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.

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

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 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

We analyse expenditure patterns for rural China, focusing on differences between families with boys and girls. The sample includes more than 5000 nuclear families from 19 Chinese provinces. Following the existing literature, we estimate Engel curves for food and for alcohol, a typical adult good. We use a flexible, partially linear specification and allow for endogeneity of total expenditures. The results are similar to those of other studies, not providing much evidence of gender differentials. We then focus on the decision to send a child to school and on the budget share spent on educational goods. Using both parametric and semiparametric estimates, we find evidence that boys are more often sent to school and that expenditures on a boy that goes to school are larger than for a school-going girl of the same age.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Gender equality is a core development objective in its own right. It is also smart development policy and sound business practice. It is integral to economic growth, business growth and good development outcomes. Gender equality can boost productivity, enhance prospects for the next generation, build resilience, and make institutions more representative and effective. In December 2015, the World Bank Group Board discussed our new Gender Equality Strategy 2016-2023, which aims to address persistent gaps and proposed a sharpened focus on more and better gender data. The Bank Group is continually scaling up commitments and expanding partnerships to fill significant gaps in gender data. The database hosts the latest sex-disaggregated data and gender statistics covering demography, education, health, access to economic opportunities, public life and decision-making, and agency.

Constrained estimates of total number of people per grid square broken down by gender and age groupings (including 0-1 and by 5-year up to 90+) for China, version v1. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are estimated number of male, female or both in each age group per grid square.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

File Descriptions:

{iso} {gender} {age group} {year} {type} {resolution}.tif

iso

Three-letter country code

gender

m = male, f= female, t = both genders

age group

• 00 = age group 0 to 12 months
• 01 = age group 1 to 4 years
• 05 = age group 5 to 9 years
• 90 = age 90 years and over

year

Year that the population represents

type

CN = Constrained , UC= Unconstrained

resolution

Resolution of the data e.q. 100m = 3 arc (approximately 100m at the equator)

Constrained estimates of total number of people per grid square broken down by gender and age groupings (including 0-1 and by 5-year up to 90+) for Hong Kong, SAR China, version v1. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are estimated number of male, female or both in each age group per grid square.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

File Descriptions:

{iso} {gender} {age group} {year} {type} {resolution}.tif

iso

Three-letter country code

gender

m = male, f= female, t = both genders

age group

• 00 = age group 0 to 12 months
• 01 = age group 1 to 4 years
• 05 = age group 5 to 9 years
• 90 = age 90 years and over

year

Year that the population represents

type

CN = Constrained , UC= Unconstrained

resolution

Resolution of the data e.q. 100m = 3 arc (approximately 100m at the equator)

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.

This dataset is about companies. It has 1 row and is filtered where the company is China Renaissance. It features 2 columns including CEO gender.

Organized original database for analization. (XLSX 195 kb)

TMNRED Dataset - Chinese Natural Reading EEG for Fuzzy Semantic Target Identification

Overview

This dataset, named TMNRED, consists of electroencephalogram (EEG) recordings obtained from 30 participants engaged in natural reading tasks. The aim is to investigate the mechanisms of semantic processing in the Chinese language within a natural reading environment.

Data Collection

• Participants: 30 healthy, right-handed individuals (average age: 22.07 years, standard deviation: 2.7 years; 18 females, 12 males) who are native Chinese speakers.
• Materials: Text ranging from 15 to 20 characters, presented as news headlines or short sentences. Materials include target semantic items and non-target semantic items.
• Procedure: Participants read sentences displayed on a screen at their own pace. Each participant completed 8 blocks of 400 trials in total, with each trial lasting approximately 2.2 seconds, including a fixation cross and inter-stimulus intervals.

