to low pandemic severity.ConclusionsUnder more severe COVID-19 pandemic conditions, pregnant women consumed less quality food, characterized by reduced consumption of vegetables, fruit, livestock/poultry meat, dairy and nuts, while the quality of the foods that pregnant women consumed in excess tended to improve, but the overconsumption of cereals/potatoes was a problem.

BackgroundPregnant women are often susceptible to anemia, which can damage the thyroid gland. However, compared with moderate and severe anemia, less attention has been paid to mild anemia. The purpose of this study was to evaluate the effect of mild anemia on the thyroid function in pregnant women during the first trimester.MethodsA total of 1,761 women in the first trimester of their pregnancy were enrolled from Shenyang, China, and divided into mild anemia and normal control groups based on their hemoglobin levels. Thyroid-stimulating hormone (TSH), free thyroxine (FT4), and free triiodothyronine (FT3) levels were compared between the two groups.ResultsThe TSH levels of pregnant women with mild anemia were higher than those of pregnant women without mild anemia (p < 0.05). Normal control women were selected to set new reference intervals for TSH, FT3, and FT4 levels during the first trimester, which were 0.11–4.13 mIU/l, 3.45–5.47 pmol/l, and 7.96–16.54 pmol/l, respectively. The upper limit of TSH 4.13 mU/l is close to the upper limit 4.0 mU/l recommended in the 2017 American Thyroid Association (ATA) guidelines, indicating that exclusion of mild anemia may reduce the difference in reference values from different regions. Mild anemia was related to 4.40 times odds of abnormally TSH levels (95% CI: 2.84, 6.76) and 5.87 increased odds of abnormal FT3 (95% CI: 3.89, 8.85). The proportion of hypothyroidism and subclinical hypothyroidism in patients with mild anemia was higher than that in those without anemia (0.6% vs. 0, p = 0.009; 12.1% vs. 1.9%, p < 0.001). Mild anemia was related to 7.61 times increased odds of subclinical hypothyroidism (95% CI: 4.53, 12.90).ConclusionsMild anemia may affect thyroid function during the first trimester, which highlights the importance of excluding mild anemia confounding when establishing a locally derived specific reference interval for early pregnancy.

Urolithiasis is one of the most common diseases in urology, with a lifetime prevalence of 14% and is more prevalent in males compared to females. We designed to explore sex disparities in the Chinese population to provide evidence for prevention measures and mechanisms of stone formation. A total of 98232 Chinese individuals who had undergone a comprehensive examination in 2017 were included. Fully adjusted odds ratios for kidney stones were measured using restricted cubic splines. Multiple imputations was applied for missing values. Propensity score matching was utilised for sensitivity analysis. Among the 98232 included participants, 42762 participants (43.53%) were females and 55470 participants (56.47%) were males. Patients’ factors might cast an influence on the development of kidney stone disease distinctly between the two genders. A risk factor for one gender might have no effect on the other gender. The risk for urolithiasis in females continuously rises as ageing, while for males the risk presents a trend to ascend until the age of around 53 and then descend. Patients’ factors might influence the development of kidney stones distinctly between the two genders. As age grew, the risk to develop kidney stones in females continuously ascended, while the risk in males presented a trend to ascend and then descend, which was presumably related to the weakening of the androgen signals.Key messagesWe found that patients’ factors might cast an influence on the development of kidney stone disease distinctly between the two sexes.The association between age and urolithiasis presents distinct trends in the two sexesThe results will provide evidence to explore the mechanisms underlying such differences can cast light on potential therapeutic targets and promote the development of tailored therapy strategies in prospect. We found that patients’ factors might cast an influence on the development of kidney stone disease distinctly between the two sexes. The association between age and urolithiasis presents distinct trends in the two sexes The results will provide evidence to explore the mechanisms underlying such differences can cast light on potential therapeutic targets and promote the development of tailored therapy strategies in prospect.