Data Structure

The dataset is organized according to the BIDS standard: - Main Folder: - dataset_description.json: Description of the dataset. - participants.tsv: Participant information. - participants.json: Details of columns in participants.tsv. - README: General information about the dataset. - data_all.mat: Labeled EEG data of all subjects in MAT format. - Derivative Data: - final_bids/: EEG data stored in JSON, TSV, and EDF formats. - preproc/: Preprocessed data, including subfolders for each subject (sub-01, etc.), with data in various formats (BDF, SET, FDT, ERP, MAT).

Technical Validation

Sensor-level EEG analyses were performed, showing distinct responses to target and non-target words at different time points, with notable changes in potential distribution across the scalp.

Distribution

The raw and preprocessed EEG data are openly available online at https://github.com/tym5049/TMNRED_Dataset under the Creative Commons Attribution 4.0 International Public License (https://creativecommons.org/licenses/by/4.0/).

Usage Notes

• Researchers should cite the dataset appropriately when using it.
• For any questions or issues, please refer to the README file or contact the corresponding authors: Yanru Bai (yr56 bai@tju.edu.cn), Guangjian Ni (niguangjian@tju.edu.cn).

Acknowledgments

This work was mainly supported by the National Key R&D Program of China (2023YFF1203503) and the National Natural Science Foundation of China (82202290). We also thank all research assistants who provided general support in participant recruiting and data collection.

Constrained estimates of total number of people per grid square broken down by gender and age groupings (including 0-1 and by 5-year up to 90+) for Taiwan, version v1. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are estimated number of male, female or both in each age group per grid square.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

File Descriptions:

{iso} {gender} {age group} {year} {type} {resolution}.tif

iso

Three-letter country code

gender

m = male, f= female, t = both genders

age group

• 00 = age group 0 to 12 months
• 01 = age group 1 to 4 years
• 05 = age group 5 to 9 years
• 90 = age 90 years and over

year

Year that the population represents

type

CN = Constrained , UC= Unconstrained

resolution

Resolution of the data e.q. 100m = 3 arc (approximately 100m at the equator)

When permitted by law, employers sometimes state the preferred age and gender of their employees in job ads. The researchers study the interaction of advertised requests for age and gender on one Mexican and three Chinese job boards, showing that firms’ explicit gender requests shift dramatically away from women and towards men when firms are seeking older (as opposed to younger) workers. This ‘age twist’ in advertised gender preferences occurs in all four of our datasets and survives controls for occupation, firm, and job title fixed effects. Chinese Data The two new Chinese data sources used are job boards serving the city of Xiamen. In part because Xiamen was one of the five economic zones established immediately after China’s 1979 economic reforms, it is highly modernized relative to other Chinese cities, with an economy based on electronics, machinery and chemical engineering. One of these job boards, XMZYJS (the Xia-Zhang-Quan city public job board) is operated directly by government employees of the local labor bureau. Like state-operated Job Centers in the U.S., XMZYJS has a history as a brick-and-mortar employment service. XMZYJS’s mandate is to serve the less-skilled portion of the area’s labor market, and operates purely as a jobposting service: workers cannot post resumes or apply to jobs on the site. In fact, while XMZYJS now posts all its job ads online, many of these ads are viewed in XMZYJS‘s offices by workers who visit in person. This is done both on individual computer terminals and on a large electronic wall display. Applications are made by calling the company that placed the ad or by coming to a specific window on XMZYJS’s premises that has been reserved by the employer at a posted date and time. The second Xiamen-based job board, XMRC , is a for-profit, privately-operated company that is sponsored by the local government. Its mandate is to serve the market for skilled workers in the Xiamen metropolitan area. XMRC operates like a typical U.S. job board: both job ads and resumes are posted online, workers can submit applications to specific jobs via the site, and firms can contact individual workers through the site as well. By design, XMZYJS aggregates job postings from all local and specialized job boards for less-skilled workers in the metropolitan area, and XMRC is the main job board for skilled workers in the area. While there is potentially some cross-posting of job ads across the two sites, descriptive statistics on the types of jobs on offer suggest the sites do, indeed, serve very different populations. Like all our data sets, XMZYJS and XMRC serve private sector employers almost exclusively. Recruiting for public sector jobs, and most recruiting for State-Owned-Enterprises (SOEs) takes place via a different process. The third Chinese database represents Zhaopin as the third-largest Internet job board in China; it operates nationally and serves workers who on average are considerably more skilled than even those on XMRC. This sample is based on all unique ads posted in four five-week observation periods in 2008-2010. In contrast to XMRC and XMZYJS where the data were supplied by the job boards, the Zhaopin data were collected by a web crawler. The sample is based on all unique ads posted in four five-week observation periods in 2008-2010. The Chinese data have 141,188, 39,727, and 1,051,038 ads in the XMZYJS, XMRC and Zhaopin samples respectively. Mexican Data The Mexican data allows to ascertain whether main results extend to a nation with different economic conditions, labor market institutions and culture. The Mexican data is a sample of job ads posted on Computrabajo. Of the new data sets explored, the Computrabajo data are most similar to Zhaopin in the sense that they come from a national online site that disproportionately serves highly skilled workers. To construct an analysis sample from the Computrabajo website, the authors collected advertisements daily for approximately 18 months between early 2011 and mid-2012 using a web crawler. Both the standardized fields and the open text portions of each ad were parsed to extract variables for the analysis. Computrabajo analysis sample contains 90,487 ads.

The dataset encompasses questionnaire and interview data focusing on Mainland Chinese high school students' English learning.The study aims to investigate the gendered English education inequality in Mainland China by adopting a sequential mixed method approach.The quantitative data includes various cultural capital and habits variables. The pilot study surveyed 265 high school students and parents, while the main study surveyed 655 students in Grade 10, 11 and 12, and 971 parents.The qualitative data were collected through semi-structured interviews with eight students and seven parents.

Gender inequality operates differently across various families in China. For example, previous research suggests that the motherhood penalty is most pronounced in patrilocal families, nil in matrilocal families (where married couples live with the wife’s parents), and moderate in nuclear families (where married couples live with the husband’s parents) (Yu and Xie 2018). However, it remains unclear how family dynamics intersect with gender in shaping individual outcomes in Chinese households. Accordingly, in this dissertation, I provide a systematic examination of this question with a mixed-methods approach. I start by examining how living in extended families reduces women’s total housework burdens (Ta et al. 2019), yet also exacerbates the gender gap (Hu and Mu 2021). Drawing on 38 in-depth interviews with married Chinese women, I find that women’s goal of making life manageable precipitates their choices of extended families, as a viable solution to navigate their work-family conflicts. This goal, however, directs their attention from the gender gap in domestic labor and fosters everyday interactions suppressing women’s intentions to resist the unequal housework division. The second chapter continues the exploration of variation in family dynamics, but is more focused on the effects of this variation on individual outcomes. Based on analyses of six-year nationally representative datasets, I find that it is necessary to make a distinction between patrilocal and matrilocal extended families. This is because the former is associated with people’s increased fertility intentions whereas the latter is related to decreased fertility intentions, as compared to nuclear families. Such an important variation is concealed if researchers combine patrilocal and matrilocal families into one category. Building on knowledge of the first two chapters, the third chapter foregrounds women’s agency to explore how they navigate the gender and family dynamics in Chinese households. Drawing on interviews with 40 married Chinese women, I show that women express an individualist ethos of self-reliance in domestics labor, rejecting the idea that their household responsibility is a result of gendered subordination. They do so by reconstructing gender status differences and reviving meanings of family works at narrative levels. Such narratives allow them to build an alternative, more reconciled story of gender inequality that they are, in fact, unable to challenge. These three chapters, although have different focuses, all contribute to a comprehensive understanding of individuals’ lives at the intersection of gender and family dynamics in a social context where gender inequality remains entrenched. In conclusion, I demonstrate how these three articles advance our knowledge of 1) individuals’ struggles between conflicting or even competing ideologies in everyday life, and 2) the connections between individual outcomes at the micro-level and social factors at the macro level.

This dataset is about companies. It has 1 row and is filtered where the company is China Chunlai Education Group. It features 2 columns including CEO gender.

Introduction

This Mandarin Chinese Call Center Speech Dataset for the Travel industry is purpose-built to power the next generation of voice AI applications for travel booking, customer support, and itinerary assistance. With over 30 hours of unscripted, real-world conversations, the dataset enables the development of highly accurate speech recognition and natural language understanding models tailored for Mandarin -speaking travelers.

Created by FutureBeeAI, this dataset supports researchers, data scientists, and conversational AI teams in building voice technologies for airlines, travel portals, and hospitality platforms.

Speech Data

The dataset includes 30 hours of dual-channel audio recordings between native Mandarin Chinese speakers engaged in real travel-related customer service conversations. These audio files reflect a wide variety of topics, accents, and scenarios found across the travel and tourism industry.

Topic Diversity

Inbound and outbound conversations span a wide range of real-world travel support situations with varied outcomes (positive, neutral, negative).

These scenarios help models understand and respond to diverse traveler needs in real-time.

Transcription

Each call is accompanied by manually curated, high-accuracy transcriptions in JSON format.

Metadata

Extensive metadata enriches each call and speaker for better filtering and AI training:

Usage and Applications

This dataset is ideal for a variety of AI use cases in the travel and tourism space:

The global gender gap index benchmarks national gender gaps on economic, political, education, and health-based criteria. In 2025, the country offering most gender equal conditions was Iceland, with a score of 0.93. Overall, the Nordic countries make up 3 of the 5 most gender equal countries in the world. The Nordic countries are known for their high levels of gender equality, including high female employment rates and evenly divided parental leave. Sudan is the second-least gender equal country Pakistan is found on the other end of the scale, ranked as the least gender equal country in the world. Conditions for civilians in the North African country have worsened significantly after a civil war broke out in April 2023. Especially girls and women are suffering and have become victims of sexual violence. Moreover, nearly 9 million people are estimated to be at acute risk of famine. The Middle East and North Africa has the largest gender gap Looking at the different world regions, the Middle East and North Africa has the largest gender gap as of 2023, just ahead of South Asia. Moreover, it is estimated that it will take another 152 years before the gender gap in the Middle East and North Africa is closed. On the other hand, Europe has the lowest gender gap in the world.

To measure how gendered job ads interact with workers’ application decisions and employers’ callback behavior, this data entails applicant and callback pools to job ads on internal records of a Chinese job board (XMRC.com), an Internet job board serving the city of Xiamen, over a six-month period in 2010. XMRC is a private firm, commissioned by the local government to serve private-sector employers seeking relatively skilled workers. Its job board has a typical U.S. structure, with posted ads and resumes, on-line job applications and a facility for employers to contact workers via the site. XMRC went online in early 2000; it is nationally recognized as dominant in Xiamen. To study the effect of gender profiling on application and callback patterns, the project began with the universe of ads that received their first application between May 1 and October 30, 2010. Those ads where then matched to all the resumes that applied to them, creating a complete set of applications. Finally, for the subset of ads that used XMRC’s internal messaging system to contact applicants, the data has indicators for which applicants were contacted after the application was submitted. This indicator serves as the measure of callbacks. The primary dataset for the paper is this subset of ads for which callback information is available, which comprises 3,637/42,744 = 8.5 percent of all ads. In all, the primary dataset comprises 229,616 applications made by 79,697 workers (resumes) to 3,637 ads, placed by 1,614 firms, resulting in 19,245 callbacks. Thus there was an average of 63 applications per ad and 5.3 callbacks per ad. One in twelve applications received a callback, while one in four resumes received a callback.

This dataset is about companies. It has 1 row and is filtered where the company is China Zheshang Bank. It features 2 columns including CEO gender.